Biodiversity in Human-Modified Landscapes: Case Studies, the State of Research, and

Biodiversity in Human-Modified Landscapes: Case Studies, the State of Research, and
Biodiversity in Human-Modified Landscapes:
Case Studies, the State of Research, and
Implications for Conservation
by
Morgan Jayne Trimble
Submitted in partial fulfillment of the requirements
for the degree of
Doctor of Philosophy (Zoology)
in the
Faculty of Natural & Agricultural Sciences
University of Pretoria
Pretoria
January 2014
Declaration
I, Morgan Jayne Trimble, declare that the thesis/dissertation, which I hereby submit for
the degree PhD Zoology at the University of Pretoria, is my own work and has not
previously been submitted by me for a degree at this or any other tertiary institution.
Signature:
Date:
Disclaimer
This thesis includes several manuscripts that were prepared for submission to different
scientific, peer-reviewed journals. Referencing has been standardized, but styles differ
somewhat according to journal specifications.
Morgan J. Trimble
i
“The last word in ignorance is the man who says of an animal or plant, "What good is it?"
If the land mechanism as a whole is good, then every part is good, whether we understand
it or not. If the biota, in the course of aeons, has built something we like but do not
understand, then who but a fool would discard seemingly useless parts? To keep every cog
and wheel is the first precaution of intelligent tinkering.”
― Aldo Leopold, Round River: From the Journals of Aldo Leopold
ii
Dedication
To my parents, Bruce and Renee Trimble.
iii
Biodiversity in Human-Modified Landscapes:
Case Studies, the State of Research, and
Implications for Conservation
Student:
Morgan Jayne Trimble
Supervisor:
Professor Rudi J. van Aarde
Conservation Ecology Research Unit
Department of Zoology & Entomology
University of Pretoria
Pretoria
0002
[email protected]
Degree:
Doctor of Philosophy (Zoology)
Abstract
Protected areas (PAs) cover 12.9% of Earth’s land, while just 5.8% has strict protection for
biodiversity (Earth’s variety of ecosystems, species, and genetic variation). Constraints of
size and configuration, mismanagement, anthropogenic pressure, and climate change
hamstring the capacity of PAs to conserve biodiversity. Increasingly, studies of
biodiversity in human-modified landscapes provide an evidence base to support policies to
make land outside of PAs as amenable as possible for biodiversity persistence.
I reviewed research on biodiversity in sub-Saharan Africa’s human-modified
landscapes within four ecosystem categorizations: rangelands, tropical forest, Cape
iv
Floristic Region, and urban and rural built environment. I found potential for humanmodified landscapes to contribute to conservation across ecosystems. Available research
could guide policy-making; nonetheless, several issues require further investment, e.g.
research deficiencies, implementation strategies, and conflict with biodiversity.
I also conducted case studies that could support land-use planning in South Africa’s
coastal forest, part of a biodiversity hotspot. By comparing herpetofaunal communities
over a land-use gradient, I found old-growth forest harbored the highest richness and
abundance. Richness was low in sugar cane cultivation and degraded forest but substantial
in acacia woodland and eucalyptus plantation. Composition differed between natural and
anthropogenic vegetation types. Functional group richness decreased monotonically along
the gradient, driven by sensitivity of fossorial herpetofauna and vegetation-dwelling frogs.
Environmental variables were good predictors of frog abundance, but less so for reptiles.
Maintaining forest and preventing degradation is important for herpetofaunal conservation
while restoration and plantations have more value than cultivation.
Old-growth remnants and post-disturbance regenerating vegetation also provide
habitat for birds. However, occurrence does not ensure persistence. I calculated population
trends for 37 bird species and general trends in overall bird density in different vegetation
types. Seventy-six percent of species assessed have declined, 57% significantly so at an
average rate of 13.9% per year. Overall, bird density fell at 12.2% per year across
vegetation types. Changes in rainfall, habitat area, and survey coverage may partly explain
trends. However, species with larger range extents declined more sharply than others and
may be responding to environmental changes on a broad scale. These results cast doubt on
the future persistence of birds in this human-modified landscape and justify further study.
v
Such studies can support sensible land-use management; however, biases in study
topics should not lead to gaps in the evidence base. By reviewing the global literature, I
demonstrated clear geographical bias among biomes and geopolitical regions and
taxonomic bias among species groups. Furthermore, distribution of published papers did
not generally reflect threats of low PA coverage, high land conversion, and high human
population density. Forests were the subject of 87% of papers, and 75% focused on the
Americas and Europe, while Africa and Asia were critically understudied.
This thesis highlights that managing human-modified landscapes for biodiversity
could contribute to conservation. However, responses to land uses are complex, locationand species-specific, and often poorly understood, hindering integration of information into
policy recommendations. Further research is needed to elucidate what, where, and how
biodiversity persists alongside humans to enhance conservation efficacy, especially in
understudied regions.
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Acknowledgements
This work is the result of three years’ worth of ideas, sweat, blood, and coffee. I’m grateful
for the support from the colleagues, friends, family, and organizations that allowed me to
create this thesis—for the most part with aplomb, punctuated only occasionally by panic
and despair (or so I would like to remember it).
Much credit is due to my supervisor, Professor Rudi van Aarde. Without his
inspiring dedication to his work and contagious passion for conservation biology, I would
not have pursued this project or, very likely, a PhD at all. Thank you for setting such an
example, and for the support, opportunities, expectations, trust, encouragement, and, most
of all, wisdom.
Thanks also to my family, especially my parents, Bruce and Renee, who are
unbelievably supportive. I’m grateful for a family that values learning, exploration,
experiences, and fulfillment beyond more traditional metrics of success. Nevertheless,
making you proud means a lot to me.
The Conservation Ecology Research Unit (CERU) has been a family away from
home since I moved to South Africa seven years ago. Thanks especially to Lilian Scholtz
for taking care of me so kindheartedly. Many CERU colleagues, current and former, helped
me on my academic journey, via mentoring, commiserating, or most commonly, a
combination of the two. Dr Sam Ferreira, Jo Fourie, Dr Rob Guldemond, and Dr Tim
Jackson provided valuable guidance, and my time as a student was greatly enriched by
Shaun D’Araujo, Alida de Flamingh, Dr. Matt Grainger, Jessi Junker, Tamara Lee, Oscar
Mohale, Michael Mole, Dr Cornelio Ntumi, Pieter Olivier, Laura Owens, Andrew Purdon,
Ashley Robson, Natasha Visser, Dr Kim Young, and the other students who have been a
vii
part of CERU. I am also grateful for many friends, both old and new, for providing
encouragement, perspective, and frequent laughs.
Funding from the United States National Science Foundation Graduate Research
Fellowship Program, the University of Pretoria, and the Conservation Ecology Research
Unit supported me during my PhD studies. Financial support for my research projects was
generously provided by Richards Bay Minerals, the National Research Foundation, and the
Department of Trade and Industry. Recognition is also due to a long list of people for data
collection efforts in the field.
viii
Table of Contents
Declaration .......................................................................................................... i
Disclaimer ........................................................................................................... i
Dedication ......................................................................................................... iii
Abstract............................................................................................................. iv
Acknowledgements .......................................................................................... vii
Table of Contents .............................................................................................. ix
List of Tables .................................................................................................. xiii
List of Figures ..................................................................................................xvi
Chapter 1. General Introduction .....................................................................................1
References...........................................................................................................7
Chapter 2. Supporting Conservation with Biodiversity Research in Sub-Saharan
Africa’s Human-Modified Landscapes .............................................................. 11
Publication Details ............................................................................................ 11
Abstract............................................................................................................. 11
Introduction....................................................................................................... 12
Methods ............................................................................................................ 17
Literature search ............................................................................................ 17
Biodiversity in Human-Modified Landscapes of African Ecosystems ................ 17
Rangelands .................................................................................................... 17
Tropical forests .............................................................................................. 26
Cape Floristic Region .................................................................................... 29
Urban and rural built environment ................................................................. 31
Constraints and Opportunities ........................................................................... 35
The science of biodiversity in human-modified landscapes ............................ 35
Implementing policies .................................................................................... 36
Living with nature ......................................................................................... 38
Conclusion ........................................................................................................ 41
Acknowledgements ........................................................................................... 42
References......................................................................................................... 43
Figures .............................................................................................................. 68
ix
Supplementary Tables ....................................................................................... 69
Supplementary References ................................................................................ 82
Chapter 3. Frog and Reptile Communities and Functional Groups Over a Land-Use
Gradient in a Coastal Tropical Forest Landscape of High Richness and
Endemicity .......................................................................................................... 93
Publication Details ............................................................................................ 93
Abstract............................................................................................................. 93
Introduction....................................................................................................... 94
Methods ............................................................................................................ 97
Study area ...................................................................................................... 97
Sampling methods ......................................................................................... 97
Analyses ........................................................................................................ 98
Results ............................................................................................................ 100
Richness, abundance, and diversity .............................................................. 101
Composition ................................................................................................ 102
Functional groups ........................................................................................ 102
Environmental predictors ............................................................................. 103
Discussion ....................................................................................................... 103
Richness, diversity, composition .................................................................. 104
Functional groups ........................................................................................ 106
Environmental predictors ............................................................................. 107
Constraints and future research .................................................................... 108
Conservation implications ............................................................................ 109
Acknowledgements ......................................................................................... 111
References....................................................................................................... 112
Figures ............................................................................................................ 120
Tables ............................................................................................................. 124
Supplementary Material .................................................................................. 130
Supplementary References .............................................................................. 149
Chapter 4. Decline of Birds in a Human-Modified Coastal Dune Forest Landscape in
South Africa ...................................................................................................... 150
Publication Details .......................................................................................... 150
x
Abstract........................................................................................................... 150
Introduction..................................................................................................... 151
Methods .......................................................................................................... 153
Bird data ...................................................................................................... 153
Trends and determinants .............................................................................. 157
Results ............................................................................................................ 160
Habitat affinity ............................................................................................ 160
Distance sampling........................................................................................ 160
Population trends and determinants .............................................................. 161
Discussion ....................................................................................................... 164
Acknowledgements ......................................................................................... 169
References....................................................................................................... 170
Figures ............................................................................................................ 177
Tables ............................................................................................................. 180
Supplementary Material .................................................................................. 182
Chapter 5. Geographical and Taxonomic Biases in Research on Biodiversity in
Human-Modified Landscapes .......................................................................... 189
Publication Details .......................................................................................... 189
Abstract........................................................................................................... 189
Introduction..................................................................................................... 190
Methods .......................................................................................................... 194
Literature search .......................................................................................... 194
Geographical distribution of research output ................................................ 195
Distribution of research output among species groups .................................. 197
Results ............................................................................................................ 198
Geographical bias ........................................................................................ 199
Taxonomic bias ........................................................................................... 201
Discussion ....................................................................................................... 202
“Natural comparison” versus “no comparison” studies ................................. 203
Patterns of geographical bias in research output ........................................... 204
Patterns of taxonomic bias ........................................................................... 207
Origins of bias and new directions for research ............................................ 208
Acknowledgements ......................................................................................... 211
xi
References....................................................................................................... 212
Figures ............................................................................................................ 220
Tables ............................................................................................................. 227
Chapter 6. General Conclusions .................................................................................. 228
References....................................................................................................... 236
Appendix A. Fences are More Than an Issue of Aesthetics ........................................ 241
Publication Details .......................................................................................... 241
Letter .............................................................................................................. 241
References....................................................................................................... 243
Appendix B. A Note on Polyvinyl Chloride (PVC) Pipe Traps for Sampling
Vegetation-Dwelling Frogs in South Africa ..................................................... 244
Publication Details .......................................................................................... 244
Introduction..................................................................................................... 244
Methods .......................................................................................................... 245
Results and Discussion .................................................................................... 246
Acknowledgements ......................................................................................... 248
References....................................................................................................... 249
Tables ............................................................................................................. 253
xii
List of Tables
Table S.2.1. Summary of studies investigating biodiversity of grazing landscapes in subSaharan African rangelands. ..................................................................................... 69
Table S.2.2. Summary of studies investigating biodiversity of agricultural mosaic
landscapes in sub-Saharan African rangelands. ......................................................... 74
Table S.2.3. Summary of studies investigating biodiversity of cropping landscapes in subSaharan African rangelands. ..................................................................................... 78
Table S.2.4. Summary of studies investigating biodiversity of agroforestry landscapes in
sub-Saharan African rangelands. ............................................................................... 80
Table 3.1. Abundance of frog and reptile species captured in trapping arrays (where *
indicates confirmation of frog species by audio recording a) across vegetation types (F
= forest, DF = degraded forest, AW = acacia woodland, P = plantation, C =
cultivation), and functional group to which species are assigned based on functional
traits. ...................................................................................................................... 124
Table 3.2. Observed species richness and abundance, abundance- and incidence-based
richness estimators, percent of predicted richness actually observed, and Shannon
diversity of frogs and reptiles across five vegetation types (F = forest, DF = degraded
forest, AW = acacia woodland, P = plantation, C = cultivation). ............................. 126
Table 3.3. Analysis of similarity (ANOSIM) results comparing frog and reptile community
composition among vegetation types based on Bray–Curtis similarity of square-roottransformed abundance data. ................................................................................... 127
Table 3.4. Functional group descriptions (Fx are frog groups, Rx are reptile groups),
number of species per group, and statistics describing significance of vegetation type
as a predictor of abundance and proportional abundance of each functional group in
Poisson (or quasi-Poisson) and binomial (or quasi-binomial) GLMs respectively (see
Table 3.1 for species composition of groups). ......................................................... 128
Table S.3.2. Top selected models (Δi < 4) relating environmental variables to (a) frog
species richness, (b) frog abundance, (c) reptile species richness, (d) reptile
abundance, and to abundance of functional groups (e) F1, (f) F2, (g) F3, (h) F4, (i) R1,
(j) R2, (k) R3, and (l) R4 (D2 = deviance explained by global models, VIF = variance
xiii
inflation factor of global model, Par. = number of parameters in the model; LL = loglikelihood; AICc = Akaike’s corrected information criterion; QAICc = Quasi-AICc; Δi
= AICc or QAICc difference from best model; wi = Akaike weights, the normalized
relative likelihood of the model given the data). ...................................................... 140
Table S.3.3. Multi-model averages (see Table S.3.2 for list of models with Δi < 4
contributing to each average model) relating environmental variables to frog species
richness, frog abundance, reptile species richness, reptile abundance, and to
abundance of functional groups F1, F2, F3, F4, R1, R2, R3, and R4. ...................... 146
Table 4.1. Population trends and covariates for relatively common species. Species names
follow Hockey et al. (2005). Pool codes are A = furtive species, B = intermediate, C =
conspicuous. * indicates statistically significant trends. Predominant habitat is the
vegetation type in which a species has the greatest proportion of sightings/km, and the
proportion is given in parentheses. Vegetation type abbreviations as follows: OG =
old-growth coastal dune forest, LW = late woodland, EW = early woodland, T =
thicket, G = grassland. OG affinity is the proportion of sighting/km in old-growth
forest. ..................................................................................................................... 180
Table S.4.1. Transects per site per year in regenerating and old-growth sites. RegX sites
are regenerating after mining; numbers in parentheses represent the regeneration age
since mining as of 2009. OG sites are old-growth forests. a indicates transect length of
250 m, b indicates transects length of 500 m, and c indicates transect length of 300 m.
............................................................................................................................... 182
Table S.4.2. Relatively rare species. Species names follow Hockey et al. (2005). ........... 183
Table S.4.3. AIC selected detection function models for species pools. See (Table 4.1) for
species pool composition. Pool A comprises furtive species, Pool B intermediate, and
Pool C conspicuous. Model details are described by P̂
a,t,
the estimated mean
probability of detection for species in the covered region a in year t; its SE; Lt, the line
length surveyed at time t; wt, the truncation distance; and the model key function and
covariates. Model abbreviations as follows: “HR” for hazard-rate key, “HN” for halfnormal key, “+ V” for vegetation type as factor covariate, and “+ O” for observer as
factor covariate. ...................................................................................................... 185
xiv
Table S.4.4. AIC model selection for validating species pooling assumption. ΔAIC = 0
indicates the most supported model of the detection function for each species pool
over the study period (years pooled with 1997 and 2006 excluded due to constraint in
setting reasonable cutpoints). See (Table 4.1) for species pool composition. Pool A
comprises furtive species, Pool B intermediate, and Pool C conspicuous. Model
abbreviations as follows: “HR” for hazard-rate key, “HN” for half-normal key, “+ V”
for vegetation type as factor covariate, “+ O” for observer as factor covariate, and “+
S” for species as factor covariate. Models including species as a factor covariate had
the highest ΔAIC in support of my species pooling assumptions. The half-normal key
model with observer as a factor covariate failed to converge for Pool A. ................. 186
Table S.4.5. AIC selected detection function models stratified by vegetation type (76
species pooled). Model details are described by P̂
a,t,
the estimated mean probability
of detection for species in the covered region a in year t; its SE; Lt, the line length
surveyed at time t; wt, the truncation distance; and the model key function and
covariates. Model abbreviations as follows: “HR” for hazard-rate key, “HN” for halfnormal key, and “+ O” for observer as factor covariate. .......................................... 187
Table 5.1. Summary of variables considered and outcomes of statistical tests with respect
to research output groupings of studies of biogeography in human-modified
landscapes published in eight major journals. .......................................................... 227
Table B.1. Vegetation-dwelling frog species expected in the area, species incidentally
recorded in the area during the survey (location of observation is denoted NW = near
water, Tr = terrestrial, Tr/NW = terrestrial and near water), and inventory of captures
in PVC pipe traps indicating array location (NW = near water, Tr = terrestrial), pipe
diameter and location (G = ground, T = tree), Snout–urostyle length (SUL) of frog,
and habitat type (AW = acacia woodland, DF = degraded forest, F = Forest). ......... 253
xv
List of Figures
Fig. 2.1. Map of sub-Saharan Africa showing ecosystem types adapted from Olson et al.
(2001): rangelands (desert and xeric shrubland, montane grassland/shrubland, flooded
grassland/savanna, and tropical/subtropical grassland/savanna/shrubland), tropical
forests (moist and dry tropical forest), the Cape Floristic Region (Mediterranean
forest/woodland/scrub), and the urban and rural built environment represented by the
human influence index (Wildlife Conservation Society and Center for International
Earth Science Information Network 2005), a dataset comprising nine data layers
incorporating population pressure (population density), human land use and
infrastructure (built-up areas, nighttime lights, land use, land cover), and human
access (coastlines, roads, railroads, navigable rivers). ............................................... 68
Fig. 3.1. Study area map indicating location of trapping arrays in five vegetation types (F =
forest, DF = degraded forest, AW = acacia woodland, P = plantation, C = cultivation);
inset shows study area location in southern Africa. ................................................. 120
Fig. 3.2. Vegetation type (F = forest, DF = degraded forest, AW = acacia woodland, P =
plantation, C = cultivation) was not a significant predictor in Poisson or quasi-Poisson
GLM for species observed per array for (a) frogs (Χ2 = 1.87, df = 4, p = 0.76) and (b)
reptiles (Χ2 = 4.73, df = 4, p = 0.32) or individuals recorded per array for (c) frogs (Φ
= 11.40, F4,25 = 2.70, p = 0.05) and (d) reptiles (Φ = 1.18, F4,25 = 1.05, p = 0.40).
Graphs illustrate mean and 95% CI. ........................................................................ 121
Fig. 3.3. Proportional abundance of functional groups for (a) frogs and (b) reptiles for each
vegetation type (F = forest, DF = degraded forest, AW = acacia woodland, P =
plantation, C = cultivation). .................................................................................... 122
Fig. S.3.1. Species accumulation curves for (a) the total frog dataset, (b) frog samples
grouped by vegetation type, (c) the total reptile dataset, and (d) reptile samples
grouped by vegetation type. Error bars represent 95% CI and in (b) and (d) are shown
only for forest. ........................................................................................................ 135
Fig. S.3.2. Rényi diversity profiles for (a) frogs and (b) reptiles in different vegetation
types (dark blue is forest (F); green is degraded forest (DF); black is acacia woodland
(AW), light blue is plantation (P), red is cultivation (C). Rényi diversity profiles are
xvi
calculated with the formula Hα = ln(Σ piα) / (1–α), where Hα is the diversity value, pi
values are the proportions of each species (which are taken to the exponent α and
summed for all species recorded), and α is a parameter taken from 0 to infinity to
generate the profile (Kindt and Coe 2005). Values of Hα reflect species richness at α =
0, are equivalent to the Shannon diversity index at α = 1, and yield the logarithm of
the reciprocal Simpson diversity index at α = 2. Profiles indicate that frog diversity is
lowest in cultivation, and reptile diversity is lowest in degraded forest and highest in
plantation. The remaining vegetation types cannot be ranked definitively as their
Rényi diversity profiles overlap. ............................................................................. 136
Fig. S.3.3. Non-metric multidimensional scaling ordination of Bray Curtis similarities
based on square-root-transformed (a) frog and (b) reptile abundance data and (c) raw
frog abundance, (d) frog incidence, and (e) raw reptile abundance data. Symbols
represent samples taken at 30 trapping array sites across five vegetation types (F =
forest, DF = degraded forest, AW = acacia woodland, P = plantation, C = cultivation),
and clustering indicates similar community composition among sites. One array site
for frogs and four array sites for reptiles were not plotted because they were outliers
with zero captures. .................................................................................................. 138
Fig. 4.1. Population trends. Change in density/ha over time for relatively common species
with fitted GLM trend line and 95% CI (stippled lines) from the original offset
estimate. Density was estimated by nt,s / 2Lt wt P̂
a,p,t
where nt,s is the number of
sighting per species per year, 2Ltwt is the area of transect coverage in hectares and P̂
a,p,t
is the detection probability over the area covered per pool per year. See Table 4.1
for trend estimates and SE’s calculated based on 999 resamples of P̂
a,p,t.
.............. 178
Fig. 4.2. Vegetation type specific trends. Change in density/ha of birds in general over time
in different vegetation types with fitted GLM trend lines of slope −0.13 ± 0.01.
Density was estimated by nt,v / 2Lt wt P̂
a,p,t
where nt,v is the number of bird sightings
per vegetation type per year, 2Ltwt is the area of transect coverage in hectares and P̂
a,p,t
is the detection probability over the area covered per vegetation type per year.
Intercepts are significantly different and trend lines are for, from highest to lowest
density, old-growth forest, late woodland, thicket, and early woodland. .................. 179
xvii
Figure S.4.6. Change in rainfall over time. Bars represent residual mean annual rainfall
from the long-term (1977–2009) mean in mm. ........................................................ 188
Fig. 5.1. Increase in studies of biogeography of human-modified landscapes over time. For
the eight journals I considered, the proportion of the total studies published comprised
by studies of the biogeography of human-modified landscapes identified in my review
increased significantly over time. ............................................................................ 220
Fig. 5.2. World map of coverage of 14 terrestrial biomes. The 14 terrestrial biomes adapted
from Olson et al. (2001). ......................................................................................... 221
Fig. 5.4. Distribution of research output, CRI, and proportion of land transformed among
geopolitical regions. Discrepancy between number of biogeography of humanmodified landscape studies per million km2 (dark green = “natural comparison”
studies; light green = “no comparison” studies; total number of studies listed to the
right of bars) and Conservation Risk Index (CRI, blue bars) per geopolitical region.
Per-region proportion of land that is transformed is listed on the right-hand axis.
Geopolitical region abbreviations: Mel./Micro./Poly. = Melanesia, Micronesia, and
Polynesia; Aust./N.Z. = Australia and New Zealand. .............................................. 223
Fig. 5.5. World map of CRI and research output per geopolitical region. Geopolitical
regions based on UN GeoScheme (UNSD 2011) colored according to their
Conservation Risk Index (CRI) from low (yellow) to high (red); blue circles are
proportional in size to the area-corrected research output per geopolitical region (refer
to Fig. 5.4 for values). ............................................................................................. 224
Fig. 5.6. Research output per biome type. Median number of studies per million km2 in
forest biomes was significantly higher than in other biomes both including and
excluding “no comparison” studies. ........................................................................ 225
Fig. 5.7. Distribution of research output and estimated richness per taxonomic group.
Discrepancy between the proportion of the 681 biogeography of human-modified
landscape studies that assessed each taxonomic group (dark green = “natural
comparison” studies; light green = “no comparison” studies) and the estimated
proportion of richness per group (blue bars). ........................................................... 226
xviii
1. General Introduction
Chapter 1. General Introduction
Conservation biology has flourished as a discipline over the past four decades in step with
the threat humanity’s activities pose to biodiversity, the variety of genetic material, species,
and ecosystems on Earth. The so-called biodiversity crisis has ethical, aesthetic, and
utilitarian consequences, the relative importance of which, if any, can be argued ad
infinitum. Beyond question, however, is the unprecedented scale of human influence on
nature and dominance over other species. Humanity has commandeered more than 40% of
Earth’s land surface for crops and pastures alone, and demand will grow (Millennium
Ecosystem Assessment 2005). Already by 1995, 83% of land on Earth was directly
influenced by humans as indicated by significant human population density, conversion to
agriculture, proximity to transport networks, and nighttime light visible to satellites
(Sanderson et al. 2002, Kareiva et al. 2007). This pervasive human footprint “suggests that
human beings are stewards of nature, whether we like it or not” (Sanderson et al. 2002).
The declaration of protected areas to maintain slices of the wild has been the
backbone of the conservation movement. Formally, a protected area is: “a clearly defined
geographical space, recognized, dedicated and managed, through legal or other effective
means, to achieve the long-term conservation of nature with associated ecosystem services
and cultural values” (Dudley 2008). Often, conservation of biodiversity within protected
areas is achieved by strict control over human access (Dudley 2008). However, a persistent
undercurrent encouraging a greater integration of conservation efforts within landscapes
dominated by humans has long inspired many conservation biologists. Perhaps Aldo
Leopold’s “Land Ethic” from A Sand County Almanac is the most famous early example
1
1. General Introduction
(Leopold 1949), but the sentiment of managing landscapes where humans live, work, and
extract resources in a manner that attempts to cater for biodiversity persistence has been
repeated with urgency (for the foundational literature of this renewal, see Daily 1999,
Daily et al. 2001, Rosenzweig 2003) in more recent publications (e.g. Ranganathan et al.
2008, DeClerck et al. 2010, Koh and Gardner 2010).
Scientific interest in the biodiversity of human-modified landscapes has recently
escalated for two predominant reasons. First, there is increasing recognition that protected
areas alone are far from sufficient to conserve much of the world’s biodiversity in the long
term (Mora and Sale 2011). At the species level of biodiversity, species–area relationships
indicate that as people make increasing areas of land inhospitable to other species, they
inflict a linear reduction on the number of species Earth can support (Rosenzweig 2003).
That is, if we protect some benchmark percentage of land for nature, e.g. 10% of Earth’s
terrestrial surface (see Brooks et al. 2004), and species cannot persist in the unprotected
90%, we can expect global species loss of 90% of the original steady-state diversity
(Rosenzweig 2003). Richness above the predicted levels maintained in protected areas
would be temporary, representative of an unpaid extinction debt (Kuussaari et al. 2009).
Mismatches between priority areas in need of conservation and the actual configuration of
the world’s protected areas create problems (Joppa and Pfaff 2009, Jenkins et al. 2013);
moreover, protected areas might not even conserve the meager percentage of species we
expect them to given their area. Ill-conceived management interventions (e.g. Konvicka et
al. 2008), inadequate protection from outside influences (Joppa et al. 2008, Newmark
2008), and climate change (Loarie et al. 2009) could all result in extinctions, even within
protected areas (see Newmark 2008).
2
1. General Introduction
Second, evidence suggests that encouraging the persistence of biodiversity beyond
protected areas will be important for maintaining ecosystem function, and thus, ecosystem
services valuable to society (Balvanera et al. 2006, Cardinale et al. 2012). Pollination, pest
control, decomposition, and nutrient cycling are examples of ecosystem services which are
important in production landscapes and contribute to economic value yet depend to varying
degrees on biodiversity maintenance (Tscharntke et al. 2005). Globally, too, humanity
depends on important services provided by nature including waste treatment, and water and
climate regulation (Turner et al. 2007).
Research on what, why, and how different components of biodiversity are able to
persist in different human land uses, under different management regimes, and in various
ecosystem types could support land-use planners and land managers that seek to make the
best possible decisions in support of biodiversity in a framework of evidence-based
conservation (Sutherland 2004). This is especially relevant in rapidly developing
landscapes where human activities are both extensifying and intensifying to support
growing populations and economies and could have dramatic consequences for
biodiversity. Such is the case in Africa, and thus, in Chapter 2, I aim to qualitatively
discuss the current state of research on biodiversity of human-modified landscapes in subSaharan Africa in relation to predominant land uses in four major ecosystem types: the
extensive rangelands, the relatively well researched tropical forests, the biologically rich
Cape Floristic Region, and the rapidly developing urban and rural built environment. This
review paper presents the available research and discusses opportunities and constraints for
further research and implementation.
3
1. General Introduction
In Chapter 3 (currently under review for publication), I present a case study
detailing patterns of herpetofauna occurrence over a land-use intensification gradient from
relatively undisturbed, old-growth coastal forest to degraded forest, regenerating forest (i.e.
acacia woodlands), eucalyptus plantations, and sugar cane cultivation. Besides traditional
metrics (i.e. abundance, richness, diversity, and community composition), I also categorize
frog and reptile species into trait-based functional groups to better understand community
responses to land use.
However, species occurrence in human-modified landscapes is not necessarily
indicative of persistence. For example, research suggests that “ecological traps”, highly
attractive habitats that are of low quality, may be relatively common in human-modified
landscapes (Battin 2004). Although they result in low fecundity and survival, they attract
individuals from surrounding high quality habitats through mismatched environmental cues
with the predicted consequence of near certain population extinction (Battin 2004).
Therefore, simply recording a species in a given land-use type may lead to the incorrect
assumption that the human-modified land provides suitable habitat for the species. It is
important, then, to assess species’ likelihood for persistence through more thorough
assessment of reproduction and survival, or their consequence, population trend. Therefore,
in Chapter 4 (published in January 2011 in the journal PLoS ONE) I aim to provide a case
study that assesses trends for bird populations in a human-modified coastal dune forest
landscape in South Africa. I assess population trends for 37 bird species and general trends
in overall bird density in different vegetation types based on a 13-year monitoring database.
I also assess species’ characteristics as potential covariates for population trends.
4
1. General Introduction
These two case studies are examples of the type of research that can support
evidence-based conservation by indicating the consequences of particular land uses within
a given ecosystem for specific components of biodiversity. On a global scale, conservation
efforts beyond protected areas could benefit from a reliable, relevant evidence base, so in
Chapter 5 (published in December 2012 in the journal Ecosphere), I present a systematic
review of the global literature on biodiversity in human-modified landscapes. The intent of
this assessment is to illustrate whether the evidence base is biased geographically among
biomes or geopolitical regions and taxonomically among species groups. Furthermore, I
assess how biases relate to geographic characteristics (i.e. area, biome type, species
richness, human population density, proportion of transformed land, and an index of
conservation importance) and, taxonomically, to the number of described species per
group. Chapter 6 presents a general discussion of the thesis and its outcomes and, along
with Chapter 5, includes ideas for future work.
As a PhD student at the University of Pretoria, I have had the opportunity to work
on several projects beyond my formal thesis chapters. In part, my interest in the topic of
conservation beyond protected areas was sparked by a controversial article published in
BioScience (Licht et al. 2010) promoting the use of South African predator conservation
tactics to protect wolves Canis lupus in the United States. The journal published my
response, which encouraged greater consideration for the ecological consequences of
fencing (Trimble and Aarde 2010); fencing continues to be a controversial topic within the
conservation community (Creel et al. 2013, Packer et al. 2013). Given the relevance, I
include this response here as Appendix 1. In conducting the field research for Chapter 3, I
carried out a preliminary investigation into the use of polyvinyl chloride pipes for trapping
5
1. General Introduction
African vegetation-dwelling frogs and showed for the first time on the continent that it
could be successful, although capture rate was low. A note on the study is included as
Appendix 2 and was published in the African Journal of Ecology (Trimble and van Aarde
2013). I was also first author on a paper that adapted age assessment techniques for Africa
elephants Loxodonta africana to aerial based surveys; it was published in October 2011in
PLoS ONE (Trimble et al. 2011). With coauthors Robert Guldemond and Matthew
Grainger, I published a response article in Restoration Ecology discussing the evidence
base for ecological restoration projects in South Africa (Guldemond et al. 2011). I also
coauthored a paper with Kim Young and Professor van Aarde on density dependence in
elephant populations, which is currently under review.
6
1. General Introduction
References
Balvanera, P., A. B. Pfisterer, N. Buchmann, J.-S. He, T. Nakashizuka, D. Raffaelli, and B.
Schmid. 2006. Quantifying the evidence for biodiversity effects on ecosystem
functioning and services. Ecology Letters 9: 1146-1156.
Brooks, T. M., et al. 2004. Coverage provided by the global protected-area system: is it
enough? Bioscience 54: 1081-1091.
Cardinale, B. J., et al. 2012. Biodiversity loss and its impact on humanity. Nature 486: 5967.
Creel, S., et al. 2013. Conserving large populations of lions – the argument for fences has
holes. Ecology Letters: 1-4.
Daily, G. C. 1999. Developing a scientific basis for managing Earth's life support systems.
Conservation Ecology 3: Art. 14.
Daily, G. C., P. R. Ehrlich, and G. A. Sanchez-Azofeifa. 2001. Countryside biogeography:
use of human-dominated habitats by the avifauna of southern Costa Rica.
Ecological Applications 11: 1-13.
DeClerck, F. A. J., R. Chazdon, K. D. Holl, J. C. Milder, B. Finegan, A. Martinez-Salinas,
P. Imbach, L. Canet, and Z. Ramos. 2010. Biodiversity conservation in humanmodified landscapes of Mesoamerica: Past, present and future. Biological
Conservation 143: 2301-2313.
Dudley, N., editor. 2008. Guidelines for Applying Protected Area Management Categories.
IUCN, Gland, Switzerland.
7
1. General Introduction
Guldemond, R. A. R., M. J. Grainger, and M. J. Trimble. 2011. Where is the evidence for
asessing evidence-based restoration? Comments on Ntshotsho et al. (2010).
Restoration Ecology 20: 7-9.
Jenkins, C., S. Pimm, and L. Joppa. 2013. Global patterns of terrestrial vertebrate diversity
and conservation. Proceedings of the National Academy of Sciences, USA 110:
E2602-E2610.
Joppa, L. N., S. R. Loarie, and S. L. Pimm. 2008. On the protection of "protected areas".
Proceedings of the National Academy of Sciences, USA 105: 6673-6678.
Joppa, L. N. and A. Pfaff. 2009. High and far: biases in the location of protected areas.
PLoS ONE 4: e8273.
Kareiva, P., S. Watts, R. McDonald, and T. Boucher. 2007. Domesticated nature: shaping
landscapes and ecosystems for human welfare. Science 316: 1866-1869.
Koh, L. and T. Gardner. 2010. Conservation in Human-modified Landscapes. Pages 236261 in N. Sodhi and P. R. Ehrlich, editors. Conservation Biology for All. Oxford
University Press, Oxford.
Konvicka, M., J. Benes, O. Cizek, F. Kopecek, O. Konvicka, and L. Vitaz. 2008. How too
much care kills species: Grassland reserves, agri-environmental schemes and
extinction of Colias myrmidone (Lepidoptera: Pieridae) from its former stronghold.
Journal of Insect Conservation 12: 519-525.
Kuussaari, M., et al. 2009. Extinction debt: a challenge for biodiversity conservation.
Trends in Ecology & Evolution 24: 564-571.
Leopold, A. 1949. A Sand County Almanac. Oxford University Press, Oxford.
8
1. General Introduction
Licht, D. S., J. J. Millspaugh, K. E. Kunkel, C. O. Kochanny, and R. O. Peterson. 2010.
Using small populations of wolves for ecosystem restoration and stewardship.
Bioscience 60: 147-153.
Loarie, S. R., P. B. Duffy, H. Hamilton, G. P. Asner, C. B. Field, and D. D. Ackerly. 2009.
The velocity of climate change. Nature 462: 1052-1055.
Millennium Ecosystem Assessment. 2005. Ecosystems and human well-being: current state
and trends. Island Press, Washington, DC, USA.
Mora, C. and P. F. Sale. 2011. Ongoing global biodiversity loss and the need to move
beyond protected areas: a review of the technical and practical shortcomings of
protected areas on land and sea. Marine Ecology Progress Series 434: 251-266.
Newmark, W. D. 2008. Isolation of African protected areas. Frontiers in Ecology and the
Environment 6: 321-328.
Packer, C., et al. 2013. Conserving large carnivores: dollars and fence. Ecology Letters 16:
635-641.
Ranganathan, J., R. J. R. Daniels, M. D. S. Chandran, P. R. Ehrlich, and G. C. Daily. 2008.
Sustaining biodiversity in ancient tropical countryside. Proceedings of the National
Academy of Sciences, USA 105: 17852-17854.
Rosenzweig, M. L. 2003. Reconciliation ecology and the future of species diversity. Oryx
37: 194-205.
Sanderson, E. W., M. Jaiteh, M. A. Levy, K. H. Redford, A. V. Wannebo, and G.
Woolmer. 2002. The human footprint and the last of the wild. Bioscience 52: 891904.
9
1. General Introduction
Sutherland, W. 2004. The need for evidence-based conservation. Trends in Ecology &
Evolution 19: 305-308.
Trimble, M. and R. van Aarde. 2013. A note on polyvinyl chloride (PVC) pipe traps for
sampling vegetation-dwelling frogs in South Africa. African Journal of Ecology:
Early view.
Trimble, M. J. and R. J. v. Aarde. 2010. Fences are more than an issue of aesthetics.
Bioscience 60: 486.
Trimble, M. J., R. J. van Aarde, S. M. Ferreira, C. F. Nørgaard, J. Fourie, P. C. Lee, and C.
J. Moss. 2011. Age determination by back length for African savanna elephants:
Extending age assessment techniques for aerial-based surveys. PLoS ONE 6:
e26614.
Tscharntke, T., A. M. Klein, A. Kruess, I. Steffan-Dewenter, and C. Thies. 2005.
Landscape perspectives on agricultural intensification and biodiversity - ecosystem
service management. Ecology Letters 8: 857-874.
Turner, W. R., K. Brandon, T. M. Brooks, R. Costanza, G. A. B. da Fonseca, and R.
Portela. 2007. Global conservation of biodiversity and ecosystem services.
Bioscience 57: 868-873.
10
2. Biodiversity in Africa’s Human-Modified Land
Chapter 2. Supporting Conservation with Biodiversity
Research
in
Sub-Saharan
Africa’s
Human-Modified
Landscapes
Publication Details
Trimble, M.J. & van Aarde, R.J. 2013. Supporting conservation with biodiversity research
in sub-Saharan Africa’s human-modified landscapes. Agriculture, Ecosystems &
Environment. In preparation.
Abstract
Protected areas cover 12% of terrestrial sub-Saharan Africa. However, given the inherent
inadequacies of these protected areas to cater for all species in conjunction with the effects
of climate change and human pressures on protected areas, the future of biodiversity
depends heavily on the 88% of land that is unprotected. The study of biodiversity patterns
and the processes that maintain them in human-modified landscapes can provide a valuable
evidence base to support science-based policy-making that seeks to make land outside of
protected areas as amenable as possible for biodiversity persistence. I discuss the literature
on biodiversity in sub-Saharan Africa’s human-modified landscapes as it relates to four
broad ecosystem categorizations (i.e. rangelands, tropical forest, the Cape Floristic Region,
and the urban and rural built environment) within which I expect similar patterns of
biodiversity persistence in relation to specific human land uses and land management
11
2. Biodiversity in Africa’s Human-Modified Land
actions. Available research demonstrates the potential contribution (and potential failures)
of biodiversity conservation in human-modified landscapes within all four ecosystem types
and goes some way towards providing general conclusions that could support policymaking. Nonetheless, conservation success in human-modified landscapes is hampered by
constraints requiring further scientific investment, e.g. deficiencies in the available
research, uncertainties regarding implementation strategies, and difficulties of coexisting
with biodiversity. However, information currently available can and should support efforts
at individual, community, provincial, national, and international levels to support
biodiversity conservation in human-modified landscapes.
Introduction
Conservation of biodiversity in Africa, like elsewhere, has historically focused on the
fortress model, whereby most protected areas (PAs) were declared to the exclusion of
people (see Adams and Hulme 2001, Siurua 2006, Carruthers 2009). Though PAs are
essential for conservation success, they are unlikely to be sufficient (Rosenzweig 2003).
For example, large mammal populations have been reduced by half in some African PAs
since 1970 (Craigie et al. 2010), probably due, in part, to increasing isolation of PAs
(Newmark 2008). Weak enforcement and ineffective management plague many of Africa’s
current PAs (Kiringe et al. 2007, Metzger et al. 2010, Pare et al. 2010), and many also fail
to cater to species with extensive spatial requirements, e.g. migratory animals (Thirgood et
al. 2004, Kirby et al. 2008, Western et al. 2009, Holdo et al. 2010) and elephants
Loxodonta africana (van Aarde and Jackson 2007). Even small-bodied species are not
necessarily safe-guarded (Pauw 2007). Additionally, the configuration of PAs within the
12
2. Biodiversity in Africa’s Human-Modified Land
continent neglects key areas for biodiversity (Chown et al. 2003, Fjeldsa et al. 2004,
Fjeldsa and Burgess 2008, Eardley et al. 2009, Beresford et al. 2011), a problem that may
escalate if climate change makes PAs inhospitable to species they once protected (Loarie et
al. 2009). If species’ ranges shift with shifting climate, the areas crucial for their
persistence will be transient (Hole et al. 2011). Furthermore, the scale of beta-diversity and
habitat heterogeneity often extends far beyond that of individual PAs (Gardner et al. 2007),
and human activities beyond PAs influence biodiversity within them (Hansen and DeFries
2007).
Therefore, there are calls for an increased focus on biodiversity beyond African
PAs (e.g. Eardley et al. 2009) on two fronts. First, conservation of some biodiversity
elements depends on how well the matrices outside of PAs cater for persistence. At the
species level, for example, the Blue Crane Anthropoides paradiseus in South Africa
(McCann et al. 2007), Ethiopia’s critically endangered Sidamo lark Heteromirafra
sidamoensis (Spottiswoode et al. 2009, Donald et al. 2010), and the last giraffes Giraffa
camelopardalis peralta in West Africa (Ciofolo 1995) all depend on human-modified
landscapes. At the ecosystem level, three biomes fall below the threshold 10% protection
status within the Afrotropic realm, i.e. tropical and subtropical dry broadleaf forests (6%),
montane grasslands and shrublands (8%), and deserts and xeric shrublands (9%), while
several ecoregions are < 5% protected, especially when limited to the IUCN I-IV
categories, e.g. Southern Congolian forest-savanna mosaic (0%), Angolan montane forestgrassland mosaic (0%), and highveld grasslands (<1%) (Jenkins and Joppa 2009). Second,
there are important links between biodiversity and ecosystem function, ecosystem services,
and human livelihoods in working landscapes (Daily et al. 2001, Rosenzweig 2003). For
13
2. Biodiversity in Africa’s Human-Modified Land
example, maintaining natural habitat in and around farms can enhance pollination and,
thus, has an economic value to production landscapes (Carvalheiro et al. 2010, Munyuli
2012), and natural systems in Africa provide economic and nutritional benefits to both rural
and urban dwellers (Schreckenberg 1999, Vanderpost 2006, Tabuti et al. 2009).
Even though, globally, scientists have neglected the biogeography of humanmodified landscapes in sub-Saharan Africa, ecologists are increasingly studying the
capacity of such landscapes to support biodiversity (Trimble and van Aarde 2012). Such
studies are required in order for policy-makers to make defensible decisions regarding land
use in relation to biodiversity conservation in the face of rapid economic development in
Africa. Agriculture in Africa has been characterized by traditional, labor-intensive,
smallholder enterprise; production has been low and has remained relatively stagnant
(Abate et al. 2000, Deininger et al. 2011). However, economic development and population
growth are driving change in African landscapes; several Africa nations sit among the
world’s fastest growing economies (IMF 2013). In 2009, the population reached 1 billion
and is predicted to double by 2050 (UN-HABITAT 2010). Urbanization is a strong force in
Africa; 40% of the current population is city-dwelling, and by 2050, 60% will be urban
(UN-HABITAT 2010). Even so, the rural population will also grow substantially, predicted
to increase by nearly 50% by mid-century (UN Population Division 2012), while growing
urban centers will depend heavily on rural resources. To meet this demand, and in the
interest of improving food security, there are calls for both intensifying smallholder
agriculture (Muriuki et al. 2005, Baiphethi and Jacobs 2009, Snapp et al. 2010, Baudron et
al. 2011) and extensifying production (Muriuki et al. 2005).
14
2. Biodiversity in Africa’s Human-Modified Land
Therefore, the interest in biodiversity in human-modified lands is timely. Although
Africa’s natural ecosystems are more intact than many other regions’, a proactive approach
to biodiversity conservation that strives for the most prudent management of the
unprotected matrices between PAs is clearly preferable to trying to reconnect and restore
already degraded ecosystems (Gardner et al. 2010). Thus, as policy-makers chart the future
course of development in Africa, they should consider the effects of different choices on
biodiversity in human-modified lands, what steps can be taken to prevent biodiversity loss,
and the benefits and costs of biodiversity persistence to people. Studies of biodiversity
patterns and the processes that maintain them in human-modified landscapes provide an
evidence base to support defensible management decisions that meet the needs of people
and biodiversity simultaneously.
The evidence base should, furthermore, provide for
relevant ecological contexts. For example, management standards for timber plantations
aim to minimize impact on biodiversity in surrounding natural forests. Yet, the same
standards have been applied in plantations embedded in grasslands with dubious efficacy
for minimizing impacts on grassland biodiversity (Pryke and Samways 2003, Lipsey and
Hockey 2010).
This scientific focus on biodiversity in human-modified landscapes is distinct from
Africa’s thirty-some-year experiment in community-based conservation (CBC, but also
known as Integrated Conservation and Development Projects, Community-Based Natural
Resource Management, and others), but these two fields can and should be amalgamated.
Promoters of CBC claim that it increases the chance of conservation success and
simultaneously reduces rural poverty by allowing community involvement in management
and profit from natural resources, especially large mammals (see Hackel 1999). The
15
2. Biodiversity in Africa’s Human-Modified Land
philosophy of linking wildlife conservation and rural economic development and the
practical successes and failures therein have been discussed in a large body of literature
(e.g. Hackel 1999, Songorwa et al. 2000, Torquebiau and Taylor 2009). However, the
discussion has focused on socioeconomics and politics with fleeting consideration for
assessing actual biodiversity persistence under different CBC models, a problem pointed
out by Caro (1999) and subsequently largely ignored.
In this review, I aimed to elucidate the current state of knowledge regarding
biodiversity in sub-Saharan Africa’s human-modified landscapes. I separate the discussion
into four major ecosystem types (see Fig. 2.1) within which I expect similar patterns to
emerge. 1) Rangelands attract the bulk of the attention as Africa’s biggest ecosystem type,
and rangeland biodiversity is perhaps the most compatible with human land-uses, so
biodiversity-conscious land-use planning in rangelands could yield huge benefits.
2)
Tropical forests are discussed briefly with a focus on Central and East African forests, and
I refer readers to an excellent review of the abundant literature from West Africa (Norris et
al. 2010). 3) The Cape Floristic Region, though small, is extremely rich in species yet
threatened by extensive commercial development, and I discuss a growing body of
literature on land-use management in the region. Finally, 4) the urban and rural built
environment will become an increasingly important concern for biodiversity conservation
in Africa where the increase of urban land cover is predicted to be the highest in the world
at nearly 600% in the first three decades of the 21st century (Seto et al. 2012); proper
management and infrastructure development could attenuate the consequences for
biodiversity. Furthermore, I discuss the constraints and opportunities for future progress of
biodiversity conservation in human-modified landscapes of Africa.
16
2. Biodiversity in Africa’s Human-Modified Land
Methods
Literature search
I searched the ISI Web of Knowledge (up to 2012) with keywords “Africa” and
“biodiversity or conservation” and each of the following terms: “agricultur*”,
“agroforestry”, “communal”, “farm*”, “game farm”, “game ranch”, “human-modified”,
“multiple-use management”, “peri$urban”, “private nature reserve”, “range$land”, “rural”,
“suburban”, and “urban”. I also searched for the terms “countryside biography”,
“reconciliation ecology”, “off-reserve conservation” (see Daily et al. 2001, Rosenzweig
2003). Additionally, I included relevant papers found coincidentally or in reference lists.
Biodiversity
in
Human-Modified
Landscapes
of
African
Ecosystems
Rangelands
Two-thirds of sub-Saharan Africa is composed of rangelands (Fig. 2.1), consisting of arid
and semi-arid grasslands, woodlands, savannas, shrublands, and deserts. The rural people
inhabiting rangelands are typically agropastoralists, combining small-scale farming and
livestock keeping, or specializing in either farming or herding. Some agricultural practices
in rangelands may be harmful to biodiversity, e.g. overcultivation, overgrazing (Kerley et
al. 1995), bush fires, cultivation of marginal and easily eroded land, and widespread use of
chemicals and pesticides (Darkoh 2003). Many people in rangelands also depend heavily
on wild resources, e.g. via hunting and gathering or by profiting from wildlife tourism
17
2. Biodiversity in Africa’s Human-Modified Land
(Homewood 2004). Game ranching is an increasingly popular land-use option across
African rangelands (McGranahan 2008), and so are “eco-estates” (Grey-Ross et al. 2009a)
as people choose to live amongst the natural beauty of African rangelands and their
considerable species diversity, especially charismatic large mammals.
The ecological mechanisms that maintain different rangeland types in different
locations, e.g. grassland versus woodland, are not fully understood though interactions
between soils, climate, fire, herbivory, and human disturbance are thought to be important
(see Bond and Parr 2010) . The biggest threats to grasslands include afforestation or bush
encroachment and clearing for agriculture (Bond and Parr 2010), while threats to the
woodlands include woodcutting, clearing for agriculture, and over-use (Schreckenberg
1999, Tabuti 2007). Many perceive that biodiversity is declining in rangeland systems; they
blame poor agricultural practices, land conversion, and over-utilization of wild resources
by rural people and worry that these patterns will increase with population growth (e.g.
Darkoh 2003, Thiollay 2006). However, documented evidence of biodiversity loss in rural
rangelands is sparse. Of course, many areas have likely lost some species, but surprisingly,
long-inhabited regions lacking formal PAs, e.g.
Kenya’s Laikipia district, maintain
abundant wildlife including large carnivores and elephants (Gadd 2005, Kinnaird and
O'Brien 2012) that might seem at odds with human occupation (Woodroffe et al. 2007).
Rangeland systems are often characterized by disturbances such as fire, unpredictable
rainfall, grazing and browsing pressure, and physical disturbance. Therefore, rangeland
biodiversity may be relatively resilient to anthropogenic disturbance due to the ability to
disperse, colonize, and persist in patchy and fluctuating environments (Homewood 2004).
18
2. Biodiversity in Africa’s Human-Modified Land
Thus, human-modified landscapes have the potential to maintain a relatively large portion
of rangeland biodiversity (see Scholes and Biggs 2005).
Nonetheless, conservation in rangelands has traditionally excluded people from
designated PAs. In South Africa, for example, conservation planning often dichotomizes
“human land-use” and conservation with little consideration for different land-use options
that may be variably amenable to biodiversity (e.g. Chown et al. 2003, Wessels et al.
2003). On the other hand, some authors have called to “mainstream” conservation into
human-modified lands (e.g. Soderstrom et al. 2003, Pote et al. 2006). O’Connor and Kuyler
(2009) used expert opinion to rank the impact of land uses in moist grasslands on overall
biodiversity integrity (in order from least to most impact: conservation, game farming,
livestock, tourism, crops, rural, dairy, timber, and urban). Empirical studies are amassing to
assess such assertions, which could support land-use planning for conservation. Here I
discuss emerging research on biodiversity in several of the most common rangeland land
uses.
Grazing
Grazing is important to the maintenance of grassland and savanna habitats, economic
development, and management for biodiversity. However, plant responses to grazing are
idiosyncratic and incompletely understood (see Watkinson and Ormerod 2001, Rutherford
et al. 2012). Overgrazing can lead to degradation and bush encroachment (the slow
proliferation of woody plants at the expense of grasses), while too little grazing can result
in succession to woodland (Watkinson and Ormerod 2001). Of course, grazing effects on
vegetation can affect higher trophic levels as well, so it is important to understand
19
2. Biodiversity in Africa’s Human-Modified Land
vegetation responses to grazing, not only for livestock production, but also because
vegetation dynamics affect many other species. However, not all grazing landscapes are
alike; unique vegetation dynamics in different ecosystems mean that different landscapes
respond differently to grazing pressure (Todd and Hoffman 2009).
Research is emerging that investigates aspects of grazing management and
biodiversity in Africa; I summarize 30 such studies in Table S.2.1. Generally, these studies
look at grazing intensity, or proxies such as bush encroachment, and show that many wild
species may be maintained depending on management and location. For example,
traditional pastoral practices, i.e. burning and boma creation, may even be necessary to
maintain avian diversity in some East African savanna areas (Gregory et al. 2010).
Contrastingly, bush encroachment due to overgrazing in Ethiopia may provoke Africa’s
first avian extinction (Spottiswoode et al. 2009, Donald et al. 2010).
Table S.2.1 shows that only about a third of studies compared biodiversity of
livestock grazing landscapes to controls with indigenous grazers such as PAs. Most studies
came from South Africa (67%) and most assessed grazing effects on plants (43%) or
insects (27%). Many areas of investigation remain open, such as the role of vegetation
structure including keystone, isolated trees in maintaining biodiversity in human land-use
areas; such trees are important for maintaining diversity in natural systems (Dean et al.
1999). A common conclusion with regards to plant diversity is that spatial heterogeneity in
grazing management that includes PAs will enhance gamma diversity because different
species thrive at different grazing intensities (e.g. Fabricius et al. 2003).
20
2. Biodiversity in Africa’s Human-Modified Land
Agricultural mosaic
While extensive grazing is common in arid-savannas and xeric shrublands, an agricultural
land-use mosaic of grazing and cropping interspersed with settlements is common in more
mesic savannas and grasslands. This mosaic effect may have important consequences for
the maintenance of biodiversity, and studies of biodiversity in agricultural mosaics (24
studies summarized in Table S.2.2) identify some common themes. Compared to strict
PAs, agricultural mosaics may actually be beneficial to some species groups. For example,
Caro (2001) illustrated greater diversity and abundance of the small mammal assemblage in
the agricultural matrix outside Katavi National Park, Tanzania than inside, a pattern that
also holds for Niokolo Koba National Park, Senegal (Konecny et al. 2010). Richness of
birds, amphibians, small mammals, butterflies, and trees is similar at 41 sites across a landuse gradient from Katavi National Park to non-intensive agricultural land; however,
composition changes along the gradient, and although the PA holds some unique species,
some species found outside the PA are absent within (Gardner et al. 2007). Thus,
agricultural mosaics may contribute to greater gamma diversity at the landscape scale;
nonetheless understanding the conservation implications of higher gamma diversity may
require a regional or global perspective on species rarity and commonness.
It is a common finding that agricultural intensification (e.g. mechanization of
agriculture, shortening fallows, destruction of remnant habitat patches, and introduction of
cash crops) can have detrimental effects on the biodiversity value of agricultural mosaics.
The mosaic effect of traditionally managed farms in KwaZulu-Natal, South Africa may
support, and even enhance, bird diversity (Ratcliffe and Crowe 2001), but intensification
results in species declines due to loss of “edge” habitats. In Burkina Faso, common
21
2. Biodiversity in Africa’s Human-Modified Land
butterfly species occur in cultivated areas, while specialists are more common in old
fallows and grazed areas, probably because grazing maintains host plants and, thus,
diversity (Gardiner et al. 2005). In this case, an agricultural mosaic of shifting fallows
could support butterfly meta-populations that allow species persistence, while
intensification could be detrimental (Gardiner et al. 2005). In Ethiopian grasslands, lowintensity agriculture supports moderate plant diversity, while larger-scale, mechanized
farms reduce tree cover and diversity (Reid et al. 1997). Similarly, In the Serengeti-Mara
ecosystem, commercial mechanized agriculture is associated with declining wildlife
populations (Homewood et al. 2001, Homewood 2004).
Cropping
Cropping is perhaps more at odds with biodiversity than grazing is because cropping
involves the direct removal of indigenous vegetation and planting of, generally, nonindigenous species. Nonetheless, crops can still harbor or support wild species, and their
conservation value may depend on the crops planted, the farming methods employed, and
the arrangement of fields with respect to natural habitat. I found relatively few studies that
assessed biodiversity in cultivated areas only (10 studies summarized in Table S.2.3), as
opposed to agricultural mosaics (Table S.2.2). This perhaps reflects the current state of
African agriculture, where most farms are smallholder or subsistence based rather than
expansive, commercial cultivation; although there are exceptions, average farm size is just
2 to 3 ha (Deininger et al. 2011). Where commercial cultivation does occur, loss of
biodiversity may be seen as a foregone conclusion not worth investigating (see Thiollay
2006). Many of the studies of biodiversity in cultivation were concerned primarily with the
22
2. Biodiversity in Africa’s Human-Modified Land
benefits of that diversity for production via pest control, fertility enhancement, or
pollination services, rather than for its value to conservation (e.g. Midega et al. 2008,
Tchabi et al. 2008, Carvalheiro et al. 2010).
Agroforestry
Agroforestry, the integration of trees into agriculturally productive landscapes, has
garnered much attention in the global conservation community because it has been shown
to provide habitat for relatively high levels of forest species diversity (see Bhagwat et al.
2008). In African rangelands, agroforestry can be divided into two types: technological and
traditional. Technological agroforestry deals with the expertise to plant and maintain tree
species that will increase productivity in agricultural production systems. Kenyan farmers,
for example, plant crops of fodder trees, which raise milk yields of cows and goats (PyeSmith 2010a). Government programs in Niger, Zambia, Malawi, and Burkina Faso support
large-scale “evergreen agriculture” projects to plant indigenous trees such as Faidherbia
albida among crops, which maintain green cover year-round, increase yields by improving
soil fertility, and provide fodder and firewood (Garrity et al. 2010). Evergreen agriculture
and other technological agroforestry projects are touted by proponents as having greater
biodiversity value than do monoculture crops (see Garrity et al. 2010, Kalaba et al. 2010,
Pye-Smith 2010a, b). Yet, evidence to support these claims remains mostly anecdotal,
warranting further research because plans are underway to expand technological
agroforestry projects throughout Africa (Garrity et al. 2010).
Traditional agroforestry, on the other hand, is a millennia-old practice, particularly
evident in the parkland savannas of West Africa, of people maintaining savanna tree
23
2. Biodiversity in Africa’s Human-Modified Land
species in pastures, fields, and villages. These trees provide shade, food, wood, and even
cash when commercially traded (e.g. shea, baobab), and traditional agroforestry may
contribute to the maintenance of tree species in addition to species for which trees provide
habitat. Many studies have enumerated tree diversity in farmlands (Table S.2.4). Even so,
the conservation value of agroforestry varies. Augusseau et al. (2006) report that in
Burkina Faso, on a farm scale, few indigenous species are important to farmers and none
are planted. Even where tree richness is maintained at a relatively high level, the
persistence of trees in traditional agroforestry can be compromised if the economic value of
totally clearing the land, e.g. for mechanized, intensive agriculture or firewood, outpaces
the value of non-timber products (Tabuti et al. 2009). Additionally, based on demographic
profiles of tree species, tree regeneration appears to be problematic in many humanmodified landscapes (e.g. Fandohan et al. 2010, Schumann et al. 2010, Venter and
Witkowski 2010). For example, a study in Benin shows that the largest shea trees are often
in villages or fields, but seedling survival is low compared to nearby PAs (Djossa et al.
2008). Regeneration potential can also be diminished when harvesting tree products affects
recruitment, as is the case for Khaya senegalensis in Benin (Gaoue and Ticktin 2008).
Where natural regeneration potential is compromised, intervention may be required to
ensure rejuvenation (Kindt et al. 2008, Ouinsavi and Sokpon 2008), especially if traditional
rotational land-use systems such as long fallow, where trees are often most capable of
regenerating, are abandoned (Schreckenberg 1999, Raebild et al. 2007).
Fortunately, agroforestry management in rangeland ecosystems is an active area of
research with regards to developing strategies to encourage tree persistence (Augusseau et
al. 2006, Kindt et al. 2008, Tabuti et al. 2009). Yet, there is a surprising lack of research to
24
2. Biodiversity in Africa’s Human-Modified Land
assess the value of savanna agroforests for faunal diversity or even non-tree plant diversity
(Table S.2.4), aspects that have been more thoroughly studied in the tropical forest context
(Bhagwat et al. 2008), and this dearth should be remedied.
Game ranching and private nature reserves
The wildlife industry, including game ranching, game farming, and private nature reserves,
has become big business, especially in southern and East African rangelands. These landuse options involve profiting from consumptive (e.g. trophy hunting, live animal sales,
meat) or non-consumptive (e.g. tourism, aesthetic value) use of wildlife on communal or
private land. South Africa alone has an estimated 9,000 private game ranches, covering
20.5 million ha, many of which were converted from traditional livestock ranches (NAMC
2006). Ranching game rather than domestic livestock may ameliorate effects of
overgrazing because indigenous species have coevolved with indigenous vegetation
(Kerley et al. 1995), and indigenous browsers may help control bush encroachment (Taylor
and Walker 1978, McGranahan 2008). Thus, the wildlife industry may be a boon to
biodiversity conservation; however, very few studies have actually assessed impacts on
biodiversity, which may be positive or negative and likely depend on management actions
(Cousins et al. 2008).
Occurrence and abundance of mammal species on private land has increased due to
game ranching (Lindsey et al. 2009). Nonetheless, some aspects of the wildlife industry are
worrying. Privatization of wildlife (and sometimes legislative requirements) begets
ubiquitous game fencing (McGranahan 2008, Lindsey et al. 2009) with substantial
ecological consequences including the interruption of natural movements, inbreeding, and
25
2. Biodiversity in Africa’s Human-Modified Land
overstocking (Hayward and Kerley 2009, Lindsey et al. 2009). Ranches are often quite
small (South African provincial averages range from 8.2 to 49.2 km2), and smaller ranches
necessitate more intensive management interventions (Bothma 2002, Lindsey et al. 2009).
Additionally, the industry’s focus on trophies may skew natural communities in favor of
valuable species and induce semi-domestication (Mysterud 2010), and it has resulted in
extra-limital introductions, questionable breeding practices, and persecution of predators
(Lindsey et al. 2009). Even within the mammal community, generally the focus of game
ranching, the full complement of species of a given ecosystem may not be maintained on
ranches despite deliberate re-introductions (Grey-Ross et al. 2009b).
Thus, much more research is needed on the biodiversity value of the wildlife
industry and what measures, e.g. promoting conservancies over single game ranches
(Lindsey et al. 2009), can improve this value. Best-practice management in terms of
grazing pressure, fire regimes, bush encroachment, wildlife ownership policies, and fencing
needs more attention (McGranahan 2008). Furthermore, surprisingly little is known about
the impacts of game ranching on species other than large mammals. Even so, game ranches
are likely more amenable to most indigenous biodiversity than are many other commercial
land-use options. For example, large eagles in South Africa’s Karoo shrublands are much
more common in areas stocking indigenous mammals than in areas with domestic livestock
and cultivation (Machange et al. 2005).
Tropical forests
Though rangelands cover the majority of Africa, tropical forests also make up a
considerable portion (~20% (Brink and Eva 2009)) (Fig. 2.1), particularly rich in
26
2. Biodiversity in Africa’s Human-Modified Land
biodiversity. Research on biodiversity in human-modified landscapes is biased towards
tropical forests (Trimble and van Aarde 2012). Nonetheless, biodiversity in humanmodified tropical forest landscapes in Africa has received much less scientific attention
than in other regions, especially South and Central America (Gardner et al. 2010). African
tropical forests tend to be in less conflict with high human population densities than
elsewhere (e.g. Southeast Asia and Brazilian Atlantic forests) (Gardner et al. 2010),
although in West Africa 80% of the original forest extent is now an agricultural-forest
mosaic home to 200 million people (Norris et al. 2010).
I do not attempt a comprehensive review of African tropical forest biodiversity in
human-modified landscapes and refer readers to Norris et al. (2010) for an excellent
treatment of the West African scenario. They lament the lack of data regarding biodiversity
in African agricultural-forest mosaics but are able to reach some general conclusions. Land
uses that maintain tree cover are more amendable to forest biodiversity than those that do
not. Species richness increases in some modified habitats, such as logged and secondary
forest, for some species groups, but endemic forest species are often lost. Additionally,
relatively high species richness in modified habitats comprises, in part, species not present
in the baseline forest comparison, so species richness alone likely overestimates the value
of modified habitats for forest species. Furthermore, habitat modification seems to affect
richness of forest plant species more negatively than of some animal groups.
Although logically, it seems more difficult to encourage the persistence of
biodiversity in human-modified landscapes embedded in tropical forests than in
rangelands, research can indicate best practices for land-use planning. In contrast to West
Africa, Central Africa still maintains large tracts of relatively undisturbed forest that are
27
2. Biodiversity in Africa’s Human-Modified Land
becoming increasingly threatened by development, and lessons learned from studying
African forest biodiversity in human-modified landscapes should be incorporated into
development policy for the region (Norris et al. 2010).
The tropical forest biome extends to East and southern Africa where forests are less
extensive; they are confined largely to high altitudes inland and a linear belt along the
coast. These geographic constraints present unique challenges for conservation and
heighten the importance of maintaining endemic species and retaining connectivity in
fragmented forests. Fewer studies consider East and southern African tropical forests than
West African forests, but work is emerging to support land-use planning in the region, and
results largely conform to those found for West Africa. Agroforestry in Ethiopian and
Tanzania supports less diversity than forests but more than other land uses (Hemp 2006,
Gove et al. 2008, Hall et al. 2011, Negash et al. 2012). While Schmitt et al. (2010) found
higher overall plant richness in Ethiopian coffee agroforests than natural forests, richness
of typical forest species was lower. In Kenya, connectivity of coastal forest fragments for
primates may be influenced by matrix structure (Anderson et al. 2007). Farmland outside
tropical forest remnants, especially structurally complex subsistence farms, support higher
bird richness than forests; however, many forest species are lost, highlighting the
importance of maintaining the forest remnants but also supporting traditional farming
techniques over commercial monocultures (Laube et al. 2008, Mulwa et al. 2012).
Furthermore, structurally diverse farmland surrounding forest remnants may enhance forest
pollinator communities (Hagen and Kraemer 2010). Similarly, South African forest
remnants embedded in various matrix types have similar bird species richness, but
abundance is highest in fragments in agricultural matrices due to the presence of forest
28
2. Biodiversity in Africa’s Human-Modified Land
generalists and open-habitat species, while forest specialists are rare (Neuschulz et al.
2011). Forest fragments and grasslands in the agricultural mosaic outside a PA in southern
Mozambique have more beetle species and higher abundance, while endemic beetle species
are better represented inside the PA (Jacobs et al. 2010).
Cape Floristic Region
While small in area (approximately 90,000 km2, see Fig. 2.1), the Cape Floristic Region
(CFR) of South Africa is a biodiversity hotspot of global significance (Myers et al. 2000)
consisting of a Mediterranean-type ecosystem with high species turnover across the
landscape and high endemicity. In-depth conservation assessments and systematic planning
have been conducted for the region and generally focused on pristine habitat that could be
formally protected (see Cowling and Pressey 2003). Because spatial turnover of species is
so high, however, successful conservation will depend heavily on efforts in humanmodified landscapes beyond PAs (Cox and Underwood 2011). Based on species-area
curves for plants and vertebrates in the CFR, practicing biodiversity friendly management
on just 25% of the land that is beyond PAs, but still in a natural or semi-natural state, might
add an additional 541 species to the 7,340 estimated to occur in PAs (Cox and Underwood
2011).
However, in contrast to many areas of Africa dominated by subsistence agriculture,
the CFR is characterized by large areas of intensively managed agricultural monocultures
with low biodiversity value (Giliomee 2006). Overall, only 26% of the CFR has been
transformed, but the CFR is made up of different habitat types, and some, especially in the
fertile lowlands, have lost much more of their area to cultivation, urbanization, and heavy
29
2. Biodiversity in Africa’s Human-Modified Land
invasion of exotic plants; for example, coast renosterveld is more than 80% transformed
(Rouget et al. 2003a). Transformation threatens not only the CFR’s
plants but also
endemic and vulnerable animals such as the Black Harrier Circus Maurus, which has been
displaced from the inland plains by cereal agriculture and now breeds, less successfully, in
the coastal strip and inland mountain habitats (Curtis et al. 2004). Though the Black Harrier
can forage in cultivated areas, it relies on intact vegetation to breed (Curtis et al. 2004).
PAs within the CFR are concentrated in areas of low agricultural value (e.g.
mountains and coastlines), so biodiversity in fertile areas depends on conservation on
privately owned land (Rouget et al. 2003b, Giliomee 2006). To increase the biodiversity
value of agricultural areas, the primary focus should be on conserving remnants of natural
vegetation on farms (Giliomee 2006). This is being attempted with some success though
incentive-driven stewardship agreements that protected almost 70,000 ha of vegetation on
private land between 2003 and 2007 (Von Hase et al. 2010). Additionally, farm
management practices may be variably amenable to biodiversity. For example, though
vineyards have very different arthropod communities than those in natural vegetation,
organic vineyards support greater diversity than do more intensively managed vineyards
(Gaigher and Samways 2010). However, these effects may be taxon dependent; for
instance, organic vineyard management benefits richness of monkey beetles (crucial
pollinators), but not bees (Kehinde and Samways 2012). Similarly, apple orchards support
less arthropod diversity than natural vegetation does, but orchards that are not sprayed with
pesticides have a higher diversity than sprayed sites (Witt and Samways 2004). On the
other hand, farms with a mixture of different crops and remnants of natural vegetation
maintain most fynbos bird species and attract several additional species, while single crop
30
2. Biodiversity in Africa’s Human-Modified Land
sites without remnant vegetation have much less bird diversity and lose many fynbos
species
(Mangnall and Crowe 2003). Clearly, maintaining remnant vegetation and
connectivity in agricultural areas of the CFR is crucial, but more research is needed to
tailor agricultural practices to better conserve CFR species in production landscapes.
Urban and rural built environment
Plant and vertebrate species richness and endemism are correlated with human population
density and human infrastructure in sub-Saharan Africa (Balmford et al. 2001, Burgess et
al. 2007, Fjeldsa and Burgess 2008), which is substantial in many regions (see Fig. 2.1).
That the pattern endures in relatively developed South Africa means either that species
persist to some degree with humans in disturbed habitats at current levels, that humandisturbed habitats actually attract more species, or that a major extinction debt is yet to be
paid (Chown et al. 2003, Fairbanks 2004). Regardless, areas with high human density,
which in Africa, are predicted to increase dramatically, outpacing growth in all other
regions in the coming decades (Seto et al. 2012), require appropriate regulations to ensure
they remain as amenable as possible to biodiversity conservation. This will be especially
important in some of Africa’s most biologically rich yet rapidly urbanizing regions; by
2030 for example, the urban area within the Eastern Afromontane and Guinean Forests of
West Africa hotspots is forecasted to be 1,900% and 920% of 2000 levels respectively
(Seto et al. 2012).
Some obvious steps include discouraging urban sprawl; providing appropriate
housing for low income populations while controlling illegal settlements in biodiversity
sensitive areas; designing relevant green spaces that include aquatic habitats and
31
2. Biodiversity in Africa’s Human-Modified Land
indigenous plants; and managing invasive species, waste, and pollutants (Muriuki et al.
2011, Puppim de Oliveira et al. 2011). Research on managing Africa’s urban and rural built
environments for biodiversity is in its infancy and is mostly constrained to South Africa.
Clearly, more research is needed, yet several studies provide pertinent information for
planners.
While urban environments might not seem particularly hospitable to biodiversity,
even small home gardens in African cities can harbor a remarkable number of species,
especially in the tropics, both intentionally cultivated and otherwise (Cumming and
Wesolowska 2004, Lubbe et al. 2010, Bigirimana et al. 2012). In South Africa,
socioeconomics, urbanicity, and ecological factors influence plant diversity and the
proportion of invasive species in home gardens (Lubbe et al. 2010, Molebatsi et al. 2010).
Gardens with a high number of non-indigenous species contribute to biotic homogenization
and pose the risk of new introductions that could prove detrimental to indigenous
ecosystems. Therefore, invasive species in the urban landscape need to be controlled
through regulation and removal, especially in threatened and fragile ecosystems (Alston
and Richardson 2006, Cilliers et al. 2008, Dures and Cumming 2010, Bigirimana et al.
2012).
Green spaces such as city parks, tree-lined streets, and even golf courses in urban
environments can support certain species. Dures and Cumming (2010) show that bird
diversity in sand fynbos in an urban gradient in Cape Town is more affected by habitat
quality than by patch metrics such as area. Thus, controlling invasive species even in highdensity housing areas may be more beneficial for birds than expanding the low quality
network of urban reserves. Alien pine tree removal helps restore invertebrate species
32
2. Biodiversity in Africa’s Human-Modified Land
diversity in Cape Town, and fragments of natural vegetation and gardens with indigenous
plants help maintain it (Pryke and Samways 2009). In the Durban Metropolitan Open
Space System, complex habitats (i.e. with trees and shrubs) support higher invertebrate
diversity than simplified habitats (i.e. mown lawns); however, simple habitats might cater
for certain rare species (Whitmore et al. 2002). Green spaces in urban Pretoria contribute to
butterfly and moth diversity (McGeoch and Chown 1997) and also support indigenous
birds (van Rensburg et al. 2009), while maintaining urban riparian vegetation is necessary
for dragonfly conservation in Pietermaritzburg (Samways and Steytler 1996). Better
ecological planning for developments such as golf courses or estates could increase the
likelihood for biodiversity persistence and minimize negative consequences, even in the
CFR (Fox and Hockey 2007). Additionally, habitat engineering, e.g. creating biotopes for
dragonflies (Steytler and Samways 1995), might be a useful tool in the urban context to
promote biodiversity, although continual management of these habitats may be necessary
to ensure persistence of species (Suh and Samways 2005).
When species are range-restricted such that a single metropolitan area may affect
most of their range, special attention is required. For example, two small forest parks in
Durban suburbs are home to the last remnant populations of the rare tree Oxyanthus
pyriformis whose specialist pollinators, the long-tongued hawkmoths, appear unable to
tolerate suburban living. Hand pollination and planting of seedlings will be necessary to
maintain the species (Johnson et al. 2004). Similarly, conservation of plants in Cape Town
is hampered by apparent sensitivity of specialist pollinator birds to urbanization, which is
concerning given the increasing urbanization in the CFR (Seto et al. 2012). Durban covers
a large portion of the range of the black-headed dwarf chameleon Bradypodion
33
2. Biodiversity in Africa’s Human-Modified Land
melanocephalum, and translocations from sites demarcated for development to sites
reserved for conservation have proven somewhat successful, dependent on adequate alienplant-control and restoration of indigenous habitat (Armstrong 2008). Unique landscape
features within urban areas may also require special attention. For example, Table
Mountain in Cape Town harbors endemic species whose conservation depends not only on
the PA of Table Mountain but also on management of lower elevation suburban woodlands
(Pryke and Samways 2010).
On the rural end of the settlement spectrum, less attention has been given to
biodiversity persistence. Some agricultural mosaic studies consider rural settlements, but a
few studies treat it explicitly. For example, similar to shifting cultivation, some cultures
practice shifting settlement, and abandoned settlements have been shown to provide
valuable seasonal resources, e.g. fruit trees, to chimpanzees Pan troglodytes in Mali
(Duvall 2008). Even road verges may provide for some species. For example, verges in the
Karoo support some plant species not found in adjacent grazing lands, though many species
from pastures are not found in verges (O'Farrell and Milton 2006). Verges also support
invertebrates and could prove valuable to conservation because verges are public spaces
that can be managed for biodiversity (Tshiguvho et al. 1999).
Understanding more about urban settlement and biodiversity may even benefit
conservation in once remote PAs where rural sprawl and infrastructure for wildlife tourism
can be dramatic (Wittemyer et al. 2008). For example, recent decades have seen substantial
increases in rural sprawl along with the construction of 60 tourist lodges, 1,200 boreholes,
and 540 km of roads in the Okavango Delta, one of Botswana’s premiere conservation
areas (Vanderpost 2006).
34
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Constraints and Opportunities
The science of biodiversity in human-modified landscapes
As others have pointed out, understanding the value of human-modified landscapes for
biodiversity, especially in Africa, is hampered by data constraints (Norris et al. 2010,
Pettorelli et al. 2010, Waltert et al. 2011, Trimble and van Aarde 2012). Many studies are
limited in temporal and spatial scale, and poor study design may result in insufficient
sampling of habitats. The focus on species richness of certain habitat types while failing to
account for the importance of species from other habitats in assigning conservation value to
different land-use options may neglect the bigger picture; Bond and Parr (2010), for
example, call for more collaboration between forest conservationists and others. More
consideration for the value of different species in terms of commonness and rarity also
needs to be developed because this review, like others highlights that human-modified
landscapes often fail to cater for endemic and specialist species (Waltert et al. 2011), and a
better understanding of beta and gamma diversity at a landscape scale is necessary.
Additionally, further investigation into the relationship between occurrence and
persistence is required, as are more studies that delve beyond species richness into the
processes that support the observed patterns of biodiversity. For example, studies of
demographic processes (e.g. Djossa et al. 2008, Schumann et al. 2010, Venter and
Witkowski 2010) and population trends (e.g. Stoner et al. 2007, Trimble and van Aarde
2011) for species inhabiting human-modified landscapes can provide insight beyond mere
patterns of occurrence. Furthermore, umbrella species are not necessarily informative for
35
2. Biodiversity in Africa’s Human-Modified Land
other taxa. As elsewhere (Gardner et al. 2010), studies of biodiversity in African humanmodified landscapes is biased towards certain taxa—and the patterns exhibited by these
species might not apply to others (Caro 2001). Also, genetic diversity, has not generally
been considered though it may be important in terms of traits valuable to humans and
valuable for conservation
(Ashley et al. 2006). Conservation in human-modified
landscapes may be particularly important in conserving genetic diversity because the
traditional fortress PA model may encompass relatively little, especially for plants (AttaKrah et al. 2004).
Many authors lament erosion of ecological knowledge to maintain species,
especially trees, medicinal plants, and wild food plants, and urge more effort towards
domestication, cultivation, and marketing to provide farmers with the means to conserve
species while easing pressure on wild stock and improving food security and economic
stability (Leakey and Tchoundjeu 2001, Dold and Cocks 2002, Dovie et al. 2007, Kindt et
al. 2008, Ntupanyama et al. 2008, Tabuti et al. 2009, Khumalo et al. 2012). However, care
must be taken to ensure that genetic diversity is maintained in the process (Lengkeek et al.
2006, Muchugi et al. 2008). Development of domestication and cultivation methods could
promote the use of native species in human-dominated lands, and these native plants may
contribute to conservation of other taxa (Dovie et al. 2007), but more research is clearly
required.
Implementing policies
Given the limitations of the available science, it is difficult to develop strategies to
encourage land uses that are of the highest conservation value. The effect of policy on
36
2. Biodiversity in Africa’s Human-Modified Land
biodiversity conservation in human-modified landscapes under different land tenure
systems and different settlement patterns needs more research because decisions are largely
opinion driven and not evidence based (Homewood 2004, Duvall 2008). Perhaps the
community-based conservation literature, which has focused heavily on implementation
and policy, could lend some insight. A review of this literature stresses that better
implementation results are achieved when there is quality governance, resilient local
institutions with local power and accountability, consideration for local context, integration
across social and ecological systems, and mutual learning involving communities and other
involved parties, e.g. outside experts (Balint and Mashinya 2008). NGO’s and foreign aid
are more likely to encourage successful conservation when projects are flexible, smallscale, and targeted at local interests, and when they prioritize innovation, learning, and
experimentation (Nelson 2009). Conservationists must also take cognizance of perspectives
and needs of local communities in both rural and urban settings in order to better engage
them in conservation management (Ferketic et al. 2010). CBC projects that are independent
of PAs are excellent opportunities to maintain biodiversity on human-modified land of
marginal use for agriculture; and expert opinion, monitoring, and ecological modeling tools
can help communities manage their natural resources (Du Toit 2002).
I have indicated several gaps in the literature on biodiversity in African humanmodified landscapes, and while much more work is required to create sensible policies that
meet conservation needs and those of governments and people (Ashley et al. 2006), as it
stands, current research can go some way towards supporting policy-making. Studies of
biodiversity persistence in different land-use options for a given region can be incorporated
into scenario modeling for future development. For example, Turpie et al. (2007)
37
2. Biodiversity in Africa’s Human-Modified Land
amalgamated studies of plants, invertebrates, birds, and mammals in human-modified
landscapes to predict how varying levels of afforestation or dairy production in the
Drakensberg grasslands of South Africa would influence alpha diversity.
Some generalities emerge from the literature that may be helpful in working
towards sensible policies. Generally, diversifying human-modified landscapes at all levels,
e.g. polyculture cropping, diverse agroforestry, and maintaining farmlands with high
heterogeneity in terms of both crops and vegetation structure, is likely to support more
species than do more homogenous land uses, while potentially also providing economic
stability against a background of fluctuating markets for specific crops (Franzen and
Mulder 2007). It is apparent that, often, endemic and specialist species cannot persist in
human-modified landscapes; thus, protected area expansion and development should be
focused within areas rich in such species (see Jenkins et al. 2013). Past and present
implementation strategies are beyond the scope of this review, yet there is literature dealing
with such strategies in Africa that may be of use, e.g. certification of sustainable and
biodiversity friendly products (Lilieholm and Weatherly 2010).
Living with nature
Maintaining biodiversity in landscapes where humans live, work, and extract resources
implies that humans will have to coexist with other species. While the consequences of
living without nature may be worse than the difficulties of living with it, certain issues
present considerable obstacles for promoting conservation beyond PAs, especially for
mammals. Human-wildlife conflict is particularly troublesome for conservation of large
mammals in human-dominated landscapes, e.g. carnivores threaten livelihoods by
38
2. Biodiversity in Africa’s Human-Modified Land
predating livestock and, occasionally, people. However, specific and practical actions can
greatly reduce the probability of carnivore attacks. For example, in Kenyan communal
lands, having a domestic dog accompany herds can reduce the risk of a carnivore attack by
63%; conversely each additional boma gate increases the risk of attack by 40% (Woodroffe
et al. 2007). However, carnivores are not the only concern. Other animals, such as baboons
and bush pigs, can damage structures and destroy crops while larger herbivores, such as
elephants, also threaten human lives. Knowledge of attitudes of people employing different
land uses can help land-use planners develop strategies to reduce conflict and negative
attitudes towards conservation. For example, crop agriculture should not be encouraged in
predominantly pastoral areas where elephants and people coexist relatively peacefully
(Gadd 2005). Furthermore, land-use planning that incorporates knowledge of which crops
are most likely to generate conflict could allow creation of buffer zones in areas with high
potential for conflict (Hockings and McLennan 2012).
The risk of disease transmission poses an additional difficulty. Diseases of domestic
animals threaten wildlife. For example, domestic dogs are carriers of canid diseases
transmissible to wild carnivores (Butler et al. 2004) and were partly responsible for
extinction of the African wild dog Lycaon pictus and decimation of lions Panthera leo in
areas of the Serengeti (see Woodroffe 1999). Additionally, livestock can transmit animal
diseases (e.g. bovine tuberculosis) to wildlife with negative conservation outcomes, while
wildlife can also transmit diseases (e.g. foot and mouth) to livestock with immense
economic consequences (Michel et al. 2006, Thomson 2009).
Fencing has been heavily used in Africa to assist people in their ability to coexist
with nature—to reduce direct conflict and disease transmission. Laws regarding fencing
39
2. Biodiversity in Africa’s Human-Modified Land
differ by country; for example, Zambia requires game fences while Namibia encourages
large-scale cooperation between game-farmers to discourage fencing (McGranahan 2008).
Obviously, fencing has serious ecological consequences (Hayward and Kerley 2009,
Trimble and van Aarde 2010) and is anathema in many ways to the goals of conservation,
especially conservation beyond PAs (Trimble and van Aarde 2010). However, nontraditional fencing technologies (see Hayward and Kerley 2009), such as fences targeted at
particular problem species (e.g. elephant fences that allow other species to pass), virtual
barriers, or fencing wildlife out of villages and fields instead of into PAs, may be
acceptable compromises. The effect of fences on the persistence of species in humanmodified landscapes certainly deserves more investigation.
Economically, wild animals provide an important resource for many people in
Africa (Bharucha and Pretty 2010), which may threaten species persistence. “Sustainable
use” is frequently discussed with relation to bushmeat hunting, but food scarcity and
population growth dictate that it will likely be impossible to enforce rules for sustainable
use unless food security issues are addressed (Fa et al. 2003). Sustainable harvesting is also
an issue for plants (Sambou et al. 2002). Community forests must be carefully managed,
e.g. by restricting harvesting of pole-sized stems to certain species, to ensure that species
are not used to extinction (Obiri et al. 2002). Additionally, rules must be assessed to ensure
that they achieve the desired goals; for example, in the Republic of Guinea, tax to the
forestry administration for harvesting palm wine counterproductively encourages
harvesters to employ lethal yet profitable methods of harvesting to compensate for the
initial investment (Sambou et al. 2002).
40
2. Biodiversity in Africa’s Human-Modified Land
Conclusion
There is clearly both necessity and great potential for human-modified land in sub-Saharan
Africa to contribute to the conservation of the continent’s biodiversity. While PAs will
remain essential, and are especially important for protecting species sensitive to human
disturbance (Devineau et al. 2009), a greater focus on biodiversity conservation beyond
their boundaries could be complementary to overall conservation goals. The information
gleaned from studies of biodiversity in human-modified landscapes in Africa discussed in
this review goes some way toward providing policy-makers with evidence to support
defensible decisions for land-use planning and conservation management beyond PAs.
Improving the amenity of human-modified landscapes for biodiversity can be encouraged
at all levels from individuals’ choices to plant indigenous home gardens, to grass roots
endeavors to manage communal resources, to communities deciding to share their land
with wildlife, to commercial farms going organic and maintaining patches of natural
habitat. Governmental intervention at the level of the city (e.g. green space planning),
region (e.g. extension agencies demonstrating biodiversity friendly agricultural practices),
nation (e.g. policy-setting for control of invasive species, pesticide or poison usage, and
land-use zoning), or even internationally (e.g. cooperative removal of boundary fences) are
also warranted.
Although several factors including lack of knowledge, implementation challenges,
and problems of coexistence with wildlife may constrain successful implementation of
biodiversity conservation in human-modified landscapes, given each constraint,
opportunity exists for progress. On the bright side, scientific interest in the topic is
increasing (Trimble and van Aarde 2012), and as research accumulates, it will allow for
41
2. Biodiversity in Africa’s Human-Modified Land
systematic reviews useful for policy decisions. Additionally, many issues associated with
human-wildlife coexistence are primarily related to large mammals and efforts to solve
these problems should continue. Meanwhile, the barriers to implementing strategies to
conserve other species groups in human-modified landscapes are far from insurmountable
and such strategies should be prioritized.
Acknowledgements
M.J.T. is supported by a National Science Foundation Graduate Research Fellowship and
R.J.v.A. through various grants to the Chair in Conservation Ecology at CERU.
42
2. Biodiversity in Africa’s Human-Modified Land
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67
2. Biodiversity in Africa’s Human-Modified Land
Figures
Fig. 2.1. Map of sub-Saharan Africa showing ecosystem types adapted from Olson et al.
(2001): rangelands (desert and xeric shrubland, montane grassland/shrubland, flooded
grassland/savanna, and tropical/subtropical grassland/savanna/shrubland), tropical forests
(moist
and
dry
tropical
forest),
the
Cape
Floristic
Region
(Mediterranean
forest/woodland/scrub), and the urban and rural built environment represented by the
human influence index (Wildlife Conservation Society and Center for International Earth
Science Information Network 2005), a dataset comprising nine data layers incorporating
population pressure (population density), human land use and infrastructure (built-up areas,
nighttime lights, land use, land cover), and human access (coastlines, roads, railroads,
navigable rivers).
68
2. Biodiversity in Africa’s Human-Modified Land
Supplementary Tables
Table S.2.1. Summary of studies investigating biodiversity of grazing landscapes in sub-Saharan African rangelands.
Reference
Country
Habitat
Taxa
Land-use variable
Control a
Biodiversity Finding
variable
Conclusion
Bergström and
Skarpe (1999)
Botswana
xeric
shrubland
large
herbivores
gradient of cattle
density with
distance to village
NA
abundance
heavy cattle and goat grazing
near villages probably excludes
wild herbivores
Blaum et al.
(2007a)
South
Africa
semiarid
savanna
5 rodent
species
increasing levels of NA
shrub encroachment
as proxy for grazing
intensity
abundance, increasing shrub cover
diversity,
affects rodents differently
community
composition
overall species richness
decreased with increasing
shrub cover
Blaum et al.
(2007b)
South
Africa
semiarid
savanna
10
mammalian
carnivores
increasing levels of NA
shrub encroachment
as proxy for grazing
intensity
abundance
species react disparately
intermediate shrub cover is best
Blaum et al.
(2009a)
South
Africa
semiarid
savanna
grounddwelling
arthropods
increasing levels of NA
shrub encroachment
as proxy for grazing
intensity
abundance,
diversity,
community
composition
mixed results for different
can use some species for
groups: abundance trends
indicators of bush
were mixed; richness
encroachment
showed bell-shaped pattern;
composition definitely
changes
Blaum et al.
(2009b)
South
Africa
semiarid
savanna
12 small and
medium
mammalian
carnivores
gradient of stocking NA
rates with and
without predator
control
abundance
abundance of all species
lowest on farms with high
stocking rate; predator
control affected species
differently
large herbivores not found
near villages; some species
more sensitive than others
need to expand research and
monitoring
69
2. Biodiversity in Africa’s Human-Modified Land
Colville et al.
(2002)
South
Africa
Succulent
Karoo
monkey
beetles
Davis et al. (2012) South
Africa
savanna
Fabricius et al.
(2003)
South
Africa
Georgiadis et al.
(2007)
contrasting grazing
histories
NA
abundance,
richness,
composition,
plant
turnover
higher abundance in
monkey beetles useful
disturbed sites generally but indicators of overgrazing
higher richness in
undisturbed sites, with
distinct assemblages at each
site
dung beetles communal grazing
Kruger
National
Park
abundance,
richness,
biomass,
structure
higher richness, abundance,
and biomass in PA than
communal grazing and
different structure
higher mammal diversity in the
PA allow for a more complex
beetle community despite
higher mammal density in the
communal land
xeric
succulent
thicket
terrestrial
arthropods,
reptiles
commercial and
subsistence
rangeland with
varying grazing
intensity
Great Fish
River
Reserve
Complex
richness,
community
similarity
greater richness generally in
nature reserve; snakes and
lizards twice as abundant in
communal grazing;
locations generally housed
2/3's of total diversity
nature reserves important, but
mixed land-use mosaic
supports greater gamma
diversity
Kenya
savanna
large
herbivores
commercial ranches, NA
communal ranches,
transitional
properties
density,
trends
many herbivores can thrive
when sharing with moderate
livestock densities, but only
few when livestock densities
are high
maintaining high wild species
diversity at landscape scale
depends on network of
unfenced areas with low or
zero livestock densities
Gregory et al.
(2010)
Kenya
savanna
birds
traditional pastoral
practices (i.e. burn
patches, abandoned
bomas)
undisturbed
matrix
species
richness,
abundance,
community
composition
greater density of birds and
unique species assemblages
on burn and boma patches
than undisturbed control
disturbances caused by
traditional pastoralism may be
critical to maintaining avian
diversity
Haarmeyer et al.
(2010)
South
Africa
Succulent
Karoo
plants
different grazing
intensities
farm with no abundance,
grazing
species
richness,
composition,
dynamics
endemic richness and
abundance decreased with
grazing, but grazed and
ungrazed plots harbor
unique species
no or moderate grazing
necessary to preserve plant
diversity and vegetation
patterns
70
2. Biodiversity in Africa’s Human-Modified Land
Hejcmanová et al. Senegal
(2010)
savanna
plants
grazing and wood
collection, 15 year
fenced, 5 year
fenced
NA
Hendricks et al.
(2005)
South
Africa
Succulent
Karoo
plants
gradient of grazing
intensity
little-grazed species
areas of
richness,
Richtersveld cover
National
Park
species richness and cover
lowest at high intensity
grazing
livestock in conservation areas
may not be compatible with
conservation goals
Kinnaird and
O'Brien (2012)
Kenya
savanna
large
mammals
livestock
management
gradient
wildlife
sanctuary
with no
livestock
fenced and group ranches
had lower richness and
occupancy than sanctuaries
and conservancies
landowners need to be
provided with incentives for
tolerating wildlife
Mayer et al.
(2006)
South
Africa
Succulent
Karoo
monkey
beetles
livestock grazing
NA
intensity (communal
versus commercial)
abundance, grazing intensity does not
changes in vegetation affects
richness,
determine abundance and
composition of beetle
composition richness; composition varies assemblages; thus, grazing
affects pollinator diversity
Mohammed and
Bekele (2010)
Ethiopia
savanna
plants
open hay-fields and NA
grazed woodlands
diversity,
biomass
production,
range
condition
Morris et al.
(2009)
Kenya
savanna
game birds
heavy grazing,
seasonal grazing,
abandoned grazing
abundance, doves most abundant in
richness,
moderate grazing; francolin,
composition spurfowl, and quail in
sanctuary; abandoned
landscape has highest
richness
wildlife
sanctuary
abundance,
richness, %
cover,
functional
diversity
occupancy,
abundance,
richness
shift towards woody species enclosures may prove useful
with time in sites where
management strategy in
grazers excluded
degraded rangelands
higher diversity in wooded management of hay-fields may
grazing land than open
reduce diversity
grassland, biomass
production follows quadratic
relationship with range
condition
maintaining a mosaic of
wildlife and livestock grazing
with patches of ungrazed
habitat will support diverse
population of game birds
71
2. Biodiversity in Africa’s Human-Modified Land
Monadjem and
Garcelon (2005)
Swaziland savanna
3 vulture
species
government cattle
conservation nest
ranches (no wildlife areas
densities
protection), cattle
ranches (with
protection for
wildlife)
O'Connor et al.
(2011)
South
Africa
grassland
plants
grazing
NA
management
(stocking rate,
cattle-to-sheep ratio)
abundance, mixed results for different
richness,
trials and groups; increaser
composition and decreaser species
identified
Reid and Ellis
(1995)
Kenya
arid
savanna
1 tree
species
livestock corrals
non-corral
sites
abundance
of seeds and
seedlings,
size class of
older trees
seedling emergence, growth, contrary to popular belief,
and survival better in
pastoralism may enhance
corrals; older tree survival
recruitment of trees
not significantly different
outside corrals
Rutherford and
Powrie (2010)
South
Africa
Succulent
Karoo
plants
low and high
grazing intensity
NA
% cover,
richness,
abundance
total number of species
declines with heavy grazing
while annuals and geophytes
increase
Rutherford and
Powrie (2011)
South
Africa
grassland
plants
heavy grazing
Tsolwana
Nature
Reserve
richness,
grazing led to higher
diversity,
richness at plot scale, but
composition plots were more similar to
each other
overall richness was similar
between grazed and ungrazed
Rutherford et al.
(2012)
South
Africa
savanna
plants,
termites
grazing gradient
NA
abundance,
richness,
composition,
cover
cover was reduced in high
grazing but no difference in
richness or diversity of
plants or termites although
composition changed
increased grazing in mopane
savanna would result in
different species assemblages
and physiognomy
Savadogo et al.
(2008)
Burkina
Faso
savannawoodlands
herbaceous
plants
grazing, fire, and
undisturbed
selective tree cutting sites
abundance,
richness
different groups respond
differently
site- and group-specific
responses require landscape
approach
Seymour and
Dean (1999)
South
Africa
Succulent
Karoo
invertebrates moderate and high
intensity grazing
NA
nest densities highest in
conservation areas, less on
cattle ranches, and
negligible on gov't ranches
vultures do not breed on
intensive ranches although
vegetation appears similar
structurally
trials suffer from lack of
baseline data and limited
replication
beta diversity across
disturbance regimes increases
gamma diversity at a landscape
level
abundance, abundance higher with high high abundances at severely
richness,
grazing but richness greater degraded areas may compound
composition at moderately grazed sites
effects of overgrazing
72
2. Biodiversity in Africa’s Human-Modified Land
Shackleton (2000) South
Africa
savanna
plants
communal grazing
areas
PAs
abundance,
richness,
beta
diversity
fewer plant species in PAs
Smart et al. (2005) South
Africa
savanna
lizards
communal
rangelands
PAs
abundance,
richness,
vegetation
communal lands have
Species used by people may
different vegetation; lizard not persist beyond PAs
richness higher in
communal lands, but
different assemblage than in
PAs
Todd and
Hoffman (2009)
South
Africa
Succulent
Karoo
plants
commercial and
communal
rangelands
NA
% cover,
richness,
community
composition,
dynamics
divergence of communities
maintained despite
vegetation changes in both
land uses
Vaudo et al.
(2012)
South
Africa
Thicket
/savanna
bees
livestock grazing
game farms
colony
density,
colony
strength
land with indigenous
more research is needed to
herbivores may have greater confirm patterns
colony density but are not
healthier
Wasiolka and
Blaum (2011)
South
Africa
xeric
shrubland
plants,
reptiles
livestock grazing
Kgalagadi
abundance, plant and reptile richness
Transfrontier richness,
and abundance higher in PA
Park
composition, than farmland
plant cover
a
communal land maintains high
diversity, but more work
should be done to ensure
persistence
longevity of shrub species
prevent quick recovery from
overgrazing in contrast to
shorter lived grassland species
livestock farming leads to
significant changes in
vegetation composition and
resources for the reptile
community
NA indicates no control; PA stands for protected area.
73
2. Biodiversity in Africa’s Human-Modified Land
Table S.2.2. Summary of studies investigating biodiversity of agricultural mosaic landscapes in sub-Saharan African rangelands.
Reference
Country
Anadón et al.
(2010)
Control a
Taxa
Land-use variable
Mauritania, savanna
Mali
raptors
settlement gradient, grassland
cultivation
abundance, richness relates positively to resident species may be
richness,
cultivation, but resident
negatively affected by habitat
composition species relate negatively to degradation
human population
Caro (1999)
Tanzania
savanna
large and
medium
mammals
gradient of human
Katavi
presence from
National
seasonal pastoralism Park
to permanent
settlements and
cultivation
densities,
densities higher in low
composition intensity use; some
mammals still occur
seasonally in high intensity
use
illegal hunting is the main
cause of lower mammal
densities
Caro (2001)
Tanzania
savanna
small
mammals
cultivation, pastures, Katavi
settlements, little
National
used areas
Park
abundance, diversity and abundance
diversity,
greater outside than inside
community park
composition
large mammals may not be
effective umbrellas for small
mammals
Devineau et al.
(2009)
Burkina
Faso
savanna
plants
agricultural mosaic
PAs
abundance,
richness,
composition,
species traits
plants are not sufficiently
protected in the agricultural
landscape, so PAs are
necessary
Eilu (2003)
Uganda
savanna
plants
cultivation, fallow,
plantation
natural
woodland/
grassland
abundance, natural habitats support
farmers should be advised how
richness,
highest diversity; banana
to maintain plant diversity in
composition crops and some annual crops agricultural landscapes
supported substantial
diversity
savanna
mammals
river segments
bordered by fields
of various sizes,
settlements, and
grazing
uninhabited
river
segments
abundance,
richness
Fritz et al. (2003) Zimbabwe
Habitat
Biodiversity Finding
variable
effect depends on land type
and plant group but
generally favors widespread
species outside PAs
Conclusion
field area affects abundance agricultural mosaics affect
and occurrence of species
most species but especially
when fields are larger than 3.2
ha
74
2. Biodiversity in Africa’s Human-Modified Land
Gardiner et al.
(2005)
Burkina
Faso
savanna
butterflies
cultivation, fallow,
grazing
30-year
fallow
Gardner et al.
(2007a)
Tanzania
savanna
small
mammals,
frogs, birds,
butterflies,
trees
gradient of human
Katavi
presence from
National
seasonal pastoralism Park
to permanent
settlements and
cultivation
abundance, richness does not decline
richness,
with land-use gradient but
composition composition in different
management areas is distinct
PAs are crucial but humanmodified landscapes can have
vital and complementary
conservation value
Happold and
Happold (1997)
Malawi
savanna
mammals
tobacco farm with
NA
mix of intense
cultivation, remnant
vegetation,
plantations, fallow
abundance,
richness
66% of species known to
occur in region occur on the
farm; large remnants are
especially important
farms that contain remnants of
natural vegetation can play an
important role in mammal
conservation
Hoare and Du
Toit (1999)
Zimbabwe
savanna
elephants
gradient of
NA
settlement and
cultivation coverage
density
elephant density declines
with increasing human
transformation
elephants coexist in human
agricultural matrix up to a
threshold of transformation
Konecny et al.
(2010)
Senegal
savanna
small
mammals
cultivation, pastures, Niokolo
fallow
Koba
National
Park
abundance, diversity and abundance
richness,
greater outside than inside
composition the park
traditional agriculture may
support species not found in
less disturbed locations
Mapinduzi et al.
(2003)
Tanzania
savanna
plants
pastoral settlement,
agro-pastoral
settlement
richness,
erosion risk
greater diversity and less
erosion risk in pastoral than
agro-pastoral settlements
traditional ecological
knowledge provides a valuable
basis for assessing rangeland
biodiversity
Moreira (2004)
South
Africa
grassland
4 bird
species
cultivation, grazing, NA
plantation, fallow
occurrence
relationship between
occurrence and land use
differs by species
afforestation and agricultural
intensification threaten bustard
species
Mworia et al.
(2008)
Kenya
savanna
large
mammals
small-scale ranches, PAs
small-scale farms,
communal grazing
abundance, wildlife density peaks at
richness,
intermediate cattle grazing;
composition small-scale agriculture not
an important factor
NA
abundance, no difference in richness;
changes in species groups
richness,
abundance highest in
relate to vegetation changes
composition cultivation yet more even in
fallow
management must maintain
heterogeneous landscape and
maintain access to water
75
2. Biodiversity in Africa’s Human-Modified Land
Nacoulma et al.
(2011)
Burkina
Faso
savanna
plants
communal
W National
cultivation, fallows, Park
remnants
abundance,
composition,
structure,
traits
O'Connor (2005)
South
Africa
grassland
plants
plantation,
commercial and
communal
cultivation/pastures
protected
grasslands
abundance, plantations have more
conservation should focus on
richness,
indigenous species than
species only found on
composition other land uses; no effect of unprotected rangelands
grazing intensity on
richness, only composition
Ratcliffe and
Crowe (2001)
South
Africa
grassland
birds
farms with various
compositions of
cultivation and
pastures
NA
abundance,
richness
species characteristic of
variegated landscapes are
lost with intensive farming
Reid et al. (1997) Ethiopia
grassland/
woodlands
trees
small- and largeholder fields and
pastures
riparian
woodlands,
wooded
grasslands
abundance,
diversity,
cover
cover and diversity high in small-holder farms may be
riparian woodlands,
compatible with conservation,
moderate in small-holder
but riparian woodlands are key
and wooded grasslands, and
low in large-holder farms
Russell and
Downs (2012)
South
Africa
grassland
frogs
plantations, sugar
cane
PAs
richness,
lower richness in plantations Land use should be considered
diversity,
and cultivation
for frog conservation
composition
Soderstrom et al.
(2003)
Burkina
Faso
savanna
birds
cultivation, fallow,
grazing
NA
abundance, richness highest on actively
richness,
disturbed land and decreases
composition with fallow age; many
species only found on
cultivated land
Stoner et al.
(2007)
Tanzania
savanna
larger
mammals
gradient of resource PAs
use restrictions
population
trends
elevation and soil determine combination of communal
vegetation type; traditional management and PAs best for
land use does not
conservation
necessarily lead to loss of
species
population declines due to
intensification of agriculture so
re-creation of a habitat mosaic
with lots of edge habitat
necessary
woody vegetation should
include many different species,
and large trees should be
maintained
declines common in all
PAs may fail some species and
land-use categories, but least more monitoring is necessary
common in strict PAs;
species commonly fared
poorly in unprotected
landscapes
76
2. Biodiversity in Africa’s Human-Modified Land
Tabuti (2007)
Uganda
savanna
16 tree
species
cultivation, fallow,
homestead,
seasonally flooded,
bush
NA
abundance,
occurrence,
population
structure
Thiollay (2006)
Burkina
Faso
savanna
nonpasserine
birds
traditional
cultivation and
fallow
PAs
abundance, some bird groups maintain hunting, habitat degradation,
composition substantial populations in
and grazing cause extinctions
cultivated areas, but raptors and declines of large birds
and large game birds mostly
absent
Wessels et al.
(2011)
South
Africa
savanna
trees
communal pastures, Kruger
cultivation
National
Park
a
most species rare, but few
widespread; some not able
to persist in some land uses
cover, height more large trees in the
communal areas but few
small trees
growing human population
threatens species persistence
large trees are probably
protected by people, but
regeneration may be
problematic
NA indicates no control; PA stands for protected area.
77
2. Biodiversity in Africa’s Human-Modified Land
Table S.2.3. Summary of studies investigating biodiversity of cropping landscapes in sub-Saharan African rangelands.
Reference
Country
Habitat
Taxa
Land-use variable
Ayuke et al.
(2011)
Malawi,
Burkina
Faso
savanna
termites &
earthworms
Carvalheiro et al.
(2010)
South
Africa
savanna
Carvalheiro et al.
(2011)
South
Africa
Fitzherbert et al.
(2006)
Control a
Biodiversity Finding
variable
Conclusion
management leading fallow
to high- and lowcarbon soils
abundance,
diversity
higher richness and
abundance under field
management that results in
high-carbon; higher worm
richness but not termite in
fallow
management that increases soil
carbon supports diversity
pollinators
orchard
distance to
natural
habitat
abundance,
richness
pollinators decline in
need to make farmland more
abundance and richness with suitable for pollinators by
distance to natural habitat
maintaining remnants of
natural habitat throughout
savanna
plants &
pollinators
sunflower fields
differing in weed
occurrence
distance to
natural
habitat
abundance, weed diversity increased
richness,
pollinator diversity
composition
Tanzania
savanna
butterflies
cultivation
areas with
little human
impact, e.g.
Katavi
National
Park
abundance, abundance and richness low increased cultivation could
richness,
in cultivation
reduce butterfly diversity
composition
Gardner et al.
(2007b)
Tanzania
savanna
amphibians
cultivation
Katavi
National
Park
abundance, cultivation decreases
richness,
diversity
composition
transformation of miombo
could threaten amphibian
species
Midega et al.
(2008)
Kenya,
South
Africa
savanna
grounddwelling
spiders
monoculture maize,
maize intercropped
with “push-pull”
crops
NA
abundance, abundance higher in the
richness,
intercrop; diversity not
composition generally greater
“push-pull” intercropping may
provide valuable pest control in
maize agro-ecosystems
natural habitat patches should
be conserved and flowering
plants maintained within fields
to maximize productivity and
conservation
78
2. Biodiversity in Africa’s Human-Modified Land
Mponela et al.
(2010)
Malawi
savanna
plants
marginal land
within cultivated
landscape
marginal
abundance,
land in
richness,
uncultivated composition
landscape
Pryke and
Samways (2012)
South
Africa
grassland
arthropods
plantations,
grassland remnants
PAs
abundance, Interior of grassland
richness,
remnant networks similar in
composition arthropod assemblage to
PAs
provided they are wide enough,
grassland remnant ecological
networks have conservation
value in human-dominated
landscapes
Sinclair et al.
(2002)
Tanzania
savanna
birds, insects cultivation
PAs
abundance, bird abundance in
richness,
agriculture much reduced;
composition half of insectivorous and
granivorous species not
recorded in cultivation;
consistent with drop in
insect abundance
many species will become
relegated to PAs unless
restoration of cultivation is
achieved
Tchabi et al.
(2008)
Benin
savanna
abuscular
mycorrhizal
fungi
natural
savanna,
long fallow
density,
spore density and species
richness,
richness higher in natural
composition savanna and yam
cultivation, intermediate in
fallow, and low in cotton
agricultural practices decrease
richness; it is not quickly
restored by fallow which could
harm soil fertility
a
cultivation
fallow areas in cultivated
marginal land in uncultivated
landscapes were rich in
areas should be spared for
disturbance tolerant species; conservation
uncultivated areas had high
conservation value species
NA indicates no control; PA stands for protected area.
79
2. Biodiversity in Africa’s Human-Modified Land
Table S.2.4. Summary of studies investigating biodiversity of agroforestry landscapes in sub-Saharan African rangelands.
Reference
Country
Habitat
Taxa
Land-use variable
Control a
Biodiversity Finding
variable
Augusseau et al.
(2006)
Burkina
Faso
savanna
trees
fallows, cultivation
NA
density, size, farmers modify species
new techniques in agroforestry
richness,
diversity towards dominance management are needed to
composition of a few useful species
encourage tree conservation
Bayala et al.
(2011)
Burkina
Faso
savanna
trees
home, village, and
bush parklands
NA
abundance, diversity was related to
richness,
farming system and many
size,
species were rare
composition
domestication and conservation
strategy are key to maintaining
parklands and threatened
species
Djossa et al.
(2008)
Benin
savanna
1 tree
species
fallow, cropland,
villages
W National
Park
abundance,
size
regeneration problem in
crops, villages, and fallows
baobabs can withstand
harvesting of NTFPs, but
future intensification may lead
to problems
Fandohan et al.
(2010)
Benin
savanna
1 tree
species
farmlands, fallow
gallery forest abundance,
size
trees less common in
farmland and fallow and
more vulnerable
introduction of seedlings to
farmlands may be necessary
Fifanou et al.
(2011)
Benin
savanna
trees
farms of different
size
NA
abundance, small land holdings had
traditional agroforestry
richness,
higher richness; people plant supports tree species richness
composition trees for food and medicine
Kindt et al. (2008) Burkina
Faso,
Mali,
Niger,
Senegal
savanna
trees
village fields, bush
fields, sylvopastoral zone
forest
reserves
abundance,
size,
richness,
composition
Ouinsavi and
Sokpon (2008)
savanna
trees
farms
NA
richness,
density and composition
abundance, depend on socioeconomic
regeneration and environmental factors
Benin
low richness in village
fields, intermediate in bush
fields and sylvo-pastoral
zone, highest in forest
reserves
Conclusion
projects to encourage farmer
assisted maintenance and
regeneration of trees may be
necessary
more evenness should be
promoted in farmlands by
encouraging management of
rarer species
80
2. Biodiversity in Africa’s Human-Modified Land
Pote et al. (2006)
South
Africa
savanna
1 tree
species
fields, villages
Raebild et al.
(2007)
Burkina
Faso
savanna
trees
fallows, cultivation, gallery forest density, size, richness highest in fallow;
plantations
richness,
regeneration low in
composition parklands
fallow important for keeping
tree diversity
Schreckenberg
(1999)
Benin
savanna
trees
cultivation, fallow,
bush
gallery forest abundance, many trees valuable to
size,
people are maintained in
composition fields and fallows
changes in the agricultural
system may result in declining
importance of valuable species
and incentive to maintain them
Schumann et al.
(2011)
Burkina
Faso
savanna
1 tree
species
fallows, crops
W National
Park
harvest
intensity,
abundance,
sprouting
healthy stands in fallows
and park but no saplings in
croplands
stands are well preserved
despite harvest due to life
history traits
savanna
1 tree
species
farmland
NA
abundance,
size
largest individuals found in
farmed land but juvenile
recruitment low
sporadic recruitment probably
enough to maintain population
due to low mortality
Venter and
Benin
Witkowski (2010)
a
plains and
rock
outcrops
abundance,
size
villages and fields have low population is stable due to low
recruitment
mortality, but seedlings are not
well protected in humanmodified areas
NA indicates no control; PA stands for protected area.
81
2. Biodiversity in Africa’s Human-Modified Land
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Chapter 3. Frog and Reptile Communities and Functional
Groups Over a Land-Use Gradient in a Coastal Tropical
Forest Landscape of High Richness and Endemicity
Publication Details
Trimble, M.J. & van Aarde, R.J. 2013. Frog and reptile communities and functional groups
over a land-use gradient in a coastal tropical forest landscape of high richness and
endemicity. Animal Conservation. In review.
Abstract
Information on the response of herpetofauna to different land uses is limited though
important for land-use planning to support conservation in human-modified landscapes.
Though transformation is dogmatically associated with extinction, species respond
idiosyncratically to land-use change, and persistence of species in habitat fragments may
depend on careful management of the human-modified matrix. I sampled herpetofauna
over a vegetation-type gradient representative of regional land uses (old-growth forest,
degraded forest, acacia woodland (i.e. new-growth forest), eucalyptus plantation, and sugar
cane cultivation) in the forest belt skirting the southeastern coast of Africa, part of a
biodiversity hotspot hosting many endemic herpetofaunal species in a highly transformed
landscape. I categorized species into trait-derived functional groups, and assessed
abundance and richness of groups and compared community metrics along the gradient. I
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further assessed the capacity of environmental variables to predict richness and abundance.
Overall, old-growth forest harbored the highest richness and abundance, and frogs and
reptiles responded similarly to the gradient. Richness was low in cultivation and,
surprisingly, in degraded forest but substantial in acacia woodland and plantation.
Composition differed between natural vegetation types (forest, degraded forest) and
anthropogenic types (plantation, cultivation), while acacia woodland grouped with the
latter for frogs and the former for reptiles. Functional group richness eroded along the
gradient, a pattern driven by sensitivity of fossorial/ground-dependent frogs (F2) and
reptiles (R2) and vegetation-dwelling frogs (F4) to habitat change. Environmental variables
were good predictors of frog abundance, particularly abundance of functional groups, but
less so for reptiles. Conserving forest and preventing degradation is essential, restoration
and plantations have intermediate conservation value, and cultivation is least amenable to
forest herpetofauna. My study demonstrates the utility of function-related assessments,
beyond
traditional
metrics
alone,
for
understanding
community
responses
to
transformation. Particularly, fossorial/ground-dependent frogs and reptiles and vegetationdwelling frogs should be closely monitored.
Introduction
Scientists are increasingly studying biodiversity in human-modified landscapes to augment
conservation efforts in protected areas with appropriate landscape management beyond
them (Daily 1999, Trimble and van Aarde 2012). This is a salient issue in the biologically
rich and unique coastal forest belt skirting the southeastern coastline of Africa, which spans
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the Maputaland Center of endemism (van Wyk 1996) and the Maputaland-PondolandAlbany biodiversity hotspot (Küper et al. 2004, Perera et al. 2011). However, mining,
tourism, agriculture, and subsistence communities have contributed to substantial forest
loss and degradation (Kyle 2004). An estimated 82% of coastal forest in KwaZulu-Natal
has been destroyed, which jeopardizes ecological integrity and threatens species
persistence, even within forest fragments (Trimble and van Aarde 2011, Olivier et al.
2013). Appropriate land-use planning could support the persistence, or at least occurrence,
of forest biodiversity in the matrix surrounding fragments and may ameliorate the impacts
of fragmentation and isolation. However, amenability of the matrix to forest species likely
depends on land use and the species in question, so teasing out which land uses are
amenable to which species could contribute to evidence-based land-use policy (see
Sutherland 2004, O'Connor and Kuyler 2009).
Herpetofauna are specialized in habitat requirements (Kanowski et al. 2006, Botts
et al. 2013), are sensitive to habitat modification, and face global extinction crises (Gibbons
et al. 2000, Stuart et al. 2008, Böhm et al. 2013). Forest conservation is particularly
important for African frogs; forests harbor two-thirds of Afrotropical Realm species, 32%
of which are threatened (Stuart et al. 2008), and range declines over the past century for
endemic frogs have been recorded in the coastal forest region (Botts et al. 2013). While
herpetofauna are important components of ecosystems as both predators and prey and can
influence whole-ecosystem processes (e.g. Beard et al. 2002, Whiles et al. 2006), they are
little studied (Trimble and van Aarde 2010), particularly in human-modified landscapes
(Trimble and van Aarde 2012), and especially in Africa (Gardner et al. 2007a).
Additionally, because habitat modification is a non-random filter for species, identifying
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characteristics of species that are sensitive to land-use change (see Suazo-Ortuno et al.
2008) can provide insight into taxonomic and functional homogenization and inform
broadly applicable conservation strategies (Smart et al. 2006, Cadotte et al. 2011, Mouillot
et al. 2013). However, function-related responses to habitat change are particularly poorly
understood for herpetofauna (Gardner et al. 2007a).
Nonetheless, frogs and reptiles do occur in human-modified landscapes, and
encouraging appropriate matrix land uses can contribute to their conservation (Anand et al.
2010, Sodhi et al. 2010). To clarify the effects of forest transformation and inform land-use
planning, I sought to document the response of herpetofaunal communities to a gradient of
land uses characteristic of the coastal forest region, which is rich in herpetofauna and
harbors many endemic and threatened species (Branch 1998, Armstrong 2001, Stuart et al.
2008, du Preez and Carruthers 2009, Measey 2011, Perera et al. 2011, IUCN 2012). I
sampled terrestrial herpetofaunal communities of five vegetation types, subjectively ranked
by structural similarity to old-growth forest: forest, degraded forest, acacia woodland (a
seral stage of forest regeneration (van Aarde et al. 1996)), eucalyptus plantation, and sugar
cane cultivation. I focused on three aims: 1) to test how abundance, richness, diversity, and
composition of frog and reptile communities change along the gradient, 2) to assign species
to functional groups, sets of species with similar ecological roles, and assess changes in
relative and proportional abundance of groups and group richness along the gradient, and
3) to quantify potential ecological drivers of community change by relating environmental
variables to overall richness and abundance of frogs and reptiles and to abundance of
functional groups.
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Methods
Study area
I sampled terrestrial herpetofauna along a 25 km section of coastline across a land-use
gradient southwest of Richards Bay, KwaZulu-Natal, South Africa, from 4 km north of the
Umlalazi River mouth to just south of the Richards Bay harbor, up to 2.3 km inland (Fig.
3.1). The region falls within the southern terminus of the East African Tropical Coastal
Forest (see van Aarde et al. 2013).
Sampling methods
I used a stratified random sample design of 30 trap arrays divided evenly among 5
vegetation types: forest, degraded forest (determined by presence of invasive plants
Lantana camara and/or Chromolaena odorata), acacia woodland (new-growth forest
dominated by Acacia karroo), eucalyptus plantation, and sugar cane cultivation. Trap
arrays were installed in three periods, two arrays per vegetation type per period, between
February 19 and March 13, 2012. I checked arrays daily for five days, identified species
captured, and released them ≥ 50 m away. Each array was operational for 120 ± 1 hrs.
Arrays were separated from each other by ≥ 500 m and from known water bodies by ≥ 300
m (Fig. 3.1).
Each array employed seven complementary sampling techniques, detailed in
Appendix S1,to represent as many species as possible (Ribeiro-Júnior et al. 2008). Arrays
consisted of three 15 m arms of 0.5-m-tall black plastic drift fence, dug 0.1 m into the
ground, spaced at 120̊, and connected at a central pitfall bucket. Arms featured pitfall
buckets at 7.5 and 15 m from the center bucket, and a funnel trap on either side between the
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outer two pitfalls. The fence guided frogs and reptiles into pitfalls and funnel traps. Four
polyvinyl chloride (PVC) pipe traps (see Trimble and van Aarde 2013) and four wooden
cover boards were installed 10 m beyond the northern-pointing fence arm and checked on
days two, four, and five. An active search was performed and audio recordings were made
in the vicinity of each array, and species found when installing or removing traps were
recorded. I measured eight environmental variables at each array and assessed the
distribution of array points along southwest—northeast and coastal distance geographic
gradients, see Appendix S1.
Analyses
I assessed sampling saturation overall and per vegetation type, separately for amphibians
and reptiles, with sample-based accumulation curves calculated in EstimateS 8.2.0 (Gotelli
and Colwell 2001, Colwell 2009). I assessed whether vegetation type affected observed
richness (species per array) and abundance (individuals per array) with Poisson generalized
linear modeling (GLM) and Χ2 analysis of deviance (or quasi-Poisson GLM and F-tests to
account for overdispersion) (Zuur et al. 2009).
I estimated richness of frogs and reptiles per vegetation type with non-parametric
richness estimators calculated in EstimateS: four abundance-based (Chao1, ACE, Jack1,
and Jack2) and two incidence-based that included frog species identified from audio
recordings (Chao2 and ICE). I calculated the range of the proportion of estimated richness
that I actually observed based on the lowest and highest of the six estimators. I used the
asymmetrical 95% CI of Chao1 and Chao2 to assess whether richness differed between
vegetation types (Colwell 2009).
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I calculated Shannon diversity overall and per vegetation type based on abundance
data for frogs and reptiles and explored differences in evenness and diversity with Rényi
diversity profiles calculated in BiodiversityR (Kindt and Coe 2005).
To assess composition, I calculated pairwise Bray-Curtis similarity on raw frog and
reptile abundance, square-root-transformed abundance (to decrease the influence of
abundant species), and frog incidence data including species identified in audio recordings
(here, Bray-Curtis simplified to Sorenson similarity) (Clarke and Gorley 2006, Anderson et
al. 2011). I used Primer 6’s (Clarke and Gorley 2006) analysis of similarity (ANOSIM) to
compare community composition among vegetation types and visualized differences with
non-metric multidimensional scaling (NMDS).
I assigned species to functional groups based on functional traits from published
information (Branch 1998, du Preez and Carruthers 2009, Pla et al. 2012). Frogs traits
comprised maximum snout-urostyle length, primary stratum of activity (fossorial, on
ground, or in vegetation), where eggs are laid (ground, water, or vegetation), and where
tadpoles develop (water or underground). Reptiles traits comprised maximum snout-ventral
length, mean clutch size, active stratum (allowing multiple options of burrowing/fossorial,
ground-active, or climbing on vegetation/rocks), reproductive strategy (viviparous or egglaying), locomotion (legs or legless), and feeding style (venomous, constrictor, or ambush).
I defined functional groups in InfoStat (Di Rienzo et al. 2011); following Pla et al. (2012), I
transformed categorical variables into a set of quantitative principal coordinates with
multidimensional scaling and retained a set of axes that explained ≥ 85% of variation, then
used Euclidian distances and the Ward linkage algorithm to create dendrograms for frogs
and reptiles separately. I retained four functional groups each for frogs and reptiles and
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used MANOVA with Hotelling post-test and Bonferroni adjustment to assess grouping
significance.
I modeled abundance of functional groups on vegetation type with Poisson GLM
and compared to the null model with Χ2 analysis of deviance (or quasi-Poisson GLM and
F-tests to account for overdispersion) (Zuur et al. 2009). Similarly I compared proportional
abundance of each functional group across vegetation types with binomial GLM (or quasibinomial to account for overdispersion) (Zuur et al. 2009). I also tallied the number of
functional groups represented per vegetation type.
I compared environmental variables among vegetation types with ANOVA. I
dropped canopy cover and height from further analyses because they were significantly
collinear with each other and temperature range, herb cover, and litter depth with
correlation coefficient magnitude ≥ 0.6 (Zuur et al. 2009); I retained the latter variables
plus litter cover, soil pH, and mean temperature. I used Poisson GLM to assess the
relationships between environmental variables and frog and reptile richness and abundance
and the abundance of functional groups. For each case, I parameterized the model set of
single-order combinations of six environmental variables and a null model. I used AICc to
compare models and performed multi-model averaging across models with AICc
differences (Δi) < 4 (Grueber et al. 2011). Where overdispersion was present, I used quasiPoisson GLMs and quasi-AICc (QAICc) (Zuur et al. 2009).
Results
I captured 436 individuals representing 17 frog and 20 reptile species (Table 3.1). Nine
frog species were recorded with audio recorders (three that were not captured in arrays),
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bringing the number of species recorded to 40. Many calls carried further than the 50m
estimated by Hilje and Mitchell Aide (2012); thus, I excluded five species recorded in
audio recordings that are only known to call from water bodies (Channing 2001, du Preez
and Carruthers 2009), resulting in 38 species considered in further analyses (Table 3.1).
Only Amietophrynus gutturalis (Table 3.1 provides common names) was recorded in every
vegetation type.
Richness, abundance, and diversity
Sampling approached but did not reach an asymptote for frogs or reptiles overall or any
vegetation type, and 95% CI for frog and reptile abundances overlapped (Fig. S.3.1). The
proportion of expected species that I observed was 71-93% for frogs and 63-84% for
reptiles and differed by vegetation type (Table 3.2). Richness estimators varied but were
similar within groups, except for reptiles in forest (Table 3.2). Incidence-based estimators
were higher than abundance-based estimators for frogs because they included auditory
records (Table 3.2).
While species and individuals recorded per array did not differ significantly
between vegetation types (Fig. 3.2), 95% CI indicated Chao1 for frogs was significantly
higher in forest, acacia woodland, and plantation than in degraded forest or cultivation.
Chao2 for frogs did not differ significantly among vegetation types. Other estimators
ranked vegetation types variably but suggested higher richness in forest, acacia woodland,
and plantation and lower richness in degraded forest and cultivation (Table 3.2). Reptile
Chao1 was significantly higher in forest, acacia woodland, and plantation than in
cultivation, while Chao2 was significantly higher in forest than degraded forest and
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cultivation (Table 3.2). Other estimators consistently ranked reptile richness highest in
forest; intermediate in acacia woodland and plantation; and lowest in degraded forest and
cultivation.
For both frogs and reptiles, Shannon diversity was highest in plantation and lowest
in cultivation and degraded forest (Table 3.2). Rényi profiles confirmed these rankings and
showed diversity rankings of other vegetation types depended on the influence of evenness,
i.e. Rényi profiles intersected (Kindt and Coe 2005) (Fig. S.3.2).
Composition
ANOSIM of square-root-transformed data indicated significant difference in composition
among vegetation types (Table 3.3). Frog community structure in forest differed
significantly from that in acacia woodland, plantation, and cultivation, while degraded
forest differed from cultivation. Reptile community structure differed significantly between
natural vegetation types (forest, degraded forest, or acacia woodland) and anthropogenic
types (cultivation or plantation), except degraded forest did not differ significantly from
plantation. NMDS ordination illustrated these patterns (Fig. S.3.3). Results based on raw
abundance and frog incidence data were similar (Fig. S.3.3, Table S.3.1).
Functional groups
Group size was similar, and group descriptions were ecologically sensible (Tables 3.1 &
3.4). Traits differed between functional groups for frogs (Wilks’ λ = 1.6x10-4, F12,29 =
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3. Herpetofauna Over a Land-Use Gradient
64.82, p < 0.001) and reptiles (Wilks’ λ = 2.4x10-5, F24,27 = 42.63, p < 0.001), and Hotelling
post-tests indicated significant differences among all functional groups.
Vegetation type was a significant predictor of abundance for functional groups F2
and R2 and of proportional abundance for F1, F2, F3, and R2 (Table 3.4). Proportional
abundance of several functional groups changed directionally along the gradient from
forest to cultivation, while number of groups represented decreased (Fig. 3.3).
Environmental predictors
Environmental variables differed significantly among vegetation types (Fig. 3.4). They
were variably effective at predicting frog and reptile richness and abundance; proportion of
deviance explained by the global model ranged from 0.06 for reptile richness to 0.67 for
abundance of functional group F2 (Table S.3.2). Generally, models performed better for
frogs than reptiles and for functional group abundance than overall richness and abundance
(Table S.3.2, S.3.3). The importance and effect of environmental variables differed among
dependent variables (Table S.3.3).
Discussion
I assessed how a rich herpetofaunal community responded to a land-use gradient to
elucidate the consequences of forest transformation and inform land-use planning. Onequarter of the species I encountered are endemic or near-endemic to Maputaland, a third to
southern Africa, and all but one to Africa (Branch 1998, du Preez and Carruthers 2009).
My study falls at the juncture of three global conservation concerns: tropical forest loss
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(Wright and Muller-Landau 2006), immense pressure on coastal habitat (Arthurton et al.
2006), and frog and reptile extinction crises (Stuart et al. 2008, Böhm et al. 2013).
Richness, diversity, composition
The number of species and individuals observed was highest in forest. Richness estimators
for both frogs and reptiles indicated higher richness in forest, acacia woodland, and
plantation and lower richness in degraded forest and cultivation. Thus, richness did not
monotonically decrease along the gradient with subjective decrease in forest similarity.
Diversity was generally highest in plantation and lowest in degraded forest and cultivation.
Community composition in forest and degraded forest differed from anthropogenic landuses, i.e. plantation and cultivation, while the acacia woodland community grouped with
the former for reptiles and the latter for frogs.
Degraded forest hosted an impoverished version of the forest assemblage for both
frogs and reptiles. This was unexpected based on studies of herpetofaunal response to
selective logging, which may be analogous to the processes that degrade forests in the
study area, e.g. physical disturbance by humans and livestock and effects from neighboring
transformed land. A recent review found no evidence for loss of herpetofaunal richness in
selectively logged areas (Gardner et al. 2007a). However, in West African forests, Hillers
et al. (2008) found that degradation, represented by structural measures, was associated
with reduced richness and altered community composition of leaf-litter frogs, possibly via
changes in microclimate. In my study, degraded forest had lower mean canopy cover and
height but higher ranges of these and of herb cover and litter depth than did forest. Thus,
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altered microclimate may drive the low abundance, richness, and diversity observed in
degraded forest.
Acacia woodland represents a seral stage of forest succession (van Aarde et al.
1996), expected to support lower richness than old-growth forest (Wassenaar et al. 2005).
My results are similar to other studies’ (Gardner et al. 2007a, Wanger et al. 2010, Hilje and
Mitchell Aide 2012) that report lower richness in new-growth but a substantial
representation of old-growth species. However, that community structure in acacia
woodland was similar to that of forest for reptiles but not for frogs hints at barriers to frog
recolonization of new-growth forest.
Plantations of exotic trees hosted structurally distinct frog and reptile communities
compared to forest but a high richness and diversity, in agreement with other studies
(Vonesh 2001, Gardner et al. 2007a). Plantation communities likely combine species
typical of forest with species characteristic of open habitats and are not necessarily
biodiversity deserts as is often assumed (see Armstrong et al. 1998). Nonetheless, some
studies have found plantations to be depauperate in amphibians (e.g. Kudavidanage et al.
2011). Inland from my study area, Russell and Downs (2012) found few frog species in
large-scale eucalyptus plantations. The plantations in my study were small-scale with
small, coppiced trees and had vegetated understories. Thus, the effects of plantation
variables, e.g. size, age, and management, require further study.
Consistent with other studies (e.g. Russell and Downs 2012), sugar cane cultivation
had few species, few individuals, and low diversity. However, cultivation harbored species
absent or rare in other vegetation types, e.g. Psammophis brevirostris, but they were wideranging, open habitat species (Branch 1998, du Preez and Carruthers 2009).
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Functional groups
A trait- rather than species-based approach is expected to better quantify and predict the
effects of disturbance on communities and the consequences for ecosystem functionality
(Mouillot et al. 2013). Functional groups are known to be differentially susceptible to
disturbance; e.g. small-bodied frogs and those that lay eggs in soil are thought to be more
disturbance-sensitive than large-bodied frogs and those that lay eggs in water (SuazoOrtuno et al. 2008). In my study, fossorial/ground-dependent frogs (F2) and reptiles (R3)
decreased along the gradient from forest to cultivation in abundance and proportional
abundance. Vegetation-dwelling frogs (F4) were not found in plantation or cultivation.
These groups appear to be particularly challenged in human-modified habitats, likely
because of changes in soil and vegetation properties, a hypothesis supported by the results
of modeling functional group abundance on environmental variables.
The number of functional groups per vegetation type declined along the gradient
from all eight recorded in forest to just five in cultivation, in line with the suggestion that
functional diversity declines monotonically along a disturbance gradient in contrast to
species richness (Mouillot et al. 2013). Few studies have investigated functional aspects of
herpetofaunal response to land-use change (Gardner et al. 2007a). Pineda et al. (2005)
found reduced frog guild richness in coffee plantations compared to forest. My results
agree with, and extend to plantations and cultivation, the observation that frog functional
diversity is lower in degraded forest than in primary forest (Ernst et al. 2006). Loss of
functional groups implies increased overlap among species’ trait profiles and, thus,
functional homogenization (Braiser and Lockwood 2011), and has consequences for
106
3. Herpetofauna Over a Land-Use Gradient
ecosystem function (e.g. Tilman et al. 2001, O'Connor and Crowe 2005, Cardinale et al.
2012). Therefore, measures of functional diversity should complement those of species
richness (Ernst et al. 2006), and the effects of functional diversity loss in herpetofaunal
communities warrant further investigation.
Environmental predictors
Environmental variables were good predictors of abundance of frog functional groups,
probably because functional groups combine species with similar roles in the ecosystem,
which are likely similarly dependent on particular conditions. F1, F2, and F3 all showed a
significant negative relationship with herb cover and mean temperature, while soil pH and
litter cover had positive effects. Abundance of F4 was positively related to litter depth,
which conceivably reflects dependence of vegetation-dwelling frogs on increased canopy
cover or vegetation density rather than litter depth per se.
The relationship between frog abundance and environmental variables suggests that
frogs respond to the vegetation-type gradient due to changes in microhabitat conditions.
Thus, environmental variables have potential for predicting frog community responses to
land uses not assessed here. Land uses resulting in soil acidification, reduced litter cover, or
increased herb cover or mean temperature appear to be generally negative for frogs
(Wyman 1988, Suazo-Ortuno et al. 2008).
Environmental variables were less effective predictors of reptile functional group
abundance. However, R1, ambush-hunting and constricting snakes, was positively
associated with litter cover as was R2, fossorial reptiles, which was also negatively
associated with mean temperature. For these species, litter cover may offer concealment
107
3. Herpetofauna Over a Land-Use Gradient
and could be associated with prey availability; however, abundance of R3, ground-active
and climbing lizards, was negatively associated with litter cover.
Un-modeled factors or a lesser dependence on specific microhabitat may explain
the weaker relationship between reptile abundance and environmental variables. Compared
to reptiles, frogs and frog eggs have more stringent moisture and temperature requirements
and are sensitive to solar radiation (Gibbons et al. 2000, Suazo-Ortuno et al. 2008).
Furthermore, reptiles often move greater lifetime distances than do frogs (Gibbons et al.
2000), so their occurrence may more often reflect mere transience.
Constraints and future research
Interpreting herpetofaunal studies requires caution due to sampling constraints, low capture
success, and limited spatial extent common in many studies, and sampling efficacy is
species- and habitat-dependent (Gardner et al. 2007a, Ribeiro-Júnior et al. 2008). Thus, I
used a combination of methods emphasizing passive techniques to reduce observer bias,
while maintaining standardized effort across vegetation types. Still, the samples are
unlikely to represent the complete community due to true rarity and furtive habits of many
species. Nonetheless, the standardized nature of my sampling enables future work to build
on my capture data by increasing the coverage extent or investigating other vegetation
types or seasons.
I experienced low capture success, a common challenge in the tropics where high
richness and rarity is expected (Gardner et al. 2007a). Concerns over cost (~32 personhours per array), introduction of seasonal effects (e.g. Gardner et al. 2007b), and the
impracticality of increasing the study area (coastal forest gives way to grassland and
108
3. Herpetofauna Over a Land-Use Gradient
savanna inland) prohibited additional trapping arrays. Nonetheless, the percentage of
species observed to estimated richness was comparable to other studies (e.g. Bell and
Donnelly 2006, Gardner et al. 2007c, Suazo-Ortuno et al. 2008), although many species
were recorded infrequently. Clearly, failure to detect a species does not imply absence, nor
does presence imply persistence solely within that habitat (Gardner et al. 2007a).
Persistence and the potential for ecological traps (Battin 2004) should be further
investigated.
Future research on species-specific responses to land-use change would be useful
because species respond idiosyncratically (Gardner et al. 2007a). The functional group
approach goes some way towards assessing differential responses of components of the
community. However, broadly defined functional groups overestimate redundancy
(Cadotte et al. 2011). Thus, loss of functional groups across the gradient likely
underestimated true functional diversity loss (Petchey and Gaston 2002).
Conservation implications
Two species in this study are of explicit conservation concern (Afrixalus spinifrons and
Hemisus guttatus (IUCN 2012)), and Botts et al. (2013) demonstrated that habitat specialist
frogs in the region have undergone range contractions, likely due to habitat loss. Therefore,
small-range, endemic species are of concern even if not formally threatened, while most
reptile species observed in this study have not even been evaluated (IUCN 2012).
My results highlight the sensitivity of fossorial/ground-dependent herpetofauna to
forest transformation. Unfortunately, this group includes many small-range species, e.g.
Leptopelis natalensis and Acontias plumbeus. Thus, although they are difficult to study
109
3. Herpetofauna Over a Land-Use Gradient
(Maritz and Alexander 2008), fossorial species warrant monitoring, especially because they
are poorly known (Böhm et al. 2013). Vegetation-dwelling frogs should also be monitored.
Maintaining old-growth forest is important for conserving herpetofauna. However,
other vegetation types did support occurrence of some species, which should be considered
in land-use planning, especially given the conservation challenges imposed by the linear
nature of the coastal forest system (Olivier et al. 2013, van Aarde et al. 2013). Degraded
forest harbored particularly low richness and diversity, so degradation must be prevented, a
concern even within protected areas because many allow access to local people for wood
collection and grazing or lack management altogether (Kyle 2004). Restoration projects
that generate acacia woodland could provide habitat and increase connectivity of forest
fragments. Plantations may hold some value for connecting not only forest fragments, but
perhaps also savanna and grassland fragments due to their diverse combination of forest
and open-habitat species including species of conservation concern, e.g. Hemisus guttatus.
However, caution is required in extrapolating my results from small- to large-scale
plantations, and hydrological impacts may negatively offset conservation value (Armstrong
et al. 1998). Finally, sugar cane cultivation was of little value for forest associated
herpetofauna.
110
3. Herpetofauna Over a Land-Use Gradient
Acknowledgements
M.J.T. is supported by an NSF Graduate Research Fellowship. I thank Richards Bay
Minerals for financial support and A. Armstrong, B. Branch, R. Guldemond, A. Harwood,
T. Lee, J. Marais, L. Minter, P. Olivier, A. Prins, L. du Preez, L. Snyman, J. Tarrant, and
G. Varrie for technical support.
111
3. Herpetofauna Over a Land-Use Gradient
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3. Herpetofauna Over a Land-Use Gradient
Figures
Fig. 3.1. Study area map indicating location of trapping arrays in five vegetation types (F =
forest, DF = degraded forest, AW = acacia woodland, P = plantation, C = cultivation); inset
shows study area location in southern Africa.
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3. Herpetofauna Over a Land-Use Gradient
a.
5
Reptile species per array
Frog species per array
8
6
4
2
0
DF
AW
P
3
2
1
C
F
c.
60
40
20
0
F
DF
AW
P
C
d.
Reptile individuals per array
Frog individuals per array
4
0
F
80
b.
DF
AW
P
C
8
6
4
2
0
F
DF
AW
P
C
Fig. 3.2. Vegetation type (F = forest, DF = degraded forest, AW = acacia woodland, P =
plantation, C = cultivation) was not a significant predictor in Poisson or quasi-Poisson
GLM for species observed per array for (a) frogs (Χ2 = 1.87, df = 4, p = 0.76) and (b)
reptiles (Χ2 = 4.73, df = 4, p = 0.32) or individuals recorded per array for (c) frogs (Φ =
11.40, F4,25 = 2.70, p = 0.05) and (d) reptiles (Φ = 1.18, F4,25 = 1.05, p = 0.40). Graphs
illustrate mean and 95% CI.
121
3. Herpetofauna Over a Land-Use Gradient
1.0
a.
0.8
F4
Proportional abundance of functional groups
0.6
F3
F2
0.4
F1
0.2
0.0
1.0
b.
F
DF
AW
P
C
0.8
0.6
R4
R3
R2
0.4
R1
0.2
0.0
F
DF
AW
P
C
Fig. 3.3. Proportional abundance of functional groups for (a) frogs and (b) reptiles for each
vegetation type (F = forest, DF = degraded forest, AW = acacia woodland, P = plantation,
C = cultivation).
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3. Herpetofauna Over a Land-Use Gradient
a.
150
8
Litter cover (%)
Litter depth (cm)
10
100
6
4
2
0
F
DF
AW
P
F
c.
9
0
AW
P
C
F
DF
AW
P
C
F
DF
AW
P
C
DF
AW
P
C
d.
7
6
5
4
F
DF
AW
P
C
e.
30
Temperature range (C)
30
Mean temperature (C)
DF
8
50
-50
28
26
24
22
20
DF
AW
P
20
10
C
g.
6
Canopy height class
150
f.
0
F
Canopy cover (%)
50
0
C
Soil pH
Herb cover (%)
100
b.
100
50
h.
4
2
0
0
F
DF
AW
P
C
F
Fig. 3.4. Environmental variables differed significantly among vegetation types (F = forest,
DF = degraded forest, AW = acacia woodland, P = plantation, C = cultivation) for (a) litter
depth (F4,25 = 4.69, p < 0.01), (b) litter cover (F4,25 = 24.70, p < 0.001), (c) herb cover (F4,25
= 6.02, p < 0.01), (d) soil pH (F4,25 = 11.08, p < 0.001), (e) mean temperature (F4,25 = 4.66,
p < 0.01), (f) temperature range (F4,25 = 15.38, p < 0.001), (g) canopy cover (F4,25 = 25.29,
p < 0.001), and (h) canopy height (in classes: 1 = 0–2 m, 2 = > 2–4 m, 3 = > 4–6 m, 4 = >
6–8 m, and 5 = > 8 m) (F4,25 = 19.83, p < 0.001). I illustrate means and 95% CI.
123
3. Herpetofauna Over a Land-Use Gradient
Tables
Table 3.1. Abundance of frog and reptile species captured in trapping arrays (where *
indicates confirmation of frog species by audio recording a) across vegetation types (F =
forest, DF = degraded forest, AW = acacia woodland, P = plantation, C = cultivation), and
functional group to which species are assigned based on functional traits.
Scientific name, common name b
F
DF
AW
P
C
Total Functional
group
Amietophrynus gutturalis, guttural toad
41
44
16
27
33
161
F3
Arthroleptis wahlbergi, bush squeaker
89
51
10
5
0
155
F2
0
0*
0*
0
10
10*
F1
3
2
2
0
2
9
F2
Phrynobatrachus mababiensis, dwarf puddle frog
6
0
0
2
0
8
F1
Afrixalus spinifrons (spinifrons), Natal leaffolding frog
2
2
0
0
0
4
F4
1
2
0
1
0
4
F3
0
0
0
3
0
3
F2
Phrynobatrachus acridoides, East African puddle
frog
0
0
0
0
3
3
F1
Afrixalus fornasinii, greater leaf-folding frog
2
0
0
0
0
2
F4
Hyperolius pusillus, water lily frog
0
0
1
0
1
2
F1
Kassina senegalensis, bubbling kassina
1*
0
0
1*
0
2*
F1
Leptopelis natalensis, Natal tree frog
1
1*
0
0
0*
2*
F2
Amietophrynus garmani, eastern olive toad
0
0
1
0
0
1
F3
Hemisus guttatus¸ spotted shovel-nosed frog
0
0
0
1
0
1
F2
Hyperolius tuberilinguis, tinker reed frog
0
0
1
0
0
1
F4
Strongylopus fasciatus, striped stream frog
0
0
0
1
0
1
F2
Ptychadena oxyrhynchus, sharp-nosed grass frog
0
0*
0*
0*
0*
0*
F3
Scelotes mossambicus, Mozambique dwarf
burrowing skink
6
5
2
0
0
13
R2
Panaspis wahlbergii, Wahlberg’s snake-eyed
skink
0
0
1
3
3
7
R3
Mabuya varia, variable skink
0
1
6
0
0
7
R3
Lygodactylus capensis (capensis), Cape dwarf
gecko
0
0
0
1
3
4
R3
Zygaspis vandami (arenicola), Van Dam’s round-
1
0
3
0
0
4
R2
Frogs
Phrynobatrachus natalensis, snoring puddle frog
Breviceps sopranus, whistling rain frog
c
Amietophrynus rangeri, raucous toad
Breviceps mossambicus, Mozambique rain frog
c
Reptiles
124
3. Herpetofauna Over a Land-Use Gradient
headed worm lizard
Mabuya striata (striata), striped skink
0
0
0
0
3
3
R3
Hemidactylus mabouia, Moreau’s tropical house
gecko
1
0
0
1
0
2
R3
Acontias plumbeus, giant legless skink
2
0
0
0
0
2
R2
Gerrhosaurus flavigularis, yellow-throated plated
lizard
0
0
0
0
1
1
R3
Psammophis brevirostris (brevirostris), shortsnouted grass snake
0
0
0
1
3
4
R4
Leptotyphlops sp., thread snakes d
0
0
0
4
0
4
R2
Crotaphopeltis hotamboeia, herald snake
0
1
0
2
0
3
R4
Psammophis mossambicus, olive grass snake
0
0
1
2
0
3
R4
Aparallactus capensis, Cape centipede eater
1
0
0
2
0
3
R2
Causus rhombeatus¸ rhombic night adder
1
0
1
0
0
2
R4
Lamprophis fuliginosus, brown house snake
0
0
0
1
0
1
R1
Philothamnus natalensis (natalensis), eastern
green snake
1
0
0
0
0
1
R1
Mehelya nyassae, black file snake
1
0
0
0
0
1
R1
Thelotornis capensis (capensis), vine snake
0
0
1
0
0
1
R4
Philothamnus hoplogaster, green water snake
1
0
0
0
0
1
R1
Total individuals observed
161
109
46
58
62
436
Total species observed (including audio
recordings)
18
9(11)
13(15) 17(18) 10(12) 37(38)
a
Audio records of guttural toad Amietophrynu gutturalis, water lily frog Hyperolius pusillus, tinker reed frog
Hyperolius tuberilinguis, painted reed frog Hyperolius marmoratus, and red-legged kassina Kassina
maculata were excluded because they only call from water bodies.
b
Scientific and common names follow nomenclature in du Preez and Carruthers (2009) and Branch (1998).
c
These Breviceps species are cryptic (Minter 2003), and while species identification was confirmed by expert
examination of photographs, only genetic identification would provide certainty; these results should be
interpreted with caution.
d
I did not identify leptotyphlops to species level because they are cryptic, and the complex is under further
revision. Currently, four species are known from the region of the study (Branch 1998).
125
3. Herpetofauna Over a Land-Use Gradient
Table 3.2. Observed species richness and abundance, abundance- and incidence-based
richness estimators, percent of predicted richness actually observed, and Shannon diversity
of frogs and reptiles across five vegetation types (F = forest, DF = degraded forest, AW =
acacia woodland, P = plantation, C = cultivation).
Species Ind.
obs.
obs.
Abundance-based estimators
Chao 1
(95% CI)
ACE Jack Jack
1
2
Incidence-based
estimators
Chao 2
ICE
Percent Shannon
observed diversity
(range)
(95% CI)
Frogs
Total
17 (18)
369
18.2 (17.1–27.4)
20.6 22.8 23.9 22.8 (18.9–46.9) 22.9 71–93%
1.35
9
146
10.0 (9.1–19.7)
12.2 12.3 13.4
10.3 (9.1–19.8) 14.6 62–90%
1.09
DF
6 (8)
102
6.0 (6.0–6.0)
6.7
8.5
10.0
9.7 (8.2–21.7)
14.2 56–100%
0.99
AW
6 (8)
31
7.5 ( 6.2–21.1)
12.0
8.5
10.0
12.2 (8.6–35.2) 18.4 43–80%
1.22
P
8 (9)
41
11.0 (8.4–31.0)
10.8 12.2 14.4
10.3 (9.1–19.8) 13.7 56–87%
1.23
C
5 (7)
49
5.0 (5.0–5.0)
5.6
8.7 (7.2–20.7)
15.6 45–100%
0.97
Total
20
67
23.8 (20.6–42.0)
23.8 27.7 31.6 25.4 (21.1–46.3) 28.5 63–84%
2.71
F
9
15
19.5 (11.0–63.2)
37.5 15.7 21.0 32.3 (15.2–96.6) 67.8 13–57%
1.9
DF
3
7
4.0 (3.1–15.9)
7.0
6.0
3.8 (3.06–14)
6.7
43–79%
0.8
AW
7
15
10.0 (7.4–30.0)
13.5 10.3 12.5
9.5 (7.3–26.6)
11.9 52–74%
1.68
P
9
17
10.5 (9.2–21.5)
12.0 13.2 14.4
10.7 (9.2–21.1) 14.6 62–86%
2.07
C
5
13
5.0 (5.0–6.6)
5.4
5.3 (5.0–10.2)
1.55
F
6.7
6.9
Reptiles
4.7
6.7
6.9
6.6 72–100%
126
3. Herpetofauna Over a Land-Use Gradient
Table 3.3. Analysis of similarity (ANOSIM) results comparing frog and reptile community
composition among vegetation types based on Bray–Curtis similarity of square-roottransformed abundance data.
Vegetation type comparison
Frogs (Global R = 0.174,
p < 0.01)
Reptiles (Global R =
0.194, p < 0.001)
R statistic a
pb
R statistic a
pb
Forest–degraded forest
−0.02
0.52
−0.05
1.00
Forest–acacia woodland
0.22
< 0.05*
0.15
0.08
Forest–plantation
0.24
< 0.05*
0.25
< 0.05*
Forest–cultivation
0.79
< 0.01**
0.38
< 0.001***
Degraded forest–acacia woodland
0.00
0.40
0.09
0.2
Degraded forest–plantation
−0.01
0.47
0.18
0.06
Degraded forest–cultivation
0.27
< 0.05*
0.28
< 0.05*
Acacia woodland–plantation
0.05
0.20
0.30
< 0.01**
Acacia woodland–cultivation
0.16
0.07
0.35
< 0.01**
Plantation–cultivation
0.11
0.10
0.09
0.17
a
ANOSIM generates an R statistic ranging from −1 (where similarities across different vegetation types are
higher than within types) to 1 (where similarities within types are higher than between types) (Clarke and
Gorley 2001).
b
Significance of each comparison is indicated by *p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.
127
3. Herpetofauna Over a Land-Use Gradient
Table 3.4. Functional group descriptions (Fx are frog groups, Rx are reptile groups),
number of species per group, and statistics describing significance of vegetation type as a
predictor of abundance and proportional abundance of each functional group in Poisson (or
quasi-Poisson) and binomial (or quasi-binomial) GLMs respectively (see Table 3.1 for
species composition of groups).
Functional
Group
Description
Number
of
species
Vegetation type
as predictor of
abundance
Vegetation type
as predictor of
proportional
abundance
F1
Small, ground-dwelling frogs (except
water lily frog) that lay eggs in water
5
Φ = 2.05, F4,25 =
1.93, p = 0.14
Χ2 = 27.05, df =
4, p < 0.001
F2
Fossorial or ground-dwelling species
(except Natal tree frog) that lay eggs in the
ground, i.e. ground dependent. Tadpoles of
three species develop in the ground
6
Φ = 7.32, F4,25 =
5.89, p < 0.01
Φ = 1.62, F4,24 =
11.60, p < 0.001
F3
Large, ground-dwelling frogs that lay eggs
in water
4
Φ = 4.82, F4,25 =
0.79, p = 0.54
Φ = 1.25, F4,24 =
7.93, p < 0.001
F4
Small, vegetation-dwelling frogs that lay
eggs in vegetation
3
Χ2 = 9.15, df = 4,
p = 0.06
Φ = 3.78, F4,24 =
0.29, p = 0.88
R1
Snakes that attack by constricting or
ambush, tend to be shorter than R4
4
Χ2 = 8.38, df = 4,
p = 0.08
Χ2 = 7.69, df = 4,
p = 0.10
R2
Legless, burrowing species, tend towards
small clutch size
5
Χ2 = 14.01, df =4,
p < 0.01
Φ = 1.69, F4,21 =
3.09, p < 0.05
R3
Ground-active and climbing lizards,
locomotion with legs, hunt by ambush
6
Φ = 1.64, F4,25 =
2.15, p = 0.10
Φ = 1.84, F4,21 =
2.56, p =0.07
R4
Venomous snakes, tend to be longer than
R1
5
Φ = 1.03, F4,25 =
1.07, p = 0.39
Φ = 1.17, F4,21 =
0.68, p = 0.61
128
3. Herpetofauna Over a Land-Use Gradient
Branch, B. 1998. Field Guide to Snakes and Other Reptiles of Southern Africa. Struik
Publishers, Cape Town.
Clarke, K. R. and R. N. Gorley. 2001. Change in Marine Communities: An Approach to
Statistical Analysis and Interpretation. Primer-E, Plymouth, United Kingtom.
du Preez, L. H. and V. Carruthers. 2009. A Complete Guide to Frogs of Southern Africa.
Struik Nature, Cape Town.
Minter, L. R. 2003. Two new cryptic species of breviceps (Anura: Microhylidae) from
Southern Africa. African Journal of Herpetology 52: 9-21.
129
3. Herpetofauna Over a Land-Use Gradient
Supplementary Material
Appendix S1
Pitfall traps:
Pitfall traps were dark plastic 20 liter buckets dug into the ground such that the rim of the
bucket was flush with ground level. Several small drainage holes (0.5 cm) were drilled in
the bottom of each bucket. Each trap array contained seven pitfall buckets, one at the
central point, and two along each arm. Bucket lids, to protect buckets from sun, rain, and
predators, were suspended 10 cm over buckets using wire stands. 3 cm of soil and leaf litter
were placed inside buckets along with a wet sponge to maintain a suitable environment for
trapped organisms. Sampling effort in pitfall traps was 35 trap nights per array, 210 trap
nights per vegetation type, and 1050 trap nights in the overall study.
Funnel traps:
I constructed funnel traps out of 0.5 cm wire mesh following Fisher et al. (2008). Funnel
traps were cylinders 90 cm long and 14 cm in diameter with inverted cone funnels with 4
cm openings inserted in each end. Funnel traps were installed along each side of each drift
fence arm with soil built up around the bottom to guide amphibians and reptiles moving
along the fence into the funnel. Funnels were covered with leaves and vegetation to provide
shade for trapped organisms. Sampling effort in funnel traps was 30 trap nights per array,
180 trap nights per vegetation type, and 900 trap nights in the overall study.
130
3. Herpetofauna Over a Land-Use Gradient
Cover boards:
Four cover boards were placed on the ground in an array 10 meters beyond the final pitfall
bucket of the northern most pointing drift fence arm. The boards were 60 cm square sheets
of 2 cm plywood.
PVC pipe traps:
Pipe trap were mounted on a tree nearest the cover boards at each array point. Each pipe
trap array consisted of four, 60 cm long, opaque white PVC pipes. I inserted two pipes, one
of 16 mm internal diameter and one of 44 mm internal diameter, into the ground near the
base of a tree. I capped one end of another two pipes, one of each of the two diameters,
fixed them together with cable ties, and hung them vertically from the tree trunk such that
the open end was at a height of 2 m. The caps allowed retention of standing water in the
bottom of the hanging pipes, and I drilled a hole in the pipes 15 cm from the bottom to
prevent the pipes from totally filling with water (following recomendations in Boughton et
al. 2000). I installed pipes on a variety of tree species with circumference at breast height
ranging from 10 cm to 200 cm (mean 53.7 cm, standard deviation 41.2 cm). In forest and
degraded forest, I commonly hung pipes on White Stinkwood Celtis africana and
Horsewood Clausena anisata trees. In acacia woodland, pipes were hung on Sweet Thorn
Acacia karroo while I used eucalyptus trees in woodlots. At five of the six sugar cane
cultivation array sites, there were no trees nearby, so all four pipes were inserted into the
ground. I hung pipes in a dead tree of unknown species at one cultivation site.
131
3. Herpetofauna Over a Land-Use Gradient
Acoustic sampling:
Automated acoustic recordings were made at each site with Song Meter SM2+ Terrestrial
Acoustic Recorders (manufactured by Wildlife Acoustics, Concord, Massachusetts).
Recorders were mounted to a tree 1 m off the ground, within a 15 m radius of the center
bucket of the array, and set to record at a sample rate of 44,100 Hz for 5 minutes every
hour, on the hour, for a 24-hour period. Acoustic detection depends on the power of each
species’ call, but estimates suggest that calling amphibians will be picked up by audio
recorders over a 50 m radius (Hilje and Mitchell Aide 2012). I analyzed audio files with
Raven Pro version 1.4 software (Bioacoustic Research Program, Cornell Lab of
Ornithology, Ithaca, New York) to visualize spectrograms concurrently to listening to
recordings. Calling amphibians were identified by comparison with species-specific
spectrograms and audio recordings provided in du Preez and Carruthers (2009). Overall, I
analyzed 720 5-min recordings, or 120 min per site.
Active search:
One active search was carried out per sampling array, and all searches were carried out by
the same individual expert observer. Each search was performed during daylight hours and
lasted 30 min, in which the observer searched an area extending roughly 50 m from the
central pitfall bucket of each array. The observer searched the area at will, focusing on
particular areas one might expect to encounter herpetofauna, e.g. under rocks, on trees, in
fallen logs, and in leaf litter. All amphibian and reptile species identified visually by the
observer were recorded.
132
3. Herpetofauna Over a Land-Use Gradient
Incidental recordings:
I recorded species found when installing or removing trap arrays, which was a relatively
standardized effort. For the most part, species found included fossorial species caught when
digging holes for the pitfalls or trenches for the drift fences.
Environmental variables:
I measured climatic and structural environmental variables to characterize study sites. At
each sampling array, I used HOBO data loggers mounted on rods 20cm from the ground to
record temperature every 10 minutes for the duration of the five days that each trapping
array was active. I then calculated a mean temperature for each array and the range in
degrees from the minimum and maximum temperature recorded on each data logger. I
recorded structural variables including canopy cover, canopy height, litter depth, litter
cover, and herb cover. Canopy cover was measured at three points, each 5 m away from the
center bucket of the trapping array, by visually estimating coverage when looking through
a 10 cm tube of 4 cm diameter. Canopy height was assigned to classes (0-2 m, > 2-4 m, >
4-6 m, > 6-8 m, and > 8 m). The other structural variables were measured in a 1 m x 1 m
PVC pipe frame at each of the three sampling points. Litter depth was measured to the
nearest cm with a ruler at the center of the frame, while litter cover (woody debris and
leaves) and herb cover (herbaceous vegetation excluding grasses and trees) were visually
estimated to the nearest 5%. For each array, I averaged the three values for each variable to
achieve a single value. To calculate soil pH, a trowel-full of soil was collected from each of
the three sampling points at each array and mixed in a bag. I oven-dried 50 g of each soil
sample for 24 hours at 70 ̊c. I combined 15 ml of each dried soil sample with 75 ml
133
3. Herpetofauna Over a Land-Use Gradient
distilled water, shook for 1 min, let sit for 1 hour, shook again, and measured pH with a
Consort c562 meter.
Geographic gradients
For each array point, I measured distance to the coast and distance along a southwest—
northeast gradient according to distance from the most southwesterly array point. I assessed
whether vegetation types differed significantly in coastal distance or southwest—northeast
gradient with ANOVA. Vegetation types differed significantly in their distance from the
coast (F4,25 = 7.40, p < 0.01), and Tukey’s multiple comparison test indicated significant
differences between plantation points and others. Besides plantations, the distance from the
coast of other vegetation types did not differ significantly from each other. Contrastingly,
there was no significant difference in southwest—northeast gradient among vegetation
types (F4,25 = 0.86, p = 0.50). Thus, I assessed if coastal distance of array points effected
observed richness (species per array) and abundance (individuals per array) with Poisson
generalized linear modeling (GLM) (z-values) or quasi-Poisson GLM (t-values) to account
for overdispersion (Zuur et al. 2009). Distance from coast was not a significant predictor
for frog richness (z-value = 1.42, p = 0.16), frog abundance (Φ = 18.99, t-value = 0.14, p =
0.89), reptile richness (z-value = 0.65, p = 0.51), or reptile abundance (Φ = 1.22, t-value =
0.73, p = 0.47).
134
3. Herpetofauna Over a Land-Use Gradient
a
c
b
d
Fig. S.3.1. Species accumulation curves for (a) the total frog dataset, (b) frog samples
grouped by vegetation type, (c) the total reptile dataset, and (d) reptile samples grouped by
vegetation type. Error bars represent 95% CI and in (b) and (d) are shown only for forest.
135
3. Herpetofauna Over a Land-Use Gradient
F
2.0
P
AW
DF
1.0
H-alpha
1.5
C
0.5
DF
AW
F
P
C
0.0
a
0
0.25
0.5
1
2
4
8
Inf
alpha
2.0
P
F
AW
C
P
DF
1.0
H-alpha
1.5
C
0.5
AW
F
0.0
DF
b
0
0.25
0.5
1
2
4
8
Inf
alpha
Fig. S.3.2. Rényi diversity profiles for (a) frogs and (b) reptiles in different vegetation
types (dark blue is forest (F); green is degraded forest (DF); black is acacia woodland
(AW), light blue is plantation (P), red is cultivation (C). Rényi diversity profiles are
calculated with the formula Hα = ln(Σ piα) / (1–α), where Hα is the diversity value, pi values
are the proportions of each species (which are taken to the exponent α and summed for all
species recorded), and α is a parameter taken from 0 to infinity to generate the profile
(Kindt and Coe 2005). Values of Hα reflect species richness at α = 0, are equivalent to the
Shannon diversity index at α = 1, and yield the logarithm of the reciprocal Simpson
136
3. Herpetofauna Over a Land-Use Gradient
diversity index at α = 2. Profiles indicate that frog diversity is lowest in cultivation, and
reptile diversity is lowest in degraded forest and highest in plantation. The remaining
vegetation types cannot be ranked definitively as their Rényi diversity profiles overlap.
137
3. Herpetofauna Over a Land-Use Gradient
Fig. S.3.3. Non-metric multidimensional scaling ordination of Bray Curtis similarities
based on square-root-transformed (a) frog and (b) reptile abundance data and (c) raw frog
abundance, (d) frog incidence, and (e) raw reptile abundance data. Symbols represent
samples taken at 30 trapping array sites across five vegetation types (F = forest, DF =
degraded forest, AW = acacia woodland, P = plantation, C = cultivation), and clustering
indicates similar community composition among sites. One array site for frogs and four
array sites for reptiles were not plotted because they were outliers with zero captures.
138
3. Herpetofauna Over a Land-Use Gradient
Table S.3.1. Analysis of similarity (ANOSIM) results comparing frog and reptile
community composition among vegetation types based on Bray Curtis similarity of raw
abundance data for frogs and reptiles and incidence data for frogs including species
identified from audio recordings.
Vegetation type
comparison
Frogs
Reptiles
Abundance data (Global
R = 0.158, p = 0.007)
Incidence data (Global R
= 0.146, p = 0.005)
Abundance data (Global
R = 0.193, p = 0.001)
R statistic a
pb
R statistic a
pb
R statistic a
pb
Forest–degraded
forest
−0.01
0.45
0.01
0.44
−0.06
0.99
Forest–acacia
woodland
0.30
0.01*
−0.03
0.61
0.14
0.11
Forest–plantation
0.28
< 0.05*
0.26
< 0.05*
0.24
< 0.05*
Forest–cultivation
0.66
< 0.01**
0.58
< 0.01**
0.38
< 0.01**
Degraded forest–
acacia woodland
0.02
0.30
−0.07
0.80
0.09
0.20
Degraded forest–
plantation
−0.03
0.55
0.17
0.09
0.18
0.05
Degraded forest–
cultivation
0.14
0.10
0.21
0.07
0.28
< 0.05*
Acacia woodland–
plantation
0.12
0.08
0.08
0.17
0.30
< 0.01**
Acacia woodland–
cultivation
0.08
0.17
0.14
0.10
0.34
< 0.01**
Plantation–
cultivation
0.11
0.09
0.22
< 0.05*
0.09
0.18
a
ANOSIM generates an R statistic ranging from −1 (where similarities across different vegetation types are
higher than within types) to 1 (where similarities within types are higher than between types) (Clarke and
Gorley 2001).
b
Significance of each comparison is indicated by * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.
139
3. Herpetofauna Over a Land-Use Gradient
Table S.3.2. Top selected models (Δi < 4) relating environmental variables to (a) frog
species richness, (b) frog abundance, (c) reptile species richness, (d) reptile abundance, and
to abundance of functional groups (e) F1, (f) F2, (g) F3, (h) F4, (i) R1, (j) R2, (k) R3, and
(l) R4 (D2 = deviance explained by global models, VIF = variance inflation factor of global
model, Par. = number of parameters in the model; LL = log-likelihood; AICc = Akaike’s
corrected information criterion; QAICc = Quasi-AICc; Δi = AICc or QAICc difference
from best model; wi = Akaike weights, the normalized relative likelihood of the model
given the data).
a) Frog species richness including audio (D2 = 0.32; VIF = 1)
Variables
Par.
LL
AICc
Δi
wi
Litter cover
2
−51.62
107.69
0.00
0.16
Herb cover + Litter cover
3
−50.77
108.46
0.77
0.11
Null
1
−53.29
108.73
1.05
0.10
Litter cover + Range temp.
3
−51.20
109.32
1.63
0.07
Mean temp
2
−52.64
109.73
2.04
0.06
Litter cover + Litter depth
3
−51.49
109.90
2.21
0.05
Litter cover + Mean temp.
3
−51.56
110.05
2.37
0.05
Litter cover + Soil p H
3
−51.57
110.07
2.38
0.05
Herb cover + Litter cover + Mean temp.
4
−50.38
110.36
2.68
0.04
Herb cover + Mean temp.
3
−51.73
110.38
2.70
0.04
Herb cover
2
−53.13
110.71
3.03
0.04
Herb cover + Litter cover + Range temp.
4
−50.60
110.79
3.11
0.03
Litter depth
2
−53.18
110.81
3.12
0.03
Soil pH
2
−53.22
110.89
3.20
0.03
Range temp
2
−53.27
110.99
3.30
0.03
Herb cover + Litter cover + Litter depth
4
−50.76
111.12
3.43
0.03
Herb cover + Litter cover + Soil pH
4
−50.77
111.13
3.45
0.03
Litter cover + Mean temp. + Range temp.
4
−50.97
111.54
3.85
0.02
Par.
LL
QAICc
Δi
wi
Herb cover + Mean temp. + Soil pH
4
−128.24
58.36
0.00
0.54
Herb cover + Litter cover + Mean temp. + Soil pH
5
−125.19
60.42
2.06
0.19
Herb cover + Litter depth + Mean temp. + Soil pH
5
−127.40
61.21
2.85
0.13
b) Frog abundance (D2 = 0.64; VIF = 5.59)
Variables
140
3. Herpetofauna Over a Land-Use Gradient
Herb cover + Mean temp. + Range temp. + Soil pH
5
−127.41
61.21
2.86
0.13
Par.
LL
AICc
Δi
wi
Null
1
−46.18
94.51
0
0.31
Litter cover
2
−45.95
96.34
1.82
0.12
Range temp.
2
−46.04
96.53
2.01
0.11
Herb cover
2
−46.13
96.71
2.2
0.1
Soil pH
2
−46.14
96.73
2.22
0.1
Litter depth
2
−46.16
96.76
2.25
0.1
Mean temp.
2
−46.18
96.81
2.3
0.1
Litter cover + Litter depth
3
−45.69
98.31
3.8
0.05
Par.
LL
QAICc
Δi
wi
Null
1
−55.15
87.08
0.00
0.32
Range temp.
2
−54.85
89.11
2.03
0.12
Litter cover
2
−54.91
89.20
2.12
0.11
Herb cover
2
−54.91
89.20
2.13
0.11
Soil pH
2
−54.96
89.28
2.20
0.11
Mean temp.
2
−55.11
89.49
2.41
0.10
Litter depth
2
−55.14
89.54
2.47
0.09
Range temp. + Soil pH
3
−54.30
90.96
3.88
0.05
Variables
Par.
LL
QAICc
Δi
wi
Herb cover
2
−41.82
56.11
0.00
0.13
Herb cover + Mean temp.
3
−39.79
56.41
0.30
0.11
Herb cover + Litter cover + Mean temp.
4
−37.35
56.43
0.32
0.11
Herb cover + Soil pH
3
−40.06
56.73
0.61
0.10
Herb cover + Mean temp. + Soil pH
4
−37.65
56.78
0.67
0.09
Herb cover + Litter cover
3
−41.10
57.95
1.84
0.05
Herb cover + Mean temp. + Range temp. + Soil pH
5
−35.97
57.96
1.85
0.05
Herb cover + Litter cover + Mean temp. + Soil pH
5
−36.02
58.02
1.91
0.05
Herb cover + Range temp.
3
−41.81
58.78
2.67
0.03
Herb cover + Litter depth
3
−41.82
58.79
2.68
0.03
c) Reptile richness (D2 = 0.06; VIF = 1)
Variables
d) Reptile abundance (D2 = 0.10; VIF = 1.33)
Variables
e) Functional group F1 (D2 = 0.40; VIF = 1.70)
141
3. Herpetofauna Over a Land-Use Gradient
Herb cover + Litter depth + Mean temp.
4
−39.70
59.20
3.09
0.03
Herb cover + Mean temp. + Range temp.
4
−39.72
59.23
3.11
0.03
Herb cover + Litter cover + Soil pH
4
−39.72
59.23
3.11
0.03
Herb cover + Litter cover + Litter depth + Mean temp.
5
−37.13
59.33
3.22
0.03
Herb cover + Range temp. + Soil pH
4
−39.88
59.41
3.30
0.03
Litter cover
2
−44.66
59.46
3.35
0.02
Herb cover + Liter cover + Mean temp. + Range Temp.
5
−37.25
59.47
3.35
0.02
Herb cover + Litter depth + Soil pH
4
−40.04
59.60
3.49
0.02
Herb cover + Litter depth + Mean temp. Soil pH
5
−37.61
59.89
3.78
0.02
Par.
LL
QAICc
Δi
wi
Litter cover + Mean temp. + Soil pH
4
−99.82
51.23
0.00
0.27
Herb cover + Litter cover + Mean temp. + Soil pH
5
−92.10
51.39
0.16
0.25
Litter cover + Litter depth + Mean temp. + Soil pH
5
−97.71
53.56
2.33
0.08
Mean temp. + Soil pH
3
−114.03
53.84
2.61
0.07
Herb cover + Mean temp. + Soil pH
4
−106.88
53.97
2.74
0.07
Litter cover + Mean temp. + Range temp. + Soil pH
5
−99.54
54.27
3.04
0.06
Mean temp. + Range temp. + Soil pH
4
−108.21
54.49
3.26
0.05
Herb cover + Mean temp. + Range temp. + Soil pH
5
−100.49
54.64
3.41
0.05
Herb cover + Litter cover + Litter depth + Mean temp. +
Soil pH
6
−91.81
54.71
3.48
0.05
Herb cover + Litter cover + Mean temp. + Range temp. +
Soil pH
6
−92.03
54.80
3.57
0.05
Par.
LL
QAICc
Δi
wi
Herb cover + Mean temp. + Soil pH
4
−77.11
78.11
0.00
0.61
Herb cover + Litter cover + Mean temp. + Soil pH
5
−77.05
81.21
3.10
0.13
Herb cover + Litter depth + Mean temp. + Soil pH
5
−77.08
81.23
3.13
0.13
Herb cover + Mean temp. + Range temp. + Soil pH
5
−77.10
81.25
3.14
0.13
Variables
Par.
LL
QAICc
Δi
wi
Litter depth
2
−15.88
36.20
0.00
0.20
Litter depth + Soil pH
3
−15.18
37.28
1.07
0.12
Litter cover + Litter depth
3
−15.36
37.65
1.44
0.10
f) Functional group F2 (D2 = 0.67; VIF = 5.15)
Variables
g) Functional group F3 (D2 = 0.56; VIF = 2.35)
Variables
h) Functional group F4 (D2 = 0.34; VIF = 1.59)
142
3. Herpetofauna Over a Land-Use Gradient
Litter depth + Range temp.
3
−15.62
38.17
1.96
0.08
Herb cover + Litter depth
3
−15.75
38.42
2.21
0.07
Litter depth + Mean temp.
3
−15.85
38.61
2.41
0.06
Litter cover
2
−17.21
38.85
2.65
0.05
Litter cover + Litter depth + Soil pH
4
−14.72
39.04
2.83
0.05
Litter cover + Soil pH
3
−16.07
39.06
2.85
0.05
Range temp.
2
−17.60
39.65
3.45
0.04
Herb cover + Litter depth + Soil pH
4
−15.12
39.84
3.63
0.03
Litter depth + Range temp. + Soil pH
4
−15.16
39.91
3.71
0.03
Litter depth + Mean temp. + Soil pH
4
−15.17
39.95
3.74
0.03
Litter cover + Range temp.
3
−16.53
39.98
3.78
0.03
Litter cover + Litter depth + Range temp.
4
−15.22
40.05
3.84
0.03
Herb cover + Litter cover + Litter depth
4
−15.24
40.09
3.88
0.03
Variables
Par.
LL
AICc
Δi
wi
Litter cover
2
−11.43
27.30
0.00
0.06
Litter cover + Mean temp.
3
−10.27
27.46
0.16
0.06
Herb cover + Litter cover
3
−10.35
27.62
0.32
0.05
Null
1
−12.75
27.65
0.35
0.05
Range temp.
2
−11.85
28.14
0.84
0.04
Litter depth
2
−11.87
28.18
0.88
0.04
Herb cover + Range temp.
3
−10.67
28.26
0.97
0.04
Litter cover + Mean temp. + Range temp.
4
−9.38
28.36
1.06
0.04
Herb cover + Litter depth
3
−10.80
28.52
1.22
0.03
Litter cover + Litter depth + Mean temp.
4
−9.46
28.53
1.23
0.03
Mean temp. + Range temp.
3
−11.00
28.92
1.62
0.03
Herb cover + Litter cover + Mean temp.
4
−9.72
29.03
1.74
0.03
Herb cover + Litter cover + Soil pH
4
−9.72
29.05
1.75
0.03
Herb cover + Litter cover + Litter depth
4
−9.80
29.20
1.90
0.02
Herb cover + Litter cover + Range temp.
4
−9.82
29.23
1.94
0.02
Herb cover
2
−12.40
29.23
1.94
0.02
Litter cover + Soil pH
3
−11.18
29.29
1.99
0.02
Litter cover + Range temp.
3
−11.19
29.31
2.01
0.02
Litter cover + Litter depth + Mean temp. + Range temp.
5
−8.41
29.32
2.02
0.02
Litter depth + Mean temp. + Range temp.
4
−9.86
29.33
2.03
0.02
i) Functional group R1 (D2 = 0.52; VIF = 1)
143
3. Herpetofauna Over a Land-Use Gradient
Soil pH
2
−12.45
29.34
2.04
0.02
Litter cover + Litter depth
3
−11.22
29.36
2.06
0.02
Litter cover + Mean temp. + Soil pH
4
−9.96
29.52
2.22
0.02
Herb cover + Litter depth + Range temp.
4
−9.99
29.58
2.28
0.02
Litter depth + Mean temp.
3
−11.33
29.59
2.29
0.02
Mean temp.
2
−12.73
29.91
2.61
0.02
Litter depth + Range temp.
3
−11.55
30.03
2.73
0.02
Herb cover + Litter cover + Litter depth + Mean temp.
5
−8.80
30.09
2.80
0.02
Litter depth + Soil pH
3
−11.74
30.40
3.11
0.01
Herb cover + Litter depth + Soil pH
4
−10.42
30.43
3.14
0.01
Herb cover + Litter cover + Mean temp. + Range temp.
5
−9.00
30.50
3.20
0.01
Herb cover + Mean temp. + Range temp.
4
−10.46
30.52
3.22
0.01
Range temp. + Soil pH
3
−11.84
30.60
3.30
0.01
Herb cover + Litter cover + Mean temp. + Soil pH
5
−9.19
30.87
3.57
0.01
Litter cover + Litter depth + Mean temp. + Soil pH
5
−9.19
30.88
3.59
0.01
Herb cover + Soil pH
3
−11.99
30.91
3.61
0.01
Herb cover + Litter cover + Litter depth + Soil pH
5
−9.21
30.92
3.62
0.01
Herb cover + Range temp. + Soil pH
4
−10.67
30.94
3.64
0.01
Herb cover + Litter depth + Mean temp.
4
−10.72
31.03
3.74
0.01
Litter cover + Mean temp. + Range temp. + Soil pH
5
−9.36
31.22
3.93
0.01
Herb cover + Litter depth + Mean temp. + Range temp.
5
−9.40
31.29
3.99
0.01
Par.
LL
QAICc
Δi
wi
Litter cover + Mean temp.
3
−32.74
64.67
0.00
0.18
Litter cover
2
−34.41
64.80
0.13
0.17
Mean temp.
2
−35.09
65.95
1.28
0.10
Herb cover + Litter cover + Mean temp.
4
−32.07
66.45
1.78
0.07
Litter cover + Soil pH
3
−34.05
66.88
2.21
0.06
Litter cover + Mean temp. + Soil pH
4
−32.51
67.20
2.52
0.05
Herb cover + Litter cover
3
−34.25
67.21
2.54
0.05
Litter cover + Litter depth + Mean temp.
4
−32.56
67.27
2.60
0.05
Litter cover + Range temp.
3
−34.32
67.33
2.66
0.05
Herb cover + Mean temp.
3
−34.37
67.42
2.75
0.05
Litter cover + Litter depth
3
−34.41
67.48
2.81
0.04
Litter cover + Mean temp. + Range temp.
4
−32.73
67.56
2.89
0.04
j) Functional group R2 (D2 = 0.38; VIF = 1.19)
Variables
144
3. Herpetofauna Over a Land-Use Gradient
Mean temp. + Soil pH
3
−34.90
68.30
3.63
0.03
Mean temp. + Range temp.
3
−34.97
68.44
3.76
0.03
Litter depth + Mean temp.
3
−35.08
68.62
3.94
0.03
Variables
Par.
LL
QAICc
Δi
wi
Litter cover
2
−36.04
46.33
0.00
0.34
Litter cover + Mean temp.
3
−35.86
48.81
2.48
0.10
Litter cover + Range temp.
3
−35.90
48.86
2.53
0.10
Herb cover + Litter cover
3
−35.96
48.92
2.59
0.09
Litter cover + Litter depth
3
−36.00
48.97
2.64
0.09
Litter cover + Soil pH
3
−36.03
49.00
2.67
0.09
Range temp.
2
−38.83
49.38
3.05
0.07
Null
1
−41.60
49.93
3.60
0.06
Litter depth
2
−39.35
49.96
3.62
0.06
Par.
LL
QAICc
Δi
wi
Range temp.
2
−24.30
53.26
0.00
0.16
Mean temp.
2
−24.42
53.50
0.24
0.14
Null
1
−25.95
53.93
0.67
0.11
Mean temp. + Range temp.
3
−23.88
55.13
1.87
0.06
Litter depth + Range temp.
3
−23.99
55.34
2.08
0.06
Litter cover
2
−25.50
55.56
2.30
0.05
Mean temp. + Soil pH
3
−24.22
55.79
2.52
0.04
Litter depth + Mean temp.
3
−24.25
55.84
2.58
0.04
Herb cover + Range temp.
3
−24.26
55.86
2.59
0.04
Range temp. + Soil pH
3
−24.29
55.93
2.66
0.04
Litter cover + Range temp.
3
−24.30
55.94
2.67
0.04
Herb cover
2
−25.74
56.01
2.75
0.04
Soil pH
2
−25.75
56.03
2.77
0.04
Herb cover + Mean temp.
3
−24.36
56.06
2.80
0.04
Litter cover + Mean temp.
3
−24.42
56.18
2.91
0.04
Litter depth
2
−25.90
56.32
3.06
0.03
Litter depth + Mean temp. + Range temp.
4
−23.32
56.98
3.72
0.02
k) Functional group R3 (D2 = 0.25; VIF = 1.83)
l) Functional group R4 (D2 = 0.18; VIF = 1.05)
Variables
145
3. Herpetofauna Over a Land-Use Gradient
Table S.3.3. Multi-model averages (see Table S.3.2 for list of models with Δi < 4
contributing to each average model) relating environmental variables to frog species
richness, frog abundance, reptile species richness, reptile abundance, and to abundance of
functional groups F1, F2, F3, F4, R1, R2, R3, and R4.
Variable a
Parameter
estimate
Unconditional SE
p
Relative
importance b
Intercept
1.2883
1.788
0.48
Litter cover
0.0067
0.004
0.09
0.67
Herb cover
−0.0051
0.004
0.26
0.33
Mean temp.
−0.0965
0.113
0.41
0.22
Range temp.
0.0164
0.026
0.55
0.16
Litter depth
−0.0077
0.054
0.89
0.12
Soil pH
−0.0299
0.123
0.82
0.11
Intercept***
15.1130
1.744
< 0.001
Herb cover***
−0.0197
0.003
< 0.001
1.00
Mean temp.***
−0.6739
0.067
< 0.001
1.00
Soil pH***
0.6205
0.082
< 0.001
1.00
Litter cover*
0.0045
0.002
0.02
0.19
Litter depth
0.0320
0.024
0.21
0.13
Range temp.
−0.0210
0.016
0.22
0.13
Intercept
0.5251
0.983
0.61
Litter cover
−0.0030
0.004
0.46
0.17
Litter depth
0.0250
0.065
0.71
0.15
Range temp.
0.0145
0.027
0.61
0.11
Herb cover
−0.0015
0.005
0.76
0.10
Soil pH
0.0474
0.167
0.79
0.10
Mean temp.
−0.0083
0.113
0.94
0.10
Intercept
0.7376
1.001
0.48
Range temp.
0.0214
0.024
0.40
0.16
Soil pH
0.1129
0.157
0.49
0.15
Litter cover
−0.0022
0.003
0.50
0.11
Herb cover
−0.0029
0.004
0.51
0.11
Frog species richness
Frog abundance
Reptile species richness
Reptile abundance
146
3. Herpetofauna Over a Land-Use Gradient
Mean temp.
−0.0294
0.100
0.78
0.10
Litter depth
−0.0068
0.052
0.90
0.09
Intercept
6.0830
7.330
0.41
Herb cover**
−0.0336
0.012
0.01
0.98
Mean temp.*
−0.4645
0.224
0.05
0.55
Soil pH
0.4299
0.243
0.09
0.39
Litter cover
−0.0102
0.006
0.10
0.32
Range temp.
0.0297
0.060
0.63
0.16
Litter depth
0.0044
0.103
0.97
0.13
Intercept*
9.7936
3.835
0.01
Mean temp.***
−0.7106
0.136
< 0.001
1.00
Soil pH***
1.1252
0.194
< 0.001
1.00
Litter cover***
0.0216
0.006
< 0.001
0.76
Herb cover***
−0.0147
0.004
< 0.001
0.46
Range temp.
−0.0633
0.055
0.26
0.21
Litter depth
−0.0673
0.050
0.20
0.13
Intercept***
13.9233
2.229
< 0.001
Herb cover***
−0.0233
0.004
< 0.001
1.00
Mean temp.***
−0.5720
0.088
< 0.001
1.00
Soil pH***
0.3597
0.096
< 0.001
1.00
Litter cover
0.0008
0.002
0.75
0.13
Litter depth
−0.0089
0.036
0.81
0.13
Range temp.
0.0036
0.022
0.87
0.13
Intercept
−6.4449
8.053
0.44
Litter depth*
0.3902
0.189
0.05
0.83
Litter cover
0.0592
0.093
0.54
0.34
Soil pH
0.8544
0.837
0.33
0.31
Range temp.
−0.0959
0.131
0.48
0.20
Herb cover
0.0062
0.013
0.66
0.13
Mean temp.
−0.0864
0.437
0.85
0.09
−18.8485
30.801
0.55
0.1255
0.179
0.50
Functional group F1
Functional group F2
Functional group F3
Functional group F4
Functional group R1
Intercept
Litter cover
0.53
147
3. Herpetofauna Over a Land-Use Gradient
Herb cover
−0.0328
0.028
0.27
0.39
Mean temp.
1.1571
1.054
0.29
0.38
Range temp.
−0.2075
0.192
0.30
0.34
Litter depth
0.2913
0.241
0.25
0.34
Soil pH
0.4639
0.759
0.56
0.19
Intercept
4.9901
7.682
0.52
Litter cover
0.0240
0.013
0.08
0.78
Mean temp.*
−0.4551
0.224
0.05
0.63
Herb cover
−0.0079
0.008
0.37
0.17
Soil pH
0.1740
0.246
0.50
0.14
Litter depth
−0.0218
0.095
0.83
0.12
Range temp.
−0.0112
0.048
0.82
0.12
Intercept
0.6984
1.755
0.70
Litter cover**
−0.0161
0.005
< 0.01
0.81
Range temp.
0.0545
0.056
0.34
0.17
Litter depth
−0.0954
0.139
0.50
0.15
Mean temp.
−0.0925
0.154
0.57
0.10
Herb cover
0.0026
0.006
0.70
0.09
Soil pH
0.0372
0.258
0.89
0.09
Intercept
−4.6615
5.326
0.39
Range temp.
0.0980
0.063
0.14
0.42
Mean temp.
0.3395
0.226
0.15
0.39
Litter depth
0.0835
0.159
0.61
0.16
Litter cover
−0.0024
0.009
0.79
0.13
Soil pH
−0.1205
0.329
0.73
0.12
Herb cover
0.0004
0.012
0.98
0.12
Functional group R2
Functional group R3
Functional group R4
a
Significance of each variable in models is indicated by * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001.
b
Relative importance reflects the sum of Akaike weights of models in each set containing each variable.
148
3. Herpetofauna Over a Land-Use Gradient
Supplementary References
Boughton, R. G., J. Staiger, and R. Franz. 2000. Use of PVC pipe refugia as a sampling
technique for hylid treefrogs. American Midland Naturalist 144: 168-177.
Clarke, K. R. and R. N. Gorley. 2001. Change in Marine Communities: An Approach to
Statistical Analysis and Interpretation. Primer-E, Plymouth, United Kingtom.
du Preez, L. H. and V. Carruthers. 2009. A Complete Guide to Frogs of Southern Africa.
Struik Nature, Cape Town.
Fisher, R., D. Stokes, C. Rochester, C. Brehme, S. Hathaway, and T. Case. 2008.
Herpetological monitoring using a pitfall trapping design in Southern California.
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Hilje, B. and T. Mitchell Aide. 2012. Recovery of amphibian species richness and
composition in a chronosequence of secondary forests, northeastern Costa Rica.
Biological Conservation 146: 170-176.
Kindt, R. and R. Coe. 2005. Tree Diversity Analysis: A manual and Software for Commom
Statistical Methods for Ecological and Biodiversity Studies. World Agroforestry
Centre, Nairobi.
Zuur, A. F., E. N. Ieno, N. J. Walker, A. A. Saveliev, and G. M. Smith. 2009. Mixed
effects models and extensions in ecology with R. Springer, New York.
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4. Decline of Coastal Dune Forest Birds
Chapter 4. Decline of Birds in a Human-Modified Coastal
Dune Forest Landscape in South Africa
Publication Details
Trimble, M.J. & van Aarde, R.J. 2011. Decline of birds in a human modified coastal dune
forest landscape in South Africa. PLoS ONE. 6: e26614. Published under Open Access.
doi:10.1371/journal.pone.0016176
Abstract
Previous studies demonstrate that old-growth forest remnants and vegetation regenerating
after anthropogenic disturbance provide habitat for birds in a human-modified coastal dune
forest landscape in northern KwaZulu-Natal, South Africa. However, occurrence does not
ensure persistence. Based on a 13-year monitoring database I calculated population trends
for 37 bird species and general trends in overall bird density in different vegetation types. I
evaluated species characteristics as covariates of population trend and assessed changes in
rainfall and proportional area and survey coverage per vegetation type. Seventy-six percent
of species assessed have declined, 57% significantly so at an average rate of 13.9% per
year. Overall, bird density has fallen at 12.2% per year across old-growth forest and woody
regenerating vegetation types. Changes in proportional area and coverage per vegetation
type may partly explain trends for a few species but are unlikely to account for most.
Below average rainfall may have contributed to bird declines. However, other possibilities
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4. Decline of Coastal Dune Forest Birds
warrant further investigation. Species with larger range extents tended to decline more
sharply than did others, and these species may be responding to environmental changes on
a broader geographical scale. My results cast doubt on the future persistence of birds in this
human-modified landscape. More research is needed to elucidate the mechanisms driving
population decline in the study area and to investigate whether the declines identified here
are more widespread across the region and perhaps the continent.
Introduction
Coastal dune forest is one of South Africa’s rarest vegetation types; restricted to the eastern
coast, it covers less than 1000 km2. It is also biogeographically important, and occurs
within the Maputaland Center of endemism (van Wyk 1996) and the Maputaland–
Pondoland–Albany biodiversity hotspot (Küper et al. 2004, Steenkamp et al. 2004). While
South African coastal dune forest is relatively well protected with 9.51% conserved, 43%
has been transformed (Low and Rebelo 1998). The coastal location on the Indian Ocean
accounts for the biggest threats to coastal dune forests—holiday resort expansion, dune
mining, and firewood collection and clearing for agriculture by local communities (Low
and Rebelo 1998). Additionally, the narrowness and linear nature of the coastal dune forest
belt might make it particularly susceptible to edge effects, fragmentation, and isolation (see
Eeley et al. 1999).
Forest conservation depends on maintaining both the land covered by forests and
the ecological processes necessary for plant regeneration and gene flow (Low and Rebelo
1998). Thus, isolated stands of protected coastal dune forests may be insufficient for their
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4. Decline of Coastal Dune Forest Birds
long-term conservation (Low and Rebelo 1998) because dispersal ability of many tree
species is constrained by distance between forest patches (Grainger et al. 2011). Due to
their vagility and role in seed dispersal (Coates-Palgrave 2003), birds may enhance
connectivity of coastal dune forest fragments (see Grainger et al. 2011). Thus, promoting
persistence of coastal dune forest birds beyond protected areas may be important for both
bird and forest conservation and is in line with recent shifts in conservation ideology from
a strictly protected area based approach to a wider consideration of biodiversity in humanmodified landscapes (Daily 2001, Daily et al. 2001). Land-use options that incorporate
coastal dune forest elements such as remnant forest patches in agricultural landscapes or
active regeneration after anthropogenic disturbances may allow bird populations to persist
beyond protected areas. This may be the case in South Africa’s northern coastal dune
forests.
North of Richards Bay, on the coast of KwaZulu-Natal province, opencast surface
mining of sand dunes has occurred since 1977 and has been followed by an active
rehabilitation program to return indigenous coastal dune vegetation to one third of the
mined area (see van Aarde et al. 1996a for program description). Earlier work showed that,
with age, bird communities in the successional sere of known-aged regenerating sites
become more similar to that of old-growth coastal dune forest (van Aarde et al. 1996a,
Kritzinger and van Aarde 1998, Wassenaar et al. 2005, Grainger and van Aarde 2012).
These observations suggest that post-mining regenerating forests and old-growth forest
remnants provide refuge for coastal dune forest birds beyond protected areas—e.g. the
Richards Bay Game Reserve ~20 km to the southwest and the iSimangaliso Wetland Park
and World Heritage Site ~5 km to the northeast. However, these studies were based on
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4. Decline of Coastal Dune Forest Birds
snapshots of bird occurrence, and occurrence of species does not ensure their persistence
(see Daily et al. 2001, Hughes et al. 2002). Assessing changes in population size over time
is a step towards understanding the processes (e.g. survival, fecundity, and dispersal (see
Hughes et al. 2002, Komar 2006)) that affect patterns of species occurrence and persistence
in human-modified landscapes.
Based on 13 years of quantitative monitoring of forest birds, I calculated population
trends for birds found commonly in old-growth coastal dune forest and woody regenerating
vegetation types. I also calculated general trends of overall bird densities over time in oldgrowth forest and woody regenerating vegetation types. I investigated how species’
characteristics known to be associated with extinction proneness of forest birds—i.e. clutch
size, habitat affinity, diet, tolerance of human-modified landscapes, and range extent (see
Sodhi et al. 2004 and references therein)—related to population trend and assessed changes
in rainfall, and proportional area and survey coverage per vegetation type as possible
determinants of population and general trends.
Methods
Bird data
I used data collected as part of a long-term monitoring program designed to assess the
success of coastal dune forest rehabilitation after dune mining (see van Aarde et al. 1996a,
Wassenaar et al. 2005 for a description of the program and map of the study area). Between
1997 and 2009, birds were surveyed via transect counts in 9 survey years at two relatively
pristine old-growth coastal dune forest sites and nine regenerating forest sites of known age
(Table S.4.1) within a mining lease area maintained by Richards Bay Minerals (RBM).
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4. Decline of Coastal Dune Forest Birds
Forest regeneration in the area follows a trajectory of vegetation types from grassland (~1–
5 years old), to thicket (~6–12 years old), to an early woodland stage dominated by Acacia
karroo (~12–20 years old), to a late woodland stage in which Acacia karroo individuals
have senesced and been replaced by coastal dune forest trees (~20–35 years old) (see van
Aarde et al. 1996a, Grainger et al. 2011). Experienced observers walked 250–500 m
transects randomly located at least 200 m apart within vegetation types (Table S.4.1) and
recorded birds seen and distance from the transect. In most years, exact distances were
recorded up to 60 m but in 1997 and 2006, distance intervals were used with cut points 2,
5, 10, 20, and 40 m and 5, 10, 15, 20, 25, and 30 m respectively. Birds flying over the
canopy and all raptors, aerial feeders, and nocturnal birds were excluded. All surveys were
conducted in the early morning under favorable weather conditions and took place between
November and February.
Throughout the study period, 102 species were represented in 7890 sightings. I
narrowed the species list to focal species typical of old-growth forest and the woody
regenerating vegetation types—thicket, early woodland, and late woodland. To do this, I
assessed the affinity of each species towards different vegetation types. For each species, I
calculated the overall number of sightings/km of transect in each vegetation type—
grassland, thicket, early woodland, late woodland, and old-growth forest. Twenty-seven
species had ≥ 60% of their sightings/km in grassland, and I excluded all but two of these
species from further analyses. I retained Red-eyed Dove and Yellow-eyed Canary because,
although the majority of sightings were in grassland, they were also quite common in oldgrowth forest with > 20% sightings/km. I also excluded Lesser-masked Weaver Ploceus
intermedius (predominantly found in thicket) from further analysis because observers in
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4. Decline of Coastal Dune Forest Birds
different years variably distinguished between Lesser-masked and other similar looking
weavers predominantly found in grassland (i.e. Village Weaver Ploceus cucullatus and
Yellow Weaver Ploceus subaureus). Thus, 6868 sightings of 76 species were retained for
further analysis. I separated these species into two groups—39 relatively rare species
(recorded ≤ 20 times throughout the study period) and 37 relatively common species
(recorded > 20 times). Common and scientific names are provided in Table 4.1 for
relatively common species and Table S.4.2 for relatively rare species.
To my knowledge, this is one of few long-term quantitative bird monitoring
datasets for Africa. However, some aspects of the survey methodology might introduce
bias. Differences in observers and vegetation types may lead to variation in the probability
of detecting birds, which could bias inferences on the change in bird densities over time
(Marques et al. 2007). I used distance sampling techniques to account for variability in
detection probability to generate more reliable density estimates than unadjusted counts
provide. Distance sampling relies on creating a detection function of the frequency of
observations on distance from the transect line to estimate the average detection probability
P̂a of observing a bird given it is within the truncation point w of the line transect
(Buckland et al. 2001).
To calculate reliable detection functions, 60–80 observations are necessary
(Buckland et al. 2001), but in my study, most species were recorded far less often than 60
times per year. Similarly detectable species can be grouped together to achieve sufficient
sample size to calculate a common detection function (Buckland et al. 2001). Thus, I
grouped the 37 relatively common species (those recorded > 20 times) into three species
pools: furtive species generally seen very close to the transect line, species that are
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4. Decline of Coastal Dune Forest Birds
intermediately visible, and conspicuous species frequently seen far from the transect line
(Table 4.1). For each of these species pools, I used the Multiple-Covariate Distance
Sampling (MCDS) engine in the program DISTANCE, version 6.0 (Thomas et al. 2009) to
fit four detection function models for each year: a half-normal key model, a hazard-rate key
model, and each with vegetation type as a factor covariate. Additionally, for 2007–2009
when two observers conducted surveys, I also fitted a half-normal and hazard-rate model
with observer as a factor covariate and with both observer and vegetation type as factor
covariates. Estimating a single detection function per year by pooling over vegetation types
and observer differences should give adequate global estimates due to the pooling
robustness property of distance sampling, but including these variables in MCDS can lead
to increased estimate precision (Marques et al. 2007). I did not use adjustment terms in the
models to avoid implausible, non-monotonic function results (Marques et al. 2007). To
achieve adequate model fit and estimator robustness, I set distance intervals and truncation
points to accommodate characteristics of species pools (e.g. shorter truncation point for
furtive species), occasional issues with distance heaping and evasive movement of birds
away from the transect line, and distance data collection intervals for 1997 and 2006.
Models were post-stratified by species, but estimates were made at the global level,
meaning that species in the same pool had a common detection function per year. I selected
the best model per year based on AIC and extracted an estimate for P̂a and its SE.
I assessed support for my assumption that species within each pool shared similar
detectability by fitting detection functions to the total dataset (years pooled). I used the
MCDS engine to fit for each species pool half-normal and hazard-rate key models, each
with vegetation type as a factor covariate, each with observer as a factor covariate and each
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4. Decline of Coastal Dune Forest Birds
with species as a factor covariate. I then compared the models with AIC to assess whether
pooling species was a reasonable assumption.
I was also interested in annual estimates specific to vegetation types. I modeled the
per year, per vegetation type detection functions for birds in general (all 76 species pooled).
I used the MCDS engine to fit for each year a half-normal key model and a hazard-rate key
model and, for 2007–2009, each with observer as a factor covariate. Again, I did not use
further adjustment terms and selected the best model per year based on AIC. Models were
post-stratified by vegetation type with estimates made at the vegetation type stratum level.
This generated an estimate for P̂a and its SE of birds in general per vegetation type per
year.
Trends and determinants
I assessed population trends over time for the 37 species recorded > 20 times. I used quasiPoisson generalized linear modeling (GLM) with log-link function and standard errors
corrected for over-dispersion (Zuur et al. 2009) and detection probability incorporated as
an offset term (Buckland et al. 2004). I fitted the model nt,s = exp(loge(2Ltwt P̂
a,p,t)
+ β0 +
β1t) + εt where nt,s is the number of birds of species s counted in year t, Lt is the line length
surveyed at time t, wt is the truncation distance, P̂
a,p,t
is the estimated mean probability of
detection for species in pool p in the covered region a in year t, and loge(2Ltwt P̂
a,p,t)
is the
offset term (modified from Buckland et al. 2004). In GLM, offsets are assumed known, but
P̂
a,p,t
is an estimate (Buckland et al. 2004). To account for uncertainty in the estimate of
P̂
a,p,t,
I randomly resampled each estimate 999 times from a lognormal distribution and
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4. Decline of Coastal Dune Forest Birds
refit the GLM to each resample. I then estimated population trend and SE as the mean
slope parameter and SE estimates from 999 fitted GLM’s for each species. Population
trends were deemed significant when population trend ± 1.96 SE did not include 0. Percent
change per year was calculated as (exp(population trend) – 1)*100.
I followed the same procedure to estimate general trends in bird density in each
vegetation type by substituting into the GLM equation nt,v, number of bird sightings per
vegetation type v in year t, and P̂
a,p,t
the estimated mean probability of detection of birds
in vegetation type v in the covered region a in year t. Subsequently, I checked for
significant differences of slopes and intercepts between vegetation types with a GLM of nt,v
on t with an offset as described previously, a categorical variable of vegetation type v, and
an interaction term between t and v. Significance of the interaction term indicates
significantly different slopes.
I only calculated population trends for species recorded > 20 times. To infer what
might be happening to the 39 relatively rare species, I assessed how commonness
influenced population trend estimates. To do this, I regressed population trend estimate and
SE on loge of the cumulative number of sightings per species throughout the study period.
Variables that are intrinsic to species might explain variation in population trends.
These include habitat affinity (Julliard et al. 2003), mean clutch size and bird weight
(proxies for life history characteristics (Saether and Bakke 2000)), diet (Sekercioglu 2002,
Sigel et al. 2006), tolerance of human-modified landscapes (Petit et al. 1999), and range
extent (Mehlman 1997). I assigned habitat affinity as a categorical variable—predominant
habitat—based on the vegetation type in which a species had the highest proportion of
sightings/km. I also quantified affinity for old-growth forest as the proportion of
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4. Decline of Coastal Dune Forest Birds
sightings/km in old-growth forest. I sourced clutch size, weight, diet, and tolerance data for
relevant species (Hockey et al. 2005). Based on the predominant food items listed, I
distinguished three diet preference classes: insects and other invertebrates; plant material;
and omnivorous/carnivorous. I considered species listed to occur in gardens, parks,
plantations, and cultivated areas tolerant of human-modified landscapes while others were
deemed intolerant. Finally, I noted the extent of each species’ resident range (IUCN 2008).
I assessed the relationship between population trend and range extent, affinity for oldgrowth forest, clutch size, and weight with linear regression. I used t-tests to compare
population trends between species with predominant habitat in old-growth forest and those
with predominant habitat in one of the regenerating vegetation types and between species
that are tolerant and intolerant of human-modified landscapes. I used ANOVA to compare
population trends between the three diet preference classes. Some caution is required in
comparing population trends among species because pooling species to calculate detection
functions means that annual density estimates from the same pool are not independent
(Buckland et al. 2001). Therefore, species pooling could influence trend estimates. Thus, I
used ANOVA to compare population trend estimates between the three species pools.
I also assessed factors that might influence both population trends and general
trends —changes in rainfall (Faaborg 1982), area of each vegetation type (Askins and
Philbrick 1987, Haskell et al. 2006), and transect coverage per vegetation type. I quantified
mean annual rainfall as the residual cumulative annual rainfall (January–December)
compared to the long-term mean annual rainfall (1977–2009). Rainfall data (provided by
RBM) was unavailable for 2008. Proportional area of each vegetation type was calculated
based on the area and age of each site in each year, and I assessed change over time with
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4. Decline of Coastal Dune Forest Birds
linear regression. Coverage per year was calculated as the proportion of km’s of transect in
each vegetation type per year. I assessed whether changes in coverage have generally
matched changes in area by regressing proportional coverage divided by proportional area
on year for each vegetation type.
Results
Habitat affinity
Of the 37 commonly observed species, 3 were only recorded in old-growth forest and 4
more had ≥ 80% of their sightings/km in old-growth forest. The majority of species (24)
were often recorded in old-growth forest (≥ 20%, < 80% sightings/km) but also frequently
seen in regenerating vegetation types. Six species were rarely seen in old-growth forest (<
20% sightings/km) including one species never recorded there (Table 4.1). Habitat
affinities should be taken as an index comparable among species rather than as an absolute
measure of species’ habitat preferences because sightings/km were not corrected for
variability in detection probability among vegetation types. I did not assess the habitat
affinities of the 39 rarely observed species (those recorded ≤ 20 times) because so few
sightings are unlikely to be representative of the species’ occurrence in different vegetation
types.
Distance sampling
I fitted detection functions for each of the three species pools in each year (Table 4.1, Table
S.4.3). Detection probability varied among species pools with furtive species being the
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4. Decline of Coastal Dune Forest Birds
least detectable and conspicuous species the most, although estimates are not directly
comparable due to variability in truncation distance (Table S.4.3). My assumption of
relatively similar bird detectability within pools was supported, and models with species as
a covariate were the least likely compared to models with a vegetation type covariate, an
observer covariate and no covariate for all three species pools (Table S.4.4). I also fitted
detection functions for birds in general (76 species pooled) for each vegetation type in each
year (Table S.4.5). As expected, detection probability was generally high in early and late
woodland, low in thicket, and intermediate in old-growth forest. There were too few
observations in grassland to fit per year detection functions.
Population trends and determinants
I estimated population trends for the 37 relatively common species (recorded > 20 times)
(Table 4.1, Fig. 4.1). Twenty-eight of these species (76%) decreased, 21 significantly so at
an average rate of 13.9% per year. Nine species (24%) increased but only one significantly
so. Population trend estimates were not significantly related to the loge of the cumulative
sightings/species (slope = −0.003, p = 0.88). However, as expected, SE of population trend
estimates decreased with an increasing loge of cumulative sightings/species (slope = −0.02,
r2 = 0.41, p < 0.01).
Population trend estimates for 30 species were acceptably reliable (SE < 0.08) for
further analyses regarding the potential determinants of population trends. I investigated
the relationship between population trends and characteristics of these species—range
extent, affinity for old-growth forest, predominant habitat, clutch size, weight, predominant
diet, and tolerance for human-modified landscapes. Range extent was significantly related
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4. Decline of Coastal Dune Forest Birds
to population trend (slope = −7.63x10−9, r2 = 0.18, p < 0.05) and was significantly
correlated with affinity for old-growth forest (Pearson r = −0.46, p < 0.05). However,
affinity for old-growth forest was not significantly related to population trend (slope =
−0.075, p = 0.23). Generally, species with larger ranges had lower population trends (i.e.
more negative) and a lower affinity for old-growth forest. Species with predominant habitat
among regenerating vegetation types had larger range extents than species with
predominant habitat in old-growth forest (mean range extent per vegetation type: oldgrowth = 3.10x106 km2, n = 20; regenerating = 9.03x106 km2, n = 10; r2 = 0.33; p < 0.01).
Furthermore, species with predominant habitat among regenerating vegetation types had
significantly lower population trends (i.e. more negative) than those with old-growth forest
as predominant habitat (mean population trend per vegetation type: old-growth = −0.08, n
= 20; regenerating = −0.16, n = 10; r2 = 0.16; p < 0.05). Weight (slope = −2.5x10−6, p =
0.99), clutch size (slope = 0.016, p < 0.57), predominant diet (mean population trend per
diet class: insects = −0.12, n = 16; plants = −0.10, n = 10; omnivorous/carnivorous = −
0.073, n = 4; p = 0.68), and tolerance for human-modified landscapes (mean population
trend per class: tolerant = −0.13, n = 13; intolerant = −0.09, n = 17; p = 0.30) were not
significantly related to population trend. Furthermore, species pool was not significantly
related to population trend (mean population trend per pool: pool A = −0.104, n = 5; pool B
= −0.127, n = 11; pool C = −0.090, n = 14; p = 0.61).
I also assessed general trends of overall bird density (76 species pooled) in the
different vegetation types—thicket, early woodland, late woodland, and old-growth forest.
Grassland had too few sightings/year to estimate detection functions. Birds declined
significantly in early woodland, late woodland, and old-growth forest with mean general
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4. Decline of Coastal Dune Forest Birds
trend and SE from 999 detection probability resamples and GLM refittings of −0.13 ± 0.03,
−0.09 ± 0.04, and −0.14 ± 0.03 respectively. Birds also declined in thicket but not
significantly so with mean general trend and SE = −0.15 ± 0.10. However, general trends in
different vegetation types did not differ significantly although the intercepts did. Thus, the
overall general trend across old−growth, late woodland, early woodland, and thicket was
−0.13 ± 0.01 (Fig. 4.2).
I assessed changes in rainfall, area of vegetation types, and transect coverage per
vegetation type over time as potential factors that could influence both population trends
and general trends of overall bird density. Mean annual rainfall did not change significantly
over time (slope = −62.10, p = 0.05). However, for 9 of 12 years for which I have rainfall
data (1997–2009 excluding 2008 when data were unavailable), mean annual rainfall was
lower than the long-term mean (Fig. S.4.6). Furthermore, mean annual rainfall has been
below the long-term mean every year since 2002. Proportional area of regenerating
vegetation types changed over time as regenerating sites aged. Proportional area increased
significantly over time for late woodland (slope = 0.019, r2 = 0.91, p < 0.01) and thicket
(slope = 0.005, r2 = 0.55, p < 0.05) and decreased for early woodland (slope = −0.005, r2 =
0.52, p < 0.05), while proportional area of grassland did not change significantly (slope =
0.001, p = 0.76). However, transect coverage per vegetation type, generally matched these
changes with proportional coverage/proportional area per vegetation type not changing
significantly over time for any vegetation type (old-growth forest: slope = −0.002, p = 0.86;
late woodland: −0.024, p = 0.74; early woodland: slope = −0.033, p = 0.31; thicket: slope =
−0.043, p = 0.08) except grassland (slope = −0.146, r2 = 0.58, p < 0.05).
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4. Decline of Coastal Dune Forest Birds
Discussion
The birds inhabiting the old-growth coastal dune forests and coastal woody regenerating
vegetation types (thicket, early woodland, and late woodland) have generally declined
since 1997. Of the 37 relatively common species, 21 have declined significantly at rates
between 7.9 and 27.8% per year while only one species has increased significantly.
Furthermore, Rudd’s Apalis, the only one of the four restricted-range bird species of the
Maputaland Centre of endemism (Steenkamp et al. 2004) to occur at the study site, has
declined significantly at a rate of 10.9% per year. None of the species for which I assessed
population trends are globally threatened (IUCN 2008), but they were, by necessity of the
trend analysis procedure, relatively common in the study area. Species with reliable
population trend estimates (SE < 0.08) tended to be the most often recorded among the
relatively common species because SE of population trend estimates decreased with
increasing cumulative records per species. However, population trend estimate itself was
not dependent on cumulative records per species, so there is no indication that populations
of the 39 relatively rare species have fared better than the relatively common species.
Earlier studies show that forest regeneration in the area results in increased bird
species diversity with regeneration age, while overall density remains relatively stable
(Kritzinger and van Aarde 1998) as the bird community undergoes a compositional shift
from grassland and pioneer species to secondary forest species (van Aarde et al. 1996a,
Grainger and van Aarde 2012). Thus, from a site-specific perspective, a few species
characteristic of early successional stages should decrease over time while many forest
species increase as the regenerating vegetation becomes more similar to old-growth coastal
dune forest. However, I took a study area wide view of population trends (necessitated by
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4. Decline of Coastal Dune Forest Birds
sample size requirements of distance sampling) rather than a site-specific approach.
Therefore, successional changes in regenerating sites should not affect population trends
unless area or transect coverage per vegetation type changes over time. While changes in
area of vegetation types could result in real changes in population densities (Askins and
Philbrick 1987, Haskell et al. 2006), changes in coverage per vegetation type could
generate false trends. Changes in coverage mirrored changes in area for all vegetation types
except grassland, which became less well represented in sampling over time. Thus,
population trend estimates for the birds found commonly in grasslands could have been
negatively biased—primarily Red-eyed Dove, Yellow-fronted Canary, and Tawny-flanked
Prinia with 68, 60, and 44% of their sightings/km in grassland respectively. Late woodland
increased substantially in proportional area (0.02 per year), and the only bird to increase
significantly, Golden-tailed Woodpecker, was also the only bird with predominant habitat
in late woodland. While thicket increased significantly and early woodland decreased
significantly in proportional area, the change was not substantial (−0.005 and +0.005 per
year respectively).
Of the species’ characteristics I assessed as potential determinants of population
trends, only predominant habitat and range extent were related to population trend. Range
extent was inversely proportional to population trend and to species’ affinity for old-growth
forest.
Additionally, species found predominantly in regenerating vegetation types had
lower population trend estimates (i.e. more negative) and larger ranges than species found
predominantly in old-growth forest. Species with large range extents tend to be generalists
and are expected to have broad environmental tolerances (Jetz et al. 2007), so specialists
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4. Decline of Coastal Dune Forest Birds
are generally more extinction prone (Sekercioglu et al. 2004). Thus, it is surprising that
species with large range extents tended to decline more sharply in my study than species
with smaller geographic distributions. One possible explanation is that habitat degradation
or destruction outside the study sites but in the local area has affected grassland, thicket,
and woodland more so than old-growth remnant patches, resulting in more severe
population declines for species found predominantly in regenerating vegetation types. In
this scenario, the significance of range extent would be largely coincidental. However,
range extent could conceivably be more directly impinging on local population trends. The
magnitude of change in bird density in response to broad-scale environmental change is
generally greatest at the edge of a species’ range, and environmental change that negatively
affects species tends to result in a contraction of the range towards the core (Mehlman
1997). Because the study site is on the Indian Ocean coast and relatively near the southern
most point of the African continent, the forests are at the edge of the range of many
species. Thus, the range extent variable generally reflects the distance between the study
site and the central point of the range. It might be that change in abundance is not only
greatest at the edge but also dependent on how far away the edge is from the core.
However, whether the central point of the ranges of species in my analysis corresponds to
core range requires further investigation, although there is some evidence that it should be
so (Lawton 1993).
That species with predominant habitat in regenerating vegetation types tended to
decline more than others did should not overshadow the conclusion that most birds,
regardless of habitat affinity, have declined. Overall density of the 76 bird species assessed
declined significantly at an alarming rate of 12.2% per year across old-growth coastal dune
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4. Decline of Coastal Dune Forest Birds
forests and woody regenerating vegetation types. Recent below average mean annual
rainfall might be expected to affect most bird species via effects on survival and breeding
success (see Faaborg 1982) and thus, might have contributed to the widespread decline
across species and vegetation types. If so, predictions of climate change induced rainfall
reductions (see de Wit and Stankiewicz 2006) are concerning. It is also possible that the
bird declines are merely temporary responses to drought. However, there are other
possibilities that warrant further research including extinction debt (Kuussaari et al. 2009),
ecological traps or sinks (Battin 2004), and broad-scale habitat change. Habitat destruction
and environmental change at a macroecological scale could be affecting population trends
at the local scale as reported elsewhere (Mehlman 1997). This hypothesis is in line with the
importance of range extent in my analysis and implies that bird population declines are
much more widespread across the region and perhaps the continent.
Severe and widespread declines of bird populations have been recorded throughout
the world (e.g. Dunn 2002, BirdLife International 2004, Gregory et al. 2005, Olsen 2008,
North American Bird Conservation Initiative 2009), and identification of these declines
was largely the result of massive survey efforts in decades-long, nationwide programs such
as the Breeding Bird Survey and Common Bird Census in the United Kingdom and the
North American Breeding Bird Survey in the United States and Canada (Peakall 2000,
North American Bird Conservation Initiative 2009). That similar declines have not been
identified in Africa might be due to a lack of monitoring data, though some studies report
declines of single species or small groups of species (Nel et al. 2002, Underhill and
Crawford 2005, Thiollay 2006), and the decline of migratory bird populations in Europe
indicate potential problems in wintering grounds in Africa (Sanderson et al. 2006).
167
4. Decline of Coastal Dune Forest Birds
Other studies show that many forest bird species occur in human-modified
landscapes that appear, from a human perspective, quite different from undisturbed forest
(e.g. Daily et al. 2001, Hughes et al. 2002, Ranganathan et al. 2008). Likewise, few species
for which I assessed habitat affinities were strictly found in old-growth coastal dune forest
while most were also found in woody regenerating vegetation. Thus, regenerating
vegetation and remnant old-growth forests at the study site might provide valuable habitat
for birds in a human-modified coastal dune forest landscape. However, the decline of birds
across the study site draws their persistence into question. While assessing population
trends over time is a step towards understanding the processes that determine occurrence
and persistence of birds in human-modified landscapes, much more research is needed to
elucidate the underlying mechanisms that generate trends—breeding success, survival, and
dispersal.
In conclusion, remnant patches of old-growth forest and sites regenerating after
mining in a human-modified coastal dune forest landscape might provide valuable habitat
for birds. Persistence of these bird communities might contribute to conservation not only
of birds but also forests by enhancing functional connectivity between coastal forests in
protected areas and other remnant patches through seed dispersal and pollination. However,
further assessment of long-term monitoring data revealed population declines of most bird
species assessed and a consistent reduction in bird density across vegetation types. Birds
are sensitive to a host of ecological threats (see Gregory et al. 2005) including habitat
degradation (Robbins et al. 1989) and fragmentation (Robinson et al. 1995), invasive
species (van Aarde 1980, Gurevitch and Padilla 2004), climate change (Veit et al. 1996,
Sekercioglu 2002, Green et al. 2008), emergent disease (Wikelski et al. 2004, LaDeau et al.
168
4. Decline of Coastal Dune Forest Birds
2007), and pollution (Fry 1995, Camphuysen and Heubeck 2001), so bird declines
identified here are a warning of environmental problems. Probable loss of valuable
ecosystem services such as pollination, seed dispersal, and nutrient recycling with bird
declines are also worrying (Sekercioglu et al. 2004) and might even threaten the coastal
dune forest rehabilitation program which relies on processes of natural succession (van
Aarde et al. 1996b, van Aarde et al. 1996a). More research is urgently needed to elucidate
the mechanisms driving the decline and to assess whether declines are a local phenomenon
or are also occurring at a broader geographical scale.
Acknowledgements
Richards Bay Minerals, the National Research Foundation, and the Department of Trade
and Industry provided support for this study. I thank the many people that collected data
and provided technical support. M.J.T. is supported by a National Science Foundation
Graduate Research Fellowship. Discussion with M. Grainger, T. Wassenaar, and R.
Guldemond contributed to the study.
Author contributions: Conceived and designed the experiments: MJT RJvA. Performed
the experiments: MJT RJvA. Wrote the paper: MJT RJvA.
169
4. Decline of Coastal Dune Forest Birds
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176
4. Decline of Coastal Dune Forest Birds
Figures
177
4. Decline of Coastal Dune Forest Birds
Fig. 4.1. Population trends. Change in density/ha over time for relatively common species
with fitted GLM trend line and 95% CI (stippled lines) from the original offset estimate.
Density was estimated by nt,s / 2Ltwt P̂
a,p,t
where nt,s is the number of sighting per species
per year, 2Ltwt is the area of transect coverage in hectares and P̂
a,p,t
is the detection
probability over the area covered per pool per year. See Table 4.1 for trend estimates and
SE’s calculated based on 999 resamples of P̂
a,p,t.
178
4. Decline of Coastal Dune Forest Birds
Fig. 4.2. Vegetation type specific trends. Change in density/ha of birds in general over time
in different vegetation types with fitted GLM trend lines of slope −0.13 ± 0.01. Density
was estimated by nt,v / 2Ltwt P̂
a,p,t
where nt,v is the number of bird sightings per vegetation
type per year, 2Ltwt is the area of transect coverage in hectares and P̂
a,p,t
is the detection
probability over the area covered per vegetation type per year. Intercepts are significantly
different and trend lines are for, from highest to lowest density, old-growth forest, late
woodland, thicket, and early woodland.
179
4. Decline of Coastal Dune Forest Birds
Tables
Table 4.1. Population trends and covariates for relatively common species. Species names
follow Hockey et al. (2005). Pool codes are A = furtive species, B = intermediate, C =
conspicuous. * indicates statistically significant trends. Predominant habitat is the
vegetation type in which a species has the greatest proportion of sightings/km, and the
proportion is given in parentheses. Vegetation type abbreviations as follows: OG = oldgrowth coastal dune forest, LW = late woodland, EW = early woodland, T = thicket, G =
grassland. OG affinity is the proportion of sighting/km in old-growth forest.
Common name
Scientific
name
Lamprotornis
corruscus
Pool
Trend
SE
0.063
Range
(km2)
350000
Predominant
habitat
OG (0.54)
OG
affinity
0.54
C
0.104
Ashy Flycatcher
Muscicapa
caerulescens
B
−0.171*
0.055
7700000
EW (0.47)
0.21
Black-backed
Puffback
Dryoscopus
cubla
C
−0.083*
0.037
5400000
OG (0.34)
0.34
Black-throated
Wattle-Eye
Platysteira
peltata
B
0.027
0.048
3100000
OG (0.81)
0.81
Blue-mantled
Crested-Flycatcher
Trochocercus
cyanomelas
A
−0.077
0.073
1200000
OG (1)
1.00
Brown-hooded
Kingfisher
Halcyon
albiventris
C
−0.008
0.067
3800000
EW (0.39)
0.08
Burchell's Coucal
Centropus
burchellii
C
−0.153*
0.067
5000000
OG (0.42)
0.42
Cape White-eye
Zosterops
virens
B
−0.090*
0.042
1300000
OG (0.38)
0.23
Collared Sunbird
Hedydipna
collaris
B
−0.132*
0.051
5500000
OG (0.52)
0.52
Dark-backed
Weaver
Ploceus
bicolor
C
0.051
0.027
1100000
OG (0.33)
0.33
Dark-capped
Bulbul
Pycnonotus
tricolor
C
−0.126*
0.029
19000000
G (0.25)
0.23
Eastern Nicator
Nicator gularis
C
−0.098
0.071
4000000
OG (0.38)
0.38
Fork-tailed Drongo
Dicrurus
adsimilis
C
−0.243*
0.071
14000000
EW (0.67)
0.09
Golden-tailed
Woodpecker
Coampethera
abingoni
C
0.377*
0.104
3880000
LW (0.56)
0.27
Green Malkoha
Ceuthmochares
aereus
C
−0.141
0.091
5400000
OG (0.82)
0.82
Black-bellied
Starling
180
4. Decline of Coastal Dune Forest Birds
Green-backed
Camaroptera
Camaroptera
brachyura
A
−0.144*
0.029
16000000
EW (0.3)
0.18
Grey Sunbird
Cyanomitra
veroxii
B
−0.160*
0.064
170000
OG (0.47)
0.47
Hadeda Ibis
Bostrychia
hagedash
C
0.270
0.153
16000000
G (0.32)
0.25
Lemon Dove
Aplopelia
larvata
A
0.082
0.089
2000000
OG (1)
1.00
Livingstone's
Turaco
Tauraco
livingstonii
C
−0.154*
0.042
5000000
OG (1)
1.00
Olive Sunbird
Cyanomitra
olivacea
B
−0.127*
0.029
570000
OG (0.45)
0.45
Red-capped Robin
Chat
Cossypha
natalensis
A
−0.137*
0.031
3600000
OG (0.58)
0.58
Red-eyed Dove
Streptopelia
semitorquata
C
−0.197
0.182
10000000
G (0.68)
0.26
Rudd's Apalis
Apalis ruddi
B
−0.116*
0.021
76000
T (0.46)
0.20
Sombre Greenbul
Andropadus
importunes
C
−0.105*
0.035
1200000
OG (0.52)
0.52
Southern Boubou
Laniarius
ferrugineus
B
−0.153*
0.046
580000
OG (0.7)
0.70
Square-tailed
Drongo
Dicrurus
ludwigii
C
−0.034
0.029
4300000
OG (0.37)
0.37
Tambourine Dove
Turtur
tympanistria
A
0.018
0.057
7400000
OG (0.41)
0.41
Tawny-flanked
Prinia
Prinia subflava
B
−0.202*
0.042
14000000
G (0.44)
0.12
Terrestrial
Brownbul
Phyllastrephus
terrestris
A
−0.181*
0.073
2400000
OG (0.9)
0.90
Trumpeter Hornbill
Bycanistes
bucinator
C
0.045
0.098
2900000
OG (0.96)
0.96
White-browed
Robin-Chat
Cossypha
heuglini
C
−0.326*
0.058
8800000
T (0.49)
0.00
White-eared Barbet
Stactolaema
leucotis
C
0.005
0.071
710000
OG (0.59)
0.59
Yellow-bellied
Greenbul
Chlorocichla
flaviventris
C
−0.095*
0.024
3800000
OG (0.49)
0.49
Yellow-breasted
Apalis
Apalis Favida
B
−0.133*
0.031
5600000
EW (0.38)
0.13
Yellow-fronted
Canary
Crithagra
mozambicus
C
−0.207
0.108
9500000
G (0.6)
0.22
Yellow-rumped
Tinkerbird
Pogoniulus
bilineatus
B
−0.142*
0.063
6600000
OG (0.75)
0.75
181
4. Decline of Coastal Dune Forest Birds
Supplementary Material
Table S.4.1. Transects per site per year in regenerating and old-growth sites. RegX sites
are regenerating after mining; numbers in parentheses represent the regeneration age since
mining as of 2009. OG sites are old-growth forests. a indicates transect length of 250 m,
b
indicates transects length of 500 m, and c indicates transect length of 300 m.
Year Observer
Transects per site
Reg1
(32)
Reg2
(29)
Reg3
(25)
Reg4
(21)
Reg5
(17)
Reg6
(13)
Reg7
(9)
Reg8
(6)
Reg9
(3)
OG1
OG2
1997
A
2a
2a
2a
2a
1a
4a
-
-
-
1a
2a
1998
A
4a
4a
4a
4a
4a
3b
-
-
-
4a
8a
2000
A
4a
4a
4a
4a
4a
3b
-
-
-
4a
4a
2001
A
4a
4a
4a
4a
4a
2b
-
-
-
4a
10 a
2004
B
2
3
2
2
3
2
3
3
-
-
5
2006
C
4
5
4
4
5
4
5
-
-
5
10
2007
D&E
6
7
7
11
4
4
7
6
3
11
9
2008
D&E
4
5
4
4
6
7
6
4
-
8
6
2009
D&E
6
6
6
4
5
6
5
4
9
4
8
182
4. Decline of Coastal Dune Forest Birds
Table S.4.2. Relatively rare species. Species names follow Hockey et al. (2005).
Common name
Scientific name
Bearded Scrub-Robin
Cercotrichas quadrivirgata
African Dusky Flycatcher
Muscicapa adusta
African Emerald Cuckoo
Chrysococcyx Cupreus
African Green-Pigeon
Treron calvus
African Paradise-Flycatcher
Terpsiphone viridis
African Pygmy-Kingfisher
Ispidina picta
Bar-throated Apalis
Apalis thoracica
Brimstone Canary
Crithagra sulphurata
Broad-billed Roller
Eurystomus glaucurus
Buff-spotted Flufftail
Sarothrura elegans
Cape Batis
Batis capensis
Cape Turtle-Dove
Streptopelia capicola
Cardinal Woodpecker
Dendropicos fuscescens
Chorister Robin-Chat
Cossypha dichroa
Common Cuckoo
Cuculus canorus
Crested Guineafowl
Guttera edouardi
Crowned Hornbill
Tockus alboterminatus
Eastern Bronze-naped Pigeon
Columba delegorguei
Eurasian Golden Oriole
Oriolus oriolus
Garden Warbler
Sylvia borin
Giant Kingfisher
Megaceryle maxima
Gorgeous Bush-Shrike
Telophorus viridis
Green Twinspot
Mandingoa nitidula
Grey Waxbill
Estrilda perreini
183
4. Decline of Coastal Dune Forest Birds
Icterine Warbler
Hippolais icterina
Klaas's Cuckoo
Chrysococcyx klaas
Lesser Honeyguide
Indicator minor
Long-billed Crombec
Sylvietta rufescens
Narina Trogon
Apaloderma narina
Neddicky
Cisticola fulvicapilla
Purple-banded Sunbird
Cinnyris bifasciatus
Purple-crested Turaco
Gallirex porphyreolophus
Red-chested Cuckoo
Cuculus solitarius
Red-fronted Tinkerbird
Pogoniulus pusillus
Southern Black Flycatcher
Melaenornis pammelaina
Spectacled Weaver
Ploceus ocularis
Spotted Flycatcher
Muscicapa striata
Swee Waxbill
Coccopygia melanotis
Woodwards' Batis
Batis fratrum
184
4. Decline of Coastal Dune Forest Birds
Table S.4.3. AIC selected detection function models for species pools. See (Table 4.1) for
species pool composition. Pool A comprises furtive species, Pool B intermediate, and Pool
C conspicuous. Model details are described by P̂
a,t,
the estimated mean probability of
detection for species in the covered region a in year t; its SE; Lt, the line length surveyed at
time t; wt, the truncation distance; and the model key function and covariates. Model
abbreviations as follows: “HR” for hazard-rate key, “HN” for half-normal key, “+ V” for
vegetation type as factor covariate, and “+ O” for observer as factor covariate.
Grouping Model
details
1997
1998
2000
2001
2004
2006
2007
2008
2009
Pool A
0.208
0.332
0.482
0.357
0.254
0.435
0.255
0.225
0.198
0.018
0.017
0.027
0.017
0.039
0.018
0.019
0.023
0.080
HR
HN + V
HN
HR
HR + O
HR
HR
P̂
a,t
SE
Best
Model
Pool B
Lt (m)
4000
9500
8500
9500
7500
13800
22500
16200
17400
w (m)
40
40
40
40
40
30
40
40
40
0.260
0.509
0.438
0.488
0.339
0.643
0.359
0.408
0.359
0.016
0.018
0.016
0.019
0.040
0.040
0.014
0.023
0.025
HN
HR
HR
HN + O
HR
P̂
a,t
SE
Pool C
HN + V HN + V
Best
Model
HN
Lt (m)
4000
9500
8500
9500
7500
13800
22500
16200
17400
w (m)
40
40
40
40
40
30
40
40
40
0.324
0.512
0.508
0.501
0.311
0.670
0.275
0.299
0.364
0.028
0.021
0.021
0.020
0.030
0.044
0.012
0.029
0.025
P̂
a,t
SE
HR + V HN + V HR + V
Best
Model
HR
HR + V HR + V HN + V HN + V
HR
HN + O HR + O HN + O
+V
+V
+V
Lt (m)
4000
9500
8500
9500
7500
13800
22500
16200
17400
w (m)
40
50
50
50
50
30
50
50
50
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4. Decline of Coastal Dune Forest Birds
Table S.4.4. AIC model selection for validating species pooling assumption. ΔAIC = 0
indicates the most supported model of the detection function for each species pool over the
study period (years pooled with 1997 and 2006 excluded due to constraint in setting
reasonable cutpoints). See (Table 4.1) for species pool composition. Pool A comprises
furtive species, Pool B intermediate, and Pool C conspicuous. Model abbreviations as
follows: “HR” for hazard-rate key, “HN” for half-normal key, “+ V” for vegetation type as
factor covariate, “+ O” for observer as factor covariate, and “+ S” for species as factor
covariate. Models including species as a factor covariate had the highest ΔAIC in support
of my species pooling assumptions. The half-normal key model with observer as a factor
covariate failed to converge for Pool A.
ΔAIC
Model
Pool A
Pool B
Pool C
HN
143.59
48.7
117.96
HN + V
102.38
5.47
48.17
HN + O
-
0
0
HN + S
138
55.38
90.84
HR
129.73
62.05
116.75
HR + V
133.45
2.61
145.27
HR + O
0
6.21
143.55
HR + S
141.16
72.21
171.47
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4. Decline of Coastal Dune Forest Birds
Table S.4.5. AIC selected detection function models stratified by vegetation type (76
species pooled). Model details are described by P̂
a,t,
the estimated mean probability of
detection for species in the covered region a in year t; its SE; Lt, the line length surveyed at
time t; wt, the truncation distance; and the model key function and covariates. Model
abbreviations as follows: “HR” for hazard-rate key, “HN” for half-normal key, and “+ O”
for observer as factor covariate.
Grouping
Old-growth
Model Details
1997
1998
2000
2001
2004
2006
2007
2008
2009
Best Model
HR
HR
HR
HR
HR
HR
HN
HR
HN + O
w (m)
40
40
40
40
40
30
40
40
40
0.270
0.467
0.571
0.465
0.380
0.579
0.335
0.430
0.382
0.016
0.022
0.025
0.019
0.039
0.030
0.015
0.030
0.030
750
3000
2000
3500
1500
4500
6000
4200
3600
0.728
0.417
0.751
0.162
0.573
0.394
0.643
0.588
SE
0.047
0.028
0.040
0.298
0.035
0.017
0.057
0.028
Lt (m)
1000
1000
2000
1500
3900
6000
3900
6600
0.346
0.674
0.787
0.676
0.450
0.712
0.348
0.388
0.614
SE
0.021
0.029
0.037
0.045
0.080
0.079
0.022
0.064
0.086
Lt (m)
1500
2000
2000
2000
1200
2700
4500
3000
1500
P̂
0.213
0.355
0.581
0.408
0.463
0.142
0.429
0.361
0.360
0.034
0.024
0.029
0.030
0.082
0.438
0.050
0.050
0.031
500
2000
2000
1000
1500
2700
3300
3900
4500
P̂
a,t
SE
Lt (m)
Late woodland
Early
woodland
Thicket
P̂
P̂
a,t
a,t
a,t
SE
Lt (m)
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4. Decline of Coastal Dune Forest Birds
Figure S.4.6. Change in rainfall over time. Bars represent residual mean annual rainfall
from the long-term (1977–2009) mean in mm.
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5. Research Biases in Human-Modified Landscapes
Chapter 5. Geographical and Taxonomic Biases in
Research on Biodiversity in Human-Modified Landscapes
Publication Details
Trimble, M.J. & van Aarde, R.J. 2012. Geographical and taxonomic biases in research on
biodiversity in human-modified landscapes. Ecosphere. 3: art 119. Published under Open
Access. doi: 10.1890/ES12-00299.1
Abstract
Biodiversity persistence in human-modified landscapes is crucial for conservation and
maintaining ecosystem services. Studies of biodiversity in landscapes where humans live,
work, and extract resources could support defensible policy-making to manage land use.
Yet, research should cover relevant regions, and biases in study topics should not lead to
gaps in the evidence base. I systematically reviewed the literature of biogeography in
human-modified landscapes published in eight eminent biogeography, conservation, and
ecology journals to assess geographical bias among biomes and geopolitical regions and
taxonomic bias among species groups. I compared research output per biome to area,
biome type, species richness, proportion of transformed land, and the ratio of transformed
to protected land. I also compared research output per geopolitical region to area,
proportion of transformed land, the ratio of transformed to protected land, and human
population density. Research output was distributed unequally among biomes, geopolitical
regions, and species groups. Biome type was a clear factor in research bias, and forest
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5. Research Biases in Human-Modified Landscapes
biomes were the subject of 87% of papers, while species richness was not generally
associated with bias. Conservation in human-modified landscapes is most important in
regions with low protected area coverage, high land conversion, and high pressure from
human populations, yet the distribution of published papers did not generally reflect these
threats. Seventy-five percent of studies focused on the Americas and Europe, while Africa
and Asia were critically understudied. Taxonomically, plants and invertebrates were the
most studied groups; however, research output was not correlated with species richness per
group. Protected areas alone will not conserve biodiversity in the long term. Thus, a strong
biogeographical evidence base is required to support policies for biodiversity maintenance
on human-modified land. Under-studied regions and species groups deserve further
research to elucidate what, where, and how biodiversity persists in human-modified
landscapes to inform conservation policy and enhance efficacy.
Introduction
Conservation actions should be based on scientific evidence to achieve the best possible
outcomes and avoid squandering resources (Sutherland 2004). However, science-based
decision-making relies on a solid foundation of relevant evidence, often an assemblage of
peer-reviewed studies (Sutherland 2004, Pullin and Knight 2009). Scientists, funding
agencies, and publishers hold sway over the composition of the evidence base through their
influence on which studies are conducted and published (Lawler et al. 2006). If research
interests are misaligned with research needs, gaps in the evidence base could compromise
conservation efforts. Thus, it is important to monitor the distribution of published research
in comparison to emerging research requirements (Lawler et al. 2006).
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5. Research Biases in Human-Modified Landscapes
I performed such an assessment for an increasingly important field of inquiry—
biogeography of human-modified landscapes—concerned with biodiversity patterns and
the processes that maintain them in areas where humans live, work, and extract resources.
Though the conservation literature has traditionally focused on large, relatively pristine
study sites (Fazey et al. 2005a, Felton et al. 2009), research on the biogeography of humanmodified landscapes is accumulating (see Daily 1999, Daily et al. 2001, Rosenzweig 2003).
In line with research needs(Chazdon et al. 2009), these studies seek to define where, what,
and how biodiversity persists in human-modified landscapes; how different aspects of
diversity co-vary; and how human actions drive these patterns.
Protected areas (PAs) are an essential part of the overall conservation strategy, but
alone, in the long-term, they are unlikely to conserve biodiversity for several reasons
including constraints of location, size, and configuration (Bengtsson et al. 2003, Brooks et
al. 2004, Joppa and Pfaff 2009); ongoing management challenges and outside pressures
(Kareiva et al. 2007); and climate change (Loarie et al. 2009). Most compellingly, if
species cannot persist beyond PA boundaries, loss of speciation rates and pools of potential
immigrants to PAs means that conserving, for example, 10% of the Earth’s surface (see
Brooks et al. 2004) is likely to result in 90% loss of species (Rosenzweig 2003). Already,
humanity has commandeered roughly 40% of Earth’s land surface for crops and pastures
alone (Millennium Ecosystem Assessment 2005), and demand will escalate for food, fiber,
fuel, shelter, space, and freshwater (Tilman 2001). Calls for conservation beyond the
boundaries of PAs are not novel (e.g. Leopold 1949), but as humans continue to transform
natural ecosystems, conservation efforts in rural villages, logging concessions, pastures,
fields, and the like will become increasingly important, not only for conservation’s sake but
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5. Research Biases in Human-Modified Landscapes
also to sustain valuable ecosystem services (e.g. pollination, decomposition, nutrient
cycling) (Tscharntke et al. 2005).
Studies on the biogeography of human-modified landscapes provide an evidence
base to support land-use planning decisions meant to render human dominated land as
amenable as possible to biodiversity. For example, studies demonstrate that agroforestry
systems maintain on average > 60% of the species richness of primary tropical forests
(Bhagwat et al. 2008), oil palm plantations support less forest biodiversity than do other
tree crops (Fitzherbert et al. 2008), and scattered remnant trees in fields or pastures help
maintain forest diversity (Dunn 2000, Fischer et al. 2010). Yet, to achieve success in
supporting policy, the evidence base must cover relevant geographical regions and
taxonomic groups and be sufficiently comprehensive; conservation decisions ought to be
based on adequate understanding of local biodiversity features and the processes that
maintain their viability rather than global generalizations (Svancara et al. 2005). While no
topic is likely “over-studied”, when scientific output is severely biased, “under-studied”
topics could hamper conservation efforts. For instance, research on human-modified forest
ecosystems guides strategies to manage plantations to encourage persistence of forest
biodiversity. However, managers have applied the same strategies to plantations embedded
in grasslands with dubious efficacy (Pryke and Samways 2003, Lipsey and Hockey 2010).
Unfortunately, biases in the topics that scientists study are common, and the drivers
of bias are varied. For example, the distribution of research output among species is uneven
(Bonnet et al. 2002, Clark and May 2002, Fazey et al. 2005a, Lawler et al. 2006, Pyšek et
al. 2008, Felton et al. 2009, Trimble and van Aarde 2010, Griffiths and Dos Santos 2012).
Threatened status, economic importance, or ecological impact drive biases somewhat, but
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5. Research Biases in Human-Modified Landscapes
so too, and apparently more so, do personal affinities of scientists, funders, and reviewers
toward certain species characteristics that may be unrelated to research needs (Bonnet et al.
2002, Lawler et al. 2006, Trimble and van Aarde 2010). There are also clear geographical
biases in ecological research. For instance, the study of invasive species is concentrated in
the Americas and Europe with little research conducted in Africa and Asia (Pyšek et al.
2008), a pattern that holds for landscape research, climate change ecology, and
conservation biology as a whole (Lawler et al. 2006, Felton et al. 2009, Conrad et al. 2011).
If studies of biogeography of human-modified landscapes are biased towards certain topics,
scientists and funding agencies may wish to refocus their efforts to ensure sufficient
science is available to support conservation in human-modified landscapes where it is most
needed. Specifically, research on biogeography in human-modified landscapes should be
prioritized in areas (or for species groups) where the topic is little studied despite high
threat.
I systematically reviewed the literature on the biogeography of human-modified
landscapes to assess the distribution of research output ecologically, among terrestrial
biomes; geopolitically, among UN GeoScheme sub-regions; and taxonomically, among
species groups. The ecological subdivision is important because I expect biodiversity to
respond more similarly to land-use management within than among ecosystem types, while
the geopolitical comparison may be more relevant to policy-makers and funding agencies. I
investigated the relationship between the distribution of research and the area and species
richness per region and biome type. I also compared the distribution of research to the
estimated importance of countryside biogeography in each region based on population
density, proportion of land converted, and the ratio of land area converted to area protected.
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5. Research Biases in Human-Modified Landscapes
These metrics provide a rough quantification of the risk of biome-wide biodiversity loss
and, thus, the importance of conservation outside PAs. I also assessed the distribution of
research among seven species groups: birds, fish, fungi, herpetofauna, mammals, plants,
and invertebrates.
Methods
Literature search
I searched the ISI Web of Knowledge (covering 1950–2010) in May 2012 with keywords
“biodiversity” or “conservation” and each of the following terms: “agricultur*”,
“agroforest*”, “crop$land”, “farm*”, “forestry”, “human$modified”, “multiple$use
management”, “range$land”, “rural”, “sub$urban”, and “urban” (“*” is a wildcard
indicating any group of characters, and “$” represents zero or one character). I limited my
assessment to eight conservation, biogeography, and ecology journals: Biodiversity and
Conservation, Biological Conservation, Conservation Biology, Diversity and Distributions,
Journal of Applied Ecology, Journal of Biogeography, Ecological Applications, and
Ecology. From the initial search, I retained primary research papers that assessed
occurrence or persistence of multi-species assemblages on human-modified land under
current use. Thus, I excluded studies of abandoned landscapes, restoration projects that
excluded human use, and fragmentation studies that did not explicitly consider biodiversity
in the human-modified areas surrounding the fragments.
The choice of keywords and journals searched was a compromise between
practicality and comprehensiveness. My keywords were, by necessity, not overly specific
to avoid biasing search results. Thus, my keywords returned many papers not relevant to
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5. Research Biases in Human-Modified Landscapes
the topic, and practicality dictated that I limit the number of journals searched to prevent an
unwieldy number of search results. I selected eight journals that I expected to be among the
least biased towards particular biome types, regions, or taxonomic groups.
To assess how research output on the topic of biogeography of human-modified
landscapes has changed over time in these journals, I calculated the number of papers per
year identified in my literature search, and I noted the total number of papers published per
year available on the ISI Web of Knowledge for each journal. I used linear regression to
assess changes over time in the proportion of the total research output that was composed
of studies on the biogeography of human-modified landscapes.
Geographical distribution of research output
I assessed the geographical distribution of research output both politically and ecologically.
For each paper identified by my search, I noted the geopolitical region or regions where the
study was carried out based on an intermediate scale of subdivision, the UN GeoScheme
categorization (UNSD 2011; Micronesia, Melanesia, and Polynesia combined to yield 19
geopolitical regions). To assess the ecological distribution, I noted the terrestrial biome or
biomes in which research was conducted (Olson et al. 2001, WWF 2001). In the few cases
where studies assessed biodiversity in aquatic ecosystems, I allotted terrestrial biomes
based on the location of the water bodies.
Many papers identified in the literature search, especially of European origin,
considered “farmland biodiversity” in semi-natural landscapes with no reference to any
natural ecosystem. These studies may not represent useful information on conserving the
biodiversity of the original biome; for example, studies of biodiversity in extensive semi195
5. Research Biases in Human-Modified Landscapes
natural grasslands under varying management regimes may or may not inform the
conservation of the biodiversity of boreal forests in which the grasslands are embedded.
Thus, to assess the effect of the inclusion of such studies on further analyses, in addition to
recording the biome in which they took place, I also categorized them as “no comparison”
(in contrast to “natural comparison”). “Natural comparison” studies compared biodiversity
patterns or processes to those of a natural baseline, either analytically or conceptually, and
“no comparison” studies did not.
I expected the area of regions to determine the distribution of research among
biomes and geopolitical units if research efforts were randomly distributed geographically.
The area covered by the largest biome, deserts and xeric shrublands, eclipses the smallest
in my study, tropical and subtropical coniferous forests, by 39 times. Similarly, the largest
geopolitical region, Northern America, is 87 times the area of the Caribbean. Thus, I
regressed the number of papers per region on area of biomes (Olson et al. 2001, WWF
2001) and geopolitical regions (UNEP 2011a). I then calculated area-corrected estimates of
research output as the number of studies per million km2 for biomes and geopolitical
regions to investigate whether other factors were related to bias in research output.
For biomes, these factors included biome type (i.e. forest or other), species richness
per biome, and the estimated importance of research in a biome. I compared the areacorrected estimates of research output between the seven forest biomes (i.e. three tropical,
two temperate, boreal, and Mediterranean forests) and the six other biomes (i.e. montane,
flooded, tropical, and temperate grasslands; tundra; and deserts). To evaluate whether
research output was skewed towards biomes with higher species richness, I used Spearman
rank correlation to compare the total studies per biome to the estimated total number of
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5. Research Biases in Human-Modified Landscapes
mammal, bird, reptile, and amphibian species per biome (Hassan et al. 2005). I also
compared area-corrected studies per biome to an estimate of the biome-specific z-value
from the power model of the species–area relationship for vascular plants calculated by
Kier et al. (2005) (I averaged the four sub-regional z-values for the tropical and subtropical
dry broadleaf forests). I reasoned that biogeography of human-modified landscapes should
be most important in biomes that have been heavily transformed and especially in those
that also have low PA coverage. Therefore, I used Spearman rank correlation to compare
area-corrected research output to the proportion of transformed land per biome and to the
ratio of converted to protected land (i.e. the Conservation Risk Index (CRI) calculated by
Hoekstra et al. (2005)). I then used Mann–Whitney U tests to compare the two species
richness metrics, CRI, and proportion of transformed land between forest and other biomes.
For geopolitical regions, I used Spearman rank correlation to assess whether areacorrected research output was correlated with the proportion of agricultural land conversion
(World Bank 2009), the geopolitical CRI (the ratio of land conversion to PA coverage
(UNSD 2010)), and population density (UNEP 2011b). I reasoned that conservation
beyond PAs would be both particularly important and challenging in geopolitical regions
with high population density.
Distribution of research output among species groups
I categorized the species group or groups assessed in each paper as birds, fish, fungi,
herpetofauna, mammals, plants, or invertebrates. I then calculated the percent of total,
“natural comparison”, and “no comparison” studies that assessed each group. I used
Spearman rank correlation to assess whether the proportion of all studies that assessed each
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5. Research Biases in Human-Modified Landscapes
species group was correlated with the proportion of described species comprised by each
group (Vie et al. 2009).
Results
I assessed the distribution of research output over time, by geo-ecological region (i.e.
biome), by geopolitical region, and by taxonomic group with respect to a number of
potential explanatory variables summarized in Table 5.1 and discussed below.
Of the 2521 references returned by my literature search, 681 assessed the
occurrence and/or persistence of biodiversity in human-modified landscapes and met my
inclusion criteria. These papers (published between 1984 and 2010) made up 4–7% of the
total papers published over the same period in Biological Conservation (214 of 4890),
Biodiversity and Conservation (168 of 2685), and Journal of Applied Ecology (110 of
2750). They comprised 1.5–2% of research output in Ecological Applications (64 of 2796),
Diversity and Distributions (16 of 657), and Conservation Biology (77 of 4132), while they
were less prevalent in Ecology (12 of 7385) and Journal of Biogeography (20 of 2837).
Prevalence has increased over time, even after accounting for the increase in publishing
output (Fig. 5.1). The yearly proportion of the total studies published by all eight journals
comprised of studies identified in my literature review increased significantly with year
(F1,18 = 219.1, p < 0.001, r2 = 0.90, y = 0.002x−3.892) although the pattern was clearly
non-linear over the study period (Fig. 5.1).
I distinguished 218 papers as “no comparison” studies and 463 as “natural
comparison”. I performed subsequent analyses both including and excluding “no
comparison” studies. Seven percent of the total studies and 8% of “natural comparison”
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5. Research Biases in Human-Modified Landscapes
studies considered biodiversity of human-modified aquatic ecosystems, e.g. streams,
wetlands, and ponds.
Geographical bias
The number of studies per biome (see Fig. 5.2) differed widely from one in tundra to 316 in
temperate broadleaf and mixed forests, while tropical and subtropical moist broadleaf
forests had the highest number of studies from the “natural comparison” category (169)
(Fig. 5.3). The seven forest biomes were the subject of 87% of studies. Contrastingly, only
13% of papers assessed the other six biomes. While “no comparison” studies came almost
exclusively from forest biomes (96%), excluding them did not remove the bias towards
forests; 83% of “natural comparison” studies were conducted in forest biomes.
Research output was also uneven among geopolitical regions (Figs. 5.4 and 5.5),
ranging from zero studies in Melanesia, Micronesia, and Polynesia to 163 in Northern
Europe. Forty-two percent of the papers studied European regions (although 68% of these
were “no comparison” studies), while a further 33% centered on regions in the Americas.
Studies conducted in Australia and New Zealand, Africa, and Asia each made up < 10% of
the total studies.
Research output was not randomly distributed among biomes or geopolitical
regions. The number of studies per biome and geopolitical region were not related to area
(biomes: F1,11 = 1.87, p = 0.20; geopolitical regions: F1,18 = 0.07, p = 0.80), even when “no
comparison” studies were excluded (biomes: F1,11 = 2.34, p = 0.15; geopolitical regions:
F1,18 = 2.40, p = 0.14).
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5. Research Biases in Human-Modified Landscapes
The median number of studies per million km2 of forest biomes (8.65) was much
higher than for other biomes (1.49) (Mann–Whitney U = 1.00, p < 0.01) (Fig. 5.6). The
difference remained substantial and significant even when “no comparison” studies were
excluded (median studies in forest biomes = 8.46, other biomes = 1.49, Mann–Whitney U
= 2.00, p < 0.01). If I categorized Mediterranean forests, woodlands, and scrub as “other”
instead of “forest”, research output remained biased towards forest in the total dataset
(median studies in forest biomes = 8.55, other biomes = 1.54, Mann–Whitney U = 6.00, p =
0.04) and when “no comparison” studies were excluded (median studies in forest biomes =
7.16, other biomes = 1.54, Mann–Whitney U = 7.00, p = 0.05).
The number of studies per biome was not significantly correlated with estimated
total mammal, bird, reptile, and amphibian richness including “no comparison” studies
(Spearman r = 0.14, p = 0.65) or excluding them (Spearman r = 0.31, p = 0.30). However,
the correlation between studies per million km2 and biome-specific z-values for vascular
plants was significant when “no comparison” studies were excluded (Spearman r = 0.56, p
= 0.05), but not when they were included (Spearman r = 0.51, p = 0.08). However, z-values
were higher in forest than other biomes (Mann–Whitney U = 5.50, p = 0.03), while total
species of mammals, birds, reptiles, and amphibians did not differ significantly (Mann–
Whitney U = 18.00, p = 0.37).
Area-corrected research output was not correlated with the per-biome CRI
(Spearman r = 0.55, p = 0.051) unless “no comparison” studies were excluded (Spearman r
= 0.63, p = 0.02) (Fig. 5.3). However, research output was correlated with proportion of
land converted per biome both with and without “no comparison” studies (Spearman r =
0.65, p = 0.02; Spearman r = 0.73, p < 0.01). However, CRI did not differ significantly
200
5. Research Biases in Human-Modified Landscapes
between forest biomes and others (Mann–Whitney U = 15.00, p = 0.43), nor did proportion
of land converted (Mann–Whitney U = 12.00, p = 0.23). Additionally, area-corrected
research output was not correlated with CRI per geopolitical region (all data: Spearman r =
−0.18, p = 0.22; “no comparison” studies excluded: Spearman r = −0.15, p = 0.26), nor
with proportion of land converted to agriculture (all data: Spearman r = 0.11, p = 0.32; “no
comparison” studies excluded: Spearman r = 0.14, p = 0.27) (Figs. 5.4 and 5.5). However,
area-corrected research output per geopolitical region was weakly correlated with
population density (all data: Spearman r = 0.47, p = 0.04; “no comparison” studies
excluded: Spearman r = 0.47, p = 0.03).
Taxonomic bias
Research output was not distributed evenly among seven major taxonomic groups (Fig.
5.7). Of the 681 papers indentified (13% of which studied multiple species groups), 36%
assessed invertebrates. Birds and plants were each assessed in 31% of papers.
Contrastingly, fewer studies assessed mammals (10%), herpetofauna (7%), fungi (3%), and
fish (0.5%).
The 218 “no comparison” studies focused even more on invertebrates (47%) and
plants (39%). Of “no comparison” studies, 25% assessed birds, while only 3%, 2%, 1%,
and 0% covered mammals, herpetofauna, fungi, and fish respectively. Therefore, among
the 463 “natural comparison” studies, research output was more evenly distributed among
taxonomic groups (Fig. 5.7): birds (33%), invertebrates (31%), plants (27%), mammals
(13%), herpetofauna (9%), fungi (4%), and fish (1%). However, the proportion of studies
that assessed each group was not correlated with the proportion of described species per
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5. Research Biases in Human-Modified Landscapes
group (all data: Spearman r = 0.34, p = 0.44; “no comparison” studies: Spearman r = 0.40,
p = 0.40; “natural comparison” studies: Spearman r = 0.11, p = 0.84).
Discussion
Biogeography of human-modified landscapes provides the evidence base required to
support defensible policy-making to encourage biodiversity conservation beyond protected
areas, an increasingly important objective. I have shown that it has been a growing subdiscipline in conservation biology over the past two decades, as reflected by publication
trends in the eight journals included in my assessment. I have also demonstrated, however,
that scientific research output is biased geo-ecologically, geopolitically, and taxonomically.
Geo-ecologically, research output for forest biomes was disproportionately higher than for
other biomes after correcting for area. In particular, temperate broadleaf and mixed forests
and tropical and subtropical moist broadleaf forests garnered a large proportion of research
output. Geopolitically, the bias was clearly towards Europe and the Americas, while
substantially fewer studies came from Africa and Asia. Taxonomically, research attention
among species groups was neither evenly distributed nor correlated with per-group
richness, and invertebrates, plants, and birds were the most studied groups.
My literature search was extensive, covering 681 papers, though not
comprehensive. I searched eight journals for a limited set of search terms because
practicality dictated that I could not assess all papers ever published. I attempted to
minimize bias as far as possible in my selection of relatively neutral keywords and
journals. Additionally, the journals I selected are preeminent in conservation, ecology, and
biogeography, and I expect them to be representative of the wider research base of high202
5. Research Biases in Human-Modified Landscapes
quality studies available to and useful for policy-makers. Nonetheless, further consideration
of less prestigious journals, the grey literature, and non-English language publications may
influence the conclusions of this study.
“Natural comparison” versus “no comparison” studies
I identified two types of studies: “natural comparison” and “no comparison”. “Natural
comparison” studies compared biodiversity between human land use and a baseline
reference from relatively natural fragments of the biome in which the study was conducted.
For example, many studies in tropical forests compared biodiversity among agroforestry
plantations, crop fields, pastures, and nearby PAs or forest remnants (e.g. Zapfack et al.
2002, Wanger et al. 2010).
On the other hand, “no comparison” studies, which came predominantly from
Europe, lacked direct reference to natural ecosystems. Many assessed the effects of
agricultural management (e.g. organic versus conventional (e.g. Batary et al. 2010) or an
intensification gradient (e.g. Kohler et al. 2007)) on “farmland biodiversity”, the suite of
species occupying traditionally managed agro-ecosystems (see Bignal and McCracken
1996), without specific reference to the biomes in which the studies were conducted. Often,
farmland itself is presented as a novel ecosystem worthy of conservation for its own sake
(e.g. Stefanescu et al. 2005, Jay-Robert et al. 2008), best accomplished by promoting the
traditional agricultural practices that created it over thousands of years (e.g. Bignal and
McCracken 1996, Pykala 2000). For example, semi-natural grasslands created by
traditional agricultural practices in Europe’s forest biomes are particularly important in
conservation schemes (see Austrheim et al. 1999, Walker et al. 2004), and many papers
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5. Research Biases in Human-Modified Landscapes
compared biodiversity among varying management options for maintaining them (e.g.
Poyry et al. 2005, Saarinen and Jantunen 2005).
Although such studies provide crucial support for conservation in Europe, their
applicability elsewhere and relevance to the biome in which they were conducted cannot be
assumed. Thus, I distinguished these studies in my analyses of research output bias and
associated factors. Nonetheless, the recognized value of farmland biodiversity in
landscapes long dominated by humans (see Bignal and McCracken 1996, Pykala 2000)
testifies to the importance of the early consideration of biogeography of human-modified
landscapes in land-use planning for regions that retain large tracts of relatively undisturbed
land (e.g. wilderness areas (Mittermeier 2003)). A comparative approach is important to
inform conservation strategies in human-modified landscapes because it allows for
consideration of community composition and functional trait richness over space and time
(e.g. Mayfield et al. 2006, Flynn et al. 2009), investigation of processes that link
occurrence to persistence (e.g. Trimble and van Aarde 2011), and, to avoid biotic
homogenization, distinction between land uses amenable to invasive or cosmopolitan
species versus more localized species (see Filippi-Codaccioni et al. 2010).
Patterns of geographical bias in research output
I expected per-region area to determine the distribution of research output if research
interest were distributed randomly geographically. However, this was not the case for
biomes or geopolitical regions. For biomes, I investigated ecosystem type (forest or other),
per-biome species richness, the proportion of transformed land, and CRI in relation to
research distribution.
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5. Research Biases in Human-Modified Landscapes
Area-corrected research output was clearly higher in forest than other biomes, a
pattern also reported by Fazey et al. (2005a) in the conservation literature. Felton et al.
(2009) subsequently found twice the degree of bias towards forests (38% of studies versus
20.5%) in the climate-change ecology literature. I found more than double that again (87%
of studies) for the literature on biogeography of human-modified landscapes. The areacorrected number of studies per biome was positively correlated with the z-value from the
power model of the species–area relationship for vascular plants (Kier et al. 2005) when
“no comparison” studies were excluded. However, z-values were also significantly higher
in forest biomes than other types. On the other hand, the per-biome number of mammal,
bird, reptile, and amphibian species was not correlated with the number of studies per
biome and did not differ significantly between forest and other types. Additionally, the
overall bias towards forests could not be explained by an estimated increased importance of
study there because forests did not tend to have a higher CRI or proportion of transformed
land than did other biomes. Thus, while the high plant richness of forests may play some
role in research bias, it seems a penchant for forests, rather than species richness or threat
status per se, drives bias. Felton et al. (2009) suggested that the bias towards forests in the
climate-change ecology literature was a result of the concomitant bias towards North
American and European study sites. However, my results indicate that the forest bias is
prominent in both temperate and tropical regions.
While research output per biome was correlated, based on rank, with CRI when “no
comparison” studies were excluded and with proportion of transformed land, there did
seem to be under-studied biomes. The temperate grasslands, savannas, and shrublands
biome was the most glaring example, and tropical and subtropical dry broadleaf forests
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warranted more attention. The Mediterranean forests, woodlands, and scrub biome had a
relatively high number of studies after correcting for area, but its CRI and the proportion of
land converted were very high, warranting more research. Among the biomes on the lower
end of the CRI scale, there was a relative excess of studies from the temperate conifer
forests and the boreal forests, which both have low proportions of converted land.
Contrastingly, tropical and subtropical grasslands, savannas, and shrublands; flooded
grasslands and savannas; and deserts and xeric shrublands were under-studied relative to
their CRI.
Geopolitical bias was towards Europe and the Americas with far less focus on
Africa and Asia, a pattern previously demonstrated for other sub-disciplines of
conservation and ecology (Pyšek et al. 2008, Felton et al. 2009, Conrad et al. 2011).
Disconcertingly, area-corrected research output per geopolitical region was not correlated
with CRI. Central Asia, Eastern Asia, Southern Asia, Northern Africa, and West Africa had
a particularly low research output considering their high CRI. Additionally, while research
output per region was weakly correlated with human population density, several regions
with high population densities had low research output including Southern, Eastern, and
South-eastern Asia and the Caribbean.
CRI, proportion of land converted, and population density are indices that I expect
to highlight regions where conservation beyond PAs will be especially important due to
extensive land conversion, little protection, and high threat. The general disparities in
patterns of research output relative to these estimates of research importance could act as a
guide to where additional research investment in the biogeography of human-modified
landscapes may be most beneficial. There are, however, caveats to consider. Estimates of
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5. Research Biases in Human-Modified Landscapes
land conversion do not account for the likelihood of future conversion or intensification,
and they likely underestimated land uses that did not totally transform the land, yet might
result in substantial degradation, e.g. grazed rangelands, hunting grounds, and selectively
logged forests (Hoekstra et al. 2005, World Bank 2009).
Additionally, the focus of this paper was terrestrial, but marine and aquatic
ecosystems also warrant consideration. While 7% of studies from my search assessed
biodiversity in human-modified ponds, streams, and wetlands, it was not feasible to include
a more targeted search in this assessment. However, I predict that similar knowledge gaps
exist regarding biogeography of human-modified aquatic biomes, an issue for future study.
Patterns of taxonomic bias
Unsurprisingly, research output was distributed unequally among seven taxonomic groups
and was not correlated with per-group richness. However, the disparities did not mirror
those found in other studies. I found that plants, invertebrates, and birds received the most
attention, while mammals, herpetofauna, fish, and fungi were studied much less often.
Contrastingly, Clark and May (2002) demonstrated that in the general conservation
literature, vertebrate species command the attention of 69% of papers, while invertebrates
(11%) and plants (20%) get less attention even though vertebrates comprise a small
fraction of known species (3%) compared to invertebrates (79%) and plants (18%).
Also in contrast to my findings, other studies have demonstrated a strong bias
towards mammals compared to other vertebrates (Bonnet et al. 2002, Clark and May 2002,
Trimble and van Aarde 2010). In a survey of papers published in nine leading ecological
journals, Bonnet et al. (2002) found that birds and mammals are over represented compared
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to their species diversity (with 44% and 27% of papers respectively yet only 20% and 9%
of vertebrate species), while fish (14% of papers, 48% of species), reptiles (7% of papers,
14% of species), and amphibians (7% of papers, 9% of species) are underrepresented.
Therefore, within the field of biogeography of human-modified landscapes,
research output among taxonomic groups may more closely mirror species richness among
groups than does the general ecological or conservation literature. However, this reduced
bias compared to other fields may be better explained by methodological constraints rather
than a sense of fairness among researchers (Pawar 2003). Invertebrates, plants, and birds
may be easier to survey than mammals, herpetofauna, and fish. The former groups may
also be more likely than the latter to persist in and, thus, be available for study in humanmodified landscapes.
Yet, species richness per taxonomic group may not be the best resource allocation
metric for research. Although one aim of promoting biodiversity persistence in humanmodified landscapes is to complement PAs in conserving species, the other is to maintain
ecosystem services (Chazdon et al. 2009). That “no comparison” studies were more biased
towards plants and invertebrates than “natural comparison” studies were is perhaps a
reflection that these groups are most closely linked with ecosystem services, such as
pollination and pest control, upon which agriculture relies (e.g. Albrecht et al. 2007, Bell et
al. 2008).
Origins of bias and new directions for research
Although biome type, geopolitical region, and species group were related to bias,
definitively determining the root cause of the biases in research output from the eight
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5. Research Biases in Human-Modified Landscapes
journals assessed here was beyond the scope of this paper. Research history and interests of
individuals and organizations, priorities of funding agencies and governments, and the
stance of journal editorial boards are all likely to play a role in influencing the type of
research conducted and published (see Fazey et al. 2005b). So too are practical constraints
such as the varying difficulty of conducting research in different geographical regions,
varying support and capacity for science in different countries, and language barriers to
publication (see Fazey et al. 2005b, Griffiths and Dos Santos 2012), in conjunction with
regional disparities in economic incentives and resources (see Pyšek et al. 2008), to name
just a few.
This systematic review has highlighted gaps in the evidence base of biogeography
of human-modified landscapes that scientists, publishers, funding agencies, and
governments may wish to consider when planning future research and making decisions
that affect research output. While research output among taxonomic groups was not free
from bias, I conclude that the geo-ecological and geopolitical biases are more immediate
hurdles for science-based conservation action (see also Pyšek et al. 2008). Some biomes
have attracted a good deal of research interest (particularly temperate broadleaf and mixed
forests and the tropical and subtropical moist broadleaf forests), while other biomes were
critically under-investigated. Similarly, research output was biased towards geopolitical
regions in Europe and the Americas, yet Asian and African regions were generally severely
underrepresented.
As conservation efforts beyond PAs become increasingly important globally, these
deficiencies could have profound consequences. Conservation success on human-modified
land depends on a sound and comprehensive evidence base and interdisciplinary
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5. Research Biases in Human-Modified Landscapes
collaboration to meet humanity’s demands for resources while allowing the persistence of
biodiversity. The evidence base to support sensible land-use policies needs to have been
generated in an appropriate geo-ecological and geopolitical context and be extensive
enough to allow systematic review or meta-analytical assessment to draw robust
conclusions regarding management actions (Pullin and Knight 2009, Segan et al. 2011,
Guldemond et al. 2012).
I hope this paper will be the first step towards rectifying the gaps in the evidence
base that I have identified. Ideally, awareness of the current biases will lead researchers,
funding agencies, editors, and publishers to choose, of their own volition, to prioritize
biogeography of human-modified landscapes in under-studied regions and biomes, while
continuing to develop and refine research and implementation strategies for the regions that
have already attracted a good deal of research. Obviously, international funding agencies
could do a great deal to support research in under-studied regions. Similarly, government
policies and funding opportunities that encourage international scientific collaboration
could help spread resources to under-studied regions, promote valuable knowledge
exchange, and build local capacity (Fazey et al. 2005b). However, given that similar
research biases have emerged repeatedly in the conservation and ecology literature (e.g.
Fazey et al. 2005b, Lawler et al. 2006, Pyšek et al. 2008, Felton et al. 2009, Conrad et al.
2011), and little progress has been made (Griffiths and Dos Santos 2012), future work
should specifically assess how to encourage research on topics in need of more attention.
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5. Research Biases in Human-Modified Landscapes
Acknowledgements
M.J.T. is supported by a National Science Foundation Graduate Research Fellowship and
R.J.v.A. through various grants to the Chair in Conservation Ecology at CERU.
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5. Research Biases in Human-Modified Landscapes
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Figures
Fig. 5.1. Increase in studies of biogeography of human-modified landscapes over time. For
the eight journals I considered, the proportion of the total studies published comprised by
studies of the biogeography of human-modified landscapes identified in my review
increased significantly over time.
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Fig. 5.2. World map of coverage of 14 terrestrial biomes. The 14 terrestrial biomes adapted from Olson et al. (2001).
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5. Research Biases in Human-Modified Landscapes
Fig. 5.3. Distribution of research output, CRI, and proportion of land transformed among biomes. Discrepancy between the number of
biogeography of human-modified landscape studies per million km2 (dark green = “natural comparison” studies; light green = “no
comparison” studies; total number of studies listed to the right of bars) and Conservation Risk Index (CRI, blue bars) per terrestrial
biome. Per-biome proportion of land that is transformed is listed on the right-hand axis. Biome abbreviations: Boreal for./taiga =
Boreal forests/taiga; Montane g./sh. = Montane grasslands and shrublands; Temp. Con. For. = Temperate conifer forests; Deserts/x.
sh. = Deserts and xeric Shrublands; Flooded g./sav. = Flooded grasslands and savannas; Trop./sub. g./sav./sh. = Tropical and
subtropical Grasslands, savannas, and shrublands; Trop./sub. moist br. for. = Tropical and subtropical moist broadleaf forests;
Trop./sub. con. for. = Tropical and subtropical coniferous forests; Temp. br./mix. for. = Temperate broadleaf and mixed forests;
Trop./sub. dry br. for. = Tropical and subtropical dry broadleaf forests; Med. for./wd./scrub = Mediterranean forests, woodlands, and
scrub; Temp. g./sav./sh. = Temperate grasslands, savannas, and shrublands.
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Fig. 5.4. Distribution of research output, CRI, and proportion of land transformed among geopolitical regions. Discrepancy between
number of biogeography of human-modified landscape studies per million km2 (dark green = “natural comparison” studies; light green
= “no comparison” studies; total number of studies listed to the right of bars) and Conservation Risk Index (CRI, blue bars) per
geopolitical region. Per-region proportion of land that is transformed is listed on the right-hand axis. Geopolitical region
abbreviations: Mel./Micro./Poly. = Melanesia, Micronesia, and Polynesia; Aust./N.Z. = Australia and New Zealand.
223
5. Research Biases in Human-Modified Landscapes
Fig. 5.5. World map of CRI and research output per geopolitical region. Geopolitical regions based on UN GeoScheme (UNSD 2011)
colored according to their Conservation Risk Index (CRI) from low (yellow) to high (red); blue circles are proportional in size to the
area-corrected research output per geopolitical region (refer to Fig. 5.4 for values).
224
5. Research Biases in Human-Modified Landscapes
Fig. 5.6. Research output per biome type. Median number of studies per million km2 in
forest biomes was significantly higher than in other biomes both including and excluding
“no comparison” studies.
225
5. Research Biases in Human-Modified Landscapes
Fig. 5.7. Distribution of research output and estimated richness per taxonomic group.
Discrepancy between the proportion of the 681 biogeography of human-modified
landscape studies that assessed each taxonomic group (dark green = “natural comparison”
studies; light green = “no comparison” studies) and the estimated proportion of richness per
group (blue bars).
226
5. Research Biases in Human-Modified Landscapes
Tables
Table 5.1. Summary of variables considered and outcomes of statistical tests with respect
to research output groupings of studies of biogeography in human-modified landscapes
published in eight major journals.
Research
output
grouping
Year
Biomes
Geopolitical
regions
Taxonomic
groups
Variables considered
Compared to
Statistical
test
Significance
All
studies
Excluding
“no
comparison”
studies
year (time)
selected studies/total
published
linear
regression
***
NA
area
studies per biome
linear
regression
NS
NS
biome type: forest or
other
area-corrected studies
per biome
Mann–
Whitney
**
**
mammal, bird, reptile,
amphibian richness
studies per biome
Spearman
NS
NS
z-value for vascular
plants
area-corrected studies
per biome
Spearman
NS
*
proportion of land
transformed
area-corrected studies
per biome
Spearman
*
**
CRI
area-corrected studies
per biome
Spearman
NS
*
area
studies per region
linear
regression
NS
NS
proportion of land
transformed
area-corrected studies
per region
Spearman
NS
NS
CRI
area-corrected studies
per region
Spearman
NS
NS
human population
density
area-corrected studies
per region
Spearman
*
*
described species per
group
studies per group
Spearman
NS
NS
Notes: CRI stands for Conservation Risk Index, the proportion of transformed to protected land. NS
is not significant, NA is not assessed, * indicated p < 0.05, ** p < 0.01, and ***p < 0.001.
227
6. General Conclusions
Chapter 6. General Conclusions
In effect, the currency of biodiversity conservation is space. Increasing the total area
dedicated to the protection of nature will, in theory (with provisos that additional areas are
selected at random, are of a sufficient scale, and have not already lost their biodiversity),
increase the proportion of the Earth’s biodiversity that is conserved at the genetic, species,
and ecosystem levels. Space encompasses environmental heterogeneity, provides the
opportunity for speciation, and buffers biodiversity from stochastic phenomena that drive
extinction; it is the underlying variable determining the steady-state diversity of Earth upon
which species–area relationships rely and island biogeography theory depends (MacArthur
and Wilson 1967, Rosenzweig 2001, Rosenzweig 2003). More space increases the
likelihood of incorporating important variations in habitat quality that give rise to source–
sink (Pulliam 1988, Liu et al. 2011), metapopulation (Hanski 1999, 2004), and patch
dynamics (Pickett and White 1985), which contribute to long-term stability of populations
and communities in temporally dynamic ecosystems (see Pickett and Thompson 1978,
Falcy and Danielson 2011). Given the importance of space, efforts to stem biodiversity loss
have focused on meeting benchmarks for protected area coverage, e.g. 10% of each
terrestrial ecological region (Brooks et al. 2004, Chape et al. 2005).
Progress towards meeting the Convention on Biological Diversity’s (CBD’s) 2010
target to reduce the rate of global biodiversity loss was measured largely in terms of
protected area coverage (Secretariat of the CBD 2010). With protected area coverage now
approaching 13% of Earth’s land surface (IUCN and UNEP-WCMC 2011), perhaps failure
to hit the 2010 target, evidenced by the growing number of species facing extinction
228
6. General Conclusions
(Butchart et al. 2010, Secretariat of the CBD 2010, IUCN 2012), can be attributed to a lack
of focus on conservation beyond protected areas. The fate of wild species in the 87% of
land area that is not formally protected will likely play a substantial role in determining the
long-term maintenance of ecosystem services valuable to humanity and the persistence of
biodiversity, even within protected areas (Daily 1999, Daily et al. 2001). Therefore, the
CBD’s proposed 2020 targets are a step in the right direction—considering biodiversity in
“areas under agriculture, aquaculture, and forestry” and focusing on “the science base and
technologies relating to biodiversity” (Perrings et al. 2010).
Conservationists now have the imperative to increase the focus of science and
implementation towards biodiversity in landscapes where humans live, work, and extract
resources (Foley 2005). That habitat degradation leads to extinctions and that protected
areas prevent them epitomize a dogmatic dichotomy in conservation biology. However,
real-world responses of species, and thus communities, to land-use change and protection
are idiosyncratic with some species tolerating or even thriving in human-modified habitats
(e.g. Sekercioglu 2012) and others succumbing to extinction despite protection (see Hansen
and DeFries 2007). Therefore, use of the classic island biogeography theory to describe the
effects of habitat fragmentation has had to evolve in recognition that the matrices
surrounding fragments can play a substantial role in determining conservation outcomes
(e.g. Pereira and Daily 2006). Ecological frameworks of source–sink, metapopulation, and
patch dynamics that have traditionally been applied in relatively pristine natural habitats
are equally, if not more, relevant to landscapes that have been substantially modified by
human activities, but in which we still seek to maintain biodiversity. The management
choices humans make in these landscapes will have profound consequences for
229
6. General Conclusions
biodiversity, and this thesis broadly focused on contributing to our understanding of the
consequences of human land use for different components of biodiversity. The general
focus in this thesis was at the species level of biodiversity; however, other levels should not
be neglected in future developments of frameworks for considering biodiversity in humanmodified landscapes.
In Chapter 2, I provided a qualitative review of the literature on biodiversity in subSaharan Africa’s human-modified landscapes in relation to four broad ecosystem
categorizations (rangelands, tropical forest, the Cape Floristic Region, and the urban and
rural built environment) within which I expect similar patterns of biodiversity persistence
in relation to specific human land uses and land management actions. The outcome of this
chapter is that, while much more work is needed, I illustrated that available research on
biodiversity in human-modified landscapes within all four ecosystem types provides
general conclusions that could support policy-making. This is especially important in light
of rapid development expected in many parts of Africa because a proactive approach to
land-use planning for biodiversity persistence is likely to be more effective and efficient
than a reactive approach requiring habitat restoration (Gardner et al. 2010). However, I also
identified several constraints to conservation success in human-modified landscapes that
require further scientific investment, including deficiencies in the available research,
uncertainties regarding implementation strategies, and difficulties of coexisting with some
species in some circumstances. However, information that is currently available can and
should be used to support efforts at individual, community, provincial, national, and
international levels to support biodiversity conservation in human-modified landscapes.
230
6. General Conclusions
Chapters 3 and 4 delved into species-specific idiosyncrasies in responses to human
land use in the forest belt skirting the southeastern coast of Africa, part of a biodiversity
hotspot hosting many endemic species in a highly transformed landscape (see van Wyk
1996, Küper et al. 2004, Perera et al. 2011, van Aarde et al. 2013). As reported in Chapter
3, I sampled a rich and highly endemic herpetofaunal assemblage over a vegetation-type
gradient representative of prevalent regional land uses (old-growth forest, degraded forest,
acacia woodland (i.e. new-growth forest), eucalyptus plantation, and sugar cane
cultivation). This topic, region, and taxon are drastically understudied even though both
frogs and reptiles face global extinction crises. Besides comparing traditional community
metrics along the gradient, I categorized species into trait-derived functional groups, and
assessed abundance and richness of groups along the gradient to elucidate ecological
underpinnings of species-specific responses. I further assessed the capacity of
environmental variables to predict richness and abundance overall and for functional
groups. The outcome was that overall, old-growth forest harbored the highest richness and
abundance, and frogs and reptiles responded similarly to the gradient. Richness was low in
cultivation and, somewhat surprisingly given other research (Gardner et al. 2007), in
degraded forest but substantial in acacia woodland and exotic plantations. Composition
differed between natural vegetation types (forest, degraded forest) and anthropogenic types
(plantation, cultivation), while acacia woodland grouped with the latter for frogs and the
former for reptiles. Functional group richness eroded along the gradient, a pattern driven by
sensitivity of fossorial frogs and reptiles and vegetation-dwelling frogs to habitat change,
which was a novel finding of the study. Environmental variables were good predictors of
frog abundance, particularly abundance of functional groups, but less so for reptiles. The
231
6. General Conclusions
implications of this research for land-use planning in the region are that conserving forest
and preventing degradation is essential, restoration and plantations have some conservation
value, and cultivation is least amenable to forest herpetofauna. Furthermore, this study
demonstrates the utility of function-related assessments, beyond traditional metrics alone,
for understanding community responses to transformation. In particular, fossorial frogs and
reptiles and vegetation-dwelling frogs should be closely monitored because they are
especially disturbance-sensitive and many are species of conservation concern (IUCN
2012, Botts et al. 2013).
One shortcoming of the herpetofaunal sampling is that recording a species’
presence in a given vegetation type does not imply that it is able to persist solely within
that vegetation type. The value of land uses to particular species found within them could
range from high, i.e. source habitat, which would be a boon to conservation; to
intermediate, a sink habitat that relies on immigrants from the source; to negative, an
ecological trap, which would result in population extinction (Battin 2004, Hansen 2011).
Unfortunately, a snapshot assessment of species occurrence does not allow for
differentiation along this gradient of habitat quality. Instead, a more in depth assessment of
the determinants of population size, e.g. survival, fecundity, and immigration, and
emigration, is required.
As discussed in Chapter 4, the avifaunal community in old-growth coastal dune
forest remnants and in vegetation types regenerating after forest clearance for mining
presents such an example where occurrence might not result in persistence. Based on
occurrence data, previous research demonstrates that forest bird diversity increases with
age along a successional sere of regenerating forest fragments and that the community
232
6. General Conclusions
becomes more similar to that of old-growth forest with time (van Aarde et al. 1996a, van
Aarde et al. 1996b, Kritzinger and van Aarde 1998, Wassenaar et al. 2005). However, I
assessed population trends for 37 bird species commonly found in old-growth forest and
trends in overall bird density in different vegetation types. The outcome was that I found
alarming population declines over a 13-year period; 76% of species I assessed declined,
57% significantly so at an average rate of 13.9% per year. Overall bird density also
declined, both in old-growth forest and in woody regenerating vegetation types, at an
average rate of 12.2% per year. These substantial declines call into question the likelihood
of persistence of these species in the region, and loss of birds may threaten ecological
processes, e.g. seed dispersal and pollination, important for functional connectivity
between remnant forest fragments and for the natural successional pathways on which the
forest restoration program depends (Grainger and van Aarde 2012).
As with the herpetofauna, I assessed species’ traits as potential correlates of
species’ responses to their environment, in this case population trends, to try to elucidate
ecological drivers of population decline. Interestingly, among the many traits I assessed,
including proxies for life history characteristics, only range extent and habitat affinity were
related to trend. Species with larger range extents (generally those with a greater affinity
for regenerating vegetation types) tended to experience more severe population declines.
Therefore, population declines in the study area could be reflecting changing
environmental conditions on a regional scale, e.g. habitat loss or changing climatic
conditions. Still the possibility that human-modified habitats may be acting as ecological
traps should be investigated, especially given that population declines were similar both
within old-growth remnants and in regenerating vegetation types (Battin 2004). It is also
233
6. General Conclusions
possible that population declines represent the realization of extinction debts following a
reduction in the area of old-growth forest (Olivier et al. 2013); however, that species found
predominantly in regenerating vegetation types tended to have lower trend estimates (more
negative) than species found predominantly in old-growth forest suggests otherwise. Then
again, more negative trends of species from regenerating vegetation types might reflect
destruction of grassland, thicket, and woodland on a regional scale.
Unfortunately, the ultimate causes of the declines remain unknown, so more work is
required to assess survival, fecundity, and dispersal rates for each species in each habitat
type. This avifaunal case study serves as a reminder of the shortcomings of occurrence data
and suggests the utility of monitoring programs for other species groups, e.g. herpetofauna,
whose persistence locally may be threatened by regional forces. The case studies presented
here contribute to the wider body of literature on biodiversity in human-modified
landscapes that can provide evidence to support conservation-conscious decision-making
by land-use planners and policy makers. Basing management decisions on scientific
evidence of efficacy can prevent wasted resources and ensure the best possible outcomes
(Sutherland 2004).
However, with limited resources for research, scientists should target the most
relevant projects, and it is crucial to monitor the distribution of published research in
comparison to emerging research needs to identify gaps (Lawler et al. 2006). Therefore, in
Chapter 5, I presented an extensive systematic review of the global literature on
biodiversity in human-modified landscapes. By comparing the research output per
geopolitical region, biome, and taxonomic group to variables that could be considered
indicative of research needs, e.g. species richness, area, human population density, and
234
6. General Conclusions
proportion of transformed and protected land, I discovered biases in the evidence base out
of sync with conservation needs and identified topics deserving of future research. In
particular, I found that 87% of research comes from forest biomes and 75% of studies were
conducted in the Americas and Europe. Therefore, a greater research focus in non-forest
biomes and in Africa and Asia is urgently required. This finding echoes calls for a greater
focus on biodiversity in African human-modified landscapes, particularly within
rangelands and the Cape Floristic Region, as discussed in Chapter 2.
In this thesis, I have provided case studies on biodiversity patterns and processes
that can inform management within the biogeographically important coastal forest belt of
southern Africa. More broadly, I have reviewed the current state of research on biodiversity
in human-modified landscapes in Africa with recommendations for future work and
discussion of the challenges of implementation. From a global perspective, I have
systematically reviewed the literature to identify geographical and taxonomic biases in
need of rectification, and ideally, this work will lead researchers, funding agencies, and
publishers to prioritize under-studied topics. Conservation success in human-dominated
land depends on a sound evidence base and collaboration between ecologists, agronomists,
economists, social and political scientists, and policy-makers to meet humanity’s demands
for resources while allowing as much biodiversity as possible to persist where we live,
work, and extract resources. The future of most species depends on it (Rosenzweig 2003).
235
6. General Conclusions
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of animal populations. Conservation Biology 18: 1482-1491.
Botts, E., B. Erasmus, and G. Alexander. 2013. Small range size and narrow niche breadth
predict range contractions in South African frogs. Global Ecology and
Biogeography 22: 567-576.
Brooks, T. M., et al. 2004. Coverage provided by the global protected-area system: is it
enough? Bioscience 54: 1081-1091.
Butchart, S. H. M., et al. 2010. Global biodiversity: indicators of recent declines. Science
328: 1164-1168.
Chape, S., J. Harrison, M. Spalding, and I. Lysenko. 2005. Measuring the extent and
effectiveness of protected areas as an indicator for meeting global biodiversity
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Daily, G. C. 1999. Developing a scientific basis for managing Earth's life support systems.
Conservation Ecology 3: Art. 14.
Daily, G. C., P. R. Ehrlich, and G. A. Sanchez-Azofeifa. 2001. Countryside biogeography:
use of human-dominated habitats by the avifauna of southern Costa Rica.
Ecological Applications 11: 1-13.
Falcy, M. R. and B. J. Danielson. 2011. When Sinks Rescue Sources in Dynamic
Environments. Pages 139-154 in J. Liu, V. Hull, A. T. Morzillo, and J. A. Wiens,
editors. Sources, Sinks, and Sustainability. Cambridge University Press,
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Foley, J. A. 2005. Global consequences of land use. Science 309: 570-574.
Gardner, T., J. Barlow, and C. A. Peres. 2007. Paradox, presumption and pitfalls in
conservation biology: the importance of habitat change for amphibians and reptiles.
Biological Conservation 138: 166-179.
Gardner, T. A., J. Barlow, N. S. Sodhi, and C. A. Peres. 2010. A multi-region assessment
of tropical forest biodiversity in a human-modified world. Biological Conservation
143: 2293-2300.
Grainger, M. J. and R. J. van Aarde. 2012. Is succession-based management of coastal
dune forest restoration valid? Ecological Restoration 30: 200-208.
Hansen, A. 2011. Contribution of Source-Sink Theory to Protected Area Science. Pages
339-360 in J. Liu, V. Hull, A. T. Morzillo, and J. A. Wiens, editors. Sources, Sinks,
and Sustainability. Cambridge University Press, Cambridge, United Kingdom.
Hansen, A. J. and R. DeFries. 2007. Ecological mechanisms linking protected areas to
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Hanski, I. 1999. Metapopulation Ecology. Oxford University Press, Oxford, United
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Hanski, I. 2004. Metapopulation theory, its use and misuse. Basic and Applied Ecology 5:
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IUCN and UNEP-WCMC. 2011. The World Database on Protected Areas (WDPA):
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IUCN. 2012. IUCN Red List of Threatened Species. Version 2012.2. IUCN, Gland,
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Kritzinger, J. J. and R. J. van Aarde. 1998. The bird communities of rehabilitating coastal
dunes at Richard's Bay, KwaZulu-Natal. South African Journal of Science 94: 7178.
Küper, W., J. H. Sommer, C. L. Jon, J. Mutke, H. P. Linder, H. J. Beentje, R. S. A. R. van
Rompaey, C. Chatelain, M. Sosef, and W. Barthlott. 2004. Africa's hotspots of
biodiversity redefined. Annals of the Missouri Botanical Garden 91: 525-535.
Lawler, J. J., et al. 2006. Conservation science: a 20-year report card. Frontiers in Ecology
and the Environment 4: 473-480.
Liu, J., V. Hull, A. T. Morzillo, and J. A. Wiens, editors. 2011. Sources, Sinks, and
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MacArthur, R. H. and E. O. Wilson. 1967. The Theory of Island Biogography. Princeton
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Olivier, P., R. van Aarde, and A. Lombard. 2013. The use of habitat suitability models and
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Africa. Diversity and Distributions 19: 1353-1365.
Pereira, H. M. and G. C. Daily. 2006. Modeling biodiversity dynamics in countryside
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Perera, S. J., D. Ratnayake-Perera, and S. Proches. 2011. Vertebrate distributions indicate a
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Pickett, S. T. and P. S. White. 1985. The Ecology of Natural Disturbance and Patch
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Pickett, S. T. A. and J. N. Thompson. 1978. Patch dynamics and the design of nature
reserves. Biological Conservation 13: 27-37.
Pulliam, H. R. 1988. Sources, sinks, and population regulation. The American Naturalist
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Rosenzweig, M. L. 2001. Loss of speciation rate will impoverish future diversity.
Proceedings of the National Academy of Sciences, USA 98: 5404-5410.
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37: 194-205.
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Sekercioglu, C. 2012. Bird functional diversity and ecosystem services in tropical forests,
agroforests and agricultural areas. Journal of Ornithology 153: S153-S161.
Sutherland, W. 2004. The need for evidence-based conservation. Trends in Ecology &
Evolution 19: 305-308.
van Aarde, R. J., S. M. Ferreira, and J. J. Kritzinger. 1996a. Successional changes in
rehabilitating coastal dune communities in northern KwaZulu/Natal, South Africa.
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van Aarde, R. J., S. M. Ferreira, J. J. Kritzinger, P. J. van Dyk, M. Vogt, and T. D.
Wassenaar. 1996b. An evaluation of habitat rehabilitation on coastal dune forests in
northern KwaZulu-Natal South Africa. Restoration Ecology 4: 334-345.
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6. General Conclusions
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Biodiversity in African Savannahs. Kluwer Academic Publishers, Dordrecht.
Wassenaar, T., R. van Aarde, S. Pimm, and S. Ferreira. 2005. Community convegence in
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240
Appendix A. Consequences of Fencing
Appendix A. Fences are More Than an Issue of Aesthetics
Publication Details
Trimble, M.J. & van Aarde, R.J. 2010. Fences are more than an issue of aesthetics.
BioScience. 60: 486. doi: 10.1525/bio.2010.60.7.20
Letter
Licht and colleagues (BioScience 60: 147–153) identify South Africa’s pioneering efforts
to reintroduce top predators to small, fenced protected areas as a conservation model
America might be wise to follow. However, South African success at large predator
reintroduction is largely the result of ubiquitous fencing that generally prevents predator
conflict with people and livestock (see Gusset et al. 2008).
The consequences of applying a similar paradigm in America are not only aesthetic,
as implied by Licht, but could also compromise the long-term success of biodiversity
conservation. A recent review of fencing for conservation concluded that fencing is an
acknowledgment that we are failing to coexist with and successfully conserve biodiversity,
and that the costs—economic and ecological—generally far exceed the benefits (Hayward
and Kerley 2009). Ecological costs include fence-line mortalities, influences on natural
behavior, impingement on natural mechanisms of population control, restriction of animal
movements in response to environmental changes (e.g. fires, climate change, drought),
241
Appendix A. Consequences of Fencing
limitation of migration and genetic flow, and impediment to recolonization and source–sink
population dynamics.
Licht and colleagues stated that there are relatively few concerns in South Africa
about the fence around Kruger National Park. This is incorrect—there are serious
ecological concerns including extinction debt and species persistence of many iconic
herbivores, even though the park covers nearly 20,000 square kilometers (Nicholls et al.
1996, Ogutu and Owen-Smith 2003). Fences around smaller protected areas can be even
more problematic.
MORGAN J. TRIMBLE
RUDI J. van AARDE
Morgan J. Trimble ([email protected]) is a research fellow with the
Conservation Ecology Research Unit, and Rudi J. van Aarde ([email protected]
is a professor of zoology, chair of conservation ecology, and director of the Conservation
Ecology Research Unit, both with the Department of Zoology and Entomology at the
University of Pretoria, South Africa.
242
Appendix A. Consequences of Fencing
References
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endangered wild dogs in South Africa. Journal of Applied Ecology 45: 100-108.
Hayward, M. W. and G. I. H. Kerley. 2009. Fencing for conservation: restriction of
evolutionary potential or a riposte to threatening processes? Biological
Conservation 142: 1-13.
Nicholls, A. O., P. C. Viljoen, M. H. Knight, and A. S. van Jaarsveld. 1996. Evaluating
population persistence of censused and unmanaged herbivore populations from the
Kruger National Park, South Africa. Biological Conservation 76: 57-67.
Ogutu, J. O. and N. Owen-Smith. 2003. ENSO, rainfall and temperature influences on
extreme population declines among African savanna ungulates. Ecology Letters 6:
412-419.
243
Appendix B. Pipe Trapping African Frogs
Appendix B. A Note on Polyvinyl Chloride (PVC) Pipe
Traps for Sampling Vegetation-Dwelling Frogs in South
Africa
Publication Details
Trimble, M.J. & van Aarde, R.J. 2013. A note on polyvinyl chloride (PVC) pipe traps for
sampling vegetation-dwelling frogs in South Africa. African Journal of Ecology, DOI:
10.1111/aje.12120.
Introduction
Vegetation-dwelling frogs are challenging to sample. They can climb out of traditional
traps, and many are furtive (Myers et al. 2007, Pittman et al. 2008). PVC pipe traps, which
mimic natural features frogs use for shelter, may provide a useful technique (e.g. Boughton
et al. 2000). Pipe trapping has been used to sample treefrogs of the family Hylidae in the
United States (e.g. Boughton et al. 2000, Liner et al. 2008, Farmer et al. 2009), but it is
increasingly used elsewhere (e.g. Laurencio and Malone 2009, Ferreira et al. 2012), even
for non-Hylids (Coqui Frog Working Group 2006).
African vegetation-dwelling frog genera, e.g. Leptopelis, Afrixalus, and Hyperolius
(see Channing 2001, du Preez and Carruthers 2009), may be attracted to artificial refugia of
PVC pipe traps. If so, pipe trapping would augment sampling techniques for African
anurans, which are little studied (Trimble and van Aarde 2010, Trimble and van Aarde
244
Appendix B. Pipe Trapping African Frogs
2012) despite conservation needs (Measey 2011), and could facilitate sampling outside the
breeding season, reduce observer and detection bias (see Bailey et al. 2004, Willson and
Gibbons 2010), and allow fundamental and applied ecological studies, e.g. habitat selection
(e.g. Johnson et al. 2007, Pittman et al. 2008), migration/dispersal (e.g. Johnson 2005), and
management effects (e.g. Muenz et al. 2006, Rice et al. 2011). In this preliminary
assessment, I provide the first evidence that it is possible to capture African frogs in PVC
pipe traps in the field. However, capture success was low, so I encourage more research on
alternate trap designs and in other habitats.
Methods
My study was conducted in the South African coastal forest within 2.3 km of the east coast,
along a 25 km section between the Umlalazi River and Richards Bay Harbour. The area
harbours a high species richness and concentration of threatened frogs (Maritz 2007,
Measey 2011) (Table B.1).
I installed 30 pipe trap arrays in terrestrial habitats ≥ 300 m from water bodies and
≥ 500 m from each other, divided evenly among five vegetation types: coastal forest,
degraded forest, acacia woodland, eucalyptus woodlot, and sugar cane cultivation. I placed
a further six arrays in coastal forest ≤ 30 m from a water body and ≥ 50 m apart. Each array
consisted of four, 60-cm-long, white PVC pipes. I inserted two pipes (one of 16 mm and 44
mm internal diameter) 10cm into the ground near the base of a tree. I attached another of
each diameter pipe together and affixed them vertically from their top at a height of 2 m up
the tree trunk. Caps on the bottom of these pipes allowed retention of standing water
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Appendix B. Pipe Trapping African Frogs
(added at installation), and a hole drilled 15 cm from the bottom prevented flooding
(Boughton et al. 2000). I installed pipes on a variety of tree species (e.g. White Stinkwood
Celtis africana, Horsewood Clausena anisata, Sweet Thorn Acacia karroo, and Eucalyptus
sp.) with circumference at breast height of 10–200cm (xˉ = 53.7 cm, sd = 41.2 cm). At five
sugar cane cultivation arrays there were no trees, so all four pipes were inserted into the
ground.
Pipe traps were installed progressively from February 17 to March 21, 2012
(summer/rainy season); I monitored arrays for 14–34 days (xˉ = 21.7, sd = 7.3). As per
agreements with landowners, arrays in cultivation and woodlots were removed after 14–15
days, while others remained for the study duration. I checked each array during daylight
hours on an intermittent schedule as logistics allowed, i.e. 5–9 times per array at intervals
of 1–9 days (xˉ = 3.4, sd = 0.7). I identified and measured frogs found in traps and released
them ≥ 50 m away. I also noted frogs observed incidentally (i.e. coincidentally or during
casual searches) during the study period.
Results and Discussion
I checked 36 arrays 219 times over 34 days (43 times for the six arrays near water and 176
for the 30 terrestrial arrays). I caught five frogs in pipes (Table B.1), a trap success of 2.3%
by array checking instances or 0.6% by pipe checking instances. One capture on the outside
of a pipe was not included in calculations (Table B.1). Sparse captures prevented statistical
analyses, but trap success appeared higher near water than away, 7% of array checking
instances versus 1.1%. I incidentally observed eight species (Table B.1). Trapping success
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Appendix B. Pipe Trapping African Frogs
was lower than reported in the Americas, e.g. 79% (Bartareau 2004), 23% (Myers et al.
2007), 2.5–4.3% (Pittman et al. 2008), and 6% (Ferreira et al. 2012) (some of these studies
included recaptures). Several factors might have contributed to my low trapping success.
(1) Pipes might not have provided attractive refugia. Frogs discriminate between
refugia attributes (e.g. Boughton et al. 2000, Bartareau 2004, Johnson et al. 2007, Johnson
et al. 2008, Hoffmann et al. 2009). Many design factors have been investigated in relation
to capture success (e.g. diameter, length, and colour), and while 44 mm diameter pipes
appeared more effective than 16 mm and ground and tree pipes both worked, other trap
designs could be investigated (see Boughton et al. 2000, Bartareau 2004, Johnson et al.
2007, Myers et al. 2007, Johnson et al. 2008, Pittman et al. 2008, Ferreira et al. 2012).
(2) Natural refugia provided by plants may have outcompeted pipes (Hoffmann et
al. 2009). Dracaena aletriformis and Strelitzia nicolai are prevalent in the undergrowth,
and their leaf axils provide hiding places for frogs (du Preez and Carruthers 2009).
(3) The sampling period may have been too short for frogs to find the pipes (Myers
et al. 2007), which could have compounded the effects of competition with natural refugia.
In conclusion, I caught three species in PVC pipe traps and found an additional
species on the outside of a pipe, demonstrating that the technique can be used to trap
African frogs of the family Hyperoliidae. However, trap success was low, and I captured
species also encountered incidentally. I encourage further assessment of PVC pipe trapping
for African vegetation-dwelling frogs to support amphibian ecological studies. Altering
trap design, using traps in areas with less abundant natural refugia, and installing traps a
few months prior to sampling should be investigated to improve success. Further
experiments could elucidate which trap designs work for which species.
247
Appendix B. Pipe Trapping African Frogs
Acknowledgements
I thank Richards Bay Minerals, Department of Trade & Industry, NSF GRFP, R.
Guldemond, T. Lee, and A. Prins.
248
Appendix B. Pipe Trapping African Frogs
References
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Demography Unit, Department of Zoology and Entomology, University of Cape
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Bailey, L. L., T. R. Simons, and K. H. Pollock. 2004. Estimating site occupancy and
species detection probability parameters for terrestrial salamanders. Ecological
Applications 14: 692-702.
Bartareau, T. M. 2004. PVC pipe diameter influcences the species and sizes of treefrogs
captured in a Florida coastal oak scrub community. Herpetological Review 35: 150152.
Boughton, R. G., J. Staiger, and R. Franz. 2000. Use of PVC pipe refugia as a sampling
technique for hylid treefrogs. American Midland Naturalist 144: 168-177.
Channing, A. 2001. Amphibians of Central and Southern Africa. Cornell University Press,
Ithaca.
Channing, A., et al. 2013. Taxonomy of the super-cryptic Hyperolius nasutus group of long
reed frogs of Africa (Anura: Hyperoliidae), with descriptions of six new species.
Zootaxa 3620: 301-350.
Coqui Frog Working Group. 2006. Coqui Frog Control for Homeowners: Methods to Stop
the Spread of Coqui Frogs in Hawai'i. University of Hawai'i at Manoa, Manoa.
du Preez, L. H. and V. Carruthers. 2009. A Complete Guide to Frogs of Southern Africa.
Struik Nature, Cape Town.
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Appendix B. Pipe Trapping African Frogs
Farmer, A., L. Smith, S. Castleberry, and J. W. Gibbons. 2009. A comparison of techniques
for sampling amphibians in isolated wetlands in Georgia, USA. Applied
Herpetology 6: 327-341.
Ferreira, E., R. G. Rocha, A. Malvasio, and C. Fonseca. 2012. Pipe refuge occupancy by
herpetofauna in the Amazonia/Cerrado ecotone. Herpetological Journal 22: 59-62.
Hoffmann, K. E., S. A. Johnson, and M. E. McGarrity. 2009. Interspecific variation in use
of polyvinyl choride (PVC) pipe refuges by hylid treefrogs: a potential source of
capture bias. Herpetological Review 40: 426-426.
Johnson, J. R. 2005. Multi-scale investigations of Gray Treefrog movements: Patterns of
migration, dispersal, and gene flow. Ph.D. thesis. University of Missouri, Columbia,
Missouri.
Johnson, J. R., J. H. Knouft, and R. D. Semlitsch. 2007. Sex and seasonal differences in the
spatial terrestrial distribution of Gray Treefrog (Hyla versicolor) populations.
Biological Conservation 140: 250-258.
Johnson, J. R., R. D. Mahan, and R. D. Semlitsch. 2008. Seasonal terrestrial microhabitat
use by Gray Treefrogs (Hyla versicolor) in Missouri oak-hickory forests.
Herpetologica 64: 259-269.
Laurencio, D. and J. H. Malone. 2009. The amphibians and reptiles of Parque Nacional
Carara, a transitional herpetofaunal assemblage in Coasta Rica. Herpetological
Conservation and Biology 4: 120-131.
Liner, A. E., L. L. Smith, S. W. Golladay, S. Castleberry, and J. W. Gibbons. 2008.
Amphibian distributions within three types of isolated wetlands in southwest
Georgia. American Midland Naturalist 160: 69-81.
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Appendix B. Pipe Trapping African Frogs
Maritz, B. 2007. The distribution and abundance of herpetofauna on a quaternary aeolian
dune deposit: Implications for strip mining. M.Sc. thesis. University of the
Witwatersrand, Johannesburg, South Africa.
Measey, G. J., editor. 2011. Ensuring a Future for South Africa's Frogs: A Strategy for
Conservation Research. South African National Biodiversity Institute, Pretoria.
Muenz, T. K., S. W. Golladay, G. Vellidis, and L. L. Smith. 2006. Stream buffer
effectiveness in an agriculturally influenced area, southwestern Georgia. Journal of
Environment Quality 35: 1924.
Myers, C. H., L. Eigner, J. A. Harris, R. Hilman, M. D. Johnson, R. Kalinowski, J. J. Muir,
M. Reyes, and L. E. Tucci. 2007. A comparison of ground-based and tree-based
polyvinyl chloride pipe refugia for capturing Pseudacris regilla in northwestern
California. Northwestern Naturalist 88: 147-154.
Pittman, S. E., A. L. Jendrek, S. J. Price, and M. E. Dorcas. 2008. Habitat selection and site
fidelity of Cope's Gray Treefrog (Hyla chrysoscelis) at the aquatic-terrestrial
ecotone. Journal of Herpetology 42: 378-385.
Rice, K. G., J. H. Waddle, M. W. Miller, M. E. Crockett, F. J. Maxxotti, and H. F. Percival.
2011. Recovery of native treefrogs after removal of nonindigenous Cuban treefrogs,
Osteopilus septentrionalis. Herpetologica 67: 105-117.
Trimble, M. J. and R. J. van Aarde. 2010. Species inequality in scientific study.
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Trimble, M. J. and R. J. van Aarde. 2012. Geographical and taxonomic biases in research
on biodiversity in human-modified landscapes. Ecosphere 3: Article 119.
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Willson, J. D. and J. W. Gibbons. 2010. Drift Fences, Coverboards, and Other Traps. Pages
229-245 in C. K. Dodd Jr., editor. Amphibian Ecology and Conservation. Oxford
University Press, Oxford.
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Appendix B. Pipe Trapping African Frogs
Tables
Table B.1. Vegetation-dwelling frog species expected in the area, species incidentally
recorded in the area during the survey (location of observation is denoted NW = near water,
Tr = terrestrial, Tr/NW = terrestrial and near water), and inventory of captures in PVC pipe
traps indicating array location (NW = near water, Tr = terrestrial), pipe diameter and
location (G = ground, T = tree), Snout–urostyle length (SUL) of frog, and habitat type (AW
= acacia woodland, DF = degraded forest, F = Forest).
Frog Atlas species a
Incidentally recorded
Pipe trap captures
NW
NW (44 mm G pipe, SUL = 35 mm, F)
Afrixalus delicates
Afrixalus fornasinii
NW (44 mm T pipe, SUL = 35 mm, F)
Afrixalus spinifrons
Tr
Hyperolius argus
NW
Hyperolius marmoratus
NW
Tr (44 mm G pipe, SUL = 23 mm, DF)
NW (outside of T pipe, F)
Hyperolius poweri
Hyperolius pickersgilli
NW
Hyperolius pusillus
Tr/NW
Hyperolius semidiscus
Hyperolius tuberilinguis
Tr/NW
NW ( 44 mm T pipe, SUL = 27 mm, F)
Tr (44 mm G pipe, SUL = 29 mm, AW)
Leptopelis mossambicus
Leptopelis natalensis b
Tr/NW
a
The South African Frog Atlas Project recorded twelve species of Leptopelis, Afrixalus, and Hyperolius in the
two quarter-degree squares spanned by the study area (ADU 2011). Nomenclature follows du Preez and
Carruthers (2009) except Hyperolius poweri (see Channing et al. 2013).
b
L. natalensis was not captured in pipes despite occurring in the area. Worth noting, however, is that on two
occasions I released incidentally caught L. natalensis individuals at the base of tree in which I had hung a set
of pipes, and both frogs climbed the tree, went into a pipe, and remained there for some time.
253
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