Evaluating the adequacy of reserves in the Maputaland, South Africa

Evaluating the adequacy of reserves in the Maputaland, South Africa
Evaluating the adequacy of reserves in the
Tembe–Tshanini Complex: a case study in
Maputaland, South Africa
J . Y . G a u g r i s and M . W . V A N R O O Y E N
Abstract The aim of this study was to determine the
minimum conservation area needed to conserve vegetation
types and their landscape and to apply it to an area
in KwaZulu-Natal, South Africa, which is within the
Maputaland Centre of Plant Endemism and part of the
Maputaland–Pondoland–Albany biodiversity hotspot. Outside conservation areas this Centre of Plant Endemism is
under threat from human utilization. We used a method
initially designed to determine minimum conservation areas
for rare plant species, which we adapted from its original
country and context, to determine minimum conservation
areas for landscape species in Maputaland’s little-documented
environment. The minimum area required for conservation
was established for the Sand Forest and Woodland vegetation
types in the region. We found that sufficient habitat is
presently conserved to preserve the Sand Forest but not the
Woodlands. The method holds promise to provide answers to
critical conservation issues in lesser-known environments
and, although relatively difficult to establish for the first time,
is an efficient and easy to use tool that can be refined once
more knowledge becomes available.
Keywords Maputaland, minimum conservation area, Sand
Forest, South Africa, sustainable utilization, woodland
vegetation
This paper contains supplementary material that can be
found online at http://journals.cambridge.org
Introduction
I
n most developing countries the human population
increase of the past 50 years has been linked with a
significant increase in natural resource exploitation
(Naughton-Treves et al., 2007) and this has been most
severe for wood (Luoga et al., 2000). Although protected
areas represent an appropriate way to conserve natural
resources they are usually inadequate in size and do not
cover the full range of features in need of conservation
J.Y. GAUGRIS* (Corresponding author) Centre for Wildlife Management,
University of Pretoria, 0002 Pretoria, South Africa. E-mail [email protected]
florafaunaman.com
M.W. VAN ROOYEN Department of Plant Science, University of Pretoria,
Pretoria, South Africa
Received 26 December 2007. Revision requested 2 April 2008.
Accepted 21 May 2008.
(Pressey et al., 2003). Consequently, there is a need to
conserve resources outside formally protected areas by
creating a network of formal conserved areas interlinked
by a matrix that creates corridors for plant and animal
dispersal (Rouget et al., 2006; Smith et al., 2006).
One of the key issues in systematic conservation planning is to determine how much needs to be protected (Eeley
et al., 2001; Sanderson et al., 2002). Setting targets has been
the crux of conservation planning since the 10% global
landmass target proposed at the 3rd World Congress on
National Parks in 1982 (Pressey et al., 2003; Tear et al.,
2005). However, such targets have promoted the protection
of land irrespective of its value for conservation.
Setting targets is currently achieved by integrating the
sum of individual targets of all landscape features (soils,
topography, climate, plants, animals) to derive a regional
target (Pressey et al., 2003). Regional targets are then used
to assess the effectiveness of current conservation areas and
develop new management plans. The most severe limitation for setting such targets lies in the lack of biodiversity
data, which in some cases could imply that short of conserving 100% of a region, conservation will fail its purpose
(Pressey et al., 2003). Moreover, targets should preferably
describe quantitative aspects, as they are more tangible and
facilitate further use and refinements, rather than qualitative
aspects. Quantitative targets are constructed from evidence,
and are therefore defensible.
Setting broad-scale conservation targets requires reviewing smaller scale targets, and assimilating these with conservation planning software (Desmet & Cowling, 2004).
Such software exists for the selection of priority areas for
conservation but tends to be most effective at a broad
geographical scale (Eeley et al., 2001; Reyers et al., 2001;
Pressey et al., 2003). Such regional overviews are based on
a review of local expert judgement within the region of
concern (Cowling et al., 2003). However, local experts
seldom have the time or budget for the detailed, longterm population viability analyses and habitat modelling
(Burgman et al., 2001; Desmet & Cowling, 2004) required
for setting scientifically and ecologically acceptable targets
at the local scale. As a consequence, expert opinion at the
local scale is usually more qualitative than quantitative.
Methods that can assist establishment of quantitative
conservation targets at the local level are therefore indispensable. Such methods should be simple, easy to use,
sufficiently adaptable to incorporate new data, and should
ª 2010 Fauna & Flora International, Oryx, 44(3), 399–410 doi:10.1017/S0030605310000438
400
J. Y. Gaugris and M. W. Van Rooyen
work with a limited amount of relevant information such as
is typically gathered from baseline ecological studies. Such
studies are often the only data available to conservation
authorities (Burgman et al., 2001).
Burgman et al. (2001) developed such a method for
setting conservation targets for plant species when there is
insufficient data and time. They applied their method to
rare plant species because such species usually require
a specific conservation action. In this study we expand this
method, evaluating its applicability for landscape species,
where landscape species are a derivative of umbrella species
(Sanderson et al., 2002). Landscape species are defined as
biological entities occurring over large, ecologically diverse
areas and having a significant impact on structure and
function of local natural ecosystems within a timespan
applicable to human management targets (Sanderson et al.,
2002). Through conservation of landscape species the
conservation of the whole landscape and the co-occurring
species could potentially be achieved.
Conserving umbrella species based on a prospective
selection of occurrence patterns and ecological traits
(Fleishman et al., 2001) may be a good short-cut for
conserving co-occurring species. However, umbrella species are often poorly selected and results may therefore fall
short of expectations. Selection based on conservation
status, failure to assess co-occurrence of similar taxonomic
groups and species richness across the target landscape, and
selection of habitat generalists are common pitfalls in
umbrella species selection. The amount of information
required to select an umbrella species properly may render
it anything but a short-cut (Seddon & Leech, 2008). The
ideal umbrella species should be neither rare nor ubiquitous, and offer a significant chance of co-occurrence with
other species (Fleishman et al., 2001). A recent revision of
the umbrella species concept narrowed the selection criteria
to seven from 17 in an attempt to provide a simpler concept
(Seddon & Leech, 2008). These seven criteria are: wellknown biology, large home range, high probability of population persistence, co-occurrence of species of conservation
interest, management needs not specifically beneficial only
to the umbrella species, moderate sensitivity to human
disturbance, and ease of monitoring. This revision is therefore
similar to the broader concept of landscape species that rely
on five criteria: area requirements, heterogeneity in habitat
utilization, ecological functionality, vulnerability to human
land-uses and socio-economic significance. Therefore, by
selecting appropriate landscape plant species we believe that
most co-occurring common and rare plant species will be
included, and to some extent the animals that depend upon
them, which may make the conservation of these landscape
plants an effective tool for conservation purposes.
By adapting and applying the method of Burgman et al.
(2001) over a geographical region identified by systematic
conservation planning as important for biodiversity con-
servation (Eeley et al., 2001; Smith et al., 2006), our aim is to
determine the minimum area of each vegetation community that needs to be fully conserved in a network of
conservation areas.
Study area
The study area lies in the Maputaland region of South Africa,
with two existing conservation sites: Tembe Elephant Park
(30,000 ha) and Tshanini Community Conservation Area
(2,420 ha) in the Manqakulane rural community. Maputaland
is the northern tip of the Maputaland–Pondoland–Albany
biodiversity hotspot (Smith et al., 2006). It consists of a sandy
plain interspersed with ancient littoral dunes that lie north–
south, covered by a mosaic of Sparse to Closed Woodland
with patches of Sand Forest (Matthews et al., 2001; Gaugris
et al., 2004; Gaugris, 2008).
The four main vegetation types are: Sand Forest, and
Closed, Open and Sparse Woodland (Gaugris, 2008; Gaugris
& Van Rooyen, 2008), the latter best described as a wooded
grassland. Sand Forest is biologically the most diverse vegetation type in Maputaland and one of the most important in
the Maputaland–Pondoland–Albany hotspot (Matthews,
2006). However, Maputaland’s woodlands are nearly as
species rich as the Sand Forest (Matthews, 2006). Vegetation
dynamics in the region are not fully understood but they
may be driven by relatively frequent small-scale disturbances
from incursions by large mammals (Gaugris, 2008). Largescale disturbances induced by increasing human populations
outside conserved areas and high densities of large mammals
inside conserved areas are challenging the resilience of both
forest and woodland ecosystems (Botes et al., 2006; Gaugris,
2008).
Methods
While keeping to the same terminology, we used and
adapted the method of Burgman et al. (2001) to suit our
approach and the conditions of our case study. This 12-step
method (Fig. 1) provides a framework within which
knowledge of each selected species can be evaluated,
thereby facilitating a discussion about how best to calculate
the area required to protect groups of associated species.
Apart from changes described immediately below, the
modifications to the original method are described in our
description of the 12 steps. We have tried to keep the
methodology as simple as possible to make it accessible.
One of the main modifications is with regards to
disturbance region, which we define to be a fixed area of
a size that is susceptible to be fully disturbed by at least one
disturbance within the time frame of that disturbance and
subject to the same decision-making governance. We thus
set the size of a disturbance region to 3,600 ha, which is
approximately half the area of a rural community’s tribal
ª 2010 Fauna & Flora International, Oryx, 44(3), 399–410
Minimum conservation areas
Mapping & qualitative process (coarse filter)
In relation to the local context
Recommended
Focus panel
Mathematical & quantitative process (fine filter)
In relation to the local context
Social scientist
Ecologist
Vegetation sepcialist
Recommended
Focus panel
Social scientist
Ecologist
Vegetation specialist
Independently of the local context
Recommended
Focus panel
Ecologist
Vegetation specialist
PREREQUISITE
Select a species to work on
Step 2
Step 1 (b)
Identify populations or groups of populations that
currently experience similar disturbance regimes
Adjust F according to local, present &
perceived future risks
Step 3
Step 5
identify map the area of potential habitat
Estimate the size density of the adult
population within the surveyed potential habitat
Step 1 (a)
Estimate the minimum population size (F)
likely to persist under the influences of
demographic & environmental uncertainty
Step 4
Outline the area of potential habitat surveyed
Step 6
Estimate a target area for protection based on
background disturbance processes
Step 7 (a)
Step 7 (b)
Identify small scale disturbances affecting the
species’ potential habitat from which the species
are expected to recover within the 50 years
management time frame
Calculate the proportion of remaining habitat
available to the species at any time
within the 50 years management time frame
Step 8 (a)
Step 8 (b)
Identify deterministic
trends that irreversibly affect the species’
potential habitat
Calculate the proportion of remaining habitat
available to the species at any time
within the 50 years management time frame
Step 9 (a)
Step 9 (b)
Evaluate the processes that permanently
reduce the density of populations within their
areas of occupancy
Adjust the target area to account for processes
that permanently reduce the density of
population within their areas of occupancy
Step 10
Identify catastrophes likely to affect the species’
potential habitat, its number of discrete
populations & its dispersal capabilities
Step 11
Combine targets across disturbance regions
& define a species / community target
Step 12
Evaluate habitat maps & evaluate the adequacy
of current strategies, set objectives accounting
for spatial & species specific constraints
FIG. 1 The process flow chart of the 12 steps in the method of Burgman et al. (2001). The method is a combination of coarse (large scale
for the regional level) and fine (local scale) considerations, with qualitative and quantitative inputs. The method should ideally be
conducted by a panel of experts or people with local knowledge, who guide the decision process and assess the various risks considered.
Step 12 is the bridge between qualitative and quantitative aspects.
land in Maputaland (Peteers, 2005). This size is also slightly
less than half of the area for reliable representation of
meteorological conditions around a meteorological station
(Yeh et al., 2000). These two considerations ensured that
each region could be considered homogeneous with respect
to human-related disturbance or localized weather events,
thereby incorporating both human and local weather
variability within their respective time frames.
ª 2010 Fauna & Flora International, Oryx, 44(3), 399–410
We view uncertainty differently to Burgman et al.
(2001), who considered that high key (all is well) and low
key (all is bad) analyses should be conducted in parallel to
provide an uncertainty range reminiscent of interval
arithmetic. To do this they noted that in most cases these
high and low keys will have to be set subjectively. In our
approach we evaluated the minimum viable population size
(F, see definition below) in an area of 36 3 42 km divided
401
402
J. Y. Gaugris and M. W. Van Rooyen
into a grid of 42 square disturbance regions of 3,600 ha, and
assumed that the variation within the 42 regions provided
a reasonable estimate of uncertainty, with upper and lower
bounds based on evaluated threats, thus avoiding running
parallel supplementary analyses. In effect, we applied the
method to each disturbance region in the area, as Burgman
et al. (2001) suggest. This approach to uncertainty was justified by the fact that most of the 42 regions have a similar
ecological potential (Gaugris et al., 2004) but are subjected
to different land tenure and land use.
Prerequisites Our method requires the selection of one or
two plant species that are characteristic of a particular
vegetation type. The species must represent the vegetation
type in such a way that their conservation ensures the conservation of the vegetation type as a whole, including its
dynamics (Burgman et al., 2001). Our choice of plant species
followed recommendations for landscape species selection
(Sanderson et al., 2002). Cleistanthus schlechteri (Pax) Hutch.
and Newtonia hildebrandtii (Vatke) Torre were chosen as
landscape tree species of the Sand Forest. Both grow into
large canopy trees, are long-lived, have a wide geographical
range in southern Africa (Pooley, 1997), and are used
for firewood and building material (Gaugris et al., 2007).
Hymenocardia ulmoides Oliv. and Sclerocarya birrea
(A. Rich.) Hochst. were the landscape tree species chosen
for the Woodlands. H. ulmoides is an abundant species,
geographically widespread in KwaZulu-Natal, and it provides good building material (Pooley, 1997). S. birrea is an
important food tree in southern Africa and is geographically
widespread on the subcontinent (Emanuel et al., 2005).
Step 1 The method firstly requires the establishment of F, the
minimum viable population size, defined as ‘the population
size that faces a 0.1% probability of falling below 50 adults at
least once in the next 50 years, assuming no detrimental
human effects’ (Burgman et al., 2001). This represents a quasi
extinction risk and provides a background risk against which
it is possible to measure the utility of conservation actions.
The 50 years benchmark reflects concerns for risks over
which current management decisions may be effective. In
the original method F is the result of a combination of life
table data and adjustment factors evaluated together. The
method was, however, originally applied to well-documented plant species, which is not the case for the plant
species of Maputaland. Because we believe that the method
should be available to non-specialists and practitioners as
well as being applicable to species for which only baseline
survey data are available, we developed an empirical method
to derive F. We first established the value of F (Step 1a),
which is the minimum viable population size independent of
the local conditions of the targeted region. In Step 1b, F is
adjusted to reflect local conditions.
To determine F we used published data from detailed
studies (Burgman et al., 2001) of F-values against life
expectancies of Banksia cuneata (Australia: F 5 6,400, life
expectancy 5 50 years), Banksia goodii (Australia: F 5 300,
life expectancy 5 300 years), Alnus incana (Northern
Europe: F 5 750, life expectancy 5 20 years) and Pentaclethra macroloba (Amazon basin: F 5 2,300, life expectancy 5 100 years). Four equations were fitted to the available
data to determine which best described the relationship: y 5
a(x) + b, y 5 a ln(x) + b, y 5 a(x)b, and y 5 aeb(x), where
a and b are constants. The regression with the best fit (i.e. the
highest coefficient of determination, R2) was selected to
calculate F for each of the landscape species selected for our
case study.
Adjusted F is based on available knowledge regarding
the species and environmental factors in the target area
(Burgman et al., 2001). Thus, based on all available
knowledge and expert opinion, F can be increased or
reduced to accommodate the effects of environmental
conditions in the target area on the species’ biology. We
consider the value of F estimated in Step 1 to be the
minimum viable population size and therefore adjusted F
could only be $ F. Our decision to set the maximum
adjustment value to 2F was subjective and based on
personal knowledge of the area. Such subjective decisions
are allowed in the original methodology as long as the
person making the decision is reasonably qualified as an
expert for the area concerned.
To establish the adjusted F-value we used the list of 25
ecological factors of Burgman et al. (2001). Each factor had
two alternative states: one related to species resilience and
one to species vulnerability (Table 1). Each factor was
investigated and a numerical value assigned per factor to
derive an ecological factor score. This score was used to
generate the adjusted F-value. If the available knowledge of
the species was insufficient to obtain a reliable answer
a value of 0 was given to both resilience and vulnerability. If
it was possible to answer a question reliably then +1 was
given for resilience or -1 for vulnerability. The ecological
factor score was obtained by summing the resilience and
vulnerability scores, and plotting the sum on a scale ranging
from 25 (all questions pertaining to resilience assigned +1),
thereby requiring no adjustment of F, to a low of -25 (all
questions pertaining to vulnerability assigned -1) requiring
maximum adjustment (F is doubled). The adjustment factor
value was calculated as a fraction of the total potential for
adjustment, based on the ecological factor score, and was
added to F to obtain the adjusted F-value (Table 1).
Step 2 The 36 3 42 km area encompasses both Tembe
Elephant Park and Tshanini Community Conservation
Area (Fig. 2). The region was not fully surveyed and
therefore only available geographical elements known from
field surveys are provided as mapped elements in Fig. 2.
The unmapped sections were evaluated by analysing
topographical maps and 1 : 50,000 aerial photographs. The
ª 2010 Fauna & Flora International, Oryx, 44(3), 399–410
ª 2010 Fauna & Flora International, Oryx, 44(3), 399–410
TABLE 1 Factors considered in the determination of the ecological factor score and thus the adjustment factor for the minimum viable population size (F), as required for Step 1 (Fig. 1) for
each of the four selected landscape tree species (see text for details) in the Tembe Elephant Park–Tshanini Conservation Area complex, Maputaland (Fig. 2).
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Total
Ecological factor score (EFS) (F+) + (F )
Adjustment factor (AF)2
1
Negative criteria (indicator of
vulnerability; F-)
Few small isolated populations
Restricted distribution
Habitat specialist
Restricted to a temporal niche
Subject to extreme habitat fluctuations
Genetic vulnerability
Weak post-disturbance regeneration
Slow weak growth
Poor competitor
Particular life stages vulnerable
Long time to set first seed or propagules
Short reproductive lifespan
Dysfunctional breeding system
Not readily pollinated
Unreliable seed production
Low seed production
Short seed or propagule viability
Seed or propagule exhausted by
disturbance
Poor dispersal
Generally killed by fire & other damage
Adversely affected by pre-1600
disturbance1
Not adapted to existing grazing, drought,
fire-regime
Unable to coppice & resprout
Vulnerable to pathogens, diseases,
insects, etc.
Dependent on vulnerable mutualist
Hymenocardia
ulmoides
Sclerocarya
birrea
Cleistanthus
schlechteri
Newtonia
hildebrandtii
F+
1
1
1
0
1
1
1
1
1
1
0
1
1
1
1
1
0
0
F0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-1
0
F+
1
1
1
0
1
1
0
0
0
1
0
1
1
1
1
1
0
0
F0
0
0
0
0
0
-1
0
0
0
0
0
0
0
0
0
-1
-1
F+
1
0
0
0
1
0
1
1
1
0
1
1
1
1
1
1
0
0
F0
-1
-1
-1
0
0
0
0
0
0
0
0
0
0
0
0
-1
0
F+
1
1
0
0
1
0
0
0
0
0
1
1
1
1
0
1
0
0
F0
0
-1
-1
0
0
-1
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
1
1
0
0
0
0
0
1
0
0
0
0
0
0
0
-1
0
0
1
0
1
0
1
0
1
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
19
-1
18
+14.00% of (F)
1
0
15
-3
12
+26.00% of (F)
1
0
15
-4
11
+28% of (F)
0
0
9
-4
5
+40.00% of (F)
Represents any large scale, landscape shaping disturbance known to have occurred prior to the colonization of South Africa by Europeans
To calculate the adjustment factor the following scale is used: if EFS 5 +25 then AF 5 +0% of F, if EFS 5 0 then AF5 +50% of F, if EFS 5 -25 then AF 5 +100% of F. For example, for N. hidebrandtii the
minimum viable population size (F) 5 186.0, the AF value (40% of 186.0) 5 74.4 individuals, and the Adjusted F value 5 186.0 + 74.4 5 260.4. As we only work with whole individuals a rounded Adjusted F of
260.0 is used.
2
Minimum conservation areas
Positive criteria (indicator
of resilience; F+)
Many large populations
Widespread distribution
Habitat generalist
Not restricted to a temporal niche
Not subject to extreme habitat fluctuations
No particular genetic vulnerability
Vigorous post-disturbance regeneration
Rapid vigorous growth
Quickly achieves site dominance
All life stages resilient
Short time to set first seed or propagules
Long reproductive lifespan
Robust breeding system
Readily pollinated
Reliable seed production
High seed production
Long seed or propagule viability
Seed or propagule not exhausted by
disturbance
Good dispersal
Generally survives fire & other damage
Not adversely affected by pre-1600
disturbance1
Adapted to existing grazing, drought,
fire-regime
Able to coppice & resprout
Not vulnerable to pathogens, diseases,
insects, etc.
Not dependent on vulnerable mutualist
403
404
J. Y. Gaugris and M. W. Van Rooyen
FIG. 2 Tembe Elephant Park and Tshanini
Community Conservation Area in Maputaland, the region evaluated for establishing
a minimum viable area for conservation
using the method of Burgman et al. (2001).
Individual grid squares are referred to in
the text using the letters and numbers (e.g.
D3). The area enclosed by the thick blue
line indicates where development is expected to occur and to modify the landscape irreversibly within the next 50 years.
The inset in the bottom right hand corner
indicates the location of Maputaland in
KwaZulu-Natal province, South Africa.
disturbance level in each of the 42 squares was categorized
as either sustainable (insignificant disturbance), light (light
to moderate disturbance linked to human activity such as
resource gathering) or heavy (moderate to heavy disturbance associated with human settlements and roads).
Step 3 The potential habitat per disturbance region (defined
as the area capable of supporting viable populations of the
selected landscape species, and characterized by climatic and/
or environmental parameters necessary to support the
successful establishment of the selected plant species) was
evaluated either directly using knowledge from fieldwork
within the surveyed areas or estimated when fieldwork-based
knowledge was unavailable. Overall it was estimated that
29,000 ha could potentially support Sand Forest vegetation
and 62,991 ha could support Woodland vegetation.
Step 4 All the potential habitat area that was physically
surveyed was mapped, and consisted of the sum of the area
of Tembe Elephant Park and of the disturbance region in
which the Tshanini Community Conservation Area occurs.
The total potential habitat surveyed amounted to 33,600 ha,
of which 5,600 ha and 28,000 ha were surveyed in Sand
Forest and Woodland vegetation respectively (Gaugris,
2008). Of the 28,000 ha of Woodland, 12,200 ha was of
Closed Woodland and 15,800 ha of Open Woodland.
Step 5 The density of mature trees per ha (D) was obtained
from detailed field surveys (Gaugris, 2008) in the Tshanini
Community Conservation Area (disturbance region B6,
Fig. 2) and Tembe Elephant Park (disturbance regions B1–
B5, C1–C5, D1–D4, E1–E3 and F1–F2; Fig. 2). To account for
human utilization, tree density outside these disturbance
regions was taken as the weighted mean of the values of
both conservation areas, reduced by 10%. To calculate D
(Step 5; Table 2) in the Sand Forest, mature C. schlechteri
and N. hildebrandtii trees were considered as those having
ª 2010 Fauna & Flora International, Oryx, 44(3), 399–410
Minimum conservation areas
TABLE 2 Baseline information required to establish the minimum conservation areas for the four vegetation types studied in Maputaland
(Fig. 2). For each of the four landscape species the following information is provided: the theoretical life expectancy (based on a literature
search), the minimum viable population size (F, the number of individuals), the adjusted F value calculated to compensate for known life
history characteristics of the species and environmental characteristics of the area (Table 1), and the mean density (number of individuals
per ha) of mature trees in the Tshanini Community Conservation Area, Tembe Elephant Park and unprotected areas.
Density of mature trees (D)
Species
Sand Forest
C. schlechteri
N. hildebrandtii
Closed Woodland
H. ulmoides
S. birrea
Open Woodland
H. ulmoides
S. birrea
Sparse Woodland
H. ulmoides
Life expectancy
(years)
F
Adjusted F
Tshanini Community
Conservation Area
Tembe Elephant
Park
Unprotected
areas
250
400
536
186
678
260
133.0
80.0
101.0
35.0
94.5
34.2
150
300
1,086
374
1,218
471
125.0
75.0
71.0
21.0
67.0
20.0
150
300
1,086
374
1,218
471
411.0
26.0
13.0
22.0
30.0
20.0
150
1,086
1,218
64.0
2.0
5.0
a diameter $ 127 mm at breast height. In the Woodland,
mature S. birrea and H. ulmoides trees were considered as
those having a diameter $ 95.5 mm and $ 16 mm at breast
height respectively.
Step 6 The determination of the minimum area required for
conservation started by establishing the ‘target area based
on background disturbance processes, also called raw target
area for reserve creation’ (Burgman et al., 2001), which was
calculated as Target Area (A0) 5 Adjusted F/D. This target
area, calculated per disturbance region, is an absolute
minimum area required to support the target species
assuming there is no threat. It is a preliminary value that
does not take into consideration other known disturbances
that can be measured and planned for.
Step 7 Sociological studies, maps and photographs were
used to determine the additional small-scale disturbances
from which species are expected to recover within a 50-year
time frame but that will effectively reduce the potential
habitat available. These disturbances were expressed as
a percentage of a disturbance region that would become
unavailable during the time frame considered, from which
the percentage of remaining habitat (S) of the disturbance
region is calculated. This represents an evaluation of
potential habitat that will become temporarily disturbed
but that is considered likely to become suitable again
provided enough time is given or appropriate management
actions are taken. We considered here all activities that
modify the canopy (cutting of trees) and sometimes the
topsoil but not the seed bank (non-mechanized slash-andburn).
Step 8 The area expected to become irreversibly damaged
within 50 years through human developments (ci) was
similarly evaluated per disturbance region and expressed as
ª 2010 Fauna & Flora International, Oryx, 44(3), 399–410
a percentage, and percentage of remaining habitat calculated as (1–ci). This represents an evaluation of potential
habitat that will become irreversibly lost and will not
become suitable again. We considered here all activities
that will permanently modify the soil structure (construction and mechanized agriculture) and the presence of a seed
bank.
Step 9 The target area per disturbance region was further
adjusted to compensate for expected density-reducing
human-related activities within the next 50 years and was
expressed as an estimated percentage of remaining habitat
(ri). This represents an evaluation of the effect of activities
that will affect the species’ density in the landscape. We
considered here the following three activities: building
construction (through wood use for construction and as
fuel), grazing (removal of seedlings), and weather effects
(droughts and floods). Once this step was completed a final
estimate of the minimum target area for conservation per
disturbance region was thus obtained.
Step 10 was not applied as no catastrophic disturbances are
expected in the region. Based on a review of historical,
geological and weather records, no volcanic eruption,
earthquake, landslide, tsunami, hurricane wind or tornado
are expected.
Step 11 was also not applicable to our case study because it
was already incorporated within our approach in 42
disturbance regions, whereby Sand Forest and Woodlands
targets were combined.
Step 12 The area of potential available habitat is divided by
the final value obtained for the minimum target area for
each of the 42 disturbance regions. This ratio gives
managers a rapid estimate of the current availability of
land for conservation.
405
406
J. Y. Gaugris and M. W. Van Rooyen
TABLE 3 Summary of the minimum area required for conservation per vegetation type and woody plant species in the whole study area,
Tembe Elephant Park, and Tshanini Community Conservation Area (Fig. 2). The sum in hectares for each species represents the sum of
all minimum areas for all disturbance regions in the site evaluated and that need to be conserved to have a satisfactory conservation
outcome. The mean area represents the mean minimum area required for conservation per disturbance region. For Tshanini
Community Conservation Area the mean could not be calculated as the site was encompassed within one disturbance region.
Whole study area
Species
Sand Forest
C. schlechteri
N. hildebrandtii
Closed Woodland
H. ulmoides
S. birrea
Open Woodland
H. ulmoides
S. birrea
Sparse Woodland
H. ulmoides
Tembe Elephant Park
Mean – SE (ha)
Sum (ha)
3,193.74
3,407.26
76.04 – 40.34
81.13 – 42.74
561.43
621.29
8,139.65
10,302.72
193.80 – 102.82
245.30 – 127.94
22,842.49
10,519.98
149,976.04
Sum (ha)
Mean – SE (ha)
Tshanini Community
Conservation Area
Sum (ha)
29.55 – 8.53
32.70 – 9.43
7.74
4.94
1,434.75
1,969.60
75.51 – 21.79
103.66 – 29.91
14.80
9.54
543.87 – 233.35
250.48 – 133.74
7,835.94
1,790.55
412.42 – 118.99
94.24 – 27.19
4.50
27.51
3,570.86 – 1,539.16
50,933.63
2,680.72 – 773.43
28.90
Results
The exponential equation (y 5 aeb(x)) provided the best fit
(R2 5 0.43) between F-value and life expectancy and was
therefore used to derive F-values for the selected species
(Table 2). H. ulmoides had the highest F-value at 1,086
individuals and N. hildebrandtii the lowest at 186. However, H. ulmoides required only a 14% increase of F to
derive the adjusted F-value, whereas N. hildebrandtii
required a 40% increase to obtain the adjusted F-value
(Table 1).
From the density (D) and adjusted F, a target area
representing the minimum viable area (Step 6) was calculated for each of the Sand Forest and Woodland species.
The size of this area was then further refined based on the
consideration of a group of reversible and irreversible
disturbances likely to affect the area (Steps 7–9). See
Appendices 1–2 for the full analysis.
The sum of all minimum and mean areas required for
conservation per disturbance region for the entire region, as
well as for the Tembe Elephant Park and Tshanini
Community Conservation Area separately, are presented
per species per vegetation unit (Table 3). The Sand Forest
species required the smallest areas, with C. schechteri
requiring a mean of 76 ha per disturbance region (range
8–1,706 ha), whereas N. hildebrandtii required a mean of 81
ha (range 5–1,808 ha). The mean minimum areas required
for the Woodland species were greater. For H. ulmoides
there was an increase in the area required from the Closed
Woodland (mean 194 ha, range 148–4,349 ha) to the Open
Woodland (mean 544 ha, range 5–9,752 ha), whereas the
area requirements for S. birrea were similar in the Closed
(245 ha; range: 10–5,411 ha) and Open Woodland (250 ha;
range 28–5,657 ha).
From Step 12 it is evident that the availability of Sand
Forest habitat far exceeds the minimum area required
to conserve this vegetation type in a 50-year time span
(Table 4). Only in disturbance region D5 (Fig. 2) is the ratio
of available to required habitat , 1. The Woodland analysis
shows that some disturbance regions do not currently have
enough habitat available (ratio , 1) for their conservation
(Table 4). Of particular importance are disturbance regions
B5, C5, D5, E2, E3 and F2, within Tembe Elephant Park,
where insufficient habitat is available for at least one
vegetation type and landscape species.
Discussion
One of the central questions in conservation science is
‘How much is enough?’ (Poiani et al., 2000; Svancara et al.,
2005; Tear et al., 2005). No universally accepted method to
obtain the answer has yet been developed. We based
our investigation of the conservation adequacy of the
Tembe–Tshanini complex using a simple population model
(Burgman et al., 2001) to set targets for conservation.
Population models have the advantage that they can deal
with processes that affect the persistence of the species
(Nicholson et al., 2006) and are therefore ideal for singlespecies investigations but not for multiple species planning
(Moilanen & Cabeza, 2002).
For both Sand Forest species, C. schlechteri and N.
hildebrandtii, it appears that sufficient habitat is already
conserved in the Tembe Elephant Park (3,400 ha) and
informally conserved in the Tshanini Community Conservation Area (1,046 ha; Gaugris, 2008) to secure the future of
both species and therefore the Sand Forest vegetation. Even
in most of the disturbance regions covering land that is not
ª 2010 Fauna & Flora International, Oryx, 44(3), 399–410
Minimum conservation areas
TABLE 4 The ratio of available to required habitat per species (Step 12 of Burgman et al., 2001) in each of the evaluated disturbance
regions (Fig. 2). A ratio . 1 indicates that sufficient available habitat exists to match the conservation requirements, whereas a ratio , 1
indicates that insufficient habitat is available. Numbers in bold indicate disturbance regions where insufficient habitat is available for
conservation of that landscape species.
Step 12, Ratio of available to required habitat (see Appendices)
Grid
square
(Fig. 2)
A1
A2
A3
A4
A5
A6
A7
B1
B2
B3
B4
B5
B6
B7
C1
C2
C3
C4
C5
C6
C7
D1
D2
D3
D4
D5
D6
D7
E1
E2
E3
E4
E5
E6
E7
F1
F2
F3
F4
F5
F6
F7
H. ulmoides
C. schlechteri
(Sand Forest)
59.17
103.55
110.94
12.33
60.43
115.41
114.56
70.63
87.65
94.39
89.96
21.01
199.95
137.54
117.99
138.64
48.36
36.27
8.16
9.58
18.92
123.61
29.02
19.34
20.94
0.19
28.65
21.92
20.87
14.38
9.53
4.61
0.00
0.00
16.42
11.70
1.42
9.67
22.46
21.14
1.77
17.62
N. hildebrandtii
(Sand Forest)
55.84
97.72
104.70
11.63
57.03
108.91
108.11
63.83
79.21
85.30
81.29
18.99
313.63
129.80
106.62
125.28
43.70
32.78
7.38
9.05
17.86
111.70
26.22
17.48
18.92
0.18
27.03
20.68
18.86
12.99
8.61
4.35
0.00
0.00
15.49
10.57
1.29
9.13
21.20
19.95
1.67
16.63
Closed
Woodland
30.8
53.8
57.7
6.4
31.4
60.0
59.6
36.6
45.5
49.0
46.7
10.9
138.7
71.5
61.2
71.9
107.2
80.4
4.2
5.0
9.8
64.1
64.3
42.9
46.4
0.2
14.9
32.7
40.8
28.1
12.4
6.0
2.8
2.5
16.1
13.7
1.1
7.6
23.5
22.1
2.8
18.4
conserved the minimum area necessary for conserving the
species is present. This implies that suitable environmental
conditions still exist beyond the conservation areas and that
there is a functional corridor between the two areas.
Disturbance region D5 is the only one in which the area
available for Sand Forest was less than the required
ª 2010 Fauna & Flora International, Oryx, 44(3), 399–410
S. birrea
Open
Woodland
13.7
24.0
25.7
2.9
14.0
26.8
26.6
6.7
8.3
9.0
8.5
2.0
455.9
31.9
11.2
13.2
19.6
14.7
0.8
2.2
4.4
11.7
11.8
7.9
8.5
0.1
6.6
14.6
7.5
5.2
2.3
2.7
1.2
1.1
7.2
2.5
0.2
3.4
10.5
9.9
1.2
8.2
Sparse
Woodland
2.1
3.6
3.9
0.4
2.1
4.1
4.0
1.0
1.3
1.4
1.3
0.3
71.0
4.8
1.7
2.0
3.0
2.3
0.1
0.3
0.7
1.8
1.8
1.2
1.3
0.0
1.0
2.2
1.2
0.8
0.4
0.4
0.2
0.2
1.1
0.4
0.0
0.5
1.6
1.5
0.2
1.2
Closed
Woodland
24.7
43.3
46.4
5.2
25.3
48.2
47.9
26.7
33.1
35.7
34.0
7.9
215.2
57.5
44.6
52.4
78.1
58.6
3.1
4.0
7.9
46.7
46.9
31.2
33.8
0.2
12.0
26.3
29.7
20.5
9.1
4.8
2.2
2.0
12.9
10.0
0.8
6.1
18.9
17.8
2.2
14.8
Open
Woodland
23.7
41.4
44.4
4.9
24.2
46.1
45.8
29.4
36.4
39.2
37.4
8.7
74.6
55.0
49.0
57.6
85.9
64.4
3.4
3.8
7.6
51.4
51.6
34.4
37.2
0.2
11.5
25.1
32.7
22.5
10.0
4.6
2.1
1.9
12.4
11.0
0.9
5.8
18.1
17.0
2.1
14.2
minimum. Most of this disturbance region (95%) is
occupied by the village of Sicabazini, where the community
resettled when they were relocated upon creation of the
Tembe Elephant Park in 1983 (Matthews et al., 2001). To
ensure the maintenance of ecological processes and overall
conservation of the Sand Forest we recommend that a suite
407
408
J. Y. Gaugris and M. W. Van Rooyen
of Sand Forest patches of varying sizes be preserved within
a network of reserves rather than in one single reserve
(Possingham et al. 2000; Van Rensburg et al., 2000a,b;
Gaugris & Van Rooyen, 2008).
For the Woodland vegetation type the minimum area
requirements for the two species investigated differed
substantially. We recommend using the species that requires the largest area. For the Closed Woodland, therefore,
at least 10,303 ha should be set aside (based on the
calculations for S. birrea), whereas 22,843 ha of the Open
Woodland should be set aside (based on the calculations for
H. ulmoides). However, within the Tembe–Tshanini complex there is only 7,386 ha of Closed Woodland and 9,576 ha
of Open Woodland and thus insufficient land is presently
conserved to ensure the persistence of these vegetation
types over the period considered. Outside these conservation areas the Closed and Open Woodlands are well
represented. Due to the marginal subsistence agriculture
value of the soils of the area (Eeley et al., 2001) and the high
rate of emigration from this area to coastal towns, where
tourism drives employment opportunities (Peteers, 2005),
these woodlands may nevertheless continue to survive
outside conserved areas.
Burgman et al. (2001) advocated the use of their method
in regions where there is little available information on
the species or vegetation dynamics, as is the case for
Maputaland (Gaugris, 2008). In this context, the method
fulfilled its promise to estimate the minimum area of each
vegetation type that should be conserved. The minimum
areas determined appeared realistic and it was possible to
determine whether the areas currently set aside for conservation are adequate.
The weakest aspect of the method is that used to
establish F, the minimum viable population size, without
specialist knowledge or extensive life table information. A
lack of detailed information (Cabeza & Moilanen, 2001;
Justus et al., 2008) or lack of reliability about the data,
leading to uncertainty (Moilanen et al. 2006a,b), are two of
the problems with which conservation planners have to
contend. Although the models fitted to the published data
did not have high R2-values, the method provided a framework to calculate F objectively. Additional information
would improve the fit. The adjusted F was derived by
considering a range of ecological factors that are important
for the long-term conservation of the species. These factors
could readily be assessed using personal and expert knowledge. However, our attempts to establish F and adjusted F
are preliminary and require revision. Future studies should
focus on devising a simple and repeatable method of setting
F without knowledge of growth, mortality and recruitment
rates. Perhaps a way of setting F from size class distribution
and abundance data could be investigated.
Species selection is crucial because the species are used
as surrogates for conservation of an entire vegetation type.
A common conservation strategy has been to use umbrella
species as surrogates for poorly known regional biota
(Simberloff, 1998), similar to the use of indicator taxa in
reserve design (Howard et al., 1998). However, evaluations
of the use of umbrella or flagship species have indicated
that they perform no better than randomly selected species
(Andelman & Fagan, 2000; Williams et al., 2000), although
encouraging results have been obtained in Uganda
(Howard et al., 1998). The selection of landscape plant
species appeared to work adequately in this study. We
recommend, however, that future studies should select one
species per vegetation unit, rather than trying to bridge
more than one vegetation unit with one species.
A major advantage of the method is its ease of use once
it has been established for an area. The wide variety of
quantitative and qualitative aspects considered allows
further refinements to be made as new knowledge becomes
available. The method can be adapted to include additional
aspects (within Steps 7–9) such as agriculture, alien plants,
urbanization and interface retention targets (Pressey et al.,
2003), and additional aspects of the population biology of
the species can be included to refine the adjusted F-value.
By changing the values in the tables (Appendices 1–2)
instantaneous results are obtained and therefore decisions
can be made without extensive consultation. Using the
tables a conservation manager can establish the minimum
area of land that should be fully preserved in each
disturbance region, and thus establish the area remaining
for sustainable use. An additional advantage of the method
lies in the possibility to scrutinize an area using a range of
disturbance region sizes and therefore to balance conservation according to the scale of definition desired. We
feel that the advantages of such a tool for conservation,
especially outside formally conserved areas, justify applying the method even if data are deficient initially, if only
to establish targets for conservation and areas for sustainable use.
A clear limitation must, however, be set on the size of
the area under scrutiny. It should remain within the grasp
and knowledge of the ecologist and experts tasked to set the
relevant minimum viable areas. Should the area become
exceedingly large the conditions are likely to be beyond the
knowledge of the experts. This method appears suitable for
setting targets by local people and experts. However, its
applicability at a larger scale is more uncertain as a lot of
definition would be lost. It would be possible to investigate
the applicability of the method for a larger scale based on
the results of many subregions, each considered as disturbance regions for the larger scale meta-analysis.
To conclude, the method described here is promising as
it sets applicable limits and is relatively easy to use, replicate, update and refine. The total area that we considered
was approximately five times larger than the combined
areas of the reserves concerned, and we feel that the local
ª 2010 Fauna & Flora International, Oryx, 44(3), 399–410
Minimum conservation areas
dynamics of the ecosystems were appropriately represented
within this total area.
Acknowledgements
This article is dedicated to Irmie Gaugris, who left us too
early. We thank Caroline Vasicek for her insight and for
reviewing the methodology, and four anonymous referees
for their valuable comments. This research was supported
by the National Research Foundation (South Africa) under
Grant Number 2047386.
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Appendices 1–2
The appendices for this article are available online at http://
journals.cambridge.org
Biographical sketches
J E R O M E G A U G R I S is an environmental consultant specializing in the
sustainable utilization of natural resources in sub-Saharan Africa. He
currently works on ecosystem service delivery in a highly populated
urban landscape in Burundi. G R E T E L V A N R O O Y E N specializes in
plant adaptations to desert environments, with a particular focus on
the Namaqualand region, and also supervises a conservation-based
rural community development programme in Maputaland.
ª 2010 Fauna & Flora International, Oryx, 44(3), 399–410
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