Old and new challenges in using species diversity for assessing biodiversity References

Old and new challenges in using species diversity for assessing biodiversity References
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Old and new challenges in using species diversity for
assessing biodiversity
Alessandro Chiarucci, Giovanni Bacaro and Samuel M. Scheiner
Phil. Trans. R. Soc. B 2011 366, doi: 10.1098/rstb.2011.0065, published 18 July 2011
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Phil. Trans. R. Soc. B (2011) 366, 2426–2437
doi:10.1098/rstb.2011.0065
Research
Old and new challenges in using species
diversity for assessing biodiversity
Alessandro Chiarucci1,*, Giovanni Bacaro1 and Samuel M. Scheiner2
1
Bioconnet, Biodiversity and Conservation Network, Department of Environmental Science ‘G. Sarfatti’,
University of Siena, 53100 Siena, Italy
2
Division of Environmental Biology, National Science Foundation, Arlington, VA 22230, USA
Although the maintenance of diversity of living systems is critical for ecosystem functioning, the
accelerating pace of global change is threatening its preservation. Standardized methods for biodiversity assessment and monitoring are needed. Species diversity is one of the most widely adopted
metrics for assessing patterns and processes of biodiversity, at both ecological and biogeographic
scales. However, those perspectives differ because of the types of data that can be feasibly collected,
resulting in differences in the questions that can be addressed. Despite a theoretical consensus on
diversity metrics, standardized methods for its measurement are lacking, especially at the scales
needed to monitor biodiversity for conservation and management purposes. We review the conceptual framework for species diversity, examine common metrics, and explore their use for biodiversity
conservation and management. Key differences in diversity measures at ecological and biogeographic
scales are the completeness of species lists and the ability to include information on species abundances. We analyse the major pitfalls and problems with quantitative measurement of species
diversity, look at the use of weighting measures by phylogenetic distance, discuss potential solutions
and propose a research agenda to solve the major existing problems.
Keywords: biodiversity assessment; biogeography; diversity measurement; ecology; spatial scale
1. INTRODUCTION
The term ‘biodiversity’ is relatively recent with its
formal introduction often pegged to the National
Forum on BioDiversity, held in Washington, DC in
September 1986. The published Proceedings of this
forum is the first book with the term ‘biodiversity’ in
its title [1]. Biodiversity has since become a central
concern in social and political discussions (e.g. the
Rio environmental summit meeting in 1992), matching its fundamental place in the science of ecology
[2]. The increasing threat of species extinction through
habitat degradation, global change and human population pressure magnified these concerns. In 2002,
the United Nations declared 2010 the International
Year of Biodiversity with a goal of halting by 2010 the
erosion of biodiversity. Despite important efforts to
prevent its loss, the biodiversity of our planet continues to decline, with no evidence of reversing this
trend [3].
Fundamental to global political concerns is the
potential relationship between biodiversity and ecosystem services [4,5] and the threat to those services from
biodiversity loss [6,7]. The link between biodiversity
and ecosystem services has been controversial, in part
because we lack a consensus about the methods to
assess and monitor biodiversity, especially at regional
* Author for correspondence ([email protected]).
One contribution of 10 to a Theme Issue ‘Biogeography and
ecology: two views of one world’.
to continental scales [6 –12]. Biodiversity can be quantified at many levels of biological organization—from
the molecular to the ecosystem [13,14]. However, it
is not practical to measure biodiversity at all levels in
all places, so biodiversity assessment methods must
be optimized to the specific level of organization and
spatial scale of interest. For example, proxy measures
(e.g. satellite imagery) of large areas can be calibrated
with other data (e.g. surveys on the ground). Therein
lies the problem—what proxies are best used for
which measures of biodiversity (e.g. [15,16])? Species
diversity is one of the most intuitive and widely
adopted measures of biodiversity [17] and it is strongly
positively correlated with diversity at other levels of
organization, such as genetic diversity and ecosystem
functioning (although mechanistic explanations are
still debated [12,17]). While the measurement of
species diversity has a long theoretical and empirical
history within ecology [18–21], problems remain in
its measurement and monitoring, especially at larger
spatial extents. These larger extents are often investigated with biogeographic approaches (see §2), even
when answering questions more appropriate to ecological approaches [22]. The acceleration of biodiversity
loss makes urgent the development of programmes to
assess and monitor biodiversity suitable for addressing
large-scale ecological questions.
We examine how species diversity is used to assess
and monitor biodiversity from an ecological perspective and discuss the resulting theoretical and practical
advantages and limitations in using such an approach
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This journal is q 2011 The Royal Society
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Measuring species diversity
at scales that are more typically considered biogeographic. To do this we (i) define how ecological and
biogeographic perspectives differ in terms of temporal
and spatial scales and types and quality of data,
(ii) review recent advances in analysing species diversity,
(iii) examine practical issues and major pitfalls connected with the measurement of species diversity at
larger spatial and temporal extents using an ecological
approach, and (iv) propose a research agenda to solve
these problems. In a complementary paper, Davies &
Buckley [23] consider the alternative use of phylogenetic diversity for focusing at broad biogeographic
scales.
Phil. Trans. R. Soc. B (2011)
2427
Table 1. Comparison of ecological and biogeographic
perspectives with regard to their typical spatial and
temporal realms of interests, data collection, data types and
data quality.
spatial scale
temporal
scale
data
collection
data types
data quality
2. ECOLOGICAL AND BIOGEOGRAPHIC
PERSPECTIVES
Because the preservation of biodiversity is an issue
from local to global scales, we must consider the
strengths and weaknesses connected with ecological
and biogeographic perspectives (table 1). The differences in these perspectives primarily relate to practical
considerations. The theoretical roots of ecology and
biogeography are well entangled; for example, the
theory of island biogeography [24] is widely considered to be fundamental in ecology [25]. Similarly,
the theoretical underpinnings of both perspectives
rely on evolutionary theory [25,26] and systematics
[27], although currently those linkages are stronger
for biogeography.
Despite these linkages, the investigation of species
diversity offers different challenges from ecological
and biogeographic perspectives. In biogeography,
species diversity is typically analysed as patterns of
species richness across large spatial extents (regional
to continental to global) or during long temporal durations (centuries to millennia to aeons). Examples
include global analyses of species– area relationships
in islands and continents (e.g. [28]) and latitudinal
patterns of species richness (e.g. [29]). In contrast,
an ecological perspective investigates species diversity
at smaller spatial scales and shorter temporal scales
(table 1). Examples include studies of assembly rules
for community organization and structure (e.g. [30]),
the relationship between species diversity and productivity (e.g. [31]), and the role of evenness and
richness in structuring communities (e.g. [32]).
The two perspectives also differ in data collection,
type and quality. Biogeographic investigations are
largely based on datasets assembled from multiple
sources, carried out over many years or decades,
because measuring species presences and abundances
over a large area by a single team of people is almost
infeasible. At biogeographic scales, data are typically
limited to measures of species richness and these can
be heterogeneous and variable in quality. For example,
combining data from species checklists may inflate
diversity by including information collected from the
same site at different times [33,34], include data collected using different taxonomic references or rely on
incomplete species lists.
In contrast, ecological studies usually rely on data
collected using specific sampling designs and criteria.
At these scales, accurate data on species abundances
A. Chiarucci et al.
ecology
biogeography
local to regional
up to decades
(centuries)
specific/single
study
richness and
abundance
high/homogeneous
regional to global
centuries to aeons
adapted/assembled
studies
richness
variable/
heterogeneous
can be collected, providing additional measures of
community structure such as evenness and dominance. For example, successional trajectories are
often characterized by changes in abundance and
dominance, rather than by changes in species composition. Such additional information can be important
for identifying the processes responsible for community structure and dynamics [35]. Finally, such data
may be more accurate (e.g. the species abundance
data from a pot experiment [36]) and precise (e.g.
abundance changes along an experimental gradient
[37]). The quality of such data is usually very high
and homogeneous, as allowed by observational data
collected by a single survey or during experimental
manipulations, under specific sampling protocols and
criteria.
Thus, as a general rule, biogeographers trade off the
use of data that are less complete and accurate, with the
ability to investigate patterns across large spatial and
temporal scales, while ecologists use more complete
and accurate data, but only in a limited set of systems,
at small spatial extents and temporal durations.
3. SPECIES DIVERSITY, RICHNESS
AND EVENNESS
Assessing species diversity so that we can use it for
natural resource assessment requires that we first
have clear definitions. Species diversity has been
defined in myriad, sometimes inconsistent or contradictory, ways that have often been based on the
method of assessment rather than a clear, conceptual
framework. Tuomisto [38] stated that a clear definition
of diversity already exists and our treatment follows
her framework. Central is the classification of species
into a series of spatially bounded assemblages, or communities. We do not imply any functional relations
among the constituting species, but we refer to an
operational definition of communities as ‘the living
organisms present within a space – time unit of any
magnitude’ [39,40].
(a) Definitions and metrics
Species diversity represents one of the major interests
in ecology and is intuitively simple yet conceptually
complex. Ask the average person to measure species
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A. Chiarucci et al.
Measuring species diversity
Table 2. Some species diversity and evenness indices (adapted from Gurevitch et al. [42]). Definitions of symbols: S, the
number of species in the sample; pi, the proportion of individuals (or biomass) in the ith species ( pi ¼ ni/N ); ni, the number
of individuals (or amount of biomass) of species i in the sample; N, the total number of individuals sampled (or total
biomass); Nmax, number of individuals (or biomass) of the most abundant species; nr, the total number of species with
abundance R; A1 and A2 are the 25% and 75% quartiles; nA1, the number of individuals in the class where A1 falls; nA2,
the number of individuals in the class where A2 falls.
name
symbol
formula
species number indices
species density
R
S
sampled area
Margalef ’s index
DMg
Menhinick’s index
DMn
emphasizes
ðS 1Þ
ln N
S
pffiffiffiffiffi
N
proportional abundance indices
X
½pi lnð pi Þ
Shannon –Weiner index
H0
inverse Simpson’s index
D
1
P 2
pi
Pielou’s index
HP
Brillouin index
HB
McIntosh’s U index
U
McIntosh’s D index
D
pffiffiffiffiffiffiffiffiffiffiffi
P 2
ðN n Þ
pffiffiffiffiffi i
ðN N Þ
common species
Berger –Parker index
d
Nmax
N
common species
Cuba’s index
DC
Q-statistic
Q
rare species
common species
P
log2 ðN!= ni !Þ
N
P
½ln N! ln ni !
N
ffiffiffiffiffiffiffiffiffiffiffiffiffi
qX
2
ni
rare species
rare species
rare species
P
jni N=Sj
2N
P
½12 nA1 þ nr þ 12 nA2
logðA2=A1Þ
Sþ1
common species
rare species
evenness indices
Shannon evenness
J
Brillouin evenness
EB
McIntosh evenness
EU
H0
ln S
HB
½ð1=NÞ lnðn!=f½N=S!gSr fð½N=S þ 1Þ!gr
pffiffiffiffiffiffiffiffiffiffiffi
P 2
½N n pffiffiffiiffi
½N N= S diversity and they will likely count the number of entities that have been given different Latin binomials.
Species richness is a simple concept. We symbolize it
as R, the number of species, although it is most
often symbolized as S; the use of R has the advantage of indicating the component of diversity we are
referring to, i.e. richness. When measured within a specified area, volume or duration, it provides a measure of
richness density. However, the issue of what counts as
a species can add complications (see §§3c, 4c). We can
expand this measure by the addition of information
about the numbers of individuals of each species, or
any other measure of abundance. Evenness (E) is ‘the
measure of how similar species are in their abundance’ [41]. Species diversity (D) is often intended as a
combination of richness and evenness [20,41].
Phil. Trans. R. Soc. B (2011)
A number of indices of species diversity and evenness have been invented (table 2 reports some
common indices). The list of species diversity indices
is continuously growing: the software Biodiverse [43]
includes more than 200 diversity indices. Two indices
are widely used: the Shannon – Weiner index based on
information theory [44] and the Simpson index [45].
The exponential form of the Shannon – Weiner index
and the inverse of the Simpson index have the property
of equalling species richness when all species are
equally abundant, and converging to unity as the set
of species approaches maximum inequality (i.e. when
all but one species are each represented by a single
individual).
Despite the existence of the unifying concept of
diversity, disagreement still exists among ecologists
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Measuring species diversity
on how to conceptualize and evaluate species diversity [46]. Various attempts to unambiguously define
species diversity have been tried [47 – 49], with some
claiming that it is impossible [19,50]. As with diversity,
many measures of evenness exist but there is no agreement on which is preferred [51,52], despite its
relevance for ecosystem stability and functioning [53].
Recently, Ricotta [49], based on work by Juhasz–
Nagy [54], classified diversity indices into four basic
families: (i) richness diversity, (ii) evenness diversity,
(iii) differentiation diversity, and (iv) abundanceweighted diversity. This classification requires three
basic elements: (i) the number of species within an
assemblage (R), (ii) the abundance distribution among
species within an assemblage [20], and (iii) a measure
of differences in species composition among subunits
within that assemblage.
The recent work by Jost [55] and Tuomisto [38] provides a framework to integrate the previous concepts
into a consistent terminology, based on the paper by
Hill [56]. This formulation, based on numbers equivalents, unifies the three most popular diversity metrics
(the number of species, the Shannon–Weiner index
and the Simpson index) as special cases of the general
index of diversity:
!1=ð1qÞ
R
X
q
pi
;
ð3:1Þ
D¼
i¼1
where R is the number of species, pi is the frequency of
the ith species and q is a parameter that determines how
species frequencies are weighted [56]. When q ¼ 0,
D ¼ R, i.e. species richness; when q ¼ 1, D becomes
the exponential Shannon – Weiner index and when
q ¼ 2, D becomes the inverse of the Simpson index.
For these indices, evenness is E ¼ D/R, or equivalently,
diversity is the product of richness and evenness
(D ¼ RE).
(b) Spatial scale
One difference between ecological and geographical
perspectives is spatial and temporal extent (table 1).
In addition to differences in extent, biogeographic
studies often use larger grain sizes (e.g. 10 10 km
blocks) than ecological ones (e.g. 1 1 m quadrats).
The effect of spatial scale on species diversity is one
of the oldest topics in ecology and biogeography
[57,58]. However, spatial scale is not simply represented by increasing area, but also by interactions
among the different scale components: grain, focus
and extent [59,60].
A challenge in measuring species diversity is to
account for the effects of scale. The idea of partitioning diversity into spatial (by analogy also temporal)
components was introduced by Whittaker [61] and
labelled a-diversity and g-diversity, namely species
diversity measured at community and landscape
extents, respectively. Whittaker later [18] expanded
that hierarchy to include point diversity (diversity
within a community) and d-diversity (diversity of a
region). Unfortunately, the meaning of each of those
levels was never made explicit. Jost [62] and Tuomisto
[63] recently proposed focusing on just two units
Phil. Trans. R. Soc. B (2011)
A. Chiarucci et al.
2429
divorced of any explicit spatial or temporal scale:
g-diversity (Dg), the total species diversity of a set of
samples, and a-diversity (Da), the mean species diversity of the individual samples. This collapsing of the
basic measures of diversity untangles scale and diversity, permitting explicit and separate analyses of scale
effects on diversity, and reunites ecological and
biogeographic perspectives by permitting diversity to
be conceptualized and measured similarly in both
disciplines.
Moreover, Toumisto [63] resolved confusion about
another measure of diversity introduced by Whittaker
in 1960: b-diversity. A huge variety of measures of
b-diversity have been proposed within three broad
categories: the ratio of regional and local diversities,
the difference in regional and local diversities where
that difference can be absolute or proportional, and
differences in species composition among samples
[64 – 68]. Of those, the first provides a measure that
has clear ecological meaning, the effective number of
sampling units (e.g. communities) in a set of samples
[55,56,62,63,69], where the ‘effective number’ is the
number of sampling units necessary to contain the
total diversity (Dg) given that each unit consists of a
set of unique species each with a mean diversity
equal to Da. Differentiation diversity is then:
Db ¼
Dg
:
Da
ð3:2Þ
These measures can then be combined with that
of evenness [38]. The evenness of an entire set of
samples is:
Eg ¼
Dg
;
Rg
ð3:3Þ
where Rg ¼ Dg for q ¼ 0; this index is contained in the
interval [0,1]. Note, however, that Ra, the mean
species richness of the individual samples, equals Da
for q ¼ 0 only when each species has the same abundance in all samples. As a result Ea ¼ Da/Ra can be
greater than unity. Finally, we can define b-richness as:
Rb ¼
Rg
Ra
ð3:3Þ
and b-evenness as:
Eb ¼
Db
:
Rb
ð3:4Þ
In this framework, the concepts of diversity, richness, evenness and differentiation are all combined
into a single scale-free formulation with a multiplicative relationship among them. Most of the other
indices of diversity and evenness are derivable within
this framework [63,70]. Of course, the grain, focus
and extent by which a particular assemblage are
measured matters, because changing the scale of the
local community amounts to changing the scale at
which the heterogeneity of the interactions between
organisms and their environment manifests itself,
resulting in a different balance between a-, b- and
g-diversity [71]. As a consequence, study design and
spatial scales need to be explicitly stated.
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A. Chiarucci et al.
Measuring species diversity
Table 3. An example of diversity partitioning across
different spatial components for floristic data from a set of
plots within protected areas of the Natura 2000 Network in
Siena Province. The different spatial components are: the
mean a-diversity among all plots (aPlot), the mean
b-diversity among plots within protected areas (bPA), the
mean a- and b-diversity among protected areas within the
entire network (aPA and bNetwork, respectively) and
g-diversity of the whole network (gNetwork).
D
spatial component
q¼0
q¼1
q¼2
aPlot
bPA
aPA
bNetwork
gNetwork
28.20
8.60
242.54
3.21
778
21.22
5.33
113.19
2.18
246.80
17.95
3.71
66.72
1.78
119.08
Despite the existence of this theoretical framework,
problems still exist in applying and modelling species
diversity data at larger scales, especially for issues connected to the use of abundance data. Species diversity
data are often both limited in the area sampled and do
not include measures of abundance. However, while
the effects of scale on species richness are known and
modelled [72,73], the same effects on evenness have
been little studied [74], largely because of practical
limitations in measuring abundance. As a consequence, species diversity data at larger scales are
usually restricted to species richness information.
The effects of how changing q can affect estimates
of diversity components and how those change with
spatial extent can be understood by considering
some empirical data (table 3). The data consist of a
floristic survey of the Natura 2000 Network of protected areas (PAs) in Siena Province. The individual
PAs varied from 8.3 to 94.6 km2 scattered over an
extent of 3820 km2 (for details see [15]). The survey
consisted of 219 plots of 10 10 m (one per squarekilometre). Abundance was measured as the frequency
of each species in 16 subplots within each plot.
A total of 778 plant species were observed. For
q ¼ 0, the ratios of diversity at the grain of each protected area and entire network to that of each
individual plot (effective number of sampling units
[63]) was 8.6 and 27.6, respectively. In contrast, for
q ¼ 2, the ratios are 3.7 and 6.6, with intermediate
values for q ¼ 1. That is, at the plot scale, diversity
of abundant species is captured to a much greater
extent than rare species. Similarly, variation among
units as measured by b-diversity is greater for q ¼ 0
than q ¼ 2, especially among plots within protected
areas. Depending on the goals regarding diversity
preservation of abundant and/or rare species, these
differences in diversity patterns could lead to different
management strategies.
(c) Not all the species are the same
An important limitation of diversity measures is that
not all species are functionally, evolutionarily and ecologically equivalent. Traditional measures of species
diversity fail to capture this variation. Vane-Wright
Phil. Trans. R. Soc. B (2011)
et al. [75] suggested that phylogenetic relationships
among species should be included in diversity
measures. They proposed a measure of taxonomic distinctiveness based on the geometry of a taxonomic tree.
This phylogenetic diversity is defined as the sum
of the lengths of the branches of a phylogenetic tree
that connects all the taxa present in a sample ([76],
see also [23]). As functional and ecological traits of
species are the result of evolution, a positive relationship
between species’ phylogenetic distances and ecological
dissimilarity is expected assuming niche conservatism
[76,77]. An overall measure of phylogenetic distances
among species within a community, thus, can provide
an index of biodiversity which captures information
about trait and functional variation. Communities composed of species spanning a greater proportion of a
phylogenetic tree are more diverse because those
species are likely to have different ecological functions,
or similar functions achieved through different phylogenetic patterns [75,78].
Many measures of phylogenetic diversity have been
proposed, on the basis either of the distance among
nodes or of branch lengths [75,79 – 82]. As is the
case for species diversity indices, phylogenetic diversity
measures use a variety of distance-based and topologybased indices [83], with disagreements about which
indices provide the best measures.
Similar to the definition of D (see §3a), Pavoine
et al. [84] proposed a parametric metric, Iq, where
selected values of q correspond to classical indices of
phylogenetic diversity. When q ¼ 0, I0 equals Faith’s
PD [80] minus the height of the phylogenetic tree
[84]. When q ¼ 1, I1 equals the Shannon phylogenetic
entropy of Allen et al. [85]. When q ¼ 2, I2 equals Rao’s
quadratic entropy [86], expressing the mean phylogenetic distance between two randomly chosen
individuals in the community.
For conservation purposes, protected areas should
not only capture current diversity, but also maintain
diversity in the face of future possible losses. To establish a reserve network that contains a phylogenetically
diverse set of species able to maintain total diversity,
we need a metric able to indicate whether diversity is
mostly in rare species that are more likely to go extinct.
Such metrics should capture both abundance and
phylogenetic information, and provide a measure of
evenness. Recently, Cadotte & Davies [87] argued
that prioritizing hotspots by incorporating evolutionary diversity metrics into prioritization schemes can
help assess the conservation values of different regions
based on evolutionary information. Using the approach
of Chao et al. [88], phylogenetic information can be
integrated into diversity measures based on Hill numbers (equation (3.1)). For a phylogenetic tree in which
branch lengths are proportional to divergence times
and branch tips are the same distance from the tree
base (such a phylogenetic tree is defined as ultrametric
and has useful mathematical properties, see Chao
et al. [88]), the mean effective number of species (or
lineages) over T years is:
Þ¼ 1
DðT
T
(
X
i[BT
Li
a q
i
T
)1=ð1qÞ
;
ð3:5Þ
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Measuring species diversity
where BT denote the set of all branches in the time interval T, Li represents the length (duration) of branch i in
the set BT and ai is the total abundance of species on
branch i. This diversity can be viewed as the effective
number of maximally distinct lineages (or species) in
the interval [2T,0] (with 0 representing the present
time and 2Ta given past reference time). From another
point of view, phylogenetic diversity of order q through T
years ago can be defined as the product of the interval
duration T, and the mean diversity over that interval,
Þ. For a set of maximally distinct species,
namely DðT
Þ reduces to Hill
all branch lengths equal T and DðT
diversity D and, if q ¼ 0, to R.
Similarly, a measure of phylogenetic evenness E(T )
can be defined to express the extent to which a set of
species are equally related through time period T.
Following our previous rationale:
EðT Þ ¼
Þ
DðT
:
RðT Þ
ð3:6Þ
Low values of E(T ) would indicate a set of species
where most are closely related and a few are more distantly related. In contrast, for q ¼ 0, E ¼ 1 when the
data consist of the maximal possible phylogenetic
diversity through the considered T period. In a
recent paper, Cadotte et al. [89] developed an entropic
index of phylogenetic diversity based on an index of
abundance-weighted evolutionary distinctiveness. In
their approach, equitability (or evenness) could be
obtained as the ratio between entropy and the number
of species (logarithmically transformed). The phylogenetic evenness measure of Cadotte et al. [89] represents
a special case of our more general parametric phylogenetic evenness (equation (3.6)). Communities with high
Þ and E(T ) thus consist of species which
values of DðT
are phylogenetically diverse, with that diversity spread
across many of the common species, so that diversity
is likely to be maintained in the face of future losses.
In recent decades, phylogenetic diversity has been
analysed mostly for its implications in conservation
biology [80,81,90,91]. Analyses at biogeographic
scales are scarce, probably because of the difficulties
in obtaining complete phylogenetic trees for a large
number of species, even if data are becoming available
for global scale analyses (see [23]).
4. MEASURING SPECIES DIVERSITY
IN THE REAL WORLD
The growing impact of human activities on natural ecosystems requires urgent development of programmes
for biodiversity assessment and monitoring [12,92],
but standard methods are missing. Ideally, we would
need data collected: (i) across relatively large spatial
extents; (ii) within relatively short time-windows; and
(iii) with the highest possible quality. Basically, we
need data collected with an ecological lens across
spatial extents typical of a biogeographic lens. However, assessment and monitoring of species diversity
across such extents are constrained by limitations in
our ability to accurately measure that diversity. Problems also arise from political and financial issues, but
that topic is beyond the scope of this paper. Instead,
Phil. Trans. R. Soc. B (2011)
A. Chiarucci et al.
2431
we focus on scientific and practical barriers. We
review these issues, with a special attention to explicitly
contrasting ecological and biogeographic scales.
(a) Limitations in assessing species richness
One of the major limitations in assessing species diversity at biogeographic extents is because of problems in
measuring species richness. Ideally, such an assessment would include the complete enumeration of the
species within an area. Often, though, it is logistically
infeasible to completely survey a large area so rare
species are probably missed [93,94]. One solution is
to use species richness estimators. A variety of estimation techniques exist, grouped into two main
classes: extrapolation of species richness relationships
(SRRs) [17,73] and using data about species occurrence and abundance within a sample. Extrapolation
of SRRs is based on one of the most consistent
relationships in ecology: increasing species richness
with area or sampling intensity [57,58,95,96]. An
SRR represents the way in which the number of
species varies as a function of the space or time over
which it is sampled. Evidence suggests the existence
of scaling relationships in SRRs, with the slope of
the curve changing with spatial scale [97]. Although
we know something about the effects of scale components (sampling unit, grain, focus and extent) on
SRRs, a general model does not exist [73,96,98,99].
The extrapolation of SRRs within ecological scales
has been investigated, but this is typically limited
to one to two orders of magnitude difference between the sampled area and the area of the estimate
[17,73,100,101]. Much less is known about bias,
accuracy and precision of SRRs when attempting
to extrapolate far beyond the sampled area, the
kind of extrapolation necessary for assessment at
biogeographic extents.
The use of SRRs for estimating species richness has
different implications for sampling designs at ecological and biogeographic scales. At ecological scales, the
data typically consist of standard survey units (e.g.
quadrats, traps) that are likely to consist of (supposedly) complete species lists and may also contain
information on species abundances. Typically such
units are arrayed in a regular, random or stratifiedrandom design and the number of sample units can
be large. In contrast, at biogeographic extents, sampling
units are more often political or geographic entities for
which species lists were obtained by heterogeneous
sources, such as surveys, herbaria records and range
distributions. As a result, the data used for SRRs at
biogeographic scales are likely to be much less accurate. Research is needed on how to incorporate such
data uncertainty into SRRs, possibly through the use
of Bayesian estimators. It may be possible to devise
sampling schemes that will provide accurate biodiversity estimates at less than continental extents, but
research is needed. For example, should one use a
greater number of sampling units of smaller size or a
lesser number of larger size? For a given area sampled,
a greater number of smaller sampling units provides
higher total species richness in comparison with a
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A. Chiarucci et al.
Measuring species diversity
lesser number of larger ones [94,98], but there are
greater costs associated with such a sampling.
The other class of methods for estimating species
richness is based on numbers of rare species or the
species abundance distribution. The underlying concept is that the number of observed rare species
provides an estimate of the number of unobserved
species. In this case, ‘rare’ refers to species for which
just one or two individuals were counted (‘singletons’
and ‘doubletons’), or which occurred in just one or
two sampling units (‘uniques’ and ‘duplicates’).
Examples of non-parametric estimators include the
first- and second-order Jackknife [102 – 104], and the
various Chao estimators [105,106]. Other estimators
are based on the entire species abundance distribution,
such as the bootstrap estimator [102,104,107]. Even
at ecological scales, such as within a single stand of
vegetation, these estimators are not free of bias
[108], and little exploration of potential bias has
been done at biogeographic scales. At intermediate
extents, such as within a nature reserve or a region,
the area surveyed can be less than 0.1 – 2% of the
total area (e.g. [109,110]); in such cases, many rare
species are missed, rendering non-parametric estimators ineffective [111]. The use of such estimators at
biogeographic scales, such as a whole country or
continent, may have an even greater probability of
undersampling rare species [93]. New non-parametric
estimators (e.g. [112]) might improve the performance
of such methods, but a theoretical basis is still lacking.
Thus, estimating species richness in an affordable way
at biogeographic scales is not yet at hand.
Because of the problems in getting affordable
data on the true total species richness, the Rg of a
region can only be assessed as the pooled species richness of a set of samples [38,55]. While this makes the
definition of the Rg of a region operational and objective, estimates of Rg are always negatively biased with
respect to the true richness and dependent on the
number of sampling units. The same problem exists
for estimates of Rb. Even temporal comparisons of
total species richness may be difficult. One solution
would be the establishment of well-defined, standardized sampling protocols, but it is unlikely that
ecologists will ever agree on a single protocol and
this may not be appropriate across different habitats
and taxa. More difficult are comparisons based on
different sampling schemes, which is highly likely at
biogeographic extents or in monitoring temporal
changes. Even at ecological scales within a network
of collaborative projects, sampling protocols can
differ [113]. Thus, methods for adjusting estimates
to common scale components are needed. However,
despite the well-known effects of sampling intensity
on diversity estimates [114,115], many comparisons still fail to correct for differences in intensity
(see [116]).
(b) Difficulties in getting adequate
abundance data
Species-abundance distributions have been investigated with regard to niche differentiation, dispersal,
speciation, density dependence and extinction
Phil. Trans. R. Soc. B (2011)
[117,118]. In conservation biology, knowledge of the
species-abundance distributions is used to predict the
probability of population persistence or community
stability in the face of global change [119].
Despite the theoretical and practical importance of
species-abundance distributions, it is difficult to
directly measure the abundance of all species at biogeographic extents. Estimators for evenness at large extents
do not exist, and are likely to prove elusive, because of
the difficulties in estimating the abundance of rare
species. As a result, the assessment of evenness, while
feasible at ecological scales, is almost impossible at biogeographic scales. Overcoming this hurdle requires
understanding the relationship between the underlying
species-abundance distribution of a large, regional
community and the observed abundance distribution
of a limited sample. Few have attempted to estimate
species abundances at large spatial extents. Green &
Plotkin [119] derived theoretical species-abundance
distributions at regional scales as functions of sample
size and the degree of conspecific spatial aggregation.
Assuming populations are randomly distributed, the
locally sampled and regional-scale species-abundance
distributions had the same functional form. However,
deviations from this pattern occurred when populations
were spatially aggregated, which is likely for most taxa.
Further research is needed on leveraging abundance
and aggregation patterns at local scales for predicting
species regional patterns.
Species abundance can be measured in various ways
depending on the taxon or the ecological question.
The most intuitive measure of species abundance is
the number of individuals. However, while this is relatively common and easy for taxa such as birds, fish or
woody plants, this measure has severe logistical limitations for clonal or tiny taxa (e.g. corals, grasses,
mosses and lichens) especially at biogeographic spatial
extents; alternative measures such as per cent cover or
biomass may be used. While such measures do not
pose a problem when used in isolation, measures
obtained with one measure of abundance differ greatly
from those obtained using another measure [120],
making it important that study designs are clearly
described [68,73].
As a general rule, most species are rare, either
because of low abundance or because they are found
in rare habitats [121,122]. Despite the potential
importance of rare species for community stability
[123], such species are difficult to assess at large
spatial and temporal extents, necessitating greater
sampling efforts. As a consequence, data collected
at large extents may under-represent rare species.
Although occurrence and abundance are generally
positively correlated, occurrence – abundance correlations are dependent on the scale of measurement;
for example, local plot abundances of breeding birds
in eastern North America are independent of the
spatial extent of the species [124]. In addition,
occurrence – abundance correlation breaks down for
habitat specialists [122]. On the other hand, spatial
distributions are also extremely important for conservation biology. Occupancy-frequency distributions
have been modelled to explain the relative abundance
of core and satellite species [125]. This type of data
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Measuring species diversity
might provide accurate estimates of species evenness,
although this needs further investigation. Because
species frequencies depend on the grain and extent
of the sample [125,126], comparisons of such estimates collected with different sampling designs
might be difficult. So, in analogy with species richness,
evenness measures obtainable at larger spatial extents
depend on the sampling design and may be inaccurate.
Ricklefs [127] argued that ecologists should focus
more on the distributions of populations in regions
rather than on plots. Although this might not be practical, it provides a more natural underlying structure
for distribution and abundance to which biodiversity
measures could be related.
(c) Taxonomy and identification
In calculating any species diversity metric, it is
assumed (implicitly or not) that all of the species in a
sample or dataset have been properly identified and
represent comparable units [128]. However, at least
two types of problem affect this practice, one connected to taxonomic and nomenclature changes and
a second connected to taxon identification and data
management.
Updates in systematics and taxonomy can involve
changes across nodes in the tree of life, from subpecies
to phyla, and the pace of such changes is increasing.
Such changes can create problems for estimates of
species richness at both ecological and biogeographic
scales, but especially the latter. One problem is how
many species are actually in a sample. Molecular techniques can both reveal unrecognized species and
collapse previously delineated groups [129,130]. The
speed at which this is occurring can affect the accuracy
of species richness estimates, especially when compared
with historical records. Such taxonomic changes have
more limited effects on measures of diversity which
incorporate abundance data (equation (3.1)) as they
are less sensitive to changes in rare species, but they
can significantly affect taxonomic and phylogenetic
measures of diversity (equation (3.5)).
Further problems come with attempts to synthesize
surveys collected by different workers if they used
different species concepts for the same group of individuals. This can be especially problematic for data
collected in different decades, with the greatest problems created by the splitting of a single taxon into
multiple units. These reclassifications affect research
on change over time and at biogeographic scales
when data are aggregated over many surveys. At minimum, species surveys must specify which taxonomic
treatises are used. Changes in species classifications
affect conservation efforts either by reducing conservation units to subspecies status or by inflating the
potential number of conservation units through the
splitting of taxa [131].
The problem connected with specimen identification is even more clear, as it deals with the human
interpretation of assigning one object (e.g. an individual)
to a category (e.g. a species). Although straightforward,
this problem becomes manifest in diversity surveys.
Ideally, one would voucher every identification for
later checks or to account for changes in taxonomy.
Phil. Trans. R. Soc. B (2011)
A. Chiarucci et al.
2433
But such vouchering typically involves just one or a
few specimens, with the assumption that all other individuals that were assessed as belonging to the same
species were correctly identified. Nor is subsequent
identification of specimens free from bias and reidentification of specimens might not be able to be
transferred to the original (field) data and/or diversity
estimations.
5. CONCLUSIONS
Based on the above discussed limitations on the assessment of biodiversity, the science of biodiversity badly
needs a research agenda. Many improvements have
been achieved to date, leading to better knowledge of
biodiversity. In particular, genetic and genomic information, nowadays more and more available, has
greatly enhanced classical surveys based on species
richness and abundance through methods such as
DNA barcoding [132,137].
Similarly, the assessment of phylogenetic diversity
will be used more often for conservation planning,
especially at larger spatial extents [23,133]. Recent
advances in sequencing technologies are improving
our capacity to easily incorporate phylogenetic information into diversity indices. This information has
significantly advanced our understanding of historical
patterns of biodiversity, phylogeography and biogeography, and can help inform current conservation
strategies and future responses to global change
[23,134]. Conservation priorities should take into
account phylogeny, with areas containing greater phylogenetic differentiation given greater conservation
priority [75,82,133].
With respect to classical biodiversity measures, we
note an increasing interest of ecologists in expanding
the analysis of diversity to the assessment of inventory
(e.g. species richness) as well as differentiation diversity (e.g. b-diversity). In this way, we can determine
how variation in community composition affects ecosystem function and services at landscape scales.
Moreover, given the important role of spatial scale in
the analysis of diversity patterns, theoretical frameworks to estimate the influence of spatial scale on
biodiversity have been proposed, and the use of
spatially explicit data is becoming common in ecology
and biogeography [59]. This represents a critical step
towards increasing our understanding on the effect of
scale components on diversity measures.
Although estimates of abundance may not be available at large extents, analyses based on occupancy or
occurrence alone may have their own intrinsic value;
for example, indicator species analysis based on occupancy frequency can give important information for
conservation actions [135]. In the same way, analyses
of species co-occurrence or species nestedness could
be performed at large spatial scales even if data on
all species were limited to occurrence data.
Finally, standardized sampling procedures and
large-scale cooperative programmes using standardized
protocols are becoming well-established for collecting
biodiversity data among the world’s biomes. Conservation International recently launched the Tropical
Ecology, Assessment, and Monitoring (TEAM)
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A. Chiarucci et al.
Measuring species diversity
Initiative to establish a network of more than 50 field
stations worldwide to assess and monitor biodiversity
[136]. This initiative is a good example of how collaborative efforts of governments, non-governmental
organizations and scientific societies can greatly improve
biodiversity conservation and knowledge.
Although we are aware that further explorations are
needed of bias, precision and accuracy of metrics of
richness, evenness and phylogenetic diversity, we
believe that these will be accomplished in the near
future through the complementary use of computer
simulations based on theoretical communities and
empirical analyses of actual data (e.g. [102,107]). Biodiversity conservation and management requires
predictive understanding of the role of diversity in ecosystem structure and function and the ways that global
change might affect those relationships. Central to our
understanding of those relationships is the measurement of biodiversity, particularly in ways that will
allow us to take information at one scale and use it
to produce knowledge at other scales. Ecological and
biogeographic approaches to this issue provide different lenses from which to view the problem, each with
its unique strengths and weaknesses. Even if biodiversity science is in its infancy, at least for applied topics,
the combination of different perspectives will allow the
necessary steps forward.
A.C. and G.B. thank the Department of Environmental
Science of the University of Siena for supporting the
Research Group ‘BIOCONNET, Biodiversity and
Conservation Network’. S.M.S. worked on this paper while
serving at the US National Science Foundation. The views
expressed in this paper do not necessarily reflect those of
the National Science Foundation or the United States
Government. G.B. worked on this work during a visiting
research period at the Institute of Hazard, Risk and
Resilience, Department of Geography, University of Durham
(UK), founded by the ‘Luigi and Francesca Brusarosco’
Foundation. We thank Jonathan T. Davies, David G. Jenkins
and Robert E. Ricklefs for useful discussion on a previous
version of the manuscript.
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