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ORIGINALARTICLE
Global patterns of specialization andcoexistence in bird assemblages
Jonathan Belmaker1*�, Cagan H. Sekercioglu2� and Walter Jetz1�
1Department of Ecology and Evolutionary
Biology, Yale University, New Haven, CT
06520, USA, 2Center for Conservation Biology,
Department of Biology, Stanford University,
Stanford, CA 94305, USA
*Correspondence: Jonathan Belmaker,
Department of Ecology and Evolutionary
Biology, Yale University, New Haven, CT 06520,
USA.
E-mail: [email protected]�These authors contributed equally.�Current address: Department of Biology,
University of Utah, Salt Lake City, UT 84112,
USA.
ABSTRACT
Aim Increased specialization has been hypothesized to facilitate local coexistence
and thus high species richness, but empirical evaluations of the richness–
specialization relationships have been relatively scant. Here, we provide a first
assessment of this relationship for terrestrial bird assemblages at global extent and
from fine to coarse grains.
Location World-wide.
Methods We use two indices of specialization that describe species-level
resource use: diet and habitat specialization. The relationship between richness
and mean assemblage-level specialization was independently assessed at realm,
biome-realm, 12,100 km2 equal-area grid cells and fine-grained scales. To identify
assemblages that are diverse relative to environmental conditions we: (1) applied
quantile regressions, (2) statistically accounted for other environmental variables
which may constrain richness, and (3) parsed the data according to the residuals
of a model relating species richness to the environmental variables.
Results Assemblage species richness increases with both measures of
specialization at all scales. Statistically, richness appears constrained by levels of
specialization, with the highest richness values only found in specialized
assemblages. Richness is positively associated with specialization even after
accounting for gradients in resource availability. Net primary productivity and
assemblage specialization have complementary statistical effects on assemblage
species richness. Contrary to expectations based on niche partitioning of local
resources, the relationship between specialization and richness is steep even at
coarse scales.
Main conclusions The results demonstrate that for an entire clade, totalling
> 9000 species, specialization and species richness are related, at least for diverse
assemblages. The strong patterns observed across scales suggest that this
relationship does not solely originate from (1) limits on coexistence in present-
day assemblages, or (2) increased specialization in richer assemblages imposed by
species’ abilities to partition ecological space. Instead, regional-scale influences on
the species pool may determine much of the observed relationship between
richness and specialization. Although causal attribution is not straightforward,
these findings support the idea that, for the scale of our analysis, specialization
may be related to the past origination of high-diversity assemblages, rather than
their contemporary assembly.
Keywords
Biodiversity, biological inventories, birds, diet breadth, habitat breadth,
macroecology, regional scale, richness–specialization hypothesis, scale, species
richness.
Journal of Biogeography (J. Biogeogr.) (2011)
ª 2011 Blackwell Publishing Ltd http://wileyonlinelibrary.com/journal/jbi 1doi:10.1111/j.1365-2699.2011.02591.x
INTRODUCTION
Establishing a mechanistic and predictive understanding of
global richness gradients is a central theme in community and
macroecology. Generally, putative mechanisms have been
approached from evolutionary or ecological perspectives.
While evolutionary approaches focus on drivers of diversifi-
cation, ecologists have emphasized the way assemblages
subdivide niche-space locally and thus facilitate coexistence
and high levels of species richness (Chase & Leibold, 2003). A
prominent niche-based hypothesis for geographic variation in
species richness asserts that certain environments, such as the
tropics, support species that are more narrowly specialized and
thus allow tighter species packing (Hutchinson, 1959; Connell
& Orias, 1964; MacArthur, 1972).
A positive relationship between specialization and species
richness has been put forward as a key explanation for global
diversity gradients (MacArthur, 1972). Specifically, it has been
argued that the latitudinal diversity gradient originates from
the stable tropical climates, which allow narrower species
tolerances and hence specialization (Klopfer & MacArthur,
1960; Pianka, 1966; MacArthur, 1972; Stevens, 1989; Jocque
et al., 2010). However, while some researchers have found
evidence for a positive relationship between specialization and
species richness (sometimes using latitude as a proxy for
richness; Pagel et al., 1991; Eeley & Foley, 1999; Cardillo, 2002;
Dyer et al., 2007; Krasnov et al., 2008b; Mason et al., 2008),
others have not (Beaver, 1979; Lappalainen & Soininen, 2006;
Novotny et al., 2006; Belmaker, 2009; Filippi-Codaccioni et al.,
2010). Moreover, a meta-analysis failed to support the
suggested richness–specialization association (Vazquez & Ste-
vens, 2004).
Despite the prominence and potential relevance of the
richness–specialization hypothesis, to date its empirical under-
standing has been limited. In this study we address this
shortcoming by providing the first global assessment of
specialization in relation to species richness for an entire large
clade. We argue that a synthetic understanding of the
purported effect of specialization on richness requires the
additional consideration of two rarely examined issues: scale
and context-dependence.
First, the strength and directionality of the richness–
specialization relationship may change across scales. While
some attention has been given to the scale-dependence of
specialization within species (Krasnov et al., 2008a; Devictor
et al., 2010), few studies have explored the scaling of commu-
nity-level specialization. If richness is limited by the ability to
partition local resources, we may expect richness–specializa-
tion associations to be strong at fine scales, but weaker at
regional scales where constraints on coexistence and effects of
resource availability are unlikely to be major forces. If positive
richness–specialization relationships were to arise under a
scenario of richness driving specialization to facilitate the
partitioning of resources (MacArthur, 1965; Rosenzweig & Ziv,
1999), we may similarly expect the patterns to be most
pronounced at fine scales where resources are most limiting.
Alternatively, richness–specialization associations at the assem-
blage level may primarily arise from regional-scale evolution-
ary diversification of lineages. For example, specialization may
be associated with reduced dispersal and hence increased
diversification rates (Jocque et al., 2010). As these evolutionary
processes are manifested at a regional scale (Ricklefs, 2006), a
positive richness–specialization relationship would accordingly
be present at both regional and fine scales.
Second, the effect of specialization on species coexistence
may depend on environmental conditions. When richness is
depressed by external conditions (e.g. disturbance, dispersal
limitation, harsh abiotic conditions), abiotic constraints are
more likely than competition to structure species assemblages.
We may therefore be more likely to observe a strong
relationship between specialization and richness in assemblages
that exist close to carrying capacity. Here we use estimates of
net primary productivity and habitat heterogeneity to repre-
sent environmental constraints. Assemblages that contain
more species than expected given environmental conditions
are more likely to be limited by their ability to divide niche
space. Thus, we expect to find context-dependence in the
richness–specialization association: specialization may limit
richness most strongly in assemblages that are species-rich
relative to environmental conditions.
In this study we use birds to provide the first global
exploration of the geography of specialization and its associ-
ation with species richness, addressing both context- and scale-
dependence. We expect richness–specialization relationships to
be strongly positive, especially in assemblages that are species-
rich relative to their environment, and steeper at fine than
coarse scales. By assessing these relationships we expect to
identify the role of specialization in global richness gradients.
MATERIALS AND METHODS
Assemblage data
Breeding season distributions for non-marine birds were
compiled from the best available sources for a given region
or taxonomic group (Jetz et al., 2007). Based on these
distributions, species occurrence was determined within six
realms (Afrotropics, Australasia, Indo-Malaya, Nearctic, Neo-
tropics and Palaearctic), 58 biome-realms (ecosystem type
nested within realms; following Olson et al., 2001) and 9731
equal-area grid cells of 12,100 km2 (110 · 110 km grids at the
equator; cells with terrestrial surface < 20% or richness < 10
were excluded). Additionally, we compiled 398 fine-grained
assemblages from an extensive survey of the literature for well-
sampled and thoroughly documented species inventories
(Meese, 2005; Belmaker & Jetz, 2011). Here we consider
assemblages from areas of 10 to 7875 km2 (median: 488 km2,
equivalent to c. 22 · 22 km grid cells) as fine-grained, with
four larger assemblages in noticeably underrepresented regions
(for an among-realm comparison see Fig. S1 in Appendix S1 in
Supporting Information). Sub-setting the assemblages by area
(30% largest vs. 30% smallest) gave qualitatively identical
J. Belmaker et al.
2 Journal of Biogeographyª 2011 Blackwell Publishing Ltd
results (Fig. S2 in Appendix S1). While these assemblages are
fine-grained relative to typical macroecological studies, data
limitations prevent us from extending these assemblages to yet
finer grains, where biological interactions may increase in
importance, without incurring geographic and environmental
biases. Variation in fine-grained assemblage area was addressed
by including area (log10-transformed area in km2) as a
covariate in multi-predictor models. Following model diag-
nostics we log10-transformed richness.
Specialization
We used two indices of specialization that describe species-
level resource use: diet and habitat specialization. Diet
specialization was estimated from a comprehensive literature
survey, but based primarily on del Hoyo et al. (2008) (see
Wilman, 2011). Diet information included the proportional
use (frequency of use relative to all other diet categories) of
each of eight dietary categories (seeds, fleshy fruits, nectar,
invertebrates, carrion, fish, other vertebrates, and other plant
material). These were combined into a single value using the
Levins’ index (Krebs, 1999):
B ¼ 1=Rp2i ð1Þ
where pi is the proportional use of dietary category i.
Habitat specialization was estimated from the number of
distinct habitats used by each species, as reported in a fine-
scale IUCN assessment (Appendix S2). Critical to the analyses
conducted here, the habitat specialization data are globally
standardized. Although simplistic, counts of commonly used
resources frequently perform as well as more detailed data
(Krebs, 1999). Note that the number of distinct habitats
returns values that are identical to those obtained from
equation 1 when all used habitats are assigned equal propor-
tions.
The maximum values of diet and habitat specialization
indices will depend on the numbers of categories. We therefore
standardized both dietary and habitat indices to vary between 0
and 1, and then subtracted this value from one so that higher
index values indicate higher specialization:
BA ¼ 1� ðB� 1Þ=ðn� 1Þ ð2Þ
where B is the unstandardized diet or habitat specialization
index for each species and n is constant across all species and
reflects the total number of dietary states (8) or the maximum
number of habitats occupied by a species (12). Specialization
indices displayed a left skew across species and assemblages,
indicating that most species are specialized, and relatively few
are generalists (Fig. 1). However, after performing a logit
transformation (Warton & Hui, 2011) skew was substantially
reduced (Fig. S3 in Appendix S1). We therefore used the mean
across species of the logit-transformed specialization to
represent assemblage-typical specialization values. Specializa-
tion values of one were given a value of 0.975 before
transformation. Habitat and dietary specialization are consid-
erably correlated (12,100 km2 grain: r = 0.68).
At least three unavoidable methodological limitations must
be acknowledged. First, treating specialization as a species-level
attribute is a simplification. However, we expect intraspecific
variation to be small relative to the strong interspecific
differences among all birds. Second, specialization would
ideally be measured at the finest ecological resolution possible.
However, there is no obvious ‘correct’ level of ecological detail
at which specialization should be measured, and using higher
resolution specialization data will inevitably entail a loss of
geographical and taxonomical coverage. Finally, specialization
may be confounded by geographical biases in human knowl-
edge; for example tropical species are typically less known than
their temperate counterparts. However, birds are by far the
best studied taxon and therefore present the least geographical
bias. These data limitations are currently unavoidable caveats
for a broad-scale view and birds provide a best-case study
system.
Auxiliary environmental variables
To estimate richness relative to environmental constraints and
to account for the possibility that assemblage-level specializa-
tion is driven by patterns of resource availability (Devictor
Num
ber
of s
peci
es
0.0 0.2 0.4 0.6 0.8 1.0
020
0040
00
(a) Speciesn = 9,061
0.0 0.2 0.4 0.6 0.8 1.0
010
0025
00
Ass
embl
ages
(b) Biome−realm
n = 58
0.82 0.88 0.92
04
812
0.38 0.62 0.82
05
1015
Ass
embl
ages
(c) 12,100km2
n = 10,011
0.82 0.88 0.92
010
0020
00
0.38 0.62 0.82
050
015
00
Diet specialization
Ass
embl
ages
(d) Fine−grained
n = 398
0.82 0.88 0.92
040
80
Habitat specialization0.38 0.62 0.82
020
60
Figure 1 (a) The frequency distribution of diet and habitat spe-
cialization among extant terrestrial bird species globally. (b–d)
Mean assemblage diet and habitat specialization for three spatial
scales: (b) biome-realm, (c) 12,100 km2 grid and (d) fine-grained.
Means were calculated following logit transformation.
Specialization and bird richness
Journal of Biogeography 3ª 2011 Blackwell Publishing Ltd
et al., 2010; Kissling et al., 2011), we extracted key environ-
mental predictors that (1) have been shown to be strong
predictors of avian richness gradients, and (2) are directly
related to resource availability. Average annual above-ground
net primary productivity was estimated using the Lund
Potsdam Jena dynamic global vegetation model, which
accounts for land use (Bondeau et al., 2007). For the finest
scale, we estimated productivity at 1 km resolution using
MODIS imagery and the MOD17 algorithm, averaged over the
years 2000–2006. We used the number of distinct land-cover
types, based on 96-class USGS 1 km resolution GLCC product
(http://edc2.usgs.gov/glcc/), as a measure of habitat heteroge-
neity. All environmental predictors were log10-transformed
prior to analyses.
Analyses
We predict that specialization will only limit richness when
richness is high relative to the local environmental constraints.
We tested this by: (1) applying quantile regressions to pinpoint
the most diverse assemblages (we used the 0.9 quantile but
other high quantiles gave similar results; Fig. S4 in Appen-
dix S1), (2) statistically accounting for environmental variables
which may constrain richness (productivity, habitat heteroge-
neity and, for fine-grained assemblages, area) by including
them in a multiple regression framework, and (3) splitting the
data into quartiles according to the residuals of a linear model
relating species richness to environmental variables. For the
latter, we postulate that the richness–specialization relation-
ship will be stronger for higher quartiles, i.e. in assemblages
which are richest relative to the environmental constraints.
As both diet and habitat specialization show strong variation
and the directionality of the relationship is not straightfor-
ward, we used reduced major axis regressions, which are
symmetrical, in addition to ordinary least squares. Sample size
differences might hinder direct comparison between scales, so
we repeated some analyses for the 12,100 km2 grid using only
cells containing fine-grained assemblages. Pseudo-R2 for
quantile regressions was determined as 1 – (sum of weighted
distances/sum of weighted distances for an intercept-only
model). All analyses were performed in R v. 2.13.0 (R
Development Core Team, 2011). Quantile regressions were
performed using the package ‘quantreg’ (Koenker, 2011) and
reduced major axis regressions using the package ‘lmodel2’
(Legendre, 2008).
To account for spatial autocorrelation we also applied
simultaneous autoregressive (SAR) models. We used row-
standardized SAR spatial error models, examined a range of
possible neighbourhood sizes (200–3800 km), and chose the
model with the lowest Akaike information criterion (AIC)
score. We calculated pseudo-R2 values for the SAR models as
the squared Pearson correlation between observed values and
those predicted by the spatial models. Here, R2fit represents the
total amount of variance explained by the model, including the
spatial component, while R2trend represents the amount of
variance explained by the non-spatial smooth component
alone. Moran’s I global test was used to determine whether
residual autocorrelation persisted after fitting the SAR model.
The significance of Moran’s I standard deviate was assessed by
randomization. SAR analyses were performed using the
package ‘spdep’ (Bivand, 2011).
When estimated as the number of habitats within the entire
species range, habitat specialization may correlate with range
size (e.g. Davies et al., 2009). In this study we reduce this
inherent correlation by using independent specialization
estimates for each species based on observed habitat utiliza-
tion. Yet, as a correlation remained (12,100 km2 grid:
r = )0.66, fine-grained assemblages: r = )0.72) we repeated
the analysis using log10 mean range size as an additional
predictor (Fig. S5 in Appendix S1). Because range size itself is a
measure of specialization (specialized species usually have
narrow ranges), further attempts to control for range-size
variation run the risk of inadvertently controlling for special-
ization patterns.
To account for sampling effects potentially causing a
spurious richness–specialization association, we constructed a
null model in which species were randomly drawn from the
global species list, weighted by their corresponding range size.
This approach preserves local richness and the global species–
specialization distribution, but assumes no dispersal limita-
tion. We compared the shape and slope of the null
relationship to empirical patterns to determine whether a
bounded species–specialization relationship is expected by
chance alone.
RESULTS
Avian diet and habitat specialization are distributed highly
unevenly between species, with many more specialized than
generalist species (Fig. 1a). Average values of assemblage
specialization show strong variation at each of the three scales
of analysis (biome-realm, 12,100 km2 grid cells and fine-
grained assemblages), with a small trend towards higher mean
specialization at coarse scales (Fig. 1b–d). Mean assemblage-
level avian diet and habitat specialization show distinct
geographical and latitudinal variation. Both habitat and diet
specialization are highest at low latitudes, decrease towards
middle latitudes, and increase again at extremely high latitudes
(Fig. 2). Comparing 12,100 km2 grid cells, mean diet special-
ization exhibits noticeably high values across South America,
much of sub-Saharan Africa, Southeast Asia and parts of
Siberia. Habitat specialization also presents clear geographic
gradients, with high values most prominently in the Neotrop-
ics, Southeast Asia and the Tibetan Plateau and low values
across most of the Palaearctic, Australia and southern Africa
(Fig. 2).
We find strong geographic similarities between assemblage-
level specialization and known richness gradients (Fig. 2).
Clear relationship between richness and mean diet and habitat
specializations exist at the realm and biome-realm scales
(Fig. 3; upper panels). Under a global null model, the
relationship between richness and specialization is funnel-
J. Belmaker et al.
4 Journal of Biogeographyª 2011 Blackwell Publishing Ltd
−60
−20
020
4060
(a) Diet specialization
0.790.880.890.90.93
−60
−20
020
4060
(b) Habitat specialization
0.290.480.540.630.8
−160 −120 −80 −40 0 40 80 120 160
−60
−20
020
4060
(c) Richness
10110170260560
Figure 2 The global distribution of mean (a) dietary specialization, (b) habitat specialization and (c) species richness of terrestrial birds
within 12,100 km2 grid cells. Points represent fine-grain assemblages. Columns to the left represent means across latitudinal bands using the
same colour scale as the maps.
Specialization and bird richness
Journal of Biogeography 5ª 2011 Blackwell Publishing Ltd
shaped, with large variability at low richness and decreasing
variance – but otherwise no trend – with increasing richness
(Fig. 3; middle panels, dashed lines). The slopes of the null
relationship between richness and specialization are low for
both ordinary least squares (OLS) [Diet: 0.0 ()0.10–0.11 95%
confidence interval (CI)); habitat, )0.02 ()0.06–0.01)] and 0.9
quantile regressions [diet: 0.0 ()0.13–0.14); habitat,
0.01()0.10–0.10)]. This contrasts strongly with empirical
patterns for 12,100 km2 grid cells, which above c. 100 species
richness becomes increasingly restricted from above at low
values of specialization (Fig. 3; middle panels) creating a
positive richness–specialization relationship [OLS: diet
0.80 ± 0.01 (SE), habitat 0.22 ± 0.01; 0.9 quantile regression:
diet 0.81 ± 0.01, habitat 0.30 ± 0.01]. The 0.9 quantile
regression slopes are steeper for the 12,100 km2 scale than
for the fine-grained assemblages [significant interaction
between the two-level categorical predictor scale (12,100 km2
grid and fine-grained assemblages) and specialization as a
continuous predictor; for habitat: t value (P) = 3.75 (< 0.001);
for diet: 5.46 (< 0.001)]. Strong richness–specialization asso-
ciations are also found within individual realms (Fig. 3; lower
panels).
We predicted that richness–specialization associations
would remain strong, even after accounting for gradients in
environmental resource availability. Our analyses confirm
previous evidence that proxies for broad-scale gradients in
resource availability, such as net primary productivity and
habitat heterogeneity, are strong predictors of species richness
(Table 1). However, additionally fitting both diet and habitat
specialization in a multi-predictor setting reveals strong,
complementary and positive effects on richness, especially at
the 12,100 km2 grid scale (Table 1). The richness–specializa-
tion relationship also remains strong when using major axis
regressions (Table 2), which are symmetrical and thus do not
assume any cause and effect relationship.
To assess these associations in more detail, we separate
assemblages according to the residuals of the richness–
environment (productivity, habitat heterogeneity and area)
relationship. Large positive residuals represent particularly
species-rich assemblages given environmental conditions, and
Ric
hnes
s(a)
1010
010
00
(b)RealmBiome−realm
Ric
hnes
s
(c)
1032
100
316
1000
(d)
12,100km2
Fine grained
Null
Diet specialization
Ric
hnes
s
(e)
0.82 0.88 0.92
100
316
1000
Habitat specialization
(f)
0.38 0.62 0.82
PalaearcticNearctic
Indo−MalayanNeotropicalAfrotropicalAustralasia
Figure 3 The relationship between global
terrestrial bird species richness and diet and
habitat specialization. (a, b) The relationships
across realms and biome-realms. Lines rep-
resent ordinary least square regressions. (c, d)
Black points represent fine-grained assem-
blages; coloured dots 12,100 km2 grid cells
(darkness increases with dot density).
Regression lines represent 0.9 quantile
regression estimates (12,100 km2 grid: pseu-
do-R2 = 0.32, 0.20; coefficient = 0.94, 0.31;
fine-grained assemblages: pseudo-R2 = 0.24,
0.12; coefficient = 0.54, 0.18; for diet and
habitat specialization, respectively);
P < 0.001 in all cases. Regions defined by the
dashed lines represent the 95% confidence
interval of the null expectation (see Materials
and Methods). Regression lines at the
12,100 km2 scale are plotted using only grid
cells in which fine-grained assemblages are
located to maintain sample size for direct
comparison. (e, f) Richness–specialization
associations for 12,100 km2 grid cells within
each of the major realms. Lines represent 0.9
quartile regressions (solid lines correspond to
significant slopes). Only assemblages with
> 100 species were included.
J. Belmaker et al.
6 Journal of Biogeographyª 2011 Blackwell Publishing Ltd
we expected to observe an especially strong signal of ecological
niche partitioning, facilitating coexistence, for these groups.
This prediction was only partially supported as the relationship
between richness and specialization is indeed steep in assem-
blages at high residual quartiles (quartiles 3 and 4; Table 2),
but also in assemblages belonging to low residual quartiles (e.g.
quartile 1).
Even after accounting for geographic variation in range size,
habitat and particularly diet specialization remain key predic-
tors of richness (Fig. S5 in Appendix S1). We also find that the
richness–specialization relationship is steeper for the 50% most
wide-ranged species than the 50% narrow-ranged ones (Fig. S6
in Appendix S1).
DISCUSSION
We demonstrate that, for an entire large clade across spatial
scales, specialization and species richness are strongly related.
Importantly, specialization remains positively associated with
richness even after accounting for known environmental
correlates of richness, such as productivity. Our results suggest
that avian dietary and habitat specialization set upper bounds
on species richness, indicating that high richness values are not
attained in generalist assemblages (Fig. 2). Conversely, species-
rich assemblages are more likely to contain specialized species
than assemblages that are species-poor. These findings con-
tradict several recent studies (Vazquez & Stevens, 2004;
Novotny et al., 2006) and suggest that the contribution of
specialization to global richness gradients may need to be
carefully reassessed, taking into consideration the grain, extent
and resolution of specialization categorization.
We would like to emphasize that if species richness and
specialization were not linked, but rather both associated with
areas containing more habitats or dietary types, we would
expect a negative, not a positive, relationship between special-
ization and richness. Simple random sampling of the environ-
ment would cause species to have wider diets and use more
habitats in heterogeneous and therefore species-rich areas.
Similarly, species would be habitat and diet specialists in
localities with limited diet or habitat options (e.g. few diet
categories used in cold or high-elevation regions might reflect
limitations of available feeding strategies). However, we find
the opposite pattern: an increase in specialization with
richness, which is more consistent with our expectation.
We find that the relationship between specialization and
species richness is steep even at coarse scales (realm and
biome-realm) and steeper for the 12,100 km2 scale than for
fine-grained assemblages. If the observed associations between
richness and specialization were simply the result of contem-
porary ecological filters (e.g. those precluding non-specialists
from entering local assemblages), we would expect to observe
shallower relationships at coarse scales. The same argument
holds also when viewing a reverse direction of causality. If
competition for limited local resources causes species in
diverse regions to specialize, we would predict shallower
richness–specialization associations at coarse scales where
resource partitioning is not a major limitation. Instead, it
appears that the positive richness–specialization relationship is
Table 1 Multi-predictor models of species richness in terrestrial birds (9061 species) globally. Ordinary least squares, quantile (0.9 quantile)
and simultaneous autoregressive (SAR) regressions are presented. For all regressions, predictors included diet specialization (Diet spec.),
habitat specialization (Habitat spec.), net primary productivity, habitat heterogeneity (Habitat heter.) and, for fine-grained assemblages only,
area. Neighbourhood sizes used for the SAR models were 3000, 200 and 1400 km for the biome-realm, 12,100 km2 grid and fine-grained
assemblages, respectively. Moran’s I values (P) were 0.70 (0.24), 21 (***) and )0.43 (0.7) for the biome-realm, 12,100 km2 grid and fine-
grained assemblages, respectively.
Scale Variable
Ordinary least squares Quantile regression Simultaneous autoregressive
Coef. (SE) t value Coef. (SE) t value Coef. (SE) z value
Biome- Realm Habitat spec. 0.27 (0.16) 1.6 0.24 (0.15) 1.6 0.63 (0.15) 4.1***
Diet spec. 0.50 (0.29) 1.7 0.93 (0.40) 2.4* 0.27 (0.36) 0.7
Productivity 0.09 (0.14) 0.6 )0.18 (0.16) )1.14 )0.06 (0.12) )0.5
Habitat heter. 0.32 (0.27) 1.2 0.26 (0.28) 0.92 0.28 (0.26) 1.0
Summary: R2adj = 0.37, P < 0.001 R2
Pseudo = 0.47, P < 0.001 R2trend = 0.36, R2
fit = 0.58
12,100 km2 grid Habitat spec. 0.05 (0.03) 7.8*** 0.08 (0.01) 11.1*** 0.18 (0.01) 29.8***
Diet spec. 0.54 (0.01) 41*** 0.52 (0.01) 37.1*** 0.04 (0.01) 3.9***
Productivity 0.22 (0.00) 58*** 0.17 (0.01) 27.0*** 0.07 (0.00) 25.6***
Habitat heter. 0.19 (0.01) 20*** 0.16 (0.01) 13.7*** 0.05 (0.00) 17.4***
Summary: R2adj = 0.60, P < 0.001 R2
Pseudo = 0.43, P < 0.001 R2trend = 0.41, R2
fit = 0.99
Fine-grained Habitat spec. )0.00 (0.03) )0.15 )0.03 (0.02) )1.6 )0.01 (0.04) )0.3
Diet spec. 0.21 (0.06) 3.5*** 0.55 (0.06) 8.8*** 0.31 (0.07) 4.1***
Productivity 0.18 (0.03) 6.9*** 0.17 (0.02) 9.0*** 0.16 (0.03) 5.6***
Habitat heter. 0.03 (0.05) 0.7 0.07 (0.03) 2.1* 0.06 (0.05) 1.2
Area 0.03 (0.02) 1.7 0.01 (0.01) 0.6 0.02 (0.02) 1.3
Summary: R2adj = 0.21, P < 0.001 R2
Pseudo = 0.33, P < 0.001 R2trend = 0.21, R2
fit = 0.27
Coef., coefficient; *, P < 0.05; ***, P < 0.001. Significant (P < 0.05) relationships are italicized and marked in bold.
Specialization and bird richness
Journal of Biogeography 7ª 2011 Blackwell Publishing Ltd
shaped by regional evolutionary processes, which are expressed
from regional to fine grains (Ricklefs, 2008). For example, the
large radiation of the Furnariidae and Tyrannidae families
(Irestedt et al., 2006; Ohlson et al., 2008), which have relatively
narrow dietary niches, exerts a signal on the high dietary
specialization of the South American assemblages (Fig. 2).
Studies conducted at a global extent are limited by the
coarseness of the available distribution data, which applies to
specialist species even more than generalist species (Jetz et al.,
2008). By including relatively fine-grained assemblages we
show that, down to a grain equivalent to c. 22 · 22 km grid
cells (median fine-grain assemblage size was 488 km2), the
richness–specialization slope does not steepen. This suggests
that any signature of local resource partitioning on assem-
blage-level specialization may be weak, or may be most
apparent at yet finer scales.
Although the richness–specialization relationship is strong
and consistent in shape across realms, considerable scatter
remains, and a wide range of richness values can be found at
each specialization level (Fig. 3), even after accounting for
environmental conditions (Table 1). In this study, specializa-
tion estimates were obtained using relatively coarse resolution
data and it is probable that most species can differentiate finer
habitat and diet delimitations. However, even a warbler that
distinguishes between different microhabitats in a single tree
(MacArthur, 1958), may use both a montane and a lowland
forest (see categories in Appendix S2). Therefore, it is unclear
what habitat and diet resolutions are the most appropriate.
Nevertheless, using coarse habitat and diet categorizations,
which are likely to be strongly conserved phylogenetically,
could have contributed to the large variability in the richness–
specialization associations. Stronger associations may be
uncovered by using more detailed specialization data acquired
at finer scales.
Specialization should ideally be measured by comparing
observed resource utilization with resource availability at the
same geographic location. However, dietary resources and
fine-grained habitats are not available globally and therefore, in
practice, such standardization is rarely performed (Vazquez &
Stevens, 2004). Here, we use habitat variety and net primary
productivity as proxies for habitat and diet availability. The
strong and positive influence of specialization on richness was
evident even after accounting for these gradients in resource
availability (Table 1). Therefore, the richness–specialization
association does not simply mirror well-established environ-
mental gradients. Productivity, in particular, has been
Table 2 The relationship between specialization and species richness in 9061 terrestrial bird species globally. Single-predictor analyses are
presented in which the response is species richness and the predictor is either habitat or diet specialization. For the 12,100 km2 grid
and fine-grained assemblages, data were partitioned and analysed separately for different quartiles (small sample size prevented this for
the coarser scales). Quartile delineation was derived from first relating species richness to environmental variables (productivity, habitat
heterogeneity and, for fine-grained assemblages, area) and then separating the assemblages according to the residuals of this model.
Quartiles range from first (lowest 25% residuals) to fourth (highest 25% residuals).
Scale Specialization Quartiles
RMA OLS
Coef. (95% CI) Coef. (SE) t value
Realm Habitat spec. all 0.6 ()0.2–3.2) 0.5 (0.3) 1.9
Diet spec. all 1.4 (0.7–3.6) 1.2 (0.3) 3.9*
Biome-Realm Habitat spec. all 0.8 (0.6–1.2) 0.5 (0.1) 5.5***
Diet spec. all 2.2 (1.6–3.6) 0.9 (0.2) 5.1***
12,100 km2 grid Habitat spec. 1 0.77 (0.74 )0.80) 0.19 (0.01) 12.8***
2 0.60 (0.58–0.62) 0.04 (0.01) 3.0**
3 0.80 (0.77–0.83) 0.16 (0.02) 9.9***
4 0.68 (0.65–0.70) 0.33 (0.01) 27.8***
Diet spec. 1 1.70 (1.64–1.76) 0.76 (0.03) 25.1***
2 1.40 (1.34–1.45) 0.21 (0.03) 7.9***
3 1.89 (1.79–1.92) 0.81 (0.03) 24.4***
4 1.54 (1.50–1.59) 1.02 (0.02) 43.8***
Fine-grained Habitat spec. 1 0.38 (0.31–0.47) 0.02 (0.04) 0.6
2 0.20 (0.17–0.25) 0.01 (0.02) 0.7
3 0.20 (0.16–0.24) 0.05 (0.02) 2.7***
4 0.30 (0.25–0.36) 0.13 (0.03) 4.9***
Diet spec. 1 0.63 (0.52–0.76) 0.24 (0.06) 4.1***
2 )0.42 ()0.52–)0.35) )0.02 (0.04) )0.4
3 0.53 (0.44–0.64) 0.18 (0.05) 3.6***
4 0.75 (0.65–0.87) 0.51 (0.05) 9.1***
Coef., coefficient; Diet spec., diet specialization; Habitat heter., habitat heterogeneity; Habitat spec., habitat specialization; *, P < 0.05; **, P < 0.01; ***,
P < 0.001. Significant (P < 0.05) relationships (for ordinary least squares regression; OLS) or slopes with 95% confidence intervals (CI) excluding
zero (for reduced major axis regression; RMA) are italicized and marked in bold.
J. Belmaker et al.
8 Journal of Biogeographyª 2011 Blackwell Publishing Ltd
suggested to promote specialization (MacArthur, 1965; Evans
et al., 2006), and hence may be a confounding factor when
analysing richness–specialization associations. This study dem-
onstrates that specialization has an independent contribution
to richness above and beyond that attributed to productivity.
Hence, and assuming richness is casually linked to specializa-
tion, gradients in resource availability (manifested as more
habitats or energetic niches, Hurlbert, 2004; Anderson & Jetz,
2005; Clarke & Gaston, 2006; Novotny et al., 2006; Belmaker,
2009; Kissling et al., 2011) may operate in tandem with
gradients in specialization to establish richness patterns.
Range size is in itself a measure of specialization (Stevens,
1989; Jocque et al., 2010). It is thus difficult to control for
range-size variation without running the risk of inadvertently
controlling for specialization patterns. Nevertheless, diet and
habitat specialization remain independent contributors to
richness, even after accounting for geographic variation in
range size (Fig. S5 in Appendix S1). Therefore, the richness–
specialization relationship does not solely reflect known
geographic variation in range sizes, most notably the decrease
in range size towards low latitudes (Stevens, 1989). Wide-
ranged species contribute disproportionally to richness gra-
dients (Jetz & Rahbek, 2002). In accordance, we find that the
richness–specialization relationship is strongest for the 50%
most wide-ranged species (Fig. S6 in Appendix S1). This
suggests that much of the global relationship may be
attributed to the replacement of wide-ranged generalists in
species-poor regions by wide-ranged specialists in species-rich
regions.
The positive but largely coarse-scale association between
richness and specialization supports several, not mutually
exclusive, hypotheses. First, specialization might facilitate the
origination of high-diversity assemblages, in addition (or
instead) of an effect on contemporary assembly. For example,
specialization may promote high diversification rates by
increasing reproductive isolation between populations (Haw-
thorne & Via, 2001; Jocque et al., 2010). In addition, a positive
richness–specialization relationship may form over evolution-
ary time and at coarse geographical scales if specialization
increases the effectiveness of resource use, consequently
reducing the within-clade ecological limits on diversification
(Phillimore & Price, 2008; Rabosky, 2009). In both of these
examples a positive assemblage-level richness–specialization
relationship emerges due to intrinsic differences among clades
in diversification rates and may thus be independent of local
environmental constraints (although the environment may
strongly influence specialization levels; Jocque et al., 2010).
This is in line with our results, which reveal only limited
support for context-dependence in the richness specialization
association, as we do not find an increase in the steepness of
the richness–specialization relationship at higher quartiles of
richness–environment residuals.
A second explanation for the positive richness–specializa-
tion relationship is that the association between the two is in
fact not causal, but that instead both are linked indepen-
dently to a third variable. We find the richness–specialization
association to be independent of prominent environmental
gradients (notably productivity), but it is impossible to
exclude the possibility that unmeasured environmental vari-
ables or life-history traits affect both specialization and
richness.
Finally, the direction of causality may in fact be reversed and
the positive richness–specialization relationship primarily
brought about by richness influencing patterns of specializa-
tion. Diverse assemblages may be more likely than species-
poor assemblages to coevolve towards increased specialization
(MacArthur, 1965; Rosenzweig & Ziv, 1999). However, we
expect such a pattern to be largely confined to fine scales where
competition for limited resources, postulated to drive such a
process, should be strongest. It has also been suggested that
asymmetrical interaction networks, where specialists tend to
interact with generalists, can cause a positive correlation
between specialization and richness (Vazquez & Stevens, 2004).
However, here too we may expect the pattern to be most
pronounced at fine scales where species interactions take place.
We cannot currently envision a mechanism by which higher
species richness will cause gradients in specialization primarily
at coarse scales. Nevertheless, lacking fully resolved phyloge-
netic trees and data on the past occurrences and interactions of
species, we are unable to directly quantify the effect of regional
diversity on the evolution of specialization across clades. Thus,
the goal of this study was a first geographic assessment and a
general test of ideas regarding niche-partitioning and com-
munity assembly. Separating the directionality of effects will
require further research that may need to be limited in
geographic and taxonomic scope.
In conclusion, we demonstrate that globally high bird
species richness is positively associated with specialization. We
interpret our findings across scales as indicating a relatively
minor role for niche-partitioning mechanisms in causing this
pattern. We suggest that our results support a regional and
evolutionary perspective for understanding how specialization
is associated with global geographic gradients in species
richness.
ACKNOWLEDGEMENTS
We would like to thank R. Meese for help with the fine-grained
data and D.M. Post, K. Mertes-Schwartz and N. Cooper for
comments. H. Wilman, C. de la Rosa and J. Simpson compiled
the bird diet data. The study was partially supported by
National Science Foundation (NSF) grants DBI 0960550 and
DEB 1026764 to W.J. C.H.S acknowledges support from the
Christensen Fund.
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SUPPORTING INFORMATION
Additional Supporting Information may be found in the
online version of this article:
Appendix S1 Additional figures detailing the relationship
between specialization and species richness of birds of the
world (Figs S1–S6).
Appendix S2 Summary of the IUCN assessment habitat
categories.
As a service to our authors and readers, this journal provides
supporting information supplied by the authors. Such mate-
rials are peer-reviewed and may be re-organized for online
delivery, but are not copy-edited or typeset. Technical support
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BIOSKETCHES
Jonathan Belmaker is a post-doctoral researcher inter-
ested in understanding how patterns of biodiversity and the
underlying processes vary across scales and environmental
gradients. He is particularly interested in the interplay
between large scale processes and local community assem-
bly.
Cagan Sekercioglu is an assistant professor at the University
of Utah. He is a conservation ecologist, ornithologist and
tropical biologist especially interested in the effects of global
change on biodiversity patterns and ecosystem services.
Walter Jetz is interested in the ecology, biogeography and
conservation of terrestrial vertebrates and plants.
Editor: Melodie McGeoch
Specialization and bird richness
Journal of Biogeography 11ª 2011 Blackwell Publishing Ltd