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Ecology, 87(5), 2006, pp. 1234–1243
Ó 2006 by the Ecological Society of America
THE INFLUENCE OF PRODUCTIVITY ON THE SPECIES RICHNESS
OF PLANTS: A CRITICAL ASSESSMENT
LEN N. GILLMAN1,3
2
AND
SHANE D. WRIGHT2
1
Division of Applied Science, AUT University, Private Bag 92006, Auckland, New Zealand
School of Biological Sciences, University of Auckland, Private Bag, Auckland, New Zealand
Abstract. Despite much scrutiny the relationship between productivity and species
richness remains controversial, and there is little agreement about causal processes. We present
the results of a survey of 159 productivity–plant species richness (P–PSR) relationships from
131 published studies. We critically assessed each study with respect to experimental design
and for the appropriateness of the surrogates used for productivity. We were able to accept
only 60 of the reported relationships as robust tests of the P–PSR relationship and a further 18
as robust tests of the biomass–species richness relationship. Previous analyses have found that
unimodal P–PSR relationships predominate. In contrast, we found that, in studies that used
data of continental to global extent, all P–PSR relationships were positive regardless of grain,
that almost all were also positive in data sets of regional extent, and that unimodal
relationships were not dominant even in studies of fine grain or small spatial extent. Our
results differ substantially from previous meta-analyses because previous studies have included
a large number of studies that do not meet basic experimental design criteria for objectively
testing P–PSR relationships. These results have important implications for theory that
attempts to explain species richness patterns. We critically review four dominant theories in
light of our results and develop new falsifiable predictions of relationship from these theories
at both small and large spatial scales.
Key words: biomass; competition; diversity; energy; evolution; precipitation; productivity; scale; species
pool; species richness.
INTRODUCTION
Understanding species richness patterns has been a
central quest for biologists at least since the times of von
Humbolt (1808), Darwin (1859), and Wallace (1878). An
extensive literature surrounds the characterization of
diversity patterns, and theoretical explorations into the
processes underpinning these patterns have been intensive and ongoing. The relationship between productivity and species richness has been one of the more
important themes in this endeavor for more than four
decades (Hutchinson 1959). Despite this, there is little
consensus as to the nature of causal processes.
Patterns
It has been suggested that the true, or ubiquitous,
form of the productivity–plant species richness (P–PSR)
relationship is unimodal (Rosenzweig and Abramsky
1993, Huston and deAngelis 1994). However, although
there are studies suggesting that the P–PSR relationship
is variable and scale dependent (Wright et al. 1993,
Weiher 1999, Chase and Leibold 2002, Scheiner and
Jones 2002, Anderson et al. 2004), the dominant form of
the relationship at any particular scale remains controversial (Whittaker and Heegaard 2003). There are
Manuscript received 3 August 2005; revised 6 October 2005;
accepted 17 October 2005; final version received 21 November
2005. Corresponding Editor: J. S. Brewer.
3
E-mail: [email protected]
several recent reviews of these relationships for different
taxa, scales, and ecosystems (Grace 1999, Waide et al.
1999, Gross et al. 2000, Cornwell and Grubb 2003,
Hawkins et al. 2003), but the most comprehensive
review to date by Mittelbach et al. (2001) found that
unimodal relationships were dominant for plants,
especially at local to landscape scales. Whittaker and
Heegaard (2003) were critical of the analysis done by
Mittelbach et al. (2001) and suggested a new metaanalysis was needed. However, there have been no
comprehensive analyses of P–PSR relationships that
have critically examined the experimental designs used
in each of the member studies.
In this review we assess all the terrestrial P–PSR
relationships included in Mittelbach et al. (2001) along
with 37 more studies either missed by them or published
since their work. Our results, following careful scrutiny
of experimental design, differ considerably from those
previously presented. These differences have important
implications for diversity theory.
Productivity–species richness theory
Long-standing theories to explain patterns of species
richness include those relating to climate stability
(Wallace 1878, Klopfer 1959, MacArthur 1965), evolutionary and ecological time (Wallace 1878, Rensch 1959,
Fischer 1960), energy and productivity (Hutchinson
1959, Wright 1983), climate predictability (Slobodkin
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PRODUCTIVITY AND PLANT SPECIES RICHNESS
and Sanders 1969), area (MacArthur and Wilson 1967),
biotic interactions (Dobzhansky 1950, Paine 1966,
Janzen 1970), disturbance (Grime 1973), and spatial
heterogeneity (Pianka 1966, Grubb 1977). A decade ago
Palmer (1994) identified .120 hypotheses that attempt
to explain patterns of species richness. Many of these,
however, are circular in the nature of their argument and
cannot explain the primary cause of diversity patterns
(Rohde 1992). Despite a plethora of theory, new theory
continues to accumulate. Recent theory includes Rapoport’s rule (Stevens 1989), the mid-domain model
(Colwell and Hurtt 1994, Willig and Lyons 1998), niche
conservatism (Ricklefs and Latham 1992, Wiens and
Donoghue 2004, Hawkins et al. 2005), the species pool
hypothesis (Taylor et al. 1990), spatial scaling effects
(Ritchie and Olff 1999), Milankovitch climate oscillation
effects (Dynesius and Jansson 2000), and history of
community assembly (Fukami and Morin 2003). Most
authors agree that productivity contributes to species
richness patterns. However, the processes responsible
for these patterns remain incompletely explained, and
many competing hypotheses remain plausible (Abrams
1995, Willig et al. 2003, Currie et al. 2004, Evans et al.
2005). It is also not known if the variety of relationships
observed at different scales can be attributed to a single
mechanism or whether several mechanisms interact in
different ways at different scales.
Competitive exclusion theory predicts a unimodal
relationship on the basis that at low productivity, stress
and a lack of resources limit the number of species that
can survive, and as productivity increases, species
richness rises, until at very high productivity, competitive exclusion reduces species richness, either because
competition is more intense (Grime 1973, 1979) or
because high productivity leads to a decrease in the
heterogeneity of limiting resources (Huston 1979, Tilman 1982, Tilman and Pacala 1993). Energy richness
theory predicts a positive relationship between productivity and species richness. The basis for this theory is
that because the probability of avoiding extinction due
to stochastic demographics depends on population size
(Coleman et al. 1982), a minimum number of individuals
is required for each species, and because every individual
requires a minimum flux of energy, environments with
more available energy can support more individuals and
more species (Wright 1983). A second theory predicting
a positive relationship is that in warmer, more productive environments, evolution has been more rapid and
therefore more species have been able to accumulate
(Rensch 1959, Rohde 1992). Greater rates of evolution
may occur in more productive environments due to
shorter generation times and/or greater mutation rates
(Rohde 1978). The greater diversity found on older
landmasses (Ashton 1992) is also consistent with this
theory. However, we are unaware of any exploration of
the possible implications of variable rates of molecular
evolution on P–PSR relationships at different spatial
scales. Terborgh (1973) suggested that reduced species
1235
richness in harsh, highly productive sites could be due to
the small spatial extent, spatial isolation, and ephemeral
existence of these habitats. This idea was formalized by
Taylor et al. (1990) in the species pool hypothesis. This
predicts that the species richness of any particular
habitat is dependent on the size of the regional species
pool that can occupy that habitat (see also Hodgson
1987, Huston 1994). There is growing support for the
species pool hypothesis (Safford et al. 2001), but see also
Weiher (1999), Hillebrand and Blenckner (2002), and
Weiher and Howe (2003).
A correlation between species richness and productivity does not necessarily mean that the processes
controlling species richness are due to productivity.
Spatial heterogeneity, historical effects, disturbance, and
physiological stress may all covary with productivity and
thereby influence the observed patterns. It is therefore
important for studies attempting to characterize the
influence of productivity on species richness not to
confound productivity with the influence of these factors.
Scale
There are two components of spatial scale that can
influence P–PSR relationships: grain and extent. Grain
is the size of the sampling unit, and extent is the
maximum distance between samples (Whittaker and
Heegaard 2003). Scheiner and Jones (2002) found that
P–PSR relationships varied as the extent and grain were
altered in repeated analyses of the same data. Species
richness studies can also be conducted at different
ecological scales (Moore and Keddy 1989), and relationships obtained from within a community type may be
influenced by processes that differ from those measured
across communities.
METHODS
Assembling the studies
We assembled all the terrestrial productivity–plant
species richness (P–PSR) studies reported in Mittelbach
et al. (2001), and then we electronically searched
Biological Abstracts from 2000 to the present using the
following key words: species richness, diversity, productivity, biomass, energy, precipitation, and AET (actual
evapotranspiration). We used the same key words to
search: American Naturalist, Austral Ecology, Ecography, Ecology, Journal of Biogeography, Journal of
Ecology, Journal of Vegetation Science, Oecologia,
Oikos, Plant Ecology, and PNAS USA. Finally, we
searched citations in published papers. Relationships
were classified according to three extents: ‘‘local to
landscape’’ (0–200 km), ‘‘regional’’ (200–4000 km), and
‘‘continental to global’’ (.4000 km). We defined two
grain sizes: fine grain representing both alpha and point
diversity (plots 10 m2 for herbs, 100 m2 for shrubs,
and 1 ha for trees) and coarse grain (plots or map
grids larger than the above areas). We critically assessed
each of the 131 published studies that we located.
Studies were excluded from the analysis if: (1) they
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LEN N. GILLMAN AND SHANE D. WRIGHT
Ecology, Vol. 87, No. 5
included sites substantially modified by human activity,
such as mowing or the grazing of stock, as these
activities can strongly influence species richness
(Schaffers 2002); (2) they were substantially modified
by exotic species; (3) the sample size was ,10; (4) the
data were duplicated from a relationship reported
elsewhere; (5) the study design, or the productivity
surrogate used, was likely to have created a bias in favor
of the reported relationship. We do not imply that the
studies excluded from our analysis amount to less than
adequate science, because in many cases the reasons for
not including the study were properly addressed by the
authors. Details of the studies we surveyed are tabulated
in Appendices A, B, and C.
Statistical issues
Mittelbach et al. (2001) used a generalized linear
model (GLIM) regression with an assumption of
Poisson errors to test species richness data for linear
and quadratic relationships with productivity. They used
this in preference to ordinary least squares (OLS)
regression because OLS regression assumes symmetric
and normally distributed errors. However, Mittelbach et
al. (2001) did not check for Poisson errors and did not
correct for overdispersion (variances that exceeded the
mean). Overdispersion using the Poisson model will tend
to lead to excessively liberal tests (Crawley 1993). It is
unlikely that all of the trend in species richness will be
explained by the predictors available, so extra-Poisson
variation (overdispersion) is almost inevitable. Species
richness data will therefore violate the assumption of
Poisson errors more often than the assumption of
normality and symmetry of errors (B. McArdle, personal
communication). In addition, Mittelbach et al. (2001)
chose a significance level of 10%. Whittaker and
Heegaard (2003) demonstrated how these procedures
could have biased the Mittelbach et al. (2001) analysis,
and the rebuttal by Mittelbach et al. (2003) failed to
demonstrate that they did not. We therefore established
the following protocol for determining the form of
relationship to report: (1) we accepted the results of
analysis carried out by Whittaker and Heegaard (2003);
(2) we only accepted results for which P , 0.05; (3) for
instances where Mittelbach et al. (2001) found conflicting results using OLS and GLIM, we either reanalyzed
the data using OLS and tested for symmetric and
normally distributed errors, or if the data were not
available, we accepted the OLS results of Mittelbach et
al. (2001). This criterion had no net effect on the number
of positive P–PSR relationships reported. However, it
decreased the number of unimodal P–PSR relationships
by one and the number of biomass–plant species
richness relationships by three. An example of one of
the data sets reported by Mittelbach et al. (2001) as
unimodal using GLIM, but not unimodal using OLS is
shown in Fig. 1. Some studies reported linear relationships but did not test for a significant quadratic term. In
such cases, we tested for a quadratic term (none were
FIG. 1. A scatter plot of data reported by Mittelbach et al.
(2001) as unimodal using a generalized linear model (Pquad ,
0.02), but for which the quadratic component was not
significant using ordinary least squares (Pquad ¼ 0.10). The
figure is redrawn from Gough et al. (1994).
significant) if the data were available; otherwise the
relationship was not included.
The appropriate use of surrogates for productivity
Actual evapotranspiration (AET), potential evapotranspiration (PET), precipitation, standing biomass,
and soil nutrient status are commonly used as indices for
productivity. AET is a measure of biologically available
energy and is closely related to potential net primary
productivity (Rosenzweig 1995). Precipitation can be
used as an estimate of productivity where it is the
limiting factor for plant growth along the full range of
precipitation values. However, productivity depends on
available energy, not water, when water exceeds the
demand for transpiration, and productivity can often
decline as precipitation increases in cool climates or as
cloud cover increases and temperature declines with
altitude (Kay et al. 1997, Rey-Banayas and Scheiner
2002, Whittaker and Heegaard 2003). Studies using
precipitation as a surrogate for productivity were not
included, therefore, in cases where a positive monotonic
relationship between precipitation and productivity
would not be expected and the expected departure from
linearity would have contributed to the reported P–PSR
relationship. Similarly, PET can be used as a surrogate
for productivity, but only when water is not the limiting
factor for plant growth (Hawkins et al. 2003). Maximum
standing biomass can correlate well with actual productivity when the vegetation is mainly composed of annual
plants, or if the aboveground vegetation dies back on an
annual basis. However, it can be a poor surrogate for
productivity where herbivory is severe (McNaughton
1985), it can be influenced by the presence or absence of
particular species, and it is likely to be decoupled from
productivity in perennial vegetation. Several studies
justified the assumption that productivity and biomass
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PRODUCTIVITY AND PLANT SPECIES RICHNESS
were well correlated. However, it was not possible to
assess the validity of using biomass as a surrogate for
productivity from the published studies where the
authors made no such claim, or where a correlation
was simply assumed. We have therefore reported those
studies that did not justify the use of biomass as a
surrogate for productivity separately.
1237
reduced (but not eliminated) when samples of constant
ramet number were used. It was not possible to tell from
published studies whether or not the quadrat size
influenced the reported relationship. We therefore
excluded only two studies in which the sampling grain
was likely to be less than the space occupied by single
plants in some plots.
Confounding productivity with environmental stress
Selective use of taxa
Terborgh (1973) pointed out that productive environments may also be harsh environments for plants due to
unusual physical conditions. Environmental stress may
create strong selection gradients allowing only a small
number of species to adapt to these conditions (Gough et
al. 1994). For example, sites with high levels of salinity,
water logging, or disturbance frequency can be stressful
for most plant species but may also have high nutrient
status, which may or may not result in high productivity
(Garcia et al. 1993, Clinebell et al. 1995, Grace 1999,
Rey-Banayas and Scheiner 2002). One biomass–species
richness study was not included for this reason.
Abrams (1995) pointed out that the use of taxa limited
to restricted portions of productivity gradients would
influence relationships due to the niche preference of the
selected taxa. For example, the replacement of droughtadapted species by rainforest taxa as annual rainfall
increases is not evidence of a negative influence of
productivity on species richness. Studies that used
restricted phylogenetic criteria for species richness
counts were not included.
Confounding productivity with historical factors
Studies were not included if there was evidence that
historical factors contributed to the reported P–PSR
relationship. For example, Mittelbach et al. (2001)
reanalyzed data from Latham and Ricklefs (1993) and
reported a unimodal relationship between AET and tree
species richness in contrast with the authors’ conclusion
that their results reflected historical factors. Most of the
data in the AET midrange in Latham and Ricklefs came
from the Eurasian continent, and all the data at the upper
end came from the southeastern United States. There are
three reasons for low diversity in the southeastern U.S.
data that are unrelated to productivity: (1) The North
American continent is smaller than Eurasia, and North
America is likely to be influenced by the diversity–area
effect; (2) richness in the southeastern U.S. has probably
been reduced due to dispersal barriers to and from lower
latitude refugia during Pleistocene glaciations (O’Brien
1998); and (3) plot areas were much smaller (Whittaker
and Heegaard 2003). Therefore this study cannot be used
to characterize the influence of productivity.
Unequal sampling effort
Some data sets were obtained using species counts
from a variable number of plots (e.g., Beatley 1969), or
variable plot area, and the P–PSR relationships extracted were artifacts of sampling effort. Studies were
not included if sampling effort was likely to have
contributed to the reported relationship. A related
problem occurs where the number of plants that can
be sampled per plot drops as the biomass and average
plant size increases. The declining phase of some
unimodal relationships obtained using small quadrats
may therefore be due to sampling effects (Abrams 1995,
Oksanen 1996, Weiher 1999). Zobel and Liira (1997)
found that the peak in a unimodal relationship was
RESULTS
AND
DISCUSSION
Patterns
We accepted 60 reported relationships as robust tests
of the influence of productivity on species richness and
18 more as robust tests of the influence of biomass on
species richness. Together these account for 63.9% of
published relationships produced from unduplicated
data and with sample sizes .10. The proportion found
to be unsuitable for representing productivity–plant
species richness (P–PSR) relationships highlights the
need for care in selecting studies for meta-analysis. The
most sophisticated statistical procedures are invalid if
the experimental designs of the member studies do not
produce representative and unbiased data.
After selecting only those studies that have used
appropriate surrogates for productivity and adequate
controls for confounding factors, positive monotonic P–
PSR relations were more prevalent in the literature than
all other relationships combined (proportion test, P ,
0.05). This result was reflected in both coarse- and finegrain studies, although fine-grain studies produced more
unimodal and nonsignificant relationships than coarsegrain studies (Fig. 2). All P–PSR relationships at
continental to global geographic extents were positive
regardless of grain (Fig. 3). Almost all were also positive
at the regional scale (proportion test for positive
relations vs. all others combined, P ¼ 0.015). At local
to landscape geographic extents our study found that
nonsignificant, unimodal, and positive relationships
occurred with similar frequencies (Fig. 3). These results
differ substantially from previous reviews (Rosenzweig
1995, Waide et al. 1999, Mittelbach et al. 2001) that have
found a predominance of unimodal relationships,
especially at small spatial scales. The proportion of
species richness variability accounted for on average in
positive monotonic relationships (R2 ¼ 55.9%) was
almost twice that of unimodal relationships (31.8%; t
test: df ¼ 34, P ¼ 0.018), and although R2 values are not
directly comparable among different studies, they give
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LEN N. GILLMAN AND SHANE D. WRIGHT
Ecology, Vol. 87, No. 5
ships were due to factors other than productivity, these
studies had to be retained in the analysis.
Competitive exclusion hypothesis
FIG. 2. Productivity–species richness relationships at (A)
coarse grain (n ¼ 29) and (B) fine grain (n ¼ 31).
some indication of the strengths of the relationships. We
therefore conclude that, although unimodal P–PSR
relationships clearly occur, their prevalence and importance appear to have been overstated.
Biomass is often used as a surrogate for productivity
(50%, 41.7%, 33.3%, and 11.1% of negative, unimodal,
nonsignificant, and positive relationships, respectfully).
However, the proportion of species richness variability
accounted for on average by biomass was less than half
(20.8%) that for other surrogates of productivity (52.6%;
t test: df ¼ 20, P , 0.001). Grace (1999) also found that
biomass accounted for a low proportion of variation in
species richness (25%). We therefore suggest caution in
the use of biomass as a surrogate for productivity when
testing species richness relationships.
Despite the number of studies eliminated in our
survey, some of the relationships we retained may have
occurred due to factors other than productivity. For
example, diversity may increase within transition zones
between structurally different communities as one
vegetation type is replaced by another (Shmida and
Wilson 1985; Rey-Benayas and Scheiner 2002), and this
may or may not be due to productivity. Scheiner and
Jones (2002) suggested that the U-shaped relationships
they found might have been due to transition zones at
the high and low productivity ranges of their data.
However, in the absence of evidence that such relation-
The predicted unimodal relationship between productivity and species richness due to competitive exclusion
occurring at higher productivities has received considerable support. Furthermore, it has been suggested that
the true form of the relationship is unimodal and that
positive relationships at continental to global scales may
only occur because such studies span multiple biomes
(Latham and Ricklefs 1993, Ricklefs and Schluter 1993).
Competitive exclusion at higher productivities may occur
due to more intense competition (Grime 1973, 1979) or
because resource supply heterogeneity is reduced (Huston
1979, Tilman 1982, Tilman and Pacala 1993). However, at
local to landscape scales our results are equivocal, as
relationships that are inconsistent with competitive
exclusion at high productivities (positive and U-shaped)
outnumber those that are consistent with it (unimodal and
negative). At regional, continental, and global extents,
positive P–PSR relationships dominate almost exclusively
regardless of grain size and regardless of whether multiple
biomes are included or not. This implies that the most
productive terrestrial environments on earth for plants
are also the most species rich (Phillips et al. 1994).
If high productivity produces low diversity at scales of
small spatial extent and fine grain due to more intense
competition, or reduced heterogeneity in resource supply,
then we would expect competitive exclusion to be greatest
at sites with the highest terrestrial productivities on the
globe and for fine-grain diversity to be especially
depressed at these sites. Therefore, theories predicting
unimodal relationships due to greater competition at
high productivities are inconsistent with the exclusively
positive monotonic relationships found in fine-grain
studies at continental to global extents. It could be
argued, however, that positive relationships at continental to global scales are due to greater spatial heterogeneity
in the wet tropics. However, there is no underlying
gradient in fine-grain spatial heterogeneity associated
with productivity at continental to global extents other
than that due to greater biotic diversity (Rohde 1992).
Steiner and Leibold (2004) suggest that gamma
diversity increases monotonically with productivity at
larger scales (in contrast to unimodal relationships at
smaller scales) due to increasing beta diversity. They
invoke cyclic assembly trajectories to explain greater
beta diversity at high productivities. However, this does
not explain why alpha diversity also increases monotonically with productivity at regional to global extents.
The species pool hypothesis
Several studies have confirmed a strong relationship
between regional species pools for habitats of particular
productivities and local diversity (Gough et al. 1994,
Safford et al. 2001). Schamp et al. (2003) argued that the
unimodal relationship they found was due to the rarity of
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PRODUCTIVITY AND PLANT SPECIES RICHNESS
1239
FIG. 3. Productivity–species richness relationships at (A) continental to global extents (n ¼ 13), (B) regional extent (n ¼ 19), and
(C) landscape to local extents (n ¼ 28). Productivity–species richness and biomass–species richness relationships were combined for
(D) regional extent (n ¼ 23) and (E) landscape to local extents (n ¼ 42). There were no biomass–species richness studies at the
continental to global extents. NS indicates nonsignificant relationships.
extremely high- and low-productivity habitats in space
and time and the consequential lack of historical
opportunity for the evolution of species adapted to these
habitat types. Less abundant habitats within a region are
also likely to be more isolated from each other, and
therefore colonization from other patches may be limited.
Similarly, the negative relationship between rainfall and
species richness in inselbergs reported by Porembski et al.
(1995) may reflect the reduced area of inselbergs
associated with greater rainfall. It is therefore possible
that some of the variety in P–PSR relationships could be
due to the effects of available habitat area and species
pool size associated with particular productivities, rather
than a direct effect due to the productivity rate.
However, for the species pool hypothesis to explain
monotonic increases in fine-grain species richness with
increasing productivity at continental to global extents,
regional species pools must also increase monotonically
in all cases. We are then left without an explanation for
the monotonic increase in regional pools unless we
invoke other mechanisms. Two such mechanisms consistent with the species pool effect (species-energy and
rates of evolution) are discussed further (see Results and
Discussion: Species-energy hypothesis and Rates of
evolution hypothesis) A third is that greater regional
pools occur in the highly productive tropics due to a
greater available biome area (Rosenzweig and Abramsky 1993). However, at the continental to global scale,
P–PSR relationships are positive even when biome area
decreases with increasing productivity (Currie and
Paquin 1987, Rohde 1997).
Species-energy hypothesis
The predominance of positive P–PSR relationships
found at global to regional scales in this review are
consistent with the species-energy hypothesis (Wright et
al. 1993). Unimodal relationships at smaller scales may
also be consistent with this theory, because the density of
plants must increase with species richness as productivity
increases from zero, and then at higher productivities
plant densities usually peak and then decline as biomass
and productivity continue to increase (Tilman and
Pacala 1993). There are, however, two problems with
this theory: (1) the differences in organism densities
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LEN N. GILLMAN AND SHANE D. WRIGHT
between tropical and extratropical regions are insufficient to explain the differences in species richness (Currie
et al. 2004); (2) the theory assumes that species richness is
in equilibrium with climate, whereas Benton (1995) has
demonstrated that despite periodic mass extinctions
there is no basis to assume equilibrium, as global
diversity has grown exponentially over the last 600 Myr
(cf. Purvis and Hector 2000).
Rates of evolution hypothesis
This theory states that evolution occurs more rapidly
in more productive environments and that this leads to
greater rates of speciation and species accumulation
(Rohde 1992, Losos and Schulter 2000, Hubbell 2001,
Wright et al. 2003, Gillooly et al. 2005). It also
encompasses the concept of evolutionary time over
which speciation has occurred (Rohde 1992). Given that
molecular evolution is necessary for speciation in
allopatric populations (Martin and McKay 2004) and
for both reproductive isolation and speciation in
sympatric populations, it might be expected that greater
rates of molecular evolution lead to greater rates of
speciation. Environments with higher productivities
may produce greater rates of molecular evolution
because metabolic rates are higher, and the rate of
nucleotide substitution is correlated with metabolic rate,
or because generation times are shorter (Rohde 1992,
Martin and Palumbi 1993, Gillooly et al. 2005).
Elevated species accumulation has been linked to rapid
rates of molecular evolution (Palumbi 1996, Barraclough and Savolainen 2001, Webster et al. 2003), and
there is some evidence to support a relationship between
rates of molecular evolution and productivity (Bleiweiss
1998, Wright et al. 2003, Gillooly et al. 2005). There is
also evidence that diversification rates in the productive
tropics is greater than at higher latitudes (Stehli and
Wells 1971, Jablonski 1993, Briggs 1999, Cardillo 1999,
Buzas et al. 2002).
At continental to global extents higher productivities
may produce greater rates of molecular evolution,
speciation, and species richness. Our result showing that
all P–PSR relationships at these scales are positive is
therefore consistent with this theory. However, at local
to regional extents, biologically available energy is
unlikely to differ sufficiently to have a significant effect
on rates of evolution. However, increasing productivity
can be associated with increasing mass per individual as
vegetation transitions from herbs to shrubs and then
trees, and greater mass is predicted to produce slower
rates of molecular evolution (Gillooly et al. 2005). In
many cases generation times may also lengthen with
productivity at these scales. Therefore, we would expect
more studies that include a mixture of herbs, shrubs, and
trees at local to regional extents to show a decline in
species richness at high productivities as average mass
increases (i.e., unimodal and negative relationships) than
those that only sampled shrubs, or trees, or herbs. As
predicted at local to regional extents, we found positive
Ecology, Vol. 87, No. 5
and U-shaped studies were less prevalent when a
combination of herbs, shrubs, or trees was included
(46.9%) than in studies in which only one of these lifeforms was included (86.7%). Conversely, unimodal and
negative relationships were more prevalent in studies that
used mixed life-forms (53.1%) than for studies that used
one life-form (13.3%) (v2 ¼ 6.714, df ¼ 1, P ¼ 0.010).
There are alternative explanations for this result.
However, future P–PSR studies that test for the influence
of the average rate of molecular evolution, metabolic
rate, mass, and generation time may be of value.
CONCLUSION
In contrast to previous studies that have found the
relationship between productivity and plant species
richness to be predominantly unimodal, this study
found that positive relationships were dominant. Furthermore the form of relationships found at continental
to global extents was exclusively positive. Variable rates
of molecular evolution provide a plausible explanation
for the observed productivity–plant species richness
patterns found at small and large extents, and yet this
mechanism has received little attention. The species pool
hypothesis also provides a plausible explanation for our
results. Therefore future investigations that measure
variables thought to influence rates of molecular
evolution, and species pools (such as regional habitat
area), may be instructive.
ACKNOWLEDGMENTS
We thank Rob Whittaker, Brad Hawkins, J. Stephen Brewer,
and one anonymous reviewer for helpful comments on an
earlier version of this paper. We thank Brian McArdle for
helpful comments and statistical advice.
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APPENDIX A
Summary assessments of productivity–plant species richness relationships included in Mittelbach et al. (2001) (Ecological
Archives (E087-073-A1).
APPENDIX B
Summary assessments of productivity–plant species richness relationships not included in Mittelbach et al. (2001) (Ecological
Archives E087-073-A2).
APPENDIX C
Citations for studies included in Appendices A and B (Ecological Archives E087-073-A3).