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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 1234 May 2006 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 1236 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 May 2006 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 1238 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 May 2006 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 1240 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. LITERATURE CITED Abrams, P. A. 1995. Monotonic or unimodal diversityproductivity gradients: what does competition theory predict? Ecology 76:2019–2027. Anderson, T. A., S. J. McNaughton, and M. E. Ritchie. 2004. Scale-dependent relationships between the spatial distribution of a limiting resource and plant species diversity in an African grassland ecosystem. Oecologia 139:277–287. Ashton, P. S. 1992. Species richness in plant communities. Pages 4–22 in S. K. Jain, editor. 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