Environmental predictors of global parrot (Aves: Psittaciformes) species richness and phylogenetic diversity

Author SummaryWhy are there more species in the tropics? This question has fascinated ecologists and evolutionary biologists for decades, generating hundreds of hypotheses, yet basic questions remain: Are rates of speciation higher in the tropics? Are rates of extinction higher in temperate regions? Do the tropics act as a source of diversity for temperate regions? We estimated rates of speciation, extinction, and range expansion associated with mammals living in tropical and temperate regions, using an almost complete mammalian phylogeny. Contrary to what has been suggested before for this class of vertebrates, we found that diversification rates are strikingly consistent with diversity patterns, with latitudinal peaks in species richness being associated with high speciation rates, low extinction rates, or both, depending on the mammalian order (rodents, bats, primates, etc.). We also found evidence for an asymmetry in range expansion, with more expansion “out of” than “into” the tropics. Taken together, these results suggest that tropical regions are not only a reservoir of biodiversity, but also the main place where biodiversity is generated.

Are polyploids really evolutionary dead-ends (again)? A critical reappraisal of Mayrose etal. ().

Latitudinal gradient in species richness

Three major patterns of diversity are defined and a general hypothesis proposed to explain them. These patterns include (a) macroevolutionary diversity, encompassing temporal changes in species richness within and among clades, (b) global diversity, in which gradients of diversity among communities or biotas vary spatially and temporally, and (c) Phanerozoic diversity, which describes largescale temporal variation in species richness within the entire biosphere. Current explanatory hypotheses for these patterns are generally formulated for systems that are assumed to be in a state of ecological equilibrium, with speciation and extinction rates being diversity-dependent. This paper describes an alternative model of diversification in which speciation and extinction rates are independent of standing diversity. It is postulated that speciation rate is controlled primarily by large-scale changes in lithospheric (geomorphological) complexity. This hypothesis is a deductive consequence of biological data showing that allopatric speciation is the general mode of differentiation, and of geological data showing that tectonic changes within the lithosphere are responsible for the formation of geographic and ecological barriers. This hypothesis makes a number of predictions about patterns of endemism, historical biogeography, and spatial gradients of diversity, and data consistent with these predictions are presented. Other potential regulators of speciation rate (degree of morphogenetic variability within species, behavioral-ecological variability within species, intensity of sexual selection) are discussed and their potential roles in shaping diversity patterns are evaluated. Although they may occasionally be important for explaining some intracladal patterns of diversification, they are insufficient by themselves to account for spatial patterns or long-term changes of diversity within biotas. Extinction rate is postulated to be controlled primarily by spatial and temporal changes in environmental harshness, particularly as the latter is manifested by gradients of temperature and moisture. Considerable neontological and paleontological data suggest that change in harshness is a major factor shaping temporal and spatial patterns of diversity through its effects on extinction rate. Other causal mechanisms of extinction, particularly the tectonic elimination of habitats, may be of importance for specific groups of organisms (e.g., marine shelf communities following continent-continent collisions) at specific localities and times (e.g., near a volcanic eruption) but are not likely to play as important a role as does change in harshness. Together, the two main controls on speciation and extinction define a diversity-independent process of diversification. The biosphere can be viewed as an open thermodynamic system that can be expected to grow in complexity (including diversity) through time as a result of the inflow of matter and energy. This increase in complexity is constrained by external factors (physical changes in the biosphere or

The global increase in species richness toward the tropics across continents and taxonomic groups, referred to as the latitudinal diversity gradient, stimulated the formulation of many hypotheses to explain the underlying mechanisms of this pattern. We evaluate several of these hypotheses to explain spatial diversity patterns in a butterfly family, the Nymphalidae, by assessing the contributions of speciation, extinction, and dispersal, and also the extent to which these processes differ among regions at the same latitude. We generate a time-calibrated phylogeny containing 2,866 nymphalid species (~45% of extant diversity). Neither speciation nor extinction rate variations consistently explain the latitudinal diversity gradient among regions because temporal diversification dynamics differ greatly across longitude. The Neotropical diversity results from low extinction rates, not high speciation rates, and biotic interchanges with other regions are rare. Southeast Asia is also characterized by a low speciation rate but, unlike the Neotropics, is the main source of dispersal events through time. Our results suggest that global climate change throughout the Cenozoic, combined with tropical niche conservatism, played a major role in generating the modern latitudinal diversity gradient of nymphalid butterflies.

GLOBAL VARIATION IN THE DIVERSIFICATION RATE OF PASSERINE BIRDS

Species richness (SR) and phylogenetic diversity (PD) are highly correlated measures of plant diversity. Each, by itself, is significantly associated with plant community biomass in biodiversity experiments. As presented by Cadotte (2015) and as we present below, reasonable but alternative analyses that attempt to control for this correlation in different ways provide contradictory or inconclusive support for the hypothesis that PD is superior to SR as a predictor of community biomass. In Venail et al. (2015), we re-analysed data from 16 experimental manipulations of grassland SR to look at how SR and PD influence variation in plant community biomass through time. Using four types of analyses, we showed that, after statistically controlling for variation in SR, PD was not related to community biomass or to the temporal stability of biomass. We did, however, find that SR tends to increase the biomass production of plant communities after controlling for PD. In his comment, Cadotte expressed two concerns about our analyses. One is that we used non-random subsets of experiments, rather than the full data set, for some of our analyses (types 2, 3). We were clear in stating these analyses were based on non-random subsets that were specifically chosen to minimize the SR–PD correlation and avoid problems associated with multicollinearity. We acknowledge that our tests are conservative, a cost of which is that they sacrifice statistical power while, at the same time, minimizing the chance of drawing an incorrect conclusion. But we disagree with Cadotte's suggestion that our use of non-random data subsets led to 'biased' conclusions, and demonstrate later in this response that his claim of bias is unsubstantiated. Cadotte's second concern was that our analyses did not account for differences in biomass across studies. This is an important criticism to consider; we made a mistake by not controlling for variation in biomass. To address this issue, Cadotte used mixed models where study was included as a random effect, and ran analyses that standardized biomass among sites. Collectively, these led Cadotte to conclude 'All analyses strongly support previous literature claims about the value of PD and I further show that: (i) PD provides a more powerful explanation of variation in biomass production than species richness; (ii) PD explains variation in biomass production after controlling for richness; and (iii) the use of data subsets inadvertently biased the conclusions'. We have two concerns with Cadotte's re-analysis. First, Cadotte's approach largely ignores the concerns we raised about multicollinearity. When two or more predictors exhibit a high degree of correlation, each predictor contains little unique information. As a result, it is difficult (if not impossible) to estimate their independent effects using statistical methods like multivariate or partial regression (Dormann et al. 2013). The consequences of multicollinearity include inflated error estimates that can alter conclusions about what predictors are significant or not, as well as unstable parameter estimates that can change in sign and magnitude with minor alterations to analyses (Graham 2003; Zuur, Ieno & Elphick 2010). Multicollinearity is a concern for the data set of Venail et al. (2015) because PD and SR are correlated with r = 0·90. We were concerned about drawing inferences from predictors that have little unique information, which is why we performed analyses that all attempted to hold one of the two predictors constant while examining the impact of the other. In contrast, Cadotte performed model selection using the full data set where the SR–PD correlation was r = 0·90. We remain sceptical of this approach because of the difficulties generating reliable estimates for strongly correlated predictors. A second issue with Cadotte's analyses, which we are guilty of for some analyses in our study, is the assumption that the relationship between biodiversity (PD or SR) and community biomass is linear. Most studies included in the Venail et al. data set have shown that the effect of biodiversity on community biomass is positive, but nonlinear and decelerating. For example, Cardinale et al. (2011) summarized the form of diversity–biomass relationships for 433 experimental manipulations of primary producer richness and concluded 'Of the studies that have shown a positive effect of producer diversity on producer biomass, 79% were best fit by some form of a positive but decelerating curve (log, power, or M-M functions, Fig. 5A)'. In contrast, only 13% of studies to date are best fit by linear relationships. We reran Cadotte's analyses after accounting for nonlinear relationships and found that most of his conclusions did not hold. Our modified analyses (provided in accompanying R-code) rerun the same analyses of Cadotte, which account for variation in biomass among studies, but using ln-transformed predictors to also account for positive, decelerating relationships. Cadotte's first set of analyses modelled biomass in experimental plots as linear functions of SR and/or PD with study included as a random effect to account for differences in biomass among sites. These produced an AIC of 10 216 and 10 194 for SR and PD, respectively, and an AIC of 10 196 for a model including both SR and PD as predictors. In contrast, the best model in our modified analyses included both ln-transformed SR and PD with an AIC of 10 184. This represents an improved fit to data compared to Cadotte's analyses, and confirms that failure to account for nonlinear relationships led to inferior models. After confirming that relationships between PD, SR and community biomass are better described by nonlinear models, we reran Cadotte's partial regression analyses which found that PD explains a significant fraction of the residual variation in biomass after accounting for effects of SR (F = 4·09, P = 0·04), but SR did not explain residual variation after accounting for effects of PD (F = 0·09, P = 0·77). Using ln-transformed predictors where the SR–PD correlation was lower (r = 0·70), we found that ln(PD) explained 0·05% of the variation unaccounted for by ln(SR) (F = 3·79, P = 0·052, R2 = 0·005). Yet, ln(SR) explained 1·4% of the residual variation in community biomass unaccounted for by ln(PD) (F = 12, P < 0·01, R2 = 0·014). Cadotte also reran our structural equation model (SEM), but used the full data set where the PD–SR correlation was r = 0·90. He accounted for variation among studies by scaling biomass to have a mean = 0 and SD = 1. Cadotte's SEM (reproduced in Fig. 1a) shows that PD explains a significant fraction of variation in scaled biomass and SD through time. In contrast, SR did not explain variation in either. We reran the same SEM on the full data set, but using ln-transformed predictors to account for nonlinear relationships. The modified SEM was a significantly improved fit over the linear version (compare χ2, P-values and AIC for Fig. 1a,b) and led to conclusions that were consistent with those from our original paper (Venail et al. 2015) where we found SR impacts community biomass, but PD does not. In contrast, PD affects the SD of biomass through time, but SR does not. In his final analysis, Cadotte tried to assess whether the five experiments included in our SEM were a 'biased' representation of the full set of 16 experiments. He chose 1000 random subsets of five experiments and, for each subset, ran two mixed effects models – one modelling biomass as a function of PD and one modelling biomass as a function of SR. He then calculated the difference in AIC for the two models. If ΔAIC was <0 (>0), this indicated PD (SR) was a better predictor of biomass for that random subset. The frequency distribution of ΔAIC values (Fig. 3 of his comment) is reproduced in Fig. 1c. The mean of this distribution was significantly <0, suggesting PD is a better predictor of biomass than SR in most random subsets of five experiments. In addition, the subset of five experiments used for our SEM was different than the overall distribution, suggesting biased selection. But Cadotte's conclusions about the 'representativeness' of the five experiments are overturned when we repeat the same analyses using ln-transformed predictor variables. Indeed, the balance of evidence favoured ln(SR) as the better model (Fig. 1d) with the distribution of ΔAIC values being significantly >0 (mean = +5·64, t = 12·06, P < 0·01). The value of ΔAIC for the subset of five experiments used in our SEM is near the centre of the distribution, indicating it was not a biased subset. So where do we stand in this exchange? Cadotte, Cardinale & Oakley (2008) found that PD was not only a significant predictor of community biomass in grassland biodiversity experiments, it explained ~2% more variation than SR. We (Venail et al. 2015) suggested that synthesis did not control for multicollinearity among predictors. When we (Venail et al. 2015) controlled for multicollinearity (but failed to account for biomass differences among studies), we found PD was not a significant predictor of community biomass or stability, whereas SR was. Cadotte argued in his comment that our new analyses were incorrect because we did not account for variation in biomass among studies, and were biased by our use of data subsets to control for multicollinearity. Cadotte's re-analyses led him to conclude that PD is not only significant, but is again a better predictor of community biomass than SR. We responded by pointing out that multicollinearity continues to be a concern about Cadotte's analyses, and his conclusions do not hold after accounting for nonlinear relationships between biodiversity and ecosystem functioning. Whether using the statistical approaches from our original paper (Venail et al. 2015) or model selection favoured by Cadotte, we are led to two conclusions: (i) either SR or PD can explain most of the variation in community biomass and stability on their own because they share so much information. However, (ii) when we examine their effects after statistically controlling for the other, there is little evidence that PD is a better predictor of ecological function than SR. SR is usually a significant predictor of community biomass and stability after controlling for variation in PD, whereas PD is often (though not always) non-significant after controlling for variation in SR. We would caution against interpreting these results as evidence that PD does not matter for ecosystem functioning. Cadotte is correct that experiments analysed to date have not been explicitly designed to test hypotheses about PD, and therefore, we will need studies that orthogonally manipulate PD and SR to fully resolve their relative importance. On the other hand, given the existing data and analyses, we think it is important that researchers refrain from claiming that phylogenetic diversity is a 'strong' predictor of ecosystem functioning, or a 'better' predictor than plant richness in grasslands. 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At present there are many animal phyla that contain only a few species. The existence of these small phyla can be used to test assumptions about speciation and extinction in multicellular animals.We first model the number of species in a monophyletic clade with a birth and death process that assumes rates of speciation and extinction are constant. If no new phyla have evolved since the Cambrian and speciation and extinction rates for minor phyla are similar to or greater than those estimated from fossils, then our model shows that the probabilities of minor phyla surviving to the present are small. Random variation in extinction and speciation rates does not improve the chances for persistence. If speciation rates exceed extinction rates at the initial radiation of the clade, but before the clade becomes large, speciation rates come to equal extinction rates and both are low, persistence from before the Ordovician up to the present becomes likely. These models show that if speciation and extinction rates are independent of the number of species in a clade, then conditions before the Ordovician strongly influence today's distribution of species among taxa.We also discuss a model in which speciation and extinction rates depend on the number of species in a clade. This alternative model can account for the persistence of phyla with few species to the present and predicts a short duration for phyla that did not exceed a threshold number of species.

Phylogenetic diversity (PD) captures the shared ancestry of species, and is increasingly being recognized as a valuable conservation currency. Regionally, PD frequently covaries closely with species richness; however, variation in speciation and extinction rates and/or the biogeographic history of lineages can result in significant deviation. Locally, these differences may be pronounced. Rapid recent speciation or high temporal turnover of lineages can result in low PD but high richness. In contrast, rare dispersal events, for example, between biomes, can elevate PD but have only small impact on richness. To date, environmental predictors of species richness have been well studied but global models explaining variation in PD are lacking. Here, we contrast the global distribution of PD versus species richness for terrestrial mammals. We show that an environmental model of lineage diversification can predict well the discrepancy in the distribution of these two variables in some places, for example, South America and Africa but not others, such as Southeast Asia. When we have information on multiple diversity indices, conservation efforts directed towards maximizing one currency or another (e.g. species richness versus PD) should also consider the underlying processes that have shaped their distributions.

The integration of Geographic Information System (GIS) methodology within a phylogenetic and statistical framework provides a background against which to evaluate the relationship between biogeographic changes and evolution in the fossil record. A case study based on patterns in Middle and Late Devonian phyllocarids (Crustacea) illustrates the usefulness of this integrated approach. Using a combined approach enhances determination of rates of biodiversity change and the relationship between biogeographic and evolutionary changes. Because the interaction between speciation and extinction rates fundamentally determines biodiversity dynamics, and speciation and extinction rates are influenced by the geographic ranges of component taxa, the relationship between biogeography and evolution is important. Furthermore, GIS makes it possible to quantify paleobiogeographic ranges.Phylogenetic biogeography resolved patterns of both vicariance and geodispersal and revealed that range expansions were more abundant than range contractions in Devonian phyllocarids. In addition, statistical tests on GIS-constrained species ranges and evolutionary-rate data revealed a relationship between increasing species' ranges and increases in both speciation and extinction rates. Extinction rate, however, increased more rapidly than speciation rate in the phyllocarids. The pattern of extinction rate increasing faster than speciation rate in the phyllocarids may illuminate aspects of the Late Devonian biodiversity crisis in particular, and protracted biodiversity crises in general.

Species‐specific diversification rates, or ‘tip rates’, can be computed quickly from phylogenies and are widely used to study diversification rate variation in relation to geography, ecology and phenotypes. These tip rates provide a number of theoretical and practical advantages, such as the relaxation of assumptions of rate homogeneity in trait‐dependent diversification studies. However, there is substantial confusion in the literature regarding whether these metrics estimate speciation or net diversification rates. Additionally, no study has yet compared the relative performance and accuracy of tip rate metrics across simulated diversification scenarios. We compared the statistical performance of three model‐free rate metrics (inverse terminal branch lengths; node density metric; DR statistic) and a model‐based approach (Bayesian analysis of macroevolutionary mixtures [BAMM]). We applied each method to a large set of simulated phylogenies that had been generated under different diversification processes. We summarized performance in relation to the type of rate variation, the magnitude of rate heterogeneity and rate regime size. We also compared the ability of the metrics to estimate both speciation and net diversification rates. We show decisively that model‐free tip rate metrics provide a better estimate of the rate of speciation than of net diversification. Error in net diversification rate estimates increases as a function of the relative extinction rate. In contrast, error in speciation rate estimates is low and relatively insensitive to extinction. Overall, and in particular when relative extinction was high, BAMM inferred the most accurate tip rates and exhibited lower error than non‐model‐based approaches. DR was highly correlated with true speciation rates but exhibited high error variance, and was the best metric for very small rate regimes. We found that, of the metrics tested, DR and BAMM are the most useful metrics for studying speciation rate dynamics and trait‐dependent diversification. Although BAMM was more accurate than DR overall, the two approaches have complementary strengths. Because tip rate metrics are more reliable estimators of speciation rate, we recommend that empirical studies using these metrics exercise caution when drawing biological interpretations in any situation where the distinction between speciation and net diversification is important.

The birth-death model is commonly used to infer speciation and extinction rates by fitting the model to phylogenetic trees with exclusively extant taxa. Recently, it was demonstrated that speciation and extinction rates are not identifiable if the rates are allowed to vary freely over time. The group of birth-death models that have the same likelihood is called a congruence class, and there is no statistical evidence to favor one model over the other. This issue has led researchers to question if and what patterns can reliably be inferred from phylogenies of only extant taxa and whether time-variable birth-death models should be fitted at all. We explore the congruence class in the context of several empirical phylogenies as well as hypothetical scenarios. For these empirical phylogenies, we assume that we inferred the true congruence class. Thus, our conclusions apply to any empirical phylogeny for which we robustly inferred the true congruence class. When we summarize shared patterns in the congruence class, we show that strong directional trends in speciation and extinction rates are shared among most models. Therefore, we conclude that the inference of strong directional trends is robust. Conversely, estimates of constant rates or gentle slopes are not robust and must be treated with caution. Interestingly, the space of valid speciation rates is narrower and more limited in contrast to extinction rates, which are less constrained. These results provide further evidence and insights that speciation rates can be estimated more reliably than extinction rates.

One of the most striking patterns observed among animals is that smaller-bodied taxa are generally much more diverse than larger-bodied taxa. This observation seems to be explained by the mere fact that smaller-bodied taxa tend to have an older evolutionary origin and have therefore had more time to diversify. A few studies, based on the prevailing null model of diversification (i.e. the stochastic constant-rate birth–death model), have suggested that this is indeed the correct explanation, and body-size dependence of speciation and extinction rates does not play a role. However, there are several potential shortcomings to these studies: a suboptimal statistical procedure and a relatively narrow range of body sizes in the analysed data. Here, we present a more coherent statistical approach, maximizing the likelihood of the constant-rate birth–death model with allometric scaling of speciation and extinction rates, given data on extant diversity, clade age and average body size in each clade. We applied our method to a dataset compiled from the literature that includes a wide range of Metazoan taxa (range from midges to elephants). We find that the higher diversity among small animals is indeed, partly, caused by higher clade age. However, it is also partly caused by the body-size dependence of speciation and extinction rates. We find that both the speciation rate and extinction rate decrease with body size such that the net diversification rate is close to 0. Even more interestingly, the allometric scaling exponent of speciation and extinction rates is approximately −0.25, which implies that the per generation speciation and extinction rates are independent of body size. This suggests that the observed relationship between diversity and body size pattern can be explained by clade age alone, but only if clade age is measured in generations rather than years. Thus, we argue that the most parsimonious explanation for the observation that smaller-bodied taxa are more diverse is that their evolutionary clock ticks faster.

Geographic patterns and climate correlates of the deviation between phylogenetic and taxonomic diversity for angiosperms in China

Assessing biodiversity with sound: Do acoustic diversity indices reflect phylogenetic and functional diversities of bird communities?