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- MATERIALS AND METHODS
Aim To analyse the global patterns in species richness of Viperidae snakes through the deconstruction of richness into sets of species according to their distribution models, range size, body size and phylogenetic structure, and to test if environmental drivers explaining the geographical ranges of species are similar to those explaining richness patterns, something we called the extreme deconstruction principle.
Methods We generated a global dataset of 228 terrestrial viperid snakes, which included geographical ranges (mapped at 1° resolution, for a grid with 7331 cells world-wide), body sizes and phylogenetic relationships among species. We used logistic regression (generalized linear model; GLM) to model species geographical ranges with five environmental predictors. Sets of species richness were also generated for large and small-bodied species, for basal and derived species and for four classes of geographical range sizes. Richness patterns were also modelled against the five environmental variables through standard ordinary least squares (OLS) multiple regressions. These subsets are replications to test if environmental factors driving species geographical ranges can be directly associated with those explaining richness patterns.
Results Around 48% of the total variance in viperid richness was explained by the environmental model, but richness sets revealed different patterns across the world. The similarity between OLS coefficients and the primacy of variables across species geographical range GLMs was equal to 0.645 when analysing all viperid snakes. Thus, in general, when an environmental predictor it is important to model species geographical ranges, this predictor is also important when modelling richness, so that the extreme deconstruction principle holds. However, replicating this correlation using subsets of species within different categories in body size, range size and phylogenetic structure gave more variable results, with correlations between GLM and OLS coefficients varying from –0.46 up to 0.83. Despite this, there is a relatively high correspondence (r = 0.73) between the similarity of GLM-OLS coefficients and R2 values of richness models, indicating that when richness is well explained by the environment, the relative importance of environmental drivers is similar in the richness OLS and its corresponding set of GLMs.
Main conclusions The deconstruction of species richness based on macroecological traits revealed that, at least for range size and phylogenetic level, the causes underlying patterns in viperid richness differ for the various sets of species. On the other hand, our analyses of extreme deconstruction using GLM for species geographical range support the idea that, if environmental drivers determine the geographical distribution of species by establishing niche boundaries, it is expected, at least in theory, that the overlap among ranges (i.e. richness) will reveal similar effects of these environmental drivers. Richness patterns may be indeed viewed as macroecological consequences of population-level processes acting on species geographical ranges.
- Top of page
- MATERIALS AND METHODS
There is a consensus among ecologists that diversity is not randomly distributed at broad scales in geographical space. For instance, it has been recognized since the 18th century that tropics possess more species than temperate regions for most kinds of organisms (Hawkins, 2001), although the detailed ecological and evolutionary mechanisms that may be underlying the so-called latitudinal diversity gradient are still debated (Hawkins et al., 2003; Willig et al., 2003; Hillebrand, 2004; Ricklefs, 2006; Mittelbach et al., 2007). Currently, a multicausal approach seems to be the most appropriate form of analysis for complex biological and ecological systems, as multiple causes can generate simultaneously the same patterns at different scales and for different groups of organisms (Elliot & Brook, 2007). In this type of approach, the relevant question is not if a given mechanism accounts for a given pattern, but rather what the relative importance of each one is to the overall patterns.
Marquet et al. (2004) also pointed out that part of the difficulty in finding overall explanations for broad-scale patterns of species richness is due to the fact that species richness is actually an ‘aggregate’ variable, usually obtained by overlapping geographical ranges (i.e. extents of occurrence) in macroecological studies. This overlap, in turn, may encompass groups of species with traits evolving in distinct ecological functions and under multiple evolutionary pathways, so that species and their overlap can present different geographical patterns and may respond in complex and different ways to environmental variation. Under niche conservatism, for example (see Wiens & Graham, 2005; Losos 2008a,b; Wiens, 2008), phylogenetically related species can have similar ecological requirements and environmental tolerances, and thus they should respond in more similar ways to environmental factors, creating as a consequence more similar patterns of geographical distribution that should overlap more frequently (increasing richness). Indeed, McPherson & Jetz (2007) showed that geographical range models vary among species with similar life-history characteristics.
Marquet et al. (2004) highlighted the advantages of partitioning richness patterns into sets of species that are defined according to particular traits (e.g. to be geographically restricted or widespread, small-bodied or large-bodied, exhibiting slow/short-dispersal or fast/long-dispersal). Under the assumption that these groups show more similar responses to intrinsic and extrinsic ecological processes, such as responses of abundance and distributions to environmental drivers, or extinction in the face of human pressures, it would be easier to interpret the factors driving species richness. Beyond other advantages, this ‘deconstructive’ approach can be used to clarify the causes underlying species richness patterns by framing questions concerning richness patterns in a clearer comparative way (see also Bini et al., 2004).
The approach of defining sets of similar species has actually been used for a long time (e.g. Badgley & Fox, 2000; Bhattarai &Vetaas, 2003), but the work by Marquet et al. (2004) tried to formalize the use of deconstructive analyses and a priori thinking in planned comparisons of richness patterns. Two examples from recent macroecological literature reinforce the utility of deconstructing species richness to help understand the mechanisms underlying its geographical patterns. Hawkins et al. (2005, 2006, 2007) used a ‘phylogenetic deconstruction’ and calculated geographical patterns of richness separately for basal and derived groups. In all cases, overall richness was more related to richness calculated from basal species, which reinforces the idea that these patterns are related to a niche conservatism process, as proposed by Wiens & Donoghue (2004). Jetz & Rahbek (2002; see also Rahbek et al., 2007) also showed that richness calculated using species with different geographical range sizes has different geographical patterns, so that richness from wide-ranging species clearly shows stronger correlations with environmental and climate variation than richness calculated for narrow-ranging species, which seem to be more randomly distributed in geographical space (see also Rahbek et al., 2007; but see Hawkins & Diniz-Filho, 2006, and Ruggiero & Hawkins, 2008, for an alternative explanation related to mountain effects). Even though these studies did not explicitly used the term ‘deconstruction’, they clearly show that the causes underlying large-scale richness patterns can be better understood by decomposing richness into historical or ecologically distinguished groups of species.
In this context, we follow here the suggestion of Marquet et al. (2004) and use several criteria to deconstruct global patterns in species richness of Viperidae snakes (Serpentes). Our previous analyses (Terribile et al., 2008) did not reveal strong evolutionary signals explaining richness patterns for this taxon and we found that present climate explains a reasonable amount of the variation in richness at global scales, as previously found for many other groups of organisms (see Hawkins et al., 2003). Thus, we initially tested the hypothesis that sets of species with different ecological and evolutionary characteristics (range size, body size and phylogenetic structure) have different richness patterns that can be explained by different sets of environmental factors.
However, the richness deconstruction is in some sense subjective, since there is not a clear criterion (or actually there may be many alternatives) for establishing sets of species based on their ecological characteristics. In the limit, each species has its own and particular ecological niche that determines its response to environment, being thus a unique ‘set’. Thus, here we have expanded and generalized the deconstructive approach to its theoretical limit by performing what can be called extreme deconstruction. The idea is to test if environmental predictors that are important to explain species geographical ranges are also important when modelling species richness. Thus, the ‘extreme deconstruction’ principle suggests that processes driving organism distributions in a lower hierarchical level (species geographical ranges) can be directly associated with environmental factors conditioning overlap of distributions at higher levels (species richness). There has been an increasing interest in techniques for modelling species geographical ranges (e.g. see Segurado & Araújo, 2004; Elith et al., 2006; Philips et al., 2006), but these techniques have been mainly used to estimate geographical ranges for conservation purposes or to predict shifts in geographical ranges or species richness after climate changes (Peterson & Vieglais, 2001; Araújo & Pearson, 2005; Thuiller et al., 2005; Araújo et al., 2006). Only a few papers have tried to relate the outputs of such models to the life history or ecological characteristics of species (e.g. see McPherson & Jetz, 2007).
Moreover, the logic of extreme deconstruction discussed here underlies recent simulation studies on richness patterns, in which environmental constraints are coupled with stochastic processes (dispersal) to define and establish limits for species geographical ranges. Although processes are established at range level, the analyses are performed to evaluate geographical patterns in species richness (Rangel & Diniz-Filho, 2005a; Storch et al., 2006; Rahbek et al., 2007; Rangel et al., 2007; see Dormann et al., 2007, and Kissling & Carl, 2008, for a completely different strategy of simulating patterns in species richness). Changing parameters of these simulations for limiting geographical ranges (environmental drivers or species tolerances to these drivers) generates different richness patterns (e.g. Rangel & Diniz-Filho, 2005b; Rangel et al., 2007).
Additionally, it is worthwhile mentioning that Araújo & Pearson (2005) showed that the current distribution of each species can be due, at least in part, to historical effects, thus increasing the list of elements that question the ‘equilibrium’ with current climate postulate (see Guisan & Thuiller, 2005), a basic assumption of species range bioclimatic modelling. So, it is expected that, for sets of species with high levels of non-equilibrium, a low environmental component for species richness is also observed. Therefore, we also tested here if lack of fit between current species geographical distribution and environment (which can in principle be interpreted as non-equilibrium) is related to a lower environmental component in species richness and, thus, indicates a larger number of historical effects explaining geographical patterns in species richness.