Hidden patterns of phylogenetic non‐stationarity overwhelm comparative analyses of niche conservatism and divergence

Abstract

Aim Despite the importance of the niche concept in ecological and evolutionary theory, there are still many discussions about its definition and operational evaluation, especially when dealing with niche divergence and conservatism in an explicit phylogenetic context. Here we evaluate patterns of niche evolution in 67 New World Carnivora species, measured using Hellinger distances based on MAXENT models of species distribution. We show how inferences on niche conservatism or divergence depend on the way phylogenetic patterns are analysed using matrix comparison techniques.Innovation Initially we used the simplest approach of Mantel tests to compare Hellinger distances (N) derived from MAXENT and phylogenetic distances (P) among species. Then we extended the Mantel test to generate a multivariate correlogram, in which phylogenetic patterns are analysed at multiple levels in the phylogeny and can reveal nonlinearity in the relationship between divergence and time. Finally, we proposed a new approach to generate 'local' (or 'specific') leverages of components for Mantel correlation, evaluating the non-stationarity in the relationship between N and P for each species. This new approach was used to show if some lineages are more prone to niche shift or conservatism than others.Main conclusions Standard Mantel tests indicated a poor correspondence between N and P matrices, discarding the idea of niche conservatism for Carnivora, but the correlogram supports that closely related species tend to be more similar than expected by chance. Moreover, the variance among Hellinger distances between pairs of closely phylogenetically related species is much larger than for the entire clade. Phylogenetic non-stationarity analysis shows that in some Carnivora families the niche tends to divergence (Mustelidae and Canidae), whereas in others it tends to conservatism (Procyonidae and Mustelidae) at short phylogenetic distances. Our analyses clearly show that misleading results may appear if niche divergence is analysed only by simple matrix correlations not taking into account complex patterns of phylogenetic nonlinearity and non-stationarity.

Quantifying community assembly processes and identifying features that impose them

“…We tested for phylogenetic signal to determine whether we could use phylogenetic turnover to make ecological inferences in our system, and to determine the most appropriate metric of phylogenetic turnover. We found significant phylogenetic signal, but only across relatively short phylogenetic distances (Figure 2), consistent with previous work (Andersson et al, 2010;Diniz-Filho et al, 2010;Hardy et al, 2012;Stegen et al, 2012). It is therefore most appropriate to quantify phylogenetic turnover among closest relatives (Stegen et al, 2012).…”

A comparison of metrics for estimating phylogenetic signal under alternative evolutionary models

“…The earliest techniques were based on statistical methods ( e.g. , phylogenetic autocorrelation coefficients, phylogenetic correlograms and autoregressive models), that quantify the level of phylogenetic autocorrelation for a given trait of interest throughout the phylogeny (see Cheverud et al , 1985; Gittleman and Kot, 1990; Diniz-Filho et al , 1998), or under non-stationary processes (Diniz-Filho et al , 2010). When more detailed and accurate phylogenies are available, it is also possible to ascertain the expected divergence between species by assuming a theoretical model of trait evolution, and thus derive model-based metrics to compare expected and observed divergences ( e.g.…”

Phylogenetic beta diversity in bacterial assemblages across ecosystems: deterministic versus stochastic processes

“…We partitioned phylogenetic distances into classes (that is, evolutionary time steps; here 0.02 units) and within each distance class we found the correlation coefficient relating between-OTU phylogenetic distances to environmental-optimum distances (DinizFilho et al, 2010). This method has the advantage of characterizing shifts in phylogenetic signal across phylogenetic distance classes (Diniz-Filho et al, 2010). An environmental-optimum for each OTU was found for each environmental variable as in Stegen et al (2012).…”

“…To evaluate phylogenetic signal across a range of phylogenetic depths, we used Mantel correlograms with 999 randomizations for significance tests (Oden and Sokal, 1986;Diniz-Filho et al, 2010) with the function 'mantel.correlog' in the R package Vegan v2.0-2 (http://vegan.r-forge.r-project.org). We partitioned phylogenetic distances into classes (that is, evolutionary time steps; here 0.02 units) and within each distance class we found the correlation coefficient relating between-OTU phylogenetic distances to environmental-optimum distances (DinizFilho et al, 2010).…”