Caroline M. Tucker

A guide to phylogenetic metrics for conservation, community ecology and macroecology

The use of phylogenies in ecology is increasingly common and has broadened our understanding of biological diversity. Ecological sub‐disciplines, particularly conservation, community ecology and macroecology, all recognize the value of evolutionary relationships but the resulting development of phylogenetic approaches has led to a proliferation of phylogenetic diversity metrics. The use of many metrics across the sub‐disciplines hampers potential meta‐analyses, syntheses, and generalizations of existing results. Further, there is no guide for selecting the appropriate metric for a given question, and different metrics are frequently used to address similar questions. To improve the choice, application, and interpretation of phylo‐diversity metrics, we organize existing metrics by expanding on a unifying framework for phylogenetic information.

Should Environmental Filtering be Abandoned

Environmental filtering, where the environment selects against certain species, is thought to be a major mechanism structuring communities. However, recent criticisms cast doubt on our ability to accurately infer filtering because competition can give rise to patterns identical to those caused by environmental filtering. While experiments can distinguish mechanisms, observational patterns are especially problematic. The environment determines community composition not only directly via survival, but also by influencing competition. If species population growth rates covary with environmental gradients, then outcomes of competitive exclusion will also vary with the environment. Here, we argue that observational studies remain valuable, but inferences about the importance of the environment cannot rely on compositional data alone, and that species abundances, population growth, or traits must be correlated with the environment.

pez: phylogenetics for the environmental sciences

UNLABELLED pez is an R package that permits measurement, modelling and simulation of phylogenetic structure in ecological data. pez contains the first implementation of many methods in R, and aggregates existing data structures and methods into a single, coherent package. AVAILABILITY AND IMPLEMENTATION pez is released under the GPL v3 open-source license, available on the Internet from CRAN (http://cran.r-project.org). The package is under active development, and the authors welcome contributions (see http://github.com/willpearse/pez). CONTACT will.pearse@gmail.com.

On the relationship between phylogenetic diversity and trait diversity

Niche differences are key to understanding the distribution and structure of biodiversity. To examine niche differences, we must first characterize how species occupy niche space, and two approaches are commonly used in the ecological literature. The first uses species traits to estimate multivariate trait space (so-called functional trait diversity, FD); the second quantifies the amount of time or evolutionary history captured by a group of species (phylogenetic diversity, PD). It is often-but controversially-assumed that these putative measures of niche space are at a minimum correlated and perhaps redundant, since more evolutionary time allows for greater accumulation of trait changes. This theoretical expectation remains surprisingly poorly evaluated, particularly in the context of multivariate measures of trait diversity. We evaluated the relationship between phylogenetic diversity and trait diversity using analytical and simulation-based methods across common models of trait evolution. We show that PD correlates with FD increasingly strongly as more traits are included in the FD measure. Our results indicate that phylogenetic diversity can be a useful surrogate for high-dimensional trait diversity, but we also show that the correlation weakens when the underlying process of trait evolution includes variation in rate and optima.

Is phylogenetic diversity a surrogate for functional diversity across clades and space

In the face of limited funding and widespread threats to biodiversity, conserving the widest possible variety of biological traits (functional diversity, FD) is a reasonable prioritization objective. Because species traits are often similar among closely related species (phylogenetic signal), many researchers have advocated for a phylogenetic gambit: maximizing phylogenetic diversity (PD) should indirectly capture FD. To our knowledge, this gambit has not been subject to a focused empirical test. Here we use data from >15,000 vertebrate species to empirically test it. We delineate >10,000 species pools and test whether prioritizing the most phylogenetically diverse set of species results in more or less FD relative to a random choice. We find that, across species pools, maximizing PD results in an average gain of 18% of FD relative to a random choice, suggesting that PD is a sound conservation prioritization strategy. However, this averaged gain hides important variability: for 10% of the species pools, maximizing PD can capture less FD than an averaged random scheme because of recent trait divergence and/or very strong trait conservatism. In addition, within a species pool, many random sets of species actually yield more FD than the PD-maximized selection, on average 36% of the time per pool. If the traits we used are representative of traits we wish to conserve, our results suggest that conservation initiatives focusing on PD will, on average, capture more FD than a random strategy, but this gain will not systematically yield more FD than random and thus can be considered risky.In the face of the biodiversity crisis, it is argued that we should prioritize species in order to capture high functional diversity (FD). Because species traits often reflect shared evolutionary history, many researchers have advocated for a “phylogenetic gambit”: maximizing phylogenetic diversity (PD) should indirectly capture FD. For the first time, we empirically test this gambit using data from >15,000 vertebrate species and ecologically-relevant traits. Maximizing PD results in an average gain of 18% of FD relative to random choice. However, this average gain hides the fact that in over 1/3 of the comparisons, maximum PD sets contain less FD than randomly chosen sets of species. These results suggest that, while maximizing PD protection can help to protect FD, it represents a risky strategy. Statement of authorship FM, MP, MC, SD, GVDR, RG, AOM, CT and WP conceived the design of the study. FM and GVDR conducted the analysis. FM, RG, MP and WP interpreted the results and wrote the first draft of the manuscript. All authors edited the final version. Data accessibility statement Most of the data is publicly available (see methods). The Fish data is available upon request. Code accessibility statement R functions developed in this paper are available at https://github.com/FloMazel/FD_PD_Max

Prioritizing phylogenetic diversity captures functional diversity unreliably

In the face of the biodiversity crisis, it is argued that we should prioritize species in order to capture high functional diversity (FD). Because species traits often reflect shared evolutionary history, many researchers have assumed that maximizing phylogenetic diversity (PD) should indirectly capture FD, a hypothesis that we name the “phylogenetic gambit”. Here, we empirically test this gambit using data on ecologically relevant traits from >15,000 vertebrate species. Specifically, we estimate a measure of surrogacy of PD for FD. We find that maximizing PD results in an average gain of 18% of FD relative to random choice. However, this average gain obscures the fact that in over one-third of the comparisons, maximum PD sets contain less FD than randomly chosen sets of species. These results suggest that, while maximizing PD protection can help to protect FD, it represents a risky conservation strategy.An ongoing conservation question is if we can maintain functional diversity by optimizing for preservation of phylogenetic diversity. Here, Mazel et al. show that functional diversity increases with phylogenetic diversity in some clades but not others, and thus could be a risky conservation strategy.

The Effect of Phylogenetic Uncertainty and Imputation on EDGE Scores

Faced with the challenge of saving as much diversity as possible given financial and time constraints, conservation biologists are increasingly prioritizing species on the basis of their overall contribution to evolutionary diversity. Metrics such as EDGE (Evolutionary Distinct and Globally Endangered) have been used to set such evolutionarily-based conservation priorities for a number of taxa, such as mammals, birds, corals, amphibians, and sharks. Each application of EDGE has required some form of correction to account for species whose position within the tree of life are unknown. Perhaps the most advanced of these corrections is phylogenetic imputation, but to date there has been no systematic assessment of both the sensitivity of EDGE scores to a phylogeny missing species, and the impact of using imputation to correct for species missing from the tree. Here we perform such an assessment, by simulating phylogenies, removing some species to make the phylogeny incomplete, imputating the position of those species, and measuring (1) how robust ED scores are for the species that are not removed and (2) how accurate the ED scores are for those removed and then imputed. We find that the EDGE ranking for species on a tree is remarkably robust to missing species from that tree, but that phylogenetic imputation for missing species, while unbiased, does not accurately reconstruct species’ evolutionary distinctiveness. On the basis of these results, we provide clear guidance for EDGE scoring in the face of phylogenetic uncertainty.

Embracing the Nonindependence of the Environmental Filter: A Reply to Responses

Unquestionably, the environment shapes the diversity and composition of ecological communities. Understanding environmental impacts on organisms has long occupied ecologists, however recent papers have argued that the traditional concept of an environmental filter that acts independent from biotic processes is difficult to quantify [1], and perhaps not meaningful to understanding community patterns [2]. Two commentaries [3,4] on our recent paper [2] reaffirm the need to consider the interdependence of environmental and biotic processes.

ecolottery: simulating and assessing community assembly with environmental filtering and neutral dynamics in R

We introduce the R package ecolottery dedicated to quick and efficient simulation of communities undergoing local neutral dynamics with environmentally filtered immigration from a reference species pool (spatially-implicit model). The package includes an Approximate Bayesian Computation (ABC) tool to estimate the parameters of these processes. We present the rationale of the approach and show examples of simulations and ABC analysis. The species in the reference pool differ in their abundances and trait values. Environmental filtering weights the probability of immigration success depending on trait values, while the descendants of established immigrants undergo neutral stochastic drift. The reference pool can be defined in a flexible way as representing, e.g., the composition of a broad biogeographical region, or available dispersers around local communities. The package provides a process-based alternative to the use of randomization-based null models. The package proposes a coalescent-based simulation algorithm that presents significant advantages over alternative algorithms. It does not require simulating community dynamics from an initial state forward in time but does still allow measurement of the influence of environmental filtering. Because of its high calculation speed, this approach allows simulating many communities within a reasonable amount of time. Diverse patterns of taxonomic, functional and phylogenetic compositions can be generated. The package can be used to explore the outcome of ecological and evolutionary processes playing at local and regional scales, and to estimate the parameters of these processes based on observed patterns. This article is protected by copyright. All rights reserved.