The future of community phylogenetics

Community phylogenetics has received plenty of criticism over the last ten years (e.g. Mayfield and Levine, 2010; Gerhold et al. 2015). Much of the criticism is tied to concerns about pattern-based inference, the use of proxy variables, and untested assumptions. These issues are hardly unique to community phylogenetics, and I think that few ideas are solely ''good or solely 'bad'. They are useful in moulding our thinking as ecologists and inspiring new directions of thought. Many influential ideas in ecology have bobbled in confidence through time, but remain valuable nonetheless [e.g. interspecific competition, character displacement (Schoener 1982; Strong 1979)]. But still, it can be hard to see exactly how to use phylogenetic distances to inform community-level analyses in a rigorous way. Fortunately, there is research showing exactly this. The key, to me at least, to avoid treating a phylogeny as just another matrix to analyze, but to consider and test the mechanisms that might link the outcome of millions of years of evolution to community-level interactions.

A couple of potential approaches to move forward questions about community phylogenetics are discussed below. The first is to consider the mechanisms behind the pattern-inference analyses and ask whether assumptions hold.

1) Phylogenies and traits - testing assumptions about proxy value
As you know, if you have read the introductory paragraph of many community phylogenetic papers, Charles Darwin was the first to highlight that two closely related species might have different interactions than two distantly related species. People have tested this hypothesis in many ways in various systems, with mixed results. The most important directions forward is to make explicit the assumptions behind such ideas and experimentally test them. I.e. Do phylogenetic distances/divergence between species capture trait and ultimately ecological divergence between species?

From Kelly et al. 2015 Fig 1b.

Because evolutionary divergence should relate to feature divergence (sensu Faith), the most direct question to ask is how functionally important trait differences increase with increasing phylogenetic distances. For example, Kelly et al. (2014) found that “close relatives share more features than distant relatives but beyond a certain threshold increasingly more distant relatives are not more divergent in phenotype”, although in a limited test based only on patristic distances. This suggests that at short distances, phylogenetic distances may be a reasonable proxy for feature divergence, but that the relationship is not useful for making predictions about distant relatives.

Phylogenies and coexistence/competition. Ecological questions about communities may not be interested in traits alone. The key assumption behind many early analyses was that closely related species shared more similar *niches*, and so competed more strongly than distantly related species. Thus the question is one step removed from trait evolution, asking instead how phylogenetic divergence correlates into fitness differences or interaction strength. Not surprisingly, current papers suggest there is a fairly mixed, less predictable relationship between phylogenetic relatedness and competitive outcomes.

Recent findings have varied from “Stabilising niche differences were unrelated to phylogenetic distance, while species’ average fitness showed phylogenetic structure” (California grassland plants, Godoy et al. 2014); to, there is no signal in fitness or niche differences (algae species, Narwani et al. 2013); to, when species are sympatric, both stabilizing and fitness differences increase with phylogenetic distance (mediterranean annual plants; Germain et al. 2016). Given constraints, tradeoffs and convergence of strategies, it is really not surprising that the idea of simply inferring the importance of competition from patterns along a phylogenetic tree is not generally possible (Kraft et al. 2015; blogpost).

2) Phylogenies and the regional species pool
Really more interesting than testing for proxy value is to think about the mechanisms that tie evolution and community dynamics together. A key role for evolution in questions about community ecology is to ask what we can learn about the regional species pool—from which local communities are assembled. What information about the history of the lineages in a regional species pool informs the composition of local composition?

The character of the regional species pool is determined in part by the evolutionary history of the region, and this can in turn greatly constrain the evolutionary history of the community (Bartish et al. 2010). The abundance of past habitat types may alter the species pool, while certain communities may act as 'museums' harbouring particular clades. For example, Bartish et al. 2016 found that the lineages represented in different habitat types in a region differ in the evolutionary history they represent, with communities in dry habitats disproportionately including lineages from dry epochs and similar for wet habitats. Here, considering the phylogeny provides insight into the evolutionary component of an ecological idea like 'environmental filtering'.

Similarly, species pools are formed by both ecological processes (dispersal and constraints on dispersal) and evolutionary ones (extinctions, speciation in situ), and one suggestion is that appropriate null models for communities may need to consider both ecological and evolutionary processes (Pigot and Etienne, 2015).
Invasive species also should be considered in the context of evolution and ecology. Gallien et al. 2016 found that “currently invasive species belong to lineages that were particularly successful at colonizing new regions in the past.”

I think using phylogenies in this way is philosophically in line with ideas like Robert Ricklef's 'regional community' concept. The recognition is that a single time scale may be limiting in terms of understanding ecological communities.

References:

1. Mayfield, Margaret M., and Jonathan M. Levine. "Opposing effects of competitive exclusion on the phylogenetic structure of communities." Ecology letters 13.9 (2010): 1085-1093.
2. Gerhold, Pille, et al. "Phylogenetic patterns are not proxies of community assembly mechanisms (they are far better)." Functional Ecology 29.5 (2015): 600-614.
3. Schoener, Thomas W. "The controversy over interspecific competition: despite spirited criticism, competition continues to occupy a major domain in ecological thought." American Scientist 70.6 (1982): 586-595. 
4. Strong Jr, Donald R., Lee Ann Szyska, and Daniel S. Simberloff. "Test of community-wide character displacement against null hypotheses." Evolution(1979): 897-913. 
5. Kelly, Steven, Richard Grenyer, and Robert W. Scotland. "Phylogenetic trees do not reliably predict feature diversity." Diversity and distributions 20.5 (2014): 600-612.
6. Godoy, Oscar, Nathan JB Kraft, and Jonathan M. Levine. "Phylogenetic relatedness and the determinants of competitive outcomes." Ecology Letters17.7 (2014): 836-844.
7. Narwani, Anita, et al. "Experimental evidence that evolutionary relatedness does not affect the ecological mechanisms of coexistence in freshwater green algae." Ecology Letters 16.11 (2013): 1373-1381.
8. Rachel M. Germain, Jason T. Weir, Benjamin Gilbert. Species coexistence: macroevolutionary relationships and the contingency of historical interactions. Proc. R. Soc. B 2016 283 20160047
9. Nathan J. B. Kraft, Oscar Godoy, and Jonathan M. Levine. Plant functional traits and the multidimensional nature of species coexistence. 2015. PNAS.
10. Bartish, Igor V., et al. "Species pools along contemporary environmental gradients represent different levels of diversification." Journal of Biogeography 37.12 (2010): 2317-2331.
11. IV Bartish, WA Ozinga, MI Bartish, GW Wamelink, SM Hennekens. 2016. Different habitats within a region contain evolutionary heritage from different epochs depending on the abiotic environment. Global Ecology and Biogeography
12. Pigot, Alex L., and Rampal S. Etienne. "A new dynamic null model for phylogenetic community structure." Ecology letters 18.2 (2015): 153-163.
13. Gallien, L., Saladin, B., Boucher, F. C., Richardson, D. M. and Zimmermann, N. E. (2016), Does the legacy of historical biogeography shape current invasiveness in pines?. New Phytol, 209: 1096–1105.

More ways to understand traits in ecology

It seems that increasingly, ecology is moving away from relying primarily on summary statistics and approximations, to considering measures that recognize the often meaningful variation in ecological data. Using only the mean of a variable, for example, may be informative in some ways, but insufficient in others. Indices of diversity increasingly reflect that ecologically relevant information is not restricted to a single moment (as seen in the framework for measuring trait diversity (Villeger et al. (2008)) and the analogous framework for phylogenetic diversity (e.g. detailed in Pavoine and Bonsall (2011); also Tucker et al. (2016)).

Particularly, the functional ecology literature has developed increasingly complex and integrative methods for measuring and comparing trait diversity. The literature has gone from descriptions of general types or traits (e.g. Whittaker 1956), to measuring measuring individual traits and relating them to particular ecologically relevant variables (e.g. Gaudet and Keddy (1988)); to calculating community-weighted values for individual traits (e.g. D Schluter, (1986)); to incorporating multiple variables into single measures (e.g. FD package); to a framework reflecting mathematical moments in data (Villeger et al. (2008); and to the use of multivariate hypervolumes to describe the multi-dimensional shape and volume of trait space to be measured (e.g. Blonder et al. 2014).

A new paper in TREE does a nice job of summarizing and integrating these developments with yet another addition: a ‘trait probability density’ approach. In  “Traits Without Borders: Integrating Functional Diversity Across Scales", Carlos P. Carmona, Francesco de Bello, Norman W.H. Mason, and Jan Lepš nicely illustrate a way to capture the complexity inherent to a concept such as the ‘functional niche’. [The "region of the functional space containing all the trait combinations displayed by the individuals of a species"].

The truth about traits is that there is meaningful variation at every scale at which we measure them (including variation between individuals, variation between populations, variation between species, and variation between communities). Often decisions are made to ignore or collapse unwanted levels of variation (such as using a mean value across several individuals to calculate a single species-level value). The authors suggest that we can instead incorporate this variation appropriately. A probability density function can be defined for the multi-trait space, with probabilities representing the relative abundances of each combination of trait values. Thus, for a species, the curve (Figure IA) would show the multivariate trait space seen across all measured individuals, with uncommon combinations of traits seen in few individuals shown at the tails of the distribution. Outliers and extreme values are incorporated but not overemphasized as they can be in convex hull approaches.

The probabilistic approach reflects that a niche *is* probabilistic for a species - after all, it is unlikely that the niche is simply a fixed set of traits that is identical for all individuals or populations. However, not all combinations of trait values (niche dimensions) are equally likely for members of a species, and these curves reflect that. And when probabilities are incorporated into trait measurements, greatly different conclusions may be made about how similar or dissimilar assemblages may be (e.g. Fig IC).

Reproduced from Fig I., Carmona et al. 2016 TREE.

One concern--one that is pretty much universal to all analyses in functional ecology--is about how the biases and limitations of available data will affect this type of measure. Some species are better described, some traits are not available for most species, some studies lack interspecific measures, some lack local measures (relying instead on general databases of trait values). In addition, some intraspecific variation arises from other sources of noise like stochasticity and measurement error. This is all part of a bigger question about sufficient data: not only do we need to know how many traits are needed to define a species, but we need to decide how much and what kind of data is necessary to understand a trait…

Fig. 2 from Carmona et al. TREE 2016. It is possible to incorporate existing measures of functional diversity (richness, evenness, divergence) into the probabilistic definition.