Valuing species on the cheap

How do we make conservation decisions for poorly known taxa (Raphael & Molina, 2007)? Evolutionary isolation – how isolated a species is on the tree of life – has been touted as a coarse-grained but relatively cheap conservation prioritization metric (Redding & Mooers, 2010). However, prices are relative: building the robust, complete, timecalibrated phylogenies that are deemed necessary to calculate evolutionary isolation is still grueling, and timeconsuming. In this issue, Curnick et al. (2015) suggest that shortcuts are possible, and that we might be able to get useful information on evolutionary isolation without needing to produce these full trees. If true, this would be very helpful. Curnick and colleagues use the roughly 1000 species of understudied but ecologically important and threatened (Carpenter et al., 2008) scleractinian corals as a test case. They report that prioritization lists based on taxonomic data combined with older and very sparse trees were similar to lists generated from a more comprehensive ‘reference’ tree. Experts armed with only taxonomic data and their natural history knowledge seemed to do well at predicting which species were most evolutionarily isolated too. Given the uncertainties associated with other measures of worth, with conservation costs, and with probabilities of success, and given the lateness of the hour, perhaps we should use the precautionary principle and start estimating evolutionary isolation values for poorly sampled groups. Curnick et al.’s measure of evolutionary isolation (often called evolutionary distinctiveness or ED) captures how many close relatives a species has: members in species-poor taxa score high, and members of species-rich taxa score low. Because random samples from larger trees should retain relative taxa sizes, smaller and sparser ‘random sample’ trees can indeed produce similar isolation rankings to full trees (consider Curnick et al.’s Fig. 2a and the grey squares in their Fig. 3a). This is a very useful observation. However, using scores from these random sample trees to impute scores for missing species did not work as well (consider Curnick et al.’s Fig. 2b and the grey squares in their Fig. 3b). For this, it seems that phylogenetic over-dispersed sampling is required. Such sampling means that very small taxa – potentially the ones containing species with highest ED – can be retained in the sparse trees; these may be needed to produce the significant correlations that Curnick et al. report. As an example, consider Curnick et al.’s smallest tree of 127 species (from Fukami et al., 2008). We found that more than 10% of the genera and families are significantly overrepresented when compared with the scleractinian taxonomy (from Huang, 2012) using hypergeometric tests. Specifically, smaller taxa are more over-represented (correlations of the degree of representation vs. taxon size: Spearman’s rho = 0.65 and 0.44, for genera and families, respectively). As suggested by Cusimano & Renner (2010), over-dispersed sampling might be common in sparse trees. This could be easily tested. The ED measure also makes use of the ages of taxa: given two genera of the same size, members of older genus would score higher because its members are more distantly related one to another. However, given that coral experts do fairly well by predicting that a particular taxon (a family, say) branches early in the tree, ED may be robust to imprecise age information. Simple simulations seem to support this conjecture: Pearson correlation coefficients of ED scores on simulated (Yule) trees versus scores on versions that have been stripped of branch length information (using the common ‘Grafen’ transformation in the R package ape) are often greater than 0.6 across a wide range of tree sizes. Taken together, patterns of over-dispersion and the information content in topologies suggest that one might be able to produce useful ED scores using only (1) the number of subtaxa (species, genera, families, etc.) in each higher taxon (genera, family, orders, etc.); (2) rough ages of those higher taxa, perhaps augmented with a simple equation or two (e.g. flowing from Stadler & Steel, 2012) linking age and species richness to expected ED. This could be tested immediately, both with simulations (e.g. using code from Stadler et al., 2014) and with existing large trees (e.g. for birds and mammals). If these inputs produce ED scores that are highly correlated with true values, we could roll up our sleeves and produce prioritization scores for many groups in short order. Invertebrates would indeed be a good place to start. bs_bs_banner