Trees for fishes: The neglected role for phylogenetic comparative methods in fisheries science

Fisheries scientists compare processes among species to estimate species productivity, management reference points, and climate sensitivities. Ecologists have developed "phylogenetic comparative methods" (PCMs) to address these questions, but there is surprisingly little application of PCM within fisheries science. Here, I bridge this gap by introducing PCM (including Brownian motion, Ornstein-Uhlenbeck, and Pagel's kappa and lambda models for species covariance), thereby showing that PCM generalizes the nested taxonomic random effects that are commonly used in fisheries science. I next summarize phylogenetic structural equation models (PSEMs), which extend the linear models that are commonly used in fisheries. Finally, I re-analyse a high-quality database used to predict mortality rates from longevity and/or growth parameters. I specifically propose a PSEM that reverts to a longevity-based prediction when longevity information is available but uses phylogenetic corrected growth parameters otherwise. Using this single PSEM replaces the common practice of fitting and predicting using separate linear models depending upon what data are available for a given species. Cross-validation suggests that the relationship between log-mortality rate and longevity does not vary based on phylogeny, and therefore, linear models and PSEM both explain 82% of variance when longevity is available. When longevity is unavailable, by contrast, the linear model explains only 37% of variance while the PSEM explains 52% of variance, where this gain occurs from conditioning predictions on phylogenetic similarities. I therefore conclude that PCM and PSEM provide a general and user-friendly replacement for linear models and can improve performance for fisheries meta-analyses that are used for fisheries management applications. Phylogenetic comparative methods (PCM) are widely used in ecology but rarely applied in fisheries science. PCM combines information about evolutionary relatedness and ecological trade-offs among traits, and could improve foundational information used by fisheries biologists (e.g., relationships among size, growth, and reproductive traits) and fisheries managers (biological reference point proxies). I introduce PCM using easy-to-use software, and illustrate improvements relative to conventional linear models when estimating natural mortality rates using size and growth information.image