Biology subjects

Luke J Harmon

Publications and source records attributed to Luke J Harmon.

3 recordsLinked to original sources

bioRxiv2015

Radiating despite a lack of character: closely related, morphologically similar, co-occurring honeyeaters have diverged ecologically

The 75 species of Australian honeyeaters (Meliphagidae) are morphologically and ecologically diverse, with species feeding on nectar, insects, fruit, and other resources. We investigated ecomorphology and community structure of honeyeaters across Australia. First, we asked to what degree morphology and ecology (foraging behavior) are concordant. Second, we estimated rates of trait evolution. Third, we compared phylogenetic and trait community structure across the broad environmental gradients of continental Australia. We found that morphology explained 37% of the variance in ecology (and 62% vice versa), and that recovered multivariate ecomorphological relationships incorporated well-known bivariate relationships. Clades of large-bodied species exhibited elevated rates of morphological trait evolution, while members of Melithreptus showed slightly faster rates of ecological trait evolution. Finally, ecological trait diversity did not decline in parallel with phylogenetic diversity along a gradient of decreasing precipitation. We employ a new method (trait fields) and extend another (phylogenetic fields) to show that while species from phylogenetically clustered assemblages co-occur with morphologically similar species, these species are as varied in foraging behavior as those from more diverse assemblages. Thus, although closely related, these arid-adapted species have diverged in ecological space to a similar degree as their mesic counterparts, perhaps mediated by competition.

Ecology↗

bioRxiv2014

A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data

Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes from comparative data is complicated by interpretational difficulties, lack of identifiability among parameter values and the common requirement that adaptive hypotheses must be assigned a priori. Here we develop a reversible-jump Bayesian method of fitting multi-optima OU models to phylogenetic comparative data that estimates the placement and magnitude of adaptive shifts directly from the data. We show how biologically informed hypotheses can be tested against this inferred posterior of shift locations using Bayes Factors to establish whether our a priori models adequately describe the dynamics of adaptive peak shifts. Furthermore, we show how the inclusion of informative priors can be used to restrict models to biologically realistic parameter space and test particular biological interpretations of evolutionary models. We argue that Bayesian model-fitting of OU models to comparative data provides a framework for integrating of multiple sources of biological data--such as microevolutionary estimates of selection parameters and paleontological timeseries--allowing inference of adaptive landscape dynamics with explicit, process-based biological interpretations.

Evolutionary Biology↗

bioRxiv2014

Model adequacy and the macroevolution of angiosperm functional traits

Making meaningful inferences from phylogenetic comparative data requires a meaningful model of trait evolution. It is thus important to determine whether the model is appropriate for the data and the question being addressed. One way to assess this is to ask whether the model provides a good statistical explanation for the variation in the data. To date, researchers have focused primarily on the explanatory power of a model relative to alternative models. Methods have been developed to assess the adequacy, or absolute explanatory power, of phylogenetic trait models but these have been restricted to specific models or questions. Here we present a general statistical framework for assessing the adequacy of phylogenetic trait models. We use our approach to evaluate the statistical performance of commonly used trait models on 337 comparative datasets covering three key Angiosperm functional traits. In general, the models we tested often provided poor statistical explanations for the evolution of these traits. This was true for many different groups and at many different scales. Whether such statistical inadequacy will qualitatively alter inferences draw from comparative datasets will depend on the context. Regardless, assessing model adequacy can provide interesting biological insights -- how and why a model fails to describe variation in a dataset gives us clues about what evolutionary processes may have driven trait evolution across time.

Evolutionary Biology↗