Applying evolutionary theory to biodiversity and conservation

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My work integrates evolution and conservation. The basis of this work is phylogenetics, the science of inferring the family tree that links species one to another. Conservation and biodiversity studies have traditionally focused on species as the fundamental unit. However, an explicit evolutionary view captures the true complexity of biodiversity in a way that simple species counts cannot. That said, we still do not have a good roadmap of how past evolution (the tree) predicts this complexity. In this Discovery Grant cycle, I will investigate whether and how we can use phylogenies to more efficiently prioritize particular populations, species or areas for conservation activity and to predict the effects of biodiversity change under climate change. Efficient approaches are necessary to manage the ongoing loss of species and associated ecosystem services. They must also be based on fundamental evolution and ecological theory. Our recent work is showing promise, but current "tree thinking" tools are still rudimentary. Here I propose to merge what we have learned in conservation phylogenetics and what we can glean from networks and network theory: such a novel mixed approach should better capture ecological variation among species, and better capture genetic variation among populations within species. At the species level, we will identify which branches of the phylogeny in select taxa are most at risk of extinction, where the species that represent those branches are currently located, to what extent they are being adequately managed, and where they may move to under climate change. We will then ask whether those branches also represent at-risk ecologies – i.e. how well does "millions of years of evolution" capture "millions of years of adaptive change?" For instance, if phylogenies capture ecologies, then graphs of these ecologies should look more tree-like than network-like, and non-random losses of species should lead to concomitant non-random shrinking of total ecological variation. In a separate project, we will test whether the past history of high diversification is associated with greater genomic and adaptive change in a lineage. This mapping of history to change is necessary if we are to use phylogenies effectively to help manage and predict the future of biodiversity. Below the species level, we will pursue two novel projects that link tree thinking to conservation. Because endangered species legislation must pay attention to and rank populations for conservation attention, new tools that can query networks (representing overall genetic or adaptive genetic variation) for populations of imperiled species may be of use to policy makers. We will test how our new network-based tools for ranking populations do at capturing total variation under different biogeographic histories of these species on the landscape. Our second project considers the current connectedness of populations: network graph theory (as applied to, e.g. computer networks) can be harnessed to measure how changes to the landscape can affect the future genetic and demographic connectivity of isolated populations. Both these network-based studies are grounded in fundamental research, yet both have downstream applications of increased relevance in the unfolding Anthropocene.