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© 2019 by Jason L. Brown

Research

Overview

Species and ecosystems are inherently connected to the environments in which they evolve. The central theme of my lab’s research aims to understand the interplay between spatial, genetic, ecological and evolutionary  processes. My lab integrates theoretical evolutionary and population genetic perspectives with geospatial, field-based and molecular genetic research to address fundamental questions regarding the evolution of molecules, organisms, populations, and ecosystems. We study the spatial patterns and processes driving community structure, distribution patterns, speciation, and biological diversity (genetic, phenotypic and behavioral). Most of the lab’s current research falls into two focal areas:

Conceptual foundations and methodological tools for quantifying the drivers of biodiversity patterns
Interplay between spatial, genetic and evolutionary processes

Understanding and predicting the spatio-temporal dynamics of populations remains a central goal in ecology. Much of my current research focuses on evaluating the genetic and demographic consequences of changes in habitat through time. The integration of geospatial and genetic data provide insights into the evolutionary mechanisms underlying recent divergences in ecology, phenotype and behavior (Brown et al. 2010 J. Biogeogr.; Brown & Knowles Mol. Ecol. 2012; Gehara, Summers & Brown 2013 Evo. Ecol.) and the genetic consequences of future climate change (Brown et al. 2016 Am. J. Botany).

 

To understand spatio-temporal dynamics of genetics and demographics, we sample the genotypes of populations using sub-genomic data (e.g. in Pika, a Penstemen flower, and anole lizards). We then couple species distribution models (SDMs), paleogeographic data, and demographic models to emulate the colonization history of focal species. Demographic models are based on empirical population data (i.e., growth and migration rates, population densities). To specify unknown demographic parameters, Approximate Bayesian Computation is used to identify the optimal parameters that are biologically relevant. Following demographic modeling, coalescent genealogies are parameterized by the simulated demographic history. The genetic simulations match conditions that correspond to empirical genetic data sets (e.g., population number and location, number and length of genes to be simulated, substitution model, and mutation and recombination rates of each gene). Using this framework (Fig. 1), researchers can create explicit predictions of demographic and genetic changes across a landscape. This framework can be used to test specific hypotheses or make predictions of spatial and/or temporal changes. For example, the explicit role of barriers (i.e., rivers, glaciers, mountains) in shaping the contemporary genetics of species can be tested, by comparing the empirical genetic data to the simulated genetic data using Bayes Factors Tests. These tests reject demographic histories that do not result in similar genetic values and marginal densities to the observed genetic data (Brown & Knowles Mol. Ecol. 2012; Prates, Brown et al. 2017 PNAS). This research provides a quantitative framework for studying speciation, geographic variation and genome evolution—providing explicit context between spatial, genetic and evolutionary process.

Quantifying Niche Divergence

Understanding the drivers of the distributions of species, communities and ecosystems remains a fundamental aim across ecology and evolution. Within this topic, one broad field of study seeks to characterize and compare the ecological niches of species, with the ultimate goal of assessing how niches evolve. In the last few years, in collaboration with A. Carnaval (City College of NY), we have been developing novel quantitative tests of niche similarity and niche divergence based on characterizations of niches in environmental space. These new quantitative methods address two major shortfalls of other methods that rely on assumptions of equilibrium distributions and are biased by the spatial abundance and availability of environments. Stay tuned for the release as an open-source R package.

Mechanisms of Biodiversity Patterns

Another focus of my research has been to measure and model biodiversity patterns of large taxonomic groups and their underlying evolutionary and ecological processes. For example, in collaboration with M. Vences (Technical U Braunschweig) and A. Yoder (Duke U), I evaluated the spatial distribution patterns of Madagascar’s amphibians, reptiles and lemurs. Madagascar contains an extraordinary number of endemic flora and fauna. Given the incredible levels of endemism and the long period of isolation from other land masses, this situation provides a unique opportunity to study the mechanisms driving divergence and diversification in situ. Furthermore, ongoing habitat destruction and climate change add urgency to studies of the flora and fauna of this remarkable and unique island country. The distribution of diverse organismal linages will depend upon the idiosyncratic factors determined by their specific organismal life-histories combined with stochastic historical factors, and therefore, various assemblages of species are under the influence of differing mechanisms. (Brown et al. 2014 Nature Comm.; Brown et al. 2016 Plos One). Thus, any model that endeavors to explain island-wide patterns must necessarily be complex.  

In collaboration with A. Carnaval, my research on this topic is extended to the incredibly diverse and threatened, Brazilian Atlantic Forest. We are studying how evolution within taxonomic groups shapes the broad-scale distribution of unique biological diversity across the landscape. In this research we move beyond traditional biodiversity measurements by quantifying and spatially modeling the phylogenetic diversity of six broad taxonomic groups spanning birds, plants to insects .  Overall, my research has identified areas, or species, of high conservative priority, many which are currently being consider by authorities or have already resulted in additional protection (e.g., Madagascar rosewoods species were CITES designated globally; Barrett, Brown & Yoder 2010 Science, 2013 Nature).  Many of our biodiversity quantification methods are released in a regularly updated and open-source, freely available ArcGIS package named SDMtoolbox (www.sdmtoolbox.org ).

Diversification, speciation, and behavior of South American poison frogs

Neotropical poison frogs ignited my passion for research and they continue to drive a core tenant of my research program. They are a species-rich family (Dendrobatidae) that exhibit a diversity of mating systems, parental care strategies, and phenotypes. These tiny frogs often use their bright coloration to warn predators of their toxicity. In many cases, with increased female parental care, female choice drives color and patterns of populations to diverge. Due to these factors, these abundant diurnal frogs are an ideal model for studying parental care and color evolution, breeding ecology, and speciation in natural systems.
 

Biogeography, Phylogenetics and Systematics

I have a keen interest in the evolutionary histories of poison frogs. Several of my current projects focus on the poison frog genera: Ameerega and Ranitomeya, which recently diverged into the east Andes versant. There they rapidly radiated into 45 aposematic species. We have already sequenced genomic data (UCEs) of over 750 tissue samples from all species in the genera from across the species’ ranges. I also have an extensive collection of acoustic and morphological data. These projects provide a rigorous framework for studying speciation, geographic variation – including widespread mimicry - and genome evolution. From a practical perspective, our systematic research clarifies the core evolutionary units, which gives meaning to the genes and genomic patterns we study and provides a starting point for biodiversity preservation.  Lastly, as with much of my research program, this provides an essential understanding why species are in their present locations.

Environmental, spatial, and behavioral factors driving species boundaries

The key factors that reinforce species boundaries for most species are poorly understood, particular of those in natural systems. We currently are working with closely related Ameerega species with distinct morphologies, distributions, and advertisement calls.  All these factors thought to reinforce species boundaries in amphibians.  Despite these distinctions, genomic data provide evidence of several pulses of historical hybridization between many species. We are currently investigating the synergy between: 1) the role of historic climate dynamism in these introgression events and 2) how the contemporary species phenotypes (acoustic and morphological) have maintained the mechanisms of speciation.  Our research on the  Ameerega petersi species complex, identified a key role for dynamism in climates, which cyclically isolated and connected sister species, resulting in bursts of introgression followed by periods of isolation (French et al., submitted). After each introgression period, the genomes of the other taxa were gradually absorbed, and largely dissolved, into the larger core population of the sister species (i.e., as observed in Neanderthals and humans)

Behavioral Ecology

The relationship between mating system and type of parental care, is of fundamental importance to understanding the ecology and evolution of breeding systems. A large part of my behavioral research examines how the nature of reproductive resources used by species influences the reproductive strategies of individuals, including mating system, form of parental care, and levels of sexual conflict. In Peru I worked with two sympatric species of closely related poison frogs that differ in parental care strategies. In collaboration with K. Summers (ECU), our results revealed that the transition to rearing tadpoles in tiny pools (mediated by competition and sexual conflict) drove the evolution of biparental care and monogamy. We also demonstrated the first case of brood parasitism (Brown et al. 2008 Biol. Lett.) and genetic monogamy in anurans (Brown et al. 2010 Am. Nat.).