Presentations and Posters
Geochemical Society Goldschmidt Conference, 2018 - Talk
Incorporating Soil, Geochemistry, and Topography, as Well as Climate in to Evolutionary Models Allows the Identification of Adaptive Processes in Plant Diversification
Soil, geochemistry, and topography are well known to be highly influential in mediating the diversity and distributions of many organisms, especially plants. But the data to explore these associations have typically been less accessible to biologists than climate data (e.g. via WorldClim). Using recently available global SoilGrids250, USGS geochemistry data, and AdaptWest land facets we show that substrate plays a significant role in explaining much of the species composition and turnover (beta diversity) in the North American daisy family (Compositae). Identifying which variables are significant and incorporating them into evolutionary and niche models allows us to identify previously obscured evolutionary processes. For example we show that similar morphotypes within the Hawaiian Silversword group of plants have evolved in parallel to occupy analogous environmental niches (common adaptive peaks), patterns that had been elusive under climate-only models. This highlights the importance of considering multiple forms of environmental data when exploring biological processes, and in continuing to work towards making harmonized, easily accessible, and interpretable soil and geochemistry data available for ecological analyses.
International Botanical Congress, 2011 - Talk
Speciation in the Australian Broadleaved Paperbark Complex (Myrtaceae)
Geochemical Society Goldschmidt Conference, 2018 - Poster
The Role of Geochemistry in the Diversification of North American Asteraceae
The largest family of flowering plants, Asteraceae, is an ideal model system to understand how the abiotic environment drives diversification, and to ask whether evolution proceeds in a predictable manner in response to environmental gradients. We used phylogenetic comparative methods to perform diversification analysis in each of multiple tribes of Asteraceae, assessing and comparing the relative role of climate, topography, and edaphic conditions, on diversification rate in North America. Calibrated phylogenies were generated for North American members of each Asteraceae tribe using publicly available sequence data. Plant species location data was extracted from digitized herbarium records, and climate, topography, soil, and geochemistry data were extracted for each location.
Individual linear and nonlinear phylogenetic generalized least squares (PGLS) regression was performed for each environmental variable of interest in each tribe, followed by model selection to determine the most appropriate shape to describe the relationship between environmental predictors and diversification rate. Finally, multiple PGLS regression and model selection were performed on variables demonstrating significant individual predictive power for diversification rate. Results give insights into environmental drivers of diversification within each tribe, and interaction among different environmental pressures.
Australian Systematic Botany Society Meeting, 2007 - Poster
Further Evidence for the non-monophyly of Melaleuca
Botany Conference, 2018 - Talk
Environmental Drivers of Species Diversity and Turnover in Large, Widespread Radiations of North American Plants (Compositae)
That the environment plays a large role in driving the diversity and distribution of species and communities is well accepted, however the specific environmental factors that drive groups of organisms to diversify remains of great interest to evolutionary and ecological research as well as for conservation and climate change studies. Typically, more readily available climate data (e.g. WorldClim) are used to investigate what predicts species distributions across large scales, however soil, geochemistry and landform, while intuitively important, are often neglected. We assembled the largest species-by-environment matrix for North America with 187 environmental variables associated with over 500,000 plant locality records and representing more than 3,000 species within 14 tribes of the Compositae (daisy family, with more than 1 in 10 flowering plants). Using these data, we have been able to identify which variables are associated with species diversity and turnover, how these interact across space and as the magnitudes of variables change, and find that soil and geochemistry both explain a large proportion of the signal of species diversity and turnover. Many tribes share broad commonalities (e.g. positive relationship between diversity and quartz concentrations) suggesting that innovations to the environment may have driven success within the entire daisy family, however different secondary variables are identified as significant within particular tribes, indicating that distinct environmental preferences may have helped drive subsequent specialization and diversification.
Botany Conference, 2018 - Poster
Patterns of Plant Endemism and Diversity in the Guiana Shield
The Guiana Shield is a vast region of mostly undisturbed forests and ancient geological formations covering nearly 2.3 million km2in northeast South America. It contains many varied ecosystems and has long been regarded as a region with high biodiversity. Yet, it has remained relatively unexplored and the spatial patterns of diversity and endemism are only broadly understood. Following 30 years of intensive collection and curation as part of the Biodiversity of the Guiana Shield (BDG) program, we use 77,973 plant occurrence records to investigate patterns of species diversity and endemism across the shield. Measures of species richness appear to be correlated such that areas of low diversity also have low endemism and vice versa, with regions in western Venezuela and eastern Guyana being highest for both. However, many areas in the study region have low or no species counts, likely an artifact of sampling effort. To better understand the spatial patterns in data-deficient areas, we use Generalized Dissimilarity Models (GDM) to predict plant diversity based on environmental variables.