Developing statistical models to understand biodiversity across macroscales
Understanding the drivers of species distributions and biodiversity at macroscales is complicated by a variety of ecological and observational complexities, such as spatial autocorrelation, nonstationarity in species-environment relationships, and species interactions. In my work, I account for these complexities to provide a more complete understanding of macroscale biodiversity and inform effective monitoring and conservation approaches across spatial scales.
During my PhD, I built a community abundance model that accounted for imperfect detection to understand forest bird abundance trends of >100 species across a network of protected forests in the Northeastern US (Doser et al. 2021 Ecological Applications), which revealed abundance trends were heterogeneous across space but consistent among species within an area. I also developed a joint species distribution model that accounts for spatial autocorrelation, imperfect detection, and species correlations to yield more accurate estimates of biodiversity (Doser, Finley, Banerjee In Review).
I am currently working on a variety of projects in which I am leveraging spatially-explicit modeling approaches to understand biodiversity patterns across macroscales in a variety of taxa. With collaborators across Europe, Israel, and Michigan State University, I am leveraging a multi-species spatially-explicit abundance framework to quantify trends in Middle Eastern butterfly communities over the last 15 years and to understand how these trends relate to climate warming. With collaborators at MSU, Audubon, and the National Park Service, I am developing a multi-species spatially varying coefficients occupancy model to understand the nonstationary effects of climate and land-use/land cover on a community of 51 forest birds across the eastern US over the last 50 years, which will provide information on how global change effects on birds vary across space, time, and species traits. I am also working with collaborators across the US to develop a similar two-stage, spatially varying coefficients model to predict species distributions and species-specific biomass of 100 tree species across the continental US while accounting for species correlations, which will yield accurate and multi-scale estimates of above-ground biomass for a variety of conservation and natural resource objectives.