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 an integrated community occupancy model that fuses multiple detection-nondetection data sources within a single statistical model to improve our ability to estimate species and biodiversity dynamics (Doser et al. 2022 Methods in Ecology and Evolution). As a postdoc, I 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 2023 Ecology). I have also developed new methodological approaches to estimate spatially-varying (or nonstationary) species-environment relationships (Doser et al. In Review JABES), as well as practical guidelines and recommendations for the use of these models in ecology and conservation (Doser et al. In Review GEB).
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 spatially-varying effects of climate and land-use change on over 300 bird species across the continental US over the last 20 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 quantify the spatially-varying relationship between vapor pressure deficit and the 50 most abundant tree species in the western US, which will provide key insight into how projected increases in vapor pressure deficit will impact forest communities across the western US.