Statistical ecology software development
Effective wildlife and natural resource management requires user-friendly software that makes state-of-the-art statistical tools accessible to natural resource managers and conservation practitioners. A key pillar of my research is developing computationally-efficient and accessible software to understand the ecological and anthropogenic drivers of species distributions, population dynamics, and biodiversity patterns.
I am the lead author and maintainer of the spOccupancy R package, which fits spatially explicit single-species, multi-species, and integrated Bayesian occupancy models (Doser et al. 2022 MEE), with an emphasis on making these complex statistical models accessible to ecologists and conservation practitioners that may lack extensive training in spatial statistics. With collaborators across the world, I am actively using spOccupancy to inform effective conservation and management approaches for birds, mammals, bats, crickets, invasive aquatic plants, and cartilaginous fish. For example, with collaborators at the University of Arkansas, we used spOccupancy within a scenario planning framework to understand the effects of multiple woodland restoration outcomes on bird communities in Arkansas, USA (Roberts et al. 2023 Restoration Ecology).
I am also the lead author and maintainer of the R package spAbundance that provides user-friendly approaches to efficiently fit spatially explicit single-species (i.e., univariate) and multi-species (i.e., multivariate) abundance-based generalized linear models, N-mixture models, and distance sampling models. spAbundance allows for robust modeling of multi-species abundance patterns, providing crucial insight into the patterns that determine population dynamics and ecological communities. I am actively using spAbundance to inform different conservation objectives for Middle Eastern and midwestern US butterfly communities, as well as forest communities across the western US. In our recent paper on integrated community models (Zipkin et al. 2023 Journal of Animal Ecology), we used spAbundance to estimate trends of 10 butterfly species across the Midwestern US using five data sources. See our recent preprint for more details on the package’s functionality.