Development and Application of Novel Spatial Modeling Approaches in Service of Biodiversity Conservation and Policy

Human-induced global change has major impacts on the spatial distribution of species. Recent technological advances in spatial modeling have helped propel efforts to preserve biodiversity faced with an ever-increasing suite of conservation concerns. These models have the power to map species distributions affected by climate and land-use change, predict ranges of non-native species, detect the environmental variables imperative to persistence, and map movement and gene flow patterns across the landscape. Two common methods used in this field include species distribution models (SDMs) that map habitat suitability for species presence and landscape connectivity models that map movement and gene flow among suitable habitats. Chapter 2 utilizes SDMs to model habitat suitability for the world’s largest community of non-native lizard species, finding that SDMs are effective in predicting where exotic species are likely to establish but less so for which species will be the most successful invaders. My third chapter utilizes landscape genomic methods to further investigate post-invasion dispersal and spatial structure within this exotic lizard hotspot, finding that spread throughout the non-native range is: 1) likely influenced by human-aided dispersal and 2) differs between species with distinct ecological traits. Chapter 4 presents a novel conservation planning toolset in the form of an R software package called Habicon, which integrates habitat suitability and connectivity models to prioritize patches and corridors for landscape conservation design and demonstrates the package’s utility with a species of high conservation concern. Finally, I present Chapter 5 with a focus on imperiled species policy, where I conducted a meta-analysis of all 50 U.S. state imperiled species acts to assess legal biodiversity conservation in the U.S. Overall, my dissertation research explores the use of modern spatial modeling and data science methods and applies them to conservation issues in novel ways to further the progress of biodiversity conservation in an era of unprecedented loss.