This doctoral dissertation research project will investigate how highly mobile vertebrate species respond to environmental conditions at different spatial scales and will develop methods to integrate species responses across scales as well as to increase the accuracy of species distribution models. Understanding and predicting species distributions are longstanding goals in biogeography, and accurate distribution information is critical for biodiversity conservation. This project will address this objective by relating the geographic locations at which a species is observed to the environmental conditions at those locations across multiple scales. The doctoral student hypothesizes that a species will respond to environmental conditions relatively consistently across a subset of spatial scales that correspond to its physiological and behavioral traits and will respond to the same environmental conditions more weakly at other scales. She will measure these scales for select East African bird species by attaching GPS tags to individuals of each species and by analyzing the recorded movement paths. The student will produce gridded environmental data, such as land cover, vegetation properties, landscape structure, and distance to critical resources, from remotely sensed imagery. Movement data, occurrence data, and environmental data at several resolutions will be combined in hierarchical models to predict species distributions across multiple scales. To evaluate whether these methods increase model accuracy, she will test models using additional occurrence data and compare them to single-scale models.
By measuring the spatial scales at which vertebrate species respond to their environment and by assessing how responses at multiple scales affect broader distribution patterns, this project will increase understanding of fundamental ecological processes. This project will facilitate further scientific investigation by contributing species occurrence data; long-term movement data; and environmental data at multiple resolutions to open-access data collections from a geographic region that has not received significant scholarly attention in the past. Project results will be distributed to local, regional, and national conservation organizations as user-friendly databases and distribution maps that serve immediate conservation needs. By engaging Kenyan students in data collection and analysis, this project will enhance technical capabilities for biodiversity assessment and conservation in East African institutions, and it will expand collaborative relationships among U.S., Kenyan, and international research communities. As a Doctoral Dissertation Research Improvement award, this project will provide the support to enable a promising student to establish an independent research career.
