Anthropogenic climate change is impacting biodiversity at all scales. Detailed spatio-temporal information about geographic distributions of species will be critical to mitigating the ramifications of these impacts. The field of distributional ecology seeks to define and explain spatial and temporal variation in species’ distributions. Correlative ecological niche modelling (e.g., ecological niche modeling, species distributional modeling), which aims to characterize species’ ecological niches in environmental space, is a popular tool used to address questions regarding species’ distributions in geographic space. These approaches are powerful, capable of rendering conservation planning more understandable and accessible to diverse stakeholders; as such, they are increasingly incorporated into natural resources management and conservation planning. The traditional modelling framework uses primary biodiversity data in a time-averaged approach wherein covariate data for a relevant time period are averaged and treated as static to estimate a species’ niche in environmental space and project that the estimation onto the geographic landscape. However, these methods impose limitations on model output quality for highly mobile, behaviorally complex, and more ephemeral species. Improved methods can enhance understanding of macroscale factors driving distributional dynamics of these species to provide crucial information that will fill important knowledge gaps necessary to project and explore future distributional potential.
Here, I present a suite of studies aimed at optimizing the current correlative niche modeling frameworks to enhance performance for highly mobile species, emphasizing improvements using open source data and platforms. Focusing on pelagic seabirds, which often behave as generalists at the species level yet exhibit high degrees of intra-specific variation in behavior, my dissertation consists of three distinct components. Chapter 1 establishes a baseline of model performance under a seasonal, time-averaged modeling approach for the Wandering Albatross (Diomedea exulans). Chapter 2 introduces modifications to the data preparation process so as to incorporate the temporal dimension into the traditional niche modeling framework, using the Wood Thrush (Hylocichla mustelina) as a case study. Finally, Chapter 3 applies the improved data preparation workflow introduced in Chapter 2 to the study species for which baseline models were developed in Chapter 1—Diomedea exulans. Improved correlative niche models will be able to inform species-level management and policy development more effectively for highly mobile and/or migratory species, as well as disease vectors of public health interest.
|Advisor:||Peterson, A. Townsend|
|Commitee:||Billings, Sharon, Cartwright, Paulyn, Moyle, Robert, Stearns, Leigh|
|School:||University of Kansas|
|Department:||Ecology & Evolutionary Biology|
|School Location:||United States -- Kansas|
|Source:||DAI-B 82/9(E), Dissertation Abstracts International|
|Subjects:||Ecology, Animal sciences, Climate Change|
|Keywords:||Distributional ecology, Temporally-explicit niche modeling, Correlative ecological niche modeling, Species distribution, Biodiversity, Pelagic seabirds, Migratory species|
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