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ORCID
https://orcid.org/0000-0003-1175-0694
Access Type
Open Access Thesis
Document Type
thesis
Degree Program
Organismic & Evolutionary Biology
Degree Type
Master of Science (M.S.)
Year Degree Awarded
2021
Month Degree Awarded
September
Abstract
Ecological inquiry is rooted fundamentally in understanding population abundance, both to develop theory and improve conservation outcomes. Despite this importance, estimating abundance is difficult due to the imperfect detection of individuals in a sample population. Further, accounting for space can provide more biologically realistic inference, shifting the focus from abundance to density and encouraging the exploration of spatial processes. To address these challenges, Spatial Capture-Recapture (“SCR”) has emerged as the most prominent method for estimating density reliably. The SCR model is conceptually straightforward: it combines a spatial model of detection with a point process model of the spatial distribution of individuals, using data collected on individuals within a spatially referenced sampling design. These data are often coarse in spatial and temporal resolution, though, motivating research into improving the quality of the data available for analysis. Here I explore two related approaches to improve inference from SCR: sampling design and data integration. Chapter 1 describes the context of this thesis in more detail. Chapter 2 presents a framework to improve sampling design for SCR through the development of an algorithmic optimization approach. Compared to pre-existing recommendations, these optimized designs perform just as well but with far more flexibility to account for available resources and challenging sampling scenarios. Chapter 3 presents one of the first methods of integrating an explicit movement model into the SCR model using telemetry data, which provides information at a much finer spatial scale. The integrated model shows significant improvements over the standard model to achieve a specific inferential objective, in this case: the estimation of landscape connectivity. In Chapter 4, I close by providing two broader conclusions about developing statistical methods for ecological inference. First, simulation-based evaluation is integral to this process, but the circularity of its use can, unfortunately, be understated. Second, and often underappreciated: statistical solutions should be as intuitive as possible to facilitate their adoption by a diverse pool of potential users. These novel approaches to sampling design and data integration represent essential steps in advancing SCR and offer intuitive opportunities to advance ecological learning about spatial processes.
DOI
https://doi.org/10.7275/23682220.0
First Advisor
Chris Sutherland
Second Advisor
Joseph Elkinton
Third Advisor
Toni Lyn Morrelli
Fourth Advisor
Daniel Sheldon
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Dupont, Gaetan L., "Statistical Improvements for Ecological Learning about Spatial Processes" (2021). Masters Theses. 1120.
https://doi.org/10.7275/23682220.0
https://scholarworks.umass.edu/masters_theses_2/1120
Included in
Applied Statistics Commons, Design of Experiments and Sample Surveys Commons, Natural Resources and Conservation Commons, Population Biology Commons, Statistical Models Commons