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Statistical Improvements for Ecological Learning about Spatial Processes

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dc.contributor.advisorChris Sutherland
dc.contributor.advisorJoseph Elkinton
dc.contributor.advisorToni Lyn Morrelli
dc.contributor.advisorDaniel Sheldon
dc.contributor.authorDupont, Gaetan L
dc.contributor.departmentUniversity of Massachusetts Amherst
dc.contributor.departmentOrganismic & Evolutionary Biology
dc.date2024-03-28T19:34:22.000
dc.date.accessioned2024-04-26T18:08:56Z
dc.date.available2024-04-26T18:08:56Z
dc.date.submittedSeptember
dc.date.submitted2021
dc.description.abstractEcological 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.
dc.description.degreeMaster of Science (M.S.)
dc.identifier.doihttps://doi.org/10.7275/23682220.0
dc.identifier.orcidhttps://orcid.org/0000-0003-1175-0694
dc.identifier.urihttps://hdl.handle.net/20.500.14394/32788
dc.relation.urlhttps://scholarworks.umass.edu/cgi/viewcontent.cgi?article=2141&context=masters_theses_2&unstamped=1
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.source.statuspublished
dc.subjectSpatial capture-recapture
dc.subjectpopulation density
dc.subjectspatial sampling
dc.subjectoptimal design
dc.subjectdata integration
dc.subjectanimal movement
dc.subjectApplied Statistics
dc.subjectDesign of Experiments and Sample Surveys
dc.subjectNatural Resources and Conservation
dc.subjectPopulation Biology
dc.subjectStatistical Models
dc.titleStatistical Improvements for Ecological Learning about Spatial Processes
dc.typeopenaccess
dc.typearticle
dc.typethesis
digcom.contributor.authorisAuthorOfPublication|email:gdupont@umass.edu|institution:University of Massachusetts Amherst|Dupont, Gaetan L
digcom.identifiermasters_theses_2/1120
digcom.identifier.contextkey23682220
digcom.identifier.submissionpathmasters_theses_2/1120
dspace.entity.typePublication
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