https://cpw.cvlcollections.org/files/original/f788889e9be56ff74ec979505e37ee57.pdf 125f45f284704fefa31746d679747c12 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal Articles Description An account of the resource CPW peer-reviewed journal publications Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Geostatistical capture–recapture models Description An account of the resource Abstract: Methods for population estimation and inference have evolved over the past decade to allow for the incorporation of spatial information when using capture–recapture study designs. Traditional approaches to specifying spatial capture–recapture (SCR) models often rely on an individual-based detection function that decays as a detection location is farther from an individual’s activity center. Traditional SCR models are intuitive because they incorporate mechanisms of animal space use based on their assumptions about activity centers. We modify the SCR model to accommodate a wide range of space use patterns, including for those individuals that may exhibit traditional elliptical utilization distributions. Our approach uses underlying Gaussian processes to characterize the space use of individuals. This allows us to account for multimodal and other complex space use patterns that may arise due to movement. We refer to this class of models as geostatistical capture–recapture (GCR) models. We adapt a recursive computing strategy to fit GCR models to data in stages, some of which can be parallelized. This technique facilitates implementation and leverages modern multicore and distributed computing environments. We demonstrate the application of GCR models by analyzing both simulated data and a data set involving capture histories of snowshoe hares in central Colorado, USA. Bibliographic Citation A bibliographic reference for the resource. Recommended practice is to include sufficient bibliographic detail to identify the resource as unambiguously as possible. Hooten, M. B., M. R. Schwob, D. S. Johnson and J. S. Ivan. 2024. Geostatistical capture-recapture models. Spatial Statistics 59:100817. doi.org/10.1016/j.spasta.2024.100817 Creator An entity primarily responsible for making the resource Hooten, Mevin B. Schwab, Michael R. Johnson, Devin S. Ivan, Jacob S. Subject The topic of the resource Guassian process MCMC Population estimation Recursive Bayes Extent The size or duration of the resource. 13 pages Rights Information about rights held in and over the resource <a href="http://rightsstatements.org/vocab/InC-NC/1.0/">IN COPYRIGHT - NON-COMMERCIAL USE PERMITTED</a> Type The nature or genre of the resource Text Format The file format, physical medium, or dimensions of the resource application/pdf Language A language of the resource English Is Part Of A related resource in which the described resource is physically or logically included. Spatial Statistics Date Accepted Date of acceptance of the resource. Examples of resources to which a Date Accepted may be relevant are a thesis (accepted by a university department) or an article (accepted by a journal). 01/30/2024 Date Issued Date of formal issuance (e.g., publication) of the resource. 02/05/2024 Date Modified Date on which the resource was changed. 01/21/2024 Date Submitted Date of submission of the resource. Examples of resources to which a Date Submitted may be relevant are a thesis (submitted to a university department) or an article (submitted to a journal). 08/25/2023