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dc.contributor.authorPaterson, J. Terrill
dc.contributor.authorProffitt, Kelly
dc.contributor.authorJimenez, Ben
dc.contributor.authorRotella, Jay J.
dc.contributor.authorGarrott, Robert A.
dc.date.accessioned2019-06-07T19:11:16Z
dc.date.available2019-06-07T19:11:16Z
dc.date.issued2019-04
dc.identifier.citationPaterson, J. Terrill, Kelly Proffitt, Ben Jimenez, Jay Rotella, and Robert Garrott. "Simulation-based validation of spatial capture-recapture models: A case study using mountain lions." PloS one 14, no. 4 (April 2019). DOI:10.1371/journal.pone.0215458.en_US
dc.identifier.issn1932-6203
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/15492
dc.description.abstractSpatial capture-recapture (SCR) models have improved the ability to estimate densities of rare and elusive animals. However, SCR models have seldom been validated even as model formulations diversify and expand to incorporate new sampling methods and/or additional sources of information on model parameters. Information on the relationship between encounter probabilities, sources of additional information, and the reliability of density estimates, is rare but crucial to assessing reliability of SCR-based estimates. We used a simulation-based approach that incorporated prior empirical work to assess the accuracy and precision of density estimates from SCR models using spatially unstructured sampling. To assess the consequences of sparse data and potential sources of bias, we simulated data under six scenarios corresponding to three different levels of search effort and two levels of correlation between search effort and animal density. We then estimated density for each scenario using four models that included increasing amounts of information from harvested individuals and telemetry to evaluate the impact of additional sources of information. Model results were sensitive to the quantity of available information: density estimates based on low search effort were biased high and imprecise, whereas estimates based on high search effort were unbiased and precise. A correlation between search effort and animal density resulted in a positive bias in density estimates, though the bias decreased with increasingly informative datasets. Adding information from harvested individuals and telemetered individuals improved density estimates based on low and moderate effort but had negligible impact for datasets resulting from high effort. We demonstrated that density estimates from SCR models using spatially unstructured sampling are reliable when sufficient information is provided. Accurate density estimates can result if empirical-based simulations such as those presented here are used to develop study designs with appropriate amounts of effort and information sources.en_US
dc.description.sponsorshipFederal Aid Wildlife Restoration grant W-163-R-1en_US
dc.rightsCC BY: This license lets you distribute, remix, tweak, and build upon this work, even commercially, as long as you credit the original creator for this work. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/legalcodeen_US
dc.titleSimulation-based validation of spatial capture-recapture models: A case study using mountain lionsen_US
dc.typeArticleen_US
mus.citation.issue4en_US
mus.citation.journaltitlePloS Oneen_US
mus.citation.volume14en_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.identifier.doi10.1371/journal.pone.0215458en_US
mus.relation.collegeCollege of Letters & Scienceen_US
mus.relation.departmentEcology.en_US
mus.relation.universityMontana State University - Bozemanen_US
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CC BY: This license lets you distribute, remix, tweak, and build upon this work, even commercially, as long as you credit the original creator for this work. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.
Except where otherwise noted, this item's license is described as CC BY: This license lets you distribute, remix, tweak, and build upon this work, even commercially, as long as you credit the original creator for this work. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.