Estimating density for species conservation: Comparing camera trap spatial count models to genetic spatial capture-recapture models

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dc.contributor.authorBurgar, Joanna M.
dc.contributor.authorStewart, Frances E.C.
dc.contributor.authorVolpe, John P.
dc.contributor.authorFisher, Jason T.
dc.contributor.authorBurton, A. Cole
dc.date.accessioned2018-10-29T23:46:32Z
dc.date.available2018-10-29T23:46:32Z
dc.date.copyright2018en_US
dc.date.issued2018
dc.description.abstractDensity estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of “marked” wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of “unmarked” wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipFunding for this project was provided by InnoTech Alberta grants to ACB and JTF; Government of Alberta (Environment and Parks), The Beaver Hills Initiative, Alberta Conservation Association grants to JTF; and NSERC (Canada), Royal Canadian Geographic Society, TD Friends of the Environment Foundation, and the Fur Institute of Canada scholarships to FECS. We thank M. Pybus, G. Hood, D. Vujnovic, and B. Eaton, for help with project oversight and design and field logistics, and M. McAdie, I. Brusselers, T. Zembal, S. Frey, N. Heim, and S. Murray for help with data collection. Research was permitted by Alberta Environment and Parks (16-004) and University of Alberta Animal Care Committee (AUP00000518).en_US
dc.identifier.citationBurgar, J.M., Stewart, F.E.C., Volpe, J.P., Fisher, J.T. & Burton, A.C. (2018). Estimating density for species conservation: Comparing camera trap spatial count models to genetic spatial capture-recapture models. Global Ecology and Conservation, 15, e00411. https://doi.org/10.1016/j.gecco.2018.e00411en_US
dc.identifier.urihttps://doi.org/10.1016/j.gecco.2018.e00411
dc.identifier.urihttp://hdl.handle.net/1828/10197
dc.language.isoenen_US
dc.publisherGlobal Ecology and Conservationen_US
dc.subjectBayesian estimation
dc.subjectCamera trap surveys
dc.subjectCost-effectiveness
dc.subjectNon-invasive genetic sampling
dc.subjectPekania pennanti
dc.subjectPopulation monitoring
dc.subjectWildlife conservation
dc.subject.departmentSchool of Environmental Studies
dc.titleEstimating density for species conservation: Comparing camera trap spatial count models to genetic spatial capture-recapture modelsen_US
dc.typeArticleen_US

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