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Dey S, Moqanaki E, Milleret C, Dupont P, Tourani M, Bischof R. Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects. Ecol Modell 2023. [DOI: 10.1016/j.ecolmodel.2023.110324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
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Dupont PPA, Bischof R, Milleret C, Peters W, Edelhoff H, Ebert C, Klamm A, Hohmann U. An evaluation of spatial capture‐recapture models applied to ungulate non‐invasive genetic sampling data. J Wildl Manage 2023. [DOI: 10.1002/jwmg.22373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
- Pierre P. A. Dupont
- Faculty of Environmental Sciences and Natural Resource Management PB 5003, NO‐1432 Ås Norway
| | - Richard Bischof
- Faculty of Environmental Sciences and Natural Resource Management PB 5003, NO‐1432 Ås Norway
| | - Cyril Milleret
- Faculty of Environmental Sciences and Natural Resource Management PB 5003, NO‐1432 Ås Norway
| | - Wibke Peters
- Bavarian State Institute for Forestry Hans‐Carl‐von‐Carlowitzplatz 1 D‐85354 Freising Germany
| | - Hendrik Edelhoff
- Bavarian State Institute for Forestry Hans‐Carl‐von‐Carlowitzplatz 1 D‐85354 Freising Germany
| | - Cornelia Ebert
- Seq‐IT GmbH & Co. KG, Department of Wildlife Genetics Pfaffplatz 10 D‐67655 Kaiserslautern Germany
| | - Alisa Klamm
- Hainich National Park Bei der Marktkirche 9 D‐99947 Bad Langensalza Germany
| | - Ulf Hohmann
- Research Institute for Forest Ecology and Forestry Hauptstrasse 16 D‐67705 Trippstadt Germany
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Marrotte RR, Howe EJ, Beauclerc KB, Potter D, Northrup JM. Explaining detection heterogeneity with finite mixture and non-Euclidean movement in spatially explicit capture-recapture models. PeerJ 2022; 10:e13490. [PMID: 35694380 PMCID: PMC9186326 DOI: 10.7717/peerj.13490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 05/03/2022] [Indexed: 01/17/2023] Open
Abstract
Landscape structure affects animal movement. Differences between landscapes may induce heterogeneity in home range size and movement rates among individuals within a population. These types of heterogeneity can cause bias when estimating population size or density and are seldom considered during analyses. Individual heterogeneity, attributable to unknown or unobserved covariates, is often modelled using latent mixture distributions, but these are demanding of data, and abundance estimates are sensitive to the parameters of the mixture distribution. A recent extension of spatially explicit capture-recapture models allows landscape structure to be modelled explicitly by incorporating landscape connectivity using non-Euclidean least-cost paths, improving inference, especially in highly structured (riparian & mountainous) landscapes. Our objective was to investigate whether these novel models could improve inference about black bear (Ursus americanus) density. We fit spatially explicit capture-recapture models with standard and complex structures to black bear data from 51 separate study areas. We found that non-Euclidean models were supported in over half of our study areas. Associated density estimates were higher and less precise than those from simple models and only slightly more precise than those from finite mixture models. Estimates were sensitive to the scale (pixel resolution) at which least-cost paths were calculated, but there was no consistent pattern across covariates or resolutions. Our results indicate that negative bias associated with ignoring heterogeneity is potentially severe. However, the most popular method for dealing with this heterogeneity (finite mixtures) yielded potentially unreliable point estimates of abundance that may not be comparable across surveys, even in data sets with 136-350 total detections, 3-5 detections per individual, 97-283 recaptures, and 80-254 spatial recaptures. In these same study areas with high sample sizes, we expected that landscape features would not severely constrain animal movements and modelling non-Euclidian distance would not consistently improve inference. Our results suggest caution in applying non-Euclidean SCR models when there is no clear landscape covariate that is known to strongly influence the movement of the focal species, and in applying finite mixture models except when abundant data are available.
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Affiliation(s)
- Robby R. Marrotte
- Wildlife Research & Monitoring Section, Ministry of Northern Development, Mines, Natural Resources and Forestry, Peterborough, Ontario, Canada
| | - Eric J. Howe
- Wildlife Research & Monitoring Section, Ministry of Northern Development, Mines, Natural Resources and Forestry, Peterborough, Ontario, Canada
| | - Kaela B. Beauclerc
- Wildlife Research & Monitoring Section, Ministry of Northern Development, Mines, Natural Resources and Forestry, Peterborough, Ontario, Canada
| | - Derek Potter
- Wildlife Research & Monitoring Section, Ministry of Northern Development, Mines, Natural Resources and Forestry, Peterborough, Ontario, Canada
| | - Joseph M. Northrup
- Wildlife Research & Monitoring Section, Ministry of Northern Development, Mines, Natural Resources and Forestry, Peterborough, Ontario, Canada,Environmental and Life Sciences Graduate Program, Trent University, Peterborough, Ontario, Canada
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