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A spatially explicit capture-recapture estimator for single-catch traps
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dc.contributor.author | Distiller, Greg | |
dc.contributor.author | Borchers, David Louis | |
dc.date.accessioned | 2015-10-21T15:40:01Z | |
dc.date.available | 2015-10-21T15:40:01Z | |
dc.date.issued | 2015-11 | |
dc.identifier.citation | Distiller , G & Borchers , D L 2015 , ' A spatially explicit capture-recapture estimator for single-catch traps ' , Ecology and Evolution , vol. 5 , no. 21 , pp. 5075-5087 . https://doi.org/10.1002/ece3.1748 | en |
dc.identifier.issn | 2045-7758 | |
dc.identifier.other | PURE: 212427725 | |
dc.identifier.other | PURE UUID: 42c9bc54-7554-4be7-b0a6-f08459486873 | |
dc.identifier.other | Scopus: 84945949562 | |
dc.identifier.other | WOS: 000364341400029 | |
dc.identifier.other | ORCID: /0000-0002-3944-0754/work/72842457 | |
dc.identifier.uri | https://hdl.handle.net/10023/7680 | |
dc.description | This work was part-funded by EPSRC grant EP/I000917/1. | en |
dc.description.abstract | 1. Single-catch traps are frequently used in live-trapping studies of small mammals. Thus far a likelihood for single-catch traps has proven elusive and usually the likelihood for multi-catch traps is used for spatially explicit capture-recapture (SECR) analyses of such data. Previous work found the multi-catch likelihood to provide a robust estimator of average density. 2. We build on a recently developed continuous-time model for SECR to derive a likelihood for single-catch traps. We use this to develop an estimator based on observed capture times and compare its performance by simulation to that of the multi-catch estimator for various scenarios with non-constant density surfaces. 3. While the multi-catch estimator is found to be a surprisingly robust estimator of average density, its performance deteriorates with high trap saturation and increasing density gradients. Moreover, it is found to be a poor estimator of the height (but not range) of the detection function. By contrast, the single catch estimators of density, distribution and detection function parameters are found to be unbiased or nearly unbiased in all scenarios considered. This gain comes at the cost of higher variance, so that despite the lower bias of the single-catch estimator of the density surface over space, its root mean squared error is similar to that of the multi-catch estimator. 4. If there is no interest in interpreting the detection function parameters themselves, and if density is expected to be fairly constantover the survey region, then the multi-catch estimator performs well with single-catch traps. However if accurate estimation of the detection function is of interest, or if density is expected to vary substantially in space, then there is merit in using the single-catch estimator when trap saturation is above about 60%. The estimator’s performance is improved if care is taken to place traps so as to span the range of variables that affect animal distribution. As a single-catch likelihood with unknown capture times remains intractable for now, researchers using single-catch traps should aim to incorporate timing devices with their traps. | |
dc.format.extent | 13 | |
dc.language.iso | eng | |
dc.relation.ispartof | Ecology and Evolution | en |
dc.rights | Copyright 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. | en |
dc.subject | Spatially explicit capture recapture (SECR) | en |
dc.subject | Density estimation | en |
dc.subject | Statistical methods | en |
dc.subject | Single-catch trap likelihood | en |
dc.subject | HA Statistics | en |
dc.subject | NDAS | en |
dc.subject.lcc | HA | en |
dc.title | A spatially explicit capture-recapture estimator for single-catch traps | en |
dc.type | Journal article | en |
dc.contributor.sponsor | EPSRC | en |
dc.description.version | Publisher PDF | en |
dc.contributor.institution | University of St Andrews. School of Mathematics and Statistics | en |
dc.contributor.institution | University of St Andrews. Marine Alliance for Science & Technology Scotland | en |
dc.contributor.institution | University of St Andrews. Scottish Oceans Institute | en |
dc.contributor.institution | University of St Andrews. Centre for Research into Ecological & Environmental Modelling | en |
dc.identifier.doi | https://doi.org/10.1002/ece3.1748 | |
dc.description.status | Peer reviewed | en |
dc.identifier.grantnumber | EP/I000917/1 | en |
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