An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou
Accurately estimating abundance is a critical component of monitoring and recovery of rare and elusive species. Spatial capture–recapture (SCR) models are an increasingly popular method for robust estimation of ecological parameters. We provide an analytical framework to assess results from empirica...
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ftpubmed:oai:pubmedcentral.nih.gov:7593142 2023-05-15T18:04:23+02:00 An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou McFarlane, Samantha Manseau, Micheline Steenweg, Robin Hervieux, Dave Hegel, Troy Slater, Simon Wilson, Paul J. 2020-09-19 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7593142/ http://www.ncbi.nlm.nih.gov/pubmed/33144989 https://doi.org/10.1002/ece3.6797 en eng John Wiley and Sons Inc. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7593142/ http://www.ncbi.nlm.nih.gov/pubmed/33144989 http://dx.doi.org/10.1002/ece3.6797 © 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. CC-BY Ecol Evol Original Research Text 2020 ftpubmed https://doi.org/10.1002/ece3.6797 2020-11-08T01:40:13Z Accurately estimating abundance is a critical component of monitoring and recovery of rare and elusive species. Spatial capture–recapture (SCR) models are an increasingly popular method for robust estimation of ecological parameters. We provide an analytical framework to assess results from empirical studies to inform SCR sampling design, using both simulated and empirical data from noninvasive genetic sampling of seven boreal caribou populations (Rangifer tarandus caribou), which varied in range size and estimated population density. We use simulated population data with varying levels of clustered distributions to quantify the impact of nonindependence of detections on density estimates, and empirical datasets to explore the influence of varied sampling intensity on the relative bias and precision of density estimates. Simulations revealed that clustered distributions of detections did not significantly impact relative bias or precision of density estimates. The genotyping success rate of our empirical dataset (n = 7,210 samples) was 95.1%, and 1,755 unique individuals were identified. Analysis of the empirical data indicated that reduced sampling intensity had a greater impact on density estimates in smaller ranges. The number of captures and spatial recaptures was strongly correlated with precision, but not absolute relative bias. The best sampling designs did not differ with estimated population density but differed between large and small ranges. We provide an efficient framework implemented in R to estimate the detection parameters required when designing SCR studies. The framework can be used when designing a monitoring program to minimize effort and cost while maximizing effectiveness, which is critical for informing wildlife management and conservation. Text Rangifer tarandus PubMed Central (PMC) Ecology and Evolution 10 20 11631 11642 |
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Original Research |
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Original Research McFarlane, Samantha Manseau, Micheline Steenweg, Robin Hervieux, Dave Hegel, Troy Slater, Simon Wilson, Paul J. An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou |
topic_facet |
Original Research |
description |
Accurately estimating abundance is a critical component of monitoring and recovery of rare and elusive species. Spatial capture–recapture (SCR) models are an increasingly popular method for robust estimation of ecological parameters. We provide an analytical framework to assess results from empirical studies to inform SCR sampling design, using both simulated and empirical data from noninvasive genetic sampling of seven boreal caribou populations (Rangifer tarandus caribou), which varied in range size and estimated population density. We use simulated population data with varying levels of clustered distributions to quantify the impact of nonindependence of detections on density estimates, and empirical datasets to explore the influence of varied sampling intensity on the relative bias and precision of density estimates. Simulations revealed that clustered distributions of detections did not significantly impact relative bias or precision of density estimates. The genotyping success rate of our empirical dataset (n = 7,210 samples) was 95.1%, and 1,755 unique individuals were identified. Analysis of the empirical data indicated that reduced sampling intensity had a greater impact on density estimates in smaller ranges. The number of captures and spatial recaptures was strongly correlated with precision, but not absolute relative bias. The best sampling designs did not differ with estimated population density but differed between large and small ranges. We provide an efficient framework implemented in R to estimate the detection parameters required when designing SCR studies. The framework can be used when designing a monitoring program to minimize effort and cost while maximizing effectiveness, which is critical for informing wildlife management and conservation. |
format |
Text |
author |
McFarlane, Samantha Manseau, Micheline Steenweg, Robin Hervieux, Dave Hegel, Troy Slater, Simon Wilson, Paul J. |
author_facet |
McFarlane, Samantha Manseau, Micheline Steenweg, Robin Hervieux, Dave Hegel, Troy Slater, Simon Wilson, Paul J. |
author_sort |
McFarlane, Samantha |
title |
An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou |
title_short |
An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou |
title_full |
An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou |
title_fullStr |
An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou |
title_full_unstemmed |
An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou |
title_sort |
assessment of sampling designs using scr analyses to estimate abundance of boreal caribou |
publisher |
John Wiley and Sons Inc. |
publishDate |
2020 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7593142/ http://www.ncbi.nlm.nih.gov/pubmed/33144989 https://doi.org/10.1002/ece3.6797 |
genre |
Rangifer tarandus |
genre_facet |
Rangifer tarandus |
op_source |
Ecol Evol |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7593142/ http://www.ncbi.nlm.nih.gov/pubmed/33144989 http://dx.doi.org/10.1002/ece3.6797 |
op_rights |
© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1002/ece3.6797 |
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Ecology and Evolution |
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10 |
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20 |
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11631 |
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11642 |
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1766175752542224384 |