An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou
Abstract 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...
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doaj-e56368fe7a794057868bd695d2c3bd1f2021-04-02T19:24:16ZengWileyEcology and Evolution2045-77582020-10-011020116311164210.1002/ece3.6797An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribouSamantha McFarlane0Micheline Manseau1Robin Steenweg2Dave Hervieux3Troy Hegel4Simon Slater5Paul J. Wilson6Environmental and Life Sciences Department Trent University Peterborough Ontario CanadaEnvironmental and Life Sciences Department Trent University Peterborough Ontario CanadaFish and Wildlife Stewardship Branch Alberta Environment and Parks Grande Prairie AB CanadaFish and Wildlife Stewardship Branch Alberta Environment and Parks Grande Prairie AB CanadaRegional Resource Management Alberta Environment and Parks Edmonton AB CanadaFish and Wildlife Stewardship Branch Alberta Environment and Parks Edmonton AB CanadaEnvironmental and Life Sciences Department Trent University Peterborough Ontario CanadaAbstract 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.https://doi.org/10.1002/ece3.6797density estimationnoninvasive genetic samplingpopulation estimationprecisionspatial capture–recapturestudy design |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Samantha McFarlane Micheline Manseau Robin Steenweg Dave Hervieux Troy Hegel Simon Slater Paul J. Wilson |
spellingShingle |
Samantha McFarlane Micheline Manseau Robin Steenweg Dave Hervieux Troy Hegel Simon Slater Paul J. Wilson An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou Ecology and Evolution density estimation noninvasive genetic sampling population estimation precision spatial capture–recapture study design |
author_facet |
Samantha McFarlane Micheline Manseau Robin Steenweg Dave Hervieux Troy Hegel Simon Slater Paul J. Wilson |
author_sort |
Samantha McFarlane |
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 |
Wiley |
series |
Ecology and Evolution |
issn |
2045-7758 |
publishDate |
2020-10-01 |
description |
Abstract 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. |
topic |
density estimation noninvasive genetic sampling population estimation precision spatial capture–recapture study design |
url |
https://doi.org/10.1002/ece3.6797 |
work_keys_str_mv |
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