Data from: 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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Main Authors: McFarlane, Samantha, Manseau, Micheline, Steenweg, Robin, Hervieux, Dave, Hegel, Troy, Slater, Simon, Wilson, Paul
Format: Other/Unknown Material
Language:unknown
Published: Zenodo 2021
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Online Access:https://doi.org/10.5061/dryad.v9s4mw6st
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spelling ftzenodo:oai:zenodo.org:5208078 2024-09-15T18:31:48+00:00 Data from: 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 2021-08-16 https://doi.org/10.5061/dryad.v9s4mw6st unknown Zenodo https://doi.org/10.1111/j.1755-0998.2012.03137.x https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.v9s4mw6st oai:zenodo.org:5208078 info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode info:eu-repo/semantics/other 2021 ftzenodo https://doi.org/10.5061/dryad.v9s4mw6st10.1111/j.1755-0998.2012.03137.x 2024-07-25T12:05:17Z 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 non-invasive 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 non-independence 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 were 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 files and R scripts for SCR analysis. Read attached README text file for additional information. This dataset is only available to the public at a summary resolution for the following reason. The spatial information held within this dataset ... Other/Unknown Material Rangifer tarandus Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language unknown
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 non-invasive 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 non-independence 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 were 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 files and R scripts for SCR analysis. Read attached README text file for additional information. This dataset is only available to the public at a summary resolution for the following reason. The spatial information held within this dataset ...
format Other/Unknown Material
author McFarlane, Samantha
Manseau, Micheline
Steenweg, Robin
Hervieux, Dave
Hegel, Troy
Slater, Simon
Wilson, Paul
spellingShingle McFarlane, Samantha
Manseau, Micheline
Steenweg, Robin
Hervieux, Dave
Hegel, Troy
Slater, Simon
Wilson, Paul
Data from: An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou
author_facet McFarlane, Samantha
Manseau, Micheline
Steenweg, Robin
Hervieux, Dave
Hegel, Troy
Slater, Simon
Wilson, Paul
author_sort McFarlane, Samantha
title Data from: An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou
title_short Data from: An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou
title_full Data from: An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou
title_fullStr Data from: An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou
title_full_unstemmed Data from: An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou
title_sort data from: an assessment of sampling designs using scr analyses to estimate abundance of boreal caribou
publisher Zenodo
publishDate 2021
url https://doi.org/10.5061/dryad.v9s4mw6st
genre Rangifer tarandus
genre_facet Rangifer tarandus
op_relation https://doi.org/10.1111/j.1755-0998.2012.03137.x
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https://doi.org/10.5061/dryad.v9s4mw6st
oai:zenodo.org:5208078
op_rights info:eu-repo/semantics/openAccess
Creative Commons Zero v1.0 Universal
https://creativecommons.org/publicdomain/zero/1.0/legalcode
op_doi https://doi.org/10.5061/dryad.v9s4mw6st10.1111/j.1755-0998.2012.03137.x
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