Data from: Using partial aggregation in Spatial Capture Recapture
1. Spatial capture-recapture (SCR) models are commonly used for analyzing data collected using non-invasive genetic sampling (NGS). Opportunistic NGS often leads to detections that do not occur at discrete detector locations. Therefore, spatial aggregation of individual detections into fixed detecto...
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ftdryad:oai:v1.datadryad.org:10255/dryad.179606 2023-05-15T16:32:21+02:00 Data from: Using partial aggregation in Spatial Capture Recapture Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J. Andrew Bischof, Richard Norway 2018-06-19T20:17:33Z http://hdl.handle.net/10255/dryad.179606 https://doi.org/10.5061/dryad.pd612qp unknown doi:10.5061/dryad.pd612qp/1 doi:10.5061/dryad.pd612qp/2 doi:10.1111/2041-210x.13030 doi:10.5061/dryad.pd612qp Milleret C, Dupont P, Brøseth H, Kindberg J, Royle JA, Bischof R (2018) Using partial aggregation in spatial capture recapture. Methods in Ecology and Evolution 9(8): 1896-1907. http://hdl.handle.net/10255/dryad.179606 wolverines Article 2018 ftdryad https://doi.org/10.5061/dryad.pd612qp https://doi.org/10.5061/dryad.pd612qp/1 https://doi.org/10.5061/dryad.pd612qp/2 https://doi.org/10.1111/2041-210x.13030 2020-01-01T16:08:59Z 1. Spatial capture-recapture (SCR) models are commonly used for analyzing data collected using non-invasive genetic sampling (NGS). Opportunistic NGS often leads to detections that do not occur at discrete detector locations. Therefore, spatial aggregation of individual detections into fixed detectors (e.g. center of grid cells) is an option to increase computing speed of SCR analyses. However, it may reduce precision and accuracy of parameter estimations. 2. Using simulations, we explored the impact that spatial aggregation of detections has on a trade-off between computing time and parameter precision and bias, under a range of biological conditions. We used three different observation models: the commonly used Poisson and Bernoulli models, as well as a novel way to partially aggregate detections (Partially Aggregated Binary model (PAB)) to reduce the loss of information after aggregating binary detections. The PAB model divides detectors into K subdetectors and models the frequency of subdetectors with more than one detection as a binomial response with a sample size of K. Finally, we demonstrate the consequences of aggregation and the use of the PAB model using NGS data from the monitoring of wolverine (Gulo gulo) in Norway. 3. Spatial aggregation of detections, while reducing computation time, does indeed incur costs in terms of reduced precision and accuracy, especially for the parameters of the detection function. SCR models estimated abundance with a low bias (< 10%) even at high degree of aggregation, but only for the Poisson and PAB models. Overall, the cost of aggregation is mitigated when using the Poisson and PAB models. At the same level of aggregation, the PAB observation models out-performs the Bernoulli model in terms of accuracy of estimates, while offering the benefits of a binary observation model (less assumptions about the underlying ecological process) over the count-based model. 4. We recommend that detector spacing after aggregation does not exceed 1.5 times the scale-parameter of the detection function in order to limit bias. We recommend the use of the PAB observation model when performing spatial aggregation of binary data as it can mitigate the cost of aggregation, compared to the Bernoulli model. Article in Journal/Newspaper Gulo gulo Dryad Digital Repository (Duke University) Norway |
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wolverines Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J. Andrew Bischof, Richard Data from: Using partial aggregation in Spatial Capture Recapture |
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wolverines |
description |
1. Spatial capture-recapture (SCR) models are commonly used for analyzing data collected using non-invasive genetic sampling (NGS). Opportunistic NGS often leads to detections that do not occur at discrete detector locations. Therefore, spatial aggregation of individual detections into fixed detectors (e.g. center of grid cells) is an option to increase computing speed of SCR analyses. However, it may reduce precision and accuracy of parameter estimations. 2. Using simulations, we explored the impact that spatial aggregation of detections has on a trade-off between computing time and parameter precision and bias, under a range of biological conditions. We used three different observation models: the commonly used Poisson and Bernoulli models, as well as a novel way to partially aggregate detections (Partially Aggregated Binary model (PAB)) to reduce the loss of information after aggregating binary detections. The PAB model divides detectors into K subdetectors and models the frequency of subdetectors with more than one detection as a binomial response with a sample size of K. Finally, we demonstrate the consequences of aggregation and the use of the PAB model using NGS data from the monitoring of wolverine (Gulo gulo) in Norway. 3. Spatial aggregation of detections, while reducing computation time, does indeed incur costs in terms of reduced precision and accuracy, especially for the parameters of the detection function. SCR models estimated abundance with a low bias (< 10%) even at high degree of aggregation, but only for the Poisson and PAB models. Overall, the cost of aggregation is mitigated when using the Poisson and PAB models. At the same level of aggregation, the PAB observation models out-performs the Bernoulli model in terms of accuracy of estimates, while offering the benefits of a binary observation model (less assumptions about the underlying ecological process) over the count-based model. 4. We recommend that detector spacing after aggregation does not exceed 1.5 times the scale-parameter of the detection function in order to limit bias. We recommend the use of the PAB observation model when performing spatial aggregation of binary data as it can mitigate the cost of aggregation, compared to the Bernoulli model. |
format |
Article in Journal/Newspaper |
author |
Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J. Andrew Bischof, Richard |
author_facet |
Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J. Andrew Bischof, Richard |
author_sort |
Milleret, Cyril |
title |
Data from: Using partial aggregation in Spatial Capture Recapture |
title_short |
Data from: Using partial aggregation in Spatial Capture Recapture |
title_full |
Data from: Using partial aggregation in Spatial Capture Recapture |
title_fullStr |
Data from: Using partial aggregation in Spatial Capture Recapture |
title_full_unstemmed |
Data from: Using partial aggregation in Spatial Capture Recapture |
title_sort |
data from: using partial aggregation in spatial capture recapture |
publishDate |
2018 |
url |
http://hdl.handle.net/10255/dryad.179606 https://doi.org/10.5061/dryad.pd612qp |
op_coverage |
Norway |
geographic |
Norway |
geographic_facet |
Norway |
genre |
Gulo gulo |
genre_facet |
Gulo gulo |
op_relation |
doi:10.5061/dryad.pd612qp/1 doi:10.5061/dryad.pd612qp/2 doi:10.1111/2041-210x.13030 doi:10.5061/dryad.pd612qp Milleret C, Dupont P, Brøseth H, Kindberg J, Royle JA, Bischof R (2018) Using partial aggregation in spatial capture recapture. Methods in Ecology and Evolution 9(8): 1896-1907. http://hdl.handle.net/10255/dryad.179606 |
op_doi |
https://doi.org/10.5061/dryad.pd612qp https://doi.org/10.5061/dryad.pd612qp/1 https://doi.org/10.5061/dryad.pd612qp/2 https://doi.org/10.1111/2041-210x.13030 |
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1766022108118253568 |