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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ftdans:oai:easy.dans.knaw.nl:easy-dataset:108297 2023-07-02T03:32:29+02:00 Data from: Using partial aggregation in Spatial Capture Recapture Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J Bischof, Richard 2018-06-19T22:17:33.000+02:00 http://nbn-resolving.org/urn:nbn:nl:ui:13-dv-6igc https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:108297 unknown doi:10.5061/dryad.pd612qp/1 doi:10.5061/dryad.pd612qp/2 doi:10.1111/2041-210x.13030 http://nbn-resolving.org/urn:nbn:nl:ui:13-dv-6igc doi:10.5061/dryad.pd612qp https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:108297 OPEN_ACCESS: The data are archived in Easy, they are accessible elsewhere through the DOI https://dans.knaw.nl/en/about/organisation-and-policy/legal-information/DANSLicence.pdf Life sciences medicine and health care 2018 ftdans https://doi.org/10.5061/dryad.pd612qp/110.5061/dryad.pd612qp/210.1111/2041-210x.1303010.5061/dryad.pd612qp 2023-06-13T13:30:22Z 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 ... Other/Unknown Material Gulo gulo Data Archiving and Networked Services (DANS): EASY (KNAW - Koninklijke Nederlandse Akademie van Wetenschappen) Norway |
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Data Archiving and Networked Services (DANS): EASY (KNAW - Koninklijke Nederlandse Akademie van Wetenschappen) |
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Life sciences medicine and health care |
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Life sciences medicine and health care Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J Bischof, Richard Data from: Using partial aggregation in Spatial Capture Recapture |
topic_facet |
Life sciences medicine and health care |
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 ... |
author |
Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J Bischof, Richard |
author_facet |
Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J 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://nbn-resolving.org/urn:nbn:nl:ui:13-dv-6igc https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:108297 |
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 http://nbn-resolving.org/urn:nbn:nl:ui:13-dv-6igc doi:10.5061/dryad.pd612qp https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:108297 |
op_rights |
OPEN_ACCESS: The data are archived in Easy, they are accessible elsewhere through the DOI https://dans.knaw.nl/en/about/organisation-and-policy/legal-information/DANSLicence.pdf |
op_doi |
https://doi.org/10.5061/dryad.pd612qp/110.5061/dryad.pd612qp/210.1111/2041-210x.1303010.5061/dryad.pd612qp |
_version_ |
1770272060158771200 |