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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Data Archiving and Networked Services (DANS)
2019
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fttriple:oai:gotriple.eu:50|dedup_wf_001::331133e96cf517f66962e8a4a9d3c412 2023-05-15T16:32:18+02:00 Data from: Using partial aggregation in Spatial Capture Recapture Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J. Andrew Bischof, Richard 2019-05-08 https://doi.org/10.5061/dryad.pd612qp undefined unknown Data Archiving and Networked Services (DANS) http://dx.doi.org/10.5061/dryad.pd612qp https://dx.doi.org/10.5061/dryad.pd612qp lic_creative-commons oai:easy.dans.knaw.nl:easy-dataset:108297 oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:108297 10.5061/dryad.pd612qp 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f re3data_____::r3d100000044 Life sciences medicine and health care Gulo gulo wolverines (:tba) envir geo Dataset https://vocabularies.coar-repositories.org/resource_types/c_ddb1/ 2019 fttriple https://doi.org/10.5061/dryad.pd612qp 2023-01-22T17:41:59Z 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 ... Dataset Gulo gulo Unknown Norway |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
unknown |
topic |
Life sciences medicine and health care Gulo gulo wolverines (:tba) envir geo |
spellingShingle |
Life sciences medicine and health care Gulo gulo wolverines (:tba) envir geo Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J. Andrew Bischof, Richard Data from: Using partial aggregation in Spatial Capture Recapture |
topic_facet |
Life sciences medicine and health care Gulo gulo wolverines (:tba) envir geo |
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 ... |
format |
Dataset |
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 |
publisher |
Data Archiving and Networked Services (DANS) |
publishDate |
2019 |
url |
https://doi.org/10.5061/dryad.pd612qp |
geographic |
Norway |
geographic_facet |
Norway |
genre |
Gulo gulo |
genre_facet |
Gulo gulo |
op_source |
oai:easy.dans.knaw.nl:easy-dataset:108297 oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:108297 10.5061/dryad.pd612qp 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f re3data_____::r3d100000044 |
op_relation |
http://dx.doi.org/10.5061/dryad.pd612qp https://dx.doi.org/10.5061/dryad.pd612qp |
op_rights |
lic_creative-commons |
op_doi |
https://doi.org/10.5061/dryad.pd612qp |
_version_ |
1766022063873589248 |