Using partial aggregation in spatial capture recapture
Abstract Spatial capture–recapture ( SCR ) models are commonly used for analysing data collected using noninvasive genetic sampling ( NGS ). Opportunistic NGS often leads to detections that do not occur at discrete detector locations. Therefore, spatial aggregation of individual detections into fixe...
Published in: | Methods in Ecology and Evolution |
---|---|
Main Authors: | , , , , , |
Other Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Wiley
2018
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1111/2041-210x.13030 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F2041-210X.13030 https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13030 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/2041-210X.13030 https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13030 |
id |
crwiley:10.1111/2041-210x.13030 |
---|---|
record_format |
openpolar |
spelling |
crwiley:10.1111/2041-210x.13030 2024-09-15T18:10:30+00:00 Using partial aggregation in spatial capture recapture Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J. Andrew Bischof, Richard Yoccoz, Nigel Miljødirektoratet Naturvårdsverket 2018 http://dx.doi.org/10.1111/2041-210x.13030 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F2041-210X.13030 https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13030 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/2041-210X.13030 https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13030 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Methods in Ecology and Evolution volume 9, issue 8, page 1896-1907 ISSN 2041-210X 2041-210X journal-article 2018 crwiley https://doi.org/10.1111/2041-210x.13030 2024-08-09T04:30:39Z Abstract Spatial capture–recapture ( SCR ) models are commonly used for analysing data collected using noninvasive 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., centre of grid cells) is an option to increase computing speed of SCR analyses. However, it may reduce precision and accuracy of parameter estimations. 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. 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 model 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. We recommend that detector spacing after aggregation does not exceed 1.5 times the scale‐parameter of ... Article in Journal/Newspaper Gulo gulo Wiley Online Library Methods in Ecology and Evolution 9 8 1896 1907 |
institution |
Open Polar |
collection |
Wiley Online Library |
op_collection_id |
crwiley |
language |
English |
description |
Abstract Spatial capture–recapture ( SCR ) models are commonly used for analysing data collected using noninvasive 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., centre of grid cells) is an option to increase computing speed of SCR analyses. However, it may reduce precision and accuracy of parameter estimations. 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. 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 model 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. We recommend that detector spacing after aggregation does not exceed 1.5 times the scale‐parameter of ... |
author2 |
Yoccoz, Nigel Miljødirektoratet Naturvårdsverket |
format |
Article in Journal/Newspaper |
author |
Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J. Andrew Bischof, Richard |
spellingShingle |
Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J. Andrew Bischof, Richard Using partial aggregation in spatial capture recapture |
author_facet |
Milleret, Cyril Dupont, Pierre Brøseth, Henrik Kindberg, Jonas Royle, J. Andrew Bischof, Richard |
author_sort |
Milleret, Cyril |
title |
Using partial aggregation in spatial capture recapture |
title_short |
Using partial aggregation in spatial capture recapture |
title_full |
Using partial aggregation in spatial capture recapture |
title_fullStr |
Using partial aggregation in spatial capture recapture |
title_full_unstemmed |
Using partial aggregation in spatial capture recapture |
title_sort |
using partial aggregation in spatial capture recapture |
publisher |
Wiley |
publishDate |
2018 |
url |
http://dx.doi.org/10.1111/2041-210x.13030 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F2041-210X.13030 https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13030 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/2041-210X.13030 https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13030 |
genre |
Gulo gulo |
genre_facet |
Gulo gulo |
op_source |
Methods in Ecology and Evolution volume 9, issue 8, page 1896-1907 ISSN 2041-210X 2041-210X |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1111/2041-210x.13030 |
container_title |
Methods in Ecology and Evolution |
container_volume |
9 |
container_issue |
8 |
container_start_page |
1896 |
op_container_end_page |
1907 |
_version_ |
1810448101876432896 |