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New technologies in the mix: Assessing N‐mixture models for abundance estimation using automated detection data from drone surveys

1. Reliable estimates of abundance are critical in effectively managing threatened species, but the feasibility of integrating data from wildlife surveys completed using advanced technologies such as remotely piloted aircraft systems (RPAS) and machine learning into abundance estimation methods such...

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Detalles Bibliográficos
Autores principales: Corcoran, Evangeline, Denman, Simon, Hamilton, Grant
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417234/
https://www.ncbi.nlm.nih.gov/pubmed/32788970
http://dx.doi.org/10.1002/ece3.6522
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author Corcoran, Evangeline
Denman, Simon
Hamilton, Grant
author_facet Corcoran, Evangeline
Denman, Simon
Hamilton, Grant
author_sort Corcoran, Evangeline
collection PubMed
description 1. Reliable estimates of abundance are critical in effectively managing threatened species, but the feasibility of integrating data from wildlife surveys completed using advanced technologies such as remotely piloted aircraft systems (RPAS) and machine learning into abundance estimation methods such as N‐mixture modeling is largely unknown due to the unique sources of detection errors associated with these technologies. 2. We evaluated two modeling approaches for estimating the abundance of koalas detected automatically in RPAS imagery: (a) a generalized N‐mixture model and (b) a modified Horvitz–Thompson (H‐T) estimator method combining generalized linear models and generalized additive models for overall probability of detection, false detection, and duplicate detection. The final estimates from each model were compared to the true number of koalas present as determined by telemetry‐assisted ground surveys. 3. The modified H‐T estimator approach performed best, with the true count of koalas captured within the 95% confidence intervals around the abundance estimates in all 4 surveys in the testing dataset (n = 138 detected objects), a particularly strong result given the difficulty in attaining accuracy found with previous methods. 4. The results suggested that N‐mixture models in their current form may not be the most appropriate approach to estimating the abundance of wildlife detected in RPAS surveys with automated detection, and accurate estimates could be made with approaches that account for spurious detections.
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spelling pubmed-74172342020-08-11 New technologies in the mix: Assessing N‐mixture models for abundance estimation using automated detection data from drone surveys Corcoran, Evangeline Denman, Simon Hamilton, Grant Ecol Evol Original Research 1. Reliable estimates of abundance are critical in effectively managing threatened species, but the feasibility of integrating data from wildlife surveys completed using advanced technologies such as remotely piloted aircraft systems (RPAS) and machine learning into abundance estimation methods such as N‐mixture modeling is largely unknown due to the unique sources of detection errors associated with these technologies. 2. We evaluated two modeling approaches for estimating the abundance of koalas detected automatically in RPAS imagery: (a) a generalized N‐mixture model and (b) a modified Horvitz–Thompson (H‐T) estimator method combining generalized linear models and generalized additive models for overall probability of detection, false detection, and duplicate detection. The final estimates from each model were compared to the true number of koalas present as determined by telemetry‐assisted ground surveys. 3. The modified H‐T estimator approach performed best, with the true count of koalas captured within the 95% confidence intervals around the abundance estimates in all 4 surveys in the testing dataset (n = 138 detected objects), a particularly strong result given the difficulty in attaining accuracy found with previous methods. 4. The results suggested that N‐mixture models in their current form may not be the most appropriate approach to estimating the abundance of wildlife detected in RPAS surveys with automated detection, and accurate estimates could be made with approaches that account for spurious detections. John Wiley and Sons Inc. 2020-06-30 /pmc/articles/PMC7417234/ /pubmed/32788970 http://dx.doi.org/10.1002/ece3.6522 Text en © 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Corcoran, Evangeline
Denman, Simon
Hamilton, Grant
New technologies in the mix: Assessing N‐mixture models for abundance estimation using automated detection data from drone surveys
title New technologies in the mix: Assessing N‐mixture models for abundance estimation using automated detection data from drone surveys
title_full New technologies in the mix: Assessing N‐mixture models for abundance estimation using automated detection data from drone surveys
title_fullStr New technologies in the mix: Assessing N‐mixture models for abundance estimation using automated detection data from drone surveys
title_full_unstemmed New technologies in the mix: Assessing N‐mixture models for abundance estimation using automated detection data from drone surveys
title_short New technologies in the mix: Assessing N‐mixture models for abundance estimation using automated detection data from drone surveys
title_sort new technologies in the mix: assessing n‐mixture models for abundance estimation using automated detection data from drone surveys
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417234/
https://www.ncbi.nlm.nih.gov/pubmed/32788970
http://dx.doi.org/10.1002/ece3.6522
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