Estimating harvest when hunting bag data are reported by area rather than individual hunters : A Bayesian autoregressive approach

Harvest estimation is a central part of adaptive management of wildlife. In the absence of complete reporting, statishods are required to extrapolate from the available data. We developed a Hierarchical Bayesian framework tailored for partial reporting where hunting areas covered by reporting huntin...

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Bibliographic Details
Published in:Ecological Indicators
Main Authors: Lindström, Tom, Bergqvist, Göran
Format: Article in Journal/Newspaper
Language:English
Published: Linköpings universitet, Ekologisk och miljövetenskaplig modellering 2022
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-187321
https://doi.org/10.1016/j.ecolind.2022.108960
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record_format openpolar
spelling ftlinkoepinguniv:oai:DiVA.org:liu-187321 2023-05-15T15:55:57+02:00 Estimating harvest when hunting bag data are reported by area rather than individual hunters : A Bayesian autoregressive approach Lindström, Tom Bergqvist, Göran 2022 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-187321 https://doi.org/10.1016/j.ecolind.2022.108960 eng eng Linköpings universitet, Ekologisk och miljövetenskaplig modellering Linköpings universitet, Tekniska fakulteten Oster Malma, Sweden; Swedish Univ Agr Sci, Sweden Elsevier Ecological Indicators, 1470-160X, 2022, 141, orcid:0000-0001-7856-2925 http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-187321 doi:10.1016/j.ecolind.2022.108960 ISI:000818658300001 info:eu-repo/semantics/openAccess Harvest statistics Voluntary reporting Autocorrelation Hierarchical Bayesian methods Environmental Sciences Miljövetenskap Article in journal info:eu-repo/semantics/article text 2022 ftlinkoepinguniv https://doi.org/10.1016/j.ecolind.2022.108960 2022-09-14T22:28:46Z Harvest estimation is a central part of adaptive management of wildlife. In the absence of complete reporting, statishods are required to extrapolate from the available data. We developed a Hierarchical Bayesian framework tailored for partial reporting where hunting areas covered by reporting hunting teams are available. The framework accounts for autocorrelation at the national, county, and hunting management precinct levels. We derived and evaluated an approximation for the probability of harvest on non-reported areas under a non-linear relationship between harvest area per team and harvest rate. We applied the framework to reports of red fox (Vulpes vulpes), wild boar (Sus scrofa), common eider (Somateria mollissima), and grey partridge (Perdix perdix) harvest in Sweden from the hunting years 1997/1998-2019/2020. The approximation was evaluated and determined to be sufficiently accurate. We showed that accounting for autocorrelation in harvest reduced uncertainty and increased predictive accuracy, particularly for game hunted in low numbers and variably between teams. The analysis also revealed that hunting rate has a sub-linear relationship with a teams area for all considered species. Further, the framework revealed substantial differences across regions in terms of parameters modeling the distribution of huntable land across teams as well as parameters modeling harvest rates. We conclude that the framework is useful to detect shifts in hunting rates and/or practices. Funding Agencies|Swedish Association of Hunting and Wildlife Management; Swedish Environmental Protection Agency Article in Journal/Newspaper Common Eider Somateria mollissima LIU - Linköping University: Publications (DiVA) Ecological Indicators 141 108960
institution Open Polar
collection LIU - Linköping University: Publications (DiVA)
op_collection_id ftlinkoepinguniv
language English
topic Harvest statistics
Voluntary reporting
Autocorrelation
Hierarchical Bayesian methods
Environmental Sciences
Miljövetenskap
spellingShingle Harvest statistics
Voluntary reporting
Autocorrelation
Hierarchical Bayesian methods
Environmental Sciences
Miljövetenskap
Lindström, Tom
Bergqvist, Göran
Estimating harvest when hunting bag data are reported by area rather than individual hunters : A Bayesian autoregressive approach
topic_facet Harvest statistics
Voluntary reporting
Autocorrelation
Hierarchical Bayesian methods
Environmental Sciences
Miljövetenskap
description Harvest estimation is a central part of adaptive management of wildlife. In the absence of complete reporting, statishods are required to extrapolate from the available data. We developed a Hierarchical Bayesian framework tailored for partial reporting where hunting areas covered by reporting hunting teams are available. The framework accounts for autocorrelation at the national, county, and hunting management precinct levels. We derived and evaluated an approximation for the probability of harvest on non-reported areas under a non-linear relationship between harvest area per team and harvest rate. We applied the framework to reports of red fox (Vulpes vulpes), wild boar (Sus scrofa), common eider (Somateria mollissima), and grey partridge (Perdix perdix) harvest in Sweden from the hunting years 1997/1998-2019/2020. The approximation was evaluated and determined to be sufficiently accurate. We showed that accounting for autocorrelation in harvest reduced uncertainty and increased predictive accuracy, particularly for game hunted in low numbers and variably between teams. The analysis also revealed that hunting rate has a sub-linear relationship with a teams area for all considered species. Further, the framework revealed substantial differences across regions in terms of parameters modeling the distribution of huntable land across teams as well as parameters modeling harvest rates. We conclude that the framework is useful to detect shifts in hunting rates and/or practices. Funding Agencies|Swedish Association of Hunting and Wildlife Management; Swedish Environmental Protection Agency
format Article in Journal/Newspaper
author Lindström, Tom
Bergqvist, Göran
author_facet Lindström, Tom
Bergqvist, Göran
author_sort Lindström, Tom
title Estimating harvest when hunting bag data are reported by area rather than individual hunters : A Bayesian autoregressive approach
title_short Estimating harvest when hunting bag data are reported by area rather than individual hunters : A Bayesian autoregressive approach
title_full Estimating harvest when hunting bag data are reported by area rather than individual hunters : A Bayesian autoregressive approach
title_fullStr Estimating harvest when hunting bag data are reported by area rather than individual hunters : A Bayesian autoregressive approach
title_full_unstemmed Estimating harvest when hunting bag data are reported by area rather than individual hunters : A Bayesian autoregressive approach
title_sort estimating harvest when hunting bag data are reported by area rather than individual hunters : a bayesian autoregressive approach
publisher Linköpings universitet, Ekologisk och miljövetenskaplig modellering
publishDate 2022
url http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-187321
https://doi.org/10.1016/j.ecolind.2022.108960
genre Common Eider
Somateria mollissima
genre_facet Common Eider
Somateria mollissima
op_relation Ecological Indicators, 1470-160X, 2022, 141,
orcid:0000-0001-7856-2925
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-187321
doi:10.1016/j.ecolind.2022.108960
ISI:000818658300001
op_rights info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.1016/j.ecolind.2022.108960
container_title Ecological Indicators
container_volume 141
container_start_page 108960
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