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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Language: | English |
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Linköpings universitet, Ekologisk och miljövetenskaplig modellering
2022
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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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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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1766391434412294144 |