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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Published in:Ecological Indicators
Main Authors: Tom Lindström, Göran Bergqvist
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier 2022
Subjects:
Online Access:https://doi.org/10.1016/j.ecolind.2022.108960
https://doaj.org/article/00d6ea43bbeb4fa5866759f73c93b1f4
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spelling ftdoajarticles:oai:doaj.org/article:00d6ea43bbeb4fa5866759f73c93b1f4 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 Tom Lindström Göran Bergqvist 2022-08-01T00:00:00Z https://doi.org/10.1016/j.ecolind.2022.108960 https://doaj.org/article/00d6ea43bbeb4fa5866759f73c93b1f4 EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S1470160X22004319 https://doaj.org/toc/1470-160X 1470-160X doi:10.1016/j.ecolind.2022.108960 https://doaj.org/article/00d6ea43bbeb4fa5866759f73c93b1f4 Ecological Indicators, Vol 141, Iss , Pp 108960- (2022) Harvest statistics Voluntary reporting Autocorrelation Hierarchical Bayesian methods Ecology QH540-549.5 article 2022 ftdoajarticles https://doi.org/10.1016/j.ecolind.2022.108960 2022-12-31T02:26:26Z 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 team’s 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. Article in Journal/Newspaper Common Eider Somateria mollissima Directory of Open Access Journals: DOAJ Articles Ecological Indicators 141 108960
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic Harvest statistics
Voluntary reporting
Autocorrelation
Hierarchical Bayesian methods
Ecology
QH540-549.5
spellingShingle Harvest statistics
Voluntary reporting
Autocorrelation
Hierarchical Bayesian methods
Ecology
QH540-549.5
Tom Lindström
Göran Bergqvist
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
Ecology
QH540-549.5
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 team’s 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.
format Article in Journal/Newspaper
author Tom Lindström
Göran Bergqvist
author_facet Tom Lindström
Göran Bergqvist
author_sort Tom Lindström
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 Elsevier
publishDate 2022
url https://doi.org/10.1016/j.ecolind.2022.108960
https://doaj.org/article/00d6ea43bbeb4fa5866759f73c93b1f4
genre Common Eider
Somateria mollissima
genre_facet Common Eider
Somateria mollissima
op_source Ecological Indicators, Vol 141, Iss , Pp 108960- (2022)
op_relation http://www.sciencedirect.com/science/article/pii/S1470160X22004319
https://doaj.org/toc/1470-160X
1470-160X
doi:10.1016/j.ecolind.2022.108960
https://doaj.org/article/00d6ea43bbeb4fa5866759f73c93b1f4
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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