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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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 |
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Open Polar |
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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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1766391438086504448 |