Bayesian hierarchical space-time models to improve multispecies assessment by combining observations from disparate fish surveys
Many wild species affected by human activities require multiple surveys with differing designs to capture behavioural response to wide ranging habitat conditions and map and quantify them. While data from for example intersecting but disparate fish surveys using different gear, are widely available,...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2012.02196 https://arxiv.org/abs/2012.02196 |
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ftdatacite:10.48550/arxiv.2012.02196 2023-05-15T17:41:30+02:00 Bayesian hierarchical space-time models to improve multispecies assessment by combining observations from disparate fish surveys Nnanatu, Chibuzor C. Thompson, Murray S. A. Spence, Michael A. Couce, Elena van der Kooij, Jeroen Lynam, Christopher P. 2020 https://dx.doi.org/10.48550/arxiv.2012.02196 https://arxiv.org/abs/2012.02196 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Methodology stat.ME Applications stat.AP FOS Computer and information sciences Article CreativeWork article Preprint 2020 ftdatacite https://doi.org/10.48550/arxiv.2012.02196 2022-03-10T15:01:56Z Many wild species affected by human activities require multiple surveys with differing designs to capture behavioural response to wide ranging habitat conditions and map and quantify them. While data from for example intersecting but disparate fish surveys using different gear, are widely available, differences in design and methodology often limit their integration. Novel statistical approaches which can draw on observations from diverse sources could enhance our understanding of multiple species distributions simultaneously and thus provide vital evidence needed to conserve their populations and biodiversity at large. Using a novel Bayesian hierarchical binomial-lognormal hurdle modelling approach within the INLA-SPDE framework, we combined and analysed acoustic and bottom trawl survey data for herring, sprat and northeast Atlantic mackerel in the North Sea. These models were implemented using INLA-SPDE techniques. By accounting for gear-specific efficiencies across surveys in addition to increased spatial coverage, we gained larger statistical power with greatly minimised uncertainties in estimation. Our statistical approach provides a methodological development to improve the evidence base for multispecies assessment and marine ecosystem-based management. And on a broader scale, it could be readily applied where disparate biological surveys and sampling methods intersect, e.g. to provide information on biodiversity patterns using global datasets of species distributions. : 32 pages, 6 Figures Article in Journal/Newspaper Northeast Atlantic DataCite Metadata Store (German National Library of Science and Technology) |
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Methodology stat.ME Applications stat.AP FOS Computer and information sciences |
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Methodology stat.ME Applications stat.AP FOS Computer and information sciences Nnanatu, Chibuzor C. Thompson, Murray S. A. Spence, Michael A. Couce, Elena van der Kooij, Jeroen Lynam, Christopher P. Bayesian hierarchical space-time models to improve multispecies assessment by combining observations from disparate fish surveys |
topic_facet |
Methodology stat.ME Applications stat.AP FOS Computer and information sciences |
description |
Many wild species affected by human activities require multiple surveys with differing designs to capture behavioural response to wide ranging habitat conditions and map and quantify them. While data from for example intersecting but disparate fish surveys using different gear, are widely available, differences in design and methodology often limit their integration. Novel statistical approaches which can draw on observations from diverse sources could enhance our understanding of multiple species distributions simultaneously and thus provide vital evidence needed to conserve their populations and biodiversity at large. Using a novel Bayesian hierarchical binomial-lognormal hurdle modelling approach within the INLA-SPDE framework, we combined and analysed acoustic and bottom trawl survey data for herring, sprat and northeast Atlantic mackerel in the North Sea. These models were implemented using INLA-SPDE techniques. By accounting for gear-specific efficiencies across surveys in addition to increased spatial coverage, we gained larger statistical power with greatly minimised uncertainties in estimation. Our statistical approach provides a methodological development to improve the evidence base for multispecies assessment and marine ecosystem-based management. And on a broader scale, it could be readily applied where disparate biological surveys and sampling methods intersect, e.g. to provide information on biodiversity patterns using global datasets of species distributions. : 32 pages, 6 Figures |
format |
Article in Journal/Newspaper |
author |
Nnanatu, Chibuzor C. Thompson, Murray S. A. Spence, Michael A. Couce, Elena van der Kooij, Jeroen Lynam, Christopher P. |
author_facet |
Nnanatu, Chibuzor C. Thompson, Murray S. A. Spence, Michael A. Couce, Elena van der Kooij, Jeroen Lynam, Christopher P. |
author_sort |
Nnanatu, Chibuzor C. |
title |
Bayesian hierarchical space-time models to improve multispecies assessment by combining observations from disparate fish surveys |
title_short |
Bayesian hierarchical space-time models to improve multispecies assessment by combining observations from disparate fish surveys |
title_full |
Bayesian hierarchical space-time models to improve multispecies assessment by combining observations from disparate fish surveys |
title_fullStr |
Bayesian hierarchical space-time models to improve multispecies assessment by combining observations from disparate fish surveys |
title_full_unstemmed |
Bayesian hierarchical space-time models to improve multispecies assessment by combining observations from disparate fish surveys |
title_sort |
bayesian hierarchical space-time models to improve multispecies assessment by combining observations from disparate fish surveys |
publisher |
arXiv |
publishDate |
2020 |
url |
https://dx.doi.org/10.48550/arxiv.2012.02196 https://arxiv.org/abs/2012.02196 |
genre |
Northeast Atlantic |
genre_facet |
Northeast Atlantic |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2012.02196 |
_version_ |
1766143100484321280 |