Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas
Understanding ecosystem dynamics within shallow shelf seas is of great importance to support marine spatial management of natural populations and activities such as fishing and offshore renewable energy production to combat climate change. Given the possibility of future changes, a baseline is neede...
Published in: | Ecological Indicators |
---|---|
Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://research.bangor.ac.uk/portal/en/researchoutputs/bayesian-network-modelling-provides-spatial-and-temporal-understanding-of-ecosystem-dynamics-within-shallow-shelf-seas(9a0c808e-b56e-4427-8aca-187b8407d46e).html https://doi.org/10.1016/j.ecolind.2021.107997 https://research.bangor.ac.uk/ws/files/39101474/Trifonova_EI1_Publication.pdf |
id |
ftuwalesbangcris:oai:research.bangor.ac.uk:publications/9a0c808e-b56e-4427-8aca-187b8407d46e |
---|---|
record_format |
openpolar |
spelling |
ftuwalesbangcris:oai:research.bangor.ac.uk:publications/9a0c808e-b56e-4427-8aca-187b8407d46e 2023-05-15T16:33:29+02:00 Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas Trifonova, Neda Scott, Beth De Dominicis, Michela Waggitt, James Wolf, Judith 2021-10-01 application/pdf https://research.bangor.ac.uk/portal/en/researchoutputs/bayesian-network-modelling-provides-spatial-and-temporal-understanding-of-ecosystem-dynamics-within-shallow-shelf-seas(9a0c808e-b56e-4427-8aca-187b8407d46e).html https://doi.org/10.1016/j.ecolind.2021.107997 https://research.bangor.ac.uk/ws/files/39101474/Trifonova_EI1_Publication.pdf eng eng info:eu-repo/semantics/openAccess Trifonova , N , Scott , B , De Dominicis , M , Waggitt , J & Wolf , J 2021 , ' Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas ' , Ecological Indicators , vol. 129 , no. 107997 , 107997 . https://doi.org/10.1016/j.ecolind.2021.107997 article 2021 ftuwalesbangcris https://doi.org/10.1016/j.ecolind.2021.107997 2021-12-26T12:07:07Z Understanding ecosystem dynamics within shallow shelf seas is of great importance to support marine spatial management of natural populations and activities such as fishing and offshore renewable energy production to combat climate change. Given the possibility of future changes, a baseline is needed to predict ecosystems responses to such changes. This study uses Bayesian techniques to find the data-driven estimates of interactions among a set of physical and biological variables and a human pressure within the last 30 years in a well-studied shallow sea (North Sea, UK) with four contrasting regions and their associated ecosystems. A hidden variable is incorporated to model functional ecosystem change, where the underlying interactions dramatically change, following natural or anthropogenic disturbance. Data-driven estimates of interactions were identified, highlighting physical (e.g. bottom temperature, potential energy anomaly) and biological variables (e.g. sandeel larvae, net primary production) to be strong indicators of ecosystem change. There was consistency in the physical and biological variables, identified as good indicators in three of the regions, however the shallower region (with depths < 50 m, that is targeted for static offshore wind developments) was the most dissimilar. The use of contrasting regions provided useful insights on responses linked to ecosystem disturbances and identified the top predators as better indicators for each region, with the harbour porpoise being a particularly valuable indicator of ecosystem change across most regions. Another important finding was the dramatic changes in the strength of many interactions over time. This suggests that physical and biological indicators should only be used with additional temporal information, as changes in strength led to the identification of two potentially significant periods of ecosystem change (after 2005 and after 2010), linked to physical pressures (e.g. cold-water anomalies, seen in bottom temperatures; salinity changes, seen in the potential energy anomaly) and primary production changes. The hidden variable also modelled a change in the early 2000s for all the regions and identified maximum chlorophyll-a and sea surface temperature as some of the better indicators of these ecosystem changes. Article in Journal/Newspaper Harbour porpoise Bangor University: Research Portal Ecological Indicators 129 107997 |
institution |
Open Polar |
collection |
Bangor University: Research Portal |
op_collection_id |
ftuwalesbangcris |
language |
English |
description |
Understanding ecosystem dynamics within shallow shelf seas is of great importance to support marine spatial management of natural populations and activities such as fishing and offshore renewable energy production to combat climate change. Given the possibility of future changes, a baseline is needed to predict ecosystems responses to such changes. This study uses Bayesian techniques to find the data-driven estimates of interactions among a set of physical and biological variables and a human pressure within the last 30 years in a well-studied shallow sea (North Sea, UK) with four contrasting regions and their associated ecosystems. A hidden variable is incorporated to model functional ecosystem change, where the underlying interactions dramatically change, following natural or anthropogenic disturbance. Data-driven estimates of interactions were identified, highlighting physical (e.g. bottom temperature, potential energy anomaly) and biological variables (e.g. sandeel larvae, net primary production) to be strong indicators of ecosystem change. There was consistency in the physical and biological variables, identified as good indicators in three of the regions, however the shallower region (with depths < 50 m, that is targeted for static offshore wind developments) was the most dissimilar. The use of contrasting regions provided useful insights on responses linked to ecosystem disturbances and identified the top predators as better indicators for each region, with the harbour porpoise being a particularly valuable indicator of ecosystem change across most regions. Another important finding was the dramatic changes in the strength of many interactions over time. This suggests that physical and biological indicators should only be used with additional temporal information, as changes in strength led to the identification of two potentially significant periods of ecosystem change (after 2005 and after 2010), linked to physical pressures (e.g. cold-water anomalies, seen in bottom temperatures; salinity changes, seen in the potential energy anomaly) and primary production changes. The hidden variable also modelled a change in the early 2000s for all the regions and identified maximum chlorophyll-a and sea surface temperature as some of the better indicators of these ecosystem changes. |
format |
Article in Journal/Newspaper |
author |
Trifonova, Neda Scott, Beth De Dominicis, Michela Waggitt, James Wolf, Judith |
spellingShingle |
Trifonova, Neda Scott, Beth De Dominicis, Michela Waggitt, James Wolf, Judith Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas |
author_facet |
Trifonova, Neda Scott, Beth De Dominicis, Michela Waggitt, James Wolf, Judith |
author_sort |
Trifonova, Neda |
title |
Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas |
title_short |
Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas |
title_full |
Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas |
title_fullStr |
Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas |
title_full_unstemmed |
Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas |
title_sort |
bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas |
publishDate |
2021 |
url |
https://research.bangor.ac.uk/portal/en/researchoutputs/bayesian-network-modelling-provides-spatial-and-temporal-understanding-of-ecosystem-dynamics-within-shallow-shelf-seas(9a0c808e-b56e-4427-8aca-187b8407d46e).html https://doi.org/10.1016/j.ecolind.2021.107997 https://research.bangor.ac.uk/ws/files/39101474/Trifonova_EI1_Publication.pdf |
genre |
Harbour porpoise |
genre_facet |
Harbour porpoise |
op_source |
Trifonova , N , Scott , B , De Dominicis , M , Waggitt , J & Wolf , J 2021 , ' Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas ' , Ecological Indicators , vol. 129 , no. 107997 , 107997 . https://doi.org/10.1016/j.ecolind.2021.107997 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1016/j.ecolind.2021.107997 |
container_title |
Ecological Indicators |
container_volume |
129 |
container_start_page |
107997 |
_version_ |
1766023183446573056 |