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...

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Autores principales:	Neda I. Trifonova, Beth E. Scott, Michela De Dominicis, James J. Waggitt, Judith Wolf
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Climate change Hidden variable Functional ecosystem change Top predator dynamics Fisheries effects Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/03274ea6071547dab34f994c7a644821
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spelling	oai:doaj.org-article:03274ea6071547dab34f994c7a6448212021-12-01T04:57:38ZBayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas1470-160X10.1016/j.ecolind.2021.107997https://doaj.org/article/03274ea6071547dab34f994c7a6448212021-10-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21006622https://doaj.org/toc/1470-160XUnderstanding 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.Neda I. TrifonovaBeth E. ScottMichela De DominicisJames J. WaggittJudith WolfElsevierarticleClimate changeHidden variableFunctional ecosystem changeTop predator dynamicsFisheries effectsEcologyQH540-549.5ENEcological Indicators, Vol 129, Iss , Pp 107997- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Climate change Hidden variable Functional ecosystem change Top predator dynamics Fisheries effects Ecology QH540-549.5
spellingShingle	Climate change Hidden variable Functional ecosystem change Top predator dynamics Fisheries effects Ecology QH540-549.5 Neda I. Trifonova Beth E. Scott Michela De Dominicis James J. Waggitt Judith Wolf Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas
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
author	Neda I. Trifonova Beth E. Scott Michela De Dominicis James J. Waggitt Judith Wolf
author_facet	Neda I. Trifonova Beth E. Scott Michela De Dominicis James J. Waggitt Judith Wolf
author_sort	Neda I. Trifonova
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
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/03274ea6071547dab34f994c7a644821
work_keys_str_mv	AT nedaitrifonova bayesiannetworkmodellingprovidesspatialandtemporalunderstandingofecosystemdynamicswithinshallowshelfseas AT bethescott bayesiannetworkmodellingprovidesspatialandtemporalunderstandingofecosystemdynamicswithinshallowshelfseas AT micheladedominicis bayesiannetworkmodellingprovidesspatialandtemporalunderstandingofecosystemdynamicswithinshallowshelfseas AT jamesjwaggitt bayesiannetworkmodellingprovidesspatialandtemporalunderstandingofecosystemdynamicswithinshallowshelfseas AT judithwolf bayesiannetworkmodellingprovidesspatialandtemporalunderstandingofecosystemdynamicswithinshallowshelfseas
_version_	1718405642382737408

Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas

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