Machine-learning approaches for modelling fish population dynamics

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. Understanding the nature of functional relationships (such as prey-predator) between species is important for building predictive models. However, modelling the interactions with external stressors over time and space is also essential for ecosystem-based approaches to fisheries management. With the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, fewer assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data and combined with existing knowledge. In this thesis, we explore Bayesian network modelling approaches, accounting for latent effects to reveal species dynamics within geographically different marine ecosystems. First, we introduce the concept of functional equivalence between different fish species and generalise trophic structure from different marine ecosystems in order to predict influence from natural and anthropogenic sources. The importance of a hidden variable in fish community change studies of this nature was acknowledged because it allows causes of change which are not purely found within the constrained model structure. Then, a functional network modelling approach was developed for the region of North Sea that takes into consideration unmeasured latent effects and spatial autocorrelation to model species interactions and associations with external factors such as climate and fisheries exploitation. The proposed model was able to produce novel insights on the ecosystem's dynamics and ecological interactions mainly because it accounts for the heterogeneous nature of the driving factors within spatially differentiated areas and their changes over time. Finally, a modified version of this dynamic Bayesian network model was used to predict the response of different ecosystem components to change in anthropogenic and environmental factors. Through the development of fisheries catch, temperature and productivity scenarios, we explore the future of different fish and zooplankton species and examine what trends of fisheries exploitation and environmental change are potentially beneficial in terms of ecological stability and resilience. Thus, we were able to provide a new data-driven modelling approach which might be beneficial to give strategic advice on potential response of the system to pressure

A new strategic framework to structure cumulative impact assessment (CIA)

Funding Information: This work was supported by Supergen Offshore Renewable Energy (ORE) Hub, funded by the Engineering and Physical Sciences Research Council (EPSRC EP/S000747/1) and the UK Department for Business, Energy & Industrial Strategy (BEIS) offshore energy Strategic Environmental Assessment Programme.Peer reviewedPostprin

A new strategic framework to structure Cumulative Impact Assessment (CIA)

Funding Information: This work was supported by Supergen Offshore Renewable Energy (ORE) Hub, funded by the Engineering and Physical Sciences Research Council (EPSRC EP/S000747/1) and the UK Department for Business, Energy & Industrial Strategy's (BEIS) offshore energy Strategic Environmental Assessment Programme. Publisher Copyright: © 2022, European Wave and Tidal Energy Conference. All rights reserved.Peer reviewedPublisher PD

Predicting ecosystem responses to changes in fisheries catch, temperature, and primary productivity with a dynamic Bayesian network model

The recent adoption of Bayesian networks (BNs) in ecology provides an opportunity to make advances because complex interactions can be recovered from field data and then used to predict the environmental response to changes in climate and biodiversity. In this study, we use a dynamic BN model with a hidden variable and spatial autocorrelation to explore the future of different fish and zooplankton species, given alternate scenarios, and across spatial scales within the North Sea. For most fish species, we were able to predict a trend of increase or decline in response to change in fisheries catch; however, this varied across the different areas, outlining the importance of trophic interactions and the spatial relationship between neighbouring areas. We were able to predict trends in zooplankton biomass in response to temperature change, with the spatial patterns of these effects varying by species. In contrast, there was high variability in terms of response to productivity changes and consequently knock-on effects on higher level trophic species. Finally, we were able to provide a new data-driven modelling approach that accounts for multispecies associations and interactions and their changes over space and time, which might be beneficial to give strategic advice on potential response of the system to pressure.We gratefully acknowledge the Natural Environment Research Council UK that has funded this research, along with support from the European Commission (OCEANCERTAIN, FP7-ENV-2013-6.1-1; no: 603773) for David Maxwell and from CEFAS for Andrew Kenny and David Maxwell

Use of our Future Seas : Relevance of Spatial and Temporal Scale for Physical and Biological Indicators

Funding This work was supported by the Supergen Offshore Renewable Energy (ORE) Hub, funded by the Engineering and Physical Sciences Research Council (EPSRC EP/S000747/1). Acknowledgments The authors would like to thank the following people for providing original images, incorporated in this work: Rory O’Hara Murray (Marine Scotland Science, United Kingdom), Ella-Sophia Benninghaus and Morgane Declerck (University of Aberdeen, United Kingdom).Peer reviewedPublisher PD

Predicting ecosystem components in the Gulf of Mexico and their responses to climate variability with a dynamic Bayesian network model

Funding: This research was carried out in part under the auspices of the Cooperative Institute for Marine and Atmospheric Studies (CIMAS), a Cooperative Institute of the University of Miami and the National Oceanic and Atmospheric Administration, cooperative agreement #NA10OAR4320143. This paper is NOAA IEA Program contribution #2018_4. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgments This research was carried out in part under the auspices of the Cooperative Institute for Marine and Atmospheric Studies (CIMAS), a Cooperative Institute of the University of Miami and the National Oceanic and Atmospheric Administration, cooperative agreement #NA10OAR4320143. This paper is a result of research, supported by the National Oceanic and Atmospheric Administration’s Integrated Ecosystem Assessment (NOAA IEA) Program. This paper is NOAA IEA Program contribution #2018_4.Peer reviewedPublisher PD

Hidden variables in a Dynamic Bayesian Network identify ecosystem level change

EU; The Academy of Finland; Projektträger Jülich (PtJ); Germany; The State Education Development Agency of Latvia; The National Centre for Research and Development, Poland; The Swedish Research Council Formas; BalticEye Stockholm University; foundation BalticSea202

A new strategic framework to structure Cumulative Impact Assessment (CIA)

In order to alleviate climate change consequences, UK governments are pioneering offshore energy developments with increasing commitment. The North Sea is a dynamic ecosystem with strong bottom-up/top-down natural and anthropogenic drivers facing rapid climate change impacts. Therefore, to ensure the compatibility of such large-scale developments with nature conservation obligations, cumulative effects need to be evaluated through cumulative impact assessments (CIA). However, by excluding climate change impacts, CIA lacks spatio-temporal appropriate baselines linking ecosystem components (e.g. physical indicators) to population dynamics which leads to uncertain predictions at populations levels. This study presents an overview of a framework for CIA using a holistic and pragmatic ecosystem approach based on spatio-temporal Bayesian network in order to identify pressure pathways, keystone components, ecosystem connectivity and resilience as well as population-level changes. We will also present potential fine-scale environmental monitoring solutions and data sources generated at MRED (Marine Renewable Energy Developments) site levels. Finally, we will discuss the usefulness of the two components that make up this framework: a database and an application of standardised shared tools that will pave the way to more transparent and multi-disciplinary collaborations. This framework will provide a multi-dimensional decision-making toolkit that would also lead towards more efficient SEAs (Strategic Environmental Assessment) as well as providing the ability to embed the CIAs of projects into regional and multinational schemes