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

Trifonova, Neda; Maxwell, David; Pinnegar, John K.; Kenny, Andrew; Tucker, Allan

doi:10.1093/icesjms/fsw231

Cited by 38 publications

(28 citation statements)

References 25 publications

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“…This is potentially highly useful in ecological analyses where complex ecological interactions change in time due to, e.g., global change or changing pressures. However, this possibility is not yet largely explored in ecological analyses, exceptions being Trifonova et al (2015Trifonova et al ( , 2017, who created spatio-temporal Bayesian networks for the North Sea food web.…”

Section: Introductionmentioning

confidence: 99%

Hidden variables in a Dynamic Bayesian Network identify ecosystem level change

Uusitalo

Tomczak

Müller‐Karulis

et al. 2018

Ecological Informatics

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Ecosystems are known to change in terms of their structure and functioning over time. Modelling this change is a challenge, however, as data are scarce, and models often assume that the relationships between ecosystem components are invariable over time. Dynamic Bayesian Networks (DBN) with hidden variables have been proposed as a method to overcome this challenge, as the hidden variables can capture the unobserved processes. In this paper, we fit a series of DBNs with different hidden variable structures to a system known to have undergone a major structural change, i.e., the Baltic Sea food web. The exact setup of the hidden variables did not considerably affect the result, and the hidden variables picked up a pattern that agrees with previous research on the system dynamics.

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Section: Introductionmentioning

confidence: 99%

Hidden variables in a Dynamic Bayesian Network identify ecosystem level change

Uusitalo

Tomczak

Müller‐Karulis

et al. 2018

Ecological Informatics

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“…A dynamic model represents the behavior of a system over time; however, many systems, including ecosystems, contain non-stationary dynamics. Hidden variables allow us to model these non-stationary system dynamics [38–40]. They are used to represent a change in the interactions of the observed ecosystem components over time.…”

Section: Methodsmentioning

confidence: 99%

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

2019

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The Gulf of Mexico is an ecologically and economically important marine ecosystem that is affected by a variety of natural and anthropogenic pressures. These complex and interacting pressures, together with the dynamic environment of the Gulf, present challenges for the effective management of its resources. The recent adoption of Bayesian networks to ecology allows for the discovery and quantification of complex interactions from data after making only a few assumptions about observations of the system. In this study, we apply Bayesian network models, with different levels of structural complexity and a varying number of hidden variables to account for uncertainty when modeling ecosystem dynamics. From these models, we predict focal ecosystem components within the Gulf of Mexico. The predictive ability of the models varied with their structure. The model that performed best was parameterized through data-driven learning techniques and accounted for multiple ecosystem components’ associations and their interactions with human and natural pressures over time. Then, we altered sea surface temperature in the best performing model to explore the response of different ecosystem components to increased temperature. The magnitude and even direction of predicted responses varied by ecosystem components due to heterogeneity in driving factors and their spatial overlap. Our findings suggest that due to varying components’ sensitivity to drivers, changes in temperature will potentially lead to trade-offs in terms of population productivity. We were able to discover meaningful interactions between ecosystem components and their environment and show how sensitive these relationships are to climate perturbations, which increases our understanding of the potential future response of the system to increasing temperature. Our findings demonstrate that accounting for additional sources of variation, by incorporating multiple interactions and pressures in the model layout, has the potential for gaining deeper insights into the structure and dynamics of ecosystems.

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“…in addition, there is a strong connection with Bayesian Networks. 12 In particular, according to the work of Helsper and Gaag, it is possible to build BNs through Ontologies, 13 and vice versa, Colace and De Santo proposed a novel algorithm for Ontology building through the use of FIGURE 1 The MuG system architecture Summing up, the need to make a decision, in a given context, can be met through the fruition of the right information delivered by the architecture. This information is featured by innovative elements based on the following:…”

Section: The Multilevel Graph Approachmentioning

confidence: 99%

“…A widely used method for representing real domains are ontologies. An ontology can adequately support pervasive context‐aware systems; in addition, there is a strong connection with Bayesian Networks . In particular, according to the work of Helsper and Gaag, it is possible to build BNs through Ontologies, and vice versa, the work of Colace and De Santo proposed a novel algorithm for Ontology building through the use of BNs …”

Section: Introductionmentioning

confidence: 99%

“…An ontology can adequately support pervasive context-aware systems 11 ; in addition, there is a strong connection with Bayesian Networks. 12 In particular, according to the work of Helsper and Gaag, it is possible to build BNs through Ontologies, 13 and vice versa, the work of Colace and De Santo proposed a novel algorithm for Ontology building through the use of BNs. 14 Addressing the aforementioned context representation methodologies and the ability of the BNs, which from experimental evidence and through probabilistic approaches are able to identify likely events, it is necessary to introduce techniques and methodologies that can manage the context in real time, to improve the quality of life in smart cities.…”

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confidence: 99%

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A multilevel graph approach for rainfall forecasting: A preliminary study case on London area

Clarizia

Colace

Santo

et al. 2019

Concurrency and Computation

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Summary Increasing populations and rapid large‐scale urbanization has created a demand to increase the quality of life through economic development, social stability, and better quality environments. These issues are addressed in the field of Smart Cities where, through the Internet of Things, efforts are being made to support added‐value services for the administration of the city and for citizens. The continuous exchange of information inevitably produces a huge amount of data, which demands analyses of data using unconventional methods within a Big Data context. How can we properly process these data? How can we properly use these data in order to increase the competitiveness and efficiency of services, and how could they contribute to social development? Services that could be useful in this field include Early Warning Systems. Information management environments, or more generally pervasive data contexts, may be supported by context representation approaches and enhanced through adopting probabilistic approaches such as Context Dimension Tree, Ontology, and Bayesian Network. The aim of this work is to introduce and explain a methodology for merging CDTs and Ontologies, and probabilistic approach based on BNs in order to help expert users handle emergencies and provide suggestions for improving the liveability of cities for their inhabitants.

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

Cited by 38 publications

References 25 publications

Hidden variables in a Dynamic Bayesian Network identify ecosystem level change

Hidden variables in a Dynamic Bayesian Network identify ecosystem level change

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

A multilevel graph approach for rainfall forecasting: A preliminary study case on London area

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