TY - GEN
T1 - A spatio-temporal Bayesian network approach for revealing functional ecological networks in fisheries
AU - Trifonova, Neda
AU - Duplisea, Daniel
AU - Kenny, Andrew
AU - Tucker, Allan
PY - 2014
Y1 - 2014
N2 - 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. Machine learning techniques can allow such complex, spatially varying interactions to be recovered from collected field data. In this study, we apply structure learning techniques to identify functional relationships between trophic groups of species that vary across space and time. Specifically, Bayesian networks are created on a window of data for each of the 20 geographically different and temporally varied sub-regions within an oceanic area. In addition, we explored the spatial and temporal variation of pre-defined functions (like predation, competition) that are generalisable by experts’ knowledge. We were able to discover meaningful ecological networks that were more precisely spatially-specific rather than temporally, as previously suggested for this region. To validate the discovered networks, we predict the biomass of the trophic groups by using dynamic Bayesian networks, and correcting for spatial autocorrelation by including a spatial node in our models
AB - 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. Machine learning techniques can allow such complex, spatially varying interactions to be recovered from collected field data. In this study, we apply structure learning techniques to identify functional relationships between trophic groups of species that vary across space and time. Specifically, Bayesian networks are created on a window of data for each of the 20 geographically different and temporally varied sub-regions within an oceanic area. In addition, we explored the spatial and temporal variation of pre-defined functions (like predation, competition) that are generalisable by experts’ knowledge. We were able to discover meaningful ecological networks that were more precisely spatially-specific rather than temporally, as previously suggested for this region. To validate the discovered networks, we predict the biomass of the trophic groups by using dynamic Bayesian networks, and correcting for spatial autocorrelation by including a spatial node in our models
U2 - 10.1007/978-3-319-12571-8_26
DO - 10.1007/978-3-319-12571-8_26
M3 - Published conference contribution
T3 - Lecture Notes in Computer Science
SP - 298
EP - 308
BT - Advances in Intelligent Data Analysis XIII
PB - Springer
ER -