title: Revealing ecological networks using Bayesian network inference algorithms.
abstract: Understanding functional relationships within ecological networks can help reveal keys to ecosystem stability or fragility. Revealing these relationships is complicated by the difficulties of isolating variables or performing experimental manipulations within a natural ecosystem, and thus inferences are often made by matching models to observational data. Such models, however, require assumptions-or detailed measurements-of parameters such as birth and death rate, encounter frequency, territorial exclusion, and predation success. Here, we evaluate the use of a Bayesian network inference algorithm, which can reveal ecological networks based upon species and habitat abundance alone. We test the algorithm's performance and applicability on observational data of avian communities and habitat in the Peak District National Park, United Kingdom. The resulting networks correctly reveal known relationships among habitat types and known interspecific relationships. In addition, the networks produced novel insights into ecosystem structure and identified key species with high connectivity. Thus, Bayesian networks show potential for becoming a valuable tool in ecosystem analysis.
authors: School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, United Kingdom.
authors: Isobel
authors: I
authors: Milns
authors: Colin M
authors: CM
authors: Beale
authors: V Anne
authors: VA
authors: Smith
mesh_terms: D000465:Algorithms; D000531:Altitude; D000818:Animals; D001499:Bayes Theorem; D001717:Birds / Q000502:physiology*; D017753:Ecosystem; D008954:Models, Biological; D006113:United Kingdom