Revealing ecological networks using Bayesian network inference algorithms

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...

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Vydané v:	Ecology (Durham) Ročník 91; číslo 7; s. 1892 - 1899
Hlavní autori:	Milns, Isobel, Beale, Colin M, Smith, V. Anne
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Washington, DC Ecological Society of America 01.07.2010
Predmet:	Algorithms Altitude Animal and plant ecology Animal, plant and microbial ecology Animals Aves avian community Bayes Theorem Bayesian analysis Bayesian network Bayesian networks Bayesian theory Biological and medical sciences Birds Birds - physiology Datasets Ecological modeling ecological network Ecosystem Ecosystem stability Ecosystem structure Ecosystems Fundamental and applied biological sciences. Psychology General aspects Grasses Habitats Inference Interspecific relationships Models, Biological mortality National parks Nonnative species Peak District National Park predation Species species distribution Statistical inference United Kingdom Vertebrates: general zoology, morphology, phylogeny, systematics, cytogenetics, geographical distribution Wildlife habitats United Kingdom Europe Peak District National Park avian community Ecology Peak District Algorithm ecologlcal network National park Vertebrata Animal community UK Bayes network species distributian Aves Bayesian network
ISSN:	0012-9658, 1939-9170
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Popis
Shrnutí:	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.
Bibliografia:	http://dx.doi.org/10.1890/09-0731.1 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	0012-9658 1939-9170
DOI:	10.1890/09-0731.1