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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Bibliographic Details
Published in:Ecology (Durham) Vol. 91; no. 7; pp. 1892 - 1899
Main Authors: Milns, Isobel, Beale, Colin M, Smith, V. Anne
Format: Journal Article
Language:English
Published: Washington, DC Ecological Society of America 01.07.2010
Subjects:
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
Online Access:Get full text
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Summary: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.
Bibliography:http://dx.doi.org/10.1890/09-0731.1
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ISSN:0012-9658
1939-9170
DOI:10.1890/09-0731.1