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BAYESIAN ANALYSIS OF METAPOPULATION DATA
Author(s) -
O'Hara R. B.,
Arjas E.,
Toivonen H.,
Hanski I.
Publication year - 2002
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/0012-9658(2002)083[2408:baomd]2.0.co;2
Subject(s) - metapopulation , markov chain monte carlo , bayesian probability , occupancy , extinction (optical mineralogy) , statistics , estimation theory , computer science , ecology , mathematics , biology , population , biological dispersal , paleontology , demography , sociology
A Bayesian approach is used to develop a method for fitting a metapopulation model (the incidence function model) to data on habitat patch occupancy, providing estimates of the five model parameters. Parameter estimation is carried out using a Markov chain Monte Carlo sampler, and data augmentation is used to include the effect of missing data in the analysis. The Bayesian approach allows us to take into account uncertainty about the parameter estimates when making predictions with the model. We demonstrate the methods of parameter estimation and prediction with simulated data. We first simulated metapopulation dynamics in real habitat patch networks with given parameter values and sampled the simulated data. Parameters were estimated both from full data sets, and from data sets with data for many years treated as missing. These estimates were then used to predict the distribution of time to extinction in modified networks, where patch areas had been reduced so that the real parameter values led to metapopulation extinction within ∼30 yr. We were successfully able to fit the model and found that, in some cases, the predictions can be sensitive to one of the parameters.