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Premium The equilibrium assumption in estimating the parameters of metapopulation models
Moilanen Atte
Publication year2000
Publication title
journal of animal ecology
Resource typeJournals
PublisherBlackwell Science Ltd
1.  The construction of a predictive metapopulation model includes three steps: the choice of factors affecting metapopulation dynamics, the choice of model structure, and finally parameter estimation and model testing. 2.  Unless the assumption is made that the metapopulation is at stochastic quasi‐equilibrium and unless the method of parameter estimation of model parameters uses that assumption, estimates from a limited amount of data will usually predict a trend in metapopulation size. 3.  This implicit estimation of a trend occurs because extinction‐colonization stochasticity, possibly amplified by regional stochasticity, leads to unequal numbers of observed extinction and colonization events during a short study period. 4.  Metapopulation models, such as those based on the logistic regression model, that rely on observed population turnover events in parameter estimation are sensitive to the implicit estimation of a trend. 5.  A new parameter estimation method, based on Monte Carlo inference for statistically implicit models, allows an explicit decision about whether metapopulation quasi‐stability is assumed or not. 6. Our confidence in metapopulation model parameter estimates that have been produced from only a few years of data is decreased by the need to know before parameter estimation whether the metapopulation is in quasi‐stable state or not. 7. The choice of whether metapopulation stability is assumed or not in parameter estimation should be done consciously. Typical data sets cover only a few years and rarely allow a statistical test of a possible trend. While making the decision about stability one should consider any information about the landscape history and species and metapopulation characteristics.
Subject(s)artificial intelligence , biological dispersal , computer science , demography , econometrics , economics , estimation , estimation theory , inference , machine learning , management , markov chain monte carlo , mathematics , metapopulation , monte carlo method , physics , population , sociology , stability (learning theory) , statistical physics , statistics
SCImago Journal Rank2.134

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