A Bayesian Modeling Approach for Determining Productivity Regimes and Their Characteristics

Oscillations in the environment result in substantial alterations to population dynamics as evidenced by time series of abundance and recruitment. Depending on the reference timescale, these oscillations are referred to as regime shifts. Regime shifts may occur on very short time scales and are ofte...

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Bibliographic Details
Published in:Ecological applications 2009-03, Vol.19 (2), p.527-537
Main Authors: Munch, S. B., Kottas, A.
Format: Article
Language:English
Subjects:
Animals
Bayes Theorem
density dependence
Ecological modeling
Ecological sustainability
Fisheries science
Fishes - physiology
hidden Markov model
Japanese sardine
Marine ecology
Markov Chains
Markov models
Models, Theoretical
Ocean fisheries
Parametric models
Population Density
Population Dynamics
Productivity
regime shift
Reproduction
Sardines
Sardinops melanostictus
Statistical variance
stock-recruitment
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Summary:Oscillations in the environment result in substantial alterations to population dynamics as evidenced by time series of abundance and recruitment. Depending on the reference timescale, these oscillations are referred to as regime shifts. Regime shifts may occur on very short time scales and are often undetected for several years. Consequently, tools that allow the estimation of regime-specific population dynamic parameters may be of great value. Using a hidden Markov model to describe the unobserved regime state, we develop methods to infer regime-specific parameters for a commonly used model of density-dependent recruitment in addition to identifying the unobserved regime state. We apply the method to recruitment data for Japanese sardine (Sardinops melanostictus).
ISSN:1051-0761
1939-5582
DOI:10.1890/07-2116.1