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Building matrix population models when individuals are non-identifiable

Abstract:

Matrix Population Models (MPM) are among the most widely used tools in ecology and evolution. These models consider the life cycle of an individual as composed by states to construct a matrix containing the likelihood of transitions between these states as well as sexual and/or asexual per-capita offspring contributions. When individuals are identifiable one can parametrize an MPM based on survival and fertility data and average development times for every state, but some of this information is absent or incomplete for non-cohort data, or for cohort data when individuals are not identifiable. Here we introduce a simple procedure for the parameterization of an MPM that can be used with cohort data when individuals are non-identifiable; among other aspects our procedure is a novelty in that it does not require information on stage development (or stage residence) times, which current procedures require to be estimated externally, and it is a frequent source of error. We exemplify the procedure with a laboratory cohort dataset from Eratyrus mucronatus (Reduviidae, Triatominae). We also show that even if individuals are identifiable and the duration of each stage is externally estimated with no error, our procedure is simpler to use and yields the same MPM parameter estimates.