Can individual variation in phenotypic plasticity enhance population viability?

Adriana A. Maldonado-Chaparro, Dwight W. Read, Daniel T. Blumstein

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In response to climatic and other sources of environmental variation, individuals within a population may adjust their behavioral, morphological or physiological responses to varying environmental conditions through phenotypic plasticity. In seasonal environments, time constraints related to seasonality, as well as variation in climatic factors, may affect body mass growth rates. To cope with the consequences of a harsh period, individuals may, for example, compensate for lost body mass by accelerating their growth rate in the following period. Phenotypically plastic responses like this can, therefore, directly affect body mass, which may affect individual fitness and, ultimately, population dynamics. Here, we use a well-studied population of yellow-bellied marmots, Marmota flaviventris, in Colorado to parametrize and develop an individual-based model (IBM) to investigate how phenotypically plastic responses in body mass growth rate may compensate for an individual's bad start after a harsh period (compensatory growth), and to explore whether individual variation in compensatory growth favors population persistence under less favorable climatic scenarios. A simulation model that allowed marmots with a body mass less than the population's average body mass to compensate their growth provided the best match with observed population sizes, suggesting the importance of trade-offs in population dynamics. We also found that compensatory growth plays an important role in decreasing the probability of extinction under both less favorable colder and random climate scenarios. Our results lead to a deeper understanding of the mechanisms that govern population fluctuations and highlight the importance of quantifying the fitness cost of phenotypically plastic responses.

Original languageEnglish (US)
Pages (from-to)19-30
Number of pages12
JournalEcological Modelling
Volume352
DOIs
StatePublished - May 24 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Ecological Modeling

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