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Climatic Change

, Volume 137, Issue 3–4, pp 525–539 | Cite as

Voltinism and resilience to climate-induced phenological mismatch

  • Robert J. KnellEmail author
  • Stephen J. Thackeray
Article

Abstract

Changes in the seasonal timing of recurring biological events are considered to be a major component of the global “fingerprint” of climate change. One effect of these changes is that ecologically important seasonal species interactions could become desynchronised as a result of these shifts (i.e. phenological mismatching), leading to reductions in fitness for some or all of the organisms concerned. One important, but unresolved, issue is the extent to which variations in voltinism (the number of generations a population of a species produces per year) may serve to exacerbate, or confer resilience to, the effects of seasonal shifts. Univoltine organisms (those with one generation per year) will always suffer the deleterious consequences of phenological mismatch, whereas multivoltine species are likely to experience at least some relief from these negative effects in generations that occur later in the season. Conversely, univoltine species will experience continual selection to adapt to changing seasonality, whereas multivoltine species will experience reduced or no selection during those generations that occur later in the season. Here, we present a new theoretical model to explore the population consequences of scenarios of changing seasonality and varying voltinism in clonal species. We find that organisms that undergo multiple generations per year show greater resilience to phenological mismatching in the spring and adapt better to changing seasonality, because of the recovery of population size and genetic diversity after each spring mismatching event. These results have clear implications for management and conservation of populations that are threatened by the effects of mismatch.

Keywords

Subsequent Generation Emergence Date Stochastic Noise Phenological Change Quantitative Genetic Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We are grateful to Aris Moustakas and Matthew Evans for helpful comments on a previous version of the manuscript, and we are grateful to two anonymous reviewers for their helpful and constructive criticisms of an earlier draft. SJT was supported by NERC grant NE/J02080X/1 (“Quantifying links between human influences on climate, shifting seasons and widespread ecosystem consequences”).

Supplementary material

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.School of Biological and Chemical SciencesQueen Mary University of LondonLondonUK
  2. 2.Lake Ecosystems Group, Centre for Ecology & Hydrology, Lancaster Environment Centre, Library Avenue, BailriggLancasterUK

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