Bulletin of Mathematical Biology

, Volume 81, Issue 11, pp 4821–4839 | Cite as

Evolutionary Rescue in a Linearly Changing Environment: Limits on Predictability

  • Maria E. OriveEmail author
  • Robert D. Holt
  • Michael Barfield
Special Issue: Modelling Biological Evolution: Developing Novel Approaches


Populations subject to substantial environmental change that decreases absolute fitness (expected number of offspring per individual) to less than one must adapt to persist. The probability of adaptive evolutionary rescue may be influenced by factors intrinsic to the organism itself, or by features specific to the individual population and its environment. An important question (given the increasing prevalence of environmental change) is the predictability of evolutionary rescue. We used an individual-based simulation model and a related analytic model to examine population persistence, given a continuously changing environment that leads to a linear change in the optimum for a phenotypic trait under selection. Population persistence was not well predicted by the population genetics at the start of environmental change, which contrasts strongly with the results shown in prior work for persistence after a sudden environmental change. Larger populations, which had a greater scope for the generation and maintenance of beneficial genetic variation, showed a clear advantage, but increasing the rate of environmental change always decreased the probability of persistence. Extinctions occurred throughout the period of continuous change, and populations that went extinct showed little sign of their eventual fate until shortly before extinction. Partially clonal populations showed less predictability and greater vulnerability to extinction when impacted by continuous change than did fully sexual populations—any advantage gained by the initial transmission of well-adapted phenotypes via clonal reproduction is lost as the phenotypic optimum continues to shift and the generation of novel variation is required for continuous adaptation.


Environmental change Extinction Individual-based model Clonality Evolutionary rescue 



MEO thanks the University of Kansas for funds in support of travel associated with this project. RDH and MB thank the University of Florida Foundation for support. This work was supported by National Science Foundation Grants DEB-1354754 to MEO and DMS-1515661 to RDH.


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Copyright information

© Society for Mathematical Biology 2018

Authors and Affiliations

  1. 1.Department of Ecology and Evolutionary BiologyUniversity of KansasLawrenceUSA
  2. 2.Department of BiologyUniversity of FloridaGainesvilleUSA

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