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Evolutionary Rescue in a Linearly Changing Environment: Limits on Predictability.

Authors :
Orive, Maria E.
Holt, Robert D.
Barfield, Michael
Source :
Bulletin of Mathematical Biology. Nov2019, Vol. 81 Issue 11, p4821-4839. 19p.
Publication Year :
2019

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00928240
Volume :
81
Issue :
11
Database :
Academic Search Index
Journal :
Bulletin of Mathematical Biology
Publication Type :
Academic Journal
Accession number :
139811398
Full Text :
https://doi.org/10.1007/s11538-018-0504-5