Evolutionary rescue under demographic and environmental stochasticity

Area of Science:

• Evolutionary biology
• Ecology
• Population genetics

Background:

• Populations face two main sources of random variation: demographic stochasticity (randomness in individual survival and reproduction) and environmental stochasticity (random fluctuations in environmental conditions affecting population growth).
• These stochastic processes can influence a population's ability to adapt and persist, particularly during environmental change.

Purpose of the Study:

• To model and compare the effects of demographic and environmental stochasticity on population survival and evolution following environmental change.
• To investigate how genetic variance and the strength of stabilizing selection interact with different types of stochasticity.
• To determine the implications for evolutionary rescue strategies.

Main Methods:

• Phenotypic selection modeling was employed to simulate evolutionary responses to abrupt environmental change.
• The study analyzed genetic and demographic dynamics under varying levels of demographic and environmental stochasticity.
• Population survival probabilities were assessed in relation to genetic variance and selection strength.

Main Results:

• Population survival probability under demographic stochasticity decreases sharply with stronger stabilizing selection, whereas under environmental stochasticity, it declines more gradually.
• The optimal genetic variance for population survival shows minimal difference between demographic and environmental stochasticity.
• Demographic stochasticity's impact is amplified in smaller populations, necessitating a slow initial decline in genetic variance for persistence.
• Environmental stochasticity's population-size-independent effects prioritize higher initial fitness for survival.

Conclusions:

• Explicitly distinguishing and measuring demographic versus environmental stochasticity is essential for accurate predictions of evolutionary rescue.
• The interaction between these stochasticity types can be synergistic, further reducing population survival.
• Management strategies for endangered populations must consider the specific type of stochasticity influencing their persistence.