Journal of Evolutionary BiologyEvolutionary BiologyPeer-reviewed journal article
Local Adaptation of Life-History Traits in a Seasonal Environment
Abstract
Populations are often spread across a spatially heterogeneous landscape, connected by migration. Consequently, the question arises whether divergent selective forces created by spatial heterogeneity can overcome the homogenising force of migration and loss of diversity through genetic drift to favour different traits across space. Such spatial heterogeneity in the population due to divergent selection is known as local adaptation. While local adaptation has been studied in a variety of settings, it remains unclear whether local adaptation of certain life-history traits can arise. Life-history traits, such as those determining an organisms fecundity (the parameter r) and ability to compete for resources (the parameter K) demonstrate unique eco-evolutionary feedback loops due to their direct relationship to individual fitness. Classic theory holds that in a constant environment, evolution maximises the populations competitive ability. Divergent selective pressures on life-history traits requires complex environmental differences, such as heterogeneous patterns of seasonality. We consider life-history evolution in a Lotka-Volterra model with three types of seasonal perturbations: repeated, sudden crashes in population size, fluctuating death rates, and fluctuating resource levels. We show that fluctuating resources cannot change the evolutionary outcome, but that sufficiently harsh population crashes or fluctuating death rates favour increased fecundity over competitive ability. Finally, we apply deterministic and stochastic modelling to study local adaptation of an island population to periodic population crashes in an island-mainland model. We find that local adaptation favouring r-selected individuals again arises when conditions are sufficiently harsh, but not so harsh that the island population cannot be sustained in the absence of migration.
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- 3
- FWCI
- 1.75
- Field percentile
- top 15%
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