For example, even if both interaction functions, predator prey and competition, are Gaussian, evolutionary multiple attractors can occur when the optimum trait values of the predator and consumer are similar.
Weak trade-offs (also called concave trade-offs) may exist if the optimum trait values for both natural and human-altered habitat are close or if habitat stringencies are low (or any combination of the two).
The OU model incorporates stabilizing selection as a constraint and quantifies the evolution of a continuous trait X as d X t) = α[ θ – X t)]d t + σd B t), where σ captures the stochastic evolution of BM, and α determines the rate of adaptive evolution towards an optimum trait value θ [ 52, 53].
Individual learners are thus unlikely to reach the optimum when traits are complex (n large), and so face a barrier to further improvement of the artifact.
We derive the stochastic learning dynamics of the distance to the optimum in trait space when choice between alternative artifacts follows the standard logit choice rule.
Moreover, mating plans (or mating allocations) have been used in animal breeding for several reasons: a) to control inbreeding; b) in situations where economic merit is not linear; c) when there is an intermediate optimum (or restricted traits); d) to increase connection among herds and, finally, e) to profit from dominance genetic effects.
Consequently, species under intense sperm competition would follow the diploid control pattern, resulting in a particular sperm trait optimum being selected.
In general, the optimum combinations were trait- and species-specific.
Owing to inevitable domestication selection in captivity, the captive-reared population has experienced a shift away from the wild optimum in quantitative trait variation related to fitness.
Environmental variations (e. g. temperature) may beconsidered as benign if their mean value is close to the functional optimum of the trait.
