Cognitive Phenotypes and the Evolution of Animal Decisions

Despite the clear fitness consequences of animal decisions, the science of animal decision making in evolutionary biology is underdeveloped compared with decision science in human psychology. Specifically, the field lacks a conceptual framework that defines and describes the relevant components of a decision, leading to imprecise language and concepts. The ‘judgment and decision-making’ (JDM) framework in human psychology is a powerful tool for framing and understanding human decisions, and we apply it here to components of animal decisions, which we refer to as ‘cognitive phenotypes’. We distinguish multiple cognitive phenotypes in the context of a JDM framework and highlight empirical approaches to characterize them as evolvable traits

Cognitive Phenotypes and the Evolution of Animal Decisions

Despite the clear fitness consequences of animal decisions, the science of animal decision making in evolutionary biology is underdeveloped compared with decision science in human psychology. Specifically, the field lacks a conceptual framework that defines and describes the relevant components of a decision, leading to imprecise language and concepts. The ‘judgment and decision-making’ (JDM) framework in human psychology is a powerful tool for framing and understanding human decisions, and we apply it here to components of animal decisions, which we refer to as ‘cognitive phenotypes’. We distinguish multiple cognitive phenotypes in the context of a JDM framework and highlight empirical approaches to characterize them as evolvable traits

Not Just a Theory--The Utility of Mathematical Models in Evolutionary Biology

Progress in science often begins with verbal hypotheses meant to explain why certain biological phenomena exist. An important purpose of mathematical models in evolutionary research, as in many other fields, is to act as “proof-of-concept” tests of the logic in verbal explanations, paralleling the way in which empirical data are used to test hypotheses. Because not all subfields of biology use mathematics for this purpose, misunderstandings of the function of proof-of-concept modeling are common. In the hope of facilitating communication, we discuss the role of proof-of-concept modeling in evolutionary biology

Adaptive divergence despite strong genetic drift: genomic analysis of the evolutionary mechanisms causing genetic differentiation in the island fox (\u3ci\u3eUrocyon littoralis\u3c/i\u3e)

The evolutionary mechanisms generating the tremendous biodiversity of islands have long fascinated evolutionary biologists. Genetic drift and divergent selection are pre- dicted to be strong on islands and both could drive population divergence and specia- tion. Alternatively, strong genetic drift may preclude adaptation. We conducted a genomic analysis to test the roles of genetic drift and divergent selection in causing genetic differentiation among populations of the island fox (Urocyon littoralis). This species consists of six subspecies, each of which occupies a different California Chan- nel Island. Analysis of 5293 SNP loci generated using Restriction-site Associated DNA (RAD) sequencing found support for genetic drift as the dominant evolutionary mech- anism driving population divergence among island fox populations. In particular, pop- ulations had exceptionally low genetic variation, small Ne (range = 2.1–89.7; median = 19.4), and significant genetic signatures of bottlenecks. Moreover, islands with the lowest genetic variation (and, by inference, the strongest historical genetic drift) were most genetically differentiated from mainland grey foxes, and vice versa, indicating genetic drift drives genome-wide divergence. Nonetheless, outlier tests identified 3.6–6.6% of loci as high FST outliers, suggesting that despite strong genetic drift, divergent selection contributes to population divergence. Patterns of similarity among populations based on high FST outliers mirrored patterns based on morphology, providing additional evidence that outliers reflect adaptive divergence. Extremely low genetic variation and small Ne in some island fox populations, particularly on San Nicolas Island, suggest that they may be vulnerable to fixation of deleterious alleles, decreased fitness and reduced adaptive potential

Mathematica code for basic model

Develops recursion equations for basic model in standard population genetic notation

Data from: Male mate choice, male quality, and the potential for sexual selection on female traits under polygyny.

Observations of male mate choice are increasingly common, even in species with traditional sex roles. In addition, female traits that bear the hallmarks of secondary sexual characters are increasingly reported. These concurrent empirical trends have led to the repeated inference that, even under polygyny, male mate choice is a mechanism of sexual selection on female traits. It is often either assumed or argued that in these cases females are competing for males of superior “quality”; females might experience sexual selection under polygyny if they compete for mates that provide either direct or indirect benefits. However, the theoretical foundation of this testable hypothesis remains largely uninvestigated. We develop a population genetic model to probe the logic of this hypothesis and demonstrate that, contrary to common inferences, male mate choice, variation in male quality (in the form of a direct fecundity benefit to females), and female ornamentation can coexist in a population without any sexual selection on female ornamentation taking place at all. Furthermore, even in a “best case scenario” where high quality males with a preference for ornamented females are able to mate disproportionately more often with them, the evolution of female traits by sexual selection may be relatively weak. We discuss the implication of these findings for ongoing empirical and theoretical research on the evolution of sexual-signaling in females

Mathematica code for basic model

Develops recursion equations for basic model in standard population genetic notation