American Society of Naturalists

Reconsidering phenotypic covariation and evolution through the lens of different trait relationships

Many organismal features provide specific performance benefits, but Darwin was one of the first to suggest that combinations of different traits may do this as well. Trait co-occurrence is common and arises at different biological scales, such as among the average types of different species and even among individuals within a population, although the two levels do not have to correspond. This raises interesting questions about how traits evolve. Why are there strong relationships between certain traits, yet none between others? Why may this change from one population to the next, or across different biological scales? It is well known that evolution is influenced by trait relationships and different trait relationships have recently been identified. But little effort has addressed whether the type of relationship between traits should influence how traits co-occur.

The authors combine three conventional methods to consider the effects of different trait relationships on trait co-occurrence. The performance paradigm – which separates how traits affect performance, and how performance affects fitness – is used to identify three fundamental categories of trait relationships. Considering the genetic control of these different relationships reveals more complex genetic effects on trait co-occurrence then is usually considered. The authors then evaluate how trait co-occurrence evolves under selection using fitness landscapes. This clarifies the distinct roles of selection in generating adaptive trait co-occurrence (within a population) and adaptive diversity (among populations and species). Finally, they consider the opportunities and limitations of comparative approaches to evaluate the evolutionary causes and consequences of trait co-occurrence. This highlights the value of evaluating trait co-occurrence within populations replicated in the same and in different selective environments. This synthesis provides a new way to consider explicit hypotheses about trait relationships that will allow researchers to generate more effective predictions about trait evolution. Read the Article