Comparative Analyses of Phenotypic Trait Covariation within and among Populations

Abstract

Many morphological, behavioral, physiological, and life-history traits covary across the biological scales of individuals, populations, and species. However, the processes that cause traits to covary also change over these scales, challenging our ability to use patterns of trait covariance to infer process. Trait relationships are also widely assumed to have generic functional relationships with similar evolutionary potentials, and even though many different trait relationships are now identified, there is little appreciation that these may influence trait covariation and evolution in unique ways. We use a trait-performance-fitness framework to classify and organize trait relationships into three general classes, address which ones more likely generate trait covariation among individuals in a population, and review how selection shapes phenotypic covariation. We generate predictions about how trait covariance changes within and among populations as a result of trait relationships and in response to selection and consider how these can be tested with comparative data. Careful comparisons of covariation patterns can narrow the set of hypothesized processes that cause trait covariation when the form of the trait relationship and how it responds to selection yield clear predictions about patterns of trait covariation. We discuss the opportunities and limitations of comparative approaches to evaluate hypotheses about the evolutionary causes and consequences of trait covariation and highlight the importance of evaluating patterns within populations replicated in the same and in different selective environments. Explicit hypotheses about trait relationships are key to generating effective predictions about phenotype and its evolution using covariance data.

Dormancy in metacommunities

“…Positive covariation could 166 arise under a number of conditions, such as when environmental favorability changes rapidly or unpredictably in both space and time (Venable and Brown 1988;Cohen and Levin 1991;Snyder 168 2006;Buoro and Carlson 2014). Positive dispersal-dormancy covariation may also be due to genetic linkage or pleiotropy (Peiman and Robinson 2017), such as when traits that increase 170 capacities for dormancy interact with traits that enhance dispersal abilities, or vice versa. Therefore, positive selection for dispersal or dormancy indirectly selects for the other strategy as 172 well.…”

Response to joint selection on germination and flowering phenology depends on the direction of selection

“…Selection on a single trait may result in the evolution of multiple traits, as trait correlations are pervasive due to shared genetic, developmental, functional, or environmental associations (Conner & Hartl, 2004; Peiman & Robinson, 2017). The strength and orientation of genetic correlations, in conjunction with the pattern of selection, can enhance or constrain evolutionary response (Agrawal & Stinchcombe, 2009; Conner et al., 2011; Lande, 1979; Simonsen & Stinchcombe, 2010; Teplitsky et al., 2014; Walling et al., 2014).…”

“…In particular, life history traits that comprise complex phenotypes such as phenology (Armbruster, Pélabon, Bolstad, & Hansen, 2014; Murren, 2012; Peiman & Robinson, 2017), may have both genetic and functional linkages. For example, in Arabidopsis thaliana , the FLOWERING LOCUS C (FLC) gene regulates flowering time and mediates germination time by affecting seed dormancy through a pleiotropic genetic correlation (Chiang et al., 2009).…”

“…Early‐flowering plants disperse seeds early, increasing the potential for fall germination of offspring, while seeds of late‐flowering plants are frequently dispersed in late fall when it is cool and are therefore more likely to germinate the following spring (Galloway, 2002; Galloway & Burgess, 2009, 2012). We predict that there is a genetic correlation between timing of germination and flowering, so they provide a coordinated influence on life history schedule (Peiman & Robinson, 2017). Furthermore, we predict that the correlation is negative as the traits represent maternal and offspring influences on life history schedule.…”

Genetic distance predicts trait differentiation at the subpopulation but not the individual level in eelgrass, Zostera marina

“…Alternatively, the strong positive correlations among many traits (Table 1) suggest that measuring relatively few simple traits might be a simpler proxy for overall trait differentiation. A better understanding of the mechanistic reason for these correlations would help assess the extent to which this is possible (Peiman & Robinson, 2017). …”