From Micro- To Macroevolution Through Quantitative Genetic Variation: Positive Evidence From Field Crickets

Abstract

. ‐Quantitative genetics has been introduced to evolutionary biologists with the suggestion that microevolution could be directly linked to macroevolutionary patterns using, among other parameters, the additive genetic variance/ covariance matrix (G) which is a statistical representation of genetic constraints to evolution. However, little is known concerning the rate and pattern of evolution of G in nature, and it is uncertain whether the constraining effect of G is important over evolutionary time scales. To address these issues, seven species of field crickets from the genera Gryllus and Teleogryllus were reared in the laboratory, and quantitative genetic parameters for morphological traits were estimated from each of them using a nested full‐sibling family design. We used three statistical approaches (T method, Flury hierarchy, and Mantel test) to compare G matrices or genetic correlation matrices in a phylogenetic framework. Results showed that G matrices were generally similar across species, with occasional differences between some species. We suggest that G has evolved at a low rate, a conclusion strengthened by the consideration that part of the observed across‐species variation in G can be explained by the effect of a genotype by environment interaction. The observed pattern of G matrix variation between species could not be predicted by either morphological trait values or phylogeny. The constraint hypothesis was tested by comparing the multivariate orientation of the reconstructed ancestral G matrix to the orientation of the across‐species divergence matrix (D matrix, based on mean trait values). The D matrix mainly revealed divergence in size and, to a much smaller extent, in a shape component related to the ovipositor length. This pattern of species divergence was found to be predictable from the ancestral G matrix in agreement with the expectation of the constraint hypothesis. Overall, these results suggest that the G matrix seems to have an influence on species divergence, and that macroevolution can be predicted, at least qualitatively, from quantitative genetic theory. Alternative explanations are discussed.

Mating System and the Evolution of Quantitative Traits: An Experimental Study of Mimulus Guttatus

“…Lande (1979) developed an explicit quantitative method for relating genetic variances and covariances within species to differences in mean trait values among species. This approach assumes constancy of the relevant genetic parameters, an assumption that has received substantial empirical scrutiny (Steppan et al 2002;Phelan et al 2003;Begin and Roff 2004;Manuel Cano et al 2004). Our results demonstrate that genetic variances and covariances are likely to change rather dramatically, over short time spans, if the mating system of a population changes simultaneously with selection.…”

What affects the predictability of evolutionary constraints using aG-matrix? The relative effects of modular pleiotropy and mutational correlation

“…Furthermore, genetic correlations among phenotypic traits may cause the reduction of genetic variance for specific combination of traits, with, in the most extreme case, complete absence of adaptive variation and thus no response to selection in a specific direction of phenotypic space on which selection may act (i.e. Similar evolutionary constraints imputed to g max have been reported in empirical studies (Blows and Higgie 2003;Bégin and Roff 2004;Marroig and Cheverud 2005;Simon et al 2016), while others argued against such a role (Merila and Bjorklund 1999;McGuigan et al 2005;Berner et al 2010;Walling et al 2014). The distribution of genetic variation in multivariate trait space is obtained from the diagonalization (eigen decomposition) of the G-matrix, with g max the first eigenvector (first principal component) and the direction of greatest genetic covariation of the traits, and thus of greatest response to selection (also called the "line of least resistance" by Schluter 1996).…”

MIPoD: A Hypothesis‐Testing Framework for Microevolutionary Inference from Patterns of Divergence

“…Nonetheless, G has been used in diverse applications that include (1) description and comparison of genetic constraints (e.g., Arnold 1992;Cheverud 1995;Steppan et al 2002), (2) assessing the consequences of alternative schemes of directional selection (e.g., Cheverud et al 1983), (3) retrospective analysis of selection using divergence data (Lande 1979;Price et al 1984;Schluter 1984;Arnold 1988;Dudley 1996;Reznick et al 1997;Jones et al 2004), (4) estimation of generalized genetic distance (Lande 1979;Schluter 1984), (5) testing for evolution along genetic lines of least resistance (Schluter 1996), and (6) testing for proportionality between G and matrices of population divergence (Lofsvold 1988;Blows and Higgie 2003;Bégin and Roff 2004;McGuigan et al 2005). Despite these applications, G has not been used in a variety of other circumstances in which it is likely to supply critical information.…”

“…As Lande (1979) pointed out, neutral divergence of multivariate population means should be proportional to the G matrix. Although a few authors have tested this prediction (e.g., Lofsvold 1988;Blows and Higgie 2003;Bégin and Roff 2004;McGuigan et al 2005;Hunt 2007), no multivariate methodology has been proposed that takes account of phylogeny and that separates the multiple hypotheses that are testable in this context. We develop our analysis in a maximum likelihood (ML) framework.…”