The joint role of coevolutionary selection and network structure in shaping trait matching in mutualisms

Coevolution can sculpt remarkable trait similarity between mutualistic partners. Yet, it remains unclear which network topologies and selection regimes enhance trait matching. To address this, we simulate coevolution in topologically distinct networks under a gradient of mutualistic selection strength. We describe three main insights. First, trait matching is jointly influenced by the strength of mutualistic selection and the structural properties of the network where coevolution is unfolding. Second, the strength of mutualistic selection determines the network descriptors better correlated with higher trait matching. While network modularity enhances trait matching when coevolution is weak, network connectance does so when coevolution is strong. Third, the structural properties of networks outrank those of modules or species in determining the degree of trait matching. Our findings suggest networks can both enhance or constrain trait matching, depending on the strength of mutualistic selection.

Core Self-Evaluation Moderates Distinctive Similarity Preference in Ideal Partner’s Personality

This research investigated the moderation effects of core self-evaluation (CSE) on singles’ ideal partner preference, concerning distinctive similarity in personality. The data were collected from singles from three countries (i.e., China, Denmark, and US), and modelled in a multilevel profile analysis. The results show that CSE moderated distinctive profile similarity preference in that people high in CSE preferred higher distinctive profile similarity with their ideal partner. In addition, CSE moderated distinctive trait similarity preference in Emotionality, Extraversion, Agreeableness, and Conscientiousness in that people high in CSE preferred their ideal partner to share higher distinctive similarity on these four traits. Implications and limitations of the research and findings are also discussed.

Effect of Water Exchange Rate on Interspecies Competition Between Submerged Macrophytes: Functional Trait Hierarchy Drives Competition

Aims: Although the relative contributions of the “competition-trait similarity” and “competition-trait hierarchy” hypotheses in predicting competitive outcomes in response to environmental variation has recently been investigated in terrestrial plants, their validity in aquatic plants remain poorly understood, particularly in terms of variation in the water exchange rate (WER). Methods: To this end, this study investigated the influence of WER variation on interspecies competition and functional traits in two pairs of submerged macrophytes (Vallisneria natans vs. Myriophyllum aquaticum and V. natans vs. Myriophyllum spicatum) under three levels of WER using the replacement series experiment. Results: Results showed that V. natans was a stronger competitor than either Myriophyllum species in static waterbodies. However, the relative competitive ability of V. natans consistently decreased with increasing WER and decreasing its planting proportion, which would eventually change it from a stronger to weaker competitor. Between species pairs, most functional traits showed competently opposite patterns to increasing WER and decreasing the planting proportion of V. natans. Conclusions: Our results indicate that WER affected the outcome of interspecies competition between submerged macrophyte species, and moreover, the relative competition ability of each species within a pair was linked strongly to species’ competition-trait hierarchy than to competition-trait similarity.

The joint role of coevolutionary selection and network structure in shaping trait complementarity in mutualisms

Coevolution can sculpt remarkable trait similarity between mutualistic partners. Yet, it remains unclear which network topologies and selection regimes enhance such trait complementarity. To address this, we simulate coevolution in topologically-distinct net-works under a gradient of mutualistic selection strength. We describe three main insights. First, trait matching is jointly influenced by the strength of mutualistic selection and the structural properties of the network where coevolution is unfolding. Second, the strength of mutualistic selection determines the network descriptors better correlated with higher trait matching. When coevolution is weak, network modularity enhances trait matching, but when it is strong, network connectance amplifies trait matching. Third, the structural properties of networks outrank those of modules or species in determining the evolved degree of trait matching. Our findings suggest networks can both enhance or constrain trait complementary, depending on the strength of mutualistic selection.

Musings on Visscher et al. (2006)

The classical twin design relies on a number of strong number of assumptions in order to yield unbiased estimates of heritability. This includes the equal environments assumption — that monozygotic and dizygotic twins experience similar degrees of environmental similarity — an assumption that is likely to be violated in practice for many traits of interest. An alternative method of estimating heritability that does not suffer from many of these limitations is to model trait similarity between sibling pairs as a function of their empirical genome-wide identity by descent sharing, estimated from genetic markers. In this review, I recount the story behind Nick Martin’s and my development of this method, our first attempts at applying it in a human population and more recent studies using the original and related methods to estimate trait heritability.