Capturing indirect genetic effects on phenotypic variability: Competition meets canalization

Genetic Variation in Male Aggression Is Influenced by Genotype of Prior Social Partners in Drosophila melanogaster

AbstractSocial behaviors can be influenced by the genotypes of interacting individuals through indirect genetic effects (IGEs) and can also display developmental plasticity. We investigated how developmental IGEs, which describe the effects of a prior social partner's genotype on later behavior, can influence aggression in male Drosophila melanogaster. We predicted that developmental IGEs cannot be estimated by simply extending the effects of contextual IGEs over time and instead have their own unique effects on behavior. On day 1 of the experiment, we measured aggressive behavior in 15 genotypic pairings (n = 600 males). On day 2, each of the males was paired with a new opponent, and aggressive behavior was again measured. We found contextual IGEs on day 1 of the experiment and developmental IGEs on day 2 of the experiment: the influence of the day 1 partner's genotype on the focal individual's day 2 behavior depended on the genotypic identity of both the day 1 partner and the focal male. Importantly, the developmental IGEs in our system produced fundamentally different dynamics than the contextual IGEs, as the presence of IGEs was altered over time. These findings represent some of the first empirical evidence demonstrating developmental IGEs, a first step toward incorporating developmental IGEs into our understanding of behavioral evolution.

Genomic prediction for root and yield traits of barley under a water availability gradient: a case study comparing different spatial adjustments

BACKGROUND

In drought periods, water use efficiency depends on the capacity of roots to extract water from deep soil. A semi-field phenotyping facility (RadiMax) was used to investigate above-ground and root traits in spring barley when grown under a water availability gradient. Above-ground traits included grain yield, grain protein concentration, grain nitrogen removal, and thousand kernel weight. Root traits were obtained through digital images measuring the root length at different depths. Two nearest-neighbor adjustments (M1 and M2) to model spatial variation were used for genetic parameter estimation and genomic prediction (GP). M1 and M2 used (co)variance structures and differed in the distance function to calculate between-neighbor correlations. M2 was the most developed adjustment, as accounted by the Euclidean distance between neighbors.

RESULTS

The estimated heritabilities ([Formula: see text]) ranged from low to medium for root and above-ground traits. The genetic coefficient of variation ([Formula: see text]) ranged from 3.2 to 7.0% for above-ground and 4.7 to 10.4% for root traits, indicating good breeding potential for the measured traits. The highest [Formula: see text] observed for root traits revealed that significant genetic change in root development can be achieved through selection. We studied the genotype-by-water availability interaction, but no relevant interaction effects were detected. GP was assessed using leave-one-line-out (LOO) cross-validation. The predictive ability (PA) estimated as the correlation between phenotypes corrected by fixed effects and genomic estimated breeding values ranged from 0.33 to 0.49 for above-ground and 0.15 to 0.27 for root traits, and no substantial variance inflation in predicted genetic effects was observed. Significant differences in PA were observed in favor of M2.

CONCLUSIONS

The significant [Formula: see text] and the accurate prediction of breeding values for above-ground and root traits revealed that developing genetically superior barley lines with improved root systems is possible. In addition, we found significant spatial variation in the experiment, highlighting the relevance of correctly accounting for spatial effects in statistical models. In this sense, the proposed nearest-neighbor adjustments are flexible approaches in terms of assumptions that can be useful for semi-field or field experiments.

Importance of interindividual interactions in eco-evolutionary population dynamics: The rise of demo-genetic agent-based models

The study of eco-evolutionary dynamics, that is of the intertwinning between ecological and evolutionary processes when they occur at comparable time scales, is of growing interest in the current context of global change. However, many eco-evolutionary studies overlook the role of interindividual interactions, which are hard to predict and yet central to selective values. Here, we aimed at putting forward models that simulate interindividual interactions in an eco-evolutionary framework: the demo-genetic agent-based models (DG-ABMs). Being demo-genetic, DG-ABMs consider the feedback loop between ecological and evolutionary processes. Being agent-based, DG-ABMs follow populations of interacting individuals with sets of traits that vary among the individuals. We argue that the ability of DG-ABMs to take into account the genetic heterogeneity-that affects individual decisions/traits related to local and instantaneous conditions-differentiates them from analytical models, another type of model largely used by evolutionary biologists to investigate eco-evolutionary feedback loops. Based on the review of studies employing DG-ABMs and explicitly or implicitly accounting for competitive, cooperative or reproductive interactions, we illustrate that DG-ABMs are particularly relevant for the exploration of fundamental, yet pressing, questions in evolutionary ecology across various levels of organization. By jointly modelling the effects of management practices and other eco-evolutionary processes on interindividual interactions and population dynamics, DG-ABMs are also effective prospective and decision support tools to evaluate the short- and long-term evolutionary costs and benefits of management strategies and to assess potential trade-offs. Finally, we provide a list of the recent practical advances of the ABM community that should facilitate the development of DG-ABMs.