Age-dependent genetic variation in aggression
Fortunato, Jennifer; Earley, Ryan L. (2023), Age-dependent genetic variation in aggression, Dryad, Dataset, https://doi.org/10.5061/dryad.k0p2ngfcd
Understanding the extent to which behavioural variance is underlain by genotypic, environmental and genotype-by-environment effects is important for predicting how behavioural traits might respond to selection and evolve. How behaviour varies both within and among individuals can change across ontogeny, leading to differences in the relative contribution of genetic and environmental effects to phenotypic variation across ages. We investigated among-individual and among-genotype variation in aggression across ontogeny by measuring, twice as juveniles and twice as adults, both approaches and attacks against a three-dimensional-printed model opponent in eight individuals from each of eight genotypes ( N = 64). Aggression was only significantly repeatable and heritable in juveniles. Additionally, how aggression changed between juvenile and adult life-history stages varied significantly among individuals and genotypes. These results suggest that juvenile aggression is likely to evolve more rapidly via natural selection than adult aggression and that the trajectory of behavioural change across the lifespan has the potential to evolve. Determining when genetic variation explains (or does not explain) behavioural variation can further our understanding of key life-history stages during which selection might drive the strongest or swiftest evolutionary response.
We collected our data by recording fish behavior and then counting approaches and bites toward the model. We then added approaches and bites to get total aggression. We used an excel document to record the data. The columns in the data set include individual (a unique individual identification number), video (a video identifier), genotype (the genotype of the individual), age (the age of the fish), time (which order the video was taken), observer (who observed the fish), approaches (the number of approaches towards a model), bites (the number of bites towards a model), latency to move, latency to approach, latency to attack (all latencies were for how long it took the fish to recover from the barrier being removed, and approach or attack the model) graphtime (the age of the fish), and aggression (total number of approaches and bites).
We used R studio (R version 3.4) to analyze the data. We used lmer from the R package lme4 to analyze our data with linear models. We used the rptGaussian function from the R package rptR to obtain heritability and repeatability.
Sigma Xia, Award: G2018031596030532
Sigma Xi, Award: G2018031596030532