Social effects of territorial neighbours on the timing of spring breeding in North American red squirrels

Abstract

Organisms can affect one another’s phenotypes when they socially interact. Indirect genetic effects occur when an individual’s phenotype is affected by genes expressed in another individual. These heritable effects can enhance or reduce adaptive potential, thereby accelerating or reversing evolutionary change. Quantifying these social effects is therefore crucial for our understanding of evolution, yet estimates of indirect genetic effects in wild animals are limited to dyadic interactions. We estimated indirect phenotypic and genetic effects, and their covariance with direct effects, for the date of spring breeding in North American red squirrels (Tamiasciurus hudsonicus) living in an array of territories of varying spatial proximity. Additionally, we estimated variance parameters and the strength of selection at low and high population densities. Social effects of neighbours on the date of spring breeding were weak at low, but stronger at high population densities. Indirect phenotypic effects accounted for a larger amount of variation in the date of breeding than direct differences among-individuals, although the genetic component to these indirect effects was not statistically significant. Nevertheless, the estimated effect size was large enough to suggest that indirect genetic effects could alter evolutionary change, resulting in less change at high densities despite stronger selection. Despite the difficulty in estimating them precisely, indirect genetic effects have clear potential to alter evolutionary trajectories in any natural systems where organisms interact.

Dynamic network partnerships and social contagion drive cooperation

“…The genetic basis of social influence could be tested in future studies that build on our approach by using genotyping to resolve the population pedigree. This will require analytical methods that can account for genetic structure in the social environment [16,18,24].…”

“…Recent experiments have demonstrated that cooperative and social behaviours can be transmitted via social contagion and dynamic network partnerships in flies, fish, and humans [12,14,15,[21][22][23]. To date, however, these laboratory studies have considered simple dyadic or small group interactions that do not reflect realistic social complexity (but see [24]), and may overestimate social influence [8,25]. Thus, a complete understanding of social influence will require collecting repeated measures of behaviour while accounting for who interacts with whom, and the frequency with which these interactions occur in a dynamic social network.…”

Indirect genetic effects clarify how traits can evolve even when fitness does not

“…V AD,W is generally expected to be low for populations in stable environments (for a review of estimates, see Hendry et al 2018), as directional selection is expected to erode genetic variance in fitness, while we are unaware of any estimates of cov A (D, I ), but have some expectations for how it should change with n or density (see above). We encourage empiricists to estimate these and, for example, to test whether IGEs get weaker as larger groups are considered (e.g., see the supplamentary materials of Fisher et al 2018).…”

Dynamic network partnerships and social contagion drive cooperation

“…The genetic basis of social influence could be tested in future studies that build on our approach by 388 using genotyping to resolve the population pedigree. This will require analytical methods that can account for genetic structure in the social environment [16,18,24]. 390…”

“…Recent experiments have demonstrated that cooperative 96 and social behaviors can be transmitted via social contagion and dynamic network partnerships in flies, fish, and humans [12,14,15,[21][22][23]. To date, however, these laboratory studies have 98 considered simple dyadic or small group interactions that do not reflect realistic social complexity [but see 24], and may overestimate social influence [8,25]. Thus, a complete 100 understanding of social influence within in a dynamic social network will require collecting repeated measures of behavior while accounting for who interacts with whom, and the frequency 102 with which these interactions occur.…”