Social structure has been widely recognized to have significant effects on genetic diversity. But the type of social structure (e.g. polygyny, monogyny, cooperative breeding) and dispersal patterns can either enhance or reduce genetic diversity. Therefore, large uncertainties still prevail about how social structure affects genetic diversity, and how species may respond to disruptions of their (social) structure is unknown. The GINS project added crucial results to this gap of knowledge. It advanced our understanding of social structure by contributing the following results:
i) a set of scripts developed in the widely used SLiM simulation language, designed to model genomic data in populations subdivided into social groups under various mating systems. These scripts are adaptable and can be readily applied by other researchers to study genetic dynamics in their own species of interest;
ii) a theoretical framework that predicts the expected distributions of Identity-by-Descent (IBD) tracts and Runs of Homozygosity (ROH) under varying social structures. These expectations serve as a critical baseline for interpreting patterns observed in empirical genomic data.
iii) Further, this work found that the first entry of the Site Frequency Spectrum (SFS) - singleton class - can be used to distinguish between certain (‘extreme’) mating systems, such as monogamy and polygyny. Since this class captures the number of segregating sites where the derived allele is present in only one individual, it is particularly sensitive to recent evolutionary events. However, it is also highly error-prone, especially in the presence of sequencing artifacts or low coverage data. As such, while these initial findings are promising, further investigation is required to fully understand the robustness and applicability of this signal. Ongoing work is focused on exploring additional summary statistics that may complement or enhance the inference of mating and dispersal systems.
iv) Findings from this project underscore the critical role of social structure in shaping genetic diversity and influencing demographic inferences. Specifically, I demonstrated that social organization can bias genetic signals, producing false indications of population expansion - even when such growth has not occurred. These biases arise because social structure affects patterns of gene flow and relatedness, which in turn distort coalescent-based demographic inferences. This result has significant implications for conservation genetics, particularly in endangered species, where misinterpreted signals of population expansion may lead to underestimating extinction risk and misallocating conservation resources.
