Wild Pedigree exploreR (wpeR): Streamlined Analysis and Visualization of Wild Pedigrees in Time and Space

Advances in non-invasive genetic sampling and long-term genetic monitoring programmes have enabled collection of large individual genotype datasets for many wildlife populations, often accompanied by rich field metadata that place the genotyped individuals in time and space. These datasets allow reconstruction of multigenerational pedigrees and have the potential to provide valuable insights into population demography, reproduction, dispersal, social structure and genetic processes. But while the tools for construction of pedigrees keep improving, their interpretation remains challenging. Integrating multigenerational pedigree data with field metadata creates significant complexity, yet specialized tools to facilitate the interpretation of such datasets remain scarce. Here we introduce wild pedigree exploreR (wpeR), an R package designed to simplify exploration, organization and interpretation of complex pedigrees. The package enables users to link reconstructed pedigrees with genetic sample metadata, enabling evaluation of biological plausibility of inferred relationships, but also allowing exploration of other characteristics of individuals and populations in spatial and temporal contexts. wpeR implements a linear workflow through which the pedigree data is imported, formatted, organized into families and integrated with field metadata. The resulting dataset can be visualized through temporal plots that track individuals and families over time, as well as with spatial outputs representing parent-offspring relationships and individual movement patterns as geographic features that can be either directly visualized on maps within R, or exported to be further explored with common GIS tools. wpeR allows exploration of lineage relationships within their ecological context, bridging the gap between statistically reconstructed pedigrees and their biological interpretation. It provides a scalable and flexible framework for analyzing these complex data, providing a practical tool for researchers and managers working with genetic monitoring datasets.