Beyond kinship
Pedigrees capture a wealth of information beyond individual relationships produced purely by genomic data (Clutton-Brock & Sheldon, 2010).  The behavioral and ecological observations required to provide inter-individual relationships results in a rich ancillary dataset that cannot be captured by genomic data alone. For example, pedigrees can discern different relationships with identical relatedness coefficients (e.g., half-siblings compared to grandparent-grandoffspring, R = 0.25), which can have different social and ecological consequences. Pedigrees also carry rich demographic data that may be inaccessible from molecular data, including sex for species without genetic sex determination (Janzen & Paukstis, 1991), cohort, number of offspring, age, and survival. Phenotypic data collected alongside ancestry is often extensive, including morphometrics (e.g., weight, size, body condition), cause of death, behaviour, and signs of inbreeding depression (e.g., disease susceptibility, infertility). On its own, the metadata captured alongside pedigrees can be used to forecast best management practices for small populations through population viability analysis (i.e., PVA; Lacy & Pollak, 2021) and provides a critical resource for understanding demography and fitness (e.g., variance in reproductive success). For example, a recent study harnessed pedigree data from 15 species (> 30K individuals) to show how generations in captivity impact survival (Farquharson, Hogg, & Grueber, 2021). Another study assessing breeding in 39 pedigreed populations of 21 wild animal species (> 35K females) concluded that many species were able to buffer annual fluctuations in optimal breeding date through phenotypic plasticity (de Villemereuil et al., 2020). Meta-analyses on this scale would be impossible to ascertain using genomic data alone, given these studies rely on life history data carried in pedigrees. Further, metadata captured in pedigrees can also be integrated with genomic approaches, for example quantitative trait locus mapping (Pelgas, Bousquet, Meirmans, Ritland, & Isabel, 2011), genome-wide association studies (GWAS; Morris et al., 2013), assessing adaptive potential (de Villemereuil et al., 2019a), genomic selection (GS) studies, and GxE studies to test genotypes for association with environmental variation (Crossa et al., 2017; see below).