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With dramatic advancements in biological data generation, genetic rescue and reproductive technologies, and inter-institutional coordination of care across entire animal populations, zoos, aquariums, and their collaborators are uniquely positioned to lead population-wide research benefiting animal wellbeing and species survival. However, procedural and inter-institutional barriers make it exceedingly difficult to access existing zoological biospecimens and data at scale. To address this, the Zoonomics Working Group, representing diverse roles across three zoological associations (AZA, EAZA, WAZA), proposes a biodiversity biobank alliance that develops and delivers shared resources to support the collection, storage, and sharing of biological samples and associated data across the zoological and conservation community. By biobank alliance, we mean a community-guided effort that develops shared resources, standards, ethos, and practices for collecting, storing, and sharing biological samples and associated data voluntarily through transparent processes, consistent with professional accreditation standards and international best practices. While initially focused on addressing the needs and regulatory landscape of U.S. institutions, the alliance is designed to create frameworks that are adaptable and adoptable for international expansion. Such a framework would help the zoological community navigate the ethical, legal, and practical challenges of managing biospecimen collections, making access more efficient, reliable, and robust. Achieving this vision requires collective agreement on ethical principles such as reciprocity, transparency, and data stewardship, ensuring that research is both feasible and proactively supported. Such coordination will drive advances in fundamental biology and accelerate progress in animal health, welfare, management, and biodiversity conservation. 

Abstract Researchers have long debated which estimator of relatedness best captures the degree of relationship between two individuals. In the genomics era, this debate continues, with relatedness estimates being sensitive to the methods used to generate markers, marker quality, and levels of diversity in sampled individuals. Here, we compare six commonly used genome‐based relatedness estimators (kinship genetic distance [KGD], Wang maximum likelihood [TrioML], Queller and Goodnight [R_xy], Kinship INference for Genome‐wide association studies [KING‐robust), and pairwise relatedness [R_AB], allele‐sharing coancestry [AS]) across five species bred in captivity–including three birds and two mammals–with varying degrees of reliable pedigree data, using reduced‐representation and whole genome resequencing data. Genome‐based relatedness estimates varied widely across estimators, sequencing methods, and species, yet the most consistent results for known first order relationships were found usingR_xy,R_AB, and AS. However, AS was found to be less consistently correlated with known pedigree relatedness than eitherR_xyorR_AB. Our combined results indicate there is not a single genome‐based estimator that is ideal across different species and data types. To determine the most appropriate genome‐based relatedness estimator for each new data set, we recommend assessing the relative: (1) correlation of candidate estimators with known relationships in the pedigree and (2) precision of candidate estimators with known first‐order relationships. These recommendations are broadly applicable to conservation breeding programmes, particularly where genome‐based estimates of relatedness can complement and complete poorly pedigreed populations. Given a growing interest in the application of wild pedigrees, our results are also applicable to in situ wildlife management.