ManyZoos: A New Collaborative Approach to Multi-Institution Research in Zoos.

Abstract

Open science and big data approaches (i.e., approaches which enable the development of large and complex data sets) facilitate comparative analyses and thus more robust, evidence-based decision-making. Whilst there has been an increase in published research arising from zoological institutions over several decades, most research has arisen from small-scale case studies, often involving one or two zoos from a small geographical radius. Data from several zoos can be combined and compared retrospectively, but this is difficult when studies adopt different methods. The benefit of wider, simultaneous multi-institution research was recently demonstrated when researchers assessed the impact of zoo closures during the COVID-19 pandemic. In this paper, we introduce a new consortium initiative called ManyZoos, which aims to address the critical need for zoo science to expand even further geographically while incorporating additional institutions and disciplines. Like other "Many X" initiatives (e.g., ManyPrimates, ManyDogs), ManyZoos aims to foster more productive research collaborations between zoological collections and other animal collections, academia, government, and nongovernment organizations. In doing so, ManyZoos will address several current limitations of zoo research including small sample sizes and siloed expertise. ManyZoos embeds collaboration at every stage of research, from study conception to dissemination of results, producing large open data sets with transparent protocols. ManyZoos has the potential to lead to more robust, evidence-based decision-making for zoo animal management and conservation.