A framework for optimising opportunistic collaborative syntheses to propel ecological conservation

Ecological data are being opportunistically synthesised at unprecedented scales in response to the global biodiversity and climate crises. Such syntheses are often only possible through large-scale, international, multidisciplinary collaborations and provide important pathways for addressing urgent conservation questions. Although large collaborative data syntheses can lead to high-impact successes, they can also be plagued with difficulties. Challenges include the standardisation of data originally collected for different purposes, integration and interpretation of knowledge sourced across different disciplines and spatio-temporal scales, and management of differing perspectives from contributors with distinct academic and cultural backgrounds. Here, we use the collective expertise of a global team of conservation ecologists and practitioners to highlight common benefits and hurdles that arise with the development of opportunistic collaborative syntheses. We outline a framework of "best practice" for developing such collaborations, encompassing the design, implementation, and deliverable phases. Our framework addresses common challenges, highlighting key actions for successful collaboration and emphasizing the support requirements. We identify funding as a major constraint to sustaining the large, international, multidisciplinary teams required to advance collaborative syntheses in a just, equitable, diverse, and inclusive way. We further advocate for thinking strategically from the outset and highlight the need for reshaping funding agendas to prioritize the structures required to propel global scientific networks. Our framework will advance the science needed for ecological conservation and the sustainable use of global natural resources by supporting proto-groups initiating new syntheses, leaders and participants of ongoing projects, and funders who want to facilitate such collaborations in the future.

DP DP210103091; FT190100599; ERC-AdG-883583