Collaboration and engagement produce more actionable science: quantitatively analyzing uptake of fish tracking...

Aquatic telemetry technology generates new knowledge about the underwater world that can inform decision-making processes and thus can improve conservation and natural resource management. Still, there is lack of evidence on how telemetry-derived knowledge can or has informed management, and what factors facilitate or deter its use. We present one of the first quantitative studies related to the science-action gap and evaluate factors that influence the uptake of fish telemetry findings into policies and practices, as well as social acceptance of these findings. We globally surveyed 212 fish telemetry researchers regarding the knowledge uptake of an applied fish telemetry research project of their choice. Respondents' personal and professional attributes, as well as the attributes of their chosen projects, were analyzed using machine learning algorithms to identify important factors that influenced the uptake (i.e., use, trust, and/or acceptance) of their findings. Researchers with extensive collaborations and who spent more time engaging in public outreach experienced greater uptake of their findings. Respondents with greater telemetry experience and commitment (e.g., more telemetry publications, higher proportion of research on fish telemetry) tended to achieve more social acceptance of their findings. Projects led by researchers who were highly involved and familiar with the fisheries management processes, and those where greater effort was devoted to research dissemination, also tended to experience greater uptake. Last, the levels of complexity and controversy of the issue addressed by the research project had a positive influence on the uptake of findings. The empirical results of this study support recent messages in the science practitioner literature for greater collaboration, knowledge co-production with partners, and public engagement to enable the transfer of knowledge and the use of evidence in decision-making and policies. Scientific organizations should consider shifting reward incentives to promote engagement and collaboration with non-scientific actors, and perhaps even rethinking hiring practices to consider personal and professional characteristics or attitudes such as altruism and networking skills given the influence of these factors in our model. Last, networks composed of both research and practice potentially have a key role in brokering and facilitating knowledge exchange and actions.