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Abstract This manuscript shares the lessons learned from providing scientific computing support to over 600 researchers and discipline experts, helping them develop reproducible and scalable analytical workflows to process large amounts of heterogeneous data.When providing scientific computing support, focus is first placed on how to foster the collaborative aspects of multidisciplinary projects on the technological side by providing virtual spaces to communicate and share documents. Then insights on data management planning and how to implement a centralized data management workflow for data‐driven projects are provided.Developing reproducible workflows requires the development of code. We describe tools and practices that have been successful in fostering collaborative coding and scaling on remote servers, enabling teams to iterate more efficiently. We have found short training sessions combined with on‐demand specialized support to be the most impactful combination in helping scientists develop their technical skills.Here we share our experiences in enabling researchers to do science more collaboratively and more reproducibly beyond any specific project, with long‐lasting effects on the way researchers conduct science. We hope that other groups supporting team‐ and data‐driven science (in environmental science and beyond) will benefit from the lessons we have learned over the years through trial and error. 

Abstract The number of protected areas that restrict or prohibit harvest of wild populations is growing. In general, protected areas are expected to increase the abundance of previously‐harvested species. Whether a protected area achieves this expectation is typically evaluated by assessing trends in abundance after implementation. However, the underlying assumption that harvest has actually ceased is rarely tested directly. Determining whether illegal harvest (poaching) has continued in a protected area is important to planning enforcement and adaptive management. Here, we estimated harvest rates for four kelp forest fish species inside marine protected areas (MPAs) and non‐MPA reference sites in the California Channel Islands, from 2003 (when MPAs were implemented) to 2017. We estimated harvest by fitting a size‐structured population model to survey data. Overall, harvest rates were effectively zero in MPAs but much higher in non‐MPA sites. This indicates successful adherence to MPA regulations, and possible displacement of fishing effort to reference sites. However, some poaching was detected in two MPA sites, highlighting the importance of assessing this quantity. This modeling approach could provide a tool to complement the long‐term management of MPA networks, particularly given the difficulty of acquiring harvest rate data at the spatial scale of individual MPAs.