Cumulative effects research in SpaDES
Natural resource management decisions benefit from cumulative effects research that efficiently integrates scientific knowledge across multiple disciplines in a timely manner. Unfortunately, researchers frequently encounter barriers to efficient integration, especially when it comes to data, models, and analyses, due to the inaccessibility and non-interoperability of these components, which contributes to disciplinary ‘siloing’ and can delay scientific outputs. Using modern tools to facilitate a continuous and adaptive workflow, I present an overview and results from two recent large scale simulation studies developed using the SpaDES package for R. I reflect on the successes and the challenges of these projects, and highlight key insights from the processes implemented for them.