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Meta-analyses have brought a significant improvement in our understanding of global biodiversity change. However, in ecology the static nature of current approaches, both in terms of the data included and the predictions presented, make meta-analyses difficult for policymakers to fully interrogate and adopt. Here we introduce Dynameta, a living-review (i.e. continually updateable) R Shiny platform for interactive ecological meta-analyses, oriented around testing the effect of anthropogenic threats on biodiversity. This platform is written as an R package and can be applied in the context of any meta-analytic PICO (Population, Intervention, Comparator, and Outcome) question concerning the effect of any threat on any taxonomic group, for any biodiversity metric and with relevance to all geographic regions. Our hope is that, alongside other existing tools, Dynameta can help encourage the broader adoption of dynamic meta-analyses in ecology.

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Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe a workflow for generating annual estimates of species’ occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, a key output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5,000 species in each of several defined “regions” (e.g., countries, protected areas, etc.) of the United Kingdom from 1970-2019. This product corresponds closely to the notion of a species distribution “Essential Biodiversity Variable” (EBV). Throughout the paper, we note where the workflow can be adapted to other situations (e.g., geographic regions or data types). We also highlight areas where the workflow can be improved; in particular, we suggest incorporation of methods to diagnose biases in the species occurrence data, to understand whether and to what extent these bias downstream products, and to mitigate them if needed. Finally, we compare the data products generated using our workflow to the first generation of species distribution EBVs and the “idealized” product as defined by others. Going forward, we hope that this paper can act as a template for research groups around the world seeking to develop similar data products.