Effective marine protected areas (MPAs) can help increase biodiversity, and the number and size of fish within their boundaries. They can also improve fisheries catches outside MPAs. To design effective MPAs decision-makers need accurate and understandable data and recommendations.
There are many MPA design guidelines scattered across reports and scientific papers. A new paper by the Environmental Markets Lab (emLab) at the University of California, Santa Barbara reviews over 300 MPA design recommendations and synthesizes these into 24 condensed guidelines to help streamline MPA planning.
These guidelines provide insights to decision makers and planners on choosing design recommendations that are the most appropriate for 1) the planning area, and 2) achieving specific conservation objectives.
Image: “Finding Harmony in MPA Guidelines” infographic by the EmLab at University of California, Santa Barbara.
Main takeaways include:
The synthesis provides practitioners and decision-makers with a one-stop source for MPA design guidelines, avoiding the need to read through many reports and papers.
The guidelines are directly linked to MPA objectives, like protecting areas of high productivity or minimizing negative impacts to fisheries.
The guidelines provide an understandable context for decision-makers to be able to choose which guidelines are most appropriate to consider, in terms of ocean region (nearshore or offshore) and the conservation objectives they'd like to achieve.
The paper provides insight into data, models, and tools that practitioners can use in the MPA planning process.
About the Paper:
In the paper “Finding Harmony in MPA Guidelines,” emLab conducted a comprehensive review of literature containing design recommendations for fully-protected MPAs and synthesized recommendations into more digestible design "guidelines."
They started with 307 unique design recommendations and ended up with 24 condensed guidelines. These guidelines are grouped by the conservation objectives that they could help to achieve. Additional information on tools, models, and datasets that can be used in MPA implementation are provided.
Read the paper here online here: https://conbio.onlinelibrary.wiley.com/doi/full/10.1111/csp2.12946