Supporting habitat restoration in the northern Gulf of Mexico through synthesis of data on multiple and interacting benefits and stressors.

Abstract

Outcomes of landscape scale restoration and conservation can be maximized when planning is based upon quantitative and decision-relevant information. Existing tools to support data-driven planning are hindered by regionally inconsistent information and a need for advanced methods to analyze data of varying spatial resolution and coverage. We present a synthesis methodology for region-wide derived metrics to characterize natural resource value, ecosystem stress, and social vulnerability to inform implementation of conservation and restoration projects. Our three-part methodology was developed and tested for the Gulf of Mexico in support of the Southeast Conservation Blueprint that was created to advance the Southeast Conservation and Adaptation Strategy. The first step included integration of prioritized natural resource metrics alongside socio-ecological metrics to create a data layer of synthesized natural resource priority across the northern Gulf of Mexico. The second component was calculation of ecosystem stress indices based on ecologically relevant thresholds and a cumulative ecosystem stress layer, in addition to analyzing correlations between individual stressors and their relative importance. The final component was development of a social vulnerability (SoVI) index. Analysis of these metrics illustrate their ability to effectively capture variability at multiple scales in the Gulf of Mexico, including expected spatial correlation of stressors such as road density and non-point source pollution in populated areas and the dominance of sea-level rise as a future stressor along the coast. Significant composite components of social vulnerability for the northern Gulf of Mexico region were identified and include economic status, professional workforce, elderly population, population stability, migrant workforce, and rural population. To demonstrate the utility of the data synthesis approach, we used the developed data layers to evaluate proposed marsh creation projects in southern Louisiana. The synthesized data layers were capable of distinguishing differences at the scale of individual habitat restoration projects, and high-value projects could be aligned with the goals of key funding streams. This pilot application illustrates how restoration programs could use the methodology developed here to maximize benefits from conservation and restoration actions along the northern Gulf of Mexico or other regions globally.