Numerical modeling efforts in support of restoration and protection activities in coastal Louisiana have traditionally been conducted externally to any stakeholder engagement processes. This separation has resulted in planning-and project-level models built solely on technical observation and analysis of natural processes. Despite its scientific rigor, this process often fails to account for the knowledge, values, and experiences of local stakeholders that often contextualizes a modeled system. To bridge this gap, a team of natural and social scientists worked directly with local residents and resource users to develop a participatory modeling approach to collect and utilize local knowledge about the Breton Sound Estuary in southeast Louisiana, USA. Knowledge capture was facilitated through application of a local knowledge mapping methodology designed to catalog local understanding of current and historical conditions within the estuary and identify desired ecological and hydrologic end states. The results of the mapping endeavor informed modeling activities designed to assess the applicability of the identified restoration solutions. This effort was aimed at increasing stakeholder buy-in surrounding the utility of numerical models for planning and designing coastal protection and restoration projects and included an ancillary outcome aimed at elevating stakeholder empowerment regarding the design of nature-based restoration solutions and modeling scenarios. This intersection of traditional science and modeling activities with the collection and analysis of traditional ecological knowledge proved useful in elevating the confidence that community members had in modeled restoration outcomes.

Louisiana faces extensive coastal land loss which threatens the livelihoods of marginalized populations. These groups have endured extreme disruptive events in the past and have survived in the region by relying on several resilient practices, including mobility. Facing environmental changes that will be wrought by deliberate coastal restoration programs, elderly residents are resisting migration while younger residents continue a decades-long inland migration. Interviews and historical records illustrate a complex intersection of resilient practices and environmental migration. The process underway conflicts to some extent with prevailing concepts in environmental migration most notably deviating from established migration patterns. In terms of social justice, selective out-migration of younger adults leaves a more vulnerable population behind, but also provides a supplementary source of income and social links to inland locales. Organized resistance to restoration projects represents a social justice response to programs that threaten the resource-based livelihoods of coastal residents while offering protection to safer inland urban residents.

No abstract

No abstract

Predictive tools are widely used to study coastal and deltaic systems in support of basic research, planning efforts, engineering design, and the implementation of restoration or protection strategies. They have been extensively used to evaluate the effectiveness of natural and nature-based solutions (NNBS) to support ecosystem functions and services of coastal ecosystems and human communities experiencing increased risk from sea-level rise and severe storms. The potential benefits of NNBS are being increasingly recognized, particularly in remote areas or areas that are either technically or financially infeasible to be protected with levees or other difficult engineering alternatives. Local communities, however, are often excluded from proposing, screening, or evaluating NNBS as restoration and protection strategies. Communities are also not sufficiently involved in the development or application of the predictive tools. This research effort outlines an approach to developing knowledge-based predictive tools and a community engagement process to evaluate NNBS strategies proposed predominantly by local communities. Incorporating knowledge from local communities benefits and potentially improves the performance of predictive tools and their ability to capture visible trends and observations. To illustrate this concept, the authors present landscape models for coastal Louisiana that successfully reproduced the frequency of flooding of local roads, rate of shoreline erosion, salinity pattern changes, and presence/absence of key species (e.g., brown shrimp, oysters, and so forth). While these qualitative measures are not a substitute for well-established rigorous and quantitative model performance assessment approaches, they offer an effective approach to engage local communities and incorporate their knowledge in the development of the predictive models and the proposed protection and restoration strategies to be examined.