Quantifying the impacts of future shoreline modification on biodiversity in a case study of coastal Georgia, United States.

People often modify the shoreline to mitigate erosion and protect property from storm impacts. The 2 main approaches to modification are gray infrastructure (e.g., bulkheads and seawalls) and natural or green infrastructure (NI) (e.g., living shorelines). Gray infrastructure is still more often used for coastal protection than NI, despite having more detrimental effects on ecosystem parameters, such as biodiversity. We assessed the impact of gray infrastructure on biodiversity and whether the adoption of NI can mitigate its loss. We examined the literature to quantify the relationship of gray infrastructure and NI to biodiversity and developed a model with temporal geospatial data on ecosystem distribution and shoreline modification to project future shoreline modification for our study location, coastal Georgia (United States). We applied the literature-derived empirical relationships of infrastructure effects on biodiversity to the shoreline modification projections to predict change in biodiversity under different NI versus gray infrastructure scenarios. For our study area, which is dominated by marshes and use of gray infrastructure, when just under half of all new coastal infrastructure was to be NI, previous losses of biodiversity from gray infrastructure could be mitigated by 2100 (net change of biodiversity of +0.14%, 95% confidence interval -0.10% to +0.39%). As biodiversity continues to decline from human impacts, it is increasingly imperative to minimize negative impacts when possible. We therefore suggest policy and the permitting process be changed to promote the adoption of NI.

Cuantificación del impacto de la futura modificación de la costa sobre la biodiversidad en un estudio de caso de la costa de Georgia, Estados Unidos Resumen Las personas modifican con frecuencia la costa para mitigar la erosión o proteger su propiedad del impacto de las tormentas. Los dos enfoques principales para la modificación son la infraestructura gris (p. ej.: mamparos y malecones) y la infraestructura verde o natural (IN) (p.ej.: costas vivientes). La infraestructura gris es más común que la IN, a pesar de que tiene efectos dañinos sobre los parámetros ambientales, como la biodiversidad. Evaluamos el impacto de la infraestructura gris sobre la biodiversidad y si la adopción de la IN puede mitigar su pérdida. Analizamos la literatura para cuantificar la relación de la infraestructura gris y la IN con la biodiversidad. También desarrollamos un modelo con datos geoespaciales temporales sobre la distribución de los ecosistemas y la modificación de la costa para proyectar la modificación costera en el futuro en nuestra localidad de estudio: la costa de Georgia, Estados Unidos. Aplicamos las relaciones empíricas derivadas de la literatura de los efectos de la infraestructura sobre la biodiversidad a las proyecciones de modificación de la costa para predecir el cambio en la biodiversidad bajo diferentes escenarios de infraestructura gris versus IN. En nuestra área de estudio, que está dominada por marismas y usa infraestructura gris, cuando un poco menos de la mitad de toda la infraestructura costera nueva debería ser IN, las pérdidas previas de biodiversidad a partir de la infraestructura gris podrían mitigarse para 2100 (cambio neto de la biodiversidad de +0.14%, 95% intervalo de confianza ­0.10% a +0.39%). Conforme la biodiversidad siga en declive por el impacto humano, cada vez es más imperativo minimizar el impacto negativo cuando sea posible. Por lo tanto, sugerimos que se modifiquen las políticas y el proceso de permisos para promover la adopción de la IN.

Policy and market forces delay real estate price declines on the US coast.

Despite increasing risks from sea-level rise (SLR) and storms, US coastal communities continue to attract relatively high-income residents, and coastal property values continue to rise. To understand this seeming paradox and explore policy responses, we develop the Coastal Home Ownership Model (C-HOM) and analyze the long-term evolution of coastal real estate markets. C-HOM incorporates changing physical attributes of the coast, economic values of these attributes, and dynamic risks associated with storms and flooding. Resident owners, renters, and non-resident investors jointly determine coastal property values and the policy choices that influence the physical evolution of the coast. In the coupled system, we find that subsidies for coastal management, such as beach nourishment, tax advantages for high-income property owners, and stable or increasing property values outside the coastal zone all dampen the effects of SLR on coastal property values. The effects, however, are temporary and only delay precipitous declines as total inundation approaches. By removing subsidies, prices would more accurately reflect risks from SLR but also trigger more coastal gentrification, as relatively high-income owners enter the market and self-finance nourishment. Our results suggest a policy tradeoff between slowing demographic transitions in coastal communities and allowing property markets to adjust smoothly to risks from climate change.

Socioeconomic and environmental predictors of estuarine shoreline hard armoring.

Rising sea levels and growing coastal populations are intensifying interactions at the land-sea interface. To stabilize upland and protect human developments from coastal hazards, landowners commonly emplace hard armoring structures, such as bulkheads and revetments, along estuarine shorelines. The ecological and economic consequences of shoreline armoring have garnered significant attention; however, few studies have examined the extent of hard armoring or identified drivers of hard armoring patterns at the individual landowner level across large geographical areas. This study addresses this knowledge gap by using a fine-scale census of hard armoring along the entire Georgia U.S. estuarine coastline. We develop a parsimonious statistical model that accurately predicts the probability of armoring emplacement at the parcel level based on a set of environmental and socioeconomic variables. Several interacting influences contribute to patterns of shoreline armoring; in particular, shoreline slope and the presence of armoring on a neighboring parcel are strong predictors of armoring. The model also suggests that continued sea level rise and coastal population growth could trigger future increases in armoring, emphasizing the importance of considering dynamic patterns of armoring when evaluating the potential effects of sea level rise. For example, evolving distributions of armoring should be considered in predictions of future salt marsh migration. The modeling approach developed in this study is adaptable to assessing patterns of hard armoring in other regions. With improved understanding of hard armoring distributions, sea level rise response plans can be fully informed to design more efficient scenarios for both urban development and coastal ecosystems.