Linking Ecological Function and Ecosystem Service Values of Estaurine Habitat Types Associated with a Barrier Island System

Ecosystem services are benefits humans receive as a function of natural processes. Many current studies seek to express these benefits as an economic value per unit of habitat type without quantifying the ecological functions that allow for the provision of ecosystem services. This study is designed to model each habitat type in an effort to explicitly link the major estuarine habitat types of Mustang Island (oyster reefs, seagrass meadows, and intertidal salt marsh) to their contribution to Nitrogen cycling services. First, a dynamic biomass model of each foundational species was created using Simile, a declarative modeling framework. Second, a monthly snapshot of Nitrogen captured in living biomass was used to quantify the contribution of each species to the Nitrogen cycling services. Finally, the amount of Nitrogen captured in living biomass was valued using a replacement cost approach. An effort was also made to link the provision of recreational fishing services provided by each aforementioned habitat type by partitioning travel costs and license sales weighted by the density of fish found in each habitat type.

It was found that oyster reefs of Mustang Island contribute $173,000 yr^-1, seagrass meadows contribute $12,054,095 yr^-1, and intertidal salt marshes contribute $5,242,755 yr^-1 in potential Nitrogen cycling services. The total value of recreational fishing services within the study site was calculated to be $83.8 million dollars yr^-1. A portion of the total value was then attributed to each habitat type: Marsh edge: $2 million; Seagrass meadows: $81 million; and Oyster: $81,000 thousand.

These efforts have been made to translate ecological function into economic benefit to improve communication among a wide variety of stakeholders that are more likely to understand economic value. Further refinement of both the models and the economic data necessary to support them, will have the potential to improve the applicability and results of these tools. These results, and the modeling framework through which they are calculated, provide a platform to evaluate management relevant scenarios in a simple, flexible manner that may be adjusted and transferred to other study sites given appropriate local data.