ABSTRACT
Ecosystem boundary retreat due to human-induced pressure is a generally observed phenomenon. However, studies that document thresholds beyond which internal resistance mechanisms are overwhelmed are uncommon. Following the Deepwater Horizon (DWH) oil spill, field studies from a few sites suggested that oiling of salt marshes could lead to a biogeomorphic feedback where plant death resulted in increased marsh erosion. We tested for spatial generality of and thresholds in this effect across 103 salt marsh sites spanning ~430 kilometers of shoreline in coastal Louisiana, Alabama, and Mississippi, using data collected as part of the natural resource damage assessment (NRDA). Our analyses revealed a threshold for oil impacts on marsh edge erosion, with higher erosion rates occurring for ~1-2 years after the spill at sites with the highest amounts of plant stem oiling (90-100%). These results provide compelling evidence showing large-scale ecosystem loss following the Deepwater Horizon oil spill. More broadly, these findings provide rare empirical evidence identifying a geomorphologic threshold in the resistance of an ecosystem to increasing intensity of human-induced disturbance.
ABSTRACT
Stranded oil covering soil and plant stems in fragile Louisiana marshes was one of the most visible impacts of the 2010 Deepwater Horizon (DWH) oil spill. As part of the assessment of marsh injury after the DWH spill, plant stem oiling was broken into five categories (0%, 0-10%, 10-50%, 50-90%, 90-100%) and used as the independent variable for estimating death of vegetation, accelerated erosion, and other metrics of injury. The length of shoreline falling into each of these stem oiling categories was therefore a key measure of the total extent of marsh injury, and its accurate estimation is the focus of this paper. First, we used geographically-weighted logistic regression (GWR) to explore and model spatially varying relationships between stem oiling field data and secondary information (oiling exposure category) collected during shoreline surveys. We then combined GWR probability estimates with field data using indicator cokriging to predict the probability of exceeding four stem oiling thresholds (0, 10, 50, and 90%) at 50 m intervals along the Louisiana shoreline. Cross-validation using Receiver Operating Characteristic (ROC) Curves demonstrate the greater prediction accuracy of the multivariate geostatistical approach relative to either aspatial regression or indicator kriging that ignores secondary information.
