We build on previous work to construct a comprehensive database of shoreline oiling exposure from the Deepwater Horizon (DWH) spill by compiling field and remotely-sensed datasets to support oil exposure and injury quantification. We compiled a spatial database of shoreline segments with attributes summarizing habitat, oiling category and timeline. We present new simplified oil exposure classes for both beaches and coastal wetland habitats derived from this database integrating both intensity and persistence of oiling on the shoreline over time. We document oiling along 2113km out of 9545km of surveyed shoreline, an increase of 19% from previously published estimates and representing the largest marine oil spill in history by length of shoreline oiled. These data may be used to generate maps and calculate summary statistics to assist in quantifying and understanding the scope, extent, and spatial distribution of shoreline oil exposure as a result of the DWH incident.

Deepwater Horizon was the largest marine oil spill in U.S. waters, oiling large expanses of coastal wetland shorelines. We compared marsh periwinkle (Littoraria irrorata) density and shell length at salt marsh sites with heavy oiling to reference conditions ∼16 months after oiling. We also compared periwinkle density and size among oiled sites with and without shoreline cleanup treatments. Densities of periwinkles were reduced by 80-90% at the oiled marsh edge and by 50% in the oiled marsh interior (∼9 m inland) compared to reference, with greatest numerical losses of periwinkles in the marsh interior, where densities were naturally higher. Shoreline cleanup further reduced adult snail density as well as snail size. Based on the size of adult periwinkles observed coupled with age and growth information, population recovery is projected to take several years once oiling and habitat conditions in affected areas are suitable to support normal periwinkle life-history functions. Where heavily oiled marshes have experienced accelerated erosion as a result of the spill, these habitat impacts would represent additional losses of periwinkles. Losses of marsh periwinkles would likely affect other ecosystem processes and attributes, including organic matter and nutrient cycling, marsh-estuarine food chains, and multiple species that prey on periwinkles.

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This paper presents an algorithm for optimal data collection in random fields, the so-called variance reduction analysis, which is an extension of kriging. The basis of variance reduction analysis is an information response function (i.e., the amount of information gain at an arbitrary point due to a measurement at another site). The ranking of potential sites is conducted using an information ranking function. The optimal number of new points is then identified by an economic gain function. The selected sequence of sites for further sampling shows a high degree of stability with respect to noisy inputs.

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