Estimating sea turtle exposures to Deepwater Horizon oil

Abstract

The Deepwater Horizon (DWH) oil spill was unprecedented in extent and duration, and affected marine natural resources, including sea turtles, throughout the northern Gulf of Mexico. Consequently, US federal and state Trustees documented and quantified oil exposure and resulting injuries to sea turtles under the DWH Natural Resource Damage Assessment. At-sea rescue operations focused on surface-pelagic juvenile sea turtles, which were especially at risk to oil exposure within oceanic convergence zones, and provided direct observations of the degree that turtles in this young life stage were exposed to DWH oil. In contrast, locations of larger neritic juvenile and adult turtles were documented during aerial surveys, but because these turtles were not captured, their oiling status could not be directly evaluated. Both the rescue operations and aerial surveys were able to observe only a small fraction of sea turtles within the vast spill footprint. We developed a spatio-temporally explicit approach that used direct observations of oiled surface-pelagic juvenile sea turtles and satellite-derived surface oil distributions to statistically estimate the probabilities of oil exposure for all sea turtles that were present within the area of the DWH spill, but whose oiling status was unknown. Our results enabled an expansion of exposure and injury quantification across the entire DWH spill area and period. This approach was conceptually straightforward and used common geospatial and statistical techniques, making it applicable to other situations in which the full extent of oil exposure for marine natural resources must be estimated from an incomplete sample.

Density and exposure of surface-pelagic juvenile sea turtles to Deepwater Horizon oil

“…This result is consistent with rapid transit of waters across the northern GoM. Under this simplification, we envisioned that each turtle at a 'fixed' location would be exposed to oil when the model of Wallace et al (2017) estimated the turtle's location had non-zero probability of heavy oiling. The results of this method provided the areal extent to which turtle densities were ap plied.…”

“…Because their model utilized satellite-based daily estimates of surface oiling, the model indirectly incorporated currents, convergence zones, and general circulation of waters in the northern Gulf during the spill period. Wallace et al (2017) found that the majority of locations, and hence the majority of fixed turtle locations within the footprint, had a positive average probability of heavy oiling during the study (Fig. 4).…”

“…Envisioning turtles as fixed points allowed us to use the results of Wallace et al (2017, this Theme Section) to approximate the 'study area. ' We were able to apply the model of Wallace et al (2017) because they estimated, via logistic regression, the average probability that fixed locations within the oil footprint became heavily oiled during the spill period. Because their model utilized satellite-based daily estimates of surface oiling, the model indirectly incorporated currents, convergence zones, and general circulation of waters in the northern Gulf during the spill period.…”

“…( 1) where p 5 is the probability of heavy oiling in a pixel (from Wallace et al 2017 tion for event e; i.e. I(e) = 1 if e true, 0 otherwise, and densities were ordered such that non-oiled had index 1 and heavy oiling had index 5.…”

δ13C and δ15N in the endangered Kemp’s ridley sea turtle Lepidochelys kempii after the Deepwater Horizon oil spill

“…These areas were heavily contaminated by oil during and after the spill (Fig. 1), and both direct observation (Wallace et al 2017) and post-spill modeling of the oil trajectory (Putman et al 2015) indicated that many Lk were exposed.…”