Applying spatiotemporal models to monitoring data to quantify fish population responses to the Deepwater Horizon oil spill in the Gulf of Mexico
Quantifying the impacts of disturbances such as oil spills on marine species can be challenging. Natural environmental variability, human responses to the disturbance (e.g., fisheries closures), the complex life histories of the species being monitored, and limited pre-spill data can make detection of effects of oil spills difficult. Using long-term monitoring data from the state of Louisiana (USA), we applied novel spatiotemporal approaches to identify anomalies in species occurrence and catch rates. We included covariates (salinity, temperature, turbidity) to help isolate unusual events. While some species showed evidence of unlikely temporal anomalies in occurrence or catch rates, we found that the majority of the observed anomalies were also before the Deepwater Horizon event. Several species-gear combinations suggested upticks in the spatial variability immediately following the spill, but most species indicated no trend. Across species-gear combinations, there was no clear evidence for synchronous or asynchronous responses in occurrence or catch rates across sites following the spill. Our results are in general agreement to other analyses of monitoring data that detected small impacts, but in contrast to recent results from ecological modeling that showed much larger effects of the oil spill on fish and shellfish.
KeywordsDeepwater Horizon oil spill Gulf of Mexico Louisiana Spatiotemporal modeling Delta—generalized linear mixed models Fisheries modeling Time series anomalies Long-term monitoring
Funding for this project was provided by the National Ocean Service’s analyses of the DWH monitoring data (Project 1CK3GC4P00). KLO was supported by a grant from The Gulf of Mexico Research Initiative to the Coastal Waters Consortium. We thank numerous staff at LDWF who contributed to the data collection and data processing for this project; without their dedicated support, long-term monitoring datasets such as the one used in our analysis would not exist. In particular, Harry Blanchet, Jason Adriance, Mike Harden, and Glenn Thomas. We are also grateful to D. Pauly, L. DePinto, M. Christman, and D. Cacela for providing feedback on earlier versions of these analyses.
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