Data from: Spatially explicit assessment of estuarine fish after Deepwater Horizon oil spill: tradeoffs in complexity and parsimony

Evaluating long-term contaminant effects on wildlife populations depends on spatial information about habitat quality, heterogeneity in contaminant exposure, and sensitivities and distributions of species integrated into a systems modeling approach. Rarely is this information readily available, making it difficult to determine the applicability of realistic models to quantify population-level risks. To evaluate the trade-offs between data demands and increased specificity of spatially explicit models for population-level risk assessments, we developed a model for a standard toxicity test species, the sheepshead minnow (Cyprinodon variegatus) exposed to oil contamination following the Deepwater Horizon oil spill and compared the output with various levels of model complexity to a standard risk quotient approach. The model uses habitat and fish occupancy data collected over five sampling periods throughout 2008-2010 in Pensacola and Choctawhatchee Bays, Florida, to predict species distribution, field-collected and publically available data on oil distribution and concentration, and chronic toxicity data from laboratory assays applied to a matrix population model. The habitat suitability model established distribution of fish within Barataria Bay, Louisiana, and the population model projected the dynamics of the species in the study area over a five-year period (October 2009 – September 2014). Vital rates were modified according to estimated contaminant concentrations to simulate oil exposure effects. To evaluate the differences in levels of model complexity, simulations varied from temporally and spatially explicit, including seasonal variation and location-specific oiling, to simple interpretations of a risk quotient derived for the study area. The results of this study indicate that species distribution, as well as spatially and temporally variable contaminant concentrations, can provide a more ecologically relevant evaluation of species recovery from catastrophic environmental impacts but might not be cost-effective or efficient for rapid assessment needs.

评估污染物对野生生物种群的长期影响，需要依托基于系统建模方法整合得到的多类空间信息：包括栖息地质量、污染物暴露异质性，以及物种的敏感性与分布特征。此类信息往往难以直接获取，导致难以确定用于量化种群水平风险的写实模型的适用性。为评估种群水平风险评估中，数据需求与空间显式模型（spatially explicit models）特异性提升之间的权衡关系，本研究针对经深水地平线漏油事件（Deepwater Horizon oil spill）后受石油污染的标准毒性试验物种——羊头鳉（Cyprinodon variegatus）构建了模型，并将不同复杂度层级的模型输出与标准风险商（risk quotient）方法进行对比。本模型依托2008至2010年间在佛罗里达州彭萨科拉湾与乔克托哈特奇湾开展的五次采样周期中收集的栖息地与鱼类占用数据，用于预测物种分布；同时整合了野外采集与公开可得的石油分布与浓度数据，以及实验室测试得到的慢性毒性数据，并将其应用于矩阵种群模型（matrix population model）。栖息地适宜性模型明确了路易斯安那州巴拉塔里亚湾内的鱼类分布，而种群模型则预测了2009年10月至2014年9月这五年间研究区域内该物种种群的动态变化。研究根据估算的污染物浓度调整种群生命率，以模拟石油暴露的影响效应。为评估模型复杂度层级间的差异，本研究开展了多组模拟：从包含季节变化与区域特定石油污染的时空显式模型，到针对研究区域推导得到的简化风险商解释模型。本研究结果表明，物种分布以及时空动态变化的污染物浓度，能够为评估物种从灾难性环境影响中恢复的情况提供更具生态学相关性的评价结果，但对于快速评估需求而言，该方法可能不具备成本效益或效率优势。