Contaminant Exposure Models (CEMs) were developed to predict population-level tissue contaminant concentrations in fishes by pairing sediment-bound contaminant concentrations (DDTs, PCBs) and fine-scale acoustic telemetry data from a habitat-associated species (Vermilion Rockfish, Sebastes miniatus), nomadic flatfish species (Hornyhead Turbot, Pleuronichthys verticalis), and nomadic benthic/midwater schooling species (White Croaker, Genyonemus lineatus) tagged near wastewater outfalls in southern California. Model results were compared to contaminant concentrations in tissue samples. The CEMs developed require further refinement before implementation into management efforts but may act as steppingstones to help shift primary monitoring methods away from the regular field collection of fish for tissue contaminant analyses and towards behavioral modeling and habitat mapping. We also developed Kernel Density Estimates that can be used by managers immediately to identify regions that contribute most to contaminant exposure in species of concern. Prioritizing remediation efforts in these areas are likely to be most effective at improving fish health.

污染物暴露模型 (CEM) 被开发用于通过将沉积物污染物浓度（DDTs、PCBs）和栖息地相关物种（Vermilion Rockfish、Sebastes miniatus）的精细声学遥测数据配对来预测鱼类的种群水平组织污染物浓度、游牧的比目鱼物种（角鲽、垂直平头鱼）和游牧的底栖/中水游牧物种（白鱼、Genyonemus lineatus）) 标记在南加州的废水排放口附近。将模型结果与组织样本中的污染物浓度进行比较。开发的 CEM 在实施到管理工作中之前需要进一步完善，但可以作为垫脚石，帮助将主要监测方法从常规的现场鱼类采集用于组织污染物分析转向行为建模和栖息地测绘。我们还开发了核密度估计值，管理人员可以立即使用它来确定对受关注物种的污染物暴露贡献最大的区域。在这些领域优先采取补救措施可能对改善鱼类健康最有效。