Evaluation of species distribution forecasts: a potential predictive tool for reducing incidental catch in pelagic fisheries Canadian Journal of Fisheries and Aquatic Sciences

Nontarget catch restrictions are becoming common in fisheries management. We test a potential tool for reducing nontargeted catch that combines species’ distribution models and ocean forecast models. We evaluated our approach for Atlantic herring (Clupea harengus), Atlantic mackerel (Scomber scombrus), alewife (Alosa pseudoharengus), and blueback herring (Alosa aestivalis). Catch of the latter two species is capped in commercial fisheries of the former two species. Ocean forecasts were derived from a data-assimilative ocean forecast model that predicts conditions 0–2 days into the future. Observed oceanographic conditions were derived from CTD casts and observed fish presence–absence was derived from fishery-independent bottom trawl collections. Species distribution models were used to predict presence–absence based on observed and forecasted oceanographic conditions, and predictions for both were very similar. Thus, most of the error in predicted distributions was generated by the species distribution models, not the oceanographic forecast model. Understanding how predictions based on forecasted conditions compare with predictions from observed conditions is key to developing an incidental catch forecast tool to help industry reduce nontarget catches.

非目标渔获限制在渔业管理中正日益普遍。我们研发了一款结合物种分布模型与海洋预报模型的工具，用于降低非目标渔获量。我们针对大西洋鲱（Clupea harengus）、大西洋鲭（Scomber scombrus）、西鲱（Alosa pseudoharengus）以及蓝背西鲱（Alosa aestivalis）评估了该方法的效果：在针对前两种鱼类的商业捕捞作业中，后两种鱼类的渔获量被设定了上限。海洋预报数据源自一款可预测未来0至2天海洋状况的数据同化海洋预报模型；实测海洋环境数据取自温盐深（CTD）剖面观测，鱼类出现与未出现的观测数据则来自独立于渔业活动的底拖网采集样本。我们通过物种分布模型，基于实测与预报的海洋环境条件预测鱼类的出现与否，且两类预测结果的一致性极高。由此可见，物种分布预测中的大部分误差源自物种分布模型本身，而非海洋预报模型。厘清基于预报海洋条件的预测结果与基于实测海洋条件的预测结果之间的差异，是开发辅助产业降低非目标渔获量的附带渔获预报工具的核心关键。