Ecol Appl. 2014 Jan;24(1):71-83. doi: 10.1890/12-0826.1.
Concerns about bycatch of protected species have become a dominant factor shaping fisheries management. However, efforts to mitigate bycatch are often hindered by a lack of data on the distributions of fishing effort and protected species. One approach to overcoming this problem has been to overlay the distribution of past fishing effort with known locations of protected species, often obtained through satellite telemetry and occurrence data, to identify potential bycatch hotspots. This approach, however, generates static bycatch risk maps, calling into question their ability to forecast into the future, particularly when dealing with spatiotemporally dynamic fisheries and highly migratory bycatch species. In this study, we use boosted regression trees to model the spatiotemporal distribution of fishing effort for two distinct fisheries in the North Pacific Ocean, the albacore (Thunnus alalunga) troll fishery and the California drift gillnet fishery that targets swordfish (Xiphias gladius). Our results suggest that it is possible to accurately predict fishing effort using < 10 readily available predictor variables (cross-validated correlations between model predictions and observed data -0.6). Although the two fisheries are quite different in their gears and fishing areas, their respective models had high predictive ability, even when input data sets were restricted to a fraction of the full time series. The implications for conservation and management are encouraging: Across a range of target species, fishing methods, and spatial scales, even a relatively short time series of fisheries data may suffice to accurately predict the location of fishing effort into the future. In combination with species distribution modeling of bycatch species, this approach holds promise as a mitigation tool when observer data are limited. Even in data-rich regions, modeling fishing effort and bycatch may provide more accurate estimates of bycatch risk than partial observer coverage for fisheries and bycatch species that are heavily influenced by dynamic oceanographic conditions.
对保护物种副渔获物的担忧已成为影响渔业管理的主要因素。然而，减轻副渔获物的努力常常因缺乏有关渔业活动和保护物种分布的数据而受阻。克服这一问题的一种方法是，将过去渔业活动的分布与通过卫星遥测和出现数据获得的已知保护物种位置进行叠加，以确定潜在的副渔获热点。然而，这种方法生成静态的副渔获物风险图，这令人质疑其对未来进行预测的能力，尤其是在处理时空动态渔业和高度洄游性副渔获物种时。在这项研究中，我们使用提升回归树来模拟北太平洋两种不同渔业的时空分布，即金枪鱼延绳钓渔业和加利福尼亚流刺网渔业，后者以箭鱼（Xiphias gladius）为目标。我们的结果表明，使用<10 个现成的预测变量（模型预测与观测数据之间的交叉验证相关性为-0.6）可以准确预测渔业活动。尽管这两种渔业在渔具和捕捞区域上有很大的不同，但它们各自的模型具有很高的预测能力，即使输入数据集仅占整个时间序列的一小部分。这对保护和管理具有重要意义：在一系列目标物种、捕捞方法和空间尺度上，即使是渔业数据的相对较短时间序列也足以准确预测未来的捕捞活动位置。结合副渔获物种的分布模型，这种方法在观察员数据有限的情况下具有作为缓解工具的潜力。即使在数据丰富的地区，对于受动态海洋条件影响较大的渔业和副渔获物种，建模渔业活动和副渔获物可能比部分观察员覆盖提供更准确的副渔获物风险估计。
