Institute of Marine Science, University of California Santa Cruz, Santa Cruz, California, 95064, USA.
Environmental Research Division, NOAA Southwest Fisheries Science Center, 99 Pacific Street, Suite #255A, Monterey, California, 93940, USA.
Ecol Appl. 2017 Dec;27(8):2313-2329. doi: 10.1002/eap.1610. Epub 2017 Oct 25.
The ocean is a dynamic environment inhabited by a diverse array of highly migratory species, many of which are under direct exploitation in targeted fisheries. The timescales of variability in the marine realm coupled with the extreme mobility of ocean-wandering species such as tuna and billfish complicates fisheries management. Developing eco-informatics solutions that allow for near real-time prediction of the distributions of highly mobile marine species is an important step towards the maturation of dynamic ocean management and ecological forecasting. Using 25 yr (1990-2014) of NOAA fisheries' observer data from the California drift gillnet fishery, we model relative probability of occurrence (presence-absence) and catchability (total catch per gillnet set) of broadbill swordfish Xiphias gladius in the California Current System. Using freely available environmental data sets and open source software, we explore the physical drivers of regional swordfish distribution. Comparing models built upon remotely sensed data sets with those built upon a data-assimilative configuration of the Regional Ocean Modelling System (ROMS), we explore trade-offs in model construction, and address how physical data can affect predictive performance and operational capacity. Swordfish catchability was found to be highest in deeper waters (>1,500 m) with surface temperatures in the 14-20°C range, isothermal layer depth (ILD) of 20-40 m, positive sea surface height (SSH) anomalies, and during the new moon (<20% lunar illumination). We observed a greater influence of mesoscale variability (SSH, wind speed, isothermal layer depth, eddy kinetic energy) in driving swordfish catchability (total catch) than was evident in predicting the relative probability of presence (presence-absence), confirming the utility of generating spatiotemporally dynamic predictions. Data-assimilative ROMS circumvent the limitations of satellite remote sensing in providing physical data fields for species distribution models (e.g., cloud cover, variable resolution, subsurface data), and facilitate broad-scale prediction of dynamic species distributions in near real time.
海洋是一个充满活力的环境，栖息着各种各样的高度洄游物种，其中许多物种都在有针对性的渔业中直接受到捕捞。海洋领域的变化时间尺度以及金枪鱼和旗鱼等海洋洄游物种的极端流动性，使渔业管理变得复杂。开发生态信息学解决方案，以便能够实时预测高度洄游海洋物种的分布，是实现动态海洋管理和生态预测成熟的重要一步。
利用美国国家海洋和大气管理局渔业局在加利福尼亚流刺网渔业 25 年（1990-2014 年）的观测数据，我们对加利福尼亚海流系统中宽吻剑鱼 Xiphias gladius 的相对出现概率（存在-不存在）和可捕性（每刺网设置的总捕获量）进行建模。我们使用免费提供的环境数据集和开源软件，探索了区域剑鱼分布的物理驱动因素。通过比较基于遥感数据集和基于区域海洋建模系统（ROMS）数据同化配置构建的模型，我们探讨了模型构建中的权衡，并解决了物理数据如何影响预测性能和操作能力的问题。
我们发现，剑鱼的可捕性在水深大于 1500 米的深水区最高，表层水温在 14-20°C 之间，等温层深度（ILD）为 20-40 米，正海面高度（SSH）异常，以及在新月期间（<20%的月照）。我们观察到中尺度变化（SSH、风速、等温层深度、涡动能）对剑鱼可捕性（总捕获量）的影响大于对存在概率（存在-不存在）的影响，这证实了生成时空动态预测的实用性。数据同化的 ROMS 避免了卫星遥感在为物种分布模型提供物理数据场方面的局限性（例如，云覆盖、可变分辨率、次表层数据），并促进了动态物种分布的广泛实时预测。
