在太平洋东北部,渔船和顶级捕食者之间有着未见的重叠
Heather Welch1,2, Tyler Clavelle3, Timothy D White3
1Institute of Marine Science, University of California, Santa Cruz, Santa Cruz, CA, USA.
Science advances
|March 6, 2024
概括
自动识别系统 (AIS) 数据中的差距隐藏了工业捕鱼对人类和野生动物的重大风险,影响了海洋捕食者保护. 解决这些数据缺口对于可持续渔业和保护鱼和海等物种至关重要.
科学领域:
- 海洋生态海洋生态学
- 渔业科学 渔业科学
- 保护生物学 保护生物学
背景情况:
- 准确的人类野生动物风险评估对于海洋顶级捕食者保护至关重要.
- 自动识别系统 (AIS) 的数据有助于绘制渔业活动的地图和估计风险.
- 由于技术或故意原因,AIS数据中的差距可能会掩盖渔船的真正活动和相关风险.
研究的目的:
- 量化由于AIS漏洞而导致渔船未见的活动在多大程度上掩盖了与海洋掠食者重叠的估计.
- 评估这些数据缺口对14个海洋捕食者群体的风险评估的影响,包括鱼,金枪鱼,哺乳动物,海鸟和皮.
- 为了突出观察到的渔业活动和自我报告的数据之间的差异,使用鱼渔业相互作用作为案例研究.
主要方法:
- 分析来自太平洋东北部渔船的自动识别系统 (AIS) 数据.
- 估计渔船活动 (由于AIS差距而导致的可见和不可见) 与14种海洋捕食者物种的分布之间的重叠.
- 估计的未见重叠与特定地区的报告捕捞数据的比较,例如在北极10度附近的水域.
主要成果:
- 由于装备有AIS的船只之间的AIS差距,多达24%的捕食者与渔船活动的重叠被发现是看不到的.
- 对于一些单独的物种,高达36%的与渔业活动的重叠是看不到的.
- 在接近10°N的海域检测到与鱼有很高的未见重叠,与报告的鱼捕获量较低形成鲜明对比,这表明渔业数据的潜在报告不足.
结论:
- 未见的渔船活动大大掩盖了人类与野生动物的风险评估,挑战了海洋顶级捕食者的保护工作.
- 考虑到这些数据缺口对于准确的风险评估和发展透明,可持续的海洋渔业至关重要.
- 在AIS衍生活动和自我报告数据之间的差异凸显了在渔业管理中改善监测和报告系统的需要.
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