探索巴伦茨海鱼生物质的短期预测和长期预测,使用动态模型数据的统计方法
Mariko Koseki1,2, Anne Britt Sandø2,3, Geir Ottersen3,4
1Geophysical Institute, University of Bergen, Bergen, Norway.
PloS one
|July 31, 2025
概括
简单的统计模型使用环境数据预测东北北极鱼生物量. 预测显示,变暖可能会增加鱼,但低排放场景可能会减少,突出需要更广泛的因素.
科学领域:
- 海洋生态海洋生态学
- 渔业科学 渔业科学
- 气候变化影响评估 气候变化影响评估
背景情况:
- 东北北极鱼种群对巴伦茨海生态系统和渔业至关重要.
- 了解影响鱼生物量的因素对于可持续管理至关重要.
- 以前的模型通常依赖于复杂的动态系统,限制了可访问性.
研究的目的:
- 评估用于预测和预测东北北极鱼生物量的简单统计模型.
- 为了确定影响鱼种群动态的关键水文和下层热带水平变量.
- 根据各种气候变化情景 (CMIP6 SSP) 预测未来的鱼生物量.
主要方法:
- 开发简单和多重线性回归模型.
- 利用了来自NEMO-NAA10km海洋模型和NORWECOM.E2E生态系统模型的数据.
- 嵌入的环境变量:温度,盐度,海冰,初级和二级生产.
- 使用CMIP6气候场景对模型性能和预测的统计评估.
主要成果:
- 几种统计模型显示了鱼生物量变化的高预测能力.
- 海洋温度升高和动物浮游生物丰富度与较大的鱼种群有积极的相关性.
- 观察到相反的趋势:由于冷却和动物浮游生物减少,生物质的高变暖趋势呈正面 (SSP5-8.5),低排放趋势呈负面 (SSP1-2.6).
- 环境变化为鱼动态提供了强大的初次近似.
结论:
- 简单的统计模型有效地捕捉了东北北极鱼生物量的关键驱动因素.
- 气候变化对鱼的影响取决于场景,受海洋学和热带变化的影响.
- 准确的鱼种群评估需要将环境因素与渔业压力和自然死亡率相结合.
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