测试预测的物种丰富性,过去的优先级和印度洋西部的海洋保护区指定之间的一致性
Tim R McClanahan1, Alan M Friedlander2,3, Julien Wickel4
1Global Marine Programs, Wildlife Conservation Society, Bronx, New York, USA.
概括
科学家们开发了一种新方法,利用环境数据和机器学习来预测印度洋西部的海洋物种多样性. 这种方法确定了关键的保护区,突出了当前海洋保护区的差距.
科学领域:
- 海洋生态和保护科学 海洋生态和保护科学
- 生物多样性信息学和预测建模
- 环境科学和空间分析
背景情况:
- 环境数据和机器学习的进步使得数据贫困地区的大规模预测成为可能.
- 准确评估海洋生物多样性对于有效的保护规划至关重要.
- 现有的保护优先事项可能无法完全捕捉到全面的生物多样性模式.
研究的目的:
- 开发一个空间分辨率的代理来预测热带近海海洋种群的数量.
- 模拟和绘制印度洋西部海洋物种丰富性的地图.
- 将新的保护重点与现有报告和保护区进行比较.
主要方法:
- 利用多样化的海洋环境空间数据库,模拟来自1000个现场地点的种群数量.
- 根据硬珊瑚和珊瑚礁鱼家族多样性之间的相关性开发了一个代理.
- 将预测应用于9个生态区域和11个国家的7039个珊瑚礁细胞,分析环境影响.
主要成果:
- 根据组合的种群数量 (珊瑚,鱼类) 和空间划分,确定了119个优先位置.
- 突出了28个在以前的报告中未确定的新型保护重点领域.
- 预测的高多样性地区和现有的海洋保护区之间只有34%的重叠.
结论:
- 环境-物种代理和建模方法有效地确定了保护优先级.
- 目前的海洋保护区可能无法充分覆盖预测物种丰富度高的地区.
- 这种预测建模可以告知和增强未来的海洋保护决策.
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