构建以使用为灵感的物种分布模型:使用多种数据类型来检查和改进模型性能
Camrin D Braun1, Martin C Arostegui1, Nima Farchadi2
1Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA.
概括
不同的数据类型可以构建可靠的物种分布模型 (SDMs),用于海洋保护. 通过组合或聚合模型将数据结合起来,可以提高生态现实性,并对蓝鱼等物种进行预测.
科学领域:
- 海洋生态海洋生态学
- 保护生物学 保护生物学
- 渔业科学 渔业科学
背景情况:
- 物种分布模型 (SDM) 对海洋保护和管理至关重要.
- 增加海洋生物多样性数据需要指导如何利用各种数据类型来实现强大的SDM.
研究的目的:
- 探索不同数据类型对SDM适应性,性能和预测能力的影响.
- 用蓝鱼 (Prionace glauca) 的渔业依赖和渔业独立数据训练的模型进行比较.
主要方法:
- 与使用传统标记回收标签,渔业观察员记录,卫星连接的电子标签和弹出式档案标签训练的SDM进行了比较.
- 评估了西北大西洋蓝的模型适合性,性能和空间预测.
- 评估模型集和聚合数据模型用于整合推理.
主要成果:
- 这四种数据类型都产生了强大的SDM,但由于采样偏差,空间预测有所不同.
- 数据采样和缺席代表的差异影响了模型的结果.
- 模型集合和聚合数据模型产生了比单个模型更具生态现实的预测.
结论:
- 为使用多种数据源开发SDM的实践者提供了指导.
- 未来的工作应该集中在整合性建模方法上,这些方法利用个人数据类型的优势,并考虑局限性.
- 无论使用哪种数据类型,在模型选择和解释中,生态现实主义至关重要.
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