基于专家地图和机会事件的海洋物种分布模型预测的差异
Zhixin Zhang1,2,3, Jamie M Kass4, Ákos Bede-Fazekas5,6
1CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, Guangdong Provincial Key Laboratory of Applied Marine Biology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China.
概括
根据数据源,物种分布模型 (SDM) 的性能不同. 与专家地图相比,机会性数据改善了海洋鱼类种群预测和可转移性,突出了数据选择的敏感性.
科学领域:
- 生态生态学 生态生态学
- 生物多样性研究的研究.
- 保护科学是一种保护科学.
背景情况:
- 物种分布模型 (SDM) 对于跟踪生物多样性变化至关重要.
- 高质量的发生数据对于准确的SDM至关重要,但往往无法获得.
- 不同数据源对SDM性能的影响,特别是在海洋环境中,需要进行调查.
研究的目的:
- 为了比较使用机会性事件记录构建的SDM的性能和预测准确性,与海洋鱼类物种的专家地图相比.
- 为了确定驱动 SDM 预测在这两种数据类型之间的差异的因素.
- 根据数据源,评估SDM对新环境的可转移性.
主要方法:
- 通过使用通用线性模型,为两个家族的233种海洋鱼类开发了SDM.
- 利用现场调查和在线存储库中的机会性事件数据.
- 使用来自IUCN红色名单数据库的专家地图作为替代数据来源.
主要成果:
- 使用机会性事件的SDM与使用专家地图的SDM相比,显示出更高的性能和可转移到新区域.
- 环境预测变量在两种数据类型之间存在差异,解释了预测差异,专家地图经常包括不适合的息地.
- 鱼类家族之间不相似性的变化表明了数据源中潜在的分类学偏差.
结论:
- 种类分布模型预测对分布数据的选择非常敏感.
- 机会主义数据为海洋物种分布建模提供了更好的可转移性.
- 研究人员应该批判性地评估专家地图的准确性,并尽量减少委员会错误,以改善生物多样性评估.
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