优先考虑海洋生物多样性数据收集,以提供可持续的海洋
Amelia E H Bridges1, Kerry L Howell1,2
1School of Biological and Marine Sciences, University of Plymouth, Drake Circus, Plymouth, Devon, UK.
概括
了解海洋生物的分布是海洋管理的关键. 这项研究开发了一条分析开放访问数据的管道,揭示了采样工作中的重大偏差,并确定了未来勘探的关键领域.
科学领域:
- 海洋生态海洋生态学
- 海洋学 海洋学 海洋学
- 生物多样性信息学 生物多样性信息学
背景情况:
- 关于海洋生物分布的未回答的生态问题阻碍了有效的海洋管理和对地球上的生命的理解.
- Benthic 和 pelagic 领域需要不同的研究方法,需要对现有数据有明确的理解,以确定知识差距.
- 开放式数据存储库对于差距分析具有价值,但在区分盆地鱼类和海底鱼类数据方面面临挑战.
研究的目的:
- 开发一条自动化数据管道,从开放存储库中提取和分离盆地鱼类和海底鱼类数据.
- 通过对基本海洋变量进行数据分层来进行关键差距分析.
- 在全球海洋数据集中识别空间和分类学偏差,并精确定位样本不足的地区.
主要方法:
- 开发一个自动化数据处理管道.
- 从开放式存储库中提取和分离盆地和海底鱼类数据.
- 对数据与海洋基本变量进行分层分层,用于差距分析.
主要成果:
- 在盆地和海全球数据集中识别显著的空间和分类学偏差.
- 观察到的偏差有利于深度小于100米,北半球和脊椎动物物种.
- 编制一套经过清理和分类的数据集,揭示慢性样本不足的地区和高度优先的勘探区域.
结论:
- 对采样工作的协调,战略优先考虑对于推进海洋建模和预测至关重要.
- 解决发现的偏见将改善我们对海洋生物多样性的理解,并支持更好的海洋管理.
- 开发的管道为分析和分类大型海洋数据集提供了可扩展的解决方案.
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