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arXiv 2107.13792q-bio.PEphysics.data-an
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利用生态监测数据估算功能多样性和物种性状

Estimation of functional diversity and species traits from ecological monitoring data

Alexey Ryabov, Bernd Blasius, Helmut Hillebrand, Irina Olenina, Thilo Gross

摘要:

气候变化和生物多样性丧失的双重危机表明,迫切需要对功能多样性进行监测。虽然高质量的生态监测数据越来越多,但迄今为止,要量化功能多样性,就需要确定物种的特征,而这方面的数据较难获得。然而,与一个物种的生态功能相关的特征也塑造了它在环境中的表现,因此应该间接地反映在它的时空分布中。因此,有可能从足够广泛的监测数据集重建这些特征。在这里,我们使用扩散图,一个确定性和事实上的无参数分析方法,重建一个代理代表的物种的性状直接从监测数据,并用它来估计功能多样性。我们证明了这种方法与模拟数据和现实世界的浮游植物监测数据从波罗的海。我们预计,这种方法更广泛地应用于现有数据可以大大推进功能生物多样性变化的分析。

英文摘要:

The twin crises of climate change and biodiversity loss define a strong need for functional diversity monitoring. While the availability of high-quality ecological monitoring data is increasing, the quantification of functional diversity so far requires the identification of species traits, for which data is harder to obtain. However, the traits that are relevant for the ecological function of a species also shape its performance in the environment and hence should be reflected indirectly in its spatio-temporal distribution. Thus it may be possible to reconstruct these traits from a sufficiently extensive monitoring dataset. Here we use diffusion maps, a deterministic and de-facto parameter-free analysis method, to reconstruct a proxy representation of the species' traits directly from monitoring data and use it to estimate functional diversity. We demonstrate this approach both with simulated data and real-world phytoplankton monitoring data from the Baltic sea. We anticipate that wider application of this approach to existing data could greatly advance the analysis of changes in functional biodiversity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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