ArticleProceedings of the National Academy of Sciences of the United States of America2022
Estimation of functional diversity and species traits from ecological monitoring data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- From data to decisions: The role of machine learning in precision ecological restoration.Ecological applications : a publication of the Ecological Society of America · 2026Review
- Five decades of seasonal phytoplankton succession examined with principal traits-An approach linking composition to function.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Microbial cohorts: bringing ecological meaning to the modularity concept of co-occurrence networks.ISME communications · 2026Article
- Mobility census for monitoring rapid urban development.Journal of the Royal Society, Interface · 2024Article
- Unsupervised pattern and outlier detection for pedestrian trajectories using diffusion maps.Physica A · 2024Article
- Quantifying microbial guilds.ISME communications · 2024Article
- Unravelling the variations of the society of England and Wales through diffusion mapping analysis of census 2011.Journal of the Royal Society, Interface · 2023Article
- Quantification of metabolic niche occupancy dynamics in a Baltic Sea bacterial community.mSystems · 2023Article
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Authors and funding
5 authors.
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Abstract
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 are 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 spatiotemporal 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 with both 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.
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