Network-informed analysis of a multivariate trait-space reveals optimal trait selection
Abstract Trait-based analyses have shown great potential to advance our understanding of terrestrial ecosystem processes and functions. However, challenges remain in adequately synthesising a multidimensional and covarying trait space. Reducing the number of studied traits while identifying the most...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-025-07940-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849734713649922048 |
|---|---|
| author | Quan Pan Marijn Bauters Marc Peaucelle David Ellsworth Jens Kattge Hans Verbeeck |
| author_facet | Quan Pan Marijn Bauters Marc Peaucelle David Ellsworth Jens Kattge Hans Verbeeck |
| author_sort | Quan Pan |
| collection | DOAJ |
| description | Abstract Trait-based analyses have shown great potential to advance our understanding of terrestrial ecosystem processes and functions. However, challenges remain in adequately synthesising a multidimensional and covarying trait space. Reducing the number of studied traits while identifying the most informative ones is increasingly recognized as a priority in functional ecology. Here, we develop a trait reduction procedure based on network analysis of a global dataset comprising 27 traits in three steps. We first construct all possible reduced networks and identify optimal reduced networks that capture the structure of the full 27-trait network. Then we apply the constraints on trait consistency to identified optimal reduced networks and establish consistent network series across ecoregions. We find the best performing networks that capture the three main dimensions of the full network (hydrological safety, leaf economic strategy, and plant reproduction and competition) and the global variance of network metrics. Finally, we find a parsimonious representation of trait covariation strategies is achieved by a 10-trait network which preserves 60% of all the original information while costing only 20.1% of the full suite of traits. Our results show the network reduction approach can improve our understanding on the main plant strategies and facilitate the future trait-based research. |
| format | Article |
| id | doaj-art-004ba129473a4fa6a2308096597d3d67 |
| institution | DOAJ |
| issn | 2399-3642 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-004ba129473a4fa6a2308096597d3d672025-08-20T03:07:44ZengNature PortfolioCommunications Biology2399-36422025-04-018111010.1038/s42003-025-07940-0Network-informed analysis of a multivariate trait-space reveals optimal trait selectionQuan Pan0Marijn Bauters1Marc Peaucelle2David Ellsworth3Jens Kattge4Hans Verbeeck5Q-ForestLab, Laboratory of Quantitative Forest Ecosystem Science, Department of Environment, Ghent UniversityQ-ForestLab, Laboratory of Quantitative Forest Ecosystem Science, Department of Environment, Ghent UniversityQ-ForestLab, Laboratory of Quantitative Forest Ecosystem Science, Department of Environment, Ghent UniversityHawkesbury Institute for the Environment, Western Sydney UniversityGerman Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-LeipzigQ-ForestLab, Laboratory of Quantitative Forest Ecosystem Science, Department of Environment, Ghent UniversityAbstract Trait-based analyses have shown great potential to advance our understanding of terrestrial ecosystem processes and functions. However, challenges remain in adequately synthesising a multidimensional and covarying trait space. Reducing the number of studied traits while identifying the most informative ones is increasingly recognized as a priority in functional ecology. Here, we develop a trait reduction procedure based on network analysis of a global dataset comprising 27 traits in three steps. We first construct all possible reduced networks and identify optimal reduced networks that capture the structure of the full 27-trait network. Then we apply the constraints on trait consistency to identified optimal reduced networks and establish consistent network series across ecoregions. We find the best performing networks that capture the three main dimensions of the full network (hydrological safety, leaf economic strategy, and plant reproduction and competition) and the global variance of network metrics. Finally, we find a parsimonious representation of trait covariation strategies is achieved by a 10-trait network which preserves 60% of all the original information while costing only 20.1% of the full suite of traits. Our results show the network reduction approach can improve our understanding on the main plant strategies and facilitate the future trait-based research.https://doi.org/10.1038/s42003-025-07940-0 |
| spellingShingle | Quan Pan Marijn Bauters Marc Peaucelle David Ellsworth Jens Kattge Hans Verbeeck Network-informed analysis of a multivariate trait-space reveals optimal trait selection Communications Biology |
| title | Network-informed analysis of a multivariate trait-space reveals optimal trait selection |
| title_full | Network-informed analysis of a multivariate trait-space reveals optimal trait selection |
| title_fullStr | Network-informed analysis of a multivariate trait-space reveals optimal trait selection |
| title_full_unstemmed | Network-informed analysis of a multivariate trait-space reveals optimal trait selection |
| title_short | Network-informed analysis of a multivariate trait-space reveals optimal trait selection |
| title_sort | network informed analysis of a multivariate trait space reveals optimal trait selection |
| url | https://doi.org/10.1038/s42003-025-07940-0 |
| work_keys_str_mv | AT quanpan networkinformedanalysisofamultivariatetraitspacerevealsoptimaltraitselection AT marijnbauters networkinformedanalysisofamultivariatetraitspacerevealsoptimaltraitselection AT marcpeaucelle networkinformedanalysisofamultivariatetraitspacerevealsoptimaltraitselection AT davidellsworth networkinformedanalysisofamultivariatetraitspacerevealsoptimaltraitselection AT jenskattge networkinformedanalysisofamultivariatetraitspacerevealsoptimaltraitselection AT hansverbeeck networkinformedanalysisofamultivariatetraitspacerevealsoptimaltraitselection |