Pan Quan, Bauters Marijn, Peaucelle Marc, Ellsworth David, Kattge Jens, Verbeeck Hans
Q-ForestLab, Laboratory of Quantitative Forest Ecosystem Science, Department of Environment, Ghent University, Gent, 9000, Belgium.
INRAE, Université de Bordeaux, Villenave-d'Ornon, Bordeaux, 33140, France.
Commun Biol. 2025 Apr 5;8(1):569. doi: 10.1038/s42003-025-07940-0.
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.
基于性状的分析在推进我们对陆地生态系统过程和功能的理解方面显示出巨大潜力。然而，在充分综合多维且相互协变的性状空间方面仍存在挑战。在功能生态学中，减少所研究性状的数量同时识别出最具信息量的性状日益被视为一项优先任务。在此，我们基于对包含27个性状的全球数据集的网络分析，分三步开发了一种性状简化程序。我们首先构建所有可能的简化网络，并识别出能够捕捉完整27性状网络结构的最优简化网络。然后我们将性状一致性约束应用于已识别的最优简化网络，并建立跨生态区域的一致网络序列。我们找到了能够捕捉完整网络三个主要维度（水文安全、叶片经济策略以及植物繁殖与竞争）和网络指标全球方差的表现最佳的网络。最后，我们发现通过一个10性状网络实现了性状协变策略的简约表示，该网络保留了所有原始信息的60%，而仅占全部性状集的20.1%。我们的结果表明，网络简化方法能够增进我们对主要植物策略的理解，并促进未来基于性状的研究。
