Simulating the Phylogenetic Diversity Metrics of Plant Communities in Alpine Grasslands of Xizang, China
文献类型：期刊论文
| 作者 | Xiang, Mingxue1; Ma, Tao1; Sun, Wei2; Li, Shaowei2; Fu, Gang2 |
| 刊名 | DIVERSITY-BASEL
 |
| 出版日期 | 2025-08-14 |
| 卷号 | 17期号:8页码:569 |
| 关键词 | alpine grasslands big data mining phylogenetic diversity Qinghai-Xizang Plateau random forest |
| DOI | 10.3390/d17080569 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Phylogenetic diversity serves as a critical complement to traditional species diversity metrics. However, the performance variations among different computational models in simulating phylogenetic diversity within plant communities in the alpine grasslands of the Qinghai-Xizang Plateau remain insufficiently characterized. Here, we evaluated nine modeling approaches-random forest (RF), generalized boosting regression (GBR), multiple linear regression (MLR), artificial neural network (ANN), generalized linear regression (GLR), conditional inference tree (CIT), extreme gradient boosting (eXGB), support vector machine (SVM), and recursive regression tree (RRT)-for predicting three key phylogenetic diversity metrics [Faith's phylogenetic diversity (PD), mean pairwise distance (MPD), mean nearest taxon distance (MNTD)] using climate variables and NDVImax. Our comprehensive analysis revealed distinct model performance patterns under grazing vs. fencing regimes. The eXGB algorithm demonstrated superior accuracy for fencing conditions, achieving the lowest relative bias (-0.08%) and RMSE (9.54) for MPD, along with optimal performance for MNTD (bias = 2.95%, RMSE = 44.86). Conversely, RF emerged as the most robust model for grazing scenarios, delivering the lowest bias (-1.63%) and RMSE (16.89) for MPD while maintaining strong predictive capability for MNTD (bias = -1.09%, RMSE = 27.59). Notably, scatterplot analysis revealed that only RF, GBR, and eXGB maintained symmetrical distributions along the 1:1 line, while other models showed problematic one-to-many value mappings or asymmetric patterns. These findings show that machine learning (especially RF and eXGB) enhances phylogenetic diversity predictions by integrating climate and NDVI data, though model performance varies by metric and management context. This study offers a framework for ecological forecasting, emphasizing multi-metric validation in biodiversity modeling. |
| URL标识 | 查看原文 |
| WOS关键词 | SPECIES RICHNESS |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001558454200001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216154]  |
| 专题 | 拉萨站高原生态系统研究中心_外文论文 |
| 通讯作者 | Fu, Gang |
| 作者单位 | 1.Qinghai Univ, State Key Lab Plateau Ecol & Agr, Xining 810018, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Lhasa Plateau Ecosyst Res Stn, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiang, Mingxue,Ma, Tao,Sun, Wei,et al. Simulating the Phylogenetic Diversity Metrics of Plant Communities in Alpine Grasslands of Xizang, China[J]. DIVERSITY-BASEL,2025,17(8):569. |
| APA | Xiang, Mingxue,Ma, Tao,Sun, Wei,Li, Shaowei,&Fu, Gang.(2025).Simulating the Phylogenetic Diversity Metrics of Plant Communities in Alpine Grasslands of Xizang, China.DIVERSITY-BASEL,17(8),569. |
| MLA | Xiang, Mingxue,et al."Simulating the Phylogenetic Diversity Metrics of Plant Communities in Alpine Grasslands of Xizang, China".DIVERSITY-BASEL 17.8(2025):569. |
入库方式： OAI收割
来源：地理科学与资源研究所
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