Predicting spatial variability of species diversity with the minimum data set of soil properties in an arid desert riparian forest

被引:5
|
作者
Li, Xiaotong [1 ,2 ,3 ]
Chen, Yudong [1 ,2 ,3 ]
Lv, Guanghui [1 ,2 ,3 ]
Wang, Jinlong [1 ,2 ,3 ]
Jiang, Lamei [1 ,2 ,3 ]
Wang, Hengfang [1 ,2 ,3 ]
Yang, Xiaodong [4 ]
机构
[1] Xinjiang Univ, Coll Ecol & Environm, Xinjiang, Peoples R China
[2] Xinjiang Univ, Key Lab Oasis Ecol, Educ Minist, Xinjiang, Peoples R China
[3] Minist Educ, Xinjiang Jinghe Observat & Res Stn Temperate Deser, Jinghe, Peoples R China
[4] Ningbo Univ, Sch Civil & Environm Engn & Geog Sci, Ningbo, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
Geo-statistics; Random Forest model; spatial distribution; spatially heterogeneous; desert riparian forests; LAND-USE TYPES; AGRICULTURAL LANDS; QUALITY ASSESSMENT; TOTAL NITROGEN; VEGETATION; ECOSYSTEM; GEOSTATISTICS; RICHNESS; RIVER; SCALE;
D O I
10.3389/fpls.2022.1014643
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Species diversity has spatial heterogeneity in ecological systems. Although a large number of studies have demonstrated the influence of soil properties on species diversity, most of them have not considered their spatial variabilities. To remedy the knowledge gap, a 1 ha (100 m x 100 m) plots of arid desert riparian forest was set up in the Ebinur Wetland Nature Reserve (ELWNR) in the NW China. Then, the minimum data set of soil properties (soil MDS) was established using the Principal Component Analysis (PCA) and the Norm Value Determination to represent the total soil property data set (soil TDS). The Geo-statistics and two models (i.e., Random Forest/RF and Multiple Linear Regression/MLR) were used to measure the spatial variability of species diversity, and predict its spatial distribution by the soil MDS, respectively. The results showed that the soil MDS was composed of soil salt content (SSC), soil total phosphorus (STP), soil available phosphorus (SAP), soil organic carbon (SOC) and soil nitrate nitrogen (SNN); which represented the soil TDS perfectly (R(2 =)0.62). Three species diversity indices (i.e., Shannon-Wiener, Simpson and Pielou indices) had a high spatial dependence (C-0/(C-0+C)< 25%; 0.72 m <= range <= 0.77 m). Ordinary kriging distribution maps showed that the spatial distribution pattern of species diversity predicted by RF model was closer to its actual distribution compared with MLR model. RF model results suggested that the soil MDS had significant effect on spatial distribution of Shannon-Wiener, Simpson and Pielou indices (Var(ex)= 56%, 49% and 36%, respectively). Among all constituents, SSC had the largest contribution on the spatial variability of species diversity (nearly 10%), while STP had least effect (< 5.3%). We concluded that the soil MDS affected spatial variability of species diversity in arid desert riparian forests. Using RF model can predict spatial variability of species diversity through soil properties. Our work provided a new case and insight for studying the spatial relationship between soil properties and plant species diversity.
引用
收藏
页数:15
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