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Predicting spatial variability of species diversity with the minimum data set of soil properties in an arid desert riparian forest
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 × 100 m) plots of ar...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691764/ https://www.ncbi.nlm.nih.gov/pubmed/36438101 http://dx.doi.org/10.3389/fpls.2022.1014643 |
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author | Li, Xiaotong Chen, Yudong Lv, Guanghui Wang, Jinlong Jiang, Lamei Wang, Hengfang Yang, Xiaodong |
author_facet | Li, Xiaotong Chen, Yudong Lv, Guanghui Wang, Jinlong Jiang, Lamei Wang, Hengfang Yang, Xiaodong |
author_sort | Li, Xiaotong |
collection | PubMed |
description | 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 × 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. |
format | Online Article Text |
id | pubmed-9691764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96917642022-11-26 Predicting spatial variability of species diversity with the minimum data set of soil properties in an arid desert riparian forest Li, Xiaotong Chen, Yudong Lv, Guanghui Wang, Jinlong Jiang, Lamei Wang, Hengfang Yang, Xiaodong Front Plant Sci Plant Science 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 × 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. Frontiers Media S.A. 2022-11-11 /pmc/articles/PMC9691764/ /pubmed/36438101 http://dx.doi.org/10.3389/fpls.2022.1014643 Text en Copyright © 2022 Li, Chen, Lv, Wang, Jiang, Wang and Yang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Li, Xiaotong Chen, Yudong Lv, Guanghui Wang, Jinlong Jiang, Lamei Wang, Hengfang Yang, Xiaodong Predicting spatial variability of species diversity with the minimum data set of soil properties in an arid desert riparian forest |
title | Predicting spatial variability of species diversity with the minimum data set of soil properties in an arid desert riparian forest |
title_full | Predicting spatial variability of species diversity with the minimum data set of soil properties in an arid desert riparian forest |
title_fullStr | Predicting spatial variability of species diversity with the minimum data set of soil properties in an arid desert riparian forest |
title_full_unstemmed | Predicting spatial variability of species diversity with the minimum data set of soil properties in an arid desert riparian forest |
title_short | Predicting spatial variability of species diversity with the minimum data set of soil properties in an arid desert riparian forest |
title_sort | predicting spatial variability of species diversity with the minimum data set of soil properties in an arid desert riparian forest |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691764/ https://www.ncbi.nlm.nih.gov/pubmed/36438101 http://dx.doi.org/10.3389/fpls.2022.1014643 |
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