Do Spatially Structured Soil Variables Influence the Plant Diversity in Tabuk Arid Region, Saudi Arabia?

Soils with more clay and dense vegetation were rich in soil carbon along Wadi Al-Sharaea, Makkah, Saudi Arabia

In arid ecosystems, lack of vegetation and nutrients can negatively impact soil carbon (C) content. In the current study, our goals were to assess soil C stocks to a depth of 50 cm in an arid ecosystem (Wadi Al-Sharaea, Saudi Arabia) and determine their relation to different vegetation cover. To address our research objective, a total of 102 quadrate (randomly selected) were established along the desert wadi. Soil samples were collected to a depth of 50 cm with 5 cm interval, then Soil Bulk Density (SBD, g/cm³), Soil Organic C Content (SOC, g C/kg), and stocks (kg C/m²) were estimated. Both soil mechanical and chemical analyses were conducted for a composite soil sample. Study sites were categorized based on their visual vegetation cover (VC) percentage (%) into three major groups: 1) scarce vegetation cover (VC less than 25%); 2) medium vegetation cover (VC is higher than 25% and less than 75%); and lastly 3) dense vegetation cover (VC is higher than 75%). Soils were characterized by higher sand content (48.2%, both fine and coarse compiled) than silt (36.7 ± 1.64%) or clay (10.1 ± 1.28%). There were significant differences among soil Calcium (Ca) and Potassium (K) content (p < 0.05), while those plant communities with medium vegetation cover showed the highest soil content of Ca and K (1.7 ± 0.24 and 0.2 ± 0.03 meq/l, respectively). Plant communities with dense vegetation cover had the lowest SBD (1.96 ± 0.03 g/cm³) and the highest SOC stocks (14.9 ± 2.1 kg C/m²). Moreover, our data analyses indicated that SBD and SOC content had strong and negative correlation, where soils with dense vegetation cover had the most significant correlation (R² = 0.95). Our results recommend that soil carbon stocks to a depth of 50 cm based on different vegetation cover of arid ecosystems should be implemented on global soil carbon budget to better elucidate factors controlling SOC content at the regional and global scales.

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 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 (C₀/(C₀+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.