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

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Abstract

Plant diversity is affected by spatial variables as well as soil physical and chemical variables. In this study, plant species and soil variables were investigated in five sites of Tabuk Province (Saudi Arabia), namely Aldesah, Alzetah, Alawz, Harra and Sharma, to understand if the spatially structured soil variables (pH, electric conductivity (EC), soil texture, calcium, potassium, phosphorus, phosphate, total organic matter (OM), bicarbonate and sodium) influence the plant diversity. A total of 163 plant species belong to 41 families and 124 genera were reported from the 5 sites. Diversity indices including the species richness (alpha), evenness, Brillouin, Menhinick, Margalef, equitability and estimated Chao-1 were significantly different among the studied sites with pronounced high values in Sharma and Aldesah. The highest value of beta diversity was reported in Aldesah (0.253) followed by Sharma (0.171). According to the principal coordinates of neighbourhood matrix (PCNM) analysis, 11 positive spatial vectors (variables) were found. However, after running the forward selection procedures (using 2 stopping criteria), only 3 spatial vectors were retained (PCNM 1 (adj–R2 = 0.043, F = 5.201, p = 0.004), PCNM 2 (adj–R2 = 0.027, F = 3.97, p = 0.006) and PCNM 3 (adj–R2 = 0.019, F = 3.36, p = 0.007)). The linear models between the selected spatial variables (PCNM vectors) and soil variables were produced to investigate their spatial structure. In the first model, the first PCNM 1 axis showed significant relationship with pH and potassium (adj–R2 = 0.175, p = 0.046). In the second model, the second PCNM 2 axis had a significant relationship with OM and sodium (adj–R2 = 0.561, p < 0.001). Lastly, sodium was the only factor significantly correlated with the third PCNM 3 axis (adj–R2 = 0.365, p = 0.002). In conclusion, the spatially structured variables of soil did not show strong influence on plant diversity except pH and potassium, which were correlated with PCNM 1, OM and sodium, which were correlated with PCNM 2, and sodium, which was correlated with PCNM 3.

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Holger Kreft, Walter Jetz (2007)

Plants, with an estimated 300,000 species, provide crucial primary production and ecosystem structure. To date, our quantitative understanding of diversity gradients of megadiverse clades such as plants has been hampered by the paucity of distribution data. Here, we investigate the global-scale species-richness pattern of vascular plants and examine its environmental and potential historical determinants. Across 1,032 geographic regions worldwide, potential evapotranspiration, the number of wet days per year, and measurements of topographical and habitat heterogeneity emerge as core predictors of species richness. After accounting for environmental effects, the residual differences across the major floristic kingdoms are minor, with the exception of the uniquely diverse Cape Region, highlighting the important role of historical contingencies. Notably, the South African Cape region contains more than twice as many species as expected by the global environmental model, confirming its uniquely evolved flora. A combined multipredictor model explains approximately 70% of the global variation in species richness and fully accounts for the enigmatic latitudinal gradient in species richness. The models illustrate the geographic interplay of different environmental predictors of species richness. Our findings highlight that different hypotheses about the causes of diversity gradients are not mutually exclusive, but likely act synergistically with water-energy dynamics playing a dominant role. The presented geostatistical approach is likely to prove instrumental for identifying richness patterns of the many other taxa without single-species distribution data that still escape our understanding.

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Distance-based tests for homogeneity of multivariate dispersions.

Marti J. Anderson (2006)

The traditional likelihood-based test for differences in multivariate dispersions is known to be sensitive to nonnormality. It is also impossible to use when the number of variables exceeds the number of observations. Many biological and ecological data sets have many variables, are highly skewed, and are zero-inflated. The traditional test and even some more robust alternatives are also unreasonable in many contexts where measures of dispersion based on a non-Euclidean dissimilarity would be more appropriate. Distance-based tests of homogeneity of multivariate dispersions, which can be based on any dissimilarity measure of choice, are proposed here. They rely on the rotational invariance of either the multivariate centroid or the spatial median to obtain measures of spread using principal coordinate axes. The tests are straightforward multivariate extensions of Levene's test, with P-values obtained either using the traditional F-distribution or using permutation of either least-squares or LAD residuals. Examples illustrate the utility of the approach, including the analysis of stabilizing selection in sparrows, biodiversity of New Zealand fish assemblages, and the response of Indonesian reef corals to an El Niño. Monte Carlo simulations from the real data sets show that the distance-based tests are robust and powerful for relevant alternative hypotheses of real differences in spread.