Computational protocol: Influence of aridity and salinity on plant nutrients scales up from species to community level in a desert ecosystem

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Protocol publication

[…] Before numerical and statistical analyses, all variables for each species were averaged at the plot level and all variables relating to the soil samples were averaged at the site level (Table ). Data were tested for normality using the Kolmogorov-Smirnov test and for equality of error variance using Levene’s test. Weighted community N and P concentration and N:P ratios were calculated according to Equation :1yj=∑i=1n(xi×ai×hi)where yj was the weighted mean of community N or P concentration (g kg−1 dry mass) or the N:P ratio of the j site, x i was the mean of foliar N or P concentration (g kg−1 dry mass) or the N:P ratio of the i species in the j site, a i was the relative abundance of the i species in the j site, and h i was the relative height of the i species in the j site. Linear or nonlinear regression was used to analyze the relationship between N:P ratio and both plant N and P concentration for each species in response to the soil water and salt gradients, and to examine the response of plant nutrient concentrations to soil factors (i.e. soil water, soil total salt and soil nutrient content). Through principal component analysis (PCA) of the environmental data, sites 1, 2, 3 and 4, sites 5, 6, 7, 8, and 9, and sites S1, S2, S3 and S4 were defined to be dry sites (low soil salt content, see Table ), humid-saline sites and humid-non-saline sites, respectively (Fig. ). One-way analysis of variance (ANOVA) was used to examine the differences in leaf N, P (content or concentration) and N:P ratios among all the plants from the dry sites, humid-saline sites and humid-non-saline sites, and differences in soil factors among all the sites. The 3-D mesh plot was also used to show the response trend in coefficient of variance (CV) of the leaf N, P and N:P ratio for each species due to soil water and salt content. We further analyzed the linear or nonlinear relationships based on the regression analysis between each of the community nutrient metrics (N, P and N:P ratio) and both soil water content and salt content. The above statistical analyses were conducted using the statistical package SPSS (PASW statistics 21.0; IBM Corporation, Armonk, NY, USA) and SigmaPlot 12.5 (SyStat Software Inc., San Jose, CA, USA). Variation in community N, P and N:P ratio was partitioned between two explanatory variable groups (soil water content [0–20 cm, 20–100 cm] and soil salt content [0–20 cm, 20–100 cm]) using a partial regression analysis with a redundancy analysis (RDA). PCA and RDA were conducted using CANOCO 5.0 (Microcomputer Power, Ithaca, NY, USA). […]

Pipeline specifications

Software tools SPSS, SigmaPlot
Application Miscellaneous