Computational protocol: Significant Phylogenetic Signal and Climate-Related Trends in Leaf Caloric Value from Tropical to Cold-Temperate Forests

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[…] We constructed a phylogenetic tree at the species and family levels by using the data from 745 species. By using the Latin name of each species as given in the Plant List (, we determined the order, family, and genus of each species based on the Angiosperm Phylogeny Group III classification (APG III). We defined a reference phylogenetic tree and resolved it to family and species level by using the freely available software Phylomatic v3 ( Branch lengths were determined using the Branch Length Adjuster algorithm in Phylocom. [...] Differences in LCV between different phylogenetic groups were tested using one-way analysis of variance with a test for least significant difference. The strength of the phylogenetic signal in LCV across the sample was quantified using Blomberg’s K statistic which tests whether the observed trait variation across a phylogeny is smaller than expected according to a Brownian motion model of trait evolution. We tested the significance of this phylogenetic signal by comparing the actual system to a null model without a phylogenetic structure. If the real value of the phylogenetic signal in the trait was greater than 95% of that of the null model (P < 0.05), the phylogenetic signal was considered significant, and vice versa. The phylogenetic signal was quantified and tested using the ‘picante’ package in R.Regression analyses were conducted to test for a latitudinal pattern in LCV and for a phylogenetic signal at the community level. Relationships between LCV and influencing factors were assessed using Pearson correlations. The relationship between LCV and leaf element content was tested using phylogenetically independent contrasts (PIC) after the phylogenetic effect was excluded. PIC correlation coefficients were calculated using the ‘pic’ package in the R. The relationship between LCV and climate used only those families that appeared in at least three forest types. The relationships between LCV at plot level and climate and soil factors were explored using linear regressions.The effect of climate (MAT and MAP), soil (STC and STN), and phylogeny (family level) on the spatial variation of LCV were further quantified using general linear models (GLMs) and partial GLMs. To avoid collinearity among explanatory variables, we removed correlated predictors by using multiple stepwise regressions (P < 0.05). Partial GLMs were then used to divide the explanatory power of these factors into independent and interactive effects.All tests used a significance level of P = 0.05. All analyses were conducted using the software SPSS 13.0 (SPSS Inc., Chicago, IL, USA, 2004) or R (version 2.15.2, R Development Core Team 2012). All figures were produced in SigmaPlot 10.0 (Washington, IL, USA, 2006). […]

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