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. 2021 Jul 30;12:679726. doi: 10.3389/fpls.2021.679726

TABLE 3.

Results of multiple linear regressions (MLRs, stepwise model selection routine) for the effects of four main climate predictors on each of eleven leaf traits.

AIC R2 P
LL = −0.770 + 0.053MTWQ − 0.0004MASH + 0.025MRH –1764.44 0.222 <0.001
LL/LW = −0.409 + 0.0003MASH –1899.05 0.010 0.007
LA = −1.752 + 0.108MTWQ − 0.001MASH + 0.055MRH –894.01 0.242 <0.001
SLA = 2.415−0.029PDQ − 0.0004MASH + 0.017MRH –2238.05 0.063 <0.001
SD = 2.233 + 0.011PDQ –1778.69 0.007 0.027
SL/SW = 0.840 − 0.010MTWQ + 0.0004MASH –1369.42 0.017 0.003
SA = 2.972 + 0.012MTWQ − 0.0003MASH –1525.42 0.020 <0.001
SPI = 4.454 + 0.011MTWQ − 0.020PDQ –1495.59 0.022 <0.001
Pmass = 0.174 − 0.023MTWQ + 0.014PDQ + 0.0002MASH –2692.53 0.233 <0.001
Nmass = 8.095 − 0.189MTWQ 238.20 0.124 <0.001
N/P = 38.743 + 0.340MTWQ − 0.577PDQ − 0.010MASH 0.48 0.136 <0.001

For each regression equation, significant climatic predictors are arranged in order of their importance (explanatory power for leaf trait variation; R2), with the strongest predictor (marked in bold) on the leftmost of the equation. Abbreviations of leaf traits and climatic factors are as specified in Table 2.