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Table 3 Regression results from least-squares linear models in the form of trait ~ variable. Table to accompany Fig. 5. Models were fit separately by forest type

From: Correction to: Morphological variation of fine root systems and leaves in primary and secondary tropical forests of Hainan Island, China

Trait (units)

Forest type

Variable (units)

β

Se

P

F(1,148)

R 2

RMSE

Specific root length (m g−1)

Secondary

Soil BS (%)

0.026

0.018

n.s

1.94

0.01

3.11

Primary

0.117

0.055

*

4.53

0.03

3.38

Secondary

Soil P (g kg−1)

2.34

8.48

n.s

0.08

 < 0.01

3.13

Primary

32.41

12.83

*

6.38

0.04

3.36

  1. *p < 0.05, **p < 0.01, ***p < 0.001
  2. aModel coefficient estimates (β), standard errors (se), and associated probabilities (p) are given for each variable by forest type (intercept terms are not shown). Regression F-statistics (F) and coefficients of determination (R2) and root-mean-squared error (RMSE) are given for each model. The F(1,148) critical value at Î±â€‰= 0.05 is 3.905. Italicized model coefficients show significant ANCOVA interaction terms between forest type and soil variable (p < 0.05). n.s. non-significant, Probabilities are denoted as follows: