Table 5 Mixed model results: estimates and standard error of the fixed effects. The response variables are the properties of organic horizons and mineral layers; the fixed effect is forest types: MF and ROF

From: Effects of Quercus rubra L. on soil properties and humus forms in 50-year-old and 80-year-old forest stands of Lombardy plain

Variable Residual model Site   Statistic Intercept MF ROF   Variable Residual model Site   Statistic Intercept MF ROF  
OCOL Non-spatial BV   Estimate 0.6534 − 01486 0   pHw3 Non-spatial BV g Estimate 1.3018 − 0.5318 0  
    SE 0.0439 0.0613 .       SE 1.0067 1.9710 .  
    Pr > |t| < .0001 < .0001 . *      Pr > |t| 0.2100 0.7900 .  
Non-spatial BG   Estimate 0.5838 − 0.2065 0    Non-spatial BG   Estimate 4.6222 − 0.0222 0  
    SE 0.0415 0.0651 .       SE 0.0351 0.0531 .  
    Pr > |t| < .0001 0.0027 . *      Pr > |t| < .0001 0.6821 .  
OCOF Non-spatial BV   Estimate 0.2796 − 0.0517 0   CN1 Sp. (Sph) BV   Estimate 15.7999 − 2.3944 0  
    SE 0.0486 0.0778 .       SE 0.5393 0.7479 .  
    Pr > |t| < .0001 0.5136 .       Pr > |t| < .0001 0.0086 . *
Non-spatial BG g Estimate 0.3855 − 0.9739 0    Sp. (Sph.) BG g Estimate 16.4088 − 0.7521 0  
    SE 0.1610 0.2558 .       SE 0.2595 0.4062 .  
    Pr > |t| 0.0207 0.0004 . *      Pr > |t| < .0001 0.0704 . +
OCO Non-spatial BV   Estimate 0.8998 − 0.2925 0   CN2 Sp. (Sph) BV   Estimate 16.3066 − 3.2997 0  
    SE 0.0672 0.09384 .       SE 0.7566 1.0508 .  
    Pr > |t| < .0001 0.0035 . *      Pr > |t| < .0001 0.0098 . *
Non-spatial BG g Estimate 0.5893 − 1.4438     Non-spatial BG   Estimate 16.0981 − 0.3535 0  
    SE 0.1262 0.1976        SE 0.2496 0.3906 .  
    Pr > |t| < .0001 < .0001   *      Pr > |t| < .0001 0.3701 .  
SOC1 Sp. (Sph) BV   Estimate 4.5447 1.7487 0   CN3 Non-spatial BV   Estimate 14.1472 − 1.1819 0  
    SE 0.8135 1.1601 .       SE 0.5447 0.8613 .  
    Pr > |t| 0.0004 0.1714 .       Pr > |t| < .0001 0.1809 .  
Non-spatial BG   Estimate 5.5028 − 0.6568 0    Non-spatial BG   Estimate 15.9222 0.0920 0  
    SE 0.3338 0.5226 .       SE 0.6984 1.0559 .  
    Pr > |t| < .0001 0.2150 .       Pr > |t| < .0001 0.9318 .  
SOC2 Non-spatial BV   Estimate 2.6605 0.4369 0   Pav Non-spatial BV   Estimate 11.5621 − 0.7432 0  
    SE 0.2131 0.2975 .       SE 1.2848 1.7710 .  
    Pr > |t| < .0001 0.1504 .       Pr > |t| < .0001 0.6772 .  
Non-spatial BG   Estimate 2.1293 − 0.5851 0    Non-spatial BG   Estimate 16.6067 − 2.5337 0  
    SE 0.0693 0.1086 .       SE 1.0558 1.6526 .  
    Pr > |t| < .0001 < .0001 . *      Pr > |t| < .0001 0.1319 .  
SOC3 Non-spatial BV   Estimate 1.0882 − 0.1119 0   BD Non-spatial BV   Estimate 0.8784 − 0.1638 0  
    SE 0.1148 0.1583 .       SE 0.0347 0.0479 .  
    Pr > |t| < .0001 0.4840 .       Pr > |t| < .0001 0.0016 .  
Non-spatial BG   Estimate 1.1722 − 0.1179 0    Non-spatial BG   Estimate 0.8424 − 0.0128 0  
    SE 0.0478 0.0722 .       SE 0.0443 0.0693 .  
    Pr > |t| < .0001 0.1250 .       Pr > |t| < .0001 0.8536 .  
SOCstock Non-spatial BV   Estimate 10.9149 0.9134 0   BD Non-spatial BV   Estimate 1.0822 0.0819 0  
    SE 0.6688 0.9340 .       SE 0.0488 0.0673 .  
    Pr > |t| < .0001 0.3345 .       Pr > |t| < .0001 0.2319 .  
Sp. (Sph) BG   Estimate 12.0364 − 3.7203 0    Non-spatial BG   Estimate 1.1673 0.0054 0  
    SE 1.3691 1.5879 .       SE 0.0348 0.0545 .  
    Pr > |t| 0.0056 0.0350 . *      Pr > |t| < .0001 0.9214 .  
pHw1 Non-spatial BV g Estimate − 0.6133 1.2496 0           
    SE 0.1777 0.2480 .           
    Pr > |t| 0.0015 < .0001 . *          
Sp. (Sph) BG   estimate 4.1371 0.2824 0           
    SE 0.0877 0.1183 .           
    Pr > |t| < .0001 0.0376 . *          
pHw2 Non-spatial BV g Estimate − 0.4729 1.3076 0           
    SE 0.4449 0.6607 .           
    Pr > |t| 0.3547 0.1245 .           
Non-spatial NF   Estimate 4.5483 − 0.0082 0           
    SE 0.0265 0.0415 .           
    Pr > |t| < .0001 0.8430 .           
  1. Sp. (Sph), the spatial covariance function of residuals was the Spherical model; non-spatial, the residuals were spatially uncorrelated
  2. *p < 0.05 and + p < 0.1 denote statistically significant differences between forest types; g defines Gaussian variable
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