Comparison of models for estimating bark thickness of Picea abies in southwest Germany: the role of tree, stand, and environmental factors

Table 2 Correlation structure of both datasets expressed by total residual variance and variances between plots, between trees, and within trees for the two tested models

Dataset	Equation	Predictor variables	\( {{\hat{\sigma}}^2}_{\mathrm{total}} \)	\( \frac{{{\hat{\sigma}}^2}_{\mathrm{plot}}}{{{\hat{\sigma}}^2}_{\mathrm{total}}} \)(%)	\( \frac{{{\hat{\sigma}}^2}_{\mathrm{tree}}}{{{\hat{\sigma}}^2}_{\mathrm{total}}} \)(%)	\( \frac{{{\hat{\sigma}}^2}_{\varepsilon}}{{{\hat{\sigma}}^2}_{\mathrm{total}}} \)(%)
1 (1970s)	(1)	d _ob	10.51	46.91	18.56	34.53
1 (1970s)	(2)	d _ob, dbh _ob	8.71	49.82	22.79	27.39
2 (2014–2016)	(1)	d _ob	12.37	28.76	35.54	35.70
2 (2014–2016)	(2)	d _ob, dbh _ob	9.73	20.72	44.47	34.81

d _ob is the diameter outside bark, dbh _ob is the diameter outside bark at breast height, \( {{\hat{\sigma}}^2}_{\mathrm{total}} \) is the total residual variance, \( {{\hat{\sigma}}^2}_{\mathrm{plot}} \) is the variance between plots, \( {{\hat{\sigma}}^2}_{\mathrm{tree}} \) is the variance between trees, and \( {{\hat{\sigma}}^2}_{\varepsilon} \) is the variance within trees

ISSN: 1297-966X