Table 2. Root mean square error (RMSE), model efficiency (ME) and Akaike's Information Criterion (AIC) of model validation for the five models and the three study species.

Models	Internal dataset						External dataset						whole dataset
	beech		oak		birch		beech		oak		birch		beech	oak	birch
	RMSE	ME	RMSE	ME	RMSE	ME	RMSE	ME	RMSE	ME	RMSE	ME	AIC
TTM	4.1	0.75	3.1	0.94	3.0	0.82	5.6	0.59	3.7	0.88	3.7	0.77	75.0	57.5	57.5
SM	3.4	0.82	2.9	0.95	2.9	0.84	4.0	0.79	3.7	0.91	3.4	0.80	72.7	67.2	65.0
PM	6.3	0.41	4.7	0.85	4.4	0.62	6.5	0.47	5.0	0.85	4.9	0.66	98.6	87.5	81.5
AM	6.5	0.41	4.0	0.9	5.3	0.48	6.5	0.46	4.7	0.85	6.2	0.33	88.1	72.2	85.6
UM	4.2	0.74	5.8	0.79	4.5	0.61	4.8	0.7	5.8	0.79	5.5	0.56	81.2	91.1	86.4

‘Internal dataset’ indicates that calibration and validation were done on the same dataset, whereas ‘external dataset’ indicate that calibration and validation were done on independent datasets. The AIC were calculated on the whole dataset. TTM is Thermal Time model, SM is sequential model, PM is Parallel model, AM is Alternating model and UM is Unified model. The smallest RMSE and highest ME values for each species are in bold.