Evaluation of regional-level forest dynamics with forest inventory data

Miquel De Cáceres

2024-04-16

Introduction

## Loading required package: sp

Goal

The aim of this article is to provide an assessment of the performance of fordyn (using either the basic and advanced sub-model) for the prediction of forest dynamics in Catalonia (NE of Spain). To this aim, we simulate forest dynamics between surveys of the Spanish National Forest Inventory and compare the model predictions of forest development against inventory data for a set of permanent plots. The evaluation focuses first on the growth (in diameter and height) of surviving trees, then turning the attention to the basal area of dead trees and overall changes in terms of basal area and density. Then, we evaluate changes in stand leaf area index and, finally, changes in shrub cover and mean shrub height.

Simulation procedure

We selected permanent plots between the second (IFN2) and the fourth (IFN4) without signs of management (i.e. the presence of stumps) and whose basal area did not decrease more than 10% between inventory surveys (to avoid the effect of disturbances).

Soil physical properties were drawn from SoilGrids (Hengl 2016), complemented by rock fragment content estimates derived from surface stoniness measurements in forest plots.

fordyn simulations were conducted for different periods:

• IFN2 - IFN3 (~ 10 years)
• IFN3 - IFN4 (~ 15 years)
• IFN2 - IFN4 (~ 25 years)

The actual simulated period varied depending on the sampling years of the target plot. Daily weather data were obtained via interpolation on plot’s coordinates using package meteoland.

Default species-specific parameters were modified using the results of the meta-modelling exercise and the growth calibration exercise. These two exercises do not provide values for all the main species included here, so it is expected that evaluation results are worse for those species not included in those exercises.

Simulations were done for both the basic (i.e. Granier) and advanced (i.e. Sperry) transpiration/photosynthesis sub-models. On a server with 20 parallel threads, computational times for the longest simulation period (25 years) are around 4 hours (i.e. 2.5 min/plot) for the basic sub-model, versus around 6 days (i.e. 1.5 hr/plot) for the advanced submodel.

In the following sections, we provide the bias, root mean squared error (in absolute and relative terms) and explained variance (R-squared) of growth and mortality predictions at the tree-level and stand-level obtained by simulations with both sub-models. Scatter plots are provided for the IFN2-IFN4 simulation to represent the relationship between predicted and observed values, as well as the factors that may influence the direction and magnitude of prediction error (i.e. initial values, environmental conditions, …).

Detailed results of growth evaluation by tree species are provided in the last section.

Growth of surviving trees

Comparison of diameter/height growth of trees (DBH >= 7.5) that survived between surveys.

Annual diameter increment

Overall predictive capacity to predict diameter increase (cm/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	25240	0.2410220	0.2510570	0.0100350	4.1635364	0.1767103	73.31710	0.1433847
2.9.3	IFN23	Sperry	25240	0.2410220	0.2402085	-0.0008135	-0.3375314	0.1812412	75.19698	0.1141433
2.9.3	IFN34	Granier	33593	0.1809126	0.2203855	0.0394729	21.8187435	0.1549733	85.66195	0.0916600
2.9.3	IFN34	Sperry	33593	0.1809126	0.2019252	0.0210126	11.6147797	0.1598018	88.33095	0.0471833
2.9.3	IFN24	Granier	22252	0.2076439	0.2166992	0.0090553	4.3609864	0.1439791	69.33944	0.1208357
2.9.3	IFN24	Sperry	22252	0.2076439	0.2020704	-0.0055735	-2.6841817	0.1515521	72.98653	0.0795355

Predictive capacity plots (IFN2-IFN4):

Relationship between diameter increase and climatic variables (MAT, P/PET and available PAR; IFN2 - IFN4):

Annual height increment

Overall predictive capacity to predict height increase (cm/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	25240	11.307232	11.956505	0.6492722	5.7420970	12.870590	113.8262	0.0648013
2.9.3	IFN23	Sperry	25240	11.307232	11.395532	0.0883002	0.7809175	12.937981	114.4222	0.0582726
2.9.3	IFN34	Granier	33593	8.225607	9.805148	1.5795402	19.2027182	12.297555	149.5033	0.0382904
2.9.3	IFN34	Sperry	33593	8.225607	8.962846	0.7372384	8.9627226	12.173948	148.0006	0.0383640
2.9.3	IFN24	Granier	22252	9.448645	9.782408	0.3337628	3.5323879	9.493789	100.4778	0.0811256
2.9.3	IFN24	Sperry	22252	9.448645	9.083228	-0.3654166	-3.8673969	9.510176	100.6512	0.0812991

Predictive capacity plots (IFN2-IFN4):

Relationship between height increase and climatic variables (MAT, P/PET and available PAR; IFN2 - IFN4):

Stand-level basal area increment

Comparison of basal area increment of surviving trees does not take into account changes in density. In other words, densities from the first inventory are used to calculate stand-level basal area of surviving trees. Hence, the comparison is meant to evaluate the effect of diameter increment of surviving trees in terms of annual stand basal area increments (m2/ha/yr) for the period evaluated.

Predictive capacity table:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1775	0.3374811	0.3527152	0.0152341	4.5140597	0.1968105	58.31751	0.4827320
2.9.3	IFN23	Sperry	1775	0.3374811	0.3418611	0.0043800	1.2978476	0.2058298	60.99003	0.4693875
2.9.3	IFN34	Granier	1774	0.3142805	0.3883378	0.0740573	23.5640720	0.2110768	67.16192	0.3651096
2.9.3	IFN34	Sperry	1774	0.3142805	0.3660654	0.0517849	16.4772808	0.2305197	73.34838	0.3543001
2.9.3	IFN24	Granier	1614	0.3033475	0.3152380	0.0118905	3.9197740	0.1783411	58.79102	0.4523729
2.9.3	IFN24	Sperry	1614	0.3033475	0.3013088	-0.0020387	-0.6720638	0.1983854	65.39874	0.4275482

Predictive capacity plots (IFN2 - IFN4):

Relationship between basal area increase and climatic variables (MAT and P/PET; IFN2 - IFN4):

Spatial error distribution (IFN2 - IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Mortality

Basal area reduction

Annual reduction of basal area (m2/ha/yr) due to trees (DBH >= 7.5) that died during the evaluation period against model’s mortality prediction. In both cases, basal area is calculated using the initial diameter of the trees, so that density reductions are the only prediction that is actually evaluated (and not the possible growth of those trees during the simulation).

Overall predictive capacity:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1775	0.0341902	0.0388506	0.0046604	13.630747	0.0801998	234.5695	0.1189968
2.9.3	IFN23	Sperry	1775	0.0341902	0.0419123	0.0077221	22.585671	0.0921206	269.4358	0.0576708
2.9.3	IFN34	Granier	1774	0.0722102	0.0667947	-0.0054155	-7.499587	0.1278666	177.0757	0.0498639
2.9.3	IFN34	Sperry	1774	0.0722102	0.0561260	-0.0160842	-22.274097	0.1082715	149.9393	0.1219296
2.9.3	IFN24	Granier	1614	0.0473227	0.0478036	0.0004809	1.016222	0.0783685	165.6045	0.1397359
2.9.3	IFN24	Sperry	1614	0.0473227	0.0411657	-0.0061570	-13.010688	0.0700971	148.1257	0.2017773

Predictive capacity plots (IFN2 - IFN4):

Relationship between dead basal area and climatic variables (MAT and P/PET; IFN2 - IFN4):

Spatial distribution of errors (IFN2 - IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Density reduction

Annual reduction of density (ind/ha/yr) due to trees (DBH >= 7.5) that died during the evaluation period against model’s mortality prediction. This is very similar to evaluating the reduction in basal area

Overall predictive capacity:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1775	2.054616	2.166542	0.1119253	5.447503	6.504228	316.5665	0.0331148
2.9.3	IFN23	Sperry	1775	2.054616	2.247157	0.1925404	9.371109	6.805936	331.2509	0.0325920
2.9.3	IFN34	Granier	1774	4.354772	3.971894	-0.3828787	-8.792163	9.760872	224.1420	0.0762814
2.9.3	IFN34	Sperry	1774	4.354772	2.706162	-1.6486106	-37.857562	7.784626	178.7608	0.1248698
2.9.3	IFN24	Granier	1614	1.636702	3.030250	1.3935474	85.143602	5.473520	334.4236	0.1094426
2.9.3	IFN24	Sperry	1614	1.636702	2.128852	0.4921496	30.069585	3.548630	216.8158	0.1776700

Ingrowth

Basal area increase

Annual increase of basal area (m2/ha/yr) due to ingrowth of trees with diameters between 7.5 cm and 12.5 cm during the evaluated period.

Predictive capacity:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1775	0.0960568	0.0659898	-0.0300670	-31.301254	0.1678272	174.7166	0.0041963
2.9.3	IFN23	Sperry	1775	0.0960568	0.0749100	-0.0211468	-22.014921	0.1712049	178.2329	0.0019911
2.9.3	IFN34	Granier	1774	0.0754149	0.0738258	-0.0015891	-2.107102	0.1431620	189.8326	0.0013542
2.9.3	IFN34	Sperry	1774	0.0754149	0.0837412	0.0083263	11.040679	0.1472762	195.2880	0.0037749
2.9.3	IFN24	Granier	1614	0.0850140	0.0654634	-0.0195505	-22.996862	0.1259979	148.2084	0.0068521
2.9.3	IFN24	Sperry	1614	0.0850140	0.0748778	-0.0101362	-11.922960	0.1272278	149.6551	0.0094581

Predictive capacity plots (IFN2 - IFN4):

Relationship between ingrowth basal area and climatic variables (MAT and P/PET; IFN2 - IFN4):

Spatial distribution of errors (IFN2 - IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Density increase

Annual increase of density (ind/ha/yr) due to ingrowth of trees with diameters between 7.5 cm and 12.5 cm during the evaluated period.

Predictive capacity:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1775	15.16134	11.53614	-3.6252060	-23.910848	26.95886	177.8132	0.0054853
2.9.3	IFN23	Sperry	1775	15.16134	12.76076	-2.4005872	-15.833603	27.34091	180.3330	0.0024200
2.9.3	IFN34	Granier	1774	12.06283	12.67282	0.6099882	5.056758	23.43540	194.2778	0.0031139
2.9.3	IFN34	Sperry	1774	12.06283	14.26701	2.2041809	18.272502	23.89215	198.0642	0.0060823
2.9.3	IFN24	Granier	1614	12.31412	10.09821	-2.2159043	-17.994826	18.54517	150.6009	0.0100449
2.9.3	IFN24	Sperry	1614	12.31412	11.33373	-0.9803904	-7.961515	18.50525	150.2767	0.0133026

Overall stand-level change

Basal area changes

This includes annual changes in basal area (m2/ha/yr) due to growth of surviving trees, mortality reductions and ingrowth derived from sapling growth. In the observed data, basal area changes include also the incorporation of trees into large diameter classes that results from the variable-radius sampling design. Since it takes into account all processes together, this evaluation is the most rellevant of all.

Overall predictive capacity:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1775	0.3972023	0.3784107	-0.0187915	-4.730976	0.3183793	80.15547	0.1203408
2.9.3	IFN23	Sperry	1775	0.3972023	0.3745223	-0.0226800	-5.709925	0.3327688	83.77818	0.1170316
2.9.3	IFN34	Granier	1774	0.3370595	0.4030425	0.0659829	19.576047	0.3257997	96.65940	0.0382718
2.9.3	IFN34	Sperry	1774	0.3370595	0.4063208	0.0692612	20.548657	0.3497075	103.75243	0.0610861
2.9.3	IFN24	Granier	1614	0.3779819	0.3485672	-0.0294147	-7.782034	0.2809288	74.32335	0.0737416
2.9.3	IFN24	Sperry	1614	0.3779819	0.3613474	-0.0166345	-4.400868	0.3087092	81.67301	0.0837204

Predictive capacity plot (IFN2 - IFN4):

Relationship between overall basal area change and climatic variables (MAT and P/PET; IFN2 - IFN4):

Spatial distribution of errors (IFN2 - IFN4):

Density changes

This includes annual changes in density (ind/ha/yr) due to growth of surviving trees, mortality reductions and ingrowth derived from sapling growth. In the observed data, changes include also the incorporation of trees into large diameter classes that results from the variable-radius sampling design.

Overall predictive capacity:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1775	13.357940	9.419832	-3.9381083	-29.481403	29.29232	219.2877	0.0040231
2.9.3	IFN23	Sperry	1775	13.357940	10.553016	-2.8049243	-20.998179	29.54857	221.2060	0.0022281
2.9.3	IFN34	Granier	1774	8.605954	8.764622	0.1586673	1.843692	26.71431	310.4166	0.0065902
2.9.3	IFN34	Sperry	1774	8.605954	11.664005	3.0580501	35.534118	26.78752	311.2672	0.0031044
2.9.3	IFN24	Granier	1614	10.933822	7.841235	-3.0925866	-28.284591	21.10788	193.0512	0.0148830
2.9.3	IFN24	Sperry	1614	10.933822	10.241405	-0.6924164	-6.332794	20.96644	191.7577	0.0078613

Changes in leaf area index of trees > 7.5 cm

Overall predictive capacity using allometric equations:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1775	0.2929417	0.2922517	-0.0006900	-0.2355397	0.4526093	154.5049	0.0326050
2.9.3	IFN23	Sperry	1775	0.2929417	0.2965507	0.0036090	1.2320009	0.4698143	160.3781	0.0309913
2.9.3	IFN34	Granier	1774	0.2826026	0.3092020	0.0265993	9.4122713	0.4412830	156.1497	0.0525377
2.9.3	IFN34	Sperry	1774	0.2826026	0.3350054	0.0524027	18.5429042	0.4377824	154.9109	0.0816851
2.9.3	IFN24	Granier	1614	0.5878670	0.5195740	-0.0682929	-11.6170699	0.6869863	116.8608	0.0876206
2.9.3	IFN24	Sperry	1614	0.5878670	0.5808761	-0.0069909	-1.1891897	0.7208363	122.6189	0.0741648

Overall predictive capacity using state variables:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1775	0.2929417	0.8129123	0.5199706	177.4997	0.9370656	319.8813	0.0081521
2.9.3	IFN23	Sperry	1775	0.2929417	0.8167653	0.5238236	178.8150	0.9894157	337.7518	0.0127528
2.9.3	IFN34	Granier	1774	0.2826026	1.0725979	0.7899952	279.5428	1.1802941	417.6515	0.0182405
2.9.3	IFN34	Sperry	1774	0.2826026	1.1535709	0.8709683	308.1954	1.4045492	497.0050	0.0237107
2.9.3	IFN24	Granier	1614	0.5878670	1.4556562	0.8677893	147.6166	1.5026004	255.6021	0.0242077
2.9.3	IFN24	Sperry	1614	0.5878670	1.5936815	1.0058146	171.0956	1.8801653	319.8284	0.0202884

Shrub cover and mean height

Shrub percent cover changes

Overall predictive capacity:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1775	22.37465	19.75818	-2.616467	-11.693892	44.13387	197.2494	0.0281254
2.9.3	IFN23	Sperry	1775	22.37465	19.25347	-3.121176	-13.949612	43.82758	195.8805	0.0059419
2.9.3	IFN34	Granier	1774	22.18602	11.87560	-10.310421	-46.472603	54.41405	245.2628	0.0509630
2.9.3	IFN34	Sperry	1774	22.18602	20.55513	-1.630895	-7.351001	53.76405	242.3330	0.0236755
2.9.3	IFN24	Granier	1614	44.61958	25.26783	-19.351747	-43.370529	63.35317	141.9851	0.0065983
2.9.3	IFN24	Sperry	1614	44.61958	27.61571	-17.003868	-38.108536	64.55488	144.6784	0.0008235

Shrub mean height changes

Overall predictive capacity:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1572	6.285700	43.62822	37.518120	596.88053	84.74814	1348.2689	0.0127112
2.9.3	IFN23	Sperry	1560	5.810655	41.73444	36.015967	619.82630	89.44012	1539.2434	0.0061212
2.9.3	IFN34	Granier	1714	32.096347	42.78995	10.815421	33.69674	81.03306	252.4682	0.0216451
2.9.3	IFN34	Sperry	1714	32.050402	39.14764	7.140503	22.27898	87.99448	274.5503	0.0015234
2.9.3	IFN24	Granier	1401	34.074337	74.52546	40.524916	118.93090	114.60069	336.3255	0.0286464
2.9.3	IFN24	Sperry	1341	33.288672	75.66733	42.656877	128.14232	139.71147	419.6967	0.0007556

Detailed evaluation by tree species

In the following we provide detailed evaluation results for the most important species.

Abies alba

Annual diameter increment

Prediction ability for diameter increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	581	0.3248858	0.1757081	-0.1491777	-45.91695	0.2990908	92.06027	0.1832686
2.9.3	IFN23	Sperry	581	0.3248858	0.1731136	-0.1517722	-46.71555	0.3032952	93.35439	0.1565168
2.9.3	IFN34	Granier	733	0.3071834	0.1711968	-0.1359865	-44.26885	0.2688735	87.52867	0.1560679
2.9.3	IFN34	Sperry	733	0.3071834	0.1589369	-0.1482465	-48.25994	0.2798596	91.10507	0.0988316
2.9.3	IFN24	Granier	554	0.3204793	0.1660305	-0.1544488	-48.19306	0.2611898	81.49975	0.2643651
2.9.3	IFN24	Sperry	554	0.3204793	0.1640309	-0.1564483	-48.81699	0.2659607	82.98843	0.2023990

Annual height increment

Prediction ability for height increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	581	16.29681	6.442982	-9.853824	-60.46476	23.65973	145.1802	0.1394643
2.9.3	IFN23	Sperry	581	16.29681	6.404699	-9.892107	-60.69967	23.81142	146.1110	0.1112480
2.9.3	IFN34	Granier	733	13.82449	6.212174	-7.612316	-55.06399	20.92261	151.3445	0.1197799
2.9.3	IFN34	Sperry	733	13.82449	5.921157	-7.903334	-57.16908	21.25651	153.7598	0.0800358
2.9.3	IFN24	Granier	554	14.71605	6.065490	-8.650559	-58.78316	17.72375	120.4382	0.2402032
2.9.3	IFN24	Sperry	554	14.71605	6.102032	-8.614016	-58.53484	17.87170	121.4436	0.1759728

Growth basal area increment

Prediction ability for basal area increase due to growth (m2/ha/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	41	0.3337317	0.1687285	-0.1650032	-49.44187	0.2406100	72.09682	0.5174678
2.9.3	IFN23	Sperry	41	0.3337317	0.1669853	-0.1667464	-49.96420	0.2402116	71.97746	0.5463756
2.9.3	IFN34	Granier	47	0.3566043	0.1863233	-0.1702810	-47.75070	0.2474370	69.38698	0.5611664
2.9.3	IFN34	Sperry	47	0.3566043	0.1761593	-0.1804450	-50.60090	0.2620951	73.49746	0.4827352
2.9.3	IFN24	Granier	41	0.3439408	0.1575496	-0.1863911	-54.19280	0.2519176	73.24446	0.5630300
2.9.3	IFN24	Sperry	41	0.3439408	0.1581637	-0.1857770	-54.01426	0.2517635	73.19967	0.5207087

Mortality

Prediction ability for basal area decrease due to mortality (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	41	0.0625212	0.0137979	-0.0487233	-77.93083	0.1223226	195.6497	0.5949484
2.9.3	IFN23	Sperry	41	0.0625212	0.0086079	-0.0539133	-86.23202	0.1392203	222.6769	0.1918869
2.9.3	IFN34	Granier	47	0.0795714	0.0498879	-0.0296834	-37.30416	0.3016182	379.0536	0.0000323
2.9.3	IFN34	Sperry	47	0.0795714	0.0094474	-0.0701239	-88.12710	0.1538457	193.3430	0.1555239
2.9.3	IFN24	Granier	41	0.0678598	0.0373322	-0.0305276	-44.98623	0.1789016	263.6341	0.0747160
2.9.3	IFN24	Sperry	41	0.0678598	0.0105811	-0.0572787	-84.40743	0.1229091	181.1221	0.0263679

Prediction ability for density decrease due to mortality (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	41	1.2737704	0.4306707	-0.8430997	-66.18930	3.153570	247.5776	0.9176891
2.9.3	IFN23	Sperry	41	1.2737704	0.1311441	-1.1426263	-89.70426	4.624829	363.0819	0.1253308
2.9.3	IFN34	Granier	47	1.2960226	0.7972839	-0.4987386	-38.48225	5.391271	415.9859	0.0000140
2.9.3	IFN34	Sperry	47	1.2960226	0.1678800	-1.1281425	-87.04652	4.373717	337.4723	0.1556176
2.9.3	IFN24	Granier	41	0.2579368	0.6663175	0.4083807	158.32590	2.266855	878.8414	0.0250615
2.9.3	IFN24	Sperry	41	0.2579368	0.3369402	0.0790035	30.62900	1.484051	575.3546	0.0033053

Ingrowth

Prediction ability for basal area increase due to ingrowth (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	41	0.0535332	0.0136456	-0.0398876	-74.509998	0.1185935	221.5326	0.0201069
2.9.3	IFN23	Sperry	41	0.0535332	0.0401618	-0.0133714	-24.977736	0.1335188	249.4131	0.0314971
2.9.3	IFN34	Granier	47	0.0280916	0.0238658	-0.0042258	-15.042922	0.0912374	324.7857	0.0229693
2.9.3	IFN34	Sperry	47	0.0280916	0.0296891	0.0015975	5.686811	0.0775698	276.1319	0.0315755
2.9.3	IFN24	Granier	41	0.0350303	0.0438875	0.0088572	25.284551	0.0778693	222.2914	0.1460852
2.9.3	IFN24	Sperry	41	0.0350303	0.0342116	-0.0008186	-2.336952	0.0833706	237.9957	0.0357364

Prediction ability for density increase due to ingrowth (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	41	8.141885	2.505699	-5.6361866	-69.2245894	19.25209	236.4573	0.0205559
2.9.3	IFN23	Sperry	41	8.141885	6.600731	-1.5411538	-18.9287101	21.57432	264.9794	0.0263007
2.9.3	IFN34	Granier	47	4.181244	4.201945	0.0207011	0.4950939	14.56676	348.3835	0.0235852
2.9.3	IFN34	Sperry	47	4.181244	5.063148	0.8819047	21.0919215	11.91431	284.9467	0.0441502
2.9.3	IFN24	Granier	41	4.512880	6.729557	2.2166766	49.1188891	10.96519	242.9755	0.1357344
2.9.3	IFN24	Sperry	41	4.512880	5.008066	0.4951856	10.9727190	11.81659	261.8413	0.0185379

Overall basal area changes

Prediction ability for overall basal area changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	41	0.2839633	0.1745975	-0.1093658	-38.51407	0.2942799	103.63307	0.2323714
2.9.3	IFN23	Sperry	41	0.2839633	0.2043301	-0.0796332	-28.04347	0.2830959	99.69452	0.2400354
2.9.3	IFN34	Granier	47	0.3158355	0.1671022	-0.1487334	-47.09203	0.4687509	148.41614	0.0124649
2.9.3	IFN34	Sperry	47	0.3158355	0.2046929	-0.1111426	-35.19003	0.3424142	108.41537	0.1132763
2.9.3	IFN24	Granier	41	0.3449944	0.1778356	-0.1671587	-48.45260	0.3404229	98.67490	0.0861575
2.9.3	IFN24	Sperry	41	0.3449944	0.2023436	-0.1426507	-41.34872	0.2930576	84.94563	0.1085571

Predictive capacity plots (IFN2-IFN4):

Relationship between basal area changes and climatic variables (MAT and P/PET; IFN2-IFN4):

Spatial distribution of errors (IFN2-IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Prediction ability for overall density changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	41	7.907260	2.075028	-5.8322324	-73.757942	20.37133	257.6281	0.0046192
2.9.3	IFN23	Sperry	41	7.907260	6.469587	-1.4376730	-18.181684	22.05840	278.9638	0.0051263
2.9.3	IFN34	Granier	47	3.873059	3.404661	-0.4683978	-12.093743	13.50186	348.6098	0.0011254
2.9.3	IFN34	Sperry	47	3.873059	4.895269	1.0222097	26.392828	12.11612	312.8308	0.0155789
2.9.3	IFN24	Granier	41	6.167707	6.321242	0.1535353	2.489341	11.23445	182.1496	0.1374349
2.9.3	IFN24	Sperry	41	6.167707	5.325178	-0.8425292	-13.660332	12.47478	202.2595	0.0142452

Fagus sylvatica

Annual diameter increment

Prediction ability for diameter increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	878	0.2623334	0.2073334	-0.0550000	-20.96571	0.2090688	79.69585	0.1336271
2.9.3	IFN23	Sperry	878	0.2623334	0.2112657	-0.0510677	-19.46672	0.2145563	81.78762	0.0755360
2.9.3	IFN34	Granier	1148	0.2237397	0.1995035	-0.0242361	-10.83230	0.1693463	75.68899	0.0996930
2.9.3	IFN34	Sperry	1148	0.2237397	0.1976363	-0.0261033	-11.66684	0.1781869	79.64027	0.0380644
2.9.3	IFN24	Granier	842	0.2499840	0.2032646	-0.0467194	-18.68896	0.1741470	69.66325	0.1524445
2.9.3	IFN24	Sperry	842	0.2499840	0.1993547	-0.0506293	-20.25303	0.1883202	75.33287	0.0495041

Annual height increment

Prediction ability for height increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	878	16.94559	9.136901	-7.808689	-46.08095	18.07897	106.68834	0.0440046
2.9.3	IFN23	Sperry	878	16.94559	9.558608	-7.386983	-43.59236	18.00341	106.24247	0.0381348
2.9.3	IFN34	Granier	1148	13.05423	8.405259	-4.648968	-35.61274	17.81713	136.48551	0.0046308
2.9.3	IFN34	Sperry	1148	13.05423	8.641139	-4.413087	-33.80581	18.11142	138.73990	0.0000127
2.9.3	IFN24	Granier	842	14.97444	8.671560	-6.302881	-42.09093	14.47779	96.68335	0.0317603
2.9.3	IFN24	Sperry	842	14.97444	8.798918	-6.175523	-41.24043	14.75573	98.53945	0.0156628

Growth basal area increment

Prediction ability for basal area increase due to growth (m2/ha/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	93	0.2141684	0.1735453	-0.0406231	-18.967814	0.1421099	66.35426	0.6168019
2.9.3	IFN23	Sperry	93	0.2141684	0.1864878	-0.0276806	-12.924694	0.1496995	69.89804	0.5946819
2.9.3	IFN34	Granier	99	0.2098880	0.1883435	-0.0215444	-10.264738	0.1246596	59.39340	0.6403028
2.9.3	IFN34	Sperry	99	0.2098880	0.1972817	-0.0126063	-6.006195	0.1408179	67.09196	0.6141650
2.9.3	IFN24	Granier	93	0.2117429	0.1732333	-0.0385095	-18.186938	0.1365205	64.47466	0.6194588
2.9.3	IFN24	Sperry	93	0.2117429	0.1813430	-0.0303998	-14.356963	0.1537594	72.61609	0.5754245

Mortality

Prediction ability for basal area decrease due to mortality (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	93	0.0128669	0.0162426	0.0033756	26.235108	0.0488393	379.5729	0.0001213
2.9.3	IFN23	Sperry	93	0.0128669	0.0137818	0.0009149	7.110223	0.0436716	339.4102	0.0059575
2.9.3	IFN34	Granier	99	0.0251491	0.0234591	-0.0016900	-6.719896	0.0557730	221.7699	0.2789124
2.9.3	IFN34	Sperry	99	0.0251491	0.0176272	-0.0075218	-29.909052	0.0509225	202.4826	0.1836860
2.9.3	IFN24	Granier	93	0.0181819	0.0199993	0.0018174	9.995436	0.0369747	203.3600	0.1968418
2.9.3	IFN24	Sperry	93	0.0181819	0.0145835	-0.0035984	-19.791089	0.0375810	206.6945	0.0681115

Prediction ability for density decrease due to mortality (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	93	0.5030180	0.7851911	0.2821732	56.096041	2.929598	582.4042	0.0059283
2.9.3	IFN23	Sperry	93	0.5030180	0.5532235	0.0502055	9.980858	1.781220	354.1066	0.1059830
2.9.3	IFN34	Granier	99	1.4949666	1.3456507	-0.1493158	-9.987904	5.561581	372.0204	0.3860488
2.9.3	IFN34	Sperry	99	1.4949666	0.6300918	-0.8648748	-57.852448	3.818657	255.4343	0.3561007
2.9.3	IFN24	Granier	93	0.6638779	1.3932259	0.7293479	109.861758	2.856867	430.3301	0.4521545
2.9.3	IFN24	Sperry	93	0.6638779	0.5848368	-0.0790411	-11.905969	1.697174	255.6455	0.2831643

Ingrowth

Prediction ability for basal area increase due to ingrowth (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	93	0.0334581	0.0384183	0.0049602	14.82521	0.1143012	341.6254	0.0005832
2.9.3	IFN23	Sperry	93	0.0334581	0.0154226	-0.0180355	-53.90480	0.1041600	311.3152	0.0104887
2.9.3	IFN34	Granier	99	0.0235301	0.0323900	0.0088599	37.65341	0.0794750	337.7587	0.0034803
2.9.3	IFN34	Sperry	99	0.0235301	0.0368692	0.0133391	56.68955	0.0879608	373.8223	0.0009118
2.9.3	IFN24	Granier	93	0.0292275	0.0369485	0.0077210	26.41682	0.0799973	273.7053	0.0166448
2.9.3	IFN24	Sperry	93	0.0292275	0.0370566	0.0078291	26.78656	0.0734826	251.4154	0.0164801

Prediction ability for density increase due to ingrowth (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	93	5.418058	7.206323	1.788266	33.00566	18.92406	349.2776	0.0012745
2.9.3	IFN23	Sperry	93	5.418058	2.877612	-2.540446	-46.88850	16.47746	304.1211	0.0116106
2.9.3	IFN34	Granier	99	4.052478	6.421286	2.368809	58.45334	14.73418	363.5844	0.0035200
2.9.3	IFN34	Sperry	99	4.052478	6.552309	2.499831	61.68650	15.49839	382.4423	0.0033742
2.9.3	IFN24	Granier	93	4.451677	6.070922	1.619245	36.37382	12.47007	280.1207	0.0120335
2.9.3	IFN24	Sperry	93	4.451677	5.714846	1.263169	28.37512	11.06609	248.5826	0.0056297

Overall basal area changes

Prediction ability for overall basal area changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	93	0.2382169	0.1937376	-0.0444794	-18.671783	0.2207718	92.67677	0.3352583
2.9.3	IFN23	Sperry	93	0.2382169	0.1860143	-0.0522026	-21.913910	0.2128521	89.35221	0.3956369
2.9.3	IFN34	Granier	99	0.1917045	0.1984485	0.0067440	3.517939	0.1882287	98.18693	0.3453787
2.9.3	IFN34	Sperry	99	0.1917045	0.2188030	0.0270985	14.135554	0.2129479	111.08136	0.2751532
2.9.3	IFN24	Granier	93	0.2270473	0.1906403	-0.0364070	-16.034968	0.1975026	86.98745	0.3074487
2.9.3	IFN24	Sperry	93	0.2270473	0.2071451	-0.0199021	-8.765640	0.2082754	91.73217	0.3333056

Predictive capacity plots (IFN2-IFN4):

Relationship between basal area changes and climatic variables (MAT and P/PET; IFN2-IFN4):

Spatial distribution of errors (IFN2-IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Prediction ability for overall density changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	93	5.246704	6.421132	1.174428	22.38411	20.14204	383.8990	0.0065094
2.9.3	IFN23	Sperry	93	5.246704	2.324388	-2.922316	-55.69813	17.45794	332.7410	0.0162710
2.9.3	IFN34	Granier	99	2.032230	5.075635	3.043405	149.75688	15.62872	769.0428	0.0175586
2.9.3	IFN34	Sperry	99	2.032230	5.922217	3.889987	191.41464	16.15671	795.0235	0.0015636
2.9.3	IFN24	Granier	93	3.810054	4.798758	0.988704	25.94987	13.62157	357.5163	0.0146571
2.9.3	IFN24	Sperry	93	3.810054	5.376227	1.566172	41.10631	11.74192	308.1825	0.0004029

Pinus halepensis

Annual diameter increment

Prediction ability for diameter increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	3290	0.3451130	0.3876252	0.0425122	12.31833	0.2157046	62.50261	0.0795771
2.9.3	IFN23	Sperry	3290	0.3451130	0.3026675	-0.0424455	-12.29902	0.2224183	64.44795	0.1042456
2.9.3	IFN34	Granier	4562	0.2441193	0.3117245	0.0676052	27.69353	0.1762431	72.19550	0.0455876
2.9.3	IFN34	Sperry	4562	0.2441193	0.1981882	-0.0459310	-18.81500	0.1813604	74.29173	0.0593012
2.9.3	IFN24	Granier	2576	0.2837014	0.3159174	0.0322160	11.35559	0.1744211	61.48053	0.0488389
2.9.3	IFN24	Sperry	2576	0.2837014	0.2070511	-0.0766503	-27.01794	0.1900243	66.98039	0.1033127

Annual height increment

Prediction ability for height increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	3290	13.83568	17.947883	4.1121997	29.7216944	14.76508	106.71740	0.0127020
2.9.3	IFN23	Sperry	3290	13.83568	13.878267	0.0425827	0.3077745	14.32631	103.54612	0.0197361
2.9.3	IFN34	Granier	4562	10.16668	13.033520	2.8668380	28.1983632	12.03005	118.32817	0.0027032
2.9.3	IFN34	Sperry	4562	10.16668	8.016958	-2.1497242	-21.1447952	11.03477	108.53856	0.0159772
2.9.3	IFN24	Granier	2576	11.40008	13.506063	2.1059807	18.4733810	10.62837	93.23062	0.0006878
2.9.3	IFN24	Sperry	2576	11.40008	8.783201	-2.6168816	-22.9549355	10.09282	88.53284	0.0377835

Growth basal area increment

Prediction ability for basal area increase due to growth (m2/ha/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	477	0.2369726	0.2699030	0.0329303	13.89626	0.1534315	64.74652	0.6785207
2.9.3	IFN23	Sperry	477	0.2369726	0.2075703	-0.0294023	-12.40746	0.1604483	67.70751	0.5965321
2.9.3	IFN34	Granier	494	0.2057077	0.2708458	0.0651381	31.66539	0.1593632	77.47072	0.6163864
2.9.3	IFN34	Sperry	494	0.2057077	0.1662921	-0.0394155	-19.16095	0.1513368	73.56885	0.4875346
2.9.3	IFN24	Granier	398	0.2031947	0.2283065	0.0251118	12.35847	0.1454044	71.55911	0.6068515
2.9.3	IFN24	Sperry	398	0.2031947	0.1411542	-0.0620406	-30.53257	0.1557230	76.63729	0.5274892

Mortality

Prediction ability for basal area decrease due to mortality (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	477	0.0171807	0.0304376	0.0132569	77.1612346	0.0606438	352.9755	0.1104570
2.9.3	IFN23	Sperry	477	0.0171807	0.0281920	0.0110112	64.0904328	0.0562628	327.4759	0.1729275
2.9.3	IFN34	Granier	494	0.0466047	0.0450994	-0.0015053	-3.2298422	0.0749353	160.7893	0.1500704
2.9.3	IFN34	Sperry	494	0.0466047	0.0410615	-0.0055432	-11.8940848	0.0703523	150.9554	0.2199615
2.9.3	IFN24	Granier	398	0.0276508	0.0322612	0.0046104	16.6736882	0.0496302	179.4890	0.2048946
2.9.3	IFN24	Sperry	398	0.0276508	0.0278328	0.0001820	0.6582254	0.0443709	160.4687	0.3105615

Prediction ability for density decrease due to mortality (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	477	0.9141541	1.598389	0.6842352	74.84900	4.945893	541.0350	0.0387912
2.9.3	IFN23	Sperry	477	0.9141541	1.340620	0.4264655	46.65138	3.958686	433.0436	0.1567807
2.9.3	IFN34	Granier	494	2.6017289	2.026513	-0.5752160	-22.10899	5.358694	205.9666	0.1621544
2.9.3	IFN34	Sperry	494	2.6017289	1.633206	-0.9685226	-37.22612	4.953363	190.3874	0.3130151
2.9.3	IFN24	Granier	398	0.9628414	1.785348	0.8225068	85.42494	3.138659	325.9788	0.1960244
2.9.3	IFN24	Sperry	398	0.9628414	1.331821	0.3689801	38.32200	1.781292	185.0037	0.4196342

Ingrowth

Prediction ability for basal area increase due to ingrowth (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	477	0.0438144	0.0327355	-0.0110789	-25.285911	0.1241979	283.4637	0.0118466
2.9.3	IFN23	Sperry	477	0.0438144	0.0413632	-0.0024512	-5.594566	0.1356866	309.6851	0.0000002
2.9.3	IFN34	Granier	494	0.0230843	0.0351662	0.0120819	52.338245	0.0870189	376.9615	0.0008095
2.9.3	IFN34	Sperry	494	0.0230843	0.0388531	0.0157689	68.309921	0.0847325	367.0572	0.0006137
2.9.3	IFN24	Granier	398	0.0286826	0.0289126	0.0002299	0.801704	0.0721259	251.4620	0.0022324
2.9.3	IFN24	Sperry	398	0.0286826	0.0323054	0.0036228	12.630480	0.0729096	254.1942	0.0029571

Prediction ability for density increase due to ingrowth (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	477	6.500345	4.790142	-1.7102030	-26.309418	17.87446	274.9771	0.0111423
2.9.3	IFN23	Sperry	477	6.500345	6.428670	-0.0716752	-1.102637	19.66642	302.5442	0.0000114
2.9.3	IFN34	Granier	494	3.354195	4.931760	1.5775647	47.032586	11.73716	349.9249	0.0000588
2.9.3	IFN34	Sperry	494	3.354195	6.334950	2.9807548	88.866471	12.74533	379.9818	0.0000714
2.9.3	IFN24	Granier	398	3.871624	4.142104	0.2704799	6.986212	9.55088	246.6892	0.0023564
2.9.3	IFN24	Sperry	398	3.871624	5.216579	1.3449552	34.738786	10.23052	264.2435	0.0008334

Overall basal area changes

Prediction ability for overall basal area changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	477	0.2439406	0.2639985	0.0200580	8.222486	0.2740271	112.3336	0.2147018
2.9.3	IFN23	Sperry	477	0.2439406	0.2169603	-0.0269802	-11.060166	0.2798736	114.7302	0.1642981
2.9.3	IFN34	Granier	494	0.1807259	0.2660785	0.0853526	47.227646	0.2833437	156.7809	0.0650272
2.9.3	IFN34	Sperry	494	0.1807259	0.1707646	-0.0099613	-5.511831	0.2766589	153.0820	0.0252954
2.9.3	IFN24	Granier	398	0.2117019	0.2431536	0.0314517	14.856608	0.2199638	103.9026	0.1765911
2.9.3	IFN24	Sperry	398	0.2117019	0.1624416	-0.0492603	-23.268695	0.2325089	109.8284	0.0740022

Predictive capacity plots (IFN2-IFN4):

Relationship between basal area changes and climatic variables (MAT and P/PET; IFN2-IFN4):

Spatial distribution of errors (IFN2-IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Prediction ability for overall density changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	477	5.7511493	3.191753	-2.5593962	-44.502343	20.49982	356.4474	0.0143372
2.9.3	IFN23	Sperry	477	5.7511493	5.214555	-0.5365943	-9.330209	21.80384	379.1214	0.0009526
2.9.3	IFN34	Granier	494	0.7589133	3.133988	2.3750746	312.957306	16.04044	2113.6067	0.0046566
2.9.3	IFN34	Sperry	494	0.7589133	4.729197	3.9702840	523.153827	16.28182	2145.4123	0.0088772
2.9.3	IFN24	Granier	398	3.0614036	3.644043	0.5826392	19.031767	12.63915	412.8548	0.0186809
2.9.3	IFN24	Sperry	398	3.0614036	4.669142	1.6077382	52.516376	12.86855	420.3480	0.0034856

Pinus nigra

Annual diameter increment

Prediction ability for diameter increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	2460	0.2558898	0.3084823	0.0525925	20.552784	0.1624684	63.49154	0.0258013
2.9.3	IFN23	Sperry	2460	0.2558898	0.2793620	0.0234722	9.172785	0.1649767	64.47177	0.0421616
2.9.3	IFN34	Granier	3321	0.1715706	0.2716321	0.1000615	58.320909	0.1665795	97.09097	0.0002235
2.9.3	IFN34	Sperry	3321	0.1715706	0.2119063	0.0403358	23.509723	0.1497371	87.27436	0.0192770
2.9.3	IFN24	Granier	2014	0.2117903	0.2669436	0.0551533	26.041464	0.1520186	71.77790	0.0002560
2.9.3	IFN24	Sperry	2014	0.2117903	0.2162414	0.0044511	2.101643	0.1449316	68.43166	0.0392910

Annual height increment

Prediction ability for height increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	2460	13.008362	18.36914	5.360779	41.21025	13.759154	105.77161	0.0202986
2.9.3	IFN23	Sperry	2460	13.008362	16.65015	3.641788	27.99574	13.278948	102.08009	0.0435860
2.9.3	IFN34	Granier	3321	9.923332	15.13471	5.211382	52.51645	12.597435	126.94764	0.0000337
2.9.3	IFN34	Sperry	3321	9.923332	11.79298	1.869648	18.84093	11.120683	112.06602	0.0289577
2.9.3	IFN24	Granier	2014	11.082126	15.07059	3.988464	35.99006	10.272929	92.69817	0.0054392
2.9.3	IFN24	Sperry	2014	11.082126	12.23322	1.151090	10.38691	8.978396	81.01691	0.0880869

Growth basal area increment

Prediction ability for basal area increase due to growth (m2/ha/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	299	0.1973055	0.2442557	0.0469502	23.795702	0.1505157	76.28562	0.6800344
2.9.3	IFN23	Sperry	299	0.1973055	0.2218152	0.0245097	12.422221	0.1498109	75.92841	0.6785288
2.9.3	IFN34	Granier	332	0.1485922	0.2477795	0.0991873	66.751334	0.1898917	127.79385	0.6286336
2.9.3	IFN34	Sperry	332	0.1485922	0.1914779	0.0428857	28.861342	0.1667533	112.22210	0.5897071
2.9.3	IFN24	Granier	253	0.1706274	0.2200110	0.0493836	28.942377	0.1568859	91.94649	0.6184239
2.9.3	IFN24	Sperry	253	0.1706274	0.1791179	0.0084905	4.976033	0.1503393	88.10969	0.6486463

Mortality

Prediction ability for basal area decrease due to mortality (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	299	0.0124471	0.0118526	-0.0005946	-4.776786	0.0445620	358.0104	0.0992300
2.9.3	IFN23	Sperry	299	0.0124471	0.0116539	-0.0007932	-6.372444	0.0444200	356.8689	0.1079484
2.9.3	IFN34	Granier	332	0.0174871	0.0157463	-0.0017408	-9.954494	0.0457070	261.3759	0.1108188
2.9.3	IFN34	Sperry	332	0.0174871	0.0148268	-0.0026603	-15.213127	0.0451960	258.4535	0.1369015
2.9.3	IFN24	Granier	253	0.0132369	0.0138174	0.0005806	4.385924	0.0352192	266.0688	0.0894560
2.9.3	IFN24	Sperry	253	0.0132369	0.0114430	-0.0017939	-13.552181	0.0319832	241.6220	0.2513125

Prediction ability for density decrease due to mortality (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	299	0.7776704	0.5297469	-0.2479235	-31.88028	3.248881	417.7710	0.0268965
2.9.3	IFN23	Sperry	299	0.7776704	0.5205545	-0.2571159	-33.06232	3.245173	417.2941	0.0292123
2.9.3	IFN34	Granier	332	1.1926192	0.6427536	-0.5498656	-46.10572	3.758500	315.1467	0.0764763
2.9.3	IFN34	Sperry	332	1.1926192	0.5615563	-0.6310629	-52.91403	3.715426	311.5350	0.1548815
2.9.3	IFN24	Granier	253	0.5679904	0.6477849	0.0797946	14.04858	2.388511	420.5197	0.0405233
2.9.3	IFN24	Sperry	253	0.5679904	0.5073832	-0.0606072	-10.67045	2.099800	369.6893	0.2159129

Ingrowth

Prediction ability for basal area increase due to ingrowth (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	299	0.0322431	0.0408395	0.0085963	26.66102	0.1202017	372.7980	0.0181666
2.9.3	IFN23	Sperry	299	0.0322431	0.0413613	0.0091182	28.27950	0.1073440	332.9206	0.0076816
2.9.3	IFN34	Granier	332	0.0176960	0.0418604	0.0241645	136.55369	0.0858957	485.3972	0.0012826
2.9.3	IFN34	Sperry	332	0.0176960	0.0386218	0.0209259	118.25231	0.0808740	457.0195	0.0019380
2.9.3	IFN24	Granier	253	0.0210942	0.0342740	0.0131798	62.48102	0.0702191	332.8842	0.0233006
2.9.3	IFN24	Sperry	253	0.0210942	0.0329578	0.0118637	56.24142	0.0719719	341.1934	0.0003215

Prediction ability for density increase due to ingrowth (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	299	4.863574	6.599629	1.736055	35.69505	18.416823	378.6685	0.0188409
2.9.3	IFN23	Sperry	299	4.863574	6.463916	1.600342	32.90465	16.463161	338.4992	0.0022273
2.9.3	IFN34	Granier	332	2.787991	6.505783	3.717792	133.35022	13.125190	470.7759	0.0005866
2.9.3	IFN34	Sperry	332	2.787991	6.250377	3.462386	124.18928	12.526857	449.3148	0.0011921
2.9.3	IFN24	Granier	253	2.916620	4.997221	2.080602	71.33607	9.795172	335.8399	0.0203836
2.9.3	IFN24	Sperry	253	2.916620	5.101795	2.185176	74.92153	10.588404	363.0368	0.0007211

Overall basal area changes

Prediction ability for overall basal area changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	299	0.2096273	0.2734867	0.0638593	30.46326	0.2517444	120.0914	0.2822120
2.9.3	IFN23	Sperry	299	0.2096273	0.2517364	0.0421090	20.08757	0.2432199	116.0249	0.3200936
2.9.3	IFN34	Granier	332	0.1577085	0.2754339	0.1177253	74.64742	0.2421147	153.5204	0.3083784
2.9.3	IFN34	Sperry	332	0.1577085	0.2188713	0.0611628	38.78215	0.2169896	137.5890	0.3103837
2.9.3	IFN24	Granier	253	0.1822697	0.2529334	0.0706637	38.76879	0.2262218	124.1138	0.3051596
2.9.3	IFN24	Sperry	253	0.1822697	0.2122525	0.0299828	16.44967	0.2097442	115.0736	0.3578467

Predictive capacity plots (IFN2-IFN4):

Relationship between basal area changes and climatic variables (MAT and P/PET; IFN2-IFN4):

Spatial distribution of errors (IFN2-IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Prediction ability for overall density changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	299	4.120112	6.069883	1.949771	47.32326	19.47738	472.7393	0.0053530
2.9.3	IFN23	Sperry	299	4.120112	5.943362	1.823251	44.25246	17.82874	432.7247	0.0040316
2.9.3	IFN34	Granier	332	2.133934	5.863030	3.729096	174.75219	14.53151	680.9728	0.0017855
2.9.3	IFN34	Sperry	332	2.133934	5.802592	3.668659	171.91998	14.63469	685.8079	0.0010471
2.9.3	IFN24	Granier	253	2.531671	5.040474	2.508803	99.09674	11.82512	467.0877	0.0094190
2.9.3	IFN24	Sperry	253	2.531671	5.176030	2.644360	104.45117	12.05899	476.3253	0.0011559

Pinus sylvestris

Annual diameter increment

Prediction ability for diameter increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	5735	0.2624103	0.2782603	0.0158500	6.040146	0.1763768	67.21412	0.0044670
2.9.3	IFN23	Sperry	5735	0.2624103	0.2880083	0.0255979	9.754925	0.1788676	68.16334	0.0044030
2.9.3	IFN34	Granier	7174	0.2004406	0.2586554	0.0582147	29.043383	0.1587823	79.21662	0.0003373
2.9.3	IFN34	Sperry	7174	0.2004406	0.2599221	0.0594815	29.675351	0.1674667	83.54928	0.0006846
2.9.3	IFN24	Granier	5067	0.2301231	0.2515565	0.0214334	9.313867	0.1447991	62.92245	0.0016348
2.9.3	IFN24	Sperry	5067	0.2301231	0.2557069	0.0255838	11.117421	0.1515710	65.86517	0.0016593

Annual height increment

Prediction ability for height increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	5735	13.532836	15.13643	1.603596	11.84967	12.588856	93.02452	0.0266769
2.9.3	IFN23	Sperry	5735	13.532836	15.68074	2.147901	15.87178	12.824279	94.76416	0.0233785
2.9.3	IFN34	Granier	7174	9.943236	13.11621	3.172975	31.91089	12.207179	122.76868	0.0175592
2.9.3	IFN34	Sperry	7174	9.943236	13.20190	3.258668	32.77271	12.439680	125.10696	0.0215288
2.9.3	IFN24	Granier	5067	11.449844	13.03288	1.583034	13.82581	9.066559	79.18500	0.0458568
2.9.3	IFN24	Sperry	5067	11.449844	13.28096	1.831117	15.99251	9.433095	82.38623	0.0378802

Growth basal area increment

Prediction ability for basal area increase due to growth (m2/ha/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	496	0.2807627	0.2930273	0.0122647	4.368336	0.1924925	68.56057	0.5639624
2.9.3	IFN23	Sperry	496	0.2807627	0.3055486	0.0247859	8.828067	0.2010509	71.60886	0.5667575
2.9.3	IFN34	Granier	522	0.2350824	0.3055257	0.0704433	29.965386	0.1935338	82.32594	0.6092187
2.9.3	IFN34	Sperry	522	0.2350824	0.3099564	0.0748740	31.850123	0.2141304	91.08739	0.6123292
2.9.3	IFN24	Granier	458	0.2545322	0.2716278	0.0170955	6.716447	0.1735631	68.18903	0.5797712
2.9.3	IFN24	Sperry	458	0.2545322	0.2790564	0.0245242	9.635011	0.1892343	74.34589	0.5674542

Mortality

Prediction ability for basal area decrease due to mortality (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	496	0.0319119	0.0406648	0.0087530	27.4286401	0.0619124	194.0106	0.3043714
2.9.3	IFN23	Sperry	496	0.0319119	0.0406586	0.0087467	27.4090824	0.0619111	194.0065	0.3043801
2.9.3	IFN34	Granier	522	0.0567274	0.0518344	-0.0048930	-8.6255267	0.0900577	158.7552	0.2164592
2.9.3	IFN34	Sperry	522	0.0567274	0.0514657	-0.0052617	-9.2754632	0.0896250	157.9924	0.2245876
2.9.3	IFN24	Granier	458	0.0416362	0.0413656	-0.0002705	-0.6497635	0.0565140	135.7330	0.3974533
2.9.3	IFN24	Sperry	458	0.0416362	0.0408292	-0.0008070	-1.9381752	0.0568289	136.4893	0.3914893

Prediction ability for density decrease due to mortality (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	496	1.666455	1.504857	-0.1615988	-9.697156	4.161693	249.7332	0.3422814
2.9.3	IFN23	Sperry	496	1.666455	1.504652	-0.1618032	-9.709423	4.161675	249.7322	0.3422736
2.9.3	IFN34	Granier	522	2.527976	1.642409	-0.8855672	-35.030675	4.946214	195.6590	0.1595086
2.9.3	IFN34	Sperry	522	2.527976	1.601697	-0.9262797	-36.641153	4.845950	191.6928	0.2146625
2.9.3	IFN24	Granier	458	1.078167	1.566766	0.4885995	45.317628	2.102586	195.0149	0.2947451
2.9.3	IFN24	Sperry	458	1.078167	1.506604	0.4284377	39.737616	2.059250	190.9955	0.2862689

Ingrowth

Prediction ability for basal area increase due to ingrowth (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	496	0.0380071	0.0365579	-0.0014492	-3.812977	0.1150870	302.8041	0.0081088
2.9.3	IFN23	Sperry	496	0.0380071	0.0393270	0.0013199	3.472892	0.1205697	317.2294	0.0000005
2.9.3	IFN34	Granier	522	0.0209448	0.0383259	0.0173811	82.985488	0.0931770	444.8701	0.0007440
2.9.3	IFN34	Sperry	522	0.0209448	0.0427661	0.0218214	104.185188	0.0975040	465.5290	0.0041890
2.9.3	IFN24	Granier	458	0.0229485	0.0421446	0.0191960	83.648118	0.0833227	363.0851	0.0003126
2.9.3	IFN24	Sperry	458	0.0229485	0.0375312	0.0145827	63.545181	0.0772080	336.4397	0.0002515

Prediction ability for density increase due to ingrowth (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	496	5.437764	5.799984	0.3622203	6.66120	16.77417	308.4755	0.0119841
2.9.3	IFN23	Sperry	496	5.437764	6.257897	0.8201326	15.08217	17.74264	326.2856	0.0000002
2.9.3	IFN34	Granier	522	3.058468	5.807781	2.7493135	89.89185	13.68525	447.4545	0.0013888
2.9.3	IFN34	Sperry	522	3.058468	6.761718	3.7032501	121.08187	14.68344	480.0913	0.0044491
2.9.3	IFN24	Granier	458	3.154621	5.925117	2.7704956	87.82340	11.33599	359.3455	0.0018194
2.9.3	IFN24	Sperry	458	3.154621	5.247110	2.0924889	66.33091	10.54285	334.2033	0.0000716

Overall basal area changes

Prediction ability for overall basal area changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	496	0.2708541	0.2873193	0.0164652	6.078993	0.2926359	108.0419	0.1853760
2.9.3	IFN23	Sperry	496	0.2708541	0.3020137	0.0311596	11.504194	0.3016487	111.3694	0.1860409
2.9.3	IFN34	Granier	522	0.1997108	0.2967895	0.0970786	48.609605	0.2905982	145.5095	0.2042864
2.9.3	IFN34	Sperry	522	0.1997108	0.3084545	0.1087437	54.450589	0.3127175	156.5852	0.2035943
2.9.3	IFN24	Granier	458	0.2462700	0.2894987	0.0432286	17.553350	0.2598092	105.4977	0.2161621
2.9.3	IFN24	Sperry	458	0.2462700	0.2967514	0.0504813	20.498358	0.2815493	114.3254	0.2159113

Predictive capacity plots (IFN2-IFN4):

Relationship between basal area changes and climatic variables (MAT and P/PET; IFN2-IFN4):

Spatial distribution of errors (IFN2-IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Prediction ability for overall density changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	496	3.5114523	4.340299	0.8288472	23.60411	18.08350	514.9864	0.0140321
2.9.3	IFN23	Sperry	496	3.5114523	4.753245	1.2417922	35.36406	19.10260	544.0086	0.0002893
2.9.3	IFN34	Granier	522	0.5269553	4.165372	3.6384168	690.46019	16.13783	3062.4658	0.0000120
2.9.3	IFN34	Sperry	522	0.5269553	5.253547	4.7265918	896.96249	17.15675	3255.8255	0.0021526
2.9.3	IFN24	Granier	458	1.7337891	5.253321	3.5195323	202.99657	13.40961	773.4279	0.0039107
2.9.3	IFN24	Sperry	458	1.7337891	4.818449	3.0846596	177.91435	13.08821	754.8903	0.0013060

Pinus uncinata

Annual diameter increment

Prediction ability for diameter increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	2298	0.2008612	0.1165946	-0.0842666	-41.95267	0.1753474	87.29777	0.0355711
2.9.3	IFN23	Sperry	2298	0.2008612	0.1348837	-0.0659775	-32.84731	0.1662416	82.76441	0.0508878
2.9.3	IFN34	Granier	2848	0.1664953	0.1176723	-0.0488229	-29.32392	0.1388770	83.41200	0.0000613
2.9.3	IFN34	Sperry	2848	0.1664953	0.1380961	-0.0283991	-17.05703	0.1339936	80.47892	0.0010126
2.9.3	IFN24	Granier	2139	0.1814906	0.1149656	-0.0665250	-36.65481	0.1332196	73.40303	0.0179076
2.9.3	IFN24	Sperry	2139	0.1814906	0.1331151	-0.0483755	-26.65454	0.1238572	68.24440	0.0399330

Annual height increment

Prediction ability for height increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	2298	9.489485	5.975979	-3.513506	-37.02526	10.731454	113.08784	0.0620335
2.9.3	IFN23	Sperry	2298	9.489485	6.922512	-2.566972	-27.05070	10.536314	111.03146	0.0532495
2.9.3	IFN34	Granier	2848	10.630800	5.811764	-4.819036	-45.33089	11.135944	104.75171	0.0601085
2.9.3	IFN34	Sperry	2848	10.630800	6.858666	-3.772134	-35.48307	10.963954	103.13386	0.0327517
2.9.3	IFN24	Granier	2139	9.933420	5.700436	-4.232983	-42.61355	8.430390	84.86896	0.0893507
2.9.3	IFN24	Sperry	2139	9.933420	6.604616	-3.328804	-33.51116	8.154796	82.09454	0.0672899

Growth basal area increment

Prediction ability for basal area increase due to growth (m2/ha/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	149	0.2780166	0.1478584	-0.1301583	-46.81672	0.2028723	72.97128	0.6633819
2.9.3	IFN23	Sperry	149	0.2780166	0.1718108	-0.1062058	-38.20125	0.1816499	65.33778	0.6734270
2.9.3	IFN34	Granier	153	0.2624445	0.1755295	-0.0869150	-33.11748	0.1702353	64.86524	0.5805546
2.9.3	IFN34	Sperry	153	0.2624445	0.2069129	-0.0555317	-21.15940	0.1568397	59.76109	0.5600639
2.9.3	IFN24	Granier	149	0.2486968	0.1410802	-0.1076167	-43.27223	0.1635645	65.76864	0.7122247
2.9.3	IFN24	Sperry	149	0.2486968	0.1655975	-0.0830993	-33.41390	0.1399465	56.27193	0.7378589

Mortality

Prediction ability for basal area decrease due to mortality (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	149	0.0685725	0.0445654	-0.0240071	-35.00984	0.0958757	139.8165	0.3726499
2.9.3	IFN23	Sperry	149	0.0685725	0.0445654	-0.0240071	-35.00984	0.0958757	139.8165	0.3726499
2.9.3	IFN34	Granier	153	0.0827843	0.0542638	-0.0285206	-34.45164	0.1124489	135.8335	0.3414284
2.9.3	IFN34	Sperry	153	0.0827843	0.0542637	-0.0285206	-34.45174	0.1124489	135.8335	0.3414283
2.9.3	IFN24	Granier	149	0.0703331	0.0436521	-0.0266811	-37.93527	0.0849146	120.7321	0.4958553
2.9.3	IFN24	Sperry	149	0.0703331	0.0436509	-0.0266823	-37.93700	0.0849152	120.7328	0.4958532

Prediction ability for density decrease due to mortality (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	149	2.258640	1.326188	-0.9324525	-41.28380	3.896957	172.5355	0.2854984
2.9.3	IFN23	Sperry	149	2.258640	1.326188	-0.9324525	-41.28380	3.896957	172.5355	0.2854984
2.9.3	IFN34	Granier	153	2.666283	1.529639	-1.1366437	-42.63027	4.388343	164.5865	0.2244071
2.9.3	IFN34	Sperry	153	2.666283	1.529637	-1.1366460	-42.63036	4.388343	164.5865	0.2244072
2.9.3	IFN24	Granier	149	1.014270	1.299109	0.2848390	28.08314	1.674879	165.1314	0.2571726
2.9.3	IFN24	Sperry	149	1.014270	1.299084	0.2848132	28.08060	1.674887	165.1322	0.2571642

Ingrowth

Prediction ability for basal area increase due to ingrowth (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	149	0.0451890	0.0436020	-0.0015870	-3.512019	0.1276956	282.5809	0.0019019
2.9.3	IFN23	Sperry	149	0.0451890	0.0529501	0.0077611	17.174688	0.1448776	320.6035	0.0249639
2.9.3	IFN34	Granier	153	0.0360767	0.0591179	0.0230412	63.867221	0.1022087	283.3092	0.0031524
2.9.3	IFN34	Sperry	153	0.0360767	0.0489456	0.0128689	35.670788	0.1078549	298.9598	0.0160268
2.9.3	IFN24	Granier	149	0.0342135	0.0616488	0.0274353	80.188432	0.0930687	272.0231	0.0000237
2.9.3	IFN24	Sperry	149	0.0342135	0.0679221	0.0337086	98.524189	0.1008855	294.8704	0.0000521

Prediction ability for density increase due to ingrowth (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	149	6.502543	8.077732	1.575189	24.22419	19.45573	299.2019	0.0004271
2.9.3	IFN23	Sperry	149	6.502543	9.367002	2.864459	44.05136	22.29634	342.8864	0.0217672
2.9.3	IFN34	Granier	153	5.197995	10.370444	5.172449	99.50855	17.00899	327.2222	0.0011182
2.9.3	IFN34	Sperry	153	5.197995	8.792194	3.594199	69.14588	17.77441	341.9474	0.0165833
2.9.3	IFN24	Granier	149	4.559891	9.596791	5.036899	110.46095	13.89822	304.7929	0.0000350
2.9.3	IFN24	Sperry	149	4.559891	9.943037	5.383145	118.05424	14.63174	320.8792	0.0027967

Overall basal area changes

Prediction ability for overall basal area changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	149	0.2853119	0.1504279	-0.1348840	-47.27598	0.3434424	120.37437	0.0895452
2.9.3	IFN23	Sperry	149	0.2853119	0.1844778	-0.1008340	-35.34168	0.3324430	116.51916	0.0988443
2.9.3	IFN34	Granier	153	0.2425902	0.1851561	-0.0574341	-23.67536	0.2754404	113.54141	0.0584804
2.9.3	IFN34	Sperry	153	0.2425902	0.2075603	-0.0350300	-14.43997	0.2787098	114.88915	0.0571897
2.9.3	IFN24	Granier	149	0.2721022	0.1664745	-0.1056277	-38.81912	0.2757251	101.33146	0.1187840
2.9.3	IFN24	Sperry	149	0.2721022	0.1998686	-0.0722336	-26.54651	0.2687846	98.78074	0.1114735

Predictive capacity plots (IFN2-IFN4):

Relationship between basal area changes and climatic variables (MAT and P/PET; IFN2-IFN4):

Spatial distribution of errors (IFN2-IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Prediction ability for overall density changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	149	5.849145	6.751544	0.902399	15.42788	22.61941	386.7131	0.0004506
2.9.3	IFN23	Sperry	149	5.849145	8.040814	2.191669	37.46990	25.06728	428.5631	0.0077463
2.9.3	IFN34	Granier	153	4.220601	8.840805	4.620203	109.46789	19.24872	456.0658	0.0103830
2.9.3	IFN34	Sperry	153	4.220601	7.262557	3.041956	72.07399	20.68930	490.1979	0.0088353
2.9.3	IFN24	Granier	149	5.007985	8.297681	3.289696	65.68902	15.62815	312.0647	0.0000220
2.9.3	IFN24	Sperry	149	5.007985	8.726725	3.718740	74.25621	16.32875	326.0542	0.0020819

Quercus faginea

Annual diameter increment

Prediction ability for diameter increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1135	0.2083410	0.1928031	-0.0155379	-7.457921	0.1413544	67.84765	0.0557608
2.9.3	IFN23	Sperry	1135	0.2083410	0.2174100	0.0090691	4.353002	0.1395385	66.97602	0.0378643
2.9.3	IFN34	Granier	579	0.1310169	0.1373183	0.0063015	4.809652	0.1112301	84.89752	0.0529122
2.9.3	IFN34	Sperry	579	0.1310169	0.1902449	0.0592280	45.206411	0.1191519	90.94393	0.0520712
2.9.3	IFN24	Granier	1037	0.1734577	0.1571122	-0.0163455	-9.423332	0.1125941	64.91160	0.0628440
2.9.3	IFN24	Sperry	1037	0.1734577	0.1967670	0.0233093	13.438054	0.1135029	65.43553	0.0267260

Annual height increment

Prediction ability for height increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1135	8.282914	6.103908	-2.1790055	-26.30723	11.354373	137.0819	0.0048432
2.9.3	IFN23	Sperry	1135	8.282914	7.221045	-1.0618686	-12.81999	11.297277	136.3925	0.0039801
2.9.3	IFN34	Granier	579	3.700627	4.410598	0.7099711	19.18516	9.304260	251.4239	0.0003450
2.9.3	IFN34	Sperry	579	3.700627	6.641037	2.9404102	79.45709	9.570765	258.6255	0.0133455
2.9.3	IFN24	Granier	1037	5.696068	4.800268	-0.8958010	-15.72665	8.253095	144.8911	0.0057538
2.9.3	IFN24	Sperry	1037	5.696068	6.481332	0.7852636	13.78606	8.516075	149.5079	0.0014925

Growth basal area increment

Prediction ability for basal area increase due to growth (m2/ha/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	236	0.1094487	0.0986754	-0.0107733	-9.843221	0.0792618	72.41914	0.5779736
2.9.3	IFN23	Sperry	236	0.1094487	0.1196542	0.0102055	9.324453	0.0862592	78.81244	0.6155523
2.9.3	IFN34	Granier	127	0.0643630	0.0659891	0.0016260	2.526356	0.0598727	93.02339	0.5888888
2.9.3	IFN34	Sperry	127	0.0643630	0.0989198	0.0345568	53.690372	0.0816951	126.92855	0.7058045
2.9.3	IFN24	Granier	210	0.1004444	0.0840434	-0.0164010	-16.328458	0.0733928	73.06811	0.5813009
2.9.3	IFN24	Sperry	210	0.1004444	0.1207059	0.0202615	20.171828	0.0963527	95.92638	0.5586032

Mortality

Prediction ability for basal area decrease due to mortality (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	236	0.0053933	0.0162797	0.0108864	201.85080	0.0566134	1049.6980	0.0024362
2.9.3	IFN23	Sperry	236	0.0053933	0.0038633	-0.0015300	-28.36931	0.0196835	364.9611	0.0478079
2.9.3	IFN34	Granier	127	0.0083747	0.0456468	0.0372721	445.05462	0.0915392	1093.0418	0.0696161
2.9.3	IFN34	Sperry	127	0.0083747	0.0036622	-0.0047125	-56.27095	0.0330105	394.1678	0.3055136
2.9.3	IFN24	Granier	210	0.0094895	0.0386425	0.0291531	307.21542	0.0711683	749.9722	0.2500400
2.9.3	IFN24	Sperry	210	0.0094895	0.0040936	-0.0053959	-56.86205	0.0228806	241.1165	0.2398281

Prediction ability for density decrease due to mortality (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	236	0.6166907	1.7984542	1.1817635	191.62985	7.460526	1209.7678	0.0045494
2.9.3	IFN23	Sperry	236	0.6166907	0.2658720	-0.3508188	-56.88731	2.664029	431.9879	0.0434564
2.9.3	IFN34	Granier	127	0.5766980	4.5915801	4.0148821	696.18453	8.995680	1559.8598	0.2849624
2.9.3	IFN34	Sperry	127	0.5766980	0.2395559	-0.3371421	-58.46077	2.228779	386.4724	0.2476570
2.9.3	IFN24	Granier	210	0.4865583	3.8358466	3.3492883	688.36320	7.747200	1592.2450	0.5504167
2.9.3	IFN24	Sperry	210	0.4865583	0.2804434	-0.2061149	-42.36181	1.474803	303.1093	0.3610187

Ingrowth

Prediction ability for basal area increase due to ingrowth (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	236	0.0130634	0.0211669	0.0081036	62.03285	0.0749454	573.7062	0.0043294
2.9.3	IFN23	Sperry	236	0.0130634	0.0389798	0.0259164	198.38992	0.0988565	756.7461	0.0069505
2.9.3	IFN34	Granier	127	0.0037973	0.0142598	0.0104625	275.52332	0.0446746	1176.4763	0.0045210
2.9.3	IFN34	Sperry	127	0.0037973	0.0298458	0.0260485	685.96971	0.0661975	1743.2693	0.0003844
2.9.3	IFN24	Granier	210	0.0063430	0.0176933	0.0113503	178.94096	0.0449992	709.4281	0.0184554
2.9.3	IFN24	Sperry	210	0.0063430	0.0427215	0.0363784	573.51892	0.0799063	1259.7516	0.0075581

Prediction ability for density increase due to ingrowth (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	236	2.0885510	4.139675	2.051124	98.20801	13.538888	648.2431	0.0052365
2.9.3	IFN23	Sperry	236	2.0885510	6.859938	4.771387	228.45441	16.902136	809.2757	0.0077781
2.9.3	IFN34	Granier	127	0.7143175	2.944675	2.230358	312.23624	9.121483	1276.9508	0.0045633
2.9.3	IFN34	Sperry	127	0.7143175	5.184057	4.469740	625.73575	11.036892	1545.0962	0.0010823
2.9.3	IFN24	Granier	210	0.9174718	3.024347	2.106875	229.63919	7.077724	771.4378	0.0175663
2.9.3	IFN24	Sperry	210	0.9174718	6.302964	5.385492	586.99265	11.415748	1244.2615	0.0055690

Overall basal area changes

Prediction ability for overall basal area changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	236	-0.1692768	0.1026599	0.2719366	160.6462	0.5161274	304.9015	0.0662988
2.9.3	IFN23	Sperry	236	-0.1692768	0.1562227	0.3254994	192.2883	0.5924392	349.9825	0.3189295
2.9.3	IFN34	Granier	127	-0.1670882	0.0295985	0.1966867	117.7143	0.4001682	239.4952	0.0530578
2.9.3	IFN34	Sperry	127	-0.1670882	0.1268826	0.2939708	175.9375	0.5055027	302.5365	0.5773309
2.9.3	IFN24	Granier	210	-0.0772680	0.0539988	0.1312669	169.8851	0.2550247	330.0521	0.0086278
2.9.3	IFN24	Sperry	210	-0.0772680	0.1725817	0.2498497	323.3546	0.4044669	523.4598	0.3110973

Predictive capacity plots (IFN2-IFN4):

Relationship between basal area changes and climatic variables (MAT and P/PET; IFN2-IFN4):

Spatial distribution of errors (IFN2-IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Prediction ability for overall density changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	236	-12.702170	2.3412212	15.043391	118.43166	32.40450	255.1100	0.0609177
2.9.3	IFN23	Sperry	236	-12.702170	6.5940659	19.296235	151.91291	38.03304	299.4216	0.0053558
2.9.3	IFN34	Granier	127	-11.564537	-1.6469046	9.917632	85.75901	22.28675	192.7163	0.1710288
2.9.3	IFN34	Sperry	127	-11.564537	4.9445013	16.509038	142.75572	29.30423	253.3973	0.0096475
2.9.3	IFN24	Granier	210	-6.818172	-0.8115001	6.006672	88.09798	12.86559	188.6955	0.3409367
2.9.3	IFN24	Sperry	210	-6.818172	6.5796198	13.397791	196.50124	21.76772	319.2604	0.0102316

Quercus ilex

Annual diameter increment

Prediction ability for diameter increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	3918	0.1489045	0.1772410	0.0283365	19.03001	0.1104943	74.20480	0.0672058
2.9.3	IFN23	Sperry	3918	0.1489045	0.2026040	0.0536995	36.06308	0.1204250	80.87402	0.0369690
2.9.3	IFN34	Granier	5772	0.1105881	0.1519348	0.0413467	37.38798	0.0989712	89.49537	0.0712142
2.9.3	IFN34	Sperry	5772	0.1105881	0.1825545	0.0719664	65.07606	0.1150158	104.00373	0.0332283
2.9.3	IFN24	Granier	3693	0.1291420	0.1565345	0.0273925	21.21115	0.0888028	68.76371	0.0915264
2.9.3	IFN24	Sperry	3693	0.1291420	0.1886585	0.0595165	46.08612	0.1050948	81.37924	0.0378757

Annual height increment

Prediction ability for height increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	3918	5.950905	5.642813	-0.3080921	-5.177231	7.874177	132.3190	0.0088152
2.9.3	IFN23	Sperry	3918	5.950905	6.636050	0.6851446	11.513284	8.011895	134.6332	0.0064293
2.9.3	IFN34	Granier	5772	3.782069	4.621783	0.8397141	22.202507	8.239339	217.8527	0.0047156
2.9.3	IFN34	Sperry	5772	3.782069	5.841157	2.0590879	54.443425	8.600617	227.4051	0.0023248
2.9.3	IFN24	Granier	3693	4.747147	4.844661	0.0975141	2.054162	5.859919	123.4408	0.0043054
2.9.3	IFN24	Sperry	3693	4.747147	6.054492	1.3073446	27.539582	6.211643	130.8500	0.0014173

Growth basal area increment

Prediction ability for basal area increase due to growth (m2/ha/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	524	0.1431107	0.1715542	0.0284435	19.87518	0.0901055	62.96212	0.7222836
2.9.3	IFN23	Sperry	524	0.1431107	0.2040681	0.0609574	42.59458	0.1261918	88.17775	0.7398702
2.9.3	IFN34	Granier	610	0.1234432	0.1705182	0.0470749	38.13488	0.1078005	87.32802	0.6474181
2.9.3	IFN34	Sperry	610	0.1234432	0.2199635	0.0965203	78.19004	0.1724030	139.66175	0.6802270
2.9.3	IFN24	Granier	489	0.1323631	0.1618460	0.0294829	22.27426	0.0822241	62.12009	0.7369705
2.9.3	IFN24	Sperry	489	0.1323631	0.2108111	0.0784480	59.26724	0.1458464	110.18660	0.7400181

Mortality

Prediction ability for basal area decrease due to mortality (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	524	0.0059090	0.0110158	0.0051068	86.423533	0.0356261	602.9080	0.0242889
2.9.3	IFN23	Sperry	524	0.0059090	0.0057865	-0.0001226	-2.074552	0.0220340	372.8867	0.0282856
2.9.3	IFN34	Granier	610	0.0154508	0.0232249	0.0077741	50.315324	0.0799641	517.5393	0.0766743
2.9.3	IFN34	Sperry	610	0.0154508	0.0074214	-0.0080294	-51.967317	0.0406859	263.3250	0.1086174
2.9.3	IFN24	Granier	489	0.0096707	0.0173101	0.0076394	78.995294	0.0428467	443.0568	0.0795685
2.9.3	IFN24	Sperry	489	0.0096707	0.0063043	-0.0033664	-34.810068	0.0214081	221.3704	0.0989045

Prediction ability for density decrease due to mortality (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	524	0.7206350	1.1965779	0.4759430	66.04494	4.545020	630.6966	0.0189368
2.9.3	IFN23	Sperry	524	0.7206350	0.5343830	-0.1862520	-25.84554	2.922075	405.4862	0.0287007
2.9.3	IFN34	Granier	610	1.7657018	2.4653587	0.6996569	39.62486	8.753283	495.7396	0.0782386
2.9.3	IFN34	Sperry	610	1.7657018	0.6222751	-1.1434267	-64.75763	5.258866	297.8343	0.0839876
2.9.3	IFN24	Granier	489	0.6802705	1.9473077	1.2670373	186.25492	5.102462	750.0637	0.0916957
2.9.3	IFN24	Sperry	489	0.6802705	0.5938822	-0.0863883	-12.69911	2.177322	320.0671	0.0313045

Ingrowth

Prediction ability for basal area increase due to ingrowth (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	524	0.0872894	0.0548725	-0.0324169	-37.13724	0.1518624	173.9757	0.0000814
2.9.3	IFN23	Sperry	524	0.0872894	0.0638605	-0.0234289	-26.84044	0.1573253	180.2341	0.0000000
2.9.3	IFN34	Granier	610	0.0744236	0.0477143	-0.0267093	-35.88823	0.1375123	184.7697	0.0000190
2.9.3	IFN34	Sperry	610	0.0744236	0.0635388	-0.0108848	-14.62552	0.1393336	187.2169	0.0007798
2.9.3	IFN24	Granier	489	0.0917714	0.0472976	-0.0444738	-48.46153	0.1348858	146.9802	0.0035816
2.9.3	IFN24	Sperry	489	0.0917714	0.0694314	-0.0223400	-24.34308	0.1353008	147.4324	0.0057408

Prediction ability for density increase due to ingrowth (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	524	14.68018	10.267661	-4.412520	-30.05767	26.45366	180.1998	0.0000534
2.9.3	IFN23	Sperry	524	14.68018	11.142326	-3.537855	-24.09953	26.97664	183.7623	0.0002184
2.9.3	IFN34	Granier	610	12.36190	8.961435	-3.400469	-27.50764	23.54030	190.4261	0.0004095
2.9.3	IFN34	Sperry	610	12.36190	11.052437	-1.309467	-10.59276	23.34720	188.8641	0.0002484
2.9.3	IFN24	Granier	489	13.71557	7.347157	-6.368415	-46.43200	20.47223	149.2627	0.0011983
2.9.3	IFN24	Sperry	489	13.71557	9.880980	-3.834592	-27.95795	19.76626	144.1155	0.0093615

Overall basal area changes

Prediction ability for overall basal area changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	524	0.2098339	0.2148253	0.0049913	2.378693	0.2024605	96.48605	0.2083278
2.9.3	IFN23	Sperry	524	0.2098339	0.2624308	0.0525969	25.065944	0.2216350	105.62400	0.2537251
2.9.3	IFN34	Granier	610	0.1902548	0.1946943	0.0044395	2.333441	0.2023414	106.35283	0.1825470
2.9.3	IFN34	Sperry	610	0.1902548	0.2796430	0.0893882	46.983423	0.2426749	127.55257	0.2318815
2.9.3	IFN24	Granier	489	0.2215166	0.1903221	-0.0311946	-14.082264	0.1807040	81.57580	0.2241293
2.9.3	IFN24	Sperry	489	0.2215166	0.2862463	0.0647297	29.221159	0.2227084	100.53802	0.2436730

Predictive capacity plots (IFN2-IFN4):

Relationship between basal area changes and climatic variables (MAT and P/PET; IFN2-IFN4):

Spatial distribution of errors (IFN2-IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Prediction ability for overall density changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	524	13.10901	9.071083	-4.0379219	-30.802657	27.63769	210.8298	0.0004681
2.9.3	IFN23	Sperry	524	13.10901	10.607943	-2.5010625	-19.078965	27.40938	209.0882	0.0002017
2.9.3	IFN34	Granier	610	10.94218	6.496076	-4.4461051	-40.632712	26.06156	238.1751	0.0016049
2.9.3	IFN34	Sperry	610	10.94218	10.430162	-0.5120195	-4.679318	24.81450	226.7783	0.0004097
2.9.3	IFN24	Granier	489	12.82621	5.477479	-7.3487353	-57.294656	21.88869	170.6559	0.0088750
2.9.3	IFN24	Sperry	489	12.82621	9.900998	-2.9252166	-22.806547	20.53866	160.1303	0.0061626

Quercus pubescens

Annual diameter increment

Prediction ability for diameter increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	256	0.2601437	0.2219477	-0.0381960	-14.68266	0.1643263	63.16751	0.1560524
2.9.3	IFN23	Sperry	256	0.2601437	0.1817317	-0.0784120	-30.14179	0.1791726	68.87446	0.1454331
2.9.3	IFN34	Granier	1936	0.1457380	0.1643728	0.0186348	12.78651	0.1209050	82.96049	0.0562826
2.9.3	IFN34	Sperry	1936	0.1457380	0.1976329	0.0518949	35.60834	0.1298686	89.11099	0.0183239
2.9.3	IFN24	Granier	236	0.2305138	0.1781990	-0.0523148	-22.69486	0.1607156	69.72060	0.1334099
2.9.3	IFN24	Sperry	236	0.2305138	0.1356624	-0.0948515	-41.14785	0.1762379	76.45438	0.1678664

Annual height increment

Prediction ability for height increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	256	11.922826	7.102031	-4.820796	-40.43333	15.139792	126.9816	0.0060401
2.9.3	IFN23	Sperry	256	11.922826	5.558861	-6.363965	-53.37631	15.687586	131.5761	0.0030231
2.9.3	IFN34	Granier	1936	3.860419	5.965957	2.105538	54.54170	11.383103	294.8670	0.0097857
2.9.3	IFN34	Sperry	1936	3.860419	7.723379	3.862960	100.06582	11.902783	308.3288	0.0096279
2.9.3	IFN24	Granier	236	9.232653	5.507702	-3.724951	-40.34540	9.322255	100.9705	0.0343227
2.9.3	IFN24	Sperry	236	9.232653	3.895550	-5.337103	-57.80682	10.317613	111.7513	0.0039732

Growth basal area increment

Prediction ability for basal area increase due to growth (m2/ha/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	79	0.0891238	0.0730349	-0.0160889	-18.052257	0.0614658	68.96674	0.6522246
2.9.3	IFN23	Sperry	79	0.0891238	0.0601712	-0.0289525	-32.485774	0.0706159	79.23352	0.6261577
2.9.3	IFN34	Granier	318	0.0901147	0.0974983	0.0073836	8.193565	0.0635313	70.50045	0.6635464
2.9.3	IFN34	Sperry	318	0.0901147	0.1318315	0.0417168	46.292963	0.0981401	108.90569	0.6820725
2.9.3	IFN24	Granier	76	0.0776113	0.0571347	-0.0204766	-26.383541	0.0590280	76.05599	0.5761773
2.9.3	IFN24	Sperry	76	0.0776113	0.0438234	-0.0337879	-43.534779	0.0717880	92.49685	0.4501398

Mortality

Prediction ability for basal area decrease due to mortality (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	79	0.0058549	0.0117108	0.0058558	100.015681	0.0379150	647.5750	0.0018919
2.9.3	IFN23	Sperry	79	0.0058549	0.0062388	0.0003839	6.556739	0.0233459	398.7403	0.0004846
2.9.3	IFN34	Granier	318	0.0101969	0.0377162	0.0275193	269.878600	0.0924166	906.3194	0.1619447
2.9.3	IFN34	Sperry	318	0.0101969	0.0051749	-0.0050220	-49.250433	0.0267774	262.6031	0.2180495
2.9.3	IFN24	Granier	76	0.0065697	0.0175178	0.0109480	166.643036	0.0460452	700.8685	0.0013526
2.9.3	IFN24	Sperry	76	0.0065697	0.0090813	0.0025116	38.229329	0.0221145	336.6117	0.0086960

Prediction ability for density decrease due to mortality (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	79	0.6973683	1.1357761	0.4384078	62.86604	4.893603	701.7244	0.0039247
2.9.3	IFN23	Sperry	79	0.6973683	0.5127621	-0.1846062	-26.47184	2.931821	420.4122	0.0005366
2.9.3	IFN34	Granier	318	1.0071161	4.0602707	3.0531546	303.15816	10.229095	1015.6818	0.2931612
2.9.3	IFN34	Sperry	318	1.0071161	0.3109872	-0.6961289	-69.12102	3.258376	323.5353	0.3351003
2.9.3	IFN24	Granier	76	0.1307881	1.8128972	1.6821090	1286.13297	5.776029	4416.3257	0.0003739
2.9.3	IFN24	Sperry	76	0.1307881	0.6057423	0.4749542	363.14784	1.358486	1038.6924	0.0031350

Ingrowth

Prediction ability for basal area increase due to ingrowth (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	79	0.000000	0.0640595	0.0640595	Inf	0.1129618	Inf	NA
2.9.3	IFN23	Sperry	79	0.000000	0.0517975	0.0517975	Inf	0.0987928	Inf	NA
2.9.3	IFN34	Granier	318	0.022931	0.0250107	0.0020797	9.06932	0.0666660	290.7242	0.0108609
2.9.3	IFN34	Sperry	318	0.022931	0.0381563	0.0152252	66.39587	0.0822106	358.5129	0.0048009
2.9.3	IFN24	Granier	76	0.000000	0.0381625	0.0381625	Inf	0.0819286	Inf	NA
2.9.3	IFN24	Sperry	76	0.000000	0.0441272	0.0441272	Inf	0.0741278	Inf	NA

Prediction ability for density increase due to ingrowth (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	79	0.00000	12.664840	12.6648403	Inf	22.12580	Inf	NA
2.9.3	IFN23	Sperry	79	0.00000	10.436225	10.4362247	Inf	19.67734	Inf	NA
2.9.3	IFN34	Granier	318	3.79738	4.653572	0.8561915	22.54690	11.43664	301.1717	0.0126221
2.9.3	IFN34	Sperry	318	3.79738	6.348719	2.5513389	67.18681	13.28263	349.7841	0.0053325
2.9.3	IFN24	Granier	76	0.00000	6.895456	6.8954564	Inf	14.15673	Inf	NA
2.9.3	IFN24	Sperry	76	0.00000	8.350982	8.3509816	Inf	14.12910	Inf	NA

Overall basal area changes

Prediction ability for overall basal area changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	79	-0.2128612	0.1259154	0.3387766	159.15374	0.4329395	203.3905	0.0778531
2.9.3	IFN23	Sperry	79	-0.2128612	0.1066803	0.3195415	150.11728	0.4128702	193.9622	0.1773117
2.9.3	IFN34	Granier	318	0.0994825	0.0818858	-0.0175967	-17.68823	0.1524520	153.2450	0.1899403
2.9.3	IFN34	Sperry	318	0.0994825	0.1673866	0.0679040	68.25725	0.1643975	165.2526	0.2432266
2.9.3	IFN24	Granier	76	-0.1022525	0.0760209	0.1782734	174.34630	0.2351245	229.9450	0.0045565
2.9.3	IFN24	Sperry	76	-0.1022525	0.0843485	0.1866010	182.49047	0.2397396	234.4585	0.0500256

Predictive capacity plots (IFN2-IFN4):

Relationship between basal area changes and climatic variables (MAT and P/PET; IFN2-IFN4):

Spatial distribution of errors (IFN2-IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Prediction ability for overall density changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	79	-13.481885	11.5290641	25.010949	185.51522	34.78344	258.0013	0.0203396
2.9.3	IFN23	Sperry	79	-13.481885	9.9234627	23.405348	173.60590	33.95803	251.8789	0.0030670
2.9.3	IFN34	Granier	318	2.610030	0.5933012	-2.016729	-77.26841	15.49942	593.8408	0.0571170
2.9.3	IFN34	Sperry	318	2.610030	6.0377320	3.427702	131.32808	14.29634	547.7464	0.0004613
2.9.3	IFN24	Granier	76	-6.282228	5.0825592	11.364787	180.90377	18.20315	289.7563	0.0854851
2.9.3	IFN24	Sperry	76	-6.282228	7.9211333	14.203361	226.08796	19.86664	316.2356	0.0053125

Quercus suber

Annual diameter increment

Prediction ability for diameter increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1243	0.1410210	0.2068347	0.0658137	46.66945	0.1669355	118.37631	0.0088297
2.9.3	IFN23	Sperry	1243	0.1410210	0.2014637	0.0604427	42.86077	0.1678270	119.00850	0.0071823
2.9.3	IFN34	Granier	1427	0.1346440	0.1886441	0.0540001	40.10588	0.1454577	108.03138	0.0001814
2.9.3	IFN34	Sperry	1427	0.1346440	0.1482393	0.0135953	10.09724	0.1489167	110.60031	0.0002041
2.9.3	IFN24	Granier	1142	0.1398422	0.1876579	0.0478158	34.19266	0.1202655	86.00090	0.0069571
2.9.3	IFN24	Sperry	1142	0.1398422	0.1689032	0.0290610	20.78129	0.1213699	86.79063	0.0050891

Annual height increment

Prediction ability for height increase (cm/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	1243	4.260807	6.553254	2.292448	53.80313	7.174093	168.3740	0.0095388
2.9.3	IFN23	Sperry	1243	4.260807	6.470349	2.209542	51.85737	7.284835	170.9731	0.0087024
2.9.3	IFN34	Granier	1427	2.408686	5.994964	3.586277	148.88933	9.121639	378.6977	0.0067776
2.9.3	IFN34	Sperry	1427	2.408686	4.785585	2.376898	98.68028	8.859291	367.8059	0.0117383
2.9.3	IFN24	Granier	1142	3.200462	5.834757	2.634295	82.30983	5.927453	185.2062	0.0094382
2.9.3	IFN24	Sperry	1142	3.200462	5.456965	2.256503	70.50553	6.076619	189.8669	0.0049343

Growth basal area increment

Prediction ability for basal area increase due to growth (m2/ha/yr) of surviving trees:

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	147	0.1162374	0.1617277	0.0454903	39.135692	0.1127966	97.03986	0.4942661
2.9.3	IFN23	Sperry	147	0.1162374	0.1596585	0.0434211	37.355530	0.1188924	102.28417	0.4631770
2.9.3	IFN34	Granier	150	0.1211765	0.1615544	0.0403779	33.321578	0.1066299	87.99555	0.5060840
2.9.3	IFN34	Sperry	150	0.1211765	0.1293733	0.0081968	6.764307	0.1180207	97.39567	0.3177576
2.9.3	IFN24	Granier	144	0.1133569	0.1423114	0.0289545	25.542776	0.0923422	81.46148	0.5237030
2.9.3	IFN24	Sperry	144	0.1133569	0.1328259	0.0194690	17.174984	0.1032834	91.11345	0.4534160

Mortality

Prediction ability for basal area decrease due to mortality (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	147	0.0103218	0.0176510	0.0073292	71.00680	0.0337549	327.0247	0.0544494
2.9.3	IFN23	Sperry	147	0.0103218	0.0176502	0.0073284	70.99938	0.0337546	327.0224	0.0544544
2.9.3	IFN34	Granier	150	0.0392110	0.0308408	-0.0083703	-21.34667	0.0750039	191.2825	0.1497010
2.9.3	IFN34	Sperry	150	0.0392110	0.0215520	-0.0176591	-45.03592	0.0760461	193.9404	0.2275668
2.9.3	IFN24	Granier	144	0.0217277	0.0316763	0.0099486	45.78772	0.0518129	238.4647	0.1317459
2.9.3	IFN24	Sperry	144	0.0217277	0.0173618	-0.0043659	-20.09357	0.0369309	169.9715	0.2237142

Prediction ability for density decrease due to mortality (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	147	0.5610858	0.7709766	0.2098909	37.40798	2.219966	395.6553	0.0937794
2.9.3	IFN23	Sperry	147	0.5610858	0.7709625	0.2098767	37.40546	2.219965	395.6551	0.0937797
2.9.3	IFN34	Granier	150	2.2370471	1.8490620	-0.3879851	-17.34363	5.463273	244.2181	0.1666712
2.9.3	IFN34	Sperry	150	2.2370471	0.8918259	-1.3452212	-60.13379	5.496417	245.6997	0.2689673
2.9.3	IFN24	Granier	144	0.9504470	2.4098230	1.4593760	153.54628	5.139984	540.7965	0.1498226
2.9.3	IFN24	Sperry	144	0.9504470	0.7626415	-0.1878055	-19.75970	2.503221	263.3731	0.2179537

Ingrowth

Prediction ability for basal area increase due to ingrowth (m2/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	147	0.0307680	0.0125718	-0.0181963	-59.14012	0.0807822	262.5525	0.0013783
2.9.3	IFN23	Sperry	147	0.0307680	0.0124342	-0.0183338	-59.58720	0.0760801	247.2698	0.0413383
2.9.3	IFN34	Granier	150	0.0152949	0.0120131	-0.0032818	-21.45651	0.0542093	354.4270	0.0046299
2.9.3	IFN34	Sperry	150	0.0152949	0.0064306	-0.0088643	-57.95570	0.0492969	322.3092	0.0022936
2.9.3	IFN24	Granier	144	0.0198601	0.0076898	-0.0121703	-61.27995	0.0430578	216.8054	0.0323280
2.9.3	IFN24	Sperry	144	0.0198601	0.0092591	-0.0106010	-53.37823	0.0511043	257.3215	0.0085565

Prediction ability for density increase due to ingrowth (ind/ha/yr):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	147	4.493781	2.243367	-2.2504136	-50.07840	11.793168	262.4331	0.0003354
2.9.3	IFN23	Sperry	147	4.493781	2.160479	-2.3333020	-51.92292	10.832612	241.0579	0.0439688
2.9.3	IFN34	Granier	150	2.243832	2.019427	-0.2244055	-10.00099	7.909450	352.4974	0.0026081
2.9.3	IFN34	Sperry	150	2.243832	1.091775	-1.1520572	-51.34329	7.053376	314.3451	0.0021810
2.9.3	IFN24	Granier	144	2.642877	1.171964	-1.4709131	-55.65576	5.800612	219.4810	0.0185027
2.9.3	IFN24	Sperry	144	2.642877	1.246779	-1.3960978	-52.82493	6.561238	248.2612	0.0154725

Overall basal area changes

Prediction ability for overall basal area changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	147	0.1361603	0.1542198	0.0180595	13.263400	0.1437150	105.5483	0.2196664
2.9.3	IFN23	Sperry	147	0.1361603	0.1524657	0.0163054	11.975129	0.1572964	115.5229	0.1748304
2.9.3	IFN34	Granier	150	0.0981880	0.1471210	0.0489329	49.835940	0.1671329	170.2172	0.1280634
2.9.3	IFN34	Sperry	150	0.0981880	0.1215039	0.0233159	23.746146	0.1769179	180.1828	0.0632521
2.9.3	IFN24	Granier	144	0.1219790	0.1118721	-0.0101069	-8.285783	0.1262743	103.5213	0.1818564
2.9.3	IFN24	Sperry	144	0.1219790	0.1297381	0.0077591	6.360996	0.1418344	116.2777	0.1887382

Predictive capacity plots (IFN2-IFN4):

Relationship between basal area changes and climatic variables (MAT and P/PET; IFN2-IFN4):

Spatial distribution of errors (IFN2-IFN4):

## Warning in get_plot_component(plot, "guide-box"): Multiple components found;
## returning the first one. To return all, use `return_all = TRUE`.

Prediction ability for overall density changes (including growth, mortality and ingrowth):

version	period	transpirationMode	n	Obs	Pred	Bias	Biasrel	RMSE	RMSErel	R2
2.9.3	IFN23	Granier	147	3.8521751	1.4723905	-2.3797846	-61.77769	11.902571	308.9831	0.0110541
2.9.3	IFN23	Sperry	147	3.8521751	1.3895162	-2.4626589	-63.92905	11.288250	293.0357	0.0431200
2.9.3	IFN34	Granier	150	-0.3761505	0.1703646	0.5465151	145.29160	10.689872	2841.9132	0.0096702
2.9.3	IFN34	Sperry	150	-0.3761505	0.1999490	0.5760995	153.15663	9.380709	2493.8708	0.0144818
2.9.3	IFN24	Granier	144	1.6882040	-1.2374874	-2.9256914	-173.30201	8.087096	479.0355	0.0541903
2.9.3	IFN24	Sperry	144	1.6882040	0.5320192	-1.1561847	-68.48608	7.363092	436.1494	0.0016765