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growing stock (stemwood) #21
Stemwood volume of living trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm with bark.

development stage #20
Stage of stand development, defined by the dominant diameter at breast height (dbhdom = dbh of the 100 largest [thickest] trees per hectare). Young growth/thicket: dbhdom <12 cm, pole timber: dbhdom 12-30 cm, young timber: dbhdom 31-40 cm, medium timber : dbhdom 41-50 cm, old timber: dbhdom >50 cm, mixed: trees of different development stages, no development stage predominant or groups of different development stages covering < 500 m². Reference: Field Survey (MID 261: Entwicklungsstufe)

altitudinal vegetation belts (NaiS; 6 classes) #2633
Altitudinal vegetation belts in the system used in the guidelines for monitoring the sustainability and performance of protection forests (NaiS; Frehner et al. 2005), reduced to six classes. The variable represents a simplification of the NaiS altitudinal vegetation belts with ten classes (NAISHSTKOMB) in which the classes «hyperinsubric» and «colline» are merged with «colline with beech» to form the class «hyperinsubric and colline» and the class «lower montane» with «upper montane» and «lower/upper montane» to form the class «lower and upper montane». The information is based on the altitudinal vegetation belts determined by experts (accessible forest sample plots of NFI4 on the 1.4-km network; Arge Frehner et al. 2020), as well as on the altitudinal vegetation belts modelled for the period 1981-2010 (other sample plots; Zischg et al. 2021).

forest district (2024) #2777
Regional demarcation with the forest districts as a unit. This variable was implemented in the year 2024 and is based on a survey with the cantonal forest services in the years 2022/2023.

accessible forest without shrub forest NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was accessible on foot.

1.4 km grid, subgrids 1-5 #1746
Sub-grids 1, 2, 3, 4 and 5 of the field surveys on the sampling grid with a mesh size of 1.4 km (base grid).

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-35calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest and the total forest (pairs of values in ), but they do in the private forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest and the total forest of the Plateau has decreased. For the private forest the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest and total forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI2
growing stock (stemwood)
development stage · altitudinal vegetation belts (NaiS; 6 classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
development stage	altitudinal vegetation belts (NaiS; 6 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	75
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	13	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	66
	lower and upper montane	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	.	0	.	0	.	0	.	67	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	172	55
	submontane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	72
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	.	24	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	70
	total	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	36	.	13	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	.	44	.	29	.	0	.	57	.	67	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	.	24	.	0	.	46	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	463	31
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	71
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	109	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	0	.	124	78	17	.	6	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.	9	100	12	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	310	42
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	152	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	272	59	0	.	9	85	355	59	77	89	0	.	0	.	0	.	106	.	0	.	0	.	0	.	0	.	0	.	0	.	35	68	0	.	0	.	0	.	12	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	.	0	.	0	.	0	.	0	.	67	92	45	.	0	.	0	.	0	.	0	.	0	.	30	100	0	.	35	.	0	.	18	.	2	.	61	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	64	58	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1496	24
	lower and upper montane	0	.	0	.	0	.	0	.	0	.	57	98	0	.	0	.	275	66	4	.	0	.	2	.	121	.	0	.	84	.	0	.	21	100	0	.	0	.	131	79	0	.	0	.	106	.	27	97	0	.	1	100	11	87	145	.	245	81	0	.	-1	.	0	.	0	.	0	.	0	.	79	92	45	81	0	.	14	100	3	100	34	100	79	.	0	.	11	80	14	.	0	.	0	.	27	100	47	100	0	.	3	.	0	.	0	.	0	.	0	.	0	.	26	.	0	.	0	.	43	76	0	.	0	.	0	100	60	.	0	.	97	71	0	.	0	.	50	100	0	.	1	.	0	.	22	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	3	.	21	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1910	22
	submontane	0	.	11	100	65	81	1	.	0	.	0	.	0	.	30	.	60	100	0	.	0	.	1	100	0	.	0	.	25	.	0	.	0	.	17	100	0	.	0	.	51	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	.	23	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	.	0	.	0	.	0	.	28	100	0	.	0	.	29	.	0	.	0	.	0	.	0	.	8	.	0	.	62	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	154	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	.	0	.	0	.	5	77	0	.	73	100	0	.	820	28
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	.	0	.	0	.	26	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	76
	total	0	.	11	100	65	81	1	.	0	.	57	98	261	65	30	100	334	57	4	.	0	.	3	72	121	.	0	.	108	81	0	.	21	100	17	100	0	.	131	79	329	51	0	0	239	60	399	53	120	61	1	100	11	87	158	92	351	64	23	100	-1	.	0	.	0	.	0	.	0	.	114	67	45	81	0	.	73	83	16	82	34	100	79	.	28	100	11	80	14	.	29	.	0	.	27	100	96	71	0	.	11	78	0	.	62	100	67	92	45	.	0	.	26	100	0	.	60	.	43	76	30	100	26	100	36	99	60	.	37	71	99	69	61	100	0	.	111	71	0	.	1	100	0	.	25	89	0	.	0	.	0	.	154	.	0	.	0	.	0	.	0	.	9	100	124	58	73	81	21	71	0	.	41	.	0	.	0	.	5	77	0	.	73	100	0	.	4658	13
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	.	0	.	300	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	238	48	17	88	46	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	612	29
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	129	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	114	84	0	.	0	.	172	57	104	62	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	111	.	132	89	112	.	0	.	0	.	48	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	192	69	4	.	12	.	0	.	0	.	53	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	64	283	59	51	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1689	21
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	303	67	89	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	.	164	.	0	.	0	.	0	.	94	.	159	85	292	47	710	36	241	45	0	.	87	.	0	.	272	74	0	.	0	.	0	.	0	.	0	.	0	.	206	.	0	.	0	.	0	.	26	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	220	.	0	.	0	.	0	.	0	.	40	72	77	.	0	.	3	.	0	.	0	.	234	87	83	89	38	.	0	.	0	.	0	.	28	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	777	31	408	46	215	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4814	15
	lower and upper montane	70	.	0	.	0	.	0	.	0	.	65	.	570	40	1016	40	228	78	877	36	0	.	46	.	76	.	0	.	220	72	223	63	406	51	320	61	0	.	393	68	106	80	0	.	13	.	280	48	0	.	1132	41	163	71	266	61	671	47	96	.	153	89	0	.	0	.	113	.	366	45	92	97	239	67	473	56	382	54	281	69	236	86	547	46	324	72	73	.	187	60	22	.	0	.	140	75	304	62	237	75	447	57	0	.	103	.	0	.	72	60	0	.	250	53	187	62	180	60	424	52	419	47	276	56	375	48	182	76	113	.	54	.	26	71	20	.	40	.	0	.	422	59	0	.	83	.	277	54	61	74	102	.	0	.	53	.	91	.	0	.	0	.	0	.	0	.	223	59	301	60	0	.	0	.	247	62	272	59	0	.	0	.	0	.	0	.	16703	8
	submontane	446	43	295	51	429	56	295	64	0	.	0	.	0	.	163	73	1949	28	63	.	0	.	187	78	210	66	0	.	0	.	387	61	0	.	0	.	0	.	0	.	104	.	7	.	0	.	0	.	0	.	647	43	182	77	253	69	0	.	47	.	0	.	241	66	0	.	0	.	0	.	0	.	0	.	21	.	32	.	76	.	195	71	0	.	528	49	32	71	60	.	187	69	0	.	11	.	0	.	0	.	177	69	435	50	520	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	102	.	0	.	0	.	0	.	51	.	0	.	0	.	88	72	80	76	23	.	7	.	0	.	220	71	357	71	99	.	127	75	209	96	0	.	0	.	0	.	0	.	108	62	73	.	80	73	367	59	195	79	182	78	254	61	42	.	181	60	0	.	11023	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	392	58	0	.	0	.	0	.	0	.	553	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	128	65	29	87	453	42	368	44	1210	27	242	53	175	58	547	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	.	0	.	0	.	63	.	0	.	0	.	0	.	391	47	98	61	134	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4841	12
	total	516	39	295	51	429	56	295	64	0	.	65	.	1002	32	1268	34	2177	26	940	34	0	.	234	65	286	55	0	.	220	72	662	42	570	46	320	61	392	58	393	68	417	45	165	81	316	44	1715	22	645	27	1779	30	431	47	519	46	948	39	143	75	153	89	241	66	0	.	113	.	477	42	430	59	351	56	494	53	414	51	432	49	431	57	547	46	852	41	106	73	246	52	208	63	0	.	152	70	523	55	237	75	624	45	435	50	623	57	40	72	469	38	33	77	718	32	555	36	1390	24	954	34	677	34	860	28	477	44	182	76	113	.	82	74	77	70	20	.	40	.	88	72	501	51	23	.	90	92	277	54	338	51	458	60	99	.	243	52	300	74	0	.	0	.	1573	21	806	32	777	32	373	52	80	73	367	59	442	49	454	47	254	61	42	.	181	60	0	.	39682	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	.	578	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	173	60	52	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	831	29
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	1222	33	43	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	71	170	73	0	.	348	71	1406	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	158	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	89	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	.	331	58	295	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4285	20
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	630	43	350	71	0	.	0	.	0	.	0	.	0	.	0	.	194	.	470	73	270	.	0	.	0	.	72	.	807	53	269	72	291	72	1611	32	2164	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	385	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	184	61	0	.	0	.	0	.	0	.	0	.	0	.	72	.	0	.	0	.	0	.	0	.	100	.	0	.	207	.	36	.	366	.	918	52	0	.	29	.	0	.	0	.	0	.	122	.	0	.	0	.	0	.	0	.	0	.	133	.	0	.	0	.	0	.	0	.	454	42	568	45	664	47	153	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11519	12
	lower and upper montane	222	72	0	.	0	.	94	.	801	46	1417	45	1054	34	1730	35	753	53	3807	22	0	.	0	.	0	.	0	.	172	.	831	54	2381	36	327	.	0	.	1296	52	480	47	370	63	63	.	0	.	0	.	2461	28	271	65	305	72	514	53	0	.	273	69	197	.	42	.	208	59	643	53	247	.	967	44	725	41	217	.	450	54	82	.	1141	56	232	71	704	49	721	50	56	.	1189	39	178	78	396	73	331	56	0	.	0	.	65	.	0	.	0	.	0	.	0	.	37	76	0	.	382	53	308	62	235	.	0	.	0	.	149	.	215	74	61	.	340	73	534	51	373	.	827	39	0	.	84	.	593	40	58	.	481	46	313	71	66	.	256	.	105	.	0	.	0	.	0	.	854	37	153	.	289	72	0	.	729	41	619	50	0	.	240	.	0	.	0	.	36712	7
	submontane	1069	35	563	48	334	72	617	51	0	.	0	.	447	.	435	58	2378	27	0	.	164	.	833	47	245	71	92	.	161	.	98	.	0	.	643	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	919	48	647	60	470	60	0	.	91	.	0	.	0	.	105	.	0	.	0	.	0	.	188	.	124	.	516	71	0	.	0	.	0	.	849	46	31	.	163	71	175	.	752	43	0	.	0	.	286	71	0	.	87	.	135	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	.	0	.	0	.	0	.	0	.	27	.	0	.	216	.	218	79	0	.	8	.	110	71	221	72	479	52	0	.	0	.	0	.	0	.	0	.	40	.	160	.	0	.	262	71	0	.	337	68	0	.	457	60	102	.	0	.	16315	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	157	.	0	.	0	.	0	.	0	.	112	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	.	111	.	547	45	0	.	83	75	182	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	.	0	.	0	.	0	.	0	.	0	.	0	.	90	.	13	.	0	.	0	.	0	.	0	.	0	.	0	.	169	.	0	.	0	.	0	.	1585	27
	total	1292	31	563	48	334	72	711	46	801	46	1417	45	3352	22	2558	28	3131	24	3807	22	164	.	833	47	245	71	92	.	526	58	1399	41	2652	34	971	46	157	.	1369	50	1433	34	809	40	355	61	2099	28	4148	19	3380	24	917	46	775	46	514	53	91	.	273	69	197	.	147	77	208	59	643	53	632	58	1314	37	849	38	734	58	450	54	82	.	1143	56	1081	39	735	47	885	43	231	80	1942	29	362	49	396	73	617	44	0	.	87	.	199	75	0	.	72	.	0	.	44	.	149	77	547	45	571	42	391	52	624	58	36	.	366	.	1125	45	215	74	90	75	340	73	534	51	400	93	949	37	216	.	302	64	593	40	144	68	591	40	667	46	546	47	256	.	105	.	0	.	791	31	964	34	1852	29	466	58	289	72	262	71	729	41	956	40	169	.	697	52	102	.	0	.	71248	5
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	.	618	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	178	61	127	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	959	32
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	856	40	101	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	136	.	359	49	198	79	1133	39	957	40	1750	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	116	.	0	.	0	.	74	.	153	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	282	71	0	.	0	.	0	.	0	.	0	.	149	.	223	71	0	.	0	.	0	.	0	.	0	.	40	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	123	.	0	.	0	.	0	.	0	.	878	38	709	61	514	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8751	13
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	2217	35	132	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	237	77	97	.	0	.	0	.	197	71	447	57	1499	38	2904	28	853	43	0	.	0	.	0	.	274	71	0	.	0	.	0	.	0	.	0	.	0	.	331	71	414	62	0	.	167	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	131	.	185	.	155	.	0	.	0	.	0	.	211	91	0	.	798	44	0	.	0	.	0	.	0	.	169	.	0	.	244	.	0	.	514	59	432	80	474	76	493	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	95	.	0	.	0	.	0	.	0	.	726	48	1084	38	862	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16343	11
	lower and upper montane	434	71	0	.	0	.	922	50	864	51	1790	36	3869	26	3850	24	3044	29	3519	23	81	.	0	.	871	50	0	.	0	.	347	62	1123	48	1682	43	0	.	1113	49	183	75	580	50	0	.	211	.	0	.	2848	26	794	52	315	77	2835	29	113	.	1141	38	293	70	190	71	1571	36	650	61	261	.	1265	62	1021	51	781	62	735	48	176	.	2659	33	942	46	267	78	359	76	645	53	279	72	628	54	1859	40	718	53	809	45	0	.	0	.	0	.	135	.	0	.	0	.	125	76	0	.	0	.	132	77	115	.	444	73	0	.	0	.	160	.	0	.	268	70	400	.	0	.	840	45	237	.	397	72	534	43	618	51	836	48	335	60	205	.	250	65	296	76	768	71	0	.	0	.	310	66	217	.	207	.	676	51	1496	40	893	47	0	.	0	.	182	.	0	.	59711	6
	submontane	718	54	854	57	505	60	2292	32	0	.	0	.	1260	46	958	47	4038	21	0	.	624	54	39	.	849	53	0	.	621	60	664	65	0	.	1445	42	0	.	0	.	380	73	114	.	0	.	0	.	0	.	1916	31	809	73	1637	40	74	.	253	58	0	.	372	76	0	.	0	.	0	.	0	.	0	.	224	.	547	73	172	.	0	.	0	.	903	42	589	58	334	78	998	53	96	.	108	.	0	.	0	.	739	59	1282	41	841	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	274	73	174	75	0	.	455	73	107	.	164	.	602	58	0	.	0	.	395	71	143	.	532	60	0	.	0	.	0	.	665	78	0	.	0	.	0	.	0	.	519	51	1364	46	225	.	681	61	1055	45	465	73	862	52	0	.	35939	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	.	0	.	0	.	0	.	0	.	0	.	138	.	19	.	69	.	177	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	122	.	114	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	721	42
	total	1152	43	854	57	505	60	3214	27	864	51	1790	36	8202	17	5040	21	7081	17	3519	23	705	49	39	.	1720	36	0	.	621	60	1011	48	1360	42	3224	30	0	.	1250	45	1119	34	1338	32	2632	27	4107	22	3220	21	4764	19	1603	45	1952	36	3183	27	366	50	1141	38	665	52	190	71	1571	36	650	61	708	52	1679	49	1246	46	1567	41	1060	40	176	.	2659	33	1845	31	856	47	693	54	1643	38	375	59	1149	37	2045	38	873	47	1549	37	1282	41	923	40	360	67	358	58	798	44	0	.	125	76	138	.	19	.	411	52	292	72	688	59	0	.	514	59	866	50	648	59	761	54	854	61	107	.	1004	41	839	50	397	72	534	43	1013	42	979	43	1084	37	205	.	250	65	296	76	1433	52	1904	27	2034	31	1687	30	217	.	726	46	2041	35	1720	37	1574	37	1055	45	465	73	1044	46	0	.	122423	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	103	.	0	.	0	.	325	60	711	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1183	33
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	155	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	.	1103	41	0	.	525	61	259	77	856	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	210	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	109	.	0	.	0	.	0	.	0	.	145	.	0	.	0	.	0	.	199	.	0	.	115	.	0	.	116	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1270	36	294	77	667	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6129	16
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	1870	36	0	.	0	.	135	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	677	62	1246	46	443	73	1236	37	2816	28	920	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	.	0	.	202	.	0	.	0	.	0	.	0	.	0	.	99	.	0	.	0	.	0	.	0	.	0	.	527	50	210	.	319	79	0	.	0	.	0	.	0	.	0	.	0	.	110	.	145	.	0	.	188	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	577	50	313	73	856	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12970	12
	lower and upper montane	0	.	93	.	0	.	0	.	636	59	789	60	2468	32	5646	22	1484	40	683	51	285	.	143	.	98	.	0	.	0	.	336	71	719	71	194	.	0	.	1123	43	444	65	841	44	132	.	84	.	0	.	1011	44	1303	49	1790	40	2298	39	457	80	352	59	308	75	693	58	1312	39	0	.	0	.	917	55	831	52	413	.	1038	53	1241	46	1662	46	0	.	349	71	513	59	223	.	451	61	571	63	594	58	1108	47	0	.	0	.	446	71	0	.	254	.	0	.	204	70	0	.	0	.	203	71	0	.	115	.	104	.	255	.	240	.	191	.	222	.	75	.	356	.	0	.	407	82	0	.	420	70	95	71	516	71	256	71	377	73	280	.	402	.	277	.	134	.	0	.	0	.	359	59	184	.	770	51	116	.	1373	42	191	.	0	.	0	.	0	.	0	.	44458	7
	submontane	1806	35	1784	37	377	72	2358	32	0	.	0	.	0	.	694	63	5869	19	0	.	664	59	231	.	122	.	0	.	273	.	313	71	0	.	275	.	0	.	0	.	0	.	441	63	0	.	0	.	0	.	817	51	1018	44	1227	45	284	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	598	77	0	.	827	47	816	63	0	.	442	74	135	.	0	.	0	.	0	.	667	61	879	52	1316	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	149	.	0	.	0	.	0	.	286	.	0	.	174	.	0	.	0	.	83	.	0	.	0	.	0	.	0	.	0	.	0	.	202	.	0	.	206	.	0	.	0	.	121	.	0	.	0	.	73	.	0	.	565	50	770	50	1048	48	626	50	0	.	28536	8
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	159	.	0	.	0	.	0	.	0	.	0	.	27	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	194	.	0	.	0	.	0	.	227	67	390	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	89	.	0	.	0	.	0	.	1087	36
	total	1806	35	1877	36	377	72	2358	32	636	59	789	60	4493	23	6340	21	7353	17	818	46	949	51	374	72	221	71	0	.	273	.	650	50	719	71	469	72	159	.	1907	34	2896	27	1725	33	1893	30	3484	24	2514	25	1828	33	2321	34	3017	30	2582	36	457	80	352	59	308	75	693	58	1312	39	0	.	0	.	917	55	831	52	413	.	1329	45	1839	40	1864	42	827	47	1165	49	513	59	665	60	586	52	670	55	594	58	1108	47	667	61	879	52	1762	34	527	50	766	52	319	79	204	70	0	.	227	67	738	43	0	.	115	.	363	58	598	59	240	.	494	53	508	71	191	65	530	75	0	.	407	82	83	.	420	70	95	71	516	71	256	71	377	73	280	.	604	75	277	.	340	72	1891	29	608	53	2004	29	184	.	770	51	189	72	1373	42	756	45	859	46	1048	48	626	50	0	.	94364	5
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	.	346	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	82	83	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	490	45	170	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1201	25
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	1235	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	.	477	50	123	82	634	43	576	52	400	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	.	0	.	104	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	172	72	406	50	325	54	0	.	0	.	0	.	162	71	54	.	0	.	81	.	93	.	62	.	0	.	83	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1082	46	631	47	383	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7268	14
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	1323	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	101	.	0	.	61	.	0	.	45	.	328	58	398	52	624	61	1007	42	58	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	.	24	.	192	77	0	.	0	.	129	78	142	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	.	93	.	0	.	0	.	0	.	95	.	169	111	591	57	149	71	0	.	0	.	136	.	61	.	231	.	0	.	0	.	45	.	0	.	158	70	40	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	.	0	.	0	.	0	.	0	.	1019	42	1017	34	962	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9384	12
	lower and upper montane	0	.	0	.	0	.	126	.	335	87	810	57	944	42	4238	21	413	59	1237	32	0	.	113	.	76	.	0	.	0	.	932	59	1312	37	216	71	0	.	454	46	151	63	286	56	0	.	123	.	0	.	1032	38	328	75	668	57	766	54	288	59	369	57	153	.	364	51	333	66	155	.	129	.	152	74	215	.	0	.	846	38	589	53	719	57	0	.	113	.	241	72	0	.	0	.	303	76	410	58	47	.	132	.	0	.	0	.	0	.	238	72	0	.	41	90	450	56	192	58	65	.	0	.	5	.	71	.	0	.	0	.	371	67	308	74	384	60	0	.	0	.	156	71	0	.	0	.	582	43	24	.	83	.	74	.	0	.	0	.	0	.	0	.	0	.	0	.	367	59	207	76	111	.	0	.	466	64	205	63	0	.	0	.	0	.	0	.	24521	8
	submontane	0	.	292	.	75	.	0	.	0	.	0	.	0	.	673	55	912	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	190	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	406	72	0	.	0	.	0	.	81	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	.	0	.	0	.	0	.	0	.	195	.	0	.	98	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	137	.	91	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	207	.	0	.	68	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	34	.	215	73	0	.	0	.	0	.	3750	22
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	.	0	.	192	.	29	.	578	44	56	71	50	.	27	.	862	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	96	31	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2024	25
	total	0	.	292	.	75	.	126	.	335	87	810	57	3502	23	4911	20	1325	37	1237	32	0	.	113	.	76	.	0	.	0	.	1032	54	1312	37	467	54	0	.	560	39	955	34	806	35	1258	37	1755	31	883	30	1032	38	328	75	1074	45	766	54	288	59	369	57	234	74	364	51	333	66	346	61	153	86	448	48	215	.	0	.	975	35	730	47	719	57	0	.	113	.	241	72	62	.	0	.	303	76	470	52	141	74	327	72	0	.	98	.	329	51	877	37	1191	36	220	53	1028	34	249	47	413	47	142	60	1098	40	152	71	93	.	243	64	462	56	549	48	424	55	0	.	0	.	156	71	0	.	0	.	582	43	24	.	83	.	131	71	0	.	207	.	0	.	68	.	2592	26	1875	25	1744	25	207	76	111	.	14	.	466	64	239	56	215	73	0	.	0	.	0	.	48147	5
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	103	.	0	.	11	.	437	47	2590	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	82	83	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1124	26	366	46	46	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4822	13
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	3704	21	143	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	304	60	2205	25	491	45	2416	25	2329	25	4550	19	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	237	71	248	67	374	59	0	.	74	.	410	64	0	.	2	.	0	.	0	.	0	.	0	.	0	.	282	71	0	.	0	.	0	.	0	.	0	.	320	60	931	33	329	53	12	.	0	.	0	.	449	47	94	71	0	.	81	.	292	75	80	80	115	.	83	81	116	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	123	.	0	.	0	.	0	.	0	.	3481	22	2271	27	1923	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28474	7
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	6528	18	570	51	0	.	148	92	0	.	0	.	0	.	0	.	194	.	622	58	672	54	158	72	0	.	795	54	2942	26	1715	30	4013	21	9403	14	4313	18	0	.	87	.	0	.	652	46	0	.	0	.	0	.	0	.	0	.	65	.	981	42	606	49	0	.	167	100	248	53	142	.	202	100	0	.	0	.	0	.	0	.	0	.	414	48	514	58	248	73	0	.	0	.	0	.	941	37	573	53	1709	32	152	69	0	.	0	.	470	56	343	57	476	65	425	64	510	77	1496	38	649	57	723	53	533	68	0	.	0	.	122	100	0	.	0	.	0	.	0	.	0	.	285	60	0	.	0	.	0	.	0	.	3553	19	3502	19	3618	20	153	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56633	5
	lower and upper montane	726	48	93	.	0	.	1142	43	2635	28	4929	22	8906	16	16480	11	6197	19	10128	12	366	81	305	62	1241	38	0	.	476	52	2668	30	5962	21	2740	31	0	.	4511	23	1365	30	2077	26	314	58	769	38	0	.	8485	14	2870	29	3556	25	7330	18	955	45	2286	24	951	42	1290	36	3536	22	1813	31	807	49	3585	29	3266	25	1807	39	3354	23	2357	30	6807	20	1498	34	1517	32	2035	28	945	43	1919	30	1862	31	3610	26	2440	28	1392	33	0	.	614	55	0	.	698	48	0	.	522	38	800	36	418	39	1117	30	859	34	745	43	994	39	497	60	502	61	1089	36	617	53	1087	36	1380	44	373	.	2654	24	237	.	1006	42	2080	21	1276	38	1758	29	1098	37	604	59	999	51	678	55	902	63	0	.	2	.	2117	23	1082	37	1376	36	792	46	4311	22	2180	27	0	.	240	.	182	.	0	.	184187	3
	submontane	4040	21	3798	24	1787	30	5563	20	0	.	0	.	1707	43	2953	26	15241	11	63	.	1452	37	1291	37	1427	36	92	.	1080	46	1463	37	0	.	2570	29	0	.	0	.	536	56	561	53	0	.	0	.	0	.	4298	20	2712	31	4007	24	359	82	415	43	0	.	693	48	105	.	0	.	0	.	0	.	188	100	369	70	1154	47	249	76	793	60	0	.	3135	22	1468	42	557	52	1894	35	984	37	119	91	0	.	286	71	1787	36	2684	27	2972	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	251	72	0	.	195	76	365	58	511	62	0	.	689	55	222	57	244	72	924	45	228	76	0	.	624	51	609	49	1006	40	606	44	618	57	0	.	939	60	0	.	0	.	269	53	233	76	599	45	2120	33	419	65	1799	32	2299	28	2012	33	1843	31	0	.	96475	4
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	709	45	0	.	0	.	0	.	0	.	665	39	106	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	.	62	.	322	65	247	79	550	36	1057	30	2239	19	702	40	354	39	1795	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	134	72	0	.	0	.	63	.	0	.	0	.	0	.	602	40	283	50	165	58	0	.	0	.	0	.	0	.	0	.	258	74	0	.	0	.	0	.	10394	9
	total	4766	19	3891	23	1787	30	6705	18	2635	28	4929	22	20845	10	20147	10	21437	9	10339	12	1817	34	1596	32	2668	26	92	.	1749	33	4753	21	6634	19	5468	21	709	45	5610	20	7150	15	4844	17	6754	16	13603	11	11559	11	12783	11	5669	21	7562	17	8345	17	1370	34	2286	24	1644	31	1396	34	3536	22	2115	28	2036	29	4753	23	3635	23	3201	28	4262	20	3292	26	7011	20	4633	18	2985	26	2592	25	2840	27	2903	23	2677	24	4123	23	2975	25	3179	25	2684	27	3668	22	1324	30	2588	22	2367	25	1236	24	1857	23	2657	17	2738	20	1650	23	3016	21	1751	29	1299	41	2273	29	2218	27	1933	31	1736	31	2070	34	594	66	3019	23	1160	41	1234	37	2080	21	2034	29	2367	25	2511	24	1273	35	1616	38	678	55	1841	43	8760	12	6423	14	8138	13	1468	31	1975	29	2912	27	4730	21	3979	20	2557	26	2252	31	2025	29	0	.	380985	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2298577. Birmensdorf, Swiss Federal Research Institute WSL

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