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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	23-25calculated 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 (pairs of values in ), but they do in the private forest and the total 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 of the Plateau has decreased. For the private forest, as well as for the total 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.

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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NFI3
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 2004/06
		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	.	194	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	-4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	327	54
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	0	0	21	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	.	0	.	0	.	0	.	0	.	0	.	274	76	32	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	106	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	445	53
	lower and upper montane	0	.	0	.	0	.	0	.	0	.	0	.	143	84	67	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	95	116	.	0	.	150	95	0	.	18	.	0	.	55	.	0	.	132	100	42	.	130	.	121	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	.	0	.	0	.	0	.	0	.	0	.	0	.	86	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	.	0	.	0	.	0	.	164	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1492	26
	submontane	0	.	51	100	0	.	0	.	0	.	0	.	0	.	0	.	46	77	0	.	0	.	0	.	0	.	0	.	0	.	146	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	.	0	.	0	.	0	.	0	.	0	.	0	.	366	48
	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	51	100	0	.	0	.	0	.	0	.	337	56	87	74	46	77	0	.	0	.	0	.	0	.	0	.	0	.	146	.	89	82	125	93	0	.	150	95	0	.	18	.	0	.	383	58	32	.	132	100	42	.	130	.	121	67	0	.	0	.	0	.	0	.	0	.	0	.	83	100	0	.	0	.	0	.	0	0	0	.	38	100	11	.	0	.	0	.	0	.	0	.	0	.	192	71	0	.	30	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	.	0	.	0	.	0	.	164	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	4	.	0	.	0	0	0	0	-4	.	0	.	24	100	0	.	65	.	0	.	0	.	0	.	0	.	0	.	0	.	2631	20
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	.	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	.	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	.	85	93
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	89	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	.	0	.	83	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	98	31	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	353	40
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	286	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	.	0	.	0	.	0	.	176	80	72	71	170	74	120	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	0	.	52	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	81	0	.	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	156	71	0	.	144	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1344	31
	lower and upper montane	0	.	0	.	0	.	144	.	0	.	13	100	130	62	183	72	112	132	136	66	0	.	15	.	153	.	0	.	0	.	5	.	65	100	38	.	0	.	298	70	18	.	0	.	0	.	0	.	0	.	0	0	0	.	3	.	82	103	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	177	83	30	.	1	.	0	.	65	100	10	.	107	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	.	0	.	66	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	82	.	4	.	0	.	0	.	0	.	0	.	0	.	19	.	8	.	16	.	0	.	185	81	3	.	0	.	0	.	0	.	0	.	0	.	2207	22
	submontane	564	45	202	47	0	.	0	.	0	.	0	.	77	.	52	.	209	43	0	.	253	61	0	.	0	.	0	.	94	.	0	.	0	.	0	.	0	.	0	.	0	.	4	.	0	.	0	.	0	.	18	.	217	71	0	.	0	.	43	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	144	79	0	.	0	.	0	.	47	.	0	.	0	.	0	.	0	.	62	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	.	0	.	0	.	19	.	0	.	0	.	0	.	184	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	148	98	0	.	210	71	4	.	65	100	209	71	0	.	2882	18
	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	.	51	82	16	.	53	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	.	0	.	0	.	0	.	191	43
	total	564	45	202	47	0	.	144	.	0	.	13	100	582	55	236	60	321	54	136	66	253	61	15	100	153	.	0	.	94	.	5	.	75	87	38	100	0	.	298	70	193	74	76	67	233	60	120	97	89	71	18	100	217	71	3	.	82	103	43	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	177	83	30	.	1	.	0	.	208	63	10	.	107	100	0	.	47	.	0	.	7	.	0	.	0	.	62	.	0	.	0	.	52	.	51	82	52	76	53	.	66	.	0	.	0	.	0	.	147	81	0	.	22	.	0	.	1	.	4	.	2	.	57	.	0	.	0	.	19	100	0	.	0	.	82	.	187	83	0	.	0	.	0	.	0	.	351	44	50	62	151	67	16	.	0	.	333	63	3	.	210	71	26	86	65	100	209	71	0	.	7064	12
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	.	31	.	0	.	114	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	384	53
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	115	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	.	0	.	0	.	101	63	223	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	.	23	.	0	.	0	.	0	.	77	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	223	62	29	.	31	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	868	30
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	362	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	72	0	.	0	.	0	.	0	.	0	.	197	84	354	44	884	32	162	58	0	.	0	.	0	.	182	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-1	.	0	.	0	.	0	.	0	.	112	65	34	.	0	.	0	.	0	.	0	.	179	.	19	100	0	.	0	.	0	.	33	.	85	88	12	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	314	53	738	48	365	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4272	16
	lower and upper montane	101	.	0	.	0	.	0	.	0	.	193	75	302	87	190	69	343	51	92	.	0	.	0	.	0	.	0	.	121	.	198	82	226	54	304	71	0	.	208	58	218	74	77	.	16	.	182	64	0	.	623	51	315	56	312	71	297	73	14	73	268	69	0	.	17	71	0	.	143	88	120	92	0	.	88	.	211	.	56	66	41	.	240	71	0	.	66	72	148	94	53	.	47	.	239	64	329	66	68	.	495	68	0	.	121	.	0	.	143	52	0	.	348	48	284	70	34	.	436	48	237	68	97	71	73	.	75	.	0	.	0	.	29	.	16	.	116	.	0	.	139	.	0	.	52	.	0	.	131	76	169	.	0	.	135	.	0	.	0	.	0	.	0	.	0	.	11	.	157	80	38	.	0	.	439	60	157	.	0	.	0	.	0	.	0	.	10395	11
	submontane	136	70	428	49	243	55	302	63	0	.	0	.	0	.	122	86	792	32	157	.	0	.	27	.	104	.	0	.	28	.	120	.	0	.	90	84	0	.	0	.	8	.	128	91	0	.	0	.	0	.	0	.	57	80	291	49	0	.	55	88	0	.	24	100	0	.	0	.	0	.	0	.	28	.	69	.	220	77	0	.	0	.	0	.	386	49	133	71	0	.	71	80	0	.	47	.	0	.	0	.	160	57	247	64	606	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	154	.	53	.	0	.	46	.	161	75	26	.	19	.	20	.	0	.	0	.	0	.	0	.	66	.	14	.	0	.	0	.	0	.	0	.	193	55	0	.	87	.	460	60	238	79	4	.	254	61	11	72	293	53	0	.	7180	12
	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	.	373	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	62	0	.	336	58	175	58	271	44	251	52	62	.	468	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	.	0	.	0	.	0	.	85	61	114	82	117	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2378	18
	total	237	58	428	49	243	55	302	63	0	.	193	75	779	43	312	54	1135	27	249	73	0	.	27	.	104	.	0	.	150	83	536	48	226	54	393	58	0	.	208	58	249	66	402	54	401	40	1541	25	499	40	623	51	372	49	603	44	479	59	69	72	268	69	24	100	17	71	0	.	143	88	120	92	28	.	157	71	432	63	79	55	41	.	240	71	386	49	199	53	148	94	124	63	47	.	286	56	327	67	68	.	655	53	247	64	726	52	112	65	268	34	23	.	684	37	459	49	305	40	942	33	318	55	565	39	73	.	75	.	33	.	239	71	94	65	16	.	162	77	161	75	165	86	19	.	71	77	0	.	131	76	169	.	0	.	280	60	14	.	0	.	0	.	839	34	881	42	716	33	157	80	125	76	460	60	676	48	160	98	254	61	11	72	293	53	0	.	25476	6
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	.	0	.	157	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	116	58	238	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	511	39
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	213	58	18	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	227	76	64	.	0	.	333	71	38	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	171	.	0	.	278	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	242	71	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	.	177	.	339	61	112	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2237	25
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	154	74	88	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	331	.	0	.	0	.	0	.	0	.	124	.	373	58	126	71	786	44	219	58	0	.	0	.	0	.	265	73	0	.	0	.	0	.	0	.	0	.	62	.	0	.	0	.	0	.	0	.	28	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	856	38	519	49	434	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4409	18
	lower and upper montane	0	.	0	.	0	.	98	.	0	.	570	71	411	58	675	48	496	60	2938	24	0	.	62	.	464	67	0	.	0	.	561	50	519	54	177	.	0	.	2238	51	14	.	160	.	0	.	236	64	0	.	1731	33	0	.	308	71	454	76	143	.	142	.	0	.	38	.	0	.	391	67	0	.	176	.	1220	40	113	.	505	58	244	.	1424	42	848	51	128	.	328	58	57	.	322	71	42	.	115	91	178	.	106	.	0	.	98	.	0	.	0	.	0	.	29	.	51	.	166	71	134	.	468	60	265	57	476	55	0	.	0	.	0	.	43	.	277	79	101	75	0	.	276	67	0	.	0	.	771	40	0	.	238	73	0	.	0	.	0	.	124	.	0	.	0	.	0	.	497	48	147	.	332	71	0	.	84	72	118	.	0	.	0	.	0	.	0	.	23328	9
	submontane	232	71	266	59	102	.	0	.	0	.	0	.	0	.	333	62	1888	31	0	.	210	.	241	58	141	.	0	.	58	.	401	72	0	.	211	.	0	.	0	.	90	.	101	.	0	.	0	.	0	.	301	58	372	58	511	71	0	.	123	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	115	.	0	.	0	.	292	71	0	.	940	43	250	69	69	.	228	58	170	.	0	.	0	.	0	.	63	.	435	61	197	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	.	0	.	163	.	0	.	0	.	34	.	0	.	29	.	0	.	0	.	129	.	436	59	136	.	183	.	270	.	0	.	0	.	0	.	0	.	0	.	0	.	115	.	299	65	0	.	280	67	0	.	111	.	433	59	0	.	11068	12
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	355	59	0	.	0	.	0	.	0	.	200	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	.	107	.	78	.	84	.	247	77	1270	30	0	.	253	97	765	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	126	73	0	.	0	.	0	.	0	.	0	.	0	.	335	58	74	71	0	.	0	.	0	.	0	.	0	.	0	.	101	.	0	.	0	.	0	.	3997	17
	total	232	71	266	59	102	.	98	.	0	.	570	71	778	38	1114	35	2384	27	2938	24	210	.	303	50	605	57	0	.	58	.	1294	40	519	54	388	71	355	59	2238	51	455	51	699	42	126	71	1555	30	415	37	2033	30	372	58	819	52	719	55	266	71	142	.	0	.	38	.	0	.	625	51	0	.	453	72	1334	37	113	.	532	56	536	60	1424	42	1788	33	378	57	397	51	286	50	491	58	42	.	115	91	178	.	169	73	435	61	295	60	0	.	349	58	78	.	140	67	298	66	1436	28	179	79	722	51	1031	34	476	55	0	.	108	.	0	.	206	82	277	79	101	75	34	.	276	67	29	.	0	.	771	40	255	62	674	46	136	.	183	.	270	.	124	.	0	.	1483	28	1170	32	1043	36	147	.	447	58	299	65	84	72	398	55	101	.	111	.	433	59	0	.	45551	6
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	.	96	73	516	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	759	35
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	781	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	.	284	66	161	.	893	47	628	59	1518	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	412	59	88	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	172	74	671	52	519	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6220	17
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	1000	42	173	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	213	.	96	.	0	.	0	.	0	.	855	48	302	75	597	54	1635	35	2722	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	218	.	0	.	0	.	161	.	0	.	149	.	0	.	0	.	0	.	0	.	0	.	0	.	221	71	0	.	0	.	0	.	0	.	0	.	33	.	97	.	943	46	0	.	0	.	0	.	91	.	140	74	185	.	274	82	378	.	1098	47	388	.	634	66	196	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	135	.	0	.	0	.	0	.	0	.	706	53	1175	39	1678	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16493	11
	lower and upper montane	330	77	0	.	0	.	278	.	649	45	966	47	2670	24	1956	35	1282	45	4714	20	147	.	127	.	615	67	0	.	398	65	616	64	879	44	681	56	0	.	1240	40	173	74	792	47	75	.	186	.	0	.	3456	24	539	59	425	72	1754	34	132	.	856	39	141	.	0	.	327	58	1305	38	0	.	1	.	1251	46	431	72	1021	34	299	75	1797	46	552	59	399	78	894	38	323	71	1083	41	101	.	660	61	768	50	308	71	0	.	552	71	0	.	125	.	0	.	0	.	0	.	139	74	0	.	293	70	310	64	115	.	0	.	140	.	324	77	64	.	599	59	301	71	87	.	1047	38	201	.	302	64	983	32	157	71	895	41	0	.	457	77	575	61	210	.	0	.	0	.	0	.	783	46	193	73	759	53	361	71	1211	41	1026	43	0	.	334	.	0	.	0	.	50146	6
	submontane	763	42	689	50	529	52	969	42	0	.	0	.	454	.	358	57	2885	26	0	.	110	.	768	52	831	58	0	.	0	.	263	79	0	.	1284	37	0	.	0	.	166	.	0	.	0	.	0	.	0	.	1273	36	873	79	608	59	0	.	50	.	0	.	249	71	0	.	0	.	0	.	0	.	124	.	0	.	494	71	0	.	0	.	0	.	1117	38	0	.	466	53	564	58	738	44	0	.	0	.	347	75	297	89	494	58	526	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	329	71	129	.	0	.	59	.	0	.	0	.	235	.	272	80	0	.	0	.	181	.	308	73	583	50	277	.	0	.	71	.	0	.	0	.	60	.	75	.	235	.	437	58	0	.	241	73	286	71	594	58	339	71	0	.	22999	9
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	148	.	0	.	0	.	0	.	0	.	235	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	.	0	.	0	.	58	.	592	50	0	.	312	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1440	28
	total	1093	37	689	50	529	52	1247	39	649	45	966	47	4906	19	2487	29	4167	22	4714	20	256	71	895	47	1446	44	0	.	398	65	1092	45	975	41	1966	31	148	.	1261	40	1478	33	1255	37	1566	34	2779	26	4756	19	4730	20	1412	53	1033	45	1754	34	182	77	856	39	391	58	0	.	327	58	1305	38	218	.	126	99	1251	46	1086	45	1021	34	448	60	1797	46	1669	32	399	78	1360	31	887	45	1821	30	322	58	660	61	1114	41	606	57	494	58	1078	44	33	.	670	43	1031	43	0	.	58	.	730	43	91	.	745	36	495	55	390	65	378	.	1238	44	1041	49	899	50	796	51	360	62	87	.	1047	38	436	71	574	51	983	32	157	71	1076	38	443	59	1040	44	852	52	210	.	71	.	1025	40	1845	31	3100	24	267	60	995	47	799	45	1211	41	1267	37	286	71	928	52	339	71	0	.	98057	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	.	132	.	0	.	0	.	351	59	722	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	560	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1765	28
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	368	72	118	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	102	.	1832	34	0	.	1348	38	921	44	2182	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	.	0	.	98	.	0	.	80	.	181	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	310	71	0	.	0	.	0	.	0	.	0	.	193	82	110	.	0	.	0	.	0	.	0	.	284	72	0	.	0	.	71	.	184	.	0	.	224	.	18	.	96	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	135	.	0	.	0	.	0	.	0	.	2120	28	418	71	1015	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12534	12
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	2750	32	155	.	0	.	0	.	0	.	0	.	0	.	0	.	196	.	0	.	204	.	309	71	0	.	841	63	1943	41	482	60	1900	35	4350	24	1353	37	0	.	0	.	0	.	194	.	0	.	0	.	0	.	0	.	0	.	0	.	269	.	453	54	0	.	0	.	23	.	0	.	243	.	0	.	0	.	0	.	0	.	0	.	366	62	206	.	130	.	0	.	0	.	0	.	478	57	253	.	487	60	88	.	0	.	0	.	0	.	240	85	321	.	0	.	160	.	195	.	224	74	57	.	0	.	0	.	0	.	117	.	0	.	0	.	0	.	0	.	0	.	185	71	0	.	0	.	0	.	0	.	1128	42	803	48	1043	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22144	10
	lower and upper montane	223	71	78	.	0	.	321	71	2465	33	3061	30	2547	32	7340	19	3392	28	925	42	0	.	124	.	47	.	0	.	0	.	588	72	1425	42	984	52	0	.	1170	48	512	56	1049	43	226	71	353	74	0	.	2144	31	1308	45	1997	36	3751	32	100	.	1188	42	583	59	814	51	868	51	208	.	0	.	2837	32	795	61	766	60	1684	39	2423	33	2846	35	0	.	725	52	187	.	206	.	525	78	1872	34	2021	35	1412	44	639	59	0	.	0	.	0	.	0	.	0	.	252	70	0	.	0	.	95	.	55	.	81	.	418	71	89	.	407	72	596	59	415	73	493	73	787	71	0	.	1117	42	0	.	467	74	229	80	1117	42	170	.	936	42	125	.	614	76	129	.	332	.	0	.	0	.	351	60	395	73	537	71	0	.	1215	41	1146	41	0	.	0	.	149	.	0	.	71444	5
	submontane	1077	41	687	48	649	58	3411	27	0	.	0	.	1014	54	1774	40	6322	18	0	.	341	71	287	88	232	73	0	.	710	58	477	63	0	.	0	.	0	.	0	.	86	.	0	.	0	.	0	.	0	.	2174	31	711	46	2355	33	399	71	0	.	0	.	391	71	35	.	0	.	0	.	0	.	0	.	264	.	370	71	124	.	537	72	0	.	955	48	1307	48	0	.	286	71	0	.	111	.	0	.	0	.	219	75	1913	39	1559	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	173	.	0	.	0	.	112	.	245	.	0	.	489	71	0	.	129	.	402	71	0	.	0	.	189	73	0	.	284	71	0	.	0	.	0	.	453	71	0	.	0	.	0	.	227	.	378	59	134	.	0	.	757	50	1421	39	922	49	404	71	0	.	37499	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	258	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	.	0	.	153	.	0	.	0	.	0	.	79	.	264	81	0	.	432	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	121	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1454	34
	total	1299	36	765	44	649	58	3732	25	2465	33	3061	30	6679	20	9387	17	9714	15	925	42	341	71	412	68	279	63	0	.	907	51	1064	49	1629	39	1293	43	258	.	2113	37	4505	23	1531	35	3474	24	5975	19	4257	21	4318	22	2020	33	4353	24	4343	29	100	.	1188	42	974	46	849	49	868	51	334	73	269	.	3388	28	1059	52	1216	44	2013	34	2960	30	3088	33	955	48	2031	36	187	.	492	59	525	78	2659	27	2227	33	1542	41	858	48	1913	39	1706	32	671	47	517	61	487	60	340	58	0	.	79	.	643	48	295	71	833	50	662	53	433	60	602	59	1156	40	735	54	589	63	1276	52	0	.	1363	37	402	71	467	74	229	80	1306	38	170	.	1540	31	125	.	614	76	129	.	785	59	3929	20	1221	40	2409	28	623	59	914	48	134	.	1215	41	1903	31	1421	39	922	49	554	58	0	.	146841	3
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	.	38	.	1280	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	.	93	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	182	57	258	46	59	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1944	19
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	1929	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	158	.	295	58	406	57	377	52	607	43	933	36	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	0	.	124	85	126	.	0	.	0	.	230	82	0	.	43	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	154	86	423	60	300	59	0	.	0	.	0	.	190	73	105	71	0	.	0	.	113	.	86	89	0	.	56	.	18	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1144	52	796	38	364	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8997	13
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	1727	30	0	.	0	.	171	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	151	73	97	85	256	63	779	39	1649	32	676	43	0	.	87	.	0	.	179	.	0	.	0	.	0	.	0	.	0	.	0	.	626	49	146	.	0	.	0	.	121	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	103	.	0	.	0	.	0	.	249	71	289	80	424	70	107	98	0	.	0	.	221	79	0	.	53	.	0	.	0	.	222	80	75	.	311	72	531	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	824	37	1348	33	566	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11988	11
	lower and upper montane	0	.	0	.	0	.	144	.	0	.	551	64	2061	34	3949	20	230	76	1404	33	0	.	0	.	109	.	0	.	0	.	338	59	1192	41	0	.	0	.	418	64	629	45	524	51	0	.	0	.	0	.	449	58	420	57	311	.	1089	38	507	60	350	70	221	73	480	52	2380	27	0	.	193	64	297	62	224	75	80	.	139	.	0	.	577	53	94	.	337	59	0	.	128	.	0	.	0	.	228	75	108	72	395	73	0	.	0	.	0	.	627	62	0	.	20	.	627	44	63	95	806	40	108	71	0	.	99	82	177	.	0	.	404	54	69	.	35	.	134	.	0	.	298	59	0	.	0	.	492	41	0	.	352	53	32	.	0	.	0	.	358	.	177	.	0	.	0	.	833	43	175	.	0	.	196	.	611	50	45	.	0	.	0	.	0	.	0	.	27297	7
	submontane	864	85	242	.	344	61	189	81	0	.	0	.	128	.	143	.	1173	44	0	.	236	71	0	.	229	69	153	.	0	.	20	.	0	.	339	71	0	.	0	.	0	.	128	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	98	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	279	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	204	.	0	.	104	.	0	.	0	.	0	.	0	.	13	.	84	.	0	.	0	.	0	.	132	.	21	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	74	0	.	80	.	0	.	5456	21
	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	.	123	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	.	0	.	0	.	0	.	92	.	75	82	165	.	190	56	634	42	526	51	171	54	0	.	111	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	208	78	-14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2281	21
	total	864	85	242	.	344	61	333	63	0	.	551	64	5845	18	4092	19	1403	39	1574	31	236	71	0	.	338	56	153	.	0	.	357	56	1192	41	339	71	0	.	728	45	1022	33	1315	31	1156	31	2294	25	3012	18	449	58	507	50	311	.	1288	35	605	53	350	70	221	73	480	52	2380	27	0	.	943	37	569	47	224	75	80	.	528	50	0	.	620	50	94	.	337	59	0	.	128	.	0	.	0	.	228	75	212	61	674	52	0	.	0	.	495	48	1450	36	982	40	316	48	1261	30	589	47	1388	29	213	50	164	75	303	72	290	72	411	53	479	48	436	55	585	68	134	.	13	.	382	51	0	.	0	.	492	41	132	.	373	50	32	.	0	.	0	.	358	.	177	.	2150	31	2610	22	1808	27	175	.	0	.	196	.	611	50	45	.	217	74	0	.	80	.	0	.	57964	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	.	132	.	0	.	31	.	485	46	2795	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	72	93	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1302	27	496	42	59	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5449	13
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	3689	21	136	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	281	67	2661	26	630	46	2682	26	2645	25	4978	18	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	296	72	206	65	501	64	0	.	80	.	411	63	0	.	43	.	0	.	0	.	0	.	0	.	0	.	310	71	0	.	0	.	0	.	0	.	0	.	347	60	1211	33	412	49	26	.	0	.	0	.	550	46	105	71	0	.	71	.	298	73	108	74	224	.	146	64	114	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	135	.	0	.	0	.	0	.	0	.	3900	22	2279	26	2040	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31536	7
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	6281	18	436	57	0	.	171	92	0	.	0	.	0	.	0	.	196	.	762	55	323	70	309	71	0	.	992	55	3194	29	1682	29	3925	21	9697	14	5165	18	0	.	87	.	0	.	819	46	0	.	0	.	0	.	0	.	0	.	62	.	1112	41	599	47	0	.	161	100	195	66	149	.	243	100	0	.	0	.	0	.	0	.	0	.	587	47	317	73	234	71	0	.	0	.	0	.	872	38	726	50	1854	32	195	70	0	.	0	.	536	50	399	57	559	67	422	60	537	76	1547	38	772	56	1015	47	732	60	0	.	0	.	117	100	0	.	0	.	0	.	0	.	0	.	320	59	0	.	0	.	0	.	0	.	3984	19	4583	19	4230	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61096	5
	lower and upper montane	654	48	78	.	0	.	985	43	3114	28	5355	22	8263	16	14360	12	5854	20	10208	12	147	100	329	57	1388	40	0	.	519	55	2305	30	4382	21	2300	31	0	.	5724	24	1563	29	2621	25	317	56	1012	37	0	.	8537	15	2625	28	3486	26	7549	19	896	42	2805	24	946	43	1349	36	3575	22	2049	30	313	53	3312	28	3578	25	1779	35	3435	23	3007	29	6922	21	1559	35	1664	32	1663	28	767	45	1976	33	2253	29	3438	25	2534	30	1943	32	0	.	770	55	0	.	895	46	0	.	685	36	962	35	467	39	1470	28	1163	34	753	36	1182	36	471	53	548	60	1324	36	621	52	1585	36	1441	44	87	.	2877	23	201	.	821	48	2476	20	1405	35	1906	27	971	40	718	56	1189	49	821	55	508	74	0	.	19	.	2481	24	1107	37	1666	36	742	48	3563	23	2492	27	0	.	334	.	149	.	0	.	186309	2
	submontane	3636	26	2566	23	1867	28	4872	21	0	.	0	.	1673	43	2782	28	13315	12	157	.	1149	35	1324	37	1537	36	153	.	891	48	1426	35	0	.	1933	30	0	.	0	.	351	59	362	55	0	.	0	.	0	.	3766	22	2231	36	3766	25	399	71	368	48	0	.	664	50	35	.	0	.	0	.	0	.	152	84	448	66	1084	43	162	80	829	53	0	.	3552	21	1690	39	535	47	1150	36	954	39	158	76	0	.	347	75	1048	37	3151	27	2888	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	376	71	0	.	212	71	595	50	589	55	0	.	594	60	265	52	239	65	685	54	311	71	0	.	456	50	638	49	913	40	832	42	565	68	0	.	524	63	0	.	0	.	253	47	302	79	815	43	1544	30	238	79	1492	32	2181	29	1704	34	1759	29	0	.	87452	4
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	760	48	0	.	0	.	0	.	0	.	808	38	123	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	.	0	.	0	.	147	.	92	.	416	49	294	64	625	38	1166	30	2738	19	686	39	628	46	1776	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	126	73	0	.	0	.	80	.	0	.	0	.	0	.	592	40	397	49	164	65	0	.	0	.	0	.	0	.	0	.	123	84	0	.	0	.	0	.	11741	8
	total	4290	23	2644	23	1867	28	5856	19	3114	28	5355	22	19906	10	17714	11	19169	10	10536	12	1296	33	1653	32	2924	27	153	.	1607	34	4493	21	4705	20	4542	20	760	48	6997	22	7901	16	5296	17	6955	15	14647	11	13060	10	12303	12	4942	22	7252	18	8786	17	1264	32	2805	24	1609	32	1384	35	3575	22	2407	27	1632	31	4564	23	4027	24	3103	26	4204	21	3985	24	7207	20	5111	18	3354	25	2199	24	1917	28	2930	25	3309	23	3756	24	3114	26	2991	25	3151	27	3806	22	1310	30	3305	21	2652	25	1531	24	2129	23	3206	17	3243	19	2294	23	3088	21	2051	28	1306	40	2414	28	2914	26	2372	28	2430	30	2035	36	351	46	3233	22	887	48	1132	40	2476	20	1987	28	2544	23	2339	25	1629	33	1754	40	821	55	1032	48	9777	12	7774	14	9227	13	1409	34	2481	28	2286	26	3801	22	3984	20	2304	28	2038	33	1908	27	0	.	383583	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. 2299090. Birmensdorf, Swiss Federal Research Institute WSL

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