Selection from the list of topics

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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 #434
Forest of which less than two-thirds is covered with shrubs that can be accessed on foot.

1.4-km grid #410
NFI's sampling grid with a mesh size of 1.4 km. The 1.4-km grid is the grid size covering all the previous terrestrial Inventories, which is why it is also called the 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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NFI4
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
grid: 1.4-km grid
state 2009/17
		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	.	28	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	176	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	.	0	.	0	.	212	58	30	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	3	.	62	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	533	33
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	206	52	48	.	0	.	8	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	0	.	0	.	43	71	367	51	25	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.	0	.	4	89	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	.	46	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	72	0	0	6	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	927	27
	lower and upper montane	0	.	0	.	0	.	0	.	0	.	0	.	34	78	45	.	0	.	5	.	0	.	0	.	0	.	0	.	2	.	0	.	138	72	0	.	0	.	0	.	0	.	13	.	0	.	0	.	0	.	0	.	17	.	8	.	31	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	81	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-11	.	0	.	0	.	0	.	0	.	38	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	445	32
	submontane	0	.	17	101	0	.	0	.	0	.	0	.	0	.	3	.	14	100	0	.	0	.	0	.	0	.	0	.	13	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	13	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	.	0	.	0	.	0	.	0	.	0	.	88	40
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.
	total	0	.	17	101	0	.	0	.	0	.	0	.	416	36	96	68	14	100	13	72	0	.	0	.	0	.	0	.	15	72	0	.	141	70	0	.	5	.	0	.	25	.	13	.	43	71	579	39	54	63	0	.	17	.	8	.	31	73	0	.	0	.	0	.	0	.	0	.	9	.	11	.	4	76	13	100	0	.	3	100	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	137	65	46	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	15	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-11	.	0	.	0	.	7	.	0	.	38	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	55	3	100	97	71	0	.	0	.	0	.	19	.	0	.	0	.	0	.	0	.	0	.	2025	17
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	.	1	.	1	72	8	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	.	4	.	0	.	0	.	34	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	70
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	112	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-4	.	120	74	12	93	0	.	198	49	106	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	.	0	.	31	81	9	.	0	.	0	.	0	.	0	.	0	.	3	100	4	.	0	.	5	73	24	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	70	7	85	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	703	25
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	305	53	50	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	58	0	.	0	.	13	100	171	56	152	82	136	97	389	39	175	65	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	73	0	.	0	.	0	.	20	96	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	76	71	0	.	0	.	0	.	0	.	3	.	34	97	1	71	13	94	0	.	0	.	0	.	0	.	0	.	82	83	0	.	23	64	89	71	77	65	31	100	7	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	131	59	76	72	276	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2400	17
	lower and upper montane	0	.	0	.	0	.	67	100	0	.	160	63	197	48	448	42	82	64	36	64	0	.	4	.	33	.	0	.	0	.	20	73	115	51	117	60	0	.	141	62	33	.	34	78	0	.	0	.	0	.	58	70	11	.	110	89	141	60	86	.	0	.	0	.	30	.	0	.	25	86	29	71	72	82	26	76	83	100	151	66	35	68	133	55	32	.	0	.	2	100	1	100	0	.	68	91	7	76	18	90	14	100	0	.	26	.	0	.	0	.	0	.	15	82	0	.	0	.	2	.	0	.	0	.	18	76	0	.	0	.	1	.	0	.	0	.	70	90	0	.	-6	.	14	.	9	77	0	0	0	.	16	100	37	86	31	.	0	.	0	.	2	100	0	.	8	.	-8	.	56	60	0	.	53	87	162	58	1	.	0	.	0	.	0	.	0	.	3126	13
	submontane	278	47	164	40	50	93	220	49	0	.	0	.	53	82	43	.	291	38	35	.	30	97	0	.	1	100	0	.	0	.	9	.	0	.	7	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	46	72	118	77	26	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	47	.	0	.	0	.	17	.	0	.	0	.	29	.	0	.	126	58	0	.	0	.	0	.	0	.	30	59	0	0	24	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	.	54	100	0	.	2	72	0	.	0	.	26	91	0	.	0	.	0	.	27	90	0	.	6	100	0	.	0	.	0	.	0	.	44	.	86	.	3	.	5	73	32	59	50	87	189	57	0	.	2219	14
	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	.	-3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	16	.	2	.	0	.	2	.	36	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	99	25	100	0	.	0	.	0	.	0	.	0	.	11	.	0	.	0	.	0	.	151	48
	total	278	47	164	40	50	93	287	44	0	.	160	63	667	30	542	37	373	33	71	59	30	97	4	100	34	98	0	.	0	.	29	59	170	39	124	57	0	.	150	59	324	41	206	62	137	97	585	31	289	47	104	50	131	70	136	74	141	60	86	.	0	.	0	.	30	100	0	.	25	86	37	57	76	78	72	70	83	100	171	59	52	56	137	53	32	100	29	.	2	100	127	57	0	.	68	91	83	65	18	90	44	51	0	0	50	70	9	57	65	64	10	92	63	61	16	100	2	.	2	.	2	100	39	67	104	66	0	.	28	54	114	59	77	65	31	100	117	64	54	100	-5	128	16	87	9	77	0	0	26	91	16	100	37	86	31	.	27	90	0	.	8	80	202	45	151	54	298	67	56	60	44	.	139	70	164	57	6	65	42	50	50	87	189	57	0	.	8647	8
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	.	11	.	0	.	0	.	23	84	321	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	50	14	107	13	.	0	.	0	.	29	58	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	124	77	93	54	22	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	731	21
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	101	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	.	0	.	0	.	11	72	41	63	119	71	216	50	124	54	602	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	152	75	0	.	36	.	0	.	46	.	26	.	16	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	17	.	41	72	14	.	24	.	0	.	0	.	109	59	33	.	0	.	0	.	0	.	5	.	63	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	343	36	58	75	90	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2362	15
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	462	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	64	151	56	71	74	818	23	647	27	296	40	0	.	0	.	0	.	98	72	0	.	0	.	0	.	0	.	0	.	34	79	75	.	0	.	0	.	6	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	.	55	89	0	.	0	.	0	.	0	.	153	51	40	.	108	52	8	.	0	.	0	.	164	56	19	96	43	111	-5	.	0	.	82	74	62	83	86	85	52	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	550	29	526	28	520	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5161	9
	lower and upper montane	35	.	0	.	0	.	0	.	97	71	145	68	361	43	331	44	411	36	317	44	0	.	21	.	153	71	0	.	115	89	109	84	178	53	79	59	0	.	362	38	146	61	19	.	11	.	230	51	0	.	675	37	446	39	351	45	270	38	47	71	165	56	0	.	21	73	117	.	71	85	30	.	154	86	59	.	206	52	128	44	34	96	138	60	92	91	80	66	37	79	32	.	59	.	377	38	430	40	105	60	163	84	0	.	63	.	0	.	117	65	0	.	276	46	129	52	134	57	513	31	222	44	206	46	45	.	192	57	31	86	41	71	111	64	118	49	84	72	9	.	252	50	0	.	54	.	0	.	131	71	180	74	35	.	124	80	88	80	0	.	7	.	15	.	2	.	399	46	164	59	83	79	8	.	387	45	128	86	0	.	37	.	0	.	0	.	11793	7
	submontane	295	49	608	33	445	35	391	50	0	.	0	.	0	.	139	78	1059	20	14	.	77	75	98	55	139	66	0	.	155	55	161	63	0	.	253	49	0	.	24	.	18	.	83	.	0	.	0	.	0	.	351	45	104	76	231	47	0	.	62	58	0	.	20	74	0	.	0	.	0	.	105	.	38	.	236	48	0	.	0	.	51	.	0	.	333	42	140	58	0	.	198	51	19	112	31	.	0	.	0	.	268	46	387	39	489	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	106	77	35	.	24	.	58	76	125	79	50	71	74	59	45	71	0	.	79	.	24	.	80	76	14	.	0	.	0	.	62	72	0	.	0	.	422	35	49	.	193	74	164	50	77	.	62	58	289	51	58	73	435	39	0	.	9544	8
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	54	.	286	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	136	45	127	58	354	41	156	40	934	24	340	34	79	56	336	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	.	0	.	0	.	0	.	86	53	77	66	46	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3059	12
	total	330	45	608	33	445	35	391	50	97	71	145	68	925	24	470	39	1470	18	331	43	77	75	119	49	292	49	0	.	270	49	270	51	249	48	332	40	10	100	420	34	366	35	292	45	1099	20	1310	20	1219	20	1026	28	549	35	582	33	368	34	109	45	165	56	20	74	21	73	117	.	257	51	209	63	228	62	294	43	258	45	154	40	101	62	138	60	425	38	219	44	37	79	229	46	79	80	429	34	484	37	105	60	431	43	387	39	551	41	170	47	408	28	263	36	675	29	286	32	1068	22	1156	19	360	32	585	29	40	114	192	57	118	56	271	44	232	47	195	36	142	53	134	73	302	44	74	59	99	63	0	.	209	58	204	66	115	61	176	60	88	80	0	.	69	65	1118	20	756	22	1499	20	214	51	275	57	172	48	463	41	190	61	289	51	95	59	435	39	0	.	32649	4
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	.	12	.	47	79	343	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	186	75	215	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	827	26
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	196	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	225	71	132	56	409	48	153	70	253	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	.	0	.	0	.	43	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	.	82	82	0	.	23	.	0	.	0	.	48	.	0	.	70	.	0	.	39	.	16	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	115	.	199	64	440	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2537	18
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	392	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	378	63	0	.	0	.	0	.	0	.	64	.	253	53	524	37	692	37	838	31	0	.	113	.	0	.	102	71	0	.	0	.	0	.	0	.	0	.	108	73	142	77	0	.	0	.	0	.	146	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	.	0	.	0	.	0	.	0	.	0	.	95	59	197	62	94	82	18	.	0	.	0	.	106	74	39	.	135	90	0	.	32	.	0	.	0	.	117	.	76	.	0	.	0	.	0	.	0	.	0	.	60	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	655	32	552	44	262	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6229	12
	lower and upper montane	119	71	0	.	34	.	318	58	134	76	366	60	787	31	508	42	321	55	1795	23	0	.	39	.	152	58	0	.	23	.	186	77	614	39	727	46	0	.	683	43	463	43	249	52	0	.	56	.	0	.	882	31	85	72	519	47	241	79	120	.	325	59	57	.	0	.	100	74	399	53	37	.	640	49	393	55	427	55	445	46	333	58	655	50	319	47	98	.	380	41	25	.	466	47	452	53	252	51	232	64	104	.	0	.	118	.	0	.	29	86	0	.	241	45	440	44	169	63	327	50	469	38	271	48	399	48	57	.	0	.	69	71	74	72	23	.	257	66	0	.	476	42	0	.	118	73	331	50	149	82	254	58	75	.	0	.	90	.	127	.	57	.	0	.	43	.	533	41	377	61	149	73	7	.	425	44	120	82	0	.	0	.	69	.	0	.	21903	7
	submontane	393	40	267	49	443	54	288	53	0	.	0	.	28	.	228	52	1423	25	28	.	182	71	167	58	85	.	0	.	134	77	314	68	0	.	77	.	0	.	0	.	37	.	2	.	0	.	0	.	0	.	243	62	243	51	124	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	.	164	77	103	72	0	.	0	.	0	.	787	30	211	56	51	71	58	.	276	53	0	.	0	.	0	.	272	63	138	81	377	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	149	.	0	.	80	.	0	.	87	.	80	.	0	.	145	73	36	.	0	.	250	59	0	.	149	59	167	78	353	63	99	71	126	89	0	.	0	.	0	.	0	.	22	.	36	.	0	.	401	43	51	.	233	60	293	55	165	74	446	46	0	.	10580	9
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	275	50	0	.	0	.	0	.	0	.	233	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	.	136	60	0	.	287	48	178	64	631	30	168	70	393	45	338	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	71	0	.	0	.	0	.	0	.	0	.	0	.	122	65	145	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3010	14
	total	512	34	267	49	477	51	606	39	134	76	366	60	1403	23	736	33	1744	22	1823	23	182	71	207	51	236	52	0	.	157	68	878	40	614	39	804	42	275	50	683	43	788	34	636	32	945	29	1181	26	1434	22	1124	28	442	41	643	41	343	60	120	.	325	59	57	.	0	.	100	74	508	44	179	65	767	43	557	45	531	46	635	39	333	58	655	50	1106	25	308	50	431	37	83	76	742	35	490	50	252	51	232	64	376	54	138	81	496	43	126	51	469	35	94	82	569	31	618	36	799	27	650	34	901	28	814	30	548	44	128	59	97	85	69	71	278	56	179	63	257	66	145	73	512	40	0	.	369	46	391	45	402	41	421	47	428	55	99	71	216	66	127	.	57	.	1078	27	1154	26	1258	26	413	56	149	73	409	43	475	41	352	49	293	55	165	74	515	42	0	.	45086	4
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	.	58	.	0	.	61	75	793	31	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	84	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1032	26
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	377	66	29	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	235	70	370	46	202	59	1032	35	749	36	1620	25	0	.	0	.	0	.	80	.	0	.	0	.	0	.	0	.	0	.	50	.	0	.	118	77	0	.	0	.	56	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	236	58	174	78	0	.	0	.	0	.	44	.	36	.	0	.	0	.	116	.	0	.	0	.	92	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	224	59	418	49	516	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6772	12
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	999	37	100	.	0	.	0	.	0	.	0	.	0	.	0	.	113	.	130	71	363	77	0	.	0	.	0	.	484	52	713	41	474	43	1584	28	1976	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	91	.	0	.	112	.	0	.	94	.	0	.	0	.	0	.	0	.	0	.	0	.	102	.	139	73	0	.	0	.	0	.	0	.	0	.	349	56	483	42	18	.	0	.	0	.	192	60	37	.	0	.	164	.	245	81	572	52	40	.	47	.	307	85	0	.	0	.	0	.	0	.	0	.	46	.	0	.	0	.	12	.	0	.	0	.	0	.	0	.	761	37	650	38	786	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12182	9
	lower and upper montane	297	53	58	.	193	68	249	66	1273	32	1402	35	2596	24	1482	27	378	52	2531	21	84	.	126	71	418	49	0	.	253	71	900	39	1525	28	542	54	0	.	1436	33	490	40	879	33	62	.	419	62	0	.	3720	17	149	71	103	.	755	38	142	73	242	63	289	60	78	.	404	45	347	52	256	71	569	47	354	50	707	42	1089	32	245	60	1942	28	492	42	366	52	809	30	52	.	742	36	307	53	752	49	980	38	499	47	0	.	55	.	0	.	0	.	0	.	257	46	102	71	118	74	263	65	186	71	531	44	104	.	126	.	86	.	236	73	45	.	955	38	140	75	50	.	590	40	145	.	226	68	828	35	112	71	243	60	0	.	340	70	71	.	168	82	55	.	0	.	0	.	668	41	347	58	471	52	95	.	530	42	699	39	0	.	142	.	0	.	0	.	42968	5
	submontane	854	31	504	39	504	40	450	44	0	.	0	.	246	72	320	64	2038	23	108	.	96	.	612	38	716	39	0	.	20	.	321	59	0	.	889	37	0	.	0	.	52	.	36	.	0	.	0	.	0	.	792	32	656	58	517	48	0	.	167	58	0	.	130	71	0	.	0	.	114	73	113	.	0	.	170	.	96	73	65	.	203	74	0	.	1472	26	166	73	554	44	535	39	533	37	95	.	0	.	232	76	413	56	849	40	762	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	116	.	0	.	209	71	46	.	103	.	142	80	0	.	0	.	159	72	152	59	0	.	0	.	323	58	198	60	329	51	0	.	0	.	71	.	0	.	0	.	0	.	140	71	192	80	462	42	0	.	494	43	596	46	707	41	110	.	0	.	20953	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	43	.	0	.	175	73	331	50	0	.	80	80	195	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	.	0	.	72	.	0	.	0	.	0	.	0	.	0	.	66	.	0	.	0	.	0	.	1173	26
	total	1151	26	562	36	697	34	699	37	1273	32	1402	35	4217	19	1930	24	2416	21	2640	21	180	71	738	34	1135	30	0	.	385	55	1351	30	1887	27	1432	31	72	.	1671	30	1396	26	1888	23	1568	27	2882	20	4389	14	4513	15	806	49	620	44	835	36	310	46	242	63	419	47	78	.	404	45	511	40	368	58	778	38	524	47	915	36	1209	29	543	42	1942	28	1964	22	532	43	1363	25	587	36	1276	26	504	43	891	43	1212	34	912	36	849	40	817	36	0	.	584	41	736	34	275	43	276	53	449	42	499	42	340	46	726	38	268	72	603	48	659	47	484	47	230	45	1365	34	281	55	50	.	590	40	305	61	378	47	874	34	112	71	567	42	211	57	669	43	71	.	168	82	126	71	1138	28	1068	30	2043	23	487	46	663	43	558	39	530	42	1193	29	662	42	849	38	110	.	0	.	85080	3
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	78	0	.	0	.	403	43	575	47	0	.	0	.	0	.	0	.	0	.	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	.	10	.	0	.	0	.	0	.	58	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	521	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1721	23
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	486	48	123	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	275	72	1221	37	0	.	1253	29	978	36	2382	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	.	0	.	0	.	95	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	203	67	0	.	0	.	0	.	0	.	0	.	202	59	305	54	156	71	0	.	0	.	0	.	147	71	27	.	0	.	0	.	0	.	0	.	238	79	89	71	264	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2204	22	716	44	599	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12042	11
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	2329	25	162	.	0	.	99	.	0	.	0	.	0	.	0	.	118	.	0	.	391	58	231	71	0	.	548	55	1977	26	1198	37	1974	27	3112	21	1070	34	0	.	0	.	0	.	11	.	0	.	0	.	0	.	0	.	0	.	87	.	477	58	278	58	0	.	113	.	75	100	0	.	170	92	0	.	0	.	0	.	0	.	0	.	168	71	439	51	77	.	0	.	0	.	0	.	339	51	344	56	783	40	52	.	0	.	0	.	90	.	17	.	226	79	0	.	386	58	291	71	457	62	780	44	499	51	0	.	0	.	66	.	0	.	0	.	0	.	91	82	91	.	61	.	0	.	0	.	0	.	0	.	2228	23	745	43	1536	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24185	7
	lower and upper montane	548	48	70	.	0	.	495	52	1206	37	3700	24	4606	19	7495	15	3113	22	3481	20	0	.	338	75	1187	36	0	.	101	.	827	47	1274	33	1402	38	0	.	1176	35	587	53	789	38	77	.	174	70	0	.	2269	23	1708	32	1950	30	2733	27	557	43	935	37	542	55	587	42	2055	31	457	53	96	.	1288	35	1169	38	651	47	1617	35	1720	29	3388	24	145	.	491	42	176	75	417	51	718	41	1781	30	963	35	1303	33	426	58	0	.	149	.	0	.	408	62	0	.	82	.	232	63	0	.	273	58	125	90	191	61	271	71	57	.	152	.	508	55	229	71	901	42	762	48	0	.	818	43	0	.	272	56	456	42	1244	31	581	38	604	41	0	.	607	51	424	63	366	58	0	.	0	.	481	48	579	45	683	51	267	74	1240	33	794	43	0	.	0	.	102	.	0	.	76648	4
	submontane	1334	29	813	40	1000	32	2492	23	0	.	0	.	688	59	735	47	5702	15	0	.	656	42	700	41	277	55	0	.	804	45	488	50	222	.	437	61	0	.	0	.	62	.	0	.	0	.	0	.	0	.	1142	33	607	44	1066	35	209	71	139	78	0	.	265	72	0	.	0	.	0	.	0	.	192	.	527	50	237	64	118	75	447	59	0	.	1111	35	1557	29	88	.	398	53	264	80	105	.	0	.	171	.	370	90	818	45	1171	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	163	.	0	.	197	75	173	88	93	.	1027	36	0	.	0	.	95	.	264	64	536	52	111	.	184	71	0	.	828	41	0	.	0	.	43	.	115	100	829	38	870	38	247	81	729	40	626	49	608	43	904	41	0	.	36056	5
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	177	82	0	.	0	.	0	.	0	.	181	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	124	71	0	.	33	.	354	69	248	46	142	77	102	.	65	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1498	27
	total	1882	25	884	37	1000	32	2987	21	1206	37	3700	24	8108	14	8515	14	8815	12	3581	20	656	42	1039	37	1464	31	0	.	1024	39	1314	35	1888	28	2071	30	177	82	1999	27	3950	19	1987	27	3303	19	4849	16	4026	22	3411	19	2315	26	3016	23	2953	25	696	38	935	37	807	44	587	42	2055	31	545	47	573	51	1837	29	1696	30	1001	36	1906	31	2167	26	3558	23	1256	33	2048	24	265	60	815	36	982	37	2257	25	1402	29	1550	31	796	52	818	45	1321	33	541	39	1231	30	949	35	168	61	585	48	248	46	709	34	271	57	482	46	271	71	443	52	444	58	1203	37	1261	33	1664	31	959	41	173	88	976	38	1027	36	272	56	456	42	1431	29	936	31	1200	31	111	.	790	42	424	63	1194	33	5025	15	1461	31	2660	21	694	41	1512	31	1137	34	1487	31	1523	29	626	49	608	43	1006	38	0	.	152150	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	.	11	.	0	.	0	.	258	39	1398	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	73	57	74	103	53	12	.	0	.	0	.	46	77	0	.	12	.	0	.	0	.	39	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	889	21	457	32	167	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3501	11
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	3028	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	250	56	962	36	642	34	773	31	1909	22	1138	25	0	.	0	.	0	.	13	.	0	.	0	.	0	.	0	.	0	.	74	.	226	55	164	58	0	.	45	.	332	52	0	.	23	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	374	41	335	45	270	55	0	.	0	.	0	.	280	47	64	88	0	.	46	.	64	.	76	90	0	.	65	62	33	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	.	0	.	0	.	0	.	0	.	1350	30	1562	24	1036	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15206	8
	high-montane	0	.	0	.	0	.	0	.	38	.	0	.	2249	23	224	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	71	193	64	92	.	0	.	114	63	513	40	721	42	1015	30	2971	18	565	41	0	.	65	.	0	.	422	41	0	.	0	.	0	.	0	.	0	.	0	.	729	37	303	52	0	.	0	.	258	54	97	95	221	71	0	.	0	.	0	.	0	.	0	.	98	75	46	95	39	74	0	.	0	.	0	.	380	47	297	62	514	40	58	87	0	.	0	.	373	39	100	.	42	.	142	73	45	.	503	45	21	.	297	53	281	64	0	.	0	.	60	.	0	.	0	.	0	.	0	.	46	.	163	60	0	.	0	.	0	.	0	.	1733	23	1891	23	878	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18940	7
	lower and upper montane	0	.	0	.	0	.	144	73	83	74	267	46	2310	20	5563	14	773	39	2447	21	0	.	0	.	37	.	0	.	0	.	280	60	1143	34	185	52	0	.	999	32	628	35	570	39	86	87	525	43	0	.	2865	18	579	42	305	41	1843	26	191	72	1125	25	265	52	782	35	1711	23	295	67	197	52	716	37	681	42	0	.	654	33	526	51	1156	38	0	.	218	49	405	52	78	.	150	58	689	40	90	77	194	58	530	45	0	.	0	.	0	.	562	41	0	.	388	63	485	38	116	50	614	29	397	51	300	43	356	47	0	.	131	81	448	47	216	80	850	41	179	71	0	.	1134	40	160	.	127	75	1280	22	235	72	699	39	202	61	23	.	73	74	77	86	0	.	0	.	0	.	1266	27	453	42	87	.	0	.	477	47	214	48	0	.	0	.	0	.	0	.	43834	5
	submontane	389	47	449	54	333	52	305	62	0	.	0	.	186	71	571	42	736	34	0	.	262	52	0	.	242	56	101	.	99	71	0	.	0	.	285	58	0	.	0	.	0	.	97	76	0	.	0	.	0	.	758	33	313	51	258	66	231	72	47	.	0	.	81	.	32	.	0	.	0	.	0	.	0	.	96	91	64	.	93	72	238	70	0	.	93	.	16	.	137	75	113	.	300	61	0	.	0	.	0	.	202	67	182	58	84	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	.	0	.	139	72	274	54	72	.	0	.	99	61	25	81	0	.	150	78	0	.	0	.	65	.	111	75	430	46	50	.	255	71	0	.	109	.	0	.	0	.	114	77	0	.	0	.	72	.	0	.	178	56	69	.	99	87	108	70	0	.	9951	9
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	.	0	.	207	58	93	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	.	47	.	48	.	173	54	78	.	152	69	518	37	755	31	587	39	364	39	559	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	159	46	114	64	17	133	0	.	0	.	0	.	0	.	0	.	94	.	0	.	0	.	0	.	3983	13
	total	389	47	449	54	333	52	449	48	121	60	267	46	7773	12	6357	13	1508	26	2447	21	262	52	0	.	279	50	101	.	99	71	422	46	1336	30	561	38	0	.	1364	26	2115	22	2049	21	1874	21	5871	12	3194	14	3623	16	957	31	563	38	2509	21	237	61	1125	25	346	46	814	34	1711	23	369	57	1152	27	1199	27	776	39	109	72	1338	24	862	38	1401	33	93	.	234	47	542	43	191	72	450	45	787	36	136	60	232	50	732	37	182	58	130	58	837	29	1424	24	965	28	609	45	1003	26	871	28	1900	18	923	29	914	29	685	36	109	72	887	31	744	35	650	38	1165	33	278	51	25	81	1194	38	310	64	127	75	1280	22	300	60	856	33	864	30	73	75	328	57	77	86	109	.	4131	15	4025	15	3477	15	453	42	87	.	72	.	477	47	393	36	163	71	99	87	108	70	0	.	95415	3
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	.	125	66	58	100	13	92	793	26	3438	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	66	206	38	163	42	59	65	0	.	0	.	133	52	7	.	12	.	0	.	0	.	39	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1831	18	766	24	189	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7889	8
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	4477	16	152	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	.	0	.	0	.	768	38	2963	21	1107	25	3684	16	4324	15	6130	15	0	.	0	.	0	.	94	87	0	.	0	.	0	.	0	.	0	.	275	52	237	52	459	37	0	.	91	71	553	38	16	.	23	.	0	.	0	.	0	.	0	.	0	.	208	65	0	.	0	.	0	.	0	.	0	.	624	32	1030	26	622	37	46	71	0	.	0	.	629	30	159	49	73	96	50	92	219	63	102	69	325	62	246	40	297	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	.	0	.	0	.	0	.	0	.	4320	16	2963	18	2747	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40155	5
	high-montane	0	.	0	.	0	.	0	.	38	.	0	.	6943	13	585	44	0	.	108	93	0	.	0	.	0	.	0	.	232	71	650	42	1005	38	323	58	0	.	697	45	3360	19	3108	20	4982	14	9762	10	4945	14	0	.	181	72	0	.	632	31	0	.	0	.	0	.	0	.	0	.	238	51	1430	28	676	36	0	.	231	69	502	41	191	69	395	56	0	.	0	.	0	.	0	.	0	.	422	42	811	33	161	58	0	.	0	.	0	.	974	27	1260	28	1983	22	168	47	0	.	0	.	925	26	211	55	446	50	382	54	707	43	1471	29	669	45	1404	29	1247	33	7	.	0	.	127	70	0	.	0	.	106	71	91	82	137	74	236	49	0	.	0	.	0	.	0	.	6158	12	4440	15	4265	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70025	3
	lower and upper montane	999	32	128	71	227	60	1273	29	2794	22	6041	17	10891	11	15872	9	5077	16	10613	10	84	100	529	51	1981	25	0	.	494	47	2322	25	4986	15	3052	23	0	.	4798	16	2348	20	2552	19	236	53	1404	27	0	.	10469	10	2995	21	3345	20	6014	16	1143	29	2793	18	1152	32	1499	25	4387	17	1595	26	644	37	3438	20	2681	22	2074	24	4084	18	2894	21	7413	15	1080	28	1252	25	1811	21	604	39	2135	21	3674	18	2574	21	2831	21	1737	26	0	.	411	51	0	.	1116	31	0	.	1259	26	1403	22	536	31	1992	18	1399	24	1500	22	1194	28	431	43	401	51	1303	30	675	38	2847	22	1481	30	59	86	3263	20	319	68	807	32	2933	16	1871	24	1973	21	953	29	518	50	928	37	796	42	488	46	15	.	53	83	3369	17	1977	23	1472	31	431	52	3221	18	1956	24	0	.	179	82	171	72	0	.	200716	2
	submontane	3544	15	2823	18	2775	18	4146	17	0	.	0	.	1200	39	2038	24	11263	9	185	64	1303	27	1577	24	1460	25	101	.	1225	32	1293	30	222	.	1948	24	0	.	24	.	168	54	225	52	0	.	0	.	0	.	3332	17	2040	26	2222	22	440	51	415	38	0	.	497	46	32	.	0	.	114	73	218	71	301	69	1253	30	501	39	277	46	956	36	0	.	3796	16	2118	23	830	34	1427	24	1393	27	231	63	0	.	403	61	1555	31	2375	23	2907	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	289	71	116	100	219	58	589	38	403	51	208	64	536	38	521	42	179	59	1419	29	447	39	0	.	414	40	890	33	1597	27	602	36	593	42	0	.	1076	34	0	.	0	.	602	30	341	48	1258	30	2056	22	396	56	1702	24	1906	24	1687	25	2193	22	0	.	89390	3
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	539	39	0	.	0	.	19	.	54	.	974	29	93	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	.	47	.	50	96	569	28	248	46	825	27	1396	25	2901	14	1237	24	1019	25	1528	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	71	0	.	0	.	38	.	0	.	0	.	0	.	508	30	396	32	161	51	0	.	0	.	0	.	0	.	0	.	170	67	0	.	0	.	0	.	12878	6
	total	4543	14	2952	17	3002	17	5418	14	2832	22	6041	17	23510	7	18647	8	16340	8	10906	10	1387	26	2107	22	3441	18	101	.	1951	25	4265	17	6285	14	5324	16	539	39	6288	14	8964	11	7070	12	8969	10	17257	7	14606	8	13802	8	5216	16	5567	15	7180	14	1558	23	2793	18	1648	26	1531	25	4387	17	2223	21	2530	20	4890	16	3934	18	2897	19	5416	15	4057	18	7831	14	4876	14	3370	17	2641	18	2031	20	3528	16	4536	16	3385	18	3395	19	3292	20	2375	23	3364	18	1687	20	4181	14	3016	17	2359	17	2799	17	3437	12	4916	11	2797	16	3560	15	1916	23	1474	27	2233	22	2885	20	2729	19	4599	17	2024	24	580	38	3569	19	1738	27	1254	25	3039	16	2480	20	3000	17	2855	18	1159	29	1520	28	796	42	1563	27	12832	8	8618	10	11333	9	2317	21	2730	21	2487	20	3617	17	3657	17	2077	23	1866	24	2363	21	0	.	421054	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. 2297010. Birmensdorf, Swiss Federal Research Institute WSL

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