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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NFI2
growing stock (stemwood)
development stage · altitudinal vegetation belts (NaiS; 6 classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
development stage	altitudinal vegetation belts (NaiS; 6 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	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	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	46	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	57
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	96	85	0	.	0	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	210	72	0	.	57	74	181	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	1	.	119	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	784	33
	lower and upper montane	0	.	0	.	0	.	0	.	0	.	32	100	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	.	0	.	2	.	0	.	24	.	0	.	0	.	0	.	37	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	37	.	0	.	0	.	0	.	0	.	0	.	54	.	0	.	0	.	0	.	0	.	0	.	20	73	0	.	46	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	.	0	.	0	.	0	.	0	.	0	.	11	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	.	0	.	0	.	0	.	308	33
	submontane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	73
	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	.	14	.	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	.	0	.	0	.	0	.	27	70
	total	0	.	0	.	0	.	0	.	0	.	32	100	96	85	0	.	19	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	210	72	2	.	57	74	204	64	17	100	0	.	32	100	37	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	113	75	0	.	0	.	0	0	0	0	0	.	54	100	0	.	0	.	0	.	0	.	0	.	20	73	0	.	46	.	0	.	0	.	0	.	0	.	0	.	14	.	13	.	0	.	24	.	0	.	0	.	0	.	0	.	0	.	11	74	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	8	92	165	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1240	23
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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	7	100	21	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-8	100	30	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	55
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	69	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	65	0	.	69	77	54	58	128	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	58	6	100	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	462	29
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	150	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	202	48	45	74	133	51	385	36	113	64	0	.	0	.	0	.	51	.	0	.	0	.	0	.	0	.	0	.	0	.	19	68	58	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	72	0	.	0	.	0	.	0	.	43	79	26	.	0	.	0	.	0	.	0	.	3	63	18	100	0	.	19	.	2	.	24	56	1	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	89	61	64	71	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1539	16
	lower and upper montane	0	.	0	.	0	.	0	.	8	.	35	98	99	60	35	64	149	67	84	66	0	.	1	.	71	.	0	.	49	.	0	.	12	100	5	.	0	.	117	57	106	85	0	.	57	.	14	96	0	.	27	62	7	88	110	80	123	79	42	100	-1	.	0	.	0	.	0	.	0	.	163	76	30	72	0	.	38	84	79	95	20	100	83	71	0	.	18	71	9	86	0	.	43	.	18	89	38	79	3	.	2	.	0	.	1	.	0	.	0	.	0	.	14	100	0	.	3	.	45	55	85	88	0	.	1	83	33	98	0	.	53	71	0	.	38	72	30	100	0	.	17	78	0	.	14	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	.	1	.	36	89	17	59	1	100	0	.	61	74	0	.	0	.	0	.	0	.	0	.	2183	15
	submontane	53	97	63	61	116	49	1	100	0	.	0	.	0	.	17	.	126	49	0	.	0	.	1	100	0	.	0	.	14	.	19	.	0	.	10	100	0	.	30	.	28	.	0	.	0	.	0	.	0	.	84	85	0	.	14	71	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	102	69	33	.	0	.	34	.	0	.	16	100	0	.	0	.	19	86	17	.	0	.	0	.	0	.	37	89	29	92	46	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	2	.	0	.	9	.	0	.	85	100	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	61	73	0	.	0	.	27	90	8	100	76	71	0	.	1229	17
	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	.	16	72	1	.	0	.	0	.	32	.	0	.	7	100	18	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	15	.	4	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	44
	total	53	97	63	61	116	49	1	100	8	.	35	98	318	39	51	54	275	42	84	66	0	.	2	72	71	.	0	.	63	81	19	.	12	100	14	74	0	.	147	49	344	39	45	74	259	40	480	30	262	46	111	66	7	88	124	71	175	63	56	79	-1	.	0	.	0	.	0	.	0	.	182	68	103	62	102	69	71	65	87	87	55	73	83	71	16	100	18	71	9	86	19	86	60	77	18	89	98	54	3	.	39	86	29	92	47	72	43	79	42	67	1	100	14	100	0	.	36	92	68	47	110	71	18	85	20	95	35	93	35	49	54	69	36	100	38	72	65	71	0	.	17	78	0	.	15	84	0	.	9	.	0	.	85	100	0	.	0	.	0	.	0	.	109	51	113	42	130	41	17	59	1	100	61	73	61	74	0	.	27	90	8	100	76	71	0	.	5578	9
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	.	38	.	6	.	6	.	346	30	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	135	47	17	66	43	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	592	22
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	109	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	74	72	62	67	33	81	265	54	133	39	0	.	0	.	0	.	1	188	0	.	0	.	0	.	0	.	0	.	54	.	78	91	126	71	0	.	36	.	26	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	.	121	61	2	.	7	.	0	.	0	.	43	65	19	.	29	.	0	.	0	.	0	.	32	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	123	51	154	59	59	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1600	17
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	393	42	43	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	96	80	.	0	.	0	.	0	.	242	68	89	84	252	33	879	25	325	34	0	.	51	.	0	.	228	56	0	.	0	.	0	.	0	.	0	.	36	.	249	71	0	.	0	.	0	.	14	.	3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	137	81	0	.	0	.	0	.	0	.	109	42	80	74	110	72	2	.	0	.	0	.	249	54	45	88	29	76	0	.	32	75	97	.	15	.	63	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	616	24	441	29	188	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5134	10
	lower and upper montane	38	.	0	.	54	75	179	75	39	72	117	64	597	27	803	34	256	51	974	27	0	.	25	.	116	74	0	.	129	72	271	48	407	43	302	44	0	.	643	44	138	48	51	.	37	83	408	42	0	.	915	30	188	62	268	48	426	43	53	.	91	83	59	.	0	.	171	74	326	38	108	70	287	46	475	42	317	42	424	39	137	86	388	41	220	59	40	.	149	47	46	79	95	76	240	53	297	45	219	52	316	50	0	.	58	.	0	.	117	44	0	.	643	45	266	43	189	44	467	34	513	39	491	30	363	41	203	49	62	.	29	.	30	61	21	71	67	74	0	.	354	43	0	.	46	.	261	40	51	60	56	.	0	.	52	71	124	72	0	.	59	.	0	.	25	.	384	38	312	46	65	83	0	.	174	54	157	59	0	.	0	.	26	.	0	.	17499	6
	submontane	376	33	364	35	518	39	510	35	0	.	0	.	5	.	94	73	1886	21	34	.	177	72	252	50	114	66	0	.	86	73	232	61	0	.	40	81	0	.	0	.	57	.	4	.	0	.	0	.	0	.	395	39	201	55	214	55	0	.	65	72	0	.	131	66	0	.	0	.	0	.	134	80	0	.	112	69	100	84	46	.	203	60	51	.	380	41	81	59	85	72	161	57	81	.	6	.	0	.	0	.	300	45	275	45	328	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	98	71	0	.	68	.	0	.	67	72	0	.	43	.	145	64	43	76	79	63	53	92	0	.	123	71	192	71	352	45	70	76	126	97	0	.	62	.	0	.	0	.	189	40	226	66	106	65	241	55	109	79	144	58	267	42	26	.	273	53	0	.	11199	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	269	51	0	.	0	.	0	.	22	.	532	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	94	51	16	86	317	36	362	32	1268	19	438	46	328	38	499	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	.	0	.	0	.	33	.	0	.	0	.	0	.	301	37	155	39	88	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4763	9
	total	414	32	364	35	572	36	689	32	39	72	117	64	1104	22	940	30	2142	19	1007	26	177	72	277	46	230	49	0	.	214	52	535	36	489	39	342	40	269	51	643	44	510	38	243	44	349	27	2090	17	804	20	1309	24	440	38	482	36	656	34	118	60	91	83	190	55	0	.	171	74	416	34	569	40	413	39	587	36	453	35	510	34	343	49	438	38	599	34	122	51	234	40	207	48	176	62	246	52	434	40	219	52	615	34	275	45	386	52	126	37	412	29	129	63	970	32	629	26	1456	17	1198	24	904	26	1048	20	461	36	235	43	227	59	77	60	159	51	28	59	110	60	145	64	397	39	79	63	100	68	261	40	207	47	248	60	352	45	155	47	250	60	0	.	120	71	1175	17	792	21	952	22	538	38	171	51	241	55	283	45	302	41	267	42	26	.	299	49	0	.	40786	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	.	0	.	58	59	527	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	268	49	28	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	882	24
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	904	28	26	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	.	167	51	415	44	0	.	591	36	1157	29	0	.	0	.	0	.	37	.	0	.	0	.	0	.	0	.	0	.	0	.	19	.	142	75	0	.	0	.	72	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	.	39	.	0	.	0	.	28	.	0	.	0	.	47	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	372	50	260	50	439	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4904	13
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	1432	35	273	58	0	.	0	.	0	.	0	.	0	.	0	.	215	71	339	62	468	59	0	.	0	.	39	.	501	51	389	50	232	57	1561	24	1737	22	0	.	94	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	0	.	225	71	0	.	0	.	0	.	109	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	106	61	92	71	36	.	0	.	0	.	0	.	0	.	121	75	72	.	0	.	0	.	0	.	54	.	0	.	110	.	71	76	199	.	604	48	0	.	16	.	83	.	0	.	0	.	92	77	0	.	0	.	0	.	0	.	0	.	156	66	0	.	0	.	0	.	0	.	918	30	412	39	515	40	74	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11367	9
	lower and upper montane	398	42	0	.	119	71	255	53	779	37	960	39	1623	24	1793	24	589	43	3189	17	0	.	0	.	96	71	0	.	100	.	476	53	1927	28	393	58	0	.	1133	40	392	40	543	40	38	.	89	.	0	.	3047	19	351	49	182	72	832	36	95	74	292	51	261	60	66	73	135	53	591	42	241	71	861	34	519	39	417	51	328	47	159	60	770	48	215	58	549	39	685	36	33	.	1223	28	576	42	691	49	193	56	188	72	0	.	39	.	0	.	0	.	0	.	154	71	94	58	0	.	265	44	256	52	423	53	112	71	0	.	81	.	277	46	37	.	843	40	432	46	209	.	822	32	113	.	144	75	490	35	84	58	404	39	184	67	140	58	155	.	56	.	0	.	0	.	0	.	796	29	131	75	166	72	0	.	826	33	403	48	0	.	145	.	0	.	0	.	36674	5
	submontane	1229	27	626	35	595	40	478	43	0	.	0	.	381	74	491	43	1837	24	0	.	209	62	610	39	493	46	53	.	323	55	201	64	0	.	457	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	728	39	500	52	345	52	0	.	49	.	0	.	92	.	57	.	0	.	80	.	0	.	185	73	497	54	295	71	108	.	65	.	0	.	801	33	174	65	222	54	335	47	611	38	86	.	0	.	167	71	284	71	298	52	447	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	.	191	58	0	.	0	.	53	.	15	.	0	.	226	63	266	52	0	.	40	89	129	62	367	43	389	44	0	.	0	.	170	74	0	.	0	.	22	.	97	.	186	80	215	58	100	.	233	59	138	71	272	60	175	75	0	.	17727	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	72	0	.	0	.	0	.	37	.	142	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	.	86	78	61	.	448	38	80	.	93	62	99	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	.	0	.	0	.	0	.	0	.	0	.	0	.	101	71	8	.	56	.	0	.	0	.	0	.	0	.	0	.	95	.	0	.	0	.	0	.	1542	20
	total	1626	22	626	35	715	35	733	33	779	37	960	39	4339	17	2583	20	2425	21	3189	17	209	62	610	39	589	40	53	.	639	40	1016	35	2395	25	850	35	150	72	1312	36	1059	29	1347	26	306	46	2442	18	3421	16	3775	17	945	35	526	42	889	34	144	59	292	51	352	51	123	61	135	53	671	39	485	48	1189	28	1016	33	712	42	618	37	224	52	771	48	1016	29	723	34	907	30	367	44	1834	22	769	34	783	44	396	41	472	51	298	52	486	44	0	.	121	75	117	73	240	53	154	53	448	38	447	36	387	39	632	42	182	52	227	89	720	42	468	36	100	62	926	38	485	43	225	93	914	30	339	53	410	43	490	35	166	44	534	33	707	32	529	36	155	.	56	.	170	74	1660	22	708	29	1828	21	303	52	352	54	215	58	926	32	635	37	233	59	417	52	175	75	0	.	73095	3
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	.	31	.	0	.	0	.	96	81	786	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	.	0	.	0	.	0	.	0	.	0	.	48	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	294	37	156	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1411	20
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	960	33	59	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	238	76	493	41	152	65	958	31	1402	26	1797	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	.	0	.	0	.	41	.	144	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	182	62	0	.	0	.	0	.	0	.	0	.	135	72	121	71	0	.	0	.	0	.	0	.	36	.	21	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	.	0	.	0	.	0	.	0	.	744	32	1028	37	567	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9209	10
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	2120	27	136	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	317	56	57	.	0	.	1	.	443	53	656	42	1254	31	2678	21	974	30	0	.	0	.	0	.	335	55	0	.	0	.	0	.	0	.	0	.	0	.	447	47	361	46	0	.	97	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	.	388	53	75	.	0	.	0	.	0	.	245	68	207	56	426	44	0	.	0	.	0	.	50	.	90	.	0	.	132	.	0	.	361	51	256	82	311	70	491	52	0	.	0	.	40	.	0	.	0	.	79	74	57	.	0	.	50	.	0	.	0	.	0	.	0	.	1266	27	1192	28	967	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16638	8
	lower and upper montane	494	51	48	.	0	.	673	43	577	46	1676	29	3513	22	4577	17	2251	25	3305	19	46	.	335	58	1066	34	0	.	242	75	412	48	1302	32	1340	34	0	.	969	37	170	61	942	32	0	.	175	76	0	.	3669	18	939	40	865	37	2344	25	196	58	1024	30	459	57	213	50	1797	26	676	46	446	59	967	48	1204	38	773	49	1140	34	512	44	2994	24	578	43	385	45	373	50	474	46	532	41	1132	37	1350	36	486	48	621	41	0	.	0	.	0	.	72	.	0	.	32	.	144	64	0	.	78	.	161	60	159	60	269	73	0	.	0	.	97	.	122	.	605	45	500	56	0	.	710	38	143	.	546	44	688	33	591	39	626	40	447	39	116	.	340	65	478	47	930	45	0	.	0	.	575	41	817	38	355	75	486	46	1141	34	961	33	0	.	0	.	110	.	0	.	62564	4
	submontane	1179	27	813	38	689	39	1881	26	0	.	0	.	797	43	650	43	4556	17	81	.	608	40	440	49	767	37	0	.	850	42	640	48	254	.	915	39	0	.	0	.	229	73	133	71	0	.	0	.	0	.	1402	27	695	54	982	39	119	73	166	51	0	.	208	76	0	.	0	.	73	.	0	.	142	.	773	42	363	66	105	.	311	74	0	.	1098	31	1149	33	371	54	782	41	471	44	66	.	0	.	0	.	802	44	954	38	868	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	163	.	0	.	155	72	102	76	50	.	596	52	289	63	162	72	784	40	77	.	0	.	217	71	332	56	425	52	58	.	158	71	0	.	1010	41	0	.	0	.	0	.	105	.	480	42	1457	31	324	61	946	41	871	37	890	36	575	46	0	.	37606	5
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	46	.	0	.	0	.	0	.	0	.	116	.	75	.	11	.	108	74	227	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	95	76	62	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	865	30
	total	1673	24	861	36	689	39	2554	22	577	46	1676	29	7390	14	5422	16	6807	14	3386	19	654	38	775	37	1833	25	0	.	1093	37	1052	35	1872	27	2312	25	51	.	1208	33	1366	27	1883	23	2213	22	4352	16	3558	15	5071	15	1634	32	1847	27	2798	22	362	39	1024	30	667	46	213	50	1797	26	749	43	957	36	1469	35	1977	28	1274	36	1388	29	824	39	2994	24	1677	25	1535	27	744	37	1256	31	1003	30	1456	31	1738	30	561	44	1424	30	954	38	914	33	380	51	401	40	426	44	32	.	260	57	75	.	222	50	381	41	386	45	402	59	163	.	361	51	509	50	535	49	1146	33	1096	38	289	63	912	33	1002	35	623	40	767	31	866	33	958	33	987	30	174	75	497	49	478	47	1940	30	2399	18	2438	21	2109	21	921	36	835	40	1942	26	1465	29	1907	26	871	37	890	36	685	42	0	.	128293	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	.	56	.	0	.	0	.	271	46	420	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	153	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	925	26
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	349	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	81	787	37	0	.	734	37	566	39	711	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	.	50	.	0	.	0	.	171	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	.	179	58	96	.	0	.	0	.	0	.	77	.	0	.	0	.	0	.	108	.	0	.	203	76	32	.	305	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1360	27	174	79	374	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6438	12
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	1846	32	122	.	0	.	74	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	132	.	0	.	474	53	1402	31	1055	38	1309	30	2313	24	962	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	.	126	.	0	.	0	.	117	66	78	.	159	77	0	.	0	.	0	.	0	.	0	.	58	.	0	.	0	.	0	.	0	.	0	.	380	45	168	72	294	54	0	.	0	.	0	.	71	71	0	.	171	90	65	.	333	59	219	73	196	60	253	59	74	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	.	0	.	0	.	0	.	0	.	0	.	1306	29	273	59	1078	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15274	9
	lower and upper montane	45	.	56	.	0	.	141	.	371	59	1490	34	3086	22	6204	16	2051	25	1568	31	160	.	82	.	332	64	0	.	186	.	476	52	529	57	558	48	0	.	816	39	420	56	490	45	80	.	103	71	0	.	872	34	1521	34	1774	28	1621	33	374	63	658	38	303	61	390	58	1266	30	76	.	0	.	544	55	870	43	241	.	1022	40	787	43	2841	26	0	.	206	67	286	59	122	.	253	61	497	54	743	41	1168	37	365	59	0	.	258	71	0	.	379	59	0	.	109	71	61	.	0	.	375	43	48	.	63	.	206	76	151	.	131	.	374	59	120	.	139	75	709	53	96	.	230	82	83	.	453	51	55	71	716	42	661	41	285	59	153	.	403	71	152	.	75	.	0	.	0	.	222	54	388	77	581	45	176	74	990	37	193	71	0	.	0	.	0	.	0	.	47081	5
	submontane	1387	29	1242	31	493	46	1971	28	0	.	0	.	0	.	702	56	5461	15	0	.	710	41	187	76	89	81	0	.	582	53	363	50	0	.	399	62	0	.	0	.	0	.	251	64	0	.	0	.	0	.	851	37	916	38	844	42	268	72	101	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	344	77	0	.	747	36	980	46	0	.	433	52	234	75	0	.	0	.	160	.	569	54	724	47	957	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	91	.	0	.	0	.	0	.	155	.	0	.	214	71	0	.	0	.	405	54	0	.	0	.	62	.	100	.	32	.	0	.	107	.	0	.	265	61	0	.	0	.	65	.	0	.	199	71	497	54	0	.	798	41	535	46	865	38	953	38	0	.	27309	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	89	.	0	.	0	.	0	.	0	.	129	.	15	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	210	61	0	.	0	.	158	.	292	41	260	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	165	76	0	.	0	.	0	.	1318	26
	total	1432	28	1298	30	493	46	2112	27	371	59	1490	34	5281	18	7028	15	7511	13	1642	29	870	38	269	61	422	54	0	.	768	47	839	37	529	57	1089	35	89	.	1372	30	2665	21	1796	27	2123	22	3383	18	2107	22	1723	25	2437	26	2618	23	1889	30	475	54	658	38	303	61	390	58	1266	30	76	.	121	74	720	46	870	43	241	.	1309	33	1210	36	3000	25	747	36	1186	39	286	59	555	46	487	48	555	49	743	41	1328	35	933	40	724	47	1215	31	420	42	961	32	390	48	109	71	220	77	292	41	783	30	48	.	234	71	362	53	593	46	350	59	773	38	560	44	518	44	924	44	96	.	230	82	487	48	453	51	55	71	779	39	847	35	317	54	153	.	510	60	152	.	340	53	2820	19	446	47	1740	25	388	77	780	38	673	45	990	37	991	35	699	39	865	38	953	38	0	.	98345	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	.	21	.	0	.	0	.	106	54	545	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	52	63	61	0	.	0	.	0	.	30	.	0	.	0	.	0	.	0	.	28	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	469	31	240	36	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1612	15
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	997	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	85	434	41	205	44	808	32	636	33	655	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	110	71	0	.	50	.	0	.	0	.	31	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	226	45	340	41	253	48	12	.	0	.	0	.	154	56	29	.	0	.	48	.	50	.	33	.	3	.	83	64	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	778	36	995	27	593	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7573	10
	high-montane	0	.	0	.	0	.	0	.	9	.	0	.	1100	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	127	62	102	82	36	.	0	.	20	139	304	45	409	37	755	38	963	29	223	44	0	.	0	.	0	.	168	60	0	.	0	.	0	.	0	.	0	.	196	58	64	84	112	77	0	.	100	.	277	53	83	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	128	59	45	.	0	.	0	.	0	.	71	77	301	52	552	39	96	61	0	.	0	.	222	45	33	.	123	.	0	.	0	.	134	82	0	.	173	52	185	57	0	.	0	.	39	.	0	.	0	.	0	.	0	.	0	.	32	.	0	.	0	.	0	.	0	.	1218	29	864	28	717	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9983	9
	lower and upper montane	0	.	0	.	0	.	77	.	452	49	888	41	1814	27	3898	16	460	46	945	27	0	.	61	.	154	77	0	.	0	.	529	55	1271	30	355	64	0	.	483	38	200	43	187	49	32	.	89	78	0	.	1441	24	199	75	383	57	1129	34	207	51	369	42	82	.	499	38	463	45	90	.	154	65	144	60	118	.	133	.	855	31	655	37	613	44	0	.	66	.	335	45	0	.	0	.	511	41	289	50	219	56	111	77	0	.	0	.	0	.	234	54	0	.	47	60	458	41	137	48	193	44	66	.	26	91	39	.	0	.	0	.	243	57	284	59	562	41	60	.	0	.	188	51	0	.	0	.	693	29	14	.	208	65	40	.	0	.	0	.	11	.	0	.	0	.	0	.	391	47	293	53	63	.	0	.	282	64	138	57	0	.	0	.	0	.	0	.	25625	6
	submontane	0	.	362	59	176	59	0	.	0	.	0	.	72	.	382	55	784	36	92	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	.	0	.	0	.	11	.	0	.	0	.	0	.	0	.	226	61	45	.	246	72	0	.	0	.	0	.	47	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	127	.	0	.	0	.	0	.	0	.	0	.	36	.	0	.	0	.	0	.	0	.	118	.	50	81	55	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	.	49	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	173	78	0	.	38	.	0	.	0	.	220	53	0	.	0	.	110	80	0	.	52	73	127	74	0	.	3	.	0	.	3783	15
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	.	0	.	0	.	23	.	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	.	33	.	86	78	104	.	85	83	394	39	305	43	124	62	15	.	571	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	.	31	95	19	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1871	18
	total	0	.	362	59	176	59	77	.	461	48	888	41	3983	17	4280	16	1244	28	1037	25	0	.	61	.	154	77	0	.	0	.	656	46	1374	28	498	51	0	.	546	35	988	24	801	25	1595	24	1817	20	1471	18	1667	22	244	64	629	45	1297	31	207	51	369	42	130	73	499	38	463	45	396	42	219	52	320	41	118	.	232	71	1290	25	738	35	613	44	0	.	66	.	335	45	36	.	0	.	511	41	417	39	264	49	229	64	50	81	55	.	329	36	1048	24	973	28	240	40	852	28	442	33	723	24	143	56	720	35	87	71	50	.	270	52	295	49	540	37	751	33	60	.	0	.	227	46	0	.	0	.	693	29	14	.	208	65	72	71	0	.	173	78	11	.	38	.	2479	19	2131	18	1949	18	293	53	63	.	110	80	282	64	190	46	127	74	0	.	3	.	0	.	50446	4
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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	62	38	.	6	.	545	30	2644	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	46	64	61	0	.	0	.	0	.	78	72	0	.	0	.	0	.	0	.	28	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1311	19	472	31	52	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5473	9
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	3388	17	85	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	503	48	1963	21	834	27	2602	18	3514	15	4600	13	0	.	0	.	0	.	39	97	0	.	0	.	0	.	0	.	0	.	164	58	203	52	383	41	0	.	77	71	443	40	0	.	1	.	0	.	0	.	0	.	0	.	0	.	182	62	0	.	0	.	0	.	0	.	0	.	412	35	761	27	352	44	19	73	0	.	0	.	378	35	108	52	29	.	48	.	186	66	44	79	238	66	162	48	309	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	.	0	.	0	.	0	.	0	.	3446	16	2623	19	2092	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30256	5
	high-montane	0	.	0	.	0	.	0	.	9	.	0	.	7137	14	573	39	0	.	82	91	0	.	0	.	0	.	0	.	215	71	498	45	967	36	224	66	0	.	535	48	3303	19	2643	21	3992	16	8960	10	4334	14	0	.	145	74	0	.	803	31	0	.	0	.	0	.	0	.	0	.	232	52	1159	29	657	35	0	.	197	71	525	38	164	69	159	77	0	.	0	.	0	.	0	.	0	.	240	48	805	32	156	61	0	.	0	.	0	.	848	29	903	28	1455	24	98	60	0	.	0	.	649	29	186	57	434	52	287	55	566	50	1439	28	502	48	851	34	840	35	0	.	0	.	171	53	0	.	0	.	79	74	57	.	86	.	238	50	0	.	0	.	0	.	0	.	5337	13	3245	15	3654	15	74	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60719	4
	lower and upper montane	974	31	104	71	173	54	1324	29	2226	22	5197	17	10731	11	17310	8	5756	15	10064	10	206	81	504	44	1835	25	0	.	707	42	2164	24	5448	15	2953	21	0	.	4169	17	1424	23	2213	20	244	47	902	28	0	.	9972	10	3205	21	3618	18	6475	15	967	31	2433	18	1164	32	1169	27	3833	17	1773	24	1150	31	2833	23	3185	21	1919	28	3848	17	2270	22	7743	15	1013	30	1265	25	1836	21	674	38	2147	20	2994	20	3407	20	2333	23	1602	25	0	.	357	55	0	.	802	34	0	.	1000	32	1023	24	353	31	1423	19	1129	25	1162	25	990	31	388	47	285	58	1073	29	594	41	2208	23	1798	29	306	75	2321	19	339	59	1203	29	2187	16	1456	26	1956	22	957	28	461	46	1022	39	697	40	1064	41	38	.	26	95	2404	17	1957	25	1231	33	661	39	3474	18	1852	22	0	.	145	100	136	83	0	.	191934	2
	submontane	4225	14	3470	17	2587	18	4841	16	0	.	0	.	1255	36	2337	24	14669	9	207	53	1704	25	1489	25	1463	26	53	.	1855	28	1455	28	254	.	1927	25	0	.	30	.	325	55	389	48	0	.	0	.	0	.	3686	16	2388	24	2646	22	387	54	395	37	0	.	478	43	57	.	0	.	153	71	134	80	327	60	1484	29	791	42	386	52	959	39	51	.	3042	17	2385	25	677	35	1765	25	1413	26	158	69	0	.	327	61	2110	25	2330	23	2700	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	189	61	163	.	178	60	396	41	324	55	50	.	940	38	449	45	205	59	1493	27	398	42	0	.	452	42	754	35	1260	26	516	36	564	42	0	.	1545	30	0	.	0	.	501	31	427	48	971	30	2581	22	534	45	2173	24	1965	22	2060	24	2054	24	0	.	98904	3
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	560	36	0	.	18	.	0	.	59	73	826	27	63	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	.	39	86	406	38	180	64	501	30	1092	25	2420	14	913	30	549	29	1414	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	.	0	.	0	.	75	71	0	.	0	.	33	.	0	.	0	.	0	.	513	30	270	34	167	47	0	.	0	.	0	.	0	.	0	.	259	61	0	.	0	.	0	.	10479	6
	total	5199	13	3574	17	2760	18	6165	14	2235	22	5197	17	22512	8	20305	8	20425	7	10353	10	1910	24	1993	22	3298	18	53	.	2776	22	4116	17	6672	14	5104	16	560	36	5236	15	7141	11	6117	12	6903	12	14768	8	11641	8	13658	8	5738	16	6264	14	7704	13	1361	24	2433	18	1642	26	1226	26	3833	17	2321	20	2646	19	4214	17	4668	17	2984	21	5202	14	3393	19	7953	14	4055	15	3649	18	2513	18	2440	21	3560	16	3575	18	4213	17	2817	20	3713	18	2330	23	3103	18	1299	22	2985	15	2050	19	1617	22	2115	17	2773	13	3441	13	1972	17	3038	16	1514	24	1303	30	1975	23	2209	20	1931	22	3408	18	2738	23	755	41	2697	18	1907	24	1601	24	2266	16	2041	21	2795	18	2520	17	1011	28	1586	29	697	40	2609	24	10645	9	6637	11	8871	9	2459	22	2202	22	3242	19	4008	17	4025	17	2225	21	2205	23	2191	23	0	.	397785	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. 2298830. Birmensdorf, Swiss Federal Research Institute WSL

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