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 NFI4/NFI5 #2282
Forest that was less than two-thirds covered with shrubs in both NFI4 (2009-2017) and NFI5 (2018-2026) and is accessible on foot.

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

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI5
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 NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	.	158	.	10	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	119	.	0	.	0	.	34	86	70	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	414	50
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	2	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	0	.	0	.	81	85	214	71	127	65	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	81	0	.	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	.	8	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	592	34
	lower and upper montane	0	.	0	.	0	.	0	.	0	.	36	.	34	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	97	.	0	.	0	.	11	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	170	.	0	.	16	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	375	54
	submontane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	.	0	.	0	.	0	.	0	.	0	.	0	.	34	.	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	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	115	.	25	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	.	0	.	0	.	0	.	0	.	52	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	384	42
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	36	.	194	83	10	.	73	100	0	.	0	.	0	.	0	.	0	.	0	.	34	.	0	.	0	.	0	.	19	90	119	100	0	.	81	85	248	62	197	48	17	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	.	0	.	0	.	111	88	0	.	0	.	11	.	0	.	0	.	0	.	115	.	25	73	0	.	0	.	59	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	.	170	.	0	.	16	.	0	.	52	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	73	8	.	10	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	1766	22
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	.	1	.	0	.	0	.	9	.	34	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	.	0	.	0	.	0	.	0	.	39	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	88	55
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	264	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	115	99	0	.	94	.	227	61	176	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	29	88	11	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	947	34
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	368	50	92	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	165	62	0	.	0	.	0	.	250	68	0	.	350	98	694	47	214	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	7	.	0	.	0	.	0	.	28	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	.	0	.	0	.	0	.	0	.	4	77	0	.	0	.	4	75	0	.	0	.	0	.	0	.	0	.	37	.	0	.	0	.	0	.	91	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	57	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2469	24
	lower and upper montane	0	.	0	.	0	.	0	.	0	.	133	72	499	67	344	47	2	.	0	.	0	.	0	.	0	.	0	.	0	.	43	.	113	97	0	.	0	.	46	100	155	71	19	.	17	100	0	.	0	.	35	73	25	.	191	.	198	68	0	.	0	.	0	.	49	.	0	.	0	.	0	.	14	77	52	.	0	.	26	100	4	.	45	.	66	.	0	.	23	100	1	.	0	.	14	.	74	77	67	.	0	.	0	.	117	.	0	.	51	.	0	.	1	.	4	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	0	.	0	.	0	.	-12	.	0	.	0	.	0	.	148	77	82	100	0	.	0	.	26	.	0	.	0	.	10	.	0	.	0	.	0	.	0	.	78	71	38	.	6	.	186	61	0	.	0	.	12	.	0	.	0	.	3025	18
	submontane	154	72	57	71	72	.	28	56	0	.	0	.	183	79	8	.	328	62	73	.	46	.	9	.	19	.	0	.	0	.	31	100	0	.	101	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	77	0	.	130	70	0	.	0	.	0	.	52	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	10	.	0	.	13	.	0	.	33	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	81	65	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	174	72	0	.	4	.	0	.	0	.	1771	21
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	60
	total	154	72	57	71	72	.	28	56	0	.	133	72	1314	34	443	42	330	61	73	100	46	.	9	.	19	.	0	.	0	.	73	72	278	54	101	.	24	71	46	100	521	44	19	100	461	77	930	38	423	55	98	56	25	100	321	66	198	68	0	.	0	.	52	.	52	95	0	.	0	.	30	81	14	77	52	.	19	100	55	70	14	77	45	.	80	85	0	.	55	72	1	.	0	.	14	.	146	63	67	.	0	.	0	.	117	.	4	77	51	100	0	.	45	87	31	88	0	.	0	.	0	.	0	.	63	72	0	.	6	100	0	.	91	82	0	.	-12	.	0	.	0	.	99	81	213	62	82	100	0	.	0	.	26	100	0	.	0	.	10	.	0	.	34	100	92	53	11	91	78	71	38	.	6	.	186	61	174	72	0	.	16	78	0	.	0	.	8352	11
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	.	38	71	0	.	10	.	89	52	227	38	0	.	0	.	0	.	0	.	0	.	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	71	10	.	0	.	0	.	0	.	17	103	25	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	563	23
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	38	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	73	12	.	93	64	169	67	626	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	.	0	.	0	.	0	.	0	.	0	.	3	.	46	.	65	.	0	.	0	.	0	.	175	73	0	.	37	.	0	.	0	.	39	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	394	48	77	98	35	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1994	21
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	801	37	0	.	0	.	17	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	239	68	97	.	643	31	894	32	184	47	0	.	0	.	0	.	160	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	.	0	.	48	.	24	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	.	0	.	0	.	0	.	0	.	184	50	164	73	5	.	1	.	0	.	0	.	23	71	2	.	0	.	27	.	0	.	299	76	47	.	181	75	152	.	0	.	0	.	39	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	407	39	646	37	632	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5998	12
	lower and upper montane	0	.	0	.	0	.	22	.	0	.	266	88	535	42	442	43	790	41	207	52	51	.	62	.	78	84	0	.	0	.	0	.	281	57	184	61	0	.	448	64	0	.	59	.	0	.	242	60	0	.	532	62	406	54	482	63	547	45	271	65	135	73	0	.	50	70	0	.	187	76	112	70	6	.	268	61	256	59	148	79	0	.	146	.	46	.	249	62	86	69	40	.	120	.	428	51	454	57	95	.	50	.	0	.	97	.	0	.	78	73	0	.	410	47	39	94	254	61	638	36	21	89	94	58	99	.	362	59	18	.	25	.	63	73	282	58	245	66	0	.	0	.	0	.	158	86	0	.	0	.	67	.	48	.	108	77	98	.	0	.	44	.	0	.	21	80	84	84	120	71	0	.	108	82	76	73	284	87	0	.	0	.	0	.	0	.	12725	9
	submontane	433	40	401	39	363	54	183	63	0	.	0	.	25	.	21	.	961	25	0	.	189	53	138	63	253	71	0	.	85	.	143	97	0	.	300	79	0	.	0	.	0	.	33	.	0	.	0	.	0	.	151	72	149	72	241	59	0	.	99	58	0	.	47	.	0	.	0	.	0	.	0	.	159	.	52	.	0	.	0	.	0	.	0	.	273	58	0	.	0	.	89	71	29	179	0	.	0	.	0	.	315	53	223	59	246	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	95	.	61	.	0	.	152	.	438	62	0	.	51	.	130	74	0	.	42	.	0	.	202	79	0	.	34	.	0	.	68	.	0	.	0	.	239	56	0	.	118	91	359	51	43	.	172	59	405	51	242	63	383	47	0	.	8835	10
	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	.	195	77	0	.	0	.	0	.	0	.	0	.	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	.	100	79	117	91	405	44	97	72	687	39	167	64	87	68	384	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	.	0	.	0	.	0	.	92	76	41	56	51	60	0	.	0	.	0	.	0	.	0	.	24	.	0	.	0	.	0	.	2526	18
	total	433	40	401	39	363	54	205	57	0	.	266	88	1399	27	463	41	1751	23	224	48	241	47	200	53	331	57	0	.	85	.	143	97	281	57	484	54	0	.	448	64	351	50	200	59	746	28	1590	24	1037	30	682	51	554	44	724	46	708	41	370	50	135	73	47	.	50	70	0	.	187	76	112	70	266	69	320	53	304	52	172	70	0	.	146	100	318	52	249	62	86	69	129	58	149	88	444	49	531	51	95	.	364	48	223	59	344	57	193	47	461	37	197	64	816	32	137	58	941	33	1020	28	135	49	515	40	126	82	362	59	356	64	167	65	305	51	434	51	397	56	438	62	39	.	51	.	289	58	0	.	42	.	67	.	250	66	180	61	132	79	0	.	113	72	968	27	785	32	1041	28	120	71	118	91	467	44	119	59	457	59	428	48	242	63	383	47	0	.	32642	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	.	0	.	458	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	.	137	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	379	60	134	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1235	29
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	275	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	166	.	0	.	0	.	0	.	225	.	131	.	0	.	26	.	89	70	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	200	.	0	.	519	77	0	.	0	.	193	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	177	76	0	.	61	.	0	.	0	.	55	.	0	.	0	.	0	.	0	.	53	.	118	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	80	184	71	188	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2746	24
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	262	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	85	.	0	.	0	.	0	.	0	.	333	71	670	56	606	46	595	52	553	53	0	.	0	.	0	.	76	.	0	.	0	.	0	.	0	.	0	.	64	.	113	.	0	.	0	.	0	.	52	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	73	39	.	124	.	0	.	131	.	0	.	141	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	118	.	0	.	0	.	0	.	0	.	879	38	785	58	507	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6262	16
	lower and upper montane	72	.	0	.	0	.	0	.	218	.	284	71	455	49	578	55	710	46	1129	33	0	.	49	.	391	58	0	.	163	.	514	56	762	49	269	.	0	.	408	52	393	72	96	.	0	.	343	60	0	.	1161	39	137	.	502	62	179	71	0	.	263	87	0	.	17	.	0	.	434	78	37	.	393	53	0	.	624	60	738	51	294	.	58	.	310	70	0	.	370	58	146	71	0	.	0	.	129	.	0	.	374	73	0	.	105	.	0	.	70	.	0	.	23	.	452	53	213	71	640	58	843	41	335	58	492	55	0	.	0	.	314	57	77	.	54	.	0	.	0	.	437	59	0	.	0	.	80	.	259	71	364	83	80	.	124	.	0	.	0	.	0	.	0	.	0	.	385	51	222	.	118	.	0	.	841	49	427	59	0	.	0	.	0	.	0	.	19957	9
	submontane	143	.	574	44	131	70	553	55	0	.	0	.	0	.	508	59	1638	31	0	.	0	.	162	.	0	.	0	.	113	.	257	76	0	.	162	82	0	.	0	.	202	76	156	96	0	.	0	.	0	.	16	405	242	.	462	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	153	.	110	.	0	.	0	.	60	.	0	.	578	45	99	.	-1	.	82	89	180	76	89	.	0	.	0	.	0	.	167	74	373	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	249	.	0	.	97	.	58	.	154	.	0	.	0	.	0	.	65	.	146	78	0	.	0	.	323	62	252	64	0	.	221	73	57	.	0	.	0	.	0	.	0	.	11	.	0	.	117	.	0	.	252	78	271	71	265	72	71	.	454	60	0	.	10273	11
	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	.	308	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	467	56	6	.	164	79	179	85	1063	35	262	58	262	68	403	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	.	0	.	0	.	0	.	0	.	0	.	0	.	193	66	43	.	31	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3453	17
	total	215	74	574	44	131	70	553	55	218	.	284	71	992	32	1086	40	2347	26	1129	33	0	.	211	80	391	58	0	.	276	72	856	42	927	44	431	70	0	.	408	52	1153	40	1053	42	659	43	1271	34	1100	33	1177	39	379	73	964	41	275	54	0	.	263	87	0	.	17	.	0	.	698	57	151	79	1065	44	110	.	624	60	982	41	354	85	58	.	889	38	99	.	369	58	228	56	180	76	89	.	129	.	0	.	374	73	167	74	478	50	0	.	714	42	80	93	386	52	631	45	1276	31	1087	38	1144	34	862	34	741	50	131	.	150	74	631	42	231	75	54	.	0	.	0	.	502	53	146	78	0	.	80	.	654	43	616	56	198	72	346	59	57	.	0	.	0	.	1517	28	1146	42	1121	34	222	.	235	71	0	.	1093	41	698	45	265	72	71	.	454	60	0	.	43925	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	66	608	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	966	36
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	588	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	97	.	580	51	317	61	872	55	169	.	780	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	367	71	30	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	280	.	54	.	402	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4634	20
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	396	74	168	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	583	74	0	.	0	.	0	.	0	.	264	85	536	63	342	72	1875	31	2204	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	.	0	.	0	.	0	.	175	.	0	.	144	.	0	.	0	.	0	.	0	.	0	.	0	.	210	.	0	.	0	.	0	.	0	.	0	.	178	72	208	71	0	.	0	.	0	.	0	.	364	71	136	71	271	.	0	.	451	.	593	71	124	.	104	.	145	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	938	52	496	61	644	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11595	13
	lower and upper montane	254	75	0	.	0	.	0	.	812	59	1000	60	1535	35	1056	45	923	59	2930	26	114	.	281	71	315	80	0	.	524	64	385	55	1152	42	258	.	0	.	1079	38	775	52	360	76	0	.	72	.	0	.	1549	32	275	59	303	60	405	73	338	75	231	73	125	.	0	.	517	63	984	53	140	85	517	71	793	59	435	75	521	52	153	.	2316	32	555	51	791	53	556	47	0	.	1019	43	163	71	530	62	758	49	0	.	0	.	97	.	0	.	0	.	0	.	60	.	86	.	45	.	334	71	154	.	367	60	285	.	277	.	142	.	279	.	93	.	221	.	308	76	0	.	1066	40	0	.	262	68	1051	36	73	.	409	59	0	.	247	.	195	.	437	71	0	.	0	.	0	.	787	46	333	71	726	50	523	74	722	45	681	50	0	.	0	.	0	.	0	.	38057	7
	submontane	1141	37	328	58	427	60	57	107	0	.	0	.	0	.	0	.	2253	28	0	.	0	.	410	73	572	53	0	.	0	.	388	.	0	.	859	49	0	.	0	.	108	.	0	.	0	.	0	.	0	.	439	62	641	62	902	52	0	.	0	.	0	.	120	.	0	.	0	.	0	.	0	.	0	.	0	.	68	.	0	.	0	.	0	.	963	41	391	58	72	.	429	52	349	78	0	.	0	.	401	72	197	71	467	51	130	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	.	0	.	0	.	300	.	277	71	111	.	0	.	46	.	0	.	0	.	210	.	134	.	257	73	180	.	0	.	0	.	0	.	0	.	0	.	300	71	101	.	387	51	0	.	71	.	137	.	382	60	278	71	0	.	15382	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	301	71	0	.	0	.	0	.	0	.	88	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	.	0	.	348	68	724	48	0	.	46	.	197	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	.	0	.	0	.	0	.	0	.	0	.	0	.	199	71	16	.	0	.	0	.	0	.	0	.	0	.	0	.	60	.	0	.	0	.	0	.	2175	25
	total	1395	33	328	58	427	60	57	107	812	59	1000	60	2520	28	1224	41	3176	26	2930	26	114	.	691	52	887	45	0	.	524	64	1356	45	1152	42	1117	44	301	71	1176	36	1727	32	1213	39	1215	44	2351	26	3593	21	1988	28	916	47	1205	42	405	73	338	75	231	73	245	71	0	.	517	63	1029	51	140	85	517	71	793	59	679	55	521	52	298	71	2316	32	1518	32	1182	40	629	43	429	52	1368	38	373	64	530	62	1159	40	197	71	467	51	227	71	178	72	575	52	134	80	60	.	434	58	769	46	698	50	337	56	835	48	285	.	729	73	735	60	499	64	293	58	366	72	608	62	277	71	1177	38	0	.	308	60	1051	36	167	71	619	51	134	.	504	61	375	71	437	71	0	.	1417	41	776	48	1834	32	632	50	827	45	910	48	722	45	752	46	197	76	382	60	278	71	0	.	72809	5
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	.	71	.	0	.	0	.	287	58	744	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	559	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1660	27
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	919	46	128	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1340	41	0	.	1270	42	1248	42	2549	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	255	72	0	.	0	.	0	.	82	.	196	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	349	72	0	.	0	.	0	.	0	.	0	.	322	72	216	75	112	.	0	.	0	.	0	.	335	72	45	.	0	.	0	.	228	.	0	.	141	.	52	.	108	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2026	29	766	50	1511	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14199	11
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	2878	31	109	.	0	.	207	.	0	.	0	.	0	.	0	.	255	.	119	.	349	74	390	71	0	.	859	63	1862	38	527	62	2148	35	4449	22	1342	40	0	.	0	.	0	.	207	.	0	.	0	.	0	.	0	.	0	.	0	.	532	75	463	58	0	.	303	.	192	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	233	76	209	.	170	.	0	.	0	.	0	.	384	71	317	.	1105	40	138	84	0	.	0	.	0	.	246	88	301	.	339	80	150	.	180	.	817	63	599	76	895	65	0	.	0	.	148	.	0	.	0	.	0	.	0	.	41	.	55	.	0	.	0	.	0	.	0	.	2115	35	1074	51	2095	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28799	9
	lower and upper montane	196	.	20	.	0	.	753	50	2430	36	2831	33	3958	27	7050	19	2460	31	3516	23	0	.	0	.	706	65	0	.	0	.	1493	45	1782	35	1443	45	0	.	1011	48	674	52	1251	37	262	71	479	72	0	.	3478	25	762	50	2167	37	2867	30	455	72	821	58	125	.	615	58	1517	40	943	51	0	.	2034	40	1989	39	912	63	1636	37	2152	39	3517	28	284	.	716	49	0	.	482	71	471	59	1583	43	1725	37	1622	40	931	50	0	.	0	.	0	.	401	.	0	.	293	72	373	64	0	.	262	73	0	.	476	69	417	71	111	.	259	.	417	73	866	55	348	71	394	71	0	.	1175	42	321	.	0	.	257	.	1114	46	285	71	602	58	112	.	459	52	97	.	510	71	0	.	0	.	711	57	734	60	467	71	0	.	1790	38	883	56	0	.	0	.	192	.	0	.	80445	5
	submontane	439	53	591	55	803	50	2912	27	0	.	0	.	737	59	1311	37	5663	18	0	.	165	.	477	61	275	63	252	.	469	72	219	.	0	.	315	72	0	.	0	.	118	.	0	.	0	.	0	.	0	.	1651	39	526	58	681	50	0	.	109	73	0	.	305	.	0	.	0	.	0	.	0	.	0	.	242	.	0	.	142	.	151	.	0	.	1199	42	924	48	79	.	532	52	0	.	189	.	0	.	0	.	695	61	1638	37	1718	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	218	.	0	.	0	.	310	.	0	.	381	75	0	.	130	.	176	.	0	.	0	.	0	.	189	.	218	.	188	.	147	.	0	.	179	.	0	.	0	.	83	.	0	.	329	71	265	75	0	.	520	59	636	56	282	60	559	48	0	.	30339	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	160	94	0	.	0	.	0	.	0	.	0	.	73	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	120	.	169	.	62	.	0	.	440	60	272	80	130	.	383	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1951	28
	total	635	48	611	53	803	50	3666	23	2430	36	2831	33	8492	18	8598	16	8123	15	3723	22	165	.	477	61	981	50	252	.	725	58	1830	39	2131	31	2148	34	160	94	1870	39	4066	24	1778	32	3680	25	6463	18	4707	21	5129	21	1287	38	2848	31	3074	29	564	60	821	58	430	76	615	58	1517	40	1198	43	532	75	2497	34	2231	37	1296	50	2166	31	2303	37	3517	28	1482	39	1640	34	79	.	1014	43	471	59	2353	33	1934	34	1791	37	1626	39	1638	37	1718	33	706	50	1055	52	1387	35	494	50	373	64	440	60	869	43	421	61	1160	45	756	53	706	52	439	72	1375	44	1828	40	1351	47	775	52	0	.	1454	36	497	74	0	.	257	.	1114	46	516	54	875	47	300	73	606	46	97	.	689	58	4842	20	1839	36	4399	22	734	60	796	51	265	75	1790	38	1403	41	636	56	282	60	752	44	0	.	157392	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	450	45	1628	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	129	75	98	80	0	.	0	.	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	490	39	475	39	85	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3393	16
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	3042	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	498	58	961	35	676	52	1030	41	1031	36	1531	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	287	74	158	.	0	.	0	.	348	99	0	.	75	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	275	66	550	51	379	66	0	.	0	.	0	.	468	45	95	83	0	.	80	.	181	.	134	89	0	.	205	66	27	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	.	0	.	0	.	0	.	0	.	2019	36	1243	31	592	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15965	10
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	2569	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	247	73	0	.	0	.	0	.	210	75	441	58	443	47	873	36	2036	27	710	45	0	.	155	.	0	.	216	74	0	.	0	.	0	.	0	.	0	.	0	.	699	52	133	72	0	.	0	.	62	.	0	.	52	.	0	.	0	.	0	.	0	.	0	.	210	72	63	.	103	72	0	.	0	.	0	.	388	72	256	.	473	58	86	.	0	.	0	.	326	60	0	.	138	81	122	.	0	.	304	64	0	.	245	78	153	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	.	0	.	0	.	0	.	0	.	855	34	2477	26	1132	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16246	9
	lower and upper montane	102	.	0	.	0	.	88	.	291	.	200	71	2566	27	4396	19	379	60	2297	25	0	.	0	.	88	.	0	.	0	.	152	.	926	39	377	71	0	.	1014	43	639	44	876	43	0	.	395	66	0	.	1944	33	937	44	304	65	2115	30	102	.	1670	30	476	52	779	49	1784	32	227	87	134	96	743	47	696	59	0	.	843	42	1044	45	1474	39	121	.	85	.	389	77	111	.	185	72	504	58	274	61	254	66	327	63	0	.	0	.	0	.	1562	83	0	.	134	63	422	51	235	76	314	54	346	59	60	.	129	.	0	.	237	.	252	71	0	.	826	55	470	.	0	.	641	46	0	.	0	.	1314	27	228	51	746	43	265	71	0	.	69	.	0	.	0	.	0	.	0	.	1235	37	251	73	0	.	0	.	212	60	132	73	0	.	0	.	0	.	0	.	42391	7
	submontane	232	70	274	54	325	53	0	.	0	.	0	.	176	.	505	51	803	46	0	.	318	61	0	.	223	66	0	.	84	.	0	.	0	.	430	72	0	.	0	.	0	.	212	74	0	.	0	.	0	.	928	40	869	53	530	61	307	74	19	.	0	.	251	72	49	.	0	.	0	.	0	.	0	.	0	.	567	63	92	.	246	.	0	.	336	71	57	.	0	.	0	.	431	52	0	.	0	.	0	.	268	71	0	.	200	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	352	.	0	.	161	.	61	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	131	.	25	.	373	71	71	.	0	.	0	.	297	.	0	.	0	.	120	.	0	.	274	.	413	58	0	.	204	73	204	71	310	68	36	.	0	.	11771	12
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	285	71	113	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	.	90	.	134	92	0	.	168	66	545	44	380	58	295	61	291	57	689	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	226	71	96	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3397	18
	total	334	57	274	54	325	53	88	.	291	.	200	71	8354	15	4902	18	1182	36	2297	25	318	61	0	.	310	55	0	.	84	.	398	59	926	39	807	51	0	.	1721	31	2041	25	2208	26	1903	28	4196	18	3981	18	2872	26	1961	32	834	45	2638	26	121	86	1670	30	727	42	828	47	1784	32	227	87	1120	39	1033	38	696	59	567	63	1345	38	1290	41	1601	37	457	58	143	72	389	77	111	.	617	42	715	46	336	53	358	52	595	47	0	.	286	59	770	45	2631	51	950	40	388	42	967	33	615	46	1404	27	732	38	908	37	683	59	181	.	837	44	313	61	450	52	1006	48	470	.	7	.	641	46	0	.	0	.	1314	27	359	49	771	41	785	43	71	.	69	.	0	.	297	.	3364	24	4421	18	3261	20	251	73	274	.	413	58	212	60	336	52	204	71	310	68	36	.	0	.	93163	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	111	69	0	.	63	85	983	29	3698	16	0	.	0	.	0	.	0	.	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	.	202	54	182	60	176	81	0	.	0	.	17	103	25	.	22	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1502	26	824	36	85	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7906	11
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	5286	19	138	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	166	.	0	.	0	.	595	51	3413	22	1136	37	3360	25	2904	24	5821	17	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	454	60	310	69	770	57	0	.	82	.	738	57	0	.	75	.	0	.	0	.	0	.	0	.	0	.	366	68	0	.	0	.	0	.	0	.	0	.	600	49	1357	32	587	48	63	97	0	.	0	.	1034	34	140	65	37	.	80	.	408	71	232	58	259	71	354	49	135	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	.	0	.	0	.	0	.	0	.	4809	21	2352	24	2739	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40899	6
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	7277	16	369	60	0	.	224	93	0	.	0	.	0	.	0	.	255	.	1033	47	514	54	390	71	0	.	1085	52	3389	25	2274	28	5044	19	10757	13	5333	18	0	.	155	100	0	.	663	48	0	.	0	.	0	.	3	.	0	.	109	72	1352	40	603	47	0	.	526	67	357	59	144	.	119	71	0	.	0	.	0	.	0	.	0	.	652	48	420	58	273	68	0	.	0	.	0	.	1138	37	945	47	1642	31	229	63	0	.	0	.	842	40	423	57	834	52	525	57	732	67	1376	40	1128	49	1219	43	1345	47	0	.	0	.	186	82	0	.	0	.	0	.	0	.	41	.	242	61	0	.	0	.	0	.	0	.	5242	19	5542	18	5009	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71961	5
	lower and upper montane	623	48	20	.	0	.	863	45	3751	28	4750	24	9582	15	13865	12	5264	20	10079	12	166	76	392	55	1576	37	0	.	687	54	2586	30	5016	19	2531	32	0	.	4009	21	2637	25	2659	25	279	67	1530	33	0	.	8698	15	2541	25	3949	25	6312	18	1167	39	3121	24	726	42	1510	35	3819	23	2775	29	424	46	3707	26	3798	26	2228	34	3913	22	3647	28	7555	18	1381	34	1840	31	1521	31	780	48	1796	30	2702	29	3187	25	2796	28	1682	35	0	.	415	50	0	.	2162	63	0	.	920	33	1376	30	747	38	2189	25	1365	31	1332	33	1447	36	751	49	656	58	1288	38	1099	45	1902	32	1405	44	16	.	3320	23	321	.	568	44	2784	21	1675	33	1871	29	1022	40	592	52	821	40	544	60	555	66	0	.	21	80	3213	23	1737	33	1349	37	637	63	3826	23	2408	29	0	.	12	.	192	.	0	.	196975	2
	submontane	2542	22	2225	22	2121	26	3734	22	0	.	0	.	1121	44	2354	26	11717	11	73	.	718	39	1197	38	1342	31	252	.	752	50	1071	47	0	.	2168	29	0	.	0	.	428	52	401	55	0	.	0	.	0	.	3265	24	2427	30	2945	24	307	74	227	44	0	.	775	49	49	.	0	.	0	.	0	.	312	70	404	67	653	56	235	72	467	63	0	.	3362	22	1472	35	197	57	1132	32	989	38	278	75	0	.	401	72	1475	35	2610	26	2692	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	602	72	218	.	258	73	311	51	577	62	0	.	833	53	722	46	307	60	524	45	241	52	0	.	496	49	677	48	927	43	738	43	417	58	0	.	544	65	0	.	0	.	452	43	300	71	939	43	1424	29	295	68	1411	30	1650	30	1292	30	1710	27	0	.	78755	4
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	485	54	0	.	0	.	0	.	0	.	876	39	185	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	.	97	93	821	39	396	57	800	31	1196	31	3294	19	997	34	816	34	2055	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	165	71	0	.	0	.	71	.	0	.	0	.	0	.	626	40	328	51	178	40	0	.	0	.	0	.	0	.	0	.	83	77	0	.	0	.	0	.	13555	7
	total	3166	20	2245	22	2121	26	4596	20	3751	28	4750	24	23266	9	16726	10	16981	10	10376	12	884	34	1589	32	2918	24	252	.	1694	35	4690	22	5695	17	5089	21	485	54	5689	18	9978	13	6471	16	8744	15	17049	9	15037	10	11963	12	5122	19	6895	17	7302	16	1393	33	3121	24	1501	32	1562	34	3819	23	3339	26	2086	29	5392	21	4202	25	3489	26	5242	19	4259	25	7750	17	4744	18	3312	23	1717	28	1913	27	2785	24	3999	22	3607	23	3470	24	3157	24	2610	26	3194	22	1851	28	5487	28	2807	23	2189	20	2572	21	4041	17	5078	15	2768	20	4280	19	2654	28	2108	34	2522	28	2985	26	3250	25	3382	26	2238	34	738	45	3813	21	845	47	809	34	2784	21	2336	27	2589	24	2267	26	1401	32	1238	33	544	60	1098	46	12179	12	9068	13	11676	12	2036	30	2287	28	2060	28	4122	22	3819	21	1733	29	1304	30	1903	26	0	.	410050	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. 2278361. Birmensdorf, Swiss Federal Research Institute WSL

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