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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.

main tree species #90
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh) with the ten most common species or species groups in Switzerland ('main tree species') categorised, and the classes 'other conifers' and 'other broadleaves' for the remaining species. The main tree species are: spruce (Picea spp.), fir (Abies spp.), pine (Pinus sylvestris, P. nigra, P. strobus, P. mugo subsp. uncinata), larch (Larix spp.), Arolla pine (Pinus cembra), beech (Fagus sylvatica), maple (Acer spp.), ash (Fraxinus spp.), oak (Quercus spp.) and chestnut (Castanea sativa). Reference: Field Survey (MID 50: Baumart)

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)
main tree species · 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
main tree species	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower and upper montane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	submontane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
spruce	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	.	13	79	0	.	1	100	147	48	256	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	139	65	2	100	54	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	.	132	48	30	81	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	777	24
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	5111	19	128	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	166	.	0	.	0	.	579	52	3192	23	947	42	3276	25	2227	26	3309	20	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	445	60	130	54	729	55	0	.	82	.	634	63	0	.	72	.	0	.	0	.	0	.	0	.	0	.	349	72	0	.	0	.	0	.	0	.	0	.	400	63	904	36	255	57	0	.	0	.	0	.	264	77	48	100	31	.	80	.	408	71	225	60	210	71	330	48	134	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	.	0	.	0	.	0	.	0	.	2032	26	1398	29	2219	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30410	7
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	5668	17	369	60	0	.	205	100	0	.	0	.	0	.	0	.	147	.	1033	47	501	55	390	71	0	.	1059	53	2966	27	1589	31	4007	22	7888	14	2680	23	0	.	151	100	0	.	639	47	0	.	0	.	0	.	3	.	0	.	92	71	1193	39	537	49	0	.	526	67	297	68	144	.	119	71	0	.	0	.	0	.	0	.	0	.	572	51	392	60	273	68	0	.	0	.	0	.	688	50	694	54	1209	36	0	.	0	.	0	.	102	80	35	100	337	56	491	58	730	67	1066	42	854	59	1049	44	720	41	0	.	0	.	186	82	0	.	0	.	0	.	0	.	41	.	216	62	0	.	0	.	0	.	0	.	2294	27	2791	24	3245	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50187	5
	lower and upper montane	29	100	0	.	0	.	104	72	2795	30	2096	30	4874	19	4512	16	1484	29	2483	17	35	100	4	100	295	64	0	.	415	67	1495	34	2067	28	1475	39	0	.	1714	27	1671	31	1221	36	111	92	328	72	0	.	1779	28	947	34	1179	33	3175	22	317	62	1495	27	209	59	518	42	1608	31	1183	44	299	53	1146	38	1142	33	1095	43	1654	30	1797	35	2942	26	441	47	328	62	277	47	35	100	273	63	885	38	1978	29	1051	36	671	46	0	.	131	71	0	.	1562	74	0	.	0	.	45	100	0	.	39	95	10	100	244	51	239	53	575	60	332	70	115	72	361	56	598	48	874	41	0	.	1035	33	0	.	218	58	1994	22	852	39	728	42	404	62	128	80	256	53	32	75	242	98	0	.	2	100	1048	34	1039	37	199	48	255	72	1150	34	1365	36	0	.	0	.	0	.	0	.	73712	4
	submontane	323	39	495	37	866	42	800	42	0	.	0	.	226	53	557	44	3114	20	70	.	10	100	288	59	136	73	0	.	212	75	162	61	0	.	399	64	0	.	0	.	21	95	159	75	0	.	0	.	0	.	460	39	124	65	746	34	42	100	0	0	0	.	307	66	26	.	0	.	0	.	0	.	139	100	59	91	65	100	17	100	0	.	0	.	947	36	503	64	17	100	236	61	85	60	19	100	0	.	0	.	306	54	769	35	695	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	122	100	34	.	25	73	36	94	123	72	0	.	44	100	137	62	0	.	138	68	35	71	0	.	3	100	12	100	35	76	37	97	44	71	0	.	182	89	0	.	0	.	0	.	0	.	554	47	439	40	232	81	493	48	88	90	439	50	578	38	0	.	17232	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	75	0	.	0	.	0	.	0	.	11	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	.	58	100	26	100	4	100	0	.	37	100	30	100	49	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	93	112	74	0	0	0	.	0	.	0	.	0	.	0	.	-4	100	0	.	0	.	0	.	381	35
	total	351	37	495	37	866	42	904	38	2795	30	2096	30	15880	10	5565	14	4598	17	2758	17	45	81	292	58	431	49	0	.	775	46	2690	26	2733	24	2264	30	4	75	3353	23	7863	15	3916	20	7396	16	10601	12	6245	14	2239	23	1222	30	1925	24	3876	20	317	62	1495	27	516	46	547	40	1608	31	1721	34	1623	30	2551	26	1201	32	1768	34	2602	26	1942	34	3134	24	1388	28	831	46	294	44	271	54	359	50	1825	28	2371	26	1324	32	977	36	769	35	826	38	1146	37	3326	38	1470	31	54	100	82	71	30	100	454	50	94	65	612	37	932	37	1748	38	1647	32	1215	44	1862	29	1451	29	918	39	137	62	1222	31	138	68	253	51	1994	22	855	38	782	40	732	40	166	65	300	47	32	75	425	68	4513	18	4332	18	6513	15	1039	37	754	37	694	37	1382	31	1858	29	84	94	439	50	578	38	0	.	172699	2
fir	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	.	10	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	.	4	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	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	67
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	1293	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	.	0	.	0	.	0	.	0	.	0	.	135	58	18	100	190	77	689	42	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	159	75	53	71	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	28	94	0	.	0	.	0	.	0	.	18	100	147	62	167	100	0	.	0	.	0	.	97	82	132	100	1	100	15	72	0	.	126	85	98	81	140	53	583	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	154	98	519	48	363	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5231	15
	lower and upper montane	36	100	0	.	0	.	145	70	609	41	1551	36	987	35	5804	15	2158	26	2733	19	14	100	24	100	309	59	0	.	187	99	366	59	946	40	565	46	0	.	501	48	158	60	375	47	100	100	314	79	0	.	2870	22	852	35	2109	27	1471	27	502	55	1167	35	271	49	575	44	1751	33	371	56	34	71	860	44	1017	37	94	64	524	42	729	49	1521	24	98	75	249	46	261	67	49	60	332	40	871	47	664	37	546	39	170	55	0	.	0	.	0	.	61	100	0	.	0	.	0	.	0	.	310	64	144	70	250	70	367	43	0	.	188	72	324	47	416	71	548	41	125	60	0	.	1028	35	166	.	247	61	380	52	466	48	586	33	270	60	246	73	331	63	140	71	9	100	0	.	0	.	571	47	333	49	112	72	150	69	1027	32	288	39	0	.	0	.	0	.	0	.	46926	5
	submontane	213	82	40	96	144	60	821	38	0	.	0	.	0	.	746	41	2686	22	0	.	19	83	112	70	71	59	0	.	52	82	0	.	0	.	35	99	0	.	0	.	89	100	36	77	0	.	0	.	0	.	1020	45	680	53	1093	30	93	84	20	100	0	.	284	50	22	.	0	.	0	.	0	.	153	100	3	100	0	.	0	.	39	100	0	.	86	94	265	50	19	100	91	64	49	83	0	.	0	.	33	100	366	81	335	39	488	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	167	100	26	.	-2	100	56	78	0	.	0	.	76	95	75	100	25	100	34	74	78	75	0	.	6	100	34	100	188	76	13	100	167	94	0	.	192	71	0	.	0	.	0	.	65	100	28	100	115	84	0	.	134	68	94	85	19	100	86	58	0	.	11878	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	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	.	18	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	2	100	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	56
	total	249	72	40	96	144	60	965	34	609	41	1551	36	2280	24	6560	14	4844	17	2733	19	32	64	136	60	380	49	0	.	347	63	366	59	946	40	600	44	4	100	501	48	383	40	429	42	291	61	1003	38	0	0	3889	20	1532	31	3202	20	1564	26	522	53	1167	35	555	35	596	42	1751	33	368	56	193	63	1066	38	1019	37	94	64	524	42	768	47	1521	24	184	59	514	34	280	62	140	46	381	36	879	46	692	36	578	38	536	58	335	39	506	58	18	100	208	52	167	100	0	.	0	.	34	100	407	53	276	60	251	70	549	42	26	100	313	55	479	37	556	55	1130	40	202	52	75	100	1054	34	200	84	325	50	380	52	472	47	619	32	458	47	259	69	498	53	140	71	200	68	162	93	541	46	938	34	398	44	140	61	265	53	1027	32	422	34	94	85	19	100	86	58	0	.	64140	4
pine	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	238	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	.	0	.	89	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	384	32
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	78	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	35	63	121	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	176	100	42	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	72	40	71	33	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	580	34
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	190	52	302	50	52	100	603	41	839	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	100	0	.	0	.	0	.	0	.	0	.	0	.	46	74	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	169	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	557	43	331	35	360	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3522	15
	lower and upper montane	0	.	0	.	0	.	74	100	0	.	0	.	83	100	109	60	88	85	400	55	8	100	0	.	22	100	0	.	0	.	28	100	0	.	0	.	0	.	0	.	0	.	213	56	0	.	0	.	0	.	304	44	18	100	0	.	0	.	0	.	29	100	0	.	0	.	44	100	0	.	0	.	0	.	41	100	0	.	58	60	0	.	53	100	44	61	64	81	61	74	2	100	87	63	0	.	8	100	0	.	72	78	0	.	54	79	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	0	0	.	0	.	0	.	16	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	100	0	.	0	.	0	.	229	59	0	.	25	100	117	71	92	60	75	73	0	.	0	.	0	.	0	.	2612	15
	submontane	26	100	21	71	21	100	0	.	0	.	0	.	229	100	0	.	360	38	0	.	38	100	66	69	163	50	0	.	21	100	17	89	0	.	265	67	0	.	0	.	55	88	61	75	0	.	0	.	0	.	79	62	0	.	229	58	0	.	50	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	286	44	35	100	36	71	2	100	32	82	0	.	0	.	0	.	12	100	117	77	148	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	.	46	100	0	.	0	.	0	.	0	.	14	100	0	.	0	.	20	100	0	.	23	71	0	.	29	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	119	59	0	.	117	50	120	56	86	73	56	100	0	.	3042	15
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	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	.	0	.	0	.	0	.	0	.	0	.	225	49	65	61	2	100	0	.	0	.	0	.	0	.	0	.	33	100	0	.	0	.	0	.	391	35
	total	26	100	21	71	21	100	74	100	0	.	0	.	393	63	109	60	449	35	400	55	45	85	66	69	186	45	0	.	21	100	45	71	0	.	265	67	67	100	0	.	264	42	576	34	52	100	639	38	1199	27	383	37	18	100	229	58	0	.	50	100	29	100	0	.	0	.	44	100	4	100	176	100	42	100	41	100	0	.	58	60	0	.	53	100	330	39	100	63	97	53	4	71	119	51	69	83	8	100	0	.	85	68	117	77	202	66	0	.	46	74	89	79	0	.	4	100	0	.	0	.	0	.	12	100	0	.	40	100	224	60	0	0	0	.	0	.	0	.	30	71	9	100	0	.	20	100	0	.	23	71	0	.	29	100	0	.	0	.	69	100	0	.	858	31	436	29	624	33	0	.	25	100	236	46	92	60	192	42	153	49	86	73	56	100	0	.	10531	8
larch	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	.	61	75	0	.	52	100	602	32	1979	21	0	.	0	.	0	.	0	.	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	.	63	65	82	89	120	75	0	.	0	.	17	103	25	.	21	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	762	29	551	42	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4358	13
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	56	187	61	82	65	578	36	2124	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	199	69	397	44	332	54	61	100	0	.	0	.	740	34	91	61	6	.	0	.	0	.	0	.	49	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2667	28	760	34	432	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8997	11
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	17	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	97	233	61	115	102	1307	24	1348	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	270	65	44	84	177	67	149	78	0	.	0	.	593	43	220	52	441	71	0	.	0	.	0	.	148	60	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1891	28	1340	29	771	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9157	11
	lower and upper montane	-4	100	0	.	0	.	73	100	0	.	0	.	48	74	0	.	50	100	0	.	0	.	0	.	27	71	0	.	0	.	0	.	0	.	6	100	0	.	50	100	39	100	109	72	32	100	33	100	0	.	0	.	5	100	13	100	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	0	.	13	100	0	.	73	98	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	57	100	0	.	0	.	27	100	0	.	42	100	0	.	100	80	0	.	81	79	305	48	166	82	125	81	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	21	100	0	.	0	.	0	.	4	100	92	58	40	100	30	100	0	.	18	100	0	.	0	.	0	.	0	.	0	.	1746	18
	submontane	34	83	55	64	106	71	22	100	0	.	0	.	94	100	0	.	40	100	0	.	0	.	0	.	0	.	0	.	0	.	57	100	0	.	34	100	0	.	0	.	89	74	0	.	0	.	0	.	0	.	22	100	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	37	100	0	.	45	100	0	.	28	71	0	.	0	.	20	100	0	.	0	.	0	.	0	.	14	100	29	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	100	0	.	0	.	0	.	66	100	0	.	22	100	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	68	100	0	.	0	.	0	.	0	.	0	.	9	100	2	100	0	.	23	71	26	100	178	54	0	.	1221	19
	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	.	9	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	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	146	89	8	100	40	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	209	64
	total	30	94	55	64	106	71	95	81	0	.	0	.	181	57	0	.	90	71	0	.	0	.	0	.	27	71	0	.	0	.	57	100	0	.	40	87	0	.	50	100	381	34	529	37	281	50	2520	17	5460	13	22	100	5	100	22	72	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	37	100	224	59	45	100	0	.	36	59	0	.	0	.	20	100	0	.	0	.	0	.	57	100	14	100	29	100	27	100	486	46	545	34	591	38	430	41	0	.	86	75	1655	23	501	37	594	56	0	.	0	.	0	.	237	46	0	.	20	100	0	.	66	100	0	.	22	100	0	.	0	.	0	.	0	.	57	73	0	.	21	100	0	.	68	100	5467	17	2662	20	1341	22	40	100	30	100	9	100	20	89	0	.	23	71	26	100	178	54	0	.	25688	6
Arolla pine	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	.	34	69	0	.	9	100	231	52	1204	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	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	.	550	34	238	44	76	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2351	15
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	67	260	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	59	96	58	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	472	30
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	90
	lower and upper montane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	submontane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	69	0	.	9	100	262	46	1463	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	.	8	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	.	618	31	334	36	95	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2836	13
other conifers	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	100	0	.	0	.	3	100	18	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	48
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	35	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	6	100	7	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	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	.	6	100	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	53
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	2	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	.	1	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	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	72
	lower and upper montane	14	100	0	.	0	.	7	100	0	.	14	74	0	.	0	.	280	58	3	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	11	74	1	100	274	87	2	100	0	.	0	.	8	100	0	.	0	.	0	.	0	.	8	100	344	98	7	100	0	0	7	79	0	.	0	.	11	100	6	100	40	77	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	27	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	4	72	47	71	9	81	0	.	0	.	0	.	0	.	1150	39
	submontane	28	100	0	.	71	100	78	77	0	.	0	.	0	.	0	.	241	66	0	.	0	.	0	.	0	.	252	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	-19	100	0	.	0	.	66	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	100	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	88	0	.	0	.	0	.	0	.	0	.	0	.	879	38
	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	.	81	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	98
	total	42	75	0	.	71	100	85	72	0	.	14	74	36	93	0	.	522	44	3	71	0	.	0	.	0	.	252	.	0	.	0	.	0	.	0	.	0	.	3	100	8	93	0	.	0	.	8	75	25	43	11	74	75	99	275	86	2	100	0	.	0	.	8	100	0	.	0	.	0	.	1	100	8	100	344	98	12	72	-19	104	7	79	0	.	66	100	11	100	6	100	40	77	0	.	5	100	0	.	4	100	0	.	0	.	5	72	0	.	0	.	0	.	0	.	0	.	0	.	81	100	1	100	0	.	0	.	0	.	7	100	0	.	10	100	0	.	0	.	56	100	27	62	0	.	6	100	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	6	100	4	100	3	64	0	.	0	.	18	71	47	71	9	81	0	.	0	.	0	.	0	.	2211	25
beech	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	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	.	24	100
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	32	96	4	100	20	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	.	0	.	0	.	78	100	0	.	0	.	30	100	37	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	232	42
	lower and upper montane	325	65	20	.	0	.	264	52	323	48	494	36	2478	28	2651	20	954	27	3573	16	104	97	256	57	662	40	0	.	22	71	570	47	1067	30	368	70	0	.	935	31	599	48	530	35	20	71	626	39	0	.	2469	19	496	43	216	59	1188	32	214	51	170	41	191	43	215	46	341	40	486	42	53	60	1313	31	702	44	727	54	1058	31	694	35	1791	27	696	39	995	39	707	33	401	55	751	35	511	32	317	67	870	40	273	46	0	.	138	61	0	.	181	58	0	.	625	41	927	38	575	41	1181	32	941	34	531	44	514	48	0	.	127	62	577	48	132	56	445	45	209	78	0	.	977	29	145	.	65	58	335	37	303	45	497	36	186	57	174	71	125	66	256	59	154	100	0	.	9	71	730	35	194	53	352	48	93	72	880	33	440	35	0	.	12	.	38	.	0	.	47752	4
	submontane	898	29	798	31	688	34	1611	34	0	.	0	.	381	60	643	44	3333	17	0	.	205	51	630	43	669	38	0	.	270	67	179	59	0	.	890	41	0	.	0	.	67	70	49	94	0	.	0	.	0	.	947	35	938	34	636	45	167	85	69	64	0	.	103	59	3	.	0	.	0	.	0	.	0	0	90	62	206	60	143	72	269	66	0	.	1463	25	449	49	86	57	507	42	618	43	17	100	0	.	208	71	317	43	351	39	602	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	230	81	32	.	131	75	43	78	0	.	0	.	405	60	71	73	151	87	20	95	15	82	0	.	52	78	449	56	359	62	418	57	110	74	0	.	56	76	0	.	0	.	148	68	120	75	146	60	422	49	6	100	441	45	746	45	340	43	394	34	0	.	24807	6
	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	.	22	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	.	53	.	0	.	2	100	0	.	153	54	399	50	168	51	23	78	80	79	16	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	926	26
	total	1223	27	818	30	688	34	1875	30	323	48	494	36	2859	25	3294	18	4287	14	3573	16	309	47	885	34	1331	27	0	.	292	63	749	39	1067	30	1258	35	0	.	935	31	675	44	611	32	25	61	668	37	0	.	3416	17	1433	27	852	37	1355	30	282	42	170	41	294	34	218	45	341	40	486	42	53	60	1313	31	793	39	932	44	1201	28	964	31	1791	27	2159	21	1444	31	794	30	907	34	1369	27	529	31	317	67	1078	35	589	31	351	39	793	34	0	.	182	58	0	.	856	32	1326	30	743	34	1259	30	1058	31	548	43	743	42	32	100	258	49	621	45	132	56	445	45	614	48	71	73	1128	28	166	88	81	49	335	37	355	40	946	32	546	45	592	45	234	50	256	59	210	76	0	.	30	74	890	31	314	43	498	38	515	42	886	33	881	28	746	45	352	42	432	32	0	.	73741	3
maple	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	.	16	100	0	.	0	.	0	.	26	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	.	42	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	24	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	121	47
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	195	39	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	17	100	63	68	2	100	73	75	77	56	3	100	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	22	79	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	71	0	.	0	.	0	.	0	.	0	.	36	96	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	1	100	1	100	27	100	22	85	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	88	0	.	0	.	0	.	0	.	9	100	54	65	138	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	832	17
	lower and upper montane	102	73	0	.	0	.	7	100	25	75	266	42	590	24	250	29	75	61	694	23	0	.	79	75	156	58	0	.	60	74	80	52	320	34	24	74	0	.	399	37	71	53	95	93	15	100	16	74	0	.	440	24	135	39	41	61	289	34	95	59	214	36	47	83	158	79	45	46	131	47	35	78	220	59	195	44	140	47	364	38	244	45	731	27	40	100	94	53	170	40	61	51	190	54	137	43	109	47	75	68	262	64	0	.	30	87	0	.	59	100	0	.	4	71	22	100	20	100	12	45	36	100	5	74	153	46	16	85	0	.	119	67	82	55	171	43	37	66	0	.	84	47	10	.	27	71	38	52	48	47	47	80	50	66	38	71	0	.	0	.	60	75	0	.	2	100	204	47	85	52	103	52	1	1787	333	29	81	57	0	.	0	.	0	.	0	.	9962	6
	submontane	264	32	182	36	53	82	28	88	0	.	0	.	96	75	215	53	323	26	0	.	140	49	38	57	71	44	0	.	0	.	2	100	0	.	66	72	0	.	0	.	28	100	54	72	0	.	0	.	0	.	68	104	63	59	10	60	0	.	5	100	0	.	36	100	0	.	0	.	0	.	0	.	0	.	-21	142	113	100	36	100	0	.	0	.	61	58	74	76	4	475	38	59	99	62	41	100	0	.	12	100	51	58	54	81	76	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	.	0	.	38	68	77	75	0	.	0	.	9	78	74	71	0	.	4	71	0	.	32	60	33	68	33	100	0	.	10	100	0	.	12	100	0	.	0	.	50	86	25	100	109	69	63	49	18	100	30	90	243	55	70	60	248	51	0	.	3603	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	28	100	0	0	18	64	6	100	56	96	27	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	159	42
	total	366	30	182	36	53	82	34	74	25	75	266	42	881	20	465	29	399	24	711	23	140	49	117	54	227	42	0	.	60	74	82	51	334	33	91	56	3	100	431	34	162	39	151	64	88	65	119	43	3	100	508	24	198	32	51	51	301	32	100	57	214	36	83	64	158	79	45	46	153	42	35	78	234	56	174	52	253	51	400	35	244	45	731	27	101	53	168	45	174	40	99	39	289	41	184	39	109	47	87	60	314	54	54	81	106	52	36	96	101	72	11	100	33	85	22	100	39	61	22	42	92	70	32	73	153	46	62	75	1	100	184	48	206	38	175	43	37	66	9	78	158	42	10	100	31	63	38	52	79	37	80	55	109	47	38	71	10	100	0	.	71	65	9	100	56	62	410	29	110	46	212	43	64	55	351	28	111	48	243	55	70	60	248	51	0	.	14678	5
ash	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	182	54	22	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	.	18	100	0	.	47	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	98	56	131	64	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	513	29
	lower and upper montane	4	100	0	.	0	.	63	100	0	.	254	55	170	70	376	54	135	64	89	48	0	.	0	0	59	66	0	.	0	.	0	0	484	42	65	67	0	.	262	44	17	100	2	100	0	.	71	82	0	.	447	33	57	64	61	55	57	54	21	100	17	94	0	.	3	100	6	100	511	59	0	.	116	76	340	49	141	62	75	54	131	84	363	38	43	100	81	68	33	73	52	63	66	71	202	60	46	79	94	83	189	47	0	.	36	100	0	.	0	.	0	.	16	83	8	100	2	695	22	71	0	.	0	.	151	78	57	64	0	.	135	63	87	73	66	61	135	100	0	.	16	100	0	.	9	77	12	100	4	100	0	.	66	100	0	.	0	.	0	.	39	100	0	.	0	.	171	60	26	100	380	55	0	0	187	41	128	53	0	.	0	.	72	.	0	.	7028	10
	submontane	254	46	380	44	32	83	10	90	0	.	0	.	0	0	75	53	510	35	0	.	137	82	34	56	113	71	0	.	166	70	409	67	0	.	220	45	0	.	0	.	42	100	8	73	0	.	0	.	0	.	322	52	363	76	37	100	0	.	48	76	0	.	4	100	0	.	0	.	0	.	0	.	0	.	244	83	130	73	30	100	7	100	0	.	188	52	57	64	23	94	125	67	26	75	132	72	0	.	148	100	90	51	262	58	194	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	46	77	55	67	0	.	185	86	97	84	8	100	1	100	0	.	0	.	29	100	89	78	143	71	68	66	47	73	0	.	31	100	0	.	0	.	24	60	89	72	60	85	48	68	14	100	98	53	173	65	71	59	47	71	0	.	6253	11
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	11	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	.	8	.	0	.	211	100	2	100	30	58	27	77	203	57	108	72	25	95	111	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	16	73	5	100	10	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	779	33
	total	258	45	380	44	32	83	74	87	0	.	254	55	177	67	451	46	645	31	89	48	137	82	34	56	172	51	0	.	166	70	409	67	484	42	284	38	9	100	262	44	60	77	10	61	182	54	104	59	0	.	770	29	419	66	99	51	57	54	70	61	17	94	4	100	3	100	6	100	511	59	0	.	116	76	584	45	270	48	105	48	138	80	363	38	231	46	138	48	56	58	176	51	92	55	333	46	46	79	242	69	279	36	262	58	238	45	18	100	211	100	49	96	46	48	35	64	205	56	130	61	25	95	111	45	161	73	57	64	0	.	181	51	144	51	66	61	320	65	97	84	24	74	1	100	9	77	12	100	32	90	89	78	208	58	70	64	47	73	0	.	70	71	113	49	136	62	213	49	115	60	440	49	48	68	201	39	226	38	173	65	71	59	119	66	0	.	14573	7
oak	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	76
	lower and upper montane	8	100	0	.	0	.	49	79	0	.	0	.	33	61	0	.	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	79	0	.	0	.	0	.	0	.	64	100	0	.	36	100	0	.	65	100	0	.	19	100	40	100	18	100	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	80	74	19	100	0	.	0	.	45	81	0	.	0	.	0	.	0	.	0	.	14	71	10	100	0	.	0	.	0	0	15	100	0	.	0	.	0	.	14	72	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	41	100	38	100	0	.	0	.	52	55	12	100	17	100	16	100	42	73	17	100	0	.	0	.	25	.	0	.	898	19
	submontane	341	57	80	70	0	.	342	54	0	.	0	.	0	.	68	73	528	23	0	.	78	100	4	100	107	57	0	.	9	100	60	100	0	.	109	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	268	50	88	91	158	73	0	.	34	100	0	.	35	100	0	.	0	.	0	.	0	.	0	.	28	100	52	89	0	.	17	71	0	.	210	47	13	100	11	100	31	86	78	74	0	.	0	.	0	.	63	70	552	74	295	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	100	0	.	36	100	0	.	23	100	0	.	108	91	2	100	35	100	129	71	82	56	0	.	239	69	25	100	5	100	187	56	0	.	0	.	3	100	0	.	0	.	45	78	0	.	0	.	34	72	0	.	81	100	133	50	110	55	67	85	0	.	5053	13
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	384	58	0	.	0	.	0	.	0	.	0	.	85	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	.	7	.	0	.	5	100	0	.	41	63	114	72	474	33	85	65	137	44	87	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	164	71	0	.	0	.	43	.	0	.	0	.	0	.	47	65	102	72	17	71	0	.	0	.	0	.	0	.	0	.	43	71	0	.	0	.	0	.	1834	19
	total	349	55	80	70	0	.	391	48	0	.	0	.	33	61	68	73	565	23	0	.	78	100	4	100	107	57	0	.	9	100	60	100	52	79	109	61	384	58	0	.	0	.	64	100	0	.	36	100	85	100	332	45	88	91	177	66	40	100	53	74	0	.	35	100	0	.	0	.	5	100	0	.	0	.	28	100	52	89	2	100	17	71	0	.	210	47	13	100	11	100	31	86	158	52	19	100	0	.	0	.	108	53	552	74	303	48	0	.	5	100	0	.	54	51	124	67	474	33	85	65	137	44	102	61	50	100	0	.	36	100	14	72	23	100	0	.	108	91	2	100	44	82	129	71	82	56	0	.	403	50	25	100	10	71	231	49	0	.	41	100	41	94	47	65	115	64	113	41	12	100	17	100	50	58	42	73	98	85	176	41	110	55	92	68	0	.	7799	10
sweet chestnut	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	22	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	.	36	72
	lower and upper montane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	100	0	.	0	.	22	100	0	0	4	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	.	70	70
	submontane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	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	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	70
	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	.	468	48	42	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	.	32	100	357	45	272	72	390	47	397	37	1190	31	464	53	257	56	1453	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	40	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5395	13
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	468	48	42	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	.	32	100	415	40	294	67	390	47	419	35	1190	31	468	52	257	56	1453	29	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	134	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5605	12
other broadleaves	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	100	0	.	0	.	0	.	2	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	.	2	100	1	100	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	5	100	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	44
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	39	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	75	3	100	1	100	3	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	4	100	0	.	0	.	0	.	4	100	0	.	3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	68	0	.	2	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	7	85	0	.	0	0	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	66	39	73	25	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	192	27
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	81	39	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	9	100	3	100	97	92	420	35	150	35	464	53	0	.	4	100	0	.	12	76	0	.	0	.	0	.	0	.	0	.	1	100	0	0	0	.	0	.	0	.	9	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	84	0	.	0	.	0	.	0	.	0	.	109	58	0	.	19	64	1	100	0	.	0	.	20	55	0	.	43	88	20	100	0	.	8	83	0	.	8	82	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	240	34	343	47	123	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2213	17
	lower and upper montane	110	93	0	.	0	.	76	85	0	.	75	69	318	41	164	32	1	100	105	32	5	100	29	93	45	58	0	.	3	100	46	85	81	42	29	60	0	.	146	49	82	58	51	78	0	.	106	50	0	.	313	41	31	55	36	93	90	52	0	.	30	50	0	.	0	.	24	60	88	52	2	100	42	45	3	100	25	67	104	51	46	73	154	34	12	100	18	68	5	75	141	69	16	87	77	63	63	54	99	66	0	0	0	.	0	.	0	.	214	75	0	.	162	48	337	48	69	52	316	53	67	54	161	51	23	62	102	69	0	.	3	71	11	71	74	65	25	68	0	.	134	70	0	.	1	100	24	53	3	100	12	79	18	100	6	100	82	100	5	100	13	100	0	.	0	.	116	49	7	63	130	68	0	.	51	50	4	100	0	.	0	.	58	.	0	.	5120	9
	submontane	162	43	174	36	140	44	22	58	0	.	0	.	96	72	50	49	582	26	3	.	91	55	25	60	12	100	0	.	20	100	185	65	0	.	148	58	0	.	0	.	31	87	34	90	0	.	0	.	0	.	81	48	97	57	27	65	5	100	0	.	0	.	7	100	-2	.	0	.	0	.	0	.	2	100	0	0	47	67	28	96	90	100	0	.	28	54	75	84	0	0	83	58	2	100	69	82	0	.	0	0	254	55	140	43	189	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	90	41	.	22	100	49	73	293	97	0	.	15	71	194	84	13	100	178	98	2	100	0	.	112	74	35	76	100	100	15	100	40	100	0	.	0	.	0	.	0	.	92	57	0	.	42	100	161	64	23	100	17	68	31	48	131	61	56	60	0	.	4683	12
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	65	0	.	0	.	0	.	0	.	363	50	49	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	.	7	100	220	61	107	85	158	54	223	38	1171	27	180	55	260	44	360	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	26	.	0	.	0	.	0	.	97	75	34	53	44	57	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	3326	13
	total	271	45	174	36	140	44	98	67	0	.	75	69	534	29	213	27	583	26	110	31	97	52	54	57	57	50	0	.	23	87	231	55	81	42	177	49	14	65	155	46	144	40	184	55	421	35	622	31	515	49	393	34	132	43	63	60	107	45	0	.	30	50	7	100	-2	100	24	60	91	51	6	76	45	43	3	100	71	49	145	41	136	70	157	34	39	48	94	69	5	75	224	48	18	77	155	48	63	54	99	66	254	55	140	43	189	64	116	55	448	47	128	71	325	36	560	33	1240	26	518	37	327	36	565	34	66	48	143	57	37	65	53	69	312	91	95	55	40	50	194	84	147	64	178	98	4	75	24	53	116	71	47	60	118	86	46	66	122	75	5	100	13	100	354	31	421	39	401	25	7	63	172	57	161	64	74	46	21	59	42	44	131	61	114	59	0	.	15548	6
indeterminable	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	.	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	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	lower and upper montane	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	submontane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0
	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	.	0	.	0	.	0	0
	total	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	.	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	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. 2279652. Birmensdorf, Swiss Federal Research Institute WSL

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