Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI2–NFI3
net increment
ownership (2 categories) · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI2/NFI3
grid: 1.4-km grid
change 1993/95–2004/06
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
ownership (2 categories)	main tree species	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%
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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.
public	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	.
	spruce	8	89	29	33	49	34	41	28	10	38	20	51	64	25	58	20	78	18	11	111	2	66	5	40	6	52	0	.	26	42	38	28	22	62	31	38	0	81	61	26	59	22	67	20	79	20	137	15	81	17	56	35	10	47	19	40	26	36	2	88	13	42	7	37	11	38	5	66	-2	360	30	29	12	102	4	126	15	47	42	30	11	51	23	50	38	29	22	45	8	72	23	37	6	57	23	40	-7	446	21	37	7	43	10	62	15	54	8	87	24	38	14	42	0	0	0	674	0	0	6	49	3	97	4	63	1	747	14	35	14	59	12	37	18	37	13	47	21	41	0	1113	13	71	5	113	7	51	25	24	7	84	17	27	10	70	6	85	18	46	0	1704	13	61	37	30	61	22	81	17	22	34	14	42	23	35	8	54	22	47	21	39	6	93	39	31	0	.	2034	4
	fir	10	55	6	49	8	38	32	30	4	68	14	46	18	34	46	20	36	23	22	39	1	74	4	137	4	64	0	.	5	45	7	55	15	55	21	43	0	.	4	86	0	337	12	28	3	149	6	42	0	72	56	17	11	49	7	55	6	47	8	43	10	45	9	37	8	45	24	33	-4	104	4	120	5	50	13	42	4	40	8	41	8	48	9	43	2	103	15	33	11	43	3	45	1	1048	5	50	8	44	14	37	8	71	3	59	2	100	0	0	4	37	2	97	0	.	0	.	0	0	3	90	8	64	0	2876	5	65	0	.	0	209	12	51	2	128	9	43	-2	220	0	.	10	53	2	144	17	42	8	53	9	41	12	28	4	43	6	66	14	47	0	966	5	75	6	48	5	55	18	32	9	46	5	47	2	73	3	65	1	105	3	71	0	100	6	61	0	.	698	6
	pine	-1	185	6	95	4	50	4	60	0	100	0	100	0	504	1	76	1	511	2	115	0	919	1	71	1	112	0	.	0	73	-1	316	0	.	-1	169	2	100	0	.	5	61	4	58	1	100	12	35	2	175	1	149	0	100	1	78	0	136	3	74	1	100	1	100	0	0	1	100	0	.	-1	177	0	107	3	90	0	.	2	177	0	100	0	0	1	167	-1	73	0	659	0	68	0	436	2	101	-1	73	0	100	1	81	1	103	2	69	0	100	2	119	0	84	0	.	0	.	0	.	0	.	0	.	0	100	0	100	0	.	2	62	1	77	0	.	0	.	0	.	0	100	0	100	-1	100	0	110	0	.	2	69	0	.	1	65	1	100	0	.	-1	100	0	.	1	468	8	30	0	2503	0	.	1	82	2	59	0	164	3	44	1	81	0	487	-1	116	0	.	83	19
	larch	0	8780	19	91	1	135	3	64	0	100	0	.	-1	267	0	100	6	55	-2	100	0	.	0	.	1	88	0	.	1	100	-1	304	0	.	0	.	0	.	0	100	4	60	2	213	5	50	28	28	61	17	0	911	0	100	1	81	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	1	74	0	71	0	100	4	91	-1	100	0	.	1	74	1	100	0	100	1	75	0	.	0	.	0	.	0	248	0	100	1	100	0	.	4	52	11	40	4	52	5	39	-2	114	0	72	23	22	-3	238	-3	140	0	.	0	.	0	100	5	45	1	73	0	.	0	100	1	107	0	.	0	71	0	0	0	.	0	334	0	.	2	66	0	.	0	.	0	.	0	100	37	29	25	18	13	25	0	100	0	.	0	100	0	.	0	.	0	631	1	85	1	61	0	.	280	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	123	16	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	32	3	59	1	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	18
	other conifers	1	91	1	71	2	100	2	53	0	100	0	.	0	.	0	.	2	63	1	89	3	100	0	.	0	.	5	.	0	110	0	.	0	.	0	.	0	.	0	.	0	83	0	.	0	.	0	388	0	196	0	100	2	100	4	89	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	100	0	100	0	.	0	.	0	100	0	.	0	100	0	100	0	96	0	70	0	.	0	100	0	.	0	100	0	.	0	.	3	97	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	100	0	.	0	.	0	100	0	.	0	100	0	.	0	.	1	100	4	95	0	100	0	75	0	.	0	.	0	.	3	85	0	.	5	100	0	.	0	.	0	100	0	0	0	100	0	.	0	100	1	55	0	.	0	100	0	.	0	.	0	.	0	.	39	24
	beech	34	19	21	28	26	45	41	24	2	118	4	43	20	38	19	30	59	24	30	31	6	71	20	34	23	28	0	.	4	48	2	207	10	32	17	31	0	.	5	136	2	76	9	34	-1	147	12	33	0	.	44	19	7	48	12	39	0	176	3	86	4	57	3	84	4	48	7	42	-5	104	3	70	3	245	13	50	4	54	2	310	22	27	-1	338	42	23	26	23	19	35	17	30	23	29	6	59	1	193	11	40	1	124	6	44	14	32	0	.	5	45	0	.	34	59	12	50	4	63	20	30	16	33	17	35	3	53	0	.	3	90	7	46	3	54	5	63	7	58	4	104	6	73	1	220	4	73	6	35	8	49	17	31	9	38	7	38	6	44	5	49	3	214	0	.	1	79	9	33	2	92	8	40	13	34	2	84	13	42	8	59	6	51	7	42	0	.	901	5
	maple	5	42	2	49	3	53	4	63	3	70	1	87	3	50	3	50	13	32	7	45	2	42	4	53	3	42	0	.	2	71	0	104	3	49	2	47	0	.	6	43	1	55	0	926	0	166	3	54	0	100	13	26	1	54	1	69	1	39	1	84	1	52	2	83	2	67	1	47	1	43	0	91	5	60	2	86	2	63	-1	113	0	425	2	50	3	47	3	78	0	1462	1	52	5	49	2	43	0	290	-1	208	2	72	4	67	1	72	2	81	1	92	0	100	0	240	0	.	0	55	2	51	1	105	0	.	3	57	0	100	0	.	0	48	2	55	3	41	5	61	0	.	2	52	1	92	1	86	1	62	2	40	2	48	2	56	0	100	2	94	0	100	0	100	1	94	1	62	3	45	1	107	2	54	4	55	2	64	0	822	6	73	1	72	3	77	0	.	171	8
	ash	4	56	1	150	4	54	2	90	0	.	0	207	1	45	4	62	12	33	4	42	5	75	3	93	4	56	0	.	2	62	5	71	5	58	5	44	0	.	4	63	4	69	0	63	1	56	4	61	0	100	9	43	1	147	0	78	0	100	0	563	1	76	0	79	0	100	0	.	2	156	0	86	2	82	6	46	7	47	3	52	1	70	0	100	5	40	2	40	1	46	3	86	2	46	2	62	0	.	2	76	2	43	3	94	0	21514	0	289	3	91	0	.	0	103	0	77	2	82	3	63	0	194	2	75	0	75	1	74	0	100	1	75	1	58	1	62	3	45	1	55	1	63	6	47	0	.	0	100	-2	126	3	56	3	54	1	100	0	64	0	.	5	101	0	72	0	470	0	1209	2	46	6	46	9	66	1	95	-1	245	-2	214	8	54	4	41	0	.	189	9
	oak	5	81	2	84	1	56	5	44	0	.	0	.	0	100	0	1493	7	27	1	100	0	799	3	100	-1	128	0	.	0	372	0	0	1	54	-1	199	5	58	0	.	0	100	0	78	0	.	2	100	0	.	2	63	-1	102	1	100	0	.	3	57	0	.	0	100	0	.	0	.	0	.	0	.	0	100	0	.	1	130	0	.	0	69	0	0	0	3676	0	72	0	90	1	48	0	3080	0	100	0	.	0	100	0	78	1	66	0	1570	0	.	0	.	0	.	1	54	0	106	0	171	-1	172	3	49	0	1336	0	100	0	.	0	.	0	224	0	215	0	.	-1	100	0	0	1	77	4	77	2	44	0	.	2	65	0	.	1	68	3	77	1	72	1	100	3	72	0	67	0	50	3	56	0	100	1	100	1	66	0	.	4	52	2	105	2	84	-1	304	0	.	68	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	65	0	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	.	-1	300	1	87	2	73	0	.	4	72	-4	125	1	141	2	190	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	100	0	.	0	.	0	.	0	.	1	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	75
	other broadleaves	8	56	1	104	5	48	5	47	0	378	-1	76	1	71	1	177	4	67	1	62	1	94	0	194	1	90	0	.	0	100	6	69	0	71	3	89	1	75	0	473	3	86	1	73	6	56	13	31	3	65	4	77	-3	112	1	114	-1	75	1	53	0	72	0	100	0	156	1	91	3	103	0	2874	2	80	0	100	-1	105	-2	184	6	56	0	83	3	81	2	92	1	52	4	48	6	45	1	67	0	485	1	61	5	62	8	56	0	610	4	65	9	35	5	66	9	44	5	54	4	59	3	103	4	46	3	69	1	86	1	101	2	88	5	79	6	87	0	86	1	100	1	189	2	54	2	88	0	79	0	85	0	372	0	65	0	120	0	534	1	89	0	100	0	89	5	64	2	95	8	39	5	91	2	52	1	115	0	100	0	72	1	112	3	80	1	68	0	.	202	10
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	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	74	19	89	28	102	23	138	16	20	34	37	38	108	18	130	15	218	13	78	24	21	34	41	31	41	25	4	.	39	33	57	27	57	34	79	23	7	56	80	24	78	19	95	17	94	19	220	11	165	12	185	14	28	38	47	28	33	36	21	33	30	31	22	32	25	30	39	27	-5	260	37	31	31	53	41	30	32	30	57	30	49	25	33	42	95	18	70	23	41	28	53	23	42	27	41	28	1	3737	49	26	28	29	38	34	37	31	18	46	58	22	27	27	51	41	16	49	14	36	56	21	32	34	26	39	13	67	16	32	22	42	41	27	33	31	30	33	33	35	6	52	38	34	21	45	32	31	40	21	28	36	52	20	36	28	24	39	48	33	5	98	29	48	97	20	105	15	138	14	42	30	39	26	56	27	15	39	43	34	40	31	26	40	59	27	0	.	4712	3
private	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	.
	spruce	3	72	1	61	8	96	-3	225	24	38	52	27	101	30	97	19	115	18	8	64	1	62	2	92	0	47	0	.	-1	249	17	39	4	250	16	32	0	.	4	50	32	28	5	56	34	31	26	31	5	96	29	31	19	51	28	41	47	56	3	100	22	28	3	79	4	63	29	29	7	65	2	203	9	56	-2	1070	9	69	5	90	7	43	47	35	3	71	2	99	-1	93	0	.	1	96	8	56	18	49	5	49	37	30	20	37	16	43	1	71	0	.	1	69	0	.	0	100	0	100	0	.	0	.	-1	116	1	207	6	48	3	75	3	68	12	38	25	31	11	80	5	73	7	52	0	1168	0	.	11	36	3	65	1	91	8	57	1	91	5	65	0	.	6	74	4	125	15	33	13	36	9	41	16	46	12	42	38	51	23	36	9	61	4	99	7	66	0	.	1166	6
	fir	3	49	1	91	7	100	0	4219	8	59	13	46	23	30	124	15	87	20	21	45	0	0	1	71	0	151	0	.	2	100	2	83	5	76	6	67	0	.	0	198	1	83	0	83	0	0	1	109	0	.	23	29	20	33	63	22	31	35	2	70	19	32	1	66	1	100	18	35	2	61	0	.	4	80	1	553	-2	133	4	64	8	51	33	32	0	.	3	86	6	77	1	71	5	43	4	75	8	54	0	264	17	45	6	44	6	69	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	1	132	-2	100	1	93	2	52	0	.	15	31	2	66	0	100	0	100	9	60	0	.	1	76	2	77	0	100	1	79	0	100	0	.	0	.	3	71	0	.	1	71	4	88	6	46	1	64	14	55	13	51	14	38	0	100	1	100	0	.	0	.	654	6
	pine	1	155	0	82	0	.	0	100	0	100	0	.	0	290	1	66	-1	196	-1	104	2	98	2	74	0	72	0	.	0	100	0	100	0	.	-1	102	-1	120	0	.	0	100	0	323	0	100	0	136	-1	121	0	100	0	.	1	100	0	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	-1	80	0	.	0	.	0	100	0	321	2	108	0	100	0	87	0	.	0	172	0	.	0	.	0	.	0	121	-2	78	3	82	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	100	0	.	0	100	0	100	0	.	0	.	0	.	0	.	0	100	0	.	0	100	0	.	0	.	0	100	0	.	0	100	0	0	0	.	0	.	0	.	3	58	1	76	2	58	0	.	0	100	-1	272	-1	137	1	89	2	61	1	117	0	183	0	.	15	49
	larch	1	73	0	100	0	.	0	100	0	100	1	100	1	163	0	100	1	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	81	0	.	0	.	4	47	1	79	0	100	3	43	2	87	0	.	0	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	100	0	.	0	.	7	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	2	100	0	100	0	100	0	.	1	100	0	.	0	100	0	100	-2	100	0	100	0	72	0	.	0	.	0	.	0	71	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	100	27	39	14	30	4	56	0	.	0	100	0	100	0	77	0	.	0	100	0	.	1	100	0	.	80	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	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	87	1	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	56
	other conifers	0	100	0	.	0	.	0	.	0	.	0	71	0	100	0	0	8	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	100	0	100	0	.	0	.	0	.	0	.	0	72	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	5	100	0	100	0	100	0	.	0	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	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	100	0	185	0	100	0	.	0	.	0	.	0	.	15	40
	beech	18	38	4	64	3	83	15	44	4	42	15	36	32	35	43	25	57	23	8	45	3	55	3	108	1	561	0	.	2	118	7	53	5	60	16	42	0	.	11	59	1	124	4	58	0	100	1	354	0	.	22	38	13	52	22	39	23	34	2	83	3	46	0	842	2	77	6	42	4	110	0	100	2	161	9	43	6	63	0	1830	7	66	12	66	7	49	9	60	2	67	0	78	14	46	4	69	2	60	5	58	11	36	3	71	15	35	0	.	0	.	0	.	1	100	10	47	6	57	2	80	4	77	2	79	3	79	0	100	-1	146	3	53	0	100	4	49	-2	129	1	83	6	40	3	101	0	.	2	66	4	70	8	49	6	51	4	60	4	54	0	.	15	70	0	100	0	100	8	37	2	96	1	71	8	45	4	103	18	40	8	57	6	42	4	55	0	.	587	7
	maple	1	93	3	62	0	100	-1	108	1	94	2	156	7	48	6	29	3	56	4	43	2	63	1	70	-5	91	0	.	1	77	0	328	1	141	3	63	0	874	3	63	1	81	0	100	1	87	1	76	0	.	0	13970	0	322	2	60	8	57	0	71	0	7265	0	.	0	.	0	57	3	72	0	100	1	54	3	68	1	665	0	229	2	42	3	132	0	100	0	100	1	64	0	71	3	64	1	74	2	46	1	75	5	58	0	107	2	71	0	.	0	.	0	.	1	91	3	67	1	80	-5	100	0	100	0	75	1	103	-3	115	0	.	1	48	3	49	2	41	0	102	0	.	2	83	2	100	0	.	0	100	0	77	1	89	0	100	0	100	0	100	0	.	1	67	0	100	1	52	3	47	0	147	1	93	1	84	-1	192	0	1139	0	.	0	123	0	.	0	.	85	16
	ash	4	71	3	88	0	100	0	100	1	159	5	70	1	134	11	37	10	32	1	93	1	65	0	100	-1	141	0	.	1	71	1	158	2	49	7	57	2	96	9	75	1	71	0	0	1	73	2	64	3	100	8	35	8	93	9	61	2	50	0	100	0	.	0	.	0	.	0	75	2	60	0	100	1	89	13	42	3	57	0	93	4	73	8	37	0	.	0	100	1	100	0	258	2	65	8	59	3	67	4	92	16	43	3	61	5	53	1	100	0	.	1	91	1	71	3	46	2	185	2	48	0	514	1	78	1	70	1	97	0	.	1	56	1	77	0	77	2	53	0	130	0	.	0	100	0	.	0	.	0	.	1	67	0	742	1	61	4	57	0	.	1	152	2	67	2	50	2	80	5	71	2	73	1	50	1	523	5	44	0	.	2	64	0	100	0	.	206	10
	oak	3	60	2	57	1	100	5	88	0	.	0	100	1	62	0	0	6	60	0	100	0	100	0	100	0	.	0	.	0	100	1	100	0	110	1	96	3	85	0	.	0	.	2	100	0	100	-1	324	0	100	-2	122	1	58	3	94	2	61	0	100	0	.	0	.	0	.	0	.	0	100	0	.	0	.	-1	127	0	.	0	.	0	.	0	100	1	85	0	.	0	100	0	.	0	58	0	631	0	.	0	.	1	60	5	73	2	39	0	.	0	.	0	.	1	100	3	51	3	61	-1	146	1	225	0	184	0	100	1	100	1	100	2	73	0	.	0	.	0	10410	0	.	0	.	-1	106	2	.	0	.	4	59	1	74	0	59	5	50	-1	100	0	.	3	77	0	87	2	70	1	60	0	.	0	.	0	71	1	90	0	100	0	100	1	78	0	.	0	.	65	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-4	86	0	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	100	4	97	-1	91	7	60	1	376	-1	596	2	104	2	61	8	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	100	0	.	-1	229	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	70
	other broadleaves	11	83	4	76	0	100	1	102	0	126	1	69	5	50	5	44	5	44	0	250	0	171	0	58	0	100	0	.	-1	95	0	852	0	143	0	169	1	89	4	101	0	121	0	173	5	80	3	67	4	64	3	51	1	130	1	138	0	445	0	.	0	74	0	.	0	.	0	102	-1	106	0	100	0	156	0	217	1	154	1	58	3	46	-1	270	0	100	0	.	0	100	-1	100	0	83	2	131	0	132	-3	58	7	52	3	74	2	77	2	71	1	100	1	64	-1	170	-1	214	13	37	0	455	8	81	1	99	0	489	0	246	0	137	1	71	0	211	1	57	0	2422	1	100	2	126	2	100	0	.	0	100	0	100	0	131	0	71	2	80	0	112	0	.	0	234	5	53	4	37	0	526	-1	136	0	100	3	81	0	407	-1	103	0	50	1	49	0	100	0	.	111	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	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	45	33	20	34	18	60	17	71	38	32	89	24	172	21	286	12	290	13	41	30	9	54	9	61	-4	167	0	.	4	61	28	33	17	72	48	27	5	89	31	52	40	26	12	39	42	27	33	28	13	57	83	21	64	27	128	22	112	31	7	64	46	24	4	102	6	61	54	24	16	51	2	197	17	50	29	95	19	50	10	69	30	33	103	23	21	53	14	55	8	66	1	333	25	38	27	39	33	35	12	58	94	22	40	29	50	25	5	60	5	97	2	125	9	50	20	39	26	39	-1	652	15	59	11	54	7	55	3	110	4	81	15	36	16	36	48	23	13	76	7	61	18	41	15	48	2	.	14	34	14	45	11	46	17	40	13	45	12	54	0	.	29	52	41	31	39	20	36	26	21	36	21	42	38	31	55	41	60	24	20	48	17	41	12	52	0	.	3005	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	.
	spruce	11	69	31	32	57	32	38	35	34	29	72	24	166	21	154	14	192	13	19	69	3	48	7	37	6	51	0	.	25	44	55	23	26	67	47	27	0	81	66	25	92	17	72	19	112	17	163	13	86	17	84	25	29	37	47	29	73	38	5	70	35	23	10	36	14	33	34	27	4	230	32	31	21	63	2	1077	24	39	46	29	18	36	70	29	40	27	24	42	6	89	23	37	6	51	31	33	11	301	26	32	44	26	30	32	31	34	9	74	24	38	15	41	0	0	0	512	0	100	6	49	3	97	3	112	1	386	19	29	17	50	15	32	29	27	37	26	32	38	5	105	20	50	5	115	7	51	37	20	10	60	18	26	17	46	7	70	23	39	0	1704	19	48	41	30	76	19	94	15	31	27	30	31	35	27	46	43	45	29	30	33	10	69	46	28	0	.	3198	3
	fir	13	44	7	44	15	49	32	31	12	45	26	32	41	23	169	12	124	15	43	29	1	74	5	111	4	61	0	.	7	43	9	46	20	45	27	37	0	.	4	96	1	219	12	28	3	149	7	39	0	72	79	15	31	28	70	21	37	30	10	37	29	26	9	35	9	42	42	24	-1	293	4	120	9	45	14	68	2	131	12	35	16	35	42	27	2	103	18	31	17	38	4	40	6	109	9	43	17	35	14	38	25	38	9	36	7	58	0	0	4	37	2	97	0	.	0	.	0	100	3	90	8	64	0	2876	6	58	-2	100	1	100	14	43	2	128	24	25	0	1406	0	100	10	51	11	56	17	42	9	48	11	37	13	28	6	38	6	65	14	47	0	966	8	54	6	48	6	50	23	31	16	33	6	42	15	49	16	44	16	36	3	68	1	85	6	61	0	.	1348	4
	pine	0	942	7	92	4	50	4	60	0	72	0	100	1	269	1	54	-1	506	1	241	2	100	3	54	1	95	0	.	0	2431	-1	198	0	.	-2	104	1	252	0	.	5	60	4	65	1	76	12	34	2	260	1	107	0	100	2	62	0	172	3	74	1	76	1	100	0	0	1	100	0	.	-1	177	0	107	2	128	0	.	2	177	0	92	0	321	3	91	-1	60	0	584	0	68	0	1407	2	101	-1	73	0	100	2	68	-1	168	5	56	0	100	2	119	0	84	0	.	0	.	1	100	0	.	0	.	1	72	0	100	0	100	2	60	1	77	0	.	0	.	0	.	0	74	0	100	-1	112	0	110	0	.	2	67	0	.	1	61	1	100	0	.	-1	100	0	.	4	179	9	28	2	120	0	.	1	102	2	106	-1	107	3	40	3	49	2	137	-1	132	0	.	98	18
	larch	1	76	20	90	1	135	3	70	0	75	1	100	0	4551	0	73	6	49	-2	100	0	.	0	.	1	88	0	.	1	100	-1	304	0	.	1	81	0	.	0	100	8	39	3	125	6	46	32	26	63	17	0	911	0	60	1	81	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	1	74	0	71	1	75	4	85	-1	100	0	.	8	83	1	100	0	100	1	75	0	.	0	.	0	.	0	248	0	129	3	71	0	100	4	53	11	40	5	47	5	39	-1	135	1	65	21	24	-3	243	-2	152	0	.	0	.	0	100	5	43	1	73	1	100	0	100	1	107	0	.	0	71	0	0	0	.	0	334	0	.	3	56	0	.	0	.	0	.	0	76	64	24	39	16	17	23	0	100	0	100	1	72	0	77	0	.	0	183	1	85	2	56	0	.	361	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	100	0	.	1	123	16	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	31	4	48	1	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	17
	other conifers	1	81	1	71	2	100	2	53	0	100	0	71	0	100	0	0	10	41	1	89	3	100	0	.	0	.	5	.	0	110	0	.	0	.	0	0	0	.	0	100	0	204	0	.	0	.	0	388	0	196	0	59	2	90	4	89	0	.	0	.	0	.	0	100	0	.	0	.	0	100	0	.	0	100	6	96	0	100	0	100	0	100	0	100	2	80	0	100	0	96	0	70	0	.	0	100	0	.	0	100	0	100	0	.	3	91	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	100	0	100	0	.	0	100	0	.	0	100	0	.	0	.	1	100	4	95	0	100	0	75	0	.	0	.	0	.	3	85	0	.	5	100	0	.	0	.	0	100	0	0	0	100	0	.	0	100	1	53	0	185	0	88	0	.	0	.	0	.	0	.	54	21
	beech	52	18	25	25	28	41	56	21	5	46	19	29	52	26	62	20	116	17	38	27	9	52	23	32	24	33	0	.	7	51	9	64	15	29	33	26	0	.	16	58	3	68	13	30	0	192	12	36	0	.	66	18	20	38	34	29	23	34	4	62	8	38	3	102	6	42	13	30	-1	752	4	66	6	159	23	35	11	43	2	418	30	26	12	74	49	21	35	23	21	32	17	29	36	25	10	45	3	69	16	33	12	35	9	38	29	24	0	.	5	45	0	.	35	58	22	34	11	42	22	28	19	31	19	32	6	49	0	100	2	135	10	35	4	49	9	40	5	100	5	77	12	40	4	97	4	73	8	31	12	40	25	26	15	30	11	32	10	34	5	49	18	67	0	100	1	78	18	25	4	67	8	37	21	27	6	74	31	29	16	41	12	33	10	33	0	.	1486	4
	maple	6	38	5	43	3	49	3	82	4	58	2	120	10	37	9	25	16	28	11	32	4	39	5	45	-2	253	0	.	3	54	0	148	4	49	5	40	0	874	9	35	2	46	0	2330	1	102	4	44	0	100	13	32	1	108	3	48	9	49	2	71	1	95	2	83	2	67	1	38	4	52	1	67	6	52	5	54	2	161	-2	102	3	61	5	88	3	48	3	77	1	208	2	42	8	39	3	40	2	51	0	859	7	45	4	65	2	54	2	81	1	92	0	100	1	176	3	67	1	54	-3	179	1	83	0	75	4	50	-3	123	0	.	1	37	5	36	5	30	5	58	0	.	4	51	3	71	1	86	1	58	2	36	2	43	3	54	0	81	2	110	0	100	1	59	1	76	2	40	6	33	1	180	3	47	5	49	1	191	0	8632	6	73	1	62	3	77	0	.	257	7
	ash	8	45	5	76	4	53	2	80	1	159	5	69	2	76	15	32	22	23	5	38	7	60	4	89	3	75	0	.	3	49	6	65	7	44	12	39	2	96	13	56	5	62	0	63	2	46	7	46	3	93	18	28	9	85	9	58	3	46	0	199	1	76	0	79	0	100	0	75	4	87	0	523	3	60	19	32	10	37	3	48	5	60	9	37	5	40	2	38	2	49	3	82	4	40	10	50	3	67	6	69	18	39	6	55	5	67	1	164	3	91	1	91	1	58	3	42	4	109	5	42	0	1536	3	61	1	57	2	63	0	100	2	45	2	47	1	49	5	34	0	167	1	63	6	45	0	.	0	100	-2	126	4	48	3	61	2	56	4	52	0	.	6	89	2	57	2	51	3	97	8	51	8	39	10	59	2	251	4	71	-2	214	10	44	4	38	0	.	394	7
	oak	7	55	4	49	1	57	10	49	0	.	0	100	1	59	0	1493	13	32	1	92	0	753	3	99	-1	128	0	.	0	918	1	100	1	74	1	282	8	48	0	.	0	100	2	79	0	100	1	223	0	100	1	330	0	491	4	75	2	61	3	55	0	.	0	100	0	.	0	.	0	100	0	.	0	100	-1	127	1	130	0	.	0	69	0	100	1	463	0	72	0	262	1	48	0	835	0	333	0	.	0	100	1	48	6	61	2	96	0	.	0	.	0	.	1	56	3	47	4	57	-2	111	4	56	0	228	0	71	1	100	0	100	2	74	0	215	0	.	-1	166	0	0	1	77	3	112	4	56	0	.	6	45	1	74	2	55	8	42	0	286	1	100	6	53	1	55	2	58	4	47	0	100	1	100	1	55	1	90	4	49	2	97	3	58	-1	304	0	.	133	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-1	308	0	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	4	126	0	539	9	49	1	376	2	383	-1	422	3	58	10	63	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	71	0	.	0	100	0	.	0	.	0	100	0	.	0	449	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	53
	other broadleaves	20	54	5	65	4	55	6	43	0	196	0	401	7	43	5	44	9	39	2	63	1	161	1	128	1	92	0	.	-1	71	6	69	0	641	3	97	1	60	4	128	3	79	0	257	11	47	16	29	7	46	7	49	-1	267	1	89	-2	86	1	53	0	54	0	100	0	156	1	73	1	266	0	4202	2	76	0	717	0	3522	-1	288	9	41	-1	336	3	77	2	92	1	51	2	93	6	42	3	75	0	144	-1	119	12	40	12	45	2	109	6	49	10	33	7	53	9	48	4	78	17	32	3	139	12	57	4	56	1	88	1	118	2	83	6	66	6	83	2	48	1	161	1	100	4	80	4	66	0	119	0	72	0	495	0	349	-1	66	2	90	1	73	0	100	0	150	10	42	5	39	8	46	4	120	2	49	4	67	0	1211	-1	122	1	79	3	65	1	64	0	.	313	9
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	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	119	17	109	24	120	21	155	16	57	24	126	20	279	15	416	9	508	9	119	18	30	29	49	27	37	32	4	.	43	31	84	21	74	31	127	17	12	49	111	22	119	15	107	16	136	15	253	10	178	12	268	12	92	22	175	17	145	25	28	29	76	18	26	31	31	27	92	18	11	146	39	31	48	38	70	43	51	26	68	27	80	20	135	20	116	17	84	22	48	26	54	23	67	22	68	23	34	102	60	24	122	18	78	22	87	19	23	38	64	22	29	26	60	35	36	31	40	28	56	24	47	30	37	32	20	47	19	32	25	38	56	22	49	24	78	19	47	33	14	40	56	26	36	33	34	30	55	18	42	28	63	18	53	23	37	30	60	28	5	98	59	35	137	17	145	12	174	12	63	23	59	22	94	20	71	33	103	20	59	26	42	29	71	24	0	.	7707	2

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. 2293214. Birmensdorf, Swiss Federal Research Institute WSL

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