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
number of stems
giant (yes/no) · main tree species
regional demarcation: forest district (2024)
unit: n/ha
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
giant (yes/no)	main tree species	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
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	.
no	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	55	25	114	32	181	28	110	19	298	17	290	16	310	6	152	8	161	12	128	13	37	41	52	62	52	34	50	.	295	24	322	16	281	12	219	17	8	70	225	18	249	11	233	11	238	9	235	7	158	9	118	15	193	19	175	16	300	11	84	55	167	21	141	24	96	24	153	16	235	25	322	15	216	17	239	17	214	26	186	15	132	29	187	15	138	20	98	21	116	35	141	37	50	43	163	21	352	13	134	16	198	21	190	26	161	34	108	24	129	17	167	20	0	.	9	63	0	100	21	41	10	48	34	32	158	22	273	21	314	21	99	24	119	18	223	18	181	22	173	33	137	20	146	33	74	31	257	10	116	26	134	29	158	22	65	57	132	38	59	26	199	26	114	11	205	12	185	10	203	24	160	28	148	21	162	28	169	22	174	27	120	25	257	26	0	.	172	2
	fir	35	35	10	38	31	51	65	19	78	26	80	22	23	17	144	8	82	12	108	12	11	69	24	38	49	27	0	.	37	50	19	33	83	21	66	29	0	.	27	36	10	37	19	24	6	42	8	29	0	100	98	11	104	18	154	12	96	19	87	32	104	20	113	24	93	24	154	21	43	26	37	41	45	31	60	19	31	37	31	25	51	27	80	20	9	40	54	36	59	29	26	40	69	21	45	28	69	21	57	32	71	26	33	35	25	67	0	.	13	36	12	99	0	.	0	.	0	100	11	45	25	37	18	59	116	23	28	97	47	47	60	31	20	44	112	25	27	34	10	96	64	33	78	36	107	22	29	33	51	26	76	20	43	31	47	34	125	45	139	33	52	66	9	44	9	45	29	27	87	28	15	42	32	36	76	19	37	32	8	61	6	98	13	57	0	.	46	3
	pine	16	43	19	35	19	61	7	38	2	98	1	99	9	32	1	52	6	27	15	40	21	54	32	45	30	36	0	.	8	55	9	59	0	.	15	50	75	81	0	.	25	38	30	32	1	100	22	24	64	18	14	33	0	.	8	64	10	72	12	69	8	89	12	98	0	.	5	99	0	.	35	89	4	70	5	54	0	.	21	52	3	83	3	53	31	39	5	53	17	37	14	78	34	44	20	50	1	76	2	98	12	41	8	54	33	37	1	99	11	68	11	91	0	.	0	.	8	99	0	.	0	.	3	75	31	74	5	97	39	49	1	99	0	.	0	.	0	.	10	80	0	99	18	92	4	81	0	.	8	63	0	.	8	48	1	96	0	.	28	95	0	.	71	21	51	24	31	26	0	.	7	80	22	33	7	50	23	29	22	40	37	32	10	43	0	.	17	7
	larch	4	55	19	65	6	81	5	67	1	98	3	99	2	38	0	74	4	55	2	100	0	.	0	.	2	75	0	.	17	98	5	79	0	.	3	89	0	.	0	99	18	33	25	25	10	51	36	15	75	9	0	70	3	77	1	99	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	1	99	1	70	8	92	20	33	1	99	0	.	10	45	0	99	3	99	1	99	0	.	0	.	0	.	2	70	4	87	11	71	1	99	27	29	45	25	37	35	26	39	3	72	1	59	68	18	49	36	25	43	0	.	0	.	1	98	12	43	2	79	2	99	2	98	14	96	0	.	2	98	0	.	0	.	4	99	0	.	10	52	0	.	0	.	0	.	0	.	110	11	90	14	38	17	1	98	1	98	3	69	2	77	0	.	7	59	4	80	8	57	0	.	18	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	100	0	.	4	37	50	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	99	0	.	3	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	24	18	27	8	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	12
	other conifers	1	70	1	69	3	99	7	85	4	98	1	99	0	100	0	.	5	32	4	100	17	98	0	.	0	.	440	.	4	68	0	.	0	.	0	.	0	.	1	99	5	50	1	99	0	.	0	.	9	43	0	100	5	61	4	80	0	.	0	.	0	.	2	98	0	.	0	.	5	99	0	.	1	99	5	79	1	99	2	100	1	99	3	99	2	84	2	99	7	84	6	59	0	.	0	.	0	.	1	99	3	99	0	.	8	76	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	2	98	0	.	16	54	0	.	1	99	0	.	0	.	29	96	17	65	4	98	4	68	0	.	0	.	0	.	2	78	0	.	39	97	0	.	0	.	3	100	0	100	1	70	0	.	2	98	8	57	15	66	1	76	0	.	0	.	0	.	0	.	2	14
	beech	155	12	100	17	104	19	156	18	68	29	95	22	62	19	82	12	123	9	183	10	82	26	181	16	238	14	0	.	122	28	101	21	96	28	126	20	0	.	103	23	31	30	48	24	3	68	16	32	0	.	127	10	123	21	70	20	94	18	63	25	76	25	112	24	74	22	69	21	132	30	23	50	133	25	60	29	145	29	81	22	180	20	131	18	226	13	141	16	205	15	116	19	223	13	111	20	28	36	88	21	54	20	59	27	110	16	0	.	17	39	0	.	154	26	141	24	81	25	121	22	175	24	128	25	102	29	1	97	29	65	85	32	40	42	62	30	90	26	38	64	127	21	81	35	67	30	58	24	79	21	168	20	77	24	185	21	91	26	105	28	107	27	0	.	5	85	53	22	49	26	108	31	58	26	102	20	105	18	95	26	84	21	62	21	0	.	83	2
	maple	37	19	18	30	19	32	22	43	54	56	27	35	22	16	12	19	10	22	35	17	19	42	53	25	27	27	0	.	21	56	11	51	27	23	13	37	11	63	41	27	6	43	3	46	11	42	4	38	0	100	27	15	25	32	8	38	15	26	18	37	16	31	41	53	33	71	7	36	39	22	13	49	26	44	22	25	22	35	31	32	32	33	46	23	17	44	17	54	32	25	30	29	48	24	32	35	24	33	23	51	56	46	20	34	15	48	12	88	3	99	1	99	5	45	7	56	10	41	7	67	5	51	0	.	40	38	13	65	3	98	41	41	65	35	55	24	50	62	0	.	24	30	26	86	13	59	6	45	17	29	27	19	31	47	6	97	9	53	7	93	13	70	2	71	4	43	16	24	24	41	23	47	27	33	43	30	17	27	45	50	28	50	13	60	0	.	18	4
	ash	33	22	27	35	22	31	11	42	14	56	46	33	7	32	13	23	12	22	11	39	65	43	30	68	23	54	0	.	9	57	19	56	33	32	28	36	22	72	28	48	5	46	2	65	16	56	6	34	5	87	37	20	31	52	20	36	14	43	16	81	13	77	4	73	0	.	2	99	43	48	15	65	9	48	63	26	53	34	13	42	27	40	30	32	15	44	3	55	13	49	28	47	33	45	39	39	20	85	13	52	37	32	37	29	29	30	13	74	5	88	4	78	10	48	19	38	41	30	11	43	5	50	14	53	18	71	22	55	6	98	20	36	11	53	13	50	55	32	26	52	8	58	21	36	0	.	2	99	9	55	37	55	33	43	13	45	43	38	0	.	76	48	4	36	8	47	12	29	46	40	29	25	29	46	33	30	30	36	18	54	33	38	12	45	0	.	17	5
	oak	20	35	26	45	5	63	24	35	0	.	0	99	1	52	1	87	13	21	1	100	18	33	33	90	20	55	0	.	8	45	1	99	6	49	11	45	498	26	0	.	0	100	5	86	0	100	5	58	1	100	7	32	7	43	5	78	5	80	60	59	0	.	5	68	0	.	0	.	2	98	0	.	0	99	14	88	8	64	0	.	4	59	1	99	24	24	2	67	9	49	8	63	27	56	2	60	0	.	3	99	12	45	32	44	15	32	0	.	0	.	0	.	12	43	27	31	44	25	20	67	32	53	12	47	3	73	6	97	2	98	29	60	21	68	0	.	14	76	0	.	14	73	40	31	104	46	0	.	89	45	4	85	12	39	126	43	18	74	0	.	43	53	3	44	9	45	9	30	1	98	1	98	3	67	2	79	6	44	9	38	14	41	8	59	0	.	10	8
	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	.	18	42	1	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	99	33	28	49	45	109	34	117	22	204	18	23	30	73	32	200	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	84	0	.	6	97	0	.	0	.	1	100	0	.	6	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	9
	other broadleaves	39	31	67	25	33	36	15	40	17	52	18	48	25	18	14	31	19	28	20	23	73	31	30	48	39	31	0	.	40	49	22	31	15	31	29	39	62	46	32	29	21	30	14	42	41	23	51	18	30	36	36	18	19	28	7	45	36	39	14	50	17	47	21	86	24	73	15	40	26	41	31	47	16	31	11	89	73	39	42	30	46	42	27	28	9	37	18	52	17	37	69	35	30	32	24	30	20	31	53	57	53	36	60	36	30	46	115	33	45	25	43	29	113	23	151	19	204	16	111	20	61	26	108	18	32	37	110	56	29	37	55	32	71	32	38	33	13	44	81	51	32	43	56	53	10	49	8	45	29	60	15	36	24	57	57	55	26	48	13	93	10	66	35	21	39	28	55	17	11	48	27	41	25	31	21	33	3	56	58	32	27	64	17	38	0	.	38	4
	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	395	6	400	11	423	12	422	7	535	11	560	10	461	5	418	4	436	5	507	5	344	14	434	14	478	10	490	.	560	13	509	9	540	8	509	8	676	18	457	11	372	9	381	7	325	7	404	5	392	5	465	5	510	9	453	8	570	6	353	14	401	10	452	9	321	16	404	8	525	11	476	13	461	10	478	8	557	12	427	10	476	12	508	9	480	8	342	9	479	10	439	11	515	7	436	11	514	10	375	10	499	9	452	12	428	12	286	15	302	9	325	11	429	11	475	9	594	8	395	8	435	12	542	8	501	11	457	15	493	13	401	13	352	15	506	13	432	12	381	18	423	9	471	11	384	13	360	9	404	11	461	10	407	11	499	13	489	18	351	17	500	13	374	5	439	6	442	6	422	12	372	12	355	9	463	11	391	9	436	11	354	12	400	16	0	.	437	1
yes	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	99	0	.	0	.	1	33	1	37	0	50	0	70	0	.	1	98	0	.	0	.	0	.	1	70	0	.	1	70	0	.	2	42	0	70	0	70	1	37	1	49	0	57	0	70	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	1	70	1	99	1	70	2	39	0	.	2	45	0	.	0	.	0	.	0	.	0	.	0	.	0	99	1	69	0	.	0	.	0	99	2	56	1	50	1	69	0	.	0	.	0	.	0	100	0	.	1	99	0	.	1	97	1	98	1	69	2	72	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	44	0	100	0	50	0	.	0	.	0	.	0	.	0	.	2	73	0	.	0	.	0	.	0	10
	fir	0	.	0	.	0	.	1	70	0	.	1	99	0	100	2	25	1	47	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	70	0	.	0	100	0	.	0	70	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	1	99	0	.	0	99	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	99	0	.	0	.	1	98	1	99	1	99	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	16
	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	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	100	1	37	0	.	0	.	0	.	0	.	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	99	0	.	0	.	0	.	0	.	0	.	1	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	39	1	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	20
	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	100	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	57	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	38
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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
	beech	0	.	0	99	0	.	0	99	0	.	0	.	0	70	0	.	0	58	0	.	2	67	0	.	0	99	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	100	0	100	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	99	0	.	0	.	1	68	0	.	0	.	0	.	0	.	0	99	0	.	2	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	99	0	.	1	99	0	.	0	.	0	.	0	.	0	24
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	62
	ash	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	72
	oak	0	99	0	99	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	1	98	1	98	1	98	0	.	0	33
	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	100	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	.	3	57	0	99	0	.	2	82	2	51	2	48	0	.	2	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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
	other broadleaves	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	98	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	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	46
	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	99	1	49	0	99	1	48	0	.	1	99	1	27	3	23	1	30	0	57	2	67	2	69	0	99	0	.	1	98	1	70	0	.	1	61	0	.	3	36	0	70	1	44	1	34	1	32	1	27	0	49	1	73	0	.	0	.	0	.	0	.	1	98	0	.	0	99	0	.	0	.	1	70	1	99	1	70	2	39	0	.	3	40	0	.	1	99	0	.	1	99	0	99	0	99	0	99	3	41	1	99	1	98	0	99	3	50	5	38	1	56	2	99	2	82	2	51	4	32	0	.	3	43	0	.	1	97	2	55	2	56	2	59	0	99	1	98	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	1	97	0	.	0	.	1	26	1	42	1	40	0	.	1	98	1	99	1	99	1	99	3	59	1	98	1	98	0	.	1	7
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	55	25	114	32	181	28	111	19	298	17	290	16	311	6	152	8	162	12	129	13	37	41	53	61	52	34	50	.	295	24	322	15	281	12	220	17	8	70	227	18	250	11	234	11	239	9	236	7	158	9	119	15	193	19	175	16	300	11	84	55	167	21	141	24	96	24	153	16	235	25	322	15	217	17	239	17	215	26	187	15	132	29	189	15	138	20	98	21	116	35	141	37	50	43	163	21	352	13	135	16	198	21	190	26	162	34	110	24	131	17	168	20	0	.	9	63	0	100	22	41	10	48	35	32	158	22	274	21	315	20	100	24	121	18	223	18	181	22	173	33	137	20	146	33	74	31	257	10	116	26	134	29	158	22	65	57	132	38	59	26	199	26	115	11	205	12	186	10	203	24	160	28	148	21	162	28	169	22	176	27	120	25	257	26	0	.	172	2
	fir	35	35	10	38	31	51	66	19	78	26	80	22	23	17	146	8	82	12	108	12	11	69	24	38	49	27	0	.	37	50	19	33	83	21	66	29	0	.	27	36	10	37	20	24	6	42	8	28	0	100	98	11	104	18	154	12	96	19	87	32	104	20	114	23	93	24	154	21	43	26	37	41	45	31	60	19	31	37	31	25	51	27	81	20	9	40	54	36	59	29	27	40	69	21	45	28	69	21	57	31	71	26	33	35	25	67	0	.	13	36	12	99	0	.	0	.	0	100	11	43	25	37	19	58	116	23	28	97	47	46	60	31	21	44	112	25	28	34	10	96	64	33	78	36	107	22	29	33	51	26	76	20	43	31	47	34	125	45	139	33	52	66	9	44	9	45	29	27	87	28	15	42	32	36	76	19	37	32	8	61	6	98	13	57	0	.	47	3
	pine	16	43	19	35	19	61	7	38	2	98	1	99	9	32	1	52	6	27	15	40	21	54	32	45	30	36	0	.	8	55	9	59	0	.	15	50	75	81	0	.	25	38	30	32	1	100	22	24	64	18	14	33	0	.	8	64	10	72	12	69	8	89	12	98	0	.	5	99	0	.	35	89	4	70	5	54	0	.	21	52	3	83	3	53	31	39	5	53	17	37	14	78	34	44	20	50	1	76	2	98	12	41	8	54	33	37	1	99	11	68	11	91	0	.	0	.	8	99	0	.	0	.	3	75	31	74	5	97	39	49	1	99	0	.	0	.	0	.	10	80	0	99	18	92	4	81	0	.	8	63	0	.	8	48	1	96	0	.	28	95	0	.	71	21	51	24	31	26	0	.	7	80	22	33	7	50	23	29	22	40	37	32	10	43	0	.	17	7
	larch	4	55	19	64	6	81	5	67	1	98	3	99	2	38	0	74	4	55	2	100	0	.	0	.	2	75	0	.	17	98	5	79	0	.	3	89	0	.	0	99	18	33	25	25	10	51	36	15	76	9	0	70	3	77	1	99	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	1	99	1	70	8	92	20	33	1	99	0	.	10	45	0	99	3	99	1	99	0	.	0	.	0	.	2	70	4	87	11	71	1	99	29	28	45	25	37	35	26	39	3	72	1	59	69	18	49	36	25	43	0	.	0	.	1	98	12	43	2	79	2	99	2	98	14	96	0	.	2	98	0	.	0	.	4	99	0	.	10	52	0	.	0	.	0	.	0	.	110	11	91	14	38	17	1	98	1	98	3	69	2	77	0	.	7	59	4	80	8	57	0	.	19	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	100	0	.	4	37	50	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	99	0	.	3	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	24	19	27	8	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	12
	other conifers	1	70	1	69	3	99	7	85	4	98	1	99	0	100	0	.	5	32	4	100	17	98	0	.	0	.	440	.	4	70	0	.	0	.	0	.	0	.	1	99	5	50	1	99	0	.	0	.	9	43	0	100	5	61	4	80	0	.	0	.	0	.	2	98	0	.	0	.	5	99	0	.	1	99	5	79	1	99	2	100	1	99	3	99	2	84	2	99	7	84	6	59	0	.	0	.	0	.	1	99	3	99	0	.	8	76	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	2	98	0	.	16	54	0	.	1	99	0	.	0	.	29	96	17	65	4	98	4	68	0	.	0	.	0	.	2	78	0	.	39	97	0	.	0	.	3	100	0	100	1	70	0	.	2	98	8	57	15	66	1	76	0	.	0	.	0	.	0	.	2	14
	beech	155	12	100	17	104	19	157	18	68	29	95	22	62	19	82	12	124	9	183	10	84	26	181	16	238	14	0	.	122	28	101	21	96	28	127	20	0	.	103	23	31	30	48	24	3	66	16	32	0	.	127	10	124	21	70	20	94	18	63	25	76	25	112	24	74	22	69	21	132	30	23	50	133	25	60	29	145	29	81	22	180	20	131	18	226	13	141	16	205	15	116	19	224	13	111	20	28	36	89	20	54	20	59	27	110	16	0	.	18	39	0	.	155	26	141	24	81	25	121	22	175	24	128	25	102	29	1	97	30	64	85	32	40	42	62	30	90	26	38	64	127	21	81	35	67	30	58	24	79	21	168	20	77	24	185	21	91	26	105	28	107	27	0	.	5	85	53	22	49	26	108	31	59	26	102	20	105	18	95	26	84	21	62	21	0	.	83	2
	maple	37	19	18	30	19	32	22	43	54	56	27	35	22	16	12	19	10	22	35	17	19	42	53	25	27	27	0	.	21	56	11	51	27	23	13	37	11	63	41	26	6	43	3	46	11	42	4	38	0	100	27	15	25	32	8	38	15	26	18	37	16	31	41	53	33	71	7	36	39	22	13	49	26	44	22	25	22	35	31	32	32	33	46	23	17	44	17	54	32	25	30	29	48	24	32	35	24	33	23	51	56	46	20	34	15	48	12	88	3	99	1	99	5	45	7	56	10	41	7	67	5	51	0	.	40	38	13	65	3	98	41	41	65	35	55	24	50	62	0	.	24	30	26	86	13	59	6	45	17	29	27	19	31	47	6	97	9	53	7	93	13	70	2	71	4	43	16	24	24	41	23	47	27	33	43	30	17	27	45	50	28	50	13	60	0	.	18	4
	ash	33	22	27	35	22	31	11	42	14	56	46	33	7	32	13	23	12	22	11	39	65	43	30	68	23	54	0	.	9	57	19	56	33	32	28	36	22	72	28	48	5	46	2	65	16	56	6	34	5	87	37	20	31	52	20	36	14	43	16	81	13	77	4	73	0	.	2	99	43	48	15	65	9	48	63	26	53	34	13	42	27	40	30	32	15	44	3	55	13	49	28	47	33	45	39	39	20	85	13	52	37	32	37	29	29	30	13	74	5	88	4	78	10	48	19	38	41	30	11	43	5	50	14	53	18	71	22	55	6	98	20	36	11	53	13	50	55	32	26	52	8	58	21	36	0	.	2	99	9	55	37	55	33	43	13	45	44	38	0	.	76	48	4	36	8	47	12	29	46	40	29	25	29	46	33	30	30	36	18	54	33	38	12	45	0	.	17	5
	oak	20	34	26	45	6	58	24	35	0	.	0	99	1	52	1	87	13	21	1	100	18	33	33	90	20	55	0	.	8	45	1	99	6	49	11	45	498	26	0	.	0	100	5	86	0	100	5	58	1	100	7	32	7	43	5	78	5	80	60	59	0	.	5	68	0	.	0	.	2	98	0	.	0	99	14	88	8	64	0	.	4	59	1	99	24	24	3	62	9	49	8	63	27	56	2	60	0	.	3	99	12	45	33	44	15	32	0	.	0	.	0	.	12	43	27	31	44	25	20	67	32	53	12	47	3	73	6	97	2	98	29	60	21	68	0	.	14	76	0	.	14	73	40	31	104	46	0	.	90	45	4	85	12	39	126	43	18	74	0	.	43	53	3	44	9	45	9	30	1	98	1	68	3	67	2	79	6	44	10	36	14	40	9	54	0	.	10	8
	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	.	18	41	1	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	99	36	28	50	45	109	34	119	22	207	18	25	29	73	32	202	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	84	0	.	6	97	0	.	0	.	1	100	0	.	6	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	9
	other broadleaves	39	31	67	25	33	36	15	40	17	52	18	48	25	18	14	31	19	27	20	23	73	31	30	48	39	31	0	.	40	49	22	31	15	31	29	39	62	46	32	29	21	30	14	42	41	23	51	18	30	36	36	18	19	28	7	45	36	39	14	50	17	47	21	86	24	73	15	40	26	41	31	47	16	31	11	89	73	39	42	30	46	42	27	28	9	37	18	52	17	37	69	35	30	32	24	30	20	31	53	57	54	36	60	36	30	46	115	33	45	25	43	29	113	23	151	19	204	16	111	20	61	26	108	18	32	37	110	56	29	37	55	32	71	32	38	33	13	44	81	51	32	43	56	53	10	49	8	45	29	60	15	36	24	57	57	55	26	48	13	93	10	66	35	21	39	28	55	17	11	48	27	41	25	31	21	33	3	56	58	32	27	64	17	38	0	.	38	4
	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	395	6	401	11	424	12	423	7	535	11	561	10	462	5	420	4	438	5	508	5	346	14	435	14	478	10	490	.	561	13	509	9	540	8	510	8	676	18	459	11	372	9	382	7	326	7	405	5	394	5	466	5	511	9	453	8	570	6	353	14	401	10	453	9	321	16	405	8	525	11	476	13	461	10	479	8	558	11	429	10	476	12	511	9	480	8	343	9	479	10	439	11	516	7	436	11	515	10	378	10	500	9	454	12	428	12	289	14	307	9	327	11	430	11	477	8	597	7	398	8	435	12	545	8	501	11	458	15	495	12	403	12	354	15	506	12	433	12	381	18	423	9	471	11	384	13	360	9	404	11	461	10	407	11	499	13	490	18	351	17	500	13	375	5	440	6	443	6	422	12	372	12	355	9	464	11	391	9	438	11	355	12	401	16	0	.	438	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. 2290988. Birmensdorf, Swiss Federal Research Institute WSL

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