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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NFI4
total 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 2009/17
		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	40	35	145	23	122	25	54	19	334	14	243	15	303	5	131	9	109	12	118	13	25	46	85	49	36	26	0	.	111	30	336	18	242	13	178	20	2	95	226	13	304	8	234	10	371	9	261	7	167	9	90	15	163	18	141	16	279	12	110	37	171	20	120	25	112	26	197	27	237	27	371	14	274	17	174	18	245	26	223	13	124	23	163	13	139	22	81	23	89	33	56	38	39	29	180	16	263	13	164	16	141	22	137	26	155	37	146	18	171	16	194	17	4	99	8	81	0	100	32	30	12	47	53	36	174	23	307	19	305	18	135	21	180	17	267	17	142	27	87	42	198	21	71	43	58	29	249	10	179	31	130	29	131	21	150	46	133	31	37	25	99	29	142	9	214	12	210	9	187	22	201	35	101	21	118	26	126	22	92	31	137	32	144	26	0	.	172	2
	fir	36	38	7	62	30	36	60	35	66	24	88	26	25	17	138	8	85	12	121	12	19	54	27	38	63	25	0	.	50	39	21	40	88	24	65	23	0	.	25	39	9	39	21	24	6	47	9	26	2	90	125	10	117	18	163	15	108	15	69	29	115	18	109	20	79	27	146	20	35	28	36	41	51	29	63	20	30	36	30	27	53	28	64	20	9	48	81	34	53	32	12	39	82	24	74	28	58	22	75	28	62	31	62	40	33	79	2	79	16	37	10	96	0	.	0	.	0	100	14	41	32	47	18	57	120	25	10	74	59	41	73	26	20	47	108	24	45	27	28	71	68	22	74	38	134	22	43	28	73	26	72	17	42	30	75	60	42	38	159	33	22	42	9	44	10	36	37	24	104	25	17	42	29	39	54	18	39	29	7	62	2	98	17	54	0	.	48	3
	pine	10	46	6	56	12	55	6	38	3	68	1	99	11	32	3	77	7	50	14	43	23	59	15	38	21	31	0	.	3	74	9	72	0	.	11	50	34	70	0	.	27	32	26	30	5	92	24	25	74	18	16	37	0	99	5	59	12	68	12	69	6	88	14	98	0	.	6	99	0	.	41	87	4	99	4	58	0	.	24	55	0	.	3	50	22	37	4	54	21	36	12	64	32	47	31	46	0	99	4	99	6	38	6	56	20	43	0	.	12	65	10	89	0	.	0	.	7	99	0	.	0	.	3	72	14	86	4	98	30	47	25	91	0	.	0	.	0	.	13	60	0	99	2	97	4	81	0	.	7	58	0	.	3	56	0	.	0	.	39	95	0	.	66	20	47	23	35	23	0	.	7	80	17	35	4	52	18	30	15	39	22	38	4	59	0	.	17	7
	larch	5	57	9	58	6	73	2	55	3	74	3	99	2	34	0	100	3	45	2	100	0	.	0	.	1	69	0	.	16	86	4	72	0	.	1	69	0	.	0	70	13	31	35	36	12	36	52	15	101	9	1	65	3	70	1	76	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	5	90	0	99	11	94	26	31	1	99	0	99	4	55	2	99	2	99	2	73	0	.	0	.	1	99	2	70	4	87	9	70	1	99	72	28	72	23	76	29	40	34	11	51	2	70	95	16	46	31	26	41	0	.	0	.	0	.	32	44	2	79	6	76	3	69	40	68	0	.	3	72	0	.	0	.	6	73	0	.	8	51	0	.	1	97	0	.	45	89	131	9	93	13	50	15	1	98	1	98	6	59	3	59	0	.	6	51	2	68	5	53	0	.	25	4
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	1	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	82	0	100	1	100	6	30	58	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	99	0	.	6	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	3	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	20	22	23	14	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	10
	other conifers	4	52	1	57	1	99	8	44	7	98	2	69	3	67	0	.	5	28	6	87	9	98	0	.	0	.	210	.	1	98	0	.	0	.	0	.	0	.	1	99	7	88	0	.	0	.	3	58	15	31	1	52	7	77	3	56	1	100	0	.	0	.	2	98	0	.	0	.	3	99	0	.	1	99	9	48	3	69	3	65	8	71	11	99	2	99	2	99	12	89	12	66	0	.	1	99	0	.	1	99	3	99	0	.	14	83	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	1	99	0	.	0	.	22	56	0	.	1	99	1	99	0	.	26	96	23	53	1	98	9	76	0	.	0	.	0	.	5	59	3	96	9	72	0	.	0	.	1	75	2	100	2	62	3	98	2	98	14	46	16	74	3	73	0	.	0	.	0	.	0	.	3	12
	beech	105	12	98	18	71	19	151	18	55	29	82	22	59	18	88	10	94	8	181	10	63	35	168	16	226	14	0	.	113	24	85	22	88	24	105	22	0	.	90	20	36	25	48	22	3	66	17	31	0	.	128	11	116	20	71	24	101	19	92	19	79	28	98	13	80	22	64	22	100	30	22	45	102	24	66	20	134	26	94	21	174	20	120	18	193	11	135	16	173	17	141	24	179	14	103	17	31	39	96	24	82	18	33	33	122	18	0	.	22	36	0	.	135	23	139	24	94	23	138	18	192	23	118	27	105	31	1	98	26	60	83	32	25	40	58	31	83	21	62	69	166	23	72	32	55	29	59	25	67	22	137	18	100	22	116	25	57	36	102	30	72	34	2	100	7	62	50	21	57	28	90	25	69	20	91	19	86	15	86	23	68	19	77	19	0	.	77	3
	maple	41	29	25	23	57	26	17	30	72	65	36	46	26	15	15	18	21	22	43	17	36	41	49	21	37	25	0	.	23	49	15	43	38	24	23	37	20	73	51	21	6	33	12	51	11	45	6	37	0	74	35	18	30	25	12	33	23	22	61	57	28	28	67	47	32	61	8	39	36	24	11	38	45	42	31	24	33	39	32	29	39	30	36	22	20	40	25	30	43	26	15	34	49	26	52	27	20	30	38	37	60	33	37	36	16	36	24	69	18	56	1	99	12	48	9	49	24	31	11	37	7	55	2	60	45	36	6	64	6	78	48	35	85	28	71	22	45	36	8	96	22	27	33	77	17	33	9	40	23	29	31	26	43	32	3	97	11	56	3	93	19	48	3	48	9	40	26	18	17	40	33	43	35	38	37	26	20	28	63	44	30	50	55	60	0	.	23	4
	ash	45	24	30	31	32	36	7	50	17	48	34	34	9	30	11	24	18	19	13	44	49	33	22	40	19	36	0	.	26	52	27	50	27	31	51	29	24	95	26	29	6	44	3	55	20	40	8	29	4	90	32	18	25	40	28	31	22	33	18	57	8	77	4	73	4	67	3	70	40	53	13	48	10	33	67	27	54	37	13	38	34	38	23	32	24	50	10	37	13	38	32	39	23	39	51	33	21	79	16	47	46	27	37	27	27	31	21	49	12	100	17	52	15	39	25	35	43	27	32	28	5	45	18	45	25	53	31	51	3	99	18	41	31	41	33	40	46	39	59	32	6	48	41	35	2	98	2	99	8	56	20	57	33	34	14	41	35	47	0	.	47	39	10	40	10	42	11	25	29	34	26	27	39	41	56	30	38	31	25	42	31	54	14	38	0	.	20	4
	oak	16	35	15	60	3	52	24	31	0	.	0	99	1	47	0	81	9	22	1	74	11	47	36	80	17	51	0	.	5	55	1	99	4	46	9	46	196	27	0	.	0	100	5	72	0	100	3	44	1	100	5	38	4	49	6	57	1	69	64	59	0	.	3	98	4	98	0	.	1	99	0	.	0	99	9	77	5	64	1	75	3	58	0	.	22	25	6	92	14	43	16	58	25	61	1	77	0	.	1	99	9	44	14	38	10	36	0	.	1	99	0	.	12	39	29	28	47	28	19	67	33	40	12	44	3	68	1	98	3	99	19	76	29	69	0	.	11	81	0	.	12	75	25	41	130	43	0	.	76	47	3	85	19	68	107	47	19	81	4	95	18	56	5	44	13	45	16	34	2	69	1	68	3	44	2	79	14	77	12	43	12	40	11	40	0	.	9	8
	sweet chestnut	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	39	0	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	.	12	99	58	32	49	40	136	30	127	25	229	18	35	29	88	32	210	24	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	81	0	.	0	.	0	.	0	.	1	100	0	.	6	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	9
	other broadleaves	61	46	57	24	33	26	23	31	17	49	21	43	37	16	17	21	26	19	27	19	40	30	27	37	42	25	0	.	50	39	22	41	15	27	42	50	116	34	35	25	28	26	20	35	94	22	55	18	26	31	38	17	28	29	9	39	33	34	20	47	19	46	13	69	23	48	14	43	30	32	37	44	30	43	13	44	34	28	46	31	48	39	24	28	17	36	37	51	19	40	47	43	16	40	48	29	27	32	73	49	48	42	72	30	28	38	167	32	71	20	92	27	115	21	203	17	260	14	122	18	89	17	130	16	31	38	133	57	46	39	76	31	65	26	76	36	47	46	107	43	34	35	52	37	15	38	12	47	28	50	19	34	20	56	55	50	25	43	13	93	15	44	41	18	65	21	73	15	8	50	32	42	42	31	24	38	12	40	26	32	34	40	27	31	0	.	48	4
	indeterminable	3	48	0	.	0	.	0	.	0	.	3	59	3	27	1	71	1	44	2	46	5	97	0	.	0	99	0	.	4	98	1	99	0	.	1	99	0	.	1	70	1	49	1	53	4	45	2	38	1	50	1	57	1	99	2	99	2	63	2	98	0	.	0	.	0	.	1	99	0	.	2	74	1	99	0	99	3	69	2	64	0	.	1	99	0	.	1	99	5	69	0	.	3	73	2	63	0	.	0	.	0	.	2	99	0	.	0	99	0	.	0	.	1	99	2	99	3	72	2	52	3	61	1	99	1	98	0	.	5	56	9	36	3	69	3	56	1	99	0	.	0	99	0	.	2	98	0	.	0	.	0	.	0	.	3	98	2	98	0	.	1	98	2	34	2	40	2	36	2	98	0	.	0	.	0	.	0	99	2	99	0	.	1	99	0	.	1	9
	total	366	10	393	11	368	9	351	10	573	10	511	9	481	4	404	4	378	5	528	5	280	14	427	12	462	8	210	.	402	12	520	11	503	8	486	10	392	18	454	8	437	6	405	7	526	7	463	4	450	5	474	5	494	9	441	8	582	6	447	9	425	10	430	11	336	12	439	12	483	13	532	12	531	10	437	9	557	13	493	9	484	11	445	10	452	8	386	10	443	11	344	15	449	9	543	8	421	9	468	11	460	9	408	12	426	14	446	14	452	9	451	8	469	10	552	8	710	7	501	7	507	10	592	9	519	14	493	17	516	11	517	8	442	12	623	10	424	12	431	20	530	11	372	15	426	18	375	9	468	14	414	11	419	9	525	20	333	19	357	22	338	17	433	5	493	6	531	5	409	11	411	15	356	9	405	11	357	8	334	12	336	13	358	12	0	.	462	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	99	1	56	0	.	0	.	1	27	1	27	0	44	1	67	1	98	1	98	0	.	0	.	1	98	0	99	1	57	0	99	0	.	3	33	1	38	2	49	2	27	1	26	0	50	0	60	0	99	0	.	0	100	0	.	0	.	0	.	0	.	1	99	0	99	1	69	2	52	0	.	1	99	1	39	1	71	3	53	0	.	0	.	0	.	0	.	0	.	1	69	0	99	2	75	0	.	1	98	1	99	2	58	3	35	2	45	0	.	0	.	0	.	0	70	0	.	1	74	0	.	3	70	1	99	2	58	4	43	1	56	1	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	1	44	1	52	1	40	0	.	0	.	1	99	0	99	0	99	1	68	2	98	0	.	0	.	1	8
	fir	0	.	0	99	1	69	1	56	0	.	1	99	0	61	1	36	1	40	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	99	0	.	1	60	0	.	1	57	0	.	0	50	0	.	0	49	0	.	0	.	0	99	1	98	1	69	1	98	0	.	1	73	0	.	0	.	0	.	0	.	1	99	0	.	0	99	1	57	0	.	0	99	0	99	0	.	0	.	0	99	0	.	1	69	0	.	0	.	1	99	0	.	0	100	0	.	0	.	0	.	0	.	0	100	1	75	0	99	0	.	0	.	1	99	1	99	0	99	0	99	1	68	0	.	1	99	0	.	0	.	0	99	0	.	0	.	0	.	0	.	1	97	3	93	1	98	0	.	0	100	0	100	0	.	0	.	1	99	1	69	0	.	0	.	0	.	0	.	0	.	0	11
	pine	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	71
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	72	0	100	0	44	1	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	100	0	99	1	71	0	.	0	.	1	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	27	1	35	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	15
	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	70	0	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	44	0	100	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	29
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	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	.	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	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	50
	beech	0	99	0	99	0	99	0	99	0	.	0	.	0	57	0	100	0	71	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	.	0	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	72	0	.	0	.	1	99	0	.	0	.	1	56	0	.	1	99	0	99	0	.	0	.	1	69	0	.	1	98	0	.	0	.	0	99	0	.	1	77	0	99	0	.	0	.	0	.	0	99	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	3	58	2	99	0	.	1	99	0	.	1	98	0	.	0	.	0	17
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	.	0	.	0	.	0	100	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	43
	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	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	45
	oak	0	99	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	99	1	99	0	.	0	.	0	.	0	99	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	1	99	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	2	68	1	98	0	.	0	24
	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	.	2	54	0	99	0	.	4	80	2	51	2	48	0	.	2	53	0	.	0	.	0	.	0	.	0	.	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	.	0	27
	other broadleaves	1	72	0	.	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	.	2	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	39
	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	1	50	1	56	2	48	2	41	0	.	1	99	2	23	2	20	2	26	1	52	2	68	3	56	1	99	0	.	2	68	0	99	2	40	1	57	0	.	4	26	1	38	4	35	2	26	3	18	2	25	1	34	0	99	0	.	1	71	1	98	1	69	1	98	2	98	2	81	1	99	1	69	2	52	1	72	2	57	1	39	2	49	4	45	1	70	2	42	0	99	1	99	0	99	1	56	0	99	5	36	1	99	3	69	5	58	3	49	6	28	3	37	2	54	4	73	2	51	3	29	1	75	3	37	0	.	3	70	2	55	3	49	5	39	2	48	3	47	0	.	1	99	4	57	0	.	0	99	1	99	0	.	1	98	0	.	3	53	3	93	1	98	3	21	2	28	1	29	0	.	4	49	3	68	2	48	1	71	2	55	4	49	1	98	0	.	2	5
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	40	35	145	23	122	25	55	19	334	14	243	15	305	5	132	9	110	12	119	13	25	44	86	48	36	26	0	.	112	29	336	18	243	13	179	20	2	95	229	13	305	8	236	10	373	9	263	7	168	9	91	15	163	18	141	16	280	12	110	37	171	20	120	25	112	26	198	26	238	27	372	14	277	17	174	18	245	26	224	13	125	23	166	13	139	22	81	23	89	33	56	38	39	29	181	15	263	13	165	16	141	22	138	26	156	37	148	18	173	15	196	17	4	99	8	81	0	100	33	30	12	47	54	35	174	23	310	19	306	18	137	21	184	16	268	17	144	27	87	42	198	21	71	43	58	29	249	10	179	31	130	29	131	21	150	46	134	31	37	25	99	29	143	9	215	12	210	9	187	22	201	35	102	21	118	26	126	22	93	31	138	32	144	26	0	.	173	2
	fir	36	38	7	59	31	35	61	34	66	24	89	26	25	17	139	8	86	11	121	12	19	54	27	38	63	25	0	.	50	39	21	40	88	24	65	23	0	.	26	38	9	39	21	24	6	47	9	26	2	90	126	10	117	18	163	15	109	16	70	29	116	18	110	20	79	27	148	20	35	28	36	41	51	29	63	20	31	37	30	27	53	28	65	20	9	48	82	33	53	32	12	39	82	24	74	28	58	22	76	27	62	31	62	40	34	77	2	79	16	37	10	96	0	.	0	.	0	100	14	40	33	46	18	56	120	25	10	74	59	40	73	26	21	47	109	24	46	27	28	71	69	22	74	38	134	22	43	28	73	26	72	17	42	30	75	60	43	36	162	32	23	41	9	44	10	35	37	24	104	25	17	42	30	38	55	18	39	29	7	62	2	98	17	54	0	.	48	3
	pine	10	46	6	56	12	55	6	38	3	68	1	99	11	32	3	77	7	50	14	43	23	59	15	38	21	31	0	.	3	74	9	72	0	.	11	50	34	70	0	.	27	32	26	30	5	92	24	25	74	18	16	37	0	99	5	59	12	68	12	69	6	88	14	98	0	.	6	99	0	.	41	87	4	99	4	58	0	.	24	55	0	.	3	50	22	37	4	54	21	36	12	64	32	47	31	46	0	99	4	99	6	38	6	56	20	43	0	.	12	65	10	89	0	.	0	.	7	99	0	.	0	.	3	72	14	86	4	98	30	47	25	91	0	.	0	.	0	.	13	60	0	99	2	97	4	81	0	.	7	58	0	.	3	56	0	.	0	.	39	95	0	.	66	20	47	23	35	23	0	.	7	80	17	35	5	48	18	30	15	39	22	38	4	59	0	.	17	7
	larch	5	57	9	58	6	73	2	55	3	74	3	99	2	34	0	100	3	45	2	100	0	.	0	.	1	69	0	.	16	86	4	72	0	.	1	69	0	.	0	70	13	31	35	36	12	37	52	15	102	9	1	65	3	70	1	76	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	5	90	0	99	11	94	26	31	1	99	0	99	4	55	3	84	2	99	2	73	0	.	0	.	1	99	2	70	4	87	9	70	1	99	73	27	72	23	77	29	40	34	11	51	2	70	96	15	46	31	26	41	0	.	0	.	0	.	32	44	2	79	6	76	3	69	40	68	0	.	3	72	0	.	0	.	6	73	0	.	8	51	0	.	1	97	0	.	45	89	133	9	94	12	50	15	1	98	1	98	6	59	3	59	0	.	6	51	2	68	5	53	0	.	25	4
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	1	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	82	0	100	1	100	7	30	59	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	99	0	.	6	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	3	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	20	23	23	14	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	10
	other conifers	4	52	1	57	1	99	8	44	7	98	2	69	3	67	0	.	5	28	6	87	9	98	0	.	0	.	210	.	1	98	0	.	0	.	0	.	0	.	1	99	7	88	0	.	0	.	3	58	15	31	1	52	7	77	3	56	1	100	0	.	0	.	2	98	0	.	0	.	3	99	0	.	1	99	9	48	3	69	3	65	8	71	11	99	2	85	2	99	12	89	12	66	0	.	1	99	0	.	1	99	3	99	0	.	14	83	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	1	99	0	.	0	.	22	56	0	.	1	99	1	99	0	.	26	96	23	53	1	98	9	76	0	.	0	.	0	.	5	57	3	96	9	72	0	.	0	.	1	75	2	100	2	62	3	98	2	98	14	46	16	74	3	73	0	.	0	.	0	.	0	.	3	12
	beech	105	12	99	18	71	19	151	18	55	29	82	22	59	18	88	10	94	8	181	10	63	34	168	16	226	14	0	.	113	24	85	22	89	24	105	22	0	.	90	20	36	25	48	22	3	66	17	31	0	.	128	11	116	20	71	24	101	19	92	19	79	28	98	13	80	22	64	22	100	30	22	45	102	24	67	20	134	26	94	21	174	20	120	18	193	11	136	15	173	17	141	24	179	13	103	17	31	39	97	23	82	18	34	33	122	18	0	.	22	36	0	.	136	23	139	24	94	23	138	18	192	23	118	27	105	31	1	98	27	58	83	32	25	40	58	31	83	21	62	69	166	23	72	32	55	29	59	25	67	22	137	18	100	22	116	25	57	36	102	30	72	34	2	100	7	62	50	21	57	28	93	25	71	20	91	19	87	15	86	23	69	19	77	19	0	.	77	2
	maple	41	29	25	23	57	26	17	30	72	65	36	46	27	15	15	18	21	22	43	17	36	41	49	21	37	25	0	.	23	49	15	43	38	24	23	37	20	73	51	21	6	33	12	51	11	45	6	37	0	74	35	18	30	25	12	33	23	22	61	57	28	28	67	47	32	61	8	39	37	24	11	38	45	42	31	24	33	39	32	29	39	30	36	22	20	40	25	30	43	26	15	34	49	26	52	27	20	30	38	37	60	33	37	36	16	36	24	69	18	56	1	99	12	48	9	49	24	31	11	37	7	55	2	60	45	36	6	64	6	78	48	35	85	28	71	22	45	36	8	96	22	27	33	77	17	33	9	40	23	29	31	26	43	32	3	97	11	56	3	93	19	48	3	48	9	40	26	18	17	40	33	43	35	38	37	26	20	28	63	44	30	50	55	60	0	.	23	4
	ash	45	24	30	31	32	36	7	50	17	48	34	34	9	30	11	24	18	19	13	44	49	33	22	40	19	36	0	.	26	52	27	50	27	31	51	29	24	95	26	29	6	44	3	55	20	40	8	29	4	90	32	18	25	40	28	31	22	33	18	57	8	77	4	73	4	67	3	70	40	53	13	48	10	33	67	27	54	37	13	38	34	38	23	32	24	50	10	37	13	38	32	39	23	39	51	33	21	79	16	47	46	27	37	27	27	31	21	49	12	100	17	52	15	39	25	35	43	27	32	28	5	45	18	45	25	53	31	51	3	99	18	41	31	41	33	40	46	39	59	32	6	48	42	34	2	98	2	99	8	56	20	57	33	34	14	41	36	47	0	.	47	39	10	40	10	42	11	25	29	34	27	27	39	41	56	30	38	31	25	42	31	54	14	38	0	.	20	4
	oak	17	34	16	59	3	52	24	31	0	.	0	99	1	47	0	81	9	21	1	74	11	47	37	78	18	49	0	.	5	55	1	99	5	44	9	44	196	27	0	.	0	100	5	72	0	100	3	44	1	100	5	37	4	49	6	57	1	69	64	59	0	.	3	98	4	98	0	.	1	99	0	.	0	99	9	77	5	58	1	75	3	58	0	.	22	25	6	92	14	43	16	58	25	61	1	77	0	.	2	99	9	44	16	42	10	34	0	.	1	99	0	.	12	39	29	28	47	28	19	67	33	40	12	44	3	68	1	98	3	99	19	76	29	69	0	.	11	81	0	.	12	75	26	41	130	43	0	.	77	47	3	85	19	68	107	47	19	81	4	95	18	56	5	44	13	45	16	34	2	69	1	68	3	44	2	79	14	77	13	41	13	40	12	37	0	.	9	8
	sweet chestnut	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	39	0	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	.	12	99	60	31	49	40	136	30	130	24	230	18	37	28	88	32	212	24	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	81	0	.	0	.	0	.	0	.	1	100	0	.	6	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	9
	other broadleaves	62	45	57	24	33	26	23	31	17	49	21	43	37	16	17	21	26	18	27	19	40	30	28	36	42	25	0	.	50	39	22	41	15	27	42	50	116	34	35	25	28	26	20	35	94	22	56	18	26	31	38	17	28	29	9	39	33	34	20	47	19	46	13	69	25	44	14	43	30	32	37	44	30	43	13	44	34	28	46	31	48	39	24	28	17	36	37	51	19	40	47	43	16	40	48	29	27	32	73	49	48	42	72	30	31	36	167	32	71	20	92	27	115	21	203	17	260	14	122	18	89	17	130	16	31	38	133	57	46	39	76	31	65	26	76	36	47	46	107	43	34	35	54	36	15	38	12	47	28	50	19	34	20	56	55	50	25	43	13	93	15	44	41	18	65	21	73	15	8	50	32	42	42	31	24	38	12	40	26	32	34	40	27	31	0	.	48	4
	indeterminable	3	48	0	.	0	.	0	.	0	.	3	59	3	27	1	71	1	44	2	46	5	97	0	.	0	99	0	.	4	98	1	99	0	.	1	99	0	.	1	70	1	49	1	53	4	45	2	38	1	50	1	57	1	99	2	99	2	63	2	98	0	.	0	.	0	.	1	99	0	.	2	74	1	99	0	99	3	69	2	64	0	.	1	99	0	.	1	99	5	69	0	.	3	73	2	63	0	.	0	.	0	.	2	99	0	.	0	99	0	.	0	.	1	99	2	99	3	72	2	52	3	61	1	99	1	98	0	.	5	56	9	36	3	69	3	56	1	99	0	.	0	99	0	.	2	98	0	.	0	.	0	.	0	.	3	98	2	98	0	.	1	98	2	34	2	40	2	36	2	98	0	.	0	.	0	.	0	99	2	99	0	.	1	99	0	.	1	9
	total	367	10	394	11	369	9	353	10	573	10	512	9	483	4	407	4	380	5	529	5	282	14	430	12	463	8	210	.	404	12	521	11	505	8	487	10	392	18	458	8	438	6	408	7	528	7	466	4	452	4	475	5	494	9	441	8	583	6	448	9	427	10	431	11	338	11	441	12	484	13	533	12	534	10	438	9	559	13	494	9	486	11	449	10	452	8	388	10	443	11	345	15	449	9	544	8	422	9	473	11	460	9	411	12	431	13	449	14	458	8	454	8	471	10	556	8	711	7	505	7	508	10	595	9	519	14	496	16	518	11	519	8	447	12	624	10	427	12	431	20	531	11	376	15	426	18	375	9	468	14	414	11	420	9	525	20	337	19	360	22	339	17	435	5	495	6	532	5	409	11	415	15	359	8	407	11	358	8	336	12	340	13	358	12	0	.	464	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. 2296847. Birmensdorf, Swiss Federal Research Institute WSL

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