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
number of stems
tree species (56 classes)
regional demarcation: forest district (2024)
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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
tree species (56 classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	.
Abies alba	459	65	46	74	147	62	526	30	317	40	924	37	1514	25	4731	14	3610	18	3113	21	43	61	97	60	538	41	0	.	297	56	148	52	1207	40	609	41	0	.	242	44	205	48	258	34	86	66	568	34	156	100	3507	18	1614	30	2227	21	1492	24	343	44	661	33	537	36	444	41	1457	34	215	39	288	58	840	37	359	35	53	67	414	34	418	49	500	28	163	56	815	47	240	49	115	48	526	46	500	54	554	36	382	47	596	46	603	50	508	79	6	100	221	51	195	100	0	.	0	.	8	100	273	76	159	67	191	70	936	37	0	.	483	48	524	37	224	52	895	40	156	55	117	76	844	31	228	69	387	57	367	37	411	40	630	30	273	40	331	82	237	49	200	68	75	61	201	88	438	46	776	31	506	47	51	47	143	53	463	30	278	35	41	72	20	100	157	68	0	.	51657	4
Larix decidua, Larix kaempferi	24	77	34	63	85	80	40	70	0	.	0	.	118	52	7	100	55	64	0	.	0	.	0	.	14	71	0	.	0	.	52	100	0	.	7	100	0	.	13	71	270	37	267	46	355	60	1255	26	3409	15	7	100	33	100	24	77	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	87	100	7	100	153	100	138	68	9	100	7	100	38	61	0	.	0	.	7	100	0	.	0	.	0	.	14	100	50	100	59	100	0	.	168	50	856	44	542	44	234	59	0	.	47	71	1472	30	423	44	323	70	0	.	0	.	0	.	359	52	0	.	88	100	0	.	117	100	0	.	8	100	0	.	0	.	17	100	0	.	20	73	0	.	7	100	0	.	333	100	4176	17	2324	20	883	25	14	100	7	100	17	100	20	100	0	.	41	72	7	100	54	55	0	.	19199	7
Picea abies	585	43	1267	39	841	50	604	31	1566	31	1922	28	13985	11	4436	16	5379	17	3126	18	18	100	381	58	184	45	0	.	542	47	2946	29	1830	26	1743	35	0	.	2966	24	6024	16	4066	21	5512	19	11351	13	7707	15	2000	22	1489	34	2196	25	4245	20	661	55	1624	32	364	38	507	44	1008	29	1849	48	2353	29	2227	33	1091	34	1624	39	2519	27	864	35	1801	25	1761	30	554	42	491	58	145	73	193	45	1461	25	2234	27	860	32	1188	37	1042	39	1429	36	1041	36	2252	27	1759	28	0	.	15	71	8	100	435	55	227	58	432	64	1019	39	1250	42	2022	32	561	38	1229	30	1345	43	935	45	93	51	1247	43	345	65	166	48	2510	23	874	57	1165	49	903	38	507	81	450	50	50	72	142	62	4981	17	6414	21	5736	16	517	40	1043	49	557	39	1349	38	1423	35	505	52	721	56	694	37	0	.	165686	3
Pinus cembra	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	276	46	2115	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	812	31	563	38	190	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4030	15
Pinus mugo subsp. uncinata	0	.	0	.	0	.	0	.	0	.	0	.	142	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	170	69	0	.	0	.	189	56	1891	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	475	100	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	100	6	100	0	.	0	.	0	.	0	.	0	.	0	.	149	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	72	35	100	194	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3646	24
Pinus mugo subsp. mugo	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	164	100	0	.	0	.	87	100	335	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	74	69	100	35	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	845	31
Pinus nigra	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	59
Pinus strobus	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	30	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	79	46
Pinus sylvestris	71	90	53	74	14	71	27	79	0	.	9	100	159	78	163	86	385	71	535	49	23	100	76	60	143	46	0	.	7	100	70	71	0	.	126	56	114	80	0	.	120	61	287	36	22	100	696	49	788	41	162	45	7	100	117	61	0	.	0	.	52	100	0	.	0	.	48	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	17	100	332	49	54	68	68	55	90	93	107	53	0	.	0	.	0	.	44	54	63	59	88	52	0	.	35	72	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	205	65	0	0	0	.	0	.	0	.	24	77	7	100	0	.	20	100	0	.	24	77	0	.	13	100	0	.	0	.	118	100	0	.	2601	34	1040	34	1101	38	0	.	48	100	139	48	34	53	186	44	113	52	60	60	13	100	0	.	10952	12
Pseudotsuga menziesii	0	.	0	.	20	100	102	71	0	.	0	.	0	.	0	.	309	33	0	.	0	.	0	.	0	.	73	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	40	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	100	0	.	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	90	100	0	.	0	.	0	.	0	.	0	.	0	.	23	100	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	100	0	.	0	.	0	.	0	.	0	.	0	.	868	23
Taxus baccata	17	100	0	.	0	.	35	100	0	.	36	71	0	.	0	.	34	100	35	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	51	74	0	.	0	.	15	100	0	.	0	.	17	100	0	.	0	.	0	.	0	.	17	100	20	100	39	71	46	100	39	71	0	.	0	.	34	100	17	100	137	68	0	.	0	.	0	.	18	100	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	195	68	0	.	51	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	71	239	82	33	100	0	.	0	.	0	.	0	.	1214	23
other conifers	24	100	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	82
Acer campestre	26	100	0	.	0	.	17	100	0	.	0	.	18	100	0	.	0	.	0	.	0	.	17	100	35	71	0	.	0	.	0	.	0	.	0	.	0	.	15	100	37	100	18	100	0	.	0	.	0	.	109	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	17	100	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	58	76	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	17	100	0	.	17	100	0	.	0	.	0	.	18	100	17	100	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	561	20
Acer opalus	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	87	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	188	77	241	69	15	100	0	.	0	.	66	69	0	.	34	70	0	.	51	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	54	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	842	31
Acer platanoides	14	71	0	.	0	.	35	100	0	.	0	.	84	60	29	77	0	.	80	78	112	69	17	100	69	79	0	.	0	.	0	.	36	100	0	.	0	.	15	100	0	.	21	100	33	100	8	100	0	.	66	61	34	100	15	100	0	.	26	78	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	21	57	92	85	75	74	0	.	21	100	0	.	0	.	7	100	0	.	48	79	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	19	100	0	.	15	71	56	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	132	75	0	.	125	100	41	72	0	.	0	.	0	.	0	.	0	.	0	.	1440	18
Acer pseudoplatanus	270	32	207	33	217	55	78	45	28	79	496	56	1239	22	494	28	529	33	968	24	69	66	180	42	188	40	0	.	105	87	143	45	575	34	177	53	0	.	574	35	196	37	111	60	159	71	346	47	0	.	498	29	249	36	91	45	452	31	278	98	319	37	218	75	252	69	53	54	230	37	26	74	482	76	268	38	219	72	433	42	220	52	608	30	166	59	218	42	315	47	137	46	409	38	221	36	147	49	167	76	400	42	198	55	158	48	146	100	123	100	18	100	18	100	85	70	114	79	54	57	48	100	16	100	367	45	7	100	0	.	293	61	484	54	429	46	136	64	0	.	199	45	0	.	0	.	92	60	80	46	145	42	212	59	15	100	17	100	0	.	57	72	36	100	160	80	599	36	124	63	114	52	68	62	387	35	172	49	541	53	217	62	571	64	0	.	20928	6
Aesculus hippocastanum	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
Alnus glutinosa	87	72	69	100	84	100	0	.	0	.	0	.	0	.	0	.	234	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	199	100	0	.	59	100	100	100	70	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	100	0	.	0	0	0	.	0	.	87	72	173	81	17	100	0	.	82	92	0	.	0	.	147	100	0	0	0	.	76	62	119	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	117	100	0	.	64	71	0	.	0	.	0	.	0	.	0	.	0	.	76	100	117	100	7	100	0	.	0	.	7	100	23	100	0	.	2239	22
Alnus incana	0	.	0	.	0	.	0	.	30	100	69	100	298	81	115	65	23	100	19	100	0	.	0	.	17	100	0	.	15	100	50	76	32	100	69	100	0	.	156	65	17	100	73	100	1180	57	334	66	829	70	107	100	59	76	0	.	104	100	0	.	0	.	34	100	0	.	0	.	0	.	61	79	0	.	0	.	86	80	260	94	21	100	0	.	0	.	0	.	0	.	34	71	0	.	88	100	17	100	249	100	73	79	0	.	0	.	368	65	0	0	49	100	0	0	0	.	0	.	198	100	40	70	0	.	0	0	129	100	0	.	0	0	0	.	15	100	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	297	66	0	.	504	59	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	6139	19
Alnus viridis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
Betula pendula	0	.	0	.	0	.	17	100	0	.	69	81	60	76	17	100	50	74	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	18	100	348	47	602	42	35	100	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	17	100	0	.	0	.	17	100	0	.	30	100	17	100	34	100	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	17	100	577	97	213	49	83	81	823	41	815	51	231	39	693	35	286	50	478	38	26	100	0	.	36	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	492	41	484	49	107	62	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	6828	14
Betula pubescens	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	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	.	23	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	52
Carpinus betulus	26	78	93	54	142	58	34	70	0	.	0	.	0	.	0	.	124	54	0	.	76	61	0	.	0	.	0	.	0	.	34	100	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	280	50	31	100	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	42	100	0	.	17	100	17	100	0	.	111	76	0	.	0	.	0	.	0	.	0	.	93	52	68	79	0	.	0	.	0	.	0	.	0	.	100	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	223	80	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	92	100	0	.	24	100	17	100	0	.	0	.	75	51	124	68	50	58	0	.	1974	17
Castanea sativa	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	739	61	42	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	131	100	742	52	611	59	1836	41	869	34	2455	28	600	51	502	48	3474	31	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	55	100	0	.	33	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12120	13
Fagus sylvatica	1242	22	699	31	568	33	1716	26	295	46	468	35	2747	24	3492	16	4526	13	5018	17	269	52	1092	31	1601	32	0	.	381	45	954	33	1579	35	877	32	0	.	1203	30	718	37	947	34	61	63	870	42	0	.	3588	18	1104	25	1215	32	1841	28	423	38	531	35	390	32	394	39	530	36	796	41	108	58	1482	32	688	33	1196	39	1573	33	1547	35	1582	29	2313	23	1454	28	1340	31	600	32	1407	30	654	32	283	51	711	34	623	29	232	41	892	28	0	.	194	69	0	.	1770	39	1857	35	1512	35	2223	34	2648	33	831	45	912	40	0	.	296	64	576	41	216	52	504	48	416	40	214	82	1311	29	193	65	271	50	565	31	347	36	1030	31	459	39	477	43	246	55	185	61	244	64	0	.	108	75	2086	30	244	49	416	42	427	35	817	28	860	28	417	38	393	33	527	31	0	.	86616	3
Fraxinus excelsior	484	44	252	46	312	56	105	70	0	.	371	47	344	48	472	31	572	24	252	46	249	50	125	59	90	51	0	.	178	72	436	52	590	38	300	42	86	100	440	39	68	84	71	61	599	59	312	45	0	.	820	29	389	51	233	40	360	45	136	59	114	78	17	100	34	70	19	100	458	57	0	.	128	48	495	40	434	50	80	51	316	62	387	43	395	61	137	43	75	57	295	46	99	58	501	46	17	100	181	54	402	37	275	42	283	43	49	100	366	100	0	.	60	64	46	61	285	52	367	57	85	60	249	53	250	54	203	64	0	.	189	46	168	57	230	70	322	59	156	55	44	62	37	100	19	100	39	100	34	71	179	82	184	58	64	53	141	68	0	.	126	70	523	63	250	68	247	37	140	53	100	43	253	67	271	36	394	45	148	66	98	47	55	49	0	.	20157	6
Fraxinus ornus	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	100	139	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	.	239	71
Ilex aquifolium	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	296	64	0	.	0	.	0	.	17	100	0	.	0	.	18	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	.	458	44
Juglans regia	0	.	0	0	17	100	0	.	0	.	0	.	11	100	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	75	37	100	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	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	.	18	100	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	266	33
Laburnum anagyroides	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	101	52
Malus sylvestris	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	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	.	71	78
Ostrya carpinifolia	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	546	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	.	353	76	1553	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2452	37
Populus alba, Populus x canescens	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	100	0	.	0	.	45	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	124	63
Populus nigra s.l.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	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	.	86	54
Populus (others)	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	85	76	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	63
Populus tremula	0	.	0	.	0	.	0	.	0	.	0	.	82	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	0	.	7	100	101	76	205	73	0	0	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	0	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	62	59	213	56	35	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	810	27
Prunus avium	36	71	17	100	40	72	17	100	0	.	0	.	72	100	24	76	220	36	36	71	0	0	0	.	0	0	0	.	0	0	0	.	36	72	0	.	0	0	34	71	35	100	69	100	0	0	49	100	108	85	17	100	0	.	18	100	53	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	72	22	77	58	73	82	76	0	.	41	85	0	.	7	100	0	.	0	0	0	.	0	.	7	100	7	100	17	100	0	.	71	64	54	100	7	100	0	.	158	47	0	.	20	100	79	79	18	100	0	.	0	.	31	100	18	100	0	.	0	.	17	100	17	100	0	.	0	.	0	.	17	100	35	70	34	100	17	100	0	.	0	.	7	100	54	58	18	100	71	78	0	.	33	82	50	72	0	.	17	100	0	.	21	75	0	.	0	.	2030	13
Prunus padus	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	128	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	162	80
Pyrus communis, Pyrus pyraster	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	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	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	75
Quercus cerris	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	70	76	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	116	51
Quercus petraea	69	70	7	100	0	.	31	64	0	.	0	.	47	71	0	.	161	53	0	.	0	.	0	.	170	62	0	.	0	.	0	.	0	.	0	.	435	52	0	.	0	.	35	100	0	.	0	0	55	100	52	48	0	.	27	100	0	.	280	92	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	21	100	0	.	143	49	0	.	51	74	0	.	326	72	0	.	0	.	0	.	0	.	29	76	57	77	0	.	0	.	0	.	170	56	73	79	461	48	387	98	164	52	199	63	34	71	0	.	41	100	44	83	123	100	0	.	0	.	0	.	55	88	27	100	89	63	0	.	575	65	7	100	26	78	562	57	0	.	14	100	0	.	0	.	35	100	284	66	0	.	7	100	7	100	0	.	27	80	79	69	84	59	55	66	0	.	5644	15
Quercus pubescens	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	56	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	.	18	100	113	62	198	100	11	100	32	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	322	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	211	66	386	58	52	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1417	33
Quercus robur	26	74	14	71	0	.	0	.	0	.	0	.	0	.	33	82	193	25	0	.	21	100	24	100	17	100	0	.	7	100	14	100	81	62	60	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	132	60	47	74	68	89	17	100	0	.	0	.	0	.	34	100	0	.	22	100	0	.	0	.	28	100	8	100	0	.	29	76	0	.	52	100	0	.	0	.	24	76	0	.	6	100	0	.	0	.	116	49	135	57	65	38	0	.	0	.	0	.	0	.	32	63	82	59	20	100	8	100	0	.	0	.	0	.	0	.	0	0	0	.	0	.	101	92	0	.	0	.	26	100	0	.	0	.	115	100	0	.	17	100	0	.	103	100	0	.	7	100	0	.	0	.	8	100	0	.	7	100	13	71	23	100	10	100	39	55	7	100	0	.	0	.	1893	14
Quercus rubra	128	100	0	.	0	.	27	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	155	84
Robinia pseudoacacia	0	.	121	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	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	.	203	83	0	.	0	.	17	100	477	65	0	.	120	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	100	42	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1091	36
Salix (others)	0	.	0	.	0	.	0	.	0	.	8	100	43	72	35	100	63	50	19	100	0	.	7	100	0	.	0	.	15	100	0	.	66	74	0	.	0	.	0	.	52	100	0	0	119	56	314	55	50	100	0	0	0	.	0	.	0	0	0	.	0	.	17	100	34	100	0	.	0	.	0	0	0	.	0	.	0	.	35	100	0	.	0	.	0	.	17	100	0	.	0	.	21	100	0	.	0	.	0	.	35	71	0	.	0	.	18	100	8	100	0	.	101	74	0	.	0	.	0	.	0	.	36	70	0	.	0	.	0	.	150	100	52	74	0	.	0	.	0	.	17	100	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	84	82	262	65	181	83	0	.	0	.	7	100	0	.	0	.	17	100	34	100	0	.	0	.	1932	19
Sorbus aria	0	.	0	.	0	.	17	100	0	.	0	.	72	51	22	100	0	.	330	38	0	.	41	72	51	74	0	.	0	.	0	.	15	100	15	100	0	.	229	72	37	100	37	100	18	100	0	0	17	100	233	64	33	100	0	.	0	.	0	.	54	58	0	.	17	100	70	70	0	.	0	.	35	71	0	.	0	.	0	.	42	100	0	.	7	100	34	100	52	58	34	71	0	0	0	.	0	.	0	.	0	.	0	.	0	.	81	100	0	.	36	71	84	52	40	100	97	59	172	57	18	100	58	58	87	67	0	.	55	74	0	.	88	82	55	100	90	80	0	.	39	100	0	.	17	100	0	.	17	100	36	71	0	.	69	100	0	.	0	.	0	.	35	100	142	50	35	71	0	.	86	82	17	100	69	71	0	.	0	.	0	.	0	.	0	.	3038	13
Sorbus aucuparia	0	.	0	.	0	.	0	.	0	0	0	.	571	37	22	100	0	.	74	62	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	71	50	0	.	18	100	17	100	17	100	19	100	32	71	0	.	101	55	0	.	97	82	0	.	42	100	39	100	0	.	81	83	0	0	55	100	0	0	68	81	0	.	37	72	0	.	0	.	0	.	0	.	0	.	55	58	22	100	15	100	0	.	0	.	0	.	18	100	0	.	49	74	34	71	0	0	0	0	18	100	0	.	0	.	51	100	0	.	0	0	0	.	35	100	71	100	17	100	17	100	19	100	0	.	0	.	153	66	0	.	19	100	0	.	69	100	0	.	0	0	0	.	23	100	69	62	345	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	2462	14
Sorbus torminalis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	17	100	17	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	.	17	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	.	17	100	0	.	0	.	0	.	86	45
Tilia cordata	17	100	149	52	7	100	0	.	0	.	19	100	225	49	40	100	155	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	88	72	0	.	0	.	395	46	0	.	79	56	0	.	8	100	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	0	.	0	0	28	71	249	92	0	.	78	78	0	.	0	.	17	100	0	.	28	100	0	.	0	.	54	100	17	100	0	.	0	.	0	.	72	79	18	100	170	67	489	45	36	71	167	95	192	73	18	100	0	.	37	100	100	100	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	135	79	0	.	19	100	0	.	18	100	0	.	18	100	0	.	0	.	0	.	93	100	0	.	0	.	0	.	50	74	0	.	3360	15
Tilia platyphyllos	70	54	0	.	0	.	52	100	0	.	0	.	15	100	77	91	75	100	14	71	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	65	83	0	.	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	100	0	.	0	.	0	.	0	.	0	.	61	89	7	100	0	.	7	100	0	.	0	.	85	100	0	.	0	.	39	71	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	259	71	17	100	155	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1173	24
Ulmus glabra	24	77	24	75	17	100	17	100	0	.	0	.	107	49	253	34	17	100	17	100	69	100	14	100	41	85	0	.	0	.	0	.	21	77	52	100	0	.	36	71	46	100	6	100	18	100	0	.	0	.	24	77	34	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	172	57	0	.	18	100	0	.	0	.	125	45	42	100	63	86	0	.	35	71	17	100	14	100	0	.	77	71	30	71	36	72	0	.	0	.	7	100	0	.	0	.	0	.	0	.	19	100	0	.	54	100	0	.	9	100	33	71	7	100	0	.	7	100	0	.	0	.	107	83	0	.	0	0	20	100	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	18	100	0	0	68	71	51	75	0	.	0	.	7	100	0	.	1911	13
Ulmus minor	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	71
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	.	28	100	0	.	0	.	0	.	0	.	0	.	0	.	118	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	.	146	83
other shrubs	19	100	33	70	0	.	0	.	0	.	0	.	56	58	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	15	100	55	75	54	58	35	100	17	100	17	100	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	99	53	0	.	0	.	18	100	248	46	96	73	0	.	36	100	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	36	71	18	100	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1092	18
indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0
total	3748	21	3086	25	2517	28	3512	19	2236	29	4390	22	22060	9	14507	10	16942	10	13742	13	991	36	2089	29	3176	24	73	.	1563	33	4990	22	6086	19	4085	22	670	50	6007	18	8388	14	6369	17	8609	17	19308	11	17831	11	11931	12	5303	20	6361	18	8723	16	2248	29	3485	24	1615	31	1818	31	3297	23	3818	30	3393	28	5359	22	3072	25	4050	28	5880	21	4019	26	5122	19	5646	19	3445	24	2770	26	1992	27	3109	25	3819	23	3327	23	2634	27	3873	24	2976	27	3657	23	2602	33	5467	20	3664	22	5218	23	4809	23	9156	17	7126	19	5041	23	6754	20	3787	28	1596	39	3308	27	3201	25	2983	26	3648	33	2281	33	939	46	4128	23	900	47	1415	41	3726	21	2827	28	3382	27	2334	26	2246	38	1310	37	586	59	990	46	15226	12	13133	14	14116	12	1561	32	2173	31	2002	27	3851	24	3451	21	2049	28	1793	31	2263	28	0	.	450759	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. 2299928. Birmensdorf, Swiss Federal Research Institute WSL

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