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 wood volume (stemwood)
main tree species · tree state (living/dead)
regional demarcation: forest district (2024)
unit: 1000 m³
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
main tree species	tree state (living/dead)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	dead	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
spruce	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	384	37	508	42	801	45	1507	33	2322	31	2875	27	14686	11	6692	13	5681	16	3344	17	3	100	315	64	404	52	0	.	815	45	2692	26	2472	27	1757	29	0	.	3817	24	6728	16	3195	19	6351	17	9358	13	5901	14	2773	22	1388	32	2064	26	4715	22	388	52	1280	28	668	42	529	37	1757	29	1342	35	1444	32	1873	27	868	38	1235	38	2484	26	2069	32	3030	28	1888	25	895	45	566	48	314	67	291	55	2179	27	1990	29	1291	33	840	36	973	38	1361	29	1051	38	2121	27	2014	31	0	.	64	71	24	100	289	61	80	65	239	58	868	42	1377	41	1734	33	846	53	1316	33	1151	33	931	38	187	51	1102	30	96	51	342	56	2029	22	769	40	863	33	1127	36	270	67	425	55	133	83	359	69	3992	18	4135	18	5527	17	506	43	1119	36	649	37	1230	30	1837	28	768	56	709	47	586	34	0	.	167969	2
	dead	56	70	55	54	41	100	66	45	76	45	152	42	2272	16	471	26	166	35	212	35	16	100	22	85	28	66	0	.	70	79	237	39	982	45	96	49	0	.	347	34	342	36	298	35	584	29	941	24	281	29	108	47	191	50	160	46	706	35	3	100	2	100	2	100	0	.	62	54	98	53	161	59	202	48	32	65	125	57	283	34	155	52	141	57	81	40	48	77	52	56	13	100	17	72	132	47	212	55	146	59	10	79	37	75	8	62	23	89	150	38	72	67	0	.	0	.	0	.	41	73	22	92	7	100	58	54	96	75	258	34	64	49	147	48	126	45	69	67	0	.	202	73	41	100	4	100	34	47	23	61	4	71	142	69	22	100	60	93	0	.	46	76	510	26	473	28	657	22	63	66	107	58	45	65	47	57	22	95	48	61	0	.	27	67	0	.	14703	6
	total	440	36	563	38	842	43	1573	32	2398	30	3027	26	16958	11	7163	13	5847	16	3556	17	19	86	336	62	433	49	0	.	885	43	2929	26	3454	24	1853	28	0	.	4164	23	7070	16	3492	19	6935	17	10300	13	6183	14	2881	21	1579	30	2223	25	5420	20	391	52	1281	28	671	42	529	37	1819	29	1441	34	1604	32	2075	26	900	38	1360	35	2767	24	2224	31	3171	28	1969	25	943	43	618	46	327	65	308	52	2310	27	2202	28	1436	33	850	36	1010	37	1368	29	1074	38	2271	26	2086	31	0	.	64	71	24	100	331	61	102	68	246	56	926	40	1473	41	1992	32	910	52	1463	32	1277	32	1000	39	187	51	1304	29	137	57	346	56	2063	22	792	39	867	33	1269	35	291	63	485	57	133	83	405	68	4502	17	4608	17	6184	17	569	40	1227	34	693	36	1277	30	1859	28	816	54	709	47	614	33	0	.	182672	2
fir	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	388	46	102	72	119	72	1278	33	557	38	1541	36	1784	26	6208	15	4675	17	2909	22	18	73	120	61	316	53	0	.	263	70	325	55	1166	36	688	49	0	.	525	49	298	36	486	42	282	66	908	38	17	100	3568	20	1478	31	2979	21	1396	26	529	53	1146	36	511	35	587	42	1351	34	331	54	170	62	898	38	862	35	133	60	464	46	550	43	1188	30	139	57	488	39	378	56	208	48	242	37	534	51	741	36	567	36	535	47	298	41	466	57	9	100	126	54	162	100	0	.	0	.	27	100	334	60	260	58	171	69	602	43	0	.	350	48	459	36	446	55	956	39	86	62	65	88	937	37	233	68	342	45	353	49	393	49	477	35	376	46	361	68	408	53	358	70	166	69	125	89	463	51	699	36	204	44	174	50	302	48	979	31	414	38	56	82	25	100	185	82	0	.	59792	4
	dead	1	100	0	.	0	.	117	52	0	.	116	64	126	44	350	31	175	39	148	51	20	100	0	.	32	71	0	.	5	72	8	71	92	69	65	74	0	.	11	71	57	66	3	73	53	78	70	56	0	.	261	39	24	68	163	41	287	42	19	81	14	100	3	100	0	.	22	64	34	100	36	73	15	67	130	61	20	100	48	61	13	100	23	77	0	.	31	94	4	73	0	.	134	55	26	86	29	77	47	77	0	.	0	.	0	.	0	.	16	100	17	100	0	.	0	.	0	.	0	.	1	100	2	100	34	58	0	.	52	58	22	88	95	87	108	85	2	100	0	.	10	52	2	100	0	.	27	76	34	66	28	89	2	100	5	100	0	.	14	100	0	.	12	87	23	100	105	61	29	90	0	.	13	100	61	56	39	100	4	100	0	.	0	.	0	.	3586	10
	total	389	45	102	72	119	72	1395	31	557	38	1657	34	1910	25	6558	14	4850	17	3057	21	38	86	120	61	348	53	0	.	267	70	333	54	1258	35	753	49	0	.	537	49	354	32	489	42	335	68	978	37	17	100	3829	19	1503	31	3142	21	1683	25	548	52	1160	36	514	35	587	42	1372	33	365	55	206	54	913	38	992	33	153	62	512	43	563	43	1211	30	139	57	518	38	383	56	208	48	376	32	560	49	770	35	614	35	535	47	298	41	466	57	9	100	142	57	178	100	0	.	0	.	27	100	334	60	261	58	174	69	636	41	0	.	402	47	481	37	540	57	1063	39	89	61	65	88	947	36	235	68	342	45	379	48	427	47	505	35	379	46	366	68	408	53	372	69	166	69	137	83	486	51	804	35	233	41	174	50	315	47	1040	30	453	35	59	83	25	100	185	82	0	.	63378	4
pine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	76	79	35	71	29	76	72	87	0	.	17	100	420	73	62	66	497	32	418	53	47	100	9	292	162	45	0	.	18	100	168	79	0	.	308	60	110	79	0	.	166	47	569	35	48	100	605	35	1167	31	369	37	16	100	170	56	0	.	107	100	27	100	0	.	0	.	24	100	0	.	147	100	16	100	31	100	0	.	0	.	0	.	43	100	360	39	99	64	104	52	6	231	122	49	37	100	8	100	0	.	89	59	159	55	193	59	0	.	64	78	58	100	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	184	62	0	.	0	.	0	.	0	.	26	71	10	100	0	.	28	100	0	.	22	71	0	.	26	100	0	.	0	.	95	100	0	.	902	31	378	31	716	34	0	.	30	100	220	48	97	58	250	42	169	53	186	65	45	100	0	.	10650	8
	dead	19	100	0	.	0	.	0	.	0	.	0	.	57	60	0	.	86	71	27	63	0	.	0	.	44	60	0	.	0	.	39	100	0	.	44	80	19	100	0	.	3	74	69	62	0	.	38	52	80	40	78	55	0	.	0	.	0	.	0	.	2	100	0	.	0	.	26	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	6	100	0	.	5	100	49	100	28	100	14	100	0	.	0	.	0	.	0	.	4	100	0	.	31	71	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	10	100	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	43	76	41	178	32	0	.	9	100	28	72	15	75	0	.	0	.	10	100	0	.	0	.	1188	13
	total	95	67	35	71	29	76	72	87	0	.	17	100	478	66	62	66	582	35	446	51	47	100	9	292	205	47	0	.	18	100	207	83	0	.	352	62	129	72	0	.	169	47	638	36	48	100	642	33	1247	30	447	37	16	100	170	56	0	.	107	100	29	100	0	.	0	.	51	100	0	.	147	100	22	100	31	100	0	.	0	.	0	.	43	100	366	39	99	64	109	50	55	111	151	49	52	100	8	100	0	.	89	59	159	55	197	59	0	.	95	73	59	100	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	184	62	13	100	0	.	0	.	0	.	36	58	10	100	0	.	37	100	0	.	22	71	0	.	26	100	0	.	0	.	95	100	0	.	967	30	454	29	894	33	0	.	39	100	248	43	112	52	250	42	169	53	196	64	45	100	0	.	11839	8
larch	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	14	73	75	68	67	98	144	69	0	.	0	.	140	62	16	100	150	59	0	.	0	.	0	.	25	72	0	.	0	.	46	100	0	.	25	100	0	.	67	87	378	42	277	40	195	64	1636	20	4796	15	20	100	4	100	15	73	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	14	100	12	100	30	100	113	71	45	100	10	100	39	66	0	.	0	.	17	100	0	.	0	.	0	.	52	100	9	100	46	100	0	.	322	62	485	32	354	42	256	55	0	.	86	72	902	28	430	39	518	60	0	.	0	.	0	.	175	43	0	.	10	100	0	.	46	100	0	.	13	100	0	.	0	.	12	100	0	.	49	71	0	.	11	100	0	.	29	100	4493	19	2342	20	1326	23	55	100	22	100	16	100	2	100	0	.	50	71	20	100	177	55	0	.	20718	7
	dead	0	.	0	.	0	.	0	.	0	.	0	.	34	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	0	.	11	100	0	.	0	.	9	76	34	85	15	88	233	39	182	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	16	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	72	51	56	11	71	40	100	0	.	0	.	7	81	1	100	10	82	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	20	100	0	.	0	.	0	.	0	.	380	30	96	36	172	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1372	14
	total	14	73	75	68	67	98	144	69	0	.	0	.	173	53	16	100	150	59	0	.	0	.	0	.	25	72	0	.	0	.	74	73	0	.	36	76	0	.	67	87	387	42	312	41	211	60	1869	20	4978	15	20	100	4	100	15	73	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	15	100	12	100	30	100	129	64	45	100	10	100	39	66	0	.	0	.	17	100	0	.	0	.	0	.	52	100	9	100	46	100	0	.	324	62	537	33	365	41	297	57	0	.	86	72	908	28	431	39	527	59	0	.	0	.	0	.	180	42	0	.	10	100	0	.	46	100	0	.	13	100	0	.	0	.	24	100	0	.	69	73	0	.	11	100	0	.	29	100	4873	18	2438	19	1499	22	55	100	22	100	16	100	2	100	0	.	50	71	20	100	177	55	0	.	22090	6
Arolla pine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	211	47	1113	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	524	31	246	38	54	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2164	15
	dead	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	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	70	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	42
	total	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	211	47	1156	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	573	31	246	38	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2270	14
other conifers	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	36	77	0	.	43	100	42	69	0	.	8	78	1	100	0	.	362	46	2	71	0	.	0	.	0	.	177	.	0	.	0	.	0	.	0	.	0	.	2	100	8	100	0	.	0	.	7	100	25	46	-5	145	60	100	198	89	1	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	4	100	294	99	-12	114	-16	100	7	86	0	.	57	100	9	100	-19	100	22	71	0	.	0	.	0	.	3	100	0	.	0	.	4	74	0	.	0	.	0	.	0	.	0	.	0	.	50	100	0	.	0	.	0	.	0	.	6	100	0	.	8	100	0	.	0	.	103	100	17	62	0	.	5	100	0	.	0	.	0	.	185	100	0	.	1	100	0	.	0	.	8	86	5	100	2	75	0	.	0	.	13	80	33	74	6	100	0	.	0	.	0	.	0	.	1770	27
	dead	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	2	100	3	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	4	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	42
	total	36	77	0	.	43	100	42	69	0	.	8	78	1	100	0	.	362	46	2	71	0	.	0	.	0	.	177	.	0	.	0	.	0	.	0	.	0	.	2	100	11	100	0	.	0	.	9	100	28	44	-5	145	60	100	198	89	1	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	4	100	294	99	-12	114	-16	100	7	86	0	.	57	100	9	100	-19	100	22	71	0	.	0	.	0	.	3	100	0	.	0	.	4	74	0	.	0	.	0	.	0	.	0	.	0	.	50	100	0	.	0	.	0	.	0	.	6	100	0	.	8	100	0	.	0	.	105	100	21	61	0	.	5	100	0	.	0	.	0	.	185	100	0	.	1	100	0	.	0	.	8	86	13	100	3	72	0	.	0	.	13	80	33	74	6	100	0	.	0	.	0	.	0	.	1793	27
beech	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	1448	27	816	31	651	32	2099	30	258	46	557	33	2506	26	2903	17	4448	14	3359	16	403	45	984	33	1333	28	0	.	269	64	756	34	1018	31	1260	35	0	.	961	30	680	46	577	31	34	66	496	42	0	.	3594	16	1363	28	823	34	1255	31	428	49	219	35	231	37	224	40	435	34	436	42	36	58	1503	31	570	38	1007	44	859	31	1092	34	1434	29	2108	21	1364	30	1059	27	1077	34	1355	28	413	32	260	85	961	37	555	31	406	40	762	32	0	.	131	63	0	.	740	34	1200	33	617	34	923	32	848	32	463	41	619	43	0	.	231	56	583	45	188	48	391	46	458	48	74	64	1102	30	129	90	109	49	308	33	383	43	1115	32	507	57	578	46	176	55	270	50	264	63	0	.	16	89	906	30	249	46	414	40	418	44	871	31	907	29	708	46	449	43	406	33	0	.	71394	3
	dead	90	80	56	54	0	.	12	53	1	100	16	86	69	46	135	42	121	43	62	47	69	100	65	80	38	48	0	.	31	71	28	100	16	100	44	58	0	.	25	50	6	75	18	91	0	.	0	.	0	.	78	43	36	65	0	.	151	76	4	100	5	100	0	.	0	.	3	100	9	77	0	.	44	56	0	.	31	65	23	56	37	62	80	59	26	85	13	100	95	79	29	74	23	67	11	100	8	100	8	76	2	100	0	.	5	100	0	.	0	.	0	.	30	64	23	71	30	60	28	67	22	91	71	71	1	72	0	.	0	.	17	71	0	.	3	74	0	.	1	100	66	47	0	.	0	.	3	100	23	72	3	100	17	100	0	.	0	.	0	.	0	.	0	.	0	.	12	54	16	100	22	100	0	.	19	59	28	60	8	76	0	.	12	100	0	.	2078	11
	total	1538	28	872	30	651	32	2111	30	259	45	573	33	2575	26	3038	17	4569	14	3421	16	472	44	1049	34	1370	28	0	.	300	60	784	33	1034	31	1304	35	0	.	986	30	685	46	594	31	34	66	496	42	0	.	3672	16	1399	27	823	34	1406	30	432	48	224	36	231	37	224	40	438	34	446	42	36	58	1547	31	570	38	1038	43	882	31	1129	33	1514	28	2133	21	1377	30	1154	27	1106	34	1378	28	424	32	268	83	968	37	557	31	406	40	767	32	0	.	131	63	0	.	769	34	1224	32	646	33	951	32	871	32	534	41	620	43	0	.	231	56	600	44	188	48	394	46	458	48	75	64	1168	30	129	90	109	49	311	33	406	42	1118	32	524	56	578	46	176	55	270	50	264	63	0	.	16	89	918	30	265	45	436	38	418	44	890	30	935	29	715	46	449	43	419	32	0	.	73472	3
maple	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	272	38	131	43	108	47	71	45	18	71	245	52	774	24	325	31	209	33	584	25	128	59	101	51	213	45	0	.	73	71	54	61	302	36	55	54	0	.	317	31	136	50	22	123	66	62	116	46	0	.	445	22	153	36	68	44	306	32	76	56	171	43	46	82	100	83	33	46	129	34	24	76	224	62	187	41	45	119	351	40	222	46	562	29	86	52	116	47	146	36	76	48	186	41	192	40	67	49	52	52	264	53	101	55	74	76	12	100	44	100	19	100	2	100	48	74	40	52	5	59	26	100	2	100	148	43	4	100	0	.	190	52	180	44	121	42	27	64	0	.	135	41	0	.	15	83	25	63	45	65	86	51	81	48	13	100	16	100	0	.	8	140	2	100	35	69	226	35	105	53	175	50	29	189	276	35	103	49	201	55	132	61	235	55	0	.	11631	5
	dead	12	100	2	100	0	.	3	71	0	.	6	100	14	60	0	.	19	58	2	100	0	.	3	100	0	.	0	.	0	.	0	.	0	.	10	100	0	.	24	60	0	.	0	.	2	100	0	.	0	.	18	67	3	100	0	.	3	100	2	100	0	.	4	100	0	.	0	.	2	100	0	.	0	.	0	.	23	73	10	82	0	.	16	100	2	100	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	2	100	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	9	77	0	.	0	.	0	.	0	.	0	.	0	.	29	60	0	.	0	.	2	100	28	75	8	100	3	100	0	.	0	.	0	.	274	18
	total	284	37	133	44	108	47	75	45	18	71	250	51	788	23	325	31	228	33	586	25	128	59	104	51	213	45	0	.	73	71	54	61	302	36	66	48	0	.	342	29	136	50	22	123	67	61	116	46	0	.	464	21	156	36	68	44	309	32	78	57	171	43	50	76	100	83	33	46	131	34	24	76	224	62	187	41	68	88	361	40	222	46	578	29	88	52	116	47	146	36	76	48	186	41	195	40	67	49	52	52	264	53	101	55	74	76	12	100	44	100	19	100	2	100	50	72	42	50	5	59	26	100	2	100	148	43	4	100	0	.	192	52	180	44	124	43	27	64	0	.	135	41	0	.	15	83	25	63	45	65	88	49	89	45	13	100	16	100	0	.	8	140	2	100	35	69	255	36	105	53	175	50	31	176	304	33	111	52	203	55	132	61	235	55	0	.	11905	5
ash	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	274	37	415	42	170	58	158	65	0	.	335	48	151	69	511	43	664	28	118	53	280	55	44	56	129	64	0	.	198	62	361	56	334	40	395	43	28	100	282	44	46	85	7	183	143	54	89	63	0	.	922	27	615	73	197	50	117	49	75	64	43	58	2	100	5	70	4	100	439	57	0	.	67	70	522	37	311	43	96	52	198	62	308	46	215	42	102	47	52	63	254	50	94	54	316	47	4	100	272	66	416	38	429	42	220	41	11	100	123	100	0	.	25	58	38	55	158	64	75	68	70	61	85	49	133	65	47	67	0	.	284	50	61	56	56	67	171	73	51	77	41	58	70	100	2	100	9	100	23	92	112	58	230	60	52	65	212	69	0	.	236	79	63	49	47	72	153	51	171	70	384	53	122	50	254	34	249	35	146	68	113	47	170	51	0	.	15676	7
	dead	8	100	44	100	0	.	2	100	0	.	26	71	18	71	11	86	19	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	1	100	0	.	14	100	0	.	0	.	13	61	14	100	0	.	24	58	0	.	31	61	6	59	2	100	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	10	100	0	.	0	.	4	100	0	.	0	.	0	.	7	71	6	100	20	83	0	.	0	.	5	100	0	.	0	.	11	100	0	.	0	.	0	.	11	100	4	100	7	100	0	.	9	100	0	.	0	.	0	.	17	92	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	73	0	.	0	.	9	100	0	.	0	.	2	100	4	100	24	77	0	.	0	.	0	.	83	68	2	100	0	.	0	.	19	100	0	.	523	19
	total	281	36	459	45	170	58	160	65	0	.	361	46	169	67	523	43	683	27	118	53	280	55	44	56	129	64	0	.	198	62	361	56	339	39	397	43	28	100	296	42	46	85	7	183	157	54	102	57	0	.	945	27	615	73	228	44	123	49	76	65	43	58	2	100	5	70	4	100	459	58	0	.	67	70	522	37	321	42	96	52	198	62	312	46	215	42	102	47	52	63	261	49	101	51	336	48	4	100	272	66	421	38	429	42	220	41	22	100	123	100	0	.	25	58	49	61	162	63	82	63	70	61	94	52	133	65	47	67	0	.	301	50	61	56	58	67	171	73	51	77	41	58	70	100	2	100	9	100	23	92	123	58	230	60	52	65	221	70	0	.	236	79	65	47	51	67	176	48	171	70	384	53	122	50	337	37	251	35	146	68	113	47	189	53	0	.	16198	6
oak	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	287	60	62	72	0	.	240	55	0	.	0	.	17	75	60	71	587	23	0	.	79	100	3	100	115	56	0	.	8	100	54	100	34	74	64	74	473	55	0	.	0	.	59	100	0	.	0	.	72	100	299	48	92	92	180	67	36	100	51	75	0	.	33	100	2	100	0	.	5	100	0	.	0	.	32	100	27	100	0	.	23	70	0	.	241	52	0	.	20	70	18	95	164	45	16	100	0	.	0	.	118	50	486	70	290	42	0	.	0	.	0	.	46	54	117	59	445	33	102	64	101	45	83	75	44	81	0	.	31	100	15	72	25	100	0	.	70	88	0	.	40	84	176	72	95	58	0	.	405	49	20	100	26	62	253	47	25	100	23	100	25	100	28	60	92	73	56	49	0	.	51	81	41	58	11	100	126	64	155	43	182	59	81	66	0	.	7336	10
	dead	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	17	89	0	.	8	100	0	.	3	100	0	.	47	82	0	.	0	.	0	.	0	.	1	100	0	.	31	88	26	100	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	6	73	0	.	2	100	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	3	100	21	55	15	88	0	.	0	.	0	.	0	.	0	.	2	100	1	100	0	.	0	.	0	.	0	.	10	100	0	.	0	.	10	71	0	.	17	100	3	70	0	.	0	.	0	.	0	.	2	100	4	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	272	25
	total	287	60	62	72	0	.	240	55	0	.	0	.	17	75	60	71	591	23	0	.	79	100	3	100	132	55	0	.	17	100	54	100	37	68	64	74	519	57	0	.	0	.	59	100	0	.	1	100	72	100	330	50	118	76	180	67	36	100	54	76	0	.	33	100	2	100	0	.	5	100	0	.	0	.	32	100	46	72	0	.	23	70	0	.	247	51	0	.	22	71	18	95	165	45	16	100	0	.	0	.	118	50	486	70	290	42	0	.	0	.	0	.	49	51	120	58	466	33	118	66	101	45	83	75	44	81	0	.	31	100	16	73	26	100	0	.	70	88	0	.	40	84	186	72	95	58	0	.	414	49	20	100	43	71	256	47	25	100	23	100	25	100	28	60	94	73	60	46	0	.	51	81	41	58	11	100	126	64	155	43	182	59	93	64	0	.	7608	10
sweet chestnut	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	406	49	43	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	271	49	251	67	402	50	488	31	1128	28	426	50	330	47	1510	31	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	26	100	0	.	64	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5391	12
	dead	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	52	0	.	0	.	0	.	0	.	0	.	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	32	62	51	75	62	49	146	50	188	31	11	73	51	59	213	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	808	16
	total	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	444	49	43	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	304	48	302	66	464	48	634	31	1316	27	437	50	381	45	1723	30	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	71	0	.	0	.	0	.	0	.	32	100	0	.	64	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6200	12
other broadleaves	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	283	42	96	38	140	49	88	77	3	100	63	75	536	30	153	28	647	34	98	32	89	53	40	58	47	57	0	.	18	80	356	75	37	47	119	53	3	100	187	47	102	45	64	45	361	42	745	28	370	44	299	38	121	56	65	61	67	51	2	100	26	50	7	100	23	63	48	78	72	59	15	68	21	67	6	100	71	43	143	37	237	62	113	40	37	47	42	50	62	65	175	51	13	100	122	46	18	66	37	143	229	62	121	47	78	61	121	61	185	38	97	82	260	40	446	39	970	26	247	32	339	50	349	41	66	51	44	100	24	80	171	65	281	90	37	65	32	61	125	78	88	57	5	82	6	80	26	56	87	70	23	55	124	86	51	57	123	67	4	100	12	100	341	33	204	30	284	34	7	100	163	63	104	57	66	53	8	58	32	62	142	60	73	66	0	.	12674	6
	dead	12	100	26	69	0	.	19	71	1	100	4	100	50	38	12	66	0	.	4	100	2	100	0	.	8	60	0	.	22	72	7	71	3	71	23	58	1	100	25	52	29	50	34	60	70	65	138	37	20	56	68	36	4	72	3	100	21	45	0	.	6	72	0	.	1	100	0	.	4	100	18	91	16	81	0	.	5	58	38	55	2	100	10	76	3	100	0	.	0	.	14	92	3	100	40	78	0	.	37	63	5	80	0	.	0	.	11	88	18	69	3	71	31	58	61	40	142	42	116	43	2	100	57	50	12	63	11	100	4	100	22	71	32	63	9	89	13	100	16	93	19	85	0	.	1	100	2	100	0	.	4	100	13	79	1	100	17	91	3	100	0	.	64	38	26	44	53	39	5	100	9	100	1	100	15	69	5	100	2	100	15	100	3	100	0	.	1626	9
	total	295	41	122	38	140	49	107	72	4	77	67	71	585	29	165	28	647	34	102	31	90	52	40	58	55	50	0	.	40	66	362	74	40	43	142	47	4	75	212	43	130	44	97	46	431	38	883	26	390	42	367	34	125	54	68	60	88	42	2	100	32	51	7	100	24	62	48	78	76	60	33	78	37	72	6	100	76	41	180	39	239	62	123	40	39	44	42	50	62	65	189	52	15	84	162	42	18	66	74	99	233	61	121	47	78	61	132	58	203	38	100	82	291	37	508	37	1112	26	363	31	342	50	406	41	78	46	55	100	28	70	193	59	313	86	45	62	45	52	141	71	107	51	5	82	7	68	28	53	87	70	27	49	136	85	52	56	140	66	6	100	12	100	405	31	229	29	337	32	12	72	172	61	106	57	81	45	13	64	34	58	158	63	76	64	0	.	14300	6
indeterminable	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	living	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	dead	4	100	0	.	0	.	0	.	0	.	11	85	141	34	6	100	16	100	1	100	4	100	0	.	13	100	0	.	2	100	1	100	0	.	10	100	0	.	9	100	18	73	5	100	19	94	16	56	3	84	2	100	4	100	3	100	28	83	0	.	0	.	0	.	0	.	9	100	0	.	13	100	0	.	0	.	2	100	14	100	0	.	6	100	0	.	0	.	37	87	0	.	0	.	13	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	2	100	11	100	0	.	22	100	0	.	2	100	30	73	4	100	1	100	3	100	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	21	76	47	59	10	69	0	.	0	.	0	.	0	.	0	.	20	100	0	.	2	100	0	.	606	15
	total	4	100	0	.	0	.	0	.	0	.	11	85	141	34	6	100	16	100	1	100	4	100	0	.	13	100	0	.	2	100	1	100	0	.	10	100	0	.	9	100	18	73	5	100	19	94	16	56	3	84	2	100	4	100	3	100	28	83	0	.	0	.	0	.	0	.	9	100	0	.	13	100	0	.	0	.	2	100	14	100	0	.	6	100	0	.	0	.	37	87	0	.	0	.	13	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	2	100	11	100	0	.	22	100	0	.	2	100	30	73	4	100	1	100	3	100	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	21	76	47	59	10	69	0	.	0	.	0	.	0	.	0	.	20	100	0	.	2	100	0	.	606	15
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	.
	living	3462	19	2241	23	2133	26	5700	19	3158	29	5641	22	21007	10	16932	10	17920	10	10833	12	1046	35	1615	31	2743	25	177	.	1662	34	4812	21	5363	19	4671	20	614	53	6159	19	8558	14	5255	17	7480	16	14576	11	13504	10	12284	12	5290	21	6759	18	7893	18	1656	32	2911	24	1505	33	1512	32	3653	22	2756	27	1835	31	4620	21	3385	25	2846	26	4492	21	4442	23	6688	19	5169	18	3113	24	2368	25	2166	28	2467	24	3809	22	3086	24	3234	25	3056	25	3018	28	3448	21	1557	30	3551	21	2960	25	1731	23	2401	23	3493	17	3354	18	2485	22	3435	21	2479	28	1472	39	2559	28	2723	27	2512	27	2721	30	1776	36	679	42	3471	22	722	51	944	36	2750	21	2138	27	2696	24	2732	25	1578	36	1397	37	883	55	1099	45	10503	12	7963	14	10012	13	1297	33	2534	29	1915	26	3819	22	3900	21	2284	29	1958	30	1960	27	0	.	387165	1
	dead	202	49	184	43	41	100	218	32	78	44	330	36	2780	14	986	20	605	21	457	28	111	67	89	61	180	40	0	.	137	63	347	38	1102	41	305	34	67	66	456	29	466	30	460	29	756	25	1491	17	613	20	667	22	288	36	360	32	1202	29	33	51	29	55	10	59	1	100	122	39	168	40	229	45	285	37	162	53	236	37	432	28	207	45	280	35	123	35	91	53	195	46	111	56	213	40	260	39	250	48	238	41	21	58	38	73	17	55	55	47	298	29	156	45	166	36	247	35	395	26	227	30	111	40	369	27	128	41	106	68	317	33	193	35	279	50	251	53	87	60	29	63	311	50	53	96	15	69	65	47	102	46	52	56	221	49	32	73	86	71	16	100	46	76	1108	19	754	22	1260	17	113	67	147	47	89	48	267	39	104	52	84	49	25	72	75	57	0	.	27167	4
	total	3664	19	2425	23	2175	26	5918	19	3236	29	5971	22	23787	9	17917	10	18525	10	11290	12	1156	35	1705	31	2923	25	177	.	1799	33	5159	21	6465	20	4976	20	680	53	6615	19	9024	14	5715	17	8237	16	16066	10	14117	10	12951	11	5578	20	7119	18	9094	17	1689	31	2940	24	1515	33	1514	32	3775	22	2924	27	2064	30	4905	21	3546	25	3082	25	4925	20	4649	23	6968	19	5292	18	3204	24	2564	24	2277	28	2680	24	4069	22	3336	24	3471	25	3077	25	3056	28	3465	21	1612	30	3850	20	3116	25	1897	23	2648	23	3888	17	3581	18	2596	22	3804	21	2607	28	1579	38	2876	28	2916	26	2791	27	2972	31	1862	36	707	41	3782	22	775	52	958	36	2815	21	2239	27	2748	24	2953	25	1610	35	1483	38	900	55	1145	44	11611	12	8717	13	11272	13	1410	32	2681	28	2004	26	4086	21	4004	21	2368	29	1984	30	2035	27	0	.	414332	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. 2299893. Birmensdorf, Swiss Federal Research Institute WSL

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