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 NFI4/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	1536	33	2526	29	2875	27	16604	11	6692	13	5681	16	3408	17	3	100	315	64	427	50	0	.	815	45	2879	25	2737	25	1757	29	0	.	4047	22	7096	16	3464	18	7198	16	10126	13	6023	14	2773	22	1529	31	2064	26	5036	21	388	52	1280	28	668	42	529	37	1757	29	1454	33	1501	31	2037	25	868	38	1490	34	2546	25	2069	32	3615	25	1888	25	895	45	566	48	314	67	318	51	2273	26	2092	27	1399	31	840	36	973	38	1361	29	1059	38	2164	27	2046	31	12	100	64	71	24	100	337	53	80	65	399	45	858	42	1480	38	1760	33	1036	46	1574	30	1369	30	931	38	187	51	1143	30	96	51	342	56	2139	21	812	38	947	32	1127	36	325	58	425	55	133	83	359	69	4101	18	4229	17	5916	16	816	38	1119	36	649	37	1241	30	1837	28	768	56	709	47	586	34	0	.	177626	2
	dead	56	70	55	54	41	100	66	45	76	45	152	42	2353	16	471	26	166	35	212	35	16	100	22	85	28	66	0	.	70	79	250	37	982	45	96	49	0	.	351	33	363	34	305	34	605	28	1015	22	284	28	108	47	191	50	160	46	783	33	3	100	2	100	2	100	0	.	62	54	105	50	161	59	204	48	32	65	125	57	326	31	155	52	141	57	81	40	48	77	52	56	13	100	17	72	132	47	238	51	146	59	10	79	37	75	8	62	35	67	151	38	72	67	0	.	0	.	0	.	67	59	22	92	7	100	58	54	97	74	258	34	75	44	169	44	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	547	25	477	28	698	21	64	64	107	58	45	65	96	58	22	95	48	61	0	.	27	67	0	.	15286	6
	total	440	36	563	38	842	43	1602	32	2603	29	3027	26	18957	10	7163	13	5847	16	3620	16	19	86	336	62	455	47	0	.	885	43	3130	25	3720	23	1853	28	0	.	4398	22	7458	16	3768	18	7803	16	11141	12	6307	14	2881	21	1720	29	2223	25	5819	20	391	52	1281	28	671	42	529	37	1819	29	1559	32	1661	31	2241	25	900	38	1615	32	2872	23	2224	31	3756	25	1969	25	943	43	618	46	327	65	335	49	2404	26	2330	27	1544	31	850	36	1010	37	1368	29	1094	37	2316	26	2118	31	12	100	64	71	24	100	404	51	102	68	406	45	916	41	1576	38	2019	32	1110	44	1743	29	1495	30	1000	39	187	51	1345	28	137	57	346	56	2173	21	835	38	951	32	1269	35	346	55	485	57	133	83	405	68	4648	17	4706	17	6614	16	880	36	1227	34	693	36	1337	29	1859	28	816	54	709	47	614	33	0	.	192913	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	1818	26	6228	15	4675	17	3040	22	18	73	120	61	318	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	476	45	550	43	1598	27	139	57	488	39	378	56	208	48	242	37	645	44	741	36	567	36	535	47	298	41	466	57	9	100	126	54	162	100	0	.	0	.	27	100	367	55	260	58	171	69	602	43	25	100	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	487	49	699	36	371	51	174	50	302	48	979	31	414	38	56	82	25	100	185	82	0	.	60763	4
	dead	1	100	0	.	0	.	117	52	0	.	116	64	143	41	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	17	100	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	57	56	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	.	3651	9
	total	389	45	102	72	119	72	1395	31	557	38	1657	34	1960	25	6578	14	4850	17	3188	21	38	86	120	61	350	52	0	.	267	70	333	54	1258	35	753	49	0	.	537	49	354	32	489	42	335	68	978	37	33	71	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	525	42	563	43	1621	27	139	57	518	38	383	56	208	48	376	32	703	42	770	35	614	35	535	47	298	41	466	57	9	100	142	57	178	100	0	.	0	.	27	100	367	55	261	58	174	69	636	41	25	100	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	510	49	804	35	400	48	174	50	315	47	1040	30	453	35	59	83	25	100	185	82	0	.	64413	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	445	69	62	66	497	32	418	53	47	100	9	292	162	45	0	.	18	100	168	79	0	.	308	60	110	79	0	.	212	43	569	35	48	100	605	35	1199	30	369	37	16	100	170	56	0	.	107	100	27	100	0	.	0	.	24	100	0	.	147	100	16	100	31	100	0	.	105	72	0	.	43	100	360	39	99	64	104	52	6	231	122	49	47	81	8	100	0	.	89	59	159	55	193	59	0	.	64	78	85	75	0	.	0	.	0	.	0	.	0	.	15	100	0	.	54	100	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	401	29	717	34	0	.	30	100	220	48	97	58	250	42	169	53	186	65	45	100	0	.	10974	8
	dead	19	100	0	.	0	.	0	.	0	.	0	.	65	54	0	.	86	71	27	63	0	.	0	.	44	60	0	.	0	.	39	100	0	.	44	80	19	100	0	.	3	74	69	62	0	.	44	47	86	38	78	55	0	.	0	.	0	.	0	.	2	100	0	.	0	.	26	100	0	.	0	.	7	100	0	.	0	.	31	72	0	.	0	.	6	100	0	.	5	100	49	100	28	100	22	70	0	.	0	.	0	.	0	.	4	100	0	.	31	71	16	90	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	79	39	188	31	0	.	9	100	28	72	15	75	0	.	0	.	10	100	0	.	0	.	1274	13
	total	95	67	35	71	29	76	72	87	0	.	17	100	510	62	62	66	582	35	446	51	47	100	9	292	205	47	0	.	18	100	207	83	0	.	352	62	129	72	0	.	215	43	638	36	48	100	648	33	1285	29	447	37	16	100	170	56	0	.	107	100	29	100	0	.	0	.	51	100	0	.	147	100	22	100	31	100	0	.	137	71	0	.	43	100	366	39	99	64	109	50	55	111	151	49	69	77	8	100	0	.	89	59	159	55	197	59	0	.	95	73	101	72	0	.	0	.	0	.	0	.	0	.	15	100	0	.	54	100	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	480	28	905	32	0	.	39	100	248	43	112	52	250	42	169	53	196	64	45	100	0	.	12248	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	.	145	60	16	100	150	59	0	.	0	.	0	.	25	72	0	.	0	.	46	100	0	.	25	100	0	.	67	87	412	39	519	39	205	61	2152	17	5106	14	20	100	4	100	15	73	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	14	100	12	100	30	100	208	59	45	100	10	100	39	66	0	.	0	.	17	100	0	.	0	.	0	.	52	100	9	100	46	100	0	.	511	46	491	32	465	40	372	42	0	.	86	72	1204	24	457	37	553	56	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	5084	17	2447	19	1387	22	55	100	22	100	16	100	2	100	0	.	50	71	20	100	177	55	0	.	23475	6
	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	314	32	192	35	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	.	4	60	51	56	11	71	46	88	0	.	0	.	38	66	3	74	10	82	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	20	100	0	.	0	.	0	.	0	.	473	26	106	34	173	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1607	13
	total	14	73	75	68	67	98	144	69	0	.	0	.	178	52	16	100	150	59	0	.	0	.	0	.	25	72	0	.	0	.	74	73	0	.	36	76	0	.	67	87	421	39	553	38	221	57	2466	17	5298	14	20	100	4	100	15	73	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	15	100	12	100	30	100	225	55	45	100	10	100	39	66	0	.	0	.	17	100	0	.	0	.	0	.	52	100	9	100	46	100	0	.	515	46	542	33	476	39	418	44	0	.	86	72	1242	23	460	37	563	55	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	5556	17	2553	19	1560	22	55	100	22	100	16	100	2	100	0	.	50	71	20	100	177	55	0	.	25082	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	.	28	73	0	.	2	100	223	45	1280	20	0	.	0	.	0	.	0	.	0	.	0	.	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	.	525	31	335	34	98	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2489	13
	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	.	55	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	41	100	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	159	38
	total	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	73	0	.	2	100	223	45	1334	19	0	.	0	.	0	.	0	.	0	.	0	.	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	.	574	31	376	37	112	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2648	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	38	96	0	.	362	46	2	71	0	.	0	.	0	.	177	.	0	.	0	.	0	.	0	.	0	.	2	100	8	94	0	.	0	.	9	77	28	42	-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	.	1813	27
	dead	0	.	0	.	0	.	0	.	0	.	0	.	3	71	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	.	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	.	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	.	31	36
	total	36	77	0	.	43	100	42	69	0	.	8	78	41	93	0	.	362	46	2	71	0	.	0	.	0	.	177	.	0	.	0	.	0	.	0	.	0	.	2	100	12	95	0	.	0	.	11	80	31	40	-5	145	60	100	198	89	1	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	4	100	294	99	-12	114	-10	160	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	.	1844	26
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	2113	30	258	46	557	33	2844	25	2903	17	4448	14	3547	16	403	45	984	33	1908	28	0	.	269	64	756	34	1018	31	1260	35	0	.	961	30	680	46	590	31	34	66	565	39	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	953	31	1092	34	1642	26	2108	21	1364	30	1059	27	1077	34	1436	27	525	30	260	85	961	37	555	31	406	40	762	32	0	.	131	63	0	.	764	33	1200	33	617	34	949	31	877	31	465	41	619	43	25	100	231	56	583	45	188	48	401	45	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	.	20	74	937	30	273	43	414	40	418	44	896	30	907	29	708	46	449	43	406	33	0	.	73286	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	11	63	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	29	49	37	62	80	59	26	85	13	100	95	79	29	74	23	67	13	88	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	42	100	12	100	0	.	2134	11
	total	1538	28	872	30	651	32	2125	30	259	45	573	33	2913	25	3038	17	4569	14	3609	16	472	44	1049	34	1946	28	0	.	300	60	784	33	1034	31	1304	35	0	.	986	30	691	45	608	31	34	66	565	39	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	982	30	1129	33	1722	26	2133	21	1377	30	1154	27	1106	34	1459	27	538	30	268	83	968	37	557	31	406	40	767	32	0	.	131	63	0	.	794	33	1224	32	646	33	977	31	900	31	537	41	620	43	25	100	231	56	600	44	188	48	403	45	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	.	20	74	949	29	289	42	436	38	418	44	915	30	935	29	715	46	491	40	419	32	0	.	75420	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	899	22	325	31	209	33	591	24	128	59	101	51	258	41	0	.	73	71	54	61	302	36	55	54	0	.	365	28	136	50	172	89	66	62	116	46	0	.	445	22	153	36	68	44	306	32	76	56	175	42	46	82	100	83	33	46	145	33	24	76	259	55	187	41	56	98	351	40	222	46	611	28	86	52	116	47	146	36	76	48	219	38	200	39	97	42	52	52	264	53	101	55	74	76	12	100	70	73	19	100	21	92	48	74	41	50	11	50	36	79	6	77	148	43	37	90	0	.	190	52	218	41	124	41	27	64	0	.	135	41	0	.	15	83	28	56	45	65	86	51	81	48	13	100	16	100	0	.	8	140	2	100	38	65	282	29	122	47	175	50	29	189	282	34	103	49	201	55	132	61	235	55	0	.	12416	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	.	2	100	0	.	0	.	2	100	2	100	2	100	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	.	278	18
	total	284	37	133	44	108	47	75	45	18	71	250	51	914	22	325	31	228	33	593	24	128	59	104	51	258	41	0	.	73	71	54	61	302	36	66	48	0	.	389	27	136	50	172	89	67	61	116	46	0	.	464	21	156	36	68	44	309	32	78	57	175	42	50	76	100	83	33	46	147	32	24	76	259	55	187	41	79	77	361	40	222	46	627	28	88	52	116	47	146	36	76	48	219	38	203	39	97	42	52	52	264	53	101	55	74	76	12	100	71	73	19	100	21	92	50	72	43	48	12	45	36	79	6	77	148	43	37	90	0	.	192	52	218	41	127	42	27	64	0	.	135	41	0	.	15	83	28	56	45	65	88	49	89	45	13	100	16	100	0	.	8	140	2	100	38	65	312	31	122	47	175	50	31	176	311	33	111	52	203	55	132	61	235	55	0	.	12694	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	226	57	511	43	664	28	118	53	280	55	44	56	189	49	0	.	198	62	361	56	334	40	395	43	28	100	297	42	46	85	7	183	172	46	89	63	0	.	922	27	615	73	197	50	117	49	75	64	43	58	2	100	5	70	4	100	439	57	0	.	73	65	522	37	311	43	110	47	198	62	376	40	215	42	102	47	52	63	254	50	94	54	337	45	102	97	272	66	416	38	429	42	220	41	11	100	123	100	38	100	53	46	38	55	158	64	102	57	70	61	85	49	133	65	49	65	0	.	284	50	121	57	56	67	171	73	51	77	41	58	71	99	2	100	9	100	23	92	112	58	230	60	52	65	212	69	0	.	236	79	96	47	101	63	240	46	171	70	395	52	122	50	257	34	249	35	146	68	113	47	170	51	0	.	16407	6
	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	.	3	75	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	244	56	523	43	683	27	118	53	280	55	44	56	189	49	0	.	198	62	361	56	339	39	397	43	28	100	312	40	46	85	7	183	186	47	102	57	0	.	945	27	615	73	228	44	123	49	76	65	43	58	2	100	5	70	4	100	459	58	0	.	73	65	522	37	321	42	110	47	198	62	380	40	215	42	102	47	52	63	261	49	101	51	357	46	102	97	272	66	421	38	429	42	220	41	22	100	123	100	38	100	53	46	49	61	162	63	109	53	70	61	94	52	133	65	49	65	0	.	301	50	121	57	58	67	171	73	51	77	41	58	71	99	2	100	9	100	23	92	123	58	230	60	52	65	221	70	0	.	236	79	99	46	105	61	264	44	171	70	395	52	122	50	340	37	251	35	146	68	113	47	189	53	0	.	16930	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	.	346	49	0	.	0	.	28	60	60	71	606	22	0	.	79	100	3	100	115	56	0	.	8	100	54	100	34	74	64	74	473	55	0	.	0	.	59	100	0	.	20	100	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	1	100	23	70	0	.	241	52	0	.	20	70	18	95	164	45	16	100	0	.	0	.	118	50	486	70	290	42	0	.	8	100	0	.	51	49	119	58	445	33	102	64	127	41	83	75	44	81	19	100	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	96	70	82	40	10	100	51	81	41	58	31	74	126	64	155	43	182	59	81	66	0	.	7615	9
	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	.	346	49	0	.	0	.	28	60	60	71	610	22	0	.	79	100	3	100	132	55	0	.	17	100	54	100	37	68	64	74	519	57	0	.	0	.	59	100	0	.	22	94	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	1	100	23	70	0	.	247	51	0	.	22	71	18	95	165	45	16	100	0	.	0	.	118	50	486	70	290	42	0	.	8	100	0	.	55	47	122	57	466	33	118	66	127	41	83	75	44	81	19	100	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	97	71	86	38	10	100	51	81	41	58	31	74	126	64	155	43	182	59	93	64	0	.	7887	9
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	277	50	251	67	402	50	556	30	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	.	168	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5569	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	46	52	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	.	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	848	15
	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	322	46	302	66	464	48	702	29	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	.	194	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6417	11
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	789	33	153	28	666	33	99	32	89	53	40	58	81	44	0	.	18	80	361	74	37	47	119	53	21	87	187	47	123	40	114	39	400	38	772	27	414	40	299	38	131	52	65	61	69	50	2	100	26	50	7	100	23	63	48	78	79	55	15	68	24	60	6	100	71	43	158	35	237	62	132	35	37	47	42	50	62	65	175	51	13	100	128	44	19	123	98	82	229	62	121	47	515	85	140	54	188	37	122	67	304	36	521	34	965	26	565	39	392	45	427	35	66	51	158	54	29	67	171	65	282	89	99	52	32	61	125	78	117	50	178	97	6	80	26	56	87	70	151	85	124	86	51	57	123	67	4	100	12	100	353	32	315	34	319	31	7	100	163	63	104	57	66	53	8	58	32	62	142	60	73	66	0	.	15029	7
	dead	12	100	26	69	0	.	19	71	1	100	4	100	74	38	12	66	0	.	4	100	2	100	0	.	8	60	0	.	22	72	7	71	3	71	23	58	13	90	25	52	29	50	34	60	73	62	140	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	.	7	51	38	55	2	100	10	76	3	100	0	.	0	.	14	92	3	100	48	67	0	.	37	63	5	80	0	.	0	.	11	88	18	69	9	72	32	56	66	38	142	42	120	41	6	72	75	42	12	63	13	82	6	77	22	71	32	63	15	67	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	58	37	5	100	9	100	1	100	15	69	5	100	2	100	15	100	3	100	0	.	1731	8
	total	295	41	122	38	140	49	107	72	4	77	67	71	862	32	165	28	666	33	104	31	90	52	40	58	88	41	0	.	40	66	368	73	40	43	142	47	35	88	212	43	152	39	148	38	473	35	911	25	434	39	367	34	135	51	68	60	90	41	2	100	32	51	7	100	24	62	48	78	83	56	33	78	40	66	6	100	78	40	196	37	239	62	142	36	39	44	42	50	62	65	189	52	15	84	176	39	19	123	135	71	233	61	121	47	515	85	151	52	206	37	131	65	336	34	586	33	1107	26	686	34	398	45	502	34	78	46	172	55	35	59	193	59	315	86	114	51	45	52	141	71	136	46	178	97	7	68	28	53	87	70	155	83	136	85	52	56	140	66	6	100	12	100	417	30	341	32	377	29	12	72	172	61	106	57	81	45	13	64	34	58	158	63	76	64	0	.	16760	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	35	60	28	79	2	100	4	100	3	100	28	83	0	.	0	.	0	.	0	.	9	100	0	.	13	100	0	.	0	.	4	73	14	100	0	.	6	100	0	.	0	.	37	87	0	.	0	.	13	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	1	100	0	.	7	100	3	73	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	49	57	10	69	0	.	0	.	0	.	0	.	0	.	20	100	0	.	2	100	0	.	654	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	35	60	28	79	2	100	4	100	3	100	28	83	0	.	0	.	0	.	0	.	9	100	0	.	13	100	0	.	0	.	4	73	14	100	0	.	6	100	0	.	0	.	37	87	0	.	0	.	13	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	1	100	0	.	7	100	3	73	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	49	57	10	69	0	.	0	.	0	.	0	.	0	.	20	100	0	.	2	100	0	.	654	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	5849	19	3362	28	5641	22	23829	9	16952	10	17958	10	11223	12	1046	35	1615	31	3482	24	177	.	1662	34	5005	21	5629	18	4671	20	632	51	6452	18	9039	14	5981	16	8408	15	15991	10	14180	10	12284	12	5441	20	6759	18	8215	17	1656	32	2915	24	1505	33	1512	32	3653	22	2890	26	1892	30	4828	21	3385	25	3112	25	4893	20	4442	23	8026	18	5169	18	3113	24	2368	25	2166	28	2607	24	4170	21	3319	23	3403	24	3056	25	3018	28	3885	22	1773	27	3642	20	3195	23	1980	21	2546	22	3490	17	4113	16	2628	21	3715	20	2469	28	1847	33	2591	28	2913	25	2870	25	3015	28	1776	36	679	42	3540	21	896	46	944	36	2863	20	2181	27	2908	23	2732	25	1633	35	1397	37	883	55	1099	45	11249	12	8475	13	10848	12	1826	31	2545	29	1915	26	3884	21	3900	21	2284	29	1958	30	1960	27	0	.	407462	1
	dead	202	49	184	43	41	100	218	32	78	44	330	36	2911	14	986	20	605	21	457	28	111	67	89	61	180	40	0	.	137	63	360	37	1102	41	305	34	79	58	460	29	493	29	467	28	780	24	1671	16	683	19	667	22	288	36	360	32	1279	28	33	51	29	55	10	59	1	100	122	39	174	39	229	45	287	36	162	53	240	36	519	25	207	45	280	35	123	35	91	53	195	46	111	56	213	40	309	35	275	45	238	41	21	58	38	73	17	55	69	42	315	28	176	40	174	34	251	34	395	26	292	29	117	38	388	26	128	41	110	66	318	32	203	33	301	47	257	52	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	1238	18	814	21	1344	16	114	66	147	47	89	48	316	36	104	52	84	49	67	68	75	57	0	.	28448	4
	total	3664	19	2425	23	2175	26	6067	18	3440	28	5971	22	26740	9	17937	10	18564	10	11680	12	1156	35	1705	31	3661	24	177	.	1799	33	5365	21	6730	19	4976	20	710	51	6912	18	9531	14	6448	16	9188	15	17662	10	14863	10	12951	11	5729	20	7119	18	9494	16	1689	31	2944	24	1515	33	1514	32	3775	22	3064	26	2121	29	5115	20	3546	25	3352	24	5412	18	4649	23	8305	18	5292	18	3204	24	2564	24	2277	28	2820	23	4479	21	3594	23	3641	24	3077	25	3056	28	3902	22	1842	27	3957	20	3371	23	2154	21	2798	22	3885	17	4405	16	2745	21	4102	19	2598	28	1957	33	2910	28	3116	25	3171	25	3272	28	1862	36	707	41	3851	21	949	46	958	36	2928	20	2282	26	2960	23	2953	25	1665	34	1483	38	900	55	1145	44	12488	11	9289	13	12191	12	1940	30	2692	28	2004	26	4200	21	4004	21	2368	29	2026	30	2035	27	0	.	435910	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. 2297595. Birmensdorf, Swiss Federal Research Institute WSL

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