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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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)
altitudinal vegetation belts (NaiS; 6 classes) · tree state (living/dead)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	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	.
upper subalpine	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	.	125	66	58	100	13	92	793	26	3438	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	66	206	38	163	42	59	65	0	.	0	.	133	52	7	.	12	.	0	.	0	.	39	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1831	18	766	24	189	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7889	8
	dead	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	68	0	.	0	.	108	45	232	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	25	62	12	70	0	.	0	.	0	.	12	73	0	.	0	.	0	.	0	.	27	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	29	58	52	28	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	639	15
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	134	65	58	100	13	92	901	27	3669	13	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	66	230	38	175	41	59	65	0	.	0	.	145	52	7	.	12	.	0	.	0	.	65	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1960	18	824	24	217	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8527	8
subalpine	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	.	4477	16	152	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	.	0	.	0	.	768	38	2963	21	1107	25	3684	16	4324	15	6130	15	0	.	0	.	0	.	94	87	0	.	0	.	0	.	0	.	0	.	275	52	237	52	459	37	0	.	91	71	553	38	16	.	23	.	0	.	0	.	0	.	0	.	0	.	208	65	0	.	0	.	0	.	0	.	0	.	624	32	1030	26	622	37	46	71	0	.	0	.	629	30	159	49	73	96	50	92	219	63	102	69	325	62	246	40	297	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	.	0	.	0	.	0	.	0	.	4320	16	2963	18	2747	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40155	5
	dead	0	.	0	.	0	.	0	.	0	.	0	.	645	24	41	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	127	52	117	32	73	52	504	27	321	24	387	21	0	.	0	.	0	.	14	77	0	.	0	.	0	.	0	.	0	.	9	67	44	93	38	64	0	.	0	.	52	58	0	.	32	.	0	.	0	.	0	.	0	.	0	.	4	71	0	.	0	.	0	.	0	.	0	.	54	48	96	39	31	51	0	.	0	.	0	.	51	64	12	92	7	100	14	100	10	100	10	65	8	100	26	61	33	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	.	0	.	0	.	0	.	0	.	531	22	322	26	313	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3974	8
	total	0	.	0	.	0	.	0	.	0	.	0	.	5122	15	193	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	.	0	.	0	.	895	37	3080	21	1180	24	4188	17	4645	14	6516	15	0	.	0	.	0	.	107	85	0	.	0	.	0	.	0	.	0	.	284	52	281	48	497	37	0	.	91	71	604	36	16	.	55	.	0	.	0	.	0	.	0	.	0	.	213	64	0	.	0	.	0	.	0	.	0	.	678	31	1126	26	653	36	46	71	0	.	0	.	679	29	172	49	80	96	64	94	229	62	112	68	333	63	272	40	330	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	118	.	0	.	0	.	0	.	0	.	4851	15	3285	18	3060	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44130	5
high-montane	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	.	38	.	0	.	6943	13	585	44	0	.	108	93	0	.	0	.	0	.	0	.	232	71	650	42	1005	38	323	58	0	.	697	45	3360	19	3108	20	4982	14	9762	10	4945	14	0	.	181	72	0	.	632	31	0	.	0	.	0	.	0	.	0	.	238	51	1430	28	676	36	0	.	231	69	502	41	191	69	395	56	0	.	0	.	0	.	0	.	0	.	422	42	811	33	161	58	0	.	0	.	0	.	974	27	1260	28	1983	22	168	47	0	.	0	.	925	26	211	55	446	50	382	54	707	43	1471	29	669	45	1404	29	1247	33	7	.	0	.	127	70	0	.	0	.	106	71	91	82	137	74	236	49	0	.	0	.	0	.	0	.	6158	12	4440	15	4265	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70025	3
	dead	0	.	0	.	0	.	0	.	4	.	0	.	1034	18	34	60	0	.	0	.	0	.	0	.	0	.	0	.	20	78	64	59	168	51	5	86	0	.	73	71	241	34	312	27	448	22	1021	17	257	26	0	.	8	100	0	.	199	49	0	.	0	.	0	.	0	.	0	.	36	75	154	41	72	58	0	.	18	84	221	41	36	97	81	82	0	.	0	.	0	.	0	.	0	.	24	70	146	46	70	71	0	.	0	.	0	.	109	46	117	37	146	33	40	60	0	.	0	.	73	45	0	.	63	89	24	86	78	58	156	38	113	51	116	40	47	59	0	.	0	.	44	73	0	.	0	.	0	.	0	.	10	100	18	90	0	.	0	.	0	.	0	.	766	18	334	22	673	22	16	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7691	6
	total	0	.	0	.	0	.	0	.	41	.	0	.	7977	12	619	44	0	.	108	93	0	.	0	.	0	.	0	.	252	71	714	41	1174	35	328	58	0	.	770	43	3602	19	3420	20	5431	14	10783	10	5202	13	0	.	190	73	0	.	831	31	0	.	0	.	0	.	0	.	0	.	274	49	1585	28	748	35	0	.	249	65	724	35	227	70	477	51	0	.	0	.	0	.	0	.	0	.	445	41	957	32	231	61	0	.	0	.	0	.	1084	26	1377	27	2129	21	208	48	0	.	0	.	998	25	211	55	509	48	407	55	785	43	1627	28	782	41	1520	28	1294	33	7	.	0	.	172	58	0	.	0	.	106	71	91	82	147	75	253	49	0	.	0	.	0	.	0	.	6925	12	4773	14	4939	15	16	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77716	3
lower and upper montane	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	999	32	128	71	227	60	1273	29	2794	22	6041	17	10891	11	15872	9	5077	16	10613	10	84	100	529	51	1981	25	0	.	494	47	2322	25	4986	15	3052	23	0	.	4798	16	2348	20	2552	19	236	53	1404	27	0	.	10469	10	2995	21	3345	20	6014	16	1143	29	2793	18	1152	32	1499	25	4387	17	1595	26	644	37	3438	20	2681	22	2074	24	4084	18	2894	21	7413	15	1080	28	1252	25	1811	21	604	39	2135	21	3674	18	2574	21	2831	21	1737	26	0	.	411	51	0	.	1116	31	0	.	1259	26	1403	22	536	31	1992	18	1399	24	1500	22	1194	28	431	43	401	51	1303	30	675	38	2847	22	1481	30	59	86	3263	20	319	68	807	32	2933	16	1871	24	1973	21	953	29	518	50	928	37	796	42	488	46	15	.	53	83	3369	17	1977	23	1472	31	431	52	3221	18	1956	24	0	.	179	82	171	72	0	.	200716	2
	dead	124	45	0	.	6	80	63	40	89	33	312	30	1024	16	740	17	234	27	455	21	20	71	2	100	98	42	0	.	15	80	107	38	672	38	182	32	0	.	296	25	108	28	202	32	23	55	105	39	0	.	560	18	196	32	122	39	766	25	35	56	37	50	39	59	1	100	86	33	98	34	78	54	134	35	107	46	115	33	228	24	114	40	150	29	25	49	58	49	117	44	43	61	132	36	271	34	189	44	133	32	31	58	0	.	4	100	0	.	97	45	0	.	44	32	151	40	28	45	104	29	135	69	113	35	82	49	10	67	89	53	60	47	73	87	265	38	185	46	18	100	199	44	25	70	38	58	106	38	85	44	64	41	111	44	9	76	39	87	39	77	33	66	1	.	8	72	357	23	108	46	44	70	2	72	211	32	86	44	0	.	0	.	18	100	0	.	11782	5
	total	1123	32	128	71	233	59	1335	29	2884	22	6352	17	11915	11	16611	9	5311	15	11068	10	104	92	531	51	2079	25	0	.	509	47	2429	25	5658	16	3235	23	0	.	5095	16	2455	20	2754	19	259	51	1509	26	0	.	11030	10	3191	21	3467	20	6780	15	1178	29	2830	18	1190	33	1499	25	4473	17	1693	26	722	34	3572	20	2787	22	2189	24	4312	17	3008	21	7563	15	1106	28	1310	25	1928	21	647	38	2267	21	3945	18	2764	21	2964	21	1768	26	0	.	415	51	0	.	1213	31	0	.	1304	26	1554	22	565	31	2096	18	1534	23	1613	22	1276	27	441	42	491	51	1362	30	748	39	3112	22	1665	30	77	74	3461	20	344	67	845	32	3039	16	1955	24	2037	21	1063	29	527	50	967	38	835	41	521	46	16	.	61	79	3726	16	2084	23	1516	30	433	52	3432	18	2042	24	0	.	179	82	189	73	0	.	212498	2
submontane	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	3544	15	2823	18	2775	18	4146	17	0	.	0	.	1200	39	2038	24	11263	9	185	64	1303	27	1577	24	1460	25	101	.	1225	32	1293	30	222	.	1948	24	0	.	24	.	168	54	225	52	0	.	0	.	0	.	3332	17	2040	26	2222	22	440	51	415	38	0	.	497	46	32	.	0	.	114	73	218	71	301	69	1253	30	501	39	277	46	956	36	0	.	3796	16	2118	23	830	34	1427	24	1393	27	231	63	0	.	403	61	1555	31	2375	23	2907	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	289	71	116	100	219	58	589	38	403	51	208	64	536	38	521	42	179	59	1419	29	447	39	0	.	414	40	890	33	1597	27	602	36	593	42	0	.	1076	34	0	.	0	.	602	30	341	48	1258	30	2056	22	396	56	1702	24	1906	24	1687	25	2193	22	0	.	89390	3
	dead	120	35	149	34	84	61	113	33	0	.	0	.	18	44	56	44	374	19	61	80	95	51	83	42	89	42	0	.	82	58	130	52	2	.	102	41	0	.	0	.	1	100	5	100	0	.	0	.	0	.	132	27	57	40	128	39	5	100	29	61	0	.	15	66	0	.	0	.	0	.	38	86	7	67	78	52	57	71	158	60	27	58	0	.	80	33	5	100	21	64	45	63	115	33	28	72	0	.	4	100	14	55	58	49	26	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	45	52	15	75	0	.	35	62	25	48	22	100	41	73	22	51	0	.	41	73	32	63	24	48	33	55	32	65	0	.	57	61	0	.	0	.	88	42	12	66	43	56	149	27	22	100	45	49	55	43	45	59	58	43	0	.	3642	7
	total	3665	16	2972	18	2859	18	4258	16	0	.	0	.	1217	38	2094	24	11636	9	247	58	1398	26	1661	24	1549	25	101	.	1307	31	1423	30	224	.	2050	24	0	.	24	.	169	54	230	51	0	.	0	.	0	.	3465	16	2097	25	2351	22	445	50	444	37	0	.	511	45	32	.	0	.	114	73	256	71	308	67	1331	29	558	38	435	46	983	36	0	.	3876	16	2123	23	851	34	1473	24	1508	26	260	63	0	.	407	61	1569	30	2433	22	2932	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	299	71	116	100	219	58	634	37	418	52	208	64	571	37	546	40	201	56	1459	29	469	39	0	.	455	41	921	34	1622	27	635	36	625	42	0	.	1133	34	0	.	0	.	690	29	353	47	1301	30	2205	21	418	54	1747	23	1961	24	1732	24	2250	22	0	.	93033	3
hyperinsubric and colline	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	.	539	39	0	.	0	.	19	.	54	.	974	29	93	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	.	50	96	569	28	248	46	825	27	1396	25	2901	14	1237	24	1019	25	1528	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	71	0	.	0	.	38	.	0	.	0	.	0	.	508	30	396	32	161	51	0	.	0	.	0	.	0	.	0	.	170	67	0	.	0	.	0	.	12878	6
	dead	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	52	0	.	0	.	0	.	0	.	109	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	39	45	31	70	73	42	180	41	415	19	133	32	111	35	189	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	0	.	127	35	60	41	52	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1576	9
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	589	40	0	.	0	.	19	.	54	.	1084	28	93	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	.	54	97	608	28	279	47	899	27	1576	25	3315	14	1370	23	1130	25	1718	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	71	0	.	0	.	40	.	0	.	0	.	0	.	635	29	456	31	213	48	0	.	0	.	0	.	0	.	0	.	170	67	0	.	0	.	0	.	14454	6
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	4543	14	2952	17	3002	17	5418	14	2832	22	6041	17	23510	7	18647	8	16340	8	10906	10	1387	26	2107	22	3441	18	101	.	1951	25	4265	17	6285	14	5324	16	539	39	6288	14	8964	11	7070	12	8969	10	17257	7	14606	8	13802	8	5216	16	5567	15	7180	14	1558	23	2793	18	1648	26	1531	25	4387	17	2223	21	2530	20	4890	16	3934	18	2897	19	5416	15	4057	18	7831	14	4876	14	3370	17	2641	18	2031	20	3528	16	4536	16	3385	18	3395	19	3292	20	2375	23	3364	18	1687	20	4181	14	3016	17	2359	17	2799	17	3437	12	4916	11	2797	16	3560	15	1916	23	1474	27	2233	22	2885	20	2729	19	4599	17	2024	24	580	38	3569	19	1738	27	1254	25	3039	16	2480	20	3000	17	2855	18	1159	29	1520	28	796	42	1563	27	12832	8	8618	10	11333	9	2317	21	2730	21	2487	20	3617	17	3657	17	2077	23	1866	24	2363	21	0	.	421054	1
	dead	244	28	149	34	90	57	175	25	93	32	312	30	2721	11	870	15	607	16	516	21	115	44	85	41	187	29	0	.	118	44	302	29	842	32	289	25	50	52	496	22	475	20	591	19	975	17	1664	12	875	14	693	15	261	26	251	27	984	22	64	41	37	50	53	47	1	100	86	33	143	30	315	29	251	27	185	34	190	30	659	22	177	34	264	33	105	28	63	46	138	38	88	44	247	25	327	29	336	32	207	31	45	43	58	49	29	57	168	34	374	20	219	25	157	26	331	29	443	18	373	19	258	39	373	23	129	37	97	48	282	29	226	30	229	35	345	31	219	40	43	50	266	36	66	52	60	41	106	38	126	38	105	32	202	36	44	45	71	56	39	77	90	46	1554	12	782	15	1512	13	135	39	87	45	152	26	233	31	132	33	55	43	45	59	76	41	0	.	29304	3
	total	4787	14	3100	17	3092	17	5594	14	2925	21	6352	17	26231	7	19517	8	16947	8	11422	10	1502	25	2192	22	3628	18	101	.	2068	24	4566	17	7127	14	5613	16	589	40	6784	14	9439	11	7662	12	9944	10	18922	7	15481	8	14494	8	5478	16	5818	15	8164	13	1622	23	2830	18	1702	27	1532	25	4473	17	2366	21	2845	19	5141	15	4119	18	3087	19	6075	14	4233	17	8095	14	4981	14	3433	17	2778	18	2119	20	3775	16	4863	16	3721	17	3602	19	3337	20	2433	22	3394	18	1856	19	4555	14	3236	16	2516	17	3131	16	3880	12	5288	11	3055	15	3932	14	2045	23	1571	27	2515	22	3111	20	2958	19	4944	17	2243	24	623	36	3834	18	1804	26	1314	25	3145	16	2606	20	3105	17	3056	18	1202	29	1592	28	835	41	1654	27	14387	8	9400	10	12844	9	2453	20	2817	21	2638	20	3850	17	3789	16	2132	23	1911	23	2439	21	0	.	450357	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. 2299345. Birmensdorf, Swiss Federal Research Institute WSL

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