Result query system

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total wood volume (stemwood) #23
Stemwood volume of all living and dead trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm with bark. The total wood volume is the sum of the growing stock and deadwood volumes.

altitudinal vegetation belts (NaiS; 6 classes) #2633
Altitudinal vegetation belts in the system used in the guidelines for monitoring the sustainability and performance of protection forests (NaiS; Frehner et al. 2005), reduced to six classes. The variable represents a simplification of the NaiS altitudinal vegetation belts with ten classes (NAISHSTKOMB) in which the classes «hyperinsubric» and «colline» are merged with «colline with beech» to form the class «hyperinsubric and colline» and the class «lower montane» with «upper montane» and «lower/upper montane» to form the class «lower and upper montane». The information is based on the altitudinal vegetation belts determined by experts (accessible forest sample plots of NFI4 on the 1.4-km network; Arge Frehner et al. 2020), as well as on the altitudinal vegetation belts modelled for the period 1981-2010 (other sample plots; Zischg et al. 2021). n/a: located above the forest boundary modelled by Zischg et al. and sometimes in the area of lakes, which were relatively roughly cut out by Zischg et al.

tree state (living/dead) #1265
Classification of trees and shrubs ≥12 cm in diameter at breast height (dbh) as living or dead. Reference: Field Survey (MID 58: Bemerkungen, MID 1018: Baumzustand)

forest district (2023) #2777
Regional demarcation with the forest districts as a unit. This variable is based on a survey of the cantonal forest services in winter 2022/2023.

accessible forest without shrub forest NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was accessible on foot.

1.4 km grid, subgrids 1-5 #1746
Sub-grids 1, 2, 3, 4 and 5 of the field surveys on the sampling grid with a mesh size of 1.4 km (base grid).

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. “protective 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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NFI3—NFI5
change: total wood volume (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · tree state (living/dead)
regional demarcation: forest district (2023)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
change 2004/07–2018/26
		forest district (2023)
		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	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	dead	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
upper subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-61	.	.	.	23	.	45	116	442	41	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	63	84	57	79	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	168	46	171	54	-6	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	903	27
	dead	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	43	.	.	.	18	.	48	77	92	51	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	100	5	94	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	35	78	4	55	8	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	252	32
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-18	.	.	.	41	.	93	54	535	35	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	63	84	62	80	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	202	35	175	54	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1155	21
subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	1025	23	4	356	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	167	64	383	54	221	59	110	306	-44	663	482	82	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	63	71	50	236	180	63	.	.	2	.	219	78	.	.	33	.	.	.	.	.	.	.	.	.	.	.	42	90	.	.	.	.	.	.	.	.	.	.	135	52	110	97	183	52	35	.	.	.	.	.	190	45	17	133	.	.	8	.	120	72	101	64	-82	.	112	65	22	72	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-56	.	.	.	.	.	.	.	.	.	608	50	18	1126	550	34	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5010	17
	dead	.	.	.	.	.	.	.	.	.	.	.	.	-54	196	-8	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-22	96	-1	5376	-30	182	361	64	47	95	221	66	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	10	100	-47	106	24	92	.	.	.	.	-10	92	.	.	-4	.	.	.	.	.	.	.	.	.	.	.	16	100	.	.	.	.	.	.	.	.	.	.	13	130	-22	111	-2	71	.	.	.	.	.	.	26	63	-1	100	.	.	-1	.	36	71	16	87	.	.	.	.	-1	155	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	24	.	.	.	.	.	.	.	.	.	165	60	68	78	40	169	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	864	39
	total	.	.	.	.	.	.	.	.	.	.	.	.	971	24	-4	563	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	145	72	382	53	191	75	471	47	3	9484	703	47	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	74	72	4	4075	203	64	.	.	2	.	209	82	.	.	28	.	.	.	.	.	.	.	.	.	.	.	58	74	.	.	.	.	.	.	.	.	.	.	148	52	88	119	181	52	35	.	.	.	.	.	216	44	16	134	.	.	7	.	156	71	117	65	-82	.	112	65	21	75	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-32	.	.	.	.	.	.	.	.	.	773	38	86	242	590	31	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5873	14
high-montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	233	280	-64	77	.	.	59	94	.	.	.	.	.	.	.	.	64	.	-78	142	126	56	84	72	.	.	93	57	169	272	347	83	540	71	144	445	84	393	.	.	71	.	.	.	-291	71	.	.	.	.	.	.	3	.	.	.	-15	.	271	52	11	1136	.	.	65	78	165	55	-3	.	-175	100	.	.	.	.	.	.	.	.	.	.	76	150	105	96	1	4304	.	.	.	.	.	.	168	123	230	48	-373	82	7	140	.	.	.	.	119	88	28	125	146	81	83	107	76	109	-162	205	243	49	202	46	475	48	.	.	.	.	72	71	.	.	.	.	.	.	.	.	.	.	-76	60	.	.	.	.	.	.	.	.	721	44	625	52	435	54	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5103	29
	dead	.	.	.	.	.	.	.	.	.	.	.	.	-167	151	50	76	.	.	.	.	.	.	.	.	.	.	.	.	-2	.	-24	101	-11	262	1	100	.	.	9	463	-65	112	-17	281	139	74	545	31	327	43	.	.	5	.	.	.	87	52	.	.	.	.	.	.	.	.	.	.	1	.	4	156	65	58	.	.	-22	166	-34	77	-5	.	45	100	.	.	.	.	.	.	.	.	.	.	8	78	-40	76	7	100	.	.	.	.	.	.	36	50	12	292	322	52	2	100	.	.	.	.	-3	126	2	307	18	102	41	64	39	73	22	311	3	607	32	161	111	82	.	.	.	.	9	100	.	.	.	.	.	.	.	.	.	.	13	110	.	.	.	.	.	.	.	.	213	51	359	36	254	50	-2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2390	20
	total	.	.	.	.	.	.	.	.	.	.	.	.	66	1103	-14	185	.	.	59	94	.	.	.	.	.	.	.	.	62	.	-102	110	114	66	85	72	.	.	101	57	105	456	330	86	679	59	689	86	411	81	.	.	76	.	.	.	-205	95	.	.	.	.	.	.	3	.	.	.	-13	.	276	53	75	147	.	.	43	75	131	61	-8	.	-130	100	.	.	.	.	.	.	.	.	.	.	83	141	65	164	8	761	.	.	.	.	.	.	204	100	242	48	-51	433	9	130	.	.	.	.	116	88	30	121	165	71	124	72	115	94	-140	259	246	53	234	42	586	50	.	.	.	.	81	72	.	.	.	.	.	.	.	.	.	.	-63	61	.	.	.	.	.	.	.	.	934	38	984	32	689	31	-2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	7494	20
lower and upper montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-26	298	-58	.	.	.	-110	121	457	67	-538	113	287	198	-348	245	-526	144	-399	148	21	280	68	81	-68	240	.	.	181	52	314	82	472	113	266	115	.	.	-1754	57	750	35	-83	360	-35	260	369	36	.	.	279	182	-138	229	510	55	-1325	59	285	47	315	54	-209	62	178	79	310	126	748	42	118	53	261	87	254	154	398	59	255	166	685	46	-404	163	-159	126	206	101	-112	239	19	1301	-155	218	115	287	-519	69	196	150	-230	97	.	.	-352	68	.	.	1321	78	.	.	175	56	356	34	275	43	439	38	139	87	435	45	284	59	183	66	97	86	-24	866	237	50	148	129	-12	4581	-71	.	279	86	124	.	-245	107	236	69	242	64	0	493020	62	321	-153	64	-358	110	-268	138	55	209	.	.	-3	.	545	50	154	107	-304	106	-100	247	200	169	-58	478	.	.	-323	.	43	.	.	.	4883	65
	dead	49	81	.	.	.	.	-5	120	8	282	162	73	239	80	239	76	149	67	692	32	-15	102	8	164	47	69	.	.	2	246	-27	186	19	690	127	67	.	.	156	62	24	223	68	180	32	167	42	72	.	.	728	31	44	223	107	64	413	65	24	62	48	61	13	100	52	86	207	66	95	48	40	143	65	114	56	116	15	265	153	38	-2	2323	69	298	-23	73	-19	329	74	79	50	74	-48	291	13	532	185	48	75	58	20	86	.	.	-18	93	.	.	118	112	.	.	3	495	-6	475	10	75	19	92	29	344	6	657	50	77	10	100	-2	2850	-36	85	-19	104	29	137	5	348	.	.	304	51	1	.	56	49	24	102	40	97	61	56	-18	279	-13	154	55	63	46	71	25	83	.	.	11	.	135	46	107	61	302	54	6	305	-29	184	13	246	.	.	.	.	14	.	.	.	5804	13
	total	23	300	-58	.	.	.	-115	121	465	69	-376	162	525	109	-109	780	-376	196	293	198	6	801	76	81	-21	812	.	.	183	53	287	92	491	112	393	77	.	.	-1598	61	774	35	-15	2073	-2	4761	411	35	.	.	1006	53	-94	339	617	48	-912	83	309	44	363	49	-196	65	230	66	517	78	842	38	158	57	326	77	311	128	413	61	408	108	683	46	-335	204	-182	114	187	104	-38	729	68	353	-203	216	127	274	-333	109	271	112	-210	105	.	.	-370	66	.	.	1440	81	.	.	178	51	349	36	284	42	457	38	168	84	442	42	334	53	193	63	95	126	-60	324	218	54	177	114	-7	8047	-71	.	583	37	125	.	-189	133	260	64	283	61	61	293	43	461	-166	65	-304	124	-222	171	79	134	.	.	8	.	679	42	262	55	-1	23964	-94	267	171	208	-45	624	.	.	-323	.	58	.	.	.	10687	30
submontane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-1062	65	-312	114	280	69	-1293	45	.	.	.	.	-542	81	-401	115	-1505	62	-84	.	-423	47	-119	191	-234	117	101	.	-132	236	-340	106	.	.	265	125	.	.	.	.	81	123	42	142	.	.	.	.	.	.	-470	93	222	199	-776	76	-86	130	-139	71	.	.	118	112	16	.	.	.	.	.	.	.	165	84	-38	66	-423	77	75	78	-362	74	.	.	-140	201	-202	146	-336	58	-1	15002	-92	103	120	74	.	.	60	83	443	50	-511	84	-285	141	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	233	81	.	.	51	116	-283	53	-5	2142	.	.	246	61	464	46	70	69	-333	92	-69	235	.	.	41	139	24	380	23	775	-86	116	-145	113	.	.	27	885	.	.	.	.	84	78	1	10520	124	55	-105	186	60	74	-58	462	-512	85	-402	71	-29	869	.	.	-8898	25
	dead	89	171	-24	307	-36	144	-163	94	.	.	.	.	35	61	49	79	225	47	-8	.	80	84	99	80	16	234	-5	.	-37	140	-73	163	.	.	37	130	.	.	.	.	2	349	-6	100	.	.	.	.	.	.	53	119	17	172	54	120	-5	100	77	59	.	.	21	88	.	.	.	.	.	.	.	.	-8	128	-1	100	93	66	-1	1416	9	164	.	.	278	61	-46	67	38	71	103	56	66	68	-7	100	.	.	2	100	34	63	10	658	146	74	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	27	75	.	.	-7	100	87	61	25	65	.	.	48	86	-21	66	27	100	-39	102	13	71	.	.	29	62	0	2364	3	558	33	71	-17	85	.	.	-17	96	.	.	.	.	28	63	.	.	-38	105	35	233	.	.	13	167	19	91	0	7594	-15	120	.	.	1449	30
	total	-973	80	-336	106	244	78	-1455	38	.	.	.	.	-507	85	-352	130	-1280	73	-92	.	-343	56	-19	875	-218	119	96	.	-169	194	-413	102	.	.	302	109	.	.	.	.	83	122	36	159	.	.	.	.	.	.	-417	103	239	184	-722	82	-91	122	-62	139	.	.	139	98	16	.	.	.	.	.	.	.	157	89	-39	65	-331	96	75	74	-353	78	.	.	138	180	-248	114	-298	61	102	112	-26	366	113	73	.	.	63	81	477	49	-501	88	-139	285	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	260	80	.	.	44	119	-197	60	20	552	.	.	294	61	443	47	97	58	-372	87	-56	290	.	.	70	103	24	399	26	684	-53	200	-162	110	.	.	10	2328	.	.	.	.	112	69	1	10520	87	78	-69	276	60	74	-45	606	-493	86	-402	74	-44	571	.	.	-7448	31
hyperinsubric and colline	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-276	64	.	.	.	.	.	.	.	.	75	94	64	72	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-61	.	-1	.	345	52	99	73	102	84	-2	4853	594	43	216	50	115	122	179	70	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	41	119	.	.	.	.	-6	.	.	.	.	.	.	.	40	167	-72	84	-10	704	.	.	.	.	.	.	.	.	.	.	-39	104	.	.	.	.	.	.	1402	33
	dead	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-64	58	.	.	.	.	.	.	.	.	32	107	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	9	.	6	.	31	73	-3	437	92	40	154	44	168	43	-3	1410	106	48	48	127	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	100	.	.	.	.	14	.	.	.	.	.	.	.	14	129	53	64	8	150	.	.	.	.	.	.	.	.	.	.	13	100	.	.	.	.	.	.	679	22
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-340	61	.	.	.	.	.	.	.	.	106	57	64	72	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-52	.	6	.	375	50	96	85	194	47	151	78	762	34	214	53	221	74	227	70	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	42	119	.	.	.	.	7	.	.	.	.	.	.	.	54	127	-18	215	-2	3340	.	.	.	.	.	.	.	.	.	.	-26	107	.	.	.	.	.	.	2081	24
total	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-1088	64	-370	97	280	69	-1402	42	457	67	-538	113	1003	99	-808	120	-2031	59	-425	141	-401	52	-51	460	-302	106	101	.	113	293	-103	441	598	90	616	74	-276	64	-1494	67	1322	44	527	83	638	81	589	122	1073	51	-191	350	155	354	-266	247	-1702	48	146	113	315	54	-90	206	197	72	310	126	796	40	439	44	616	51	216	181	42	962	715	65	320	130	-546	125	-299	115	4	8597	-448	74	18	1544	-246	143	353	102	-413	90	258	118	212	149	-511	84	-698	68	303	72	2070	51	-34	970	320	42	353	46	869	32	964	25	299	63	760	34	607	44	378	45	87	405	-145	201	545	36	645	46	234	237	393	57	421	59	-209	158	-314	98	236	69	324	53	24	839	-47	579	-245	57	-503	85	-268	138	81	322	1536	29	740	54	1598	26	155	130	-179	184	-205	154	261	131	-117	332	-551	79	-725	59	14	1804	.	.	8403	50
	dead	138	114	-24	307	-36	144	-168	91	8	282	162	73	53	633	330	57	375	39	684	32	64	107	107	75	63	79	-5	.	-37	142	-125	105	7	1802	164	59	-64	58	143	75	4	2969	15	977	550	47	713	25	640	32	781	30	66	156	161	58	495	55	102	47	48	61	33	66	52	86	207	66	106	44	-2	3402	145	59	55	118	86	95	108	61	2	3185	110	191	255	67	-65	107	112	58	153	45	17	840	29	241	145	65	84	52	54	51	10	658	137	80	55	46	138	102	321	52	98	41	147	50	178	40	40	108	137	82	73	105	117	43	86	46	30	314	53	120	39	150	139	72	53	84	-21	66	340	46	-38	104	69	42	24	102	71	60	61	59	22	278	34	99	38	97	46	71	8	324	426	35	494	29	472	34	105	63	265	63	41	205	-29	184	26	148	32	68	0	7594	-1	2376	.	.	11439	9
	total	-950	82	-394	92	244	78	-1570	37	465	69	-376	162	1056	99	-478	201	-1656	72	259	228	-337	58	57	318	-239	130	96	.	76	454	-228	224	605	91	780	58	-340	61	-1351	73	1325	45	542	82	1188	39	1302	53	1713	30	589	117	221	248	-105	633	-1208	65	247	65	363	49	-57	329	249	61	517	78	903	36	437	52	761	44	271	147	128	318	822	58	322	131	-436	160	-44	736	-61	566	-336	99	171	156	-229	196	381	99	-268	141	342	92	266	121	-501	88	-560	83	358	61	2208	54	287	90	417	32	501	34	1046	27	1003	25	436	50	834	32	724	40	464	42	117	335	-92	298	584	35	784	45	287	196	372	59	760	30	-247	141	-245	122	260	64	395	49	85	239	-26	1061	-211	72	-465	90	-222	171	89	286	1963	24	1234	31	2070	20	261	71	85	395	-164	193	232	155	-91	435	-519	82	-725	60	13	1976	.	.	19842	22

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. 2316745. Birmensdorf, Swiss Federal Research Institute WSL
https://doi.org/10.21258/1947714