Selection from the list of topics

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total number of stems #3
Number of stems of all living and dead trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm. The total number of stems is the sum of the number of stems and the number of deadwood stems.

altitudinal vegetation belts (NaiS, 3 classes) #2668
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 three classes. The variable represents a simplification of the NaiS altititudinal vegetation belts in six classes (NAISHSTKOMB6KL) in which the class «hyperinsubric and colline» is mearged with «submontane» to form the class «hyperinsubric, colline, submontane», the class «lower and upper montane» with «high montane» to form the class «montane» and the class «subalpine» with «upper subalpine» to form the class «subalpine». 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).

diameter at breast height (10 classes) #1317
Diameter at breast height (dbh) of the trees and shrubs ≥12 cm dbh – in ten classes. Reference: Field Survey (MID 60: Brusthöhendurchmesser, MID 62: Umfang)

forest district (2024) #2777
Regional demarcation with the forest districts as a unit. This variable was implemented in the year 2024 and is based on a survey with the cantonal forest services in the years 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	23-25calculated 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 number of stems
altitudinal vegetation belts (NaiS, 3 classes) · diameter at breast height (10 classes)
regional demarcation: forest district (2024)
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
altitudinal vegetation belts (NaiS, 3 classes)	diameter at breast height (10 classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	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	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.
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	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	2585	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	243	86	1395	35	157	48	826	30	2152	30	5100	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	482	100	318	74	505	72	0	.	0	.	337	58	0	.	241	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	165	71	597	53	490	41	0	.	0	.	0	.	396	53	90	100	0	.	0	.	36	71	351	79	0	.	40	71	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	.	0	.	0	.	0	.	0	.	3065	20	2146	26	1182	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23028	8
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	1721	21	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	100	618	34	263	55	610	29	1358	29	3102	16	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	0	.	286	86	226	92	292	67	0	.	0	.	140	69	0	.	153	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	675	39	481	41	65	.	0	.	0	.	270	55	36	71	0	.	35	.	108	71	131	71	35	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	.	0	.	0	.	0	.	0	.	1849	23	1308	28	756	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14732	8
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	1373	21	61	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	60	642	28	238	50	639	31	789	26	1820	16	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	0	.	94	94	122	78	137	75	0	.	0	.	158	68	0	.	39	.	0	.	0	.	0	.	0	.	0	.	33	81	0	.	0	.	0	.	0	.	0	.	104	73	386	37	145	40	6	.	0	.	0	.	90	56	25	77	0	.	0	.	98	77	42	75	32	.	70	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	.	0	.	0	.	0	.	0	.	955	24	876	25	640	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9740	7
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	899	20	40	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	66	404	25	98	43	486	31	445	25	1190	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	83	58	67	57	71	0	.	14	.	79	60	0	.	9	.	0	.	0	.	0	.	0	.	0	.	44	100	0	.	0	.	0	.	0	.	0	.	43	71	214	39	93	56	0	.	0	.	0	.	78	52	22	75	0	.	0	.	44	85	14	100	0	.	38	78	35	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	.	0	.	0	.	0	.	0	.	815	23	378	26	327	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6051	7
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	291	23	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	64	238	29	69	53	268	28	224	28	643	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	7	100	10	100	0	.	7	.	21	74	0	.	9	.	0	.	0	.	0	.	0	.	0	.	22	74	0	.	0	.	0	.	0	.	0	.	69	59	155	39	43	63	0	.	0	.	0	.	64	61	0	.	0	.	14	.	43	71	7	100	7	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	378	22	274	30	304	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3295	7
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	94	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	97	37	28	50	103	40	64	40	221	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	10	100	0	.	0	.	21	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	74	0	.	0	.	0	.	0	.	0	.	14	71	36	60	22	58	0	.	0	.	0	.	14	100	14	71	0	.	7	.	14	71	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	206	31	76	35	70	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1167	11
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	36	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	98	40	0	.	21	58	28	50	72	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	14	71	7	100	0	.	0	.	0	.	0	.	22	58	7	100	0	.	7	.	0	.	0	.	0	.	14	71	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	146	28	14	71	29	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	560	14
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100	7	100	37	52	22	58	50	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	39	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	210	21
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	9	100	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	58	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	35
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	41
	total	0	.	0	.	0	.	0	.	0	.	0	.	7006	19	154	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	483	75	3508	25	861	45	3004	26	5091	23	12213	13	0	.	0	.	0	.	44	.	0	.	0	.	0	.	0	.	0	.	937	88	732	75	1011	65	0	.	29	.	771	56	0	.	451	.	0	.	0	.	0	.	0	.	0	.	173	76	0	.	0	.	0	.	0	.	0	.	409	53	2077	33	1273	39	71	.	0	.	0	.	934	47	194	79	0	.	62	.	344	72	545	74	88	.	172	66	96	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	257	.	0	.	0	.	0	.	0	.	7500	17	5085	23	3308	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58886	6
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	.
	12-20 cm	191	75	0	.	0	.	272	61	558	41	1490	30	7218	14	5030	13	2782	27	5797	15	0	.	189	56	843	36	0	.	431	50	1514	30	2749	21	698	40	0	.	2523	23	1934	23	2179	23	3838	25	6423	15	2816	25	4243	17	2129	33	1339	29	4687	19	705	50	1696	30	276	45	845	33	1427	26	1092	31	1573	31	1617	29	1095	37	774	41	2391	28	1310	38	1402	25	448	45	1220	36	1039	39	510	55	476	43	1528	29	1606	30	1017	42	615	36	0	.	218	60	1589	43	974	39	521	44	1658	33	1061	39	899	38	2427	29	1919	36	1048	36	1638	34	650	56	1401	35	1472	34	888	37	1798	35	732	46	34	.	1786	27	66	.	297	65	1704	24	918	47	1226	36	496	37	492	72	507	53	58	100	108	73	3667	19	4370	21	5363	18	549	42	718	57	194	52	1358	33	629	46	0	.	33	.	33	.	0	.	132040	3
	21-30 cm	191	61	33	.	0	.	236	50	517	38	1325	25	4139	13	3095	16	1154	25	3983	15	17	100	138	53	415	41	0	.	275	62	1098	36	1970	22	607	41	0	.	1919	23	1360	26	1421	26	1568	24	3554	16	1732	22	1954	18	732	29	644	29	2613	20	337	60	737	25	184	64	302	41	820	31	1000	31	903	36	1222	28	447	33	735	36	1343	28	710	43	1332	23	417	45	509	37	551	37	272	60	461	36	769	33	1072	38	643	38	509	39	0	.	256	77	334	45	660	33	374	39	797	39	893	35	429	42	1409	29	1061	32	615	41	899	35	260	53	796	33	354	35	297	36	1054	48	363	58	0	.	1092	29	50	.	109	71	967	26	524	50	688	40	259	53	340	68	252	65	146	88	86	100	2112	20	2367	24	3311	19	484	39	348	48	36	71	1029	34	538	37	0	.	33	.	17	.	0	.	77601	3
	31-40 cm	101	48	0	.	0	.	71	65	542	30	1045	24	2968	13	1937	14	632	29	2433	15	31	100	83	65	215	45	0	.	201	55	626	33	1042	27	461	30	0	.	687	26	958	21	866	22	568	22	2287	16	1361	22	1601	18	453	31	528	37	1261	21	181	50	593	35	151	54	290	42	510	26	720	31	357	35	639	27	405	32	597	35	756	23	574	32	963	24	334	37	445	42	614	35	56	58	267	37	502	30	640	27	484	31	459	42	0	.	94	59	100	57	252	39	376	50	160	47	353	36	233	47	404	30	294	34	400	39	396	35	240	57	432	38	337	33	303	39	502	35	254	50	0	.	767	26	83	.	88	62	635	25	233	34	395	34	201	52	131	65	106	64	230	57	39	82	1265	25	834	22	1669	18	175	37	241	37	46	63	613	27	430	31	0	.	40	.	23	.	0	.	46867	3
	41-50 cm	77	55	7	.	0	.	110	44	478	28	618	24	1861	13	1283	13	486	24	1487	14	49	86	42	100	174	49	0	.	103	58	401	29	613	27	326	31	0	.	713	21	738	22	497	20	415	24	1183	15	772	20	1119	17	250	30	383	28	955	19	116	44	331	29	105	48	147	39	469	23	244	33	169	37	490	25	366	30	327	37	326	26	263	40	657	23	153	47	188	35	234	32	71	59	264	31	385	29	349	27	306	31	149	38	0	.	48	55	28	61	169	41	382	35	63	51	109	34	45	53	239	37	154	34	181	34	197	45	148	63	280	40	256	43	246	39	291	36	81	54	21	.	397	26	27	.	67	52	399	24	95	38	315	31	204	36	75	68	110	54	89	68	36	73	456	23	581	24	880	18	104	53	142	44	73	61	356	25	319	31	0	.	46	.	13	.	0	.	28969	3
	51-60 cm	77	67	0	.	0	.	80	50	236	32	367	30	1323	14	992	14	318	24	638	18	7	100	7	100	104	44	0	.	27	62	178	30	436	26	201	33	0	.	391	25	297	27	356	23	344	27	791	17	293	26	457	18	132	32	250	30	506	22	43	57	178	37	43	70	80	42	250	26	104	46	85	47	267	30	193	35	68	45	228	28	268	33	418	22	56	61	133	41	43	58	91	59	89	38	175	31	267	30	221	31	109	48	0	.	21	73	50	55	151	42	110	41	57	43	36	66	15	71	90	30	94	40	102	39	94	45	48	64	123	36	166	41	131	40	182	37	66	51	7	.	174	28	27	.	45	47	144	28	148	37	123	47	145	46	46	74	36	59	66	51	36	73	262	26	330	25	552	20	88	44	58	45	33	72	238	30	120	40	0	.	0	.	27	.	0	.	16457	3
	61-70 cm	14	100	0	.	0	.	20	58	90	37	148	36	456	19	404	18	112	33	123	29	0	.	7	100	28	61	0	.	13	71	87	42	192	32	64	43	0	.	125	34	106	33	151	28	129	29	317	19	67	49	151	29	119	42	145	35	189	28	38	53	46	47	29	71	21	75	146	28	40	46	32	60	98	38	58	39	44	53	112	35	83	51	227	29	7	100	27	61	22	58	15	71	38	69	132	37	53	49	74	43	14	71	0	.	20	74	35	82	37	52	78	47	35	66	22	58	21	100	61	40	7	100	72	56	46	47	31	63	37	53	62	43	61	46	109	49	27	50	0	.	125	44	7	.	30	79	49	38	53	55	7	100	35	53	0	.	52	53	0	.	7	100	78	61	114	34	154	30	7	100	80	56	7	100	97	33	27	61	0	.	0	.	7	.	0	.	6310	4
	71-80 cm	0	.	0	.	0	.	0	.	22	74	19	73	138	28	206	21	83	35	30	50	0	.	7	100	0	.	0	.	0	.	26	62	101	46	26	62	0	.	92	44	61	36	47	38	41	41	112	29	42	46	78	35	45	57	43	47	71	43	22	74	15	71	0	.	30	60	15	71	0	.	27	63	64	42	51	50	14	72	61	44	88	44	125	34	0	.	14	100	0	.	8	100	0	.	62	44	6	100	29	79	7	100	0	.	7	100	28	61	34	60	46	53	7	100	7	100	0	.	22	58	14	71	21	58	26	50	14	100	29	79	21	58	21	58	6	100	7	100	0	.	20	74	0	.	8	100	15	71	16	100	8	100	21	58	0	.	0	.	16	100	29	80	71	42	21	58	77	37	7	100	14	71	7	100	13	71	13	71	0	.	0	.	0	.	0	.	2594	6
	81-90 cm	0	.	0	.	0	.	7	100	0	.	0	.	76	40	77	33	37	45	22	76	0	.	0	.	0	.	0	.	0	.	7	100	13	71	0	.	0	.	27	50	14	71	21	58	19	58	79	30	22	74	14	71	0	.	0	.	0	.	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	7	100	16	72	42	100	0	.	0	.	7	100	0	.	0	.	7	100	7	100	14	71	7	100	0	.	0	.	0	.	19	72	8	100	14	71	8	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	15	71	14	71	0	.	13	71	0	.	0	.	0	.	0	.	7	100	0	.	0	.	8	100	0	.	0	.	8	100	0	.	27	100	27	60	21	58	0	.	14	71	0	.	7	100	0	.	0	.	0	.	0	.	0	.	785	12
	91-100 cm	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	8	100	15	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58	0	.	0	.	6	100	7	100	0	.	7	100	7	100	0	.	0	.	0	.	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	8	100	0	.	10	100	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	14	71	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	190	20
	>100 cm	0	.	7	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	16	71	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	14	100	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	105	29
	total	649	50	46	.	0	.	803	47	2444	29	5012	22	18179	11	13032	12	5620	20	14527	13	105	93	472	51	1778	35	0	.	1051	48	3937	29	7116	19	2384	30	0	.	6504	19	5468	19	5537	20	6929	20	14767	13	7104	19	9631	15	3869	27	3332	24	10282	16	1449	39	3612	24	794	44	1714	32	3636	23	3200	28	3146	29	4396	23	2626	27	2559	34	5231	23	3319	30	5173	19	1415	34	2536	30	2510	30	1022	47	1596	32	3568	25	4000	25	2801	29	1869	34	0	.	664	59	2186	38	2308	32	1894	33	2791	31	2497	33	1642	38	4662	25	3543	30	2440	31	3295	31	1399	46	3106	30	2682	29	1969	27	3942	33	1543	41	62	.	4361	24	258	.	645	51	3920	21	1987	38	2762	31	1369	35	1083	66	1070	49	613	59	342	71	7946	17	8658	19	12035	16	1413	35	1615	41	403	49	3711	25	2076	30	0	.	152	.	119	.	0	.	311918	2
hyperinsubric, colline, 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	.
	12-20 cm	1666	28	1509	27	1322	37	969	28	0	.	0	.	452	44	1078	29	5354	16	0	.	553	39	751	44	576	37	0	.	288	51	643	44	0	.	984	50	175	46	0	.	268	77	400	47	0	.	1253	52	88	84	1301	25	639	39	1744	31	248	77	662	48	0	.	491	53	52	.	0	.	0	.	0	.	503	87	226	80	1017	57	308	95	515	54	0	.	1860	29	530	48	238	69	343	44	742	40	315	95	0	.	0	.	1137	39	1521	36	1305	34	159	.	1077	48	370	52	2347	35	1450	35	5659	21	1323	43	828	45	3159	30	196	74	0	.	139	75	449	54	639	60	0	.	386	66	536	56	87	72	399	62	611	71	0	.	497	57	303	54	430	47	601	57	51	75	0	.	392	90	1420	46	716	44	1064	41	116	77	277	50	772	37	199	58	576	38	762	35	637	57	1002	37	0	.	63655	5
	21-30 cm	580	27	973	37	509	32	656	41	0	.	0	.	233	48	609	32	2950	16	35	.	160	48	479	41	538	41	17	.	189	50	486	39	0	.	492	34	293	51	0	.	109	75	168	51	0	.	735	45	67	71	809	25	434	34	643	28	129	85	72	62	0	.	138	59	52	.	0	.	0	.	0	.	69	79	265	62	461	47	123	73	353	71	0	.	1290	30	135	68	153	57	294	45	582	45	46	100	0	.	36	100	561	36	629	40	894	33	88	.	352	46	232	60	526	49	1020	31	2490	22	613	41	568	46	1506	27	288	80	0	.	132	72	341	57	219	61	0	.	293	56	323	57	87	72	231	55	85	53	0	.	536	47	211	51	206	61	371	44	201	54	0	.	73	71	516	44	316	46	377	49	75	71	190	51	366	41	259	74	260	34	686	36	338	44	667	35	0	.	33490	4
	31-40 cm	468	31	381	32	369	33	419	29	0	.	0	.	202	49	457	33	1396	14	0	.	90	48	273	44	397	37	14	.	151	59	333	44	0	.	391	33	186	56	0	.	27	62	55	77	0	.	369	43	24	79	656	30	339	34	547	37	57	71	67	60	0	.	90	67	7	.	0	.	0	.	0	.	52	100	59	86	206	54	71	71	89	55	0	.	637	25	133	38	125	54	276	36	360	40	39	71	0	.	107	73	271	46	421	30	423	35	18	.	68	53	136	64	207	42	451	38	769	23	152	52	231	43	543	40	110	77	0	.	64	80	126	58	106	84	0	.	77	58	121	61	73	71	22	58	82	60	0	.	138	45	137	46	186	53	149	41	63	65	0	.	74	59	173	46	151	50	50	52	42	100	74	71	280	40	76	80	376	37	377	33	344	40	240	38	0	.	17317	4
	41-50 cm	354	27	161	34	168	36	427	24	0	.	0	.	266	49	176	35	1255	14	0	.	44	52	182	49	132	37	14	.	127	51	148	50	0	.	157	34	91	57	0	.	53	74	40	55	0	.	95	46	32	81	510	23	323	37	303	29	34	72	84	54	0	.	90	52	14	.	0	.	0	.	0	.	0	.	16	71	81	58	43	71	51	63	0	.	452	26	61	53	121	59	177	39	130	48	18	100	0	.	43	74	98	58	217	34	205	32	14	.	65	51	63	78	35	59	167	33	310	35	55	43	71	46	186	34	31	80	0	.	28	81	60	71	71	65	0	.	98	65	79	51	14	100	65	68	27	71	0	.	42	47	78	59	119	44	128	48	67	68	0	.	56	66	65	55	43	63	59	49	68	72	70	59	199	34	14	100	181	39	170	32	205	39	183	33	0	.	10178	4
	51-60 cm	142	33	156	29	87	41	309	28	0	.	0	.	94	52	159	33	760	15	0	.	74	48	42	58	70	44	21	.	46	62	96	42	0	.	123	33	21	100	0	.	21	76	24	58	0	.	31	61	12	100	261	27	200	51	169	34	7	100	49	59	0	.	49	74	0	.	0	.	0	.	0	.	0	.	44	100	52	59	18	100	51	52	0	.	139	32	127	46	34	72	62	70	28	61	18	100	0	.	14	71	38	45	181	35	169	29	0	.	9	100	0	.	28	59	59	46	87	53	32	59	27	100	102	39	23	100	0	.	0	.	7	100	64	80	0	.	22	76	7	100	35	72	13	100	7	100	0	.	14	71	52	63	80	56	40	57	13	71	0	.	50	60	52	74	0	.	8	100	7	100	75	54	96	37	0	.	138	41	174	38	109	41	135	36	0	.	5564	5
	61-70 cm	78	37	53	39	34	45	158	29	0	.	0	.	34	73	80	40	339	20	0	.	52	52	28	79	14	100	0	.	13	100	21	58	0	.	27	50	7	100	0	.	0	.	0	.	0	.	16	71	7	100	77	35	62	43	92	36	20	100	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	75	0	.	108	38	54	47	0	.	34	52	0	.	6	100	0	.	7	100	13	71	44	46	47	62	7	.	7	100	0	.	7	100	23	58	41	45	8	100	8	100	22	58	8	100	0	.	0	.	6	100	6	100	0	.	8	100	0	.	7	100	9	100	0	.	0	.	0	.	0	.	13	100	14	71	13	100	0	.	35	83	0	.	0	.	7	100	20	100	41	53	13	100	0	.	16	72	33	53	20	74	47	55	0	.	2014	7
	71-80 cm	31	50	20	58	30	60	63	39	0	.	0	.	0	.	28	61	102	32	0	.	9	100	7	100	14	71	7	.	0	.	14	71	0	.	8	100	0	.	0	.	6	100	0	.	0	.	0	.	0	.	14	71	0	.	7	100	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	9	100	19	72	0	.	26	75	0	.	23	58	27	50	0	.	0	.	0	.	0	.	0	.	0	.	7	100	37	63	20	74	0	.	6	100	16	100	0	.	15	71	16	100	16	100	13	100	24	57	8	100	0	.	7	100	7	100	6	100	0	.	8	100	0	.	0	.	9	100	0	.	0	.	0	.	8	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	71	13	71	7	100	0	.	739	11
	81-90 cm	7	100	7	100	13	71	20	74	0	.	0	.	0	.	0	.	27	50	0	.	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	33	60	0	.	14	100	8	100	0	.	0	.	8	100	7	100	0	.	20	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	8	100	0	.	0	.	7	100	0	.	10	100	14	71	14	71	0	.	0	.	288	17
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	30	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	14	100	0	.	0	.	0	.	8	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	90	40
	>100 cm	0	.	0	.	0	.	8	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	19	100	0	.	23	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	39
	total	3326	22	3259	25	2533	28	3029	21	0	.	0	.	1280	44	2600	26	12213	12	35	.	989	37	1768	35	1741	34	73	.	815	45	1738	36	0	.	2189	32	772	48	0	.	485	60	694	47	0	.	2500	41	230	72	3636	21	1997	28	3505	26	494	71	955	44	0	.	872	43	125	.	0	.	0	.	0	.	625	77	619	61	1835	47	563	79	1121	52	0	.	4510	22	1075	36	671	51	1186	33	1843	38	444	77	0	.	207	76	2124	33	3079	26	3096	26	287	.	1614	43	825	52	3150	35	3184	30	9395	19	2226	37	1745	40	5593	26	663	74	0	.	371	71	998	52	1111	57	0	.	892	55	1066	47	302	62	756	52	813	59	0	.	1226	47	788	47	1033	41	1303	43	422	55	0	.	687	59	2227	39	1226	43	1574	37	327	73	727	46	1734	31	547	63	1556	27	2228	28	1680	33	2281	29	0	.	133407	3
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	.
	12-20 cm	1857	27	1509	27	1322	37	1241	25	558	41	1490	30	10255	11	6108	12	8137	14	5797	15	553	39	940	37	1420	26	0	.	719	36	2157	25	2749	21	1682	33	175	46	2766	22	3597	19	2736	20	4664	21	9829	14	8004	14	5544	14	2768	27	3083	21	4934	19	1367	35	1696	30	768	37	897	32	1427	26	1574	37	1891	29	2625	28	1321	33	1791	37	3036	25	1825	31	1643	26	2309	25	1751	29	1277	34	853	37	1218	30	1861	29	1606	30	1017	42	1752	28	1521	36	1523	30	1914	37	2648	27	1381	26	4005	25	2511	26	6558	19	4146	22	2836	27	4208	24	1834	32	686	53	1891	30	1921	29	1567	32	1842	35	1118	38	570	53	1873	26	465	55	908	52	1704	24	1416	36	1530	31	993	28	1093	45	558	48	58	100	500	73	8152	14	7231	15	7609	15	665	37	996	43	967	32	1557	30	1205	30	762	35	671	55	1035	36	0	.	218723	2
	21-30 cm	771	25	1006	36	509	32	892	32	517	38	1325	25	6093	11	3739	14	4104	13	4017	15	177	44	617	34	953	29	17	.	464	42	1583	27	1970	22	1099	28	293	51	2005	23	2088	20	1852	22	2178	19	5647	14	4901	13	2764	14	1167	22	1288	20	2764	19	409	51	737	25	322	44	354	38	820	31	1287	31	1129	34	1584	25	712	31	1196	28	1606	25	1063	37	1486	23	1707	25	645	33	704	31	565	37	1043	30	833	31	1072	38	679	36	1069	27	629	40	1150	31	422	41	1688	22	1087	26	1388	29	1913	23	2919	19	2291	22	1664	25	2121	22	1221	32	368	43	1059	28	730	32	516	33	1054	48	656	40	323	57	1179	28	281	49	194	46	967	26	1060	34	899	33	515	37	711	40	453	43	146	88	159	63	4477	14	3991	17	4445	16	558	35	538	36	402	38	1287	31	798	27	686	36	371	41	683	34	0	.	125822	2
	31-40 cm	568	27	381	32	369	33	490	27	542	30	1045	24	4543	11	2456	13	2028	13	2433	15	122	44	356	37	611	28	14	.	351	40	959	26	1042	27	852	22	186	56	721	25	1627	16	1160	20	1207	19	3445	13	3206	13	2257	15	792	23	1075	26	1340	20	247	40	593	35	241	42	297	41	510	26	814	29	480	33	827	25	464	30	803	29	984	21	663	29	1002	23	971	21	578	34	739	30	332	31	627	28	574	27	640	27	591	29	730	32	421	30	517	30	222	43	706	25	657	33	373	31	804	26	1001	20	647	24	551	25	944	28	506	32	338	46	538	32	495	28	479	32	502	35	331	40	121	61	840	25	105	80	171	43	635	25	371	27	532	28	453	35	280	37	169	47	230	57	113	48	2393	17	1861	16	2359	15	217	36	314	33	327	36	689	26	806	24	377	33	384	37	263	36	0	.	73924	2
	41-50 cm	431	24	168	33	168	36	537	21	478	28	618	24	3026	11	1499	12	1741	12	1487	14	93	51	223	44	306	32	14	.	230	38	548	25	613	27	483	24	91	57	747	20	1195	16	634	17	901	20	1723	12	1994	12	1629	13	573	24	686	20	989	18	200	34	331	29	195	35	161	37	469	23	289	31	227	33	548	23	382	29	422	31	448	23	314	35	666	23	606	23	249	29	355	29	249	33	394	26	448	27	349	27	349	29	247	32	217	34	253	28	85	44	449	25	538	28	98	39	276	24	355	31	372	26	247	26	368	24	227	41	192	52	323	35	316	37	355	31	326	33	178	43	100	46	411	26	91	56	95	42	399	24	136	30	392	27	370	27	203	39	177	42	89	68	93	49	1336	16	1002	17	1266	15	172	43	212	35	272	30	370	24	501	24	170	32	251	36	197	32	0	.	45198	2
	51-60 cm	219	32	156	29	87	41	389	25	236	32	367	30	1708	12	1169	13	1077	13	638	18	81	45	49	51	173	31	21	.	74	45	274	25	436	26	325	24	21	100	447	23	556	19	449	20	612	19	1047	14	948	14	717	14	332	33	419	22	513	22	92	41	178	37	91	51	80	42	250	26	128	41	92	44	276	30	237	34	127	35	267	26	320	29	427	22	195	28	260	31	77	45	153	45	117	32	216	27	267	30	235	30	147	37	181	35	190	27	118	41	316	28	153	34	84	34	95	38	102	47	186	27	121	38	204	27	131	38	91	47	130	34	181	38	205	36	182	37	88	42	14	71	209	26	40	74	52	43	144	28	162	34	175	38	246	34	86	48	49	47	66	51	86	46	691	17	604	19	864	17	95	41	134	36	129	33	238	30	257	28	174	38	109	41	162	34	0	.	25316	2
	61-70 cm	92	35	53	39	34	45	178	26	90	37	148	36	583	16	484	17	451	17	123	29	52	52	35	66	42	53	0	.	26	61	108	36	192	32	91	33	7	100	132	33	203	25	179	25	232	24	397	17	295	21	229	22	182	31	237	25	208	27	59	49	46	47	29	71	21	75	146	28	46	42	32	60	107	36	58	39	44	53	133	32	110	43	227	29	115	36	81	37	22	58	49	42	38	69	161	32	53	49	81	40	27	50	44	46	67	49	57	55	80	37	100	39	43	57	45	41	62	45	84	35	30	50	94	45	60	40	46	49	37	53	82	37	67	43	109	49	35	45	0	.	132	42	15	71	30	79	49	38	53	55	7	100	54	43	14	71	66	47	0	.	42	71	285	28	189	24	231	26	27	78	120	41	20	74	97	33	43	46	33	53	20	74	54	50	0	.	9491	3
	71-80 cm	31	50	20	58	30	60	63	39	22	74	19	73	175	24	235	20	185	23	30	50	9	100	14	70	14	71	7	.	0	.	40	47	101	46	34	53	0	.	99	41	165	27	47	38	63	33	140	25	114	30	92	32	45	57	50	43	71	43	22	74	15	71	14	71	30	60	15	71	0	.	27	63	64	42	60	45	40	45	69	41	114	38	125	34	23	58	41	47	0	.	8	100	0	.	77	41	6	100	29	79	13	71	37	63	27	61	42	47	48	48	62	47	7	100	22	58	16	100	59	40	34	52	46	41	41	41	14	100	37	66	28	50	42	41	15	72	15	71	0	.	20	74	9	100	8	100	15	71	16	100	16	71	21	58	0	.	7	100	16	100	29	80	217	23	35	45	106	31	7	100	14	71	7	100	13	71	13	71	13	71	13	71	7	100	0	.	3893	5
	81-90 cm	7	100	7	100	13	71	27	61	0	.	0	.	76	40	77	33	64	33	22	76	7	100	7	100	0	.	0	.	0	.	7	100	13	71	0	.	0	.	34	45	21	58	35	45	56	40	100	26	71	40	21	58	0	.	0	.	0	.	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	7	100	25	59	42	100	0	.	7	100	7	100	0	.	0	.	7	100	7	100	14	71	7	100	13	100	33	60	0	.	41	52	16	71	14	71	8	100	8	100	7	100	0	.	20	73	0	.	0	.	7	100	15	71	14	71	9	100	13	71	0	.	0	.	9	100	0	.	7	100	0	.	0	.	8	100	0	.	0	.	8	100	7	100	85	42	34	51	29	50	0	.	14	71	7	100	7	100	10	100	14	71	14	71	0	.	0	.	1284	9
	91-100 cm	0	.	0	.	0	.	7	100	0	.	0	.	7	100	7	100	38	67	15	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	50	9	100	0	.	6	100	7	100	8	100	7	100	7	100	0	.	0	.	0	.	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	16	100	0	.	7	100	14	100	0	.	0	.	8	100	8	100	10	100	0	.	7	100	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	29	50	22	58	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	340	16
	>100 cm	0	.	7	100	0	.	8	100	0	.	0	.	0	.	14	71	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	14	71	23	58	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	14	100	11	100	0	.	0	.	0	.	8	100	19	100	0	.	23	58	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	220	20
	total	3975	20	3306	24	2533	28	3831	19	2444	29	5012	22	26465	9	15786	10	17834	10	14562	13	1094	34	2240	30	3519	24	73	.	1866	33	5675	23	7116	19	4573	22	772	48	6987	18	9461	14	7092	17	9933	16	22358	11	19547	11	13267	12	5866	20	6837	17	10821	16	2404	29	3612	24	1667	31	1839	31	3636	23	4137	29	3878	27	6032	22	3246	25	4423	28	6565	20	4440	26	5625	19	5925	19	3611	24	3181	26	2208	28	3439	25	4184	23	4000	25	3008	27	3993	24	3079	26	3760	23	2881	31	5999	20	3992	22	6012	23	5681	22	11037	17	7821	19	5482	23	8033	20	4020	28	1743	40	4022	26	3768	25	3253	26	4038	32	2434	33	1128	45	4663	23	1014	47	1458	40	3920	21	3214	29	3550	26	2660	26	2386	38	1491	38	613	59	1029	46	17672	11	14969	14	16917	13	1740	32	2342	31	2137	27	4258	23	3632	21	2228	28	1833	31	2400	28	0	.	504211	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. 2280610. Birmensdorf, Swiss Federal Research Institute WSL

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