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total number of stems NFI1 #243
Number of stems of all living and dead trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm according to the method used in NFI1. In NFI1, dead trees were only included if their wood was still usable. The total number of stems NFI1 is the sum of the number of stems and the number of deadwood stems NFI1.

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 #434
Forest of which less than two-thirds is covered with shrubs that can be accessed on foot.

1.4-km grid #410
NFI's sampling grid with a mesh size of 1.4 km. The 1.4-km grid is the grid size covering all the previous terrestrial Inventories, which is why it is also called the 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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NFI1
total number of stems NFI1
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
grid: 1.4-km grid
state 1983/85
		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	.	90	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	90	.
	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	.	20	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.
	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	.	110	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	110	.
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	.	1811	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	68	883	30	581	44	1085	21	2054	19	5719	13	0	.	0	.	0	.	117	91	0	.	0	.	0	.	0	.	0	.	358	71	220	91	424	54	0	.	119	100	300	44	0	.	117	.	0	.	0	.	0	.	0	.	0	.	20	71	0	.	0	.	0	.	0	.	0	.	57	62	356	30	234	39	38	.	0	.	0	.	251	45	19	100	69	.	0	.	49	67	133	59	79	73	33	58	86	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	.	0	.	0	.	0	.	0	.	2318	16	1956	19	1145	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20775	6
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	1577	20	67	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	80	849	26	412	35	817	27	1539	20	2926	12	0	.	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	85	88	62	71	291	51	0	.	20	100	142	47	0	.	74	.	0	.	0	.	0	.	0	.	0	.	30	75	0	.	0	.	0	.	0	.	0	.	105	54	318	41	185	40	0	.	0	.	0	.	116	44	28	74	39	.	10	.	49	72	102	68	10	100	102	65	59	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	.	0	.	0	.	0	.	0	.	1678	17	1565	19	1244	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14687	6
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	1070	19	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	65	353	26	159	31	653	23	1098	17	1922	12	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	0	.	23	73	40	78	97	45	0	.	10	100	127	53	0	.	13	.	0	.	0	.	0	.	0	.	0	.	38	78	0	.	0	.	0	.	0	.	0	.	48	67	122	38	102	46	0	.	0	.	0	.	104	43	27	63	0	.	0	.	37	74	80	80	40	100	39	61	30	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	.	0	.	0	.	0	.	0	.	1110	15	738	19	516	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8761	6
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	423	21	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	43	286	25	80	40	363	21	611	17	1013	12	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	40	82	22	52	0	.	4	100	53	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	75	0	.	0	.	0	.	0	.	0	.	58	44	158	28	27	47	0	.	0	.	0	.	61	48	11	58	0	.	4	.	32	89	20	60	32	89	16	50	64	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	.	0	.	0	.	0	.	0	.	558	14	410	20	408	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4909	5
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	151	23	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	61	148	26	56	34	169	25	220	20	395	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	12	74	13	74	0	.	8	71	12	74	0	.	4	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	27	51	77	37	19	66	0	.	0	.	0	.	27	51	4	100	0	.	4	.	12	74	21	100	24	75	8	71	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	272	17	221	22	123	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2093	6
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	99	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	75	40	35	16	78	46	33	103	25	99	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	8	71	5	100	0	.	4	100	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	75	0	.	0	.	0	.	0	.	0	.	15	61	15	61	4	100	0	.	0	.	0	.	12	58	8	100	0	.	4	.	4	100	0	.	0	.	0	.	20	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	153	24	12	58	43	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	745	10
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	16	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	20	53	4	100	32	40	16	50	51	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	9	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	58	15	61	4	100	0	.	0	.	0	.	8	71	0	.	0	.	4	.	4	100	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	26	12	58	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	329	13
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	4	100	4	100	11	58	4	100	25	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	8	71	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	45	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	111	19
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	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	.	12	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	33
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	4	100	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	35
	total	0	.	0	.	0	.	0	.	0	.	0	.	5151	16	97	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	460	55	2582	21	1310	32	3184	19	5649	15	12163	10	0	.	0	.	0	.	165	94	0	.	0	.	0	.	0	.	0	.	497	70	388	71	860	47	0	.	165	93	658	39	0	.	209	.	0	.	0	.	0	.	0	.	0	.	127	62	0	.	0	.	0	.	0	.	0	.	328	39	1070	27	579	36	38	.	0	.	0	.	578	36	97	65	108	.	27	.	187	68	356	57	192	82	197	56	283	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	.	0	.	0	.	0	.	0	.	6224	13	4926	17	3483	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52478	4
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	369	39	50	72	156	82	615	34	1212	29	2020	22	8178	10	6327	10	1724	21	6588	11	11	100	174	55	781	31	0	.	500	56	1768	23	3580	17	1642	26	0	.	2917	20	2066	21	2549	19	2192	19	5575	12	2092	18	5369	12	1495	26	1401	23	4348	16	434	40	1703	26	383	40	791	32	1522	21	1387	26	1314	23	2182	21	1193	34	1150	25	2549	22	1197	32	2405	18	1120	33	579	32	1302	27	524	50	1060	29	2013	21	2171	23	1244	27	771	38	0	.	121	65	558	32	1304	26	686	32	1501	24	1103	28	690	36	3160	20	1680	27	1679	25	994	27	650	45	1176	28	1065	29	753	33	2169	24	829	36	10	100	1497	24	182	63	463	33	1559	19	581	26	1057	28	497	32	340	45	442	52	137	50	239	47	5021	15	3231	17	5272	14	925	31	848	45	91	89	1271	26	762	30	0	.	90	100	30	100	0	.	139356	2
	21-30 cm	341	32	30	100	98	60	404	39	1150	25	1817	22	4711	10	3714	11	1208	20	3837	12	20	100	187	46	436	29	0	.	243	44	862	29	2000	17	997	25	0	.	1838	20	1239	19	1769	18	1129	21	2982	13	1626	18	3286	12	945	23	543	23	2170	16	214	36	758	23	508	35	338	31	1023	21	951	25	805	25	1056	21	949	23	879	29	1617	17	705	25	2019	16	543	35	391	39	833	24	100	58	848	24	1050	21	1311	21	650	25	401	28	0	.	98	62	177	50	506	26	428	33	577	26	678	25	414	35	947	22	705	29	645	25	606	28	216	43	812	26	403	32	340	31	1171	22	433	35	48	100	904	22	104	71	434	32	836	21	426	25	673	26	406	33	142	59	133	55	164	40	259	50	2487	15	1395	17	2576	13	396	33	404	40	110	45	932	24	693	25	0	.	60	100	20	100	0	.	80289	2
	31-40 cm	275	34	0	.	28	77	283	33	638	24	965	19	3460	9	2756	11	926	18	2152	12	32	88	161	48	282	28	0	.	104	48	571	27	1268	17	707	27	0	.	984	18	918	18	767	19	716	19	1761	13	1250	17	2007	12	587	24	850	22	1234	16	195	39	506	21	268	43	167	31	651	22	470	23	548	25	714	21	574	25	491	28	1022	18	588	27	1054	18	239	38	333	31	516	24	97	48	515	24	610	23	835	20	458	26	271	32	0	.	52	58	150	51	318	32	376	27	166	30	290	26	99	41	249	23	192	32	284	34	318	29	178	49	367	27	237	34	313	31	617	22	248	34	96	88	524	22	44	72	182	32	642	20	264	25	481	23	310	27	95	52	229	51	101	42	198	53	1118	16	875	18	1313	13	393	28	195	50	140	43	723	21	441	25	0	.	78	82	12	100	0	.	49710	2
	41-50 cm	78	37	0	.	0	.	114	32	284	26	580	20	2118	9	1897	10	606	18	916	12	9	100	48	59	238	25	0	.	118	48	246	24	610	17	264	25	0	.	503	17	461	17	558	16	614	18	1041	12	464	17	982	12	310	25	443	21	733	17	150	32	320	21	129	35	103	30	430	18	216	29	248	25	407	23	280	27	199	28	560	20	350	23	723	16	62	47	185	28	209	27	139	44	219	24	386	21	471	20	227	24	192	32	0	.	30	63	92	39	164	26	175	24	55	42	108	40	43	41	192	24	108	34	96	27	142	30	74	48	137	29	148	35	165	29	312	22	198	31	12	74	245	25	39	64	136	33	278	20	168	29	228	26	211	27	65	62	65	44	66	43	106	45	593	17	328	20	695	14	251	28	122	34	112	41	420	23	191	30	0	.	19	71	20	100	0	.	27054	2
	51-60 cm	28	51	0	.	0	.	53	51	61	38	259	22	1089	10	755	12	289	18	344	16	17	100	24	58	43	35	0	.	21	60	88	34	228	21	101	34	0	.	180	25	284	18	277	20	260	22	526	13	185	21	342	14	248	27	185	23	245	22	33	39	100	31	40	49	69	34	235	22	64	31	139	28	209	25	136	28	88	38	234	23	102	35	383	19	23	47	67	40	33	40	12	58	37	52	143	25	180	23	154	30	38	55	0	.	40	64	67	41	114	27	62	35	30	50	20	52	11	100	96	27	25	52	79	36	62	44	28	48	62	32	120	32	93	37	136	26	83	36	13	100	122	34	11	100	85	38	99	27	86	31	84	35	79	39	16	71	25	63	17	61	53	46	346	20	175	22	373	20	113	33	50	41	46	60	138	25	55	37	0	.	4	100	0	.	0	.	11768	3
	61-70 cm	0	.	8	100	0	.	8	71	12	76	9	71	245	17	252	15	77	30	82	25	9	100	4	100	15	50	0	.	13	100	20	54	111	26	31	50	0	.	82	40	82	31	99	25	119	25	217	19	52	39	107	23	39	48	24	47	54	31	24	41	24	46	0	.	24	47	38	33	20	54	76	33	49	47	36	43	44	41	64	32	42	40	113	28	8	100	20	83	11	58	4	100	0	.	52	39	40	41	57	34	10	72	0	.	9	71	13	76	48	49	25	54	16	50	8	71	4	100	29	43	17	50	20	45	12	58	35	71	19	60	27	38	19	45	29	54	37	46	0	.	29	38	0	.	33	43	8	71	47	41	29	43	15	49	0	.	25	58	9	70	0	.	59	34	41	40	102	25	17	61	31	47	9	100	16	50	12	58	0	.	4	100	0	.	0	.	3380	4
	71-80 cm	0	.	0	.	0	.	0	.	8	71	6	100	70	28	114	23	22	45	24	48	0	.	0	.	4	100	0	.	0	.	8	71	17	61	4	100	0	.	35	43	23	41	40	40	34	45	94	24	21	45	12	58	0	.	0	.	14	58	17	61	20	66	4	100	21	53	24	70	0	.	4	100	17	50	0	.	20	74	13	59	16	50	60	46	4	100	0	.	0	.	0	.	4	100	20	53	13	60	37	43	0	.	0	.	0	.	4	100	22	67	20	54	0	.	4	100	0	.	11	57	17	78	12	76	0	.	12	100	7	100	12	58	30	53	0	.	7	100	0	.	9	100	0	.	4	100	13	58	4	100	9	70	12	75	0	.	0	.	9	100	0	.	46	30	13	58	45	43	4	100	8	71	0	.	13	58	4	100	0	.	0	.	0	.	0	.	1192	7
	81-90 cm	0	.	0	.	0	.	8	71	0	.	0	.	19	45	39	33	5	100	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	61	0	.	8	71	12	58	16	50	9	71	4	100	4	100	4	100	0	.	0	.	0	.	0	.	0	.	4	100	4	100	4	100	0	.	5	100	0	.	12	75	4	100	8	71	0	.	0	.	4	100	0	.	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	18	59	0	.	0	.	0	.	4	100	8	71	9	71	4	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	71	12	58	12	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	288	12
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	8	71	5	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	8	71	0	.	4	100	0	.	13	73	4	100	4	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	4	100	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	0	.	0	.	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	105	20
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	4	100	0	.	5	100	4	100	0	.	0	.	10	73	0	.	12	76	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	27
	total	1090	31	88	71	282	62	1485	30	3366	22	5655	18	19908	8	15859	8	4857	15	13956	10	97	71	598	41	1800	26	0	.	999	40	3562	22	7814	14	3750	21	0	.	6563	16	5078	15	6072	15	5074	15	12228	10	5703	15	12114	10	3627	20	3450	18	8799	14	1068	30	3431	19	1336	32	1512	28	3935	18	3113	21	3139	21	4637	18	3172	22	2870	22	6076	16	3003	22	6766	15	1998	30	1574	26	2908	21	876	39	2683	22	4277	18	5022	18	2840	20	1683	27	0	.	350	55	1068	28	2485	22	1778	24	2348	22	2215	23	1260	32	4713	18	2743	24	2828	21	2134	24	1197	36	2598	24	2021	24	1720	25	4434	20	1843	27	179	85	3333	19	379	62	1337	28	3435	16	1576	22	2561	21	1529	25	658	40	919	40	503	38	855	39	9684	13	6074	14	10388	12	2099	24	1663	33	507	41	3513	19	2157	23	0	.	255	88	82	100	0	.	313215	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	1941	18	2533	20	1852	26	1647	23	0	.	0	.	517	43	889	27	6340	13	94	72	350	34	930	33	652	35	109	.	521	37	542	43	110	.	633	31	915	43	0	.	250	59	183	53	50	100	1378	28	19	100	1849	22	695	41	1230	29	290	52	268	46	0	.	441	49	84	.	0	.	10	100	470	73	242	58	389	39	873	49	234	59	703	43	84	.	1955	21	397	32	467	42	926	31	913	31	174	61	0	.	50	82	918	33	1354	25	1188	29	66	100	354	32	283	49	1206	33	2074	25	5507	16	1012	34	1376	33	2190	24	381	73	0	.	226	60	425	42	414	55	31	.	465	38	324	59	58	72	1064	32	385	55	0	.	643	45	529	42	958	33	635	42	485	53	0	.	631	45	1128	30	763	39	1236	32	338	57	137	49	799	33	235	48	689	28	1232	27	713	27	1229	28	0	.	68880	3
	21-30 cm	1315	18	992	20	860	26	955	20	0	.	0	.	337	45	687	30	3440	12	104	68	292	37	555	30	562	29	10	.	330	38	591	31	31	.	584	27	188	37	20	.	138	54	143	52	20	100	389	37	9	100	1341	19	623	30	788	24	117	60	178	37	0	.	215	45	47	.	0	.	94	74	10	100	137	62	412	38	340	46	178	55	245	45	17	.	1114	23	432	28	356	36	401	30	812	28	99	83	0	.	99	59	478	33	707	27	657	25	30	73	126	44	181	54	354	35	641	26	1537	17	305	36	304	30	865	27	190	60	0	.	166	60	227	44	148	60	0	.	254	38	130	56	103	75	478	36	319	51	0	.	235	38	407	34	540	32	269	35	141	50	0	.	605	32	391	35	219	38	350	37	138	56	208	48	592	26	154	54	411	34	552	26	534	31	575	27	0	.	35131	3
	31-40 cm	820	17	824	19	426	23	811	20	0	.	0	.	255	37	297	29	2200	11	25	62	218	32	238	32	385	28	0	.	355	29	281	31	43	.	378	28	61	54	8	.	92	66	107	50	0	.	103	37	17	100	822	19	411	22	590	24	110	59	164	44	0	.	113	56	17	.	0	.	75	79	37	100	70	62	257	32	148	64	61	59	163	50	0	.	613	23	312	28	248	43	329	30	341	28	82	74	0	.	66	68	291	34	376	30	500	25	0	.	25	62	55	56	100	46	210	30	315	27	136	37	124	40	248	26	17	80	0	.	44	64	80	53	50	59	29	.	190	40	111	55	36	60	360	29	111	43	0	.	88	50	177	38	285	34	146	43	55	44	0	.	311	36	159	33	55	47	89	43	68	60	183	43	523	25	130	52	362	25	520	26	414	27	519	27	0	.	20469	3
	41-50 cm	442	19	444	20	184	23	559	19	0	.	0	.	159	43	215	33	1561	11	24	86	149	33	148	33	129	34	8	.	181	33	146	36	21	.	220	28	32	78	16	.	39	62	60	56	8	100	50	39	19	72	375	19	234	26	285	24	62	62	80	49	0	.	35	58	0	.	0	.	12	100	8	100	30	65	144	35	59	53	29	72	67	51	0	.	228	22	318	25	48	46	202	32	149	33	11	100	0	.	29	59	171	33	292	29	187	24	0	.	24	62	13	57	18	61	62	43	117	31	80	41	51	52	97	38	15	100	0	.	5	100	28	47	13	73	8	.	94	42	54	65	39	73	124	32	39	69	0	.	56	42	64	45	81	36	29	56	39	53	0	.	104	36	49	48	8	100	60	37	29	56	133	35	360	24	76	45	289	30	219	26	231	25	281	26	0	.	10857	3
	51-60 cm	166	24	209	24	109	27	244	23	0	.	0	.	59	43	90	35	684	12	0	.	81	32	39	40	47	42	4	.	75	36	33	52	7	.	77	41	12	75	4	.	23	74	25	61	12	74	22	53	0	.	164	28	103	34	91	34	25	62	19	66	0	.	15	71	0	.	0	.	4	100	4	100	4	100	57	46	7	100	7	100	36	42	0	.	112	28	122	30	0	.	64	36	20	69	0	.	0	.	12	100	85	40	89	35	98	28	0	.	16	50	9	70	10	72	34	46	35	39	29	63	24	51	39	42	8	100	0	.	5	100	16	61	5	100	0	.	23	45	6	100	7	100	53	34	11	74	0	.	12	100	17	50	23	46	8	71	12	58	0	.	37	48	8	71	4	100	44	39	12	74	47	54	83	33	13	58	132	30	80	31	104	34	83	30	0	.	4210	4
	61-70 cm	70	30	46	37	16	62	25	47	0	.	0	.	20	62	33	50	184	18	0	.	31	43	8	100	4	100	4	.	12	58	15	50	7	.	34	60	0	.	8	.	4	100	0	.	0	.	4	100	4	100	35	51	8	71	21	64	6	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	25	67	4	100	18	100	0	.	24	41	50	44	4	100	8	71	10	73	0	.	0	.	6	100	15	50	16	61	48	36	4	100	4	100	0	.	0	.	0	.	9	70	17	62	4	100	12	58	0	.	0	.	5	100	0	.	0	.	0	.	24	59	4	100	0	.	31	53	8	71	0	.	0	.	0	.	4	100	0	.	12	74	0	.	4	100	4	100	0	.	12	58	0	.	4	100	30	49	4	100	32	44	20	53	32	43	35	37	0	.	1144	7
	71-80 cm	12	58	11	74	8	71	41	44	0	.	0	.	0	.	30	51	69	24	0	.	8	71	0	.	0	.	0	.	4	100	7	71	0	.	8	71	0	.	0	.	0	.	4	100	0	.	0	.	0	.	8	100	8	71	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	72	0	.	4	100	0	.	0	.	0	.	0	.	4	100	4	100	15	50	0	.	39	65	0	.	0	.	4	100	21	52	8	100	0	.	16	59	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	71	0	.	0	.	4	100	0	.	0	.	0	.	4	100	0	.	4	100	0	.	0	.	4	100	0	.	0	.	9	100	0	.	10	73	11	58	4	100	4	100	0	.	425	12
	81-90 cm	4	100	9	72	4	100	0	.	0	.	0	.	0	.	12	58	34	43	0	.	4	100	4	100	0	.	0	.	0	.	4	100	0	.	9	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	14	58	0	.	0	.	0	.	9	71	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	8	71	0	.	4	100	0	.	143	18
	91-100 cm	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	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	.	4	100	0	.	8	71	0	.	0	.	0	.	12	74	0	.	0	.	11	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	51	31
	>100 cm	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	4	100	30	53	0	.	12	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	.	71	30
	total	4770	14	5075	16	3459	21	4281	16	0	.	0	.	1347	37	2253	23	14515	9	246	63	1134	27	1927	26	1779	26	135	.	1478	30	1620	29	219	.	1946	24	1209	38	56	.	545	50	523	46	90	72	1957	27	68	80	4593	16	2086	26	3011	21	610	52	709	36	0	.	823	40	148	.	0	.	196	71	531	71	482	58	1260	28	1453	41	513	52	1232	38	101	.	4051	18	1652	22	1123	32	1934	24	2245	26	366	61	0	.	263	58	1961	24	2843	21	2696	21	100	90	616	27	540	49	1689	30	3026	24	7565	14	1617	29	1883	28	3495	22	615	64	0	.	451	59	776	42	630	51	69	.	1050	36	629	44	244	56	2123	28	872	43	0	.	1037	38	1194	33	1890	27	1087	34	748	46	0	.	1696	30	1740	29	1050	35	1795	28	585	49	712	34	2400	21	613	41	1928	23	2642	21	2033	23	2730	22	0	.	141381	2
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	2310	17	2583	20	2008	25	2262	19	1212	29	2020	22	10506	8	7216	10	8065	11	6682	11	361	33	1104	29	1433	23	109	.	1020	33	2310	20	3690	17	2275	21	915	43	3018	19	3199	16	3314	17	3327	14	9097	10	7830	10	7218	11	2191	22	2631	18	4755	15	702	30	1703	26	824	32	876	31	1522	21	1756	25	2004	25	2848	19	1582	27	2142	25	3083	19	1900	26	2607	17	3075	18	976	23	1768	22	1450	27	1973	21	2206	20	2171	23	1294	26	1690	25	1354	25	1309	27	681	28	2014	19	1203	23	2745	20	3177	19	6197	14	4422	16	3074	21	3938	17	1375	28	699	42	1535	23	1569	23	1200	28	2286	23	1295	27	333	57	1555	23	1246	29	848	31	1559	19	1224	26	1587	23	1500	24	975	32	927	37	137	50	870	35	8467	11	5950	12	7653	11	1263	27	985	39	890	31	1506	23	1451	20	1232	27	804	27	1259	27	0	.	229101	2
	21-30 cm	1656	16	1022	19	958	24	1359	18	1150	25	1817	22	6624	9	4468	10	4647	10	3941	12	312	36	742	25	998	21	10	.	574	28	1453	21	2031	17	1581	18	188	37	1998	19	2226	15	2324	15	1966	16	4930	10	4562	10	4627	10	1568	18	1331	17	2313	16	392	26	758	23	723	28	384	30	1023	21	1130	23	878	23	1484	19	1361	20	1239	24	1938	15	950	22	2110	16	1657	19	823	24	1189	20	501	27	1660	18	1179	20	1311	21	749	23	879	22	707	27	755	23	312	34	950	20	794	23	931	21	1319	18	1950	15	1369	18	1037	21	1550	18	806	25	265	37	1080	23	640	26	589	26	1230	21	687	26	178	49	1007	21	581	32	753	28	836	21	662	21	1080	20	963	23	411	31	274	37	164	40	864	27	4556	11	3180	12	4170	11	535	28	612	31	702	23	1086	22	1104	20	552	26	595	30	595	26	0	.	130128	2
	31-40 cm	1094	15	824	19	454	22	1094	17	638	24	965	19	4786	8	3065	10	3126	9	2177	12	250	30	399	27	667	20	0	.	460	25	852	21	1311	17	1085	20	61	54	1077	17	1363	15	1033	15	1369	15	2962	10	3189	10	2829	10	999	17	1440	16	1366	16	360	29	506	21	381	34	183	29	651	22	569	22	626	23	881	18	830	20	649	26	1209	17	751	24	1067	17	852	19	645	21	764	21	426	25	856	18	730	21	835	20	524	24	562	23	376	30	552	23	198	41	466	24	533	22	266	25	499	19	413	23	489	18	342	23	532	22	335	28	215	42	492	25	357	28	402	26	676	21	438	26	207	50	560	21	404	27	292	26	642	20	352	23	658	20	641	21	241	33	284	42	101	42	509	30	2388	10	1668	13	1917	12	460	25	378	33	663	22	854	19	802	18	520	26	493	26	531	26	0	.	78940	1
	41-50 cm	520	17	444	20	184	23	673	17	284	26	580	20	2700	8	2125	10	2167	9	940	12	157	31	196	29	368	20	8	.	299	27	393	20	631	17	485	19	32	78	606	16	786	14	698	15	984	14	1702	10	1496	10	1357	10	544	18	727	16	795	16	230	27	320	21	164	30	103	30	430	18	246	27	297	24	458	21	424	21	262	24	642	18	417	21	723	16	290	20	503	19	257	24	341	26	369	20	409	20	471	20	255	22	362	23	292	29	217	22	150	29	346	18	215	21	74	35	171	30	160	26	334	19	171	27	193	23	162	28	105	43	162	25	207	29	193	25	384	20	292	25	67	55	283	24	163	29	174	30	278	20	223	24	291	22	317	22	94	46	104	34	66	43	210	29	1200	11	746	14	1164	11	280	26	255	24	472	20	496	20	480	22	219	26	250	24	301	25	0	.	42820	2
	51-60 cm	194	22	209	24	109	27	297	21	61	38	259	22	1299	9	849	11	973	10	344	16	98	32	62	33	90	27	4	.	96	31	121	28	235	21	178	26	12	75	200	23	454	15	359	17	441	16	768	11	580	13	507	13	351	21	277	19	270	21	52	35	100	31	55	40	69	34	235	22	77	28	156	26	226	24	193	24	103	34	253	21	138	28	387	19	135	24	190	24	33	40	77	32	57	41	159	25	180	23	166	29	122	32	89	35	138	27	93	33	207	21	90	29	40	41	54	35	46	38	152	23	52	35	119	28	74	39	40	40	87	33	160	27	105	33	151	25	106	30	18	76	129	33	64	33	97	34	99	27	97	30	100	31	109	31	24	53	37	47	17	61	90	33	626	13	401	15	540	15	126	31	97	34	129	30	151	23	187	24	80	31	108	33	83	30	0	.	18071	2
	61-70 cm	70	30	54	35	16	62	33	40	12	76	9	71	365	14	285	14	261	15	82	25	40	40	12	74	19	45	4	.	25	59	35	37	118	25	65	39	0	.	103	34	126	23	115	24	164	20	324	15	156	20	142	21	46	42	45	39	60	30	24	41	24	46	4	100	24	47	38	33	25	48	84	31	54	43	36	43	73	34	76	28	60	41	113	28	32	39	70	39	15	50	12	58	10	73	64	34	40	41	63	32	26	42	16	61	56	33	32	44	68	38	29	48	16	50	8	71	12	58	58	30	29	43	32	35	16	50	39	65	25	52	27	38	19	45	50	40	61	36	4	100	29	38	31	53	41	37	8	71	47	41	29	43	18	44	0	.	38	46	9	70	4	100	216	20	53	33	157	21	17	61	35	43	38	44	20	45	43	35	20	53	35	40	35	37	0	.	5269	3
	71-80 cm	12	58	11	74	8	71	41	44	8	71	6	100	86	25	145	21	90	21	24	48	8	71	0	.	4	100	0	.	4	100	15	50	17	61	11	58	0	.	43	38	43	33	48	35	66	30	110	22	72	25	20	53	8	71	6	100	14	58	17	61	20	66	4	100	21	53	24	70	0	.	9	71	25	47	0	.	20	74	18	51	16	50	60	46	4	100	21	72	0	.	4	100	4	100	20	53	13	60	37	43	4	100	4	100	15	50	15	50	76	40	24	48	0	.	8	71	21	52	27	43	17	78	28	47	8	71	16	80	7	100	20	53	30	53	0	.	7	100	0	.	9	100	9	71	4	100	13	58	8	71	9	70	12	75	0	.	4	100	9	100	4	100	140	20	25	41	53	38	4	100	8	71	9	100	13	58	14	60	11	58	4	100	4	100	0	.	1946	6
	81-90 cm	4	100	9	72	4	100	8	71	0	.	0	.	19	45	51	29	39	40	8	71	4	100	4	100	0	.	0	.	0	.	4	100	0	.	9	71	0	.	24	47	4	100	12	58	23	41	20	45	34	35	4	100	8	71	4	100	0	.	0	.	0	.	0	.	0	.	4	100	4	100	4	100	0	.	5	100	0	.	20	53	4	100	8	71	4	100	0	.	4	100	0	.	0	.	4	100	0	.	4	100	0	.	4	100	0	.	4	100	26	41	4	100	0	.	0	.	9	71	18	59	0	.	4	100	0	.	4	100	8	71	9	71	4	100	4	100	0	.	0	.	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	38	20	45	12	58	0	.	0	.	5	100	0	.	0	.	8	71	0	.	4	100	0	.	542	9
	91-100 cm	0	.	4	100	0	.	0	.	0	.	0	.	11	58	5	100	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	8	71	0	.	4	100	8	71	13	73	8	71	4	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	4	100	8	71	13	58	0	.	0	.	4	100	12	74	0	.	0	.	11	73	0	.	0	.	8	71	0	.	0	.	0	.	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	58	4	100	0	.	0	.	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	192	15
	>100 cm	0	.	4	100	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	0	.	0	.	19	51	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	4	100	6	100	5	100	4	100	0	.	4	100	40	44	0	.	24	47	0	.	0	.	0	.	0	.	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	58	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	175	18
	total	5860	13	5163	15	3741	20	5766	14	3366	22	5655	18	26406	7	18209	8	19372	8	14202	10	1231	25	2525	22	3578	18	135	.	2477	24	5182	17	8033	14	5696	16	1209	38	7080	15	8205	12	7905	13	8348	12	19944	8	17934	8	16707	8	5714	16	6461	14	9573	13	1777	23	3431	19	2159	25	1660	27	3935	18	3805	20	4058	20	5979	16	4432	17	4487	20	7246	14	4235	19	7076	14	6049	15	3226	17	4030	18	2810	21	4928	17	4771	17	5022	18	3102	19	3645	18	2843	21	3046	20	1496	22	4170	15	2898	19	4075	18	5240	17	8825	13	6908	14	4723	18	6432	15	2776	23	1384	32	3405	21	2989	20	2548	21	4786	19	2893	22	808	39	3577	18	2503	25	2209	24	3435	16	2613	20	3755	18	3560	18	1745	26	1667	30	503	38	2551	24	17647	9	12050	10	15667	9	2685	21	2375	25	2907	19	4126	17	4085	16	2642	21	2287	22	2812	21	0	.	507185	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. 2298497. Birmensdorf, Swiss Federal Research Institute WSL

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