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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 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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NFI1
total number of stems NFI1
altitudinal vegetation belts (NaiS, 3 classes) · diameter at breast height (10 classes)
regional demarcation: forest district (2024)
unit: n/ha
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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)	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
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	.	171	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	115	35	209	33	119	18	162	25	178	12	0	.	0	.	0	.	50	.	0	.	0	.	0	.	0	.	0	.	716	45	327	71	480	41	0	.	0	.	209	38	0	.	550	.	0	.	0	.	0	.	0	.	0	.	25	71	0	.	0	.	0	.	0	.	0	.	10	91	114	21	112	19	200	.	0	.	0	.	134	39	0	.	0	.	0	.	101	35	175	41	150	.	22	82	25	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	250	.	0	.	0	.	0	.	0	.	110	19	188	20	125	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	152	7
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	156	18	173	10	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	95	22	134	51	91	30	103	24	99	13	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	207	58	96	41	301	40	0	.	0	.	79	37	0	.	350	.	0	.	0	.	0	.	0	.	0	.	50	71	0	.	0	.	0	.	0	.	0	.	21	52	121	35	75	29	0	.	0	.	0	.	58	37	25	71	0	.	50	.	77	69	66	54	0	.	106	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	.	0	.	0	.	0	.	0	.	75	17	133	21	141	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	105	7
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	84	19	33	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	41	48	21	48	36	88	23	72	21	75	11	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	12	62	59	66	42	0	.	0	.	48	47	0	.	60	.	0	.	0	.	0	.	0	.	0	.	95	34	0	.	0	.	0	.	0	.	0	.	34	75	46	32	55	39	0	.	0	.	0	.	35	35	35	30	0	.	0	.	61	69	99	82	0	.	36	44	10	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	260	.	0	.	0	.	0	.	0	.	57	14	59	20	69	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	6
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	40	21	37	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	8	35	19	30	34	53	15	37	18	30	12	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	84	62	63	15	38	0	.	20	.	33	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	71	0	.	0	.	0	.	0	.	0	.	41	38	48	21	15	39	0	.	0	.	0	.	23	62	20	0	0	.	20	.	81	52	27	41	20	.	13	41	30	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	.	0	.	0	.	0	.	0	.	29	14	37	24	53	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	6
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	10	27	9	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	47	19	27	20	41	21	28	13	25	16	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	84	13	84	8	76	0	.	20	.	8	89	0	.	20	.	0	.	0	.	0	.	0	.	0	.	40	71	0	.	0	.	0	.	0	.	0	.	17	63	20	42	5	60	0	.	0	.	0	.	17	52	10	71	0	.	20	.	30	24	33	82	40	.	7	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	.	0	.	0	.	0	.	0	.	16	18	18	25	21	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	8
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	5	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	81	6	32	0	.	7	37	9	34	4	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	84	6	84	7	85	0	.	20	.	8	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	71	0	.	0	.	0	.	0	.	0	.	4	91	3	96	0	.	0	.	0	.	0	.	7	58	20	70	0	.	20	.	10	72	0	.	0	.	0	.	10	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	31	0	.	11	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	12
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	1	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	82	4	57	3	91	3	53	2	55	2	36	0	.	0	.	0	.	0	.	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	88	0	.	0	.	0	.	0	.	0	.	3	91	0	.	0	.	20	.	10	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	30	2	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	17
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	82	1	98	0	.	2	67	1	98	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	69	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	27
	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	.	2	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	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	.	1	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	44
	>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	.	1	98	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	45
	total	0	.	0	.	0	.	0	.	0	.	0	.	467	13	253	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	21	323	14	445	29	386	15	399	14	403	7	0	.	0	.	0	.	50	.	0	.	0	.	0	.	0	.	0	.	1053	37	565	47	877	32	0	.	60	.	393	21	0	.	980	.	0	.	0	.	0	.	0	.	0	.	250	23	0	.	0	.	0	.	0	.	0	.	130	41	354	16	262	20	200	.	0	.	0	.	277	24	110	19	0	.	130	.	369	41	399	37	210	.	184	50	95	11	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	790	.	0	.	0	.	0	.	0	.	304	10	438	15	420	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	382	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	210	27	150	.	0	.	257	19	192	36	228	23	180	10	208	10	149	20	286	10	26	68	163	52	256	31	0	.	515	37	345	16	242	18	317	28	0	.	208	20	128	15	173	22	166	21	192	12	172	17	246	15	184	19	251	19	311	13	177	43	264	30	123	45	225	30	180	22	268	22	243	21	209	24	208	36	248	20	184	29	229	36	186	18	416	25	153	31	206	28	221	45	147	35	170	24	237	34	219	28	283	36	0	.	144	44	131	28	186	21	95	38	216	33	283	26	326	23	291	22	308	24	219	21	198	24	386	40	338	21	178	30	96	28	352	31	150	35	50	.	232	20	250	.	128	22	169	19	95	22	233	29	161	29	132	35	196	57	69	61	193	38	300	16	232	17	250	15	185	39	212	39	0	.	165	28	152	32	0	.	450	.	150	.	0	.	216	3
	21-30 cm	140	21	0	.	0	.	137	20	201	17	243	22	107	9	114	10	105	19	162	11	48	74	101	53	100	32	0	.	142	27	170	27	123	17	193	19	0	.	153	22	110	18	120	20	90	25	113	13	133	18	124	14	94	22	64	25	154	16	80	33	97	24	177	20	103	25	108	18	190	21	120	25	138	17	155	17	176	24	126	15	91	32	179	15	205	21	108	43	155	21	42	59	184	19	91	19	159	23	105	20	114	25	0	.	87	56	25	56	49	38	103	38	58	21	133	23	154	22	122	21	134	25	84	33	130	25	151	28	219	17	69	34	74	29	149	24	107	52	250	.	114	17	250	.	151	14	95	20	93	19	124	19	118	29	117	62	85	44	140	16	135	25	124	16	108	18	137	12	37	36	157	37	87	31	129	21	145	20	0	.	300	.	100	.	0	.	125	2
	31-40 cm	98	42	0	.	0	.	147	22	134	15	112	15	81	10	81	11	93	15	97	10	77	55	60	69	74	28	0	.	26	60	91	27	72	18	165	23	0	.	84	16	78	16	45	16	52	20	64	14	93	15	91	13	58	24	118	19	82	15	73	44	49	24	75	34	41	29	85	19	78	19	78	28	98	17	90	23	107	28	49	19	89	35	87	17	102	30	90	26	76	19	49	39	103	14	44	21	85	17	95	22	74	34	0	.	64	29	21	60	38	56	113	22	20	49	44	28	48	36	19	30	32	41	52	31	74	28	109	49	76	24	46	37	75	30	79	30	81	23	430	.	78	16	90	.	54	25	49	19	37	28	108	18	96	18	40	50	74	34	89	18	163	68	49	19	68	17	69	11	63	34	41	20	110	19	92	15	91	17	0	.	310	.	60	.	0	.	76	2
	41-50 cm	40	37	0	.	0	.	31	24	73	18	64	19	56	8	56	8	60	14	35	11	21	68	15	87	69	15	0	.	13	34	42	21	38	17	49	21	0	.	44	15	36	16	37	16	47	18	41	13	33	16	40	13	32	25	47	23	50	13	48	34	35	21	54	23	20	31	46	12	35	28	30	30	56	21	38	23	40	25	39	23	62	19	56	13	26	42	49	22	41	28	88	29	56	15	30	23	59	17	33	20	43	28	0	.	37	36	32	33	27	31	48	16	10	48	12	41	15	46	17	32	17	45	20	28	34	27	38	25	38	23	32	39	36	23	36	24	44	36	40	.	33	20	100	.	43	35	30	19	28	19	33	28	57	22	54	64	20	40	36	42	66	8	25	24	29	19	38	13	43	28	47	25	75	23	52	22	31	30	0	.	40	.	100	.	0	.	42	2
	51-60 cm	8	89	0	.	0	.	17	52	18	34	24	24	28	11	23	12	21	16	13	19	42	68	10	50	11	41	0	.	9	87	14	45	16	25	8	41	0	.	17	27	20	19	24	19	20	25	18	14	13	22	13	18	32	29	23	21	17	25	13	40	9	41	23	37	17	34	23	28	10	35	25	28	28	25	21	29	17	52	22	24	25	33	26	23	10	43	20	38	8	40	9	43	6	95	19	31	18	28	29	29	6	71	0	.	26	65	20	46	18	32	12	43	10	48	3	96	7	94	11	30	4	70	14	54	16	41	14	48	16	31	22	31	18	50	19	29	27	27	0	.	22	31	0	.	32	38	13	28	23	30	8	57	28	40	21	48	9	91	5	85	34	16	9	31	13	22	15	21	14	48	18	37	12	35	15	24	7	52	0	.	0	.	0	.	0	.	18	3
	61-70 cm	0	.	40	.	0	.	3	91	4	72	1	99	5	21	7	17	6	41	3	35	21	68	5	86	2	94	0	.	0	.	2	67	9	27	1	97	0	.	9	48	6	33	5	35	7	31	7	24	4	48	4	31	6	56	2	67	4	33	7	46	1	98	0	.	5	51	4	45	1	97	11	44	7	52	8	40	9	53	4	37	11	37	8	43	4	95	8	78	2	67	0	.	0	.	7	46	1	98	7	41	5	66	0	.	11	48	2	96	9	70	10	44	4	51	3	65	2	94	3	54	2	68	3	66	3	66	13	92	5	68	5	44	1	97	0	.	7	46	0	.	4	40	0	.	6	59	1	99	10	51	1	96	3	65	0	.	15	54	5	85	0	.	3	51	1	56	5	27	4	64	16	39	0	.	2	55	3	65	0	.	0	.	0	.	0	.	5	6
	71-80 cm	0	.	0	.	0	.	0	.	3	66	0	.	2	39	4	25	1	69	1	61	0	.	0	.	0	.	0	.	0	.	2	68	1	69	0	.	0	.	4	50	1	56	2	59	1	56	3	30	1	56	1	70	0	.	0	.	1	68	5	62	2	68	3	92	6	53	2	68	0	.	0	.	1	68	0	.	8	69	2	56	5	44	6	55	2	95	0	.	0	.	0	.	0	.	2	97	1	98	5	44	0	.	0	.	0	.	2	96	5	97	5	69	0	.	2	96	0	.	1	98	1	97	1	97	0	.	0	.	0	.	2	67	4	72	0	.	4	95	0	.	2	98	0	.	0	.	2	54	2	96	0	.	4	95	0	.	0	.	11	85	0	.	3	39	1	69	2	50	2	96	2	94	0	.	2	68	1	97	0	.	0	.	0	.	0	.	2	9
	81-90 cm	0	.	0	.	0	.	3	92	0	.	0	.	0	71	1	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	59	0	.	1	99	1	56	0	70	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	1	96	0	.	0	.	1	98	1	99	0	.	0	.	1	97	0	.	0	.	0	.	0	.	1	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	4	91	2	96	1	97	1	96	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	1	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	18
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	99	0	.	0	99	0	.	0	.	0	.	0	.	0	.	3	92	0	.	1	98	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	2	96	0	.	0	.	0	.	2	95	0	.	0	.	0	.	0	.	0	.	0	.	2	96	0	.	0	.	0	.	2	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	2	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	26
	>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	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	96	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	1	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	41
	total	496	14	190	.	0	.	596	16	625	15	672	14	459	6	495	5	436	10	596	6	235	2	354	32	514	20	0	.	704	29	666	14	502	11	733	11	0	.	521	14	380	10	407	12	384	15	438	8	449	11	519	9	409	13	505	8	619	9	403	21	456	19	458	15	417	21	450	12	583	12	508	18	538	13	521	15	606	13	427	16	513	22	548	11	765	16	427	20	488	15	409	26	496	13	362	15	561	19	495	13	523	19	0	.	370	36	237	19	333	18	387	21	317	23	482	18	552	17	466	17	498	19	393	17	454	16	714	21	696	14	355	19	307	18	636	23	423	26	770	.	486	12	690	.	414	9	358	13	286	13	508	12	467	16	363	17	400	38	355	14	592	14	514	12	453	11	516	9	348	21	494	24	284	9	457	14	430	15	0	.	1100	.	410	.	0	.	484	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	129	21	199	23	193	33	148	26	0	.	0	.	273	31	207	18	207	13	0	.	117	40	230	35	216	35	550	.	149	54	171	51	0	.	146	26	701	24	0	.	282	44	153	40	0	.	480	24	46	76	218	25	172	43	215	21	276	29	104	47	0	.	343	33	450	.	0	.	0	.	0	.	175	71	139	40	382	37	451	31	289	34	0	.	229	22	71	37	241	20	207	29	245	29	334	26	0	.	25	71	221	31	218	19	183	33	350	.	115	28	298	24	431	26	501	14	501	13	240	45	349	39	421	18	800	48	0	.	463	12	264	32	373	43	0	.	208	34	184	73	84	59	321	21	126	54	0	.	444	30	421	28	216	40	428	28	448	46	0	.	202	35	377	25	229	22	249	26	156	22	50	58	224	33	207	47	186	22	154	24	168	27	173	28	0	.	242	4
	21-30 cm	107	21	89	23	80	34	118	15	0	.	0	.	158	34	124	27	121	10	0	.	73	37	140	30	171	23	50	.	137	36	155	30	0	.	157	19	100	14	0	.	146	36	115	40	0	.	164	36	23	76	140	17	144	24	150	16	114	37	87	18	0	.	136	27	250	.	0	.	0	.	0	.	50	71	139	43	216	35	276	6	93	52	0	.	139	26	70	34	219	17	79	27	256	22	47	76	0	.	175	10	61	36	93	24	73	26	100	.	42	60	171	36	77	34	142	17	136	17	51	40	61	17	169	23	309	23	0	.	341	2	99	31	178	40	0	.	132	28	17	91	150	69	228	34	139	76	0	.	143	16	231	19	227	27	129	22	111	51	0	.	224	49	155	28	108	34	45	37	161	43	58	51	103	21	54	78	96	31	90	26	137	32	86	28	0	.	121	4
	31-40 cm	71	16	61	18	49	29	106	15	0	.	0	.	145	17	70	21	75	9	0	.	63	33	58	41	96	24	0	.	150	14	72	27	0	.	106	19	22	72	0	.	113	42	73	31	0	.	32	47	41	76	96	17	92	12	113	15	124	45	106	35	0	.	52	40	90	.	0	.	0	.	0	.	85	71	69	55	109	51	72	2	49	40	0	.	66	20	38	44	121	48	97	26	107	22	70	76	0	.	145	22	46	42	55	25	40	22	0	.	8	94	58	37	29	50	41	32	35	30	13	34	24	54	44	21	32	77	0	.	69	48	60	58	58	40	0	.	94	25	20	91	40	47	109	21	65	35	0	.	36	58	43	46	112	25	74	41	37	41	0	.	110	55	41	33	30	41	15	45	105	26	48	39	98	29	79	68	60	24	93	24	61	21	79	22	0	.	69	4
	41-50 cm	41	22	47	22	19	24	70	16	0	.	0	.	99	29	49	27	45	11	0	.	32	42	19	40	25	49	40	.	54	32	38	41	0	.	59	22	28	76	0	.	50	38	52	43	0	.	14	38	51	14	46	14	46	21	51	15	77	48	56	43	0	.	26	49	0	.	0	.	0	.	0	.	20	71	40	63	41	41	47	71	32	66	0	.	32	21	79	26	28	48	56	34	36	32	28	76	0	.	40	0	42	39	47	20	28	22	0	.	3	94	11	46	4	95	10	38	9	42	16	36	17	64	16	33	37	77	0	.	14	64	26	33	17	51	0	.	59	17	0	.	40	81	24	38	35	71	0	.	29	31	13	58	25	44	9	64	30	50	0	.	78	20	14	78	0	.	12	36	18	79	49	34	52	28	22	78	53	36	42	23	53	20	44	22	0	.	37	4
	51-60 cm	14	29	27	24	11	35	22	25	0	.	0	.	31	36	19	29	18	12	0	.	23	30	9	52	14	47	20	.	27	33	10	43	0	.	18	28	8	90	0	.	28	56	25	41	0	.	9	44	0	.	12	28	24	33	17	32	36	29	7	58	0	.	13	58	0	.	0	.	0	.	0	.	0	.	17	82	5	93	0	.	17	31	0	.	12	32	26	42	0	.	17	39	2	94	0	.	0	.	0	.	17	39	20	30	15	27	0	.	0	.	5	85	2	96	6	54	3	47	0	.	13	39	5	46	19	77	0	.	0	.	20	36	6	85	0	.	11	47	0	.	0	.	17	35	5	87	0	.	9	93	9	55	0	.	6	59	10	50	0	.	13	56	2	94	3	92	15	36	0	.	7	91	11	33	6	84	23	29	12	33	25	37	13	22	0	.	14	5
	61-70 cm	7	37	4	49	4	56	3	60	0	.	0	.	7	64	6	47	5	22	0	.	7	66	4	95	2	96	20	.	7	58	4	63	0	.	6	55	0	.	0	.	5	86	0	.	0	.	0	.	11	65	3	46	3	66	5	60	13	65	0	.	0	.	3	91	0	.	0	.	0	.	0	.	0	.	0	.	15	67	0	.	19	89	0	.	2	69	8	39	0	.	2	95	0	.	0	.	0	.	0	.	3	65	4	56	7	44	20	.	0	.	0	.	0	.	0	.	1	99	4	95	0	.	3	53	0	.	0	.	14	64	0	.	0	.	0	.	22	53	0	.	0	.	6	93	5	87	0	.	0	.	0	.	0	.	0	.	5	87	0	.	0	.	0	.	0	.	5	50	0	.	0	.	5	71	0	.	7	45	4	57	5	50	5	57	0	.	4	9
	71-80 cm	1	98	1	98	1	97	3	57	0	.	0	.	0	.	5	45	2	30	0	.	2	93	0	.	0	.	0	.	0	.	4	63	0	.	3	65	0	.	0	.	0	.	4	90	0	.	0	.	0	.	0	.	2	66	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	96	1	98	3	46	0	.	14	94	0	.	0	.	0	.	2	55	0	.	0	.	4	54	9	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	90	0	.	0	.	0	.	0	.	0	.	0	.	5	87	0	.	0	.	0	.	0	.	2	95	0	.	0	.	0	.	0	.	3	68	1	97	1	96	1	97	0	.	1	16
	81-90 cm	0	.	0	.	1	97	0	.	0	.	0	.	0	.	2	67	1	70	0	.	2	93	2	95	0	.	0	.	0	.	2	95	0	.	3	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	4	94	0	.	0	.	0	.	1	69	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	1	24
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	3	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	96	0	.	0	.	0	.	0	.	0	42
	>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	.	4	94	0	.	0	.	0	.	0	.	5	96	0	.	3	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	49
	total	370	11	429	12	358	19	468	10	0	.	0	.	713	19	482	11	474	6	0	.	320	23	462	21	525	18	680	.	523	24	456	24	0	.	498	13	859	15	0	.	624	21	421	25	0	.	698	21	172	41	515	10	484	19	553	9	640	6	359	19	0	.	575	16	790	.	0	.	0	.	0	.	330	71	404	40	768	24	847	15	499	27	0	.	481	16	293	16	609	7	458	12	646	18	478	4	0	.	385	1	392	16	439	10	351	19	470	.	188	23	543	26	543	23	700	12	690	10	330	35	464	28	668	13	1206	32	0	.	901	8	469	33	632	30	0	.	527	21	221	59	314	34	713	18	376	48	0	.	661	19	718	18	580	20	645	14	648	33	0	.	627	26	590	21	370	23	343	20	440	14	213	26	493	13	369	8	430	15	397	15	451	12	401	15	0	.	488	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	140	18	198	22	193	33	166	20	192	36	228	23	182	9	204	9	193	11	282	10	100	40	212	30	234	24	550	.	309	35	287	17	242	18	231	22	701	24	192	21	133	16	175	18	151	17	203	11	174	10	237	13	179	22	233	14	303	13	147	34	264	30	229	30	240	27	180	22	323	24	257	23	235	21	199	33	278	21	200	23	242	28	198	17	281	17	118	26	214	22	212	25	187	23	170	22	237	34	200	29	250	24	218	19	177	29	111	28	144	14	135	21	308	21	398	14	465	12	253	19	300	22	335	15	253	30	315	39	323	17	193	23	137	32	303	33	168	25	165	71	216	20	312	19	127	23	169	19	227	28	279	22	186	23	317	28	284	38	69	61	199	26	213	12	215	12	222	12	181	34	148	37	162	36	169	25	168	19	154	24	187	24	172	26	0	.	215	2
	21-30 cm	112	18	87	23	80	34	121	13	201	17	243	22	121	8	117	10	117	9	159	11	68	34	130	26	140	19	50	.	139	23	165	21	123	17	175	14	100	14	141	22	107	13	121	17	90	19	114	11	109	11	129	11	117	17	108	15	148	16	83	20	97	24	157	17	113	23	108	18	192	20	116	22	149	17	153	16	180	21	127	14	91	28	185	14	157	18	92	31	170	16	65	26	213	15	84	18	159	23	112	18	86	21	93	24	75	23	29	38	72	26	106	23	64	21	138	14	140	14	93	18	102	22	133	20	146	22	133	27	206	16	71	27	97	24	127	26	115	35	50	67	118	17	231	29	147	27	95	20	112	13	150	16	157	21	124	26	94	33	140	16	185	36	102	11	117	13	125	11	57	37	118	33	99	18	121	21	121	18	90	26	149	28	87	26	0	.	121	2
	31-40 cm	75	15	59	18	49	29	112	13	134	15	112	15	85	8	78	10	79	8	95	10	66	28	58	35	86	18	0	.	96	24	85	21	72	18	135	17	22	72	81	15	69	13	49	14	64	16	63	12	80	9	93	10	73	13	116	12	82	15	87	29	49	24	64	26	45	26	85	19	76	18	75	26	94	16	87	22	103	26	50	17	81	31	86	17	76	17	68	23	86	21	79	23	104	12	50	19	85	17	100	20	59	27	55	25	44	19	23	48	34	32	85	18	23	35	43	21	38	24	20	21	30	30	47	18	65	28	97	43	79	25	46	32	67	25	69	31	85	17	78	72	74	16	107	19	58	21	49	19	37	28	92	18	110	15	61	34	61	30	89	18	133	45	52	11	61	13	61	13	70	27	44	21	101	21	91	15	76	14	93	24	78	26	78	21	0	.	73	2
	41-50 cm	41	20	46	23	19	24	63	15	73	18	64	19	54	8	55	8	49	9	34	11	30	38	18	36	45	21	40	.	36	33	41	19	38	17	54	15	28	76	46	14	36	12	38	14	49	13	38	10	31	9	41	10	39	16	49	13	50	13	52	27	35	21	40	24	19	31	46	12	36	27	35	29	50	21	38	22	39	21	38	21	56	19	54	14	30	19	62	18	38	25	68	23	48	15	29	21	59	17	34	18	42	25	47	20	29	19	33	26	29	20	31	18	7	44	11	28	10	33	18	24	17	34	18	22	33	25	48	30	34	21	30	32	29	21	35	21	49	24	6	93	34	21	34	36	40	32	30	19	28	17	28	27	50	21	26	60	24	32	36	42	73	13	26	13	29	16	38	11	39	28	48	20	58	20	49	22	42	26	42	23	52	19	47	20	0	.	40	2
	51-60 cm	14	28	26	24	11	35	21	23	18	34	24	24	23	10	22	11	19	10	13	19	26	30	9	40	13	33	20	.	19	35	13	35	16	25	13	24	8	90	17	25	21	15	23	16	20	19	16	12	14	14	13	15	29	22	20	18	17	24	10	34	9	41	18	32	16	34	23	28	11	33	23	27	24	25	20	27	14	44	19	24	23	29	26	22	12	26	23	29	6	42	14	32	4	78	19	30	18	28	26	30	12	35	20	30	16	26	18	39	14	28	9	35	6	46	4	49	4	47	9	28	7	34	9	40	17	35	18	32	17	36	22	24	15	44	16	31	22	25	0	.	20	32	15	38	23	40	13	28	17	31	8	44	19	40	11	43	10	61	5	85	22	29	12	16	14	17	17	16	12	49	13	36	11	25	14	24	15	27	12	33	24	38	12	23	0	.	16	3
	61-70 cm	6	38	5	43	4	56	3	52	4	72	1	99	5	18	7	16	5	20	2	35	10	51	4	70	2	67	20	.	4	64	3	47	9	27	4	50	0	.	9	45	6	23	4	36	7	24	7	20	4	26	4	26	5	46	3	47	5	31	4	51	1	98	2	96	4	51	4	45	2	67	11	41	7	48	7	41	12	39	4	32	13	38	7	43	2	58	8	47	2	68	1	97	0	.	8	41	1	98	6	41	4	47	4	56	8	36	3	53	5	60	5	49	2	54	2	68	1	69	4	40	3	58	3	41	3	53	12	75	5	55	4	46	1	98	1	96	12	39	0	.	4	41	5	94	6	49	1	99	6	54	1	97	2	67	0	.	12	49	5	85	0	.	6	27	1	57	6	23	3	65	9	45	4	73	2	55	5	38	4	57	4	51	5	58	0	.	5	5
	71-80 cm	1	99	1	98	1	97	2	58	3	66	0	.	1	34	4	22	2	28	1	61	2	95	0	.	0	.	0	.	0	.	3	47	1	69	1	68	0	.	4	45	2	42	3	45	2	39	3	27	2	30	0	70	1	69	1	99	1	69	3	66	2	68	2	96	6	54	2	68	0	.	0	.	1	69	0	.	5	72	2	56	4	46	6	56	1	99	0	.	0	.	0	.	0	.	2	98	1	98	4	45	1	98	1	98	3	46	2	66	5	71	3	72	0	.	1	98	2	55	1	69	1	98	3	48	2	67	2	94	0	.	2	68	3	73	0	.	3	96	0	.	2	98	3	91	0	.	2	54	1	98	0	.	2	97	0	.	2	96	11	85	0	.	4	25	1	43	1	45	2	96	1	97	0	.	1	68	2	57	1	97	1	97	1	97	0	.	2	7
	81-90 cm	0	.	0	.	1	97	1	99	0	.	0	.	0	71	1	37	1	70	0	.	2	95	1	97	0	.	0	.	0	.	1	98	0	.	2	68	0	.	3	50	0	99	0	99	2	43	0	57	0	70	0	.	1	69	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	1	97	0	.	1	99	1	98	1	99	1	99	0	.	1	98	0	.	0	.	0	.	0	.	1	96	0	.	1	98	0	.	0	.	1	70	0	.	0	.	0	.	1	69	1	99	0	.	1	99	0	.	3	94	1	97	1	98	1	98	1	96	0	.	0	.	1	98	3	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	70	1	70	0	70	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	13
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	1	99	0	.	0	.	1	70	0	100	0	.	0	99	0	.	0	.	0	.	0	.	0	.	2	96	0	.	1	98	0	.	0	.	0	.	0	.	0	.	1	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	1	98	1	97	0	.	0	.	0	.	1	98	1	99	0	.	0	.	2	71	0	.	0	.	1	97	0	.	0	.	0	.	1	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	99	0	.	0	.	1	97	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	20
	>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	70	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	1	99	0	.	0	.	0	.	0	.	2	72	0	.	2	55	0	.	0	.	0	.	0	.	1	97	1	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	26
	total	387	9	421	12	358	19	489	9	625	15	672	14	473	5	489	5	465	5	588	6	304	20	433	18	520	13	680	.	602	19	597	13	502	11	615	9	859	15	493	14	374	8	413	11	385	11	445	7	415	6	518	7	443	11	530	6	605	9	385	15	456	19	514	11	443	20	450	12	641	14	517	17	560	13	505	14	630	13	442	14	510	18	563	10	560	12	370	15	516	11	439	12	557	11	361	13	561	19	484	12	454	13	439	10	353	17	221	17	306	12	374	15	410	17	597	11	661	9	399	15	459	16	552	11	519	19	629	21	667	12	370	16	350	18	555	24	455	18	299	45	468	11	710	16	400	17	358	13	429	16	559	11	525	12	539	14	487	27	355	14	612	16	417	8	440	8	470	8	363	18	382	22	435	12	447	13	430	11	397	15	496	14	401	14	0	.	473	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. 2299013. Birmensdorf, Swiss Federal Research Institute WSL

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