Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
total number of stems
altitudinal vegetation belts (NaiS, 3 classes) · diameter at breast height (10 classes)
regional demarcation: forest district (2024)
unit: n/ha
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
altitudinal vegetation belts (NaiS, 3 classes)	diameter at breast height (10 classes)	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	.	168	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	756	.	0	.	0	.	179	54	161	23	54	25	153	15	172	19	202	12	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	443	73	247	42	444	36	0	.	0	.	144	37	0	.	550	.	0	.	0	.	0	.	0	.	0	.	137	66	0	.	0	.	0	.	0	.	0	.	84	47	204	32	174	27	160	78	0	.	0	.	166	20	161	44	147	1	0	.	50	0	182	51	150	71	74	57	50	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	200	.	0	.	0	.	0	.	0	.	148	14	170	19	146	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	170	5
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	116	14	53	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	378	.	0	.	0	.	72	61	65	23	91	38	104	20	97	20	108	12	0	.	0	.	0	.	50	.	0	.	0	.	0	.	0	.	0	.	263	56	151	80	266	28	0	.	0	.	56	51	0	.	350	.	0	.	0	.	0	.	0	.	0	.	13	87	0	.	0	.	0	.	0	.	0	.	0	.	127	28	129	27	122	53	0	.	0	.	100	29	38	28	0	.	100	.	150	0	60	51	175	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	.	0	.	0	.	0	.	0	.	88	17	101	22	96	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	98	6
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	92	14	95	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	.	0	.	0	.	37	21	59	19	82	33	75	21	58	17	57	11	0	.	0	.	0	.	50	.	0	.	0	.	0	.	0	.	0	.	99	51	98	48	103	49	0	.	0	.	70	46	0	.	90	.	0	.	0	.	0	.	0	.	0	.	28	47	0	.	0	.	0	.	0	.	0	.	44	62	75	25	42	23	5	88	0	.	0	.	21	38	23	39	0	.	0	.	136	29	17	65	105	10	40	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	200	.	0	.	0	.	0	.	0	.	45	18	69	17	81	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	5
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	60	13	61	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	40	39	16	40	22	53	23	30	17	35	11	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	43	39	47	38	48	0	.	40	.	36	37	0	.	20	.	0	.	0	.	0	.	0	.	0	.	30	87	0	.	0	.	0	.	0	.	0	.	22	47	38	30	26	43	0	.	0	.	0	.	14	42	16	53	17	67	0	.	61	46	9	64	0	.	34	34	40	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	.	0	.	0	.	0	.	0	.	37	18	32	17	38	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	5
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	21	18	28	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	37	22	21	20	39	31	18	17	19	19	13	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	65	5	89	6	82	0	.	20	.	10	49	0	.	20	.	0	.	0	.	0	.	0	.	0	.	15	55	0	.	0	.	0	.	0	.	0	.	39	29	29	28	13	47	0	.	0	.	0	.	16	43	5	88	0	.	40	.	60	0	3	93	10	71	18	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	.	0	.	0	.	0	.	0	.	17	17	22	24	35	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	6
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	8	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	56	8	33	7	40	12	32	5	32	6	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	89	5	88	6	82	0	.	0	.	7	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	55	0	.	0	.	0	.	0	.	0	.	6	58	6	54	7	39	0	.	0	.	0	.	4	72	10	47	0	.	20	.	20	0	0	.	20	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	9	28	6	29	8	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	10
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	4	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	89	9	34	2	96	2	54	2	48	2	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	84	0	.	0	.	0	.	20	.	3	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	87	0	.	0	.	0	.	0	.	0	.	15	39	1	97	2	95	0	.	0	.	0	.	4	52	5	85	0	.	20	.	0	.	0	.	0	.	12	37	10	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	24	1	69	4	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	12
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	54	1	98	2	95	5	42	2	45	1	46	0	.	0	.	0	.	0	.	0	.	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	97	0	.	7	84	0	.	0	.	2	67	0	.	0	.	0	.	0	.	0	.	20	71	0	.	10	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	37	1	69	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	17
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	89	1	98	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	56	1	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	33
	>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	68	1	69	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	38
	total	0	.	0	.	0	.	0	.	0	.	0	.	469	10	237	12	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1302	.	0	.	0	.	386	37	364	13	297	23	438	10	383	13	431	7	0	.	0	.	0	.	100	.	0	.	0	.	0	.	0	.	0	.	900	54	545	48	864	27	0	.	80	.	328	34	0	.	1030	.	0	.	0	.	0	.	0	.	0	.	247	30	0	.	0	.	0	.	0	.	0	.	210	25	481	15	394	18	294	60	0	.	0	.	326	19	259	28	164	6	180	.	477	14	269	48	480	34	178	28	110	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	770	.	0	.	0	.	0	.	0	.	354	10	403	14	408	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	400	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	110	61	0	.	0	.	136	46	116	30	169	22	199	9	155	9	241	21	219	10	0	.	137	27	274	12	0	.	265	24	223	19	188	15	137	31	0	.	209	15	154	15	179	15	288	15	215	10	204	18	204	12	304	22	181	18	289	12	241	36	206	23	125	20	181	19	161	18	197	16	272	21	176	21	163	29	166	29	239	19	222	30	114	17	129	32	269	23	183	31	204	42	114	33	242	17	203	20	181	29	165	20	0	.	156	36	364	31	192	30	126	28	228	22	214	25	286	16	317	16	283	25	201	21	308	22	451	33	285	22	253	23	167	24	387	21	236	33	100	.	213	17	200	.	115	54	167	15	211	38	212	26	127	22	326	45	203	37	40	85	101	44	226	13	276	14	279	11	141	26	231	46	113	24	157	25	116	38	0	.	100	.	100	.	0	.	211	2
	21-30 cm	110	41	100	.	0	.	118	29	105	26	151	16	114	10	97	12	100	18	149	11	24	74	100	18	168	26	0	.	170	45	154	28	142	16	119	33	0	.	137	18	104	19	121	18	110	18	124	11	114	15	94	13	102	19	87	19	159	13	115	48	90	15	83	50	64	30	93	24	190	18	156	28	128	20	67	25	152	24	131	19	120	35	96	16	120	32	112	25	97	28	109	49	110	22	109	22	140	27	123	30	137	26	0	.	183	60	87	30	130	19	80	26	110	29	198	22	137	21	182	21	158	20	135	24	170	23	113	25	163	18	69	22	51	28	164	40	117	47	0	.	126	20	150	.	42	60	93	19	125	40	123	30	66	44	242	38	101	54	99	74	81	83	130	14	158	16	162	13	126	21	112	35	21	53	114	28	99	27	0	.	100	.	50	.	0	.	122	3
	31-40 cm	58	19	0	.	0	.	35	51	106	15	119	15	78	9	60	11	55	24	91	11	43	74	60	42	69	25	0	.	124	35	94	23	69	22	91	16	0	.	51	20	68	14	69	14	38	18	74	11	90	15	77	13	73	21	71	30	82	14	62	36	72	29	68	35	62	31	58	16	119	20	62	27	65	19	60	23	128	23	70	15	97	22	75	16	96	19	98	31	108	25	22	45	64	24	66	20	71	18	68	24	123	30	0	.	67	35	35	43	50	30	71	43	25	34	65	23	71	32	51	22	41	25	67	27	72	25	92	37	87	27	63	20	53	30	73	28	82	38	0	.	88	16	250	.	34	49	64	16	57	16	71	22	51	43	72	50	42	53	157	29	36	59	72	20	57	15	79	12	66	28	77	17	27	46	67	19	79	19	0	.	120	.	70	.	0	.	72	2
	41-50 cm	44	32	20	.	0	.	55	16	92	12	70	14	48	9	40	9	42	17	56	9	68	52	30	87	55	30	0	.	64	38	55	19	40	23	64	18	0	.	54	15	55	15	38	12	23	20	39	11	49	14	54	11	34	20	52	19	61	11	40	26	40	21	47	25	31	28	53	11	40	24	29	30	50	16	55	21	65	26	34	18	45	32	58	15	44	35	41	22	41	21	29	46	63	13	51	18	37	20	44	22	40	24	0	.	34	24	10	44	33	33	73	23	11	37	18	24	14	40	24	30	23	24	29	22	36	38	50	47	56	29	48	32	43	29	44	28	26	43	60	.	44	18	80	.	26	35	42	13	22	25	56	17	52	20	41	54	44	40	61	46	34	48	28	18	35	19	40	13	41	34	46	29	42	43	38	17	59	19	0	.	140	.	40	.	0	.	44	2
	51-60 cm	44	50	0	.	0	.	40	29	48	17	42	23	33	10	31	10	27	18	26	14	10	74	5	87	35	27	0	.	17	40	24	21	29	22	40	22	0	.	27	20	22	22	28	15	20	23	26	13	18	22	22	13	18	24	34	21	31	16	15	45	23	29	19	58	17	32	28	17	17	39	15	42	27	24	29	27	15	31	19	23	45	22	39	14	16	53	29	31	8	52	36	46	21	25	22	23	31	23	31	23	29	38	0	.	15	53	11	47	30	33	23	29	7	37	6	62	5	62	10	24	13	33	15	31	17	38	15	54	25	24	28	34	22	32	28	29	21	39	20	.	19	20	80	.	17	28	14	21	36	20	24	36	37	36	25	60	14	47	45	10	34	48	15	21	20	20	25	15	23	26	19	31	19	58	27	22	22	32	0	.	0	.	80	.	0	.	25	3
	61-70 cm	8	89	0	.	0	.	10	41	18	25	17	31	11	16	12	16	10	28	5	26	0	.	5	86	13	46	0	.	8	54	11	36	13	28	13	35	0	.	9	30	8	28	13	21	8	25	10	16	4	46	7	26	16	36	20	27	12	22	13	39	6	42	13	58	4	70	16	20	7	41	6	56	10	31	9	31	10	39	9	32	14	46	21	27	2	95	6	55	4	52	6	59	9	62	16	30	6	41	10	36	4	64	0	.	14	57	8	77	7	45	16	35	4	63	4	52	7	95	6	35	1	98	11	51	8	41	10	54	7	45	11	36	13	34	20	38	9	38	0	.	14	39	20	.	12	69	5	30	12	47	3	66	9	43	0	.	21	36	0	.	6	83	4	60	7	31	7	25	6	73	26	45	4	90	11	25	5	56	0	.	0	.	20	.	0	.	10	4
	71-80 cm	0	.	0	.	0	.	0	.	4	69	2	71	3	26	6	19	7	31	2	39	0	.	5	86	5	95	0	.	0	.	3	58	7	44	5	56	0	.	6	41	4	33	4	33	4	35	4	27	3	44	4	33	6	52	6	42	4	40	7	65	2	68	0	.	6	53	2	68	0	.	5	60	6	38	8	44	3	67	5	41	15	37	9	29	0	.	3	96	0	.	3	91	0	.	7	40	1	99	4	76	2	96	0	.	5	88	6	54	7	54	9	46	1	98	1	97	0	.	2	55	2	68	3	53	5	44	4	95	6	74	3	53	5	43	1	98	2	94	0	.	2	71	0	.	3	93	1	68	4	95	1	97	5	50	0	.	0	.	11	85	28	59	4	40	1	56	4	33	4	66	4	63	4	90	1	68	2	66	0	.	0	.	0	.	0	.	4	6
	81-90 cm	0	.	0	.	0	.	3	91	0	.	0	.	2	40	2	32	3	41	1	75	0	.	0	.	0	.	0	.	0	.	1	98	1	70	0	.	0	.	2	48	1	69	2	49	1	56	2	28	1	73	1	70	0	.	0	.	1	99	3	93	1	98	0	.	0	.	0	.	0	.	0	.	0	.	2	97	0	.	1	98	3	68	4	79	0	.	0	.	1	97	0	.	0	.	1	98	1	98	2	67	2	96	0	.	0	.	0	.	4	66	3	65	2	67	1	96	0	.	0	.	0	.	1	97	0	.	0	.	1	96	3	67	5	56	0	.	4	63	0	.	0	.	0	.	0	.	1	98	0	.	0	.	2	95	0	.	0	.	5	85	0	.	2	77	2	58	1	56	0	.	5	62	0	.	1	98	0	.	0	.	0	.	0	.	0	.	1	11
	91-100 cm	0	.	0	.	0	.	3	92	0	.	0	.	0	100	0	.	1	98	1	70	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	2	55	0	.	0	.	0	99	0	70	0	.	0	99	1	98	0	.	0	.	0	.	1	98	3	92	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	1	98	0	99	0	.	0	.	0	.	0	.	0	.	1	98	0	.	1	98	0	.	0	.	0	.	2	96	0	.	0	.	0	.	1	96	0	.	1	98	0	.	0	.	0	.	3	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	93	0	.	0	.	0	99	1	69	0	.	0	.	0	.	4	90	0	.	0	.	0	.	0	.	0	.	0	.	0	18
	>100 cm	0	.	20	.	0	.	0	.	0	.	0	.	0	.	0	100	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	1	57	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	3	96	2	97	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	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	26
	total	374	23	140	.	0	.	400	24	490	14	569	10	487	7	404	6	486	11	548	7	144	64	342	11	619	9	0	.	647	21	565	18	489	11	468	16	0	.	497	10	416	9	456	10	492	12	495	7	484	10	462	8	554	14	451	9	640	7	495	18	440	12	360	16	366	17	411	11	569	11	543	17	464	14	391	14	541	20	507	13	563	18	418	9	408	13	558	13	442	17	409	31	381	15	516	12	490	14	465	17	502	17	0	.	475	34	525	23	456	20	400	17	388	19	508	18	519	15	593	13	521	18	461	14	617	17	737	19	630	14	479	14	360	11	716	18	497	25	180	.	506	13	780	.	249	35	387	11	466	24	490	18	351	19	708	39	429	33	417	32	320	43	482	10	557	11	597	8	406	16	518	24	234	19	416	16	382	18	0	.	460	.	360	.	0	.	489	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	152	23	148	20	215	28	100	20	0	.	0	.	184	22	163	17	156	13	0	.	174	22	197	33	139	20	0	.	139	32	189	30	0	.	191	43	113	30	0	.	195	58	224	14	0	.	451	37	131	40	143	17	117	30	225	22	320	25	314	24	0	.	238	34	150	.	0	.	0	.	0	.	725	51	216	56	441	39	500	64	321	19	0	.	206	22	156	36	141	53	83	33	210	24	477	59	0	.	0	.	274	27	208	28	159	27	285	41	325	30	211	22	565	19	300	21	496	13	344	27	205	30	474	19	289	16	0	.	205	17	321	18	456	33	0	.	271	43	260	39	83	43	166	47	448	51	0	.	204	43	147	35	176	27	254	44	38	55	0	.	280	72	447	32	238	26	274	26	156	22	132	25	174	25	184	7	112	27	123	27	132	50	171	28	0	.	218	4
	21-30 cm	53	21	96	32	83	22	62	37	0	.	0	.	95	31	92	22	88	12	100	.	50	35	126	29	123	29	50	.	91	30	143	23	0	.	96	23	123	33	0	.	79	53	94	25	0	.	265	27	100	0	89	17	80	23	83	18	167	52	34	46	0	.	67	42	150	.	0	.	0	.	0	.	100	36	254	23	200	22	200	18	220	50	0	.	143	23	40	60	90	36	71	35	169	31	70	76	0	.	50	71	135	22	86	33	109	26	124	71	100	29	135	32	138	34	184	18	218	14	165	24	135	33	207	16	425	32	250	.	195	18	244	27	157	33	0	.	206	27	157	40	83	43	92	40	62	17	0	.	220	29	102	30	84	49	157	24	150	20	0	.	52	53	162	29	100	31	98	32	100	0	88	27	82	30	239	50	51	23	111	28	70	35	114	25	0	.	113	4
	31-40 cm	43	26	38	26	60	23	40	24	0	.	0	.	82	31	69	24	40	11	0	.	28	36	72	33	94	22	40	.	72	43	98	31	0	.	76	21	74	44	0	.	20	36	31	62	0	.	133	23	36	29	72	23	62	23	71	31	74	3	32	44	0	.	44	54	20	.	0	.	0	.	0	.	75	71	56	63	89	37	115	3	56	25	0	.	71	17	39	21	74	31	67	21	104	24	59	12	0	.	150	19	65	36	58	22	52	28	25	71	17	43	74	45	50	28	83	28	67	16	37	43	67	24	75	33	162	23	72	.	94	45	90	29	75	66	0	.	54	34	59	45	70	41	11	34	60	34	0	.	56	24	66	22	76	38	63	16	47	42	0	.	53	34	54	31	48	38	21	41	56	63	31	60	63	30	70	59	73	26	61	24	71	31	41	29	0	.	58	4
	41-50 cm	32	20	16	29	27	28	42	16	0	.	0	.	108	31	27	25	36	11	0	.	14	40	48	39	29	26	40	.	61	33	43	40	0	.	30	24	36	45	0	.	39	56	22	32	0	.	34	29	48	44	56	14	59	27	39	20	44	6	40	36	0	.	44	33	40	.	0	.	0	.	0	.	0	.	15	41	35	44	70	10	32	40	0	.	50	18	18	43	72	39	43	27	43	31	28	76	0	.	60	24	24	51	30	28	27	24	20	71	17	41	34	62	8	51	30	20	27	32	13	30	19	31	26	26	45	43	152	.	42	34	43	52	51	40	0	.	69	45	38	31	13	81	29	50	20	50	0	.	17	29	38	42	49	24	54	29	50	46	0	.	40	46	20	43	14	53	14	36	91	8	29	45	45	21	13	84	35	30	27	23	42	29	31	23	0	.	34	4
	51-60 cm	13	27	15	22	14	34	30	21	0	.	0	.	38	37	24	24	22	12	0	.	23	35	11	49	18	30	60	.	22	48	28	29	0	.	24	22	8	93	0	.	15	57	13	36	0	.	11	48	18	76	29	20	37	45	22	27	9	76	23	43	0	.	24	62	0	.	0	.	0	.	0	.	0	.	42	82	23	46	30	71	32	18	0	.	15	26	37	34	20	57	15	64	12	42	28	76	0	.	20	0	9	33	25	28	21	21	0	.	2	95	0	.	6	52	11	38	8	51	12	42	6	96	14	32	34	77	22	.	0	.	5	85	46	66	0	.	16	61	3	91	33	43	8	66	5	86	0	.	6	60	49	42	33	43	17	43	10	51	0	.	36	37	16	65	0	.	2	97	9	79	31	38	22	26	0	.	27	32	28	31	22	32	23	28	0	.	19	5
	61-70 cm	7	32	5	35	6	38	16	21	0	.	0	.	14	64	12	33	10	17	0	.	16	40	7	73	7	53	0	.	6	92	6	49	0	.	5	43	3	93	0	.	0	.	0	.	0	.	6	60	11	65	9	29	11	35	12	29	25	65	10	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	55	0	.	12	33	16	35	0	.	8	44	2	95	9	76	0	.	10	71	3	65	6	41	6	59	10	71	2	95	0	.	2	96	4	52	4	41	2	95	2	96	3	53	11	77	0	.	0	.	4	90	4	90	0	.	6	82	0	.	7	81	3	91	0	.	0	.	0	.	0	.	5	92	6	59	10	87	0	.	25	68	0	.	0	.	7	78	27	79	17	37	3	96	0	.	3	67	5	48	4	70	8	50	0	.	7	7
	71-80 cm	3	47	2	55	5	55	6	36	0	.	0	.	0	.	4	57	3	30	0	.	3	95	2	95	5	52	20	.	0	.	4	63	0	.	2	97	0	.	0	.	4	90	0	.	0	.	0	.	0	.	2	68	0	.	1	98	0	.	0	.	0	.	7	58	0	.	0	.	0	.	0	.	0	.	8	82	8	59	0	.	16	59	0	.	3	55	8	39	0	.	0	.	0	.	0	.	0	.	0	.	2	96	5	59	2	72	0	.	2	96	9	88	0	.	3	66	1	99	4	95	3	96	3	52	11	77	0	.	11	64	5	85	4	90	0	.	5	89	0	.	0	.	3	91	0	.	0	.	0	.	4	91	0	.	0	.	5	87	0	.	0	.	0	.	0	.	2	96	0	.	0	.	0	.	0	.	0	.	2	67	4	51	1	97	0	.	3	11
	81-90 cm	1	98	1	98	2	67	2	73	0	.	0	.	0	.	0	.	1	49	0	.	2	95	2	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	90	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	89	0	.	0	.	2	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	98	4	56	0	.	4	95	4	88	0	.	0	.	1	99	2	96	0	.	3	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	88	0	.	0	.	2	96	0	.	0	.	1	96	0	.	2	97	2	67	3	66	0	.	0	.	1	17
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	1	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	98	0	.	0	.	4	95	0	.	0	.	0	.	1	99	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	94	0	.	0	.	0	.	0	.	0	.	0	.	5	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	37
	>100 cm	0	.	0	.	0	.	1	98	0	.	0	.	0	.	1	98	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	.	4	98	0	.	2	96	0	.	0	.	0	.	1	99	5	97	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	40
	total	303	14	321	17	413	15	299	11	0	.	0	.	520	22	393	12	356	7	100	.	310	17	465	19	415	16	210	.	392	21	510	19	0	.	425	20	356	25	0	.	352	31	389	14	0	.	900	19	344	10	401	9	366	13	452	15	640	1	453	17	0	.	422	15	360	.	0	.	0	.	0	.	900	32	592	21	795	24	915	35	699	16	0	.	500	11	316	17	396	26	287	17	539	20	672	26	0	.	290	29	512	17	421	15	385	15	463	52	475	25	467	22	769	17	614	12	823	9	583	19	437	22	809	13	978	16	496	.	547	4	713	15	794	25	0	.	627	25	518	24	290	24	322	33	596	33	0	.	503	28	405	18	423	15	551	22	314	23	0	.	491	28	700	21	400	26	418	21	438	11	328	20	390	15	506	30	304	9	360	16	349	18	389	16	0	.	453	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	146	21	144	20	215	28	106	19	116	30	169	22	190	8	154	8	177	11	216	10	141	26	181	27	200	13	0	.	194	22	212	16	201	15	164	28	113	30	205	15	159	13	158	13	241	13	213	9	202	10	185	10	224	20	203	15	284	12	272	22	206	23	180	24	178	18	161	18	234	26	267	18	240	20	170	26	238	28	232	17	243	22	127	18	185	19	220	20	173	27	129	30	158	21	242	17	203	20	164	30	223	20	208	28	159	24	269	28	232	18	157	16	332	16	255	16	449	12	280	12	243	19	333	16	290	20	345	36	246	20	256	18	193	22	349	22	247	27	237	38	200	16	170	40	230	44	167	15	208	29	195	22	149	17	285	31	145	38	40	85	203	60	201	10	230	10	246	10	143	23	188	34	157	21	160	22	114	24	123	27	130	48	167	28	0	.	207	2
	21-30 cm	61	20	96	31	83	22	71	28	105	26	151	16	114	8	95	10	91	10	149	11	45	33	119	23	143	19	50	.	125	31	150	21	148	15	107	21	123	33	130	17	86	14	112	15	108	13	123	9	110	9	92	11	92	15	85	13	157	13	81	43	90	15	75	34	70	27	93	24	201	19	155	27	144	17	92	23	159	18	120	17	142	29	104	16	137	19	81	25	96	23	85	30	137	21	94	22	140	27	116	29	136	17	86	33	120	25	63	31	121	15	106	17	120	21	191	15	200	12	155	15	137	18	164	14	193	21	131	18	134	17	109	20	57	27	145	40	145	30	134	43	121	19	99	34	49	35	93	19	159	26	117	24	77	30	194	24	118	32	99	74	65	52	110	11	130	12	137	11	122	19	101	24	65	30	127	25	76	21	111	28	72	32	110	25	0	.	116	2
	31-40 cm	45	22	36	26	60	23	39	22	106	15	119	15	82	8	62	10	44	11	89	11	31	33	69	27	83	17	40	.	95	28	95	18	71	21	83	13	74	44	50	19	62	11	68	13	51	14	72	9	67	9	75	11	68	16	71	21	81	13	49	30	72	29	56	31	59	31	58	16	116	19	69	24	71	18	60	21	112	20	71	14	88	20	76	16	78	13	73	26	100	21	50	22	82	18	61	18	71	18	75	21	93	24	58	22	54	24	37	34	51	19	61	27	31	23	73	19	68	14	40	18	48	17	68	22	78	22	98	27	66	25	72	15	54	25	65	29	73	30	50	48	86	16	39	68	43	31	64	16	57	14	70	17	68	27	67	25	44	37	157	29	46	31	57	13	61	11	71	11	64	26	57	22	53	27	68	18	76	16	61	24	74	28	43	27	0	.	67	2
	41-50 cm	34	18	16	28	27	28	44	13	92	12	70	14	53	8	38	9	38	9	55	9	24	42	43	36	41	22	40	.	62	25	51	17	39	23	47	16	36	45	52	14	47	11	37	10	34	16	36	9	39	9	54	9	45	18	45	14	58	11	40	21	40	21	46	20	32	25	53	11	42	21	31	26	46	16	49	21	56	23	36	15	42	28	57	15	49	16	31	21	48	20	38	24	54	15	47	18	37	20	45	19	31	25	30	28	28	20	14	31	31	20	50	21	9	30	24	16	24	28	19	22	21	18	27	17	35	34	61	33	40	28	43	29	43	21	43	25	39	34	41	26	41	18	35	40	24	29	42	13	20	20	51	16	55	16	49	27	46	30	61	46	38	34	32	13	32	13	36	11	48	27	39	25	44	19	35	17	47	17	27	23	49	27	32	22	0	.	40	2
	51-60 cm	17	27	15	22	14	34	32	18	48	17	42	23	30	9	29	9	23	10	25	14	21	34	9	44	26	22	60	.	20	34	26	17	28	22	32	16	8	93	28	18	22	15	25	14	24	15	22	11	19	11	24	11	26	29	28	17	30	17	18	32	23	29	21	43	16	32	28	17	19	34	13	40	23	24	31	27	17	25	18	21	43	19	38	14	16	24	33	23	10	40	23	39	17	23	21	20	31	23	30	21	19	31	25	28	20	19	19	30	22	22	16	26	6	31	8	34	7	44	12	21	10	32	14	22	20	31	23	33	16	27	23	32	25	27	25	30	20	33	6	60	21	19	16	56	13	30	14	21	25	23	31	28	37	26	21	38	13	37	45	10	35	29	16	13	19	16	24	14	21	26	24	26	21	24	24	23	24	23	28	31	21	32	26	26	0	.	23	2
	61-70 cm	7	31	5	35	6	38	15	20	18	25	17	31	10	14	12	14	10	15	5	26	13	43	7	61	10	35	0	.	7	54	10	31	12	28	9	29	3	93	9	28	8	22	11	19	9	20	8	14	6	20	8	20	14	27	16	21	13	21	12	41	6	42	7	64	4	70	16	20	6	37	5	49	9	30	7	32	7	42	8	29	15	37	21	27	9	32	10	31	3	53	7	36	6	56	16	26	6	41	10	33	3	46	6	41	7	45	8	51	5	34	10	30	3	55	4	37	4	42	4	31	2	47	6	42	9	33	11	39	4	48	10	32	9	35	17	39	8	35	0	.	13	38	5	62	8	72	5	30	8	50	2	67	8	36	3	65	17	35	0	.	17	62	6	26	6	22	8	23	9	56	22	33	3	71	10	26	4	43	5	48	4	70	9	44	0	.	9	3
	71-80 cm	2	48	2	55	5	55	5	36	4	69	2	71	3	22	6	18	4	22	2	39	2	96	3	66	5	52	20	.	0	.	4	44	7	44	3	50	0	.	6	38	6	24	3	31	3	29	3	24	2	29	3	30	3	54	3	40	4	40	4	68	2	68	3	65	6	54	2	68	1	98	4	61	5	39	8	40	5	40	4	38	15	32	9	29	2	56	5	43	0	.	1	97	0	.	7	37	1	99	4	76	2	68	5	59	3	58	9	35	3	45	6	39	1	99	2	55	1	99	3	38	3	50	3	38	6	35	3	96	5	63	4	46	6	29	2	68	3	65	0	.	2	71	3	92	2	95	1	68	2	97	2	67	3	53	0	.	2	96	11	85	12	72	5	21	1	44	4	26	3	67	2	66	1	97	1	69	1	69	2	67	4	52	1	97	0	.	4	5
	81-90 cm	1	99	1	99	2	67	2	59	0	.	0	.	1	40	2	32	1	32	1	75	2	96	1	97	0	.	0	.	0	.	1	99	1	70	0	.	0	.	3	38	1	57	2	40	2	35	2	24	1	39	1	57	0	.	0	.	1	99	2	96	1	98	0	.	0	.	0	.	0	.	0	.	0	.	2	98	0	.	0	99	3	55	4	79	0	.	1	98	1	98	0	.	0	.	1	98	1	98	2	68	1	98	2	98	3	57	0	.	3	50	2	55	2	56	1	98	1	99	1	57	0	.	2	58	0	.	0	.	1	98	4	57	3	58	1	97	3	66	0	.	0	.	5	61	0	.	1	98	0	.	0	.	1	97	0	.	0	.	5	85	3	94	2	38	1	45	1	44	0	.	3	66	1	97	1	98	1	99	2	67	3	66	0	.	0	.	1	8
	91-100 cm	0	.	0	.	0	.	1	99	0	.	0	.	0	71	0	100	1	66	1	70	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	2	48	0	99	0	.	0	100	0	70	0	100	0	100	1	99	0	.	0	.	0	.	1	98	2	96	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	1	98	0	99	0	.	0	.	0	.	0	.	0	.	1	98	0	.	1	98	0	.	2	98	0	.	1	98	1	99	0	.	0	.	1	98	1	99	0	99	0	.	0	99	0	.	2	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	94	0	.	0	.	0	.	0	.	0	.	0	.	4	65	0	.	0	.	1	49	1	49	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	15
	>100 cm	0	.	1	99	0	.	1	98	0	.	0	.	0	.	0	70	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	99	0	.	0	.	1	70	1	44	0	100	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	69	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	4	98	2	98	1	72	0	.	0	.	0	.	1	99	1	99	0	.	2	56	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	21
	total	313	13	315	17	413	15	315	10	490	14	569	10	484	5	399	5	389	6	543	7	279	18	432	15	508	10	210	.	504	16	548	14	507	11	446	13	356	25	485	10	391	7	417	9	474	9	480	6	446	6	443	6	474	11	451	9	627	7	478	13	440	12	390	11	366	16	411	11	620	16	544	17	538	12	418	13	593	16	490	12	592	14	436	9	475	9	455	12	432	15	333	17	454	13	489	11	490	14	449	16	507	12	421	15	398	14	421	19	470	11	409	11	504	14	559	11	755	8	516	10	464	14	619	11	632	15	673	17	513	15	520	11	391	14	647	19	538	18	469	25	483	13	375	28	369	27	387	11	479	18	467	14	398	12	619	22	389	25	417	32	417	25	431	7	481	8	527	7	411	14	436	18	346	13	426	14	344	11	360	16	356	17	388	16	0	.	466	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. 2282179. Birmensdorf, Swiss Federal Research Institute WSL

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