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: 1000 n
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)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	2777	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	271	.	0	.	0	.	307	70	1889	29	212	39	1417	25	2972	23	7208	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	482	100	371	65	669	60	0	.	0	.	408	51	0	.	241	.	0	.	0	.	0	.	0	.	0	.	200	83	0	.	0	.	0	.	0	.	0	.	199	60	1143	40	670	40	213	92	0	.	0	.	971	31	220	66	101	71	0	.	36	71	455	63	106	100	131	73	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	.	0	.	0	.	0	.	0	.	3515	18	2294	25	1293	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30881	7
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	1922	20	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	135	.	0	.	0	.	123	76	758	29	354	48	966	28	1674	24	3837	15	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	0	.	286	86	226	92	401	56	0	.	0	.	158	62	0	.	153	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	709	38	499	40	162	72	0	.	0	.	585	38	52	58	0	.	35	.	108	71	149	63	123	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	.	0	.	0	.	0	.	0	.	2081	21	1363	27	851	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17837	7
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	1522	20	61	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	.	0	.	0	.	64	49	692	26	318	44	700	29	997	22	2032	14	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	0	.	108	83	147	67	155	68	0	.	0	.	198	57	0	.	39	.	0	.	0	.	0	.	0	.	0	.	40	69	0	.	0	.	0	.	0	.	0	.	104	73	418	35	163	37	6	100	0	.	0	.	121	45	31	64	0	.	0	.	98	77	42	75	74	71	70	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	.	0	.	0	.	0	.	0	.	1065	22	935	24	715	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11066	6
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	995	20	40	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	59	460	24	157	37	497	30	516	22	1250	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	68	58	67	57	71	0	.	14	.	102	52	0	.	9	.	0	.	0	.	0	.	0	.	0	.	44	100	0	.	0	.	0	.	0	.	0	.	51	61	214	39	100	52	0	.	0	.	0	.	85	49	22	75	12	100	0	.	44	85	21	74	0	.	61	56	35	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	.	0	.	0	.	0	.	0	.	873	21	430	24	334	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6651	6
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	343	23	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	58	259	27	76	49	289	27	286	24	665	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	81	7	100	10	100	0	.	7	.	29	60	0	.	9	.	0	.	0	.	0	.	0	.	0	.	22	74	0	.	0	.	0	.	0	.	0	.	91	48	162	38	50	56	0	.	0	.	0	.	92	49	6	100	0	.	14	.	43	71	7	100	7	100	33	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	413	21	291	28	311	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3656	7
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	131	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	71	97	37	28	50	114	37	79	35	221	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	7	100	10	100	0	.	0	.	21	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	74	0	.	0	.	0	.	0	.	0	.	14	71	36	60	29	50	0	.	0	.	0	.	21	75	14	71	0	.	7	.	14	71	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	213	30	82	33	70	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1270	10
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	68	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	105	38	7	100	21	58	28	50	72	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	36	55	7	100	7	100	0	.	0	.	0	.	22	58	7	100	0	.	7	.	0	.	0	.	0	.	22	58	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	160	27	14	71	38	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	673	13
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	7	100	7	100	48	46	41	47	50	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	10	100	0	.	0	.	13	70	0	.	0	.	0	.	0	.	0	.	14	100	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	39	14	71	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	299	18
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	9	100	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	58	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	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	.	14	71	14	71	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	38
	total	0	.	0	.	0	.	0	.	0	.	0	.	7765	18	154	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	466	.	0	.	0	.	662	58	4277	22	1159	38	4066	22	6607	19	15349	12	0	.	0	.	0	.	44	.	0	.	0	.	0	.	0	.	0	.	979	85	818	68	1302	55	0	.	29	.	930	49	0	.	451	.	0	.	0	.	0	.	0	.	0	.	362	58	0	.	0	.	0	.	0	.	0	.	496	46	2697	29	1519	35	392	77	0	.	0	.	1909	30	353	57	112	71	62	.	344	72	675	61	339	78	316	53	96	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	257	.	0	.	0	.	0	.	0	.	8407	16	5437	22	3619	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72451	5
montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	191	75	0	.	0	.	272	61	629	38	1490	30	8828	13	5030	13	2782	27	5894	15	0	.	189	56	1046	32	0	.	431	50	1692	28	2843	21	698	40	0	.	2977	22	2216	22	2395	22	5113	21	7152	15	3253	24	4243	17	2236	31	1339	29	4810	19	705	50	1696	30	276	45	845	33	1427	26	1193	29	1573	31	1767	27	1095	37	891	37	2972	26	1310	38	1658	23	448	45	1220	36	1039	39	510	55	476	43	2084	27	1956	28	1285	37	615	36	0	.	218	60	1624	42	974	39	670	38	1799	30	1296	34	935	37	3444	24	2020	34	1356	31	1693	33	1459	47	1419	34	1490	33	979	34	2656	31	732	46	34	.	1939	26	66	.	297	65	1742	24	918	47	1226	36	496	37	590	62	507	53	58	100	108	73	4276	19	4683	20	6357	16	662	39	718	57	194	52	1467	32	629	46	0	.	33	.	33	.	0	.	146594	3
	21-30 cm	191	61	33	.	0	.	236	50	571	36	1325	25	5072	13	3129	15	1154	25	4022	15	17	100	138	53	640	39	0	.	275	62	1167	34	2158	22	607	41	0	.	1953	23	1496	24	1614	24	1963	22	4128	15	1824	21	1954	18	750	29	644	29	2648	19	337	60	737	25	184	64	302	41	820	31	1148	30	903	36	1285	27	447	33	813	33	1628	25	710	43	1399	22	417	45	509	37	551	37	272	60	461	36	939	30	1352	33	875	37	509	39	0	.	256	77	387	41	660	33	424	36	866	36	1200	33	447	40	1975	27	1126	31	912	33	935	34	365	42	814	32	407	33	297	36	1123	45	363	58	0	.	1143	28	50	.	109	71	967	26	543	48	707	39	259	53	437	57	252	65	146	88	86	100	2453	20	2678	22	3687	18	592	35	348	48	36	71	1065	33	538	37	0	.	33	.	17	.	0	.	85009	3
	31-40 cm	101	48	0	.	0	.	71	65	575	28	1045	24	3455	13	1937	14	632	29	2441	15	31	100	83	65	263	39	0	.	201	55	711	30	1042	27	461	30	0	.	728	25	982	21	923	21	682	22	2451	15	1428	21	1601	18	539	30	528	37	1358	20	181	50	593	35	151	54	290	42	510	26	720	31	357	35	656	26	405	32	690	33	866	22	574	32	1093	22	334	37	445	42	614	35	56	58	267	37	571	28	682	25	484	31	459	42	0	.	94	59	157	51	252	39	376	50	197	40	393	34	233	47	559	28	294	34	451	36	396	35	298	50	432	38	373	32	310	38	502	35	254	50	0	.	801	26	83	.	88	62	664	24	248	33	411	33	201	52	131	65	106	64	230	57	39	82	1363	24	964	21	1790	17	310	39	241	37	46	63	632	27	430	31	0	.	40	.	23	.	0	.	49678	3
	41-50 cm	77	55	7	.	0	.	110	44	501	27	618	24	2115	13	1283	13	486	24	1512	14	49	86	42	100	208	42	0	.	103	58	415	28	613	27	326	31	0	.	767	21	788	21	512	20	415	24	1284	15	779	20	1119	17	250	30	383	28	1007	18	116	44	331	29	105	48	147	39	469	23	244	33	169	37	504	24	366	30	351	35	420	24	263	40	840	21	153	47	188	35	234	32	71	59	264	31	441	27	355	27	313	31	149	38	0	.	48	55	43	52	169	41	388	34	84	42	109	34	45	53	261	35	161	33	195	32	197	45	162	58	280	40	284	40	254	38	298	35	81	54	21	.	397	26	27	.	67	52	435	22	95	38	322	30	204	36	75	68	110	54	89	68	36	73	531	22	596	24	905	18	193	43	142	44	73	61	356	25	319	31	0	.	46	.	13	.	0	.	30371	3
	51-60 cm	77	67	0	.	0	.	80	50	259	30	367	30	1482	13	992	14	318	24	696	17	7	100	7	100	135	41	0	.	27	62	185	29	436	26	201	33	0	.	391	25	311	26	379	22	351	27	874	16	293	26	457	18	132	32	250	30	522	22	43	57	186	36	43	70	80	42	250	26	104	46	85	47	274	30	193	35	80	41	235	28	268	33	560	21	56	61	133	41	43	58	91	59	89	38	188	29	296	29	221	31	109	48	0	.	21	73	50	55	151	42	123	38	57	43	36	66	15	71	105	30	94	40	102	39	94	45	48	64	123	36	166	41	131	40	191	36	66	51	7	.	174	28	27	.	45	47	144	28	155	35	139	43	145	46	46	74	36	59	66	51	36	73	288	25	345	25	566	19	108	38	58	45	33	72	253	29	120	40	0	.	0	.	27	.	0	.	17243	3
	61-70 cm	14	100	0	.	0	.	20	58	97	35	148	36	482	18	404	18	112	33	130	28	0	.	7	100	48	55	0	.	13	71	87	42	192	32	64	43	0	.	125	34	114	32	178	26	136	28	331	19	67	49	151	29	119	42	145	35	205	26	38	53	46	47	29	71	21	75	146	28	40	46	32	60	105	36	58	39	56	47	112	35	83	51	310	31	7	100	27	61	22	58	15	71	38	69	139	36	60	45	74	43	14	71	0	.	20	74	35	82	37	52	86	44	35	66	22	58	21	100	61	40	7	100	72	56	46	47	31	63	37	53	62	43	76	42	135	44	27	50	0	.	125	44	7	.	30	79	56	35	53	55	15	71	35	53	0	.	52	53	0	.	7	100	78	61	114	34	170	28	28	79	80	56	7	100	105	31	27	61	0	.	0	.	7	.	0	.	6668	4
	71-80 cm	0	.	0	.	0	.	0	.	22	74	19	73	145	27	206	21	83	35	46	41	0	.	7	100	21	100	0	.	0	.	26	62	101	46	26	62	0	.	92	44	61	36	58	36	63	37	119	28	42	46	78	35	45	57	43	47	71	43	22	74	15	71	0	.	30	60	15	71	0	.	27	63	64	42	51	50	14	72	61	44	88	44	134	32	0	.	14	100	0	.	8	100	0	.	62	44	6	100	29	79	7	100	0	.	7	100	28	61	34	60	46	53	7	100	7	100	0	.	22	58	14	71	21	58	26	50	14	100	29	79	21	58	29	50	6	100	7	100	0	.	20	74	0	.	8	100	15	71	16	100	8	100	21	58	0	.	0	.	16	100	29	80	71	42	21	58	84	35	18	72	14	71	7	100	13	71	13	71	0	.	0	.	0	.	0	.	2713	6
	81-90 cm	0	.	0	.	0	.	7	100	0	.	0	.	76	40	77	33	37	45	22	76	0	.	0	.	0	.	0	.	0	.	7	100	13	71	0	.	0	.	27	50	14	71	32	52	19	58	79	30	22	74	14	71	0	.	0	.	9	100	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	7	100	16	72	55	80	0	.	0	.	7	100	0	.	0	.	7	100	7	100	14	71	7	100	0	.	0	.	0	.	19	72	15	71	14	71	8	100	0	.	0	.	0	.	7	100	0	.	0	.	7	100	15	71	29	62	0	.	13	71	0	.	0	.	0	.	0	.	7	100	0	.	0	.	8	100	0	.	0	.	8	100	0	.	37	78	27	60	21	58	0	.	14	71	0	.	7	100	0	.	0	.	0	.	0	.	0	.	858	11
	91-100 cm	0	.	0	.	0	.	7	100	0	.	0	.	6	100	0	.	8	100	15	71	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	22	58	0	.	0	.	6	100	14	71	0	.	7	100	7	100	0	.	0	.	0	.	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	6	100	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	8	100	0	.	10	100	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	14	71	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	220	18
	>100 cm	0	.	7	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	24	58	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	14	100	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	120	26
	total	649	50	46	.	0	.	803	47	2654	28	5012	22	21660	11	13066	12	5620	20	14777	13	105	93	472	51	2360	31	0	.	1051	48	4290	27	7407	18	2384	30	0	.	7089	18	5980	18	6092	19	8749	18	16454	12	7707	18	9631	15	4080	26	3332	24	10631	16	1449	39	3619	24	794	44	1714	32	3636	23	3447	26	3146	29	4655	22	2626	27	2903	31	6309	21	3319	30	6063	17	1415	34	2536	30	2510	30	1022	47	1596	32	4438	23	4715	24	3309	28	1869	34	0	.	664	59	2345	36	2308	32	2128	30	3060	28	3078	30	1696	37	6437	22	3716	29	3118	27	3386	30	2385	38	3143	30	2816	28	2112	26	4911	29	1543	41	62	.	4599	24	258	.	645	51	4029	21	2028	37	2828	30	1369	35	1279	58	1070	49	613	59	342	71	9107	17	9442	18	13588	14	1910	32	1615	41	403	49	3898	24	2076	30	0	.	152	.	119	.	0	.	339473	2
hyperinsubric, colline, submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	1666	28	1509	27	1322	37	1063	27	0	.	0	.	452	44	1078	29	5388	16	0	.	553	39	751	44	635	34	0	.	288	51	643	44	0	.	984	50	284	48	0	.	268	77	400	47	0	.	1253	52	88	84	1301	25	639	39	1744	31	248	77	662	48	0	.	491	53	52	.	0	.	0	.	0	.	503	87	226	80	1017	57	308	95	515	54	0	.	1860	29	530	48	238	69	343	44	742	40	315	95	0	.	0	.	1137	39	1521	36	1305	34	204	81	1265	43	389	50	2530	34	1660	33	5675	21	1428	40	927	41	3448	29	196	74	0	.	139	75	449	54	639	60	0	.	386	66	536	56	87	72	415	59	611	71	0	.	497	57	303	54	430	47	601	57	51	75	0	.	392	90	1420	46	752	42	1368	37	116	77	316	46	772	37	199	58	576	38	762	35	637	57	1002	37	0	.	65498	5
	21-30 cm	580	27	973	37	509	32	656	41	0	.	0	.	233	48	609	32	3033	15	35	.	160	48	479	41	562	39	17	.	189	50	486	39	0	.	492	34	311	49	0	.	109	75	168	51	0	.	735	45	67	71	809	25	434	34	643	28	129	85	72	62	0	.	138	59	52	.	0	.	0	.	0	.	69	79	265	62	461	47	123	73	353	71	0	.	1290	30	135	68	153	57	294	45	600	44	46	100	0	.	36	100	561	36	629	40	894	33	88	100	390	42	250	56	618	45	1020	31	2490	22	682	38	611	43	1506	27	288	80	92	.	132	72	341	57	219	61	0	.	293	56	323	57	87	72	231	55	85	53	0	.	536	47	211	51	206	61	371	44	201	54	0	.	73	71	516	44	316	46	488	41	75	71	209	47	366	41	259	74	260	34	686	36	338	44	667	35	0	.	34116	4
	31-40 cm	468	31	381	32	369	33	419	29	0	.	0	.	202	49	457	33	1396	14	0	.	90	48	273	44	431	35	14	.	151	59	333	44	0	.	391	33	186	56	0	.	27	62	55	77	0	.	369	43	24	79	656	30	339	34	547	37	57	71	67	60	0	.	90	67	7	.	0	.	0	.	0	.	52	100	59	86	206	54	71	71	89	55	0	.	637	25	133	38	125	54	276	36	367	39	39	71	0	.	107	73	271	46	421	30	423	35	18	100	68	53	136	64	226	40	457	38	769	23	152	52	301	37	543	40	110	77	27	.	64	80	126	58	106	84	0	.	77	58	121	61	73	71	29	50	82	60	0	.	138	45	137	46	186	53	149	41	63	65	0	.	74	59	173	46	151	50	106	48	42	100	74	71	280	40	76	80	376	37	377	33	344	40	240	38	0	.	17542	4
	41-50 cm	354	27	161	34	168	36	448	24	0	.	0	.	266	49	176	35	1255	14	0	.	44	52	182	49	132	37	14	.	127	51	148	50	0	.	157	34	91	57	0	.	53	74	40	55	0	.	95	46	32	81	510	23	323	37	303	29	34	72	84	54	0	.	90	52	14	.	0	.	0	.	0	.	0	.	16	71	81	58	43	71	51	63	0	.	452	26	61	53	121	59	177	39	151	43	18	100	0	.	43	74	98	58	217	34	223	31	14	100	65	51	63	78	35	59	167	33	310	35	55	43	87	42	186	34	31	80	56	.	28	81	60	71	71	65	0	.	98	65	79	51	14	100	72	62	27	71	0	.	42	47	78	59	119	44	128	48	67	68	0	.	56	66	65	55	43	63	68	45	68	72	70	59	199	34	14	100	181	39	170	32	205	39	183	33	0	.	10327	4
	51-60 cm	142	33	156	29	87	41	320	27	0	.	0	.	94	52	159	33	760	15	0	.	74	48	42	58	80	41	21	.	46	62	96	42	0	.	123	33	21	100	0	.	21	76	24	58	0	.	31	61	12	100	261	27	200	51	169	34	7	100	49	59	0	.	49	74	0	.	0	.	0	.	0	.	0	.	44	100	52	59	18	100	51	52	0	.	139	32	127	46	34	72	62	70	42	52	18	100	0	.	14	71	38	45	181	35	169	29	0	.	9	100	0	.	28	59	59	46	87	53	48	51	27	100	102	39	23	100	8	.	0	.	7	100	64	80	0	.	22	76	7	100	35	72	20	74	7	100	0	.	14	71	102	59	80	56	40	57	13	71	0	.	50	60	52	74	0	.	8	100	7	100	75	54	96	37	0	.	138	41	174	38	109	41	135	36	0	.	5681	5
	61-70 cm	78	37	53	39	34	45	169	28	0	.	0	.	34	73	80	40	339	20	0	.	52	52	28	79	32	60	0	.	13	100	21	58	0	.	27	50	7	100	0	.	0	.	0	.	0	.	16	71	7	100	77	35	62	43	92	36	20	100	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	75	0	.	108	38	54	47	0	.	34	52	7	100	6	100	0	.	7	100	13	71	44	46	47	62	7	100	7	100	0	.	7	100	23	58	41	45	8	100	8	100	22	58	8	100	0	.	0	.	6	100	6	100	0	.	8	100	0	.	7	100	9	100	0	.	0	.	0	.	0	.	13	100	14	71	13	100	0	.	35	83	0	.	0	.	36	82	20	100	41	53	13	100	0	.	16	72	33	53	20	74	47	55	0	.	2078	7
	71-80 cm	31	50	20	58	30	60	63	39	0	.	0	.	0	.	28	61	102	32	0	.	9	100	7	100	25	59	7	.	0	.	14	71	0	.	8	100	0	.	0	.	6	100	0	.	0	.	0	.	0	.	14	71	0	.	7	100	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	9	100	19	72	0	.	26	75	0	.	23	58	27	50	0	.	0	.	0	.	0	.	0	.	0	.	7	100	37	63	20	74	0	.	6	100	16	100	0	.	15	71	16	100	16	100	13	100	24	57	8	100	0	.	7	100	7	100	6	100	0	.	8	100	0	.	0	.	9	100	0	.	0	.	0	.	8	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	13	71	21	58	7	100	0	.	766	11
	81-90 cm	7	100	7	100	13	71	20	74	0	.	0	.	0	.	0	.	27	50	0	.	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	33	60	0	.	14	100	8	100	0	.	0	.	8	100	7	100	0	.	20	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	8	100	0	.	0	.	7	100	0	.	10	100	14	71	14	71	0	.	0	.	295	17
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	30	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	14	100	0	.	0	.	0	.	8	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	97	38
	>100 cm	0	.	0	.	0	.	8	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	6	100	0	.	0	.	0	.	8	100	19	100	0	.	23	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	115	40
	total	3326	22	3259	25	2533	28	3165	21	0	.	0	.	1280	44	2600	26	12330	12	35	.	989	37	1768	35	1896	32	73	.	815	45	1738	36	0	.	2189	32	900	44	0	.	485	60	694	47	0	.	2500	41	230	72	3636	21	1997	28	3505	26	494	71	955	44	0	.	872	43	125	.	0	.	0	.	0	.	625	77	619	61	1835	47	563	79	1121	52	0	.	4510	22	1075	36	671	51	1186	33	1909	37	444	77	0	.	207	76	2124	33	3079	26	3151	25	331	88	1846	40	861	50	3443	33	3401	28	9411	19	2416	35	1975	36	5882	26	663	74	183	.	371	71	998	52	1111	57	0	.	892	55	1066	47	302	62	806	49	813	59	0	.	1226	47	838	45	1033	41	1303	43	422	55	0	.	687	59	2227	39	1261	42	2093	34	327	73	784	43	1734	31	547	63	1556	27	2228	28	1688	32	2281	29	0	.	136515	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	1857	27	1509	27	1322	37	1335	25	629	38	1490	30	12056	11	6108	12	8170	14	5894	15	553	39	940	37	1680	24	0	.	719	36	2335	23	3114	21	1682	33	284	48	3284	21	4373	17	3007	19	6531	17	11377	12	10549	13	5544	14	2876	26	3083	21	5058	18	1367	35	1696	30	768	37	897	32	1427	26	1674	35	1944	28	2939	26	1321	33	1908	35	3687	23	1825	31	1899	24	2309	25	1751	29	1277	34	853	37	1218	30	2600	25	1956	28	1285	37	1752	28	1521	36	1523	30	2028	35	3382	24	1729	24	4542	22	2956	24	6610	19	5844	18	3167	25	4905	22	1888	31	1495	46	2013	28	2045	27	1749	30	2699	30	1118	38	570	53	2025	25	481	53	908	52	1742	24	1416	36	1530	31	993	28	1191	42	558	48	58	100	500	73	9211	13	7728	14	9019	13	778	35	1034	42	967	32	1666	29	1205	30	762	35	671	55	1035	36	0	.	242973	2
	21-30 cm	771	25	1006	36	509	32	892	32	571	36	1325	25	7227	11	3772	14	4187	13	4056	15	177	44	617	34	1201	28	17	.	464	42	1653	27	2293	21	1099	28	311	49	2076	22	2363	18	2136	20	2929	18	6537	12	5728	12	2764	14	1185	22	1288	20	2799	19	409	51	737	25	322	44	354	38	820	31	1434	29	1129	34	1756	23	712	31	1274	27	1909	23	1063	37	1552	22	1707	25	645	33	704	31	565	37	1061	29	1004	28	1352	33	911	36	1069	27	629	40	1150	31	475	38	1759	21	1173	24	1646	26	2220	23	2937	19	3242	20	1789	24	2418	21	1258	31	565	34	1096	27	871	29	516	33	1123	45	656	40	323	57	1230	27	281	49	194	46	967	26	1078	34	918	32	515	37	809	37	453	43	146	88	159	63	5051	13	4356	16	5027	14	667	32	557	35	402	38	1324	30	798	27	686	36	371	41	683	34	0	.	136962	2
	31-40 cm	568	27	381	32	369	33	490	27	575	28	1045	24	5179	10	2456	13	2028	13	2441	15	122	44	356	37	694	26	14	.	351	40	1044	25	1102	26	852	22	186	56	791	23	1701	16	1297	19	1382	18	3817	12	3484	12	2257	15	878	23	1075	26	1437	19	247	40	593	35	241	42	297	41	510	26	828	29	505	32	863	24	464	30	896	28	1135	20	663	29	1132	21	971	21	578	34	739	30	332	31	634	27	651	25	682	25	591	29	730	32	421	30	517	30	278	40	738	24	675	32	429	28	850	25	1001	20	832	22	627	24	995	27	506	32	423	40	538	32	572	26	486	32	502	35	331	40	121	61	874	24	111	75	171	43	664	24	386	26	548	27	453	35	280	37	169	47	230	57	113	48	2602	16	2050	15	2611	14	351	36	314	33	327	36	707	25	806	24	377	33	384	37	263	36	0	.	78286	2
	41-50 cm	431	24	168	33	168	36	559	21	501	27	618	24	3376	10	1499	12	1741	12	1512	14	93	51	223	44	340	29	14	.	230	38	562	24	613	27	483	24	91	57	833	20	1302	16	708	17	911	20	1895	12	2060	12	1629	13	573	24	686	20	1041	18	200	34	331	29	195	35	161	37	469	23	303	30	227	33	562	23	382	29	447	30	566	21	314	35	848	21	606	23	249	29	355	29	249	33	415	25	503	25	355	27	356	28	247	32	217	34	271	27	108	38	449	25	551	27	119	34	276	24	355	31	401	25	270	24	393	23	227	41	262	44	330	35	345	35	385	29	333	32	178	43	100	46	411	26	98	53	95	42	435	22	136	30	400	27	370	27	203	39	177	42	89	68	93	49	1468	15	1068	16	1307	15	261	37	212	35	272	30	370	24	501	24	170	32	251	36	197	32	0	.	47350	2
	51-60 cm	219	32	156	29	87	41	400	24	259	30	367	30	1919	11	1169	13	1077	13	696	17	81	45	49	51	215	30	21	.	74	45	281	24	436	26	325	24	21	100	453	23	591	18	479	19	640	19	1192	13	971	14	717	14	332	33	419	22	530	21	92	41	186	36	91	51	80	42	250	26	135	40	92	44	283	29	237	34	139	33	282	25	320	29	569	21	195	28	260	31	77	45	153	45	131	31	228	26	296	29	235	30	147	37	181	35	190	27	141	36	323	27	173	32	84	34	95	38	102	47	245	24	127	36	204	27	131	38	99	44	130	34	181	38	228	33	191	36	88	42	14	71	209	26	47	65	52	43	144	28	169	33	240	35	246	34	86	48	49	47	66	51	86	46	754	16	636	18	885	17	115	36	134	36	129	33	253	29	257	28	174	38	109	41	162	34	0	.	26579	2
	61-70 cm	92	35	53	39	34	45	189	25	97	35	148	36	647	16	484	17	451	17	130	28	52	52	35	66	80	41	0	.	26	61	108	36	192	32	91	33	7	100	138	31	210	24	206	23	250	23	425	16	295	21	229	22	182	31	237	25	225	25	59	49	46	47	29	71	21	75	146	28	46	42	39	53	114	34	58	39	56	47	133	32	110	43	310	31	115	36	81	37	22	58	49	42	45	60	167	31	60	45	81	40	27	50	44	46	67	49	57	55	80	37	114	35	43	57	45	41	62	45	90	33	30	50	94	45	60	40	46	49	37	53	82	37	82	39	135	44	35	45	0	.	132	42	15	71	30	79	56	35	53	55	15	71	54	43	14	71	66	47	0	.	42	71	292	27	196	24	276	25	48	62	120	41	20	74	105	31	43	46	33	53	20	74	54	50	0	.	10016	3
	71-80 cm	31	50	20	58	30	60	63	39	22	74	19	73	213	23	235	20	185	23	46	41	9	100	14	70	45	56	7	.	0	.	40	47	101	46	34	53	0	.	99	41	172	27	65	34	84	31	147	25	114	30	92	32	45	57	50	43	71	43	22	74	15	71	14	71	30	60	15	71	7	100	27	63	64	42	60	45	40	45	69	41	114	38	134	32	23	58	41	47	0	.	8	100	0	.	77	41	6	100	29	79	13	71	37	63	27	61	65	41	48	48	69	43	7	100	22	58	16	100	59	40	34	52	46	41	41	41	14	100	37	66	28	50	57	35	15	72	15	71	0	.	20	74	9	100	8	100	15	71	16	100	16	71	21	58	0	.	7	100	16	100	29	80	231	22	35	45	132	28	18	72	14	71	7	100	13	71	13	71	13	71	21	58	7	100	0	.	4153	5
	81-90 cm	7	100	7	100	13	71	27	61	0	.	0	.	76	40	77	33	64	33	22	76	7	100	7	100	0	.	0	.	0	.	7	100	13	71	0	.	0	.	41	41	21	58	46	42	67	37	120	25	71	40	21	58	0	.	0	.	9	100	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	7	100	25	59	55	80	0	.	7	100	7	100	0	.	0	.	7	100	7	100	14	71	7	100	13	100	33	60	0	.	41	52	23	58	24	58	8	100	8	100	20	58	0	.	27	60	0	.	0	.	7	100	29	61	29	62	9	100	13	71	0	.	0	.	15	71	0	.	7	100	0	.	0	.	8	100	0	.	0	.	8	100	7	100	95	39	41	46	36	45	0	.	14	71	7	100	7	100	10	100	14	71	14	71	0	.	0	.	1451	8
	91-100 cm	0	.	0	.	0	.	7	100	0	.	0	.	13	71	7	100	38	67	15	71	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	29	50	9	100	0	.	6	100	14	71	8	100	7	100	7	100	0	.	0	.	0	.	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	6	100	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	16	100	0	.	7	100	14	100	0	.	0	.	8	100	8	100	10	100	0	.	7	100	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	29	50	29	50	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	383	15
	>100 cm	0	.	7	100	0	.	8	100	0	.	0	.	0	.	14	71	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	14	71	38	45	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	37	100	14	100	18	73	0	.	0	.	0	.	8	100	19	100	0	.	23	58	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	285	21
	total	3975	20	3306	24	2533	28	3968	19	2654	28	5012	22	30705	9	15820	10	17951	10	14812	13	1094	34	2240	30	4256	22	73	.	1866	33	6029	22	7873	18	4573	22	900	44	7751	17	10742	13	7945	16	12815	14	25561	10	23286	10	13267	12	6077	20	6837	17	11169	15	2404	29	3619	24	1667	31	1839	31	3636	23	4426	28	3964	27	6581	20	3246	25	4767	26	7802	19	4440	26	6515	18	5925	19	3611	24	3181	26	2208	28	3505	25	5244	21	4715	24	3516	27	3993	24	3079	26	3815	23	3173	29	6851	19	4507	21	6894	21	6479	20	11107	17	10763	16	6043	21	9112	19	4111	28	2912	33	4188	25	4153	23	3539	24	5007	28	2434	33	1128	45	4901	22	1064	45	1458	40	4029	21	3254	29	3666	26	2660	26	2582	36	1491	38	613	59	1029	46	19740	11	16140	13	19300	12	2237	29	2399	31	2137	27	4445	23	3632	21	2228	28	1840	31	2400	28	0	.	548438	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. 2281017. Birmensdorf, Swiss Federal Research Institute WSL

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