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
grid: 1.4-km grid
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	.	2582	17	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	.	0	.	0	.	466	37	1603	23	685	38	2284	21	3422	17	7844	11	0	.	0	.	0	.	74	77	0	.	0	.	0	.	0	.	0	.	362	74	230	61	459	50	0	.	129	100	430	37	89	.	117	.	0	.	0	.	0	.	0	.	0	.	110	83	0	.	0	.	0	.	0	.	0	.	385	33	1191	28	777	33	201	68	0	.	0	.	666	27	192	51	196	74	0	.	20	71	342	53	129	62	102	56	77	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	.	0	.	0	.	0	.	0	.	4096	14	2115	17	1701	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33275	5
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	1655	16	34	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	.	0	.	0	.	211	49	770	24	560	32	1463	20	1981	15	3940	11	0	.	0	.	0	.	83	77	0	.	0	.	0	.	0	.	0	.	239	64	157	82	318	46	0	.	40	100	211	47	20	.	74	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	155	46	566	30	409	33	124	60	0	.	0	.	419	32	124	71	89	100	21	100	68	62	115	53	97	61	21	100	91	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	.	0	.	0	.	0	.	0	.	2540	13	1690	17	1262	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19659	5
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	1345	16	36	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	.	0	.	0	.	125	52	598	20	307	32	758	21	1297	15	2037	11	0	.	0	.	0	.	21	71	0	.	0	.	0	.	0	.	0	.	54	80	95	63	192	47	0	.	8	100	177	44	0	.	19	.	0	.	0	.	0	.	0	.	0	.	35	57	0	.	0	.	0	.	0	.	0	.	163	40	269	31	205	35	4	100	0	.	0	.	139	32	25	54	22	100	0	.	86	61	61	69	57	58	57	51	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	.	0	.	0	.	0	.	0	.	1176	16	1115	17	896	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11455	5
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	763	16	30	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	106	39	364	21	156	26	478	20	726	15	1374	12	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	0	.	34	69	41	57	86	48	0	.	8	100	68	44	0	.	4	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	55	41	194	28	112	36	4	100	0	.	0	.	134	40	27	52	10	73	4	100	31	69	30	67	37	90	41	47	48	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	.	0	.	0	.	0	.	0	.	886	14	536	17	481	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6937	5
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	325	19	29	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	43	234	24	84	32	307	19	400	17	833	18	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	18	80	4	100	22	52	0	.	4	100	29	47	0	.	4	.	0	.	0	.	0	.	0	.	0	.	12	74	0	.	0	.	0	.	0	.	0	.	77	36	130	31	73	34	6	100	0	.	0	.	74	39	8	71	0	.	8	100	23	71	4	100	29	88	26	52	35	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	.	0	.	0	.	0	.	0	.	467	15	327	20	265	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3932	6
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	134	28	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	62	69	32	33	35	135	26	129	23	282	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	12	75	9	71	0	.	4	100	25	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	74	0	.	0	.	0	.	0	.	0	.	19	45	42	40	25	41	0	.	0	.	0	.	23	47	8	71	0	.	4	100	8	71	4	100	16	60	4	100	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	228	19	114	23	67	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1454	8
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	71	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	60	82	36	8	71	32	40	59	32	79	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	0	.	0	.	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	29	43	8	71	13	58	0	.	0	.	0	.	16	50	4	100	0	.	4	100	0	.	0	.	4	100	16	50	13	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	165	21	20	45	30	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	698	10
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	4	100	4	100	30	42	31	39	44	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	19	53	12	74	6	100	0	.	0	.	8	71	0	.	0	.	0	.	0	.	0	.	12	74	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	30	12	58	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	278	13
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	5	100	4	100	4	100	0	.	23	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	41	12	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	23
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	8	71	8	71	12	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	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	.	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	28
	total	0	.	0	.	0	.	0	.	0	.	0	.	6888	14	148	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	259	.	0	.	0	.	1024	35	3729	18	1841	27	5498	17	8054	13	16469	9	0	.	0	.	0	.	204	76	0	.	0	.	0	.	0	.	0	.	716	64	544	62	1094	42	0	.	196	92	951	35	109	.	219	.	0	.	0	.	0	.	0	.	0	.	212	55	0	.	0	.	0	.	0	.	0	.	896	29	2423	22	1626	28	344	60	0	.	0	.	1478	24	387	48	316	81	41	91	236	61	555	52	381	55	267	42	294	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	137	.	0	.	0	.	0	.	0	.	9640	11	5941	15	4708	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77827	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	270	47	0	.	74	60	409	37	1178	28	1412	22	9665	9	5558	9	2415	20	5572	12	0	.	168	43	831	26	0	.	305	43	1252	24	2574	16	1061	28	0	.	2801	17	2713	15	2225	16	5677	16	7108	11	2969	18	5487	12	1994	24	1539	24	3912	15	527	41	1439	23	315	41	674	26	1692	26	1021	23	1458	23	2451	20	807	30	1159	31	2854	19	1354	27	2026	22	480	45	888	31	1120	29	300	53	690	29	2485	20	1846	19	1468	24	548	28	0	.	236	56	1592	31	1664	25	1388	24	1762	22	1441	24	753	30	3920	16	2377	24	1811	24	1160	28	919	41	1377	25	1130	26	1128	28	2787	20	656	32	94	82	1921	21	149	59	322	44	1690	19	937	33	991	28	516	30	394	53	307	50	95	53	174	57	4785	13	4283	15	6645	12	877	27	581	46	154	43	1301	25	586	32	0	.	118	85	59	74	0	.	149850	2
	21-30 cm	231	38	20	100	75	55	309	40	720	24	1210	20	5127	10	3154	11	1016	20	3487	12	10	100	88	49	639	29	0	.	204	50	900	29	1999	16	742	28	0	.	1781	18	1712	17	1706	17	2033	16	3766	11	1736	18	2631	12	857	23	788	25	2088	16	332	39	736	20	237	39	286	31	834	23	880	25	924	26	1459	21	469	28	862	26	1567	19	698	31	1395	19	336	36	472	32	650	26	212	50	532	27	1117	20	1352	21	811	27	428	31	0	.	182	64	451	29	906	23	564	24	904	24	1019	24	350	32	1915	18	1129	24	1059	22	761	27	319	35	665	26	395	26	511	31	1253	26	452	35	0	.	1186	27	93	67	203	40	993	20	501	36	569	30	258	39	267	52	218	55	161	51	137	74	2671	14	2325	16	3582	12	545	26	263	42	52	52	851	27	465	28	0	.	56	73	68	87	0	.	83918	2
	31-40 cm	136	36	12	100	64	54	220	49	562	23	868	20	3315	9	2152	10	572	21	2103	12	18	100	99	50	317	27	0	.	143	47	624	24	1217	19	566	24	0	.	762	18	1060	18	1113	15	807	16	2151	12	1238	16	2128	12	502	23	474	27	1138	16	171	37	515	27	279	37	234	32	512	19	541	26	427	24	558	21	416	26	555	26	1058	17	473	26	993	18	301	30	297	36	540	25	72	51	443	24	781	21	570	20	396	27	358	33	0	.	56	59	290	36	457	27	380	32	357	25	427	24	263	35	662	20	264	27	479	25	334	28	251	37	354	30	297	26	279	30	638	22	301	33	4	100	782	21	100	64	135	38	691	18	268	28	427	27	203	33	95	53	106	51	151	49	74	56	1457	15	864	17	1683	13	298	28	212	32	42	54	491	22	357	26	0	.	24	100	31	71	0	.	49436	2
	41-50 cm	134	33	24	85	39	73	132	31	409	22	647	19	2073	9	1476	10	440	19	1324	11	28	86	38	65	256	28	0	.	113	53	384	23	716	17	370	24	0	.	713	16	706	16	650	16	429	18	1302	12	698	15	1349	11	252	23	320	24	865	14	144	34	297	21	150	35	143	30	465	18	182	28	207	26	405	20	283	25	331	27	541	18	271	27	823	16	106	41	181	27	277	25	60	46	258	23	415	20	422	21	304	24	149	28	0	.	28	55	40	39	236	25	305	27	170	26	144	26	55	37	311	21	158	28	207	23	180	32	131	43	227	30	272	29	222	28	467	21	103	37	12	100	428	20	31	71	102	39	468	17	199	29	308	25	172	27	62	55	100	44	65	53	25	64	674	14	469	20	835	13	256	30	138	35	64	52	291	21	252	27	0	.	28	100	12	75	0	.	30548	2
	51-60 cm	90	42	4	100	12	74	64	40	195	26	431	21	1403	10	1153	11	272	20	661	13	4	100	39	74	130	33	0	.	34	51	155	29	472	18	205	25	0	.	316	19	352	20	422	16	365	19	726	13	262	20	521	13	164	26	217	25	442	18	94	33	200	24	64	43	80	31	229	20	83	37	136	31	300	24	179	28	76	32	312	21	240	25	518	16	37	54	88	37	100	51	56	55	156	27	262	21	284	21	188	25	117	36	0	.	12	74	45	39	193	26	98	30	43	38	78	37	24	52	190	21	81	32	122	26	91	34	38	49	92	30	149	31	108	31	303	21	80	39	4	100	206	26	26	72	63	35	160	21	158	29	154	33	124	34	26	75	49	48	87	43	37	54	452	17	293	20	453	16	186	31	58	41	28	59	201	24	108	31	0	.	0	.	16	100	0	.	17541	2
	61-70 cm	39	49	0	.	0	.	20	45	60	34	184	27	481	13	477	13	121	25	163	27	0	.	16	61	57	42	0	.	8	71	63	37	151	25	83	33	0	.	117	25	146	23	135	23	124	25	336	14	76	34	181	20	118	32	114	29	177	22	25	47	55	29	16	71	31	50	170	21	27	42	56	39	82	30	42	34	58	33	116	26	79	35	292	23	4	100	24	53	16	50	8	71	41	48	152	26	68	32	75	32	24	52	0	.	12	75	27	61	59	32	75	34	19	66	34	57	11	100	66	28	13	57	54	42	31	43	25	49	32	43	73	33	59	35	102	35	20	45	4	100	108	35	9	71	29	55	61	27	88	33	44	36	32	41	0	.	42	43	13	74	16	79	167	25	92	27	187	22	44	42	50	52	12	76	105	26	28	43	0	.	0	.	4	100	0	.	6757	3
	71-80 cm	0	.	0	.	0	.	20	83	18	60	41	37	176	20	183	17	79	29	87	30	0	.	8	71	15	78	0	.	9	100	28	48	61	45	42	37	0	.	78	33	43	32	81	27	94	27	151	22	39	34	80	25	47	37	25	47	63	31	29	56	22	53	0	.	33	39	32	47	0	.	27	44	43	39	34	46	19	52	39	41	63	38	121	27	0	.	8	100	0	.	8	71	0	.	53	36	29	49	43	55	12	58	0	.	4	100	20	52	41	42	29	48	4	100	15	61	0	.	23	41	35	52	23	41	15	50	35	63	39	53	27	43	46	37	40	41	33	53	0	.	20	52	0	.	4	100	8	71	31	52	12	58	12	58	0	.	0	.	13	74	18	81	110	35	40	43	116	33	27	41	8	71	8	71	17	50	12	58	0	.	0	.	0	.	0	.	2967	5
	81-90 cm	0	.	0	.	0	.	8	71	0	.	6	100	67	29	80	24	25	41	20	54	0	.	0	.	0	.	0	.	0	.	4	100	12	58	4	100	0	.	19	45	19	53	34	36	21	45	56	27	16	61	29	38	0	.	0	.	10	71	4	100	4	100	0	.	0	.	12	75	7	100	8	71	0	.	11	73	7	100	8	71	9	72	49	56	0	.	0	.	4	100	0	.	0	.	8	71	4	100	16	50	4	100	0	.	0	.	0	.	14	59	12	58	8	71	4	100	0	.	14	78	8	71	8	71	0	.	8	100	4	100	9	71	27	43	13	58	12	58	0	.	7	100	0	.	0	.	4	100	0	.	0	.	4	100	0	.	4	100	4	100	0	.	32	54	27	42	28	43	0	.	16	61	0	.	17	50	0	.	0	.	0	.	0	.	0	.	871	8
	91-100 cm	0	.	0	.	0	.	4	100	0	.	0	.	23	47	4	100	5	100	8	71	0	.	0	.	0	.	0	.	0	.	0	.	9	72	0	.	0	.	16	50	0	.	4	100	4	100	19	45	8	71	8	71	4	100	0	.	0	.	0	.	4	100	4	100	0	.	8	100	0	.	0	.	16	78	0	.	5	100	4	100	4	100	10	71	0	.	0	.	0	.	0	.	4	100	4	100	0	.	4	100	0	.	0	.	0	.	4	100	4	100	4	100	0	.	4	100	0	.	9	72	4	100	0	.	0	.	5	100	4	100	0	.	7	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	13	58	14	59	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	270	13
	>100 cm	0	.	4	100	0	.	0	.	0	.	0	.	12	75	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	58	4	100	27	86	4	100	21	52	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	5	100	0	.	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	0	.	0	.	8	100	19	50	0	.	11	100	0	.	0	.	8	71	0	.	12	75	0	.	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	199	19
	total	901	32	64	71	265	55	1186	33	3144	21	4798	17	22341	8	14241	8	4949	15	13425	10	60	93	456	40	2244	23	0	.	816	39	3411	22	7210	14	3074	21	0	.	6615	14	6756	13	6397	13	9558	14	15636	9	7042	14	12419	10	3938	20	3476	19	8696	13	1326	29	3272	18	1066	32	1490	24	3953	19	2741	22	3243	21	5319	18	2239	22	3071	24	6504	16	3191	22	6231	16	1264	31	1958	25	2707	22	716	40	2125	23	5275	16	4575	16	3313	20	1641	26	0	.	530	51	2477	27	3592	21	2855	20	3277	19	3166	21	1456	29	7119	15	4070	22	3776	20	2573	25	1731	31	2798	23	2352	21	2390	23	5601	18	1661	27	118	76	4657	19	409	62	858	35	4076	16	2181	27	2506	23	1321	26	843	49	829	40	589	41	481	44	10362	11	8407	14	13541	11	2233	23	1326	33	363	41	3275	20	1808	24	0	.	226	72	190	73	0	.	342358	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	2135	21	2121	19	1276	25	1583	26	0	.	0	.	457	36	771	26	5173	12	143	75	445	31	980	33	639	29	0	.	507	33	587	38	42	.	1048	35	323	40	111	.	145	77	234	45	110	.	1362	31	49	83	1552	21	660	29	1187	28	294	54	541	37	0	.	290	52	28	.	0	.	21	100	220	73	419	66	819	31	595	55	264	70	429	41	0	.	1578	22	663	32	261	48	720	39	769	29	232	75	0	.	26	100	932	29	1240	28	1382	30	109	81	1077	31	344	46	2027	28	1632	26	5755	16	1487	27	1316	26	2413	25	111	73	0	.	78	74	625	46	531	52	237	59	475	41	488	43	75	58	497	33	656	46	0	.	435	43	331	39	679	30	403	48	114	44	0	.	470	50	1247	34	793	35	1739	25	203	57	342	40	985	26	189	48	705	30	637	28	577	38	734	30	0	.	65877	3
	21-30 cm	911	23	1060	23	668	20	679	28	0	.	0	.	255	42	498	30	2843	12	117	66	223	36	488	29	405	32	10	.	293	31	413	33	0	.	502	28	213	41	0	.	59	75	93	51	40	.	709	30	38	71	849	21	381	28	451	25	166	59	100	51	0	.	86	53	28	.	0	.	10	100	199	75	84	56	488	34	261	47	93	61	241	59	0	.	970	23	241	43	183	41	316	35	504	34	88	75	0	.	21	100	568	32	626	28	864	26	47	100	395	30	177	47	485	33	847	25	2280	16	679	26	787	28	1165	23	161	79	50	100	98	61	378	40	150	52	180	74	259	43	218	48	65	59	235	37	255	48	0	.	379	40	155	41	407	32	228	41	127	51	0	.	170	45	460	33	435	35	642	27	152	52	264	59	492	27	174	63	376	28	611	27	357	30	571	27	0	.	33253	3
	31-40 cm	656	21	418	22	500	24	429	22	0	.	0	.	234	42	325	29	1422	12	46	61	150	38	245	31	387	31	8	.	222	34	264	36	23	.	333	29	124	49	0	.	16	62	52	54	0	.	304	31	14	78	667	21	286	28	392	33	107	64	56	46	0	.	66	56	4	.	0	.	19	100	31	72	63	71	218	36	146	47	115	52	159	41	0	.	589	19	205	28	164	36	244	27	350	29	95	78	0	.	87	59	295	32	338	25	438	25	9	100	61	42	87	57	255	38	441	28	853	18	277	30	298	26	426	30	62	75	14	100	54	65	139	44	88	65	25	63	70	45	87	51	43	65	94	35	160	49	0	.	153	38	137	39	309	30	121	36	64	45	0	.	155	34	144	35	158	37	166	30	53	61	111	42	289	28	89	54	327	31	368	27	247	34	224	34	0	.	17966	3
	41-50 cm	434	18	209	25	236	23	378	19	0	.	0	.	164	46	175	36	1087	11	54	67	120	33	210	31	159	29	8	.	140	36	127	40	25	.	128	29	86	42	0	.	32	74	30	48	0	.	86	33	19	82	432	18	249	30	237	26	32	52	89	42	0	.	65	47	7	.	0	.	21	72	37	100	4	100	108	35	57	50	81	47	86	42	0	.	443	20	107	32	159	38	173	29	199	28	19	72	0	.	37	60	148	38	211	28	242	26	8	100	50	40	51	61	40	41	189	28	294	25	102	27	111	29	147	29	18	81	31	100	21	65	55	50	43	60	23	78	79	51	66	41	8	100	147	32	38	53	0	.	46	39	98	42	159	28	103	40	68	48	0	.	89	37	66	38	39	49	91	36	47	61	83	40	194	26	39	63	178	28	168	26	178	32	187	26	0	.	10501	3
	51-60 cm	161	22	188	23	143	23	251	23	0	.	0	.	82	42	120	31	667	13	8	71	105	34	80	36	94	34	12	.	72	41	91	33	8	.	100	32	24	71	4	.	12	75	17	50	0	.	35	46	7	100	199	22	158	39	124	30	4	100	30	53	0	.	39	60	0	.	0	.	8	71	0	.	17	100	70	47	39	52	34	60	46	40	0	.	184	24	148	29	44	44	79	39	69	31	11	100	0	.	8	71	65	45	119	32	151	24	0	.	14	58	4	100	26	45	58	34	75	38	81	32	45	59	81	31	14	100	4	100	5	100	27	54	38	82	12	100	34	44	14	60	31	59	98	39	7	71	0	.	11	58	105	40	118	35	36	46	20	53	0	.	63	38	53	49	17	100	16	60	12	74	111	36	125	26	29	86	124	31	129	33	121	32	146	28	0	.	5832	4
	61-70 cm	80	28	80	34	59	35	136	23	0	.	0	.	20	73	51	38	301	16	0	.	57	36	27	55	18	60	0	.	28	47	16	50	8	.	39	47	4	100	0	.	0	.	4	100	0	.	18	61	4	100	62	35	64	35	76	35	23	61	12	100	0	.	0	.	0	.	0	.	4	100	4	100	12	100	36	43	0	.	4	100	19	63	0	.	100	31	80	31	4	100	36	40	18	81	4	100	0	.	10	72	20	66	33	42	44	45	4	100	17	62	4	100	24	54	26	47	52	40	9	71	8	71	28	42	5	100	0	.	0	.	4	100	4	100	0	.	13	58	13	100	4	100	35	47	0	.	0	.	0	.	4	100	15	71	12	58	24	53	0	.	60	50	4	100	0	.	28	62	12	100	42	37	61	37	4	100	34	49	28	47	20	53	84	35	0	.	2294	5
	71-80 cm	39	37	36	44	21	52	46	35	0	.	0	.	0	.	16	62	83	27	0	.	9	71	8	71	14	59	4	.	5	100	11	58	4	.	19	63	4	100	0	.	4	100	0	.	4	.	9	100	0	.	17	50	8	71	4	100	9	100	4	100	0	.	8	71	0	.	0	.	0	.	0	.	0	.	9	71	11	71	4	100	23	60	0	.	20	45	40	37	0	.	0	.	11	73	0	.	0	.	12	100	4	100	26	56	12	74	0	.	16	50	9	100	6	100	13	58	22	53	27	52	7	100	18	50	5	100	0	.	4	100	4	100	4	100	0	.	5	100	0	.	0	.	28	51	0	.	0	.	0	.	4	100	4	100	0	.	20	53	0	.	4	100	0	.	0	.	5	100	0	.	4	100	24	41	4	100	8	100	12	58	16	50	20	45	0	.	851	8
	81-90 cm	14	60	4	100	16	50	12	74	0	.	0	.	0	.	4	100	38	37	0	.	8	71	4	100	6	100	0	.	4	100	0	.	4	.	5	100	0	.	0	.	0	.	4	100	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	5	100	0	.	7	71	20	45	0	.	4	100	0	.	0	.	0	.	6	100	0	.	8	100	20	60	0	.	21	60	4	100	0	.	15	77	8	71	9	71	0	.	11	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	4	100	0	.	4	100	14	77	0	.	10	72	8	71	20	53	0	.	0	.	351	12
	91-100 cm	4	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	17	80	0	.	0	.	6	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	8	100	0	.	0	.	0	.	8	71	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	4	100	0	.	0	.	0	.	81	28
	>100 cm	0	.	4	100	0	.	4	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	8	71	0	.	0	.	22	100	9	70	25	60	0	.	13	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	132	29
	total	4434	17	4119	17	2920	18	3518	18	0	.	0	.	1211	35	1968	23	11631	9	367	65	1117	26	2054	25	1721	24	42	.	1275	27	1510	30	115	.	2179	26	778	35	115	.	268	59	433	42	154	.	2532	26	131	72	3782	17	1806	23	2471	22	636	51	831	35	0	.	555	40	67	.	0	.	83	71	491	72	599	55	1747	27	1113	45	594	52	1009	38	0	.	3892	17	1503	24	814	34	1571	27	1920	26	450	61	0	.	207	61	2032	25	2611	21	3175	22	177	87	1665	28	680	42	2864	26	3243	22	9357	14	2696	23	2572	24	4306	22	375	73	100	100	260	61	1231	38	857	49	477	62	934	36	887	38	226	53	1152	27	1116	40	0	.	1029	37	835	33	1691	25	903	37	441	39	0	.	1015	33	1974	29	1441	32	2691	24	480	49	961	36	2188	20	527	49	1762	21	1965	22	1537	24	1970	22	0	.	137137	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	2405	19	2121	19	1350	24	1992	22	1178	28	1412	22	12704	8	6342	9	7588	10	5715	11	445	31	1148	29	1470	19	0	.	812	26	1839	20	2766	16	2108	22	323	40	3378	15	4462	13	3144	14	8071	13	11892	9	10862	10	7039	10	2654	20	2726	18	4281	14	1068	28	1439	23	605	33	702	26	1692	26	1403	25	1907	21	3329	18	1625	21	1883	27	3547	17	1873	22	2143	21	2058	20	1552	22	1381	25	1020	32	1458	21	2827	19	1846	19	1494	24	1481	21	1240	28	1618	27	2086	25	3931	16	2509	18	3990	17	3073	17	6508	15	6073	13	3885	17	4420	17	1271	27	939	40	1797	22	1884	22	1761	24	3101	19	1131	25	583	38	1996	20	647	29	978	34	1690	19	1372	26	1322	23	1230	21	797	36	421	38	95	53	643	40	10128	9	7191	11	10085	10	1081	24	923	33	1139	23	1490	23	1290	22	637	28	695	35	793	28	0	.	249002	1
	21-30 cm	1141	20	1080	23	744	19	989	23	720	24	1210	20	7037	8	3686	10	3859	10	3603	12	233	35	576	25	1044	22	10	.	497	27	1313	22	2074	16	1244	20	213	41	1992	17	2542	13	2359	14	3536	12	6457	9	5715	9	3481	10	1238	18	1239	18	2338	15	433	32	736	20	324	32	314	29	834	23	1130	24	1280	24	1861	18	956	22	1163	22	1872	17	959	27	1470	19	1305	20	713	26	833	22	528	29	1036	22	1215	19	1352	21	831	26	996	22	626	28	1047	24	653	24	1867	16	1149	18	1514	18	1867	17	2630	14	3013	14	2040	17	2313	15	942	26	437	30	878	22	869	22	681	26	1524	23	711	27	218	48	1251	26	328	33	458	32	993	20	880	27	724	25	691	24	495	34	346	40	161	51	307	41	5670	9	4449	11	5486	10	697	23	526	36	544	25	1025	25	841	20	611	27	413	28	639	26	0	.	136830	1
	31-40 cm	793	18	430	22	564	22	649	22	562	23	868	20	4894	8	2513	9	1994	10	2148	12	168	36	344	26	704	21	8	.	365	27	889	20	1273	18	900	18	124	49	887	17	1674	14	1472	13	1564	13	3752	9	3289	9	2795	10	788	18	866	21	1266	16	227	30	515	27	346	32	237	31	512	19	614	24	553	22	812	19	633	21	709	22	1350	15	633	22	1013	18	890	16	502	24	704	21	316	24	793	18	911	20	570	20	484	24	653	23	338	25	494	23	463	27	787	19	672	22	616	21	867	18	1116	16	1079	15	587	18	927	19	396	26	352	31	468	25	493	21	423	25	672	21	371	28	91	49	825	20	194	37	296	32	691	18	421	22	564	22	548	22	215	31	169	36	151	49	229	29	2777	10	2137	11	2744	10	351	25	323	25	331	25	580	20	684	20	368	27	271	33	256	31	0	.	78857	1
	41-50 cm	568	16	233	24	275	22	509	16	409	22	647	19	3000	8	1680	9	1527	10	1377	11	148	31	248	28	414	20	8	.	253	31	511	20	741	17	498	20	86	42	819	15	1101	13	836	14	907	14	2115	9	2091	9	1781	9	501	19	557	18	918	14	232	27	297	21	216	28	150	29	465	18	237	24	286	24	495	18	391	21	395	24	690	15	357	23	827	16	549	18	288	21	436	21	233	25	457	18	458	19	422	21	341	22	297	24	211	28	270	24	102	28	479	17	469	20	214	22	333	19	349	22	547	16	296	19	365	18	202	29	193	35	277	26	364	25	306	23	538	20	182	30	77	38	436	20	179	29	140	32	468	17	245	24	407	22	356	19	165	32	168	32	65	53	114	32	1626	10	1044	12	1407	11	303	27	221	26	258	23	330	20	430	19	168	26	206	31	199	25	0	.	47987	1
	51-60 cm	251	21	192	23	154	22	315	20	195	26	431	21	1810	9	1302	10	939	11	668	13	109	33	119	34	224	24	12	.	105	32	246	22	481	18	305	20	24	71	380	17	598	15	523	14	672	14	1161	10	1102	14	720	11	321	23	341	19	450	17	124	28	200	24	103	35	80	31	229	20	110	32	140	30	338	22	249	24	119	27	375	18	286	22	522	16	221	22	236	23	144	38	134	32	226	21	285	20	284	21	196	24	182	28	119	32	163	23	123	27	336	19	176	22	75	28	136	26	99	31	345	16	133	28	203	20	113	31	66	39	100	28	205	26	172	28	350	20	114	30	18	51	236	24	125	34	70	32	160	21	169	27	259	25	253	24	62	41	69	37	87	43	100	31	972	11	637	14	734	13	198	29	169	28	153	24	229	24	233	22	129	33	121	32	162	27	0	.	27305	2
	61-70 cm	119	24	80	34	59	35	156	21	60	34	184	27	634	12	534	13	422	13	163	27	57	36	43	41	75	35	0	.	36	40	79	31	159	24	122	27	4	100	133	23	215	18	172	19	259	18	483	12	362	19	243	17	182	24	189	22	201	20	36	45	55	29	16	71	31	50	170	21	34	36	73	33	103	27	78	27	62	32	144	22	98	31	292	23	104	30	104	27	20	45	44	35	59	41	168	24	68	32	85	29	44	41	33	42	56	39	51	38	118	23	105	26	43	42	59	38	64	38	98	23	29	38	82	31	40	37	33	41	36	40	93	28	67	32	119	33	33	35	17	79	112	34	44	40	29	55	61	27	88	33	48	34	50	34	12	58	66	33	13	74	76	43	399	15	206	18	282	18	57	39	93	33	72	33	109	26	62	33	28	47	20	53	88	34	0	.	10504	2
	71-80 cm	39	37	36	44	21	52	66	35	18	60	41	37	246	17	199	16	162	20	87	30	9	71	16	50	29	50	4	.	15	73	40	38	65	42	62	32	4	100	99	29	129	26	89	25	130	22	219	18	118	22	97	23	55	33	29	43	72	29	32	51	22	53	8	71	33	39	32	47	4	100	31	41	43	39	43	39	33	39	48	36	86	32	121	27	20	45	48	35	0	.	8	71	11	73	61	34	29	49	55	48	16	50	26	56	16	61	49	33	64	30	51	36	10	73	27	42	22	53	66	28	46	43	41	32	24	41	35	63	43	49	36	37	66	29	53	36	37	48	0	.	20	52	28	51	4	100	8	71	31	52	17	50	16	50	0	.	20	53	13	74	21	69	275	19	60	32	151	27	27	41	12	58	32	35	21	45	20	53	12	58	16	50	20	45	0	.	4515	4
	81-90 cm	14	60	4	100	16	50	20	53	0	.	6	100	71	28	84	23	63	27	20	54	8	71	4	100	6	100	0	.	4	100	4	100	16	50	9	71	0	.	27	38	23	47	42	32	51	31	91	22	60	30	33	35	0	.	0	.	10	71	4	100	4	100	0	.	0	.	12	75	7	100	8	71	9	100	11	73	11	73	12	58	14	58	49	56	7	71	20	45	4	100	4	100	0	.	8	71	4	100	22	45	4	100	8	100	20	60	8	100	54	33	28	42	13	59	20	64	8	71	31	45	8	71	20	52	0	.	8	100	4	100	21	52	27	43	17	50	12	58	0	.	7	100	13	58	0	.	4	100	0	.	0	.	4	100	0	.	4	100	4	100	4	100	84	27	40	34	40	35	0	.	20	53	14	77	17	50	10	72	8	71	20	53	0	.	0	.	1500	6
	91-100 cm	4	100	0	.	0	.	4	100	0	.	0	.	28	42	8	71	21	66	8	71	0	.	6	100	0	.	0	.	4	100	0	.	9	72	0	.	0	.	24	41	5	100	8	71	7	71	19	45	31	45	8	71	4	100	0	.	0	.	0	.	4	100	4	100	0	.	8	100	0	.	0	.	16	78	0	.	5	100	4	100	4	100	10	71	0	.	0	.	0	.	0	.	4	100	4	100	0	.	4	100	0	.	9	100	0	.	4	100	16	61	4	100	0	.	4	100	8	71	9	72	4	100	4	100	0	.	5	100	4	100	0	.	7	100	0	.	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	0	.	37	33	26	42	4	100	0	.	0	.	9	71	0	.	0	.	4	100	0	.	0	.	0	.	439	11
	>100 cm	0	.	8	71	0	.	4	100	0	.	0	.	16	62	8	71	4	100	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	15	50	4	100	27	86	12	58	33	39	12	58	4	100	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	5	100	0	.	0	.	8	71	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	0	.	22	100	12	72	27	41	0	.	11	100	22	100	9	70	33	49	0	.	24	47	0	.	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	383	15
	total	5335	15	4183	17	3184	17	4703	16	3144	21	4798	17	30440	7	16357	8	16581	8	13792	10	1177	25	2510	22	3965	17	42	.	2092	22	4921	18	7583	14	5252	16	778	35	7754	13	10753	10	8672	11	15210	10	26222	7	23642	7	16201	8	5744	16	5947	14	9535	12	2157	22	3272	18	1620	25	1557	24	3953	19	3540	21	4278	19	7011	16	3987	17	4381	20	8049	14	4309	18	6450	16	5156	15	3461	18	3521	19	2287	22	4044	17	5937	15	4575	16	3520	19	3673	18	2611	21	3704	20	3550	20	7680	13	5162	15	6486	15	6409	15	10814	13	11293	11	7029	16	8398	14	2989	23	2067	27	3613	20	3964	18	3514	20	6373	17	2595	22	1005	35	4883	18	1561	26	1974	27	4076	16	3210	21	3341	19	3149	18	1746	30	1271	30	589	41	1496	27	21976	7	15789	9	20940	8	2713	21	2287	24	2552	18	3802	18	3570	16	1965	22	1762	23	2160	21	0	.	557321	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. 2280583. Birmensdorf, Swiss Federal Research Institute WSL

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