Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
total number of stems
altitudinal vegetation belts (NaiS, 3 classes) · diameter at breast height (10 classes)
regional demarcation: forest district (2024)
unit: n/ha
evaluation area: accessible forest without shrub forest
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)	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	166	13	23	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	756	.	0	.	0	.	166	25	171	17	137	32	216	15	165	14	212	9	0	.	0	.	0	.	123	55	0	.	0	.	0	.	0	.	0	.	445	52	185	46	245	38	0	.	325	71	152	25	450	.	550	.	0	.	0	.	0	.	0	.	0	.	111	69	0	.	0	.	0	.	0	.	0	.	118	22	204	22	209	24	173	56	0	.	0	.	131	19	143	34	248	54	0	.	34	40	214	40	163	36	73	41	74	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	200	.	0	.	0	.	0	.	0	.	158	11	159	12	164	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	177	4
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	106	12	59	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	378	.	0	.	0	.	75	41	82	19	112	25	139	15	95	12	107	9	0	.	0	.	0	.	138	55	0	.	0	.	0	.	0	.	0	.	295	39	126	70	170	32	0	.	100	71	74	39	100	.	350	.	0	.	0	.	0	.	0	.	0	.	10	89	0	.	0	.	0	.	0	.	0	.	47	40	97	24	110	25	106	45	0	.	0	.	83	26	92	60	112	87	50	71	117	23	72	41	123	37	15	92	87	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	.	0	.	0	.	0	.	0	.	98	10	127	13	122	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	105	4
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	86	11	62	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	.	0	.	0	.	45	45	64	14	62	25	72	15	62	11	55	8	0	.	0	.	0	.	35	44	0	.	0	.	0	.	0	.	0	.	67	61	76	48	103	33	0	.	20	71	63	35	0	.	90	.	0	.	0	.	0	.	0	.	0	.	36	36	0	.	0	.	0	.	0	.	0	.	50	32	46	25	55	27	3	92	0	.	0	.	28	25	18	38	27	87	0	.	147	19	38	59	72	32	41	35	8	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	200	.	0	.	0	.	0	.	0	.	45	13	84	12	86	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	4
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	49	12	51	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	29	39	15	31	17	45	15	35	11	37	9	0	.	0	.	0	.	35	86	0	.	0	.	0	.	0	.	0	.	42	50	33	41	46	35	0	.	20	71	24	36	0	.	20	.	0	.	0	.	0	.	0	.	0	.	24	89	0	.	0	.	0	.	0	.	0	.	17	34	33	22	30	27	3	92	0	.	0	.	26	35	20	35	13	52	10	71	54	37	19	57	47	74	29	29	46	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	.	0	.	0	.	0	.	0	.	34	12	40	12	46	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	4
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	21	15	49	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	34	25	19	17	25	29	14	19	14	23	16	0	.	0	.	0	.	6	86	0	.	0	.	0	.	0	.	0	.	23	67	3	91	12	40	0	.	10	71	10	39	0	.	20	.	0	.	0	.	0	.	0	.	0	.	12	59	0	.	0	.	0	.	0	.	0	.	24	27	22	25	20	26	5	91	0	.	0	.	15	34	6	60	0	.	20	71	40	40	2	94	36	72	19	36	34	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	.	0	.	0	.	0	.	0	.	18	12	25	16	26	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	5
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	9	25	10	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	57	7	29	7	29	13	22	6	21	8	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	90	10	63	5	62	0	.	10	71	9	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	59	0	.	0	.	0	.	0	.	0	.	6	38	7	35	7	34	0	.	0	.	0	.	5	43	6	60	0	.	10	71	13	40	3	93	20	34	3	93	17	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	9	17	9	20	6	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	7
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	5	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	54	9	33	2	68	3	37	3	31	2	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	86	3	91	0	.	0	.	10	71	1	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	89	0	.	0	.	0	.	0	.	0	.	9	36	1	68	4	54	0	.	0	.	0	.	3	46	3	92	0	.	10	71	0	.	0	.	5	86	12	33	13	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	19	2	43	3	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	9
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	65	0	99	1	98	3	40	1	38	1	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	94	0	.	0	.	1	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	97	3	49	3	71	5	91	0	.	0	.	2	68	0	.	0	.	0	.	0	.	0	.	15	55	0	.	4	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	28	1	56	1	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	13
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	66	1	99	1	98	0	99	0	.	1	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	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	40	1	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	23
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	1	69	0	70	0	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	28
	total	0	.	0	.	0	.	0	.	0	.	0	.	442	8	253	9	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1302	.	0	.	0	.	365	22	397	11	369	19	521	9	388	8	446	5	0	.	0	.	0	.	338	55	0	.	0	.	0	.	0	.	0	.	881	39	437	46	586	27	0	.	495	59	336	23	550	.	1030	.	0	.	0	.	0	.	0	.	0	.	213	32	0	.	0	.	0	.	0	.	0	.	273	16	414	13	438	16	296	45	0	.	0	.	292	15	287	31	401	64	100	57	406	18	348	38	483	22	191	20	283	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	770	.	0	.	0	.	0	.	0	.	372	7	447	9	454	11	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	414	3
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	116	37	0	.	97	36	121	29	215	21	151	16	215	7	159	6	213	15	220	8	0	.	122	20	204	14	0	.	169	29	198	17	176	12	165	22	0	.	201	12	189	10	150	11	315	13	226	8	198	14	217	8	288	17	200	18	263	10	172	32	188	17	131	29	153	16	189	21	168	15	229	16	245	15	137	23	204	25	228	14	202	20	145	18	166	37	210	22	194	22	151	43	135	22	266	13	170	14	215	17	147	17	0	.	239	36	376	22	248	18	223	17	202	16	235	16	253	15	321	11	323	17	257	17	234	20	289	33	287	15	215	17	206	21	326	14	163	23	231	38	228	14	261	13	104	36	156	14	191	26	167	21	136	21	288	39	140	39	58	39	108	44	227	9	277	10	280	8	170	18	208	37	109	20	160	20	124	25	0	.	311	51	149	24	0	.	212	2
	21-30 cm	99	26	50	71	98	25	91	32	131	15	129	14	114	7	90	8	90	15	138	9	24	73	63	31	157	20	0	.	113	40	142	23	137	11	115	22	0	.	128	14	119	12	115	12	113	12	120	8	116	14	104	9	124	16	102	19	140	11	108	30	96	13	99	27	65	22	93	18	145	17	145	19	146	15	79	21	152	18	125	14	104	25	100	15	116	26	111	23	113	19	107	39	104	19	120	14	125	16	119	20	115	21	0	.	185	47	106	20	135	16	90	17	103	19	166	16	118	19	157	14	153	18	150	15	153	19	100	24	139	16	75	18	93	25	146	21	113	27	0	.	141	22	162	32	66	31	91	15	102	31	96	24	68	34	195	37	99	47	98	37	85	66	127	11	151	12	151	9	105	16	94	31	37	36	104	22	98	20	0	.	147	24	173	51	0	.	119	2
	31-40 cm	58	22	30	71	84	23	65	44	103	13	93	14	74	7	62	7	50	17	83	8	43	73	71	34	78	17	0	.	79	35	99	17	83	15	88	16	0	.	55	14	74	14	75	10	45	13	68	9	83	11	84	9	72	16	62	21	76	12	56	26	67	23	116	24	53	24	57	13	89	18	67	18	56	16	71	19	98	19	85	12	71	20	71	14	104	15	70	29	94	18	36	41	87	15	84	15	53	15	58	21	96	24	0	.	56	39	68	30	68	21	61	28	41	20	69	15	89	23	54	16	36	22	68	19	67	20	79	28	74	22	56	17	51	24	75	17	75	25	10	67	93	14	175	24	44	28	64	13	55	19	72	20	53	25	69	40	48	42	92	34	46	44	69	12	56	12	71	9	58	20	76	17	30	38	60	16	75	17	0	.	64	66	80	9	0	.	70	2
	41-50 cm	57	17	60	47	51	53	39	20	75	11	69	13	46	7	42	7	39	14	52	7	68	52	28	54	63	17	0	.	63	42	61	14	49	13	57	17	0	.	51	11	49	12	44	12	24	15	41	9	47	11	53	8	36	15	42	18	58	9	47	23	39	14	63	20	32	21	52	10	30	21	33	20	40	14	48	17	58	19	43	13	40	21	59	11	37	31	43	17	48	18	30	34	50	13	44	14	39	16	45	17	40	16	0	.	28	31	9	33	35	19	49	20	19	22	23	19	19	27	25	17	22	23	29	17	36	25	41	35	47	23	52	22	40	21	55	15	26	29	29	74	51	13	55	38	33	30	43	11	41	21	52	17	45	16	45	42	45	32	40	40	16	54	32	11	30	17	35	10	49	23	49	22	46	36	36	15	53	17	0	.	74	66	30	23	0	.	43	2
	51-60 cm	39	30	10	71	15	55	19	32	36	18	46	15	31	8	33	8	24	15	26	10	10	73	29	63	32	25	0	.	19	38	24	23	32	14	32	18	0	.	23	16	25	16	28	12	20	16	23	10	18	16	21	9	24	19	28	19	30	14	31	20	26	19	26	32	18	22	26	14	14	33	21	27	30	19	30	20	13	26	25	17	36	19	37	11	13	48	21	29	17	47	28	45	31	20	28	16	26	16	28	19	31	27	0	.	12	58	11	32	29	20	16	25	5	35	13	33	8	45	16	17	11	28	17	20	18	28	12	43	19	22	28	24	20	25	35	16	20	31	10	74	24	21	46	41	20	23	15	16	32	20	26	28	33	27	19	65	22	38	53	24	23	41	21	14	19	16	19	13	36	23	21	31	20	45	25	19	23	24	0	.	0	.	41	69	0	.	25	2
	61-70 cm	17	40	0	.	0	.	6	37	11	28	20	24	11	11	14	11	11	22	6	26	0	.	11	48	14	36	0	.	4	63	10	32	10	22	13	28	0	.	8	23	10	19	9	20	7	23	11	12	5	33	7	18	17	27	15	24	12	18	8	40	7	25	7	64	7	46	19	15	4	39	9	35	8	27	7	29	10	28	9	23	12	31	21	19	1	96	6	48	3	46	4	63	8	43	16	21	6	29	11	26	7	47	0	.	12	60	6	57	9	26	12	28	2	65	6	54	4	97	5	25	2	55	8	39	6	39	8	42	7	38	14	27	11	29	12	32	5	39	10	67	13	31	16	39	9	49	6	24	18	25	7	31	8	36	0	.	19	30	8	65	10	71	8	24	6	25	8	21	9	36	18	44	8	67	13	21	6	38	0	.	0	.	10	69	0	.	10	3
	71-80 cm	0	.	0	.	0	.	6	79	3	56	4	34	4	19	5	15	7	26	3	29	0	.	6	60	4	75	0	.	5	92	5	44	4	43	7	33	0	.	6	30	3	30	5	24	5	25	5	21	3	32	3	24	7	33	3	44	4	28	9	50	3	50	0	.	7	33	4	44	0	.	4	41	4	36	6	42	3	49	3	40	9	34	9	24	0	.	2	98	0	.	4	63	0	.	6	34	3	48	6	52	3	53	0	.	4	89	5	48	6	38	5	45	0	99	2	58	0	.	2	39	5	50	3	37	3	46	11	58	8	49	5	39	8	32	5	38	8	48	0	.	2	49	0	.	1	97	1	69	6	47	2	55	3	53	0	.	0	.	8	65	11	73	5	34	3	41	5	32	5	36	3	66	5	61	2	47	3	54	0	.	0	.	0	.	0	.	4	5
	81-90 cm	0	.	0	.	0	.	2	67	0	.	1	99	1	29	2	23	2	38	1	53	0	.	0	.	0	.	0	.	0	.	1	98	1	57	1	98	0	.	1	43	1	51	2	34	1	44	2	26	1	60	1	37	0	.	0	.	1	70	1	97	1	99	0	.	0	.	1	73	1	99	1	68	0	.	2	71	1	98	1	70	1	70	4	55	0	.	0	.	1	98	0	.	0	.	1	69	0	99	2	47	1	97	0	.	0	.	0	.	2	56	2	55	1	69	1	98	0	.	1	77	1	69	1	69	0	.	3	97	1	98	2	68	5	39	1	56	3	54	0	.	1	99	0	.	0	.	0	99	0	.	0	.	1	97	0	.	2	95	3	93	0	.	2	53	2	41	1	42	0	.	6	55	0	.	2	48	0	.	0	.	0	.	0	.	0	.	1	8
	91-100 cm	0	.	0	.	0	.	1	97	0	.	0	.	1	46	0	100	0	99	0	70	0	.	0	.	0	.	0	.	0	.	0	.	1	71	0	.	0	.	1	49	0	.	0	99	0	100	1	44	1	70	0	70	1	99	0	.	0	.	0	.	1	99	2	96	0	.	1	99	0	.	0	.	2	76	0	.	1	98	0	99	1	99	1	70	0	.	0	.	0	.	0	.	1	98	0	99	0	.	1	98	0	.	0	.	0	.	1	98	1	98	1	99	0	.	1	98	0	.	1	71	1	98	0	.	0	.	1	97	1	98	0	.	1	98	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	96	0	.	0	.	1	57	1	58	0	100	0	.	0	.	3	93	0	.	0	.	0	.	0	.	0	.	0	.	0	13
	>100 cm	0	.	10	71	0	.	0	.	0	.	0	.	0	74	0	100	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	57	0	99	2	85	0	100	1	52	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	2	98	0	.	0	.	0	.	1	99	0	.	0	.	0	99	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	69	0	.	0	.	0	.	2	98	3	48	0	.	1	99	0	.	0	.	1	70	0	.	2	73	0	.	0	.	1	98	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	19
	total	386	15	160	9	345	25	350	24	573	10	512	9	497	5	408	4	436	8	530	5	145	63	330	14	552	9	0	.	453	22	539	14	494	8	477	12	0	.	476	9	471	7	431	7	530	9	497	5	471	8	490	6	568	11	452	9	585	6	432	13	427	10	444	15	337	12	441	12	450	12	510	12	531	11	380	13	542	15	520	10	477	13	447	10	437	17	462	13	470	13	361	26	415	13	565	8	422	9	486	11	440	13	0	.	537	26	584	17	536	13	458	10	375	12	515	11	490	12	582	8	552	15	536	11	518	15	545	18	584	11	447	10	436	13	655	10	414	16	290	26	552	12	715	19	277	25	375	9	445	18	423	14	348	15	616	34	377	27	360	22	299	28	491	6	545	8	570	6	432	11	475	18	258	16	402	12	382	13	0	.	596	17	483	18	0	.	484	1
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	175	17	208	14	162	20	159	22	0	.	0	.	186	21	161	15	160	10	189	57	118	21	220	26	142	20	0	.	150	22	188	29	200	.	241	29	163	25	550	.	185	60	168	25	550	.	329	22	127	42	177	15	141	21	205	21	334	19	310	16	0	.	214	35	150	.	0	.	50	71	525	17	336	51	255	18	337	42	278	54	218	23	0	.	186	16	142	25	120	38	155	34	198	18	401	47	0	.	41	82	219	21	195	22	182	26	283	41	255	23	240	28	523	16	304	17	470	11	292	19	257	18	384	18	285	17	0	.	133	46	397	28	541	27	388	14	230	26	254	29	99	32	139	25	428	30	0	.	223	30	154	26	192	19	206	37	72	26	0	.	168	42	365	24	250	24	334	16	140	44	126	30	173	18	158	25	135	24	109	21	120	32	130	24	0	.	214	3
	21-30 cm	75	19	104	19	85	13	68	24	0	.	0	.	104	31	104	21	88	9	153	45	59	29	110	20	90	25	50	.	87	20	132	22	0	.	116	18	108	28	0	.	76	56	67	35	200	.	172	21	100	0	97	15	81	20	78	18	189	34	57	38	0	.	64	37	150	.	0	.	25	71	476	26	67	38	152	23	148	30	98	43	122	49	0	.	114	18	52	39	84	28	68	30	130	26	153	47	0	.	33	82	134	24	99	23	114	20	122	72	94	21	124	27	125	24	158	15	186	10	134	17	154	20	186	14	415	33	124	72	167	20	240	19	153	27	295	48	126	30	113	37	86	32	66	29	167	34	0	.	194	24	72	28	115	22	117	27	81	37	0	.	61	37	134	22	137	25	123	20	104	36	97	53	86	20	146	49	72	21	104	20	74	23	101	20	0	.	108	3
	31-40 cm	54	17	41	17	64	19	43	17	0	.	0	.	95	31	68	21	44	9	60	35	40	31	55	24	86	24	40	.	66	24	85	26	110	.	77	20	62	38	0	.	20	35	37	39	0	.	73	22	35	30	76	16	61	19	68	27	122	39	32	32	0	.	49	42	20	.	0	.	45	71	75	14	50	58	68	26	83	31	121	27	81	23	0	.	69	12	44	19	75	21	53	18	90	19	164	51	0	.	140	15	69	23	53	18	58	20	24	72	14	36	61	42	66	31	82	20	70	13	54	22	58	17	68	24	159	24	35	72	91	31	88	26	89	46	42	25	34	33	45	40	57	43	26	26	105	34	0	.	79	22	64	25	87	19	62	17	40	28	0	.	55	22	42	25	50	27	32	24	37	47	41	32	51	21	74	37	62	25	63	19	52	28	40	28	0	.	58	3
	41-50 cm	36	14	20	21	30	16	38	13	0	.	0	.	67	36	37	29	34	9	71	43	32	25	47	23	35	21	40	.	41	27	41	32	120	.	30	21	43	28	0	.	41	54	21	31	0	.	21	24	49	43	49	11	53	22	41	18	36	13	51	26	0	.	48	28	40	.	0	.	50	14	90	71	3	91	33	24	32	37	85	15	43	27	0	.	52	13	23	25	73	23	37	21	51	18	33	43	0	.	59	16	35	32	33	22	32	20	20	72	12	34	36	47	10	35	35	20	24	22	20	19	22	22	23	23	47	42	75	72	35	30	35	36	44	39	37	51	38	40	34	26	10	85	41	21	25	40	0	.	24	23	46	30	45	15	53	24	43	33	0	.	32	26	19	29	12	42	17	31	32	47	30	29	34	18	33	49	34	21	29	19	37	25	33	19	0	.	34	3
	51-60 cm	13	19	18	18	18	17	25	19	0	.	0	.	33	31	25	23	21	11	10	49	28	26	18	29	21	27	60	.	21	33	29	22	40	.	23	24	12	64	20	.	15	56	12	33	0	.	8	40	19	73	23	17	34	33	21	24	5	88	17	42	0	.	29	47	0	.	0	.	20	0	0	.	13	91	22	39	22	39	35	41	23	23	0	.	22	19	31	20	20	31	17	33	18	22	19	83	0	.	13	41	15	39	19	27	20	18	0	.	3	54	3	92	7	39	11	29	6	36	16	25	9	56	13	26	36	73	11	72	8	81	17	42	39	69	20	81	16	31	7	51	40	30	28	31	5	62	0	.	6	49	49	27	33	26	18	32	13	39	0	.	23	28	15	42	5	97	3	58	8	65	41	24	22	19	24	76	24	24	22	27	25	25	26	21	0	.	19	3
	61-70 cm	7	25	8	32	8	31	14	18	0	.	0	.	8	67	11	32	9	14	0	.	15	29	6	51	4	56	0	.	8	40	5	44	40	.	9	42	2	95	0	.	0	.	3	93	0	.	4	56	10	69	7	32	14	28	13	30	27	33	7	95	0	.	0	.	0	.	0	.	10	71	10	71	10	91	11	34	0	.	4	90	10	53	0	.	12	27	17	24	2	95	8	35	5	77	6	83	0	.	16	43	5	62	5	38	6	43	10	72	4	57	3	92	6	49	5	43	4	38	2	68	2	68	4	38	12	73	0	.	0	.	2	94	4	90	0	.	6	48	7	95	5	85	10	41	0	.	0	.	0	.	2	95	4	67	6	48	15	39	0	.	22	43	1	97	0	.	5	59	8	93	16	26	11	32	3	93	6	45	5	44	4	49	15	30	0	.	7	5
	71-80 cm	3	35	4	42	3	50	5	32	0	.	0	.	0	.	3	58	3	26	0	.	2	67	2	68	3	55	20	.	2	97	4	52	20	.	4	59	2	95	0	.	5	88	0	.	20	.	2	97	0	.	2	48	2	67	1	98	10	88	2	95	0	.	6	60	0	.	0	.	0	.	0	.	0	.	3	67	6	62	5	88	12	51	0	.	2	42	8	31	0	.	0	.	3	69	0	.	0	.	19	82	1	98	4	53	2	73	0	.	4	45	6	92	2	97	2	54	2	51	5	49	1	98	3	46	12	73	0	.	7	81	3	94	4	90	0	.	2	95	0	.	0	.	8	45	0	.	0	.	0	.	2	96	1	97	0	.	13	39	0	.	1	97	0	.	0	.	1	98	0	.	1	96	4	37	3	93	2	98	2	54	3	46	4	40	0	.	3	8
	81-90 cm	1	58	0	99	2	47	1	73	0	.	0	.	0	.	1	98	1	36	0	.	2	67	1	98	1	98	0	.	1	97	0	.	20	.	1	98	0	.	0	.	0	.	3	93	0	.	1	98	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	94	0	.	3	95	0	.	1	69	4	40	0	.	1	98	0	.	0	.	0	.	10	82	0	.	1	98	3	58	0	.	5	55	3	92	0	.	3	75	1	70	2	68	0	.	2	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	96	0	.	0	.	1	98	0	.	1	96	2	75	0	.	2	69	1	68	4	49	0	.	0	.	1	12
	91-100 cm	0	99	0	.	0	.	0	.	0	.	0	.	0	.	1	98	1	79	0	.	0	.	1	98	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	2	98	0	.	0	.	0	.	1	70	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	3	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	1	98	0	.	0	.	0	.	0	28
	>100 cm	0	.	0	99	0	.	0	99	0	.	0	.	0	.	1	98	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	99	0	.	2	67	0	.	0	.	4	98	1	70	5	57	0	.	2	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	29
	total	363	12	404	11	372	10	354	11	0	.	0	.	494	20	412	10	360	6	483	42	297	14	461	14	383	14	210	.	378	13	483	17	550	.	502	16	392	18	570	.	341	32	310	19	770	.	612	14	341	10	431	9	385	11	426	13	722	8	476	12	0	.	410	14	360	.	0	.	200	4	1176	13	480	37	544	11	632	27	627	28	511	17	0	.	458	9	321	14	374	16	338	18	494	13	777	19	0	.	331	20	478	13	411	12	418	15	459	53	395	18	476	18	739	13	603	11	765	8	530	12	503	14	686	13	966	16	245	72	441	20	782	13	873	20	782	25	452	19	460	22	297	18	322	15	729	19	0	.	527	20	390	15	478	9	462	20	280	15	0	.	363	21	577	15	454	20	516	14	330	31	353	25	384	9	441	28	336	10	336	12	320	13	349	13	0	.	446	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	165	16	200	14	157	19	150	19	215	21	151	16	202	6	158	6	174	8	219	8	107	22	197	22	172	12	0	.	157	18	195	15	184	12	195	18	163	25	200	11	182	9	148	11	280	10	211	7	208	8	206	7	228	14	202	14	262	9	222	19	188	17	161	23	153	15	189	21	192	19	238	14	253	14	178	16	240	21	218	13	211	16	149	18	181	15	174	17	174	20	154	27	162	15	259	13	170	14	201	17	186	15	195	22	188	23	264	19	234	12	220	13	290	13	267	12	428	10	271	9	281	13	313	13	221	19	225	34	258	14	247	15	224	18	304	13	186	18	250	25	217	14	156	20	211	28	156	14	200	20	164	17	164	14	240	27	112	30	58	39	146	33	201	7	225	8	256	7	163	17	167	27	160	16	160	18	129	17	109	21	134	29	131	22	0	.	207	1
	21-30 cm	79	16	102	19	86	12	74	20	131	15	129	14	112	6	92	8	88	8	138	9	56	28	99	18	122	16	50	.	96	20	139	17	138	11	115	15	108	28	118	14	104	10	111	11	123	9	115	7	109	7	102	7	106	13	92	14	143	10	90	25	96	13	86	23	68	21	93	18	154	17	159	19	142	14	105	16	149	15	115	13	108	22	102	15	115	15	80	21	105	16	80	24	115	16	111	14	125	16	112	20	125	16	99	23	122	19	83	18	111	12	101	13	110	14	162	11	173	9	135	11	148	13	164	11	163	18	105	20	126	14	114	15	87	21	149	19	117	20	94	39	136	21	79	25	99	25	91	15	128	21	90	20	92	19	149	24	92	33	98	37	70	36	112	7	139	8	139	7	105	15	95	31	77	19	110	20	84	15	104	20	80	20	106	19	0	.	114	1
	31-40 cm	55	15	41	17	65	17	49	19	103	13	93	14	78	6	62	7	46	8	82	8	40	29	59	20	82	15	40	.	71	20	94	14	85	14	83	13	62	38	52	14	68	10	69	10	54	10	67	7	63	7	82	8	68	12	64	17	77	11	47	22	67	23	92	22	52	23	57	13	84	17	69	16	62	14	70	15	91	16	83	11	71	16	71	14	78	10	56	19	89	15	48	17	88	12	84	14	53	15	65	18	82	17	53	18	57	18	59	22	47	16	59	18	45	18	75	13	73	12	48	12	42	14	66	15	69	19	84	22	67	19	65	14	54	20	66	17	61	22	39	39	90	14	47	30	64	24	64	13	61	15	70	16	73	15	65	20	45	28	92	34	52	21	55	9	67	8	70	8	53	19	59	17	47	19	62	15	69	15	63	19	52	26	42	25	0	.	66	1
	41-50 cm	39	12	22	20	32	16	38	11	75	11	69	13	48	6	42	7	35	7	53	7	35	23	42	22	48	14	40	.	49	24	54	14	49	13	46	14	43	28	48	11	45	9	39	10	32	11	38	7	40	7	52	6	43	13	41	13	56	9	48	17	39	14	57	17	33	20	52	10	32	18	36	19	38	13	43	14	50	18	42	11	40	17	58	11	48	12	32	15	55	14	35	18	51	11	42	14	39	16	46	15	37	18	33	22	31	18	13	23	29	14	41	16	16	19	29	14	23	19	24	13	21	15	26	13	35	23	46	27	40	20	48	19	39	17	53	14	30	24	33	25	47	13	43	19	30	24	43	11	36	18	50	15	47	11	50	22	44	23	40	40	26	24	32	8	33	10	36	8	46	21	40	18	36	16	35	14	43	14	29	19	40	24	33	17	0	.	40	1
	51-60 cm	17	17	18	18	18	17	24	16	36	18	46	15	29	7	32	7	22	9	26	10	26	25	20	29	26	19	60	.	20	26	26	16	32	14	28	14	12	64	22	14	24	12	25	11	23	11	21	8	21	13	21	8	28	19	25	15	28	14	26	19	26	19	27	27	17	23	26	14	15	27	17	27	26	18	27	19	15	21	23	15	32	16	36	11	19	18	26	17	18	34	20	27	25	16	26	15	26	16	26	18	23	23	19	27	19	17	16	22	20	16	15	18	5	25	12	22	6	29	15	13	10	25	14	16	20	25	16	32	14	22	27	21	22	23	34	15	19	24	8	43	26	19	30	27	15	24	15	16	25	21	32	20	34	18	19	33	18	29	53	24	23	23	19	9	20	12	19	11	30	23	31	20	22	17	25	19	23	17	22	27	23	26	27	20	0	.	23	2
	61-70 cm	8	22	8	32	7	31	12	17	11	28	20	24	10	10	13	11	10	12	6	26	14	30	7	37	9	32	0	.	7	35	8	28	11	22	11	24	2	95	8	21	9	16	8	17	9	16	9	10	7	18	7	16	16	20	14	19	12	17	8	40	7	25	4	67	7	46	19	15	5	33	9	30	8	25	9	22	8	28	9	20	11	27	20	20	9	27	12	23	3	42	7	31	7	38	15	20	6	29	11	24	6	38	5	38	6	36	6	35	7	20	9	23	3	40	5	36	4	36	4	21	2	36	6	29	7	32	8	34	5	36	12	24	8	28	12	30	5	30	7	74	12	30	11	33	6	51	6	24	13	27	6	30	7	31	4	52	17	24	8	65	17	37	8	14	6	16	7	17	9	35	17	27	10	29	12	21	6	30	5	44	4	49	15	29	0	.	9	2
	71-80 cm	3	35	3	42	2	50	5	33	3	56	4	34	4	16	5	15	4	19	3	29	2	68	3	46	3	48	20	.	3	70	4	35	4	41	6	30	2	95	6	27	5	24	4	24	5	20	4	17	2	21	3	22	5	31	2	41	4	27	7	46	3	50	2	67	7	34	4	44	1	99	4	38	3	37	5	36	4	35	3	35	10	29	8	24	2	43	5	32	0	.	1	68	1	72	6	32	3	48	7	45	2	47	4	53	2	59	6	30	4	28	4	33	1	72	2	40	1	52	3	27	3	42	3	29	4	37	8	59	6	46	5	34	8	25	5	33	6	44	0	.	2	49	7	46	1	98	1	69	4	49	2	48	2	47	0	.	5	47	8	65	5	65	5	18	2	31	4	26	4	37	2	55	5	32	2	42	2	51	2	54	3	46	3	41	0	.	4	4
	81-90 cm	1	59	0	99	2	48	1	52	0	.	1	99	1	28	2	23	1	27	1	53	2	68	1	98	1	99	0	.	1	98	0	99	1	49	1	70	0	.	2	36	1	46	2	31	2	30	2	21	1	30	1	35	0	.	0	.	1	71	1	98	1	99	0	.	0	.	1	73	1	99	1	69	1	99	1	72	1	71	1	57	2	57	3	55	1	70	2	42	0	99	1	99	0	.	1	69	0	99	3	42	1	99	1	98	2	58	1	99	3	31	2	40	1	57	2	63	1	70	1	44	1	70	1	51	0	.	2	98	1	99	3	49	3	40	2	48	2	55	0	.	1	99	3	53	0	.	0	99	0	.	0	.	1	98	0	.	1	97	3	93	1	98	2	27	1	33	1	34	0	.	4	49	2	75	2	48	1	71	1	68	4	49	0	.	0	.	1	6
	91-100 cm	0	99	0	.	0	.	0	99	0	.	0	.	0	42	0	70	0	66	0	70	0	.	1	99	0	.	0	.	1	98	0	.	1	71	0	.	0	.	1	39	0	100	0	70	0	70	0	44	1	45	0	70	0	99	0	.	0	.	0	.	1	99	1	98	0	.	1	99	0	.	0	.	1	77	0	.	1	99	0	100	0	99	1	70	0	.	0	.	0	.	0	.	0	99	0	99	0	.	1	99	0	.	1	99	0	.	0	99	1	60	0	99	0	.	0	99	1	70	0	72	0	99	0	99	0	.	1	98	1	99	0	.	1	99	0	.	1	98	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	2	67	0	.	0	.	1	33	1	41	0	100	0	.	0	.	1	69	0	.	0	.	1	98	0	.	0	.	0	.	0	11
	>100 cm	0	.	1	70	0	.	0	99	0	.	0	.	0	62	0	70	0	100	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	99	0	.	1	49	0	100	1	86	0	57	1	39	0	57	0	100	0	.	0	.	0	.	0	.	0	.	0	.	2	98	0	.	0	.	0	.	0	99	0	.	0	.	0	70	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	69	0	.	0	.	3	99	2	71	2	40	0	.	1	99	2	99	1	70	1	48	0	.	2	45	0	.	0	.	1	99	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	71	0	.	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	15
	total	367	10	394	11	369	9	353	10	573	10	512	9	483	4	407	4	380	5	529	5	282	14	430	12	463	8	210	.	404	12	521	11	505	8	487	10	392	18	458	8	438	6	408	7	528	7	466	4	452	4	475	5	494	9	441	8	583	6	448	9	427	10	431	11	338	11	441	12	484	13	533	12	534	10	438	9	559	13	494	9	486	11	449	10	452	8	388	10	443	11	345	15	449	9	544	8	422	9	473	11	460	9	411	12	431	13	449	14	458	8	454	8	471	10	556	8	711	7	505	7	508	10	595	9	519	14	496	16	518	11	519	8	447	12	624	10	427	12	431	20	531	11	376	15	426	18	375	9	468	14	414	11	420	9	525	20	337	19	360	22	339	17	435	5	495	6	532	5	409	11	415	15	359	8	407	11	358	8	336	12	340	13	358	12	0	.	464	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. 2281767. Birmensdorf, Swiss Federal Research Institute WSL

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