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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (main diameter classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (main diameter classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	.
	12-15 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	.
	16-23 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	.
	24-35 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	.
	36-51 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	.
	=52 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	.
upper 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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	90	0	.	2	71	22	44	92	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	72	14	61	1	100	0	.	0	.	0	1773	2	.	3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	35	31	59	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	218	16
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	1	100	26	52	379	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	55	17	104	27	100	0	.	0	.	3	100	10	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	170	62	49	37	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	717	19
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	80	0	.	16	100	189	32	763	20	0	.	0	.	0	.	0	.	0	.	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	70	56	63	-4	100	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	324	31	154	47	50	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1627	13
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	78	0	.	6	100	232	36	1051	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	68	34	64	0	.	0	.	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	452	30	255	39	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2134	14
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	100	0	.	0	.	297	47	1196	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	.	14	100	56	58	61	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	.	447	35	165	43	25	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2379	20
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	179	80	0	.	25	93	765	30	3481	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	.	120	52	177	53	85	78	0	.	0	.	11	64	13	.	22	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1416	26	655	35	91	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7075	12
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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	87	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	.	0	.	0	.	11	59	53	34	10	48	60	36	119	48	159	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	14	61	17	61	0	.	0	.	11	63	0	.	7	.	0	.	0	.	0	.	0	.	0	.	8	87	0	.	0	.	0	.	0	.	0	.	6	68	39	66	6	61	0	.	0	.	0	.	25	48	6	100	2	.	0	.	0	.	17	62	4	100	3	100	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	0	.	92	36	114	53	32	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	926	13
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	211	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	.	0	.	0	.	35	82	162	36	51	66	148	33	208	29	448	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	94	42	79	75	64	0	.	0	.	44	62	0	.	22	.	0	.	0	.	0	.	0	.	0	.	5	72	0	.	0	.	0	.	0	.	0	.	4	71	75	41	14	146	0	.	0	.	0	.	127	43	6	77	3	.	0	.	11	79	53	75	0	100	8	59	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	300	24	158	34	93	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2434	9
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	1013	21	28	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	.	0	.	0	.	12	167	449	34	203	45	425	35	560	35	936	21	0	.	0	.	0	.	27	.	0	.	0	.	0	.	0	.	0	.	126	91	103	87	148	67	0	.	0	.	163	68	0	.	18	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	8	100	305	40	82	55	30	100	0	.	0	.	151	45	14	100	0	.	16	.	86	79	30	71	63	71	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	.	0	.	0	.	0	.	0	.	572	31	536	28	328	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6569	8
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	2133	21	74	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	0	.	0	.	100	62	1010	24	436	38	1282	31	1009	32	1987	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	62	78	55	89	72	0	.	31	.	276	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	92	0	.	0	.	0	.	0	.	0	.	146	64	408	35	191	58	9	100	0	.	0	.	142	43	43	67	0	.	0	.	108	78	87	76	26	77	132	54	36	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	.	0	.	0	.	0	.	0	.	1089	30	902	27	846	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12937	7
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	1452	28	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	312	52	1688	33	273	47	1412	29	797	27	1749	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	73	37	77	56	100	0	.	50	.	120	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	215	89	0	.	0	.	0	.	0	.	0	.	363	48	485	39	94	60	0	.	0	.	0	.	356	45	56	63	0	.	62	.	127	71	0	.	130	71	140	47	79	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	.	0	.	0	.	0	.	2242	25	717	34	1062	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14265	8
	total	0	.	0	.	0	.	0	.	0	.	0	.	4896	20	151	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	.	0	.	0	.	469	53	3361	24	973	36	3328	24	2693	24	5278	17	0	.	0	.	0	.	27	.	0	.	0	.	0	.	0	.	0	.	420	62	274	67	385	51	0	.	81	.	614	54	0	.	48	.	0	.	0	.	0	.	0	.	0	.	358	69	0	.	0	.	0	.	0	.	0	.	527	49	1313	32	387	49	39	100	0	.	0	.	801	35	125	64	5	.	78	.	331	74	187	70	223	71	298	49	119	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	.	0	.	0	.	0	.	0	.	4296	22	2427	25	2362	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37132	6
high-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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	188	24	2	100	0	.	1	100	0	.	0	.	0	.	0	.	0	.	21	53	0	0	0	0	0	.	11	89	54	48	47	38	229	24	189	19	152	29	0	.	5	100	0	.	24	55	0	.	0	.	0	.	0	.	0	.	4	73	60	48	7	47	0	.	1	100	14	54	1	.	6	100	0	.	0	.	0	.	0	.	0	.	7	60	48	83	7	66	0	.	0	.	0	.	74	47	15	65	27	43	2	58	0	.	0	.	15	41	6	83	5	100	11	58	5	83	23	45	13	72	24	54	17	74	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	175	30	153	23	142	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1788	7
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	414	21	-15	100	0	.	3	100	0	.	0	.	0	.	0	.	7	.	105	50	10	71	9	78	0	.	21	89	78	34	202	36	519	26	737	18	418	24	0	.	20	80	0	.	93	48	0	.	0	.	0	.	0	.	0	.	34	74	143	43	13	71	0	.	32	71	24	57	8	.	0	0	0	.	0	.	0	.	0	.	0	.	26	53	46	74	20	74	0	.	0	.	0	.	189	47	98	51	78	38	13	100	0	.	0	.	42	41	34	69	60	76	7	372	31	62	143	42	44	74	34	47	87	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	90	0	.	0	.	0	.	0	.	530	24	781	24	527	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5686	7
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	1252	23	66	71	0	.	0	.	0	.	0	.	0	.	0	.	24	.	338	63	15	75	61	71	0	.	109	76	364	40	502	34	756	25	1758	18	1324	21	0	.	50	100	0	.	162	50	0	.	0	.	0	.	0	.	0	.	57	73	371	40	122	49	0	.	124	71	40	60	38	.	0	0	0	.	0	.	0	.	0	.	0	.	33	65	74	74	32	100	0	.	0	.	0	.	87	50	100	66	367	42	54	72	0	.	0	.	213	46	91	58	151	65	88	70	149	68	469	41	109	52	114	54	207	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	.	66	100	0	.	0	.	0	.	0	.	1182	21	1268	23	875	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13275	6
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	2394	21	276	60	0	.	10	100	0	.	0	.	0	.	0	.	83	.	387	50	171	64	125	71	0	.	171	62	1394	28	453	31	1191	25	3384	15	2135	21	0	.	46	100	0	.	216	53	0	.	0	.	0	.	0	.	0	.	17	100	329	43	210	56	0	.	176	75	21	81	83	.	153	71	0	.	0	.	0	.	0	.	0	.	308	58	129	90	84	76	0	.	0	.	0	.	185	62	138	55	864	34	82	63	0	.	0	.	200	47	94	61	136	60	217	65	314	79	528	45	282	72	474	51	264	64	0	.	0	.	0	0	0	.	0	.	0	.	0	.	22	.	129	59	0	.	0	.	0	.	0	.	1498	22	1440	22	1443	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22255	6
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	3577	20	83	100	0	.	181	95	0	.	0	.	0	.	0	.	89	.	43	72	335	62	142	72	0	.	728	58	1225	29	627	41	2098	25	4277	17	1232	27	0	.	-5	100	0	.	161	52	0	.	0	.	0	.	0	.	0	.	0	.	386	56	254	52	0	.	52	100	120	82	31	.	306	72	0	.	0	.	0	.	0	.	0	.	274	59	65	100	92	100	0	.	0	.	0	.	581	49	397	63	946	35	83	79	0	.	0	.	145	59	163	67	360	74	197	64	145	100	321	52	417	56	404	44	481	56	0	.	0	.	121	100	0	.	0	.	0	.	0	.	50	.	94	87	0	.	0	.	0	.	0	.	1531	27	1450	24	1809	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26067	6
	total	0	.	0	.	0	.	0	.	0	.	0	.	7823	17	414	57	0	.	196	93	0	.	0	.	0	.	0	.	203	.	894	49	531	59	337	71	0	.	1041	53	3115	25	1832	29	4794	20	10345	13	5261	18	0	.	116	96	0	.	655	42	0	.	0	.	0	.	0	.	0	.	111	72	1289	40	606	48	0	.	385	71	217	63	160	.	465	71	0	.	0	.	0	.	0	.	0	.	647	46	363	71	234	72	0	.	0	.	0	.	1116	36	748	51	2281	30	234	58	0	.	0	.	615	41	389	55	713	57	520	64	645	65	1483	39	865	54	1050	44	1056	53	0	.	0	.	123	98	0	.	0	.	0	.	0	.	84	.	314	60	0	.	0	.	0	.	0	.	4917	18	5092	18	4795	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69071	5
lower and upper 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	.
	12-15 cm	5	100	0	.	0	.	9	46	-1	1697	49	44	267	29	152	31	122	41	261	19	0	.	17	55	49	33	0	.	4	72	39	44	106	28	19	47	0	.	79	25	65	48	50	34	10	100	54	36	0	.	183	20	94	38	94	37	137	23	34	61	70	31	9	57	39	38	74	40	35	34	17	57	52	33	33	38	24	48	88	34	44	62	65	27	40	59	62	39	61	45	18	56	17	64	80	34	23	139	24	103	38	59	0	.	4	100	0	.	19	59	0	.	63	39	37	40	48	37	119	36	68	41	49	35	45	40	67	54	5	74	49	55	16	52	97	41	24	56	0	.	75	35	3	.	15	62	77	29	29	48	54	41	14	47	36	61	22	70	0	.	10	74	0	.	8	76	147	45	33	44	33	52	-26	154	56	30	26	42	0	.	0	.	0	.	0	.	4131	5
	16-23 cm	41	83	4	.	0	.	51	76	91	35	298	29	572	20	843	19	439	29	914	18	0	.	17	63	172	37	0	.	50	73	231	41	547	25	123	53	0	.	294	29	175	50	142	36	14	99	123	36	0	.	651	19	220	30	117	30	435	20	82	55	238	32	48	48	101	38	224	35	211	40	64	45	216	32	159	41	129	46	340	28	253	46	183	31	103	50	119	42	156	46	65	64	95	39	275	33	235	38	202	44	145	43	0	.	70	66	0	.	41	56	0	.	235	39	358	39	86	39	448	31	293	36	113	48	177	39	125	52	39	58	85	49	33	48	191	42	93	70	9	.	243	28	17	.	31	64	274	28	130	58	194	41	32	51	130	69	121	58	8	100	14	100	0	.	9	84	275	25	139	43	97	56	0	.	190	37	144	65	0	.	23	.	10	.	0	.	14684	4
	24-35 cm	105	55	9	.	0	.	91	56	580	32	1028	27	1771	19	1940	16	810	30	2867	17	13	100	98	65	292	40	0	.	293	64	364	42	1092	25	423	46	0	.	867	35	480	34	314	35	0	.	365	35	0	.	1374	18	383	31	651	39	1347	23	285	58	641	35	107	59	259	41	493	29	772	35	67	72	820	28	437	35	472	40	796	29	447	38	1061	23	411	38	346	42	426	38	53	58	303	50	430	29	739	31	443	34	564	39	0	.	117	76	0	.	161	67	0	.	217	38	373	40	229	51	480	38	385	38	298	37	446	43	88	81	14	100	210	44	137	47	426	43	333	61	0	.	954	28	66	.	77	71	718	29	243	37	404	42	88	59	170	52	88	91	192	62	42	100	0	.	0	0	764	27	287	48	209	42	54	63	771	28	494	32	0	.	12	.	20	.	0	.	37493	4
	36-51 cm	257	50	14	.	0	.	325	46	1417	29	2308	24	3313	17	4110	14	1708	24	4363	14	135	100	127	86	545	38	0	.	227	61	940	33	1567	25	907	35	0	.	2050	23	879	30	904	27	82	76	318	41	0	.	3582	17	917	29	1178	26	2596	20	262	46	998	27	299	48	502	38	1415	25	776	33	93	50	1251	29	1181	30	909	36	1144	23	813	36	2305	21	631	38	525	36	840	32	243	54	765	31	830	31	937	28	975	31	527	37	0	.	205	54	0	.	187	46	0	.	164	41	309	36	117	54	422	35	305	45	330	43	336	40	133	60	284	64	499	45	153	47	485	38	307	50	58	.	1003	25	77	.	189	54	1103	23	435	40	897	29	379	41	354	78	347	54	251	55	64	108	0	.	0	.	1050	28	517	39	521	41	198	61	1243	24	1196	30	0	.	199	.	39	.	0	.	67349	3
	=52 cm	242	59	67	.	0	.	476	45	1274	34	1959	28	3596	20	6759	14	2659	23	2597	17	0	.	78	100	841	47	0	.	71	74	1002	38	1658	29	728	41	0	.	1652	25	497	40	1420	28	155	72	453	42	0	.	2619	20	1324	39	1769	31	2691	26	486	50	968	37	340	53	552	42	1448	25	564	42	87	72	1326	32	1204	39	435	46	1502	32	1735	32	3899	23	209	53	564	42	313	53	450	50	327	44	1319	35	1022	31	1126	39	531	46	0	.	201	72	0	.	542	59	0	.	188	50	216	54	100	84	333	44	154	50	270	48	362	56	245	63	293	85	474	49	605	53	641	48	477	52	22	.	866	40	77	.	377	58	690	30	793	38	392	40	674	49	101	71	342	45	433	68	297	76	0	.	0	.	765	31	523	47	859	49	191	60	1392	30	454	49	0	.	0	.	99	.	0	.	71446	4
	total	651	47	96	.	0	.	952	42	3362	28	5641	22	9519	15	13804	12	5738	20	11002	12	148	100	337	53	1900	36	0	.	644	55	2575	30	4969	20	2200	31	0	.	4942	21	2096	25	2830	24	261	66	1314	33	0	.	8409	15	2937	26	3809	25	7207	19	1149	40	2915	24	804	43	1453	34	3653	22	2358	29	329	53	3665	25	3014	27	1969	33	3870	23	3292	28	7513	18	1394	35	1616	31	1796	28	829	45	1507	31	2935	26	2956	25	2771	27	1804	34	0	.	595	57	0	.	950	47	0	.	867	33	1293	32	580	40	1803	26	1204	32	1060	33	1367	35	658	48	635	59	1317	37	944	45	1840	34	1234	45	88	.	3140	23	240	.	689	45	2863	20	1630	33	1942	27	1187	39	790	58	919	47	883	55	427	70	0	.	17	80	3001	23	1499	35	1719	37	417	56	3652	22	2315	29	0	.	234	.	168	.	0	.	195103	2
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	.
	12-15 cm	46	87	90	56	142	50	50	31	0	.	0	.	25	58	62	30	280	18	0	.	26	53	38	60	31	35	0	.	64	84	49	68	0	.	78	80	0	.	0	.	16	78	12	50	0	.	0	.	0	.	57	32	37	38	67	32	8	71	39	52	0	.	10	158	-5	.	0	.	0	.	0	.	29	94	10	82	41	58	19	91	32	64	0	.	90	33	28	47	7	100	58	85	11	126	31	96	0	.	0	.	44	43	174	41	45	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	75	0	.	15	96	21	67	39	58	0	.	10	65	15	78	-4	127	21	66	36	74	0	.	14	79	10	131	25	53	7	72	2	100	0	.	24	100	0	.	0	.	40	53	4	76	30	71	26	37	9	71	31	40	31	35	42	76	62	43	0	.	2361	8
	16-23 cm	227	30	190	37	202	44	253	46	0	.	0	.	50	46	126	36	888	17	0	.	73	45	85	47	112	50	0	.	56	50	101	40	0	.	299	56	0	.	0	.	15	78	55	56	0	.	0	.	0	.	227	31	138	52	297	34	20	77	47	60	0	.	31	64	8	.	0	.	0	.	0	.	47	79	39	71	144	59	33	88	102	64	0	.	362	30	64	57	33	68	40	54	104	40	15	100	0	.	8	100	134	41	198	37	313	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	80	15	.	42	71	49	56	64	58	0	.	72	67	93	58	3	100	36	62	25	58	0	.	85	54	49	65	55	60	89	62	3	100	0	.	17	74	0	.	0	.	137	51	28	88	56	44	142	51	64	81	62	40	243	42	86	37	233	43	0	.	6950	6
	24-35 cm	408	30	531	36	442	35	521	38	0	.	0	.	212	49	583	34	1867	16	25	.	149	55	246	44	365	39	2	.	154	62	472	42	0	.	338	37	0	.	0	.	43	80	69	73	0	.	0	.	0	.	634	29	358	35	560	36	90	74	37	77	0	.	120	47	17	.	0	.	0	.	0	.	95	100	86	59	121	76	19	138	134	71	0	.	871	28	143	43	96	55	258	43	413	45	43	76	0	.	94	78	536	41	566	33	519	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	211	82	39	.	95	80	159	74	158	77	0	.	113	71	155	65	85	73	43	53	56	68	0	.	137	68	112	50	254	51	178	45	101	51	0	.	97	70	0	.	0	.	137	61	63	76	122	74	241	40	144	75	386	41	342	39	392	42	348	33	0	.	16410	5
	36-51 cm	1101	25	522	26	668	34	1385	25	0	.	0	.	818	46	632	30	3861	13	0	.	142	52	573	42	524	35	106	.	379	61	575	45	0	.	754	33	0	.	0	.	147	68	65	59	0	.	0	.	0	.	1749	23	1110	48	1108	27	71	74	202	51	0	.	293	51	39	.	0	.	0	.	0	.	0	.	91	75	215	68	55	73	230	58	0	.	1392	25	237	38	361	57	607	36	422	39	74	100	0	.	183	73	354	49	798	33	843	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	121	71	137	.	96	73	190	66	45	78	0	.	166	57	306	51	97	76	239	61	138	74	0	.	91	55	493	50	407	43	344	46	189	71	0	.	176	64	0	.	0	.	77	63	121	71	158	73	653	34	14	200	557	36	550	35	687	38	475	35	0	.	29213	5
	=52 cm	1029	29	812	34	680	37	2688	24	0	.	0	.	486	53	1179	39	5324	15	0	.	507	43	336	75	550	42	70	.	162	77	339	60	0	.	665	33	0	.	0	.	68	81	144	72	0	.	0	.	0	.	1208	26	744	37	918	33	137	75	182	73	0	.	247	85	0	.	0	.	0	.	0	.	0	.	145	100	157	55	67	100	492	56	0	.	1060	30	1024	45	75	75	373	56	150	60	68	100	0	.	113	77	184	54	1282	40	1487	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	101	100	22	.	37	100	88	76	271	83	0	.	180	70	20	100	97	98	317	63	0	.	0	.	38	71	218	61	361	50	151	55	184	88	0	.	359	64	0	.	0	.	90	66	109	100	460	48	436	41	0	.	551	44	981	44	517	39	674	40	0	.	31414	6
	total	2811	21	2146	23	2133	26	4897	21	0	.	0	.	1591	47	2583	28	12221	12	25	.	898	38	1278	37	1582	31	177	.	815	50	1536	37	0	.	2134	29	0	.	0	.	289	53	346	59	0	.	0	.	0	.	3875	21	2387	33	2951	25	326	71	507	49	0	.	701	51	60	.	0	.	0	.	0	.	172	73	371	67	678	45	192	75	990	50	0	.	3775	21	1497	38	572	53	1337	36	1099	38	231	79	0	.	398	76	1252	36	3018	28	3207	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	505	71	214	.	286	71	508	54	578	57	0	.	541	56	590	47	278	66	656	51	255	56	0	.	364	54	882	45	1102	42	770	42	478	61	0	.	672	58	0	.	0	.	482	45	326	72	826	44	1498	29	231	70	1586	31	2147	31	1724	32	1792	28	0	.	86349	4
hyperinsubric and colline	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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	61	0	.	0	.	0	.	0	.	107	80	2	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	.	6	.	9	90	25	113	15	49	89	42	68	46	188	30	60	37	57	56	141	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	5	.	0	.	0	.	0	.	48	54	33	43	11	56	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	881	14
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	52	0	.	0	.	0	.	0	.	104	49	23	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	.	14	84	186	46	70	53	248	40	196	44	688	24	175	39	164	51	376	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	73	0	.	0	.	34	.	0	.	0	.	0	.	145	41	81	48	35	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2620	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	204	54	0	.	0	.	0	.	0	.	341	48	26	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	.	23	100	48	120	112	64	249	42	432	36	827	25	186	40	420	41	449	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	71	0	.	0	.	33	.	0	.	0	.	0	.	132	58	103	59	27	71	0	.	0	.	0	.	0	.	0	.	33	100	0	.	0	.	0	.	3767	10
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	285	56	0	.	0	.	0	.	0	.	187	45	63	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	.	18	100	109	53	127	58	62	59	322	34	782	28	163	39	168	41	458	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	71	0	.	0	.	0	.	0	.	0	.	0	.	195	47	67	57	14	347	0	.	0	.	0	.	0	.	0	.	103	83	0	.	0	.	0	.	3197	11
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	100	0	.	0	.	0	.	0	.	135	61	47	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	.	30	100	143	69	25	100	108	51	235	38	425	36	300	62	88	79	491	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	101	84	0	.	30	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2267	17
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	632	51	0	.	0	.	0	.	0	.	873	39	160	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	.	93	96	511	42	349	56	756	34	1253	31	2910	19	884	33	897	36	1915	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	186	71	0	.	0	.	72	.	0	.	0	.	0	.	620	40	284	50	117	54	0	.	0	.	0	.	0	.	0	.	137	87	0	.	0	.	0	.	12732	8
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	.
	12-15 cm	51	78	90	56	142	50	58	27	-1	1697	49	44	566	17	217	23	401	18	262	19	26	53	55	44	80	24	0	.	67	80	109	36	114	27	97	65	14	61	101	23	199	24	119	21	300	20	492	23	404	20	240	17	136	28	161	25	169	20	73	40	70	31	19	87	34	46	74	40	52	34	91	35	105	32	43	35	66	40	131	27	78	44	78	26	130	29	90	31	68	42	76	67	28	62	125	32	71	72	31	81	82	36	174	41	55	35	90	40	111	37	62	27	155	29	105	33	236	25	219	23	140	31	201	23	66	31	72	51	61	35	87	37	82	34	114	36	34	44	15	78	72	37	25	59	51	55	77	29	45	39	64	40	42	35	48	47	24	65	0	.	34	74	338	20	340	22	374	21	37	40	63	43	0	14262	65	27	57	29	32	33	42	76	62	43	0	.	10305	3
	16-23 cm	267	29	195	37	202	44	304	41	91	35	298	29	1246	12	955	17	1327	15	916	17	73	45	102	40	285	30	0	.	113	41	437	26	604	24	431	42	43	52	350	27	436	25	451	22	683	21	1197	13	1268	14	879	16	378	26	414	26	548	18	129	41	238	32	79	38	110	36	224	35	322	35	248	30	352	26	198	36	304	35	441	24	363	37	205	29	464	26	183	34	189	40	105	45	199	28	321	29	281	34	230	39	279	29	198	37	411	34	206	44	423	25	179	30	523	26	553	30	773	22	795	20	508	26	551	25	245	36	183	38	277	29	178	33	139	31	280	34	165	49	102	54	246	27	53	53	56	44	274	28	227	39	243	35	116	37	253	43	123	57	8	100	30	61	1145	16	1077	18	1072	18	168	39	153	39	142	51	254	34	206	47	243	42	109	36	243	41	0	.	33091	2
	24-35 cm	514	26	540	35	442	35	612	34	580	32	1028	27	4248	12	2618	14	2677	14	2892	17	161	51	344	36	658	28	2	.	471	45	1174	28	1163	24	822	29	204	54	987	32	1365	20	1088	21	1197	20	3212	13	3049	12	2008	15	791	23	1211	27	1627	20	322	52	641	35	227	38	276	39	493	29	954	31	541	33	1185	23	523	31	717	32	1017	25	618	32	1079	23	1282	22	490	32	523	33	312	37	716	33	529	25	813	29	568	30	1099	28	566	33	647	31	118	42	656	28	617	29	546	26	806	27	1056	22	1038	22	910	25	898	23	762	35	362	40	607	34	541	31	425	36	634	43	446	49	155	65	1039	26	109	64	133	50	718	29	492	30	528	34	451	36	381	32	189	50	192	62	139	57	2211	15	2062	17	2183	15	350	42	331	38	295	35	916	26	880	25	375	36	404	40	368	32	0	.	79141	2
	36-51 cm	1358	22	537	26	668	34	1711	22	1417	29	2308	24	8657	11	5093	12	5569	12	4374	14	277	55	700	38	1069	26	106	.	688	41	1902	23	1755	24	1786	23	285	56	2321	21	3479	15	1858	18	2562	20	5131	12	5236	12	5331	13	2073	29	2286	19	2883	19	465	34	998	27	592	35	541	36	1415	25	868	30	500	31	1550	25	1273	28	1331	29	1495	21	1126	29	2458	20	2023	21	762	27	1201	28	850	30	1187	24	1320	25	1067	27	1243	27	881	29	798	33	1063	24	349	42	871	22	1216	27	316	29	631	25	899	25	927	21	610	27	944	25	674	32	692	44	995	31	997	33	804	33	786	32	473	38	364	45	1100	24	316	52	327	44	1103	23	586	32	1412	25	981	26	699	45	535	43	251	55	240	55	3234	15	2664	15	3438	15	638	34	679	36	851	29	1257	24	1753	24	653	32	886	37	514	33	0	.	137086	2
	=52 cm	1271	26	879	32	680	37	3164	21	1274	34	1959	28	9112	12	8069	13	7984	13	2779	17	507	43	414	64	1391	33	70	.	322	50	1383	31	1993	26	1535	25	87	100	2692	23	3560	19	2464	20	3666	18	5958	13	4224	16	3827	15	2063	28	2687	23	2989	24	668	42	968	37	588	47	552	42	1448	25	693	37	511	44	1636	27	1349	37	694	33	1809	28	2257	28	4205	22	1269	26	1588	32	388	45	823	37	477	35	1876	28	1087	30	1331	34	715	37	1282	40	1711	33	1010	33	1581	29	1121	30	440	32	452	32	526	33	1134	26	461	33	1121	32	722	37	539	43	651	46	1110	31	1419	30	1201	35	657	42	42	71	1083	35	394	54	377	58	690	30	831	37	660	32	1142	34	252	44	526	42	433	68	657	49	4322	16	2333	18	3781	17	632	42	1319	36	627	34	1392	30	1005	32	981	44	517	39	773	37	0	.	147839	2
	total	3462	19	2241	23	2133	26	5849	19	3362	28	5641	22	23829	9	16952	10	17958	10	11223	12	1046	35	1615	31	3482	24	177	.	1662	34	5005	21	5629	18	4671	20	632	51	6452	18	9039	14	5981	16	8408	15	15991	10	14180	10	12284	12	5441	20	6759	18	8215	17	1656	32	2915	24	1505	33	1512	32	3653	22	2890	26	1892	30	4828	21	3385	25	3112	25	4893	20	4442	23	8026	18	5169	18	3113	24	2368	25	2166	28	2607	24	4170	21	3319	23	3403	24	3056	25	3018	28	3885	22	1773	27	3642	20	3195	23	1980	21	2546	22	3490	17	4113	16	2628	21	3715	20	2469	28	1847	33	2591	28	2913	25	2870	25	3015	28	1776	36	679	42	3540	21	896	46	944	36	2863	20	2181	27	2908	23	2732	25	1633	35	1397	37	883	55	1099	45	11249	12	8475	13	10848	12	1826	31	2545	29	1915	26	3884	21	3900	21	2284	29	1958	30	1960	27	0	.	407462	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. 2293430. Birmensdorf, Swiss Federal Research Institute WSL

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