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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NFI1
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		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	.	0	.	0	.	1	.	2	100	92	25	0	.	0	.	0	.	0	.	0	.	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	100	5	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	43	2	71	3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	20
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	.	0	.	2	.	6	75	219	27	0	.	0	.	0	.	0	.	0	.	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	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	.	134	45	53	57	8	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	444	21
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	.	0	.	0	.	65	57	580	24	0	.	0	.	0	.	0	.	0	.	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	100	39	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	314	33	79	80	31	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1138	17
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	.	0	.	0	.	111	42	834	26	0	.	0	.	0	.	0	.	0	.	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	76	23	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	364	32	46	53	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1423	18
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	.	0	.	0	.	249	63	689	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	299	35	62	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1364	24
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	101	.	0	.	3	.	432	50	2415	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	.	53	83	66	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1136	26	242	47	51	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4500	14
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	.	59	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	49	13	49	19	38	53	37	94	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	10	82	29	90	0	.	0	.	15	61	0	.	15	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	53	7	59	1	.	0	.	0	.	10	61	0	.	0	.	0	.	0	.	5	100	3	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	39	31	58	44	16	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	498	12
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	307	23	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	134	37	37	60	115	38	187	30	476	25	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	48	86	21	100	49	68	0	.	0	.	45	53	0	.	12	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	0	72	46	14	49	1	.	0	.	0	.	28	77	0	.	0	.	5	.	10	80	15	58	2	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	150	40	275	29	165	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2196	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	1154	26	45	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	346	28	115	69	496	36	451	32	1043	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	63	62	118	64	0	.	0	.	146	74	0	.	52	.	0	.	0	.	0	.	0	.	0	.	39	78	0	.	0	.	0	.	0	.	0	.	23	75	204	48	82	56	0	.	0	.	0	.	51	68	12	100	0	.	0	.	66	100	81	92	0	.	49	101	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	495	29	538	32	520	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6268	10
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	1477	26	49	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	166	65	840	28	133	53	932	27	878	30	1920	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	85	144	91	66	71	0	.	15	.	163	86	0	.	18	.	0	.	0	.	0	.	0	.	0	.	56	75	0	.	0	.	0	.	0	.	0	.	151	63	362	37	109	58	0	.	0	.	0	.	134	70	27	74	0	.	10	.	98	87	75	88	11	.	33	72	47	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	.	0	.	0	.	0	.	0	.	990	22	944	30	666	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10604	8
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	509	29	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	111	59	745	35	120	61	719	33	506	32	895	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	100	30	100	50	71	0	.	49	.	105	59	0	.	-7	.	0	.	0	.	0	.	0	.	0	.	117	100	0	.	0	.	0	.	0	.	0	.	88	65	311	60	21	76	0	.	0	.	0	.	167	53	55	76	0	.	54	.	112	84	92	100	36	.	4	100	133	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	1374	26	444	36	455	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7383	10
	total	0	.	0	.	0	.	0	.	0	.	0	.	3506	23	112	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	285	62	2090	25	418	47	2281	26	2076	25	4428	19	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	225	71	268	74	312	59	0	.	63	.	475	58	0	.	90	.	0	.	0	.	0	.	0	.	0	.	219	74	0	.	0	.	0	.	0	.	0	.	262	59	957	35	232	55	2	.	0	.	0	.	391	51	94	72	0	.	70	.	285	73	267	89	52	.	97	68	181	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	.	0	.	0	.	0	.	0	.	3049	21	2259	25	1821	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26948	7
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	.	97	26	5	59	0	.	4	100	0	.	0	.	0	.	0	.	4	.	29	63	18	66	3	75	0	.	7	71	27	43	52	44	79	33	163	21	134	25	0	.	11	.	0	.	25	56	0	.	0	.	0	.	7	.	0	.	1	.	39	51	2	71	0	.	4	100	2	100	1	.	0	0	0	.	0	.	0	.	0	.	0	.	5	65	49	85	0	.	0	.	0	.	0	.	20	43	17	60	9	65	3	74	0	.	0	.	14	71	5	81	13	100	6	100	7	71	35	45	2	100	7	44	20	74	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	215	26	134	23	99	33	3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1375	8
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	347	20	29	60	0	.	4	100	0	.	0	.	0	.	0	.	13	.	103	63	84	72	6	100	0	.	34	74	120	33	199	39	318	32	595	22	390	30	0	.	13	.	0	.	137	67	0	.	0	.	0	.	0	.	0	.	0	.	141	51	34	54	0	.	4	100	13	63	22	.	15	100	0	.	0	.	0	.	0	.	0	.	14	53	101	88	3	100	0	.	0	.	0	.	19	46	46	52	80	43	9	70	0	.	0	.	24	51	40	60	38	78	15	63	28	75	197	51	23	66	15	61	84	94	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	26	100	0	.	0	.	0	.	0	.	499	26	446	23	450	35	8	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4792	8
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	1262	21	228	61	0	.	26	100	0	.	0	.	0	.	0	.	20	.	204	67	200	84	61	76	0	.	96	65	767	33	289	38	763	34	1872	20	1637	23	0	.	22	.	0	.	133	50	0	.	0	.	0	.	24	.	0	.	46	.	259	53	174	61	0	.	43	100	62	50	107	.	14	100	0	.	0	.	0	.	0	.	0	.	71	51	176	64	60	71	0	.	0	.	0	.	105	60	88	70	367	46	19	73	0	.	0	.	83	59	67	74	98	69	201	61	238	81	368	46	97	70	265	78	181	53	0	.	0	.	40	78	0	.	0	.	0	.	0	.	0	.	115	71	0	.	0	.	0	.	0	.	966	23	844	23	655	23	19	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13429	7
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	2888	20	225	62	0	.	4	100	0	.	0	.	0	.	0	.	52	.	175	64	145	56	38	100	0	.	98	61	1240	31	521	34	1883	24	3208	17	1601	21	0	.	-5	.	0	.	215	53	0	.	0	.	0	.	0	.	0	.	40	.	264	50	224	56	0	.	95	104	279	78	77	.	162	89	0	.	0	.	0	.	0	.	0	.	165	55	169	60	105	86	0	.	0	.	0	.	240	52	127	57	552	33	41	73	0	.	0	.	8	100	130	85	126	67	166	60	155	75	369	40	342	65	295	45	250	52	0	.	0	.	124	71	0	.	0	.	0	.	0	.	0	.	43	64	0	.	0	.	0	.	0	.	897	30	1149	23	1333	23	81	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20295	6
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	1765	23	33	73	0	.	111	107	0	.	0	.	0	.	0	.	9	.	0	.	84	78	20	94	0	.	539	65	958	34	493	52	1375	29	2573	19	640	30	0	.	0	.	0	.	33	73	0	.	0	.	0	.	0	.	0	.	0	.	221	47	188	56	0	.	150	100	310	53	6	.	284	71	0	.	0	.	0	.	0	.	0	.	91	71	0	.	110	76	0	.	0	.	0	.	386	52	270	58	331	59	54	75	0	.	0	.	229	82	61	64	146	79	61	100	77	100	121	59	174	51	112	52	104	54	0	.	0	.	110	85	0	.	0	.	0	.	0	.	0	.	61	100	0	.	0	.	0	.	0	.	803	33	549	28	1050	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14690	8
	total	0	.	0	.	0	.	0	.	0	.	0	.	6360	18	520	53	0	.	149	85	0	.	0	.	0	.	0	.	98	.	511	61	531	58	128	73	0	.	773	57	3112	27	1554	32	4418	21	8411	15	4400	19	0	.	41	.	0	.	542	46	0	.	0	.	0	.	31	.	0	.	87	.	923	42	621	51	0	.	295	71	666	57	213	.	474	76	0	.	0	.	0	.	0	.	0	.	346	48	496	60	278	78	0	.	0	.	0	.	769	40	548	50	1340	32	126	70	0	.	0	.	358	62	302	63	420	64	449	55	505	73	1090	39	638	56	694	49	638	53	0	.	0	.	280	72	0	.	0	.	0	.	0	.	0	.	245	58	0	.	0	.	0	.	0	.	3379	20	3122	19	3587	20	110	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54581	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	15	51	2	.	0	.	29	47	43	54	86	32	210	25	315	17	108	32	382	17	0	0	13	68	45	42	0	.	43	59	64	31	168	29	67	43	0	.	122	33	34	41	30	31	15	60	45	41	0	.	240	23	48	34	99	30	203	26	21	80	90	43	3	71	41	55	62	38	53	44	37	54	77	58	98	50	62	37	83	41	40	56	104	27	72	42	30	40	64	46	15	61	25	47	43	44	59	46	71	45	47	61	0	.	7	71	0	.	19	50	0	.	48	48	66	42	50	47	93	43	65	38	28	43	35	39	36	87	13	71	47	45	15	60	54	47	17	54	0	.	74	35	-2	.	13	54	79	30	17	37	57	49	21	53	11	77	32	76	3	73	6	118	0	.	1	.	104	37	32	60	42	55	0	.	72	41	39	37	0	.	11	.	2	.	0	.	4831	4
	16-23 cm	55	54	2	.	0	.	88	50	234	41	591	29	658	18	1174	15	426	31	1306	16	6	100	80	75	81	67	0	.	82	61	297	31	467	32	233	42	0	.	532	27	148	35	143	37	7	75	108	39	0	.	821	23	134	34	281	31	609	23	81	47	285	33	83	52	125	45	199	30	318	35	88	41	281	32	309	32	166	39	392	30	205	47	443	27	286	41	123	63	201	38	63	61	189	34	119	44	212	36	244	35	162	42	0	.	46	59	0	.	58	57	0	.	83	65	187	48	125	49	192	40	196	43	81	47	112	49	53	76	8	79	75	51	46	66	124	47	102	56	27	.	210	30	18	.	69	50	265	30	54	52	180	34	85	44	26	63	79	64	39	69	23	73	0	.	0	.	280	26	42	48	39	57	12	72	271	36	233	40	0	.	48	.	4	.	0	.	16627	4
	24-35 cm	198	55	0	.	0	.	263	49	903	33	1644	28	1802	18	2713	15	1309	26	2891	15	61	100	46	80	246	41	0	.	54	100	539	42	1240	26	994	39	0	.	1217	28	315	39	312	35	35	71	183	53	0	.	2422	19	572	33	757	31	1348	22	150	76	418	33	299	48	220	39	842	28	519	39	184	57	984	29	919	29	491	44	672	28	372	36	1416	26	480	38	427	50	667	34	79	62	529	32	287	34	843	31	585	35	295	43	0	.	90	62	0	.	47	59	0	.	63	55	209	39	141	47	147	46	265	42	121	57	291	41	62	58	107	60	151	46	143	50	340	44	321	56	211	.	652	28	43	.	252	43	548	25	269	38	682	33	203	52	134	80	179	51	160	54	216	88	0	.	0	.	620	28	189	51	356	49	216	47	1054	28	635	30	0	.	138	.	23	.	0	.	43020	4
	36-51 cm	261	59	0	.	0	.	307	46	1124	31	2069	26	3838	17	6276	14	2603	22	3114	15	99	83	91	87	630	41	0	.	37	79	745	33	1658	25	968	35	0	.	1619	24	410	50	797	31	90	66	132	53	0	.	2814	17	809	34	1481	30	2740	20	419	41	813	27	340	51	276	43	1222	24	610	38	347	47	1432	33	1117	31	635	39	1111	28	941	34	2028	21	331	46	723	34	695	35	569	48	734	32	587	35	1570	28	754	32	501	42	0	.	167	57	0	.	201	48	0	.	77	69	110	41	143	64	292	39	151	51	224	54	259	50	122	60	136	64	193	43	257	58	332	39	491	46	75	.	729	28	73	.	365	47	747	25	384	37	601	38	604	40	194	77	264	54	194	56	275	67	0	.	0	.	634	28	504	41	565	42	472	47	1747	26	846	34	0	.	52	.	102	.	0	.	65049	3
	=52 cm	39	100	64	.	0	.	172	52	455	52	707	37	2638	19	4466	15	1216	25	1037	25	145	100	65	79	134	46	0	.	0	.	400	47	1202	33	99	81	0	.	836	35	323	47	729	34	122	63	293	45	0	.	1161	23	864	39	550	32	1036	31	269	48	286	36	277	55	552	45	958	35	164	43	430	62	755	35	723	40	422	70	734	33	855	38	1395	39	205	48	441	50	273	43	69	59	79	100	578	42	520	33	841	39	162	58	0	.	202	76	0	.	239	81	0	.	38	69	157	56	127	100	172	44	71	100	97	86	231	65	146	100	191	89	355	48	316	61	81	53	599	54	0	.	646	47	0	.	301	59	470	30	475	42	152	54	308	49	37	100	283	78	150	85	94	61	0	.	0	.	261	36	237	66	452	45	56	71	538	31	213	48	0	.	0	.	0	.	0	.	37437	5
	total	568	52	68	.	0	.	858	43	2760	29	5097	23	9147	14	14944	12	5663	19	8730	13	311	77	296	55	1136	38	0	.	215	63	2046	32	4736	22	2362	32	0	.	4326	21	1230	34	2012	26	270	61	761	41	0	.	7458	14	2428	30	3168	26	5936	19	939	42	1892	23	1002	42	1213	35	3284	22	1665	33	1087	47	3529	28	3165	25	1776	37	2992	23	2412	29	5386	20	1374	33	1745	35	1900	27	795	43	1555	30	1615	31	3204	26	2495	28	1167	35	0	.	511	64	0	.	562	51	0	.	309	40	729	36	586	48	896	29	748	36	551	44	928	43	418	59	455	63	821	39	777	48	930	37	1530	41	313	.	2311	25	132	.	999	42	2109	21	1200	36	1673	28	1220	37	403	64	836	49	546	54	614	68	0	.	1	.	1899	23	1004	40	1454	35	756	45	3682	23	1966	28	0	.	249	.	132	.	0	.	166964	3
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	66	32	136	32	72	48	94	34	0	.	0	.	43	51	85	32	471	20	0	.	16	67	65	67	28	53	5	.	14	63	50	70	0	.	66	40	0	.	0	.	21	66	9	64	0	.	0	.	0	.	113	36	68	62	127	36	5	71	6	76	0	.	44	56	10	.	0	.	0	.	0	.	3	100	6	91	39	61	14	91	10	55	0	.	142	32	22	49	20	54	36	48	38	54	9	87	0	.	2	100	61	44	86	33	96	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	92	0	.	16	80	12	67	12	71	0	.	13	65	24	85	3	72	33	47	4	100	0	.	46	57	36	55	15	69	24	58	40	79	0	.	14	58	0	.	0	.	32	61	8	71	6	72	41	51	12	80	56	40	40	46	32	51	77	46	0	.	2835	7
	16-23 cm	312	32	248	29	219	33	397	29	0	.	0	.	116	53	240	32	1448	15	0	.	91	41	166	42	163	41	24	.	71	79	119	47	0	.	247	37	0	.	0	.	40	66	53	72	0	.	0	.	0	.	318	25	205	52	231	35	34	77	35	62	0	.	71	49	12	.	0	.	0	.	0	.	20	100	45	71	86	56	31	72	79	68	0	.	371	32	21	62	80	46	158	43	165	42	28	71	0	.	22	82	121	42	296	36	222	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	90	0	.	43	72	37	85	77	79	0	.	86	64	19	92	19	72	142	49	51	80	0	.	112	53	146	52	136	54	121	49	71	68	0	.	59	68	0	.	0	.	60	53	31	100	17	100	150	37	27	85	186	38	97	38	150	37	148	35	0	.	8657	5
	24-35 cm	1021	26	711	30	411	36	1220	24	0	.	0	.	332	47	698	33	3421	14	0	.	202	52	334	43	529	39	0	.	377	47	301	49	0	.	796	31	0	.	0	.	152	74	111	60	0	.	0	.	0	.	1112	26	637	32	1233	25	109	72	125	56	0	.	154	59	75	.	0	.	0	.	0	.	96	100	80	71	482	54	103	70	116	74	0	.	863	31	161	45	277	60	376	40	326	43	19	100	0	.	177	72	178	46	419	33	422	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	95	0	.	86	81	80	64	86	74	0	.	191	52	33	100	43	78	395	53	122	81	0	.	92	57	193	58	404	43	194	52	46	73	0	.	256	79	0	.	0	.	36	62	76	71	130	59	476	39	63	100	389	41	511	33	424	42	511	38	0	.	23015	5
	36-51 cm	1578	27	1602	27	502	35	2506	23	0	.	0	.	781	43	999	32	6003	14	0	.	350	46	311	41	416	53	40	.	516	49	527	46	0	.	963	34	0	.	0	.	229	59	203	59	0	.	0	.	0	.	1642	23	960	32	1452	27	167	76	352	62	0	.	196	63	8	.	0	.	0	.	0	.	40	100	125	91	349	52	86	94	177	76	0	.	1008	26	906	41	200	53	766	44	347	45	78	100	0	.	81	75	665	45	1182	29	705	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	25	77	102	59	68	72	0	.	210	51	39	100	146	97	183	47	152	73	0	.	203	63	101	61	360	52	152	69	131	75	0	.	325	61	0	.	0	.	57	68	95	92	333	52	924	38	112	92	932	39	876	33	950	34	884	32	0	.	36390	5
	=52 cm	866	33	1099	33	430	39	1023	31	0	.	0	.	316	50	819	31	3442	18	0	.	508	57	235	72	158	52	59	.	209	45	277	67	0	.	644	44	0	.	0	.	128	71	151	60	0	.	0	.	0	.	455	36	518	41	629	43	113	81	18	100	0	.	65	75	0	.	0	.	0	.	0	.	0	.	51	100	182	84	0	.	212	75	0	.	418	38	418	47	0	.	280	53	0	.	0	.	0	.	0	.	279	52	571	42	874	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	31	100	67	70	25	100	0	.	184	63	0	.	0	.	248	54	46	100	0	.	44	100	17	100	0	.	33	74	90	80	0	.	54	74	0	.	0	.	191	55	0	.	24	100	192	53	21	100	655	42	428	36	442	46	439	40	0	.	18726	6
	total	3843	21	3795	23	1634	28	5241	20	0	.	0	.	1588	42	2842	26	14785	11	0	.	1167	38	1111	36	1294	36	128	.	1187	44	1275	39	0	.	2716	28	0	.	0	.	570	54	527	53	0	.	0	.	0	.	3640	21	2387	27	3672	23	428	72	537	54	0	.	530	52	105	.	0	.	0	.	0	.	158	100	308	78	1138	52	234	74	594	60	0	.	2801	23	1528	38	577	52	1615	36	876	38	134	86	0	.	281	73	1305	38	2554	26	2318	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	193	71	0	.	201	71	298	62	267	67	0	.	685	52	114	71	212	80	1001	43	376	66	0	.	496	50	494	49	916	43	524	47	378	56	0	.	707	63	0	.	0	.	376	48	211	72	510	50	1783	33	234	82	2218	31	1951	29	1998	31	2060	28	0	.	89623	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	.	81	52	0	.	0	.	0	.	0	.	68	50	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	0	.	16	47	14	62	73	43	163	32	275	26	43	72	51	55	115	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	82	0	.	0	.	25	.	0	.	0	.	0	.	56	45	15	50	22	56	0	.	0	.	0	.	0	.	0	.	7	85	0	.	0	.	0	.	1048	11
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	55	0	.	0	.	0	.	0	.	201	49	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	.	30	44	100	59	192	46	256	31	568	25	65	53	164	53	363	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	73	0	.	0	.	23	.	0	.	0	.	0	.	130	50	47	53	10	62	0	.	0	.	0	.	0	.	0	.	34	87	0	.	0	.	0	.	2373	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	57	0	.	0	.	0	.	0	.	109	44	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	48	80	64	68	120	55	306	34	541	30	51	57	63	43	353	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	72	0	.	0	.	11	.	0	.	0	.	0	.	154	39	111	54	56	74	0	.	0	.	0	.	0	.	0	.	129	97	0	.	0	.	0	.	2210	12
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	115	89	0	.	0	.	0	.	0	.	45	51	57	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	.	69	.	0	.	8	100	54	88	51	68	104	40	243	46	96	44	122	67	223	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	150	64	21	59	36	71	0	.	0	.	0	.	0	.	0	.	81	74	0	.	0	.	0	.	1480	17
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	0	.	0	.	0	.	0	.	23	67	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	.	169	100	14	100	12	100	53	60	274	59	219	88	54	57	325	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	77	62	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	1361	25
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	435	51	0	.	0	.	0	.	0	.	447	41	96	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	.	52	.	271	71	246	60	449	38	883	30	1900	20	473	48	454	38	1379	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	73	0	.	0	.	59	.	0	.	0	.	0	.	491	40	219	48	202	50	0	.	0	.	0	.	0	.	0	.	272	80	0	.	0	.	0	.	8473	9
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	80	28	138	31	72	48	123	28	43	54	86	32	410	16	405	14	579	17	386	17	16	67	77	57	73	33	5	.	61	44	143	31	186	27	136	29	81	52	129	32	109	24	104	25	113	25	331	17	320	15	353	19	127	37	227	24	233	23	28	64	90	43	48	52	58	44	62	38	69	40	86	34	111	46	105	47	105	32	113	33	51	45	120	27	214	25	52	31	83	37	51	38	63	38	57	36	108	46	72	44	108	36	86	33	108	41	20	43	59	27	35	34	125	31	230	25	325	23	160	32	122	31	156	27	81	49	43	73	70	33	64	36	33	38	75	39	30	42	24	85	77	34	31	50	17	48	79	30	82	38	93	36	38	40	60	49	72	55	3	73	20	54	336	19	210	19	276	20	43	47	48	49	41	51	84	37	95	28	46	41	42	46	80	45	0	.	10717	3
	16-23 cm	366	28	250	29	219	33	485	25	234	41	591	29	1428	11	1446	13	1874	13	1310	16	97	39	246	37	245	35	24	.	165	46	518	24	551	29	486	27	129	55	566	26	452	19	432	24	442	25	1097	16	1090	16	1139	18	352	33	511	23	782	22	116	38	285	33	154	36	136	42	199	30	367	32	250	33	384	26	354	29	256	31	481	25	306	36	470	26	657	25	145	55	281	30	221	35	353	27	168	34	313	37	269	32	284	30	296	36	267	28	42	58	219	25	193	36	285	36	443	27	693	22	309	28	400	30	483	22	201	41	91	50	262	40	137	37	148	47	207	47	187	42	46	70	236	28	160	45	120	45	265	30	198	35	326	30	252	34	170	39	150	46	39	69	81	53	913	18	821	16	974	19	81	47	56	50	162	35	298	34	420	28	132	36	198	37	152	34	0	.	35089	2
	24-35 cm	1219	23	711	30	411	36	1484	22	903	33	1644	28	4550	12	3684	13	4730	12	2916	15	263	46	380	39	774	29	0	.	451	41	1044	29	1440	25	1851	25	64	57	1322	27	1598	20	827	22	1295	24	2680	16	3268	15	3534	15	1231	22	1991	20	1589	19	275	49	418	33	453	38	318	36	842	28	613	34	506	35	1371	24	999	27	1015	33	983	23	595	32	1483	25	1342	24	588	38	944	30	455	35	855	25	416	27	1019	28	822	30	473	32	419	33	512	33	135	48	398	31	552	33	202	38	516	25	682	26	331	28	408	31	572	25	513	33	366	57	643	32	328	33	544	43	521	34	512	40	244	88	735	25	438	49	374	39	548	25	378	31	875	29	744	30	338	43	225	43	160	54	472	58	1928	15	1571	17	1918	19	283	40	486	39	692	30	1117	27	1024	24	640	33	562	40	535	36	0	.	89081	2
	36-51 cm	1840	25	1602	27	502	35	2812	21	1124	31	2069	26	8984	11	7549	12	8606	11	3118	15	449	40	403	37	1045	32	40	.	605	43	1448	25	1803	23	1970	24	115	89	1883	21	2726	19	1654	20	2906	18	4374	14	4411	12	4456	13	1764	23	2934	20	3122	19	771	36	813	27	536	39	284	42	1222	24	699	34	755	33	1762	28	1242	30	1094	29	1640	25	1194	30	2207	21	1339	23	1630	27	895	29	1335	32	1081	26	886	27	1738	26	940	28	1166	31	1182	29	941	26	391	40	726	24	738	27	170	41	214	29	386	38	530	29	429	37	573	30	445	37	375	43	605	31	648	37	653	31	629	29	701	36	114	74	998	26	256	44	517	39	747	25	591	32	702	34	1050	29	347	53	394	44	194	56	600	45	2402	16	2160	18	2735	15	680	35	898	33	1397	30	1859	25	1778	26	957	31	1002	33	986	30	0	.	135241	2
	=52 cm	905	32	1163	31	430	39	1195	28	455	52	707	37	5230	13	5334	13	4658	15	1148	25	653	49	300	59	292	35	59	.	218	44	678	39	1286	31	763	38	46	100	1486	31	2219	20	1493	25	2215	21	3644	15	2250	18	1616	19	1383	29	1178	27	1181	28	287	46	286	36	343	46	552	45	958	35	229	42	681	42	993	29	774	37	802	46	1150	26	1072	34	1671	34	623	30	858	34	273	43	349	44	79	100	786	35	520	33	951	35	441	39	571	42	1076	30	496	42	989	36	366	53	105	47	210	44	401	51	787	37	240	40	568	34	398	45	334	57	434	48	632	32	458	44	317	37	784	44	0	.	756	42	248	54	347	52	470	30	519	39	169	49	377	44	70	63	372	62	150	85	148	47	2476	18	1080	21	2035	19	237	66	476	43	247	44	559	30	868	34	449	35	442	46	439	40	0	.	80960	3
	total	4411	19	3863	22	1634	28	6099	18	2760	29	5097	23	20601	10	18418	10	20447	9	8878	12	1477	34	1407	30	2429	26	128	.	1500	36	3832	23	5266	21	5206	21	435	51	5385	19	7103	16	4510	18	6971	16	12126	11	11339	11	11098	11	4856	20	6841	17	6908	17	1476	33	1892	23	1533	32	1348	33	3284	22	1977	29	2278	29	4621	23	3473	24	3272	28	4367	20	3220	25	5951	19	4175	19	3274	26	2477	24	2410	28	2431	23	2314	24	3700	24	3054	25	2472	26	2554	26	2905	23	1083	32	2391	23	1883	25	886	25	1612	23	2486	19	2117	21	1598	23	2351	22	1639	30	1209	40	2014	29	1808	28	1836	29	1749	28	2214	33	427	76	2803	23	1133	39	1375	35	2109	21	1768	28	2166	24	2461	25	986	37	1214	38	546	54	1321	46	8055	12	5842	14	7936	13	1324	33	1964	29	2540	27	3917	22	4184	21	2224	28	2246	30	2191	27	0	.	351089	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. 2293598. Birmensdorf, Swiss Federal Research Institute WSL

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