Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI2
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
grid: 1.4-km grid
state 1993/95
		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	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.
	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	.	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	.
	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	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.
	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	.	10	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	.
	=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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.
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	.	1	100	0	.	0	.	4	38	88	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	.	3	71	5	77	0	.	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	100	15	36	3	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	20
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	72	3	.	6	.	21	43	314	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	.	13	70	8	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	32	35	38	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	544	14
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	58	11	.	0	.	133	36	651	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	59	18	84	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	264	26	187	43	31	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1403	12
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	70	9	.	0	.	154	33	834	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	74	15	76	0	.	0	.	0	.	28	100	0	.	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	476	22	85	33	13	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1668	11
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	15	.	0	.	233	40	757	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	.	54	73	17	71	0	.	0	.	0	.	39	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	431	25	150	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1730	14
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	62	38	.	6	.	545	30	2644	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	46	64	61	0	.	0	.	0	.	78	72	0	.	0	.	0	.	0	.	28	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1311	19	472	31	52	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5473	9
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	.	49	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	7	66	40	41	16	39	34	26	61	24	113	31	0	.	0	.	0	.	4	88	0	.	0	.	0	.	0	.	0	.	6	72	11	64	13	58	0	.	6	100	19	43	0	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	14	40	8	50	1	100	0	.	0	.	6	51	2	83	3	.	0	.	1	100	4	100	3	58	2	80	4	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	55	22	47	48	39	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	578	10
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	234	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	118	115	27	73	40	155	23	259	19	385	20	0	.	0	.	0	.	6	92	0	.	0	.	0	.	0	.	0	.	31	60	35	94	36	54	0	.	9	100	15	83	0	.	-3	.	0	.	0	.	0	.	0	.	0	.	4	74	0	.	0	.	0	.	0	.	0	.	5	71	28	53	41	56	13	71	0	.	0	.	32	56	8	90	9	.	0	.	11	66	8	100	23	65	2	100	11	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	191	24	251	22	179	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2187	7
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	864	20	29	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	109	397	25	205	38	559	24	823	21	902	20	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	0	.	36	90	24	79	144	50	0	.	11	100	121	49	0	.	-5	.	0	.	0	.	0	.	0	.	0	.	42	73	0	.	0	.	0	.	0	.	0	.	42	45	142	42	83	50	0	.	0	.	0	.	60	47	25	60	16	.	3	.	67	75	5	100	9	100	74	58	41	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	.	0	.	0	.	0	.	0	.	696	23	620	22	605	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6750	7
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	1320	20	36	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	175	54	713	24	364	42	1001	21	1478	18	1996	14	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	32	66	97	69	98	48	0	.	13	100	165	56	0	.	10	.	0	.	0	.	0	.	0	.	0	.	37	85	0	.	0	.	0	.	0	.	0	.	151	44	308	31	105	49	5	100	0	.	0	.	164	45	47	63	0	.	9	.	54	84	16	100	70	82	53	50	98	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	.	0	.	0	.	0	.	0	.	1104	20	1071	20	784	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11604	6
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	921	22	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	239	49	698	29	176	39	853	23	893	20	1204	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	84	36	100	91	63	0	.	38	78	123	48	0	.	-7	.	0	.	0	.	0	.	0	.	0	.	99	77	0	.	0	.	0	.	0	.	0	.	214	38	269	38	115	73	0	.	0	.	0	.	116	43	27	73	0	.	36	.	54	82	11	100	133	61	31	74	155	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	.	0	.	0	.	0	.	0	.	1400	20	634	27	485	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9138	7
	total	0	.	0	.	0	.	0	.	0	.	0	.	3388	17	85	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	503	48	1963	21	834	27	2602	18	3514	15	4600	13	0	.	0	.	0	.	39	97	0	.	0	.	0	.	0	.	0	.	164	58	203	52	383	41	0	.	77	71	443	40	0	.	1	.	0	.	0	.	0	.	0	.	0	.	182	62	0	.	0	.	0	.	0	.	0	.	412	35	761	27	352	44	19	73	0	.	0	.	378	35	108	52	29	.	48	.	186	66	44	79	238	66	162	48	309	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	.	0	.	0	.	0	.	0	.	3446	16	2623	19	2092	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30256	5
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	.	1	.	0	.	190	36	5	51	0	.	1	100	0	.	0	.	0	.	0	.	3	81	11	48	54	63	0	100	0	.	6	47	32	39	41	30	101	23	189	18	141	25	0	.	9	73	0	.	20	43	0	.	0	.	0	.	0	.	0	.	-7	128	27	37	6	59	0	.	4	71	7	56	2	74	0	0	0	.	0	.	0	.	0	.	0	.	3	66	21	46	2	100	0	.	0	.	0	.	40	32	28	39	24	34	0	100	0	.	0	.	31	39	3	80	5	77	6	64	7	54	37	38	4	74	19	48	21	53	0	.	0	.	1	100	0	.	0	.	1	100	0	.	0	.	2	73	0	.	0	.	0	.	0	.	176	21	149	21	108	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1530	8
	16-23 cm	0	.	0	.	0	.	0	.	5	.	0	.	520	18	34	60	0	.	0	.	0	.	0	.	0	.	0	.	4	100	85	44	109	48	6	82	0	.	8	168	159	43	258	32	251	22	633	16	397	22	0	.	26	72	0	.	110	44	0	.	0	.	0	.	0	.	0	.	24	57	142	38	44	40	0	.	14	73	34	57	12	66	6	71	0	.	0	.	0	.	0	.	0	.	13	60	106	43	4	73	0	.	0	.	0	.	52	33	86	36	91	36	13	53	0	.	0	.	95	33	31	55	24	54	12	84	36	49	190	40	19	45	48	70	97	55	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	15	83	0	.	0	.	0	.	0	.	555	16	443	18	331	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5142	6
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	1380	18	140	48	0	.	5	100	0	.	0	.	0	.	0	.	66	71	172	55	265	46	13	75	0	.	53	77	639	28	484	30	589	20	1675	15	1187	17	0	.	82	77	0	.	205	42	0	.	0	.	0	.	0	.	0	.	93	61	278	35	91	48	0	.	66	71	144	58	34	72	-9	100	0	.	0	.	0	.	0	.	0	.	52	52	198	41	34	73	0	.	0	.	0	.	109	47	114	58	347	35	11	101	0	.	0	.	156	42	42	83	64	77	58	52	101	86	388	37	41	54	163	63	212	43	0	.	0	.	32	74	0	.	0	.	24	100	0	.	28	.	71	68	0	.	0	.	0	.	0	.	1367	16	753	19	722	21	11	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12751	5
	36-51 cm	0	.	0	.	0	.	0	.	4	.	0	.	2930	15	264	41	0	.	0	.	0	.	0	.	0	.	0	.	110	74	233	52	414	35	64	59	0	.	105	51	1167	22	715	22	1411	20	3160	12	1676	16	0	.	28	72	0	.	338	34	0	.	0	.	0	.	0	.	0	.	94	59	437	31	279	43	0	.	89	74	108	55	70	75	100	80	0	.	0	.	0	.	0	.	0	.	96	54	326	35	87	62	0	.	0	.	0	.	245	44	268	33	548	28	44	60	0	.	0	.	138	37	51	90	80	65	134	59	186	62	425	30	268	63	283	40	272	38	0	.	0	.	77	54	0	.	0	.	52	71	36	.	46	.	101	50	0	.	0	.	0	.	0	.	1532	16	1159	18	1227	17	63	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21541	4
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	2118	18	130	67	0	.	76	92	0	.	0	.	0	.	0	.	32	100	-4	100	126	53	139	81	0	.	362	53	1306	25	1146	30	1641	20	3303	14	934	27	0	.	0	.	0	.	130	53	0	.	0	.	0	.	0	.	0	.	28	100	274	33	239	43	0	.	24	100	232	42	46	86	63	79	0	.	0	.	0	.	0	.	0	.	76	75	155	54	29	72	0	.	0	.	0	.	402	40	408	36	444	33	29	73	0	.	0	.	230	48	59	58	261	66	78	71	235	59	400	41	170	44	338	39	239	42	0	.	0	.	61	62	0	.	0	.	0	.	21	.	12	.	49	80	0	.	0	.	0	.	0	.	1708	19	741	23	1267	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19754	5
	total	0	.	0	.	0	.	0	.	9	.	0	.	7137	14	573	39	0	.	82	91	0	.	0	.	0	.	0	.	215	71	498	45	967	36	224	66	0	.	535	48	3303	19	2643	21	3992	16	8960	10	4334	14	0	.	145	74	0	.	803	31	0	.	0	.	0	.	0	.	0	.	232	52	1159	29	657	35	0	.	197	71	525	38	164	69	159	77	0	.	0	.	0	.	0	.	0	.	240	48	805	32	156	61	0	.	0	.	0	.	848	29	903	28	1455	24	98	60	0	.	0	.	649	29	186	57	434	52	287	55	566	50	1439	28	502	48	851	34	840	35	0	.	0	.	171	53	0	.	0	.	79	74	57	.	86	.	238	50	0	.	0	.	0	.	0	.	5337	13	3245	15	3654	15	74	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60719	4
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	26	52	2	100	5	70	19	66	44	35	66	31	291	19	269	14	63	25	300	16	0	0	16	75	45	31	0	.	51	60	44	30	160	32	72	35	0	.	159	38	50	38	36	26	15	54	129	60	0	.	289	17	110	39	63	24	151	18	20	41	72	24	10	48	32	40	85	35	58	43	15	96	80	25	48	33	55	35	70	36	43	32	92	33	51	43	49	52	78	38	19	47	41	33	102	28	68	32	43	32	18	127	0	.	36	79	0	.	26	44	0	.	71	33	46	33	30	42	119	33	135	51	89	34	51	40	70	62	14	68	33	35	14	47	60	27	40	47	0	.	78	22	18	72	15	46	70	23	35	41	51	34	9	50	18	73	29	95	54	64	21	64	0	.	5	80	108	21	50	33	14	47	8	87	106	50	29	67	0	.	2	100	17	95	0	.	5200	4
	16-23 cm	76	41	5	100	36	61	92	40	163	30	369	25	710	14	981	12	284	20	1153	13	0	.	11	52	157	37	0	.	90	53	343	32	503	22	290	29	0	.	494	23	186	28	175	24	35	52	138	31	0	.	900	16	174	31	266	26	553	18	76	50	287	27	84	38	89	33	212	26	242	28	75	40	308	26	143	30	196	28	324	27	158	37	448	20	158	34	98	31	197	28	73	51	198	30	280	28	267	26	150	36	125	39	0	.	27	66	0	.	83	35	0	.	182	34	154	28	73	39	308	24	265	33	213	27	126	32	52	45	10	75	117	46	54	43	229	27	150	36	32	71	306	27	32	61	51	36	238	23	86	30	149	31	46	37	46	43	94	57	32	53	70	56	0	.	13	100	374	20	151	35	75	45	16	66	193	32	120	31	0	.	14	100	10	100	0	.	16566	3
	24-35 cm	233	36	23	100	89	59	343	36	731	25	1190	22	2038	15	2803	11	911	21	2709	12	4	100	163	47	363	28	0	.	125	50	484	32	1489	18	674	26	0	.	1003	30	299	32	492	28	11	100	239	37	0	.	2634	12	668	24	432	24	1445	18	155	44	509	25	344	38	205	28	816	22	579	27	213	47	541	27	840	24	507	34	819	20	482	24	1394	17	409	33	249	41	646	26	62	63	645	24	597	25	681	27	391	28	430	28	0	.	60	73	0	.	127	40	0	.	268	36	339	28	158	41	294	26	246	31	334	31	302	32	52	54	78	59	197	38	90	54	606	25	260	40	70	100	662	23	87	71	198	33	671	19	326	28	464	27	151	43	107	71	224	46	116	40	169	54	0	.	8	100	725	20	364	28	325	37	36	85	789	25	470	27	0	.	60	100	23	100	0	.	42562	3
	36-51 cm	470	33	22	100	43	78	549	30	862	24	2395	18	3951	12	6801	10	2283	16	3856	11	59	77	177	53	669	29	0	.	206	56	769	27	1909	17	1328	27	0	.	1582	19	539	32	810	24	105	59	162	41	0	.	3966	11	1029	24	1710	21	2767	17	425	32	923	21	492	37	421	29	1328	18	585	30	421	36	1163	25	1362	24	584	32	1538	19	935	26	2759	16	244	41	508	27	712	25	415	44	1018	23	1153	24	1371	24	621	24	622	30	0	.	97	59	0	.	180	42	0	.	174	40	277	32	76	38	343	26	292	32	290	36	238	37	80	51	45	61	280	37	208	52	839	27	715	29	131	93	700	23	133	68	427	31	873	21	578	28	737	27	411	30	229	50	331	47	287	42	544	43	0	.	0	.	730	22	758	29	423	42	348	40	1458	21	882	25	0	.	70	100	72	100	0	.	72875	2
	=52 cm	169	48	51	100	0	.	322	40	426	41	1177	22	3741	15	6456	10	2215	17	2047	16	143	100	137	53	601	34	0	.	234	73	523	33	1388	22	588	30	0	.	931	24	349	35	700	27	78	53	235	33	0	.	2183	16	1224	29	1146	22	1560	20	291	40	642	27	234	46	422	42	1392	21	309	36	426	36	740	32	792	29	576	44	1097	25	652	31	3049	19	151	39	360	42	203	39	105	55	245	36	862	27	1019	24	1129	30	406	40	0	.	136	66	0	.	386	46	0	.	305	77	206	39	15	100	359	30	191	40	235	40	273	56	133	91	138	83	446	43	228	52	474	33	634	39	72	100	574	33	69	84	513	36	334	25	431	36	555	33	339	32	62	80	343	54	208	56	259	49	38	.	0	.	468	28	634	38	394	41	254	43	928	24	351	33	0	.	0	.	15	100	0	.	54731	3
	total	974	31	104	71	173	54	1324	29	2226	22	5197	17	10731	11	17310	8	5756	15	10064	10	206	81	504	44	1835	25	0	.	707	42	2164	24	5448	15	2953	21	0	.	4169	17	1424	23	2213	20	244	47	902	28	0	.	9972	10	3205	21	3618	18	6475	15	967	31	2433	18	1164	32	1169	27	3833	17	1773	24	1150	31	2833	23	3185	21	1919	28	3848	17	2270	22	7743	15	1013	30	1265	25	1836	21	674	38	2147	20	2994	20	3407	20	2333	23	1602	25	0	.	357	55	0	.	802	34	0	.	1000	32	1023	24	353	31	1423	19	1129	25	1162	25	990	31	388	47	285	58	1073	29	594	41	2208	23	1798	29	306	75	2321	19	339	59	1203	29	2187	16	1456	26	1956	22	957	28	461	46	1022	39	697	40	1064	41	38	.	26	95	2404	17	1957	25	1231	33	661	39	3474	18	1852	22	0	.	145	100	136	83	0	.	191934	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	102	24	146	28	151	37	83	36	0	.	0	.	14	41	38	32	420	19	21	82	52	46	59	35	21	35	0	.	31	54	29	42	4	.	40	34	0	.	0	.	8	74	11	50	0	.	0	.	0	.	96	29	47	36	83	32	13	55	13	57	0	.	10	60	1	.	0	.	1	100	22	77	9	63	31	39	55	42	16	69	36	41	0	.	94	26	29	38	20	50	63	49	45	38	1	113	0	.	2	72	46	29	64	40	113	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	65	0	.	0	6744	8	58	17	53	1	.	12	51	30	45	8	93	43	43	14	63	0	.	20	60	12	50	55	48	21	58	18	60	0	.	11	48	0	.	0	.	46	43	9	56	14	49	18	76	62	91	76	53	101	39	17	32	102	65	0	.	2861	6
	16-23 cm	358	17	361	27	355	27	317	25	0	.	0	.	98	45	172	33	1331	14	43	61	88	37	162	43	150	35	7	.	136	40	150	42	4	.	95	33	0	.	0	.	53	63	31	47	0	.	0	.	0	.	283	25	221	43	195	34	43	56	43	49	0	.	43	49	15	.	0	.	4	100	28	71	56	59	108	38	70	54	39	58	81	47	7	.	288	22	91	33	80	44	170	33	152	35	16	60	0	.	8	100	224	32	196	29	297	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	77	30	.	34	61	71	40	49	87	7	.	62	40	51	52	11	63	105	32	60	51	0	.	88	53	90	52	182	35	82	51	86	59	0	.	84	53	0	.	0	.	138	35	111	65	63	56	138	25	54	53	115	38	198	30	134	32	199	32	0	.	8977	4
	24-35 cm	1024	18	568	21	740	22	723	23	0	.	0	.	262	45	468	26	2724	12	62	59	212	37	411	28	473	32	38	.	285	34	361	33	76	.	383	29	0	.	11	.	58	72	96	54	0	.	0	.	0	.	909	20	552	34	631	27	48	54	79	42	0	.	95	44	29	.	0	.	93	75	0	0	76	63	356	37	261	48	121	58	247	43	32	.	606	23	249	33	209	36	384	29	545	29	76	91	0	.	104	63	464	31	550	27	590	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	65	41	.	64	62	102	48	85	80	12	.	226	43	43	71	54	71	305	37	192	50	0	.	114	43	241	35	376	32	148	38	90	59	0	.	550	35	0	.	0	.	109	48	83	51	168	48	509	31	93	63	447	35	348	27	377	34	242	36	0	.	21358	4
	36-51 cm	1513	17	1373	20	669	24	2099	19	0	.	0	.	592	40	756	27	5166	10	60	93	592	31	555	31	619	29	8	.	876	31	600	36	69	.	856	31	0	.	18	.	154	58	138	54	0	.	0	.	0	.	1456	19	1064	28	1112	25	158	52	180	39	0	.	242	48	12	.	0	.	45	73	66	100	126	58	634	32	289	49	168	60	307	50	12	.	1335	20	972	26	348	43	734	29	508	29	48	81	0	.	92	67	847	31	925	28	824	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	76	66	.	44	60	156	45	143	59	30	.	366	43	236	51	91	75	574	32	102	49	0	.	111	64	366	39	538	33	239	47	155	52	0	.	570	33	0	.	0	.	84	52	129	59	525	34	1128	24	259	53	774	27	824	26	890	25	846	29	0	.	38476	3
	=52 cm	1228	22	1022	24	671	23	1618	22	0	.	0	.	289	42	903	41	5028	12	21	71	759	35	303	44	199	39	0	.	527	32	315	42	101	.	553	42	0	.	0	.	51	100	113	58	0	.	0	.	0	.	943	22	503	34	624	29	125	76	80	64	0	.	89	60	0	.	0	.	10	100	19	100	60	100	355	36	115	73	43	75	287	60	0	.	720	23	1044	33	20	98	415	39	164	65	16	100	0	.	122	91	529	35	596	32	877	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	26	.	36	71	59	52	30	89	0	.	274	46	89	74	41	76	466	38	30	100	0	.	119	54	45	59	109	43	27	73	215	52	0	.	330	42	0	.	0	.	124	52	96	63	200	56	787	29	66	51	760	33	494	30	643	33	665	30	0	.	27233	4
	total	4225	14	3470	17	2587	18	4841	16	0	.	0	.	1255	36	2337	24	14669	9	207	53	1704	25	1489	25	1463	26	53	.	1855	28	1455	28	254	.	1927	25	0	.	30	.	325	55	389	48	0	.	0	.	0	.	3686	16	2388	24	2646	22	387	54	395	37	0	.	478	43	57	.	0	.	153	71	134	80	327	60	1484	29	791	42	386	52	959	39	51	.	3042	17	2385	25	677	35	1765	25	1413	26	158	69	0	.	327	61	2110	25	2330	23	2700	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	189	61	163	.	178	60	396	41	324	55	50	.	940	38	449	45	205	59	1493	27	398	42	0	.	452	42	754	35	1260	26	516	36	564	42	0	.	1545	30	0	.	0	.	501	31	427	48	971	30	2581	22	534	45	2173	24	1965	22	2060	24	2054	24	0	.	98904	3
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	.	47	54	0	.	2	.	0	.	6	100	89	33	1	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	.	2	.	1	100	-9	304	11	50	70	49	77	27	290	19	71	40	63	43	85	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	4	82	0	.	0	.	7	.	0	.	0	.	0	.	48	33	29	36	8	132	0	.	0	.	0	.	0	.	0	.	4	71	0	.	0	.	0	.	909	10
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	177	48	0	.	5	.	0	.	9	100	203	27	5	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	.	19	.	14	100	53	43	25	72	147	35	267	27	695	17	129	33	198	35	275	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	25	93	0	.	0	.	17	.	0	.	0	.	0	.	120	32	87	38	19	44	0	.	0	.	0	.	0	.	0	.	22	68	0	.	0	.	0	.	2524	8
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	151	41	0	.	11	.	0	.	5	100	265	33	3	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	.	9	74	59	53	51	52	145	38	307	29	795	19	111	47	119	30	430	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	.	0	.	0	.	44	76	0	.	0	.	9	.	0	.	0	.	0	.	155	38	83	41	54	50	0	.	0	.	0	.	0	.	0	.	71	68	0	.	0	.	0	.	2888	8
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	52	0	.	0	.	0	.	18	100	94	46	47	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	.	25	.	3	100	49	42	41	77	84	51	185	40	298	25	155	29	115	45	251	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	135	35	57	50	71	77	0	.	0	.	0	.	0	.	0	.	89	64	0	.	0	.	0	.	1826	11
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	72	0	.	0	.	0	.	21	75	174	53	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	255	49	53	83	55	62	256	65	342	33	446	45	55	60	374	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	55	15	100	15	100	0	.	0	.	0	.	0	.	0	.	74	83	0	.	0	.	0	.	2332	15
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	560	36	0	.	18	.	0	.	59	73	826	27	63	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	.	39	86	406	38	180	64	501	30	1092	25	2420	14	913	30	549	29	1414	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	.	0	.	0	.	75	71	0	.	0	.	33	.	0	.	0	.	0	.	513	30	270	34	167	47	0	.	0	.	0	.	0	.	0	.	259	61	0	.	0	.	0	.	10479	6
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	128	22	149	27	157	36	102	32	44	34	66	31	545	16	312	13	483	17	321	16	52	46	74	32	67	24	0	.	85	41	84	22	220	28	112	25	47	54	172	35	133	22	105	17	157	17	473	19	342	16	384	14	167	28	147	21	188	15	33	34	72	24	20	38	33	38	85	35	59	46	76	34	108	21	79	25	120	26	111	26	81	25	98	31	145	22	78	36	98	32	82	39	86	25	106	27	89	26	46	30	65	42	64	40	151	35	42	31	61	53	48	23	141	29	123	21	321	17	226	22	202	37	182	24	64	33	79	56	55	32	48	27	52	28	85	23	52	38	30	45	87	21	63	36	28	39	72	23	60	32	63	29	67	40	46	41	47	62	54	64	32	46	290	15	246	17	312	17	59	29	28	34	26	60	168	46	105	42	105	38	19	30	119	57	0	.	11207	3
	16-23 cm	434	16	366	27	390	25	409	21	168	29	369	25	1561	10	1188	11	1616	12	1197	13	88	37	173	40	307	25	7	.	230	31	579	23	615	20	392	23	177	48	520	22	522	19	539	18	455	15	1259	10	1100	12	1183	13	421	26	461	21	712	16	119	37	287	27	127	30	104	31	212	26	300	24	280	26	444	21	250	24	290	23	411	23	251	28	457	20	446	19	190	23	277	24	243	27	350	22	314	26	373	22	162	33	349	25	196	29	343	34	71	32	263	19	164	27	355	23	421	20	768	15	564	16	502	22	521	17	202	32	129	33	249	32	230	27	153	39	344	24	212	28	84	42	317	26	151	28	111	32	240	22	199	29	239	28	248	27	145	34	181	41	32	53	154	38	994	11	829	13	1045	12	262	34	139	35	154	23	247	28	236	24	220	28	148	30	209	31	0	.	35944	2
	24-35 cm	1257	16	591	20	829	21	1066	19	731	25	1190	22	4544	10	3440	10	3635	10	2775	12	216	36	574	24	836	22	38	.	476	26	1017	21	1829	17	1071	19	151	41	1131	28	1451	16	1288	17	1163	15	3140	11	2742	11	3543	10	1302	20	1063	19	1720	16	234	32	509	25	439	31	234	27	816	22	801	22	515	27	858	20	1196	20	845	26	1205	17	764	21	1412	17	1014	19	498	26	855	22	445	26	1189	18	768	22	879	23	529	25	894	21	550	27	650	23	160	34	466	23	499	27	425	26	646	20	953	17	632	18	432	21	844	19	418	26	262	43	556	28	348	27	412	34	871	21	486	29	113	67	748	21	402	32	390	30	695	19	484	23	733	21	619	24	264	36	314	36	116	40	720	30	2482	11	1650	13	2247	12	458	24	493	30	546	30	882	23	917	22	419	25	436	32	265	34	0	.	87715	2
	36-51 cm	1984	15	1395	20	712	23	2648	16	866	24	2395	18	8792	8	7857	9	7449	8	3916	11	651	29	732	27	1288	20	8	.	1192	26	1602	20	2393	15	2248	20	107	52	1881	17	2599	14	2036	15	2535	14	5059	9	4554	9	5422	9	2122	18	2823	16	3269	15	605	25	923	21	733	29	433	29	1328	18	757	25	1020	22	1674	19	1996	19	976	25	1979	17	1312	22	2880	16	1579	18	1480	19	1060	22	1149	24	1526	18	1334	22	1696	20	800	21	1469	22	925	28	946	21	399	32	823	18	709	24	308	28	462	25	374	22	829	16	505	24	621	22	396	30	385	38	530	25	774	28	688	26	1240	20	1080	24	367	47	869	21	707	29	529	27	925	20	728	25	1149	22	1074	21	468	34	486	36	287	42	1113	27	3248	10	2371	13	2908	11	950	26	948	27	1476	21	1717	19	1656	18	913	24	959	24	917	28	0	.	148000	1
	=52 cm	1396	20	1073	23	671	23	1939	19	426	41	1177	22	7069	10	7509	10	7242	9	2144	16	903	33	440	35	801	27	0	.	793	30	834	26	1614	20	1281	24	77	72	1532	21	2437	17	2149	19	2593	15	4838	10	2903	13	3125	13	1727	23	1770	18	1816	18	370	35	642	27	323	37	422	42	1392	21	405	31	754	24	1130	24	1147	23	753	36	1495	20	985	27	3105	19	871	20	1404	27	224	36	520	33	409	34	1053	24	1175	22	1280	28	936	26	596	32	1012	24	629	29	1373	21	629	28	389	61	462	40	357	32	1190	22	332	28	870	26	433	40	449	43	585	35	808	27	627	28	867	24	908	30	162	61	676	29	585	33	543	35	334	25	571	29	612	30	513	24	89	60	558	39	208	56	589	32	3630	12	1541	16	2359	15	730	34	594	33	1041	24	994	23	1111	25	567	28	643	33	680	29	0	.	114918	2
	total	5199	13	3574	17	2760	18	6165	14	2235	22	5197	17	22512	8	20305	8	20425	7	10353	10	1910	24	1993	22	3298	18	53	.	2776	22	4116	17	6672	14	5104	16	560	36	5236	15	7141	11	6117	12	6903	12	14768	8	11641	8	13658	8	5738	16	6264	14	7704	13	1361	24	2433	18	1642	26	1226	26	3833	17	2321	20	2646	19	4214	17	4668	17	2984	21	5202	14	3393	19	7953	14	4055	15	3649	18	2513	18	2440	21	3560	16	3575	18	4213	17	2817	20	3713	18	2330	23	3103	18	1299	22	2985	15	2050	19	1617	22	2115	17	2773	13	3441	13	1972	17	3038	16	1514	24	1303	30	1975	23	2209	20	1931	22	3408	18	2738	23	755	41	2697	18	1907	24	1601	24	2266	16	2041	21	2795	18	2520	17	1011	28	1586	29	697	40	2609	24	10645	9	6637	11	8871	9	2459	22	2202	22	3242	19	4008	17	4025	17	2225	21	2205	23	2191	23	0	.	397785	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. 2293481. Birmensdorf, Swiss Federal Research Institute WSL

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