Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
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 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (main diameter classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	2	71	70	28	0	.	0	.	0	.	0	.	0	.	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	8	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	.	21	37	29	63	3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	134	21
	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	.	9	68	314	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	.	12	80	22	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	.	168	62	43	41	6	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	575	23
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	.	0	.	16	.	74	47	634	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	87	49	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	.	296	33	139	51	47	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1308	15
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	.	0	.	6	.	131	45	941	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	.	27	72	25	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	.	441	31	195	46	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1808	16
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	.	0	.	0	.	221	58	1180	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	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	.	447	35	132	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2077	22
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	141	.	0	.	23	.	437	44	3140	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	.	71	73	119	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	.	1373	27	538	40	62	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5903	13
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	.	83	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	75	43	40	6	64	24	41	119	49	129	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	11	71	10	77	0	.	0	.	9	75	0	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	68	27	84	6	61	0	.	0	.	0	.	11	63	6	100	0	.	0	.	0	.	14	74	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	0	.	86	38	110	55	30	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	762	16
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	190	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	138	41	34	85	60	42	206	28	339	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	94	38	87	55	80	0	.	0	.	35	73	0	.	22	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	4	71	66	45	14	146	0	.	0	.	0	.	60	65	5	100	0	.	0	.	11	79	42	93	0	.	4	71	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	257	26	154	35	104	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1954	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	881	23	28	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	415	37	171	50	359	40	566	34	877	22	0	.	0	.	0	.	27	.	0	.	0	.	0	.	0	.	0	.	126	91	88	100	109	83	0	.	0	.	137	79	0	.	18	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	8	100	297	41	82	55	30	.	0	.	0	.	109	58	14	100	0	.	16	.	86	79	30	71	28	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	.	0	.	0	.	0	.	0	.	531	33	523	29	312	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5965	9
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	1922	22	74	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	87	966	25	243	48	1229	33	981	33	1979	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	77	69	61	89	72	0	.	31	.	223	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	92	0	.	0	.	0	.	0	.	0	.	146	64	408	35	191	58	9	.	0	.	0	.	126	48	37	75	0	.	0	.	108	78	78	83	18	.	94	71	36	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	.	0	.	0	.	0	.	0	.	1038	31	877	27	794	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12044	8
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	1112	25	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	259	61	1600	34	237	52	1231	30	611	29	1711	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	100	11	100	56	100	0	.	50	.	107	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	215	89	0	.	0	.	0	.	0	.	0	.	251	53	485	39	94	60	0	.	0	.	0	.	293	53	48	71	0	.	62	.	127	71	0	.	60	.	78	58	79	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	.	0	.	0	.	0	.	2158	26	693	35	998	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12771	9
	total	0	.	0	.	0	.	0	.	0	.	0	.	4187	20	151	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	374	64	3163	25	692	46	2903	26	2483	26	5035	18	0	.	0	.	0	.	27	.	0	.	0	.	0	.	0	.	0	.	338	73	217	80	319	58	0	.	81	.	511	61	0	.	48	.	0	.	0	.	0	.	0	.	0	.	348	71	0	.	0	.	0	.	0	.	0	.	415	56	1284	33	387	49	39	.	0	.	0	.	599	44	111	71	0	.	78	.	331	74	164	79	107	.	192	64	119	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	.	0	.	0	.	0	.	0	.	4069	23	2357	25	2239	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33496	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	.	148	28	2	100	0	.	1	100	0	.	0	.	0	.	0	.	0	.	18	60	0	0	0	0	0	.	1	100	44	54	47	38	171	29	175	20	128	31	0	.	5	.	0	.	20	63	0	.	0	.	0	.	0	.	0	.	2	.	60	48	7	47	0	.	0	0	14	54	1	.	0	.	0	.	0	.	0	.	0	.	0	.	7	60	48	83	5	82	0	.	0	.	0	.	72	48	15	65	20	52	2	71	0	.	0	.	9	53	6	83	5	100	9	66	5	82	23	45	13	72	24	54	17	74	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	164	31	142	25	105	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1543	8
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	350	23	-15	100	0	.	3	100	0	.	0	.	0	.	0	.	7	.	78	63	10	71	9	78	0	.	16	113	75	35	202	36	406	30	661	19	391	25	0	.	15	.	0	.	77	54	0	.	0	.	0	.	0	.	0	.	12	.	143	43	13	71	0	.	18	100	24	57	8	.	0	0	0	.	0	.	0	.	0	.	0	.	26	53	46	74	7	100	0	.	0	.	0	.	179	50	98	51	70	41	13	100	0	.	0	.	19	56	34	69	50	88	25	80	25	74	143	42	35	89	32	50	87	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	90	0	.	0	.	0	.	0	.	459	26	692	26	374	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4941	7
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	1026	23	66	71	0	.	0	.	0	.	0	.	0	.	0	.	24	.	307	69	15	75	61	71	0	.	103	80	364	40	489	35	587	26	1670	19	1289	22	0	.	50	.	0	.	126	58	0	.	0	.	0	.	0	.	0	.	35	.	371	40	122	49	0	.	52	100	40	60	38	.	0	0	0	.	0	.	0	.	0	.	0	.	33	65	74	74	32	100	0	.	0	.	0	.	53	51	100	66	351	43	33	100	0	.	0	.	187	52	91	58	124	76	81	76	114	84	469	41	91	59	114	54	207	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	100	0	.	0	.	0	.	0	.	1057	22	1191	25	839	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12145	7
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	2114	22	276	60	0	.	10	100	0	.	0	.	0	.	0	.	83	.	255	65	171	64	125	71	0	.	171	62	1376	29	441	32	1155	26	3111	16	2127	21	0	.	46	.	0	.	194	58	0	.	0	.	0	.	0	.	0	.	17	.	329	43	210	56	0	.	121	100	21	81	83	.	87	100	0	.	0	.	0	.	0	.	0	.	308	58	129	90	74	85	0	.	0	.	0	.	166	68	138	55	857	35	60	78	0	.	0	.	123	56	94	61	130	63	217	65	280	87	528	45	235	84	462	53	264	64	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	129	59	0	.	0	.	0	.	0	.	1341	24	1347	23	1431	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20838	6
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	3332	21	83	100	0	.	181	95	0	.	0	.	0	.	0	.	89	.	43	72	287	71	142	72	0	.	728	58	1209	29	455	43	1974	26	3960	18	1232	27	0	.	-5	.	0	.	117	62	0	.	0	.	0	.	0	.	0	.	0	.	386	56	254	52	0	.	0	0	120	82	31	.	179	100	0	.	0	.	0	.	0	.	0	.	274	59	65	100	92	100	0	.	0	.	0	.	581	49	397	63	811	39	83	79	0	.	0	.	126	67	163	67	319	83	197	64	145	100	321	52	417	56	404	44	319	67	0	.	0	.	121	100	0	.	0	.	0	.	0	.	0	.	94	87	0	.	0	.	0	.	0	.	1419	28	1399	25	1737	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24281	7
	total	0	.	0	.	0	.	0	.	0	.	0	.	6971	18	414	57	0	.	196	93	0	.	0	.	0	.	0	.	203	.	701	60	483	64	337	71	0	.	1019	54	3069	25	1634	30	4294	21	9577	14	5168	18	0	.	112	.	0	.	535	47	0	.	0	.	0	.	0	.	0	.	66	.	1289	40	606	48	0	.	191	100	217	63	160	.	266	100	0	.	0	.	0	.	0	.	0	.	647	46	363	71	210	79	0	.	0	.	0	.	1052	38	748	51	2110	32	190	70	0	.	0	.	464	49	389	55	629	63	529	63	569	73	1483	39	791	58	1035	44	894	59	0	.	0	.	123	98	0	.	0	.	0	.	0	.	0	.	314	60	0	.	0	.	0	.	0	.	4440	19	4772	19	4487	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63748	5
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	5	100	0	.	0	.	9	46	-6	333	49	44	221	32	152	31	122	41	255	19	0	.	17	55	40	37	0	.	4	72	39	44	106	28	19	47	0	.	69	25	55	54	39	39	10	100	51	38	0	.	183	20	84	41	94	37	137	23	34	61	70	31	9	57	39	38	74	40	29	35	17	57	44	37	33	38	19	55	72	39	44	62	60	29	40	59	62	39	61	45	18	56	17	64	56	38	21	37	24	103	38	59	0	.	4	100	0	.	19	59	0	.	60	41	32	43	48	38	100	41	66	42	40	41	45	40	31	71	5	74	49	55	13	60	48	47	24	56	0	.	73	36	3	.	15	62	76	30	29	48	54	41	14	47	29	71	22	70	0	.	10	74	0	.	6	.	133	49	28	48	33	52	-26	154	47	30	26	42	0	.	0	.	0	.	0	.	3787	5
	16-23 cm	41	83	4	.	0	.	51	76	81	37	298	29	497	21	843	19	439	29	908	18	0	.	17	63	113	42	0	.	50	73	231	41	497	26	123	53	0	.	280	31	149	56	106	35	14	99	105	41	0	.	651	19	220	30	117	30	431	21	82	55	238	32	48	48	101	38	224	35	211	40	64	45	208	33	159	41	123	48	297	31	253	46	170	32	103	50	119	42	156	46	65	64	95	39	227	37	172	43	141	45	145	43	0	.	70	66	0	.	41	56	0	.	217	42	275	44	84	40	317	35	272	38	68	66	177	39	75	68	30	69	85	49	25	55	117	55	93	70	9	.	220	29	17	.	31	64	270	28	124	60	194	41	32	51	113	77	121	58	8	100	14	100	0	.	7	.	249	27	100	46	97	56	0	.	188	37	144	65	0	.	23	.	10	.	0	.	13586	4
	24-35 cm	105	55	9	.	0	.	91	56	542	34	1028	27	1450	18	1920	16	810	30	2858	17	13	100	98	65	257	44	0	.	293	64	364	42	1053	26	423	46	0	.	858	35	471	35	249	36	0	.	318	38	0	.	1374	18	383	31	651	39	1347	23	285	58	641	35	107	59	259	41	493	29	772	35	67	72	769	30	437	35	472	40	657	33	447	38	1017	24	411	38	346	42	426	38	53	58	303	50	357	32	646	34	359	35	564	39	0	.	117	76	0	.	161	67	0	.	210	39	369	41	236	49	319	40	372	39	230	41	446	43	88	81	14	100	210	44	137	47	426	43	333	61	0	.	909	29	66	.	77	71	718	29	243	37	404	42	88	59	138	59	88	91	192	62	42	100	0	.	0	.	704	28	206	41	209	42	54	63	737	28	494	32	0	.	12	.	20	.	0	.	35921	4
	36-51 cm	257	50	14	.	0	.	325	46	1347	30	2308	24	2866	17	4110	14	1708	24	4302	14	135	100	127	86	471	42	0	.	227	61	940	33	1567	25	907	35	0	.	1906	24	784	32	824	28	82	76	298	44	0	.	3582	17	780	30	1178	26	2466	21	262	46	998	27	299	48	502	38	1415	25	776	33	93	50	1223	30	1181	30	902	36	1044	24	813	36	1845	22	631	38	525	36	840	32	243	54	765	31	690	34	893	29	975	31	527	37	0	.	205	54	0	.	187	46	0	.	143	46	279	39	117	54	422	35	296	46	311	46	336	40	133	60	284	64	499	45	140	50	476	38	307	50	58	.	1003	25	77	.	189	54	1015	24	418	42	897	29	379	41	354	78	347	54	251	55	64	108	0	.	0	.	1030	29	341	45	521	41	198	61	1243	24	1196	30	0	.	199	.	39	.	0	.	64909	3
	=52 cm	242	59	67	.	0	.	476	45	1193	36	1959	28	3225	22	6759	14	2659	23	2288	17	0	.	78	100	527	45	0	.	71	74	1002	38	1658	29	728	41	0	.	1652	25	437	44	1365	29	155	72	434	44	0	.	2619	20	1324	39	1769	31	2623	26	486	50	964	37	340	53	552	42	1448	25	564	42	87	72	1279	33	1204	39	381	50	1502	32	1735	32	3282	26	209	53	564	42	313	53	450	50	327	44	1253	36	991	32	1126	39	531	46	0	.	201	72	0	.	542	59	0	.	188	50	216	54	100	84	333	44	154	50	270	48	362	56	245	63	293	85	474	49	392	61	641	48	477	52	22	.	866	40	77	.	377	58	670	31	774	39	392	40	674	49	101	71	342	45	433	68	297	76	0	.	0	.	700	34	295	47	859	49	191	60	1372	31	454	49	0	.	0	.	99	.	0	.	68783	4
	total	651	47	96	.	0	.	952	42	3158	29	5641	22	8259	15	13784	12	5738	20	10612	13	148	100	337	53	1408	39	0	.	644	55	2575	30	4880	20	2200	31	0	.	4766	21	1897	27	2583	26	261	66	1205	36	0	.	8409	15	2791	27	3809	25	7004	19	1149	40	2911	24	804	43	1453	34	3653	22	2352	29	329	53	3523	26	3014	27	1896	34	3572	24	3292	28	6374	20	1394	35	1616	31	1796	28	829	45	1507	31	2583	28	2724	26	2626	28	1804	34	0	.	595	57	0	.	950	47	0	.	818	35	1171	34	585	40	1492	28	1159	33	918	37	1367	35	572	54	626	59	1317	37	707	50	1708	37	1234	45	88	.	3070	23	240	.	689	45	2750	21	1587	34	1942	27	1187	39	735	62	919	47	883	55	427	70	0	.	13	.	2815	24	971	37	1719	37	417	56	3587	22	2315	29	0	.	234	.	168	.	0	.	186986	2
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	46	87	90	56	142	50	47	32	0	.	0	.	25	58	62	30	278	18	0	.	26	53	38	60	30	36	0	.	64	84	49	68	0	.	78	80	0	.	0	.	16	78	12	50	0	.	0	.	0	.	57	32	37	38	67	32	8	71	39	52	0	.	10	158	-5	.	0	.	0	.	0	.	29	94	10	82	41	58	19	91	32	64	0	.	90	33	28	47	7	100	58	85	11	126	31	96	0	.	0	.	44	43	174	41	45	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	75	0	.	15	96	21	67	39	58	0	.	10	65	15	78	-4	127	21	68	36	74	0	.	14	79	10	131	25	53	7	72	2	100	0	.	24	100	0	.	0	.	27	63	4	76	30	71	26	37	9	71	31	40	31	35	42	76	62	43	0	.	2343	8
	16-23 cm	227	30	190	37	202	44	242	48	0	.	0	.	50	46	126	36	870	17	0	.	73	45	85	47	105	54	0	.	56	50	101	40	0	.	299	56	0	.	0	.	15	78	55	56	0	.	0	.	0	.	227	31	138	52	297	34	20	77	47	60	0	.	31	64	8	.	0	.	0	.	0	.	47	79	39	71	144	59	33	88	102	64	0	.	362	30	64	57	33	68	40	54	89	44	15	100	0	.	8	100	134	41	198	37	313	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	80	0	.	42	71	49	56	64	58	0	.	72	67	93	58	3	100	36	62	25	58	0	.	85	54	49	65	55	60	89	62	3	100	0	.	17	74	0	.	0	.	114	58	28	88	45	49	142	51	64	81	62	40	243	42	86	37	233	43	0	.	6848	6
	24-35 cm	408	30	531	36	442	35	521	38	0	.	0	.	212	49	583	34	1848	16	25	.	149	55	246	44	349	40	2	.	154	62	472	42	0	.	338	37	0	.	0	.	43	80	69	73	0	.	0	.	0	.	634	29	358	35	560	36	90	74	37	77	0	.	120	47	17	.	0	.	0	.	0	.	95	100	86	59	121	76	19	138	134	71	0	.	871	28	143	43	96	55	258	43	413	45	43	76	0	.	94	78	536	41	566	33	519	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	211	82	0	.	95	80	159	74	158	77	0	.	113	71	155	65	85	73	43	53	56	68	0	.	137	68	112	50	254	51	178	45	101	51	0	.	97	70	0	.	0	.	106	73	63	76	122	74	241	40	144	75	386	41	342	39	392	42	348	33	0	.	16305	5
	36-51 cm	1101	25	522	26	668	34	1330	25	0	.	0	.	818	46	632	30	3861	13	0	.	142	52	573	42	509	36	106	.	379	61	575	45	0	.	754	33	0	.	0	.	147	68	65	59	0	.	0	.	0	.	1749	23	1110	48	1108	27	71	74	202	51	0	.	293	51	39	.	0	.	0	.	0	.	0	.	91	75	215	68	55	73	230	58	0	.	1392	25	237	38	361	57	607	36	374	42	74	100	0	.	183	73	354	49	798	33	805	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	121	71	0	.	96	73	190	66	45	78	0	.	166	57	306	51	97	76	212	68	138	74	0	.	91	55	430	55	407	43	344	46	189	71	0	.	176	64	0	.	0	.	46	81	121	71	158	73	653	34	14	200	557	36	550	35	687	38	475	35	0	.	28801	5
	=52 cm	1029	29	812	34	680	37	2608	25	0	.	0	.	486	53	1179	39	5324	15	0	.	507	43	336	75	342	45	70	.	162	77	339	60	0	.	665	33	0	.	0	.	68	81	144	72	0	.	0	.	0	.	1208	26	744	37	918	33	137	75	182	73	0	.	247	85	0	.	0	.	0	.	0	.	0	.	145	100	157	55	67	100	492	56	0	.	1060	30	1024	45	75	75	373	56	73	64	68	100	0	.	113	77	184	54	1282	40	1087	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	101	100	0	.	37	100	88	76	271	83	0	.	180	70	20	100	97	98	170	78	0	.	0	.	38	71	153	76	361	50	151	55	184	88	0	.	359	64	0	.	0	.	36	71	109	100	460	48	436	41	0	.	551	44	981	44	517	39	674	40	0	.	30361	6
	total	2811	21	2146	23	2133	26	4748	21	0	.	0	.	1591	47	2583	28	12182	12	25	.	898	38	1278	37	1335	33	177	.	815	50	1536	37	0	.	2134	29	0	.	0	.	289	53	346	59	0	.	0	.	0	.	3875	21	2387	33	2951	25	326	71	507	49	0	.	701	51	60	.	0	.	0	.	0	.	172	73	371	67	678	45	192	75	990	50	0	.	3775	21	1497	38	572	53	1337	36	960	41	231	79	0	.	398	76	1252	36	3018	28	2770	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	505	71	0	.	286	71	508	54	578	57	0	.	541	56	590	47	278	66	482	59	255	56	0	.	364	54	754	50	1102	42	770	42	478	61	0	.	672	58	0	.	0	.	329	47	326	72	815	44	1498	29	231	70	1586	31	2147	31	1724	32	1792	28	0	.	84658	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	.	6	61	0	.	0	.	0	.	0	.	107	80	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	8	.	25	113	13	54	78	46	60	50	186	30	52	40	55	58	128	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	5	.	0	.	0	.	0	.	48	54	32	44	8	67	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	822	15
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	52	0	.	0	.	0	.	0	.	104	49	23	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	.	11	.	170	49	67	55	219	43	185	46	688	24	168	40	164	51	362	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	73	0	.	0	.	34	.	0	.	0	.	0	.	145	41	81	48	31	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2534	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	193	57	0	.	0	.	0	.	0	.	341	48	26	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	.	23	.	83	56	112	64	219	46	432	36	827	25	150	44	381	45	449	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	71	0	.	0	.	33	.	0	.	0	.	0	.	132	58	103	59	12	100	0	.	0	.	0	.	0	.	0	.	33	100	0	.	0	.	0	.	3670	10
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	285	56	0	.	0	.	0	.	0	.	187	45	63	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	.	18	.	109	53	127	58	62	59	318	34	782	28	130	43	138	45	458	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	71	0	.	0	.	0	.	0	.	0	.	0	.	195	47	67	57	-1	5538	0	.	0	.	0	.	0	.	0	.	103	83	0	.	0	.	0	.	3113	12
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	100	0	.	0	.	0	.	0	.	135	61	47	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	.	30	.	111	84	25	100	108	51	235	38	425	36	300	62	88	79	491	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	101	84	0	.	30	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2236	17
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	614	53	0	.	0	.	0	.	0	.	873	39	160	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	.	90	.	498	43	344	57	685	36	1230	32	2908	19	799	35	826	39	1888	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	186	71	0	.	0	.	72	.	0	.	0	.	0	.	620	40	283	51	80	64	0	.	0	.	0	.	0	.	0	.	137	87	0	.	0	.	0	.	12375	8
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	51	78	90	56	142	50	56	28	-6	333	49	44	478	18	217	23	400	18	257	19	26	53	55	44	70	26	0	.	67	80	106	37	106	28	97	65	6	61	78	23	158	27	105	23	205	25	454	24	329	23	240	17	126	30	161	25	165	21	73	40	70	31	19	87	34	46	74	40	44	38	89	35	90	36	43	35	60	43	113	30	78	44	67	28	130	29	90	31	68	42	76	67	28	62	93	39	69	58	29	86	82	36	174	41	55	35	87	41	87	45	47	31	139	31	92	36	234	25	172	27	133	32	173	25	65	32	36	62	58	37	83	38	77	36	65	40	34	44	15	78	70	38	24	61	51	55	76	30	45	39	64	40	42	35	41	53	24	65	0	.	34	74	318	21	319	23	306	25	33	43	63	43	0	14262	56	28	57	29	32	33	42	76	62	43	0	.	9391	4
	16-23 cm	267	29	195	37	202	44	293	42	81	37	298	29	1087	13	955	17	1309	15	910	18	73	45	102	40	218	33	0	.	113	41	409	28	507	25	431	42	43	52	324	28	378	28	398	23	481	26	1085	14	1067	15	879	16	374	26	414	26	529	19	129	41	238	32	79	38	110	36	224	35	300	37	244	30	323	28	198	36	285	37	389	26	363	37	192	31	464	26	183	34	189	40	105	45	184	29	270	32	218	37	157	42	279	29	198	37	411	34	194	46	387	27	174	31	449	29	459	32	771	22	564	24	475	28	480	28	263	33	111	50	257	31	170	35	125	34	207	40	165	49	102	54	223	28	53	53	56	44	270	28	221	40	243	35	116	37	237	46	123	57	8	100	30	61	1029	17	978	20	878	18	128	41	142	41	142	51	252	34	206	47	243	42	109	36	243	41	0	.	30439	3
	24-35 cm	514	26	540	35	442	35	612	34	542	34	1028	27	3568	12	2598	14	2658	14	2884	17	161	51	344	36	606	30	2	.	471	45	1143	29	1067	25	822	29	193	57	976	32	1315	20	979	22	962	22	2968	14	2826	13	2008	15	791	23	1211	27	1591	20	322	52	641	35	227	38	276	39	493	29	933	32	527	34	1096	25	523	31	645	34	853	28	618	32	1035	24	1282	22	490	32	523	33	312	37	716	33	456	27	720	31	484	31	1099	28	566	33	647	31	84	43	672	27	594	30	491	28	801	27	1063	22	766	24	858	26	803	25	755	35	288	47	607	34	488	33	425	36	634	43	446	49	155	65	994	27	109	64	133	50	718	29	492	30	516	34	451	36	349	34	189	50	192	62	139	57	2016	15	1956	17	2020	16	270	36	331	38	295	35	882	27	880	25	375	36	404	40	368	32	0	.	75312	2
	36-51 cm	1358	22	537	26	668	34	1655	22	1347	30	2308	24	7720	11	5093	12	5569	12	4313	14	277	55	700	38	980	27	106	.	688	41	1770	24	1739	24	1786	23	285	56	2142	22	3310	16	1573	19	2472	20	4707	13	5111	12	5331	13	1937	30	2286	19	2731	19	465	34	998	27	592	35	541	36	1415	25	834	31	491	31	1522	25	1273	28	1269	30	1342	22	1126	29	1932	21	2023	21	762	27	1201	28	850	30	1139	25	1180	27	1022	27	1233	27	881	29	798	33	1025	25	330	44	870	22	1200	27	273	32	597	25	899	25	800	23	564	29	900	26	674	32	522	52	986	32	942	34	741	36	777	32	473	38	364	45	1100	24	290	56	327	44	1015	24	568	33	1327	26	981	26	699	45	535	43	251	55	240	55	3015	16	2485	16	3306	16	462	38	679	36	851	29	1257	24	1753	24	653	32	886	37	514	33	0	.	131513	2
	=52 cm	1271	26	879	32	680	37	3084	22	1193	36	1959	28	8155	13	8069	13	7984	13	2470	17	507	43	414	64	869	32	70	.	322	50	1383	31	1945	27	1535	25	87	100	2639	23	3396	20	2201	21	3361	19	5361	14	4170	16	3827	15	2063	28	2687	23	2877	24	668	42	964	37	588	47	552	42	1448	25	645	39	485	46	1589	28	1349	37	588	37	1796	28	2257	28	3461	25	1269	26	1588	32	388	45	823	37	400	38	1810	29	1056	30	1331	34	715	37	1282	40	1311	31	862	37	1536	30	946	34	378	33	452	32	526	33	1052	28	454	34	1079	33	722	37	517	45	651	46	1040	33	1145	33	1039	37	657	42	42	71	1083	35	247	62	377	58	670	31	812	38	545	36	1142	34	252	44	526	42	433	68	657	49	4125	17	2225	19	3502	18	404	44	1319	36	627	34	1372	31	1005	32	981	44	517	39	773	37	0	.	140509	2
	total	3462	19	2241	23	2133	26	5700	19	3158	29	5641	22	21007	10	16932	10	17920	10	10833	12	1046	35	1615	31	2743	25	177	.	1662	34	4812	21	5363	19	4671	20	614	53	6159	19	8558	14	5255	17	7480	16	14576	11	13504	10	12284	12	5290	21	6759	18	7893	18	1656	32	2911	24	1505	33	1512	32	3653	22	2756	27	1835	31	4620	21	3385	25	2846	26	4492	21	4442	23	6688	19	5169	18	3113	24	2368	25	2166	28	2467	24	3809	22	3086	24	3234	25	3056	25	3018	28	3448	21	1557	30	3551	21	2960	25	1731	23	2401	23	3493	17	3354	18	2485	22	3435	21	2479	28	1472	39	2559	28	2723	27	2512	27	2721	30	1776	36	679	42	3471	22	722	51	944	36	2750	21	2138	27	2696	24	2732	25	1578	36	1397	37	883	55	1099	45	10503	12	7963	14	10012	13	1297	33	2534	29	1915	26	3819	22	3900	21	2284	29	1958	30	1960	27	0	.	387165	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. 2293427. Birmensdorf, Swiss Federal Research Institute WSL

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