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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NFI5
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (main diameter classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	.	7	53	0	.	4	100	24	38	127	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	.	2	.	20	76	11	55	6	82	0	.	0	.	9	59	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	59	19	41	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	289	19
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	79	0	.	6	100	84	35	353	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	.	1	.	21	66	21	59	20	100	0	.	0	.	0	13282	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	181	59	60	58	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	785	18
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	71	0	.	45	100	226	30	792	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	.	72	61	71	71	112	100	0	.	0	.	8	100	11	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	286	34	164	43	33	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1846	14
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	83	0	.	7	100	281	38	1195	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	69	49	63	0	.	0	.	0	.	0	.	0	.	21	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	459	33	362	49	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2495	14
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	0	.	0	.	368	44	1232	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	.	14	.	16	100	30	71	38	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	.	519	36	219	43	27	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2491	17
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	111	69	0	.	63	85	983	29	3698	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	202	54	182	60	176	81	0	.	0	.	17	103	25	.	22	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1502	26	824	36	85	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7906	11
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	.	77	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	14	73	55	32	9	47	38	36	54	36	131	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	87	11	64	19	53	0	.	0	.	5	79	0	.	2	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	5	71	33	55	7	61	2	100	0	.	0	.	12	41	1	100	12	.	0	.	19	100	24	57	3	100	3	75	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	0	.	128	34	83	43	26	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	808	12
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	274	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	.	0	.	0	.	55	77	216	33	36	38	132	35	225	32	475	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	92	47	76	104	62	0	.	0	.	13	105	0	.	25	.	0	.	0	.	0	.	0	.	0	.	17	72	0	.	0	.	0	.	0	.	0	.	26	65	88	44	72	56	0	.	0	.	0	.	109	38	14	78	11	.	0	.	15	75	91	67	19	100	16	66	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	381	26	174	31	142	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2873	9
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	984	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	.	0	.	0	.	47	84	429	30	216	55	570	35	595	34	1186	22	0	.	0	.	0	.	11	.	0	.	0	.	0	.	0	.	0	.	127	87	119	88	309	58	0	.	0	.	215	75	0	.	38	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	13	100	299	43	128	56	41	100	0	.	0	.	284	41	15	71	8	.	20	.	75	71	30	95	98	72	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	623	30	505	31	402	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7476	9
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	2192	20	79	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	.	0	.	0	.	130	49	1070	25	523	38	1043	31	1095	28	1871	20	0	.	0	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	113	60	99	58	267	80	0	.	0	.	318	51	0	.	10	.	0	.	0	.	0	.	0	.	0	.	100	88	0	.	0	.	0	.	0	.	0	.	141	69	495	35	196	52	21	100	0	.	0	.	210	37	52	65	6	.	0	.	151	74	74	65	61	73	155	61	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	1248	29	737	27	894	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13468	7
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	1759	24	60	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	348	50	1644	30	352	48	1577	29	935	28	2158	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	138	72	34	74	71	100	0	.	82	.	187	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	237	83	0	.	0	.	0	.	0	.	0	.	415	48	442	40	184	58	0	.	0	.	0	.	419	45	57	63	0	.	59	.	149	71	13	100	79	100	166	45	109	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	.	0	.	0	.	0	.	0	.	2429	24	853	32	1275	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16274	8
	total	0	.	0	.	0	.	0	.	0	.	0	.	5286	19	138	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	166	.	0	.	0	.	595	51	3413	22	1136	37	3360	25	2904	24	5821	17	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	454	60	310	69	770	57	0	.	82	.	738	57	0	.	75	.	0	.	0	.	0	.	0	.	0	.	366	68	0	.	0	.	0	.	0	.	0	.	600	49	1357	32	587	48	63	97	0	.	0	.	1034	34	140	65	37	.	80	.	408	71	232	58	259	71	354	49	135	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	.	0	.	0	.	0	.	0	.	4809	21	2352	24	2739	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40899	6
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	172	21	1	100	0	.	4	71	0	.	0	.	0	.	0	.	0	.	20	47	0	.	0	.	0	.	9	61	63	42	41	42	180	24	315	30	93	27	0	.	4	100	0	.	23	59	0	.	0	.	0	.	1	.	0	.	4	75	48	42	9	50	0	.	13	93	20	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	63	20	83	6	78	0	.	0	.	0	.	41	40	25	61	23	44	2	61	0	.	0	.	19	45	4	82	2	81	1	646	7	65	28	47	31	59	14	80	66	73	0	.	0	.	23	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	148	21	166	27	168	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1825	8
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	446	22	5	100	0	.	4	100	0	.	0	.	0	.	0	.	9	.	121	48	3	100	11	79	0	.	42	53	158	31	219	42	530	23	814	18	358	25	0	.	11	100	0	.	79	51	0	.	0	.	0	.	2	.	0	.	8	100	150	40	15	60	0	.	63	63	59	53	3	.	3	100	0	.	0	.	0	.	0	.	0	.	18	67	62	65	24	72	0	.	0	.	0	.	182	39	102	58	70	36	9	100	0	.	0	.	44	39	27	64	70	86	17	154	44	59	115	43	64	70	55	55	126	61	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	26	88	0	.	0	.	0	.	0	.	605	25	677	25	404	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5869	7
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	1151	19	34	71	0	.	0	.	0	.	0	.	0	.	0	.	34	.	253	71	36	72	41	74	0	.	129	71	322	40	709	36	867	24	1721	17	851	25	0	.	64	100	0	.	129	56	0	.	0	.	0	.	0	.	0	.	64	97	390	43	93	49	0	.	65	65	60	56	14	.	0	.	0	.	0	.	0	.	0	.	0	.	41	70	135	57	48	82	0	.	0	.	0	.	114	51	147	59	244	42	37	100	0	.	0	.	248	44	87	59	204	57	93	59	159	77	427	42	139	56	83	42	190	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	35	100	0	.	0	.	0	.	0	.	1115	24	1466	24	1013	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13064	6
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	1817	20	239	66	0	.	11	100	0	.	0	.	0	.	0	.	67	.	522	47	131	59	128	71	0	.	144	65	1272	29	610	28	1154	24	3291	15	2491	20	0	.	30	100	0	.	167	58	0	.	0	.	0	.	0	.	0	.	34	100	341	40	195	55	0	.	262	71	64	56	102	.	10	100	0	.	0	.	0	.	0	.	0	.	294	57	135	83	89	71	0	.	0	.	0	.	271	52	136	51	591	38	69	57	0	.	0	.	293	47	137	56	183	57	194	64	373	81	420	46	352	64	524	59	354	55	0	.	0	.	17	100	0	.	0	.	0	.	0	.	9	.	79	71	0	.	0	.	0	.	0	.	1582	24	1584	22	1359	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22124	6
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	3691	20	89	84	0	.	205	95	0	.	0	.	0	.	0	.	146	.	118	57	344	62	210	72	0	.	761	55	1574	30	694	43	2312	26	4617	16	1540	25	0	.	47	100	0	.	266	56	0	.	0	.	0	.	0	.	0	.	0	.	422	55	290	51	0	.	123	100	153	84	25	.	106	71	0	.	0	.	0	.	0	.	0	.	292	60	69	100	106	100	0	.	0	.	0	.	530	49	535	57	714	35	112	71	0	.	0	.	238	51	169	67	375	69	221	62	148	75	386	48	542	56	544	41	610	51	0	.	0	.	130	100	0	.	0	.	0	.	0	.	19	.	102	62	0	.	0	.	0	.	0	.	1790	25	1650	23	2065	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29079	6
	total	0	.	0	.	0	.	0	.	0	.	0	.	7277	16	369	60	0	.	224	93	0	.	0	.	0	.	0	.	255	.	1033	47	514	54	390	71	0	.	1085	52	3389	25	2274	28	5044	19	10757	13	5333	18	0	.	155	100	0	.	663	48	0	.	0	.	0	.	3	.	0	.	109	72	1352	40	603	47	0	.	526	67	357	59	144	.	119	71	0	.	0	.	0	.	0	.	0	.	652	48	420	58	273	68	0	.	0	.	0	.	1138	37	945	47	1642	31	229	63	0	.	0	.	842	40	423	57	834	52	525	57	732	67	1376	40	1128	49	1219	43	1345	47	0	.	0	.	186	82	0	.	0	.	0	.	0	.	41	.	242	61	0	.	0	.	0	.	0	.	5242	19	5542	18	5009	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71961	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	11	56	0	.	0	.	11	54	9	49	50	36	190	20	309	25	186	45	228	26	19	100	18	61	23	56	0	.	2	73	37	32	126	29	26	46	0	.	40	29	31	36	40	38	7	71	47	37	0	.	203	23	111	46	85	41	135	29	22	64	50	30	10	57	35	50	74	48	36	38	45	81	61	32	52	52	16	47	106	43	24	49	52	27	30	78	71	48	49	44	46	61	21	51	82	36	36	36	33	40	-11	278	0	.	12	75	0	.	53	80	0	.	68	35	31	39	21	68	166	43	66	39	43	35	27	39	36	49	5	68	34	45	18	54	50	38	14	67	0	.	56	37	3	.	34	60	47	40	13	100	55	54	19	56	19	76	14	80	17	73	21	84	0	.	10	88	99	32	31	37	14	49	23	55	60	34	57	49	0	.	0	.	0	.	0	.	4123	5
	16-23 cm	18	88	0	.	0	.	50	61	98	53	184	42	587	26	852	15	511	29	853	18	33	100	21	57	132	47	0	.	28	82	203	37	371	52	116	52	0	.	230	26	168	41	124	37	18	60	136	38	0	.	712	21	241	34	222	42	430	21	49	86	241	30	44	50	155	41	305	32	266	45	51	52	223	30	168	43	113	43	331	37	157	44	256	26	52	47	169	35	159	38	75	49	101	47	281	34	153	37	191	41	73	48	0	.	40	71	0	.	51	54	0	.	194	39	197	39	118	43	374	31	262	38	135	40	190	45	156	49	59	92	108	40	52	52	205	43	37	60	0	.	261	32	20	.	38	71	250	24	79	46	146	42	54	49	35	72	38	57	10	100	50	65	0	.	11	75	352	29	79	42	71	45	21	172	218	30	142	59	0	.	0	.	8	.	0	.	14014	4
	24-35 cm	105	65	20	.	0	.	80	65	588	33	1034	30	1766	17	1945	17	757	27	2315	17	18	100	86	59	371	38	0	.	208	64	255	40	1271	24	399	50	0	.	430	43	604	34	316	32	7	100	405	36	0	.	1220	19	381	34	512	35	1244	22	203	72	586	27	149	57	232	46	536	33	796	35	79	62	727	29	583	35	341	42	713	29	653	52	1045	24	325	47	482	39	371	35	95	58	329	41	359	31	727	30	253	43	415	45	0	.	90	72	0	.	559	84	0	.	280	41	479	34	299	47	760	33	471	35	201	44	555	42	68	75	44	61	258	40	116	49	538	41	326	55	0	.	759	29	75	.	112	69	623	26	282	42	361	53	72	51	163	57	99	76	100	72	29	100	0	.	0	0	761	27	362	35	124	53	57	71	701	31	387	37	0	.	12	.	27	.	0	.	36491	4
	36-51 cm	189	50	0	.	0	.	204	54	1439	28	1643	26	3202	19	3689	15	1509	28	3816	14	48	100	180	80	425	40	0	.	310	56	884	31	1518	24	1060	37	0	.	1462	24	1111	30	544	28	98	76	366	40	0	.	3370	17	710	31	1097	27	1925	21	282	46	975	31	289	45	489	40	1152	25	1026	34	141	46	1194	29	1214	29	1034	34	1267	23	865	38	2069	20	707	37	535	38	774	33	214	56	761	34	685	35	1005	26	966	31	577	36	0	.	77	63	0	.	565	70	0	.	167	37	340	32	199	61	425	32	388	41	573	41	357	39	194	73	231	72	445	44	210	49	515	38	365	46	16	.	1189	25	110	.	208	53	1135	21	368	34	778	29	322	44	254	60	215	53	254	63	82	88	0	.	0	.	1097	25	616	36	549	43	215	63	1105	25	996	30	0	.	0	.	18	.	0	.	63596	3
	=52 cm	299	63	0	.	0	.	517	51	1618	32	1840	29	3837	19	7070	15	2301	25	2867	17	48	100	86	100	626	49	0	.	138	72	1207	37	1730	25	929	35	0	.	1846	25	723	36	1635	28	148	71	577	43	0	.	3192	20	1098	33	2032	29	2578	23	610	50	1270	32	234	52	599	44	1751	26	651	44	108	65	1502	33	1781	34	724	55	1495	29	1948	31	4133	20	268	53	583	41	168	74	350	60	583	35	1296	34	1265	31	1353	36	627	45	0	.	196	60	0	.	935	59	0	.	212	50	329	49	110	84	465	36	178	47	380	43	318	59	296	65	318	82	444	53	703	51	594	40	664	49	0	.	1055	34	114	.	177	52	728	33	932	41	531	38	554	49	120	71	454	44	162	59	372	73	0	.	0	.	903	34	648	41	592	50	321	76	1742	30	825	41	0	.	0	.	140	.	0	.	78751	3
	total	623	48	20	.	0	.	863	45	3751	28	4750	24	9582	15	13865	12	5264	20	10079	12	166	76	392	55	1576	37	0	.	687	54	2586	30	5016	19	2531	32	0	.	4009	21	2637	25	2659	25	279	67	1530	33	0	.	8698	15	2541	25	3949	25	6312	18	1167	39	3121	24	726	42	1510	35	3819	23	2775	29	424	46	3707	26	3798	26	2228	34	3913	22	3647	28	7555	18	1381	34	1840	31	1521	31	780	48	1796	30	2702	29	3187	25	2796	28	1682	35	0	.	415	50	0	.	2162	63	0	.	920	33	1376	30	747	38	2189	25	1365	31	1332	33	1447	36	751	49	656	58	1288	38	1099	45	1902	32	1405	44	16	.	3320	23	321	.	568	44	2784	21	1675	33	1871	29	1022	40	592	52	821	40	544	60	555	66	0	.	21	80	3213	23	1737	33	1349	37	637	63	3826	23	2408	29	0	.	12	.	192	.	0	.	196975	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	149	30	83	29	15	47	56	35	0	.	0	.	21	49	47	32	391	20	3	.	50	49	44	52	43	46	2	.	22	72	39	64	0	.	59	52	0	.	0	.	13	61	13	76	0	.	0	.	0	.	10	484	54	38	121	51	9	80	20	58	0	.	47	57	-5	.	0	.	0	.	0	.	36	100	8	127	27	84	12	100	6	74	0	.	78	30	16	63	2	100	51	56	29	132	54	97	0	.	0	.	35	49	59	36	48	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	71	0	.	3	78	6	66	28	63	0	.	14	56	30	48	3	100	16	56	38	71	0	.	18	62	15	51	47	72	7	52	1	100	0	.	13	85	0	.	0	.	28	70	0	.	13	50	22	41	11	76	117	52	32	48	39	40	32	34	0	.	2304	8
	16-23 cm	255	29	217	34	273	39	149	33	0	.	0	.	40	46	196	31	946	17	0	.	106	45	148	41	99	54	0	.	40	54	140	56	0	.	295	65	0	.	0	.	26	75	49	53	0	.	0	.	0	.	178	28	144	48	237	33	42	95	35	58	0	.	68	55	9	.	0	.	0	.	0	.	112	93	6	644	112	58	32	85	71	78	0	.	212	31	127	62	-7	223	99	42	81	43	32	100	0	.	0	0	156	38	254	40	148	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	75	0	.	37	71	84	54	36	76	0	.	65	63	52	72	19	73	87	68	33	72	0	.	95	60	18	132	104	49	58	80	5	100	0	.	61	96	0	.	0	.	97	51	9	100	69	52	78	46	49	62	102	55	117	40	137	70	145	36	0	.	6721	6
	24-35 cm	364	32	548	33	334	33	527	39	0	.	0	.	153	44	385	40	1786	17	22	.	96	55	177	49	320	37	-5	.	106	64	350	49	0	.	435	35	0	.	0	.	71	71	144	58	0	.	0	.	0	.	390	35	392	40	660	32	77	82	64	66	0	.	105	71	16	.	0	.	0	.	0	.	152	88	72	61	209	68	39	100	113	89	0	.	722	29	133	53	26	72	152	46	331	48	45	100	0	.	0	.	530	38	361	31	486	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	239	77	48	.	45	73	108	78	106	70	0	.	197	56	132	59	78	71	109	67	40	63	0	.	194	56	112	56	128	59	161	52	112	52	0	.	63	100	0	.	0	.	111	72	68	71	117	54	204	44	175	79	223	43	336	41	158	41	511	32	0	.	14667	6
	36-51 cm	985	28	527	26	637	33	997	27	0	.	0	.	583	45	741	29	3418	14	48	.	105	62	509	47	525	36	38	.	351	59	222	54	0	.	657	35	0	.	0	.	134	58	71	60	0	.	0	.	0	.	1260	26	786	36	887	30	92	80	36	70	0	.	210	50	29	.	0	.	0	.	0	.	13	100	128	71	144	57	59	78	138	56	0	.	1144	25	249	36	164	55	423	37	406	38	48	91	0	.	253	71	472	48	692	30	624	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	156	74	75	.	130	72	61	69	102	69	0	.	308	56	305	60	60	58	24	80	130	58	0	.	160	55	294	55	414	47	313	49	135	75	0	.	158	71	0	.	0	.	98	50	118	87	250	58	470	36	60	72	519	45	450	34	420	38	421	35	0	.	24438	5
	=52 cm	790	28	850	30	861	39	2004	26	0	.	0	.	325	59	985	33	5177	15	0	.	361	53	318	62	355	40	218	.	233	68	320	49	0	.	722	32	0	.	0	.	184	59	124	66	0	.	0	.	0	.	1427	28	1050	37	1041	31	86	78	71	71	0	.	345	72	0	.	0	.	0	.	0	.	0	.	190	100	161	59	93	100	138	81	0	.	1207	30	946	44	11	100	408	52	142	69	99	100	0	.	148	81	282	47	1243	41	1386	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	167	100	94	.	43	100	52	77	306	89	0	.	250	62	203	75	147	80	288	66	0	.	0	.	28	71	238	53	233	52	198	49	163	74	0	.	250	72	0	.	0	.	118	63	104	85	490	49	650	36	0	.	450	42	714	41	537	37	601	34	0	.	30625	5
	total	2542	22	2225	22	2121	26	3734	22	0	.	0	.	1121	44	2354	26	11717	11	73	.	718	39	1197	38	1342	31	252	.	752	50	1071	47	0	.	2168	29	0	.	0	.	428	52	401	55	0	.	0	.	0	.	3265	24	2427	30	2945	24	307	74	227	44	0	.	775	49	49	.	0	.	0	.	0	.	312	70	404	67	653	56	235	72	467	63	0	.	3362	22	1472	35	197	57	1132	32	989	38	278	75	0	.	401	72	1475	35	2610	26	2692	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	602	72	218	.	258	73	311	51	577	62	0	.	833	53	722	46	307	60	524	45	241	52	0	.	496	49	677	48	927	43	738	43	417	58	0	.	544	65	0	.	0	.	452	43	300	71	939	43	1424	29	295	68	1411	30	1650	30	1292	30	1710	27	0	.	78755	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	.	23	57	0	.	0	.	0	.	0	.	63	59	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	.	8	.	8	75	49	51	15	55	91	54	84	66	151	27	46	39	24	45	110	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	3	.	0	.	0	.	0	.	40	53	34	38	19	57	0	.	0	.	0	.	0	.	0	.	7	78	0	.	0	.	0	.	778	13
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	85	0	.	0	.	0	.	0	.	115	52	17	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	.	22	83	171	44	52	53	237	33	128	35	645	25	166	36	141	41	364	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	.	0	.	0	.	0	.	143	41	76	52	39	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2428	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	54	0	.	0	.	0	.	0	.	353	49	34	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	.	17	100	234	45	117	57	270	46	355	36	1003	25	256	41	336	42	635	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	74	0	.	0	.	35	.	0	.	0	.	0	.	139	52	118	62	25	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4110	10
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	61	0	.	0	.	0	.	0	.	229	49	45	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	.	12	100	179	51	165	58	108	42	385	33	986	25	156	36	193	38	433	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	71	0	.	0	.	0	.	0	.	0	.	0	.	167	48	81	66	66	55	0	.	0	.	0	.	0	.	0	.	57	75	0	.	0	.	0	.	3593	10
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	113	100	0	.	0	.	0	.	0	.	115	55	86	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	.	0	.	0	.	0	.	37	100	188	59	47	100	94	50	243	38	509	37	373	61	122	70	514	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	136	80	20	100	29	100	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	2646	16
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	485	54	0	.	0	.	0	.	0	.	876	39	185	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	.	86	.	97	93	821	39	396	57	800	31	1196	31	3294	19	997	34	816	34	2055	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	165	71	0	.	0	.	71	.	0	.	0	.	0	.	626	40	328	51	178	40	0	.	0	.	0	.	0	.	0	.	83	77	0	.	0	.	0	.	13555	7
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	160	28	83	29	15	47	67	31	9	49	50	36	460	12	358	22	577	20	236	25	69	45	62	40	66	36	2	.	24	65	96	30	133	28	85	39	23	57	64	26	169	20	103	25	229	20	502	21	353	21	214	32	169	33	206	34	167	25	42	43	50	30	57	48	32	57	74	48	55	35	104	40	124	34	60	48	56	48	143	34	30	42	54	27	107	31	87	41	50	43	97	41	50	79	148	41	56	37	40	36	24	141	59	36	68	31	56	32	179	31	56	26	170	32	115	50	172	25	252	29	96	29	168	22	34	43	62	42	60	32	73	33	63	37	118	44	28	44	30	48	82	38	19	50	72	47	47	40	34	52	70	43	69	51	29	52	16	73	17	73	34	61	374	18	312	19	342	17	31	37	27	35	45	34	71	31	174	38	39	42	39	40	32	34	0	.	10126	3
	16-23 cm	274	27	217	34	273	39	199	29	98	53	184	42	1346	14	1054	13	1457	15	857	18	139	41	170	37	231	35	0	.	77	43	464	27	398	49	423	47	61	85	327	23	582	19	429	25	687	19	1373	13	1203	15	890	18	396	27	459	26	551	19	84	55	241	30	113	39	165	39	305	32	334	39	249	30	454	31	174	47	288	31	434	30	231	38	284	25	264	27	297	33	152	41	174	32	182	32	347	29	215	32	215	37	229	30	254	40	205	32	232	32	434	25	216	26	460	24	325	27	763	22	692	19	458	25	579	24	243	38	216	38	303	33	274	29	160	32	336	35	101	46	52	72	295	29	107	58	70	50	250	24	174	39	164	40	190	33	127	49	43	52	10	100	111	60	1311	16	998	18	1043	16	88	39	140	34	98	51	267	27	244	41	117	40	137	70	153	34	0	.	32690	3
	24-35 cm	469	29	568	32	334	33	608	35	588	33	1034	30	4054	11	2364	15	2542	14	2337	17	114	49	264	38	691	27	-5	.	348	44	857	31	1374	23	875	29	71	54	607	35	1452	19	1385	22	1489	19	3300	13	2863	13	1610	17	837	25	1172	23	1462	20	267	57	586	27	254	44	247	44	536	33	987	31	589	34	1280	24	655	32	616	34	1028	26	781	45	1083	23	1047	25	615	33	397	33	247	36	661	31	453	27	861	26	301	38	946	29	361	31	607	33	144	43	1310	38	560	27	739	28	834	25	1302	22	1555	20	920	24	1048	22	907	33	350	43	546	34	602	28	319	31	728	36	523	40	132	59	838	27	184	57	152	53	623	26	557	31	486	42	236	39	360	36	212	45	100	72	92	75	2163	16	2253	17	2345	14	431	32	241	38	261	38	877	30	611	28	336	41	170	39	537	31	0	.	77653	2
	36-51 cm	1174	24	527	26	637	33	1202	24	1439	28	1643	26	7794	11	4748	13	4927	13	3875	14	153	52	690	40	950	26	38	.	728	38	1628	24	1715	22	1845	25	217	61	1736	21	3621	16	1749	17	2302	19	5262	12	5602	12	4629	14	1526	24	1984	20	2193	19	318	41	975	31	499	34	518	38	1152	25	1173	30	581	28	1670	25	1342	27	1440	28	1708	20	1105	32	2089	20	1850	21	784	28	938	29	637	31	1167	26	1128	27	1140	25	1308	27	1049	29	692	30	732	28	424	40	1448	31	1000	27	365	24	725	23	1184	23	1083	19	770	25	1216	24	706	31	794	45	854	32	918	33	991	35	888	31	672	36	321	57	1266	23	134	84	338	39	1135	21	609	27	1081	25	838	30	567	38	350	44	254	63	240	55	3457	16	2764	16	3529	14	734	33	798	34	685	31	1165	24	1515	25	508	31	420	38	439	34	0	.	129714	2
	=52 cm	1089	27	850	30	861	39	2521	23	1618	32	1840	29	9612	12	8203	13	7477	13	3072	17	409	48	404	53	980	34	218	.	517	46	1645	29	2074	23	1861	23	113	100	2956	22	4154	18	2805	20	4037	19	6612	12	5016	14	4619	16	2195	24	3074	22	2929	21	682	45	1270	32	579	48	599	44	1751	26	789	39	564	43	1863	28	1971	32	1089	40	1929	25	2112	29	4240	20	1475	26	1529	31	179	70	757	39	725	31	1923	27	1334	30	1607	32	909	34	1243	41	1582	30	996	33	2116	31	975	28	455	32	572	32	620	34	1495	24	526	32	1270	29	765	38	687	38	760	43	1116	35	1718	30	1312	31	914	39	203	75	1332	30	402	55	177	52	728	33	961	40	788	30	934	33	318	40	618	37	162	59	622	52	4874	15	2742	17	4418	16	752	37	1081	35	971	35	1742	30	1275	30	733	40	537	37	741	33	0	.	159867	2
	total	3166	20	2245	22	2121	26	4596	20	3751	28	4750	24	23266	9	16726	10	16981	10	10376	12	884	34	1589	32	2918	24	252	.	1694	35	4690	22	5695	17	5089	21	485	54	5689	18	9978	13	6471	16	8744	15	17049	9	15037	10	11963	12	5122	19	6895	17	7302	16	1393	33	3121	24	1501	32	1562	34	3819	23	3339	26	2086	29	5392	21	4202	25	3489	26	5242	19	4259	25	7750	17	4744	18	3312	23	1717	28	1913	27	2785	24	3999	22	3607	23	3470	24	3157	24	2610	26	3194	22	1851	28	5487	28	2807	23	2189	20	2572	21	4041	17	5078	15	2768	20	4280	19	2654	28	2108	34	2522	28	2985	26	3250	25	3382	26	2238	34	738	45	3813	21	845	47	809	34	2784	21	2336	27	2589	24	2267	26	1401	32	1238	33	544	60	1098	46	12179	12	9068	13	11676	12	2036	30	2287	28	2060	28	4122	22	3819	21	1733	29	1304	30	1903	26	0	.	410050	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. 2276631. Birmensdorf, Swiss Federal Research Institute WSL

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