Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI2
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (main diameter classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (main diameter classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	1	71	94	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	.	2	100	8	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	.	24	42	8	41	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	25
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	11	.	6	76	255	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	.	18	89	13	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	114	53	24	62	8	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	453	20
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	.	0	.	0	.	69	50	557	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	.	14	100	31	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	248	33	163	62	29	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1140	16
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.	0	.	0	.	129	45	880	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	.	29	75	18	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	.	414	31	91	42	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1577	17
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	.	0	.	0	.	232	63	804	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	325	34	79	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1511	22
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	103	.	0	.	11	.	437	47	2590	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	82	83	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1124	26	366	46	46	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4822	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	.	61	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	35	76	11	47	14	41	39	42	115	52	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	8	100	17	72	18	71	0	.	0	.	14	65	0	.	11	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	13	51	9	55	0	.	0	.	0	.	7	67	1	100	0	.	0	.	2	100	7	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	68	28	33	40	34	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	526	15
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	275	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	33	49	63	98	31	198	29	392	28	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	39	76	55	100	45	69	0	.	0	.	9	170	0	.	-6	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	0	29	72	46	66	12	.	0	.	0	.	50	64	0	.	0	.	0	.	17	76	15	100	23	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	152	33	268	29	161	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2046	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	1004	25	49	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	373	32	156	62	457	36	530	33	960	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	100	30	100	165	63	0	.	0	.	81	74	0	.	-9	.	0	.	0	.	0	.	0	.	0	.	70	81	0	.	0	.	0	.	0	.	0	.	36	71	245	44	87	54	0	.	0	.	0	.	56	56	12	100	0	.	6	.	84	100	10	100	0	.	55	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	.	0	.	0	.	0	.	0	.	557	29	493	31	426	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6053	9
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	1605	27	60	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	63	812	28	154	53	1032	29	923	32	1808	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	80	147	81	67	73	0	.	24	.	189	83	0	.	21	.	0	.	0	.	0	.	0	.	0	.	57	100	0	.	0	.	0	.	0	.	0	.	157	60	413	37	161	56	0	.	0	.	0	.	179	61	32	76	0	.	15	.	91	92	29	100	26	.	17	72	50	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	.	0	.	0	.	0	.	0	.	1118	28	971	31	667	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10982	8
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	759	27	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	211	63	878	36	121	65	816	31	639	31	1275	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	100	0	0	79	74	0	.	50	.	117	71	0	.	-14	.	0	.	0	.	0	.	0	.	0	.	153	89	0	.	0	.	0	.	0	.	0	.	127	62	232	47	26	77	0	.	0	.	0	.	158	54	50	73	0	.	61	.	99	82	18	100	66	.	11	227	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	.	0	.	0	.	0	.	0	.	1586	26	506	39	635	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8868	9
	total	0	.	0	.	0	.	0	.	0	.	0	.	3704	21	143	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	304	60	2205	25	491	45	2416	25	2329	25	4550	19	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	237	71	248	67	374	59	0	.	74	.	410	64	0	.	2	.	0	.	0	.	0	.	0	.	0	.	282	71	0	.	0	.	0	.	0	.	0	.	320	60	931	33	329	53	12	.	0	.	0	.	449	47	94	71	0	.	81	.	292	75	80	80	115	.	83	81	116	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	123	.	0	.	0	.	0	.	0	.	3481	22	2271	27	1923	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28474	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	.	89	39	4	75	0	.	2	100	0	.	0	.	0	.	0	.	4	.	12	67	12	65	1	100	0	.	6	73	17	52	52	38	104	33	151	20	142	36	0	.	10	.	0	.	17	67	0	.	0	.	0	.	0	.	0	.	-14	.	33	49	1	100	0	.	3	100	4	100	1	.	0	0	0	.	0	.	0	.	0	.	0	.	5	66	19	79	3	100	0	.	0	.	0	.	42	45	26	50	12	42	1	100	0	.	0	.	14	66	5	81	7	100	9	71	4	100	47	49	3	100	10	48	18	92	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	181	33	176	25	65	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1297	10
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	381	24	31	58	0	.	0	.	0	.	0	.	0	.	0	.	6	.	101	58	115	76	11	82	0	.	29	71	88	38	320	43	262	33	577	21	357	28	0	.	18	.	0	.	107	59	0	.	0	.	0	.	0	.	0	.	3	.	152	42	35	46	0	.	15	100	10	79	12	.	5	100	0	.	0	.	0	.	0	.	0	.	22	60	103	80	3	100	0	.	0	.	0	.	43	46	64	51	64	42	16	62	0	.	0	.	58	57	57	56	30	74	21	84	31	71	235	51	23	59	10	73	95	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	503	23	481	26	415	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4935	8
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	1076	20	170	62	0	.	9	100	0	.	0	.	0	.	0	.	63	.	214	72	153	74	15	100	0	.	87	81	621	41	341	42	565	28	1989	21	1282	23	0	.	40	.	0	.	192	65	0	.	0	.	0	.	0	.	0	.	48	.	244	49	76	75	0	.	49	100	42	60	35	.	-16	100	0	.	0	.	0	.	0	.	0	.	90	52	165	63	71	72	0	.	0	.	0	.	49	75	18	264	375	49	28	73	0	.	0	.	122	65	78	83	103	87	85	59	169	94	532	44	55	63	219	77	144	66	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	76	100	0	.	0	.	0	.	0	.	1039	24	710	26	675	25	23	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12143	7
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	2795	21	289	59	0	.	0	.	0	.	0	.	0	.	0	.	65	.	301	64	290	51	82	71	0	.	130	64	1153	30	454	32	1579	26	3392	17	1826	21	0	.	19	.	0	.	293	52	0	.	0	.	0	.	0	.	0	.	29	.	321	45	328	64	0	.	99	100	19	100	81	.	132	100	0	.	0	.	0	.	0	.	0	.	168	53	194	58	111	79	0	.	0	.	0	.	244	50	146	56	756	36	54	75	0	.	0	.	43	62	96	90	134	72	171	73	228	85	488	39	394	70	334	53	91	80	0	.	0	.	46	100	0	.	0	.	0	.	0	.	0	.	113	62	0	.	0	.	0	.	0	.	988	26	1393	23	1287	22	130	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21283	6
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	2187	22	77	100	0	.	138	92	0	.	0	.	0	.	0	.	55	.	-6	100	102	78	49	74	0	.	543	61	1064	36	547	51	1504	27	3293	17	707	29	0	.	0	.	0	.	43	71	0	.	0	.	0	.	0	.	0	.	0	.	231	48	166	60	0	.	0	0	175	71	12	.	82	100	0	.	0	.	0	.	0	.	0	.	129	75	33	100	60	72	0	.	0	.	0	.	564	45	319	61	502	46	54	73	0	.	0	.	232	78	107	59	201	89	137	71	78	100	194	51	175	53	149	47	185	80	0	.	0	.	56	100	0	.	0	.	0	.	0	.	0	.	71	100	0	.	0	.	0	.	0	.	841	31	742	26	1177	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16974	7
	total	0	.	0	.	0	.	0	.	0	.	0	.	6528	18	570	51	0	.	148	92	0	.	0	.	0	.	0	.	194	.	622	58	672	54	158	72	0	.	795	54	2942	26	1715	30	4013	21	9403	14	4313	18	0	.	87	.	0	.	652	46	0	.	0	.	0	.	0	.	0	.	65	.	981	42	606	49	0	.	167	100	248	53	142	.	202	100	0	.	0	.	0	.	0	.	0	.	414	48	514	58	248	73	0	.	0	.	0	.	941	37	573	53	1709	32	152	69	0	.	0	.	470	56	343	57	476	65	425	64	510	77	1496	38	649	57	723	53	533	68	0	.	0	.	122	100	0	.	0	.	0	.	0	.	0	.	285	60	0	.	0	.	0	.	0	.	3553	19	3502	19	3618	20	153	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56633	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	33	70	0	.	0	.	17	62	25	52	53	47	277	30	286	19	52	32	291	18	0	0	26	80	44	41	0	.	85	62	52	31	173	45	47	40	0	.	124	33	51	40	29	38	15	70	50	38	0	.	250	25	82	40	64	29	176	21	23	56	90	31	4	60	37	58	101	51	58	52	36	55	62	37	49	41	43	56	62	34	41	47	118	33	70	53	40	53	69	62	30	54	27	48	83	47	64	49	28	54	18	222	0	.	64	80	0	.	23	50	0	.	43	43	59	43	42	50	101	63	67	38	47	56	73	49	23	83	24	72	38	48	15	56	25	49	38	73	0	.	77	28	18	.	9	65	86	27	35	64	58	49	15	54	24	100	10	262	5	100	20	82	0	.	2	.	83	29	57	47	16	64	12	100	94	71	47	73	0	.	4	.	1	.	0	.	4810	5
	16-23 cm	72	54	8	.	0	.	74	47	151	46	409	30	610	21	1065	17	292	26	1341	17	0	.	15	60	111	54	0	.	151	54	504	35	594	28	275	42	0	.	459	26	254	36	141	32	28	68	147	41	0	.	806	26	130	37	341	32	686	23	106	62	386	34	63	59	110	43	249	37	269	34	85	64	272	29	152	39	191	40	314	36	183	48	430	28	235	40	101	42	147	38	72	58	150	41	130	53	262	34	195	40	149	51	0	.	45	69	0	.	55	53	0	.	153	64	165	40	108	45	287	39	248	45	113	42	110	46	55	63	19	75	79	40	48	58	157	47	92	40	32	.	370	36	20	.	38	62	249	31	93	40	189	41	47	50	31	59	118	70	36	77	34	72	0	.	0	.	277	27	122	43	68	50	12	72	286	36	130	38	0	.	23	.	0	.	0	.	16824	4
	24-35 cm	205	48	0	.	0	.	245	46	691	31	1348	30	1845	22	2818	16	1015	29	2961	15	7	100	108	62	265	45	0	.	154	61	637	41	1623	24	685	37	0	.	1089	41	327	33	313	40	0	.	211	47	0	.	2185	18	551	31	445	31	1491	22	191	58	490	34	244	50	266	35	798	30	634	33	116	66	754	33	881	28	407	42	758	28	421	35	1444	24	536	38	269	60	705	35	40	75	601	34	357	41	751	31	516	34	479	38	0	.	101	72	0	.	69	54	0	.	106	46	241	39	144	50	214	39	268	42	178	54	316	38	68	70	135	62	185	54	90	58	375	42	192	71	124	.	762	27	64	.	159	45	649	25	307	40	500	35	208	53	173	77	243	61	132	54	113	81	0	.	0	.	685	28	197	43	291	45	75	61	933	29	537	32	0	.	98	.	38	.	0	.	41846	4
	36-51 cm	341	56	0	.	0	.	521	44	1111	29	2289	24	3494	18	6145	13	2641	21	4023	15	104	77	90	102	458	44	0	.	86	73	891	31	1934	22	1408	38	0	.	1904	23	408	42	732	30	149	68	101	73	0	.	3327	16	888	32	1601	29	3067	20	367	49	788	28	363	48	391	41	1236	24	566	39	331	57	1562	30	1316	30	536	42	1127	26	842	38	2441	24	421	41	595	34	801	33	683	47	893	32	521	32	1625	29	681	30	539	43	0	.	168	60	0	.	212	58	0	.	84	55	146	45	95	44	296	35	251	50	250	59	217	53	124	58	72	69	278	39	184	73	399	36	607	47	217	.	783	28	135	.	351	46	703	24	462	41	741	41	440	40	291	67	259	47	268	56	447	68	0	.	0	.	706	28	406	45	459	40	450	47	1851	25	1104	30	0	.	115	.	118	.	0	.	70025	3
	=52 cm	75	100	84	.	0	.	285	47	657	45	828	30	2680	19	6166	13	2197	22	1512	21	255	100	67	100	364	46	0	.	0	.	584	47	1637	29	324	49	0	.	936	31	325	48	862	33	122	59	260	45	0	.	1917	21	1218	40	1105	31	1911	26	267	54	532	35	276	52	486	59	1151	31	287	45	240	51	935	39	869	37	631	64	1093	33	871	39	2374	28	236	42	510	50	313	44	120	64	248	50	771	37	908	29	1021	39	207	54	0	.	235	65	0	.	339	69	0	.	136	52	189	61	28	100	220	49	24	100	158	61	278	65	226	91	253	83	509	53	281	66	131	42	451	47	0	.	661	45	0	.	449	53	394	32	379	56	270	48	387	43	86	100	369	73	238	79	288	64	0	.	0	.	367	32	300	49	542	47	243	49	1147	28	361	43	0	.	0	.	25	.	0	.	50682	4
	total	726	48	93	.	0	.	1142	43	2635	28	4929	22	8906	16	16480	11	6197	19	10128	12	366	81	305	62	1241	38	0	.	476	52	2668	30	5962	21	2740	31	0	.	4511	23	1365	30	2077	26	314	58	769	38	0	.	8485	14	2870	29	3556	25	7330	18	955	45	2286	24	951	42	1290	36	3536	22	1813	31	807	49	3585	29	3266	25	1807	39	3354	23	2357	30	6807	20	1498	34	1517	32	2035	28	945	43	1919	30	1862	31	3610	26	2440	28	1392	33	0	.	614	55	0	.	698	48	0	.	522	38	800	36	418	39	1117	30	859	34	745	43	994	39	497	60	502	61	1089	36	617	53	1087	36	1380	44	373	.	2654	24	237	.	1006	42	2080	21	1276	38	1758	29	1098	37	604	59	999	51	678	55	902	63	0	.	2	.	2117	23	1082	37	1376	36	792	46	4311	22	2180	27	0	.	240	.	182	.	0	.	184187	3
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	100	29	105	43	90	54	49	46	0	.	0	.	17	50	52	36	480	24	31	.	30	60	43	47	26	43	0	.	11	57	27	46	0	.	35	42	0	.	0	.	15	76	20	49	0	.	0	.	0	.	118	38	46	44	111	36	12	79	20	66	0	.	17	59	2	.	0	.	0	.	0	.	8	100	7	100	81	46	18	89	31	51	0	.	91	37	21	55	15	60	75	65	46	58	0	748	0	.	2	100	42	41	70	60	169	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	78	0	.	0	1080	8	85	20	63	0	.	4	83	35	57	14	100	8	72	8	74	0	.	33	55	12	77	38	87	35	63	27	66	0	.	10	79	0	.	0	.	25	69	8	73	19	61	30	44	106	94	65	83	129	51	8	60	42	44	0	.	2823	8
	16-23 cm	271	26	395	40	190	48	291	34	0	.	0	.	107	55	237	36	1233	19	32	.	75	49	206	56	169	49	12	.	55	60	139	63	0	.	99	40	0	.	0	.	76	76	45	54	0	.	0	.	0	.	361	32	207	56	261	39	25	80	30	59	0	.	73	51	28	.	0	.	0	.	0	.	39	100	63	64	68	54	42	72	78	59	0	.	281	32	39	51	62	51	132	39	99	48	19	74	0	.	0	.	162	42	271	35	384	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	93	0	.	34	78	55	54	58	124	0	.	33	78	76	61	16	75	86	52	20	61	0	.	131	61	126	67	78	65	121	60	115	72	0	.	33	56	0	.	0	.	77	71	35	71	27	55	123	38	74	64	144	48	145	38	154	43	165	39	0	.	8369	6
	24-35 cm	845	30	540	30	488	35	775	29	0	.	0	.	313	54	646	29	3057	14	0	.	210	48	448	38	490	38	67	.	149	66	353	42	0	.	533	33	0	.	0	.	98	71	113	63	0	.	0	.	0	.	997	26	688	41	955	29	51	77	89	55	0	.	137	49	54	.	0	.	0	.	0	.	67	100	82	77	381	54	65	74	136	63	0	.	613	30	71	55	187	52	421	39	418	41	12	71	0	.	130	78	285	43	514	34	545	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	84	0	.	92	74	70	80	138	90	0	.	158	53	42	100	55	100	302	57	106	100	0	.	159	52	211	52	258	58	129	45	107	76	0	.	341	71	0	.	0	.	40	73	63	71	55	64	463	40	51	65	429	41	354	35	407	46	224	49	0	.	20334	5
	36-51 cm	1517	23	1464	29	504	37	2785	23	0	.	0	.	841	47	955	31	5491	13	0	.	433	55	385	52	499	48	14	.	571	50	609	53	0	.	1242	36	0	.	0	.	263	58	182	62	0	.	0	.	0	.	1866	23	1257	37	1749	26	115	75	217	49	0	.	332	54	22	.	0	.	0	.	0	.	74	100	125	80	452	52	123	85	182	57	0	.	1404	26	777	42	292	64	854	40	380	41	61	100	0	.	135	76	789	45	1004	31	767	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	42	75	140	60	239	60	0	.	179	66	69	95	109	100	338	54	94	71	0	.	130	90	225	60	583	45	271	61	159	66	0	.	413	61	0	.	0	.	31	84	105	80	401	51	1058	36	163	100	530	39	1009	31	789	34	895	34	0	.	38720	5
	=52 cm	1307	31	1295	31	515	36	1663	27	0	.	0	.	429	47	1063	35	4980	15	0	.	704	53	210	90	245	51	0	.	294	51	335	56	0	.	661	46	0	.	0	.	84	100	202	58	0	.	0	.	0	.	956	26	515	40	930	30	155	100	59	57	0	.	134	68	0	.	0	.	0	.	0	.	0	.	92	100	172	80	0	.	366	77	0	.	747	30	560	52	1	100	413	58	41	71	27	100	0	.	20	100	509	41	825	38	1107	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	100	0	.	28	100	92	58	55	90	0	.	315	63	0	.	50	100	191	62	0	.	0	.	171	63	35	100	49	100	50	71	209	72	0	.	142	53	0	.	0	.	96	89	22	100	98	79	445	43	25	100	632	45	662	36	655	48	518	42	0	.	26228	6
	total	4040	21	3798	24	1787	30	5563	20	0	.	0	.	1707	43	2953	26	15241	11	63	.	1452	37	1291	37	1427	36	92	.	1080	46	1463	37	0	.	2570	29	0	.	0	.	536	56	561	53	0	.	0	.	0	.	4298	20	2712	31	4007	24	359	82	415	43	0	.	693	48	105	.	0	.	0	.	0	.	188	100	369	70	1154	47	249	76	793	60	0	.	3135	22	1468	42	557	52	1894	35	984	37	119	91	0	.	286	71	1787	36	2684	27	2972	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	251	72	0	.	195	76	365	58	511	62	0	.	689	55	222	57	244	72	924	45	228	76	0	.	624	51	609	49	1006	40	606	44	618	57	0	.	939	60	0	.	0	.	269	53	233	76	599	45	2120	33	419	65	1799	32	2299	28	2012	33	1843	31	0	.	96475	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	.	64	67	0	.	0	.	0	.	0	.	76	55	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	.	4	.	2	.	-25	200	16	59	120	45	88	36	284	25	83	57	26	59	99	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	83	0	.	0	.	14	.	0	.	0	.	0	.	57	43	21	47	4	426	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	947	14
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	266	55	0	.	0	.	0	.	0	.	173	43	9	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	.	26	.	59	55	40	80	218	43	351	34	648	23	129	51	116	52	350	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	94	0	.	0	.	32	.	0	.	0	.	0	.	151	41	75	52	21	57	0	.	0	.	0	.	0	.	0	.	15	71	0	.	0	.	0	.	2760	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	192	52	0	.	0	.	0	.	0	.	244	45	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	78	68	57	58	112	47	386	36	708	26	62	52	95	42	531	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	75	0	.	0	.	17	.	0	.	0	.	0	.	201	47	112	51	81	57	0	.	0	.	0	.	0	.	0	.	83	89	0	.	0	.	0	.	3047	11
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	76	0	.	0	.	0	.	0	.	146	51	78	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	.	5	.	32	61	55	100	68	77	159	38	363	34	97	39	57	86	282	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	154	49	46	55	32	75	0	.	0	.	0	.	0	.	0	.	131	75	0	.	0	.	0	.	1824	14
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	115	84	0	.	0	.	0	.	0	.	27	71	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	.	178	100	79	100	32	71	72	57	236	53	331	66	60	74	532	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	28	100	27	100	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	0	.	1817	21
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	709	45	0	.	0	.	0	.	0	.	665	39	106	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	.	62	.	322	65	247	79	550	36	1057	30	2239	19	702	40	354	39	1795	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	134	72	0	.	0	.	63	.	0	.	0	.	0	.	602	40	283	50	165	58	0	.	0	.	0	.	0	.	0	.	258	74	0	.	0	.	0	.	10394	9
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	133	28	105	43	90	54	66	38	25	52	53	47	443	21	342	16	532	22	323	19	30	60	68	42	70	30	0	.	101	53	92	24	185	42	84	29	64	67	137	30	117	31	113	22	133	27	318	18	352	24	368	20	137	29	175	25	206	19	43	43	90	31	21	49	39	55	101	51	51	66	85	33	89	31	56	38	127	35	98	29	72	34	129	32	161	31	61	40	84	52	105	49	73	40	88	45	83	42	33	47	59	72	70	60	237	39	43	44	39	134	46	30	164	34	147	27	326	23	206	39	99	30	154	37	88	42	28	69	77	39	49	40	45	35	44	46	43	66	35	57	91	28	25	73	17	49	86	27	76	39	70	42	57	61	73	48	37	85	5	100	29	61	330	20	241	19	213	26	65	42	35	44	42	42	200	60	112	57	133	50	11	52	43	43	0	.	10544	4
	16-23 cm	343	23	403	39	190	48	365	29	151	46	409	30	1373	13	1334	15	1525	16	1373	16	75	49	220	52	279	36	12	.	212	41	744	28	710	27	384	32	266	55	487	25	530	22	555	27	398	23	1101	15	1013	16	1168	20	355	35	602	24	821	20	136	50	386	34	136	39	137	40	249	37	311	31	292	34	391	24	215	33	274	31	375	32	273	37	428	28	516	25	140	34	209	30	205	32	248	31	174	41	365	33	198	40	311	33	271	35	462	43	69	47	225	27	163	32	398	34	516	26	756	21	523	26	422	31	492	23	226	46	103	42	303	41	180	28	117	66	252	45	125	36	108	52	386	35	105	46	58	45	249	31	270	36	315	36	156	39	185	44	233	50	36	77	67	46	921	16	847	18	959	17	157	37	95	39	135	35	359	31	274	31	159	35	177	39	165	39	0	.	35386	3
	24-35 cm	1050	26	540	30	488	35	1020	24	691	31	1348	30	4238	13	3682	13	4072	13	2970	15	216	47	556	33	754	29	67	.	366	41	1204	28	1776	23	1234	25	192	52	1190	38	1448	21	923	24	1022	22	3042	15	2804	15	3183	14	1279	26	1399	22	1734	21	279	43	490	34	382	37	320	34	798	30	748	30	389	37	1063	26	963	26	837	32	946	24	592	29	1419	25	1149	24	341	49	892	30	461	36	1019	26	529	31	916	28	717	29	764	29	514	34	647	28	91	49	424	32	549	36	246	30	627	26	853	23	454	27	453	30	812	27	464	30	322	58	768	34	310	39	502	44	519	35	350	46	166	79	837	26	367	50	265	48	649	25	542	29	712	29	582	35	318	46	350	48	132	54	454	57	2045	16	1479	18	1936	17	283	35	346	39	538	35	984	27	966	25	437	33	505	42	262	44	0	.	84563	2
	36-51 cm	1858	22	1464	29	504	37	3306	20	1111	29	2289	24	8735	12	7449	12	8132	11	4023	15	537	47	475	46	956	32	14	.	722	42	1801	26	2224	21	2732	26	71	76	2105	21	2645	18	1523	19	2760	19	4692	14	4592	12	5192	13	2164	25	3351	19	3474	18	585	36	788	28	695	36	413	39	1236	24	636	35	798	33	2031	26	1441	28	1111	31	1458	24	1105	31	2594	23	1825	22	1372	28	1093	30	1537	31	1274	25	807	25	1818	27	927	26	1328	32	1004	31	979	24	407	38	832	25	990	29	206	39	305	29	458	28	614	25	436	37	667	31	421	41	442	50	632	32	837	37	774	34	540	30	786	39	285	79	938	27	472	48	445	39	703	24	596	37	965	34	1180	28	562	45	419	38	268	56	860	46	2673	16	2501	17	2729	15	641	37	859	32	1508	29	2014	25	1634	23	1140	29	903	32	1013	33	0	.	144411	2
	=52 cm	1382	29	1380	30	515	36	1948	24	657	45	828	30	6055	12	7340	12	7176	12	1649	21	959	47	277	72	609	34	0	.	349	46	913	36	1740	28	1034	33	115	84	1690	27	2410	22	1731	24	2442	20	4450	14	2798	16	2873	16	1733	30	2035	21	2109	24	327	45	532	35	410	41	486	59	1151	31	369	42	471	35	1180	33	961	34	853	50	1385	28	1249	35	2442	28	983	25	1071	36	313	44	532	47	289	44	1080	31	941	28	1101	37	716	33	825	38	1342	28	715	37	1068	34	620	40	222	38	261	47	264	49	941	33	240	37	891	32	552	41	404	58	493	47	842	35	495	42	381	45	766	38	0	.	767	40	191	62	449	53	394	32	550	44	305	44	536	35	135	68	578	54	238	79	430	46	2791	18	1355	21	2301	19	322	46	640	41	688	33	1173	28	992	33	687	35	655	48	543	40	0	.	106081	3
	total	4766	19	3891	23	1787	30	6705	18	2635	28	4929	22	20845	10	20147	10	21437	9	10339	12	1817	34	1596	32	2668	26	92	.	1749	33	4753	21	6634	19	5468	21	709	45	5610	20	7150	15	4844	17	6754	16	13603	11	11559	11	12783	11	5669	21	7562	17	8345	17	1370	34	2286	24	1644	31	1396	34	3536	22	2115	28	2036	29	4753	23	3635	23	3201	28	4262	20	3292	26	7011	20	4633	18	2985	26	2592	25	2840	27	2903	23	2677	24	4123	23	2975	25	3179	25	2684	27	3668	22	1324	30	2588	22	2367	25	1236	24	1857	23	2657	17	2738	20	1650	23	3016	21	1751	29	1299	41	2273	29	2218	27	1933	31	1736	31	2070	34	594	66	3019	23	1160	41	1234	37	2080	21	2034	29	2367	25	2511	24	1273	35	1616	38	678	55	1841	43	8760	12	6423	14	8138	13	1468	31	1975	29	2912	27	4730	21	3979	20	2557	26	2252	31	2025	29	0	.	380985	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. 2293485. Birmensdorf, Swiss Federal Research Institute WSL

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