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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NFI3
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (main diameter classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		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	.	3	65	1	100	0	.	7	37	103	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	100	6	52	6	57	0	.	0	.	0	.	19	63	1	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	47	30	51	5	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	226	16
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-1	506	4	100	2	.	22	32	288	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	11	61	22	57	0	.	0	.	0	.	-4	186	4	.	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	144	37	56	29	6	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	560	14
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	75	19	100	14	.	130	33	652	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	24	75	16	100	0	.	0	.	0	.	41	64	0	.	0	.	0	.	0	.	26	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	292	25	191	36	42	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1488	11
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	67	12	100	0	.	244	38	985	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	42	17	78	6	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	547	21	208	32	18	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2130	11
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	100	15	100	0	.	311	35	963	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	67	66	43	51	0	.	0	.	0	.	42	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	587	23	181	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2272	13
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	103	75	51	100	17	.	715	28	2990	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	76	136	45	103	46	6	.	0	.	0	.	133	58	5	.	0	.	0	.	0	.	34	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1614	18	667	26	70	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6676	9
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	63	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	7	63	33	27	23	47	72	33	86	25	137	28	0	.	0	.	0	.	3	76	0	.	0	.	0	.	0	.	0	.	13	76	5	74	18	78	0	.	5	100	10	53	0	.	4	.	0	.	0	.	0	.	0	.	0	.	2	80	0	.	0	.	0	.	0	.	0	.	5	40	18	48	16	60	1	100	0	.	0	.	15	36	3	68	7	89	0	.	1	100	10	66	5	62	1	100	2	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	85	23	39	32	61	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	755	9
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	252	24	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	.	0	.	0	.	47	66	154	29	82	42	175	24	292	20	422	18	0	.	0	.	0	.	17	81	0	.	0	.	0	.	0	.	0	.	39	84	15	82	51	52	0	.	6	100	34	55	0	.	10	.	0	.	0	.	0	.	0	.	0	.	5	84	0	.	0	.	0	.	0	.	0	.	13	59	44	40	32	54	12	100	0	.	0	.	44	54	19	72	6	100	0	.	12	60	23	74	19	71	7	69	10	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	0	.	0	.	0	.	0	.	222	21	241	24	247	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2585	7
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	847	18	13	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	.	0	.	0	.	47	75	435	28	261	33	496	24	994	20	928	16	0	.	0	.	0	.	9	72	0	.	0	.	0	.	0	.	0	.	52	74	42	85	173	59	0	.	0	100	113	50	0	.	2	.	0	.	0	.	0	.	0	.	0	.	43	59	0	.	0	.	0	.	0	.	0	.	58	52	176	37	77	60	12	100	0	.	0	.	112	37	29	61	16	100	6	.	40	65	0	.	55	61	21	62	25	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	0	.	0	.	0	.	0	.	541	24	686	22	643	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6988	7
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	1411	17	53	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	210	52	809	23	280	31	1080	21	1585	17	2123	14	0	.	0	.	0	.	25	100	0	.	0	.	0	.	0	.	0	.	35	62	71	57	171	51	0	.	-6	237	154	53	0	.	5	.	0	.	0	.	0	.	0	.	0	.	44	100	0	.	0	.	0	.	0	.	0	.	113	47	308	31	158	44	9	73	0	.	0	.	226	46	48	55	3	100	0	.	85	80	24	82	81	75	95	46	66	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	.	0	.	0	.	0	.	0	.	1190	18	1025	21	1018	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12528	5
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	1169	21	95	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	224	48	1080	27	214	36	1142	22	1156	20	1601	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	77	42	80	83	78	0	.	48	81	118	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	88	0	.	0	.	0	.	0	.	0	.	349	34	350	34	201	46	0	.	0	.	0	.	145	41	27	62	0	0	37	.	58	82	13	100	190	58	92	51	156	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	0	.	0	.	0	.	0	.	1806	19	834	26	682	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12100	6
	total	0	.	0	.	0	.	0	.	0	.	0	.	3742	16	167	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	.	0	.	0	.	535	44	2510	20	860	27	2966	17	4114	15	5212	12	0	.	0	.	0	.	53	84	0	.	0	.	0	.	0	.	0	.	216	55	174	51	495	44	0	.	53	85	428	40	0	.	21	.	0	.	0	.	0	.	0	.	0	.	186	65	0	.	0	.	0	.	0	.	0	.	538	32	896	28	484	38	34	71	0	.	0	.	541	33	127	47	32	97	42	.	197	62	70	63	350	56	216	41	260	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	.	0	.	0	.	0	.	0	.	3844	16	2825	20	2652	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34955	5
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	1	.	0	.	154	20	8	78	0	.	1	100	0	.	0	.	0	.	0	.	1	100	33	60	12	48	1	100	0	.	7	51	25	60	50	31	176	22	287	22	103	22	0	.	6	81	0	.	22	41	0	.	0	.	0	.	0	.	0	.	4	61	30	35	5	48	0	.	1	73	7	63	4	65	0	0	0	.	0	.	0	.	0	.	0	.	5	63	4	178	2	74	0	.	0	.	0	.	77	39	22	41	49	36	3	53	0	.	0	.	26	35	5	66	13	67	2	272	6	51	25	40	17	85	27	50	29	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	73	0	.	0	.	0	.	0	.	174	19	148	21	111	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1684	7
	16-23 cm	0	.	0	.	0	.	0	.	9	.	0	.	367	17	25	71	0	.	1	100	0	.	0	.	0	.	0	.	5	72	75	46	32	52	6	73	0	.	23	51	91	34	242	25	415	18	665	14	405	21	0	.	18	76	0	.	87	37	0	.	0	.	0	.	0	.	0	.	29	56	140	34	19	81	0	.	17	73	18	48	8	72	0	0	0	.	0	.	0	.	0	.	0	.	0	2473	100	35	4	73	0	.	0	.	0	.	120	29	99	36	144	25	12	71	0	.	0	.	88	39	21	71	28	48	10	50	22	52	133	33	17	55	71	46	54	49	0	.	0	.	3	100	0	.	0	.	4	72	0	.	0	.	14	84	0	.	0	.	0	.	0	.	490	19	635	17	414	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5181	5
	24-35 cm	0	.	0	.	0	.	0	.	4	.	0	.	1320	20	93	63	0	.	9	100	0	.	0	.	0	.	0	.	23	75	223	51	171	61	34	71	0	.	65	80	507	27	622	27	637	20	1630	14	1118	18	0	.	73	77	0	.	180	46	0	.	0	.	0	.	0	.	0	.	40	71	337	35	77	48	0	.	73	72	109	62	37	74	-36	100	0	.	0	.	0	.	0	.	0	.	44	70	77	62	15	100	0	.	0	.	0	.	133	44	213	43	292	32	32	74	0	.	0	.	229	38	59	69	57	62	45	69	151	60	364	34	52	47	161	42	236	48	0	.	0	.	6	100	0	.	0	.	17	100	0	.	28	83	57	84	0	.	0	.	0	.	0	.	1228	15	894	19	793	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12531	5
	36-51 cm	0	.	0	.	0	.	0	.	5	.	0	.	2412	16	254	44	0	.	0	.	0	.	0	.	0	.	0	.	138	71	295	51	356	40	75	64	0	.	103	57	1192	25	658	23	1174	20	3270	12	1850	16	0	.	60	73	0	.	312	36	0	.	0	.	0	.	0	.	0	.	39	58	377	32	208	38	0	.	80	74	153	65	55	77	86	75	0	.	0	.	0	.	0	.	0	.	166	48	334	36	91	57	0	.	0	.	0	.	128	49	321	34	577	27	71	55	0	.	0	.	272	31	47	72	62	77	130	50	185	63	437	34	236	70	330	45	331	39	0	.	0	.	20	85	0	.	0	.	57	71	40	100	57	78	108	63	0	.	0	.	0	.	0	.	1421	15	1377	18	1105	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21054	4
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	3030	18	171	62	0	.	83	93	0	.	0	.	0	.	0	.	37	89	9	138	262	45	161	65	0	.	475	50	1348	23	1195	32	2003	19	3901	13	1124	25	0	.	-3	100	0	.	191	45	0	.	0	.	0	.	0	.	0	.	51	100	354	37	249	39	0	.	25	100	170	55	62	76	199	57	0	.	0	.	0	.	0	.	0	.	165	63	151	69	42	71	0	.	0	.	0	.	394	42	492	36	671	28	43	76	0	.	0	.	228	45	85	64	337	62	98	65	297	60	481	43	293	45	619	35	461	34	0	.	0	.	84	80	0	.	0	.	18	71	46	71	52	71	73	63	0	.	0	.	0	.	0	.	2226	18	1150	20	1580	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25183	5
	total	0	.	0	.	0	.	0	.	20	.	0	.	7284	15	549	42	0	.	94	92	0	.	0	.	0	.	0	.	204	71	635	43	833	37	277	58	0	.	672	47	3162	20	2766	21	4404	15	9753	10	4600	14	0	.	155	75	0	.	792	32	0	.	0	.	0	.	0	.	0	.	163	55	1237	30	558	35	0	.	197	70	458	46	166	69	248	64	0	.	0	.	0	.	0	.	0	.	380	43	666	35	154	57	0	.	0	.	0	.	852	27	1148	27	1733	22	161	49	0	.	0	.	843	26	216	56	496	52	286	52	662	46	1441	29	615	46	1209	31	1111	33	0	.	0	.	113	62	0	.	0	.	95	71	86	78	137	75	254	51	0	.	0	.	0	.	0	.	5539	13	4204	14	4004	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65633	4
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	45	74	0	0	1	100	21	38	43	50	54	35	268	28	252	13	71	28	251	16	0	.	8	50	39	28	0	.	8	52	25	32	76	25	51	33	0	.	90	24	68	30	36	26	11	55	54	28	0	.	263	15	85	29	64	31	155	19	13	54	80	24	17	44	47	38	84	27	32	33	11	58	112	34	22	67	40	33	72	26	37	67	63	36	26	50	33	33	38	35	10	51	28	36	101	33	55	28	48	31	41	61	0	.	19	57	0	.	32	45	0	.	79	29	64	32	27	33	132	21	122	29	57	27	27	65	35	54	7	105	8	226	19	43	76	28	30	38	0	.	76	25	7	78	23	54	61	26	31	57	41	28	13	41	27	54	16	56	7	73	19	84	0	.	8	65	151	29	44	30	24	46	4	65	53	27	42	69	0	.	9	100	7	90	0	.	4444	4
	16-23 cm	61	40	2	100	20	56	99	36	148	27	291	22	686	15	811	13	265	25	760	15	0	.	10	79	142	32	0	.	61	55	154	37	398	22	260	31	0	.	476	28	226	27	132	25	34	52	184	29	0	.	739	14	214	27	186	23	440	19	28	75	180	22	80	49	85	34	268	23	204	35	73	39	273	28	155	35	211	31	230	27	136	41	342	23	142	41	114	33	246	29	58	49	126	28	250	27	257	30	165	33	159	40	0	.	63	72	0	.	88	35	0	.	208	28	203	27	28	75	372	23	357	30	229	28	126	30	83	45	16	105	148	39	51	41	224	29	109	41	0	.	232	26	62	80	72	40	250	21	115	34	190	35	40	41	79	63	69	55	33	49	64	56	24	.	20	89	426	19	145	39	69	37	11	77	170	49	108	35	0	.	47	94	39	89	0	.	15147	3
	24-35 cm	146	41	4	100	45	63	288	46	703	26	1007	22	1875	14	2469	12	892	22	2477	12	6	100	105	56	381	27	0	.	129	62	421	29	1130	21	652	26	0	.	1203	27	350	27	421	25	7	137	333	30	0	.	2265	12	499	26	481	29	1376	19	163	42	579	25	254	38	247	35	585	21	535	33	138	38	548	25	651	25	438	27	853	20	480	27	1285	18	483	32	252	38	455	26	98	53	618	27	500	25	538	25	312	32	512	33	0	.	70	62	0	.	193	38	0	.	358	27	338	30	175	39	438	25	273	34	353	27	341	34	108	58	26	75	308	33	93	45	544	27	265	34	0	.	727	22	124	70	121	41	692	21	262	28	405	27	92	46	100	50	177	47	88	53	64	59	0	.	11	71	866	20	290	30	260	35	66	53	672	24	454	28	0	.	13	100	41	86	0	.	39597	3
	36-51 cm	497	33	44	82	67	62	538	30	1117	24	2237	18	3669	12	5494	10	1766	18	3941	11	78	100	144	57	740	26	0	.	179	45	777	27	1658	18	1256	25	0	.	1949	19	705	27	955	22	82	48	290	29	0	.	4403	11	845	24	1150	23	2557	17	404	31	1008	20	486	34	341	30	1262	19	392	30	208	39	894	24	1327	23	712	30	1456	20	958	26	2454	18	354	36	432	29	628	25	246	45	1017	23	1074	21	1078	24	820	22	648	27	0	.	114	58	0	.	176	34	0	.	213	41	308	27	84	58	423	26	356	33	353	29	252	35	84	43	120	56	534	35	138	53	955	27	460	34	49	100	947	21	127	63	331	37	1126	18	519	27	812	25	270	33	273	56	425	47	280	43	219	52	20	.	4	100	965	20	792	26	397	36	210	48	1168	21	882	25	0	.	133	100	48	100	0	.	69906	2
	=52 cm	254	40	33	100	0	.	460	39	800	28	2305	23	3991	15	7136	11	2522	18	2687	16	0	.	195	63	768	34	0	.	278	66	767	30	1006	24	925	29	0	.	1350	22	504	32	970	25	110	55	366	35	0	.	2980	17	1298	29	1290	24	2020	20	363	38	952	25	329	40	563	34	1868	19	297	39	400	45	938	28	893	28	406	36	1313	24	1027	28	3477	18	158	46	439	38	571	48	174	46	481	37	1149	26	977	26	1307	30	682	35	0	.	197	67	0	.	526	38	0	.	531	82	294	40	57	68	482	27	278	37	271	34	228	50	114	65	143	85	445	41	350	50	903	28	791	38	0	0	732	31	143	68	491	45	479	25	656	32	777	26	394	40	67	84	473	48	369	48	325	59	0	.	0	.	687	25	865	34	513	43	305	48	1114	23	489	33	0	.	12	100	30	100	0	.	68310	3
	total	1003	31	82	71	132	60	1405	27	2810	22	5895	17	10488	11	16163	9	5517	16	10116	10	83	100	461	51	2070	26	0	.	655	43	2144	24	4270	16	3144	21	0	.	5068	19	1853	22	2514	19	244	46	1226	25	0	.	10651	10	2941	21	3171	20	6549	15	970	29	2800	18	1166	31	1283	26	4067	17	1459	26	831	37	2764	21	3048	20	1806	24	3924	17	2638	22	7620	15	1164	29	1269	26	1939	22	586	39	2269	22	3074	19	2904	21	2652	23	2043	25	0	.	462	54	0	.	1014	31	0	.	1389	34	1206	23	370	34	1848	19	1386	23	1263	22	974	30	424	38	311	57	1443	27	651	40	2703	22	1656	30	49	100	2714	19	463	60	1038	33	2606	16	1583	25	2225	21	808	29	547	43	1160	39	777	39	691	45	45	.	43	76	3095	17	2136	24	1263	32	595	44	3177	18	1975	23	0	.	214	95	166	71	0	.	197403	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	74	22	106	24	63	42	86	33	0	.	0	.	14	37	43	31	241	16	14	79	26	33	48	34	27	29	0	.	18	38	48	48	3	.	49	38	0	.	2	.	11	63	-5	186	0	.	0	.	0	.	94	25	37	44	95	38	-3	331	10	84	0	.	9	63	1	.	0	.	2	100	8	.	18	66	32	51	31	58	13	83	22	47	0	.	100	26	49	39	13	45	29	45	22	53	14	74	0	.	1	100	57	31	68	32	129	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	75	1	100	2	86	26	49	31	51	12	77	19	41	18	54	1	73	19	107	25	48	0	.	23	55	10	97	51	37	21	60	3	59	0	.	19	67	0	.	0	.	52	39	14	56	16	55	57	49	1	74	34	55	55	43	35	45	56	32	0	.	2324	5
	16-23 cm	335	23	355	24	256	24	242	28	0	.	0	.	74	43	196	37	939	14	75	85	120	37	105	34	78	45	0	.	79	40	110	36	4	.	163	31	0	.	0	.	29	61	25	62	0	.	0	.	0	.	243	22	143	35	251	29	52	54	29	63	0	.	28	56	9	.	0	.	6	75	39	.	32	50	106	36	105	47	24	63	82	50	0	.	333	25	87	42	39	73	136	38	106	44	12	59	0	.	5	100	173	31	268	29	317	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	85	12	100	13	90	85	48	34	60	4	100	24	123	46	54	0	.	92	40	61	59	0	.	82	49	41	53	114	39	84	54	34	48	0	.	31	46	0	.	0	.	111	36	43	46	89	55	148	32	66	71	109	33	215	29	132	35	183	36	0	.	7708	4
	24-35 cm	772	23	437	25	750	24	571	25	0	.	0	.	223	44	420	27	2511	13	60	56	202	38	347	33	386	28	32	.	258	39	298	37	14	.	359	34	0	.	0	.	54	65	77	61	0	.	0	.	0	.	722	22	281	32	578	28	99	58	53	49	0	.	75	50	0	.	0	.	28	100	22	.	97	60	288	37	244	45	78	78	187	39	16	.	694	23	219	29	162	36	275	30	454	31	103	83	0	.	51	65	389	33	484	27	510	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	74	32	100	68	65	172	42	110	56	6	100	224	43	70	47	52	78	159	40	182	48	0	.	132	43	160	38	340	33	188	42	127	58	0	.	289	47	0	.	0	.	199	35	80	56	116	40	422	35	127	59	333	32	373	30	271	41	319	31	0	.	18526	4
	36-51 cm	1431	20	1109	22	807	24	1297	20	0	.	0	.	536	38	663	28	3996	11	61	65	417	30	533	30	629	30	38	.	508	36	448	38	113	.	555	29	0	.	8	.	128	71	95	54	0	.	0	.	0	.	1292	20	845	40	897	26	50	60	131	44	0	.	217	46	9	.	0	.	79	71	0	.	84	66	539	32	339	42	152	64	377	48	0	.	1380	20	722	26	424	38	600	29	514	29	60	72	0	.	117	60	557	40	769	26	772	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	77	77	100	71	58	234	42	88	54	61	89	305	54	88	44	43	70	576	32	192	50	0	.	86	59	396	38	590	30	347	38	342	55	0	.	479	34	0	.	0	.	96	40	127	62	412	40	702	24	66	56	574	28	582	27	660	29	644	29	0	.	32158	4
	=52 cm	1309	21	897	22	777	25	1840	21	0	.	0	.	402	39	829	34	5204	12	11	100	631	31	444	40	322	37	18	.	407	38	226	46	105	.	637	38	0	.	0	.	52	71	91	60	0	.	0	.	0	.	780	22	595	30	906	35	210	56	104	45	0	.	142	65	0	.	0	.	11	100	0	.	75	100	447	35	130	51	130	52	311	47	0	.	844	24	1320	29	74	65	310	38	279	45	19	100	0	.	187	71	193	47	814	35	924	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	34	100	9	100	96	47	140	83	14	100	279	48	75	76	54	85	692	37	0	.	0	.	120	59	145	55	176	39	51	54	312	47	0	.	516	43	0	.	0	.	69	55	52	100	479	36	882	29	126	65	724	34	750	33	524	32	872	30	0	.	30248	4
	total	3920	17	2905	17	2653	19	4036	17	0	.	0	.	1249	36	2150	24	12891	9	221	53	1398	26	1477	26	1442	26	87	.	1270	30	1131	30	239	.	1763	25	0	.	9	.	274	51	282	49	0	.	0	.	0	.	3132	17	1901	28	2727	23	408	50	328	37	0	.	470	45	19	.	0	.	126	75	69	.	306	63	1412	28	850	35	397	52	978	36	16	.	3352	16	2396	24	712	35	1350	24	1374	25	208	65	0	.	360	61	1368	31	2403	23	2652	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	216	71	156	99	163	59	612	37	404	50	96	90	852	43	297	41	151	59	1538	27	460	40	0	.	443	40	752	36	1271	26	691	35	818	43	0	.	1334	36	0	.	0	.	527	29	316	52	1113	30	2211	22	385	48	1774	26	1976	23	1622	26	2075	25	0	.	90964	3
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	53	0	.	0	.	0	.	2	100	94	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	.	5	100	-6	339	19	55	72	41	64	34	212	25	50	33	42	39	117	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	35	37	27	36	16	41	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	831	12
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	45	0	.	0	.	0	.	20	100	194	27	12	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	.	6	.	16	72	95	37	40	57	191	32	206	28	602	16	150	28	204	37	313	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	76	0	.	0	.	28	.	0	.	0	.	0	.	92	33	103	39	45	34	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	2398	7
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	239	43	0	.	14	.	15	.	4	100	289	32	10	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	.	0	.	0	.	9	.	12	100	92	37	66	53	199	38	348	26	844	19	204	32	287	32	406	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	73	0	.	0	.	10	.	0	.	0	.	0	.	112	40	139	38	26	67	0	.	0	.	0	.	0	.	0	.	56	71	0	.	0	.	0	.	3435	8
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	173	51	0	.	10	.	0	.	4	100	122	38	23	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	.	0	.	0	.	8	.	16	71	78	32	46	64	66	45	306	30	651	25	69	59	155	33	291	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	151	39	101	40	77	80	0	.	0	.	0	.	0	.	0	.	69	61	0	.	0	.	0	.	2427	10
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	73	0	.	0	.	8	.	24	71	192	56	26	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	.	12	.	14	100	258	43	42	73	97	46	357	51	331	31	488	41	67	61	348	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	84	52	18	100	38	58	0	.	0	.	0	.	0	.	0	.	68	100	0	.	0	.	0	.	2535	14
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	565	39	0	.	24	.	23	.	55	75	891	26	72	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	.	0	.	0	.	83	.	63	81	516	30	213	51	625	28	1281	24	2640	14	962	27	755	28	1475	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	73	0	.	0	.	42	.	0	.	0	.	0	.	475	31	388	33	202	46	0	.	0	.	0	.	0	.	0	.	206	72	0	.	0	.	0	.	11627	6
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	119	31	106	24	64	42	106	28	44	49	54	35	499	16	302	12	312	14	266	16	26	33	56	30	66	20	0	.	27	30	106	30	94	22	101	25	29	53	105	21	140	20	103	22	262	18	527	15	343	14	357	13	128	24	158	26	176	18	23	48	80	24	26	36	48	37	84	27	50	29	54	28	152	27	54	41	77	29	102	22	62	43	67	34	127	23	82	27	52	28	39	36	49	31	123	29	59	29	51	30	98	31	68	32	195	34	87	35	72	41	90	25	155	24	128	23	239	22	242	15	174	22	194	19	34	55	43	44	46	31	55	48	77	29	119	22	50	28	18	54	77	24	27	80	48	36	61	26	55	40	51	29	67	29	52	38	20	48	7	73	38	54	338	13	253	15	395	14	58	27	40	35	61	46	54	26	76	45	57	41	44	41	64	30	0	.	10264	2
	16-23 cm	396	20	357	24	276	23	341	22	157	26	291	22	1380	10	1037	13	1204	12	836	15	120	37	115	32	220	26	0	.	144	32	340	23	455	20	429	22	62	45	547	25	499	17	485	16	645	14	1357	10	1127	11	983	12	375	21	437	19	596	16	57	49	180	22	108	39	93	32	268	23	278	29	267	26	376	23	261	25	340	25	306	22	226	31	351	23	475	21	200	26	285	27	193	30	231	25	267	25	357	24	174	32	332	25	268	29	385	26	154	24	337	18	238	20	422	20	408	19	630	15	650	16	605	22	576	17	183	30	129	32	185	27	268	27	163	27	293	24	133	40	46	54	235	26	153	40	133	35	254	21	204	27	232	30	173	28	192	38	102	40	33	49	94	41	973	12	1055	12	1250	12	189	32	158	35	159	30	236	40	217	24	226	28	180	36	222	34	0	.	33579	2
	24-35 cm	917	20	441	24	795	22	859	23	707	26	1007	22	4265	9	2995	11	3403	11	2546	12	208	37	452	28	766	19	32	.	410	32	942	21	1336	19	1045	20	239	43	1315	25	1391	15	1414	16	1158	15	3376	10	2709	10	2987	10	853	19	1059	20	1664	17	216	34	579	25	329	31	247	35	585	21	654	28	538	25	902	20	939	20	755	22	1153	17	703	21	1267	18	1178	19	471	24	618	21	374	26	1072	20	689	23	614	23	378	28	901	24	484	27	589	27	206	32	698	20	452	24	601	21	686	20	1019	17	1024	16	648	21	831	18	487	29	332	36	484	28	587	22	386	26	811	23	489	27	70	47	786	21	283	38	303	33	709	20	448	22	593	21	506	26	299	31	304	37	88	53	353	40	2173	11	1921	13	2569	12	369	27	376	27	488	31	799	22	787	21	429	28	285	39	360	29	0	.	82564	2
	36-51 cm	1929	17	1153	21	874	22	1835	16	1122	24	2237	18	8028	8	6463	9	5763	9	4002	11	495	30	677	27	1370	20	38	.	826	27	1519	20	2128	16	1885	19	173	51	2269	17	2870	14	1999	14	2341	14	5511	9	4981	9	5695	10	1751	22	2048	17	2944	15	535	26	1008	20	703	27	350	30	1262	19	545	24	656	23	1355	18	1865	19	1124	23	1915	17	1390	22	2545	17	1735	18	1154	20	1052	21	846	24	1531	18	1343	18	1411	20	1028	20	1205	23	769	26	894	21	257	32	911	17	797	22	365	27	614	20	735	23	1025	17	605	22	710	20	404	28	431	37	657	26	1085	25	652	27	1413	21	765	29	137	46	1010	20	703	29	523	30	1183	17	654	24	1264	20	999	21	620	33	767	36	280	43	699	28	3330	10	2715	12	3279	11	919	24	809	27	912	21	1234	20	1456	19	651	25	793	29	693	28	0	.	140203	1
	=52 cm	1563	19	930	21	777	25	2300	18	800	28	2305	23	8592	10	8232	10	7726	10	2781	16	631	31	639	33	1090	26	18	.	722	34	1002	25	1374	21	1723	22	61	73	2049	19	3027	15	2495	19	3280	14	5927	10	3714	12	3760	14	1890	22	2196	20	2422	17	467	31	952	25	471	34	563	34	1868	19	436	32	796	28	1344	22	1341	22	609	27	1731	20	1400	24	3676	17	1003	22	1758	24	645	43	484	29	760	29	1425	23	1128	24	1536	27	875	29	814	35	1133	24	776	26	1693	18	957	22	671	65	650	33	388	28	1385	19	458	27	955	27	410	35	503	40	646	37	1025	25	1202	25	1535	21	1071	31	75	76	870	28	836	32	491	45	496	24	822	27	975	23	660	27	117	53	785	35	369	48	841	35	4703	12	2183	15	3057	14	918	32	992	28	1187	25	1240	21	1213	24	819	31	536	31	902	29	0	.	140648	2
	total	4924	15	2987	17	2785	18	5441	14	2830	22	5895	17	22763	8	19029	8	18408	8	10431	10	1481	25	1938	23	3512	18	87	.	2129	23	3909	17	5386	15	5184	16	565	39	6284	16	7927	11	6496	12	7686	11	16699	7	12873	8	13783	8	4997	17	5898	15	7803	13	1298	23	2800	18	1636	25	1301	26	4067	17	1964	21	2310	21	4129	17	4460	17	2905	19	5206	14	3782	18	7906	15	4516	14	3666	18	2651	19	1936	21	3643	16	3847	17	3570	18	3167	20	3411	19	2403	23	3196	18	1481	20	3710	14	2534	17	2215	23	2487	17	3010	13	4326	12	2490	16	3267	15	1517	24	1438	28	2019	23	3020	19	2479	20	4171	17	2508	25	346	38	2979	17	2001	25	1498	26	2702	15	2184	20	3114	17	2405	18	1280	27	1979	29	777	39	2025	28	11517	8	8126	10	10550	9	2452	22	2375	22	2806	20	3563	17	3749	17	2182	22	1837	26	2241	24	0	.	407258	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. 2293317. Birmensdorf, Swiss Federal Research Institute WSL

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