Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI5
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (main diameter classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (main diameter classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	2	82	104	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	.	1	100	4	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	.	56	61	12	49	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	182	27
	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	.	15	66	263	24	0	.	0	.	0	.	0	.	0	.	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	16	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	.	177	61	58	60	8	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	543	24
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	.	0	.	45	.	81	46	592	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	75	71	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	.	261	37	142	48	27	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1288	16
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	.	0	.	7	.	157	43	1028	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	82	42	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	.	450	33	283	58	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2068	16
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	.	0	.	0	.	273	54	1211	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	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	.	510	37	166	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2206	18
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	.	0	.	52	.	529	45	3197	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	120	77	150	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1454	27	660	42	52	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6286	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	.	69	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	90	43	35	4	79	30	43	49	39	102	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	87	7	81	13	62	0	.	0	.	3	116	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	71	18	57	7	61	0	.	0	.	0	.	9	54	1	100	0	.	0	.	19	100	21	65	0	.	1	100	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	0	.	118	37	79	45	23	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	656	14
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	245	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	96	165	39	24	47	68	54	208	34	329	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	92	43	82	81	75	0	.	0	.	3	302	0	.	25	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	24	69	66	48	72	56	0	.	0	.	0	.	52	59	11	100	0	.	0	.	15	75	80	75	0	.	7	74	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	340	28	156	34	136	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2266	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	930	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	365	33	188	61	331	46	569	35	1015	22	0	.	0	.	0	.	11	.	0	.	0	.	0	.	0	.	0	.	127	87	103	100	244	68	0	.	0	.	185	86	0	.	38	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	13	100	290	44	128	56	41	.	0	.	0	.	159	56	15	71	0	.	20	.	75	71	26	108	41	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	551	33	489	32	348	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6365	9
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	1970	21	79	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	59	947	26	340	45	934	34	1023	30	1827	20	0	.	0	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	66	74	89	63	267	80	0	.	0	.	242	59	0	.	10	.	0	.	0	.	0	.	0	.	0	.	100	88	0	.	0	.	0	.	0	.	0	.	141	69	495	35	196	52	21	.	0	.	0	.	179	42	45	73	0	.	0	.	151	74	64	73	22	.	131	71	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	.	0	.	0	.	0	.	0	.	1159	30	709	27	845	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12177	8
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	1432	25	60	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	274	60	1484	33	281	55	1391	30	720	31	2117	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	84	100	12	100	71	100	0	.	82	.	187	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	237	83	0	.	0	.	0	.	0	.	0	.	290	54	442	40	184	58	0	.	0	.	0	.	335	54	49	71	0	.	59	.	149	71	13	100	79	.	102	58	109	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	.	0	.	0	.	0	.	0	.	2296	25	834	33	1205	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14621	8
	total	0	.	0	.	0	.	0	.	0	.	0	.	4646	20	138	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	443	64	3004	24	837	46	2753	27	2568	26	5391	18	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	354	71	255	81	676	63	0	.	82	.	621	65	0	.	75	.	0	.	0	.	0	.	0	.	0	.	349	72	0	.	0	.	0	.	0	.	0	.	473	56	1311	33	587	48	61	.	0	.	0	.	734	44	121	73	0	.	80	.	408	71	204	66	141	.	255	62	135	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	.	0	.	0	.	0	.	0	.	4464	22	2267	25	2557	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36086	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	.	131	24	1	100	0	.	4	71	0	.	0	.	0	.	0	.	0	.	12	62	0	.	0	.	0	.	6	75	49	46	41	42	145	28	300	31	79	29	0	.	4	.	0	.	18	70	0	.	0	.	0	.	1	.	0	.	1	.	48	42	9	50	0	.	12	100	20	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	63	20	83	6	78	0	.	0	.	0	.	37	43	25	61	16	53	1	72	0	.	0	.	11	61	4	82	2	100	1	646	3	75	28	47	31	59	14	80	62	77	0	.	0	.	23	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	132	23	148	30	114	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1572	9
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	356	25	5	100	0	.	4	100	0	.	0	.	0	.	0	.	9	.	66	62	3	100	11	79	0	.	28	61	141	33	219	42	438	27	741	19	309	27	0	.	11	.	0	.	68	58	0	.	0	.	0	.	2	.	0	.	8	.	150	40	15	60	0	.	52	73	59	53	3	.	0	0	0	.	0	.	0	.	0	.	0	.	18	67	62	65	10	100	0	.	0	.	0	.	170	41	102	58	59	40	9	100	0	.	0	.	20	55	27	64	66	90	4	515	28	72	115	43	61	73	53	57	122	63	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	26	88	0	.	0	.	0	.	0	.	539	27	601	27	323	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5130	7
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	967	20	34	71	0	.	0	.	0	.	0	.	0	.	0	.	34	.	212	83	36	72	41	74	0	.	129	71	322	40	689	37	606	28	1564	19	803	26	0	.	64	.	0	.	76	66	0	.	0	.	0	.	0	.	0	.	2	.	390	43	93	49	0	.	28	72	60	56	14	.	0	.	0	.	0	.	0	.	0	.	0	.	41	70	135	57	38	100	0	.	0	.	0	.	90	59	147	59	230	44	37	100	0	.	0	.	193	53	87	59	153	69	94	58	114	100	427	42	121	62	83	42	190	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	933	26	1335	25	862	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11509	7
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	1563	22	239	66	0	.	11	100	0	.	0	.	0	.	0	.	67	.	277	61	131	59	128	71	0	.	144	65	1253	30	598	29	1012	24	2954	16	2438	21	0	.	30	.	0	.	141	66	0	.	0	.	0	.	0	.	0	.	34	.	341	40	195	55	0	.	132	100	64	56	102	.	10	100	0	.	0	.	0	.	0	.	0	.	294	57	135	83	73	83	0	.	0	.	0	.	204	62	136	51	584	39	41	72	0	.	0	.	233	55	137	56	156	66	178	69	326	91	420	46	255	79	524	59	354	55	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	79	71	0	.	0	.	0	.	0	.	1387	25	1454	24	1304	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20154	6
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	3407	21	89	84	0	.	205	95	0	.	0	.	0	.	0	.	146	.	106	63	273	74	210	72	0	.	761	55	1560	30	448	39	2208	27	4148	17	1540	25	0	.	47	.	0	.	219	64	0	.	0	.	0	.	0	.	0	.	0	.	422	55	290	51	0	.	0	.	153	84	25	.	58	100	0	.	0	.	0	.	0	.	0	.	292	60	69	100	106	100	0	.	0	.	0	.	530	49	535	57	573	40	112	71	0	.	0	.	190	58	169	67	319	80	221	62	129	85	386	48	536	56	527	43	465	59	0	.	0	.	130	100	0	.	0	.	0	.	0	.	0	.	102	62	0	.	0	.	0	.	0	.	1651	26	1601	24	1984	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26942	6
	total	0	.	0	.	0	.	0	.	0	.	0	.	6425	18	369	60	0	.	224	93	0	.	0	.	0	.	0	.	255	.	673	61	442	61	390	71	0	.	1068	52	3325	26	1996	29	4409	21	9707	13	5169	18	0	.	155	.	0	.	522	54	0	.	0	.	0	.	3	.	0	.	45	.	1352	40	603	47	0	.	223	81	357	59	144	.	68	100	0	.	0	.	0	.	0	.	0	.	652	48	420	58	233	78	0	.	0	.	0	.	1031	39	945	47	1463	34	200	71	0	.	0	.	648	48	423	57	697	61	499	60	601	79	1376	40	1004	53	1200	44	1192	52	0	.	0	.	186	82	0	.	0	.	0	.	0	.	0	.	242	61	0	.	0	.	0	.	0	.	4641	20	5140	19	4587	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65306	5
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	11	56	0	.	0	.	11	54	6	52	50	36	144	19	307	25	186	45	222	26	19	100	18	61	19	64	0	.	2	73	37	32	120	30	26	46	0	.	39	29	27	38	27	37	7	71	47	37	0	.	203	23	104	49	85	41	135	29	22	64	50	30	10	57	35	50	74	48	30	41	45	81	54	34	52	52	12	53	91	49	24	49	42	30	30	78	71	48	49	44	46	61	21	51	67	41	23	42	33	40	-11	278	0	.	12	75	0	.	53	80	0	.	61	38	23	48	20	70	151	47	61	42	38	38	27	39	23	65	4	81	34	45	15	60	27	48	14	67	0	.	48	40	3	.	34	60	44	42	13	100	55	54	19	56	19	76	14	80	17	73	21	84	0	.	9	.	88	35	28	40	14	49	23	55	56	36	57	49	0	.	0	.	0	.	0	.	3849	6
	16-23 cm	18	88	0	.	0	.	50	61	95	54	184	42	531	28	852	15	511	29	849	18	33	100	21	57	127	48	0	.	28	82	203	37	356	54	116	52	0	.	222	27	133	44	96	39	18	60	123	41	0	.	712	21	241	34	222	42	423	22	49	86	241	30	44	50	155	41	305	32	262	45	51	52	202	32	168	43	110	44	319	38	157	44	226	27	52	47	169	35	159	38	75	49	101	47	210	40	110	39	151	44	73	48	0	.	40	71	0	.	51	54	0	.	174	44	186	41	114	44	299	36	254	39	90	49	190	45	74	55	59	92	108	40	37	61	117	54	37	60	0	.	215	33	20	.	38	71	241	25	79	46	146	42	54	49	35	72	38	57	10	100	50	65	0	.	8	.	336	30	75	44	71	45	21	172	193	32	142	59	0	.	0	.	8	.	0	.	13165	4
	24-35 cm	105	65	20	.	0	.	80	65	535	35	1034	30	1466	18	1918	17	757	27	2295	17	18	100	86	59	287	40	0	.	208	64	255	40	1110	23	399	50	0	.	421	44	549	37	284	35	7	100	308	40	0	.	1220	19	381	34	512	35	1244	22	203	72	586	27	149	57	232	46	536	33	796	35	79	62	662	30	583	35	328	44	615	32	653	52	984	25	325	47	482	39	371	35	95	58	329	41	300	34	666	32	253	43	415	45	0	.	90	72	0	.	559	84	0	.	280	41	438	37	290	48	611	37	424	38	162	52	555	42	59	85	27	77	258	40	116	49	462	46	326	55	0	.	635	29	75	.	112	69	623	26	274	43	361	53	72	51	142	64	99	76	100	72	29	100	0	.	0	.	707	28	308	40	124	53	57	71	688	32	387	37	0	.	12	.	27	.	0	.	34633	4
	36-51 cm	189	50	0	.	0	.	204	54	1380	28	1643	26	2706	18	3689	15	1509	28	3769	14	48	100	180	80	373	44	0	.	310	56	884	31	1478	24	1060	37	0	.	1420	24	962	33	487	29	98	76	336	43	0	.	3370	17	627	33	1097	27	1847	21	282	46	975	31	289	45	489	40	1152	25	1026	34	141	46	1174	29	1214	29	1026	35	1134	24	865	38	1756	23	707	37	535	38	774	33	214	56	761	34	573	40	907	28	914	32	577	36	0	.	77	63	0	.	565	70	0	.	126	45	320	33	199	61	368	34	384	41	499	45	357	39	194	73	231	72	445	44	200	52	505	38	365	46	16	.	1174	25	110	.	208	53	1070	22	347	36	778	29	322	44	245	62	215	53	254	63	82	88	0	.	0	.	1050	26	427	41	549	43	215	63	1072	25	996	30	0	.	0	.	18	.	0	.	61131	3
	=52 cm	299	63	0	.	0	.	517	51	1504	34	1840	29	3592	20	7070	15	2301	25	2525	18	48	100	86	100	497	56	0	.	138	72	1207	37	1730	25	929	35	0	.	1812	25	619	39	1618	28	148	71	552	45	0	.	3192	20	1098	33	2032	29	2491	23	610	50	1236	33	234	52	599	44	1751	26	651	44	108	65	1446	35	1781	34	673	58	1483	29	1948	31	3421	22	268	53	583	41	168	74	350	60	583	35	1191	37	1178	32	1353	36	627	45	0	.	196	60	0	.	935	59	0	.	212	50	329	49	110	84	465	36	167	50	380	43	318	59	296	65	318	82	444	53	482	59	594	40	664	49	0	.	1055	34	114	.	177	52	697	34	913	42	531	38	554	49	120	71	454	44	162	59	372	73	0	.	0	.	801	37	406	45	592	50	321	76	1715	30	825	41	0	.	0	.	140	.	0	.	75948	4
	total	623	48	20	.	0	.	863	45	3520	29	4750	24	8439	15	13836	12	5264	20	9661	13	166	76	392	55	1304	42	0	.	687	54	2586	30	4794	19	2531	32	0	.	3914	21	2290	28	2513	26	279	67	1366	35	0	.	8698	15	2452	26	3949	25	6141	18	1167	39	3088	24	726	42	1510	35	3819	23	2765	30	424	46	3537	27	3798	26	2150	36	3642	23	3647	28	6429	19	1381	34	1840	31	1521	31	780	48	1796	30	2341	32	2884	26	2703	28	1682	35	0	.	415	50	0	.	2162	63	0	.	853	35	1296	31	733	39	1894	27	1289	33	1169	37	1447	36	646	55	638	59	1288	38	850	50	1704	35	1405	44	16	.	3126	23	321	.	568	44	2675	22	1626	34	1871	29	1022	40	561	55	821	40	544	60	555	66	0	.	16	.	2982	24	1244	37	1349	37	637	63	3724	23	2408	29	0	.	12	.	192	.	0	.	188726	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	149	30	83	29	15	47	53	37	0	.	0	.	21	49	47	32	391	20	3	.	50	49	44	52	42	47	2	.	22	72	39	64	0	.	59	52	0	.	0	.	13	61	13	76	0	.	0	.	0	.	10	484	54	38	121	51	9	80	20	58	0	.	47	57	-5	.	0	.	0	.	0	.	36	100	8	127	27	84	12	100	6	74	0	.	78	30	16	63	2	100	51	56	50	63	54	97	0	.	0	.	35	49	59	36	48	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	71	0	.	3	78	6	66	28	63	0	.	14	56	30	48	3	100	12	67	38	71	0	.	18	62	13	58	47	72	7	52	1	100	0	.	13	85	0	.	0	.	26	76	0	.	12	54	22	41	11	76	117	52	32	48	35	43	32	34	0	.	2306	8
	16-23 cm	255	29	217	34	273	39	141	34	0	.	0	.	40	46	196	31	944	17	0	.	106	45	148	41	95	57	0	.	40	54	140	56	0	.	295	65	0	.	0	.	26	75	49	53	0	.	0	.	0	.	178	28	144	48	237	33	42	95	35	58	0	.	68	55	9	.	0	.	0	.	0	.	112	93	6	644	112	58	32	85	71	78	0	.	212	31	127	62	-7	223	99	42	79	44	32	100	0	.	0	0	156	38	254	40	148	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	75	0	.	37	71	84	54	36	76	0	.	65	63	52	72	19	73	87	68	33	72	0	.	95	60	18	132	104	49	58	80	5	100	0	.	61	96	0	.	0	.	88	55	9	100	59	59	78	46	49	62	102	55	117	40	137	70	145	36	0	.	6686	6
	24-35 cm	364	32	548	33	334	33	518	39	0	.	0	.	153	44	385	40	1758	17	22	.	96	55	177	49	298	39	-5	.	106	64	350	49	0	.	435	35	0	.	0	.	71	71	144	58	0	.	0	.	0	.	390	35	392	40	660	32	77	82	64	66	0	.	105	71	16	.	0	.	0	.	0	.	152	88	72	61	209	68	39	100	113	89	0	.	722	29	133	53	26	72	152	46	322	50	45	100	0	.	0	.	530	38	361	31	486	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	239	77	0	.	45	73	108	78	106	70	0	.	197	56	132	59	78	71	109	67	40	63	0	.	194	56	112	56	128	59	161	52	112	52	0	.	63	100	0	.	0	.	85	89	68	71	117	54	204	44	175	79	223	43	336	41	158	41	511	32	0	.	14525	6
	36-51 cm	985	28	527	26	637	33	963	28	0	.	0	.	583	45	741	29	3406	14	48	.	105	62	509	47	525	36	38	.	351	59	222	54	0	.	657	35	0	.	0	.	134	58	71	60	0	.	0	.	0	.	1260	26	786	36	887	30	92	80	36	70	0	.	210	50	29	.	0	.	0	.	0	.	13	100	128	71	144	57	59	78	138	56	0	.	1144	25	249	36	164	55	423	37	353	41	48	91	0	.	253	71	472	48	692	30	605	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	156	74	0	.	130	72	61	69	102	69	0	.	308	56	305	60	60	58	24	80	130	58	0	.	160	55	294	55	414	47	313	49	135	75	0	.	158	71	0	.	0	.	75	58	118	87	250	58	470	36	60	72	519	45	450	34	420	38	421	35	0	.	24220	5
	=52 cm	790	28	850	30	861	39	1866	27	0	.	0	.	325	59	985	33	5177	15	0	.	361	53	318	62	321	43	218	.	233	68	320	49	0	.	722	32	0	.	0	.	184	59	124	66	0	.	0	.	0	.	1427	28	1050	37	1041	31	86	78	71	71	0	.	345	72	0	.	0	.	0	.	0	.	0	.	190	100	161	59	93	100	138	81	0	.	1207	30	946	44	11	100	408	52	48	58	99	100	0	.	148	81	282	47	1243	41	1292	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	167	100	0	.	43	100	52	77	306	89	0	.	250	62	203	75	147	80	116	71	0	.	0	.	28	71	215	58	233	52	198	49	163	74	0	.	250	72	0	.	0	.	58	77	104	85	490	49	650	36	0	.	450	42	714	41	537	37	601	34	0	.	29918	5
	total	2542	22	2225	22	2121	26	3541	23	0	.	0	.	1121	44	2354	26	11676	12	73	.	718	39	1197	38	1281	32	252	.	752	50	1071	47	0	.	2168	29	0	.	0	.	428	52	401	55	0	.	0	.	0	.	3265	24	2427	30	2945	24	307	74	227	44	0	.	775	49	49	.	0	.	0	.	0	.	312	70	404	67	653	56	235	72	467	63	0	.	3362	22	1472	35	197	57	1132	32	853	41	278	75	0	.	401	72	1475	35	2610	26	2579	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	602	72	0	.	258	73	311	51	577	62	0	.	833	53	722	46	307	60	347	45	241	52	0	.	496	49	652	50	927	43	738	43	417	58	0	.	544	65	0	.	0	.	332	47	300	71	928	44	1424	29	295	68	1411	30	1650	30	1288	30	1710	27	0	.	77654	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	.	14	67	0	.	0	.	0	.	0	.	63	59	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	.	5	.	44	55	14	58	83	58	77	72	149	27	43	41	22	47	101	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	3	.	0	.	0	.	0	.	40	53	30	41	17	61	0	.	0	.	0	.	0	.	0	.	7	78	0	.	0	.	0	.	725	14
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	85	0	.	0	.	0	.	0	.	115	52	17	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	.	18	.	159	47	52	53	207	35	106	37	645	25	155	38	133	43	348	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	.	0	.	0	.	0	.	143	41	76	52	35	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2321	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	54	0	.	0	.	0	.	0	.	353	49	34	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	.	17	.	223	46	112	59	234	51	351	37	1003	25	179	42	301	46	581	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	74	0	.	0	.	35	.	0	.	0	.	0	.	139	52	118	62	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3879	10
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	61	0	.	0	.	0	.	0	.	229	49	45	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	.	12	.	179	51	165	58	108	42	380	34	986	25	142	39	157	41	412	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	71	0	.	0	.	0	.	0	.	0	.	0	.	167	48	81	66	57	62	0	.	0	.	0	.	0	.	0	.	57	75	0	.	0	.	0	.	3508	10
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	113	100	0	.	0	.	0	.	0	.	115	55	86	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	.	151	69	47	100	94	50	243	38	509	37	373	61	122	70	502	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	136	80	20	100	29	100	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	2596	17
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	475	55	0	.	0	.	0	.	0	.	876	39	185	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	.	90	.	757	42	391	57	726	33	1156	32	3293	19	892	36	735	37	1944	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	165	71	0	.	0	.	71	.	0	.	0	.	0	.	626	40	324	52	154	44	0	.	0	.	0	.	0	.	0	.	83	77	0	.	0	.	0	.	13029	8
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	160	28	83	29	15	47	64	32	6	52	50	36	365	13	356	22	577	20	230	25	69	45	62	40	61	38	2	.	24	65	88	32	120	30	85	39	14	67	57	28	133	22	84	27	183	24	460	22	287	24	214	32	162	34	206	34	162	26	42	43	50	30	57	48	32	57	74	48	47	39	101	42	111	37	60	48	51	52	126	38	30	42	45	29	107	31	87	41	50	43	97	41	71	47	128	46	42	45	40	36	24	141	59	36	68	31	47	36	141	37	42	31	146	37	99	57	169	25	214	34	89	31	141	24	34	43	45	54	56	35	71	34	58	39	92	54	28	44	30	48	74	41	15	57	72	47	44	42	34	52	67	45	69	51	29	52	16	73	17	73	34	61	346	19	278	21	270	19	28	40	26	36	45	34	66	32	174	38	39	42	35	43	32	34	0	.	9290	4
	16-23 cm	274	27	217	34	273	39	191	30	95	54	184	42	1172	16	1054	13	1455	15	853	18	139	41	170	37	222	37	0	.	77	43	409	28	359	53	423	47	61	85	294	25	464	21	389	26	524	23	1202	15	918	16	890	18	396	27	459	26	533	20	84	55	241	30	113	39	165	39	305	32	331	40	245	31	410	33	174	47	274	33	412	31	231	38	251	26	264	27	297	33	152	41	174	32	179	33	264	34	172	34	160	42	229	30	254	40	205	32	212	35	383	27	200	28	390	27	292	29	759	22	526	24	424	27	505	26	230	40	117	40	292	34	252	30	133	35	243	41	101	46	52	72	249	30	107	58	70	50	241	25	174	39	164	40	190	33	127	49	43	52	10	100	111	60	1199	18	898	20	927	18	83	40	130	36	98	51	242	28	244	41	117	40	137	70	153	34	0	.	30110	3
	24-35 cm	469	29	568	32	334	33	598	35	535	35	1034	30	3516	11	2337	15	2515	14	2317	17	114	49	264	38	585	28	-5	.	348	44	817	32	1146	23	875	29	71	54	589	35	1319	20	1306	23	989	24	2876	14	2445	14	1610	17	837	25	1172	23	1409	20	267	57	586	27	254	44	247	44	536	33	926	32	573	35	1151	26	655	32	565	36	900	29	781	45	1022	25	1047	25	615	33	397	33	247	36	651	32	394	29	801	28	291	39	946	29	361	31	607	33	120	47	1275	39	542	28	591	29	789	26	1293	22	1142	24	827	26	896	24	908	33	248	54	525	35	528	30	319	31	652	39	523	40	132	59	714	27	184	57	152	53	623	26	549	31	472	43	236	39	339	38	212	45	100	72	92	75	1884	17	2084	18	2045	16	377	35	241	38	261	38	863	30	611	28	336	41	170	39	537	31	0	.	72200	2
	36-51 cm	1174	24	527	26	637	33	1167	25	1380	28	1643	26	6823	11	4748	13	4914	13	3828	14	153	52	690	40	898	27	38	.	728	38	1383	25	1609	23	1845	25	217	61	1639	22	3325	16	1497	18	2051	20	4700	13	5338	12	4629	14	1443	24	1984	20	2089	19	318	41	975	31	499	34	518	38	1152	25	1126	31	570	28	1650	25	1342	27	1302	30	1499	21	1105	32	1776	22	1850	21	784	28	938	29	637	31	1115	26	1016	29	1041	27	1240	28	1049	29	692	30	712	29	357	44	1434	31	986	27	297	27	699	24	1184	23	923	22	723	26	1067	26	690	31	672	52	844	33	783	36	957	36	878	31	672	36	321	57	1251	23	134	84	338	39	1070	22	587	28	1072	26	838	30	558	39	350	44	254	63	240	55	3163	17	2527	17	3345	15	545	37	798	34	685	31	1132	24	1515	25	508	31	420	38	439	34	0	.	123258	2
	=52 cm	1089	27	850	30	861	39	2383	23	1504	34	1840	29	8757	12	8203	13	7477	13	2730	18	409	48	404	53	818	38	218	.	517	46	1633	29	2002	24	1861	23	113	100	2847	23	3876	18	2472	21	3747	20	5808	13	4954	14	4619	16	2195	24	3074	22	2797	21	682	45	1236	33	579	48	599	44	1751	26	735	41	542	45	1807	29	1971	32	916	45	1917	25	2112	29	3479	22	1475	26	1529	31	179	70	757	39	632	33	1818	29	1247	31	1607	32	909	34	1243	41	1488	31	857	36	2063	32	821	31	417	33	572	32	620	34	1363	26	506	33	1201	31	765	38	574	43	760	43	1110	35	1417	32	1167	32	914	39	203	75	1332	30	230	61	177	52	697	34	941	41	747	31	934	33	318	40	618	37	162	59	622	52	4592	16	2620	18	4078	17	510	40	1081	35	971	35	1715	30	1275	30	733	40	537	37	741	33	0	.	152231	2
	total	3166	20	2245	22	2121	26	4404	20	3520	29	4750	24	20632	9	16697	10	16939	10	9958	12	884	34	1589	32	2585	26	252	.	1694	35	4330	23	5236	18	5089	21	475	55	5426	19	9118	14	5748	17	7494	16	15046	10	13943	10	11963	12	5033	19	6895	17	6990	17	1393	33	3088	24	1501	32	1562	34	3819	23	3164	27	2030	30	5129	22	4202	25	3108	28	4855	20	4259	25	6573	19	4744	18	3312	23	1717	28	1913	27	2648	24	3620	24	3304	24	3337	25	3157	24	2610	26	3080	22	1593	31	5295	29	2591	24	1840	22	2452	22	4026	17	4168	18	2569	22	3810	20	2627	28	1655	40	2476	28	2743	27	2883	27	3031	29	2238	34	738	45	3620	21	669	53	809	34	2675	22	2286	27	2523	25	2267	26	1370	33	1238	33	544	60	1098	46	11185	13	8408	14	10664	13	1544	33	2276	28	2060	28	4019	22	3819	21	1733	29	1299	30	1903	26	0	.	387089	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. 2276614. Birmensdorf, Swiss Federal Research Institute WSL

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