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 (“±”).

top

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).

top

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).

top

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.

top

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.

top

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

top

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.

top

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.

top

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.

top

NFI4
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (main diameter classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (main diameter classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	91	1	100	1	71	17	35	106	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	83	11	52	12	50	0	100	0	.	0	.	1	161	1	.	1	.	0	.	0	.	3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	33	26	41	6	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	225	12
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	5	100	1	100	25	36	343	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	22	41	20	71	16	100	0	.	0	.	14	53	6	.	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	196	32	71	27	15	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	742	12
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	65	23	100	8	100	151	32	755	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	77	37	58	41	53	-2	100	0	.	0	.	32	48	0	.	0	.	0	.	0	.	26	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	322	24	189	31	86	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1726	10
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	64	13	100	3	100	248	30	1069	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	48	40	60	12	100	0	.	0	.	34	87	0	.	11	.	0	.	0	.	6	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	563	21	273	28	45	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2418	10
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	100	16	100	0	.	352	32	1165	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	.	22	100	78	66	49	45	34	100	0	.	0	.	53	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	721	23	207	31	37	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2778	13
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	66	58	100	13	92	793	26	3438	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	66	206	38	163	42	59	65	0	.	0	.	133	52	7	.	12	.	0	.	0	.	39	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1831	18	766	24	189	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7889	8
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	.	78	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	14	39	44	26	21	37	81	24	123	31	136	28	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	11	72	7	62	11	54	0	.	5	100	11	43	2	.	4	.	0	.	0	.	0	.	0	.	0	.	4	87	0	.	0	.	0	.	0	.	0	.	14	45	30	52	7	41	1	100	0	.	0	.	17	42	14	79	18	93	0	.	0	.	10	61	5	72	3	71	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	91	25	81	42	40	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	893	9
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	239	20	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	.	0	.	0	.	38	58	146	27	116	43	296	32	314	19	468	18	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	52	73	28	72	47	64	0	.	7	100	53	47	9	.	11	.	0	.	0	.	0	.	0	.	0	.	3	72	0	.	0	.	0	.	0	.	0	.	17	46	72	32	35	67	9	100	0	.	0	.	88	39	19	73	14	88	0	.	6	79	29	75	6	70	6	51	9	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	302	18	188	23	157	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2825	7
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	948	18	17	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	.	0	.	0	.	82	54	471	27	247	30	612	22	897	19	1043	15	0	.	0	.	0	.	33	72	0	.	0	.	0	.	0	.	0	.	93	68	66	82	138	51	0	.	16	100	150	48	5	.	9	.	0	.	0	.	0	.	0	.	0	.	21	71	0	.	0	.	0	.	0	.	0	.	38	51	203	35	139	53	25	76	0	.	0	.	84	45	30	79	25	100	10	100	73	62	16	71	41	62	15	71	16	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	.	0	.	0	.	0	.	0	.	621	24	690	21	577	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7505	6
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	1796	17	53	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.	0	.	0	.	217	50	965	24	426	29	1234	21	1639	17	2081	15	0	.	0	.	0	.	42	100	0	.	0	.	0	.	0	.	0	.	43	63	67	45	139	48	0	.	24	76	196	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	86	0	.	0	.	0	.	0	.	0	.	161	44	262	31	205	39	12	71	0	.	0	.	201	42	58	54	16	100	4	100	71	66	47	75	96	79	99	45	72	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	.	0	.	0	.	0	.	0	.	1252	19	1068	20	1082	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13738	5
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	1416	21	78	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	417	41	1336	29	297	34	1461	20	1352	20	2401	25	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	76	73	69	73	123	67	0	.	39	77	142	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	117	89	0	.	0	.	0	.	0	.	0	.	394	33	462	32	236	42	0	.	0	.	0	.	239	39	38	53	0	.	37	100	69	71	0	.	176	61	124	43	199	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	2053	19	936	23	891	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15195	7
	total	0	.	0	.	0	.	0	.	0	.	0	.	4477	16	152	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	.	0	.	0	.	768	38	2963	21	1107	25	3684	16	4324	15	6130	15	0	.	0	.	0	.	94	87	0	.	0	.	0	.	0	.	0	.	275	52	237	52	459	37	0	.	91	71	553	38	16	.	23	.	0	.	0	.	0	.	0	.	0	.	208	65	0	.	0	.	0	.	0	.	0	.	624	32	1030	26	622	37	46	71	0	.	0	.	629	30	159	49	73	96	50	92	219	63	102	69	325	62	246	40	297	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	.	0	.	0	.	0	.	0	.	4320	16	2963	18	2747	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40155	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	.	164	19	4	58	0	.	1	100	0	.	0	.	0	.	0	.	0	0	15	47	13	55	0	0	0	.	16	49	53	32	37	32	225	21	257	27	162	31	0	.	4	88	0	.	17	43	0	.	0	.	0	.	0	.	0	.	7	67	42	41	12	49	0	.	1	71	11	50	4	65	4	86	0	.	0	.	0	.	0	.	0	.	9	62	38	56	4	55	0	.	0	.	0	.	80	41	25	37	54	35	2	50	0	.	0	.	23	35	3	82	5	65	6	58	6	61	29	33	11	52	26	42	21	43	0	.	0	.	3	71	0	.	0	.	1	100	0	.	0	.	2	72	0	.	0	.	0	.	0	.	182	19	125	18	134	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1839	7
	16-23 cm	0	.	0	.	0	.	0	.	8	.	0	.	406	16	6	226	0	.	2	100	0	.	0	.	0	.	0	.	4	100	72	45	35	43	5	78	0	.	24	53	119	26	205	25	621	20	657	14	368	19	0	.	24	65	0	.	75	43	0	.	0	.	0	.	0	.	0	.	33	49	122	33	34	50	0	.	20	65	25	45	8	72	4	100	0	.	0	.	0	.	0	.	0	.	26	52	81	36	10	75	0	.	0	.	0	.	150	35	103	37	144	25	9	80	0	.	0	.	87	31	19	68	41	61	9	173	22	53	123	32	28	66	69	47	68	49	0	.	0	.	0	.	0	.	0	.	0	5071	0	.	0	.	18	68	0	.	0	.	0	.	0	.	540	16	611	18	441	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5475	5
	24-35 cm	0	.	0	.	0	.	0	.	11	.	0	.	1126	17	107	63	0	.	0	.	0	.	0	.	0	.	0	.	27	71	240	54	187	56	35	71	0	.	71	65	415	28	525	25	812	18	1605	13	1151	17	0	.	65	71	0	.	138	41	0	.	0	.	0	.	0	.	0	.	53	59	393	31	101	41	0	.	76	68	81	55	41	71	0	0	0	.	0	.	0	.	0	.	0	.	28	55	127	43	15	100	0	.	0	.	0	.	176	36	257	40	329	28	49	52	0	.	0	.	228	36	49	58	79	68	59	60	131	51	350	35	74	45	187	40	281	48	0	.	0	.	14	100	0	.	0	.	20	100	0	.	7	100	48	84	0	.	0	.	0	.	0	.	1287	15	1045	18	793	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12895	5
	36-51 cm	0	.	0	.	0	.	0	.	17	.	0	.	2232	15	241	46	0	.	6	100	0	.	0	.	0	.	0	.	132	73	297	42	343	41	82	64	0	.	102	58	1227	22	839	23	1149	19	3218	12	2006	16	0	.	91	76	0	.	245	35	0	.	0	.	0	.	0	.	0	.	59	62	413	32	222	40	0	.	103	75	191	56	71	75	122	59	0	.	0	.	0	.	0	.	0	.	199	52	297	38	86	63	0	.	0	.	0	.	167	42	376	33	702	27	65	53	0	.	0	.	258	31	50	60	74	59	163	54	241	58	439	36	192	64	404	38	342	42	0	.	0	.	24	100	0	.	0	.	60	71	23	100	59	82	110	46	0	.	0	.	0	.	0	.	1650	15	1256	19	1199	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21848	4
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	3015	16	227	68	0	.	100	94	0	.	0	.	0	.	0	.	69	79	26	72	428	41	200	66	0	.	483	50	1546	22	1502	28	2175	19	4026	14	1258	23	0	.	-3	100	0	.	157	48	0	.	0	.	0	.	0	.	0	.	85	89	460	36	308	40	0	.	30	100	194	46	68	78	266	56	0	.	0	.	0	.	0	.	0	.	160	59	269	43	44	100	0	.	0	.	0	.	401	41	500	38	753	28	44	79	0	.	0	.	328	33	89	66	247	62	145	54	306	58	530	44	364	46	719	33	534	36	7	.	0	.	86	80	0	.	0	.	25	74	68	77	71	72	58	77	0	.	0	.	0	.	0	.	2500	18	1402	20	1699	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27968	5
	total	0	.	0	.	0	.	0	.	38	.	0	.	6943	13	585	44	0	.	108	93	0	.	0	.	0	.	0	.	232	71	650	42	1005	38	323	58	0	.	697	45	3360	19	3108	20	4982	14	9762	10	4945	14	0	.	181	72	0	.	632	31	0	.	0	.	0	.	0	.	0	.	238	51	1430	28	676	36	0	.	231	69	502	41	191	69	395	56	0	.	0	.	0	.	0	.	0	.	422	42	811	33	161	58	0	.	0	.	0	.	974	27	1260	28	1983	22	168	47	0	.	0	.	925	26	211	55	446	50	382	54	707	43	1471	29	669	45	1404	29	1247	33	7	.	0	.	127	70	0	.	0	.	106	71	91	82	137	74	236	49	0	.	0	.	0	.	0	.	6158	12	4440	15	4265	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70025	3
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	9	56	0	.	2	74	14	40	30	57	42	33	289	19	265	20	102	29	253	16	0	.	16	44	37	27	0	.	3	59	31	35	91	22	82	49	0	.	95	32	79	27	52	33	12	61	47	28	0	.	272	20	82	29	98	30	116	19	14	100	63	23	14	54	30	32	69	31	28	29	22	39	85	25	26	32	31	35	72	28	51	40	72	27	32	51	41	35	58	32	12	52	25	35	124	33	30	63	36	49	28	50	0	.	23	91	0	.	30	40	0	.	79	31	49	34	37	31	126	23	110	26	55	30	28	38	39	50	9	58	31	48	18	45	105	30	22	40	5	100	73	31	7	61	11	49	82	24	30	36	38	33	26	50	24	53	14	65	3	81	12	47	4	.	14	72	127	30	47	32	46	48	-15	157	72	45	24	35	0	.	5	100	2	100	0	.	4496	4
	16-23 cm	50	50	3	100	10	72	46	59	145	33	275	22	692	15	881	13	383	22	816	13	0	.	10	64	153	29	0	.	40	56	152	37	407	23	169	36	0	.	305	24	217	30	158	28	13	170	152	32	0	.	785	13	230	24	209	30	349	17	52	57	199	24	36	38	91	29	294	33	153	33	107	41	302	28	122	33	144	35	306	23	186	37	248	22	82	41	128	36	167	32	36	63	114	27	359	22	184	28	209	28	129	32	0	.	66	56	0	.	82	38	0	.	217	28	277	29	63	32	404	21	281	26	182	31	136	32	73	49	37	46	99	40	58	52	225	27	106	41	5	100	242	26	45	76	39	43	266	23	117	41	143	32	34	36	76	63	75	55	17	50	26	63	3	.	19	75	449	20	132	33	73	46	10	77	214	28	132	43	0	.	33	71	15	72	0	.	14770	3
	24-35 cm	127	41	6	100	71	58	235	46	500	25	924	21	1843	13	2191	11	733	22	2406	13	7	100	96	50	397	26	0	.	199	56	424	32	1153	19	683	31	0	.	886	24	604	27	344	25	6	106	380	30	0	.	2051	13	417	26	641	27	1071	20	209	46	567	26	248	41	203	32	578	21	515	33	87	45	756	23	472	28	489	28	753	22	454	32	1078	19	380	31	243	35	392	29	78	49	485	29	638	22	575	25	421	29	423	31	0	.	84	63	0	.	216	39	0	.	368	25	353	28	222	39	464	26	340	29	477	25	410	32	83	63	30	76	201	34	113	41	468	29	381	38	0	.	938	27	97	65	129	39	673	23	236	35	361	32	131	41	108	46	115	68	133	50	89	59	5	.	9	100	881	20	288	33	194	38	69	61	560	24	433	27	0	.	21	74	51	80	0	.	38169	3
	36-51 cm	434	35	66	88	108	62	415	32	1222	23	2221	19	3563	12	4846	10	1496	19	4124	11	77	100	164	56	629	26	0	.	142	57	868	27	1750	18	1086	26	0	.	1902	18	789	23	933	22	91	53	401	38	0	.	4390	11	842	23	984	23	2137	17	331	34	905	20	498	37	449	29	1466	19	544	28	180	39	994	23	1006	24	865	27	1368	19	829	25	2300	17	443	33	444	28	830	24	193	44	951	22	919	21	886	27	875	24	533	27	0	.	121	55	0	.	189	31	0	.	271	27	414	26	136	42	466	23	321	35	405	30	362	31	95	50	166	60	492	36	139	43	953	25	384	34	37	89	1044	21	90	71	258	40	1239	19	584	28	867	25	282	33	255	64	348	44	211	42	55	76	3	.	12	100	1110	20	672	26	512	34	179	52	1032	21	898	26	0	.	120	100	43	71	0	.	68251	2
	=52 cm	378	37	54	80	36	74	562	40	896	30	2579	22	4502	15	7689	11	2363	19	3014	15	0	.	244	64	766	35	0	.	110	68	846	31	1585	21	1031	31	0	.	1610	20	659	33	1066	24	114	60	423	33	0	.	2971	14	1423	30	1414	26	2341	20	536	34	1059	25	356	39	725	31	1980	21	355	40	248	58	1301	25	1055	30	546	34	1585	24	1375	25	3715	18	142	47	396	36	364	37	286	46	560	33	1634	26	900	25	1290	29	624	34	0	.	118	74	0	.	599	39	0	.	324	68	310	38	79	61	532	27	349	37	381	32	258	49	140	65	160	85	480	39	347	53	1096	29	587	44	12	100	964	32	80	72	369	39	672	23	903	29	563	28	480	40	56	71	377	40	431	50	306	59	0	.	0	.	801	25	838	28	647	42	189	54	1343	24	469	35	0	.	0	.	60	100	0	.	75031	3
	total	999	32	128	71	227	60	1273	29	2794	22	6041	17	10891	11	15872	9	5077	16	10613	10	84	100	529	51	1981	25	0	.	494	47	2322	25	4986	15	3052	23	0	.	4798	16	2348	20	2552	19	236	53	1404	27	0	.	10469	10	2995	21	3345	20	6014	16	1143	29	2793	18	1152	32	1499	25	4387	17	1595	26	644	37	3438	20	2681	22	2074	24	4084	18	2894	21	7413	15	1080	28	1252	25	1811	21	604	39	2135	21	3674	18	2574	21	2831	21	1737	26	0	.	411	51	0	.	1116	31	0	.	1259	26	1403	22	536	31	1992	18	1399	24	1500	22	1194	28	431	43	401	51	1303	30	675	38	2847	22	1481	30	59	86	3263	20	319	68	807	32	2933	16	1871	24	1973	21	953	29	518	50	928	37	796	42	488	46	15	.	53	83	3369	17	1977	23	1472	31	431	52	3221	18	1956	24	0	.	179	82	171	72	0	.	200716	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	81	37	134	31	98	42	87	32	0	.	0	.	20	46	43	28	258	14	7	79	22	40	47	38	32	31	0	.	33	95	36	56	2	.	76	54	0	.	11	.	8	78	7	50	0	.	0	.	0	.	81	25	35	30	47	28	13	53	30	41	0	.	7	140	-3	.	0	.	2	100	6	73	25	68	29	33	25	55	12	85	23	52	0	.	74	26	39	32	13	59	81	49	21	50	20	80	0	.	2	100	37	31	123	35	77	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	74	0	.	8	96	22	49	30	50	12	63	21	45	17	48	-1	247	38	51	32	53	0	.	18	50	4	282	36	34	7	48	4	73	0	.	22	64	0	.	0	.	43	39	9	61	23	57	30	34	33	80	48	35	55	53	28	63	51	41	0	.	2421	6
	16-23 cm	280	23	293	23	214	29	292	31	0	.	0	.	56	38	115	34	870	14	15	81	56	38	115	29	87	38	0	.	90	31	108	40	3	.	266	41	0	.	7	.	8	78	33	53	0	.	0	.	0	.	260	28	128	38	182	32	39	59	44	44	0	.	18	65	5	.	0	.	6	100	34	71	36	65	163	37	81	58	29	66	71	54	0	.	295	23	62	44	38	43	103	43	92	32	13	72	0	.	5	100	125	29	194	28	335	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	79	8	100	24	72	70	40	50	50	54	62	59	50	80	45	3	71	51	36	49	54	0	.	67	44	44	46	107	32	52	58	10	56	0	.	37	50	0	.	0	.	147	34	49	53	67	47	161	30	41	71	97	29	181	34	113	30	196	35	0	.	7117	5
	24-35 cm	611	21	563	25	662	27	519	28	0	.	0	.	196	42	401	30	1911	12	56	60	189	36	239	32	323	34	1	.	203	34	337	37	12	.	314	30	0	.	0	.	23	80	38	73	0	.	0	.	0	.	633	21	282	31	415	31	149	55	51	53	0	.	85	43	9	.	0	.	9	100	78	90	84	75	231	39	81	65	54	66	158	45	0	.	753	22	238	36	124	35	210	33	344	38	70	70	0	.	52	83	511	32	491	26	494	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	118	80	21	100	78	65	178	48	126	61	51	86	109	50	109	55	46	73	106	46	191	51	0	.	154	45	102	44	358	32	114	42	68	46	0	.	191	41	0	.	0	.	233	34	76	48	177	62	250	29	108	62	367	31	365	30	277	36	307	28	0	.	16478	4
	36-51 cm	1312	18	664	23	837	22	1218	19	0	.	0	.	563	40	610	33	3514	11	107	68	354	33	612	28	560	27	60	.	442	40	466	38	82	.	583	28	0	.	6	.	88	69	57	53	0	.	0	.	0	.	1420	18	878	36	834	25	60	55	162	40	0	.	206	45	21	.	0	.	53	74	91	87	44	100	434	34	167	54	90	45	287	42	0	.	1449	19	410	28	518	37	575	28	569	26	86	71	0	.	128	63	563	40	760	27	900	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	72	74	100	88	58	217	44	35	65	62	83	141	44	209	44	53	80	489	32	163	48	0	.	102	43	463	36	616	28	301	40	203	49	0	.	285	35	0	.	0	.	117	37	123	49	338	43	609	26	94	73	538	27	595	28	616	31	572	26	0	.	30013	3
	=52 cm	1261	21	1169	24	963	23	2029	20	0	.	0	.	365	44	870	34	4710	12	0	0	682	33	564	37	458	35	40	.	458	40	346	42	123	.	709	35	0	.	0	.	41	82	91	63	0	.	0	.	0	.	938	21	717	31	745	32	180	52	129	58	0	.	182	70	0	.	0	.	46	74	8	100	111	100	396	41	147	51	92	56	416	44	0	.	1225	22	1370	27	136	43	458	34	368	45	42	100	0	.	217	73	319	50	807	37	1101	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	100	12	100	20	100	101	52	161	84	29	100	206	46	106	80	78	75	735	34	13	100	0	.	72	73	278	40	480	36	128	43	308	49	0	.	540	40	0	.	0	.	62	56	84	81	653	33	1007	29	119	66	651	34	710	38	654	32	1067	27	0	.	33360	4
	total	3544	15	2823	18	2775	18	4146	17	0	.	0	.	1200	39	2038	24	11263	9	185	64	1303	27	1577	24	1460	25	101	.	1225	32	1293	30	222	.	1948	24	0	.	24	.	168	54	225	52	0	.	0	.	0	.	3332	17	2040	26	2222	22	440	51	415	38	0	.	497	46	32	.	0	.	114	73	218	71	301	69	1253	30	501	39	277	46	956	36	0	.	3796	16	2118	23	830	34	1427	24	1393	27	231	63	0	.	403	61	1555	31	2375	23	2907	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	289	71	116	100	219	58	589	38	403	51	208	64	536	38	521	42	179	59	1419	29	447	39	0	.	414	40	890	33	1597	27	602	36	593	42	0	.	1076	34	0	.	0	.	602	30	341	48	1258	30	2056	22	396	56	1702	24	1906	24	1687	25	2193	22	0	.	89390	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	.	16	44	0	.	0	.	0	.	5	.	81	58	1	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	.	3	.	5	90	25	65	11	41	69	45	59	32	186	20	65	29	44	41	91	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	3	.	0	.	0	.	0	.	43	39	38	31	11	41	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	757	10
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	46	0	.	0	.	0	.	20	.	123	30	13	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	.	7	84	174	32	45	47	236	30	187	31	570	18	199	29	211	31	268	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	72	0	.	0	.	18	.	0	.	0	.	0	.	103	34	81	35	37	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2359	8
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	134	48	0	.	0	.	7	.	18	.	336	30	15	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	.	12	100	75	56	72	55	221	33	375	27	900	18	261	28	385	30	387	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	71	0	.	0	.	18	.	0	.	0	.	0	.	102	44	165	36	19	63	0	.	0	.	0	.	0	.	0	.	57	75	0	.	0	.	0	.	3627	7
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	251	42	0	.	0	.	4	.	0	.	197	33	38	88	0	.	0	.	0	.	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	.	9	100	96	38	94	50	144	43	326	28	819	20	226	27	251	28	387	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	71	0	.	0	.	0	.	0	.	0	.	0	.	149	38	70	41	53	97	0	.	0	.	0	.	0	.	0	.	70	71	0	.	0	.	0	.	3226	8
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	62	0	.	0	.	9	.	12	.	237	61	27	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	.	13	.	16	100	199	46	27	70	155	46	449	53	425	27	486	37	129	61	395	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	53	41	100	41	72	0	.	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	2909	14
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	539	39	0	.	0	.	19	.	54	.	974	29	93	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	.	47	.	50	96	569	28	248	46	825	27	1396	25	2901	14	1237	24	1019	25	1528	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	71	0	.	0	.	38	.	0	.	0	.	0	.	508	30	396	32	161	51	0	.	0	.	0	.	0	.	0	.	170	67	0	.	0	.	0	.	12878	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	90	33	134	31	100	42	102	28	32	55	42	33	551	12	312	17	360	13	262	16	22	40	63	30	69	21	0	.	36	88	82	30	111	20	159	36	16	44	137	25	192	16	116	19	323	16	525	18	405	16	354	16	120	21	144	22	147	16	44	43	63	23	21	58	27	37	69	31	47	26	78	26	133	21	55	23	62	29	107	22	80	30	79	25	106	23	80	24	70	28	93	43	46	30	157	28	67	42	42	42	66	28	123	35	103	42	100	34	121	23	84	24	151	26	108	23	223	17	232	15	173	21	171	20	40	30	46	44	59	25	70	28	78	26	139	24	43	30	22	44	75	31	45	44	43	41	83	24	50	28	42	40	65	27	34	39	18	52	3	81	34	45	348	13	284	16	362	15	56	28	69	37	15	182	105	40	72	26	56	53	33	56	53	39	0	.	10631	3
	16-23 cm	329	21	296	22	225	28	338	28	153	31	275	22	1392	9	1006	12	1253	12	833	13	56	38	125	28	240	23	0	.	134	27	333	24	471	20	441	28	45	46	374	21	494	17	517	16	951	16	1270	10	1191	10	1044	12	382	20	391	22	470	15	95	37	199	24	54	33	95	28	294	33	244	27	291	23	420	23	286	25	252	28	413	18	274	29	262	21	377	20	190	28	206	27	138	36	206	21	401	20	265	22	224	27	254	22	194	28	416	31	177	30	453	17	245	20	486	20	464	21	633	17	792	14	536	18	506	19	179	30	109	35	217	23	203	25	183	28	356	22	166	32	85	43	246	26	96	40	88	36	266	24	191	30	187	27	161	24	146	41	85	49	17	50	63	39	1145	11	970	13	1246	12	181	28	140	33	171	29	255	26	229	28	181	34	145	28	210	33	0	.	33289	2
	24-35 cm	737	19	569	24	733	25	754	24	511	25	924	21	4114	9	2716	10	2644	11	2461	13	196	35	335	27	719	21	1	.	429	31	1001	22	1383	17	1032	23	134	48	1039	22	1549	15	1185	15	1456	14	3368	9	2965	9	2684	11	764	19	1056	21	1391	17	260	39	567	26	332	32	213	31	578	21	671	27	624	25	1085	19	703	23	662	24	1039	18	657	25	1087	19	1133	17	481	25	516	23	288	27	829	23	758	20	702	22	489	27	933	22	491	26	584	24	240	29	788	20	581	22	661	18	728	19	1122	16	1069	16	804	19	968	17	596	28	309	32	501	27	493	24	441	27	816	24	490	31	109	55	998	25	203	39	320	34	693	22	453	25	469	26	560	24	239	29	183	46	133	50	280	33	2336	11	2099	12	2588	11	364	28	371	35	318	26	669	22	799	20	422	27	298	33	358	27	0	.	80400	2
	36-51 cm	1746	16	730	22	945	21	1633	16	1240	23	2221	19	8154	8	5750	9	5010	9	4236	11	431	32	776	25	1189	19	60	.	715	30	1631	19	2184	16	1752	19	251	42	2228	16	3103	13	2272	13	2477	14	5704	9	5194	9	5810	9	1812	21	1818	17	2484	15	493	26	905	20	703	29	471	28	1466	19	699	24	750	23	1409	18	1440	20	1159	23	1845	16	1188	21	2422	16	1893	16	854	20	1348	20	768	24	1520	17	1268	19	1182	23	1089	21	1096	24	760	27	1029	21	338	29	979	17	1041	20	503	20	741	19	955	18	1185	14	680	20	892	19	599	25	482	36	746	26	998	25	677	25	1429	20	525	27	245	40	1121	20	579	29	421	31	1299	18	743	23	1389	20	1044	20	556	36	550	33	211	42	341	32	3617	10	2679	12	3606	11	795	23	850	27	787	23	1126	20	1436	19	665	26	736	31	615	25	0	.	139493	1
	=52 cm	1639	18	1223	23	999	22	2591	18	896	30	2579	22	9299	9	8863	10	7073	10	3114	15	682	33	809	32	1224	26	40	.	637	32	1218	25	2136	18	1941	22	94	62	2510	17	3627	15	2980	17	3761	13	6390	10	4852	15	3909	12	2138	22	2158	20	2687	18	665	30	1059	25	538	35	725	31	1980	21	562	31	786	29	1843	20	1451	24	762	27	2012	20	1858	21	3981	17	1367	21	1766	23	500	29	744	27	928	26	1952	23	1169	21	1551	27	943	28	807	37	1232	29	832	25	1839	19	1065	22	558	42	758	35	504	25	1638	16	605	27	1023	23	502	33	527	39	710	38	1121	25	1350	25	1859	22	799	34	119	72	1128	28	815	32	382	38	698	23	1043	26	912	22	1025	25	183	37	684	31	431	50	845	33	5386	11	2586	14	3531	14	922	26	1300	27	1195	25	1462	23	1121	25	753	36	654	32	1127	26	0	.	157240	2
	total	4543	14	2952	17	3002	17	5418	14	2832	22	6041	17	23510	7	18647	8	16340	8	10906	10	1387	26	2107	22	3441	18	101	.	1951	25	4265	17	6285	14	5324	16	539	39	6288	14	8964	11	7070	12	8969	10	17257	7	14606	8	13802	8	5216	16	5567	15	7180	14	1558	23	2793	18	1648	26	1531	25	4387	17	2223	21	2530	20	4890	16	3934	18	2897	19	5416	15	4057	18	7831	14	4876	14	3370	17	2641	18	2031	20	3528	16	4536	16	3385	18	3395	19	3292	20	2375	23	3364	18	1687	20	4181	14	3016	17	2359	17	2799	17	3437	12	4916	11	2797	16	3560	15	1916	23	1474	27	2233	22	2885	20	2729	19	4599	17	2024	24	580	38	3569	19	1738	27	1254	25	3039	16	2480	20	3000	17	2855	18	1159	29	1520	28	796	42	1563	27	12832	8	8618	10	11333	9	2317	21	2730	21	2487	20	3617	17	3657	17	2077	23	1866	24	2363	21	0	.	421054	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. 2293496. Birmensdorf, Swiss Federal Research Institute WSL

back