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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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

NFI3
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (main diameter classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (main diameter classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	3	83	74	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	4	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	60	38	68	3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	178	26
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	4	.	7	60	269	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	.	13	81	27	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	149	61	30	44	8	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	512	22
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	.	0	.	26	.	85	49	562	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	.	24	100	27	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	.	279	33	172	57	41	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1235	16
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	.	0	.	0	.	123	43	925	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	.	19	72	21	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	.	409	33	132	48	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1662	17
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	.	0	.	0	.	267	58	965	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	411	33	124	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1862	21
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	132	.	0	.	31	.	485	46	2795	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	72	93	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1302	27	496	42	59	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5449	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	.	60	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	27	40	4	74	17	52	62	37	82	52	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	19	100	9	76	27	93	0	.	0	.	2	215	0	.	8	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	53	14	72	24	67	0	.	0	.	0	.	16	54	1	100	0	.	0	.	2	100	11	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	0	.	72	32	47	45	28	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	548	13
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	240	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	159	43	47	56	60	37	219	31	386	27	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	0	.	66	101	22	90	61	67	0	.	0	.	12	168	0	.	20	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	54	41	39	64	0	.	0	.	0	.	34	102	10	100	0	.	0	.	18	71	40	79	0	.	4	71	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	.	0	.	0	.	0	.	0	.	200	32	227	36	125	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2098	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	834	23	23	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	321	34	172	53	379	38	686	32	853	22	0	.	0	.	0	.	10	.	0	.	0	.	0	.	0	.	0	.	81	93	58	100	210	76	0	.	0	.	104	80	0	.	4	.	0	.	0	.	0	.	0	.	0	.	52	72	0	.	0	.	0	.	0	.	0	.	0	100	268	43	51	54	20	.	0	.	0	.	94	50	14	100	0	.	10	.	39	84	0	.	54	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	.	0	.	0	.	0	.	0	.	478	30	573	31	349	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5780	9
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	1582	22	74	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	83	916	27	252	47	1092	31	1045	31	2096	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	77	100	70	158	79	0	.	12	.	183	74	0	.	11	.	0	.	0	.	0	.	0	.	0	.	80	100	0	.	0	.	0	.	0	.	0	.	136	61	471	36	176	63	6	.	0	.	0	.	202	57	36	75	0	.	0	.	132	93	35	100	33	.	72	72	35	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	.	0	.	0	.	0	.	0	.	1106	27	803	30	712	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11701	8
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	972	28	40	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	195	63	1238	36	155	56	1133	31	633	28	1561	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	100	17	100	46	100	0	.	68	.	111	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	170	88	0	.	0	.	0	.	0	.	0	.	207	61	404	39	121	54	0	.	0	.	0	.	204	51	43	71	0	.	62	.	107	82	22	100	138	.	58	60	78	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	.	0	.	0	.	0	.	0	.	2044	26	629	36	826	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11408	9
	total	0	.	0	.	0	.	0	.	0	.	0	.	3689	21	136	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	281	67	2661	26	630	46	2682	26	2645	25	4978	18	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	296	72	206	65	501	64	0	.	80	.	411	63	0	.	43	.	0	.	0	.	0	.	0	.	0	.	310	71	0	.	0	.	0	.	0	.	0	.	347	60	1211	33	412	49	26	.	0	.	0	.	550	46	105	71	0	.	71	.	298	73	108	74	224	.	146	64	114	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	135	.	0	.	0	.	0	.	0	.	3900	22	2279	26	2040	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31536	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	.	109	25	2	100	0	.	1	100	0	.	0	.	0	.	0	.	2	.	37	81	2	100	1	100	0	.	3	71	29	45	47	43	135	33	271	24	104	30	0	.	8	.	0	.	27	50	0	.	0	.	0	.	0	.	0	.	2	.	35	46	3	71	0	.	0	0	3	58	1	.	0	.	0	.	0	.	0	.	0	.	0	.	9	62	11	66	1	100	0	.	0	.	0	.	80	59	14	70	9	73	4	71	0	.	0	.	13	55	9	66	15	100	2	453	2	100	32	47	28	93	20	54	20	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	72	0	.	0	.	0	.	0	.	157	31	151	31	95	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1499	9
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	262	22	10	71	0	.	2	100	0	.	0	.	0	.	0	.	5	.	79	65	3	100	11	73	0	.	25	70	108	44	255	37	356	28	580	19	433	25	0	.	9	.	0	.	95	50	0	.	0	.	0	.	0	.	0	.	4	.	160	40	3	534	0	.	18	100	16	62	8	.	0	0	0	.	0	.	0	.	0	.	0	.	1	1158	69	68	8	72	0	.	0	.	0	.	103	40	86	48	68	44	10	100	0	.	0	.	17	52	38	72	20	76	12	59	22	79	171	39	22	71	27	43	50	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	452	27	610	26	407	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4655	7
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	844	21	60	71	0	.	17	100	0	.	0	.	0	.	0	.	27	.	283	66	18	71	58	71	0	.	122	76	523	37	491	36	470	25	1762	19	1365	23	0	.	36	.	0	.	191	69	0	.	0	.	0	.	0	.	0	.	33	.	306	42	103	51	0	.	51	100	44	75	41	.	0	0	0	.	0	.	0	.	0	.	0	.	58	85	90	84	31	100	0	.	0	.	0	.	25	77	84	81	316	46	44	74	0	.	0	.	187	54	109	69	84	77	68	81	170	92	484	41	70	57	141	61	185	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	100	0	.	0	.	0	.	0	.	1032	21	1039	26	849	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11992	7
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	1992	21	279	59	0	.	0	.	0	.	0	.	0	.	0	.	107	.	347	65	100	77	114	71	0	.	149	71	1405	35	408	34	1090	27	3278	16	2112	20	0	.	39	.	0	.	326	52	0	.	0	.	0	.	0	.	0	.	23	.	283	44	228	51	0	.	91	100	1	2130	67	.	100	100	0	.	0	.	0	.	0	.	0	.	237	55	113	97	106	77	0	.	0	.	0	.	147	73	166	63	737	35	58	88	0	.	0	.	125	56	88	73	130	66	182	58	231	90	570	42	344	79	467	54	238	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	75	0	.	0	.	0	.	0	.	1059	26	1626	22	1250	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20556	6
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	3073	22	85	100	0	.	150	94	0	.	0	.	0	.	0	.	56	.	15	138	200	74	125	76	0	.	693	59	1130	32	482	44	1874	25	3806	18	1151	27	0	.	-5	.	0	.	180	59	0	.	0	.	0	.	0	.	0	.	0	.	328	55	262	49	0	.	0	0	131	89	31	.	143	100	0	.	0	.	0	.	0	.	0	.	283	63	36	100	87	71	0	.	0	.	0	.	516	51	376	65	723	40	80	76	0	.	0	.	194	80	154	64	310	84	156	71	112	100	291	43	308	53	361	46	239	65	0	.	0	.	117	100	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	0	.	1283	30	1157	25	1629	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22395	7
	total	0	.	0	.	0	.	0	.	0	.	0	.	6281	18	436	57	0	.	171	92	0	.	0	.	0	.	0	.	196	.	762	55	323	70	309	71	0	.	992	55	3194	29	1682	29	3925	21	9697	14	5165	18	0	.	87	.	0	.	819	46	0	.	0	.	0	.	0	.	0	.	62	.	1112	41	599	47	0	.	161	100	195	66	149	.	243	100	0	.	0	.	0	.	0	.	0	.	587	47	317	73	234	71	0	.	0	.	0	.	872	38	726	50	1854	32	195	70	0	.	0	.	536	50	399	57	559	67	422	60	537	76	1547	38	772	56	1015	47	732	60	0	.	0	.	117	100	0	.	0	.	0	.	0	.	0	.	320	59	0	.	0	.	0	.	0	.	3984	19	4583	19	4230	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61096	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	70	86	0	.	0	.	12	61	15	186	64	42	279	46	245	16	75	39	291	21	0	.	12	57	37	36	0	.	6	51	33	38	98	28	32	52	0	.	111	31	36	53	34	37	10	77	49	39	0	.	224	25	100	38	53	32	171	23	15	76	96	30	14	53	36	37	67	30	42	39	7	148	70	46	37	41	20	43	83	33	30	52	45	70	32	64	44	40	39	46	18	51	17	56	65	45	63	40	30	50	45	47	0	.	16	59	0	.	22	63	0	.	70	45	41	39	33	40	83	40	79	38	27	48	40	78	28	83	13	68	25	45	10	59	18	46	26	46	0	.	71	33	2	.	21	80	65	33	30	94	46	37	15	44	33	72	19	68	3	100	4	100	0	.	5	.	76	26	17	41	30	57	4	82	48	30	62	81	0	.	0	.	1	.	0	.	4057	6
	16-23 cm	57	54	3	.	0	.	44	64	98	38	306	31	566	23	801	20	253	35	906	19	0	.	18	78	105	49	0	.	93	61	225	36	462	28	142	45	0	.	557	36	151	38	94	38	20	61	94	42	0	.	641	22	208	29	156	28	574	25	41	84	199	29	70	79	76	41	279	32	289	40	46	53	241	33	141	48	204	42	196	40	170	55	260	32	158	57	110	39	177	40	91	55	123	36	144	50	252	41	224	38	215	46	0	.	100	79	0	.	37	55	0	.	181	43	207	40	48	59	312	34	333	44	104	44	137	39	33	83	35	84	101	43	36	51	162	53	60	76	0	.	205	31	17	.	49	62	258	28	110	51	242	48	29	52	126	71	93	63	14	71	27	100	0	.	4	.	272	27	170	48	73	45	6	100	170	80	105	53	0	.	73	.	8	.	0	.	14220	5
	24-35 cm	66	58	6	.	0	.	85	57	612	35	1147	28	1687	20	2399	17	904	28	2774	17	10	100	89	73	264	46	0	.	207	66	413	33	1024	27	400	39	0	.	1370	36	367	34	298	30	14	100	274	37	0	.	1628	18	417	31	636	36	1634	24	201	54	669	34	140	51	302	48	519	28	787	37	96	64	696	31	666	30	452	39	804	27	427	37	1312	24	627	38	353	46	506	36	57	68	503	36	243	45	609	32	341	42	687	42	0	.	120	61	0	.	206	59	0	.	162	41	307	46	198	50	360	38	333	44	172	47	394	42	119	88	40	88	184	47	122	50	439	43	267	51	0	.	682	28	67	.	99	60	695	27	213	40	349	36	103	66	109	72	190	62	116	66	27	100	0	.	10	.	668	27	211	47	284	44	71	61	821	28	546	34	0	.	22	.	11	.	0	.	38435	4
	36-51 cm	296	56	15	.	0	.	449	43	1276	29	2231	23	3048	17	4436	14	2072	23	4207	15	137	100	140	87	512	44	0	.	190	63	809	32	1596	25	1049	37	0	.	2219	25	615	35	926	28	95	63	182	46	0	.	3621	17	743	32	1202	28	2908	20	339	47	992	27	342	48	348	42	1287	25	536	34	95	49	1144	29	1724	28	727	41	1141	26	975	38	2263	25	536	41	492	39	601	32	356	52	905	32	818	31	1240	28	824	30	576	38	0	.	196	59	0	.	151	49	0	.	87	67	213	39	124	51	424	38	329	44	230	46	279	44	99	55	197	62	413	50	117	52	481	40	341	52	87	.	1069	25	70	.	307	56	910	22	475	37	811	29	253	44	349	75	348	49	311	54	101	74	0	.	0	.	961	27	401	45	491	37	261	54	1351	26	1146	29	0	.	219	.	80	.	0	.	66916	3
	=52 cm	165	63	54	.	0	.	394	46	1113	32	1606	27	2683	22	6479	14	2549	24	2029	18	0	.	70	100	470	49	0	.	24	100	826	39	1202	32	677	39	0	.	1467	27	394	47	1269	30	179	60	413	44	0	.	2423	21	1156	43	1439	31	2261	27	300	52	849	31	379	53	587	49	1424	27	393	44	70	71	1160	35	1010	37	377	55	1211	33	1405	35	3041	27	207	50	665	42	341	47	246	52	428	62	984	38	1274	31	1116	42	420	49	0	.	339	66	0	.	479	57	0	.	185	51	194	60	64	100	291	44	89	70	220	46	331	56	192	66	263	85	600	48	336	64	484	47	748	51	0	.	850	39	45	.	345	59	547	32	577	49	458	40	571	48	100	100	539	64	377	74	351	80	0	.	0	.	504	34	308	54	788	46	401	56	1173	29	633	39	0	.	20	.	49	.	0	.	62681	4
	total	654	48	78	.	0	.	985	43	3114	28	5355	22	8263	16	14360	12	5854	20	10208	12	147	100	329	57	1388	40	0	.	519	55	2305	30	4382	21	2300	31	0	.	5724	24	1563	29	2621	25	317	56	1012	37	0	.	8537	15	2625	28	3486	26	7549	19	896	42	2805	24	946	43	1349	36	3575	22	2049	30	313	53	3312	28	3578	25	1779	35	3435	23	3007	29	6922	21	1559	35	1664	32	1663	28	767	45	1976	33	2253	29	3438	25	2534	30	1943	32	0	.	770	55	0	.	895	46	0	.	685	36	962	35	467	39	1470	28	1163	34	753	36	1182	36	471	53	548	60	1324	36	621	52	1585	36	1441	44	87	.	2877	23	201	.	821	48	2476	20	1405	35	1906	27	971	40	718	56	1189	49	821	55	508	74	0	.	19	.	2481	24	1107	37	1666	36	742	48	3563	23	2492	27	0	.	334	.	149	.	0	.	186309	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	66	33	64	42	58	72	86	51	0	.	0	.	12	42	55	32	246	19	21	.	24	49	34	53	37	35	0	.	9	55	69	56	0	.	32	53	0	.	0	.	20	64	-11	177	0	.	0	.	0	.	84	30	39	64	91	36	-15	113	10	137	0	.	15	62	2	.	0	.	0	.	0	.	21	88	13	100	49	63	19	92	21	74	0	.	133	33	47	55	6	84	14	50	25	46	20	93	0	.	0	0	65	41	47	32	103	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	76	0	.	4	87	21	64	36	58	0	.	17	67	20	72	1	100	23	74	22	79	0	.	26	68	16	62	31	58	31	71	4	71	0	.	1	100	0	.	0	.	32	50	11	76	21	68	48	63	2	100	24	65	37	53	11	45	67	36	0	.	2149	7
	16-23 cm	266	35	302	38	211	35	230	41	0	.	0	.	93	56	199	37	1024	18	116	.	104	55	72	46	97	62	0	.	32	50	132	40	0	.	150	41	0	.	0	.	41	74	31	80	0	.	0	.	0	.	247	31	123	39	334	34	28	72	23	108	0	.	43	62	16	.	0	.	0	.	0	.	26	73	51	64	150	55	26	87	95	68	0	.	420	33	47	62	51	59	76	46	108	44	16	72	0	.	8	100	191	38	284	38	338	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	85	86	0	.	5	222	100	69	32	72	0	.	14	245	53	74	0	.	30	56	33	58	0	.	108	62	60	60	77	68	150	54	43	60	0	.	17	71	0	.	0	.	79	60	35	71	57	53	117	52	101	81	70	37	219	41	129	48	148	37	0	.	7565	6
	24-35 cm	692	38	374	35	413	35	574	31	0	.	0	.	238	55	572	30	2401	17	21	.	203	54	384	44	405	38	55	.	160	52	414	42	0	.	416	43	0	.	0	.	47	84	77	73	0	.	0	.	0	.	798	30	312	41	780	32	110	81	67	64	0	.	120	55	0	.	0	.	0	.	0	.	64	100	122	59	330	54	9	288	164	52	0	.	756	32	141	41	136	51	270	42	397	48	33	72	0	.	72	76	298	51	692	31	577	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	167	75	0	.	103	72	149	57	140	67	0	.	180	61	103	54	97	78	111	68	81	89	0	.	134	57	215	49	218	56	176	47	147	79	0	.	92	68	0	.	0	.	76	63	27	100	93	63	356	46	129	83	273	42	208	43	405	46	266	40	0	.	17638	5
	36-51 cm	1327	30	1019	32	579	39	1586	24	0	.	0	.	761	45	800	33	4438	13	0	.	234	46	543	42	642	43	67	.	440	56	557	43	0	.	738	33	0	.	0	.	199	76	124	65	0	.	0	.	0	.	1596	24	1116	51	1391	28	73	71	157	58	0	.	277	50	17	.	0	.	0	.	0	.	41	100	133	75	441	53	55	82	162	71	0	.	1372	26	514	41	312	55	513	40	345	45	60	100	0	.	157	72	341	42	899	31	738	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	76	0	.	83	71	209	57	145	57	0	.	179	65	74	69	64	82	360	64	174	78	0	.	42	114	246	61	442	43	401	47	178	75	0	.	165	63	0	.	0	.	50	87	142	87	238	57	635	33	6	166	579	36	697	33	669	37	578	32	0	.	31157	5
	=52 cm	1285	30	806	31	607	38	2396	25	0	.	0	.	570	45	1155	37	5206	15	0	.	585	39	290	77	356	49	31	.	250	68	255	58	0	.	598	37	0	.	0	.	44	100	141	68	0	.	0	.	0	.	1042	25	639	36	1170	35	203	81	111	52	0	.	208	76	0	.	0	.	0	.	0	.	0	.	129	100	114	72	53	100	386	59	0	.	871	31	941	47	29	83	277	50	79	61	31	100	0	.	110	80	153	58	1230	38	1133	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	100	0	.	16	100	116	59	237	82	0	.	204	77	14	100	77	103	161	74	0	.	0	.	146	75	101	79	144	59	73	62	193	93	0	.	248	67	0	.	0	.	15	100	86	100	406	48	388	37	0	.	546	42	1020	38	490	39	700	37	0	.	28943	6
	total	3636	26	2566	23	1867	28	4872	21	0	.	0	.	1673	43	2782	28	13315	12	157	.	1149	35	1324	37	1537	36	153	.	891	48	1426	35	0	.	1933	30	0	.	0	.	351	59	362	55	0	.	0	.	0	.	3766	22	2231	36	3766	25	399	71	368	48	0	.	664	50	35	.	0	.	0	.	0	.	152	84	448	66	1084	43	162	80	829	53	0	.	3552	21	1690	39	535	47	1150	36	954	39	158	76	0	.	347	75	1048	37	3151	27	2888	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	376	71	0	.	212	71	595	50	589	55	0	.	594	60	265	52	239	65	685	54	311	71	0	.	456	50	638	49	913	40	832	42	565	68	0	.	524	63	0	.	0	.	253	47	302	79	815	43	1544	30	238	79	1492	32	2181	29	1704	34	1759	29	0	.	87452	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	.	32	77	0	.	0	.	0	.	0	.	94	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	85	.	10	.	-25	128	14	63	95	48	66	53	260	30	62	42	43	61	152	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	50	42	32	46	15	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	992	16
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	92	53	0	.	0	.	0	.	0	.	130	44	21	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	.	11	.	18	.	99	58	71	58	220	43	209	41	639	21	140	42	158	62	380	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	77	0	.	0	.	53	.	0	.	0	.	0	.	118	41	81	52	39	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2493	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	371	48	0	.	0	.	0	.	0	.	313	46	18	74	0	.	0	.	0	.	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	.	22	.	88	49	105	61	200	44	435	32	748	26	124	43	285	48	496	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	95	72	0	.	0	.	20	.	0	.	0	.	0	.	144	52	158	52	36	83	0	.	0	.	0	.	0	.	0	.	46	100	0	.	0	.	0	.	3720	10
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	190	71	0	.	0	.	0	.	0	.	155	43	39	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	15	.	83	45	54	83	23	119	284	36	822	33	65	66	95	58	337	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	201	49	92	52	27	210	0	.	0	.	0	.	0	.	0	.	78	77	0	.	0	.	0	.	2593	14
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	100	0	.	0	.	0	.	0	.	116	55	45	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	.	26	.	171	80	49	100	86	66	173	41	269	39	296	68	47	71	411	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	82	33	100	47	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1942	19
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	760	48	0	.	0	.	0	.	0	.	808	38	123	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	.	147	.	92	.	416	49	294	64	625	38	1166	30	2738	19	686	39	628	46	1776	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	126	73	0	.	0	.	80	.	0	.	0	.	0	.	592	40	397	49	164	65	0	.	0	.	0	.	0	.	0	.	123	84	0	.	0	.	0	.	11741	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	136	47	64	42	58	72	98	46	15	186	64	42	460	28	302	14	321	17	313	21	24	49	46	42	74	25	0	.	17	37	138	36	100	27	66	36	32	77	120	29	111	25	74	41	163	28	480	20	261	22	308	20	148	32	144	26	186	24	26	72	96	30	29	41	38	36	67	30	63	39	51	39	121	37	50	40	69	47	107	30	53	42	53	61	165	29	91	34	45	41	32	36	42	35	93	38	74	36	31	49	110	31	47	32	204	48	95	51	25	150	52	38	169	33	107	36	293	27	174	25	133	30	194	29	52	64	32	73	59	34	74	42	66	37	40	44	43	38	20	72	72	33	25	68	43	56	65	33	56	59	62	32	53	37	71	47	23	57	3	100	5	77	332	20	273	22	249	16	28	39	52	44	52	58	50	29	87	61	37	53	11	45	68	35	0	.	9424	4
	16-23 cm	323	30	304	38	211	35	274	36	98	38	306	31	1161	14	1010	17	1277	16	1024	20	104	55	89	40	202	39	0	.	130	46	436	25	465	28	302	30	92	53	609	33	465	23	427	25	440	23	1030	14	1109	15	887	18	341	23	489	25	705	22	64	66	199	29	113	54	92	38	279	32	359	37	229	31	331	28	192	39	372	32	250	33	273	41	280	31	578	29	157	33	228	34	167	36	231	28	167	44	321	35	240	35	407	30	284	38	449	32	121	37	290	27	206	29	411	30	416	29	687	20	503	25	539	33	504	25	234	39	72	48	250	32	223	37	100	32	212	44	75	77	53	74	205	31	47	51	82	44	258	28	232	38	301	40	138	43	329	40	136	47	14	71	44	67	920	19	952	19	931	16	205	41	130	34	122	50	271	58	175	35	219	41	202	47	156	36	0	.	31543	3
	24-35 cm	759	35	380	35	413	35	659	28	612	35	1147	28	3603	12	3054	15	3305	14	2812	17	213	51	474	38	668	29	55	.	394	41	1110	26	1043	26	874	27	371	48	1491	34	1276	20	1038	22	890	21	3120	14	2797	14	2425	15	765	24	1416	24	1944	22	268	43	669	34	260	37	302	48	519	28	902	33	460	33	1072	26	788	27	833	30	961	24	633	29	1316	24	1383	24	494	35	642	30	327	36	900	29	385	33	699	30	443	35	985	33	692	31	713	36	48	62	671	27	499	33	427	27	742	27	946	23	765	24	740	28	752	26	639	33	328	58	627	34	456	30	414	34	623	40	446	39	103	54	779	26	178	57	180	52	695	27	443	30	564	29	434	38	304	38	337	49	116	66	119	57	1933	15	1952	18	2020	16	238	43	377	36	427	40	950	27	818	26	254	40	427	43	277	39	0	.	78801	2
	36-51 cm	1623	26	1034	31	579	39	2035	21	1276	29	2231	23	7383	11	5589	12	6511	11	4207	15	371	47	683	38	1154	31	67	.	737	40	1712	24	1696	24	1900	24	190	71	2420	23	3162	19	1710	19	2277	20	4782	13	5171	12	5216	13	1898	32	2593	20	3308	19	496	37	992	27	619	34	365	41	1287	25	596	31	478	31	1571	24	1857	26	1270	31	1380	24	1204	32	2374	24	1907	21	1006	28	913	28	869	32	1250	26	1195	25	1353	27	1087	26	916	28	899	31	947	26	298	46	890	24	988	29	174	47	497	26	946	30	816	26	549	31	697	27	500	33	462	53	885	31	999	36	801	34	754	32	519	41	161	62	1133	24	430	56	481	45	910	22	533	34	1057	26	895	28	750	43	526	41	311	54	266	48	2776	16	2654	16	3006	15	543	40	728	31	896	28	1357	26	1724	22	774	31	888	37	658	31	0	.	134585	2
	=52 cm	1450	27	860	30	607	38	2791	23	1113	32	1606	27	7299	13	7759	13	7755	13	2180	18	585	39	360	65	826	35	31	.	330	55	1097	32	1402	29	1400	25	74	100	2355	25	2887	21	2047	22	3185	19	5235	14	3722	16	3466	16	1790	30	2609	23	2644	24	411	40	849	31	588	43	587	49	1424	27	487	40	415	45	1469	29	1140	35	559	41	1506	28	1823	30	3184	26	1078	27	1606	32	370	44	522	36	507	53	1468	30	1310	30	1313	36	573	39	1230	38	1493	28	749	39	1430	29	907	33	351	36	366	37	333	37	984	31	333	38	941	36	625	38	411	46	592	44	1162	31	991	33	801	36	952	43	14	100	1043	34	206	62	345	59	547	32	723	42	559	36	819	36	174	63	733	53	377	74	599	54	3816	17	1943	19	3021	18	394	48	1194	34	789	34	1173	29	1180	29	1020	38	510	37	750	35	0	.	129231	2
	total	4290	23	2644	23	1867	28	5856	19	3114	28	5355	22	19906	10	17714	11	19169	10	10536	12	1296	33	1653	32	2924	27	153	.	1607	34	4493	21	4705	20	4542	20	760	48	6997	22	7901	16	5296	17	6955	15	14647	11	13060	10	12303	12	4942	22	7252	18	8786	17	1264	32	2805	24	1609	32	1384	35	3575	22	2407	27	1632	31	4564	23	4027	24	3103	26	4204	21	3985	24	7207	20	5111	18	3354	25	2199	24	1917	28	2930	25	3309	23	3756	24	3114	26	2991	25	3151	27	3806	22	1310	30	3305	21	2652	25	1531	24	2129	23	3206	17	3243	19	2294	23	3088	21	2051	28	1306	40	2414	28	2914	26	2372	28	2430	30	2035	36	351	46	3233	22	887	48	1132	40	2476	20	1987	28	2544	23	2339	25	1629	33	1754	40	821	55	1032	48	9777	12	7774	14	9227	13	1409	34	2481	28	2286	26	3801	22	3984	20	2304	28	2038	33	1908	27	0	.	383583	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. 2293309. Birmensdorf, Swiss Federal Research Institute WSL

back