Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI2
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · ownership (2 categories)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	ownership (2 categories)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	.
	public	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.
	private	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.
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	.
	public	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	78	0	.	6	.	530	31	2585	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	46	64	61	0	.	0	.	0	.	78	72	0	.	0	.	0	.	0	.	28	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1140	20	253	48	52	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4939	10
	private	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	98	38	.	0	.	15	.	59	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	171	59	219	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	535	28
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	62	38	.	6	.	545	30	2644	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	46	64	61	0	.	0	.	0	.	78	72	0	.	0	.	0	.	0	.	28	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1311	19	472	31	52	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5473	9
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	public	0	.	0	.	0	.	0	.	0	.	0	.	1512	28	85	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	503	48	1166	28	698	31	2330	20	3332	16	4600	13	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	79	203	52	383	41	0	.	77	71	376	46	0	.	1	.	0	.	0	.	0	.	0	.	0	.	111	78	0	.	0	.	0	.	0	.	0	.	412	35	761	27	277	49	19	73	0	.	0	.	378	35	108	52	29	.	0	.	78	74	44	79	238	66	144	51	145	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	.	0	.	0	.	0	.	0	.	3018	17	2384	21	2092	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25640	5
	private	0	.	0	.	0	.	0	.	0	.	0	.	1876	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	797	32	136	55	272	53	182	60	0	.	0	.	0	.	0	.	39	97	0	.	0	.	0	.	0	.	0	.	92	82	0	.	0	.	0	.	0	.	67	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	.	0	.	0	.	0	.	0	.	0	.	0	.	48	.	108	.	0	.	0	.	18	141	165	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	428	43	239	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4616	13
	total	0	.	0	.	0	.	0	.	0	.	0	.	3388	17	85	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	503	48	1963	21	834	27	2602	18	3514	15	4600	13	0	.	0	.	0	.	39	97	0	.	0	.	0	.	0	.	0	.	164	58	203	52	383	41	0	.	77	71	443	40	0	.	1	.	0	.	0	.	0	.	0	.	0	.	182	62	0	.	0	.	0	.	0	.	0	.	412	35	761	27	352	44	19	73	0	.	0	.	378	35	108	52	29	.	48	.	186	66	44	79	238	66	162	48	309	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	.	0	.	0	.	0	.	0	.	3446	16	2623	19	2092	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30256	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	.
	public	0	.	0	.	0	.	0	.	9	.	0	.	2721	26	410	47	0	.	74	.	0	.	0	.	0	.	0	.	113	.	214	61	622	50	167	81	0	.	535	48	2432	22	2369	22	2983	19	8085	11	3815	15	0	.	145	74	0	.	257	51	0	.	0	.	0	.	0	.	0	.	160	63	1035	30	657	35	0	.	197	71	466	41	164	69	30	.	0	.	0	.	0	.	0	.	0	.	240	48	805	32	75	.	0	.	0	.	0	.	832	30	903	28	1354	25	98	60	0	.	0	.	616	30	186	57	434	52	287	55	357	55	1332	29	359	61	685	39	214	67	0	.	0	.	171	53	0	.	0	.	0	.	57	.	86	.	238	50	0	.	0	.	0	.	0	.	4034	15	2740	16	3011	17	74	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46849	4
	private	0	.	0	.	0	.	0	.	0	.	0	.	4416	17	163	71	0	.	7	.	0	.	0	.	0	.	0	.	102	.	284	65	345	45	57	.	0	.	0	.	872	34	275	59	1010	30	876	32	520	35	0	.	0	.	0	.	546	39	0	.	0	.	0	.	0	.	0	.	72	90	124	.	0	.	0	.	0	.	58	88	0	.	129	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	71	0	.	0	.	0	.	16	72	0	.	101	78	0	.	0	.	0	.	33	.	0	.	0	.	0	.	209	95	107	.	143	73	166	62	627	42	0	.	0	.	0	.	0	.	0	.	79	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1303	27	505	35	644	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13870	8
	total	0	.	0	.	0	.	0	.	9	.	0	.	7137	14	573	39	0	.	82	91	0	.	0	.	0	.	0	.	215	71	498	45	967	36	224	66	0	.	535	48	3303	19	2643	21	3992	16	8960	10	4334	14	0	.	145	74	0	.	803	31	0	.	0	.	0	.	0	.	0	.	232	52	1159	29	657	35	0	.	197	71	525	38	164	69	159	77	0	.	0	.	0	.	0	.	0	.	240	48	805	32	156	61	0	.	0	.	0	.	848	29	903	28	1455	24	98	60	0	.	0	.	649	29	186	57	434	52	287	55	566	50	1439	28	502	48	851	34	840	35	0	.	0	.	171	53	0	.	0	.	79	74	57	.	86	.	238	50	0	.	0	.	0	.	0	.	5337	13	3245	15	3654	15	74	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60719	4
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	public	483	37	104	71	173	54	1206	30	828	34	1455	31	4256	17	4945	15	1456	25	7042	12	206	81	442	48	1415	28	0	.	606	46	653	35	3875	19	1976	26	0	.	3237	20	1057	25	2029	21	206	52	781	31	0	.	6639	12	1095	35	1042	36	1168	33	786	36	595	33	748	35	1002	30	1499	24	867	35	1150	31	1932	24	1246	32	1274	34	3141	19	1285	29	1765	30	949	32	1123	26	1595	23	577	41	1357	25	1753	23	1939	27	1875	26	323	57	0	.	59	98	0	.	596	41	0	.	978	33	489	31	225	37	1299	20	1003	27	1123	25	708	38	236	68	216	71	553	36	274	63	941	31	1452	31	96	.	1556	25	226	73	1203	29	1656	19	1117	30	1956	22	903	29	461	46	866	43	697	40	1064	41	0	.	25	.	1508	22	1317	32	609	41	309	58	514	42	393	43	0	.	145	.	26	.	0	.	103955	3
	private	491	51	0	.	0	.	119	.	1398	29	3742	20	6476	15	12365	10	4300	18	3023	20	0	.	62	98	421	56	0	.	100	.	1511	31	1573	26	977	36	0	.	932	36	367	53	184	62	38	.	122	58	0	.	3333	19	2110	27	2577	22	5308	16	180	58	1837	22	415	63	167	58	2335	23	906	34	0	.	901	50	1938	27	645	48	707	40	985	33	5977	17	64	.	142	92	241	46	97	.	789	35	1241	36	1468	31	459	49	1280	28	0	.	298	63	0	.	206	61	0	.	22	89	534	38	127	54	124	72	125	69	39	.	282	50	152	61	69	90	520	46	320	54	1268	32	346	72	209	.	764	32	113	.	0	.	530	35	339	54	0	.	53	.	0	.	155	.	0	.	0	.	38	.	1	.	896	28	640	40	621	50	353	52	2960	20	1459	26	0	.	0	.	110	.	0	.	87979	3
	total	974	31	104	71	173	54	1324	29	2226	22	5197	17	10731	11	17310	8	5756	15	10064	10	206	81	504	44	1835	25	0	.	707	42	2164	24	5448	15	2953	21	0	.	4169	17	1424	23	2213	20	244	47	902	28	0	.	9972	10	3205	21	3618	18	6475	15	967	31	2433	18	1164	32	1169	27	3833	17	1773	24	1150	31	2833	23	3185	21	1919	28	3848	17	2270	22	7743	15	1013	30	1265	25	1836	21	674	38	2147	20	2994	20	3407	20	2333	23	1602	25	0	.	357	55	0	.	802	34	0	.	1000	32	1023	24	353	31	1423	19	1129	25	1162	25	990	31	388	47	285	58	1073	29	594	41	2208	23	1798	29	306	75	2321	19	339	59	1203	29	2187	16	1456	26	1956	22	957	28	461	46	1022	39	697	40	1064	41	38	.	26	95	2404	17	1957	25	1231	33	661	39	3474	18	1852	22	0	.	145	100	136	83	0	.	191934	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	.
	public	3142	17	2184	19	2237	20	3587	18	0	.	0	.	949	44	803	39	8388	11	207	53	1229	29	1071	28	894	27	53	.	1362	33	765	34	254	.	665	39	0	.	30	.	172	63	389	48	0	.	0	.	0	.	2774	19	837	35	198	54	0	.	297	44	0	.	478	43	57	.	0	.	153	71	103	.	185	73	323	62	498	55	340	58	548	53	0	.	2599	19	1713	28	677	35	1666	25	892	31	152	71	0	.	236	75	795	40	904	37	1626	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	.	0	.	103	74	277	50	169	54	50	.	473	50	129	69	205	59	915	32	349	46	0	.	390	46	345	55	873	32	256	53	200	70	0	.	619	38	0	.	0	.	311	36	278	62	687	34	1510	28	82	74	1394	29	1012	30	1174	31	1400	28	0	.	59686	4
	private	1083	29	1285	32	349	51	1254	35	0	.	0	.	306	58	1534	31	6281	14	0	.	475	48	418	52	568	51	0	.	493	51	690	45	0	.	1262	33	0	.	0	.	153	93	0	.	0	.	0	.	0	.	912	34	1551	33	2448	24	387	54	97	71	0	.	0	.	0	.	0	.	0	.	31	.	142	100	1161	33	292	64	46	.	411	58	51	.	443	42	672	51	0	.	100	.	522	46	6	.	0	.	92	.	1315	33	1426	30	1074	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	133	75	163	.	74	.	119	71	155	.	0	.	467	57	321	57	0	.	578	49	49	.	0	.	62	.	409	45	387	42	261	49	364	53	0	.	926	44	0	.	0	.	190	56	150	74	284	62	1071	35	451	51	779	45	954	33	886	36	655	45	0	.	39218	5
	total	4225	14	3470	17	2587	18	4841	16	0	.	0	.	1255	36	2337	24	14669	9	207	53	1704	25	1489	25	1463	26	53	.	1855	28	1455	28	254	.	1927	25	0	.	30	.	325	55	389	48	0	.	0	.	0	.	3686	16	2388	24	2646	22	387	54	395	37	0	.	478	43	57	.	0	.	153	71	134	80	327	60	1484	29	791	42	386	52	959	39	51	.	3042	17	2385	25	677	35	1765	25	1413	26	158	69	0	.	327	61	2110	25	2330	23	2700	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	189	61	163	.	178	60	396	41	324	55	50	.	940	38	449	45	205	59	1493	27	398	42	0	.	452	42	754	35	1260	26	516	36	564	42	0	.	1545	30	0	.	0	.	501	31	427	48	971	30	2581	22	534	45	2173	24	1965	22	2060	24	2054	24	0	.	98904	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	.
	public	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	195	59	0	.	18	.	0	.	37	.	379	37	15	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	86	296	43	180	64	192	48	72	58	642	29	390	43	281	38	216	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	.	0	.	0	.	75	71	0	.	0	.	33	.	0	.	0	.	0	.	310	37	167	47	167	47	0	.	0	.	0	.	0	.	0	.	165	76	0	.	0	.	0	.	3944	11
	private	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	365	45	0	.	0	.	0	.	22	.	447	40	48	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	.	0	.	110	78	0	.	309	39	1020	27	1779	16	523	41	268	45	1198	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	202	50	103	48	0	.	0	.	0	.	0	.	0	.	0	.	95	.	0	.	0	.	0	.	6534	9
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	560	36	0	.	18	.	0	.	59	73	826	27	63	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	.	46	.	39	86	406	38	180	64	501	30	1092	25	2420	14	913	30	549	29	1414	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	.	0	.	0	.	75	71	0	.	0	.	33	.	0	.	0	.	0	.	513	30	270	34	167	47	0	.	0	.	0	.	0	.	0	.	259	61	0	.	0	.	0	.	10479	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	.
	public	3625	15	2289	19	2411	19	4793	15	837	34	1455	31	9437	12	6243	13	9844	10	7323	11	1435	27	1512	24	2309	20	53	.	2081	26	1632	23	4750	18	2808	21	195	59	4305	17	4921	14	5484	13	5562	13	13127	8	11014	8	9413	10	2077	24	1240	31	1425	28	1083	28	595	33	1227	27	1059	29	1499	24	1251	27	2491	20	3171	17	1569	29	2047	26	4323	15	1997	24	1796	29	3548	16	2835	20	2273	19	2243	22	2249	19	2256	20	2744	21	2186	24	1118	33	904	37	1685	25	1283	23	2669	16	1874	20	1287	26	561	28	867	23	2761	14	1578	20	1801	21	1051	30	670	39	1724	25	1428	25	1272	27	1349	26	1925	26	225	58	1932	21	1216	28	1552	25	1656	19	1639	24	2386	20	2080	20	751	34	1066	37	697	40	1683	29	8503	10	5569	12	7141	11	1669	28	1297	27	1819	25	597	37	1786	24	1176	28	1320	30	1426	28	0	.	245033	2
	private	1574	25	1285	32	349	51	1372	33	1398	29	3742	20	13074	10	14062	10	10582	11	3030	20	475	48	481	47	989	38	0	.	695	41	2484	24	1921	23	2296	24	365	45	932	36	2221	20	632	34	1341	26	1642	22	627	31	4245	16	3661	21	5024	16	6279	15	278	45	1837	22	415	63	167	58	2335	23	1071	31	155	82	1043	45	3099	21	937	39	879	34	1397	29	6157	17	507	39	814	45	241	46	197	71	1311	28	1319	34	1468	31	631	39	2595	22	1426	30	1418	27	16	72	316	48	175	62	331	37	1554	22	1906	16	680	35	393	37	1237	24	463	39	633	46	251	58	782	35	660	39	2059	25	813	45	530	53	764	32	691	44	49	.	609	32	402	48	409	45	441	39	261	49	519	48	0	.	926	44	2142	20	1068	23	1730	21	790	35	905	39	1423	29	3411	19	2238	23	1048	31	886	36	765	41	0	.	152752	2
	total	5199	13	3574	17	2760	18	6165	14	2235	22	5197	17	22512	8	20305	8	20425	7	10353	10	1910	24	1993	22	3298	18	53	.	2776	22	4116	17	6672	14	5104	16	560	36	5236	15	7141	11	6117	12	6903	12	14768	8	11641	8	13658	8	5738	16	6264	14	7704	13	1361	24	2433	18	1642	26	1226	26	3833	17	2321	20	2646	19	4214	17	4668	17	2984	21	5202	14	3393	19	7953	14	4055	15	3649	18	2513	18	2440	21	3560	16	3575	18	4213	17	2817	20	3713	18	2330	23	3103	18	1299	22	2985	15	2050	19	1617	22	2115	17	2773	13	3441	13	1972	17	3038	16	1514	24	1303	30	1975	23	2209	20	1931	22	3408	18	2738	23	755	41	2697	18	1907	24	1601	24	2266	16	2041	21	2795	18	2520	17	1011	28	1586	29	697	40	2609	24	10645	9	6637	11	8871	9	2459	22	2202	22	3242	19	4008	17	4025	17	2225	21	2205	23	2191	23	0	.	397785	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. 2296315. Birmensdorf, Swiss Federal Research Institute WSL

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