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	23-35calculated 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).

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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 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).

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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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NFI1
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (main diameter classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		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	±%
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	.
	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	.
	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	.
	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	.
	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	.
	=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	.
	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	.
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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	64	0	.	0	.	30	38	0	.	0	.	130	20
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	237	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	91	0	.	0	.	195	35	0	.	0	.	444	21
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	664	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	72	0	.	0	.	424	29	0	.	0	.	1138	17
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	953	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	52	0	.	0	.	419	28	0	.	0	.	1423	18
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1003	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	361	30	0	.	0	.	1364	24
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2951	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	120	57	0	.	0	.	1429	22	0	.	0	.	4500	14
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	.
	12-15 cm	0	.	0	.	0	.	59	26	0	.	0	.	0	.	0	.	206	18	0	.	0	.	0	.	16	100	39	69	30	59	0	.	0	.	0	.	0	.	25	33	5	100	5	63	112	26	0	.	0	.	498	12
	16-23 cm	0	.	0	.	0	.	310	23	0	.	0	.	0	.	0	.	948	16	0	.	2	.	0	.	48	86	70	56	58	47	0	.	0	.	7	100	0	.	115	35	30	43	18	66	590	20	0	.	0	.	2196	10
	24-35 cm	0	.	0	.	0	.	1198	26	0	.	0	.	0	.	9	100	2451	16	0	.	0	.	0	.	48	100	181	47	199	60	0	.	0	.	39	78	0	.	373	31	147	68	71	76	1553	23	0	.	0	.	6268	10
	36-51 cm	0	.	0	.	0	.	1526	25	0	.	0	.	0	.	166	65	4703	12	0	.	0	.	0	.	49	85	210	66	195	73	0	.	0	.	56	75	0	.	783	25	182	59	135	45	2600	16	0	.	0	.	10604	8
	=52 cm	0	.	0	.	0	.	525	29	0	.	0	.	0	.	111	59	2985	15	0	.	0	.	0	.	65	100	80	58	147	54	0	.	0	.	117	100	0	.	642	34	258	55	181	56	2273	19	0	.	0	.	7383	10
	total	0	.	0	.	0	.	3618	22	0	.	0	.	0	.	285	62	11292	11	0	.	2	.	0	.	225	71	580	46	628	47	0	.	0	.	219	74	0	.	1937	22	622	52	410	42	7128	14	0	.	0	.	26948	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	.
	12-15 cm	0	.	0	.	0	.	107	24	0	.	54	41	0	.	7	71	455	13	0	.	36	50	7	.	1	.	41	49	6	64	0	.	0	.	55	77	0	.	79	27	48	36	30	51	448	16	3	.	0	.	1375	8
	16-23 cm	0	.	0	.	0	.	380	19	0	.	206	44	0	.	34	74	1623	13	0	.	150	62	0	.	0	.	175	42	54	52	0	.	0	.	118	75	0	.	256	22	240	43	153	55	1395	16	8	.	0	.	4792	8
	24-35 cm	0	.	0	.	0	.	1515	20	0	.	485	46	0	.	96	65	5327	12	0	.	155	45	24	.	46	.	432	40	226	53	0	.	0	.	307	41	0	.	826	25	808	35	699	36	2464	13	19	.	0	.	13429	7
	36-51 cm	0	.	0	.	0	.	3117	19	0	.	410	37	0	.	98	61	8452	10	0	.	210	54	0	.	40	.	488	37	613	47	0	.	0	.	438	37	0	.	1224	21	690	31	1054	29	3379	14	81	.	0	.	20295	6
	=52 cm	0	.	0	.	0	.	1909	22	0	.	113	61	0	.	539	65	6039	13	0	.	33	73	0	.	0	.	408	36	749	40	0	.	0	.	201	52	0	.	1477	26	259	47	560	29	2402	18	0	.	0	.	14690	8
	total	0	.	0	.	0	.	7029	16	0	.	1268	36	0	.	773	57	21895	9	0	.	584	43	31	.	87	.	1544	32	1649	37	0	.	0	.	1120	36	0	.	3863	18	2045	30	2496	26	10088	11	110	.	0	.	54581	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	.
	12-15 cm	45	35	43	54	86	32	1014	10	58	36	342	19	0	.	122	33	124	21	240	23	350	18	218	24	53	44	114	43	388	19	72	42	134	26	173	26	53	54	369	17	84	42	444	14	105	37	32	60	166	25	4831	4
	16-23 cm	145	36	234	41	591	29	3564	9	168	48	1079	19	0	.	532	27	407	21	821	23	1024	17	772	17	318	35	369	26	1515	15	286	41	576	23	576	22	208	35	922	18	172	39	1421	12	280	26	42	48	608	24	16627	4
	24-35 cm	461	37	903	33	1644	28	8716	9	353	35	2827	19	0	.	1217	28	845	23	2422	19	2678	16	1928	17	519	39	1168	26	3870	14	480	38	1702	21	1714	20	385	36	993	18	460	31	4503	12	620	28	189	51	2423	18	43020	4
	36-51 cm	568	37	1124	31	2069	26	15831	8	820	34	3409	17	0	.	1619	24	1429	23	2814	17	5030	15	3070	14	610	38	1779	28	5831	13	331	46	2721	18	2911	19	668	34	1197	19	517	33	5778	11	634	28	504	41	3784	17	65049	3
	=52 cm	275	42	455	52	707	37	9358	10	344	48	1701	26	0	.	836	35	1467	22	1161	23	2450	20	2343	20	164	43	1185	31	4128	19	205	48	862	31	1939	22	364	49	900	31	568	48	4267	16	261	36	237	66	1258	22	37437	5
	total	1494	32	2760	29	5097	23	38483	7	1743	30	9358	15	0	.	4326	21	4272	18	7458	14	11531	13	8330	12	1665	33	4616	24	15732	11	1374	33	5995	16	7313	16	1678	31	4382	14	1801	30	16413	10	1900	23	1004	40	8239	14	166964	3
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	368	18	0	.	0	.	600	17	113	42	130	34	0	.	0	.	31	50	113	36	200	31	61	44	0	.	3	100	69	40	142	32	116	27	11	74	243	24	0	.	56	69	277	21	32	61	8	71	264	20	2835	7
	16-23 cm	1176	15	0	.	0	.	1804	13	444	24	437	27	0	.	0	.	93	49	318	25	470	29	118	36	0	.	20	100	241	34	371	32	424	25	50	54	639	21	0	.	112	62	1075	18	60	53	31	100	776	16	8657	5
	24-35 cm	3363	14	0	.	0	.	4451	12	1065	25	1475	23	0	.	0	.	263	50	1112	26	1979	19	353	38	0	.	96	100	781	37	863	31	1140	24	196	66	1019	22	0	.	108	67	2136	19	36	62	76	71	2504	16	23015	5
	36-51 cm	6188	14	0	.	0	.	7783	12	1117	27	2006	24	0	.	0	.	432	42	1642	23	2579	20	556	45	0	.	40	100	738	36	1008	26	2219	24	159	62	2552	20	0	.	36	62	2171	19	57	68	95	92	5011	15	36390	5
	=52 cm	3418	17	0	.	0	.	4577	15	960	36	1131	31	0	.	0	.	279	46	455	36	1259	28	83	62	0	.	0	.	445	51	418	38	697	35	0	.	1723	23	0	.	82	73	809	26	191	55	0	.	2199	19	18726	6
	total	14513	11	0	.	0	.	19215	10	3699	20	5178	20	0	.	0	.	1097	38	3640	21	6487	17	1172	35	0	.	158	100	2273	33	2801	23	4596	20	415	57	6177	16	0	.	393	50	6467	16	376	48	211	72	10754	13	89623	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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	81	52	0	.	68	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	750	13	0	.	44	67	93	31	0	.	7	85	1048	11
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	129	55	0	.	206	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1761	12	0	.	55	60	188	37	0	.	34	87	2373	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	64	57	0	.	117	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1553	14	0	.	27	59	321	29	0	.	129	97	2210	12
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	115	89	0	.	102	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	.	900	20	0	.	5	100	208	48	0	.	81	74	1480	17
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	46	100	0	.	49	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1142	29	0	.	0	.	102	53	0	.	21	100	1361	25
	total	0	.	0	.	0	.	0	.	0	.	0	.	435	51	0	.	542	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	.	6106	10	0	.	131	60	912	27	0	.	272	80	8473	9
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	.
	12-15 cm	413	17	43	54	86	32	1780	8	172	30	526	15	81	52	129	32	977	9	353	19	587	15	285	21	69	40	197	30	493	16	214	25	250	18	238	26	302	21	1230	9	194	28	799	11	821	11	43	47	436	15	10717	3
	16-23 cm	1321	14	234	41	591	29	6058	7	612	21	1721	15	129	55	566	26	3513	8	1139	18	1645	14	890	15	367	32	634	20	1867	13	657	25	1000	17	750	21	847	18	3067	8	554	25	2722	10	2708	10	81	47	1418	13	35089	2
	24-35 cm	3824	13	903	33	1644	28	15880	6	1418	21	4787	14	64	57	1322	27	9666	8	3534	15	4811	12	2305	15	613	34	1877	20	5075	13	1342	24	2842	15	2257	17	1405	19	3795	9	1522	22	7436	10	5417	10	283	40	5056	12	89081	2
	36-51 cm	6756	13	1124	31	2069	26	28257	6	1937	21	5825	13	115	89	1883	21	16072	7	4456	13	7819	12	3626	14	699	34	2516	22	7377	11	1339	23	4940	15	3564	16	3289	17	4156	10	1426	21	9141	9	7297	9	680	35	8877	11	135241	2
	=52 cm	3693	16	455	52	707	37	16370	7	1304	29	2945	19	46	100	1486	31	11822	8	1616	19	3742	16	2427	19	229	42	1674	24	5469	16	623	30	1559	23	2257	20	2087	21	4160	14	1167	29	5817	12	5590	11	237	66	3479	14	80960	3
	total	16007	10	2760	29	5097	23	68345	5	5442	16	15804	11	435	51	5385	19	42049	6	11098	11	18604	10	9533	12	1977	29	6898	18	20282	10	4175	19	10592	12	9067	14	7930	14	16408	6	4862	18	25917	8	21833	7	1324	33	19265	9	351089	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. 1234199. Birmensdorf, Swiss Federal Research Institute WSL

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