Selection from the list of topics

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growing stock (stemwood) #21
Stemwood volume of living trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm with bark.

altitudinal vegetation belts (NaiS; 6 classes) #2633
Altitudinal vegetation belts in the system used in the guidelines for monitoring the sustainability and performance of protection forests (NaiS; Frehner et al. 2005), reduced to six classes. The variable represents a simplification of the NaiS altitudinal vegetation belts with ten classes (NAISHSTKOMB) in which the classes «hyperinsubric» and «colline» are merged with «colline with beech» to form the class «hyperinsubric and colline» and the class «lower montane» with «upper montane» and «lower/upper montane» to form the class «lower and upper montane». The information is based on the altitudinal vegetation belts determined by experts (accessible forest sample plots of NFI4 on the 1.4-km network; Arge Frehner et al. 2020), as well as on the altitudinal vegetation belts modelled for the period 1981-2010 (other sample plots; Zischg et al. 2021).

main tree species #90
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh) with the ten most common species or species groups in Switzerland ('main tree species') categorised, and the classes 'other conifers' and 'other broadleaves' for the remaining species. The main tree species are: spruce (Picea spp.), fir (Abies spp.), pine (Pinus sylvestris, P. nigra, P. strobus, P. mugo subsp. uncinata), larch (Larix spp.), Arolla pine (Pinus cembra), beech (Fagus sylvatica), maple (Acer spp.), ash (Fraxinus spp.), oak (Quercus spp.) and chestnut (Castanea sativa). Reference: Field Survey (MID 50: Baumart)

canton #827
Regional demarcation with the cantons as a unit, with the two half-cantons, Basel-Land and Basel-Stadt, combined into one canton for statistical reasons.

accessible forest without shrub forest NFI4/NFI5 #2282
Forest that was less than two-thirds covered with shrubs in both NFI4 (2009-2017) and NFI5 (2018-2026) and is accessible on foot.

1.4 km grid, subgrids 1-5 #1746
Sub-grids 1, 2, 3, 4 and 5 of the field surveys on the sampling grid with a mesh size of 1.4 km (base grid).

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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NFI4—NFI5
change: growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
change 2009/17–2018/26
		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)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	fir	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	larch	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	ash	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
upper subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	95	37	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	69	100	.	.	.	.	31	51	.	.	.	.	195	40
	fir	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	10	470	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	310	.	.	.	.	7	108	.	.	.	.	20	244
	larch	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	62	379	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	102	83	.	.	.	.	98	83	.	.	.	.	261	101
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	236	33	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	100	.	.	.	.	110	44	.	.	.	.	348	26
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-2	97	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-2	97
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	ash	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	4	74	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	2573	.	.	.	.	4	95	.	.	.	.	8	66
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	405	65	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	177	77	.	.	.	.	249	42	.	.	.	.	831	38
subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	317	79	.	.	37	.	.	.	123	53	661	82	.	.	-8	.	.	.	29	124	368	68	147	70	.	.	.	.	0	2727	.	.	270	47	123	59	45	165	522	46	.	.	.	.	2634	27
	fir	.	.	.	.	.	.	22	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-3	100	.	.	.	.	19	118
	pine	.	.	.	.	.	.	16	64	.	.	.	.	.	.	.	.	-9	200	.	.	.	.	.	.	4	100	54	70	.	.	.	.	.	.	3	79	.	.	.	.	.	.	.	.	4	200	.	.	.	.	73	61
	larch	.	.	.	.	.	.	18	100	.	.	.	.	.	.	.	.	331	51	.	.	.	.	.	.	.	.	.	.	7	90	.	.	.	.	.	.	.	.	325	32	.	.	14	100	286	59	.	.	.	.	980	27
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	5653	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-20	138	.	.	.	.	-19	350
	other conifers	.	.	.	.	.	.	-3	106	.	.	.	.	.	.	.	.	-2	131	.	.	.	.	.	.	.	.	1	100	.	.	.	.	.	.	5	100	.	.	.	.	.	.	.	.	0	173	.	.	.	.	1	547
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	4	100	.	.	.	.	.	.	.	.	.	.	4	100
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	100	10	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	18	88	.	.	-4	108	2	100	.	.	.	.	29	68
	ash	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other broadleaves	.	.	.	.	.	.	6	153	.	.	.	.	.	.	.	.	10	70	.	.	.	.	.	.	1	100	-1	100	-1	141	.	.	.	.	.	.	.	.	2	144	1	101	0	1085	25	107	.	.	.	.	45	67
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	.	.	0	0
	total	.	.	.	.	.	.	377	67	.	.	37	.	.	.	126	52	1001	61	.	.	-8	.	.	.	34	102	422	66	153	68	.	.	.	.	8	133	.	.	620	29	124	59	55	142	816	39	.	.	.	.	3767	22
high-montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	-809	81	.	.	201	94	.	.	32	187	838	83	.	.	39	279	3	.	5	469	60	108	-106	270	.	.	.	.	67	194	.	.	-375	103	-44	809	338	56	608	59	0	.	.	.	858	143
	fir	.	.	.	.	.	.	280	34	.	.	30	100	.	.	.	.	30	269	.	.	.	.	.	.	-12	100	-4	864	0	0	.	.	.	.	17	93	.	.	131	46	-5	741	249	46	187	37	.	.	.	.	903	22
	pine	.	.	.	.	.	.	0	100	.	.	.	.	.	.	.	.	82	155	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	20	100	.	.	-21	164	23	82	.	.	-96	95	.	.	.	.	8	2139
	larch	.	.	.	.	.	.	-4	129	.	.	.	.	.	.	.	.	360	38	.	.	.	.	.	.	.	.	.	.	24	100	.	.	.	.	.	.	.	.	195	64	.	.	64	60	195	72	.	.	.	.	835	28
	Arolla pine	.	.	.	.	.	.	19	100	.	.	.	.	.	.	.	.	0	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	19	102
	other conifers	.	.	.	.	.	.	1	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	100	.	.	.	.	.	.	.	.	.	.	1	100	.	.	-1	99	.	.	.	.	1	157
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	18	139	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	6	232	.	.	.	.	8	91	.	.	.	.	32	93
	maple	.	.	.	.	.	.	-62	109	.	.	3	100	.	.	11	100	9	237	.	.	3	100	.	.	3	82	3	112	.	.	.	.	.	.	0	92	.	.	25	92	2	73	16	50	64	53	.	.	.	.	78	107
	ash	.	.	.	.	.	.	1	100	.	.	.	.	.	.	-1	100	13	504	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	15	71	.	.	1	100	-24	312	.	.	.	.	5	2052
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	4	89	.	.	.	.	4	89
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	4	75	.	.	.	.	.	.	.	.	.	.	4	75
	other broadleaves	.	.	.	.	.	.	10	125	.	.	-5	100	.	.	3	100	101	165	.	.	4	64	.	.	1	100	0	0	0	13049	.	.	.	.	-2	89	.	.	-22	116	8	76	1	442	44	201	.	.	.	.	144	132
	indeterminable	.	.	.	.	.	.	0	0	.	.	0	0	.	.	.	.	0	0	.	.	.	.	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	0	0	0	0	0	0	0	0	.	.	.	.	0	0
	total	.	.	.	.	.	.	-563	124	.	.	228	83	.	.	44	134	1450	53	.	.	46	243	3	.	-3	1079	60	137	-81	353	.	.	.	.	102	123	.	.	-41	1003	-14	2542	670	34	989	45	0	.	.	.	2890	46
lower and upper montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-81	114	269	65	-780	51	-1760	42	51	101	274	165	.	.	-850	48	489	54	-2	9693	-1555	45	-22	883	233	57	116	91	159	345	-37	159	70	105	-325	114	-112	108	1097	78	123	63	-672	50	-79	224	228	45	-254	163	-3422	53
	fir	-88	86	52	158	10	2124	237	248	53	58	-250	148	.	.	-25	310	43	158	199	95	385	76	425	45	48	94	150	42	290	96	18	74	24	689	178	116	-94	55	101	81	-52	175	244	145	150	55	6	858	-3	5225	2101	49
	pine	13	100	.	.	-17	100	29	225	8	92	4	100	.	.	.	.	-6	335	39	168	1	100	22	90	.	.	.	.	-27	189	-25	98	2	642	1	100	-24	90	4	100	9	100	-26	102	-2	650	.	.	-47	108	-41	322
	larch	-32	72	.	.	.	.	-12	162	2	94	6	100	.	.	-16	72	16	111	.	.	6	70	-2	100	.	.	.	.	-24	64	-1	100	.	.	6	100	27	100	132	46	.	.	-6	271	6	53	-15	100	24	75	116	72
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	5	76	.	.	5	71	78	55	.	.	.	.	.	.	0	100	.	.	16	90	78	78	0	100	.	.	4	100	71	71	.	.	45	65	1	100	.	.	.	.	.	.	-169	110	-1	100	.	.	20	89	155	135
	beech	-16	202	64	98	-63	113	74	575	-342	78	142	121	.	.	-26	421	109	91	-79	224	-29	457	-123	120	50	135	-173	58	719	40	25	234	-145	159	154	59	-41	166	889	23	86	79	135	129	-8	890	12	296	103	107	1517	53
	maple	-5	365	7	99	22	210	332	29	-35	85	55	82	.	.	52	131	-47	143	96	49	-17	290	127	45	4	790	-17	292	231	39	10	100	180	40	24	273	9	462	53	64	17	131	192	45	63	58	-8	275	148	47	1495	18
	ash	-17	119	.	.	-81	113	-50	167	0	3795	94	117	.	.	-34	165	20	56	-106	76	-72	80	-23	91	72	129	43	99	46	299	4	100	2	2323	-83	87	-33	111	-8	294	35	106	-76	143	35	96	13	116	-114	68	-334	95
	oak	-1	1133	.	.	.	.	21	80	.	.	18	89	.	.	.	.	21	80	40	138	9	71	2	184	0	100	.	.	1	100	-8	100	-19	179	3	100	-33	78	5	124	.	.	-8	417	21	86	2	100	23	45	97	90
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-18	316	.	.	.	.	.	.	.	.	.	.	-18	316
	other broadleaves	29	65	-3	100	12	87	-223	97	12	249	88	52	.	.	-35	117	-42	119	85	71	44	61	-38	78	10	289	12	107	18	283	1	100	29	79	65	78	0	0	78	199	-24	143	34	225	32	95	0	3512	19	120	205	153
	indeterminable	.	.	.	.	0	0	0	0	0	0	0	0	.	.	0	0	0	0	0	0	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	-193	67	389	60	-891	64	-1273	92	-250	125	431	167	.	.	-933	50	604	59	289	141	-1150	71	369	93	417	48	136	136	1484	57	-13	826	188	187	23	2177	-302	65	2332	44	194	86	-352	197	216	106	237	50	-81	706	1872	140
submontane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-530	81	.	.	.	.	-1332	48	-26	218	-238	105	.	.	.	.	30	64	-532	55	-82	176	-120	55	.	.	91	100	-141	128	-463	53	-407	69	15	100	-467	65	.	.	26	126	-176	135	.	.	-5	100	-850	57	-5206	22
	fir	-401	75	.	.	.	.	117	224	40	46	37	89	.	.	.	.	42	107	122	112	60	342	43	143	.	.	45	100	17	87	27	110	-24	314	2	100	166	57	.	.	-32	73	137	111	.	.	21	100	-79	174	339	158
	pine	-82	49	.	.	.	.	-117	70	71	59	-167	83	.	.	.	.	21	124	-25	174	59	77	-57	100	.	.	.	.	.	.	-4	946	-12	157	.	.	-13	347	.	.	-5	305	-1	911	-1	100	.	.	-75	122	-411	55
	larch	17	370	.	.	.	.	-42	161	.	.	20	71	.	.	.	.	5	138	2	100	4	100	.	.	.	.	4	100	6	108	-2	582	3	100	.	.	-12	143	.	.	.	.	75	65	.	.	.	.	-26	94	55	202
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	72	59	.	.	.	.	82	63	75	100	.	.	.	.	.	.	.	.	.	.	16	94	.	.	.	.	.	.	0	1135	9	100	.	.	.	.	0	86	.	.	.	.	-46	102	.	.	.	.	3	79	210	54
	beech	-408	70	.	.	.	.	196	148	-428	60	-80	206	.	.	.	.	8	181	-99	122	229	50	-110	127	.	.	0	0	-302	87	26	309	-276	58	25	123	31	413	.	.	72	112	-47	397	-48	76	30	78	9	1759	-1172	60
	maple	58	109	.	.	.	.	161	50	32	136	24	94	.	.	.	.	60	62	-34	160	37	57	42	86	.	.	.	.	144	109	4	210	-6	887	6	97	24	326	.	.	12	100	47	100	20	96	-3	100	-6	1332	623	40
	ash	-257	53	.	.	.	.	-108	91	-169	51	-39	332	.	.	.	.	6	201	-46	96	-282	65	-6	148	.	.	.	.	-101	132	12	207	-42	121	-6	512	-255	64	.	.	2	100	-229	114	4	372	-69	105	-60	161	-1645	28
	oak	124	39	.	.	.	.	-49	187	-8	98	53	48	.	.	.	.	.	.	-7	216	-13	70	-1	221	.	.	.	.	15	171	-23	160	29	80	.	.	106	72	.	.	-9	300	-11	678	17	77	.	.	-112	84	111	170
	sweet chestnut	-6	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-3	71	-36	101	.	.	.	.	-40	93
	other broadleaves	48	116	.	.	.	.	-61	198	-13	240	-105	149	.	.	.	.	18	90	9	237	-11	276	-8	88	.	.	0	100	-109	102	2	375	20	358	9	202	-281	120	.	.	7	189	-109	141	13	148	.	.	113	56	-457	99
	indeterminable	0	0	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	0	0	0	0	.	.	.	.	.	.	.	.	.	.	0	0	0	0	0	0	.	.	.	.	0	0	0	0	.	.	0	0	0	0
	total	-1365	48	.	.	.	.	-1154	74	-427	75	-494	89	.	.	.	.	194	45	-610	63	15	2588	-217	88	.	.	141	75	-471	95	-413	67	-715	54	51	115	-701	79	.	.	73	178	-362	139	-30	216	-27	196	-1083	53	-7594	23
hyperinsubric and colline	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	.	.	.	.	.	.	4	75	.	.	1	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-23	.	21	89	.	.	.	.	29	81	.	.	-18	100	14	295
	fir	.	.	.	.	.	.	.	.	.	.	.	.	4	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	7	.	7	100	.	.	.	.	-3	181	.	.	.	.	15	77
	pine	.	.	.	.	.	.	.	.	.	.	.	.	-43	59	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-13	208	.	.	-36	100	-92	56
	larch	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-4	100	.	.	.	.	-32	84	.	.	.	.	-34	80
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	32	96	.	.	.	.	1	100	.	.	.	.	33	94
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-26	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	21	.	67	99	.	.	.	.	11	100	.	.	.	.	74	103
	maple	.	.	.	.	.	.	.	.	.	.	.	.	3	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	30	136	.	.	.	.	3	73	.	.	.	.	37	112
	ash	.	.	.	.	.	.	.	.	.	.	.	.	-19	100	.	.	3	70	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	145	73	.	.	0	100	10	51	.	.	.	.	141	76
	oak	.	.	.	.	.	.	.	.	.	.	.	.	-88	155	.	.	14	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-4	.	40	181	.	.	-25	67	28	72	.	.	6	73	-30	536
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	62	59	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	22	1036	.	.	.	.	6	87	.	.	.	.	90	258
	other broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	-8	261	.	.	-27	250	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	544	35	.	.	2	98	70	62	.	.	-7	100	575	36
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	0	0	0	0	.	.	.	.	0	0
	total	.	.	.	.	.	.	.	.	.	.	.	.	-147	100	.	.	27	210	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	.	904	37	.	.	-22	73	111	59	.	.	-54	107	823	46
total	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-611	72	269	65	-780	51	-3584	34	24	317	274	201	4	75	-695	60	2114	44	-534	65	-1606	45	-139	148	267	52	636	46	58	1124	-500	50	-337	86	-243	161	-602	54	1081	88	229	162	-465	99	1112	42	223	46	-1122	56	-4927	52
	fir	-489	63	52	158	10	2124	656	99	92	38	-183	203	4	100	-25	310	114	100	321	73	444	80	468	43	37	127	191	44	307	91	45	72	0	69983	197	105	79	137	239	43	-89	114	630	64	330	32	27	215	-81	249	3377	35
	pine	-69	61	.	.	-17	100	-71	148	80	54	-163	85	-43	59	.	.	98	143	14	562	60	76	-36	170	4	100	54	70	-27	189	-30	163	-10	236	23	86	-37	135	-14	261	27	98	-27	104	-102	96	.	.	-157	70	-443	72
	larch	-15	461	.	.	.	.	-40	183	2	94	26	60	.	.	-16	72	776	41	2	100	10	58	-2	100	.	.	4	100	13	227	-3	381	3	100	6	100	15	215	750	25	.	.	148	45	552	43	-15	100	-2	1759	2213	21
	Arolla pine	.	.	.	.	.	.	19	100	.	.	.	.	.	.	.	.	236	42	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	100	.	.	.	.	89	62	.	.	.	.	347	33
	other conifers	77	56	.	.	5	71	158	42	75	100	.	.	.	.	0	100	-4	83	16	90	93	67	0	100	.	.	5	84	71	71	9	100	45	65	6	84	0	86	32	96	1	100	-215	89	-2	98	.	.	22	78	398	60
	beech	-424	68	64	98	-63	113	270	191	-770	48	63	381	.	.	-26	421	109	98	-178	120	199	88	-233	87	50	135	-173	58	417	93	51	195	-421	67	179	54	11	1288	967	22	158	67	88	291	-37	225	41	101	111	168	454	235
	maple	53	127	7	99	22	210	431	33	-3	1984	82	62	3	100	66	106	32	248	62	114	23	241	169	40	8	456	-14	355	375	48	15	94	174	52	30	213	34	268	126	47	31	82	251	39	154	35	-12	196	142	76	2262	17
	ash	-274	50	.	.	-81	113	-158	82	-168	52	55	309	-19	100	-35	159	42	159	-152	60	-354	54	-28	78	72	129	43	99	-55	345	16	158	-40	155	-90	89	-287	58	152	72	37	100	-303	94	25	328	-56	131	-174	71	-1834	32
	oak	124	40	.	.	.	.	-28	334	-8	98	70	42	-88	155	.	.	35	62	33	175	-3	325	1	377	0	100	.	.	17	159	-31	122	11	374	3	100	68	118	45	161	-9	300	-44	192	70	43	2	100	-83	114	184	142
	sweet chestnut	-6	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	66	55	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	8	2830	.	.	-3	71	-30	125	.	.	.	.	36	667
	other broadleaves	77	77	-3	100	12	87	-267	93	-1	4724	-21	760	-8	261	-32	129	63	298	94	68	37	109	-46	66	12	228	11	115	-91	134	3	216	50	154	73	74	-281	120	603	41	-8	468	-72	239	188	57	0	3512	126	54	520	119
	indeterminable	0	0	.	.	0	0	0	0	0	0	0	0	.	.	0	0	0	0	0	0	0	0	0	0	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	-1557	43	389	60	-891	64	-2613	62	-677	66	202	429	-147	100	-762	62	3682	29	-321	174	-1096	83	155	254	449	45	758	47	1085	93	-426	69	-527	99	184	280	-1000	59	3992	30	376	112	-12	7612	2351	26	211	62	-1218	67	2588	136

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. 1382445. Birmensdorf, Swiss Federal Research Institute WSL

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