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 NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was 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	23-25calculated 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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NFI3—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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
change 2004/07–2018/26
		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	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	96	44	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	53	96	.	.	.	.	28	70	.	.	.	.	177	39
	fir	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	2659	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	9	100	.	.	.	.	9	104	.	.	.	.	20	218
	larch	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	69	233	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	51	79	.	.	.	.	192	44	.	.	.	.	313	60
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	233	35	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5	100	.	.	.	.	83	66	.	.	.	.	320	30
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	4	56	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	4	56
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	ash	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	100	.	.	.	.	-2	107	.	.	.	.	2	146
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	407	48	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	120	58	.	.	.	.	310	37	.	.	.	.	837	28
subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	938	23	.	.	.	.	.	.	162	64	860	65	.	.	0	.	.	.	52	71	110	102	243	70	.	.	.	.	39	92	.	.	355	35	236	45	20	521	796	35	.	.	.	.	3811	19
	fir	.	.	.	.	.	.	2	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	87
	pine	.	.	.	.	.	.	18	68	.	.	.	.	.	.	.	.	-19	108	.	.	.	.	.	.	4	100	114	83	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	7	156	.	.	.	.	124	79
	larch	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	136	128	.	.	.	.	.	.	.	.	.	.	1	100	.	.	.	.	.	.	.	.	278	41	-22	100	-35	100	250	97	.	.	.	.	608	53
	Arolla pine	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	-36	301	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-15	229	.	.	.	.	-51	222
	other conifers	.	.	.	.	.	.	1	100	.	.	.	.	.	.	.	.	-1	218	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-1	101	.	.	.	.	-1	201
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	6	100	.	.	.	.	.	.	.	.	.	.	6	100
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	12	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	104	.	.	.	.	.	.	15	83
	ash	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other broadleaves	.	.	.	.	.	.	1	1900	.	.	.	.	.	.	.	.	4	74	.	.	.	.	.	.	1	100	-1	100	0	293	.	.	.	.	.	.	.	.	-2	131	1	100	1	100	32	90	.	.	.	.	38	84
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	.	.	0	0
	total	.	.	.	.	.	.	959	23	.	.	.	.	.	.	162	64	956	66	.	.	0	.	.	.	57	71	223	72	244	70	.	.	.	.	39	92	.	.	636	28	215	47	-10	1198	1068	37	.	.	.	.	4550	19
high-montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	-168	371	.	.	140	105	.	.	72	66	432	210	.	.	-232	92	3	.	-12	.	215	75	23	875	.	.	.	.	145	114	.	.	-52	707	-136	248	508	36	805	45	0	.	.	.	1743	77
	fir	.	.	.	.	.	.	252	50	.	.	26	100	.	.	.	.	200	60	.	.	-10	100	.	.	-11	.	25	166	-8	100	.	.	.	.	32	73	.	.	72	131	74	90	305	52	252	42	.	.	.	.	1208	24
	pine	.	.	.	.	.	.	0	100	.	.	.	.	.	.	.	.	-161	79	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-9	100	.	.	-40	83	14	92	.	.	43	212	.	.	.	.	-152	106
	larch	.	.	.	.	.	.	-1	203	.	.	.	.	.	.	.	.	303	45	.	.	.	.	.	.	.	.	.	.	23	100	.	.	.	.	.	.	.	.	237	57	.	.	95	51	430	43	.	.	.	.	1087	25
	Arolla pine	.	.	.	.	.	.	0	100	.	.	.	.	.	.	.	.	0	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	96
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	100	.	.	.	.	.	.	.	.	.	.	2	100	.	.	0	100	.	.	.	.	2	84
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	10	86	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	23	65	.	.	.	.	18	96	.	.	.	.	52	48
	maple	.	.	.	.	.	.	32	96	.	.	5	100	.	.	.	.	44	59	.	.	9	100	.	.	5	.	5	98	.	.	.	.	.	.	3	71	.	.	24	97	1	100	-46	139	53	47	.	.	.	.	132	63
	ash	.	.	.	.	.	.	7	100	.	.	.	.	.	.	.	.	-27	154	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	7	100	.	.	1	100	-9	779	.	.	.	.	-21	402
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5	100	.	.	.	.	5	100
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	13	73	.	.	.	.	.	.	.	.	.	.	13	73
	other broadleaves	.	.	.	.	.	.	9	100	.	.	0	0	.	.	5	100	142	135	.	.	5	77	.	.	1	.	-1	100	6	64	.	.	.	.	-4	75	.	.	-17	123	15	80	6	105	-26	302	.	.	.	.	141	148
	indeterminable	.	.	.	.	.	.	0	0	.	.	0	0	.	.	.	.	0	0	.	.	.	.	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	0	0	0	0	0	0	0	0	.	.	.	.	0	0
	total	.	.	.	.	.	.	131	500	.	.	170	91	.	.	76	68	944	103	.	.	-229	95	3	.	-17	.	244	73	45	452	.	.	.	.	167	96	.	.	267	155	-30	1158	869	31	1571	32	0	.	.	.	4210	35
lower and upper montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-121	89	216	107	-758	53	-2356	36	51	100	580	86	.	.	-978	70	679	51	-14	1031	-2136	34	-180	180	410	61	62	228	76	804	-155	92	100	136	-932	47	-315	66	1127	74	297	42	-1159	60	-12	1460	103	107	-377	110	-5792	37
	fir	-75	81	120	79	174	126	826	87	59	70	-104	260	.	.	45	134	138	72	231	124	746	45	800	37	96	65	195	47	177	175	15	76	-57	360	365	70	-121	65	163	62	16	610	851	45	288	47	29	270	102	216	5076	23
	pine	19	100	.	.	-14	100	-189	76	6	138	1	100	.	.	.	.	-8	204	-24	150	3	100	21	89	.	.	.	.	19	77	-21	113	-22	195	1	100	-34	67	.	.	9	100	41	96	-20	116	.	.	-89	88	-303	62
	larch	-74	86	.	.	.	.	9	290	-9	130	6	100	.	.	2	100	-87	99	.	.	-2	361	0	100	.	.	.	.	-36	69	-11	100	.	.	6	100	27	100	255	35	.	.	-87	93	-27	74	-15	100	27	75	-19	891
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	8	71	.	.	8	71	-13	1153	.	.	.	.	.	.	0	100	.	.	6	74	91	76	1	100	.	.	5	100	109	90	.	.	28	63	1	100	.	.	.	.	.	.	-116	122	-9	100	.	.	38	77	159	151
	beech	-28	250	96	56	-72	143	52	998	-133	119	246	80	.	.	-285	85	176	69	-41	629	66	219	-41	309	109	68	37	393	529	65	-15	404	-177	156	296	45	-41	197	1157	21	137	54	196	126	123	70	-5	1178	-247	83	2135	46
	maple	36	63	-22	156	88	74	260	44	7	367	154	36	.	.	16	343	53	48	63	74	71	96	183	33	4	1180	-20	191	331	39	16	100	115	49	33	183	-12	443	44	71	39	103	182	47	64	72	11	204	43	179	1758	17
	ash	-30	143	.	.	-49	227	-26	339	-2	790	137	58	.	.	-419	91	26	50	-98	109	18	318	-16	171	68	90	81	78	-180	75	-2	100	-46	244	-68	107	-76	137	-4	774	64	61	18	590	75	77	14	100	-127	86	-642	81
	oak	5	219	.	.	.	.	16	78	.	.	20	103	.	.	.	.	13	110	5	438	-19	180	2	100	1	100	.	.	-11	100	-7	100	-35	93	4	100	-41	84	10	95	.	.	2	1413	9	129	.	.	-22	226	-48	193
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	29	106	.	.	.	.	.	.	.	.	.	.	29	106
	other broadleaves	48	93	-3	71	18	60	-64	139	20	64	52	99	.	.	-191	84	-56	82	34	101	45	83	-30	83	29	50	-24	124	-69	116	2	100	-40	200	-2	1364	-3	100	220	75	-30	97	74	55	7	472	1	355	28	132	65	466
	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	-211	77	407	73	-605	99	-1485	94	-2	10857	1091	61	.	.	-1810	55	934	45	162	310	-1117	80	739	63	717	42	336	69	945	100	-178	114	-134	398	-297	191	-616	53	3001	35	531	41	1	67906	498	53	137	117	-626	108	2417	129
submontane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-625	78	.	.	.	.	-2038	38	-336	89	-432	93	.	.	.	.	46	118	-588	58	-759	60	-54	120	.	.	80	161	-218	87	-359	68	-476	58	15	100	-646	70	.	.	10	284	-405	78	.	.	-6	100	-998	56	-7790	19
	fir	-299	94	.	.	.	.	-184	203	107	49	56	74	.	.	.	.	54	85	200	67	-39	858	59	162	.	.	73	100	8	75	43	105	12	1043	8	100	146	136	.	.	35	108	95	280	.	.	24	100	-34	349	365	196
	pine	-569	47	.	.	.	.	-1	8438	8	531	-215	71	.	.	.	.	-92	78	-25	133	32	80	-64	100	.	.	.	.	.	.	1	5774	-73	67	.	.	-18	236	.	.	13	100	-66	76	.	.	.	.	-316	39	-1384	26
	larch	-17	390	.	.	.	.	-36	152	.	.	17	176	.	.	.	.	-9	116	2	100	7	100	.	.	.	.	10	100	17	93	4	351	6	100	.	.	-8	137	.	.	.	.	141	63	.	.	.	.	-69	70	66	210
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	95	57	.	.	.	.	118	67	99	100	.	.	.	.	.	.	.	.	.	.	32	97	.	.	.	.	.	.	1	507	-4	100	.	.	.	.	-3	128	.	.	.	.	29	91	.	.	.	.	7	79	374	38
	beech	-938	48	.	.	.	.	-596	87	-375	63	93	194	.	.	.	.	5	345	19	820	357	47	30	182	.	.	-2	100	-494	66	110	102	-199	68	51	62	100	149	.	.	206	73	230	70	-9	342	69	89	231	78	-1113	83
	maple	46	191	.	.	.	.	180	61	18	284	35	75	.	.	.	.	68	62	-32	172	51	68	12	183	.	.	.	.	14	671	19	65	-29	195	30	72	59	86	.	.	.	.	70	65	33	95	7	100	213	47	795	30
	ash	-145	78	.	.	.	.	-117	100	-107	80	80	224	.	.	.	.	3	439	-87	131	-365	64	18	113	.	.	.	.	-27	393	61	70	24	88	59	80	-281	56	.	.	3	100	-88	297	19	62	-77	120	-119	131	-1144	46
	oak	-117	169	.	.	.	.	-13	682	-29	122	19	234	.	.	.	.	.	.	1	2653	-64	101	-13	105	.	.	.	.	-4	841	-3	559	54	75	.	.	143	105	.	.	7	220	-84	116	16	100	.	.	-40	250	-128	250
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-8	109	4	100	.	.	.	.	1	894
	other broadleaves	57	147	.	.	.	.	-18	684	-98	53	87	92	.	.	.	.	36	75	8	178	33	125	-4	258	.	.	0	100	-61	114	-62	100	4	1704	12	269	84	139	.	.	-2	329	-8	2123	16	94	-19	100	96	88	160	197
	indeterminable	.	.	.	.	.	.	.	.	0	0	0	0	.	.	.	.	.	.	0	0	0	0	.	.	.	.	.	.	.	.	.	.	0	0	.	.	0	0	.	.	.	.	0	0	0	0	.	.	0	0	0	0
	total	-2512	39	.	.	.	.	-2703	42	-715	59	-260	221	.	.	.	.	116	98	-501	87	-716	104	-16	1003	.	.	160	84	-765	56	-189	147	-676	57	174	57	-423	148	.	.	272	71	-94	635	79	81	-2	4976	-1027	65	-9798	23
hyperinsubric and colline	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	.	.	.	.	.	.	4	75	.	.	1	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-21	.	72	60	.	.	.	.	-46	119	.	.	-20	100	-11	693
	fir	.	.	.	.	.	.	.	.	.	.	.	.	4	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	13	.	3	100	.	.	.	.	1	318	.	.	.	.	22	67
	pine	.	.	.	.	.	.	.	.	.	.	.	.	-48	62	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-14	.	.	.	.	.	.	.	-59	124	.	.	-26	100	-147	57
	larch	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	9	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-15	100	.	.	.	.	-5	378	.	.	.	.	-11	248
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	40	97	.	.	.	.	1	100	.	.	.	.	41	95
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-5	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-7	.	156	55	.	.	.	.	-15	100	.	.	.	.	130	68
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	11	204	.	.	.	.	-11	129	.	.	.	.	-1	4212
	ash	.	.	.	.	.	.	.	.	.	.	.	.	-34	100	.	.	-8	120	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-37	.	326	41	.	.	-16	100	17	59	.	.	.	.	249	58
	oak	.	.	.	.	.	.	.	.	.	.	.	.	-213	76	.	.	29	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	4	.	77	136	.	.	44	104	21	139	.	.	5	74	-34	604
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	104	54	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	371	72	.	.	.	.	14	67	.	.	.	.	489	55
	other broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	3	72	.	.	0	70440	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	520	41	.	.	2	71	35	134	.	.	1	100	561	41
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	0	0	.	.	.	.	.	.	0	0
	total	.	.	.	.	.	.	.	.	.	.	.	.	-284	63	.	.	130	64	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-61	.	1563	25	.	.	30	156	-48	235	.	.	-40	104	1289	36
total	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-746	67	216	107	-758	53	-3623	37	-285	106	288	228	4	75	-744	93	2114	53	-602	61	-3128	28	-231	143	450	57	468	58	124	556	-514	55	-376	82	-733	64	-981	51	1554	60	408	92	-1036	76	1570	31	97	114	-1395	50	-7861	38
	fir	-374	77	120	79	174	126	895	91	166	40	-23	1186	4	100	45	134	391	41	431	73	697	68	859	36	86	75	293	42	177	175	58	81	-45	524	405	64	38	560	238	58	125	98	1251	39	541	32	54	155	68	367	6672	21
	pine	-550	48	.	.	-14	100	-172	89	13	311	-213	71	-48	62	.	.	-277	55	-49	100	36	73	-43	154	4	100	114	83	19	77	-20	359	-96	69	-8	109	-66	75	-31	112	36	56	-26	246	-20	608	.	.	-431	34	-1842	25
	larch	-91	101	.	.	.	.	-27	222	-9	130	23	136	.	.	2	100	421	68	2	100	5	184	0	100	.	.	10	100	5	768	-8	222	6	100	6	100	19	156	807	25	-22	100	114	117	839	38	-15	100	-42	123	2043	25
	Arolla pine	.	.	.	.	.	.	0	100	.	.	.	.	.	.	.	.	197	68	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5	100	.	.	.	.	67	97	.	.	.	.	269	56
	other conifers	103	53	.	.	8	71	106	159	99	100	.	.	.	.	0	100	3	108	6	74	123	61	1	100	.	.	5	100	111	89	-4	100	28	63	1	100	-3	128	40	97	2	100	-86	166	-10	93	.	.	46	65	579	49
	beech	-966	48	96	56	-72	143	-544	135	-509	56	340	79	.	.	-285	85	187	66	-21	1429	423	52	-11	1283	109	68	34	420	35	1367	96	132	-376	82	346	40	52	324	1342	19	342	49	426	69	117	81	65	127	-17	1636	1209	111
	maple	81	111	-22	156	88	74	472	34	25	228	193	32	.	.	16	343	177	32	31	232	131	59	195	33	9	571	-16	250	345	46	35	58	87	93	65	98	47	160	78	57	40	100	208	56	139	46	18	127	255	49	2699	14
	ash	-175	70	.	.	-49	227	-136	107	-109	80	217	90	-34	100	-419	91	-5	962	-185	84	-347	69	3	1240	68	90	81	78	-207	83	58	73	-22	525	-10	914	-393	49	329	42	67	58	-85	330	103	91	-64	148	-245	77	-1559	49
	oak	-112	177	.	.	.	.	3	3267	-29	122	39	125	-213	76	.	.	42	77	6	612	-84	88	-11	125	1	100	.	.	-15	228	-10	187	20	264	4	100	106	145	87	120	7	220	-38	292	52	69	.	.	-57	195	-205	190
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	108	52	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	412	65	.	.	-8	109	19	57	.	.	.	.	531	51
	other broadleaves	106	91	-3	71	18	60	-73	211	-78	69	139	68	3	72	-186	86	129	166	42	89	83	67	-35	79	32	47	-26	116	-124	85	-60	103	-36	306	6	729	81	145	724	38	-17	189	76	234	62	165	-19	104	125	74	968	56
	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
	total	-2723	37	407	73	-605	99	-3098	62	-717	64	1002	89	-284	63	-1571	64	3487	36	-340	196	-2063	57	725	68	757	40	963	37	469	229	-367	94	-810	81	83	722	-1101	64	5586	21	988	47	797	148	3478	20	135	148	-1693	56	3506	120

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. 1408387. Birmensdorf, Swiss Federal Research Institute WSL

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