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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NFI1—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 1983/85–2018/26
		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	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	90	59	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	44	95	.	.	.	.	48	83	.	.	.	.	181	43
	fir	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	18	240	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	17	100	.	.	.	.	-97	71	.	.	.	.	-61	136
	larch	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	490	37	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	81	74	.	.	.	.	472	34	.	.	.	.	1043	24
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	284	60	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	8	100	.	.	.	.	315	37	.	.	.	.	606	34
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	14	57	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	14	57
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	ash	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	100	.	.	.	.	0	445185	.	.	.	.	4	89
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	898	31	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	151	53	.	.	.	.	738	33	.	.	.	.	1787	21
subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	1206	34	.	.	.	.	.	.	158	86	2635	29	.	.	18	.	.	.	123	75	209	135	154	126	.	.	.	.	130	71	.	.	588	42	187	86	176	77	913	47	.	.	.	.	6497	17
	fir	.	.	.	.	.	.	10	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	10	97
	pine	.	.	.	.	.	.	-42	124	.	.	.	.	.	.	.	.	-7	578	.	.	.	.	.	.	4	100	140	81	.	.	.	.	.	.	.	.	.	.	.	.	-56	100	.	.	55	64	.	.	.	.	93	159
	larch	.	.	.	.	.	.	-37	100	.	.	.	.	.	.	.	.	585	37	.	.	.	.	.	.	.	.	.	.	-9	100	.	.	.	.	.	.	.	.	742	28	-63	100	20	100	1124	46	.	.	.	.	2362	25
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	3378	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-1	7289	.	.	.	.	2	4727
	other conifers	.	.	.	.	.	.	1	100	.	.	.	.	.	.	.	.	19	53	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	4	71	.	.	.	.	23	44
	beech	.	.	.	.	.	.	-1	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	14	100	.	.	.	.	.	.	.	.	.	.	14	105
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	18	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	341	.	.	.	.	.	.	19	97
	ash	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other broadleaves	.	.	.	.	.	.	30	97	.	.	.	.	.	.	.	.	8	54	.	.	.	.	.	.	1	100	2	100	4	77	.	.	.	.	.	.	.	.	5	79	1	100	1	100	64	58	.	.	.	.	117	41
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	.	.	0	0
	total	.	.	.	.	.	.	1166	36	.	.	.	.	.	.	158	86	3261	26	.	.	18	.	.	.	129	75	350	94	150	130	.	.	.	.	130	71	.	.	1350	25	70	352	198	74	2159	33	.	.	.	.	9138	14
high-montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	-689	124	.	.	405	89	.	.	286	70	721	181	.	.	76	276	-28	.	-42	.	301	83	-889	53	.	.	.	.	123	206	.	.	658	75	270	148	789	36	1986	36	-100	.	.	.	3869	51
	fir	.	.	.	.	.	.	539	35	.	.	80	100	.	.	.	.	251	57	.	.	-5	100	.	.	-7	.	102	73	-5	100	.	.	.	.	35	75	.	.	151	103	67	92	369	59	397	56	-10	.	.	.	1963	22
	pine	.	.	.	.	.	.	2	100	.	.	.	.	.	.	.	.	284	53	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	25	100	.	.	-109	72	68	89	.	.	187	83	.	.	.	.	458	52
	larch	.	.	.	.	.	.	14	94	.	.	.	.	.	.	.	.	701	35	.	.	.	.	.	.	.	.	.	.	30	100	.	.	.	.	.	.	.	.	628	33	.	.	136	54	1261	28	.	.	.	.	2770	18
	Arolla pine	.	.	.	.	.	.	5	100	.	.	.	.	.	.	.	.	0	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-3	100	.	.	.	.	2	269
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	100	.	.	.	.	.	.	.	.	.	.	3	175	.	.	-10	106	.	.	.	.	-7	171
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	45	50	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	57	63	.	.	.	.	22	97	.	.	.	.	124	38
	maple	.	.	.	.	.	.	85	71	.	.	9	100	.	.	.	.	122	44	.	.	12	100	.	.	6	.	7	85	.	.	.	.	.	.	5	71	.	.	35	98	1	100	26	72	85	44	.	.	.	.	392	25
	ash	.	.	.	.	.	.	7	100	.	.	.	.	.	.	.	.	23	166	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	10	100	.	.	1	100	88	63	.	.	.	.	129	53
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	8	100	.	.	.	.	8	100
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	25	105	.	.	.	.	.	.	.	.	.	.	25	105
	other broadleaves	.	.	.	.	.	.	27	46	.	.	-1	100	.	.	9	100	566	51	.	.	11	72	0	.	1	.	0	0	7	78	.	.	.	.	-3	272	.	.	88	68	23	88	8	161	258	34	.	.	.	.	994	31
	indeterminable	.	.	.	.	.	.	0	0	.	.	0	0	.	.	.	.	0	0	.	.	.	.	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	.	.	0	0	0	0	0	0	.	.	.	.	0	0
	total	.	.	.	.	.	.	-10	8519	.	.	492	77	.	.	295	69	2713	51	.	.	93	236	-28	.	-42	.	411	72	-857	55	.	.	.	.	186	134	.	.	1544	37	431	94	1329	32	4279	22	-110	.	.	.	10726	20
lower and upper montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-43	177	567	69	-943	69	-4461	29	-135	156	-384	270	.	.	-815	55	1502	32	-16	2454	-1516	56	-51	899	581	65	-625	61	669	123	-186	44	-211	141	-674	79	44	712	1264	80	373	60	-833	109	180	137	172	111	-91	682	-5631	52
	fir	-59	71	100	67	519	60	806	114	172	54	180	284	.	.	104	90	251	78	766	46	1425	30	1507	35	88	81	-64	472	854	50	44	71	-178	172	433	95	75	100	290	59	36	443	1870	29	273	54	-17	631	-44	594	9432	18
	pine	-13	217	.	.	-8	100	-120	146	-13	170	0	100	.	.	.	.	-64	69	-24	247	18	100	31	113	.	.	.	.	-14	191	-108	60	-101	55	-3	100	18	204	.	.	9	100	3	523	27	125	.	.	-74	87	-437	53
	larch	-19	84	.	.	.	.	-84	72	-2	71	6	100	.	.	13	100	-50	127	.	.	16	71	8	100	.	.	.	.	-68	115	-5	100	.	.	18	100	27	100	292	56	.	.	-45	132	-4	732	-12	100	33	71	122	177
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	-1	1087	.	.	11	71	92	175	.	.	.	.	.	.	2	100	.	.	10	73	180	80	5	100	.	.	8	100	212	94	.	.	46	57	3	100	.	.	.	.	.	.	-112	134	4	100	.	.	32	51	492	68
	beech	149	75	90	54	-91	176	1558	38	2	11121	650	31	.	.	8	3062	341	64	32	979	502	31	162	104	80	162	-79	417	1576	22	253	55	-69	500	613	34	123	91	2578	16	325	45	1030	35	331	40	43	170	-50	526	10155	12
	maple	-45	110	6	345	68	108	584	24	74	68	341	25	.	.	173	58	62	41	114	57	259	30	329	27	26	178	55	208	462	34	28	138	265	36	128	51	83	78	108	58	96	57	377	28	149	48	24	117	93	94	3859	10
	ash	-3	2011	.	.	42	178	185	76	7	257	327	44	.	.	61	184	89	66	156	57	62	114	-7	667	277	73	70	89	255	85	-22	77	133	49	105	66	102	52	31	74	154	55	171	69	86	71	26	100	163	98	2470	20
	oak	18	93	.	.	.	.	18	127	.	.	42	83	.	.	.	.	29	104	49	134	23	226	6	100	3	100	.	.	-9	100	-8	88	28	72	8	100	-42	78	25	72	.	.	53	92	18	98	.	.	-52	88	211	61
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-5	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-38	173	.	.	.	.	.	.	.	.	.	.	-43	154
	other broadleaves	29	101	-3	72	57	76	138	57	13	233	79	64	.	.	43	88	20	336	152	48	42	165	-11	200	46	55	-20	117	-2	3911	10	113	27	284	-15	344	-9	71	464	49	-64	90	25	249	34	90	6	73	71	91	1132	29
	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
	total	11	1509	760	58	-346	244	-1283	143	119	293	1239	101	.	.	-412	142	2175	28	1240	57	1011	100	1979	36	1100	45	-655	104	3935	30	7	2432	-58	1115	615	130	419	97	5014	26	929	44	2538	49	1099	35	241	117	82	1080	21762	18
submontane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-2970	32	.	.	.	.	-3689	28	-239	100	-1573	38	.	.	.	.	57	74	-414	61	-1224	42	-24	245	.	.	26	707	-657	74	-97	389	-1098	49	19	100	-921	63	.	.	94	102	-845	61	-95	100	.	.	-1875	42	-15525	13
	fir	-355	104	.	.	.	.	22	2281	112	57	66	66	.	.	.	.	100	72	489	47	-152	252	82	111	.	.	131	100	41	94	85	95	-42	344	19	100	564	58	.	.	98	102	222	139	.	.	42	100	-465	65	1059	92
	pine	-735	37	.	.	.	.	-158	84	-2	2578	-325	56	.	.	.	.	-315	55	-26	195	57	63	-20	100	.	.	.	.	.	.	36	212	-294	40	.	.	-91	76	.	.	15	172	-278	77	.	.	.	.	-541	36	-2677	19
	larch	-9	1024	.	.	.	.	-113	87	-63	100	-72	170	.	.	.	.	-58	120	5	100	5	210	.	.	.	.	18	100	42	89	-2	843	20	100	.	.	21	74	.	.	.	.	203	56	.	.	.	.	-79	100	-83	306
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	168	54	.	.	.	.	221	67	222	100	-35	100	.	.	.	.	.	.	.	.	52	98	.	.	.	.	.	.	-14	140	63	100	.	.	.	.	3	120	.	.	.	.	62	91	.	.	.	.	3	481	746	41
	beech	-137	391	.	.	.	.	-964	59	-297	123	173	190	.	.	.	.	-178	97	-302	96	73	428	13	688	.	.	-22	100	-130	177	454	45	229	71	29	320	304	73	.	.	184	74	520	46	20	398	117	75	285	133	372	323
	maple	-1	18287	.	.	.	.	340	41	60	122	68	70	.	.	.	.	67	59	-19	318	50	80	33	111	.	.	.	.	92	141	42	63	48	112	20	72	102	64	.	.	.	.	160	46	47	89	9	100	490	34	1610	21
	ash	-154	110	.	.	.	.	83	207	42	190	399	58	.	.	.	.	9	66	0	485987	190	105	-65	149	.	.	.	.	229	59	74	57	104	64	159	63	172	75	.	.	8	100	145	142	13	87	-62	88	20	603	1367	39
	oak	120	177	.	.	.	.	109	100	-33	157	7	691	.	.	.	.	.	.	-35	89	58	143	-34	126	.	.	.	.	-31	106	-85	95	45	71	.	.	320	91	.	.	45	75	135	141	21	129	.	.	-59	237	586	80
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	6	100	7	100	.	.	.	.	18	58
	other broadleaves	-10	1282	.	.	.	.	158	74	-54	72	103	124	.	.	.	.	46	71	-72	66	82	62	-107	63	.	.	2	100	-87	98	-8	311	43	196	18	234	12	868	.	.	22	67	316	104	-58	119	-17	71	172	71	560	83
	indeterminable	.	.	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	0	0	0	0	.	.	.	.	.	.	.	.	.	.	0	0	.	.	0	0	.	.	.	.	0	0	.	.	.	.	0	0	0	0
	total	-4084	30	.	.	.	.	-3991	34	-251	198	-1187	69	.	.	.	.	-268	97	-375	115	-808	92	-122	225	.	.	154	103	-514	114	561	89	-943	61	264	66	486	149	.	.	467	65	645	127	-44	289	89	83	-2048	49	-11968	23
hyperinsubric and colline	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	.	.	.	.	.	.	3	121	.	.	11	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-30	.	140	48	.	.	.	.	-1	3669	.	.	-42	100	81	124
	fir	.	.	.	.	.	.	.	.	.	.	.	.	4	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	18	.	13	100	.	.	.	.	15	65	.	.	.	.	49	49
	pine	.	.	.	.	.	.	.	.	.	.	.	.	22	201	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-39	.	.	.	.	.	.	.	-49	237	.	.	-100	100	-166	99
	larch	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	9	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-4	504	.	.	.	.	12	229	.	.	.	.	17	211
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	82	98	.	.	.	.	1	100	.	.	.	.	83	98
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	13	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	53	.	404	40	.	.	.	.	-5	100	.	.	.	.	466	36
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	66	75	.	.	.	.	1	1749	.	.	.	.	66	75
	ash	.	.	.	.	.	.	.	.	.	.	.	.	3	100	.	.	11	72	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	4	.	570	42	.	.	-2	100	19	48	.	.	.	.	605	39
	oak	.	.	.	.	.	.	.	.	.	.	.	.	3	5322	.	.	51	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	6	.	269	37	.	.	104	65	115	50	.	.	-13	148	535	41
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	229	52	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1333	31	.	.	.	.	7	118	.	.	.	.	1569	27
	other broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	5	150	.	.	193	68	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1005	26	.	.	3	178	79	60	.	.	-34	73	1252	24
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	0	0	.	.	.	.	.	.	0	0
	total	.	.	.	.	.	.	.	.	.	.	.	.	40	427	.	.	518	36	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	11	.	3878	14	.	.	105	63	192	78	.	.	-189	81	4556	14
total	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-3013	32	567	69	-943	69	-7632	25	-374	85	-1552	80	3	121	-371	138	5017	31	-430	108	-2647	39	-102	449	662	59	-89	636	-722	150	-283	136	-1308	47	-402	148	-908	72	2694	42	924	53	-713	151	3031	29	71	301	-2008	50	-10529	39
	fir	-414	89	100	67	519	60	1377	77	284	39	326	159	4	100	104	90	602	42	1256	34	1268	45	1589	34	81	88	169	201	890	48	129	67	-220	154	487	85	657	51	454	50	201	98	2461	27	685	39	14	842	-508	78	12513	16
	pine	-748	37	.	.	-8	100	-319	71	-15	340	-325	56	22	201	.	.	-84	288	-51	156	75	53	11	379	4	100	140	81	-14	191	-73	138	-394	33	22	114	-112	78	-92	88	37	233	-275	78	122	174	.	.	-714	32	-2792	23
	larch	-29	332	.	.	.	.	-220	55	-65	97	-66	184	.	.	13	100	1677	23	5	100	21	72	8	100	.	.	18	100	-5	1821	-8	288	20	100	18	100	48	65	1739	19	-63	100	314	48	2865	22	-12	100	-46	180	6232	14
	Arolla pine	.	.	.	.	.	.	5	100	.	.	.	.	.	.	.	.	287	67	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	8	100	.	.	.	.	311	40	.	.	.	.	611	38
	other conifers	166	55	.	.	11	71	314	70	222	100	-35	100	.	.	2	100	33	39	10	73	232	65	5	100	.	.	8	100	199	101	63	100	46	57	3	100	3	120	82	98	3	175	-50	322	-1	946	.	.	35	66	1351	34
	beech	11	4781	90	54	-91	176	593	138	-295	147	823	47	.	.	8	3062	221	126	-271	156	575	60	175	109	80	162	-101	327	1446	28	707	35	160	239	642	35	480	53	3053	14	509	39	1550	28	368	42	161	71	236	195	11130	15
	maple	-46	298	6	345	68	108	1008	20	134	66	419	24	.	.	173	58	269	27	95	94	321	27	362	26	31	146	62	183	554	37	70	67	313	35	154	43	185	50	209	41	97	56	563	23	282	33	33	88	583	33	5947	9
	ash	-157	116	.	.	42	178	275	81	49	167	726	38	3	100	61	184	132	54	156	83	252	84	-72	149	277	73	70	89	484	53	53	86	237	39	264	46	278	50	611	39	162	52	315	75	206	40	-36	167	183	110	4571	17
	oak	138	154	.	.	.	.	128	87	-33	157	49	126	3	5322	.	.	80	74	14	516	81	121	-28	156	3	100	.	.	-39	86	-93	87	74	52	8	100	283	103	295	34	45	75	293	71	162	41	.	.	-123	120	1341	40
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	229	52	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1319	32	.	.	6	100	14	77	.	.	.	.	1568	28
	other broadleaves	18	736	-3	72	57	76	353	41	-41	120	180	76	5	150	52	75	836	39	81	108	135	64	-118	60	49	52	-16	145	-78	154	2	1190	70	163	0	14115	3	3626	1564	22	-18	339	353	95	378	35	-11	113	208	67	4058	18
	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	-4072	31	760	58	-346	244	-4118	60	-133	455	545	282	40	427	41	1539	9298	19	866	96	313	406	1830	42	1187	43	261	318	2714	52	569	93	-1001	86	1195	72	917	91	11937	12	1896	37	4816	32	8423	15	220	141	-2155	62	36000	15

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. 1408376. Birmensdorf, Swiss Federal Research Institute WSL

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