Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI3—NFI5
change: total wood volume (stemwood)
tree species (5 classes) · tree state (living/dead)
regional demarcation: forest district (2024)
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
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
tree species (5 classes)	tree state (living/dead)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	dead	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
spruce	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-189	109	80	187	123	124	-759	53	216	107	-758	53	594	137	-1690	34	-1777	46	-750	44	44	83	-38	190	-291	100	.	.	-109	243	-242	123	194	195	445	82	4	75	-744	93	1000	54	575	70	358	138	-110	603	292	124	-602	61	-371	62	-679	69	-2078	34	110	85	145	71	-302	54	20	667	-203	107	450	57	291	48	176	134	-91	332	276	74	267	146	68	304	-395	97	-514	55	-215	99	-202	96	19	536	22	214	-132	208	-437	76	-163	111	59	255	-394	61	-647	64	203	92	1341	63	-316	98	.	.	22	79	13	100	95	54	35	66	163	49	173	50	358	44	-124	267	-109	134	322	35	159	114	-292	164	4	2388	-4	6064	-172	139	-224	91	43	285	224	42	-102	136	-238	79	-117	85	-352	76	-151	64	-28	291	733	36	125	225	712	44	97	114	-302	93	-58	314	-18	751	154	181	-906	47	-291	96	25	529	.	.	-7861	38
	dead	-1	2144	-30	82	-33	152	-151	90	14	156	89	110	-187	158	126	106	119	51	367	50	-16	100	17	92	14	144	-5	.	-64	69	-122	55	-22	607	3	944	.	.	-22	317	-67	125	-87	114	496	50	431	37	207	43	263	47	85	101	61	103	330	47	8	71	13	176	11	103	43	97	154	81	13	105	-45	111	139	39	-62	48	45	140	57	117	-35	68	-139	132	179	94	-49	60	-11	124	-2	100	16	72	3	1777	82	98	60	64	17	79	-3	823	147	65	23	82	85	138	302	55	.	.	.	.	-13	100	9	144	-4	85	-7	100	36	83	75	51	-11	777	6	311	33	159	154	65	56	79	-3	100	108	82	-29	100	38	64	26	92	15	98	52	61	18	192	4	176	19	71	16	100	7	393	316	40	357	38	233	50	37	72	223	72	-11	743	-54	81	-7	134	19	90	7	100	-26	88	.	.	4564	18
	total	-191	109	50	301	90	178	-910	45	230	107	-668	62	407	215	-1564	37	-1657	50	-383	72	28	76	-21	314	-278	99	-5	.	-173	168	-364	89	172	230	448	83	4	75	-766	90	933	59	488	83	853	52	321	193	499	70	-339	110	-286	78	-618	77	-1747	39	118	82	158	72	-291	56	62	218	-50	404	463	55	246	68	315	75	-153	199	321	62	324	120	33	627	-534	80	-335	76	-264	80	-213	91	17	601	37	119	-128	221	-355	94	-103	175	77	206	-397	62	-500	81	225	86	1426	67	-15	1532	.	.	22	79	-1	3404	104	56	30	66	155	51	209	40	434	42	-135	271	-103	149	356	32	312	58	-236	203	1	14910	104	180	-201	121	-186	100	69	189	239	41	-50	274	-220	82	-113	88	-333	79	-135	65	-21	277	1049	28	482	53	946	31	134	87	-78	311	-69	272	-72	201	147	191	-887	47	-285	98	-1	25155	.	.	-3297	91
fir	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-113	83	-65	95	49	76	-244	107	120	79	174	126	551	32	394	130	57	912	-106	305	16	68	78	66	72	58	.	.	113	55	52	132	-152	142	-37	393	4	100	45	134	122	66	-16	278	55	68	235	54	-6	100	431	73	194	154	274	103	229	103	29	299	185	61	101	89	70	113	473	54	86	75	22	207	271	43	183	77	-53	80	8	751	232	80	-194	98	58	81	15	1197	-92	131	-24	157	56	170	320	55	-25	596	111	103	6	1920	-42	391	74	86	18	100	85	59	21	100	.	.	.	.	3	100	1	13872	6	389	103	79	64	160	.	.	61	111	71	156	115	79	255	58	143	62	43	100	299	55	-43	428	-40	444	96	71	115	50	188	38	207	54	-110	62	-18	556	-154	152	86	77	45	100	77	89	419	36	54	155	-21	358	-93	160	268	49	-51	152	10	85	15	100	-60	175	.	.	6672	21
	dead	62	77	.	.	-5	100	-27	87	2	100	-7	437	102	73	112	73	167	55	196	44	0	107	.	.	-23	110	.	.	3	84	-8	66	0	7084	110	70	.	.	5	94	-20	99	43	162	-23	157	13	170	36	100	122	63	-32	134	66	99	192	54	20	104	25	72	16	82	.	.	23	61	-8	230	30	135	-34	167	54	109	3	171	1	2798	-14	100	113	83	.	.	-13	450	11	133	63	71	-143	93	-10	86	26	67	15	71	6	151	.	.	.	.	.	.	-14	76	12	100	.	.	.	.	.	.	8	100	-34	213	3	100	34	109	.	.	48	68	-23	154	-21	90	6	742	2	100	.	.	90	104	0	100	9	100	0	100	17	110	9	114	2	100	.	.	30	87	-3	100	.	.	5	90	30	76	4	718	56	61	.	.	2	757	19	237	32	107	3	100	.	.	.	.	.	.	1596	23
	total	-52	164	-65	95	44	85	-271	95	122	78	167	137	652	32	506	98	224	215	90	356	16	67	78	66	48	100	.	.	116	55	45	159	-152	142	73	119	4	100	50	116	102	81	27	196	32	61	249	49	30	100	553	58	161	188	340	80	422	55	49	151	210	54	117	77	70	113	496	53	78	72	52	98	236	56	237	64	-50	81	9	624	218	85	-81	205	58	81	1	10998	-81	149	39	44	-87	208	309	54	1	22687	126	92	12	1069	-42	391	74	86	18	100	71	60	33	100	.	.	.	.	3	100	9	1057	-28	260	106	78	98	94	.	.	109	70	48	219	94	95	260	61	145	62	43	100	388	36	-43	428	-31	590	96	70	133	52	197	36	210	53	-110	62	12	825	-157	149	86	77	50	91	107	59	423	38	109	78	-21	358	-90	164	288	50	-19	338	13	73	15	100	-60	175	.	.	8268	17
other conifers	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-132	83	-393	63	44	130	-56	159	.	.	-6	237	-57	151	4	1258	-48	410	8	1026	-2	774	2	2322	4	405	99	.	5	100	-115	117	.	.	-80	98	-48	62	2	82	-79	106	-142	81	72	67	88	149	405	74	-41	120	37	83	123	59	3	71	-64	100	2	100	1	100	0	100	18	100	4	100	92	100	37	67	112	87	24	70	-17	201	2	101	14	100	-31	229	-15	105	8	449	-6	561	-47	94	-9	100	1	100	7	100	-2	1026	-26	146	-23	177	57	48	4	1814	132	54	29	129	.	.	0	4709	460	30	33	155	106	92	.	.	.	.	16	185	-43	221	4	100	20	100	.	.	90	70	15	63	10	100	-13	124	.	.	-10	113	.	.	-205	78	.	.	27	81	39	100	68	100	640	42	287	68	-50	204	-15	100	-34	127	-29	168	55	73	-138	56	-35	108	-152	60	-94	64	.	.	1049	73
	dead	-124	89	.	.	.	.	.	.	.	.	.	.	6	400	4	100	19	137	27	91	11	100	.	.	25	72	.	.	.	.	-61	71	.	.	-6	265	-12	100	.	.	51	86	54	100	23	79	161	37	332	38	27	86	.	.	-2	100	.	.	.	.	2	100	.	.	.	.	9	100	.	.	1	100	1	165	.	.	5	100	-7	89	.	.	.	.	23	63	.	.	0	100	13	81	22	71	-2	100	.	.	.	.	16	100	-25	132	-6	100	0	104	31	91	9	77	2	100	.	.	.	.	16	70	0	100	20	93	.	.	.	.	-1	371	16	86	.	.	4	100	.	.	-13	84	1	95	.	.	9	100	.	.	-3	445	.	.	10	100	.	.	.	.	26	100	1	100	68	94	66	46	146	52	.	.	46	100	33	57	-18	76	12	100	.	.	-19	100	.	.	.	.	1047	23
	total	-257	84	-393	63	44	130	-56	159	.	.	-6	237	-51	163	8	671	-29	674	35	252	9	86	2	2322	29	90	99	.	5	100	-176	97	.	.	-86	87	-60	61	2	82	-28	213	-88	120	95	63	249	50	737	37	-14	388	37	83	122	59	3	71	-64	100	4	100	1	100	0	100	28	100	4	100	93	100	39	65	112	87	29	74	-24	144	2	101	14	100	-8	906	-15	105	8	447	7	388	-26	182	-11	100	1	100	7	100	14	68	-51	122	-29	142	57	48	35	208	141	52	32	121	.	.	0	4709	476	29	33	155	127	74	.	.	.	.	15	196	-27	344	4	100	24	100	.	.	77	69	16	62	10	100	-5	174	.	.	-13	181	.	.	-195	77	.	.	27	81	65	100	70	100	708	38	353	54	95	84	-15	100	11	79	5	1016	37	124	-126	61	-35	108	-172	61	-94	64	.	.	2096	37
beech	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-625	54	-101	157	105	79	-344	73	96	56	-72	143	-353	113	223	139	-664	73	250	89	-227	57	-127	158	-156	100	.	.	72	104	8	1249	168	105	91	171	.	.	-285	85	51	117	11	700	-19	94	144	51	.	.	-21	1429	388	45	-6	1827	41	196	3	1350	-82	70	48	141	67	88	-47	162	109	68	32	59	2	7589	131	58	-319	84	313	53	12	1618	-102	279	96	132	-31	324	-209	73	-33	549	-102	162	88	69	27	200	231	48	132	65	20	253	-100	136	.	.	20	78	.	.	227	48	354	34	216	45	186	58	175	55	165	66	247	63	.	.	96	63	83	84	-17	449	104	59	188	57	40	59	64	72	39	79	-92	95	41	127	-104	92	-92	108	147	44	21	264	16	403	-19	615	9	610	.	.	20	89	97	96	65	127	51	87	32	272	-128	135	-109	132	114	55	-24	231	48	191	.	.	1209	111
	dead	189	53	-40	171	.	.	0	16316	-6	84	24	74	30	211	-15	246	38	196	90	63	8	735	69	104	52	71	.	.	40	91	-15	198	30	84	14	102	.	.	40	56	47	81	3	165	-14	112	24	78	.	.	46	101	1	1463	2	155	-49	176	54	67	5	100	.	.	.	.	-3	100	9	77	8	100	24	112	6	100	50	75	45	57	-5	459	94	59	45	58	-3	651	48	71	56	70	108	46	13	100	42	99	2	932	.	.	.	.	-6	129	.	.	.	.	.	.	13	94	13	207	6	256	10	139	56	82	-4	950	8	78	.	.	.	.	8	184	.	.	3	63	4	128	1	100	121	54	2	74	3	100	-1	100	31	66	0	27761	15	85	17	94	.	.	5	100	.	.	.	.	.	.	71	52	12	100	-8	100	.	.	41	60	-24	66	-5	122	6	100	6	100	.	.	1613	18
	total	-436	76	-141	118	105	79	-344	67	90	57	-49	206	-323	123	208	145	-626	78	340	65	-219	69	-57	253	-104	154	.	.	112	62	-7	1687	198	90	105	151	.	.	-245	99	99	79	13	562	-33	76	168	47	.	.	25	1220	390	44	-4	2852	-9	1089	57	73	-77	71	48	141	67	88	-51	157	118	63	40	60	26	530	137	56	-269	97	358	50	7	2822	-8	3395	141	92	-34	310	-161	97	23	810	6	2573	102	64	69	54	232	51	132	65	20	253	-106	128	.	.	20	78	.	.	240	45	367	33	222	44	195	57	232	44	160	53	255	61	.	.	96	63	91	70	-17	449	106	59	192	57	41	59	185	34	41	79	-89	99	40	130	-73	122	-92	110	162	42	38	159	16	403	-14	842	9	610	.	.	20	89	168	57	76	99	43	102	32	272	-87	195	-133	109	110	57	-18	310	54	172	.	.	2822	47
other broadleaves	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-64	383	81	114	-68	153	-49	131	-24	136	58	220	-8	1525	53	353	201	97	20	520	-243	49	20	165	32	107	.	.	6	823	133	111	320	27	128	133	-245	74	-589	93	122	55	23	210	73	123	41	229	192	90	-106	164	-157	174	-69	188	9	1085	52	104	32	107	44	73	21	94	3	843	109	84	-39	72	79	69	-160	86	77	167	80	83	-41	294	43	371	24	306	205	43	14	511	40	226	-210	70	44	208	-16	150	38	99	-30	701	-100	176	-30	376	5	723	540	44	101	70	52	150	-52	245	589	41	184	55	26	593	185	68	93	84	-9	175	12	64	-172	89	88	88	64	113	164	68	209	67	12	510	-52	187	47	62	20	61	74	71	-15	177	18	525	-53	147	-189	84	9	106	-70	242	-11	1068	126	48	259	45	-65	180	101	69	-78	157	41	174	-21	535	246	37	-285	51	75	138	.	.	2435	50
	dead	12	234	45	73	2	100	10	96	-3	100	48	68	87	63	93	46	36	127	10	115	58	57	22	65	-17	69	.	.	-15	99	83	85	0	1401	38	84	-52	68	125	59	-5	275	-15	71	46	59	109	44	78	85	308	51	4	243	31	53	37	90	20	66	4	132	6	100	-6	100	17	100	56	64	-4	193	13	88	53	49	-14	165	11	239	55	88	34	87	8	94	.	.	33	74	23	65	15	97	24	110	-5	72	7	155	15	103	38	118	3	911	33	53	46	70	-1	1517	83	46	134	51	189	32	-1	4434	119	46	77	83	16	69	10	100	-4	71	34	72	27	61	-30	61	-6	214	-6	111	11	100	-9	124	10	89	-2	88	9	71	0	5426	-24	158	14	204	-11	120	2	100	.	.	32	55	19	83	38	73	0	100	4	132	17	85	-16	152	6	366	-6	111	6	64	17	91	.	.	2421	13
	total	-51	496	126	69	-65	158	-39	164	-27	123	106	111	80	154	146	125	237	80	30	340	-185	62	41	80	15	265	.	.	-9	547	215	68	321	27	166	103	-297	72	-465	111	117	54	8	645	119	76	150	54	270	60	202	98	-153	180	-38	353	46	239	72	84	36	100	50	76	15	115	20	66	165	50	-43	74	92	64	-107	125	63	201	90	65	14	897	77	216	33	231	205	43	47	116	64	150	-195	75	68	140	-21	127	45	89	-15	1435	-62	306	-28	413	37	72	586	44	100	80	135	59	82	142	777	32	183	54	145	113	262	61	109	72	1	411	8	74	-138	103	114	77	35	224	158	68	203	69	24	282	-61	177	57	63	17	70	83	65	-15	171	-6	1777	-39	208	-200	85	11	88	-70	242	21	520	145	39	297	40	-64	181	105	67	-62	203	25	321	-15	817	240	38	-279	52	92	121	.	.	4856	25
indeterminable	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	dead	.	.	.	.	.	.	.	.	.	.	8	136	14	216	9	72	-4	100	-6	101	4	100	.	.	13	100	.	.	-1	100	-1	100	.	.	6	117	.	.	-5	100	-2	273	18	86	23	87	-25	99	-13	81	14	89	8	80	2	100	-16	79	.	.	.	.	.	.	15	100	8	100	35	72	8	108	1	100	4	100	-4	104	1	100	2	210	8	100	.	.	.	.	31	86	.	.	.	.	.	.	.	.	.	.	.	.	0	811	.	.	.	.	-9	100	.	.	.	.	.	.	-4	119	-2	85	.	.	-15	100	22	100	.	.	-1	100	11	174	0	3732	3	100	-3	100	.	.	9	100	-3	100	.	.	1	100	2	100	.	.	.	.	-1	100	.	.	.	.	.	.	6	322	21	89	-20	82	.	.	.	.	.	.	.	.	7	100	21	100	.	.	1	100	.	.	199	44
	total	.	.	.	.	.	.	.	.	.	.	8	136	14	216	9	72	-4	100	-6	101	4	100	.	.	13	100	.	.	-1	100	-1	100	.	.	6	117	.	.	-5	100	-2	273	18	86	23	87	-25	99	-13	81	14	89	8	80	2	100	-16	79	.	.	.	.	.	.	15	100	8	100	35	72	8	108	1	100	4	100	-4	104	1	100	2	210	8	100	.	.	.	.	31	86	.	.	.	.	.	.	.	.	.	.	.	.	0	811	.	.	.	.	-9	100	.	.	.	.	.	.	-4	119	-2	85	.	.	-15	100	22	100	.	.	-1	100	11	174	0	3732	3	100	-3	100	.	.	9	100	-3	100	.	.	1	100	2	100	.	.	.	.	-1	100	.	.	.	.	.	.	6	322	21	89	-20	82	.	.	.	.	.	.	.	.	7	100	21	100	.	.	1	100	.	.	199	44
total	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-1125	62	-399	90	253	75	-1453	41	407	73	-605	99	727	136	-1017	95	-2229	54	-579	103	-412	51	-64	363	-340	95	99	.	87	376	-164	276	531	101	547	82	-284	63	-1571	64	1217	47	451	96	538	95	399	179	882	61	-340	196	91	599	-357	183	-1796	46	129	125	283	58	-108	173	178	78	244	156	757	40	398	46	565	55	175	220	4	9597	651	71	273	150	-635	107	-367	94	-42	851	-481	68	-4	6814	-282	125	311	115	-452	83	223	135	166	188	-542	79	-725	65	283	75	1990	51	-62	529	309	43	323	49	821	34	925	25	275	68	722	35	576	46	350	46	62	567	-171	169	511	37	601	49	203	271	386	58	387	64	-218	151	-322	95	200	81	299	56	-21	932	-71	383	-259	54	-516	83	-277	133	66	394	1408	31	634	62	1437	28	135	148	-205	161	-226	138	218	154	-165	234	-571	76	-739	58	-6	4412	.	.	3506	120
	dead	138	114	-24	307	-36	144	-168	91	8	282	162	73	53	633	330	57	375	39	684	32	64	107	107	75	63	79	-5	.	-37	142	-125	105	7	1802	164	59	-64	58	143	75	4	2969	15	977	550	47	713	25	640	32	781	30	66	156	161	58	495	55	102	47	48	61	33	66	52	86	207	66	106	44	-2	3402	145	59	55	118	86	95	108	61	2	3185	110	191	255	67	-65	107	112	58	153	45	17	840	29	241	145	65	84	52	54	51	10	658	137	80	55	46	138	102	321	52	98	41	147	50	178	40	40	108	137	82	73	105	117	43	86	46	30	314	53	120	39	150	139	72	53	84	-21	66	340	46	-38	104	69	42	24	102	71	60	61	59	22	278	34	99	38	97	46	71	8	324	426	35	494	29	472	34	105	63	265	63	41	205	-29	184	26	148	32	68	0	7594	-1	2376	.	.	11439	9
	total	-986	79	-423	85	218	87	-1621	36	415	75	-443	136	780	133	-687	140	-1855	64	106	554	-347	57	43	416	-277	113	94	.	51	679	-288	175	538	102	712	62	-348	61	-1428	69	1220	48	466	94	1088	42	1112	61	1523	33	441	155	157	347	-196	336	-1302	60	231	68	331	52	-75	252	229	65	451	86	863	36	396	55	710	46	231	171	90	448	758	63	275	151	-525	132	-112	288	-107	320	-369	89	149	178	-265	170	340	110	-306	123	307	101	220	144	-532	83	-588	79	338	63	2128	54	259	98	407	33	471	35	998	28	965	25	412	53	795	33	693	41	435	43	92	426	-118	229	550	36	740	47	257	217	365	60	726	31	-256	135	-253	118	224	74	370	51	40	502	-50	546	-225	67	-478	88	-231	163	74	343	1834	25	1128	34	1909	21	240	76	60	562	-185	170	190	187	-139	282	-539	79	-739	59	-7	3840	.	.	14944	28

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. 2285623. Birmensdorf, Swiss Federal Research Institute WSL

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