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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NFI4—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 NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
change 2009/17–2018/26
		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	-32	341	-13	1392	65	155	-631	59	269	65	-780	51	-725	116	-1127	43	-1083	60	-650	46	43	84	-23	211	4	1121	.	.	-40	183	-189	159	-4	9089	507	41	4	75	-695	60	767	45	453	67	197	258	475	109	222	152	-534	65	-307	64	-138	128	-1160	58	-71	67	215	49	-152	72	18	364	-149	75	267	52	122	75	514	54	333	35	277	85	56	668	-127	151	-481	88	-500	50	-64	223	-272	83	-43	255	41	58	-447	66	279	70	-74	223	137	56	-205	73	-535	52	86	174	1161	75	-577	61	43	100	18	100	6	100	117	48	14	67	213	35	74	112	268	58	-113	288	179	51	287	36	82	228	-13	699	-51	135	79	124	42	178	-88	94	-145	91	43	111	-165	64	-395	58	-159	74	-126	103	-101	86	65	64	412	64	103	256	597	48	223	46	-366	87	45	376	141	107	20	949	-685	59	-270	80	-8	830	.	.	-4927	52
	dead	-27	88	-18	93	-19	233	18	280	15	136	131	69	109	216	54	210	48	112	316	55	-16	100	9	186	0	37217	.	.	-36	89	-118	54	-254	90	15	261	.	.	17	418	70	153	30	199	481	51	166	75	250	41	232	47	44	142	0	10158	9	1373	5	88	22	99	11	104	43	97	117	104	9	146	-31	100	143	56	-20	78	24	189	47	174	-12	134	81	65	191	90	-7	128	-12	81	0	100	9	109	-23	218	128	67	53	72	7	82	-4	682	161	58	22	83	107	103	282	49	.	.	.	.	.	.	31	58	3	121	10	120	23	97	36	67	4	1340	-6	110	41	136	130	73	43	96	.	.	10	84	-41	100	34	71	27	86	-4	140	64	53	16	171	-12	160	-33	117	16	100	-7	201	246	40	307	42	123	83	10	119	181	88	31	202	-4	938	-3	288	-6	459	7	100	-24	80	.	.	4130	17
	total	-59	183	-31	576	46	239	-613	56	284	66	-649	60	-616	139	-1073	46	-1035	63	-334	75	27	78	-14	366	4	981	.	.	-76	104	-308	106	-259	177	522	41	4	75	-678	63	838	48	483	63	678	58	641	74	471	70	-302	113	-263	67	-138	126	-1151	57	-66	73	237	45	-141	77	61	115	-32	399	276	49	91	106	657	52	313	36	301	78	103	358	-139	139	-400	100	-309	58	-71	200	-284	80	-42	257	50	43	-471	71	406	58	-21	750	144	56	-208	71	-374	70	108	143	1268	77	-295	93	43	100	18	100	6	100	148	45	17	73	223	35	97	79	304	56	-109	333	173	53	329	33	212	83	30	272	-51	135	89	110	1	7541	-54	135	-117	110	39	125	-101	97	-379	58	-171	68	-159	100	-85	84	58	57	658	45	410	52	720	38	233	45	-185	147	76	214	137	113	18	1097	-690	58	-263	82	-32	235	.	.	-798	324
fir	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-139	77	-62	109	25	140	-313	90	52	158	10	2124	463	30	331	126	169	172	-307	125	14	80	16	53	62	52	.	.	84	53	41	197	-220	148	-88	177	4	100	-25	310	85	73	-57	76	9	269	94	85	-17	100	321	73	54	404	223	89	167	120	-7	840	20	452	44	181	10	345	400	37	37	127	23	158	168	45	157	61	-39	95	48	79	218	66	-77	280	45	72	26	416	-98	120	-68	71	140	47	234	57	-49	273	12	708	1	6631	38	113	40	96	10	100	81	61	5	100	.	.	.	.	7	100	40	76	16	271	80	88	-53	176	1	100	-37	106	20	564	110	72	175	66	115	58	10	205	117	117	-33	319	-18	607	28	255	79	48	143	41	82	76	-102	77	90	69	-218	109	34	100	37	108	54	66	239	39	27	215	-34	202	-37	234	48	176	8	891	38	89	-5	100	-99	128	.	.	3377	35
	dead	86	60	.	.	.	.	-1	975	2	100	-20	135	86	92	68	109	82	65	156	50	3	97	.	.	12	102	.	.	3	84	-5	82	-5	158	87	78	.	.	2	350	-14	84	67	104	-29	93	25	112	30	125	64	127	2	1007	13	356	135	62	5	530	11	88	13	100	.	.	4	258	9	123	36	101	6	250	-14	165	-2	100	-25	100	-13	100	120	78	.	.	9	473	13	117	63	71	-106	64	-18	67	20	76	14	67	8	100	.	.	.	.	.	.	-2	100	10	100	.	.	.	.	.	.	8	100	31	101	0	100	38	99	.	.	36	60	3	651	-16	101	9	461	0	100	.	.	91	104	0	100	9	100	-19	71	-5	87	9	110	0	100	-5	100	53	73	-3	100	.	.	-6	95	8	122	15	151	30	78	.	.	16	91	-6	503	-5	100	0	100	.	.	.	.	.	.	1297	24
	total	-54	170	-62	109	25	140	-314	89	54	152	-10	2170	549	33	399	101	251	114	-151	247	17	83	16	53	74	52	.	.	87	53	36	230	-225	144	-1	12344	4	100	-23	317	71	89	9	684	-20	232	119	61	13	160	386	69	56	404	236	86	302	59	-2	2371	31	299	57	146	10	345	404	38	45	106	59	98	174	46	143	65	-41	91	23	135	205	71	43	437	45	72	35	265	-85	137	-5	441	33	285	216	61	-29	468	25	326	9	1045	38	113	40	96	10	100	79	62	15	100	.	.	.	.	7	100	48	61	46	76	80	88	-15	588	1	100	-1	2824	23	481	94	89	184	74	115	58	10	205	208	45	-33	319	-8	1330	9	779	74	49	152	38	82	76	-107	73	143	57	-221	108	34	100	31	127	61	58	254	40	57	116	-34	202	-21	422	41	190	3	2375	38	89	-5	100	-99	128	.	.	4674	25
other conifers	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-28	159	-34	62	59	93	-4	1513	.	.	-12	149	2	3771	30	164	51	206	-18	261	-2	688	58	68	26	54	75	.	3	100	-112	117	.	.	-29	184	-43	59	-16	73	28	298	18	302	87	58	440	35	535	59	32	249	18	82	143	54	2	71	-57	100	2	100	0	100	-2	100	20	100	4	100	29	97	34	78	62	77	31	72	-35	164	-1	183	0	11165	-24	175	4	60	19	143	18	111	-3	515	28	76	1	100	7	100	0	15586	-58	71	36	105	-25	164	37	172	132	48	59	143	4	100	0	1944	481	28	46	70	38	155	.	.	-14	100	42	55	62	70	3	100	10	100	.	.	-23	210	9	63	10	100	-7	113	.	.	-12	106	.	.	-174	107	.	.	15	77	-26	100	39	100	431	40	248	69	-140	69	-15	100	2	471	15	259	28	90	-55	100	-44	91	-94	81	12	135	.	.	2516	25
	dead	-19	100	.	.	.	.	.	.	.	.	.	.	16	148	4	100	-2	1721	32	64	11	100	.	.	-1	384	.	.	.	.	-67	72	.	.	-31	67	-4	100	.	.	52	84	27	112	28	72	107	42	256	44	-19	62	.	.	.	.	.	.	.	.	0	100	.	.	.	.	-3	100	.	.	1	100	7	90	.	.	5	100	-11	88	.	.	.	.	23	63	.	.	0	100	-6	150	6	159	1	62	.	.	.	.	16	100	7	100	-4	100	14	102	8	259	-5	79	-7	84	.	.	.	.	8	221	-1	205	32	85	.	.	.	.	8	73	11	124	.	.	4	100	.	.	-12	87	-1	174	.	.	-1	100	.	.	-5	319	.	.	-8	100	.	.	.	.	26	100	1	100	38	130	26	94	84	76	.	.	37	100	15	95	-5	92	12	100	.	.	-10	100	.	.	.	.	701	27
	total	-47	103	-34	62	59	93	-4	1513	.	.	-12	149	18	334	34	155	50	209	14	322	9	88	58	68	25	47	75	.	3	100	-179	96	.	.	-59	97	-48	60	-16	73	80	69	45	147	115	55	547	28	791	37	13	573	18	82	143	54	2	71	-57	100	2	100	0	100	-2	100	16	100	4	100	30	97	41	80	62	77	36	67	-46	141	-1	183	0	11165	-1	2751	4	60	19	143	12	229	3	374	28	75	1	100	7	100	16	64	-51	77	33	120	-11	321	45	128	127	50	51	166	4	100	0	1944	489	28	45	72	70	77	.	.	-14	100	50	51	73	57	3	100	14	100	.	.	-35	135	9	60	10	100	-8	112	.	.	-16	160	.	.	-182	107	.	.	15	77	0	100	41	100	469	38	274	62	-57	169	-15	100	39	83	30	128	23	115	-43	127	-44	91	-104	74	12	135	.	.	3216	19
beech	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-225	92	2	4858	37	209	-238	66	64	98	-63	113	14	2037	391	60	-161	179	26	786	-94	70	-99	173	-577	56	.	.	24	153	-8	1524	49	279	-2	7437	.	.	-26	421	-5	1074	20	326	-9	96	103	59	.	.	-178	120	70	194	29	323	100	59	-146	96	-48	71	62	96	-7	884	-94	124	50	135	18	64	-190	52	223	52	-74	276	247	48	-128	144	149	144	51	195	80	140	-265	60	-169	102	-67	155	4	1390	57	71	117	57	34	216	-54	89	31	378	.	.	51	70	.	.	91	45	125	73	126	49	310	44	181	44	83	101	125	77	7	100	27	162	37	134	-56	106	44	124	156	51	-3	529	27	184	37	89	-29	104	27	162	-27	265	-169	78	38	229	15	255	58	53	-14	667	-54	85	.	.	10	75	-47	178	41	101	84	37	97	58	-10	807	-26	426	39	191	-97	69	25	183	.	.	454	235
	dead	121	75	-10	283	.	.	60	70	2	99	24	73	31	180	-34	102	9	675	60	81	23	208	29	71	52	69	.	.	9	304	-15	197	34	71	-5	439	.	.	46	50	46	84	-1	215	2	100	24	78	.	.	27	169	-8	231	4	81	-15	485	50	73	-1	100	.	.	.	.	-3	100	8	104	8	100	7	147	6	100	58	60	43	64	2	508	34	61	30	56	-1	2868	-31	159	34	110	85	45	1	150	41	102	11	106	-2	100	.	.	-3	155	.	.	.	.	.	.	-6	281	11	270	-6	302	2	775	55	54	-38	87	7	86	.	.	.	.	7	242	.	.	2	122	5	100	0	100	89	56	2	74	3	100	0	100	14	93	1	315	-2	252	17	94	.	.	5	100	.	.	.	.	0	100	69	56	-5	100	-7	100	.	.	30	83	-19	80	-7	85	-36	119	-6	100	.	.	1081	23
	total	-104	186	-8	1210	37	209	-178	70	66	95	-39	154	45	623	357	65	-152	184	86	227	-71	126	-70	229	-526	62	.	.	33	62	-23	562	83	167	-7	2377	.	.	20	509	41	151	20	340	-7	96	127	52	.	.	-150	140	63	214	33	283	85	102	-96	114	-49	71	62	96	-7	884	-97	123	57	113	26	59	-184	52	229	51	-17	1277	291	46	-126	150	183	117	81	124	80	151	-296	53	-136	126	18	507	6	1020	98	48	129	58	33	227	-54	89	28	424	.	.	51	70	.	.	85	54	136	59	120	53	312	43	235	35	45	146	132	73	7	100	27	162	44	97	-56	106	46	120	161	51	-3	530	116	40	39	87	-25	117	27	164	-13	503	-168	79	36	246	32	123	58	53	-9	1060	-54	85	.	.	10	73	23	330	37	117	77	40	97	58	20	403	-45	254	32	242	-133	58	20	238	.	.	1535	69
other broadleaves	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	128	58	112	63	-199	53	-67	144	4	199	-47	218	-318	81	148	97	47	399	101	57	-122	80	21	170	-79	55	.	.	-39	125	-46	404	243	39	29	309	-112	135	-1	12729	64	86	57	176	53	161	-53	155	117	112	37	419	-154	138	-121	50	-23	261	18	130	17	218	42	92	30	134	-11	252	92	122	2	650	38	142	43	274	182	110	33	171	-146	139	132	65	2	1884	153	50	-35	168	8	820	68	132	10	624	0	19508	6	629	-72	270	-130	85	-264	127	7	438	514	39	53	57	17	386	-122	103	411	43	18	611	-116	103	152	109	39	147	-1	1878	13	70	-227	62	37	125	56	135	204	75	126	65	42	196	-107	119	8	339	11	51	72	92	-128	86	-16	253	15	266	-196	69	19	91	-86	221	50	119	178	47	180	57	-65	116	57	71	26	303	32	281	-28	323	101	67	-188	59	13	377	.	.	1168	89
	dead	28	94	39	69	2	100	-10	115	-1	100	56	54	91	65	92	44	43	93	18	62	58	57	27	79	-5	90	.	.	-11	99	93	75	-2	189	22	153	-38	73	120	65	1	855	-14	70	20	196	61	97	80	83	271	54	-16	172	24	65	35	92	13	74	3	100	2	100	-1	100	17	100	45	59	-10	119	0	1699	60	44	-29	79	31	64	59	82	33	72	6	141	.	.	32	74	22	63	18	64	-11	385	2	100	23	71	12	129	58	75	18	100	20	69	18	165	12	78	49	54	84	65	153	37	46	98	107	45	73	75	13	81	21	76	-4	72	20	141	37	54	-11	61	14	82	-6	109	-8	237	4	416	8	103	0	100	1	81	10	192	16	104	30	63	-24	101	-1	100	.	.	-4	399	16	81	36	89	-1	100	-4	100	17	82	-22	120	9	257	-3	91	-9	150	-1	615	.	.	2202	14
	total	157	53	150	50	-197	54	-77	124	3	249	9	1006	-227	104	241	57	90	199	119	47	-65	147	49	98	-84	51	.	.	-50	94	47	327	241	39	51	176	-150	116	119	82	65	84	42	238	73	138	8	821	197	58	309	87	-169	125	-97	64	12	418	31	95	20	187	43	93	29	136	6	198	137	78	-8	257	38	134	104	111	153	128	64	84	-87	242	165	49	8	575	153	50	-2	1752	30	219	86	106	-1	5632	3	2803	29	101	-60	325	-72	174	-246	136	27	101	533	41	65	56	66	105	-38	286	564	33	64	181	-10	1171	226	71	52	115	20	117	9	107	-207	66	74	85	45	167	218	72	119	69	34	246	-103	134	16	201	10	52	73	90	-117	82	0	17144	45	97	-220	69	18	95	-86	221	45	125	194	41	217	43	-66	114	52	73	42	178	10	952	-20	508	98	68	-197	57	12	404	.	.	3370	31
indeterminable	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	dead	-4	100	.	.	.	.	.	.	.	.	0	254	2	1005	3	123	-5	100	0	100	-1	100	.	.	0	100	.	.	-1	100	-1	100	.	.	-2	100	.	.	-9	100	10	76	13	120	15	156	4	164	-5	130	13	100	4	158	-2	100	-19	85	.	.	.	.	.	.	15	100	-1	100	35	72	-5	100	1	100	4	100	0	70	5	71	3	100	3	380	.	.	.	.	-6	91	.	.	.	.	-13	100	.	.	.	.	.	.	0	849	.	.	.	.	.	.	.	.	-1	100	.	.	-3	213	0	72	-11	100	.	.	0	100	.	.	-2	100	-7	82	2	122	1	224	-3	100	.	.	-1	100	.	.	.	.	1	100	2	100	.	.	.	.	-1	100	.	.	.	.	.	.	16	97	0	195	-1	327	.	.	.	.	.	.	.	.	7	100	0	100	.	.	0	100	.	.	54	113
	total	-4	100	.	.	.	.	.	.	.	.	0	254	2	1005	3	123	-5	100	0	100	-1	100	.	.	0	100	.	.	-1	100	-1	100	.	.	-2	100	.	.	-9	100	10	76	13	120	15	156	4	164	-5	130	13	100	4	158	-2	100	-19	85	.	.	.	.	.	.	15	100	-1	100	35	72	-5	100	1	100	4	100	0	70	5	71	3	100	3	380	.	.	.	.	-6	91	.	.	.	.	-13	100	.	.	.	.	.	.	0	849	.	.	.	.	.	.	.	.	-1	100	.	.	-3	213	0	72	-11	100	.	.	0	100	.	.	-2	100	-7	82	2	122	1	224	-3	100	.	.	-1	100	.	.	.	.	1	100	2	100	.	.	.	.	-1	100	.	.	.	.	.	.	16	97	0	195	-1	327	.	.	.	.	.	.	.	.	7	100	0	100	.	.	0	100	.	.	54	113
total	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-296	102	4	6099	-13	1124	-1253	42	389	60	-891	64	-563	176	-226	353	-977	87	-847	65	-162	93	-26	789	-564	64	75	.	32	403	-315	159	67	940	418	74	-147	100	-762	62	939	42	490	75	337	155	1059	54	857	61	-321	174	-319	133	135	255	-913	80	-263	69	206	72	-4	3319	50	255	166	162	449	45	194	59	564	60	818	35	377	117	349	118	-183	214	-275	232	-426	69	199	125	-651	54	-253	96	178	94	-172	204	288	99	67	365	101	219	-409	66	-692	69	78	223	1845	55	-387	92	209	68	25	573	551	34	965	24	140	106	565	40	184	111	261	60	-69	480	72	312	380	41	367	74	463	41	59	219	273	79	-51	435	-134	139	-79	208	155	78	-319	68	-465	71	-232	78	-159	148	-340	100	-1	31776	930	37	593	61	829	43	211	62	-257	146	145	176	238	112	-81	352	-550	78	-654	47	-57	295	.	.	2588	136
	dead	185	63	10	401	-17	265	68	102	17	118	189	58	334	82	187	86	176	65	581	36	78	86	65	56	56	79	.	.	-36	145	-113	113	-227	101	85	101	-43	66	175	63	165	76	122	81	517	49	387	37	610	35	588	36	27	277	40	170	144	157	73	60	34	68	26	72	56	79	130	93	105	38	-1	5366	164	53	37	112	55	109	90	109	39	136	270	44	250	71	1	3445	-4	1004	113	48	12	655	-64	127	191	52	101	45	42	58	61	86	172	62	55	48	131	104	299	46	34	96	95	67	144	45	95	58	183	36	78	92	82	56	57	51	42	151	28	224	64	97	135	76	59	75	-18	68	182	64	-35	149	54	50	9	302	4	644	85	49	21	159	29	64	-4	1705	43	72	-6	248	289	41	357	38	326	43	35	78	207	77	79	78	-7	675	2	1677	-15	184	-48	96	-31	82	.	.	9463	10
	total	-111	255	14	1742	-30	457	-1185	42	406	60	-702	78	-229	435	-38	2087	-801	105	-266	197	-84	167	39	525	-507	72	75	.	-4	2945	-428	127	-161	482	503	60	-189	89	-587	82	1105	40	612	62	854	49	1445	37	1467	33	268	239	-292	142	175	200	-769	92	-190	77	241	62	22	565	106	119	296	95	554	37	193	69	729	55	855	34	432	103	439	94	-145	277	-6	10384	-176	129	200	123	-655	53	-141	166	190	89	-236	167	479	68	168	146	142	156	-347	79	-519	89	133	130	1976	58	-88	325	243	60	120	109	695	28	1060	23	323	47	644	34	266	80	318	54	-27	1358	99	219	445	36	502	64	522	43	41	319	454	45	-86	303	-80	235	-70	228	159	76	-234	87	-444	75	-203	90	-163	173	-297	114	-7	3641	1219	30	949	34	1155	29	246	56	-51	675	224	113	231	118	-80	366	-566	75	-703	44	-88	193	.	.	12051	30

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. 2297115. Birmensdorf, Swiss Federal Research Institute WSL

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