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 number of stems
altitudinal vegetation belts (NaiS; 6 classes) · tree state (living/dead)
regional demarcation: forest district (2024)
unit: n/ha
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
altitudinal vegetation belts (NaiS; 6 classes)	tree state (living/dead)	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
n/a	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	dead	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
upper subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-90	.	.	.	-80	.	2	779	27	121	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	61	12	140	10	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-13	338	18	286	-110	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	13	156
	dead	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	90	.	.	.	90	.	12	65	25	57	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	50	71	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	12	40	12	62	100	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	22	34
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	10	.	14	87	52	61	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	61	12	190	26	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-1	2908	31	181	-10	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	35	56
subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	20	83	-87	20	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	222	60	37	73	9	336	-11	359	-27	110	-16	175	.	.	.	.	.	.	-130	.	.	.	.	.	.	.	.	.	.	.	120	0	-20	227	88	94	.	.	0	.	0	6038	.	.	0	.	.	.	.	.	.	.	.	.	.	.	-45	24	.	.	.	.	.	.	.	.	.	.	104	59	15	145	41	55	40	.	.	.	.	.	7	251	61	105	.	.	50	.	48	147	380	39	-273	.	66	69	52	67	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-100	.	.	.	.	.	.	.	.	.	60	42	-2	1801	15	199	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	19	47
	dead	.	.	.	.	.	.	.	.	.	.	.	.	5	156	-27	66	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-7	81	13	121	-36	128	43	67	29	42	24	60	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	59	58	6	599	59	88	.	.	0	.	0	4642	.	.	-20	.	.	.	.	.	.	.	.	.	.	.	10	71	.	.	.	.	.	.	.	.	.	.	4	88	1	416	-8	91	0	.	.	.	.	.	15	50	0	.	.	.	0	.	46	39	74	56	0	.	0	.	1	71	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	50	.	.	.	.	.	.	.	.	.	22	30	20	69	17	60	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	17	23
	total	.	.	.	.	.	.	.	.	.	.	.	.	25	62	-113	31	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	215	63	49	62	-28	194	32	81	2	1451	8	322	.	.	.	.	.	.	-130	.	.	.	.	.	.	.	.	.	.	.	179	19	-14	534	147	65	.	.	0	.	0	12903	.	.	-20	.	.	.	.	.	.	.	.	.	.	.	-35	10	.	.	.	.	.	.	.	.	.	.	108	57	16	144	33	88	40	.	.	.	.	.	22	55	61	105	.	.	50	.	93	57	454	25	-273	.	66	69	52	66	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-50	.	.	.	.	.	.	.	.	.	82	32	18	224	32	102	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	36	25
high-montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	24	129	-64	28	.	.	45	70	.	.	.	.	.	.	.	.	0	.	-179	61	35	45	-35	51	.	.	43	73	0	22810	-43	193	41	130	-10	274	-87	35	.	.	-80	.	.	.	-228	89	.	.	.	.	.	.	100	.	.	.	70	.	32	70	16	358	.	.	435	60	237	39	-110	.	-80	71	.	.	.	.	.	.	.	.	.	.	-3	1242	71	418	5	71	.	.	.	.	.	.	33	122	66	146	-66	55	-36	15	.	.	.	.	-23	110	-18	186	21	180	133	65	-10	140	-99	99	196	52	29	255	248	96	.	.	.	.	552	65	.	.	.	.	.	.	.	.	.	.	-173	27	.	.	.	.	.	.	.	.	-19	177	-7	310	-16	182	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	4474
	dead	.	.	.	.	.	.	.	.	.	.	.	.	-4	338	43	63	.	.	0	.	.	.	.	.	.	.	.	.	-50	.	-59	61	-69	50	0	.	.	.	0	.	4	247	-7	278	12	129	22	40	48	49	.	.	150	.	.	.	90	51	.	.	.	.	.	.	0	.	.	.	20	.	0	2532	43	38	.	.	-4	375	-7	186	-40	.	-10	71	.	.	.	.	.	.	.	.	.	.	39	24	-77	74	25	71	.	.	.	.	.	.	56	53	40	45	55	24	0	.	.	.	.	.	-14	89	9	198	7	82	-10	204	3	932	-18	306	10	93	17	153	116	53	.	.	.	.	10	69	.	.	.	.	.	.	.	.	.	.	-10	171	.	.	.	.	.	.	.	.	8	151	36	32	48	33	10	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	19	21
	total	.	.	.	.	.	.	.	.	.	.	.	.	20	170	-21	77	.	.	45	70	.	.	.	.	.	.	.	.	-50	.	-238	38	-34	94	-35	51	.	.	43	73	4	1474	-50	173	52	100	12	226	-39	79	.	.	70	.	.	.	-138	135	.	.	.	.	.	.	100	.	.	.	90	.	32	92	59	96	.	.	432	58	231	37	-150	.	-90	71	.	.	.	.	.	.	.	.	.	.	37	81	-5	6277	30	47	.	.	.	.	.	.	88	42	106	101	-11	302	-36	15	.	.	.	.	-37	78	-9	321	27	128	123	74	-7	552	-117	122	207	47	46	153	364	66	.	.	.	.	562	65	.	.	.	.	.	.	.	.	.	.	-183	34	.	.	.	.	.	.	.	.	-11	313	30	57	32	77	10	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	19	60
lower and upper montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-78	88	-90	.	.	.	-59	75	-81	39	-63	62	-8	353	-25	99	78	99	-114	28	406	81	74	80	-1	6063	.	.	-201	43	-59	84	16	241	-45	87	.	.	-275	37	14	171	-64	34	-83	61	32	158	.	.	-43	63	-72	77	60	171	-80	67	8	928	-71	42	-70	20	19	201	-55	40	13	338	237	83	-39	99	16	364	-107	66	-89	62	-6	663	-117	39	-73	163	107	81	-41	128	108	142	-84	88	-20	220	-142	32	-46	83	-213	28	.	.	-284	72	.	.	426	86	.	.	50	88	-30	117	120	76	52	56	-43	128	53	63	10	273	13	383	-138	29	7	408	62	96	-38	111	-31	307	-60	.	-69	47	-20	.	66	210	-12	236	-44	160	-70	81	46	154	-322	65	-97	185	-24	331	334	70	.	.	273	.	-18	164	-7	726	-164	59	174	45	-67	71	5	1214	.	.	-660	.	-20	.	.	.	-36	22
	dead	8	333	0	.	.	.	25	41	19	87	24	57	16	49	7	109	6	223	26	36	-10	68	-8	171	39	48	.	.	0	.	5	383	-2	510	31	72	.	.	33	89	-4	420	-23	128	37	75	13	247	.	.	45	28	10	259	33	44	17	75	31	79	8	73	3	92	-3	613	24	42	17	86	-15	225	31	62	-21	76	13	302	27	64	14	183	12	99	-28	74	0	7218	18	76	37	53	19	76	12	155	17	90	20	82	6	162	.	.	-110	75	.	.	41	188	.	.	-1	2535	-19	144	10	97	18	61	46	58	-3	541	43	24	76	86	55	79	12	152	-7	171	-4	451	8	347	0	.	34	52	50	.	23	33	1	396	12	268	22	48	-6	400	-2	899	17	62	75	52	37	14	.	.	50	.	4	426	52	31	106	72	-21	164	16	69	-20	62	.	.	0	.	20	.	.	.	15	15
	total	-70	107	-90	.	.	.	-34	139	-62	40	-40	116	8	349	-18	138	84	98	-89	38	395	81	66	97	38	118	.	.	-201	46	-54	80	14	309	-14	307	.	.	-241	43	10	325	-87	41	-46	143	45	112	.	.	1	1988	-61	114	93	109	-63	86	39	241	-63	46	-67	21	17	303	-31	82	30	137	222	95	-9	349	-5	1388	-95	63	-62	92	8	485	-104	50	-102	131	106	83	-23	234	145	101	-65	120	-8	554	-125	37	-26	154	-207	30	.	.	-395	69	.	.	467	94	.	.	49	81	-49	67	129	69	70	46	2	1482	50	79	54	58	89	41	-82	4	19	109	55	108	-42	116	-23	468	-60	.	-35	94	30	.	89	157	-11	263	-32	294	-48	118	41	204	-324	60	-80	223	50	189	371	62	.	.	323	.	-14	214	45	113	-57	67	154	37	-51	99	-15	375	.	.	-660	.	0	.	.	.	-20	40
submontane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-15	421	28	182	-85	74	-128	38	.	.	.	.	-111	93	-42	116	-21	163	-1420	.	3	3310	38	219	-71	52	-30	.	-15	619	-212	26	.	.	101	74	.	.	.	.	-123	157	25	365	.	.	.	.	.	.	-92	62	-13	403	-61	105	65	9	-74	41	.	.	272	80	200	.	.	.	.	.	.	.	30	521	-55	217	-296	33	-60	130	-277	31	.	.	-160	47	-86	62	-274	19	50	194	29	468	198	66	.	.	-75	61	-29	188	-57	130	-176	54	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-49	205	.	.	-128	41	-265	41	-101	76	.	.	96	63	14	664	60	90	-10	1400	187	103	.	.	-17	358	-48	149	109	200	-201	46	-179	78	.	.	202	144	.	.	.	.	39	259	-210	30	-91	81	-64	136	-24	879	75	111	-109	39	16	726	-37	130	.	.	-47	26
	dead	16	99	1	531	-1	570	-12	80	.	.	.	.	11	62	16	57	5	100	-100	.	72	37	23	80	14	89	-50	.	42	86	19	221	.	.	-4	419	.	.	.	.	27	137	0	.	.	.	.	.	.	.	11	92	15	54	2	625	0	.	91	27	.	.	33	68	0	.	.	.	.	.	.	.	40	18	17	81	15	248	30	47	14	306	.	.	35	28	-27	75	50	69	27	28	41	66	-23	76	.	.	0	.	34	54	0	7954	16	101	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	94	36	.	.	-27	64	97	11	62	45	.	.	48	32	-58	59	7	81	-28	122	30	35	.	.	47	48	9	192	8	225	36	64	-37	89	.	.	2	852	.	.	.	.	75	38	0	.	19	98	16	59	0	.	26	99	-6	169	21	61	-1	740	.	.	13	19
	total	0	48699	29	167	-87	72	-140	35	.	.	.	.	-100	107	-26	186	-17	211	-1520	.	76	173	61	137	-57	53	-80	.	27	369	-194	41	.	.	98	85	.	.	.	.	-96	225	25	365	.	.	.	.	.	.	-81	75	2	2775	-60	113	65	9	17	135	.	.	305	71	200	.	.	.	.	.	.	.	70	234	-38	335	-280	42	-30	213	-263	46	.	.	-126	59	-113	58	-224	19	77	123	70	182	174	85	.	.	-75	61	5	980	-57	145	-160	58	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	45	300	.	.	-155	23	-168	68	-39	236	.	.	144	46	-44	240	67	76	-38	420	217	85	.	.	30	185	-39	203	117	193	-165	59	-215	79	.	.	204	149	.	.	.	.	114	86	-210	30	-72	80	-48	186	-24	879	101	94	-115	40	37	328	-38	136	.	.	-34	37
hyperinsubric and colline	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-236	37	.	.	.	.	.	.	.	.	-63	60	47	37	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	50	.	-130	.	120	39	117	38	-10	1102	-75	103	-57	70	-57	81	15	346	-16	244	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-99	79	.	.	.	.	-150	.	.	.	.	.	.	.	-11	594	28	150	-27	346	.	.	.	.	.	.	.	.	.	.	28	325	.	.	.	.	.	.	-28	64
	dead	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-75	71	.	.	.	.	.	.	.	.	107	27	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	100	.	29	43	37	137	131	39	67	45	59	39	43	66	53	30	56	66	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	25	70	.	.	.	.	150	.	.	.	.	.	.	.	53	28	91	32	-17	116	.	.	.	.	.	.	.	.	.	.	9	75	.	.	.	.	.	.	56	17
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-311	45	.	.	.	.	.	.	.	.	45	64	47	37	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	50	.	-30	.	149	35	154	62	120	76	-9	802	2	1728	-14	246	68	77	39	55	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-74	130	.	.	.	.	0	.	.	.	.	.	.	.	42	174	120	33	-44	245	.	.	.	.	.	.	.	.	.	.	38	225	.	.	.	.	.	.	28	62
total	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	living	-24	237	24	204	-85	74	-117	36	-81	39	-63	62	4	377	-30	69	4	851	-127	28	78	151	48	133	-40	75	-30	.	-77	82	-125	30	18	189	29	150	-236	37	-196	43	7	338	-37	87	14	242	-12	147	-25	70	-58	44	-45	82	-2	3168	-89	55	-27	174	-71	42	96	121	38	95	-55	40	29	126	99	80	-12	261	7	822	-107	70	-22	195	-68	62	-110	38	-136	47	24	241	-95	50	72	117	-38	188	-3	1085	-104	66	-42	71	-116	39	-57	130	-184	46	43	79	149	63	8	333	14	369	-54	82	-20	190	5	377	-11	260	10	259	34	93	16	199	-26	288	-5	1088	20	190	60	137	9	803	4	2262	-8	653	-11	1064	108	105	-12	236	-43	112	-65	72	28	317	-242	39	-126	101	-24	331	259	75	8	253	4	463	-8	221	-39	118	-135	49	3	2695	-63	77	39	128	-93	43	-30	394	-36	126	.	.	-25	19
	dead	15	95	1	531	-1	570	-6	140	19	87	24	57	7	79	9	66	5	97	24	39	57	43	15	98	25	45	-50	.	19	122	1	1478	-9	115	12	108	-75	71	26	91	8	107	-16	91	24	54	28	25	32	32	35	27	16	95	17	53	23	52	56	34	8	73	18	67	-2	613	24	42	22	60	-5	318	36	39	-16	90	11	212	18	66	12	180	10	113	17	60	-12	99	26	54	31	29	28	51	14	91	0	8631	18	70	21	53	0	7954	-3	598	44	47	24	80	36	36	55	49	26	85	49	38	20	48	38	34	32	71	34	33	50	71	10	316	27	43	16	82	32	81	21	101	-50	61	30	50	-18	176	25	24	1	396	23	94	19	48	2	826	32	59	-2	1015	75	52	17	71	17	34	35	23	28	31	40	33	72	67	6	217	14	69	2	627	-4	220	19	61	0	11895	.	.	18	8
	total	-9	703	26	187	-87	72	-123	34	-62	40	-40	116	11	148	-21	100	9	390	-104	36	135	95	62	101	-14	193	-80	.	-58	120	-124	32	9	445	41	115	-311	45	-170	51	15	176	-54	67	39	85	16	117	7	252	-24	110	-30	149	15	419	-67	73	30	187	-63	46	113	104	36	127	-31	82	51	71	95	90	24	112	-9	643	-95	77	-5	938	-56	85	-101	47	-119	55	13	490	-69	67	103	79	-10	713	11	302	-103	70	-24	133	-95	49	-57	145	-188	46	87	35	173	64	44	67	70	63	-28	143	28	121	26	74	26	94	42	44	68	47	66	44	-15	618	22	219	36	105	92	100	30	268	-46	195	22	229	-29	479	133	85	-11	263	-19	315	-46	103	30	318	-210	45	-127	103	50	189	276	72	25	82	38	43	20	88	2	2981	-63	51	8	827	-48	105	41	135	-97	45	-11	1106	-36	136	.	.	-7	67

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. 2284543. Birmensdorf, Swiss Federal Research Institute WSL

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