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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NFI1—NFI5
change: number of stems
altitudinal vegetation belts (NaiS; 6 classes) · conifers and broadleaves
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 1983/85–2018/26
		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)	conifers and broadleaves	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	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
upper subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-100	.	.	.	120	.	41	56	69	60	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	150	45	300	14	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	25	183	118	54	-10	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	66	37
	broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	0	.	0	.	3	99	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	25	71	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-3	97	0	.	50	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	89
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	0	.	0	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	0	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-100	.	.	.	120	.	41	56	72	57	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	150	45	325	19	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	23	201	118	54	40	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	68	36
subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	conifers	.	.	.	.	.	.	.	.	.	.	.	.	-24	179	-113	22	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	384	81	64	55	-22	327	-35	158	35	153	-35	133	.	.	.	.	.	.	20	.	.	.	.	.	.	.	.	.	.	.	244	43	9	1531	242	81	.	.	20	.	-19	296	.	.	-310	.	.	.	.	.	.	.	.	.	.	.	-30	0	.	.	.	.	.	.	.	.	.	.	158	44	118	76	178	32	60	.	.	.	.	.	164	41	137	85	.	.	30	.	101	137	334	51	40	.	45	56	-3	274	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-50	.	.	.	.	.	.	.	.	.	114	36	-90	61	1	8863	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	30	52
	broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	9	122	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	7	54	0	.	3	97	1	98	0	.	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	20	84	0	.	0	.	.	.	0	.	10	90	.	.	0	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	0	.	7	66	0	.	0	.	.	.	.	.	8	91	0	.	.	.	0	.	0	.	16	82	0	.	-31	153	25	71	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	13	76	19	74	20	98	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	7	37
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	0	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	0	.	0	.	0	.	.	.	0	.	0	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	0	.	0	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	-15	305	-113	22	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	384	81	70	49	-22	327	-32	173	36	148	-35	133	.	.	.	.	.	.	20	.	.	.	.	.	.	.	.	.	.	.	264	33	9	1531	242	81	.	.	20	.	-9	711	.	.	-310	.	.	.	.	.	.	.	.	.	.	.	-30	0	.	.	.	.	.	.	.	.	.	.	158	44	126	75	178	32	60	.	.	.	.	.	173	39	137	85	.	.	30	.	101	137	351	47	40	.	13	216	22	127	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-50	.	.	.	.	.	.	.	.	.	127	36	-71	82	21	337	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	36	44
high-montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	conifers	.	.	.	.	.	.	.	.	.	.	.	.	-44	110	-295	30	.	.	-89	63	.	.	.	.	.	.	.	.	-170	.	-168	18	-421	45	20	142	.	.	2	1369	-23	214	65	137	29	203	15	284	-88	47	.	.	-150	.	.	.	-311	103	.	.	.	.	.	.	-100	.	.	.	-163	.	135	53	-48	155	.	.	360	67	119	70	-870	.	-335	41	.	.	.	.	.	.	.	.	.	.	3	1638	-242	218	30	237	.	.	.	.	.	.	59	94	188	118	-72	59	-3	357	.	.	.	.	35	158	-15	208	149	103	56	197	-124	42	-169	103	263	57	21	257	276	79	.	.	.	.	278	112	.	.	.	.	.	.	.	.	.	.	-142	50	.	.	.	.	.	.	.	.	-96	46	12	352	-6	769	-320	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-20	75
	broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	28	34	0	.	.	.	26	207	.	.	.	.	.	.	.	.	0	.	0	.	-10	93	0	.	.	.	20	90	-2	243	-24	148	58	101	9	130	34	87	.	.	0	.	.	.	50	84	.	.	.	.	.	.	0	.	.	.	150	.	0	.	8	87	.	.	-20	84	33	46	0	.	0	.	.	.	.	.	.	.	.	.	.	.	39	43	-57	83	0	.	.	.	.	.	.	.	175	56	22	89	28	104	1	1609	.	.	.	.	14	94	10	293	41	81	74	87	0	.	11	62	-10	91	68	105	-19	85	.	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	-19	79	.	.	.	.	.	.	.	.	33	73	71	35	22	111	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	28	25
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	0	.	0	.	.	.	0	.	.	.	.	.	.	.	.	.	0	.	0	.	0	.	0	.	.	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.	.	.	0	.	.	.	.	.	.	.	0	.	.	.	0	.	0	.	0	.	.	.	0	.	0	.	0	.	0	.	.	.	.	.	.	.	.	.	.	.	0	.	0	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	0	.	0	.	0	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	-16	323	-295	30	.	.	-64	172	.	.	.	.	.	.	.	.	-170	.	-168	18	-430	43	20	142	.	.	22	225	-25	187	41	212	87	99	24	186	-54	99	.	.	-150	.	.	.	-260	132	.	.	.	.	.	.	-100	.	.	.	-13	.	135	53	-40	184	.	.	339	77	152	53	-870	.	-335	41	.	.	.	.	.	.	.	.	.	.	41	116	-299	191	30	237	.	.	.	.	.	.	234	49	210	105	-45	132	-2	928	.	.	.	.	50	133	-5	1191	190	98	130	89	-124	42	-157	110	253	60	89	127	258	86	.	.	.	.	278	112	.	.	.	.	.	.	.	.	.	.	-161	41	.	.	.	.	.	.	.	.	-63	85	83	60	16	375	-320	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	8	213
lower and upper montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	conifers	-56	46	-40	.	.	.	-153	64	-166	25	-174	47	-92	34	-99	28	81	87	-77	29	272	68	37	56	-8	683	.	.	-85	183	-132	64	-83	72	-334	26	.	.	-131	45	104	59	-71	54	-11	479	-7	390	.	.	-66	58	32	256	-41	192	-91	61	-108	56	-32	244	-87	61	-16	274	-76	39	11	656	121	93	-57	102	-27	311	-92	104	-49	87	-23	99	-106	48	-270	44	52	154	-114	30	118	73	-23	233	38	108	-160	46	-121	29	-83	55	.	.	51	186	.	.	309	85	.	.	-4	96	0	.	-17	94	28	68	-19	47	39	68	99	64	31	258	-221	40	-1	1470	-19	278	-69	53	-21	452	-750	.	-35	165	50	.	-53	256	-15	149	138	47	-14	279	-91	78	78	126	-100	147	-42	176	-289	44	.	.	298	.	-31	89	-21	468	-111	43	102	88	-39	130	-24	331	.	.	-600	.	0	.	.	.	-53	14
	broadleaves	-96	96	-100	.	.	.	-105	58	-45	42	-29	93	18	120	-20	90	-59	35	-48	62	35	240	-22	600	17	444	.	.	-221	51	-70	51	62	39	-21	176	.	.	-65	75	-16	250	0	4822	-13	162	30	354	.	.	-15	160	2	1154	-11	175	-10	259	97	82	-27	120	-43	95	12	600	-18	113	40	118	-88	88	-64	125	-60	89	-82	67	4	1243	26	234	-57	64	-119	64	91	96	5	1162	-20	508	-70	29	19	134	5	327	-54	58	-34	171	.	.	-53	195	.	.	165	80	.	.	216	33	-32	190	67	201	104	70	70	100	5	1004	50	94	-66	161	81	62	36	137	27	257	24	385	-12	476	0	.	-34	67	10	.	-25	261	15	127	-57	68	-87	29	-68	78	14	646	-30	124	54	147	335	63	.	.	300	.	64	59	27	163	-38	188	-8	347	-49	87	-21	306	.	.	-300	.	-90	.	.	.	-10	54
	indeterminable	0	.	0	.	.	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.	0	.	0	.	0	.	.	.	0	.	0	.	0	.	0	.	.	.	.	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.	0	.	.	.	0	.
	total	-152	52	-140	.	.	.	-258	36	-211	25	-203	40	-74	51	-119	27	22	341	-125	30	307	87	15	947	9	861	.	.	-306	79	-202	39	-21	324	-354	24	.	.	-196	39	88	95	-70	67	-24	266	24	504	.	.	-81	50	34	278	-52	159	-102	64	-11	915	-59	145	-129	23	-4	2239	-95	40	51	166	33	450	-121	76	-87	118	-174	52	-45	175	3	2163	-163	33	-389	40	143	88	-109	58	98	137	-93	65	58	94	-155	48	-175	30	-117	61	.	.	-2	7521	.	.	474	83	.	.	212	34	-32	190	50	247	132	62	50	141	45	154	149	54	-34	93	-139	96	35	153	9	986	-45	237	-34	351	-750	.	-70	92	60	.	-78	161	-1	5093	81	79	-101	51	-159	50	92	170	-130	140	12	816	45	194	.	.	598	.	33	149	6	1781	-149	47	95	86	-88	77	-45	218	.	.	-900	.	-90	.	.	.	-64	15
submontane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	conifers	-39	96	-68	67	-7	1061	-163	31	.	.	.	.	-112	111	-90	67	-90	36	130	.	8	301	-40	164	-53	101	-380	.	-97	90	-132	82	.	.	-33	287	.	.	.	.	-188	57	-45	188	.	.	.	.	.	.	-93	67	-106	77	-88	74	-24	285	-46	80	.	.	67	297	-450	.	.	.	.	.	.	.	490	96	15	312	62	362	-200	71	2	1349	.	.	-60	106	-38	136	-189	59	-95	72	-38	55	214	66	.	.	0	.	52	155	-57	99	-54	135	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-90	59	.	.	-168	15	36	156	91	85	.	.	12	140	97	46	33	82	-326	57	-88	190	.	.	-62	77	-11	56	-167	66	-40	108	-274	80	.	.	-1	1393	.	.	.	.	-13	92	-4	79	27	237	-236	32	132	92	-88	88	-149	21	-1	14171	-111	57	.	.	-70	16
	broadleaves	-4	1227	-61	67	-24	186	-54	73	.	.	.	.	-159	55	-26	226	-25	90	100	.	32	332	-8	1391	-81	66	-20	.	-67	165	-39	119	.	.	-30	199	.	.	.	.	-49	300	20	462	.	.	.	.	.	.	-100	36	-27	184	-15	147	-2	6255	-59	195	.	.	-36	143	120	.	.	.	.	.	.	.	-10	427	113	72	-319	67	79	398	-165	46	.	.	-54	81	26	277	-269	29	-1	9808	6	1846	65	238	.	.	-195	9	30	255	-13	270	-32	161	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-165	156	.	.	-276	61	25	608	-85	131	.	.	142	57	109	154	-50	286	-45	158	325	88	.	.	-59	107	-324	33	223	99	-159	76	-168	26	.	.	-122	95	.	.	.	.	25	457	-156	2	56	100	44	107	-1	1120	-23	248	69	88	-112	39	40	91	.	.	-30	32
	indeterminable	0	.	0	.	0	.	0	.	.	.	.	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.	.	.	.	.	0	.	0	.	.	.	.	.	.	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.	0	.	.	.	.	.	.	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.	0	.	0	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	0	.	0	.	0	.	.	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.	.	.	.	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.
	total	-44	155	-129	38	-31	290	-217	26	.	.	.	.	-271	57	-117	70	-114	34	230	.	40	261	-48	305	-134	47	-400	.	-164	88	-170	76	.	.	-62	167	.	.	.	.	-236	84	-25	255	.	.	.	.	.	.	-193	34	-133	74	-103	62	-26	197	-105	102	.	.	32	519	-330	.	.	.	.	.	.	.	480	107	128	99	-256	142	-121	141	-163	58	.	.	-114	69	-12	803	-458	14	-96	97	-31	370	279	106	.	.	-195	9	82	145	-69	88	-86	115	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-256	122	.	.	-444	33	62	319	6	1365	.	.	154	52	206	88	-17	815	-372	60	237	170	.	.	-122	55	-335	31	55	339	-199	59	-442	48	.	.	-123	103	.	.	.	.	12	964	-160	0	83	100	-192	42	130	93	-111	91	-80	93	-112	96	-70	86	.	.	-100	14
hyperinsubric and colline	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-73	114	.	.	.	.	.	.	.	.	3	93	9	76	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	50	.	190	.	-4	92	13	85	0	.	0	.	-1	99	49	95	6	95	-1	98	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	.	.	0	.	.	.	.	.	.	.	-35	521	-94	78	-4	526	.	.	.	.	.	.	.	.	.	.	-234	75	.	.	.	.	.	.	-7	191
	broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-495	45	.	.	.	.	.	.	.	.	9	1741	173	69	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	290	.	-30	.	374	38	23	499	71	190	-216	66	-8	947	111	79	-39	166	-15	617	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-366	18	.	.	.	.	-300	.	.	.	.	.	.	.	26	206	210	23	-16	777	.	.	.	.	.	.	.	.	.	.	-210	0	.	.	.	.	.	.	-4	792
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	.	.	0	.	.	.	.	.	.	.	0	.	0	.	0	.	.	.	.	.	.	.	.	.	.	.	0	.	.	.	.	.	.	.	0	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-568	37	.	.	.	.	.	.	.	.	12	1323	183	62	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	340	.	160	.	369	39	37	318	71	190	-216	66	-9	837	160	58	-33	196	-16	580	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-366	18	.	.	.	.	-300	.	.	.	.	.	.	.	-10	1995	116	49	-20	657	.	.	.	.	.	.	.	.	.	.	-444	40	.	.	.	.	.	.	-11	328
total	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	conifers	-42	79	-67	66	-7	1061	-161	28	-166	25	-174	47	-61	37	-105	23	-47	65	-75	29	57	90	-19	256	-33	117	-380	.	-100	73	-136	43	-119	50	-161	41	-73	114	-75	75	29	96	-14	281	8	491	17	155	-19	133	-74	44	-35	165	-65	78	-108	53	-82	47	-32	244	-12	855	-52	90	-76	39	31	206	109	54	9	639	-21	342	7	1389	-25	139	-52	74	-126	38	-118	49	14	382	-131	28	-15	394	-29	114	39	82	-175	64	-90	35	-12	410	-57	99	-34	179	94	46	149	54	33	110	0	1636	0	.	-4	81	56	38	1	1790	26	70	64	75	9	659	-90	120	83	62	25	122	43	184	-11	612	-25	482	-4	1437	-276	61	-65	162	-15	149	69	69	-13	224	-125	42	8	607	-161	78	-42	176	-126	64	-3	1078	-10	283	-12	201	-46	169	-56	76	-142	49	-20	233	-55	102	-159	22	-42	230	-105	57	.	.	-38	14
	broadleaves	-17	282	-63	63	-24	186	-62	56	-45	42	-29	93	11	98	-20	87	-33	52	-44	66	32	268	-12	755	-37	122	-20	.	-113	68	-51	49	54	41	-24	140	-495	45	-49	79	-5	258	-6	289	35	101	9	183	15	69	-41	50	-11	250	-13	112	-4	652	31	221	-27	120	-39	82	19	334	-18	113	43	92	-33	92	-41	128	-37	134	-146	53	13	294	-16	316	-52	65	-72	53	63	94	-58	92	-8	744	-39	124	25	86	-6	283	-62	44	0	140901	-13	270	-23	199	115	59	134	40	20	126	124	54	-129	64	8	839	79	51	16	223	-3	1644	29	163	-33	165	-7	419	19	200	13	315	12	450	36	144	93	155	-34	72	-38	165	96	124	15	127	-90	41	-145	27	49	183	-107	74	-78	44	54	147	76	189	20	59	65	24	31	61	-4	884	-1	6784	30	120	-44	87	-22	197	36	162	-125	34	33	106	.	.	-6	64
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	.	.	0	.
	total	-59	102	-129	37	-31	290	-224	23	-211	25	-203	40	-50	52	-125	23	-80	44	-118	31	89	116	-31	366	-70	73	-400	.	-213	55	-187	33	-66	103	-185	38	-568	37	-124	57	23	141	-20	195	43	129	26	121	-4	657	-115	30	-46	149	-78	67	-111	58	-50	147	-59	145	-51	165	-34	226	-95	40	74	99	76	90	-32	246	-58	158	-139	96	-12	480	-68	98	-178	28	-190	39	77	110	-189	31	-23	347	-68	88	64	71	-181	66	-152	30	-12	611	-69	88	-58	148	209	38	283	40	53	86	124	54	-129	64	3	1920	135	34	17	228	22	279	93	78	-23	208	-98	116	102	65	39	135	55	175	25	353	69	293	-37	166	-314	64	31	543	-1	5093	-22	258	-158	33	-75	109	-99	97	-239	61	12	816	-50	174	17	196	54	62	19	169	-50	181	-57	109	-112	62	-64	98	-77	91	-123	61	-167	68	-71	80	.	.	-44	15

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2283797. Birmensdorf, Swiss Federal Research Institute WSL

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