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number of stems (standing living trees) with microhabitat #402
Number of standing living trees and shrubs ≥12 cm in diameter at breast height (dbh) on which a microhabitat was found. Since various microhabitats at a time may be found on the same tree, the tree may contribute to the number of stems (standing-living) of several different microhabitats. Microhabitats are defined as: fungal fruiting bodies; areas on the stem where moss, lichen or ivy covers more than 10%; woodpecker breeding cavity; shallow tree cavity; wide crack; bark pocket; fresh break; dendrotelm; canker or burr; buttress-root concavity; deep tree cavity; area of exposed bare wood larger than a hand; stag-headedness; resin flow; insect damage (foot of trunk /trunk/stem); >20% dead branches.

microhabitats (20 classes) #2360
Type of microhabitat that occurs at least once on a tree or shrub ≥12 cm in diameter at breast height (dbh). Reference: Field Survey (MID 819: Bemerkung zu Ex-LFI-Probebaum, MID1027: Baumschadenart, MID 1035-1049: Baummikrohabitate)

altitudinal vegetation belts (NaiS; 6 classes) #2633
Altitudinal vegetation belts in the system used in the guidelines for monitoring the sustainability and performance of protection forests (NaiS; Frehner et al. 2005), reduced to six classes. The variable represents a simplification of the NaiS altitudinal vegetation belts with ten classes (NAISHSTKOMB) in which the classes «hyperinsubric» and «colline» are merged with «colline with beech» to form the class «hyperinsubric and colline» and the class «lower montane» with «upper montane» and «lower/upper montane» to form the class «lower and upper montane». The information is based on the altitudinal vegetation belts determined by experts (accessible forest sample plots of NFI4 on the 1.4-km network; Arge Frehner et al. 2020), as well as on the altitudinal vegetation belts modelled for the period 1981-2010 (other sample plots; Zischg et al. 2021).

canton #827
Regional demarcation with the cantons as a unit, with the two half-cantons, Basel-Land and Basel-Stadt, combined into one canton for statistical reasons.

accessible forest without shrub forest NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was accessible on foot.

1.4 km grid, subgrids 1-5 #1746
Sub-grids 1, 2, 3, 4 and 5 of the field surveys on the sampling grid with a mesh size of 1.4 km (base grid).

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI5
number of stems (standing living trees) with microhabitat
microhabitats (20 classes) · altitudinal vegetation belts (NaiS; 6 classes)
regional demarcation: canton
unit: n/ha
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
microhabitats (20 classes)	altitudinal vegetation belts (NaiS; 6 classes)	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	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	.
	upper subalpine	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	.
	subalpine	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	.
	high-montane	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	.
	lower and upper montane	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	.
	submontane	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	.
	hyperinsubric and colline	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	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	.
woodpecker breeding cavity	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	1	57
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	58
	high-montane	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	2	96	2	68	1	60	0	.	0	.	1	34
	lower and upper montane	0	.	0	.	0	.	0	58	0	.	0	.	0	.	0	.	0	99	0	.	0	99	0	.	0	.	0	.	0	100	0	.	0	.	0	99	0	.	0	100	0	.	0	75	0	.	0	.	0	99	0	32
	submontane	1	53	0	.	0	.	0	70	1	99	2	76	0	.	0	.	0	.	1	98	0	99	0	.	0	.	0	.	0	.	0	.	2	75	5	86	1	74	0	.	0	.	2	62	0	.	0	.	1	57	1	24
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	46	0	.	0	.	1	97	0	.	0	.	2	41
	total	1	53	0	.	0	.	0	41	0	99	1	78	0	.	0	.	0	35	0	100	0	70	0	.	0	.	0	.	0	100	0	.	1	76	1	70	1	74	1	41	1	99	1	42	1	44	0	.	1	49	1	15
cavity (<5 cm depth)	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	65	0	.	0	.	1	98	0	.	0	.	3	42
	subalpine	0	.	0	.	0	.	2	66	0	.	0	.	0	.	0	.	2	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	11	72	2	38	0	.	0	.	2	24
	high-montane	0	.	0	.	0	.	3	45	0	.	5	95	0	.	14	62	4	41	0	.	0	.	0	.	0	.	0	.	9	96	0	.	0	.	0	.	0	.	8	50	5	70	0	.	5	36	0	.	0	.	4	20
	lower and upper montane	2	96	3	66	3	74	3	26	3	97	3	48	0	.	12	41	2	61	5	51	2	55	0	99	11	46	6	61	6	28	14	55	3	52	4	57	1	97	16	30	2	98	4	30	15	76	10	95	1	99	5	12
	submontane	2	40	0	.	0	.	2	37	3	52	4	59	0	.	0	.	6	94	2	69	4	50	4	96	0	.	0	.	0	.	2	98	11	47	17	61	11	66	0	.	19	56	2	60	12	92	0	.	2	53	4	20
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	21	0	.	0	.	4	60	0	.	0	.	13	19
	total	2	38	3	66	3	74	3	18	3	47	3	35	0	.	11	37	4	24	4	46	2	37	1	76	9	47	4	62	5	28	5	51	6	39	4	47	9	64	13	16	4	44	3	25	5	28	8	96	1	47	5	8
cavity (>5 cm depth)	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	88	0	.	0	.	4	49	0	.	0	.	2	38
	subalpine	0	.	0	.	0	.	4	54	0	.	0	.	0	.	0	.	2	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	38	0	.	0	.	4	48	0	.	0	.	3	25
	high-montane	0	.	0	.	0	.	5	57	0	.	0	.	0	.	0	.	3	35	0	.	0	.	0	.	0	.	4	75	0	.	0	.	0	.	4	65	0	.	5	46	4	52	6	42	2	47	0	.	0	.	3	19
	lower and upper montane	5	69	1	96	2	98	3	21	3	97	3	40	0	.	11	46	4	48	7	43	3	54	3	47	10	51	12	41	7	25	0	.	9	59	2	44	6	77	16	25	9	60	3	31	19	34	0	.	4	44	6	10
	submontane	2	37	0	.	0	.	2	36	5	54	3	69	0	.	0	.	2	96	4	62	1	99	2	96	0	.	0	.	12	59	2	98	16	36	0	.	6	69	0	.	0	.	2	74	0	.	0	.	3	37	4	17
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	21	0	.	0	.	7	98	0	.	0	.	18	20
	total	3	33	1	96	2	98	3	17	4	47	3	35	0	.	9	47	3	22	6	37	2	49	2	44	8	53	8	38	6	24	1	99	12	33	2	37	6	57	16	15	5	45	3	25	4	24	0	.	3	29	5	7
damage from insects	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	1	70
	subalpine	0	.	0	.	0	.	2	63	0	.	0	.	0	.	0	.	1	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	94	1	51	0	.	0	.	1	30
	high-montane	0	.	0	.	0	.	0	99	0	.	6	95	0	.	0	.	1	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	1	97	1	98	2	39	0	.	0	.	1	26
	lower and upper montane	0	.	0	.	0	.	1	52	0	.	1	73	0	.	0	.	1	70	1	99	0	99	0	.	0	.	0	.	1	60	0	.	0	.	0	99	0	.	0	100	0	.	1	54	0	.	0	.	0	.	1	27
	submontane	0	.	0	.	0	.	0	100	1	99	4	73	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	1	99	0	.	0	.	0	99	0	42
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	97	0	.	0	.	0	70
	total	0	.	0	.	0	.	1	40	0	99	2	49	0	.	0	.	1	29	1	76	0	100	0	.	0	.	0	.	1	60	0	.	0	.	0	99	0	99	0	58	0	99	1	41	2	30	0	.	0	100	1	15
dendrotelm	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	.	0	.	0	.	0	.	0	.	9	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	66
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	94	0	.	2	70	0	.	0	.	1	78	0	.	0	.	1	35
	lower and upper montane	5	69	0	.	0	.	1	39	3	97	3	40	0	.	4	61	8	62	3	50	0	70	1	100	1	97	6	98	1	54	0	.	8	40	2	51	0	.	25	20	1	98	4	30	7	73	0	.	4	67	4	12
	submontane	1	52	0	.	0	.	1	49	4	50	0	.	0	.	0	.	0	.	2	98	0	99	0	.	0	.	0	.	3	97	3	58	3	99	0	.	5	68	0	.	17	89	7	39	20	75	0	.	2	49	2	20
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	26	0	.	23	51	0	.	0	.	0	.	13	24
	total	2	42	0	.	0	.	1	33	4	44	2	41	0	.	3	61	3	32	3	44	0	57	1	100	1	98	3	99	2	43	2	59	6	38	2	45	4	68	15	16	2	80	4	23	1	47	0	.	3	48	3	9
buttress-root concavity	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	56	0	.	0	.	67	41	0	.	0	.	57	19
	subalpine	0	.	0	.	0	.	91	17	0	.	0	.	0	.	33	59	68	12	0	.	20	0	0	.	75	28	209	54	102	32	0	.	0	.	45	39	0	.	47	17	98	32	44	43	59	15	0	.	0	.	70	8
	high-montane	0	.	0	.	0	.	66	15	0	.	84	41	0	.	153	33	45	11	0	.	83	41	0	.	190	0	82	28	73	33	0	.	0	.	48	48	0	.	62	20	104	23	83	22	33	14	0	.	0	.	54	6
	lower and upper montane	48	33	116	21	83	21	57	8	49	33	41	14	0	.	96	17	55	14	48	18	60	15	45	15	148	23	94	19	76	10	52	30	52	18	64	15	49	26	80	11	130	28	61	11	71	27	57	19	56	14	63	3
	submontane	24	14	0	.	0	.	26	13	22	29	12	48	0	.	0	.	18	55	31	37	27	27	11	43	0	.	25	71	40	38	34	25	30	26	56	35	24	21	0	.	54	32	34	20	65	46	20	0	23	17	26	6
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	4	90	0	.	36	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	17	0	.	49	48	12	53	0	.	0	.	49	16
	total	26	13	116	21	83	21	53	6	31	23	37	14	4	90	99	16	52	6	43	16	50	13	40	15	141	20	103	19	73	9	39	20	43	15	60	14	29	17	66	8	109	17	55	9	46	10	46	22	36	11	53	2
bark loss	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	70	0	.	0	.	31	25	0	.	0	.	30	15
	subalpine	0	.	0	.	0	.	26	21	0	.	0	.	0	.	0	.	18	20	0	.	20	0	0	.	8	84	66	71	0	.	0	.	0	.	0	.	0	.	26	34	38	91	4	90	31	16	0	.	0	.	23	11
	high-montane	0	.	0	.	0	.	18	29	0	.	50	44	0	.	8	54	26	15	0	.	54	37	0	.	120	0	14	51	42	44	0	.	0	.	5	95	0	.	26	21	32	26	19	32	26	18	0	.	0	.	25	8
	lower and upper montane	18	58	17	36	27	40	24	10	30	37	25	27	0	.	46	21	35	19	22	17	23	16	13	19	64	39	35	28	25	16	6	68	26	24	19	26	25	57	31	26	40	30	21	14	47	25	9	54	27	22	25	5
	submontane	17	16	0	.	0	.	17	13	33	32	24	23	0	.	0	.	25	47	27	23	23	31	9	55	0	.	25	71	51	21	19	27	18	30	31	68	18	22	0	.	21	63	13	24	8	92	0	.	18	18	20	6
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	11	0	.	23	51	26	34	0	.	0	.	65	11
	total	17	15	17	36	27	40	21	8	32	25	27	18	0	.	37	21	26	10	24	13	24	14	12	18	60	35	33	26	28	13	15	26	23	19	17	24	19	23	49	10	35	22	18	11	29	10	7	56	21	15	26	3
fire scar	n/a	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	.
	upper subalpine	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	.
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100
	high-montane	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	.
	lower and upper montane	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	.
	submontane	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	.
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	80	0	.	0	.	0	.	0	.	0	.	4	71
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	81	0	.	0	.	0	.	0	.	0	.	0	69
bark shelter/pocket	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	84	0	.	0	.	21	47	0	.	0	.	15	30
	subalpine	0	.	0	.	0	.	7	34	0	.	0	.	0	.	0	.	8	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	36	0	.	5	94	18	34	0	.	0	.	11	18
	high-montane	0	.	0	.	0	.	8	35	0	.	0	.	0	.	4	89	10	31	0	.	11	86	0	.	0	.	8	84	0	.	0	.	0	.	0	.	0	.	19	35	5	74	9	72	12	40	0	.	0	.	10	18
	lower and upper montane	9	65	1	97	3	71	5	24	7	64	4	52	0	.	9	42	7	47	7	32	4	37	1	63	4	97	2	98	8	29	2	95	9	35	3	46	1	97	7	29	2	67	8	34	1	98	0	.	5	48	6	10
	submontane	2	40	0	.	0	.	2	29	3	61	6	63	0	.	0	.	22	62	5	36	3	80	0	.	0	.	0	.	0	.	4	60	10	53	0	.	2	82	0	.	25	84	3	54	0	.	0	.	1	57	3	17
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	22	0	.	7	82	11	79	0	.	0	.	25	21
	total	3	35	1	97	3	71	5	16	4	45	4	40	0	.	8	40	10	17	6	26	4	32	1	63	3	97	3	67	6	29	3	52	9	31	3	47	2	70	20	16	5	53	7	27	13	22	0	.	2	41	7	7
fresh stem/large branch breakage	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	84	0	.	0	.	5	52	0	.	0	.	5	36
	subalpine	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	2	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	69	0	.	0	.	7	40	0	.	0	.	3	28
	high-montane	0	.	0	.	0	.	1	99	0	.	2	95	0	.	4	89	4	29	0	.	0	.	0	.	0	.	0	.	5	95	0	.	0	.	0	.	0	.	3	49	1	97	1	98	3	39	0	.	0	.	3	19
	lower and upper montane	6	96	0	.	6	59	2	35	5	72	4	39	0	.	8	51	4	49	4	55	5	58	2	60	1	97	4	68	6	31	5	95	2	65	1	99	3	66	6	37	0	.	1	50	3	54	0	.	3	48	3	12
	submontane	3	45	0	.	0	.	2	35	4	51	1	99	0	.	0	.	0	.	7	41	1	70	2	96	0	.	0	.	3	97	2	68	10	54	5	86	7	55	0	.	7	89	3	53	3	92	0	.	2	47	3	17
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	20	0	.	0	.	2	67	0	.	0	.	8	19
	total	3	41	0	.	6	59	2	25	4	42	3	35	0	.	7	47	3	20	5	36	3	52	2	53	1	97	2	69	5	28	2	61	6	45	1	74	6	51	7	16	1	70	2	35	4	24	0	.	2	34	3	8
broad crack	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	99
	subalpine	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	98	0	.	0	.	3	51	0	.	0	.	1	40
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	75	2	96	2	98	2	46	0	.	0	.	1	27
	lower and upper montane	0	.	0	.	0	.	0	71	0	.	1	70	0	.	3	70	3	56	1	99	0	100	0	.	0	.	0	.	1	76	0	.	0	99	0	.	1	97	3	55	0	.	1	53	1	98	0	.	0	.	1	22
	submontane	1	57	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	0	.	0	.	4	96	0	.	0	.	13	86	2	69	0	.	0	.	0	.	0	.	0	.	0	.	1	36
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	23	0	.	0	.	0	.	0	.	0	.	6	23
	total	0	57	0	.	0	.	0	52	0	.	0	70	0	.	2	71	1	31	1	77	0	100	0	.	0	.	0	.	1	61	0	.	0	99	1	99	2	61	5	20	1	99	1	47	2	32	0	.	0	.	1	12
crown deadwood	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	88	0	.	0	.	29	58	0	.	0	.	39	25
	subalpine	0	.	0	.	0	.	64	25	0	.	0	.	0	.	17	82	53	22	0	.	0	.	0	.	231	43	178	52	33	65	0	.	0	.	35	71	0	.	50	34	93	91	5	94	49	26	0	.	0	.	56	13
	high-montane	0	.	0	.	0	.	50	30	0	.	92	35	0	.	95	74	59	16	0	.	72	37	0	.	0	.	85	36	11	53	0	.	0	.	26	62	0	.	44	32	60	32	54	31	73	28	0	.	0	.	60	11
	lower and upper montane	8	64	93	39	93	44	51	19	40	35	57	29	0	.	13	38	52	37	29	35	78	29	36	31	97	56	19	35	60	21	40	59	41	39	37	26	64	69	20	27	65	45	46	23	63	27	47	49	43	48	48	8
	submontane	24	22	0	.	0	.	58	20	33	42	78	59	0	.	0	.	19	58	28	41	68	36	87	78	0	.	575	71	65	78	60	35	20	35	69	79	57	30	0	.	32	61	50	36	8	92	0	.	39	39	48	11
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	14	90	0	.	27	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	0	77	13	0	.	0	.	68	52	0	.	0	.	68	13
	total	23	22	93	39	93	44	54	12	35	30	67	25	14	90	24	46	53	11	29	28	74	22	43	32	109	45	79	33	55	21	54	31	33	31	37	23	59	28	50	11	64	29	46	17	59	17	35	52	40	30	51	5
canker or burrs	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	84	0	.	0	.	11	55	0	.	0	.	11	31
	subalpine	0	.	0	.	0	.	15	31	0	.	0	.	0	.	17	82	14	27	0	.	0	.	0	.	8	84	21	60	3	93	0	.	0	.	25	71	0	.	26	32	28	42	20	94	3	34	0	.	0	.	13	15
	high-montane	0	.	0	.	0	.	11	35	0	.	5	97	0	.	34	63	8	26	0	.	7	94	0	.	0	.	12	68	0	.	0	.	0	.	10	57	0	.	35	28	31	52	13	54	9	25	0	.	0	.	12	13
	lower and upper montane	3	96	10	52	12	50	10	20	3	66	9	28	0	.	18	39	17	34	6	39	11	25	4	36	9	55	12	40	10	26	0	.	19	45	6	39	5	73	66	19	36	58	10	22	37	53	13	52	3	43	14	9
	submontane	3	33	0	.	0	.	2	38	3	59	3	70	0	.	0	.	11	56	23	72	7	50	0	.	0	.	0	.	1	96	4	52	10	48	11	90	4	45	0	.	27	47	11	34	46	46	0	.	5	30	6	16
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	21	0	.	0	.	16	45	0	.	0	.	42	18
	total	3	31	10	52	12	50	8	15	3	46	7	26	0	.	20	32	12	15	11	47	9	22	4	36	9	50	13	31	8	26	3	53	15	35	8	29	4	39	49	12	32	33	11	18	11	20	10	55	4	25	13	6
perennial fungal fruiting body	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	70
	subalpine	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	98	0	.	4	94	1	70	0	.	0	.	1	45
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	62	0	.	7	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	1	63	0	.	0	.	1	43
	lower and upper montane	0	.	0	.	2	68	0	50	0	.	1	99	0	.	1	99	0	99	0	.	1	76	0	99	4	97	1	98	0	99	0	.	0	.	1	99	0	.	1	71	0	.	0	.	0	.	0	.	0	99	0	25
	submontane	1	57	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	53	0	.	0	99	0	.	0	.	1	73	0	.	0	.	0	99	0	31
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	100
	total	0	57	0	.	2	68	0	47	0	.	1	99	0	.	0	99	1	48	0	.	1	60	0	99	3	97	0	99	0	100	0	.	1	55	1	99	0	99	1	47	0	.	0	59	0	48	0	.	0	70	1	17
ephemeral fungal fruiting body	n/a	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	.
	upper subalpine	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	.
	subalpine	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	.
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	100
	lower and upper montane	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	99	1	99	0	.	0	.	9	81	0	.	0	100	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	70	0	40
	submontane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	98	2	75	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	54
	hyperinsubric and colline	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	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	100	1	99	0	.	0	.	7	81	0	.	0	100	1	99	1	60	0	.	0	.	0	.	0	.	0	100	0	100	0	.	0	70	0	32
bryophytes >10%	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	0	.	1	70
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	47	0	.	0	.	0	.	20	84	0	.	0	.	0	.	0	.	0	.	0	.	2	75	3	91	11	64	2	53	0	.	0	.	3	31
	high-montane	0	.	0	.	0	.	27	34	0	.	6	93	0	.	0	.	23	27	0	.	32	86	0	.	0	.	10	53	4	96	0	.	0	.	4	97	0	.	12	50	13	97	10	50	21	27	0	.	0	.	19	15
	lower and upper montane	88	23	8	83	40	36	93	7	44	30	104	16	0	.	105	22	46	25	121	15	124	13	90	18	203	45	85	23	86	12	62	39	80	18	79	23	50	32	17	32	98	32	87	11	131	19	50	28	78	16	86	4
	submontane	89	12	0	.	0	.	104	13	55	20	104	21	0	.	0	.	15	42	135	17	75	17	199	38	0	.	225	24	35	38	103	19	118	22	233	56	76	18	0	.	59	41	181	16	130	42	142	32	76	16	101	5
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	151	46	0	.	339	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	220	0	124	16	0	.	344	36	91	33	0	.	118	33	138	13
	total	89	11	8	83	40	36	80	7	52	17	95	13	151	46	83	23	26	20	125	11	103	11	105	17	170	45	58	23	69	12	92	17	96	14	73	23	72	16	58	15	51	30	104	10	31	16	60	29	78	11	70	3
lichens >10%	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	282	12	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	370	7	0	.	0	.	240	16	0	.	0	.	271	9
	subalpine	0	.	0	.	0	.	223	17	0	.	0	.	0	.	143	25	229	10	0	.	50	0	0	.	1200	38	369	51	297	32	0	.	0	.	70	71	0	.	312	15	348	33	166	30	232	12	0	.	0	.	247	6
	high-montane	0	.	0	.	0	.	108	19	0	.	164	21	0	.	171	45	121	13	0	.	133	38	0	.	170	0	182	33	93	46	0	.	0	.	64	41	0	.	114	19	157	33	161	19	152	14	0	.	0	.	130	7
	lower and upper montane	0	.	1	96	0	.	16	17	1	97	26	28	0	.	14	46	20	53	23	39	9	75	107	17	15	67	19	45	19	33	0	.	10	46	15	30	0	.	34	33	2	66	104	12	43	57	0	.	1	70	32	7
	submontane	3	58	0	.	0	.	1	50	3	68	2	99	0	.	0	.	0	.	1	98	3	63	22	66	0	.	0	.	1	97	1	98	2	75	0	.	0	.	0	.	0	.	1	99	12	65	0	.	3	50	2	23
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	4	90	0	.	13	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	41	0	.	0	.	18	48	0	.	80	66	17	32
	total	3	58	1	96	0	.	41	13	3	60	33	21	4	90	44	34	151	7	16	39	13	41	93	17	169	71	110	31	38	26	1	99	7	42	24	26	0	.	76	13	105	29	84	11	159	8	0	.	3	47	68	4
ivy >10%	n/a	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	.
	upper subalpine	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	.
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	100
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	1	98	1	62	0	.	0	.	1	43
	lower and upper montane	7	57	0	.	7	72	2	29	4	70	5	42	0	.	1	99	0	.	33	39	4	54	1	99	23	79	12	99	14	24	15	95	8	55	11	81	68	43	0	.	1	97	3	37	6	73	13	65	18	30	7	14
	submontane	26	30	0	.	0	.	17	21	28	44	51	36	0	.	0	.	0	.	54	35	38	41	24	37	0	.	0	.	34	57	13	47	23	30	59	57	37	22	0	.	24	89	50	18	22	50	29	24	39	25	31	9
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	34	68	0	.	14	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	0	21	27	0	.	87	35	22	53	0	.	9	75	23	21
	total	24	29	0	.	7	72	6	19	21	42	17	32	34	68	1	99	0	78	39	27	15	36	4	40	19	80	6	99	14	24	14	43	14	28	12	61	44	20	9	27	3	83	16	18	3	34	14	47	30	20	11	7
resinosis	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	46	0	.	0	.	85	35	0	.	0	.	46	26
	subalpine	0	.	0	.	0	.	99	16	0	.	0	.	0	.	47	23	89	12	0	.	50	0	0	.	30	58	59	52	15	49	0	.	0	.	20	71	0	.	58	21	110	37	20	62	66	12	0	.	0	.	76	7
	high-montane	0	.	0	.	0	.	62	15	0	.	113	40	0	.	110	44	74	10	0	.	47	35	0	.	50	0	66	27	51	51	0	.	0	.	42	60	0	.	56	24	105	24	53	19	63	13	0	.	0	.	67	6
	lower and upper montane	12	69	42	28	19	31	21	12	12	62	29	23	0	.	27	23	33	20	11	27	26	17	21	22	19	59	30	45	25	16	19	55	20	44	24	24	39	50	10	30	39	22	21	14	23	44	22	38	12	28	22	5
	submontane	17	25	0	.	0	.	15	33	4	84	10	41	0	.	0	.	11	62	6	49	18	43	7	66	0	.	75	71	7	68	17	43	3	59	0	.	25	30	0	.	7	89	14	45	0	.	40	79	20	25	15	11
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	10	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	89	0	.	0	.	23	42	0	.	0	.	5	39
	total	17	24	42	28	19	31	31	9	7	51	32	21	10	90	39	24	64	7	9	24	25	15	19	21	22	44	46	22	24	15	17	34	13	40	26	24	28	26	20	15	70	18	22	12	58	9	23	37	16	20	33	3
other microhabitats	n/a	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	308	11	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	394	9	0	.	0	.	270	15	0	.	0	.	298	8
	subalpine	0	.	0	.	0	.	264	14	0	.	0	.	0	.	160	31	254	9	0	.	70	0	0	.	1229	35	423	44	313	33	0	.	0	.	125	8	0	.	330	15	397	30	171	29	270	11	0	.	0	.	278	6
	high-montane	0	.	0	.	0	.	178	13	0	.	208	17	0	.	205	34	190	9	0	.	176	34	50	0	220	0	234	26	137	35	0	.	0	.	104	23	0	.	231	14	234	21	227	17	242	9	0	.	0	.	206	5
	lower and upper montane	162	24	94	22	131	18	181	5	166	24	194	10	0	.	202	16	188	14	223	10	189	8	210	9	362	28	233	19	184	8	119	22	184	12	160	14	153	20	365	9	265	22	241	6	326	13	100	20	157	12	207	2
	submontane	149	9	0	.	0	.	173	8	203	13	195	15	0	.	0	.	131	27	203	14	165	13	267	27	0	.	325	38	233	26	159	18	208	16	332	44	155	12	0	.	240	13	285	11	255	37	210	30	149	9	185	4
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	204	33	0	.	568	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	290	0	473	7	0	.	395	32	242	15	0	.	118	33	430	6
	total	150	8	94	22	131	18	186	4	192	11	196	8	204	33	199	14	228	5	217	8	179	7	216	9	462	28	261	15	193	8	148	15	194	10	159	13	157	10	383	5	270	13	250	5	260	5	108	22	151	7	222	2
total	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	upper subalpine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	subalpine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	high-montane	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	lower and upper montane	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	submontane	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	hyperinsubric and colline	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.

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. 1395869. Birmensdorf, Swiss Federal Research Institute WSL

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