Forest Ecology and Management 154 (2001) 105±115
The impact of logging intensity on ®eld-layer
vegetation in Swedish boreal forests
Johan Bergstedt*, Per Milberg
Department of Biology-IFM, LinkoÈping University, SE-581 83 LinkoÈping, Sweden
Received 6 December 1999; received in revised form 13 June 2000; accepted 26 September 2000
Abstract
The relationship between logging intensity and changes in ground cover vegetation was studied in 16 species and groups of
species recorded at 10- or 11-year intervals in mature conifer-dominated forests. The 789 plots located in northern and central
Sweden had been surveyed by the National Forest Inventory and the National Survey of Forest Soil and Vegetation. Thirtyseven percent of the plots had been subjected to a thinning or clear-cutting between the inventories. A principal components
analysis showed that, of the variables considered, logging intensity had the highest explanatory power regarding change in
ground cover vegetation between the inventories (the other variables were sum of temperatures, age of stand, timber volume,
percentage Pinus sylvestris and site productivity). A multivariate direct gradient analysis technique (Redundancy analysis)
showed that the logging intensity signi®cantly affected the change in cover. This analysis also ranked the species in their
responsiveness to logging. Epilobium angustifolium, narrow-leaved grasses and broad-leaved grasses, increased most with
logging intensity. The response was not linear and only detectable at high logging intensities (>80%). In contrast, Vaccinium
myrtillus seemed to decrease linearly with increased logging intensity. There was several years time-lag in the response to
logging of E. angustifolium, V. myrtillus and narrow-leaved grasses. Several species and groups of species seemed unaffected
by the logging. In sample plots unaffected by logging the cover of most species decreased. # 2001 Elsevier Science B.V. All
rights reserved.
Keywords: Clear cut; Community; Cutting; Multivariate analysis; Sweden; Thinning
1. Introduction
The composition of the ground vegetation in a
forest is, to a large extent, shaped by the density
and composition of the tree layer. Temperature and
light are among the most important environmental
variables that are governed by the tree stratum
(BraÊkenhielm and Persson, 1980; Foster, 1985; Brumelis and Carleton, 1989; Nygaard and édegaard,
*
Corresponding author. Tel.: ‡46-1328-1332;
fax: ‡46-1328-2611.
E-mail address: [email protected] (J. Bergstedt).
1999). Consequently, changes in the tree layer are
likely to lead to the changes in the ground vegetation.
More speci®cally, a reduction in the tree canopy can
lead to the death of some individuals, to the establishment from seeds, and to a shift in competitiveness
among the species because of the increase in soil
moisture, nutrients, light and temperature during the
growth season (Foster, 1985; Zobel, 1993). Although
the effect of clear-cutting, thinning and selective felling on ground vegetation in boreal forests has been
studied (e.g. Brumelis and Carleton, 1989; Nieppola,
1992; Hannerz and HaÊnell, 1997; BraÊkenhielm and
Liu, 1998), we are not aware of any study exploring
0378-1127/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 1 1 2 7 ( 0 0 ) 0 0 6 4 2 - 3
106
J. Bergstedt, P. Milberg / Forest Ecology and Management 154 (2001) 105±115
several intensities of logging. In recent years, forestry
practices in Sweden have started to change because of
the Forestry Act of 1994 (Skogsstyrelsen, 1994) and
because of in¯uence from the certi®cation of Swedish
forest companies by the Forest Stewardship Council
(FSC; http://www.fsc-sweden.org/). These changes
are likely to lead to larger areas being subjected to
logging that is more intense than today's thinning but
not as intense as a complete clear-cut. Therefore it is of
interest to quantify the changes in ground-cover vegetation in response to the logging intensity.
In the present study, we investigated the response of
the ®eld-layer vegetation in boreal coniferous forest to
logging of varying intensities. Data originated from
789 sample plots where the vegetation had been
recorded with 10- or 11-year intervals. More speci®cally, we tested whether logging intensity affects the
changes in cover of ®eld-layer ¯ora between the two
inventories. If this was so, we wanted to describe the
responses of individual species to logging intensity.
Finally, we expected a time lag in the response to the
logging.
2. Materials and methods
2.1. The data
In 1983 the National Forest Inventory (NFI) of
Sweden started to establish permanent plots with an
aim to revisit them at 5-year intervals. Parallel to this,
the plots were also subjected to a soil and vegetation
inventory by the National Survey of Forest Soils and
Vegetation (NSFV). Both inventories have been conducted by the Swedish University of Agricultural
Sciences. The main objective was to re®ne the Swedish system for estimating site productivity by means of
easily assessed variables including vegetation, slope,
soil type and soil moisture (HaÈgglund and Lundmark,
1981).
Statistically the inventory is a strati®ed systematic
clustered sampling with repeated measurements. For
logistic reasons the sample plots are located along the
sides of a quadrat that corresponds to one day's work
by an inventory team. A quadrat contains one sample
plot at each corner of the quadrat and one in the middle
of each side, i.e. eight in total per cluster (Lindroth,
1995).
The sample plots are circular with a radius of 10 or
20 m for the forest variables. Vegetation is surveyed in
a 100 m2 plot, i.e. a circular plot with a radius of
5.64 m. All plots have the same centre.
The sample plots used in this study were established
between 1983 and 1987. NFI revisited the plots for the
®rst time in 1988±1990, but lack of funding precluded
vegetation or soil inventories. In 1993, the vegetation
and soil inventories were resumed, but funding limited
the investigation to only half of the plots each year.
The sample plots were thus revisited in 1993±1998 for
the second vegetation and soil inventory. The interval
between the inventories was 10 or 11 years. The
second inventory had environmental monitoring as
an additional object, which meant that more species
were recorded in more detail (Odell and StaÊhl, 1998).
The data are compiled by the NSFV and kept in a
public database.
2.2. Inclusion criteria
We decided to concentrate on the boreal coniferous
forests of northern and central Sweden, a vast but still
relatively homogeneous area (Kempe et al., 1992).
More precisely, we included plots from the `southern
boreal' and `middle boreal' regions (Anon., 1977).
Both regions are dominated by P. sylvestris L. and
Picea abies (L.) H. Karst., but the `southern boreal'
has a small amount of nemoral broad-leaved tree
species lacking further north.
For several reasons, we decided to increase the
homogeneity of the vegetation under consideration.
First, to exclude plots where forestry is not conducted,
we eliminated plots not classi®ed as ``productive
forests''. Second, we excluded stands with more than
15% of the standing timber being of deciduous tree
species at the ®rst inventory, since logging strategies in
such stands might be selectively removing deciduous
trees. Third, we excluded young stands, i.e. plots
where trees had a mean diameter at breast height less
than 100 mm at the ®rst inventory (excluding maturity
classes C1±D2, according to the Field instruction for
the NFI; Anon., 1983±1998), because these would not
normally be subjected to logging. Fourth, since different logging strategies are often used on dry and wet
soils compared with intermediate types, we excluded
plots that had been classi®ed as ``dry'' or ``moist'' or
that were on organogenic soils (Karltun et al., 1996).
J. Bergstedt, P. Milberg / Forest Ecology and Management 154 (2001) 105±115
Since the sample plots are permanent and the
sample design is systematic, some sample plots were
divided by different kinds of boundaries among the
land use classes, stand maturity classes and moisture
classes. All such plots were excluded.
We also excluded a few cases where two different
logging operations had taken place between the inventories and one particular case where logging had not
been recorded, although some trees had been removed.
These inclusion criteria lead to 789 sample plots
from 541 clusters being selected for the present study
(Fig. 1).
2.3. Vegetation
The objective of the NFSV was originally to
improve the system for classi®cation of forests
according to site productivity, which is re¯ected
in the species and groups of species subjected to
the inventory. After excluding species that were
present on less than 10 occasions under the two
Fig. 1. Locations of the 541 clusters of sample plots in central and
northern Sweden.
107
inventories, 16 species and groups of species were
considered:
Narrow-leaved grasses Ð grasses with folded and
thread-like leaves. In the boreal forest this group is
dominated by Deschampsia flexuosa (L.) Trin. with
the occasional presence of Festuca ovina L. and
Nardus stricta L.
`Broad-leaved grasses' Ð all grasses that are not
`narrow-leaved grasses'; dominated by Calamagrostis spp. and Deschampsia cespitosa (L.) P.
Beauv. with some Agrostis capillaris L., Melica
nutans L. and Poa spp.
`Carex-Luzula' Ð species of these genera found in
dry or mesic habitats; dominated by Luzula pilosa
(L.) Willd., L. campestris (L.) DC., L. multiflora
(Ehrh.) Lej., Carex digitata L. and C. pallescens L.
`Herbs' Ð 27 species and one group of species,
tall-grown ferns (mainly Dryopteris filix-mas (L.)
Schott and Athyrium filix-femina (L.) Roth). The
most common of the 27 species are Geranium
sylvaticum L., Filipendula ulmaria (L.) Maxim.,
Rumex acetosa L. and Cirsium palustre (L.) Scop.
in decreasing order of abundance (Odell and Drakenberg, 1991).
`Equisetum sylvaticum L., Carex globularis L. and
Menyanthes trifoliata L.' Ð grouped together
because previous studies have shown them to indicate similar site productivity.
`Andromeda polifolia L. and Vaccinium oxycoccus
L.' Ð grouped together because previous studies
have shown them to indicate similar site productivity.
`Calluna vulgaris' Ð consists of Calluna vulgaris
(L.) Hull and Erica tetralix L. but the latter species
is very rare in the geographic area considered here.
The other species used in the present analysis were
Empetrum spp., Epilobium angustifolium L.,
Ledum palustre L., Pteridium aquilinum (L.) Kuhn,
Rubus chamaemorus L., Vaccinium myrtillus L. V.
uliginosum L., V. vitis-idaea L. and the family
Lycopodiaceae.
The long interval between the inventories, the
yearly weather variations and the problem of reinventing the plots at the same time of the year can cause
vegetation cover data variations other than the actual
changes in species' abundance. Therefore, according to the ®eld instructions, the vegetation cover is
108
J. Bergstedt, P. Milberg / Forest Ecology and Management 154 (2001) 105±115
estimated as the maximum cover during the veget
ation season. This overcomes part of the problem,
as indicated by the analysis where exclusion of those
plots visited very early and very late in the season
caused no apparent shifts in the data (Odell and StaÊhl,
1998). Before each ®eld season, and once during
the season, crews met to cross-calibrate their cover
estimates.
The vegetation survey was made for a representative area of the sample plot by excluding areas that had
been subject to disturbance such as soil scari®cation
and walking paths. Parts of the sample area that
differed in other aspects such as rock outcrops,
stumps, heaps of slash residues, etc., were also omitted
from the vegetation inventory.
The methods for the vegetation survey differed in
1980s and 1990s and more species were identi®ed and
records were made on a more detailed scale in the
1990s. The vegetation cover estimates in the 1990s,
which were made on a 100-point scale (0.1, 1, 2,
3,. . .,100 m2), were transformed to the classes 0.1, 1,
3, 6, 9, 12, 15, 20, 25, 30, 40, 50, 60, 80 and 100 m2
that were used in the 1980s. All values were then
transformed to percent vegetation cover with the
following formula:
PC ˆ
V
100
A
(1)
where PC is the percent vegetation cover, V the
vegetation cover in square metres and A the representative area.
In the present study, we used the difference in
vegetation cover between the two inventories, i.e.
the percent cover at the second inventory minus that
of the ®rst.
2.4. Other variables
Numerous variables have been recorded by the NFI
and NSFV and some of these were used in the present
analyses either directly or indirectly to calculate new
variables. Apart from the variables listed in Table 1,
we used `time since logging' and `logging intensity'.
The time was calculated from two occasions on which
forest inventories had been performed, i.e. 5 and 10±
11 years after establishing the plots. On each occasion
in the ®eld, the time from logging was estimated and
placed in four classes; ongoing growth season (season
0), previous season (season 1), season 2 (seasons 3±5).
A season is the time between two subsequent shoot
growth episodes. This is more easily determined in the
®eld than the particular year trees were felled. The
intensity of logging (I) was calculated from the data
collected on two of the three forest inventories (0, 5
and 10±11 years from establishment) and was
expressed as percent felled volume per standing
volume before logging. This was calculated as
Iˆ
…Vp ‡ Gu tt † …Va Ga tf †
100
…Vp ‡ Gu tt †
(2)
where Vp is the volume at the inventory prior to
logging, Va the volume at the inventory after logging,
Gu the growth per year between the inventory prior to
logging and logging, Ga the growth per year between
the logging and the inventory after logging, tt the
seasons between the inventory prior to logging and
after logging, and tf the seasons between logging and
the inventory after logging
In some cases, this formula calculated negative
`logging intensity' values. This was an artefact
because of the problem of determining the time of
Table 1
Description of environmental variables recorded and used in the present study
Variable
Description
`Age'
`Percent P. sylvestris'
`Site productivity'
Age of the stand
Volume P. sylvestris per total tree volume
Productivity was estimated according to HaÈgglund and Lundmark (1981), i.e. from easily assessed variables
like vegetation composition, slope, soil type and moisture. According to this system, one value each is
calculated for P. sylvestris and P. abies; the higher of the two was used
Sum of temperature ‡5 C; calculated for each cluster of plots from climatological data, height above sea
level and latitude
Stem volume of all the trees 1.3 m hight
`Temperature sum'
`Volume'
J. Bergstedt, P. Milberg / Forest Ecology and Management 154 (2001) 105±115
109
logging. Furthermore, the formula used to calculate
the volume and growth per year can amplify possible
small mismeasurements.
3. Results
2.5. Data analyses
3.1.1. Ranking of environmental variables
The eigenvalues of the two ®rst principal components in the initial PCA were 0.100 and 0.079. The
results clearly showed that logging intensity was the
variable that explained most of the ®rst principal
component (PC), while inter-set correlations with
the second PC were low (Table 2).
An initial principal components analysis (PCA) of
the data on change in cover was conducted with the
CANOCO 4 software (ter Braak and Smilauer, 1998;
default options except `centering/standardization by
species'). PCA is an indirect gradient method where
the samples are arranged according to species data.
The ordination scores were then correlated with the
environmental data as a way to rank the explanatory
power of the environmental variables.
We used a direct gradient analysis technique for the
remaining analyses, where samples are directly related
to measured environmental variables. The species'
change in cover was expected to increase or decrease
with logging intensity; thus redundancy analysis
(RDA) was chosen because it assumes linear
responses of species to environmental gradients.
The data were subjected to RDA with the CANOCO
4 software using default options. The statistical signi®cance of the ordination was evaluated with a Monte
Carlo test with 2000 permutations (reduced model).
The analysis consisted of three steps (Hallgren et al.,
1999), all calculated using certain environmental variables (see Table 1) as covariables (hence eliminating
the variation that they can explain). First, 10% of the
data was randomly picked and used in an exploratory
phase to decide on, e.g. type of analysis, transformations and possible exclusion of samples or species.
Second, the remaining 90% was used to test the
hypothesis under consideration. We kept these two
steps separate to prevent a circularity from occurring
when the ``data-diving'' needed to make the decision
on the type of analysis and data formatting is done
with the same data that is used for testing a hypothesis
(Hallgren et al., 1999). Third, a full analysis (100% of
the data) was done to retrieve ordination scores for
species based on the maximum amount of information
available.
To test for a potential time-lag in the response to
logging, we plotted change in cover of selected species
as a function of logging intensity and time period from
logging. This was done using the 294 plots that had
been subjected to logging between the inventories.
3.1. Community responses to logging
3.1.2. Testing if logging intensity affects change
in cover
The RDA performed on the 90% subsample was
calculated with `temperature sum', `volume', `age',
`site productivity' and `percent P. sylvestris' as covariables and `logging intensity' as the only environmental variable. Although this resulted in a relatively
low eigenvalue (0.026) of the ®rst ordination axis, i.e.
the logging intensity axis, the Monte Carlo test clearly
showed that logging intensity signi®cantly affected
the change in ground vegetation (P < 0:005).
3.2. Species responses to logging intensity
The RDA conducted on the full data set resulted in
a ranking of the species and groups of species according to their responses to logging intensity (Table 3).
One species and three groups of species had high
positive scores (>0.1), E. angustifolium, `narrowleaved grasses', `E. sylvaticum, M. trifoliata and C.
globularis' and `broad-leaved grasses'. The species
and groups of species all increased when logging inten
sity was above 80% (Figs. 2 and 3). They seemed
Table 2
Inter-set correlations between environmental variables and principal components (PC). The PCA was conducted with change in
cover between two inventories (10±11 years) for 16 species and
groups of species
Environmental variable
Logging intensity
Age
Volume
Site productivity
Temperature sum
Percent P. sylvestris
PC1
0.483
0.172
0.283
0.048
0.029
0.258
PC2
0.015
0.007
0.116
0.143
0.016
0.114
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J. Bergstedt, P. Milberg / Forest Ecology and Management 154 (2001) 105±115
Table 3
Ranking of the species and groups of species by scores from an
RDA performed on change in cover between two inventories (10±
11 years) with `age', `percent P. sylvestris', `site productivity',
`temperature sum' and `volume' as covariables, where axis 1
represents logging intensity
Species
E. angustifolium
`Narrow-leaved grasses'
E. sylvaticum, C. globularis and M. trifoliata
`Broad-leaved grasses'
Carex-Luzula
P. aquilinum
Empetrum spp.
R. chamaemorus
V. uliginosum
C. vulgaris
Lycopodiaceae
L. palustre
`Herbs'
V. vitis-idaea
A. polifolia and V. oxycoccus
V. myrtillus
Ordination axis 1
0.383
0.334
0.131
0.126
0.087
0.084
0.045
0.045
0.038
0.031
0.030
0.023
0.0070
0.010
0.066
0.139
indifferent to low logging intensities, with the exception of `broad-leaved grasses' where a slight decrease
could be detected. In sample plots not subjected to
logging, E. angustifolium seemed stable in abundance
while the others decreased. One species, V. myrtillus,
had a large negative score (< 0:1) and its response to
logging seemed to be negative and linear. There was
also a decrease in cover in plots unaffected by logging.
The dynamics of cover change in V. myrtillus was
dramatic when compared with other species (Fig. 3).
The other species and groups of species tended to be
more or less unaffected by logging and had scores
between 0.1 and ±0.1 (Table 3). On sample plots
unaffected by logging between the inventories, many
species decreased (exceptions were Lycopodiaceae, E.
angustifolium, P. aquilinum and the group with A.
polifolia and V. oxycoccus).
3.3. Time lag in the response
The three species with the most extreme ordination
scores in the RDA (Table 3) were used to illustrate the
potential time lag in response to logging. In all three
species, the response was unclear after 0±2 seasons,
apparent after 3±6 seasons and most profound after 7±
11 seasons (Fig. 4).
4. Discussion
This study was performed on a survey materials
collected by numerous persons from vast areas where
the sample plots have been subjected to various management methods and natural events. This contributes
a lot of variation in the data, but it also gives an idea of
the overall situation in a large geographical area.
Considering that these factors add to the variability,
it is perhaps surprising that the results were so clear
and interpretable. The main problems seem to have
been that the actual management methods and timing
of them has to be determined by person in the ®eld
(re¯ected by the negative `logging intensity' in
Figs. 2±4), and the fact that species in some cases
had been lumped in groups. For example, `herbs'
consisted of a high number of species that potentially
might respond differently to logging and succession.
The ®nding in the initial PCA, that logging intensity
was the variable with the highest explanatory power
regarding the change in vegetation, is not surprising.
Logging was the only drastic event occurring in the
sample plots while other variables had not changed at
all or, as for the timber volume and age of the stand,
just slowly increased. The RDA clearly con®rmed that
logging intensity signi®cantly affected the change in
vegetation.
It is worth noting that the large number of sample
plots (789) used in the present study actually enabled
us to draw reasonable conclusions also about the lack
of a response, at least for species that were fairly
abundant.
4.1. Response to high intensities of logging
It is important to note that the vegetation inventory
was performed on the `representative area', i.e. the
area of the sample plot that was unaffected by disturbances such as soil scari®cation, tractor tracks or
heaps of logging residues. Hence, our study does not
re¯ect the vegetation dynamics in gaps in the ®eld
layer vegetation that are created accidentally or intentionally by scari®cation to facilitate tree seedling
establishment (IngeloÈg, 1974; Hannerz and HaÊnell,
1993; Halpern and Spies, 1995). In such gaps, several
euphemeral species, e.g. Galeopsis spp. and Senecio
spp., can ¯ourish for a couple of years (Dyrness, 1973;
Hintikka, 1987) and gaps can also be important for the
J. Bergstedt, P. Milberg / Forest Ecology and Management 154 (2001) 105±115
111
Fig. 2. Changes in cover of species and groups of species plotted against logging intensity. Cover change calculated as percent cover at the
second inventory minus the percent cover at the ®rst. The logging intensity was calculated from the data on timber volume before and after
logging, annual growth and seasons from logging. The formula sometimes leads to negative logging intensities because of the problem of
determining the time of logging and calculating annual growth.
establishment of new long-lived individuals of the
other species.
`Narrow-leaved grasses' and E. angustifolium had
the highest positive values in the RDA ranking
(Table 3) and showed a similar response to high
logging intensities, both expanding only when >80%
of the standing timber had been removed (Fig. 3). They
are opportunistic species that increase after ®res
(Dyrness, 1973; Schimmel and GranstroÈm, 1996)
and fertilizer application (Kellner, 1993) and are
well-known examples of species increasing after
clear-cuts (SjoÈrs, 1971; IngeloÈg, 1974; Corns and La
Roi, 1976; Kielland-Lund, 1981; Zobel, 1989; Nieppola, 1992; Hannerz and HaÊnell, 1997). However, the
fact that
they only responded to very high logging
intensities has not been shown previously. The reason
for this non-linear response to tree removal is unclear.
The direct environmental changes due to logging
involves mainly more light reaching the ground, higher
summer temperatures, increased availability of soil
nutrients and a rise in the groundwater table (Brumelis
and Carleton, 1989; Hannerz and HaÊnell, 1997). Of
these variables, it is possible that only light increases
linearly with logging intensity (cf. Martens et al.,
2000) while the other variables might increase exponentially.
The group with E. sylvaticum, C. globularis and M.
trifoliata was constructed because the three species
were considered to indicate similar site productivity
conditions. It is nevertheless an ecologically heterogeneous group whose species might respond differently to logging intensity. In the present analyses, the
group was probably not represented by the wetland
species M. trifoliata since moist plots were excluded.
Of the remaining two species, C. globularis is considered as a pioneer species (KellomaÈki and VaÈisaÈnen,
1991) which ®ts the response pattern in Fig. 3, i.e.
large increase at high logging intensities.
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J. Bergstedt, P. Milberg / Forest Ecology and Management 154 (2001) 105±115
Fig. 3. Changes in the cover of species and groups of species plotted against logging intensity. Cover change was calculated as percent cover
at the second inventory minus the percent cover at the ®rst. The logging intensity was calculated from data on timber volume before and after
logging, annual growth and seasons from logging. The formula sometimes leads to negative logging intensities because of the problem of
determining the time of logging and calculating annual growth. V. myrtillus decreased more than 100% in two plots (not indicated).
In contrast, C. vulgaris showed a slight decrease in
cover at high logging intensities (Fig. 3) and has
previously been reported to die off immediately in
response to logging (BraÊkenhielm and Persson, 1980).
Some of the species in Figs. 2 and 3 (A. polifolia/V.
oxycoccus, L. palustre R. chamaemorus and V. uliginosum) are most common in moist or wet areas (Odell
and Drakenberg, 1991) and their cover in the mesic
forests types included in our study was low. Consequently, the absolute changes in cover was small.
These species as well as several others (Figs. 2 and
3) seemed more or less unaffected by high logging
intensities and most notable was the very abundant V.
vitis-idaea (Fig. 3).
What will happen in Swedish boreal forests if most
logging operations will leave more trees? First of all,
we predict a less pronounced vegetation dynamic
after logging, i.e. the post-harvest phase will be less
dominated by species like grasses and E. angustifolium. Second, only one species (V. myrtillus) clearly
decreased in response to a clear cut, but reducing the
amount of complete clear cuts in the landscape is still
not likely to have much effect on this species since it
was also negatively affected by intermediate logging
intensities. Third, for a full appreciation of the impact
of logging, we need information on individual species
as well as on the less abundant forest species.1
4.2. Response to low and intermediate intensities
of logging
In a study spanning a long time interval silvicultural thinning could not explain changes in cover
1
The present analysis only considered 16 species and groups of
species.
J. Bergstedt, P. Milberg / Forest Ecology and Management 154 (2001) 105±115
113
Fig. 4. Changes in the cover of `narrow-leaved grasses', E. angustifolium and V. myrtillus after three different time periods from logging (0±2
seasons, 3±6 seasons and 7±11 seasons). Cover change was calculated as percent cover at the second inventory minus the percent cover at the
®rst. The logging intensity was calculated from data on the timber volume before and after logging, annual growth and seasons from logging.
The formula sometimes leads to negative logging intensities because of the problem of determining the time of logging and calculating annual
growth.
(Nieppola, 1992). Our study, which spanned a shorter
time interval and included many more samples, con®rmed that intermediate intensities of cuts had little
effect on the cover of individual species (Figs. 2 and
3). The only exception was V. myrtillus (see Section
4.4).
4.3. Vegetation change in sample plots not
subjected to logging
In sample plots not subjected to logging, most
species decreased as would be expected when the tree
canopy becomes denser (e.g. Nygaard and édegaard,
1999). The clear-cut opportunist E. angustifolium
seemed to be indifferent to a closing canopy cover
(Fig. 3). It is possible that its decrease from a postlogging abundance situation might already have taken
place within a few decades from logging (i.e. well
before the ®rst inventory). The family Lycopodiaceae
and `Carex-Luzula' also seemed unaffected by the
closing of the tree canopy (Fig. 2), which suggests
that they are very persistent in the boreal forest. Their
stability in response to both clear cut and succession
makes them excellent candidates as the indicators of
site productivity. The cover of species is otherwise
dif®cult to use to predict productivity (Nieppola,
1993).
4.4. Vaccinium myrtillus
V. myrtillus was the only species with a clear
negative score in the RDA (Table 3), and exhibited
very dramatic dynamics irrespective of logging intensity (Fig. 3). There seemed to be a general decrease
with increasing logging intensity, but the variation was
very large around a potential regression line. Although
this species' sensitivity to clear cutting is well documented (IngeloÈg, 1974; Kardell, 1980; Atlegrim and
SjoÈberg, 1996; Hannerz and HaÊnell, 1997), the large
variability suggests interactions between logging and
other parameters. This means that conclusions regarding the response of V. myrtillus from a single site (e.g.
Atlegrim and SjoÈberg, 1996; Nygaard and édegaard,
1999) might have low generality.
114
J. Bergstedt, P. Milberg / Forest Ecology and Management 154 (2001) 105±115
V. myrtillus is deciduous and this can increase the
dif®culty of estimating the cover as ``maximum cover
under the vegetation season''. Furthermore, its cover
might have been overestimated in logged plots, where
it might be dif®cult to distinguish between dead
lea¯ess branches and alive ones. It is also worth
pointing out that V. myrtillus is the dominant species
in the ®eld layer of the boreal forest in Sweden (Odell
and Drakenberg, 1991). Thus, absolute changes in
cover over time will appear profound and methodological problems, like observer differences in cover
estimates, will result in large variability. Another
explanation of the dynamics could be that its cover
varies between years (LaÈhde and Nieppola, 1987). V.
myrtillus is used by many animals, e.g. moose (Alces
alces), roe deer (Capreolus capreolus), voles, capercaillie (Tetrao urogallus) and many insect species
(Atlegrim and SjoÈberg, 1996) and their exploitation
can vary over time and possibly cause interannual
variations in cover.
4.5. Time-lag in response to logging
A quick response to a logging can be expected if it
concerns the dying off of individuals, and to be slow
when an increase is at hand (dying can be instantaneous while individuals increase their biomass over
time). The increase of E. angustifolium and `narrowleaved grasses' became most apparent after 3±6 seasons (Fig. 4), conforming to this model and to previous
reports (Hannerz and HaÊnell, 1993, 1997). The
decrease of V. myrtillus was not quick in the present
study (Fig. 4), and it seemed that the reduction in cover
continued over several years after a clear cut. This
suggests a slow process of being out-competed by
other species rather than instantly dying off as reported
by BraÊkenhielm and Persson (1980) in a dry P. sylvestris forest. However, the interpretation of the present results is complicated by the problem of
estimating the cover of V. myrtillus as ``maximum
cover during the season''; dead or dying branches
without leaves could have been taken for living ones
and result in an overestimation of the actual cover.
4.6. Conclusions
Our major ®ndings were (i) most species showed
non-linear response to logging intensity, (ii) many
species lacked a response to intermediate levels of
logging, (iii) most species decreased in the absence of
logging and (iv) that V. myrtillus exhibited large and
puzzling variation.
Acknowledgements
We thank Gunnar Odell and Evert Carlsson (SLU)
for various efforts and for providing us with excerpts
from the database and Mike Palmer for commenting
on the analyses and manuscript. This study would not
have been possible without the devoted work by
numerous persons in the ®eld during the last decades.
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