3.1. 再造林放棄地での森林再生に対する地形と管理履歴の影響
J For Res (2006) 11:99–106
DOI 10.1007/s10310-005-0192-5
© The Japanese Forest Society and Springer-Verlag Tokyo 2006
ORIGINAL ARTICLE
Hiromi Yamagawa · Satoshi Ito · Yasushi Mitsuda
Kazuro Fukuzato
Effects of topography and management history on natural forest recovery in
abandoned forest after clear-cutting in Miyazaki, Japan
Received: April 29, 2005 / Accepted: November 11, 2005
Abstract We investigated factors limiting the recovery of
natural forest in former large-scale conifer plantations
abandoned after clear-cutting in southwestern Japan. We
analyzed forest recovery status (“recovered” sites covered
by evergreen broad-leaved trees, and “unrecovered” sites
covered by pioneer community or nonvegetated sites) using
aerial photographs and field survey. We applied logistic
regression analyses to evaluate the effects of topography,
construction of harvesting roads, distance from remnant
forest, stand condition before clear-cutting, and prior landuse history on forest recovery. Human factors, i.e., land use
and clear-cutting age, were found to affect to forest recovery more than environmental factors such as topography.
Harvesting roads had the strongest negative impact on forest recovery. Forest recovery after clear-cutting of young
sugi plantations also took longer than after clear-cutting of
old sugi plantations or evergreen broad-leaved forests. Furthermore, areas formerly utilized as meadows recovered
less successfully than those that had been managed as coppices. The influences of these factors were thought to be
promoted by the advance reproduction as the regeneration
sources for forest recovery. The influence of stand age before logging suggested an effect of thinning, which might
alter the abundance of advanced reproduction in the understory. However, distance from remnant forest appeared to
be less important. An influence of topography was also
detected, but this could be partly explained by the existence
of advance reproduction in the understory in certain topographic positions. Thus, our analysis suggests that regenera-
H. Yamagawa
Faculty of Agriculture, University of Miyazaki, Miyazaki, Japan
S. Ito (*)
Faculty of Agriculture, University of Miyazaki, 1-1 Gakuen
Kibanadai Nishi, Miyazaki 889-2192, Japan
Tel. +81-985-58-7178; Fax +81-985-58-7178
e-mail: [email protected]
Y. Mitsuda
Forestry and Forest Product Research Institute, Tsukuba, Japan
K. Fukuzato
Miyazaki Prefectural Forestry Technology Center, Miyazaki, Japan
tion sources originating from advanced reproduction in
plantations play a significant role for the recovery of natural
forest after clear-cutting.
Key words Clear-cutting · Aerial photographs · Forest
recovery · Land-use history · Regeneration sources
Introduction
The abandonment of conifer plantations after clear-cutting
has been increasing throughout Japan for a number of
reasons: economic deterioration of Japanese forestry, aging
and decline in number of forestry workers, frequent typhoon disasters, and herbivore damage by sika deer (Sakai
2003). Increased abandonment of forests after clear-cutting
directly results in a decline in the potential for domestic
timber production. Furthermore, it is also feared that the
public utility functions of forest ecosystems, such as soil and
water conservation or biodiversity conservation, would
decline when forest recovery is unsuccessful or delayed
(Sakai 2003). Therefore, there is a pressing need to investigate the current status of abandoned forests and to clarify
the mechanisms of vegetation recovery so that countermeasures can be put in place to meet these problems (Yoshida
2003).
Generally, vegetation recovery and regeneration of trees
after clear-cutting of forests are known to be affected by
many factors, such as topography, seed dispersal, seed tree
proximity, presence of seed dispersal agents, soil conditions,
microclimate, competition with other species (Coates 2000;
Löf 2000), and advance reproduction (Smale et al. 2001). In
addition, the influence of human activities such as land-use
history (Grashof-Bokdam and Geertsema 1998) and construction of harvesting roads (Pinard et al. 2000; Buckley
et al. 2003) have also been identified as determinants of
forest recovery after logging. Recent studies on forest abandoned after clear-cutting in northern Kyushu, Japan
(Yoshida 2003; Nagashima et al. 2004), reported several
cases of succession to tree communities with reference to
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100
the influence of elevation and time after clear-cutting. Sakai
et al. (2004) reported the importance of hardwood
coppicing for forest recovery from an investigation in
Shikoku. However, information on the factors of vegetation
recovery (regeneration sources, environment, and human
activities) is still limited. In particular, the influence of human activities such as forest operations and land use, which
would strongly affect forest recovery after clear-cutting of
conifer plantations (García-Montiel and Scatena 1994;
Gusriguata and Ostertag 2001; Chinea and Helmer 2003), is
not well documented.
The specific objective of this study was to investigate the
relative importance of several human and natural factors
that could limit forest recovery on land abandoned after
clear-cutting of large-scale plantations. We used aerial photograph interpretation and field survey to investigate the
recovering forests in a warm-temperate, evergreen broadleaved forest region. We analyzed the data for the effects of
topography, harvesting roads, distance from remnant forests, stand conditions before clear-cutting, and prior landuse history on forest recovery status at the early stage of the
recovery process.
Materials and methods
Study site
The study site was a former conifer plantation (ca. 70 ha)
that was abandoned and left to regenerate naturally after
clear-cutting in 1997 in Saigo village, Miyazaki, located in
southwestern Japan (131°28′N, 32°25′E). The study site was
situated on a north-facing slope (28° average slope) within
an elevation range from 100 to 500 m asl. Annual mean
temperature and precipitation at this site are 15.1°C and
2600 mm, respectively. These conditions correspond to
those of the warm-temperate region where the natural vegetation is evergreen broad-leaved forest dominated by
Fagaceae and Lauraceae species (Miyawaki 1981). Parts of
this area had previously been utilized as meadows for hay
on the upper slopes or coppices for fuelwood on the lower
slopes, but since the 1960s most of the meadows and coppices were converted to sugi (Cryptomeria japonica D.
Don) plantations. However, part of the meadows and coppices seem to have been abandoned and have since regenerated naturally to secondary forest consisting of evergreen or
deciduous broad-leaved trees. Most of the site was clear-cut
in 1996–1997 accompanied by the construction of a large
number of harvesting roads.
Patch classification for forest recovery status
In this study, apart from the field survey data, we prepared
datasets of forest recovery status and possible limiting factors such as topography, existence of harvesting roads,
distance from remnant forests, stand conditions before
clear-cutting, and prior land use obtained from aerial photographs and maps and using geographic information sys-
126
tem (GIS) software (TNTmips, Microimages, Lincoln, NE,
USA.). The datasets were prepared as raster data at 10-m
resolution covering the whole study site (6072 pixels).
The natural forest communities at the early stage of forest succession of the region are generally characterized by
high dominance of pioneers, which mostly have deciduous
leaf habit, and develop into evergreen broad-leaved communities with the progress of forest succession (Miyawaki
1981; Ito 1996). Thus, in this study, we divided the forest
recovery status into two categories: (1) recovered sites covered by evergreen broad-leaved trees, and (2) unrecovered
sites covered by pioneer community dominated by deciduous pioneer trees and nonvegetated areas. This was to allow
the assessment of the initial recovery rate at the early stage
of forest recovery.
We used TNTmips to process an orthophotograph of a
color aerial photograph taken in April 2001 at an altitude of
1200 m. We identified areas that could be clearly interpreted
as evergreen broad-leaved community, pioneer community,
and nonvegetated by using stereograph hypostatization of
original aerial photographs to serve as reference values for
use as sample pixels of known ground cover for image
analysis. Based on these reference values, the vegetation
in each pixel of the study site was classified as recovered
or unrecovered by using the “supervised classification”
(Muchoney et al. 2000) menu function of TNTmips with the
method of maximum likelihood.
To validate the patch classification performed by image
analysis, a field survey was conducted by establishing 10
randomly selected plots (10 × 10 m for each plot) in patches
classified by image analysis as recovered and 12 randomly
selected plots in patches classified as unrecovered. The
name and degree of coverage of each species were recorded
for the tree layer (canopy layer exceeding ca. 5 m in height)
and shrub layers according to the Braun-Blanquet method
(Braun-Blanquet 1964) within each of the 22 plots.
Interpretation of site condition
Factors that were assumed to affect forest recovery were
measured using the following methods. Slope angle and
slope convexity indices were obtained as measures of topography. Contours from 1 : 25 000 scale topography map images (Geographical Survey Institute) were captured as line
vectors and converted to a digital elevation model (DEM)
with a resolution of 10 × 10 m. The slope angle (SLOPE)
was computed from the DEM using TNTmips. The slope
convexity indices of transverse profiles (TC) and longitudinal profiles (LC) were computed as the difference between
the elevation of the target pixel and the average elevation
of the fifth pixel from target pixel (i.e., 100-m scale); positive values indicated convex slopes. TC indicates watergathering or water-spreading properties of the slope (Troeh
1965), which might affect the surface soil stability after logging. LC has been reported to be related to soil sedimentation properties (Takeshita 1964).
We adopted stand conditions just before clear-cutting
(LAND-USE1, Fig. 1A) and land use before establishment
101
Fig. 1A,B. Distribution of
stand conditions just before
clear-cutting (LAND-USE1)
(A) and land use before
establishment of the logged
plantations (LAND-USE2) (B)
determined from
orthophotographs created from
aerial photographs taken in
May 1994 and March 1947,
respectively
N
N
A
B
Evergreen broadleaved
Meadow
Old sugi plantation
Coppice
Young sugi plantation
of the logged plantations (LAND-USE2, Fig. 1B) as factors
of land-use history for analysis. The stand conditions just
before clear-cutting (LAND-USE1) were determined from
orthophotographs created from aerial photographs taken in
May 1994 at an altitude of 700 m. Stand conditions were
classified into evergreen broad-leaved forests and sugi
plantations. For sugi plantations, planting years were determined by comparing aerial photographs taken at intervals
of about 5 years, commencing in 1961. We were thus able to
classify sugi plantations into two age classes: young plantations (ca. 30 years old) and old plantations (ca. 40 years
old). To estimate the tree density of young and old sugi
plantations just before logging, we established three plots
(400 m2 for each) for each of the young and old sugi plantations on the aerial photograph (taken in 1994), and counted
the number of canopy trees within the plots. The tree density of these plantations was mostly uniform within each of
the young or old stands. The estimated tree densities of the
young and old sugi plantations were 2492 ± 153 and 1133 ±
52 trees/ha (mean ± standard deviation), respectively. This
implied that the young and old sugi plantations in our categories would correspond to the situations before and after
thinning, respectively.
The land use before establishment of the logged plantations (LAND-USE2) was derived from orthophotographs
created from aerial photographs taken in March 1947 at an
altitude of 4700 m. LAND-USE2 was classified as either
meadow or coppice and captured as polygon data before
being converted into raster data of the same size and
arrangement as the DEM to be used in the analysis. The
harvesting road data were derived from orthophotographs
created from aerial photographs taken immediately after
clear-cutting in May 1999 at an altitude of 1500 m. We created line vectors of the harvesting roads by interpreting the
aerial photographs; we converted these vectors to raster
data to form an attribute called ROAD. All the pixels
crossed by the road vector were designated as ROAD. The
distance from the nearest remnant forest patch within and
surrounding (DIST) was computed as the distance from the
forest edge interpreted from an orthophotograph of the
1999 aerial photographs. Most remnant forest patches were
conifer plantation.
Data analysis
We verified the classification of patches obtained by image
analysis by using the following methods. The field survey
coverage data, which were in six classes (Braun-Blanquet
1964), were converted into percentage cover (5: 87.5%; 4:
62.5%; 3: 37.5%; 2: 17.5%; 1: 5.0%; +: 0.5%) using the
midpoint value of the percentage ranges covered by each
class. In each plot, the representative percentage cover of
each species was obtained by averaging the values recorded
separately for tree and shrub layers. In each plot, we
summed the average percentage cover of evergreen broadleaved tree species and percentage cover of pioneer tree
species to determine the proportion of the whole plot coverage represented by each of these two species categories.
Pioneer species and nonpioneer species were classified according to descriptions in the literature (Kitamura and
Murata 1979; Okuda 1997; Mogi et al. 2000). We then compared the results of the classification obtained by image
analysis (recovered and unrecovered patches) with the pro-
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102
Table 1. Summary of environmental variables (numerical variables)
for logistic analyses
Variables
Average
Minimum
Maximum
SLOPE (°)
TC
LC
DIST (m)
28
−1
0
51
2
−74
−50
10
59
64
49
187
SLOPE, degree of slope angle; TC, slope convexity index at a transverse profile over 100 m; LC, slope convexity index at a longitudinal
profile over 100 m; DIST, distance from the nearest remnant forest
patch
Table 2. Summary of environmental variables (categorical variables)
for logistic analyses
Variables
Category
LAND-USE1
Evergreen broad-leaved
Old sugi plantation
Young sugi plantation
Meadow
Coppice
Road
No road
LAND-USE2
ROAD
(bro)
(sugi_o)
(sugi_y)
(1)
(0)
(1)
(0)
Stand ages of young and old sugi plantations at clear-cutting were ca.
30 and >40 years, respectively
LAND-USE1, stand condition just before clear-cutting interpreted
by the aerial photographs taken in 1994 (3 years before clear-cut);
LAND-USE2, land-use condition before establishment of the logged
plantations interpreted by the aerial photographs taken in 1947 (50
years before clear-cut); ROAD, road patches interpreted by the aerial
photograph taken in 1999 (2 years after clear-cut)
portions of evergreen trees and pioneer trees obtained from
the field survey.
We also analyzed the species composition of the surveyed plots by TWINSPAN (Hill 1979) using the average
percentage cover of each species in each plot as the dominance value. The results from the TWINSPAN analysis
were compared with the image analysis classification of the
pixels corresponding to the plots.
The effects of topography, land use, and road construction on recovery were analyzed by logistic regression
analysis (GLM). The environmental variables used in this
analysis are shown in Tables 1 and 2. In the GLM, forest
recovery status (recovered patch: 1, unrecovered patch: 0)
was used as the dependent variable. Slope angle (SLOPE),
the slope convexity indices of the transverse profile (TC)
and longitudinal profile (LC), and distance from the nearest
remnant forest patch (DIST) were used as numerical explanatory variables. The following categorical explanatory
variables were also computed: stand condition just before
clear-cutting (LAND-USE1; evergreen broad-leaved forest: bro; young sugi plantations: sugi_y; old sugi plantations:
sugi_o), land-use condition before establishment of the
plantation (LAND-USE2; meadows: 1; coppice: 0), and
presence of harvesting road (ROAD, presence: 1, no presence: 0). The analyses were performed using stepwise selection of explanatory variables.
128
Results
Image analysis and validation of patch classification
From the image analysis, 3567 of the 6072 pixels (59%)
were classified as recovered patches and 2505 pixels (41%)
were classified as unrecovered patches (Fig. 2). The field
survey counted 60 tree species within the tree and shrub
layers, including 18 pioneer species. The TWINSPAN
analysis classified the surveyed plots into two vegetation
types: evergreen broad-leaved type and pioneer type
(Tables 3 and 4). The vegetation of the recovered, evergreen broad-leaved type was characterized by the following indicator species: Neolitsea sericea, Daphniphyllum
macropodum, and Quercus glauca. The vegetation of the
unrecovered, pioneer type had the following indicator species: Fagara ailanthoides, Weigela japonica, and Fagara
mantchurica. Mallotus japonicus and Persea thunbergii were
observed in most stands.
According to the results of the TWINSPAN analysis, the
vegetation of all 10 plots classified by image analysis as
recovered patches was evergreen broad-leaved type. The
vegetation of 9 out of the 12 plots classified as unrecovered
patches by image analysis was classified by TWINSPAN
analysis as pioneer type. The vegetation of the other 3 plots
was classified as evergreen broad-leaved type. The accuracy
of the classification by image analysis was therefore more
than 85%.
The proportions of evergreen trees and pioneer trees are
shown as box plots in Figs. 3 and 4. The quartile range of the
proportion of evergreen trees classified by image analysis
was 76%–88% for recovered patches and 1%–11% for unrecovered patches (Fig. 3). The quartile range of the proportion of pioneer trees classified by image analysis was
2%–13% for recovered patches and 42%–89% for unrecovered patches (Fig. 4).
Logistic regression analysis
In the logistic regression analysis (GLM), all explanatory
variables were adopted using the stepwise procedure (Table
5). The order of the contributions of the explanatory variables, based on their standardized partial regression coefficient (SRC), was as follows. The harvesting road (ROAD)
had the highest SRC (−0.2994, P < 0.001), indicating a
strong negative effect on forest recovery. The stand condition just before clear-cutting (LAND-USE1) also showed a
high negative SRC, with young sugi plantations (−0.2787, P
< 0.001) having a more negative influence than old sugi
plantations (−0.1948, P < 0.001). The proportions of recovered patches were significantly different between young
sugi plantations and old sugi plantations (chi-square test, P
< 0.001), between young sugi plantations and evergreen
broad-leaved areas (P < 0.001), and between old sugi plantations and evergreen broad-leaved areas (P < 0.001). Land
use before the original establishment of the plantations
(LAND-USE2) was negative for meadows (−0.1383, P <
0.001) compared with coppices. Slope steepness (SLOPE)
103
was a positive factor, indicating greater forest recovery on
steeper slopes (0.1200, P < 0.001). Both slope convexity
indices, transverse profile (TC) and longitudinal profile
(LC), appeared to be positive factors (0.0735, P < 0.001 and
0.0657, P < 0.001, respectively) indicating greater recovery
in patches on convex slopes. The distance from the nearest
remnant forest patch (DIST) was detected as a negative
factor, but the SRC was small (−0.0302, P = 0.019)
compared with other variables selected by the stepwise
procedure.
N
21
20
19
18
14
17
15
16
10
12 9
23
22 13
Discussion
2 1
3
4
8
5
7 6
Recovered pixels
Unrecovered pixels
Field survey plots
Fig. 2. Distribution of forest patches classified by image analysis of
color aerial photographs taken in 2001 (4 years after clear-cut)
We were able to classify the forest recovery status of sugi
(Cryptomeria japonica) plantations abandoned after clearcutting by using image analysis of color aerial photographs.
The vegetation types present in the study area (i.e., evergreen broad-leaved trees dominating in developed forests
contrasting with the deciduous habit of most pioneer trees)
facilitated the analysis of aerial photographs taken in
spring. This appears to be an effective method for investi-
Table 3. Abundance of tree species in each plot of the two vegetation groups classified by TWINSPAN
Species
Evergreen broad-leaved trees type
Plot number
Classification of forest patches
by image analysis
Mallotus japonicusa
Persea thunbergii
Neolitsea sericea
Daphniphyllum macropodum
Quercus glauca
Castanea crenata
Eurya japonica
Castanopsis caspidata
Rhus succedaneaa
Cinnamomum insularimontanum
Symplocos lucida
Ilex macropodaa
Euscaphis japonica
Callicarpa mollis
Viburnum erosum
Hydrangea luteovenosa
Clethra barvbnervis
Symplocos myrtacea
Fagara ailanthoidesa
Weigela japonicaa
Fagara mantchuricaa
Styrax japonica
Lindera erythrocarpa
Cornus macrophylla
Aralia elataa
Rhus javanicaa
Rubus palmatusa
Callicarpa japonica
10
Ev
12
Pi
20
Ev
2
Ev
2
2b
1b
3b
3
5
4
3b
3b
4b
4
3
2b
1b
1
2
3
4
2b
1b
3
1
1
1
3
3
2
1
21
Ev
23
Ev
4
Ev
7
Ev
18
Ev
9
Pi
14
Ev
15
Ev
22
Pi
1
Pi
17
Pi
19
Pi
3
Pi
8
Pi
13
Pi
16
Pi
5
Pi
6
Pi
2
4
3b
1b
1b
2
2
2
1
2
1
4
1b
2b
2b
1
2
2
1
1
2
3b
5b
1
3
3b
5b
2
2
2b
5b
2b
3
2
2b
3
4
3b
1b
3b
3
3
2b
3b
2b
1
2
2b
2b
3b
3
2
3
1
1
1
4
2
2
2
1
2
2
4
1
4
1
1
1
1
1
3
1
1
1
1
2
3
1
1
1
1
1
2
1
2
1
1
1
1
1
1
1
3
1
1
3
1
2
1
1
1
1
1
1
2
1
1
1
3
1
1
1
1
1
1
2
1
2
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
2
4b
1b
1
1
1
2
2
2
2
1
2
1b
2b
1b
1
1
1
2
1
3b
4
2
1b
2
1
2
2
3
1
1
1
1
1
1
1
3
1
1
1
1
1
2
1
2
1
1
1
1
2
1
2
1
1
2
1
1
1
2
1
2
1
1
3b
1
1
1
2
Pioneer trees type
1
1
1
1
1
3b
2b
2b
1
2
5b
4b
1b
1
4
1
1
2
2
3
1
2b
5b
2b
1
2
4b
3b
1b
1
2
1
1
4b
3b
1b
1
1
4
1
1
4b
4b
3
3
1
2
Figures are pseudo-species abundance values corresponding to the percentage cover averaged for the tree layer and shrub layer. 1: less than 2.0%,
2: 2.0%–4.9%, 3: 5.0%–9.9%, 4: 10.0%–19.9%, 5: more than 20%. Species that occurred in less than four plots, shown in Table 4, are excluded
from this table
Ev, recovered patches classified by image analysis; Pi, unrecovered patches classified by image analysis
a
Pioneer species. For detail of the definition of pioneer species, see text
b
Abundance of indicator species detected by TWINSPAN
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104
Table 4. List of infrequent species that occurred in less than 4 of the 22 plots
Category
Species
Pioneer
Clerodendrum trichotomum
Rhus trichocarpa
Sambucus racemosa subsp. Sieboldiana
Stachyurus praecox
Boehmeria spicata
Pinus densiflora
Rhus sylvestris
Lespedeza bicolor
Deutzia scabra
Nonpioneer
Aucuba japonica
Ligustrum japonicum
Ilex integra
Persea japonica
Prunus spinulosa
Ehretia ovalifolia
Pourthiaea villosa var. laevis
Helwingia japonica
Phyllanthus flexuosus
Viburnum dilatarum
Acer ginnala
Kalopanax pictus
Acer palmatum
Evodiopanax innovans
Litsea lancifolia
Quercus serrata
Magnolia obovata
Prunus jamasakura
Cephalotaxus harringtonia drupacea
Ampelopsis brevipedunculata
Cryptomeria japonica
Premna microphylla
Table 5. Summary of the results of the logistic regression analysis
Variables
Levels
Intercept
Categorical
ROAD
LAND-USE1
LAND-USE2
Non-road
Road
bro
sugi_y
sugi_o
Coppice
Meadow
Numerical
SLOPE
TC
LC
DIST
No. of pixels
4055
2017
1384
1286
3402
4245
1827
6072
6072
6072
6072
Forested area (%)
69.4
37.2
83.9
40.3
55.5
65.7
42.6
SRC
P value
0.1515
<0.001
−0.2994
<0.001
−0.2787
−0.1948
<0.001
<0.001
−0.1383
<0.001
0.1200
0.0735
0.0657
−0.0302
<0.001
<0.001
<0.001
0.019
80
60
40
20
0
Ev
Pi
gating the recovery status of large-scale forests in warmtemperate regions. Some misclassification of pioneer tree
patches (three stands identified by TWINSPAN as evergreen broad-leaved type) might have arisen because of the
time lag of 3 years between the aerial photography (2001)
and the field survey (2004). Presumably, these three stands
might have developed far enough along the succession during these 3 years to have exceeded the threshold between
pioneer and evergreen vegetation types.
130
Proportion of pioneer trees (%)
80
60
40
20
Proportion of evergreen trees (%)
100
Fig. 4. Box plots of the
proportion of the summed
average percentage cover of
pioneer trees to the total
cover of all species in
surveyed plots (n = 22).
Boxes are for recovered
patches (Ev) and unrecovered patches (Pi) as
classified by image analysis
0
Fig. 3. Box plots of the
proportion of the summed
average percentage cover of
evergreen broad-leaved trees
to the total cover of all
species in surveyed plots
(n = 22). Boxes are for
recovered patches (Ev) and
unrecovered patches (Pi) as
classified by image analysis
100
SRC, standardized partial regression coefficient
Ev
Pi
The importance of limiting factors for forest recovery in
forest abandoned after clear-cutting was recognized to be
higher for human factors, i.e., land use, plantation management, clear-cutting age, and logging practices, than for
environmental factors such as topography. Amongst the
forestry practices, construction of harvesting roads demonstrated the largest influence, probably because of soil compaction caused by heavy machinery (Pinard et al. 1996;
Guariguata and Dupuy 1997) and greater surface erosion
105
leading to topsoil removal (Tálamo and Caziani 2003).
Soil compaction might have prevented seedling establishment, particularly for evergreen, late-successional species. Similar results of prevented seedling establishment
for late-successional species were reported in northern
parts of Japan (Tsushima et al. 1992). Surface erosion
would also have resulted in the loss of regeneration sources,
that is, material for vegetative reproduction and buried
seeds. This suggests that changes in the physical environment can be partly associated with the lack of regeneration
sources.
A delay in forest recovery after clear-cutting of young
sugi plantations (ca. 30 years old) compared with older
plantations (ca. >40 years old) was detected. This difference
seemed to be related to the difference in canopy tree density of two age categories. Kiyono (1990) noted that quantitative and qualitative declines of ground vegetation have
commonly been reported in 20- to 30-year-old sugi and
hinoki stands in which thinning has been delayed. Ito et al.
(2003) reported that the occurrence of evergreen broadleaved tree species in the understory of conifer plantations
in the warm-temperate region strongly depends on
stand age. They observed a large variation in the number of
evergreen species in the understory of 30- to 40-year-old
sugi plantation. In our study site, young sugi plantations can
be presumed to have contained a low density of advance
reproduction (i.e., regeneration sources) before clearcutting because of dense canopy. In contrast, the evergreen
broad-leaved forests and older sugi plantations might have
had a relative abundance of regeneration sources in the
understory (and the canopy in natural forests); thus,
contributing to rapid forest recovery. Furthermore, our
analysis revealed that areas formerly utilized as meadows
did not recover successfully. Ito et al. (2004) reported that
former meadow sites lacked evergreen forest species
compared with former coppice sites, even after establishment of conifer plantations. Therefore, we can surmise that
the unsuccessful recovery on former meadows in our study
site was a consequence of the scarcity of regeneration
sources for forest recovery at clear-cutting. From these results, we suggest that the land-use history has a significant
effect on forest recovery through the density of regeneration sources, such as trees that could survive and resprout
after clear-cutting.
The positive effect of the nearest remnant forest patch
acting as a seed source was less than expected. This was
similar to the result reported by Ito et al. (2003), who
observed limited edge effects from natural forest acting
on sugi plantations. However, Euskirchen et al. (2001) reported that forest edges facing large-scale clear-cutting
areas in temperate forest usually express an effect up to 50–
60 m, and up to a maximum of about 100 m. Our study area
was a large-scale clear-cut where most logged pixels were
far distant from the remnant forest patches, and most forests surrounding the study site were conifer plantations. It is
likely that these factors exerted little influence on the
logged area when compared with natural forests. These
features of our study site may partly explain the small edge
effect. Thus, the influence of edge effects from remnant
forest should be examined in smaller clear-cuts and under a
condition being adjacent to natural evergreen broad-leaved
forests.
An influence of topography on forest recovery was also
identified. We suggest that topography directly influences
recovery through differences in physical site conditions
such as soil moisture regimes or surface soil stability. However, it has been reported that the understory of sugi plantations on ridges contains more evergreen broad-leaved
tree species with gravity- dispersed and frugivore-dispersed
seed than plantations on valleys or lower slopes (Nakagawa
et al. 1998). Another study, conducted in evergreen broadleaved forests in the warm-temperate region, pointed out
that early successional species naturally occur within valleys
that suffer frequent disturbances, and late-successional
species occur along ridges (Sakai and Ohsawa 1994).
Therefore, we suggest the possibility that more evergreen
broad-leaved trees existed on convex slopes and more pioneer trees existed on concave slopes before clear-cutting.
Furthermore, a higher density of understory trees on
steeper slopes has been reported for cool-temperate forests
(Ito 2002). Thus, we assume that the difference in forest
recovery promoted by topographic variations was partly
affected by the distribution of understory trees prior
to clear-cutting in addition to differences in the physical
environment.
In conclusion, our analysis suggests that the regeneration
sources originating from advance reproduction in the understory of the plantation have a significant role in the
successful recovery of evergreen forest on plantation land
abandoned after clear-cutting. In this study, it was difficult
to distinguish the individual trees originating from the advance reproduction during the field survey. Thus, the actual
contribution of advance reproduction to forest recovery
should be evaluated quantitatively by prelogging observation of understory vegetation and postlogging monitoring.
On the other hand, the strong influence of human activities
on forest recovery before and during clear-cutting detected
in our analysis would suggest that there is potential for
improving the recovery process by altering prior management practices and logging methods.
The best measures to encourage the recovery of forest
on these abandoned sites are those that enhance the maintenance and promotion of regeneration sources before
clear-cutting. For example, effective methods would include a longer rotation period to avoid clear-cutting of
young forest and maintaining and promoting advance reproduction in the understory by stand thinning. Also, the
logging method, including the establishment of harvesting
roads, must be selected and executed with a view toward
conserving sources of regeneration as well as the physical
environment.
In this study, we evaluated the initial rate of forest recovery recognized at the early stage after logging. From the
viewpoint of the long-term recovery process, the progress of
forest succession from the pioneer type to evergreen type
would vary due to recolonization rate of evergreen species
under possible constraints of external seed sources and
altered soil conditions. Therefore, long-term observation
131
106
concerning the change in detailed species composition will
be needed in further studies.
Acknowledgments Part of the work presented in this article was performed as a Research Project for Utilizing Advanced Technologies in
Agriculture, Forestry and Fisheries from AFFRC of Japan (no. 1614),
and by a Grant-in-Aid for Scientific Research from JSPS (no.
15380110).
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