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Climatic Controls on Natural Forest Distribution and Predicting
the Impact of Climate Warming: Especially referring to
Buna (Fagus crenata) Forests
Nobuyuki TANAKA1*, Tetsuya MATSUI2, Tsutomu YAGIHASHI3 and Hirosi TAODA1
1

Forestry and Forest Products Research Institute (FFPRI)
1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japane-mail: ntanaka@ffpri.affrc.go.jp
2
FFPRI, Hokkaido Research Center
7 Hitsujigaoka, Toyohira, Sapporo, Hokkaido 062-8516, Japan
3
Japan International Research Center for Agricultural Sciences
1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, Japan*corresponding author

Abstract
This paper illustrates studies on impacts of climate change on buna (Fagus crenata) forests inJapan and discusses issues for further study. The occurrence probability of buna forests was predictedby tree-based models. In the models, the response variable was presence/absence data on actual distribution of buna forests derived from the Third Mesh Vegetation Database (MVDB). Predictor variables
comprised four climatic ones, i.e., the Warmth Index (WI), the mean minimum daily temperature of thecoldest month (TMC), the precipitation in the warm season (PRS) and that in the cold season (PRW);
and five non-climatic variables, i.e., topography, surface geology, soil, slope aspect and inclination.
According to the models developed, DWS values, which evaluate the contribution of each predictor
variable to a model, were the highest for PRW, followed by WI and TMC. They were low for PRS as
well as for the non-climatic variables. The models showed climatic conditions for varying occurrence
probability for buna forests, indicating that the variables controlling buna forest distribution and theirthresholds vary among regions. Suitable habitats for buna forests, defined as the areas with over 0.5 inoccurrence probability, cover 26,220 km2 under the current climate. The area of suitable habitats ispredicted to decrease to 9% of the current area under the CCSR/NIES climate change scenario and 37%
under the RCM20 scenario. Under both scenarios, suitable habitats disappear in Kyushu, Shikoku and
the Pacific Ocean side of Honshu, and decline greatly even in the Tohoku area of northern Honshu withthe most extensive suitable habitats. The vulnerability index, which indicates the degree to whichclimatic conditions become worse for buna with climate change, is defined as the reciprocal of theoccurrence probability, and its map was depicted. Buna forests in the areas which become unsuitable
after climate change may gradually decline along with the death of the buna trees. This method is useful for predicting distributions of plants and vegetation on large spatial and temporal scales. The
MVDB provides distribution data for vegetation types but not for plant species. It is necessary to construct databases on plant species distributions in Japan in order to predict habitats for various plant
species.
Key words: climate change scenario, potential distribution, suitable habitat, tree model,
vulnerability index

1. Introduction
Global warming has been proceeding, according toan IPCC report (IPCC, 2001). The global averagesurface temperature has increased by 0.6°C over the
20th century. That was projected to increase by 1.4
to 5.8°C over the period from 1990 to 2100. Precipitation was projected to change along with the temGlobal Environmental Research
10(2)/2006: 151-160
printed in Japan

perature. Assessing the impact of climate change on
natural ecosystems is an important issue in the IPCC
program (IPCC, 2001).
Much research on predicting the impact of climatechange on potential distributions of wild plants has
been conducted in Europe and the United States. For
example, current and potential future distributions of
80 common trees of the Eastern United States were©2006 AIRIES152

N. TANAKA et al.

predicted by bioclimatic models (Iverson et al., 1999).
Those of 33 plants of England were predicted, andecosystems vulnerable and/or sensitive to climatechange were determined (Harrison et al., 2001; Berryet al., 2003). Those of 1,350 plants of the European
continent were predicted under seven climate change
scenarios (Thuiller et al., 2005).
Developing such bioclimatic models enables theprediction of potential distributions of wild plant species under current and future climates. As models
provide qualitative predictions, the potential distribution can be classified in some areas with different
magnitudes of suitability for plant species, such as“potential distribution areas,” where a plant species
can occur, “suitable habitat,” where it can occur inhigh densities, and “optimum habitat,” where it canoccur in the highest densities. This kind of researchenables prediction of changes of habitat for a plant
species after climate change, and determining theareas which will continue to be habitat for the species
after climate change as well as those which become
non-habitat. These results are useful to the preparation of management plans for conserving plants andplant communities in the age of climate change.
There has been a little research on the impact ofclimate change on distributions of plants and vegetation in East Asia including Japan. The researchstarted in the 1990s in Japan. Potential distributionsof six forest types, i.e., Picea jezoensis forests,
Quercus crispula forests, Zelkova serrata forests,
Quercus acuta forests and Castanopsis cuspidata forests (Uchijima et al., 1992), and those of natural vegetation zones (Tsunekawa et al., 1993) were predicted
to shift northward as the climate warmed. Thesestudies used only the index of accumulated temperature, the Warmth Index (Kira, 1977), as the predictor
variable. We reviewed the research on climate andforests in Japan (Ohsawa et al., 1998; Omasa et al.,
2003; Tanaka et al., 2003), developed models predicting distributions of buna (Fagus crenata) forests andQuercus crispula forests, using environmental predic-

Fig. 1 A buna forest in a suitable habitat.
(Fujisato-machi, Akita Prefecture)

tor variables (Yagihashi et al., 2005; Matsui et al.,2004a; Matsui et al., 2004c; Matsui et al., 2005), andassessed the impact of climate change on buna forests(Matsui et al., 2004b; Matsui et al., in press).
The buna forest is one of the main climax forest
types in Japan, covering 23,000km2 (17% of the total
natural forest area). The forests are important aswater sources and habitats for wild plants and animals.
The Shirakami Mountains located in northern Honshu
are designated as a World Natural Heritage Area forthe largest area of primeval buna forests (Fig. 1).
Buna ranges from southern Hokkaido (42°38’ N inlatitude) in the north to Mt. Takakuma, Kagoshima
Prefecture (31°29’ N) in the south. Buna forests
cover extensive areas in southern Hokkaido, Tohoku
and the Sea of Japan side of Honshu, whereas they are
limited to upper parts of mountains on the Pacific
Ocean side of Honshu, Shikoku and Kyushu.
This paper illustrates our researches on the impact
of climate change on buna forest distribution andrefers to issues for further study.

2. Predicting the Impact of Climate Warming on Buna Forests
2.1 Current climatic data and climate change
scenarios
Current climatic data for all the 345,175 Third
Standard Area Grid cells were extracted from the current climatic database generated by the Japan Meteorological Agency (1996). The Third Standard Area
Grid is a framework of cells of 30” in latitude by 45”
in longitude (ca. 1 km × 1 km) with ID numbers, covering all of Japan (Japan Map Center, 1998). The
original dataset was obtained from weather stations
established nationwide, and the dataset was interpolated into the cells by multiple regression analysis(Okamura et al., 1989). The averaged period for temperature is from 1953 to 1982 and for precipitation,from 1953 to 1976.
From this current climatic database, the present
studies extracted the following four climatic variables
for every cell and used them as predictor variables forpredictive distribution models. They were considered likely to be major factors critically affecting thesurvival and growth of plants in Japan. (A) The
Warmth Index (Kira, 1977), defined as the annual sumof positive differences between monthly means and+5°C, is a measure of effective heat for plants.
Isopleths of WI correspond well with boundaries offorest zones in Japan since precipitation is sufficientfor plant growth (Kira, 1977). WI has often beenused for forest zonation studies in East Asia rather
than mean annual temperature (Kira, 1977; Box 1995).
(B) The monthly mean daily minimum temperature
for the coldest month (TMC) is an index of the cold
extreme. Northern limits of plant distributions coincide with their tolerance to cold (Sakai, 1995).
Absolute minimum temperature may have a more di-Climatic Controls on Natural Forest Distribution and Predicting the Impact of Climate Warming

rect effect on survival, but suitable data were unavailable. (C) Winter (December-March) precipitation(PRW) is an index of dryness and snowfall. The Sea
of Japan side of Honshu has one of the heaviest snowfalls in the world. There are many plants whose
distributions correspond well to snow cover (Sakai,
1995; Ohsawa et al., 1998). Snow cover is advantageous and disadvantageous for various plant species todifferent degrees. (D) Summer (May-September)precipitation (PRS) is an index of water supply during
the growing season. The summer precipitation is
generally considered to be sufficient for plants inJapan. However, it may be critical for some plant
species in some regions. For example, it is suggested
to be critical for buna in southern Hokkaido (Tsukada,1982) and inland areas of Honshu (Yagihashi et al.,2005).
Various climate change scenarios (Global Climate
Model scenarios) based on emissions scenarios forgreenhouse gases such as CO2 and N2O have been
proposed by different institutes. Because of the steep
and complex topography of Japan, it is apparently

153

necessary to analyze the distribution of plants andforests in as small spatial resolutions as 0.1, 1 or
10 km for accurate and useful prediction. The two
climate change scenarios, i.e., the CCSR/NIES
scenario (spatial resolution: 5’ latitude × 7.5’ longitude,
ca. 10 km × 10 km), (Yokozawa et al., 2003), basedon greenhouse gas emission scenario IS92a (IPCC,
1992), and the RCM20 scenario (spatial resolution:
20 km × 20 km; Japan Metrological Agency, 2004)
based on emission scenario SRES-A2 (IPCC, 2001),
were referred to for constructing databases of futureclimates with a resolution of ca. 1 km (the Third Standard Area Grid) by means of spatial interpolation(Matsui et al., 2004b).
Comparing the current climate with the CCSR/
NIES (2091-2100) and the RCM20 (2081-2100)
scenarios shows larger difference in temperature thanin precipitation (Fig. 2). The average annual temperature for all of Japan increases by 4.9°C in the CCSR/
NIES scenario and by 2.9°C in the RCM20 scenario.
WI and TMC increase by 42°C·month and 5.1°C in
the CCSR/NIES scenario, and by 23°C·month andFig. 2 Maps of four climatic variables under the current climate
and future climate scenarios, i.e., the CCSR/NIES
scenario of 2091-2100 and the RCM20 scenario of2081-2100.
WI: Warmth Index, TMC: monthly mean daily minimum temperature for the coldest month, PRW: winter (December-March)
precipitation, PRS: summer (May-September) precipitation.

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4.1°C in the RCM20 scenario, respectively. While
PRS increases for both scenarios, PRW changes little
for both.
The averaging period differs between the two
scenarios. In this paper, the CCSR/NIES scenario for2091-2100 and the RCM20 scenario for 2081-2100
are expressed by those for 2100.
2.2 Environmental data other than climatic data
The distributional frequency and dominance ofplants are affected not only by climatic factors but
also by land factors such as geology, topography andsoil. The five non-climatic factors, i.e., topography,
surface geology, soil, slope aspect and inclination,
were used as predictor variables in predicting buna
forest distribution (Matsui et al., 2004b). The data
sets of topography, surface geology and soil were extracted from Digital National Land Information (Japan
Map Center, 1979), based on 1:200,000 maps covering the entire nation (National Land Agency, 19701978). In these data sets, classification categories forfactors sometimes differed among prefectures, necessitating standardization of the classification categories
over the nation. After that was done, 15 topography,
21 surface geology and 18 soil types were classified(Yoshinaga et al., 1994). The data on slope aspect
and inclination were calculated from 1.5” × 2.25” (ca.
50 m × 50 m) data from the Digital Elevation Model(Geographical Survey Institute, 2000).
2.3 Vegetation data
A statistical model incorporating vegetation distribution as a response variable and environment factors
as predictor variables enable prediction of habitats forthe vegetation analyzed. Information on the geographical distribution of buna forests was obtained
from the Third Mesh Vegetation Database (MVDB),
which includes data on vegetation types in every Third
Standard Area Grid cell. The MVDB was made fromthe 1:50,000 vegetation maps of the entire nation produced under the Third National Survey on the Natural
Environment (NSNE) conducted by the Ministry of
the Environment, Japan (Asia Air Survey, 1988).
We recognized 345,167 cells, 23,432 of whichwere buna forests (Fig. 6a). The presence/absence
datum of the buna forest for every cell was the response variable for the models. For development of
the model, we excluded 188,363 cells of anthropogenic vegetation types, such as plantation forests, agricultural lands and urban areas, to avoid having anthropogenic effects excessively affecting the model. The
model was developed using the remaining 156,804
cells, and used to calculate the probability of buna
forest occurring in each of the 345,167 cells, producing a complete map of occurrence probability of buna
forests for the entire nation.
2.4 Comparison of models
The predictive models examined were statisticalmodels to determine the potential distribution of agiven species (Guisan & Zimmermann, 2000). Three
popular predictive distribution models (MathSoft Inc.,
2000), i.e., Generalized Linear Models (GLMs),
Generalized Additive Models (GAMs) and Tree-based
Models (TMs), were used to determine the potential
distribution of buna forests and their performance wascompared (Matsui et al., 2004c). For GLMs，two
sets of predictor variables were applied, one of which
was based solely on the four climatic terms(GLM-Simple), while the other included two-1evel
interaction terms and quadratic polynomial terms(GLM-Complex). The models’ performance wascompared with AIC (Akaike’s information criterion),
residual deviance, and accuracy measures, including
Kohen’s kappa statistic and overall prediction success,which are often used in predictive modelling studies.
The resulting values all indicated that TMs performed
best, followed by GAMs, GLM-Complex, and GLMSimple. The superiority of TMs may be due to theirbinary recursive partitioning approach to split a data
set into subsets based on a single predictor variablechosen to minimize the deviance in the response
variables in each of the resulting subsets until eachsubset becomes homogeneous. TMs incorporate
possible interactions among predictor variables toform prediction rules. TMs have a high capacity tomodel relationships between the buna forest distribution and the non-homogeneous Japanese climatic patterns nationwide. In addition, TMs have anotheradvantage that the predictor variables and their threshold values in partitioning can be interpreted ecologically.
2.5 Variables and threshold values relevant tobuna forest distribution
The ranges for all buna forest cells are between
30.5 and 109.4°C·month in WI, between –15.5 and
0.0°C in TMC, between 440 and 2,750 mm in PRS,
and between 165 and 1,593 mm in PRW (Fig. 3).
Along the PRS axis, buna forest cells occurredFig. 3 Distributions of buna forests in climate spaces definedby the axes of four climatic variables (WI, TMC, PRW
and PRS).
Black and gray circles respectively indicate Standard Area Gridcells of buna forests and those of other vegetation. Abbreviations of climatic variables are as in Fig. 2.

Climatic Controls on Natural Forest Distribution and Predicting the Impact of Climate Warming

throughout the whole range of available cells. Alongthe PRW axis, buna forest cells occurred up to the
maximum value but did not occur under 165 mm.
Ninety-nine percent of buna forest cells occurred between 42.9 and 92.6°C·month in WI, between –13.0
and –2.6°C in TMC, between 552 and 2,256 mm in
PRS, and between 251 and 1,485 mm in PRW in the
climate space.
Potential distributions of buna forests were predicted by three TMs, i.e., TM CLIMATE, which incorporated only the four climatic variables; TM SPATIAL,
which incorporated spatial variables (latitude andlongitude) as well as the four climatic variables; andTM ENVI, which incorporated five land variables aswell as the four climatic ones (Matsui et al., 2004a, b).
There was little difference in prediction accuracy between TM SPATIAL and TM CLIMATE. Deviance
Weight Score (DWS) values, defined as the sum of the
reduction of deviance between the parent nodes andthe respective children nodes generated by each predictor variable (Matsui et al., 2004a), were low for
both latitude and longitude, which contributed only
locally to TM SPATIAL. This indicates that the four
climatic variables are able to explain the actual buna
forest distributions, and suggests that buna forests are
distributed in almost all areas which are suitable forthem.
The DWS values of TM ENVI were highest for
PRW, followed by WI and TMC, and lowest for PRS
among the four climatic variables, while the DWS

155values for the land variables were all low (Matsui etal., 2004b). This result indicates that the three climatic variables with high DWS values greatly influence buna forest distributions on the condition ofmodelling the whole range of buna forests on a 1-km
spatial resolution.
Figure 4 shows the diagram for TM CLIMATE
based on the four climatic variables. The intermediate node above the terminal nodes from 47 to 68
with high occurrence probability indicates a main areaof suitable habitats located on the Sea of Japan side of
Honshu and southern Hokkaido. This node showed
0.667 in probability and was defined by a climate withhigh precipitation both in winter and summer(564 < PRW, 731 < PRS) and intermediate temperatures (48.9 < WI < 77.2, –12.3 < TMC). Among thesuitable habitat areas, the optimum habitat area withthe highest probability, 0.857, (terminal node 51) was
located in areas from Toyama Prefecture northward,
being defined by a climate with 564 < PW <1,059,
916 < PS < 1,151, 52.6 < WI < 65.2, –12.3 < TMC.
Most of the buna forest areas in southwestern Japansuch as Kyushu, Shikoku and the Kii Peninsula were
included in terminal nodes 25 and 46, both of whichshowed 0.45 in probability. Terminal node 25 wasdefined by 331 < PRW < 494, 1,789 < PRS, WI < 74.0,–10.0 < TMC; and node 46, by 494 < PRW < 564,
1,941 < PRS, 48.9 < WI < 77.2, –12.3 < TMC.
The variables apparently limiting buna forest
distributions and their threshold values are indicatedFig. 4 Diagram of the Tree-based Model (TM) CLIMATE. (Matsui et al., 2004a)
If the condition shown at the top of a branch is met, follow the left branch, otherwise follow
the right branch. The length of the vertical line below each yes-no split corresponds to thechange in magnitude of deviance between parent and children nodes. The identification
numbers for some of the 85 terminal nodes are shown in parentheses. Values following theterminal node identification numbers indicate the probability of occurrence of buna forests.
Abbreviations of climatic variables are as in Fig. 2.

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by the climatic conditions for the areas with low probability as shown in the TM (Fig. 5). The low probability for the majority of Hokkaido (Fig. 5(A)) is due
to coldness and drought in winter (TMC < –12.5,
PRW < 494); that for the inland areas of Honshu, andAbukuma and Kitakami mountains in northern
Honshu (Fig. 5(B)) is due to drought in both winterand summer (PRW < 331, PRS < 795); that for thearea southward from Miyagi Prefecture along the
Pacific side of Honshu, Shikoku and Kyushu(Fig. 5(C)) is due to summer heat and winter drought(89.6 < WI, PRW < 494), and that for the area southward from Niigata Prefecture along the Sea of Japanside (Fig. 5(D)) is due to summer heat (95.2 < WI).
These results indicate that the climatic variables
limiting buna forest distributions and their thresholdvalues vary among regions. Iverson and Prasad(1998) obtained similar results with environmental
variables related to range boundaries of a tree species
differing geographically in the Eastern US.

Fig. 5 Climatic conditions of the areas with low occurrence
probability of buna forests. (modified from Matsui etal. 2004a)
Four terminal nodes with large areas are selected from TM
CLIMATE in Fig. 4.

2.6 Impact of climate change on buna forest
distribution
Three maps showing actual distribution and probability distribution under two future climates in 2100
for buna forests are shown in Fig. 6. When thethreshold value of probability was set from 0.3 to 0.5,
the Kappa value as an index of prediction accuracy
was maximized (more than 0.6), indicating a good
agreement. Assuming that suitable habitats for buna
forests are areas with more than 0.5 in probability, thetotal area of suitable habitats is 26,220 km2 under thecurrent climate. The total area of suitable habitats
decreases into 9% of that under the CCSR/NIES
scenario (Matsui et al., 2004b), and 37% under the
RCM20 scenario. Under both scenarios, the area ofsuitable habitats almost vanishes in Kyushu, Shikoku
and the Pacific side of Honshu, and greatly decreases
even in northern Honshu, which has extensive suitable
habitats at present. The Shirakami Mountains, designated as the World Natural Heritage Area for the largest buna forest reserve are, unfortunately, no exception(Matsui et al., in press). In Honshu, suitable habitats
are predicted to shift to higher elevations on highmountains with sub-alpine coniferous forests.
With climate warming, buna trees in low elevation
areas will be replaced by tree species which occur inlower elevation areas under the current climate. The
candidate species for replacement are Quercus serrata,
Q. crispula and Castanea crenata in low elevation
areas along the Sea of Japan side of Honshu; and additionally evergreen oak species (Cyclobalanopsis spp.),
Fagus japonica and Abies firma in Kyushu, Shikoku
and the Pacific side of Honshu. It will take about
100 years for trees of other species to replace bunatrees by growing in canopy gaps formed by the deathof buna trees.
With climate warming, buna forests surrounded byartificial forests may be succeeded by thickets ofshrub and dwarf-bamboo species after the death ofbuna trees since there are few seed sources of succeeding tree species. With climate warming, the extinction of buna may occur most easily in buna forests
isolated on summits of low mountains which are under marginal climate conditions with high temperatures and low precipitation (Fig. 7). In the marginalsites, buna may regenerate poorly after the death ofthe mother trees. On the other hand, in subalpine
zones on high mountains, buna may colonize gaps
formed after the death of coniferous trees, resulting ina gradual increase of buna.
2.7 Movement of the northern limit
The current northern limit of buna distribution is
located in the Kuromatsunai lowland in southern
Hokkaido. Under both climate change scenarios(CCSR/NIES and RCM20) in 2100, suitable habitat
will expand into central and northern Hokkaido beyond Kuromatusnai. This suggests that buna willcolonize the new regions with climate warming. The

Climatic Controls on Natural Forest Distribution and Predicting the Impact of Climate Warming(Map A)

157(A) Actual(B) Current climate(C) CCSR/NIES(2091-2100)(D) RDM20(2081-2100)

Fig. 6 Distribution of buna forests.
(A) actual distribution, (B) predicted occurrence probability under the current climate, (C) that under the CCSR/NIES
scenario of 2091-2100, (D) that under the RCM20 scenario of 2081-2100. Red areas with more than 0.5 in probability
are defined as suitable habitats for buna forests.

Fig. 7 A vulnerable buna forest under a marginal habitat withlow values in PRW and PRS and high values in WI.
(Mt. Tsukuba, Ibaraki Prefecture)
After the trees die, dense dwarf-bamboo (Sasamorphaborealis) thickets sometimes cover the land due to poor
regeneration of buna.

northward migration of buna after the last glacial
period was estimated to be 233 m/yr (Tsukada, 1982),
20 m/yr (Igarashi, 1994) and 11 m/yr (Takiya & Hagiwara,
1997). Isopleths for the TMC, which is an importantvariable relevant to the northern limit of buna forests,
will shift northward or eastward 10 to 50 km in
Hokkaido over the next 100 years according to bothclimate change scenarios. Assuming the migration
speed to be 11 to 233 m/yr, the migration distancebecomes 1.1 to 23.3 km in 100 years. This suggeststhat buna cannot keep pace with the speed of climatechange projected by either scenario in many places in
Hokkaido.
There need to be continuous natural forests fromsouth to north in order for smooth migration of tree
species to occur. Since natural forests are fragmented by artificial land-use areas such as plantation
forests, agricultural fields and cities, it is not easy forbuna to migrate. Northward migration of buna will
be interrupted, especially by the extensive Ishikari
lowland including many cities and agricultural fields.
This lowland is also unsuitable for buna, due to too
low values in TMC, PRW and PRS under the current
climate, and due to both high values in WI and low
values in PRW and PRS under the future climate
scenarios.
2.8 Vulnerability map
The IPCC report of 2001 pointed out the importance of assessing vulnerability to climate change. In

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N. TANAKA et al.

order to prevent the decline of forest ecosystems, it isimportant to predict the location of forests vulnerable
to climate change and to monitor the forests so that
adaptive measures can be taken if necessary. Therefore a map showing the location of vulnerable forests
would be useful.
Low occurrence probabilities indicate inferior
habitat conditions for buna. The vulnerability index(VI), which is the reciprocal of the occurrence probability, was devised for assessing inferiority of habitat
conditions for buna, and was calculated for every cellof the actual buna forest distribution under the current
and future climate conditions (Fig. 8). Buna forests
in Kyushu, Shikoku and the Pacific Ocean side of
Honshu showed high VI values under both climatechange scenarios in 2100. Buna forests in southern
Hokkaido showed high VI values under the CCSR/
NIES scenario and low VI values under the RCM20
scenario.
2.9 Adaptation
Climate change induces changes in species composition in forest communities through extinction andinvasion of plant species. With climate warming,
human society has to decide whether to accept thechanges in forests or to take some adaptive measures
to conserve declining species. In either case, conservation management plans incorporating possible impacts of climate change will be important for every
region in the future. For example, adaptations relevant to buna could include (1) controlling evergreen
tree species invading buna forests occurring at theFig. 8 Distribution of vulnerability index (VI) for existing buna
forests under (A) the current climate, (B) the CCSR/
NIES scenario of 2091-2100, and (C) the RCM20
scenario of 2081-2100.
Buna forests in high VI areas are judged more vulnerable toclimate warming. Buna forests in areas with less than 2 in VI
are judged to have the lowest risk of forest decline. VI is thereciprocal of occurrence probability.

upper limit of evergreen lucidophyll forests in westernJapan, (2) constructing ecological corridors as migration paths for buna in mountainous areas in central
and northern Honshu, and (3) controlling buna invasion into boreal forests beyond the northern limit of
the current buna distribution in Hokkaido.

3. Issues for Further Study
3.1 Significance and issues regarding predicting
habitats
Changes in potential distribution areas of wild
plant species due to climate change have been intensively studied in the US (Iverson & Prasad, 1998;
Iverson et al., 1999), Europe (Huntley et al., 1996;
Sykes et al., 1996; Kienast et al., 1998; Harrison et al.,
2001; Berry et al., 2003; Thuiller et al., 2005) and
New Zealand (Leathwick et al., 1996). These studies
developed statistical models predicting habitats forplant species using data on actual distributions of thespecies and current climate, and predicted habitat distributions under future climate scenarios both on large
scales (e.g., the eastern US, England and Europe) andon long time scales (e.g., 50 and 100 years). The
predictions are useful for nature conservation.
Assessments of vulnerability of ecosystems and species to climate change should be taken into account inplanning conservation management. Predicted shifts
in habitats are useful information when selecting
monitoring sites for detecting the impact of climatechange.
Few studies on the impact of climate change onplant distributions have been done in East Asia.
Since Japan and other East Asian countries share floristic regions, such as the Sino-Japanese floral region,
distributions of many species cover more than two
countries. It would be desirable to conduct a collaborative study covering East Asia.
There are time lags between shifts in habitats andplant migration. It is necessary to develop dynamic
models predicting plant migration. Models predicting shifts of plant species under climate change havebeen developed, incorporating data on potential
migration rates, approximate time to reproductive
maturity and potential migration paths on fragmented
landscape (Iverson et al., 2005). No individualbased models of plant community dynamics which
predict shifts in plant species on such a large scale
have been developed yet.
3.2 Constraints in predicting distribution due todata
Among the data necessary for making a predictivemodel, it is most difficult to obtain accurate data on
the distribution of wild plant species. Fortunately,
data on buna forest distribution are available in the
MVDB, which was made from vegetation maps
covering Japan. Since buna is a typical dominant
species in natural forests, buna forests were con-

Climatic Controls on Natural Forest Distribution and Predicting the Impact of Climate Warming

sciously identified when making vegetation maps.
However, since buna forests have been replaced bysecondary forests dominated by other tree species including pioneers after frequent logging in the past, thecells occupied by these secondary forests are not identified as buna forests. The area of buna forests hasapparently decreased in low elevation areas, where
human impacts such as logging were more intensive.
Therefore the statistical models, which were based on
the actual distribution of buna forests, inherently
underestimated the potential distribution in low elevation areas.
The MVDB, which provides information on the
distribution of vegetation types in 1-km resolution
covering all of Japan, is useful for predicting the distribution of some vegetation types. Since the MVDB
provides no distributional information on plant species,
it is not useful for predicting distributions of plant
species. Distributions of plant species have been
predicted in the US and Europe, based on digital
information on plant distributions (Huntley et al.,
1995; Iverson & Prasad, 1998; Thompson et al., 1999;
Bakkenes et al., 2002; Thuiller et al., 2005). In
Japan, we are constructing a Phytosociological Relevé
Database (PRDB) in order to predict the distributionof many plant species (Tanaka et al., 2005). The
PRDB includes standardized relevé data collectedusing a consistent survey method (Braun-Blanquet,1964) for various types of vegetation throughout
Japan for the last 50 years. Each relevé includes dataon location (Standard Area Grid Code), environmental
conditions, community stratification, and the nameand dominance of each plant. When the PRDB isready for analysis, it will enable us to parameterize
models predicting distributions of a variety of plant
species under current and future climates. Predictingchanges in potential distribution for plant species is
expected to provide a warning to human societies
about risks and be a useful tool for conservation ofplants and vegetation in the age of global warming.

Acknowledgement
This study was funded by the Global Environmental Research of Japan (B-11 and S-4) program of
the Ministry of the Environment, and the Cooperative
System in Supporting Priority Research Program of
the Japan Science and Technology Corporation.
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