Environmental factors control and climate change impact on forest type: Dong PraYa Yen-KhaoYai world heritage in Thailand ()
1. INTRODUCTION
Forest area use is an optimum option for climate change mitigation. In developing countries, however, forest degradation is in direct conflict with controlling climate change. Exercising a natural forest reserve strategy is an option that will protect and preserve existing forests. Reforestation should also be employed. Forest ecosystems are vital for the welfare of living things and mankind [1]. Forests are sources of raw materials and provide basic human needs such as food, clothing, housing, and medicine [2]. Additionally, forests balance the environment. Deforestation impacts soil and water resources, leading to direct and indirect socioeconomic problems.
The IPCC [3] report on the impact of the Global Climate Model (GCM) on tropical forests does not especially focus on Thailand. Thai natural forest resources may experience different impacts of climate change than reported. This research is the first of its kind that downscales the global climate model to a regional one, specifically, to a 25 km × 25 km grid size of forestry covering Thailand [4]. We selected physical factors that account for every forest type in Thailand. The objectives of our research are the following: first, to identify environmental factors that control forest type; to calculate total carbon content of each forest type; and to examine the impact of climate change on forest type.
2. STUDY AREA
The total area of the Dong PraYa Yen-Khao Yai forest complex is 6152.13 sq. km2. It is situated at 14˚00' - 14˚33'N and 101˚05' - 103˚14'E in northeastern Thailand, covering 6 provinces including Saraburi, Nakhon Nayok, Nakhon Rachasrima, Prachin Buri, Sakaew, and Buri Rum. Dong PraYa Yen-KhaoYai Forest Complex is the second UNESCO world heritage site created in Thailand, and is comprised of four national parks and a wildlife sanctuary (Figure 1). Khao Yai National Park consists of hill evergreen forest(KY_HEF), moist evergreen forest (KY_MEF), dry evergreen forest(KY_DEF), mixed deciduous forest (KY_MDF), secondary forest (KY_SF), grasslands (KY_GL), and deciduous dipterocarp forest (KY_DDF). Tab Lan National Park consists of hill evergreen forest (TL_HEF), moist evergreen forest (TL_ MEF), dry evergreen forest (TL_DEF), mixed deciduous forest (TL_MDF), deciduous dipterocarp forest (TL_ DDF), and palm forest (TL_PF). Pang Srida National Park consists of dry evergreen forest (PD_DEF), mixed deciduous forest (PD_MDF), deciduous dipterocarp forest (PD_DDF), secondary forest (PD_SF), and grassland (PD_GL). Ta Phraya National Park consists of dry evergreen forest (TY_DEF), mixed deciduous forest (TY_ MDF), deciduous dipterocarp forest (TY_DDF), and grassland (TY_GL). Dong Yai Wildlife Sanctuary consists of deciduous dipterocarp forest (DY_DDF), mixed deciduous forest (DY_MDF), deciduous dipterocarp forest (DY_DDF), and grassland (DY_GL). This world heri tage site encompasses all major habitat types and at least 2500 plant species (16 endemic) of the 20,000 - 25,000 species estimated for Thailand [5].
3. METHODOLOGY
Dong PraYaYen-KhaoYai forest complex consists of eight ecosystems: hill evergreen forest, moist evergreen forest, dry evergreen forest, mixed deciduous forest, dipterocarp deciduous forest, secondary forest, palm forest, and grassland. For each forest type, three plot sizes including 40 × 40 m2, 4 × 4 m2, and 1 × 1 m2 were plotted. All seventy-four plots were examined. Above and below ground biomass were calculated by using allometric equations (protocols described in the works of [6,7]). Soil was randomly chosen for 1 subsamples in 3 samples. Total soil sampling included 27 pits from our total study area. Soil was collected in 4 levels: 0 - 30 cm, 30 - 60, 60 - 90, and >90 cm, for analyzing texture, bulk density, soil moisture, soil reaction (pH), soil organic matter (SOM), % organic carbon (%OC), cation exchange capacity (CEC), total nitrogen, available phosphorus (avai.P), available potassium (avai.K), and total organic carbon (TOC) by using standard methods [8]. To reduce the number of sample plots, similar forest types were grouped. Twenty-four plots were further analyzed for cluster and canonical correspondence analysis (CCA) to examine the relationship between forest type and physio-chemical
Figure 1. Map of Thailand and study area.
soil properties [9]. We applied fuzzy probability theory [10] to model each forest type. The predicted forest types were compared with the actual classifications by the Royal Forest Department in Thailand, 2002 [11]. Finally, climate scenario under A2 and B2 [4] during the years 2000-2040 was performed with the predicted forest model.
4. RESULTS
4.1. Plant Species
Plant species were identified according to the guidelines of Gardner et al. [12]. A local expert and plant taxonomist from the Royal Forest Department found that TL was the most abundant species (332 species), followed by PD (293 species), KY (271 species), DY (169 species), and TY (99 species), respectively.
4.2. Important Value Index (IVI) in Plant Communities
There exists high forestry biodiversity in each national park. Therefore, we presented only the top five of the highest IVI values of each species in a study area as follows (Table 1).
4.3. Cluster Analysis
By using IVI values to assign sample units to groups based on redundant response patterns, we classified our samples units into 7 groups (Figure 2).
Group 1 is the largest and comprises types KY, TL, PD, DY, and TY. These include KY-HEF, KY-DEF, KY-MEF; TL-MEF, TL-MDF, TL-DEF, TL-PF and TL-HEF; PDMEF, PD-DEF; DY-DEF, DY-MDF; TY-DEF. Most forest types of this group are characteristically high in moisture. Group 2 consists of only KY_DDF, Group 3 of only DY_GL, Group 4 of PD_MDF and TY_MDF. Moreover, Group 5 is composed of mostly dry dipterocarp forest (DDF) except the dipterocarp forest in KY that is placed in Group 2. Group 6 consists of KY_MDF, PD_SF, KY_SF, and KY_GL. Group 7 incorporates PD_GL and TY_GL.
By using two-way cluster analysis, we discerned that the dominant species of each evergreen forest is distinct. This infers that each forest type is the same, but the dominant plant species is different, highlighting species diversity (Figure 3).
4.4. Canonical Correspondence Analysis (CCA)
Fourteen soil parameters in four depths (0 - 30, 30 - 60, 60 - 90, >90 cm) were tested by correlation coefficient to find the best representative parameter before further analysis (CCA). We found that OM, OC, TOC, and N ex-
Table 1. List of the top five of the highest IVI values.
hibited high correlation (r > 0.9; p < 0.01) for every soil depth parameter. OM was there representative parameter used to calculate the correlation. pH and phosphorus content have high positive correlations (r > 0.6; p < 0.01) for every soil depth. We thus selected pH as a representative parameter to calculate the correlation. CEC, sand and clay also exhibit high correlations (r > −0.7; p < 0.01) for every soil depth, and we subsequently chose sand as a parameter to calculate further correlations. In summary,
Figure 3. Two-way analyses for evergreen forest.
all parameters used for further calculations are pH, OM, K, Mg, Ca, sand, silt, and bulk density (Tables 2 and 3).
We selected the higher correlation value with plot score, pH (R = 0.939 at Axis 2), and OM (R = −0.699 at Axis 1). For the second calculation, we included above ground carbon (ABGtc) for each forest type.
Figure 4 shows, unambiguously, that forest types HEF, DEF, MEF, MDF, DDF, GL, SF, and PF were located from left to right, a placement consistent with organic matter (OM). The correlation between Axis1 and aboveground carbon is negative (R = −0.797) and pH is positive (R = 0.835) (Tables 2 and 3). The evergreen forest had higher moisture content, consistent with potential to store high amounts of organic matter.