Projecting Forest Tree Distributions and Adaptation to Climate Change in Northern Thailand
Total Page：16
File Type：pdf, Size：1020Kb
Journal of Ecology and Natural Environment Vol. 1(3), pp. 055-063, June, 2009 Available online at http://www.academicjournals.org/JENE © 2009 Academic Journals Full Length Research Paper Projecting forest tree distributions and adaptation to climate change in northern Thailand Yongyut Trisurat1* Rob Alkemade2 and Eric Arets2 1Faculty of Forestry, Kasetsart University Bangkok 10900, Thailand 2The Netherlands Environmental Assessment Agency P.O. Box 303, 3720 AH Bilthoven, Netherlands. Accepted 18 May, 2009 Climate change is a global threat to biodiversity because it has the potential to cause significant impacts on the distribution of species and the composition of habitats. The objective of this research is to evaluate the consequence of climate change in distribution of forest tree species, both deciduous and evergreen species. We extracted the HadCM3 A2 climate change scenario (regionally-oriented economic development) for the year 2050 in northern Thailand. A machine learning algorithm based on maximum entropy theory (MAXENT) was employed to generate ecological niche models of forest plants. Six evergreen species and 16 deciduous species were selected using the criteria developed by the Asia Pacific Forest Genetic Resources Programme (APFORGEN) for genetic resources conservation and management. Species occurrences were obtained from the Department of National Park, Wildlife and Plant Conservation. The accuracy of each ecological niche model was assessed using the area under curve of a receiver operating characteristic (ROC) curve. The results show that the total extent of occurrence of all selected plant species is not substantially different between current and predicted climate change conditions. However, their spatial configuration and turnover rate are high, especially evergreen tree species. Ten plant species will loss their ecological niches (suitable locations) ranging from 2 - 13%, while the remaining 12 species will gain substantial suitable habitats. The assemblages of evergreen species or species richness are likely to shift toward the north where low temperature is anticipated for year 2050. In contrast, the deciduous species will expand their distribution ranges. Based on the IUCN Red List criteria, 10 plant species will be categorized as near threatened (NT) and 12 species will be listed as concerned status. An important point is that species distribution models were found to depend significantly on extreme climate variables such as minimum temperature of coldest months, and precipitation of driest and coldest quarters. Key words: Northern Thailand, climate change, forest tree species distribution model. INTRODUCTION Forest cover in Thailand had declined from 53% of the pical countries is decreasing due to most of remaining fo- country area in 1961 to approximately 25% in 1998, rest cover is located in protected areas and rugged ter- which was an annual loss of between 1.5 and 2% on ave- rain which are strongly restricted by laws and not easy to rage (Charuphat, 2000). The impacts of deforestation access, respectively (RFD, 2007). have been recognized as critical threats to species loss Several studies indicate that climate change has be- (Fox and Vogler, 2005). Not only does it cause habitat come a global threat to biodiversity in recent years and in loss but also habitat fragmentation, diminishing patch the future (Young et al., 2002; Miles et al., 2004; Cuesta- size and core area and isolation of suitable habitats Camach et al., 2006) because climatic variables are im- (MacDonald, 2003). However, the recent trends indicate portant environmental factors that determine ecological that the deforestation rate in Thailand, including other tro- niches of tree species and their patterns of distribution (Avise, 2000; IPCC, 2001). By using species-distribution models (SDMs) and predicted global climate data, Miles *Corresponding author. Email: [email protected]. Tel (662) 579- et al. (2004) indicated that up to 43% of a sample of tree 0176. Fax: (662) 942-8107. species in Amazonia could become non-viable by 2095. 056 J. Ecol. Nat. Environ. Figure 1. Location of provinces in northern Thailand. In addition, approximately 59% of plant and 37% of bird 2007). According to Charuphat (2000), forest cover in northern Thailand species in the Northern Tropical Andes will become ex- declined from 68% in 1961 to 43% in 1998. In addition, eight percent of the forest cover was removed between 1982 and1998, which was the tinct or classified as critically endangered species by the highest deforestation rate in Thailand. year 2080 (Peralvo, 2004). The Fourth Inter-governmental Panel on Climate Change (IPCC) Assessment Report indicated that mean Data on land use, socio-economic and biophysical factors temperature in Thailand will raise by 2.0 - 5.5°C under A set of environmental variables for plants that may directly or indirectly the HadCM3 A2 scenario (regionally-oriented economic affect the patterns of abundance and distribution in northern Thailand development) (IPCC, 2007). It is expected that the pre- were created. These variables were four topographic factors (altitude, dicted climate change will have potential impacts on the slope, aspect and proximity to stream), bio-climate variables, three an- thropogenic factors or threats to species loss (population density, dis- distribution of tree species in Thailand. The objectives of tance to villages, and distance to roads), two biotic factors (vegetation this research are to predict forest tree distributions in nor- type and patch size), and three soil characteristics (texture, drainage thern Thailand, and assess the spatial patterns of their and depth). In addition, we assumed that environmental variable were distribution changes and species loss under the predicted stable, except climatic variables. Altitude, aspect and slope were extracted and interpolated from 20 m climate change. interval contour lines. Distance to main road and distance to streams and rivers were digitized and buffered from topographic maps at scale 1:50,000. Current climate variables were generated from data recorded MATERIALS AND METHODS from weather stations across the north. Population data and a soil map at scale 1:100,000 were obtained from the Local Administration Depart- Study area ment and Land Development Department, respectively. The predicted monthly temperature and rainfall values of TYN SC Northern Thailand is situated between latitudes 14° 56’ 17” - 20° 27’ 5” 2.0 climate datasets in 2050 generated at a spatial resolution of 0.5° N and longitudes 97° 20’ 38” - 101° 47’ 31” E. It covers 17 provinces 2 (approximately 45 km) (Mitchell et al., 2004) were converted to ESRI and encompasses an area of 172,277 km or 30% of the country’s land ASCII grids (*.asc). Then, we resampled the coarse resolution climatic area (Figure 1). The dominant topography is mountainous oriented variables to a resolution of 500 m using spline interpolation method north-south. The average annual temperature ranges from 20 - 34°C (ESRI, 1996). The 500 m resolution was chosen as an appropriate size depending on location. Similarly, the average annual rainfall varies bet- for regional assessment. In addition, it was relevant to general vege- ween 600 and 1,000 mm in low areas to more than 1,000 mm in moun- tation classification and topographic variation. These data were calibra- tainous areas. The rainy season is from May to October. ted with latitude, longitude and digital elevation model (DEM) in the mo- Northern Thailand was formerly covered by dense forest. Dominant del because temperature and rainfall are often highly correlated with to- vegetation includes dry dipterocarp and mixed deciduous forests in low pographic variables (Hutchinson, 1995). Later the adjusted monthly and moderate altitudes, while pine forest, hill evergreen forest and tropi- temperature and rainfall grids were used to generate 19 biological cli- cal montane cloud forest are dominant in high altitudes (Santisuk, mate variables (bioclim) which were more biologically meaningful varia- 1988). Forest fires occur across the region in the dry season and contri- bles. The bioclimate variables represent annual trends, seasonality and bute to the degradation of hill evergreen forest (Saipunkaew et al., extreme or limiting environmental factors (see details in http://cre.anu. Trisurat et al. 057 edu.au/outputs/ anuclim/doc/bioclim.html). Meanwhile, vegetation types Where, T = species turnover rate; G = species gain; L = species loss, were derived from a 1: 50,000 land use map for 2002 (Land Develop- and SR = current species distribution. A turnover rate of 0 indicates that ment Department, 2003). the species assemblage does not change, whereas a turnover rate of 100 indicates that they are completely different from previous cond- itions. Species distribution modeling The processes for mapping species niche distributions include three Species vulnerability: Based on the IUCN Red List criteria 2001 main steps: (a) selection of species; (b) collection of plant presence (IUCN, 2004), six quantitative criteria have been developed to evaluate points; and (c) generation of species distribution models. the status of threatened species. In this study, we used criterion A3(c) as follows: Extinct (EX) is a species with a projected suitable habitat Selection of species: We used the criteria and justification developed loss of 100% in 50 years; Critically endangered (CR) has projected loss by the Asia Pacific Forest Genetic Resources Programme (APFOR of 80 to 100%;