(Department of Agriculture, 2015). The natural habitat of Water Onion is severely fragmented due to deforestation, land use change and dredging. Based on limited survey, Soothornnawaphat (2010) indicated that the remaining habitat of Water Onion in Thailand covered only 1.90 rai or less than 1 ha. It is most likely however that this species may exist in remote streams in Ranong, Phang-Nga and nearby provinces. In connection to this, there is a global and national concern about over exploitation of Water Onion and its conservation status. The objectives of this research were to investigate the current extent of occurrence and to assess the conservation status of Water Onion in Thailand using species distribution models and the IUCN Red List categories and criteria version 3.1 II. METHOD 2.1 Study area and sampling The research was conducted in Ranong and Phang-Nga Provinces in southern Thailand. The study area was sub-divided into 3 parts. The first part covers three districts in upper Ranong, namely Kraburi, Laoun and Mueng Ranong. The second part is located in four districts (Kraper, Suksamran, Kruraburi and Takaopha) of lower Ranong and upper Phang- Nga provinces. In addition, the third part area covers 6 districts in lower Phang-Nga province, including Kapong, Mueng, Thaimeung, Thabpud, Takouthung and Kaoyoa. Actual ground survey was carried out in parts 1 and 3 because these areas have not been investigated. Occurrence data in part 2 were gathered from previous studies (Ranong Provincial Natural Resources and Environment office, 2012; Soothornnawaphat, 2010; Thailand Institute of Scientific and Technological Research, 2013). Seventy-five percentage of the sample point data was used to generate species distribution models, while the remaining 25% was kept as independent data to test the omission errors. Assuming that the surveyed localities without occurrence data for each species have a higher probability of being considered as reliable absences, we randomly selected a similar number of these probable absences to estimate commission errors. 2.2 Environmental factors for species distribution Potential environmental factors that may contribute to the distributions include 17 variables from 4 datasets. Physical factors include elevation, slope, aspect, soil group and accumulated flow. Bio-physical factors consist of land use and normalized difference vegetation index (NDVI) derived from Landsat-TM data. Six annual and seasonal bioclimatic variables important in species distribution include annual mean temperature, maximum temperature of warmest month, mean temperature of driest quarter, annual precipitation, precipitation of wettest quarter and precipitation of driest quarter. In addition, human pressures that influences its distribution include distance to road, distance to village and distance to dredging. All environmental variables were geo-referenced and resampled to grid cell of 30 x 30 m resolution for spatial analyses. 2.3 Species distribution modeling Two species distribution models were selected to map the predicted extent of Water Onion. 1. Logistic regression is a multivariate statistical technique to predict a binary dependent variable (presence or absence) before entering them in the model (Atkinson and Massari, 1988). It is one of common species distribution modeling techniques (Trisurat et. al., 2011). In this research, it was used to estimate the probability of the occurrence of Water Onion in the landscape. The logistic regression model is written as: Prob event = eZi 1+ezi Proceedings of the International Conference on Climate Change, Biodiversity and Ecosystem Services for the 31 Sustainable Development Goals (SDGs): Policy and Practice 27-29 June 2016, Cha-am, Phetchaburi, Thailand
Proceedings of International Conference on Climate Change, Biodiversity and Ecosystem Services for the Sustainable Development Goals : Policy and Practice 27-29 June 2016 at the Sirindhorn International Environmental Park, Cha-am, Phetchaburi, Thailand
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