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Download Download Environment and Natural Resources Journal 2021; 19(3): 220-229 Spatial Modeling of Forage Crops for Tiger Prey Species in the Area Surrounding Highway 304 in the Dong Phayayen-Khao Yai Forest Complex 1 1 1 2 1* Paanwaris Paansri , Natcha Sangprom , Warong Suksavate , Aingorn Chaiyes , and Prateep Duengkae 1Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, Bangkok 10900, Thailand 2School of Agriculture and Cooperatives, Sukhothai Thammathirat Open University, Nonthaburi 11120, Thailand ARTICLE INFO ABSTRACT Received: 26 Nov 2020 Spatial modeling is an analytical procedure that simulates real-world conditions Received in revised: 6 Feb 2021 using remote sensing and geographic information systems. The field data in this Accepted: 8 Feb 2021 study were collected from 318 survey plots in the area surrounding highway Published online: 10 Mar 2021 304 in the Dong Phayayen-Khao Yai Forest Complex (DPKY-FC) during the DOI: 10.32526/ennrj/19/2020234 2019 rainy season. Forage-crop biomass was collected from all plots. We Keywords: focused on sambar deer (Rusa unicolor) and gaur (Bos gaurus), which are the Wildlife habitat/ Remote sensing/ main prey for tigers in this area. We created spatial models using generalized Geographic information system/ linear models with stepwise regression. The results indicated that the Spatial model/ Highway 304 normalized difference vegetation index (NDVI) varied directly with grass biomass but inversely with shrub biomass (p<0.05). Elevation varied directly * Corresponding author: with forb biomass but inversely with shrub biomass (p<0.05). The probability E-mail: [email protected] of occurrence of sambar deer varied directly with distance from disturbance variables, distance from the stream, and grass biomass (p<0.001), but inversely with NDVI (p<0.05). The occurrence of gaur varied directly with NDVI (p=0.08), but varied inversely with slope, distance from the road, and distance from the stream (p<0.05). Our results demonstrate that spatial modeling can be an effective tool for wildlife habitat management in the area surrounding highway 304. 1. INTRODUCTION binomial distribution and logit link that can fit higher- The factors affecting species distributions were degree polynomials (Hirzel et al., 2001). GLMs first described by Grinnell (1917) and comprise provide good predictions for virtual species locally measured variables such as food, vegetation, distributions because they can deal with many types of soil, and climate. The advent of geographic predictors (Brotons et al., 2004; Engler et al., 2004). information systems and remote sensing have Duangchatrasiri et al. (2019) assessed whether human provided spatial data covering large areas that can be activity had an impact on the occurrence of wildlife by used to elucidate the distribution of species. The using the distance from villages and roads because major contribution of habitat-suitability models wildlife is sensitive to hunting and a range of other (HSMs) to niche theory has been the ability to test the environmental conditions such as disturbance relevance of such variables for species and variables inside protected areas (Lynam et al., 2012). environmental relationships (Grinnell, 1917; Guisan In addition, distance to rivers and streams have also and Zimmermann, 2000). There are many available been included as covariates that reflect habitat quality methods for generating HSMs. One of the main near streams (Linkie et al., 2006). Land-cover data differences among them is dependence on the quality have the most diverse influences on ecological niches of data needed. Generalized linear models (GLMs) are (Schadt et al., 2002; Sachot et al., 2003; Seoane et al., a generalization of multiple regression analyses with a 2004). The Landsat 8 satellite provides ecologists with Citation: Paansri P, Sangprom N, Suksavate W, Chaiyes A, Duengkae P. Spatial modeling of forage crops for tiger prey species in the area surrounding highway 304 in the Dong Phayayen-Khao Yai Forest Complex. Environ. Nat. Resour. J. 2021;19(3):220-229. (https://doi.org/10.32526/ennrj/19/2020234) 220 Paansri P et al. / Environment and Natural Resources Journal 2021; 19(3): 220-229 infrared imagery, which allows computation of the This study used plot observations from a plant- normalized difference vegetation index (NDVI), a community survey to investigate habitat composition proxy for plant biomass (Estrada‐Peña et al., 2006). and factors affecting the quality and quantity of habitat The Dong Phayayen-Khao Yai Forest Complex in the DPKY-FC. We used multiple scales, including (DPKY-FC) in Thailand was declared a UNESCO plot, patch, and landscape (Cushman and McGarigal, World Heritage Site in 2005. However, recent reports 2002), by selecting random points to represent the have shown that tigers in the DPKY-FC were found study area. Information regarding habitat size, only in Thap Lan National Park and not in Khao Yai fragmentation, and resources provides the baseline for National Park (DNP, 2016; Ash et al., 2020). The last devising appropriate wildlife-management strategies report of tigers in Khao Yai National Park was from at both the operational and policy levels. This is September 19th, 1996. Gaur, the main prey for tigers in urgently needed to restore populations of tigers and this area, are still found on both sides of highway other endangered wildlife. 304 (Simcharoen et al., 2018), but they cannot travel between sites. Also, sambar deer, which are found 2. METHODOLOGY along the forest edge, still do not appear to use the 2.1 Study site highway 304 wildlife corridor. Therefore, the most The study was conducted in a 450.35 km2 area important landscape-level issue for the DPKY-FC is of the DPKY-FC comprising parts of Khao Yai the lack (in both quality and quantity) of wildlife National Park and Thap Lan National Park in Nakhon habitat. Highway 304 passes through this forest, Ratchasima and Prachin Buri Provinces (14°05′- resulting in threat factors that prevent large wildlife 14°30′N, 101°45′-102°00′E) (Figure 1). The elevation from being able to move safely back and forth across was 38-877 m.a.s.l. Field data were collected from the the road. immediate area surrounding highway 304. Figure 1. Study area showing the wildlife corridor and sampling points surrounding highway 304 in Khao Yai National Park and Thap Lan National Park. 221 Paansri P et al. / Environment and Natural Resources Journal 2021; 19(3): 220-229 2.2 Survey design study area, including the density of prey and tiger We surveyed sampling points in 318 plots to population; three forest types, including evergreen study the grasslands according to the abundance of forest (Ever), deciduous forest (Deci), and mixed sambar deer and gaur, which are the main prey for deciduous forest (Misc); elevation; and slope. The tigers. The coordinates of the centers of the plots were value of each factor was normalized from 0 to 100. recorded as the sampling points. We used tiger and The study area was then divided into 10 clusters prey occurrence data from the Wildlife Research according to the scores of each factor using K-means Division, Department of National Parks, Wildlife and clustering (Table 1). Cluster characteristics can be Plant Conservation, to create species distribution described according to the scores of various factors as models (SDMs) of tigers (Panthera tigris) and their follows: for wildlife habitat suitability, a value of 0 main prey species, particularly sambar deer (Rusa indicated that the area was not suitable, whereas a unicolor), gaur (Bos gaurus), and wild boar (Sus value of 100 indicated that the area was most suitable; scrofa). A model was created using the SDMs with for the forest type, a value of 0 indicated that the area maximum entropy (MaxEnt) (Phillips et al., 2006). did not have a specific forest type, whereas a value of The coordinates of the sampling points were randomly 100 indicated that the area had a specific forest type; selected to represent the study area. The following and for elevation and slope, a value of 0 indicated that factors were considered for each sampling point: the the area was flat, whereas a value of 100 indicated that distribution or occurrence of tigers and prey in the the area had a very high elevation and a steep slope. Table 1. The characteristics of 10 clusters based on the scoring of factors considered at the sample plots. Clusters Tiger Gaur Sambar deer Elevation Slope Ever Deci Misc 1 12.12 0.00 0.00 70.70 100.00 100.00 0.00 0.00 2 100.00 22.45 11.59 100.00 46.83 99.84 0.00 0.19 3 24.96 4.65 11.54 87.23 76.65 0.00 100.00 0.00 4 0.30 23.22 24.81 6.20 29.59 100.00 0.00 0.00 5 8.23 46.40 48.08 79.74 26.59 100.00 0.00 0.00 6 1.66 56.20 54.35 20.06 5.63 0.00 0.00 100.00 7 1.70 100.00 100.00 38.83 0.00 100.00 0.00 0.00 8 0.00 14.78 7.76 0.00 34.21 0.00 100.00 0.00 9 32.74 86.18 97.43 48.76 13.69 0.00 100.00 0.00 10 84.71 78.10 94.91 63.97 7.66 100.00 0.00 0.00 2.3 Field data collection methods weight and calculate the moisture content (percentage) The coordinates of the center point of the sample of the biomass following Pattanakiat (1988). In plots were recorded using the UTM WGS84 coordinate addition, direct sightings, tracks, and fresh dung of system. Grassland data were collected in four steps: (1) sambar deer and gaur were recorded at every sub-plot at each sample plot, four sub-plots (1 × 1 m) were at each sample plot.
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