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Your search keyword '"Paansri, Paanwaris"' showing total 19 results
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19 results on '"Paansri, Paanwaris"'

1. Home range and habitat utilization of gaur (Bos gaurus) in transition zone between protected forest and human-dominated landscape, Eastern Thailand

Author

Prayoon, Umphornpimon, Suksavate, Warong, Chaiyes, Aingorn, Paansri, Paanwaris, Siriaroonrat, Boripat, Utara, Yongchai, Tipkantha, Wanlaya, Baicharoen, Sudarath, Jairak, Waleemas, Kaewkhunjob, Erngsiri, Chaisonkhram, Chavin, Maikaew, Umaporn, Ieamsaard, Gittiyaporn, Sripiboon, Supaphen, Winitpornsawan, Supagit, Thunhikorn, Somying, Wanna, Nattawut, Sriracha, Sila, Sornsa, Thiti, Chokcharoen, Ratchanee, Buanual, Annop, Pattanakiat, Sura, Srikulnath, Kornsorn, and Duengkae, Prateep

Published
2024
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5. Species diversity, temporal pattern and habitat use of carnivorous mammals in the Khao Yai National Park, Thailand.

Author

Bangthong, Phanitsupha, Sukmasuang, Ronglarp, Khoewsree, Noraset, Plaard, Mananya, Paansri, Paanwaris, Kaewdee, Bunyatiporn, Chairat, Rattanawat, Duengkae, Prateep, and Siripattaranugul, Kittiwara

Subjects
SPECIES diversity, HABITATS, CARNIVOROUS animals, WILDLIFE conservation, 'UTTHAYAN haeng Chat Khao Yai (Thailand)
Abstract

Large and meso-carnivores play crucial roles in ecosystems. A systematic camera trap study conducted in Khao Yai National Park has confirmed that this area serves as a habitat for at least 18 significant carnivorous wildlife species from 6 families and 14 genera. Most of this wildlife is active during the night. The study identified 2 species as endangered, 6 species as vulnerable, and 2 species as near-threatened according to the IUCN Red List. When considering the conservation status at the national level, there is 1 species in the critically endangered category, 5 species in the endangered category, 6 species in the vulnerable category, and 2 species in the near-threatened category. The study's results have highlighted the close correlation between the distribution of wild animals in the area and human activities. Therefore, it is crucial to enforce strict regulations to avoid disrupting natural behaviors within this ecosystem. [ABSTRACT FROM AUTHOR]

Published
2023
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6. Genetic Monitoring of the Last Captive Population of Greater Mouse-Deer on the Thai Mainland and Prediction of Habitat Suitability before Reintroduction

Author

Wongloet, Wongsathit, primary, Kongthong, Prach, additional, Chaiyes, Aingorn, additional, Singchat, Worapong, additional, Suksavate, Warong, additional, Ariyaraphong, Nattakan, additional, Panthum, Thitipong, additional, Lisachov, Artem, additional, Jaisamut, Kitipong, additional, Sonongbua, Jumaporn, additional, Budi, Trifan, additional, Wannakan, Wannapa, additional, Thienpreecha, Worawit, additional, Paansri, Paanwaris, additional, Ahmad, Syed Farhan, additional, Sribuarod, Kriangsak, additional, Prayoon, Umphornpimon, additional, Aramsirirujiwet, Pakpoom, additional, Chamchumroon, Wiyada, additional, Muangmai, Narongrit, additional, Duengkae, Prateep, additional, and Srikulnath, Kornsorn, additional

Published
2023
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7. Effect of enviromental factors at multiple landscape scales on bird community in riparian ecosystem at Mun-Chi River confluence, Thailand

Author

Chaleekarn, Warut, primary, Duengkae, Prateep, additional, Pongcharoen, Chattraphas, additional, Sutummawong, Nantida, additional, Nakmuenwai, Pisut, additional, Siripin, Sasithon, additional, Chirachitmichai, Chanon, additional, Kummoo, Wuttisak, additional, Paansri, Paanwaris, additional, and Suksavate, ‪Warong, additional

Published
2022
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8. Coincidence of low genetic diversity and increasing population size in wild gaur populations in the Khao Phaeng Ma Non-Hunting Area, Thailand: A challenge for conservation management under human-wildlife conflict

Author

Duengkae, Prateep, primary, Ariyaraphong, Nattakan, additional, Tipkantha, Wanlaya, additional, Jairak, Waleemas, additional, Baicharoen, Sudarath, additional, Nguyen, Dung Ho My, additional, Korboon, Onjira, additional, Singchat, Worapong, additional, Panthum, Thitipong, additional, Ahmad, Syed Farhan, additional, Kaewkhunjob, Erngsiri, additional, Chaisonkhram, Chavin, additional, Maikaew, Umaporn, additional, Muangmai, Narongrit, additional, Ieamsaard, Gittiyaporn, additional, Sripiboon, Supaphen, additional, Paansri, Paanwaris, additional, Suksavate, Warong, additional, Chaiyes, Aingorn, additional, Winitpornsawan, Supagit, additional, Prayoon, Umphornpimon, additional, Sornsa, Thiti, additional, Chokcharoen, Ratchanee, additional, Buanual, Annop, additional, Siriaroonrat, Boripat, additional, Utara, Yongchai, additional, and Srikulnath, Kornsorn, additional

Published
2022
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11. Monitoring of diversity, abundance, activity period and habitat use of wildlife species around the wildlife corridor that connects the natural world heritage site of Thailand

Author

PLA-ARD, MANANYA, primary, KHIOESREE, NORASET, additional, KEAWDEE, BUNYATIPORN, additional, HUNGHENG, WIPAVEE, additional, CHATTRAKULDEE, PATTANAPHON, additional, PENGTHONG, PHANITSUPHA, additional, THONGBANTHUM, JIDAPA, additional, PAANSRI, PAANWARIS, additional, CHARASPET, KHWANRUTAI, additional, PANGANTA, TANAPONE, additional, CHANACHAI, YUWARAK, additional, DUENGKAE, PRATEEP, additional, MAROD, DOKRAK, additional, UTHAIRASMEE, SUWIMOL, additional, KAEWKRACHANG, THARNRAT, additional, BHUMPAKPHAN, NARIS, additional, TRISURAT, YONGYUT, additional, SUKSAVATE, WARONG, additional, SUNGKAEW, SARAWOOD, additional, PONGPATTANANURAK, NANTACHAI, additional, RACHARAK, PHRUET, additional, WIWATWITTAYA, DECHA, additional, TASEN, WATTANACHAI, additional, and Sukmasuang, Ronglarp, additional

Published
2021
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12. Abundance, diversity and daily activity of terrestrial mammal and bird species in disturbed and undisturbed limestone habitats using camera trapping, Central Thailand

Author

PLA-ARD, MANANYA, primary, HOONHEANG, WIPAVEE, additional, KAEWDEE, BUNYATHIPORN, additional, PANGANTA, TARAPON, additional, CHARASPET, KHWANRUTAI, additional, KHOIESRI, NORASET, additional, PAANSRI, PAANWARIS, additional, KANKA, PEMIKA, additional, CHANACHAI, YUWARAK, additional, THONGBANTHUM, JIDAPHA, additional, BANGTHONG, PHANITSUPHA, additional, and Sukmasuang, Ronglarp, additional

Published
2021
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14. Use of Bayesian, Lasso Binary Quantile Regression to Identify Suitable Habitat for Tiger Prey Species in Thap Lan National Park, Eastern Thailand.

Author

Paansri, Paanwaris, Suksavate, Warong, Chaiyes, Aingorn, Chanteap, Prawatsart, and Duengkae, Prateep

Subjects
*HABITATS, *QUANTILE regression, *TIGERS, *NORMALIZED difference vegetation index, *NATIONAL parks & reserves, *FOREST biomass
Abstract

A Bayesian approach was used to develop binary quantile regression models featuring the lasso penalty. The models afford the advantages of all quantile regression models, such as robustness and detailed insights into covariate effects; they also handle issues associated with overfitting well. Thus, this model was used to investigate habitat suitability for the management of tiger prey species. Field data were collected from 150 sampling sites (2,416 sub-plots) in Thap Lan National Park of the Dong Phayayen-Khao Yai Forest Complex (DPKY) from August 2019 to March 2021. We focused on sambar deer (Rusa unicolor) and gaur (Bos gaurus) because they are the principal prey species of tigers. Vegetation was sampled for biomass and nutrient content to identify suitable habitat. The "bayesQR" package of R was used to identify habitats appropriate for these species. The correlation between forage crop biomass and the normalized difference vegetation index (NDVI) was significantly associated with tiger prey species presence. The habitat can be improved by increasing grass and forb biomasses as the prey species prefer open habitats, such as grassland and open areas of dry evergreen forest. Habitat management has ensured that the grass biomass of open forest is significantly higher than that of dense forest. In addition, the hemicellulose content of open forest was significantly greater than that of dense forest. We found that spatial modeling combined with Bayesian, lasso binary quantile regression could aid wildlife habitat management in a Thai National Park. [ABSTRACT FROM AUTHOR]

Published
2022
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17. Monitoring the diversity, abundance, activity period and habitat use of wildlife species around the wildlife corridor that connects the natural world heritage site in Thailand.

Author

PLA-ARD, MANANYA, KHIOESREE, NORASET, KEAWDEE, BUNYATIPORN, HUNGHENG, WIPAVEE, CHATTRAKULDEE, PATTANAPHON, PENGTHONG, PHANITSUPHA, THONGBANTHUM, JIDAPA, PAANSRI, PAANWARIS, CHARASPET, KHWANRUTAI, PANGANTA, TANAPONE, CHANACHAI, YUWARAK, DUENGKAE, PRATEEP, MAROD, DOKRAK, UTHAIRASMEE, SUWIMOL, KAEWKRACHANG, THARNRAT, BHUMPAKPHAN, NARIS, TRISURAT, YONGYUT, SUKSAVATE, WARONG, SUNGKAEW, SARAWOOD, and PONGPATTANANURAK, NANTACHAI

Published
2021
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19. Spatial Modeling of Forage Crops for Tiger Prey Species in the Area Surrounding Highway 304 in the Dong Phayayen-Khao Yai Forest Complex.

Author

Paansri, Paanwaris, Sangprom, Natcha, Suksavate, Warong, Chaiyes, Aingorn, and Duengkae, Prateep

Subjects
*NORMALIZED difference vegetation index, *GEOGRAPHIC information systems, *WILDLIFE management areas, *TIGERS, *REMOTE sensing
Abstract

Spatial modeling is an analytical procedure that simulates real-world conditions using remote sensing and geographic information systems. The field data in this study were collected from 318 survey plots in the area surrounding highway 304 in the Dong Phayayen-Khao Yai Forest Complex (DPKY-FC) during the 2019 rainy season. Forage-crop biomass was collected from all plots. We focused on sambar deer (Rusa unicolor) and gaur (Bos gaurus), which are the main prey for tigers in this area. We created spatial models using generalized linear models with stepwise regression. The results indicated that the normalized difference vegetation index (NDVI) varied directly with grass biomass but inversely with shrub biomass (p<0.05). Elevation varied directly with forb biomass but inversely with shrub biomass (p<0.05). The probability 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. [ABSTRACT FROM AUTHOR]

Published
2021
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