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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/57612
Title: Predicting potential distribution of an endemic butterfly lizard, Leiolepis ocellata (Squamata: Agamidae)
Authors: Pattarapon Promnun
Chalita Kongrit
Nontivich Tandavanitj
Supatchaya Techachoochert
Jenjit Khudamrongsawat
Chulalongkorn University
Mahidol University
Keywords: Agricultural and Biological Sciences;Earth and Planetary Sciences;Environmental Science
Issue Date: 1-Apr-2020
Citation: Tropical Natural History. Vol.20, No.1 (2020), 60-71
Abstract: © 2020 by Chulalongkorn University. Predicting available habitat and the fine scale distribution of an endemic butterfly lizard, Leiolepis ocellata, is important for conservation since its populations are experiencing threats from anthropogenic activities. We conducted field surveys in northern Thailand and detected L. ocellata at 15 of 16 surveyed localities. We then used Maxent to predict its potential distribution based on presence-only data collected from field surveys and museum specimens and predictor variables (19 bioclimatic variables). The most optimal model confirmed its geographic distribution in northern Thailand but absent from other regions in the country supported by high AUC (0.875±0.076). The most important predictor variable was isothermality followed by temperature annual range and precipitation seasonality. Its occurrence was corresponded with known sites for the species. One further site, from which an old specimen was collected and recorded in 1967, was not predicted by the model and disappeared during field surveys. Most predicted distribution range located outside protected areas, which may potentially disturb L. ocellata populations. Our results presented updated information on current occurrence and predicted distribution of L. ocellata in northern Thailand, which is useful for developing conservation strategy of this species.
URI: http://repository.li.mahidol.ac.th/dspace/handle/123456789/57612
metadata.dc.identifier.url: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85087343148&origin=inward
ISSN: 15139700
Appears in Collections:Scopus 2020

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