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Habitat suitability assessment for the endangered Nilgiri Laughingthrush: A multiple logistic regression approach

Present address: Centre for Biodiversity Studies, Baba Ghulam Shah Badshah University, Rajouri, India
Current science (Impact Factor: 0.93). 06/2008; 94(11):1487-1494.

ABSTRACT Application of remote sensing and Geographic Informa-tion System (GIS) tools has assumed an increasingly important role in conservation biology and wildlife management by providing means for modelling poten-tial distributions of species and their habitats, unlike the conventional ground surveys. We present here a predic-tive model of habitat suitability for the Nilgiri Laugh-ingthrush, Garrulax cachinnans based on a synergistic use of field surveys and digitally processed satellite im-agery combined with features mapped using GIS data layers. Collateral data were created in a GIS framework based on ground surveys comprising layers such as land-use, measures of proximity to likely features of distur-bance and a digital terrain model. Multiple binomial logistic regression approach was used for modelling, and the model performance was assessed by the area under the receiver operating characteristics (ROC) curve. About 320 km 2 , 25.12% of the area of the Nilgiris considered for modelling was predicted to be suitable for the Nilgiri Laughingthrush. The area under the ROC curve was found to be 0.984 ± 0.003 (R 2 : 0.93 at P < 0.0001), implying a highly effective model. The as-sessed suitable habitat was highly fragmented and comprised of 1352 patches (natural as well as man-made) distributed all over the study area. The smallest suitable patch identified by the model was 400 m 2 and the largest patch 17.65 km 2 . Also, ca. 92% of all patches were smaller than 0.5 km 2 . We presume that some suit-able habitat patches may be unoccupied due to strong fidelity of the species to shola (montane wet temperate forest) patches, low colonization rates, or large inter-fragment distances. Also, larger fragments might serve as source or 'exporters' of surplus individuals to main-tain sink populations throughout the rest of the range. We discuss the implication of habitat fragmentation and narrow geographical range and anthropogenic pressure for the conservation of the Nilgiri Laughing-thrush.

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    • "RS and GIS also help in monitoring areas of land for 35 their suitability to endangered species, through integration of various habitat variables of both spatial and non-spatial nature (Davis et al. 1990). The outputs of such models are usually simple, easily understandable and can be used for the assessment of 40 environmental impacts or prioritisation of conservation efforts in a timely and cost-effective manner (Kushwaha et al. 2004; Zarri et al. 2008). Indian gaur (Bos gaurus) belongs to the Bovidae family and is one among the nine species of wild oxen 45 found in the world. "
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    • "If the value of the index is high on a particular location, then the chance of species occurrence is greater. HSI models use geo-statistics (Kushwaha and others 2004; Habib and others 2010), logistic regression (Zarri and others 2008; Imam and others 2009), refined logistic regression (Singh and Kushwaha 2011), multi-criteria analysis using analytical hierarchy process (AHP) (Nekhaya and others 2009; Goswami 2010) and other data integration techniques to calculate an index of species occurrence (Clark and others 1999; Brown and others 2000) and provide an efficient and inexpensive method for determining habitat quality (Schamberger and Krohn 1982). "
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    • "Thus quantifying the amount of potential habitat available. In India recently, Kushwaha et al. (2004), Aditya (2004), Quadri (2004), Braunisch et al. (2008) and Zarri et al. (2008) have used " binomial multiple logistic regression " to analyze habitat suitability for Cervus unicolor and Muntiacus muntjac at Ranikhet, muntjac in Binsor Wildlife Sanctuary, tiger in Corbett Tiger Reserve, edge effect on two population of capercaillie (Tetrao urogallus) and Nilgiri laughingthrush (Garrulax cachinnans) in Western Ghats respec- tively. "
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