Introduction conservation priority sites by delineating species distribution using models on habitat suitability [1, 3-5].
1. Introduction conservation priority sites by delineating species distribution using models on habitat suitability [1, 3-5].
Conservation planning requires basic information GIS-based habitat models are usually based on an about distribution and status of natural resources. exclusively deductive or inductive approach, but few Knowledge of presence or absence of wildlife species habitat modelling studies have integrated both and their distribution across a landscape is critical for techniques. A deductive multivariate model of habitat making sound wildlife management decisions. of female black bear (Ursus americanus) was Therefore, predicting species distribution has become developed in the Ozark National Forest based on forest an important component of conservation planning in cover and several topographic and spatial parameters recent years, and wide variety of modelling techniques [6]. Black bear habitat was modeled on a regional basis have been developed for this purpose [1]. Habitat for the entire southeastern United States using models are simplified representations of complex deductive rules based on Forest inventory analysis ecological processes and cannot include all factors that surveys from the U.S. Forest Service [7]. Landsat influences a species occurrence or abundance [2]. The Thematic Mapper (TM) data was used to model sage rapid pace of development of GIS (Geographical grouse (Centrocercus urophasianus) habitat in Information System) has assisted in identification of Northern Utah [8]. GIS was used to predict potential
Corresponding author: Ambica Paliwal, Ph.D., research habitat for the small whorled pogonia (Isotria field: wildlife science. E-mail: [email protected].
medeoloides ), the rarest orchid in eastern North Co-author: Vinod Bihari Mathur, Ph.D. dean, research field: wildlife science. E-mail: [email protected].
America north of Florida [9]. Habitat suitability
Predicting Potential Distribution of Gaur (Bos gaurus) in Tadoba-Andhari Tiger Reserve, Central India
1042 analysis was conducted using empirical evaluation
models and models based on expertise in GIS [10]. Brown bear was modeled in the Central Apennines [11]. GIS based habitat model was developed for the Virginia northern flying squirrel in West Virginia [12]. Suitability of habitat was predicted for the large grazing African ungulates using inductive approach [13]. Predictive habitat models attempt to provide detailed predictions of distributions by relating presence or absence of a species to a set of environmental predictors that are likely to influence the suitability of the environment for the focal species [14]. Attempts were made to quantify important niche components and to predict a population’s response to change in those components [15]. Habitat models are based on the premise that the individuals select a site that offer a suitable set of environmental conditions.
In the absence of the reliable absence data, approaches which require only presence data are advantageous in the practical conservation management [16]. Ecological Niche Factor Analysis (ENFA) is one such approach [17]. It is an envelope based approach to habitat suitability modelling. The ENFA is based on Hutchinson’s definition of an ecological niche [18]. It was developed using Biomapper 3.1 [17]. It assumes that the environmental conditions are optimal where species is most frequently found [19]. This hypothesis implies that the species are at some sort of equilibrium with their environment. ENFA is based on the principle of Principal component analysis (PCA). ENFA computes the habitat suitability maps by comparing environmental response of the species to the environmental characteristics of the entire study area. It summarizes all predictor variables in few independent factors, first factor is called as “marginality” factor and other factors are “specialization” factors. MF (Marginality factor) describes how far the species optimum is from the mean environmental conditions in study area. It passes through the centroid of all species observation and centroid of all background cells in the study area. A
high MF value indicates that the species requirements are significantly different from average habitat conditions. SF (Specialization factor) describes the narrowness of the species niche. A high SF value indicates restricted ecological tolerance as compared to the prevailing conditions in the area. We have opted for the environmental envelope approach because absence of evidence cannot be equated with evidence of absence [14, 17, 20, 21].
In this paper we have used ENFA to study the distribution status of Gaur (Bos gaurus), commonly referred as the Indian bison. It is the largest living bovine, confined to the Oriental bio-geographic region [22]. The Gaur has been recorded in Bhutan, Nepal, Myanmar, Thailand, Southern China, Vietnam, Cambodia, Peninsular Malaysia [23, 24]. The global population of the Gaur is estimated at 13,000-30,000 animals of these, only 5,200-18,000 are reproductively active individuals [25]. The population has declined overall by at least 50% between 1950 and 2002 [24]. As
a consequence, Gaur is categorized as “Vulnerable” in the IUCN Red List of Threatened Species, 2009 [26]. It is protected under Schedule I of the Wildlife (Protection) Act 1972 of India (highest level of protection) and is listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES).The current distribution of Gaur is restricted to scattered pockets in India, Nepal, Bhutan, Cambodia, China, the Lao PDR, peninsular Malaysia, Mayanmar, Thailand and Vietnam. The actual distribution is highly fragmented and the species exist in small pockets in the areas of presence.