Environmental Monitoring and Assessment

, Volume 155, Issue 1–4, pp 555–567 | Cite as

Development of tiger habitat suitability model using geospatial tools—a case study in Achankmar Wildlife Sanctuary (AMWLS), Chhattisgarh India

  • R. Singh
  • P. K. JoshiEmail author
  • M. Kumar
  • P. P. Dash
  • B. D. Joshi


Geospatial tools supported by ancillary geo-database and extensive fieldwork regarding the distribution of tiger and its prey in Anchankmar Wildlife Sanctuary (AMWLS) were used to build a tiger habitat suitability model. This consists of a quantitative geographical information system (GIS) based approach using field parameters and spatial thematic information. The estimates of tiger sightings, its prey sighting and predicted distribution with the assistance of contextual environmental data including terrain, road network, settlement and drainage surfaces were used to develop the model. Eight variables in the dataset viz., forest cover type, forest cover density, slope, aspect, altitude, and distance from road, settlement and drainage were seen as suitable proxies and were used as independent variables in the analysis. Principal component analysis and binomial multiple logistic regression were used for statistical treatments of collected habitat parameters from field and independent variables respectively. The assessment showed a strong expert agreement between the predicted and observed suitable areas. A combination of the generated information and published literature was also used while building a habitat suitability map for the tiger. The modeling approach has taken the habitat preference parameters of the tiger and potential distribution of prey species into account. For assessing the potential distribution of prey species, independent suitability models were developed and validated with the ground truth. It is envisaged that inclusion of the prey distribution probability strengthens the model when a key species is under question. The results of the analysis indicate that tiger occur throughout the sanctuary. The results have been found to be an important input as baseline information for population modeling and natural resource management in the wildlife sanctuary. The development and application of similar models can help in better management of the protected areas of national interest.


Geospatial tools Habitat Model Prey Suitability Tiger 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Braunisch, C., Bullmann, K., Graf, R. F., & Hirzel, A. H. (2008). Living on the edge—Modeling habitat suitability for species at the edge of their fundamental niche. Ecological Modelling, 214(2–4), 153–167. doi:10.1016/j.ecolmodel.2008.02.001.CrossRefGoogle Scholar
  2. Burgman, M. A., & Lindenmayer, D. B. (1998). Conservation biology for the Australian environment. Sydney: Surrey Beatty and Sons.Google Scholar
  3. Burgman, M. A., Breininger, D. R., Duncan, B. W., & Ferson, S. (2001). Setting reliability bounds on habitat suitability indices. Ecological Applications, 11, 70– 78. doi:10.1890/1051-0761(2001)011[0070:SRBOHS]2.0.CO;2.CrossRefGoogle Scholar
  4. Drury, K. L. S., & Candelaria, J. F. (2008). Using model identification to analyze spatially explicit data with habitat, and temporal, variability. Ecological Modelling, 214(2–4), 305–315. doi:10.1016/j.ecolmodel.2008.02.009.CrossRefGoogle Scholar
  5. Dzeroski, S., Grbovic, J., Walley, W. J., & Kompare, B. (1997). Using machine learning techniques in the construction of models: II data analysis with rule induction. Ecological Modelling, 95, 95–111. doi:10.1016/S0304-3800(96)00029-4.CrossRefGoogle Scholar
  6. ESRI (1999). ArcView GIS vers. 3.2. Redlands, CA, USA: Environmental Systems Research Institute, Inc.Google Scholar
  7. Fieberg, J., & Jenkins, K. J. (2005). Assessing uncertainty in ecological systems using global sensitivity analyses: A case example of simulated wolf reintroduction effects on elk. Ecological Modelling, 187, 259–280. doi:10.1016/j.ecolmodel.2005.01.042.CrossRefGoogle Scholar
  8. Hackett, C., & Vamnclay, J. K. (1998). Mobilizing expert knowledge of tree growth with the PLANTGRO and INFER systems. Ecological Modelling, 106, 233–246. doi:10.1016/S0304-3800(97)00185-3.CrossRefGoogle Scholar
  9. Hirzel, A. H., Hausser, J., & Perrin, N. (2006). Biomapper 3.2. Lab. For conservation biology. University of Lausanne, Lausanne.
  10. Hirzel, A. H., Helfer, V., & M’etral, F. (2001). Assess ing habitat-suitability models with a virtual species. Ecological Modelling, 145, 111–121. doi:10.1016/S0304-3800(01)00396-9.CrossRefGoogle Scholar
  11. Horst, H. S., Dijkhuizen, A. A., Huirne, R. B. M., & De Leeuw, P. W. (1998). Introduction of contagious animal diseases into The Netherlands: Elicitation of expert opinions. Livestock Production Science, 53, 253–264. doi:10.1016/S0301-6226(97)00098-5.CrossRefGoogle Scholar
  12. Mackenzie, D. I., & Royle, J. A. (2005). Designing occupancy studies: General advice and allocating survey effort. Journal of Applied Ecology, 42, 1105–1114. doi:10.1111/j.1365-2664.2005.01098.x.CrossRefGoogle Scholar
  13. Marcot, B. G. (2006). Characterizing species at risk I: Modeling rare species under the Northwest Forest Plan. Ecology and Society, 11, 10.Google Scholar
  14. Möltgen, J., Schmidt, B., & Kuhn, W. (1999). Landscape editing with knowledge-based measure deductions for ecological planning. In P. Agouris & A. Stefanidis (Eds.), ISD’99—Integrated spatial databases. Lecture notes in computer science 1737. Berlin: Springer.CrossRefGoogle Scholar
  15. Pearce, J. L., & Boyce, M. S. (2006). Modelling distribution and abundance with presence-only data. Journal of Applied Ecology, 43, 405–412. doi:10.1111/j.1365-2664.2005.01112.x.CrossRefGoogle Scholar
  16. Smith, C., Felderhof, L., & Bosch, O. J. H. (2007). Adaptive management: Making it happen through participatory systems analysis. Systems Research and Behavioral Science, 24, 567–587.CrossRefGoogle Scholar
  17. SPSS (1988). SPSS-X user’s guide (3rd ed). Chicago: SPSS Inc.Google Scholar
  18. Stoms, D. M., Davis, F. W., & Cogan, C. B. (1992). Sensitivity of wildlife habitat models to uncertainties in GIS data. Photogrammetric Engineering and Remote Sensing, 58, 843–850.Google Scholar
  19. USFWS (1980). Habitat evaluation procedures report ESM 102. Washington, DC, USA: United States Fish and Wildlife Service.Google Scholar
  20. USFWS (1996) Habitat evaluation procedures report 870 FW 1. Washington, DC, USA: United States Fish and Wildlife Service.Google Scholar
  21. Venterink, H. G. M. O., & Wassen, M. J. (1997). A comparison of six models predicting vegetation response to hydrological habitat change. Ecological Modelling, 101, 347–361. doi:10.1016/S0304-3800(97)00062-8.CrossRefGoogle Scholar
  22. Wightmann, R. (1995). GIS-based forest management planning in New Brunswick. Proceedings of ninth annual symposium on geographic information systems in natural resources management (Vol. 2, pp. 503–506) Vancouver, BC, Canada. GIS World.Google Scholar
  23. Yamada, K., Elith, J., McCarthy, M., & Zerger, A. (2003). Eliciting and integrating expert knowledge for wildlife habitat modeling. Environmental Modelling, 165, 251–264.Google Scholar
  24. Zaniewski, A. E., Lehmann, A., & Overton, J. M. (2002). Predicting species spatial distributions using presence-only data: A case study of native New Zealand ferns. Ecological Modelling, 157, 261–280. doi:10.1016/S0304-3800(02)00199-0.CrossRefGoogle Scholar
  25. Zhu, A. X. (1999). A personal construct-based knowledge acquisition process for natural resource mapping. International Journal of Geographical In formation Science, 13, 119–141. doi:10.1080/136588199241382.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • R. Singh
    • 1
  • P. K. Joshi
    • 2
    Email author
  • M. Kumar
    • 3
  • P. P. Dash
    • 3
  • B. D. Joshi
    • 4
  1. 1.Wildlife Institute of IndiaDehradunIndia
  2. 2.TERI UniversityNew DelhiIndia
  3. 3.Indian Institute of Remote SensingDehradunIndia
  4. 4.Gurukula Kangri UniversityHaridwarIndia

Personalised recommendations