Abstract
Fragmentation has now emerged as a major global problem, with anthropogenic activities regarded as one of the main causes, primarily for affecting the habitat suitability. Habitat suitability is influenced by several elements, the most significant of which are the structural components of land use and topography. With the aid of remote sensing and GIS tools, habitats were assessed using a multi-criteria approach and the habitat suitability modelling. Land Use/Land Cover and topographic characteristics were used in MaxEnt distribution model to assess the habitat suitability in the heterogeneous landscape of Central India. Significant overlaps of potential habitats were observed between the species mostly within the protected areas.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baldwin RA (2009) Use of maximum entropy modeling in wildlife research. Entropy 11(4):854–866. https://doi.org/10.3390/e11040854
Boral D, Moktan S (2021) Predictive distribution modeling of Swertia bimaculata in Darjeeling-Sikkim eastern Himalaya using MaxEnt: current and future scenarios. Ecol Process 10(1). https://doi.org/10.1186/s13717-021-00294-5
Gebrewahid Y, Abrehe S, Meresa E, Eyasu G, Abay K, Gebreab G, Kidanemariam K, Adissu G, Abreha G, Darcha G (2020) Current and future predicting potential areas of Oxytenanthera abyssinica (A. Richard) using MaxEnt model under climate change in Northern Ethiopia. Ecol Process 9(1). https://doi.org/10.1186/s13717-019-0210-8
Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29(5):773–785. https://doi.org/10.1111/j.0906-7590.2006.04700.x
Jackson CR, Robertson MP (2011) Predicting the potential distribution of an endangered cryptic subterranean mammal from few occurrence records. J Nat Conserv 19(2):87–94. https://doi.org/10.1016/j.jnc.2010.06.006
Kumar S, Singh R (2021) Geospatial applications in land use/land cover change detection for sustainable regional development: the case of Central Haryana, India. Geo Environ Eng 15(3):81–98. https://doi.org/10.7494/geom.2021.15.3.81
Lissovsky AA, Dudov SV (2021) Species-distribution Modeling: advantages and limitations of its application. 2. MaxEnt. Biol Bull Rev 11(3):265–275. https://doi.org/10.1134/s2079086421030087
Mcdonald RI, Kareiva P, Forman RTT (2008) The implications of current and future urbanization for global protected areas and biodiversity conservation. Biol Conserv 141(6):1695–1703. https://doi.org/10.1016/j.biocon.2008.04.025
Nduati EW, Mundia CN, Ngigi MM (2013) Effects of vegetation change and land use/land cover change on land surface temperature in the Mara ecosystem. Int J Sci Res 2(8):22–28
Phillips SB, Aneja VP, Kang D, Arya SP (2006) Modelling and analysis of the atmospheric nitrogen deposition in North Carolina. Int J Global Environ Issues 6(2–3):231–252. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Prajapati RK, Triptathi S, Mishra RM (2015) Habitat suitability analysis for chital (Axis axis) using geo-spatial technology pf Panna National Park (M.P.) India. Int J Adv Res Sci Technol 4(6):427–434
Soberon J, Peterson AT (2005) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers Inform 2(0):1–10. https://doi.org/10.17161/bi.v2i0.4
Teng SY, Su NJ, Lee MA, Lan KW, Chang Y, Weng JS, Wang YC, Sihombing RI, Vayghan AH (2021) Modeling the habitat distribution of Acanthopagrus schlegelii in the coastal waters of the eastern Taiwan strait using maxent with fishery and remote sensing data. JMSE 9(12):1442. https://doi.org/10.3390/jmse9121442
Wang R, Jiang C, Liu L, Shen Z, Yang J, Wang Y, Hu J, Wang M, Hu J, Lu X, Li Q (2021) Prediction of the potential distribution of the predatory mite Neoseiulus californicus McGregor in China using MaxEnt. Global Ecol Conserv 29(July):e01733. https://doi.org/10.1016/j.gecco.2021.e01733
WorldClim. (n.d.). Historical climate data. https://www.worldclim.org/
Yang X, ** X, Zhou Y (2021) Wildfire risk assessment and zoning by integrating maxent and GIS in Hunan Province, China. Forests 12(10):1299. https://doi.org/10.3390/f12101299
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Areendran, G., John, A.C., Abhijitha, C.S., Raj, K., Ranjan, K. (2023). Evaluating Potential Habitats of Chital, Sloth Bear and Jungle Cat in Selected Areas of Central Indian Landscape. In: Dhyani, S., Adhikari, D., Dasgupta, R., Kadaverugu, R. (eds) Ecosystem and Species Habitat Modeling for Conservation and Restoration. Springer, Singapore. https://doi.org/10.1007/978-981-99-0131-9_16
Download citation
DOI: https://doi.org/10.1007/978-981-99-0131-9_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0130-2
Online ISBN: 978-981-99-0131-9
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)