Abstract
Biodiversity loss due to habitat degradation, exploitation of natural deposits, rapid change of environment and climate, and various anthropogenic phenomenon throughout the last few decades in the quest of development have led to rise in safeguarding species ecological domain. With natural habitat of the endangered Panthera Tigris Tigris fast declining, coupled with factors such as loss in genetic diversity and disruption of ecological corridors, there is an urgent need to conserve and reintroduce it to newer geographic locations. The study aims to predict and model the distribution of the species Panthera Tigris Tigris by combining various climatic, human influence, and environmental factors so as to predict alternate ecological niche for the already dwindling tiger habitats in India. 19 Bioclimatic variables, Elevation level, 17 Land Cover classes, Population Density, and Human Footprint data were taken. MAXENT, SVM, Random Forest, and Artificial Neural Networks were used for modeling. Sampling bias on the species was removed through spatial thinning. These variables were tested for Pearson correlation and those having coefficient greater than 0.70 were removed. Kappa statistic and AUC were used to study the results of the methodology implemented. Testing data comprises 25% of the presence only points and test AUC value of MAXENT was found to be the highest at 0.963, followed by RF at 0.931, ANN at 0.906, and lastly SVM at 0.898. These indicated a high degree of accuracy for prediction. The most recent datasets were taken into consideration for the above variables increasing accuracy in both time and spatial domain.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Grinnell, J. (1917). The niche-relationships of the California thrasher. Auk, 34, 427–433.
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259.
Thatte, P., Joshi, A., Vaidyanathan, S., Landguth, E., & Ramakrishnan, U. (2018). Maintaining tiger connectivity and minimizing extinction into the next century: Insights from landscape genetics and spatially-explicit simulations. Biological Conservation, 218, 181–191.
Kywe, T. Z. (2012). Habitat suitability modeling for tiger(Panthera tigris) in the Hukaung Valley Tiger Reserve, Northern Myanmar, Niedersächsische Staats (157 pp.). Germany: Nund Universitätsbibliothek Göttingen.
Hernandez, P. A., Graham, C. H., Master, L. L., & Albert, D. L. (2006). The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 5, 773–785.
Kulloli, R. N., & Kumar, S. (2014). Comparison of Bioclimatic, NDVI and elevation variables in assessing extent of Commiphora wightii (Arnt.) Bhand. ISPRS—International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-8, 589–595.
Duan, R.-Y., Kong, X.-Q., Huang, M.-Y., Fan, W.-Y., & Wang, Z.-G. (2014). The predictive performance and stability of six species distribution models. PLoS ONE, 9(11), e112764. https://doi.org/10.1371/journal.pone.0112764.
Qing, Z., Zhang, Y., Sun, G., Duo, H., Wen, L., & Lei, G. (2015). Using species distribution model to estimate the wintering population size of the endangered scaly-sided merganser in China. PLoS ONE, 10, e0117307. https://doi.org/10.1371/journal.pone.0117307.
Hansen, J., Sato, M., Ruedy, R., Lacis, A., & Oinas, V. (2000). Global warming in the twenty-first century: An alternative scenario. Proceedings of the National Academy of Sciences, 97, 9875–9880. https://doi.org/10.1073/pnas.170278997.
Elith, J., Graham, C. H., Anderson, R. P., et al. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129–151.
Petitpierre, B., Kueffer, C., Broennimann, O., Randin, C., Daehler, C., et al. (2012). Climatic niche shifts are rare among terrestrial plant invaders. Science, 335, 1344–1348.
Freeman, E. A., & Moisen, G. G. (2008). A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecological Modelling, 217, 48–58.
Yackulic, C. B., et al. (2013). Presence-only modelling using MAXENT: When can we trust the inferences? Methods in Ecology and Evolution, 4, 236–243.
Adhikari, D., Barik, S. K., & Upadhaya, K. (2012). Habitat distribution modelling for reintroduction of Ilex khasiana Purk., a critically endangered tree species of northeastern India. Ecological Engineering, 40, 37–43.
Elith, J. H., Graham, C. P., Anderson, R., Dudík, M., Ferrier, S., et al. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129–151.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bajaj, S., Geraldine Bessie Amali, D. (2019). Species Environmental Niche Distribution Modeling for Panthera Tigris Tigris ‘Royal Bengal Tiger’ Using Machine Learning. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 882. Springer, Singapore. https://doi.org/10.1007/978-981-13-5953-8_22
Download citation
DOI: https://doi.org/10.1007/978-981-13-5953-8_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5952-1
Online ISBN: 978-981-13-5953-8
eBook Packages: EngineeringEngineering (R0)