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Abstract
Prediction of the potential geographic distribution of species is essential concerning various purposes in protection and conservation. The present study focused on predicting the distribution of Pinus roxburghii Sarg. (chir pine) in Uttarakhand Himalayas using the MaxEnt model. The model produced AUC curve with significant value of 0.882 (± 0.023). The study results showed that 426200 ha (5.91%) cover highly potential habitat area for chir pine. Whereas 833900 ha (11.56%), 1019200 ha (14.13%) and 4936000 ha (68.41%) cover good potential, moderately potential and least potential habitat areas, respectively. Based on the jacknife test, it was observed that temperature seasonality (bio4), precipitation of seasonality (bio15) and precipitation of driest month (bio14) are the significant contributors to the occurrence of chir pine in Uttarakhand Himalayas. This study exemplifies the usefulness of the prediction model of species distribution and offers a more effective way to manage chir pine forest by all means, which is beneficial for both the wildlife and human beings for future prospects.
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References
- Baldwin, R. A. (2009). Use of maximum entropy modeling in wildlife research. Entropy, 11(4), 854-866. DOI: https://doi.org/10.3390/e11040854
- Chakraborty, A., Joshi, P. K., & Sachdeva, K. (2016). Predicting distribution of major forest tree species to potential impacts of climate change in the central Himalayan region. Ecological Engineering, 97, 593-609. DOI: https://doi.org/10.1016/j.ecoleng.2016.10.006
- Ferrier, S. (2002). Mapping spatial pattern in biodiversity for regional conservation planning: where to from here?. Systematic Biology, 51(2), 331-363. DOI: https://doi.org/10.1080/10635150252899806
- Graham, C. H., Ferrier, S., Huettman, F., Moritz, C., & Peterson, A. T. (2004). New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology & Evolution, 19(9), 497-503. DOI: https://doi.org/10.1016/j.tree.2004.07.006
- Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of climatology, 25(15), 195-204. DOI: https://doi.org/10.1002/joc.1276
- Merow, C., Smith, M. J., & Silander, Jr. J. A. (2013). A practical guide to MaxEnt for modeling species distributions: what it does, and why inputs and settings matter. Ecography, 36(10), 1058-1069. DOI: https://doi.org/10.1111/j.1600-0587.2013.07872.x
- Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2), 161-175. DOI: https://doi.org/10.1111/j.0906-7590.2008.5203.x
- Phillips, S. J., Miroslav, D., & Schapire, R. E. (2004). Maxent Software for Species Distribution Modeling. http://cs.princeton.edu/?schapire/Maxent/ DOI: https://doi.org/10.1145/1015330.1015412
- Yang, X. Q., Kushwaha, S. P. S., Saran, S., Xu, J., & Roy, P. S. (2013). Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in lesser Himalayan foothills. Ecological Engineering, 51, 83-87. DOI: https://doi.org/10.1016/j.ecoleng.2012.12.004
References
Baldwin, R. A. (2009). Use of maximum entropy modeling in wildlife research. Entropy, 11(4), 854-866. DOI: https://doi.org/10.3390/e11040854
Chakraborty, A., Joshi, P. K., & Sachdeva, K. (2016). Predicting distribution of major forest tree species to potential impacts of climate change in the central Himalayan region. Ecological Engineering, 97, 593-609. DOI: https://doi.org/10.1016/j.ecoleng.2016.10.006
Ferrier, S. (2002). Mapping spatial pattern in biodiversity for regional conservation planning: where to from here?. Systematic Biology, 51(2), 331-363. DOI: https://doi.org/10.1080/10635150252899806
Graham, C. H., Ferrier, S., Huettman, F., Moritz, C., & Peterson, A. T. (2004). New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology & Evolution, 19(9), 497-503. DOI: https://doi.org/10.1016/j.tree.2004.07.006
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of climatology, 25(15), 195-204. DOI: https://doi.org/10.1002/joc.1276
Merow, C., Smith, M. J., & Silander, Jr. J. A. (2013). A practical guide to MaxEnt for modeling species distributions: what it does, and why inputs and settings matter. Ecography, 36(10), 1058-1069. DOI: https://doi.org/10.1111/j.1600-0587.2013.07872.x
Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2), 161-175. DOI: https://doi.org/10.1111/j.0906-7590.2008.5203.x
Phillips, S. J., Miroslav, D., & Schapire, R. E. (2004). Maxent Software for Species Distribution Modeling. http://cs.princeton.edu/?schapire/Maxent/ DOI: https://doi.org/10.1145/1015330.1015412
Yang, X. Q., Kushwaha, S. P. S., Saran, S., Xu, J., & Roy, P. S. (2013). Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in lesser Himalayan foothills. Ecological Engineering, 51, 83-87. DOI: https://doi.org/10.1016/j.ecoleng.2012.12.004