OALibJ  Vol.2 No.6 , June 2015
Predicting Potential Habitat Distribution of Sorrel (Rumex vesicarius L.) in India from Presence-Only Data Using Maximum Entropy Model
Abstract: Sorrel (Rumex vesicarius L.) is an underutilized, underexploited, traditional, valuable medicinal and vegetable herb. It is wildly distributed as an environmental weed and is sparsely cultivated in market and truck gardens as a minor leafy vegetable crop in south India. Concerning nutritional and health security of developing country like India, increasing production either by introducing its cultivation in non-traditional areas or by enhancing its productivity can be an important issue in near future. It is, therefore, most essential to predict possible potential new growing areas for sorrel in India. Habitat suitability modeling provides a tool for researchers and managers to understand the potential extent of concerned species spread. One dataset for sorrel presence locations (n = 21 points) in Karnataka and Andhra Pradesh states of south India was generated following two field surveys organized by the National Bureau of Plant Genetic Resources Regional Station, Rajendranagar in collaboration with Vegetable Research Station, Dr. Y. S. R. Horticultural University, Rajendranagar during 2010-2011. WorldClim dataset comprising of 19 bioclimatic data layers representing current climatic conditions was downloaded from Sorrel presence locations dataset and WorldClim dataset were used with maximum entropy (MaxEnt) modeling to develop preliminary habitat suitability map for sorrel in India. MaxEnt model was able to precisely predict current suitable sorrel habitat (training AUC = 0.993 and test AUC = 0.985). Further study is needed to examine the potential for sorrel to cultivate beyond its current range. Habitat suitability modeling provides an essential tool for enhancing our understanding of sorrel species spread.
Cite this paper: Reddy, M. , Begum, H. , Sunil, N. , Rao, P. , Sivaraj, N. and Kumar, S. (2015) Predicting Potential Habitat Distribution of Sorrel (Rumex vesicarius L.) in India from Presence-Only Data Using Maximum Entropy Model. Open Access Library Journal, 2, 1-11. doi: 10.4236/oalib.1101590.

[1]   Rechinger, K.H. (1984) Rumex (Polygonaceae) in Australia Are Consideration. Nuytsia, 5, 75-122.

[2]   Kirtikar, K.R. and Basu, B.D. (1999) Indian Medicinal Plants. 2nd Edition, Dehradun.

[3]   Batanouny, K.H. (1999) Wild Medicinal Plants in Egypt. Academy of Scientific Research and Technology, Egypt. The World Conservation Union (IUCN), Switzerland, 166-167.

[4]   AI-Quran, S. (2009) Ethanopharmacological Survey of Wild Medicinal Plants in Showbak, Jordan. Journal of Ethnopharmacology, 123, 45-50.

[5]   Belanger, J., Balakrishna, M., Latha, P. and Katumalla, S. (2010) Contribution of Selected Wild and Cultivated Leafy Vegetables from South India to Lutein and β-Carotene Intake. Asia Pacific Journal of Clinical Nutrition, 19, 417-424.

[6]   AI-Rumaih, M., May, F.A., Saad, A.I. and Warsy, A.S. (2002) Seasonal Variation in Mineral Content of Different Organs during Development of Rumex vesicarius L. Saudi Journal of Biological Sciences, 9, 69-79.

[7]   Alam, E.A. (2012) In Vitro Studies on Rumex vesicarius L. (Polygonaceae) for the Production of Some Active Constituents. Ph. D. Thesis, Helwan University, Egypt.

[8]   Mandle, V.S., Salunke, S.D., Gaikwad, S.M., Dande, K.G. and Patil, M.M. (2012) Study of Nutritional Value of Some Unique Leafy Vegetable Grown in Latur District. Journal of Animal Science Advances, 2, 296-298.

[9]   Vermani, K. and Sanjay, G. (2002) Herbal Medicines for Sexually Transmitted Diseases and AIDS. Journal of Ethnopharmacology, 80, 49-66.

[10]   Orhan, D., Deliorman, O. and Berrin, O. (2009) Antiviral Activity and Cytotoxicity of the Lipophilic Extracts of Various Edible Plants and Their Fatty Acids. Food Chemistry, 115, 701-705.

[11]   Huxley, A. (1992) The New RHS Dictionary of Gardening. MacMillan Press, London.

[12]   Franklin, J. (2009) Mapping Species Distributions, Spatial Inference and Prediction. Cambridge University Press, Cambridge.

[13]   Graham, C.H., Ferrier, S., Huettman, F., Moritz, C. and Peterson, A.T. (2004) New Developments in Museum-Based Informatics and Applications in Biodiversity Analysis. Trends in Ecology & Evolution, 19, 497-503.

[14]   Elith, J. and Leathwick, J.R. (2009) Species Distribution Models, Ecological Explanation and Prediction across Space and Time. Annual Review of Ecology and Systematics, 40, 677-697.

[15]   Elith, J., Phillips, S.J., Hastie, T., Dudik, M., Chee, Y.E. and Yates, C.J. (2011) A Statistical Explanation of MaxEnt for Ecologists. Diversity and Distributions, 17, 43-57.

[16]   Thuiller, W., Richardson, D.M., Pysek, P., Midgley, G.F., Hughes, G.O. and Rouget, M. (2005) Niche-Based Modelling as a Tool for Predicting the Risk of Alien Plant Invasions at a Global Scale. Global Change Biology, 11, 2234-2250.

[17]   Jarnevich, C.S. and Reynolds, L.V. (2011) Challenges of Predicting the Potential Distribution of a Slow-Spreading Invader, a Habitat Suitability Map for an Invasive Riparian Tree. Biological Invasions, 3, 153-163.

[18]   Jarnevich, C.S., Stohlgren, T.J., Barnett, D. and Kartesz, J. (2006) Filling in the Gaps: Modelling Native Species Richness and Invasions Using Spatially Incomplete Data. Diversity and Distributions, 12, 511-520.

[19]   Phillips, S.J., Anderson, R.P. and Schapire, R.E. (2006) Maximum Entropy Modeling of Species Geographic Distributions. Ecological Modelling, 190, 231-259.

[20]   Elith, J., Graham, H.C., Anderson, P.R., Dudik, M., Ferrier, S., Guisan, A., Hijmans, J.R., Huettmann, F., Leathwick, R.J., Lehmann, A., Li, J., Lohmann, G.L., Loiselle, A.B., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M., Townsend, P.A., Phillips, J.S., Richardson, K., Scachetti, P.R., Schapire, E.R., Soberon, J., Williams, S., Wisz, S.M. and Zimmermann, E.N. (2006) Novel Methods Improve Prediction of Species’ Distributions from Occurrence Data. Ecography, 29, 129-151.

[21]   Evangelista, P.H., Kumar, S., Stohlgren, T.J., Jarnevich, C.S., Crall, A.W., Norman, J.B. and Barnett, D.T. (2008) Modelling Invasion for a Habitat Generalist and a Specialist Plant Species. Diversity and Distributions, 14, 808-817.

[22]   Phillips, S.J. and Dudik, M. (2008) Modeling of Species Distributions with MaxEnt: New Extensions and a Comprehensive Evaluation. Ecography, 31, 161-175.

[23]   Reddy, M.T., Begum, H., Sunil, N., Pandravada, S.R. and Sivaraj, N. (2015) Assessing Climate Suitability for Sustainable Vegetable Roselle (Hibiscus sabdariffa var. sabdariffa L.) Cultivation in India Using MaxEnt Model. Agricultural and Biological Sciences Journal, 1, 62-70.

[24]   Reddy, M.T., Begum, H., Sunil, N., Pandravada, S.R., Sivaraj, N. and Kumar, S. (2015) Mapping the Climate Suitability Using MaxEnt Modeling Approach for Ceylon Spinach (Basella alba L.) Cultivation in India. The Journal of Agricultural Sciences, 10, 87-97.

[25]   Ponder, W.F., Carter, G.A., Flemons, P. and Chapman, R.R. (2001) Evaluation of Museum Collection Data for Use in Biodiversity Assessment. Conservation Biology, 15, 648-657.

[26]   Stockwell, D.R.B. and Peterson, A.T. (2002) Controlling Bias in Biodiversity Data. In: Scott, J.M., Heglund, P.J., Morrison, M.L., Haufler, J.B., Raphael, M.G., Wall, W.A. and Samson, F.B., Eds., Predicting Species Occurrences: Issues of Accuracy and Scale, Island Press, Washington DC, 537-546.

[27]   Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A. (2005) Very High Resolution Interpolated Climate Surfaces for Global Land Areas. International Journal of Climatology, 25, 1965-1978.

[28]   Nix, H.A. (1986) A Biogeographic Analysis of Australian Elapid Snakes. In: Longmore, R., Ed., Atlas of Elapid Snakes of Australia. Australian Flora and Fauna Series No. 7, Australian Government Publishing Service, Canberra, 4-15.

[29]   Kumar, S., Spaulding, S.A., Stohlgren, T.J., Hermann, K., Schmidt, T. and Bahls, L. (2009) Potential Habitat Distribution for the Freshwater Diatom Didymosphenia geminata in the Continental US. Frontiers in Ecology and Environment, 7, 415-420.

[30]   Sundar, S.S. and Mitsuko, C. (2005) A Geographical Information System for the Analysis of Biodiversity Data. Gis Resource Document [WWW Document].

[31]   Eitzinger, A., Laderach, P., Carmona, S., Navarro, C. and Collet, L. (2013) Prediction of the Impact of Climate Change on Coffee and Mango Growing Areas in Haiti. Full Technical Report, Centro Internacional de Agricultura Tropical (CIAT), Cali, Colombia.

[32]   Hanley, J.A. and McNeil, B.J. (1982) The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve. Radiology, 143, 29-36.

[33]   Manel, S., Williams, H.C. and Ormerod, S.J. (2001) Evaluating Presence-Absence Models in Ecology: The Need to Account for Prevalence. Journal of Applied Ecology, 38, 921-931.

[34]   Swets, J.A. (1988) Measuring the Accuracy of Diagnostic Systems. Science, 240, 1285-1293.

[35]   Hoffman, J.D., Narumalani, S., Mishra, D.R., Merani, P. and Wilson, R.G. (2008) Predicting Potential Occurrence and Spread of Invasive Plant Species along the North Platte River, Nebraska. Invasive Plant Science and Management, 1, 359-367.

[36]   Hanczar, B., Hua, J., Sima, C., Weinstein, J., Bittner, M. and Dougherty, E.R. (2010) Small-Sample Precision of ROC-Related Estimates. Bioinformatics, 26, 822-830.

[37]   Fielding, A.H. and Bell, J.F. (1997) A Review of Methods for the Assessment of Prediction Errors in Conservation Presence/Absence Models. Environmental Conservation, 24, 38-49.

[38]   Hernandez, P.A., Graham, C.H., Master, L.L. and Albert, D.L. (2006) The Effect of Sample Size and Species Characteristics on Performance of Different Species Distribution Modeling Methods. Ecography, 29, 773-785.

[39]   Pearson, R.G., Raxworthy, C.J., Nakamura, M. and Peterson, A.T. (2007) Predicting Species’ Distributions from Small Numbers of Occurrence Records: A Test Case Using Cryptic Geckos in Madagascar. Journal of Biogeography, 34, 102-107.

[40]   Phillips, S.J. (2008) Transferability, Sample Selection Bias and Background Data in Presence-Only Modelling: A Response to Peterson et al. (2007). Ecography, 31, 272-278.

[41]   Phillips, S.J., Dudik, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J. and Ferrier, S. (2009) Sample Selection Bias and Presence-Only Distribution Models: Implications for Background and Pseudo-Absence Data. Ecological Applications, 19, 181-197.

[42]   Hernandez, P.A., Franke, I., Herzog, S.K., Pacheco, V., Paniagua, L., Quintana, H.L., Soto, A., Swenson, J.J., Tovar, C., Valqui, T.H., Vargas, J. and Young, B.E. (2008) Predicting Species Distributions in Poorly-Studied Landscapes. Biodiversity and Conservation, 17, 1353-1366.

[43]   Anderson, R.P. and Martinez-Meyer, E. (2004) Modelling Species’ Geographic Distributions for Conservation Assessments: An Implementation with the Spiny Pocket Mice (Heteromys) of Ecuador. Biological Conservation, 116, 167-179.

[44]   Stohlgren, T.J. (2007) Measuring Plant Diversity: Lessons from the Field. Oxford University Press, Oxford, 408.