Spatial Modeling of Species Distribution and predicting potential distribution of the Iranian long-legged wood frog Abstract

Document Type : علمی - پژوهشی

Authors

1 Assistant of Biodiversity and Ecosystem Management, Environmental Sciences Research Institute

2 M. Sc. in Biodiversity and Ecosystem Management, Environmental Sciences Research Institute, Shahid Beheshti University, G.C., Evin, Tehran, Iran

Abstract

Today, it is well-known that predicting the distribution potential of endangered species by usingspatial modeling methods is highly beneficial and using these methods can greatly contribute toecological conservation and management. Rana pesudodalmatina is one of the Iranian endemicamphibian species of Iran. In order to predict the potential geographic distribution of the species itsoccurrence points were collected through field work and 19 so-called bioclim climate variables asspatial environmental predictors were extracted from the Worldclim database. By applying Pearsoncorrelation test, the highly correlated variables with correlation coefficient of 0.75 were eliminated.Species distribution modeling was done using newly published R package which includes GLM,GAM, RF, MARS, CART, FDA, BRT and SVM models. All individual models were compound as anensemble to reduce the uncertainty which increase the accuracy and predictive power. The resultsrevealed that the long-legged wood frog has maximum distribution potential in Hyrcanian forest ofIran. Also, the results of the valuation of the models showed that the AUC and TSS had better statusand the SVM model was the most credible. In addition, the results of measuring the importance ofeach of the variables showed that BIO6 had the highest and BIO19 had the least importance for this.

Keywords

References

  1. حسین زاده، م.، آبادیان، م. و پویانی، ا، 1394، ارزیابی پراکنش جغرافیایی کنونی و آینده گونه معرفی شده جکوی شکم زرد خانگی Hemidactylus flaviviridis Ruppell, 1840 درایران با استفاده از مدلسازی پراکنش گونه‌ایی. مجله پژوهشهای جانوری (مجله زیست شناسی ایران). 28(4): 431-440.
  2. صابر فر، ر.، فلاحتکار س و کیا ح، 1395، مروری بر مدل‌های توزیع گونه‌ای با معرفی مدل نمادین .سومین کنفرانس بین المللی مدیریت در قرن21 .کفاش، ا.، یوسفی، م.، احمدی، م.، کلهر، گ.، کابلی، م، 1392، پیش بینی اثر تغییرات اقلیمی بر خزندگان مناطق بیابانی ایران ( مطالعه موردی سوسمار دم تیغی بین النهرین Saara loricata). سومین کنفرانس برنامه ریزی و مدیریت محیط زیست، دانشگاه تهران.
  3. کمی، ح. ق. و ابراهیمی، م، 1383 ، مطالعۀ ریزساختار‌های سطحی دهان لارو قورباغه جنگلی با استفاده از میکروسکوپ الکترونی نگاره. دوازدهمین کنفرانس سراسری زیست شناسی ایران، دانشگاه بوعلی سینای همدان.
  4. کمی، ح. ق.، اسماعیلی، ح. و ابراهیمی، م، 1381 ، .بررسی صفات مورفومتریک، رابطه طول و وزن و نسبت جنسی در قورباغۀ جنگلی در Rana macrocnemis pseudodalmatina استان گلستان .اولین کنفرانس علوم و تنوع زیستی جانوری، دانشگاه شهید باهنرکرمان.
  5. مومنی م. و زحمتکش، ی، 1383 ، بررسی امکان تکثیر و پرورش گونه Rana ridibunda در تالاب انزلی. اولین کنگرة علوم دام و آبزیان کشور دانشگاه تهران.مجله علمی پژوهشی دانشگاه اصفهان. (6)35: 222-209
  6. میرزاجانی، ع.، کیابی، ب. و باقری، س، 1385 ، بررسی رشد لارو قورباغه مردابی و برآورد جعمیت گونۀ Rana ridibunda در تالاب انزلی، مجله زیست شناسی ایران. (2)19: 202-191.
  7. یوسفی‌سیاهکلرودی، س.، سعیدی، ه.، بهفر،م، 1395 ، اطلس دوزیستان ایران. انتشارات جهاد دانشگاهی واحد خوارزمی.
  8. Araújo, M. B., and Peterson, A. T, 2012, Uses and misuses of bioclimatic envelope the Past, a Hope for the Future. Springer-Verlag, London.
  9. Austin, M. P, 2002, Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological modelling, 157(2-3), 101-118.
  10. Behjou, F. K., Majnounian, B., Dvořak, J., Namiranian, M., Saeed, A., & Feghhi, J, 2009, Productivity and cost of manual felling with a chainsaw in Caspian forests. Journal of Forest Science, 55(2), 96-100.
  11. Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. and Courchamp, F, 2012, Impacts of climate change on the future of biodiversity. Ecology letters. 15(4), 365-377.
  12. Bosso, L., Di Febbraro, M., Cristinzio, G., Zoina, A., and Russo, D, 2016, Shedding light on the effects of climate change on the potential distribution of Xylella fastidiosa in the Mediterranean basin. Biological invasions, 18(6), 1759-1768.
  13. Bosso, L., Luchi, N., Maresi, G., Cristinzio, G., Smeraldo, S., and Russo, D, 2017, Predicting current and future disease outbreaks of Diplodia sapinea shoot blight in Italy: species distribution models as a tool for forest mana gement planning. Forest Ecology and Management, 400, 655-664.
  14. Breiman, L, 1984, Classification and regression trees. – Wadsworth International Group, Belmont, CA, USA.
  15. Breiman, L, 2001, Random forests. Machine learning. 45(1): 5-32.
  16. Bui, D. T., Ho, T. C., Revhaug, I., Pradhan, B., and Nguyen, D. B, 2014, Landslide susceptibility mapping along the national road 32 of Vietnam using GIS-based J48 decision tree classifier and its ensembles. In Cartography from pole to pole (pp. 303-317). Springer Berlin Heidelberg. doi: 10.1007/978-3-642-32618-9_22.
  17. Chen, W., Pourghasemi, H. R., Kornejady, A., and Zhang, N, 2017, Landslide spatial modeling: introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma, 305, 314-327.
  18. Clark, J. S., Gelfand, A. E., Woodall, C. W., and Zhu, K, 2014, More than the sum of the parts: forest climate response from joint species distribution models. Ecological Applications, 24(5), 990-999.
  19. Drake, J. M., Randin, C., & Guisan, A, 2006, Modelling ecological niches with support vector machines. Journal of applied ecology, 43(3), 424-432.
  20. Eiselt, J., Schmidtler, J.F. and Schmidtler, F, 1971, Vorläufige Mitteilung über zwei neue Subspezies von Amphibia salientia aus dem Iran. Annalen des Naturhistorischen Museums in Wien. 75: 383-385.
  21. Elith, J., Graham, C. H., Anderson, R. P., Dudik, M., Ferrier, S., Guisan, A., ... and Li, J, 2006, Novel methods improve prediction of species' distributions from occurrence data. Ecography, 129-151.
  22. Felicisimo, A. M., Cuartero, A., Remondo, J., and Quiros, E, 2013, Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides. 10(2): 175-189.
  23. Friedman, J. H, 2001, Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232.
  24. Genard, M., and Lescourret, F, 2013, Combining niche and dispersal in a simple model (NDM) of species distribution. PloS one, 8(11), e79948.
  25. Guisan, A., and Theurillat, J. P, 2000, Assessing alpine plant vulnerability to climate change: a modeling perspective. Integrated assessment, 1(4), 307-320.
  26. Guisan, A., and Thuiller, W, 2005, Predicting species distribution: offering more than simple habitat models. Ecology letters, 8(9), 993-1009.
  27. Guisan, A., and Zimmermann, N. E, 2000, Predictive habitat distribution models in ecology. Ecological modelling, 135(2-3), 147-186.
  28. Gutt, J., Zurell, D., Bracegridle, T., Cheung, W., Clark, M., Convey, P., ... and Griffiths, H, 2012, Correlative and dynamic species distribution modelling for ecological predictions in the Antarctic: a cross-disciplinary concept. Polar Research, 31(1), 11091.
  29. Harris, D. J, 2015, Generating realistic assemblages with a joint species distribution model. Methods in Ecology and Evolution, 6(4), 465-473.
  30. Hastie, T. J., and Tibshirani, R. J, 1990, Generalized additive models, volume 43 of Monographs on Statistics and Applied Probability.
  31. Hastie, T., Tibshirani, R., and Buja, A, 1994, Flexible discriminant analysis by optimal scoring. Journal of the American statistical association, 89(428), 1255-1270.
  32. Hayes, B. J., Cogan, N. O., Pembleton, L. W., Goddard, M. E., Wang, J., Spangenberg, G. C., and Forster, J. W, 2013, Prospects for genomic selection in forage plant species. Plant Breeding, 132(2), 133-143.
  33. Hays, J.D., Imbrie, J. and Shackleton, N.J, 1976, Variations in the Earth's orbit: pacemaker of the ice ages. Science. 194(4270):121-1132.
  34. Hewitt, G.M. (2004). The structure of biodiversity–insights from molecular phylogeography. Frontiers in zoology. 1(1):1.
  35. Hijmans, RJ., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A, 2005, Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. 25: 1965–1978.
  36. Jebur, M. N., Pradhan, B., and Tehrany, M. S, 2014, Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sensing of Environment. 152: 150-165.
  37. Lee, C. O., Arge, C. N., Odstrčil, D., Millward, G., Pizzo, V., Quinn, J. M., and Henney, C. J, 2013, Ensemble modeling of CME propagation. Solar Physics. 285(1-2): 349-368.
  38. Marvi mohadjer, M.R, 2006, Silviculture. University of Tehran press. Tehran.
  39. McCullagh, P, 1973, Nelder. JA (1989), Generalized Linear Models. CRC Monographs on Statistics and Applied Probability, Springer Verlag, New York.
  40. Mieszkowska, N., Milligan, G., Burrows, M. T., Freckleton, R., and Spencer, M, 2013, Dynamic species distribution models from categorical survey data. Journal of Animal Ecology, 82(6), 1215-1226.
  41. Naimi, B., and Araujo, M. B, 2016, sdm: a reproducible and extensible R platform for species distribution modelling. Ecography. 39(4): 368-375.
  42. O’Connor, R. J, 2002, The conceptual basis of species distribution modeling: time for a paradigm shift. Predicting species occurrences: issues of accuracy and scale. Island Press, Washington, DC, USA, 25-33.
  43. Ovaskainen, O., Abrego, N., Halme, P., and Dunson, D, 2016, Using latent variable models to identify large networks of species‐to‐species associations at different spatial scales. Methods in Ecology and Evolution, 7(5), 549-555.
  44. Pearson, R. G, 2007, Species’ distribution modeling for conservation educators and practitioners. Synthesis. American Museum of Natural History, 50.
  45. Pearson, R.G., Raxworthy, C.J., Nakamura, M. and Townsend Peterson, A, 2007, Predicting species distribution from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography. 34(1):102-117
  46. Phillips, S. J., Anderson, R. P., and Schapire, R. E, 2006, Maximum entropy modeling of species geographic distributions. Ecological modelling, 190(3-4), 231-259.
  47. Poorzady, M., & Bakhtiari, F, 2009, Spatial and temporal changes of Hyrcanian forest in Iran. iForest-Biogeosciences and Forestry, 2(5), 198.Renner, I. W., and Warton, D. I, 2013, Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology. Biometrics, 69(1), 274-281.
  48. Pounds, J. A., Fogden, M. P., and Campbell, J. H, 1999, Biological response to climate change on a tropical mountain. Nature, 398(6728), 611-615.Guisan, A., and Thuiller, W, 2005, Predicting species distribution: offering more than simple habitat models. Ecology letters, 8(9), 993-1009.
  49. Pourmajidian, M. R., & Rahmani, A, 2009, The influence of single-tree selection cutting on silvicultural properties of a northern hardwood forest in Iran. American-Eurasian J Agric Environ Sci, 5(4), 526-532.
  50. Roelants, K., Gower, D.J., Wilkinson, M., Loader, S.P., Biju, S.D., Guillaume, K. and Bossuyt, F, 2007, Global patterns of diversification in the history of modern amphibians. Proceedings of the National Academy of Sciences. 104(3):887-892.
  51. Rokach, L, 2010, Ensemble-based classifiers. Artificial Intelligence Review. 33(1): 1-39.
  52. Royle, J. A., Chandler, R. B., Yackulic, C., and Nichols, J. D, 2012, Likelihood analysis of species occurrence probability from presence‐only data for modelling species distributions. Methods in Ecology and Evolution, 3(3), 545-554.
  53. Sagheb-Talebi, Kh., Sajedi, T., Pourhashemi, M., 2014, Forests of Iran, A Treasure from
  54. Sechrest, W., Brooks, T. M., da Fonseca, G. A., Konstant, W. R., Mittermeier, R. A., Purvis, A., ... & Gittleman, J. L, 2002, Hotspots and the conservation of evolutionary history. Proceedings of the National Academy of Sciences, 99(4), 2067-2071.‏
  55. Set of tutorials on SVM’s and kernel methods [Online]. Available: http://www.kernel-machines.org/tutorial.html.
  56. Stuart, S. N., Chanson, J. S., Cox, N. A., Young, B. E., Rodrigues, A. S., Fischman, D. L., and Waller, R. W, 2004, Status and trends of amphibian declines and extinctions worldwide. Science, 306(5702), 1783-1786.
  57. Stuart, S. N., Chanson, J. S., Cox, N. A., Young, B. E., Rodrigues, A. S., Fischman, D. L., & Waller, R. W, 2004, Status and trends of amphibian declines and extinctions worldwide. Science, 306(5702), 1783-1786.‏
  58. Taberlet, P., Fumagalli, L., Wust-Saucy, A.G. and Cosson, J.F, 1998, Comparative phylogeography and postglacial colonization routes in Europe. Molecular Ecology. 7:453–464.
  59. Tikhonov, G., Abrego, N., Dunson, D., and Ovaskainen, O, 2017, Using joint species distribution models for evaluating how species‐to‐species associations depend on the environmental context. Methods in Ecology and Evolution, 8(4), 443-452.
  60. Vane-Wright, R.I., Humphries, C.J., Williams, P.H, 1991,What to protect? – Systematics and the agony of choice. Biological Conservation, 55, 235-254.
  61. Vapnik., V, 1995, The Nature of Statistical Learning Theory, Springer- Verlag, New York.
  62. Vapnik., V, 1995, The Nature of Statistical Learning Theory, Springer- Verlag, New York.
  63. Veith, M., Kosuch, J. and Vences, M, 2003, Climatic oscillations triggered post-Messinian speciation of Western Palearctic brown frogs (Amphibia, Ranidae). Molecular phylogenetics and evolution. 26(2):310-327.
  64. Vences, M., Hauswaldt, J.S., Steinfartz, S., Rupp, O., Goesmann, A., Künzel, S. and Laugsch, C, 2013, Radically different phylogeographies and patterns of genetic variation in two European brown frogs, genus Rana. Molecular phylogenetics and evolution. 68(3):657-670.
  65. Vences, M., Hauswaldt, J.S., Steinfartz, S., Rupp, O., Goesmann, A., Künzel, S. and Laugsch, C, 2013, Radically different phylogeographies and patterns of genetic variation in two European brown frogs, genus Rana. Molecular phylogenetics and evolution. 68(3):657-670.
  66. Wake, D. B., and Vredenburg, V. T, 2008, Are we in the midst of the sixth mass extinction? A view from the world of amphibians. Proceedings of the National Academy of Sciences, 105(Supplement 1), 11466-11473.
  67. Walter., S.D, 2002, Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data. Stat Med. 21: 1237–1256.
  68. Williams, P. H., & Gaston, K. J, 1998, Biodiversity indicators: graphical techniques, smoothing and searching for what makes relationships work. Ecography, 551-560.
  69. Yackulic, C. B., Chandler, R., Zipkin, E. F., Royle, J. A., Nichols, J. D., Campbell Grant, E. H., and Veran, S, 2013, Presence‐only modelling using MAXENT: when can we trust the inferences?. Methods in Ecology and Evolution, 4(3), 236-243.
  70. Zurell, D., Jeltsch, F., Dormann, C. F., and Schröder, B, 2009, Static species distribution models in dynamically changing systems: how good can predictions really be., Ecography, 32(5), 733-744.
Iranian Journal of Remote Sensing & GIS
Volume 10, Issue 2 - Serial Number 2
September 2018
Pages 95-108

Files

Share

How to cite

Statistics