Modelling the spatial distribution of snake species in northwestern Tunisia using maximum entropy (Maxent) and Geographic Information System (GIS)

Abstract

We used GIS and maximum entropy to predict the potential distribution of six snake species belong to three families in Kroumiria (Northwestern Tunisia): Natricidae (Natrix maura and Natrix astreptophora), Colubridae (Hemorrhois hippocrepis, Coronella girondica and Macroprotodon mauritanicus), and Lamprophiidae (Malpolon insignitus). The suitable habitat for each species was modelled using the maximum entropy algorithm, combining presence field data (collected during 16 years: 2000–2015) with a set of seven environmental variables (mean annual precipitation, elevation, slope gradient, aspect, distance to watercourses, land surface temperature and normalized Differential Vegetation Index. The relative importance of these environmental variables was evaluated by jackknife tests and the predictive power of our models was assessed using the area under the receiver operating characteristic. The main explicative variables of the species distribution were distance from streams and elevation, with contributions ranging from 60 to 77 and from 10 to 25%, respectively. Our study provided the first habitat suitability models for snakes in Kroumiria and this information can be used by conservation biologists and land managers concerned with preserving snakes in Kroumiria.

Keywords

Species distribution modelling / Maxent / Snakes / Kroumiria / Tunisia

References

[1] Anadón JD, Giménez A, Martínez M, Palazón JA, Esteve MA. Assessing changes in habitat quality due to land use changes in the spur-thighed tortoise Testudo graeca using hierarchical predictive habitat models. Divers Distrib, 2007, 13: 324-331.

[2] Anadón JD, Giménez A, Graciá E, Perez I, Ferrández M, Fahd S, El Mouden H, Kalboussi M, Jdeidi T, Larbes S, Rouag R, Slimani T, Znari M, Fritz U. Distribution of Testudo graeca in the western Mediterranean according to climatic factors. Amphib Reptil, 2012, 33: 285-296.

[3] Bombi P, Luiselli L, Capula M, Salvi D. Predicting elusiveness: potential distribution model of the southern smooth snake, Coronella girondica, in Italy. Acta Herpetol, 2009, 4: 7-13.

[4] Bons J, Geniez P. Amphibiens et Reptiles du Maroc (Sahara Occidental compris). Atlas biogéographique, 1996, Barcelona: Associación Herpetológica Española.

[5] Boyd D, Foody GM. An overview of recent remote sensing and GIS based research in ecological informatics. Ecol Inform, 2011, 6: 25-36.

[6] Brito JC, Feriche M, Herrera T, Kaliontzopoulou A, Martínez-Freiría F, Nesbitt D, Omolo D, Ontiveros D, Quiñoz L, Pleguezuelos JM, Santos X, Sillero N. En los límites de su distribución: anfibios y reptiles paleárticos en el noroeste de Túnez. Bol Asoc Herpetol Esp, 2008, 19: 75-82.

[7] Brito JC, Fahd S, Geniez P, Martinez-Freiría F, Pleguezuelos JM, Trape JF. Biogeography and conservation of viperids from North-West Africa: an application of ecological niche-based models and GIS. J Arid Environ, 2011, 75: 1029-1037.

[8] Brotons L, Thuiller W, Araújo MB, Hirzel AH. Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography, 2004, 27: 437-448.

[9] Brown JL. SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol Evol, 2014, 5: 694-700.

[10] Busby JR. Margules CR, Austin MP. BIOCLIM—a bioclimatic analysis and prediction system. Nature conservation: cost effective biological surveys and data analysis, 1991, Canberra: CISIRO 64 68

[11] Carpenter G, Gillison AN, Winter J. DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodivers Conserv, 1993, 2: 667-680.

[12] Chpakowsky N, Chnéour A. Les serpents de Tunisie. Bull Soc Sci Nat Tunis, 1953, 6: 125-146.

[13] de Souza Muñoz ME, De Giovanni R, de Siqueira MF, Sutton T, Brewer P, Pereira RS, Canhos DAL, Canhos VP. openModeller: a generic approach to species’ potential distribution modelling. GeoInformatica, 2011, 15: 111-135.

[14] Domergue CA. Liste des ophidiens de Tunisie, de l’Algérie et du Maroc. Arch Inst Pasteur Tunis, 1959, 36: 157-161.

[15] Elith J, Graham C, Anderson R, Dudik M, Ferrier S, Guisan A, Hijmans R, Huettmann F, Leathwick J, Lehmann A, Li J, Lohmann L, Loiselle B, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton J, Peterson A, Phillips S, Richardson K, Scachetti-Pereira R, Schapire R, Soberon J, Williams S, Wisz M, Zimmermann N. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 2006, 29: 129-151.

[16] Garcia-Rosello E, Guisande C, Gonzalez-Dacosta J, Heine J, Pelayo-Villamil P, Manjarras-Hernandez A, Vaamonde A, Granado-Lorencio C. ModestR: a software tool for managing and analyzing species distribution map databases. Ecography, 2013, 36: 1202-1207.

[17] Gibson L, Barrett B, Burbidge A. Dealing with uncertain absences in habitat modelling: a case study of a rare ground-dwelling parrot. Divers Distrib, 2007, 13: 704-713.

[18] Graham CH, Elith J, Hijmans RJ, Guisan A, Townsend Peterson A, Loiselle BA, Anderson RP, Dudk M, Ferrier S, Huettmann F, Leathwick J, Lehmann A, Li J, Lohmann L, Loiselle B, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton J, Phillips S, Richardson K, Pereira RS, Schapire R, Soberón J, Williams S, Wisz M, Zimmermann N. The influence of spatial errors in species occurrence data used in distribution models. J Appl Ecol, 2008, 45: 239-247.

[19] Guisan A, Hofer U. Predicting reptile distributions at the mesoscale: relation to climate and topography. J Biogeogr, 2003, 30: 1233-1243.

[20] Guisan A, Zimmermann NE. Predictive habitat distribution models in ecology. Ecol Model, 2000, 135: 147-186.

[21] Harris DJ, Carretero MA, Brito JC, Kaliontzopoulou A, Pinho C, Perera A, Vasconcelos R, Barata M, Barbosa D, Carvalho S, Fonseca MM, Pérez-Lanuza G, Rato C. Data on the distribution of the terrestrial herpetofauna of Morocco: records from 2001–2006. Herpetol Bull, 2008, 103: 19-28.

[22] Hemsing LØ (2010) GIS modelling of potential natural vegetation (PNV): a methodological case study from south-central Norway. Master thesis, Norwegian University of Life Sciences. https://brage.bibsys.no. Accessed 02.09.16

[23] Hirzel AH, Hausser J, Chessel D, Perrin N. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data?. Ecology, 2002, 83: 2027-2036.

[24] Joger U. Reptiles and amphibians of southern Tunisia. Kaupia, 2003, 12: 71-88.

[25] Kaliontzopoulou A, Brito JC, Carretero MA, Larbes S, Harris DJ. Modelling the partially unknown distribution of wall lizards (Podarcis) in North Africa: ecological affinities, potential areas of occurrence, and methodological constraints. Can J Zool, 2008, 86: 992-1001.

[26] Kramer-Schadt S, Niedballa J, Pilgrim JD, Schroder B, Lindenborn J, Reinfelder V, Stillfried M, Heckmann I, Scharf AK, Augeri DM, Cheyne SM, Hearn AJ, Ross J, Macdonald DW, Mathai J, Eaton J, Marshall AJ, Semiadi G, Rustam R, Bernard H, Alfred R, Samejima H, Duckworth JW, Breitenmoser-Wuersten C, Belant JL, Hofer H, Wilting A. The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib, 2013, 19: 1366-1379.

[27] Leathwick JR, Elith J, Hastie T. Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol Model, 2006, 199: 188-196.

[28] Lehmann A, Overton JM, Leathwick JR. GRASP: generalized regression analysis and spatial prediction. Ecol Model, 2003, 160: 165-183.

[29] Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecol Biogeogr 17:145–151

[30] Mechergui T, Pardos M, Boussaidi N, Hasnaoui B, Jacobs DF. Development of cork oak (Quercus suber L.) seedlings in response to tree shelters and mulching in northwestern Tunisia. J For Res, 2013, 24: 193-204.

[31] Muthoni FK (2010) Modelling the spatial distribution of snake species under changing climate scenario in Spain. Master thesis, Faculty of Geo-information Science and Earth Observation

[32] Nouira S, Blanc CP, Ktari MH. Biodiversité de l’herpétofaune tunisienne. I. Les ophidiens. Bull Soc Sci Nat Tunis, 1995, 24: 76-94.

[33] Pastorelli C, Laghi P, Melloni L. Distribuzione di Coronella girondica (Daudin, 1803) in Romagna (Reptilia: Squamata: Colubridae). Acta Herpetologica, 2007, 2: 121-217.

[34] Pearson RG (2010) Species’ distribution modelling for conservation educators and practitioners. Lessons in conservation. American Museum of Natural History, pp 54–89. http://ncep.amnh.org. Accessed 05.09.16

[35] Pearson RG, Dawson TP, Berry PM, Harrison PA. SPECIES: a spatial evaluation of climate impact on the envelope of species. Ecol Model, 2002, 154: 289-300.

[36] Phillips SB, Aneja VP, Kang D, Arya SP. Modelling and analysis of the atmospheric nitrogen deposition in North Carolina. Int J Glob Environ, 2006, 6: 231-252.

[37] Pokrant F, Kindler C, Ivanov M, Cheylan M, Geniez P, Böhme W, Fritz U. Integrative taxonomy provides evidence for the species status of the Ibero-Maghrebian grass snake Natrix astreptophora. Biol J Lin Soc, 2016, 118: 873-888.

[38] Posner SD (1988) Biological diversity and tropical forests in Tunisia. Washington

[39] Rouag R, Benyacoub S. Inventaire et écologie des reptiles du Parc national d’El Kala (Algérie). Bull Soc Herpétol Fr, 2006, 117: 25-40.

[40] Santos X, Brito JC, Caro J, Abril AJ, Lorenzo M, Sillero N, Pleguezuelos JM. Habitat suitability, threats and conservation of isolated populations of the smooth snake (Coronella austriaca) in the southern Iberian Peninsula. Biol Cons, 2009, 142: 344-352.

[41] Schleich HH, Kästle W, Kabisch K. Amphibians and Reptiles of North Africa, 1996, Koenigstein: Koeltz Sci. ed..

[42] Segura C, Feriche M, Pleguezuelos JM, Santos X. Specialist and generalist species in habitat use: implications for conservation assessment in snakes. J Nat Hist, 2007, 41: 2765-2774.

[43] Senay GB, Friedrichs M, Singh RK, Velpuri NM. Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin. Remote Sens Environ, 2016

[44] Sillero N, Tarroso P. Free GIS for herpetologists: free data sources on Internet and comparison analysis of proprietary and free/open source software. Acta Herpetol, 2010, 5: 63-85.

[45] Silva-Rocha I, Salvi D, Sillero N, Mateo JA, Carretero MA. Snakes on the Balearic Islands: an invasion tale with implications for native biodiversity conservation. PLoS ONE, 2015, 10: e0121026.

[46] Soberón J, Peterson TA. Interpretation of Models of Fundamental Ecological Niches and Species’ Distributional Areas. Biodivers Inform, 2005, 2: 1-10.

[47] Sow AS, Martinez-Freiria F, Dieng H, Fahd S, Brito JC. Biogeographical analysis of the Atlantic Sahara reptiles: environmental correlates of species distribution and vulnerability toclimate change. J Arid Environ, 2014, 109: 65-73.

[48] Stockwell D, Peters D. The GARP modelling system: problems and solutions to automated spatial prediction. Int J Geogr Inf Sci, 1999, 13: 143-158.

[49] Suarez-Seoane S, Garcia de la Morena EL, Morales Prieto MB, Osborne PE, de Juana E. Maximum entropy niche-based modelling of seasonal changes in little bustard (Tetrax tetrax) distribution. Ecol Model, 2008, 219: 17-29.

[50] Syfert MM, Smith MJ, Coomes DA. The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLoS ONE, 2013, 8: e55158.

[51] Thuiller W. BIOMOD—optimizing predictions of species distributions and projecting potential future shifts under global change. Glob Change Biol, 2003, 9: 1353-1362.

[52] Thuiller W, Münkemüller T. Møller AP, Fiedler W, Berthold P. Habitat suitability modelling. Effects of climate change on birds, 2010, New York: Oxford University Press 77 85

[53] Thuiller W, Lafourcade B, Engler R, Araújo MB. BIOMOD—a platform for ensemble forecasting of species distributions. Ecography, 2009, 32: 369-373.

[54] USGS (2015) Landsat 8 (L8) data users handbook. Earth Resources Observation and Science (EROS) Center 8, 97

[55] Veloz SD. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. J Biogeogr, 2009, 36: 2290-2299.

[56] Wen L, Saintilan N, Yang XH, Hunter S, Mawer D. MODIS NDVI based metrics improve habitat suitability modelling in fragmented patchy floodplains. Remote Sens Appl Soc Environ, 2015, 1: 85-97.

[57] Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, Elith J, Dudík M, Ferrier S, Huettmann F, Leathwick JR, Lehmann A, Lohmann L, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Phillips SJ, Richardson KS, Scachetti-Pereira R, Schapire RE, Soberón J, Williams SE, Zimmermann NE. Effects of sample size on the performance of species distribution models. Divers Distrib, 2008, 14: 763-773.

[58] Yackulic CB, Chandler R, Zipkin EF, Royle JA, Nichols JD, Campbell Grant EH, Veran S. Presence-only modelling using MAXENT: when can we trust the inferences?. Methods Ecol Evol, 2013, 4: 236-243.

[59] Zaniewski E, Lehmann A, Overton JM, McC J. Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol Model, 2002, 157: 261-280.