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A web-based GIS system for wildlife species: a case study from Khouzestan Province, Iran

  • Zeinab ObeidaviEmail author
  • Kazem Rangzan
  • Mostafa Kabolizade
  • Rouhollah Mirzaei
Research Article
  • 84 Downloads

Abstract

Recent efforts to aggregate, process, and use biodiversity information have appended novel opportunities and challenges for the field, and a rapid increase in studies that integrate and analyze data in the biological-ecological realm. We developed a web-based GIS system for the wildlife of Khouzestan Province that provides potential distribution maps and other spatial and nonspatial data on the wildlife of Khouzestan Province and its protected areas. We used MaxEnt and a fuzzy inference system to model distributions of species. Our application was structured using a client/server architecture, and the database design and construction was carried out using PostgreSQL/PostGIS, and GeoServer to serve maps. The mapping interface was developed using OpenLayers; ASP.NET was selected for designing the user interface. We used qualitative-quantitative methods to develop, design, refine, and finalize our system particularly as regards usability. The design approach resulted in a user-friendly interface that allows both specialists and non-specialists to quickly and efficiently run models to estimate potential distributions of species. Our application highlights what can be accomplished with a biodiversity-oriented web application.

Keywords

Biodiversity Potential distribution MaxEnt Fuzzy inference system Web application 

Notes

Acknowledgements

We are most grateful to the data support of the Khouzestan Department of Environment, the Khouzestan Natural Resources and Watershed, and the Khouzestan Department of Management and Planning.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Aguiar LMS, Bernard E, Ribeiro V, Machado RB, Jones G (2016) Should I stay or should I go? Climate change effects on the future of Neotropical savannah bats. Glob Ecol Conserv 5(2016):22–33CrossRefGoogle Scholar
  2. Alley N, Stohlgren TJ, Evangelista PH et al (2004) Iterative model development for natural resource managers: a case example in Utah’s Grand-Staircase-Escalante National Monument. Geograph Inform Sci 10:1–9Google Scholar
  3. Bai W, Connor T, Zhang J et al (2018) Long-term distribution and habitat changes of protected wildlife: giant pandas in Wolong Nature Reserve, China. Environ Sci Pollut Res.  https://doi.org/10.1007/s11356-018-1407-6
  4. Brooke J (1996) SUS: A “Quick and Dirty” usability scale. Usability evaluation in industry. Taylor and Francis London UK pp 189–194 49.Google Scholar
  5. Chatterjee S, Hadi AS (eds) (2006) Regression analysis by example. Wiley, New YorkGoogle Scholar
  6. Duff TJ, Bell TL, York A (2014) Recognising fuzzy vegetation pattern: the spatial prediction of floristically defined fuzzy communities using species distribution modelling methods. J Veg Sci 25:323–337CrossRefGoogle Scholar
  7. Elith J, Phillips SJ, Hastie T et al (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43 47CrossRefGoogle Scholar
  8. Engler R, Guisan A, Rechsteiner L (2004) An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Appl Ecol 41:263–274CrossRefGoogle Scholar
  9. Evangelista PH, Norman J, Berhanu L et al (2008) Predicting habitat suitability for the endemic mountain nyala (Tragelaphus buxtoni) in Ethiopia. Wildl Res 35:409–416CrossRefGoogle Scholar
  10. Fa JE, Funk SM, O’Connell D (2011) Zoo Conservation Biology. Cambridge University Press ISBN: 978-0-521-82763-8Google Scholar
  11. Ferreira N, Lins L, Fink D (2011) BirdVis: visualizing and understanding bird populations. IEEE Trans Vis Comput Graph 17(12):2374–2383CrossRefGoogle Scholar
  12. Flemons P, Guralnick R, Krieger J (2007) A web-based GIS tool for exploring the world’s biodiversity: the global biodiversity information facility mapping and analysis portal application (GBIF-MAPA). Ecol Inform 2(1):49–60CrossRefGoogle Scholar
  13. Fourcade Y, Engler JO, Rodder D et al (2014) Mapping species distributions with MaxEnt using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS One 9(5):e97122CrossRefGoogle Scholar
  14. Franklin J, Miller JA (2009) Mapping species distributions: spatial inference and prediction. Cambridge University Press, New YorkGoogle Scholar
  15. Fulton EA, Smith AD, Smith DC et al (2011) Human behavior: the key source of uncertainty in fisheries management. Fish Fisher 12:2–17Google Scholar
  16. Gass G, Kumar S, Evangelista PH (2009) Ponderosa Pine in the Interior West: current condition and land management legacies. Technical Report to U.S. Department of Agriculture, Forest Service, Missoula MT USA 2009 p. 106Google Scholar
  17. Giovanelli JGR, De-Siqueira MF, Haddad CFB et al (2010) Modeling a spatially restricted distribution in the Neotropics: how the size of calibration area affects the performance of five presence-only methods. Ecol Model 221:215–224Google Scholar
  18. Golbarg F, Nabi Bidhend G, Hoveidi H (2018) Environmental management of oil pipelines risks in the wetland areas by Delphi and MCDM techniques: case of Shadegan international wetland, Iran. Pollution 4(2):195–210Google Scholar
  19. Gonzales R, Cardille JA, Parrott L (2009) SFMN geoSearch: an interactive approach to the visualization and exchange of point-based ecological data. Ecol Inform 4(4):196–205CrossRefGoogle Scholar
  20. Goodwin ZA, Harris DJ, Filer D et al (2015) Widespread mistaken identity in tropical plant collections. Curr Biol 25:1066–1067CrossRefGoogle Scholar
  21. Graham M, Kennedy J (2014) Vesper: visualizing species archives. Ecol Inform 24:132–147CrossRefGoogle Scholar
  22. Graham J, Newman G, Kumar S et al (2010) Bringing modelling to the masses: a web based system to predict potential species distributions. Future Internet 2010(2):624–634CrossRefGoogle Scholar
  23. Guralnick RP, Hill A (2009) Biodiversity informatics: automated approaches for documenting global biodiversity patterns and processes. Bioinformatics 25(4):421–428CrossRefGoogle Scholar
  24. Guralnick RP, Wieczorek J, Hijmans RJ et al (2006) Biogeomancer: automated georeferencing to map the world’s biodiversity data. PLoS Biol 4:1908–1909CrossRefGoogle Scholar
  25. Guralnick RP, Hill AW, Lane M (2007) Towards a collaborative, global infrastructure for biodiversity assessment. Ecol Lett 10(8):663–672CrossRefGoogle Scholar
  26. Helali H (2001) Design and Implementation of a Web GIS for the City of Tehran. Master’s thesis, Department of Geodesy and Geomatics Engineering K. N. Toosi University of TechnologyGoogle Scholar
  27. Hemmati T (2015) Workshop on Biodiversity, Khouzestan Department of Environment. https://www.doe.ir/portal/home/?news/168127/168146/173845/ (Accessed 10.01.18)
  28. Hijmans RJ, Cameron SE, Parra JL et al (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  29. Hilborn R (2007) Managing fisheries is managing people: what has been learned? Fish Fisher 8:285–296Google Scholar
  30. IUCN (2016) The IUCN Red List of Threatened Species. Version 2016-3. http://www.iucnredlist.org/ (Accessed 10.01.17)
  31. Janicki J, Narula N, Ziegler M, Guénard B, Economo EP (2016) Visualizing and interacting with large volume biodiversity data using client–server web-mapping applications: The design and implementation of antmaps.org. Ecol Inform 32(2016):185–193CrossRefGoogle Scholar
  32. Jarnevich CS, Evangelista PH, Stohlgren TJ et al (2010) An update to the national tamarisk map. West North Am Naturalist 2010 in pressGoogle Scholar
  33. Jazirian I, Alesheikh AA, Helali H (2007) Web-GIS technology and implementation method. Geogr Res Q 57:127–183Google Scholar
  34. Kommana K (2013) Implementation of a Geoserver application for GIS data distribution and manipulation. Master’s thesis, Department of Physical Geography and Quaternary Geology, Stockholm UniversityGoogle Scholar
  35. Kumar S, Stohigren TJ (2009) MaxEnt modelling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. J Ecol Nat Environ 4:94–98Google Scholar
  36. Legendre P, Fortin MJ (1989) Spatial pattern and ecological analysis. Kluwer Academic Publishers 80:107–138Google Scholar
  37. Lu CY, Gu W, Dai AH et al (2012) Assessing habitat suitability based on geographic information system (GIS) and fuzzy: a case study of Schisandra sphenanthera Rehd. et Wils. In Qinling Mountains, China. Ecol Model 242:105–115CrossRefGoogle Scholar
  38. McCarthy JL, Wibisono HT, McCarthy KP et al (2015) Assessing the distribution and habitat use of four felid species in Bukit Barisan Selatan National Park, Sumatra, Indonesia, Glob Ecol Conserv 3:210–221Google Scholar
  39. Michaelis C (2013) Get started with GeoServer and its REST API; an open source data management system with a full API and built-in WMS and WFS server. https://www.ibm.com/developerworks/web/library/os-geoserver/os-geoserver-pdf.pdf. Accessed 21 July 2015
  40. Mirzaei R, Hemami MR, Esmaili Sari A et al (2013) Determination of common buzzard (Buteo buteo) distribution and influencing factors in Golestan province using Maximum Entropy algorithms. Proceedings of 1st international conference of IALE-Iran Isfahan Iran 10 p [In Persian]Google Scholar
  41. Mocq J, St-Hilairea A, Cunjak RA (2013) Assessment of Atlantic salmon (Salmo salar) habitat quality and its uncertainty using a multiple-expert fuzzy model applied to the Romaine River (Canada). Ecol Model 265:14–25CrossRefGoogle Scholar
  42. Montello DR, Sutton PC (2013) An introduction to scientific research methods in geography. 2nded Thousand Oaks: SAGE 2013 314 ppGoogle Scholar
  43. Morisette JT, Jarnevich CS, Ullah A et al (2006) A tamarisk habitat suitability map for the continental United States. Front Ecol Environ 4:11–17CrossRefGoogle Scholar
  44. Mraz M (2010) Dynamic Server Map System. http://gis.vsb.cz/GISacek/GISacek_2010/sborniky/ing/Mraz.pdf. Accessed 30 July 2015
  45. Nabavi SMB, Behrouzi-Rad B, Padash A (2011) Atlas of birds and mammals distribution in Khouzestan. Department of Environment Islamic Republic of Iran Khouzestan Province Office 460 ppGoogle Scholar
  46. National Iranian Oil Company (NIOC) (2018) http://nioc.ir/portal/home/?news/100193/100207/ 117019 (Accessed 11.02.18)
  47. Nielsen J (1993) Usability engineering. Academic Press, BostonCrossRefGoogle Scholar
  48. Obeidavi Z, Mirzaei R, Jalalinasab AR et al (2017a) Assessing the autumn habitat suitability of Marmaronetta angustirostris in Shadegan wetland. Proceedings of the 1st international conference of SilkGIS 24-26 May 2017 Isfahan Iran 7p. [In Persian]Google Scholar
  49. Obeidavi Z, Rangzan K, Mirzaei R et al (2017b) Habitat suitability modelling of brown bear (Ursus arctos) in Shimbar protected area, Khuzestan Province. IJAE 5(18):61–72 [In Persian]CrossRefGoogle Scholar
  50. Obeidavi Z, Rangzan K, Mirzaei R et al (2017c) Wildlife habitats suitability modelling using fuzzy inference system: a case study of Persian leopard (Panthera pardus saxicolor) in Shimbar Protected Area. IJAE 6(1):57–67 [In Persian]CrossRefGoogle Scholar
  51. Obeidavi Z, Rangzan K, Mirzaei R et al (2018) Potential distribution modelling of wildlife species based on ecological knowledge of local communities compared with machine learning methods: a case study of Gazella subgutturosa in Mishdagh protected area. JNE 70(4):893–906 [In Persian]Google Scholar
  52. Paton A (2009) Biodiversity informatics and the plant conservation baseline. Trends Plant Sci 14(11):629–637CrossRefGoogle Scholar
  53. Pearson RG, Raxworthy CJ, Nakamura M et al (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Biogeography 34:102–117CrossRefGoogle Scholar
  54. Phillips SJ, Dudik M (2008) Modelling of species distributions with MaxEnt: new extensions and a comprehensive evaluation. Ecography 31:161–175CrossRefGoogle Scholar
  55. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modelling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  56. Robertson MP, Villet MH, Palmer AR (2004) A fuzzy classification technique for predicting species’ distributions: applications using invasive alien plants and indigenous insects. Divers Distrib 10:461–474CrossRefGoogle Scholar
  57. Statistical Yearbook of Khouzestan Province (2013) Management and Planning Department of KhouzestanGoogle Scholar
  58. Vallecillo S, Maes J, Polce C, Lavalle C (2016) A habitat quality indicator for common birds in Europe based on species distribution models. Ecol Indic 69(2016):488–499 55CrossRefGoogle Scholar
  59. Verbruggen H, Tyberghein L, Belton MF et al (2013) Improving transferability of introduced species’ distribution models: new tools to forecast the spread of a highly invasive seaweed. PLoS One 8(6):e68337CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zeinab Obeidavi
    • 1
    Email author
  • Kazem Rangzan
    • 1
  • Mostafa Kabolizade
    • 1
  • Rouhollah Mirzaei
    • 2
  1. 1.Department of Remote Sensing and Geographic Information System, Faculty of Earth SciencesShahid Chamran University of AhvazAhvazIran
  2. 2.Department of Environment, Faculty of Natural Resources and Earth SciencesUniversity of KashanKashanIran

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