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

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.

[1]  T. Connor,et al.  Long-term distribution and habitat changes of protected wildlife: giant pandas in Wolong Nature Reserve, China , 2018, Environmental Science and Pollution Research.

[2]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[3]  R. Guralnick,et al.  BioGeomancer: Automated Georeferencing to Map the World's Biodiversity Data , 2006, PLoS biology.

[4]  David C. Smith,et al.  Human behaviour: the key source of uncertainty in fisheries management , 2011 .

[5]  M. F. Siqueira,et al.  Modeling a spatially restricted distribution in the Neotropics: How the size of calibration area affects the performance of five presence-only methods , 2010 .

[6]  Jakob Nielsen,et al.  Chapter 4 – The Usability Engineering Lifecycle , 1993 .

[7]  C. Lavalle,et al.  A habitat quality indicator for common birds in Europe based on species distribution models , 2016 .

[8]  Jennifer A. Miller,et al.  Mapping Species Distributions: Spatial Inference and Prediction , 2010 .

[9]  A. York,et al.  Recognising fuzzy vegetation pattern: the spatial prediction of floristically defined fuzzy communities using species distribution modelling methods , 2014 .

[10]  R. Hilborn Managing fisheries is managing people: what has been learned? , 2007 .

[11]  Miroslav Dudík,et al.  Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation , 2008 .

[12]  Alan Paton,et al.  Biodiversity informatics and the plant conservation baseline. , 2009, Trends in plant science.

[13]  Jakob Nielsen,et al.  Usability engineering , 1997, The Computer Science and Engineering Handbook.

[14]  Daniel R. Montello,et al.  An introduction to scientific research methods in geography & environmental studies , 2006 .

[15]  Karteek Kommana Implementation of a Geoserver Applicatoin For GIS Data Distribution and Manipulation , 2013 .

[16]  A. St‐Hilaire,et al.  Assessment of Atlantic salmon (Salmo salar) habitat quality and its uncertainty using a multiple-expert fuzzy model applied to the Romaine River (Canada) , 2013 .

[17]  G. Hoarau,et al.  Improving Transferability of Introduced Species’ Distribution Models: New Tools to Forecast the Spread of a Highly Invasive Seaweed , 2013, PloS one.

[18]  R. Guralnick,et al.  Biodiversity informatics: automated approaches for documenting global biodiversity patterns and processes , 2009, Bioinform..

[19]  Gareth Jones,et al.  Should I stay or should I go? Climate change effects on the future of Neotropical savannah bats , 2016 .

[20]  Jeffrey A. Pedelty,et al.  A tamarisk habitat suitability map for the continental United States , 2006 .

[21]  J. Fa,et al.  Zoo Conservation Biology , 2011 .

[22]  T. Fuller,et al.  Assessing the distribution and habitat use of four felid species in Bukit Barisan Selatan National Park, Sumatra, Indonesia , 2015 .

[23]  Jeffrey A. Cardille,et al.  SFMN GeoSearch: An interactive approach to the visualization and exchange of point-based ecological data , 2009, Ecol. Informatics.

[24]  M. Fortin,et al.  Spatial pattern and ecological analysis , 1989, Vegetatio.

[25]  Sunil Kumar,et al.  Bringing Modeling to the Masses: A Web Based System to Predict Potential Species Distributions , 2010, Future Internet.

[26]  Steve Kelling,et al.  BirdVis: Visualizing and Understanding Bird Populations , 2011, IEEE Transactions on Visualization and Computer Graphics.

[27]  S. Chatterjee,et al.  Regression Analysis by Example , 1979 .

[28]  G. Bidhendi,et al.  Environmental Management of Oil Pipelines Risks in the Wetland Areas by Delphi and MCDM Techniques: Case of Shadegan International Wetland, Iran , 2018 .

[29]  Harris David,et al.  A statistical explanation of MaxEnt for ecologists , 2013 .

[30]  J. Engler,et al.  Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias , 2014, PloS one.

[31]  Miguel Nakamura Savoy Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar , 2007 .

[32]  Sunil Kumar,et al.  Predicting habitat suitability for the endemic mountain nyala (Tragelaphus buxtoni) in Ethiopia , 2008 .

[33]  A. Guisan,et al.  An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data , 2004 .

[34]  Jessie Kennedy,et al.  Vesper: Visualising species archives , 2014, Ecol. Informatics.

[35]  Robert P Guralnick,et al.  Towards a collaborative, global infrastructure for biodiversity assessment , 2007, Ecology letters.

[36]  Paul Evangelista,et al.  Iterative Model Development for Natural Resource Managers: A Case Example in Utah's Grand Staircase-Escalante National Monument , 2004, Ann. GIS.

[37]  Chunyan Lu,et al.  Assessing habitat suitability based on geographic information system (GIS) and fuzzy: A case study of Schisandra sphenanthera Rehd. et Wils. in Qinling Mountains, China , 2012 .

[38]  Matt Ziegler,et al.  Visualizing and interacting with large-volume biodiversity data using client-server web-mapping applications: The design and implementation of antmaps.org , 2016, Ecol. Informatics.

[39]  D. Harris,et al.  Widespread mistaken identity in tropical plant collections , 2015, Current Biology.

[40]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

[41]  Martin Mráz Dynamic Server Map System , 2010 .

[42]  Robert P. Guralnick,et al.  A web-based GIS tool for exploring the world's biodiversity: The Global Biodiversity Information Facility Mapping and Analysis Portal Application (GBIF-MAPA) , 2007, Ecol. Informatics.

[43]  Sunil Kumar,et al.  Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia , 2009 .

[44]  A. Palmer,et al.  A fuzzy classification technique for predicting species’ distributions: applications using invasive alien plants and indigenous insects , 2004 .

[45]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .