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

Document Type : Research Paper

Authors

1 j

2 Department of RS & GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Iran

3 Department of Environment, Faculty of Natural Resources and Earth Sciences, University of Kashan, Iran.

4 Department of Fisheries and Environmental Scinces, Faculty of Natural Resources and Earth Scinces, Shahrekord University, Iran

Abstract

Monitoring and managing the wildlife populations and habitats required to model the species distribution and habitat suitability. So, Gazella subgutturosa potential distribution in Mishdagh Protected Area was modeled using fuzzy (based on ecological knowledge of local communities) and MaxEnt (based on species occurrence records) approaches; thus, in addition to model the species distribution using maximum entropy algorithm (MaxEnt approach) and fuzzy inference system (fuzzy approach), we can also assess and compare the performance of each approach. In addition, the accuracy of predictive models was tested using jackknife test. Also, we applied threshold of 10%. Based on results of fuzzy and MaxEnt approaches, the most important variables for species potential distribution modelling were land use, distance to farms and distance to water sources. Also, 47.45% and 14.08% of study area predicted as species potential presence area in fuzzy and MaxEnt approaches, respectively. According to results of jackknife test, success rates of fuzzy and MaxEnt approaches were 80.95% and 66.66%, respectively (p<0.01). Findings of this research confirmed the high performance of fuzzy inference system and maximum entropy algorithm to model species potential distribution. This study emphasized the necessity of attention to fuzzy approach for potential distribution modelling of wildlife species in Iran, and emphasized also the necessity of attention to the ecological knowledge of local communities.

Keywords

Volume 70, Issue 4
February 2018
Pages 893-906
  • Receive Date: 04 May 2017
  • Revise Date: 22 January 2018
  • Accept Date: 30 October 2017
  • First Publish Date: 22 December 2017