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

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OBEIDAVI ZEINAB; RANGZAN KAZEM; RANGZAN KAZEM; MIRZAEI ROUHOLLAH; ASHRAFZADEH MOHAMMAD REZA

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Journal: Journal of Natural Environment Year:2018 | Volume:70 | Issue:4 Page(s): 893-906

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Title

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

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Keywords

ecological knowledgeQ1

local communitiesQ1

potential distributionQ1

fuzzy inference systemQ1

maximum entropy algorithmQ1

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.

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APA: Copy

OBEIDAVI, ZEINAB, RANGZAN, KAZEM, MIRZAEI, ROUHOLLAH, & ASHRAFZADEH, MOHAMMAD REZA. (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. JOURNAL OF NATURAL ENVIRONMENT (IRANIAN JOURNAL OF NATURAL RESOURCES), 70(4 ), 893-906. SID. https://sid.ir/paper/195007/en

Vancouver: Copy

OBEIDAVI ZEINAB, RANGZAN KAZEM, MIRZAEI ROUHOLLAH, ASHRAFZADEH MOHAMMAD REZA. 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. JOURNAL OF NATURAL ENVIRONMENT (IRANIAN JOURNAL OF NATURAL RESOURCES)[Internet]. 2018;70(4 ):893-906. Available from: https://sid.ir/paper/195007/en

IEEE: Copy

ZEINAB OBEIDAVI, KAZEM RANGZAN, ROUHOLLAH MIRZAEI, and MOHAMMAD REZA ASHRAFZADEH, “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,” JOURNAL OF NATURAL ENVIRONMENT (IRANIAN JOURNAL OF NATURAL RESOURCES), vol. 70, no. 4 , pp. 893–906, 2018, [Online]. Available: https://sid.ir/paper/195007/en

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