Modeling the spatial distribution of wolf (Canis lupus pallipes) attacks on human using genetic algorithm (GARP) in Hamedan province

In recent decades due to steady human population growth coupled with increased use of resources and habitat degradation, conflicts between humans and carnivores have greatly been expanded. In order to mitigate these conflicts based on a clear understanding of conflict patterns, applying the species...

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Main Authors: N Behdarvand, M Kaboli, R Ebrahimpour, B Jabbarian Amiri
Format: Article in Journal/Newspaper
Language:unknown
Published: Isfahan University of Technology 2012
Subjects:
Online Access:https://doaj.org/article/5830beec31464214a16df5382493b6df
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spelling fttriple:oai:gotriple.eu:oai:doaj.org/article:5830beec31464214a16df5382493b6df 2023-05-15T15:50:30+02:00 Modeling the spatial distribution of wolf (Canis lupus pallipes) attacks on human using genetic algorithm (GARP) in Hamedan province N Behdarvand M Kaboli R Ebrahimpour B Jabbarian Amiri 2012-06-01 https://doaj.org/article/5830beec31464214a16df5382493b6df other unknown Isfahan University of Technology 2476-3128 2476-3217 https://doaj.org/article/5830beec31464214a16df5382493b6df undefined Iranian Journal of Applied Ecology, Vol 1, Iss 1, Pp 4-14 (2012) Hamedan province human-wolf conflict genetic algorithm for rule-set prediction (GARP) modeling distribution attacks envir manag Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2012 fttriple 2023-01-22T19:30:37Z In recent decades due to steady human population growth coupled with increased use of resources and habitat degradation, conflicts between humans and carnivores have greatly been expanded. In order to mitigate these conflicts based on a clear understanding of conflict patterns, applying the species distribution models as helpful methods has been suggested. Occurring the recent conflict between wolves and local communities in Hamedan province is a clear case of this problem. In this study, capabilities of the genetic algorithm (GARP) were assessed in the modeling spatial distribution of wolf attacks in Hamedan province during 2006-2012. The area under the receiver operating characteristic curve (ROC) was used to evaluate performance of the model. Findings indicated that the applied modelingapproach has a very good performance (area under curve=0.856) inpredicting the spatial distribution of wolf attacks on humans. In addition, based on the results of sensitivity analysis, land-cover t ype, human population density and distance from main road were the most effective parameters. Findings of the present study can be applied in formulation of an adaptive management plan for wolf conservation and mitigation of the conflicts with local communities. Article in Journal/Newspaper Canis lupus Unknown
institution Open Polar
collection Unknown
op_collection_id fttriple
language unknown
topic Hamedan province
human-wolf conflict
genetic algorithm for rule-set prediction (GARP)
modeling distribution attacks
envir
manag
spellingShingle Hamedan province
human-wolf conflict
genetic algorithm for rule-set prediction (GARP)
modeling distribution attacks
envir
manag
N Behdarvand
M Kaboli
R Ebrahimpour
B Jabbarian Amiri
Modeling the spatial distribution of wolf (Canis lupus pallipes) attacks on human using genetic algorithm (GARP) in Hamedan province
topic_facet Hamedan province
human-wolf conflict
genetic algorithm for rule-set prediction (GARP)
modeling distribution attacks
envir
manag
description In recent decades due to steady human population growth coupled with increased use of resources and habitat degradation, conflicts between humans and carnivores have greatly been expanded. In order to mitigate these conflicts based on a clear understanding of conflict patterns, applying the species distribution models as helpful methods has been suggested. Occurring the recent conflict between wolves and local communities in Hamedan province is a clear case of this problem. In this study, capabilities of the genetic algorithm (GARP) were assessed in the modeling spatial distribution of wolf attacks in Hamedan province during 2006-2012. The area under the receiver operating characteristic curve (ROC) was used to evaluate performance of the model. Findings indicated that the applied modelingapproach has a very good performance (area under curve=0.856) inpredicting the spatial distribution of wolf attacks on humans. In addition, based on the results of sensitivity analysis, land-cover t ype, human population density and distance from main road were the most effective parameters. Findings of the present study can be applied in formulation of an adaptive management plan for wolf conservation and mitigation of the conflicts with local communities.
format Article in Journal/Newspaper
author N Behdarvand
M Kaboli
R Ebrahimpour
B Jabbarian Amiri
author_facet N Behdarvand
M Kaboli
R Ebrahimpour
B Jabbarian Amiri
author_sort N Behdarvand
title Modeling the spatial distribution of wolf (Canis lupus pallipes) attacks on human using genetic algorithm (GARP) in Hamedan province
title_short Modeling the spatial distribution of wolf (Canis lupus pallipes) attacks on human using genetic algorithm (GARP) in Hamedan province
title_full Modeling the spatial distribution of wolf (Canis lupus pallipes) attacks on human using genetic algorithm (GARP) in Hamedan province
title_fullStr Modeling the spatial distribution of wolf (Canis lupus pallipes) attacks on human using genetic algorithm (GARP) in Hamedan province
title_full_unstemmed Modeling the spatial distribution of wolf (Canis lupus pallipes) attacks on human using genetic algorithm (GARP) in Hamedan province
title_sort modeling the spatial distribution of wolf (canis lupus pallipes) attacks on human using genetic algorithm (garp) in hamedan province
publisher Isfahan University of Technology
publishDate 2012
url https://doaj.org/article/5830beec31464214a16df5382493b6df
genre Canis lupus
genre_facet Canis lupus
op_source Iranian Journal of Applied Ecology, Vol 1, Iss 1, Pp 4-14 (2012)
op_relation 2476-3128
2476-3217
https://doaj.org/article/5830beec31464214a16df5382493b6df
op_rights undefined
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