Cargando…
Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor
Snakebite envenoming is an important public health problem in Iran, despite its risk not being quantified. This study aims to use venomous snakes’ habitat suitability as an indicator of snakebite risk, to identify high-priority areas for snakebite management across the country. Thus, an ensemble app...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582189/ https://www.ncbi.nlm.nih.gov/pubmed/33093515 http://dx.doi.org/10.1038/s41598-020-74682-w |
_version_ | 1783599138571550720 |
---|---|
author | Yousefi, Masoud Kafash, Anooshe Khani, Ali Nabati, Nima |
author_facet | Yousefi, Masoud Kafash, Anooshe Khani, Ali Nabati, Nima |
author_sort | Yousefi, Masoud |
collection | PubMed |
description | Snakebite envenoming is an important public health problem in Iran, despite its risk not being quantified. This study aims to use venomous snakes’ habitat suitability as an indicator of snakebite risk, to identify high-priority areas for snakebite management across the country. Thus, an ensemble approach using five distribution modelling methods: Generalized Boosted Models, Generalized Additive Models, Maximum Entropy Modelling, Generalized Linear Models, and Random Forest was applied to produce a spatial snakebite risk model for Iran. To achieve this, four venomous snakes’ habitat suitability (Macrovipera lebetinus, Echis carinatus, Pseudocerastes persicus and Naja oxiana) were modelled and then multiplied. These medically important snakes are responsible for the most snakebite incidents in Iran. Multiplying habitat suitability models of the four snakes showed that the northeast of Iran (west of Khorasan-e-Razavi province) has the highest snakebite risk in the country. In addition, villages that were at risk of envenoming from the four snakes were identified. Results revealed that 51,112 villages are at risk of envenoming from M. lebetinus, 30,339 from E. carinatus, 51,657 from P. persicus and 12,124 from N. oxiana. Precipitation seasonality was identified as the most important variable influencing distribution of the P. persicus, E. carinatus and M. lebetinus in Iran. Precipitation of the driest quarter was the most important predictor of suitable habitats of the N. oxiana. Since climatic variables play an important role in shaping the distribution of the four venomous snakes in Iran, thus their distribution may alter with changing climate. This paper demonstrates application of species distribution modelling in public health research and identified potential snakebite risk areas in Iran by using venomous snakes’ habitat suitability models as an indicating factor. Results of this study can be used in snakebite and human–snake conflict management in Iran. We recommend increasing public awareness of snakebite envenoming and education of local people in areas which identified with the highest snakebite risk. |
format | Online Article Text |
id | pubmed-7582189 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75821892020-10-23 Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor Yousefi, Masoud Kafash, Anooshe Khani, Ali Nabati, Nima Sci Rep Article Snakebite envenoming is an important public health problem in Iran, despite its risk not being quantified. This study aims to use venomous snakes’ habitat suitability as an indicator of snakebite risk, to identify high-priority areas for snakebite management across the country. Thus, an ensemble approach using five distribution modelling methods: Generalized Boosted Models, Generalized Additive Models, Maximum Entropy Modelling, Generalized Linear Models, and Random Forest was applied to produce a spatial snakebite risk model for Iran. To achieve this, four venomous snakes’ habitat suitability (Macrovipera lebetinus, Echis carinatus, Pseudocerastes persicus and Naja oxiana) were modelled and then multiplied. These medically important snakes are responsible for the most snakebite incidents in Iran. Multiplying habitat suitability models of the four snakes showed that the northeast of Iran (west of Khorasan-e-Razavi province) has the highest snakebite risk in the country. In addition, villages that were at risk of envenoming from the four snakes were identified. Results revealed that 51,112 villages are at risk of envenoming from M. lebetinus, 30,339 from E. carinatus, 51,657 from P. persicus and 12,124 from N. oxiana. Precipitation seasonality was identified as the most important variable influencing distribution of the P. persicus, E. carinatus and M. lebetinus in Iran. Precipitation of the driest quarter was the most important predictor of suitable habitats of the N. oxiana. Since climatic variables play an important role in shaping the distribution of the four venomous snakes in Iran, thus their distribution may alter with changing climate. This paper demonstrates application of species distribution modelling in public health research and identified potential snakebite risk areas in Iran by using venomous snakes’ habitat suitability models as an indicating factor. Results of this study can be used in snakebite and human–snake conflict management in Iran. We recommend increasing public awareness of snakebite envenoming and education of local people in areas which identified with the highest snakebite risk. Nature Publishing Group UK 2020-10-22 /pmc/articles/PMC7582189/ /pubmed/33093515 http://dx.doi.org/10.1038/s41598-020-74682-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Yousefi, Masoud Kafash, Anooshe Khani, Ali Nabati, Nima Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor |
title | Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor |
title_full | Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor |
title_fullStr | Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor |
title_full_unstemmed | Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor |
title_short | Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor |
title_sort | applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582189/ https://www.ncbi.nlm.nih.gov/pubmed/33093515 http://dx.doi.org/10.1038/s41598-020-74682-w |
work_keys_str_mv | AT yousefimasoud applyingspeciesdistributionmodelsinpublichealthresearchbypredictingsnakebiteriskusingvenomoussnakeshabitatsuitabilityasanindicatingfactor AT kafashanooshe applyingspeciesdistributionmodelsinpublichealthresearchbypredictingsnakebiteriskusingvenomoussnakeshabitatsuitabilityasanindicatingfactor AT khaniali applyingspeciesdistributionmodelsinpublichealthresearchbypredictingsnakebiteriskusingvenomoussnakeshabitatsuitabilityasanindicatingfactor AT nabatinima applyingspeciesdistributionmodelsinpublichealthresearchbypredictingsnakebiteriskusingvenomoussnakeshabitatsuitabilityasanindicatingfactor |