Climate change impacts on the distribution of venomous snakes and snakebite risk in Mozambique

Zacarias, Daniel; Loyola, Rafael

doi:10.1007/s10584-018-2338-4

Cited by 37 publications

(56 citation statements)

References 58 publications

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“…In another study, Zacarias and Loyola 58 modelled current and future distributions of 13 snakes in Mozambique and showed that venomous snake distribution will change under climate change. They concluded patterns of snakebite risk may change due to climatic changes 58 . Results of current research revealed that 62 .…”

Section: Discussionmentioning

confidence: 99%

Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor

Yousefi

Kafash

Khani³

et al. 2020

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor

Yousefi

Kafash

Khani³

et al. 2020

Sci Rep

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“…Global warming is one of the major forces driving such change, as it is altering the distribution of nature [48,49], the distribution of people [50] and the orientation of people to interact with nature [51]. In Mozambique, for example, it is estimated that climate change will alter the spatial patterns of snake bite risk due to changes in the distribution of venomous snake species [52]. Such knowledge allows policy makers to develop risk management plans to minimize the consequences of negative human-nature interactions.…”

Section: Dynamics (A) Spatial Dynamicsmentioning

confidence: 99%

The ecology of human–nature interactions

2020

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The direct interactions between people and nature are critically important in many ways, with growing attention particularly on their impacts on human health and wellbeing (both positive and negative), on people's attitudes and behaviour towards nature, and on the benefits and hazards to wildlife. A growing evidence base is accelerating the understanding of different forms that these direct human–nature interactions take, novel analyses are revealing the importance of the opportunity and orientation of individual people as key drivers of these interactions, and methodological developments are increasingly making apparent their spatial, temporal and socio-economic dynamics. Here, we provide a roadmap of these advances and identify key, often interdisciplinary, research challenges that remain to be met. We identified several key challenges, including the need to characterize individual people's nature interactions through their life course, to determine in a comparable fashion how these interactions vary across much more diverse geographical, cultural and socio-economic contexts that have been explored to date, and to quantify how the relative contributions of people's opportunity and orientation vary in shaping their nature interactions. A robust research effort, guided by a focus on such unanswered questions, has the potential to yield high-impact insights into the fundamental nature of human–nature interactions and contribute to developing strategies for their appropriate management.

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“…They found that the snakes’ suitable climate spaces will increase in human populated areas of the country. In another study, Zacarias & Loyola [57] modeled current and future distributions of 13 snakes in Mozambique and showed that venomous snake distribution will change under climate change. They concluded patterns of snakebite risk may change due to climatic changes [57].…”

Section: Discussionmentioning

confidence: 99%

“…In another study, Zacarias & Loyola [57] modeled current and future distributions of 13 snakes in Mozambique and showed that venomous snake distribution will change under climate change. They concluded patterns of snakebite risk may change due to climatic changes [57]. Results of current research revealed that climatic variables play an important role in shaping the distribution of four venomous snakes in Iran, thus their distribution may alter with changing climate.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Developing snakebite risk model using venomous snake habitat suitability as an indicating factor: An application of species distribution models in public health research

Yousefi

Kafash

Khani³

et al. 2020

Preprint

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22Snakebite envenoming is an important public health problem in Iran, despite its risk not 23 being quantified. This study aims to use venomous snakes' habitat suitability as an 24 indicator of snakebite risk, to identify high-priority areas for snakebite management 25 across the country. Thus, an ensemble approach using five distribution modeling 26 methods: Generalized Boosted Models, Generalized Additive Models, Maximum Entropy 27 Modeling Generalized Linear Models, and Random Forest was applied to produce a 28 spatial snakebite risk model for Iran. To achieve this, four venomous snakes' habitat 29 suitability (Macrovipera lebetina, Echis carinatus, Pseudocerastes persicus and Naja 30 oxiana) were modeled and then multiplied. These medically important snakes are 31 responsible for the most snakebite incidents in Iran. Multiplying habitat suitability 32 models of the four snakes showed that the northeast of Iran (west of Khorasan-e-Razavi 33 province) has the highest snakebite risk in the country. In addition, villages that were at 34 risk of envenoming from the four snakes were identified. Results revealed that 51,112 35 villages are at risk of envenoming from M. lebetina, 30,339 from E. carinatus, 51,657 36 from P. persicus and 12,124 from N. oxiana. This paper demonstrates application of 37 species distribution modeling in public health research and identified potential snakebite 38 risk areas in Iran by using venomous snakes' habitat suitability models as an indicating 39 factor. Results of this study can be used in snakebite and human-snake conflict 40 management in Iran. We recommend increasing public awareness of snakebite 3 41 envenoming and education of local people in areas which identified with the highest 42 snakebite risk. 43 4 62 identifying environmental drivers of species distribution [42−45] and predicting impacts 63 of climate change on biodiversity [46−51]. SDMs are successfully used to identify 64 suitable habitats of species even in areas with no distribution records [52−54]. Thus, 65 these models can be used to identify suitable habitats of venomous snakes as proxies of 66 snakebite risk [12, 55−57] in data poor regions like Iran. 67 The main goal of this paper was to apply SDMs and produce a spatial risk model 68 for snakebite in Iran. To do this, firstly, five distribution modeling methods [38]: 69 Generalized Boosted Models, Generalized Additive Models, Maximum Entropy 70 Modeling Generalized Linear Models, and Random Forest and distribution data of M. 71 lebetina, E. carinatus, P. persicus and N. oxiana were to produce their habitat suitability 72 models. Secondly, the five habitat suitability models [38−58] of each species were 73combined by ensemble approach and finally the four species ensemble models were 74 multiplied to identify potential snakebite risk. We also in addition, number of villages 75 that are at risk of envenoming by these four snakes determined in Iran. 76 77 Materials and methods 78 Occurrence data 79 Distribution records of the M. lebetina, E. carinatus, P. per...

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Climate change impacts on the distribution of venomous snakes and snakebite risk in Mozambique

Cited by 37 publications

References 58 publications

Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor

Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor

The ecology of human–nature interactions

Developing snakebite risk model using venomous snake habitat suitability as an indicating factor: An application of species distribution models in public health research

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