Venomous snakes and climate change: ophidism as a dynamic problem

Nori, Javier; Carrasco, Paola A.; Leynaud, Gerardo C.

doi:10.1007/s10584-013-1019-6

Cited by 57 publications

(59 citation statements)

References 48 publications

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“…TSS was the only helpful accuracy measure to assess the performance of SDMs (Allouche et al, 2006), with the same pattern as FGM. Nori et al (2013) predicted similar values in the north of Misiones, throughout Buenos Aires and in a disjunct area in Tucumán. Conversely, in our study, model predictions (except DOMAIN and GLM) did not achieve high suitability values for these areas.…”

Section: Comparison Between Models and Real Distributionsupporting

confidence: 53%

“…As a model for this study, we selected B. alternatus, a poisonous snake species with public health importance. Thorough and continuous efforts have been made for decades to perform unbiased samplings throughout its distribution area in Argentina (e.g., Arzamendia & Giraudo, 2009;Bellini, Giraudo, Arzamendia, & Etchepare, 2015;Giraudo, 2001;Giraudo & Arzamendia, 2014;Giraudo et al, 2008;Nori, Carrasco, & Leynaud, 2013). Besides, B. alternatus: (a) presents (Bellini et al, 2015;Giraudo, 2012;Giraudo et al, 2008;Scrocchi, Moreta, & Kretzschmar, 2006); (e) presents peculiarities in its distribution which constitute real challenges in modeling; for example, it has not been found in humid forests to the northeast of its distribution (Giraudo, 2001) and two disjunctive and isolated populations occur in coastal areas and the Pampean hills in Buenos Aires province (South distribution).…”

Section: As Inmentioning

confidence: 99%

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Species distribution models and empirical test: Comparing predictions with well‐understood geographical distribution of Bothrops alternatus in Argentina

Sarquis

Cristaldi

Arzamendia

et al. 2018

Ecology and Evolution

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Species distribution models (SDMs) estimate the geographical distribution of species although with several limitations due to sources of inaccuracy and biases. Empirical tests arose as the most important steps in scientific knowledge to assess the efficiency of model predictions, which are poorly rigorous in SDMs. A good approach to the empirical distribution (ED) of a species can be obtained from comprehensive empirical knowledge, that is, well‐understood distributions gathered from large amount of data generated with appropriate spatial and temporal sample coverage. The aims of this study were to (a) compare different SDMs predictions with an ED; and (b) evaluate if fuzzy global matching (FGM) could be used as an index to compare SDMs predictions and ED. Six algorithms with 5 and 20 variables were used to assess their accuracy in predicting the ED of the venomous snake Bothrops alternatus (Viperidae). Its entire distribution is known, thanks to thorough field surveys across Argentina, with 1,767 records. ED was compared with SDMs predictions using Map Comparison Kit. SDMs predictions showed important biases in all methods used, from 70% sub‐estimation to 40% over‐estimation of ED. BIOCLIM predicted ≈31% of B. alternatus ED. DOMAIN predicted 99% of ED, but over‐estimated 40% of the area. GLM with five variables calculated 75% of ED, while Genetic Algorithm for Rule‐set Prediction showed ≈60% of ED; the last two presenting overpredictions in areas with favorable climatic conditions but not inhabited by the species. MaxEnt and RF were the only methods to detect isolated populations in the southern distribution of B. alternatus. Although SDMs proved useful in making predictions about species distribution, predictions need validation with expert maps knowledge and ED. Moreover, FGM showed a good performance as an index with values similar to True Skill Statistic, so that it could be used to relate ED and SDMs predictions.

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Section: Comparison Between Models and Real Distributionsupporting

confidence: 53%

Section: As Inmentioning

confidence: 99%

Species distribution models and empirical test: Comparing predictions with well‐understood geographical distribution of Bothrops alternatus in Argentina

Sarquis

Cristaldi

Arzamendia

et al. 2018

Ecology and Evolution

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“…human activities, type of housing, population's age) that are known to be causal of snakebites [1][2][3]. However, previous evidence has shown that ecological niche modelling is usually successful at inferring geographic patterns of ophidism at continental [5], country [26] and regional scales [7]. This is particularly useful to infer potential snakebite risk areas when hospital data are scarce or biased.…”

Section: Discussionmentioning

confidence: 99%

Estimating geographic patterns of ophidism risk in Ecuador

Yáñez‐Arenas

Díaz‐Gamboa

Patrón-Rivero

et al. 2018

Neotropical Biodiversity

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“…SDMs are successfully used to identify suitable habitats of species even in areas with no distribution records [52-54]. Thus, these models can be used to identify suitable habitats of venomous snakes as proxies of snakebite risk [12, 55-57] in data poor regions like Iran.…”

Section: Introductionmentioning

confidence: 99%

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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Venomous snakes and climate change: ophidism as a dynamic problem

Cited by 57 publications

References 48 publications

Species distribution models and empirical test: Comparing predictions with well‐understood geographical distribution of Bothrops alternatus in Argentina

Species distribution models and empirical test: Comparing predictions with well‐understood geographical distribution of Bothrops alternatus in Argentina

Estimating geographic patterns of ophidism risk in Ecuador

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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