Prediction of the potential global distribution for Biomphalaria straminea, an intermediate host for Schistosoma mansoni

Abstract

BackgroundSchistosomiasis is a snail-borne parasitic disease and is endemic in many tropical and subtropical countries. Biomphalaria straminea, an intermediate host for Schistosoma mansoni, is native to the southeastern part of South America and has established in other regions of South America, Central America and southern China during the last decades. S. mansoni is endemic in Africa, the Middle East, South America and the Caribbean. Knowledge of the potential global distribution of this snail is essential for risk assessment, monitoring, disease prevention and control.Methods and findingsA comprehensive database of cross-continental occurrence for B. straminea was compiled to construct ecological models. We used several approaches to investigate the distribution of B. straminea, including direct comparison of climatic conditions, principal component analysis and niche overlap analyses to detect niche shifts. We also investigated the impacts of bioclimatic and human factors, and then used the bioclimatic and footprint layers to predict the potential distribution of B. straminea at global scale. We detected niche shifts accompanying the invasions of B. straminea in the Americas and China. The introduced populations had enlarged its habitats to subtropical regions where annual mean temperature is relatively low. Annual mean temperature, isothermality and temperature seasonality were identified as most important climatic features for the occurrence of B. straminea. Additionally, human factors improved the model prediction (P<0.001). Our model showed that under current climate conditions the snail should mostly be confined to the tropic and subtropic regions, including South America, Central America, Sub-Saharan Africa and Southeast Asia.ConclusionsOur results confirmed that niche shifts took place in the invasions of B. straminea, in which bioclimatic and human factors played an important role. Our model predicted the global distribution of B. straminea based on habitat suitability, which would help for prioritizing monitoring and management efforts for B. straminea control in the context of ongoing climate change and human disturbances.

Risk prediction of two types of potential snail habitats in Anhui Province of China: Model-based approaches

“…While Maxent and GARP are machine-learning programs, the former conducted by the stand-alone Maxent software, version 3.4.1 [27], and the latter based on biological evolution theory, which also has its stand-alone software, i.e. Desktop GARP, version 1.1.6 [28]. The other eight models, were presence/absence models which need both presence and absence records: ANN and SVM were built in R, version 3.5.1 by the nnet package and the e1071 package, respectively, while the other six presence/absence models were carried out using the Biomod2 package for R [29] (more details are shown in appendix S1 Text).…”

Currently Available Monitoring and Surveillance Systems for Taenia spp., Echinococcus spp., Schistosoma spp., and Soil-Transmitted Helminths at the Control/Elimination Stage: A Systematic Review

“…For the M&S of schistosomiasis at the post-control/elimination stage, 15 articles obtained in this review indeed mentioned the use of spatial technology, out of which the majority focused on using it for monitoring snail density [12,15,18,[99][100][101][103][104][105][106][107][108][109][110][111]. At present, snail surveys are extremely time-consuming as they are conducted manually and their quality can be influenced by many factors, such as temperature, light, and vegetation.…”

“…Therefore, changes in the current applied M&S strategies are definitely required. This generates a chance for the introduction of spatial technology in M&S systems in order to improve survey quality and meet the requirements of current monitoring work [99].…”

Padrão de distribuição da diversidade genética molecular e espacial de Biomphalaria spp. e sua relação com a ocorrência da esquistossomose na região do Médio Paranapanema, estado de São Paulo

“…O modelo de distribuição potencial foi estimado utilizando-se um algoritmo de máxima entropia no programa MaxEnt 3.4.1 (PHILLIPS et al, 2006;PHILLIPS e DUDÍK, 2008). Os dados foram computados com os um subconjunto de treinamento de 75%, 25% de teste e 25 repetições e a saída foi computada como logistic, teste de jackknife e através das curvas de resposta em MaxEnt (ZABURLÍN et al, 2016;RUMI et al, 2017;YANG et al, 2018).…”

“…No Brasil, a utilização dos SIGs aplicados em estudos de distribuição de caramujos hospedeiros intermediários de S. mansoni em relação a ocorrência da esquistossomose, foram principalmente utilizados em áreas de alta endemicidade no estado da Bahia (BAVIA et al, 1999;CARDIM et al, 2011), Pernambuco (ALMEIDA et al, 2003GOMES et al, 2012GOMES et al, , 2018, Minas Gerais (GUIMARÃES et al, 2009(GUIMARÃES et al, , 2012FONSECA et al, 2014) BARBOSA et al, 2016, CARVALHO et al, 2017GOMES et al, 2018). Outros exemplos de sua aplicação são os estudos da distribuição da espécie Biomphalaria no Uruguai, Argentina (RUMI et al, 2017), China e na África (PEDERSEN et al, 2014;HABIB et al, 2016;YANG et al, 2018;FAN et al, 2018). MARENGO et al (2012MARENGO et al ( , 2014 fizeram estudos de previsão de aumento de precipitação para as próximas décadas no Brasil, em função das alterações climáticas globais, que provavelmente terão como consequência o aumento do volume de água de todas as bacias hidrográficas brasileiras.…”