Modelling the potential geographic distribution of triatomines infected by Triatoma virus in the southern cone of South America

Particle and Fibre Toxicology

Gerardo Aníbal Marti²  David Eladio Gorla³  María Gabriela Echeverria¹  María Laura Susevich²  Agustín Balsalobre²  Soledad Ceccarelli² 

[1] Cátedra de Virología, Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, (CONICET), La Plata, Buenos Aires, Argentina;Centro de Estudios Parasitológicos y de Vectores (CEPAVE-CCT-La Plata-CONICET – UNLP), Boulevard 120 e/61 y 62, La Plata, 1900, Argentina;Centro Regional de Investigaciones Científicas y Transferencia Tecnológica (CRILAR - CONICET), La Rioja, Argentina

关键词: AVHRR imagery;    WorldClim;    MaxEnt;    Ecological Niche Modelling;    Triatominae;    Triatoma virus;   

Others  :  1146656
DOI  :  10.1186/s13071-015-0761-1

 received in 2014-11-05, accepted in 2015-02-20,  发布年份 2015

【 摘 要 】

Background

Triatoma virus (TrV) is the only entomopathogenous virus identified in triatomines. We estimated the potential geographic distribution of triatomine species naturally infected by TrV, using remotely sensed and meteorological environmental variables, to predict new potential areas where triatomines infected with TrV may be found.

Methods

Detection of TrV infection in samples was performed with RT-PCR. Ecological niche models (ENM) were constructed using the MaxEnt software. We used 42 environmental variables derived from remotely sensed imagery (AVHRR) and 19 bioclimatic variables (Bioclim). The MaxEnt Jackknife procedure was used to minimize the number of environmental variables that showed an influence on final models. The goodness of fit of the model predictions was evaluated by the mean area under the curve (AUC).

Results

We obtained 37 samples of 7 species of triatomines naturally infected with TrV. Of the TrV positive samples, 32% were from sylvatic habitat, 46% came from peridomicile habitats and 22% from domicile habitats. Five of the seven infected species were found only in the sylvatic habitat, one species only in the domicile and only Triatoma infestans was found in the three habitats. The MaxEnt model estimated with the Bioclim dataset identified five environmental variables as best predictors: temperature annual range, mean diurnal range, mean temperature of coldest quarter, temperature seasonality and annual mean temperature. The model using the AVHRR dataset identified six environmental variables: minimum Land Surface Temperature (LST), minimum Middle Infrared Radiation (MIR), LST annual amplitude, MIR annual amplitude annual, LST variance and MIR variance. The potential geographic distribution of triatomine species infected by TrV coincides with the Chaco and the Monte ecoregions either modelled by AVHRR or Bioclim environmental datasets.

Conclusions

Our results show that the conditions of the Dry Chaco ecoregion in Argentina are favourable for the infection of triatomine species with TrV, and open the possibility of its use as a potential agent for the biological control of peridomestic and/or sylvatic triatomine species. Results identify areas of potential occurrence that should be verified in the field.

【 授权许可】

   
2015 Ceccarelli et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]Sustaining the Drive to Overcome the Global Impact of Neglected Tropical Diseases. Second WHO Report on Neglected Tropical Diseases. 2013.140. WHO/HTM/NTD/2013. 1
• [2]Jannin J, Salvatella R. Estimación cuantitativa de la enfermedad de Chagas en las Américas/Quantitative estimation of Chagas Disease in the Americas. Organización Panamericana de la Salud. 2006. OPS/HDM/CD/425-06
• [3]Marti GA, Echeverria MG, Susevich ML, Becnel JJ, Pelizza SA, García JJ. Prevalence and distribution of parasites and pathogens of triatominae from Argentina, with emphasis on Triatoma infestans and Triatoma virus TrV. J Invertebr Pathol. 2009; 102:233-237.
• [4]Erlandson M. Insect Pest Control by Viruses. In: Encyclopedia of Virology. 3rd ed. Mahy BW, Regenmortel MH, editors. Elsevier, San Diego; 2008: p.125-133.
• [5]Agirre J, Aloria K, Arizmendi JM, Iloro I, Elortza F, Sánchez-Eugenia R et al.. Capsid protein identification and analysis of mature Triatoma virus (TrV) virions and naturally occurring empty particles. Virology. 2011; 409:91-101.
• [6]Squires G, Pous J, Agirre J, Rozas-Dennis GS, Costabel MD, Marti GA et al.. Structure of the Triatoma virus capsid. Acta Crystallogr D Biol Crystallogr. 2013; 69:1026-1037.
• [7]Gordon KH, Waterhouse PM. Small RNA Viruses of Insects: Expression in Plants and RNA Silencing. In: Advances in virus Research. Volume 68 Insect viruses: Biotechnological Applications. Bonning BC, editor. Elsevier, London; 2006: p.459-502.
• [8]Bonning BC, Miller WA. Dicistroviruses. Annu Rev Entomol. 2010; 55:129-150.
• [9]Snijder J, Uetrecht C, Rose RJ, Sanchez-Eugenia R, Marti G, Agirre J et al.. Probing the biophysical interplay between a viral genome and its capsid. Nat Chem. 2013; 5:502-509.
• [10]Muscio OA. Virus entomopatogenos y su posible uso como agentes de biocontrol. Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata; 1988.
• [11]Muscio OA, La Torre J, Bonder MA, Scodeller EA. Triatoma virus pathogenicity in laboratory colonies of Triatoma infestans (Hemiptera:Reduviidae). J Med Entomol. 1997; 34:253-256.
• [12]Muscio OA, Bonder MA, La Torre JL, Scodeller EA. Horizontal transmission of Triatoma virus through the fecal-oral route in Triatoma infestans (Hemiptera: Triatomidae). J Med Entomol. 2000; 37:271-275.
• [13]Schaub GA, Boker CA, Jensen C, Reduth D. Cannibalism and Coprophagy are Modes of Transmission of Blastocrithidia triatomae (Trypanosomatidae) between Triatomines. J Protozoo. 1989; 36:171-175.
• [14]Muscio OA, La Torre JL, Scodeller EA. Small Nonoccluded Viruses from Triatomine Bug Triatoma infestans (Hemiptera: Reduviidae). J Invertebr Pathol. 1987; 49:218-220.
• [15]Susevich ML, Marti GA, Serena MS, Echeverría MG. New Triatoma virus hosts in wild habitats of Argentina. J Invertebr Pathol. 2012; 110(3):405-407.
• [16]Marti GA, Echeverría MG, Susevich ML, Ceccarelli S, Balsalobre A, Rabinovich JE et al.. Exploration for Triatoma virus (TrV) infection in laboratory-reared triatomines of Latin America: a collaborative study*. Int J Trop Insect Sci. 2013; 33:294-304.
• [17]Franklin J, Wejnert KE, Hathaway SA, Rochester CJ, Fisher RN. Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California. Divers Distrib. 2009; 15:167-177.
• [18]Tsoar A, Allouche O, Steinitz O, Rotem D, Kadmon R. A comparative evaluation of presence-only methods for modelling species distribution. Divers Distrib. 2007; 13:397-405.
• [19]Peterson AT. Ecologic niche modeling and spatial patterns of disease transmission. Emerg Infect Dis. 2006; 12(12):1822-1826.
• [20]Abad-Franch F, Noireau F, Paucar A, Aguilar HM, Carpio C. The use of live-bait traps for the study of sylvatic Rhodnius populations (Hemiptera: Reduviidae) in palm trees. Trans R Soc Trop Med Hyg. 2000; 94:629-630.
• [21]Lent H, Wygodzinsky P. Revision of the triatominae (Hemiptera: Reduviidae), and their significance as vectors of Chagas’ disease. Bull Am Museum Nat Hist. 1979; 163:123-520.
• [22]Marti GA, González ET, García JJ, Viguera AR, Guérin DMA, Echeverría MG. AC-ELISA and RT-PCR assays for the diagnosis of triatoma virus (TrV) in triatomines (Hemiptera: Reduviidae) species. Arch Virol. 2008; 153:1427-1432.
• [23]Stockwell DRB, Peterson AT. Effects of sample size on accuracy of species distribution models. Ecol Modell. 2002; 148:1-13.
• [24]Holben BN. Characteristics of maximum-value composite images from temporal AVHRR data. Int J Remote Sens. 1986; 7:1417-1434.
• [25]Boyd DS, Wicks TE, Curran PJ. Use of middle infrared radiation to estimate the leaf area index of a boreal forest. Tree Physiol. 2000; 20:755-760.
• [26]Rogers DJ, Randolph SE. Studying the global distribution of infectious diseases using GIS and RS. Nat Rev Microbiol. 2003; 1:231-237.
• [27]Hay SI, Tatem AJ, Graham AJ, Goetz SJ, Rogers DJ. Global environmental data for mapping infectious disease distribution. Adv Parasitol. 2006; 62:37-77.
• [28]Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. 2005; 25:1965-1978.
• [29]Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Modell. 2006; 190:231-259.
• [30]Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A et al.. Novel methods improve prediction of species’ distributions from occurrence data. Ecography (Cop). 2006; 29:129-151.
• [31]Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J et al.. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl. 2009; 19:181-197.
• [32]Lobo JM, Jiménez-Valverde A, Real R. AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr. 2008; 17:145-151.
• [33]Terribile LC, Diniz-Filho JAF, De Marco JR P. How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end. Brazilian J Biol. 2010; 70(2):263-269.
• [34]Burkart R, Bárbaro N, Sánchez O, Gómez DA. Eco-Regiones de la Argentina. 1999.
• [35]Noireau F, Diosque P, Jansen AM. Trypanosoma cruzi: adaptation to its vectors and its hosts. Vet Res. 2009; 40:26.
• [36]Buitrago R, Waleckx E, Bosseno M-F, Zoveda F, Vidaurre P, Salas R et al.. First report of widespread wild populations of Triatoma infestans (Reduviidae, Triatominae) in the valleys of La Paz, Bolivia. Am J Trop Med Hyg. 2010; 82:574-579.
• [37]Marti GA, Echeverría MG, Waleckx E, Susevich ML, Balsalobre A, Gorla DE. Triatominae in furnariid nests of the Argentine Gran Chaco. J Vector Ecol. 2014; 39:66-71.
• [38]Peterson AT, Nakazawa Y. Environmental data sets matter in ecological niche modelling: an example with Solenopsis invicta and Solenopsis richteri. Glob Ecol Biogeogr. 2008; 17:135-144.
• [39]Pereira JM, De Almeida PS, De Sousa AV, De Paula AM, Machado RB, Gurgel-Gonçalves R. Climatic factors influencing triatomine occurrence in Central-West Brazil. Mem Inst Oswaldo Cruz. 2013; 108:335-341.
• [40]Gorla D. Variables ambientales registradas por sensores remotos como indicadores de la distribución geográfica de Triatoma infestans (Heteroptera: Reduviidae). Ecol Austral. 2002; 12:117-127.
• [41]Kalluri S, Gilruth P, Rogers D, Szczur M. Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review. PLoS Pathog. 2007; 3(10):e116.
• [42]Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL et al.. The global distribution and burden of dengue. Nature. 2013; 496:504-507.