Species-specific ecological niche modelling predicts different range contractions for Lutzomyia intermedia and a related vector of Leishmania braziliensis following climate change in South America

Parasites and Vectors

Volumn 10, Issue 1, 2017, Pages

  McIntyre, Shannon ^a,b   Rangel, Elizabeth F ^a   Ready, Paul D ^b   Carvalho, Bruno M ^a  

^a INSTITUTO OSWALDO CRUZ   (Brazil)

^b LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

Author keywords

Climate change; Cutaneous leishmaniasis; Ecological niche modelling; Lutzomyia intermedia; Lutzomyia neivai; Range changes; South America

Indexed keywords

ARGENTINA; ARTICLE; BRAZIL; CARBON FOOTPRINT; CLIMATE CHANGE; CONTROLLED STUDY; ECOLOGICAL NICHE; ENTROPY; ENVIRONMENTAL TEMPERATURE; GEOGRAPHICAL VARIATION (SPECIES); LEISHMANIA BRAZILIENSIS; LUTZOMYIA; LUTZOMYIA INTERMEDIA; NONHUMAN; PARASITE TRANSMISSION; POPULATION DISPERSION; PREDICTIVE VALUE; RANDOM FOREST; SEASONAL VARIATION; SOUTH AMERICA; SPECIES DIFFERENCE; SUPPORT VECTOR MACHINE; TEMPERATURE SENSITIVITY; ANIMAL; GROWTH, DEVELOPMENT AND AGING; INSECT VECTOR; PSYCHODIDAE; SPATIAL ANALYSIS; THEORETICAL MODEL;

ANIMALS; CLIMATE CHANGE; INSECT VECTORS; MODELS, THEORETICAL; PSYCHODIDAE; SOUTH AMERICA; SPATIAL ANALYSIS; SPECIES SPECIFICITY;

EID: 85016098478     PISSN: None     EISSN: 17563305     Source Type: Journal    
DOI: 10.1186/s13071-017-2093-9     Document Type: Article

References (70)

1. IPCC. Summary for Policymakers. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM, editors. Climate Change 2013: The Physical Science Basis Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. New York, USA, Cambridge, UK: Cambridge University Press (CUP); 2013. p. 1-30.
2. Campbell-Lendrum D, Manga L, Bagayoko M, Sommerfeld J. Climate change and vector-borne diseases: what are the implications for public health research and policy? Philos T R Soc Lon B. 2015;370:20130552.
3. Lane RP. Sandflies (Phlebotominae). In: Medical Insects and Arachnids: Springer Nature; 1993: 78-119.
4. Ready P. Biology of phlebotomine sand flies as vectors of disease agents. Annu Rev Entomol. 2013;58(1):227-50.
5. Hijmans RJ, Phillips S, Leathwick J, Elith J. dismo: Species distribution modeling. R package version 1.0-15. 2016. https://CRAN.R-project.org/package=dismo.
6. Marcondes CB. A redescription of Lutzomyia (Nyssomyia) intermedia (Lutz & Neiva, 1912), and resurrection of L. neivai (Pinto, 1926) (Diptera, Psychodidae, Phlebotominae). Mem I Oswaldo Cruz. 1996;91(4):457-62.
7. Alvar J, Vélez ID, Bern C, Herrero M, Desjeux P, Cano J, et al. Leishmaniasis worldwide and global estimates of its incidence. PLoS One. 2012;7(5), e35671.
8. WHO. World Health Organization. Control of the leishmaniases: report of a meeting of the WHO Expert Committee on the Control of Leishmaniases, Geneva, 22-26 March 2010. WHO Technical Report Series, no. 949. Geneva: WHO Press; 2010.
9. Rangel EF, Lainson R. Proven and putative vectors of American cutaneous leishmaniasis in Brazil: aspects of their biology and vectorial competence. Mem I Oswaldo Cruz. 2009;104(7):937-54.
10. Rangel EF, Souza NAD, Wermelinger ED, Barbosa AF. Infecção natural de Lutzomyia intermedia Lutz & Neiva, 1912, em área endêmica de leishmaniose tegumentar no Estado do Rio de Janeiro. Mem I Oswaldo Cruz. 1984;79(3):395-6.
11. Pita-Pereira D, Souza GD, Zwetsch A, Alves CR, Britto C, Rangel EF. First report of Lutzomyia (Nyssomyia) neivai (Diptera: Psychodidae: Phlebotominae) naturally infected by Leishmania (Viannia) braziliensis in a periurban area of south Brazil using a multiplex polymerase chain reaction assay. Am J Trop Med Hyg. 2009;80(4):593-5.
12. Córdoba-Lanús E, De Grosso ML, Piñero JE, Valladares B, Salomón OD. Natural infection of Lutzomyia neivai with Leishmania spp. in northwestern Argentina. Acta Trop. 2006;98(1):1-5.
13. Andrade Filho JD, Galati EA, Falcao AL. Nyssomyia intermedia (Lutz & Neiva, 1912) and Nyssomyia neivai (Pinto, 1926) (Diptera: Psychodidae: Phlebotominae) geographical distribution and epidemiological importance. Mem I Oswaldo Cruz. 2007;102(4):481-7.
14. Afonso MMDS, Gomes AC, Meneses CRV, Rangel EF. Studies on the feeding habits of Lutzomyia (N.) intermedia (Diptera, Psychodidae), vector of cutaneous leishmaniasis in Brazil. Cad Saude Publica. 2005;21(6):1816-20.
15. Dias-Sversutti ADC, Scodro RBDL, Reinhold-Castro KR, Neitzke HC, Teodoro U. Preliminary study on feeding preference of Nyssomyia neivai (Pinto) and Nyssomyia whitmani (Antunes & Coutinho) (Diptera: Psychodidae) in a rural area of the state of Paraná, South Brazil. Neotrop Entomol. 2007;36(6):953-9.
16. Peterson A, Shaw J. Lutzomyia vectors for cutaneous leishmaniasis in southern Brazil: ecological niche models, predicted geographic distributions, and climate change effects. Int J Parasitol. 2003;33(9):919-31.
17. Gonzalez C, Wang O, Strutz SE, Gonzalez-Salazar C, Sanchez-Cordero V, Sarkar S. Climate change and risk of leishmaniasis in North America: predictions from ecological niche models of vector and reservoir species. PLoS Neglect Trop D. 2010;4(1), e585.
18. Quintana M, Salomon O, Guerra R, De Grosso ML, Fuenzalida A. Phlebotominae of epidemiological importance in cutaneous leishmaniasis in northwestern Argentina: risk maps and ecological niche models. Med Vet Entomol. 2013;27(1):39-48.
19. Carvalho BM, Rangel EF, Ready PD, Vale MM. Ecological niche modelling predicts southward expansion of Lutzomyia (Nyssomyia) flaviscutellata (Diptera: Psychodidae: Phlebotominae), vector of Leishmania (Leishmania) amazonensis in South America, under climate change. PLoS One. 2015;10(11), e0143282.
20. Sanchez I, Liria J, Feliciangeli MD. Ecological niche modeling of seventeen sandflies species (Diptera, Psychodidae, Phlebotominae) from Venezuela. Int J Zool. 2015;2015:1-9.
21. Meneguzzi V, Santos C, Leite G, Fux B, Falqueto A. Environmental niche modelling of phlebotomine sand flies and cutaneous leishmaniasis identifies Lutzomyia intermedia as the main vector species in southeastern Brazil. PLoS One. 2016;11(10), e0164580.
22. González C, Paz A, Ferro C. Predicted altitudinal shifts and reduced spatial distribution of Leishmania infantum vector species under climate change scenarios in Colombia. Acta Trop. 2014;129:83-90.
23. Peterson A. Predicting species' geographic distributions based on ecological niche models. Condor. 2007;103:599-605.
24. Elith J, Graham CH. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography. 2009;32.
25. Carvalho BM, Rangel EF, Vale MM. Evaluation of the impacts of climate change on disease vectors through ecological niche modelling. B Entomol Res. 2016. doi: 10.1017/S0007485316001097.
26. Martins AV, Falcão AL, Williams P. American sand flies (Diptera: Psychodidae, Phlebotominae). Rio de Janeiro: Academia Brasileira de Ciências; 1978.
27. Young D, Duncan M. Guide to the identification and geographic distribution of Lutzomyia sand flies in Mexico, the West Indies, Central and South America (Diptera: Psychodidae). Florida, USA: Associated Publishers American Entomological Institute; 1994.
28. Marcondes CB, Lozovei AL, Vilela JH. Distribuição geográfica de flebotomíneos do complexo Lutzomyia intermedia (Lutz & Neiva, 1912) (Diptera, Psychodidae). Rev Soc Bras Med Trop. 1998;31(1):51-8.
29. Marcondes C: Morfometria e DNA mitocondrial de populações sul americanas de Lutzomyia (Nyssomyia) intermedia (Lutz & Neiva, 1912) (Diptera, Psychodidae, Phlebotominae). Universidade Federal do Paraná, PhD Thesis; 1997.
30. ESRI: http://www.esri.com. 2016. Accessed on 19 Dec 2016.
31. Aiello-Lammens ME, Boria RA, Radosavljevic A, Vilela B, Anderson RP: spThin: Functions for spatial thinning of species occurrence records for use in ecological models. R package version 0.1.0. 2014. https://cran.r-project.org/package=spThin.
32. Lobo JM, Tognelli MF. Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data. J Nat Conserv. 2011;19(1):1-7.
33. Booth TH, Nix HA, Busby JR, Hutchinson MF. bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Divers Distrib. 2013;20(1):1-9.
34. Hijmans R, Cameron S, Parra J, Jones P, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. 2005;25(15):1965-78.
35. IPCC. Scenario processes for AR5. 2014. http://sedac.ipcc-data.org/ddc/ar5-scenario-process/RCPs.html. Accessed 30 July 2016.
36. Thomson AM, Calvin KV, Smith SJ, Kyle GP, Volke A, Patel P, et al. RCP4.5: a pathway for stabilization of radiative forcing by 2100. Clim Change. 2011;109(1-2):77-94.
37. Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G, et al. RCP 8.5 - A scenario of comparatively high greenhouse gas emissions. Clim Change. 2011;109(1-2):33-57.
38. Yin L, Fu R, Shevliakova E, Dickinson RE. How well can CMIP5 simulate precipitation and its controlling processes over tropical South America? Clim Dynam. 2012;41(11-12):3127-43.
39. Pearson RG, Dawson TP. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecol Biogeogr. 2003;12(5):361-71.
40. R Core Team. R: A language environment for statistical computing. Viennna: R Foundation for Statistical Computing; 2016. https://cran.r-project.org.
41. Araujo MB, Pearson RG, Thuiller W, Erhard M. Validation of species-climate impact models under climate change. Glob Change Biol. 2005;11(9):1504-13.
42. Hijmans R, Graham C. The ability of climate envelope models to predict the effect of climate change on species distributions. Glob Change Biol. 2006;12(12):2272-81.
43. Beaumont L, Graham E, Englert Duursma D, Wilson P, Cabrelli A, Baumgartner J, et al. Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges? Ecol Model. 2016;342:135-46.
44. Guisan A, Edwards Jr TC, Hastie T. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model. 2002;157:89-100.
45. Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Model. 2006;190(3-4):231-59.
46. Franklin J. Mapping Species Distributions: Spatial Inference and Prediction. Cambridge: Cambridge University Press; 2010.
47. Stockwell D. The GARP modelling system: problems and solutions to automated spatial prediction. Int J Geogr Inf Sci. 1999;13(2):143-58.
48. Anderson R, Lew D, Peterson A. Evaluating predictive models of species' distributions: criteria for selecting optimal models. Ecol Model. 2003;162:211-32.
49. Liu C, Berry PM, Dawson TP, Pearson RG. Selecting thresholds of occurrence in the prediction of species distributions. Ecography. 2005;28(3):385-93.
50. Soberón J, Peterson A. Interpretation of models of fundamental ecological niches and species' distributional areas. Biodivers Inform. 2005;2:1-10.
51. Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher S, Peterson A, et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Model. 2011;222:1810-9.
52. McPherson JM, Jetz W, Rogers DJ. The effects of species' range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? J Appl Ecol. 2004;41(5):811-23.
53. Allouche O, Tsoar A, Kadmon R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol. 2006;43(6):1223-32.
54. Araujo M, New M. Ensemble forecasting of species distributions. Trends Ecol Evol. 2007;22(1):42-7.
55. Peterson A, Papes M, Eaton M. Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent. Ecography. 2007;30:550-60.
56. Met-Office: Met Office climate prediction model: HadGEM2 family. 2016. http://www.metoffice.gov.uk/research/modelling-systems/unified-model/climate-models/hadgem2. Accessed on 30 Aug 2016.
57. Bellouin N, Collins WJ, Culverwell ID, Halloran PR, Hardiman SC, Hinton TJ, et al. The HadGEM2 family of Met Office Unified Model climate configurations. Geosci Model Dev. 2011;4(3):723-57.
58. Forattini OP, Pattoli DB, Rabello EX, Ferreira OA. Infecção natural de flebotomíneos em foco enzoótico de leishmaniose tegumentar no Estado de São Paulo, Brasil. Rev Saude Publ. 1972;6(4):431-3.
59. Casanova C, Mayo RC, Rangel O, Mascarini LM, Pignatti MG, Galati EAB, et al. Natural Lutzomyia intermedia (Lutz & Neiva) infection in the Valley of the Mogi Guaçú River, State of São Paulo, Brazil. Bol Malar Salud Ambi. 1995;35 Suppl 1:77-84.
60. Pita-Pereira D, Alves CR, Souza MB, Brazil RP, Bertho AL, de Figueiredo Barbosa A, et al. Identification of naturally infected Lutzomyia intermedia and Lutzomyia migonei with Leishmania (Viannia) braziliensis in Rio de Janeiro (Brazil) revealed by a PCR multiplex non-isotopic hybridisation assay. T Roy Soc Trop Med H. 2005;99(12):905-13.
61. Rocha LS, Falqueto A, dos Santos CB, Ferreira AL, da Graça GC, Grimaldi G, et al. Survey of natural infection by Leishmania in sand fly species collected in southeastern Brazil. T Roy Soc Trop Med H. 2010;104(7):461-6.
62. Forattini OP, Santos MD. Nota sobre infecção natural de Phlebotomus intermedius Lutz & Neiva, 1912, por formas em leptomonas, em foco de leishmaniose tegumentar americana. Arch Hyg Saude Publ. 1952;17:171-4.
63. Marcondes C, Bittencourt I, Stoco P, Eger I, Grisard E, Steindel M. Natural infection of Nyssomyia neivai (Pinto, 1926) (Diptera: Psychodidae, Phlebotominae) by Leishmania (Viannia) spp. In Brazil. T Roy Soc Trop Med H. 2009;103:1093-7.
64. Galati EAB, Marassá AM, Gonçalves-Andrade RM, Consales CA, Bueno EF. Phlebotomines (Diptera, Psychodidae) in the Ribeira Valley Speleological Province-1. Parque Estadual Intervales, state of São Paulo, Brazil. Rev Bras Entomol. 2010;54(2):311-21.
65. Galati EAB, Marassá AM, Fonseca MB, Gonçalves-Andrade RM, Consales CA, Bueno EF. Phlebotomines (Diptera, Psychodidae) in the Speleological Province of the Ribeira Valley: 3. Serra district-area of hostels for tourists who visit the Parque Estadual do Alto Ribeira (PETAR), state of São Paulo, Brazil. Rev Bras Entomol. 2010;54(4):665-76.
66. Saraiva L, Carvalho GMDL, Sanguinette CDC, Carvalho DAAD, Falcão AL, Andrade Filho JD. Sandflies (Diptera: Psychodidae: Phlebotominae) collected on the banks of the Velhas River in the state of Minas Gerais, Brazil. Mem I Oswaldo Cruz. 2008;103(8):843-6.
67. Carvalho BM, Maximo M, Costa WA, de Santana ALF, da Costa SM, da Costa Rego TAN, et al. Leishmaniasis transmission in an ecotourism area: potential vectors in Ilha Grande, Rio de Janeiro State, Brazil. Parasit Vectors. 2013;6(1):325.
68. Salomón OD, Orellano PW, Quintana MG, Perez S, Estani SS, Acardi S, et al. Transmision de la leishmaniasis tegumentaria en la Argentina. Medicina-Buenos Aire. 2006;66:211-9.
69. Andrade Filho JD, Silva ACLD, Falcão AL. Phlebotomine sand flies in the state of Piauí, Brazil (Diptera: Psychodidae: Phlebotominae). Mem I Oswaldo Cruz. 2001;96(8):1085-7.
70. Pullan RL, Sturrock HJ, Magalhaes RJS, Clements AC, Brooker SJ. Spatial parasite ecology and epidemiology: a review of methods and applications. Parasitology. 2012;139(14):1870-87.