REFERENCES
01. Soares AC, Sant Anna MRV, Gontijo NF, Araújo RN, Pessoa GCD, Koerich LB, et al. Features of interaction between triatomines and vertebrates based on bug feeding parameters. In: Guarneri A, Lorenzo M, eds. Triatominae - The biology of Chagas disease vectors. Switzerland: Springer; 2021. p. 239-64.
02. Costa J, Dornak LL, Almeida CE, Peterson AT. Distributional potential of the Triatoma brasiliensis species complex at present and under scenarios of future climate conditions. Parasite Vectors. 2014; 7: 238.
03. Garza M, Feria Arroyo TP, CasillSupplas EA, Sanchez-Cordero V, Rivaldi C-L, Sarkar S. Projected future distributions of vectors of Trypanosoma cruzi in North America under climate change scenarios. PLoS Negl Trop Dis. 2014; 8(5): e2818.
04. Medone P, Ceccarelli S, Parham PE, Figuera A, Rabinovich J. The impact of climate change on the geographic distribution of two vectors of Chagas disease: implications for the force of infection. Philos Trans R Soc Lond B Biol Sci. 2015; 370(1665): 20130560.
05. Garrido R, Bacigalupo A, Peña-Gómez F, Bustamante RO, Cattan PE, Gorla DE, et al. Potential impact of climate change on the geographical distribution of two wild vectors of Chagas disease in Chile: Mepraia spinolai and Mepraia gajardoi. Parasite Vectors. 2019; 12: 478.
06. Eberhard FE, Cunze S, Kochmann J, Klimpel S. Modelling the climatic suitability of Chagas disease vectors on a global scale. Elife. 2020; 9: e52072.
07. Gurgel-Gonçalves R, Galvao C, Costa J, Peterson AT. Geographic distribution of Chagas disease vectors in Brazil based on ecological niche modeling. J Trop Med. 2012; 2012: 705326.
08. Parra-Henao G, Suárez-Escudero LC, González-Car S. Potential distribution of Chagas disease vectors (Hemiptera, Reduviidae, Triatominae) in Colombia, based on ecological niche modeling. J Trop Med. 2016; 2016: 1439090.
09. Moo-Llanes DA, Montes de Oca-Aguilar AC, Rodríguez-Rojas JJ. Pattern of climate connectivity and equivalent niche of Triatominae species of the Phyllosoma complex. Med Vet Entomol. 2020; 34(4): 440-51.
10. Campos-Soto R, Diaz-Campusano G, Rives-Blanchard N, Cianferoni F, Torres-Perez F. Biogeographic origin and phylogenetic relationships of Mepraia (Hemiptera, Reduviidae) on islands of northern Chile. PLoS One. 2020; 15(6): e0234056.
11. Zuluaga S, Mejía P, Velez-Mira A, Quintero J, Triana-Chavez O, Cantillo-Barraza O. Updated geographical distribution and natural infection of Panstrongylus geniculatus (Latreille, 1811) in Antioquia department, Colombia. Parasite Epidemiol Control. 2021; 15: e00226.
12. Gómez-Palacio A, Arboleda S, Dumonteil E, Peterson AT. Ecological niche and geographic distribution of the Chagas disease vector, Triatoma dimidiata (Reduviidae: Triatominae): evidence for niche differentiation among cryptic species. Infect Genet Evol. 2015; 36: 15-22.
13. de Souza RCM, Campolina-Silva GH, Bezerra CM, Diotaiuti L, Gorla DE. Does Triatoma brasiliensis occupy the same environmental niche space as Triatoma melanica? Parasite Vectors. 2015; 8(1): 1-14.
14. de la Vega GJ, Medone P, Ceccarelli S, Rabinovich J, Schilman PE. Geographical distribution, climatic variability and thermo-tolerance of Chagas disease vectors. Ecography. 2015; 38(8): 851-60.
15. Gorla D, Noireau F. Geographic distribution of Triatominae vectors in America. In: Telleria J, Tibayrenc M, editors. American trypanosomiasis Chagas disease. One hundred years of research. 2nd ed. Elsevier; 2017. p. 197-221.
16. Eduardo AA, Santos LABO, Reboucas MC, Martinez PA. Patterns of vector species richness and species composition as drivers of Chagas disease occurrence in Brazil. Int J Environ Health Res. 2018; 28(6): 590-8.
17. Bender A, Python A, Lindsay SW, Golding N, Moyes CL. Modelling geospatial distributions of the triatomine vectors of Trypanosoma cruzi in Latin America. PLoS Negl Trop Dis. 2019; 14(8): 0008411.
18. Miles MA, de Souza AA, Povoa MM. Chagas’s disease in the Amazon basin. III. Ecotopes of ten triatomine bug species (Hemiptera: Reduviidae) from the vicinity of Belém, Pará State, Brazi. J Med Entomol. 1981; 18(4): 266-78.
19. Canals M, Cruzat L, Molina MC, Ferreira A, Cattan PE. Blood host sources of Mepraia spinolai (Heteroptera: Reduviidae), wild vector of Chagas disease in Chile. J Med Entomol. 2001; 38(2): 303-7.
20. Peterson AT, Sánchez-Cordero V, Beard CB, Ramsey JM. Ecologic niche modeling and potential reservoirs for Chagas disease, Mexico. Emerg Infect Dis. 2002; 8(7): 662-7.
21. Rabinovich JE, Kitron UD, Obed Y, Yoshioka M, Gottdenker N, Chaves LF. Ecological patterns of blood-feeding by kissing bugs (Hemiptera: Reduviidae: Triatominae). Mem Inst Oswaldo Cruz. 2011; 106(4): 479-94.
22. Testai R, Ferreira de Siqueira M, Rocha DSB, Roque ALR, Jansen AM, Xavier SCDC. Space-environment relationship in the identification of potential areas of expansion of Trypanosoma cruzi infection in Didelphis aurita in the Atlantic Rainforest. PLoS One. 2023; 18(7): e0288595.
23. De Lucena DT. Ecología dos Triatomineos do Brasil. Rev Bras Malariol Doenças Trop. 1959; 11(4): 577-633.
24. Diniz-Filho JAF, Ceccarelli S, Hasperué W, Rabinovich J. Geographical patterns of Triatominae (Heteroptera: Reduviidae) richness and distribution in the Western Hemisphere. Insect Conserv Divers. 2013; 6(6): 704-14.
25. De La Vega GJ, Schilman PE. Ecological and physiological thermal niches to understand distribution of Chagas disease vectors in Latin America. Med Vet Entomol. 2018; 32(1): 1-13.
26. Lazzari CR. Temperature preference in Triatoma infestans (Hemiptera: Reduviidae). Bull Entomol Res. 1991; 81: 273-6.
27. Schilman PE, Lazzari CR. Temperature preference in Rhodnius prolixus, effects and possible consequences. Acta Trop. 2004; 90: 115-22.
28. Canals M, Solis R, Valderas J, Ehrenfeld M, Cattan PE. Preliminary studies on temperature selection and activity cycles of Triatoma infestans and T. spinolai (Heteroptera: Reduviidae), Chilean vectors of Chagas’ disease. J Med Entomol. 1997; 34: 11-7.
29. Clark N. The effect of temperature and humidity upon eggs of the bug, Rhodnius prolixus (Heteroptera, Reduviidae). J Anim Ecol. 1953; 1: 82-7.
30. Lazzari CR, Nuñez JA. The response to radiant heat and the estimation of the temperature of distant sources in Triatoma infestans. J Insect Physiol. 1989; 35: 525-9.
31. Fresquet N, Lazzari CR. Daily variation of the response to heat in Rhodnius prolixus: The roles of light and temperature as synchronisers. J Insect Physiol. 2014; 70: 36-40.
32. Okasha AYK. Effects of high temperature in Rhodnius prolixus (Stal). Nature. 1964; 204 :1221-2.
33. Ceccarelli S, Rabinovich JE. Global climate change effects on Venezuela´s vulnerability to Chagas disease is linked to the geographic distribution of five Triatomine species. J Med Entomol. 2015; 52: 1333-43.
34. Badel-Mogollón J, Rodríguez-Figueroa L, Parra-Hena G. Análisis espacio-temporal de las condiciones biofísicas y ecológicas de Triatoma dimidiata (Hemiptera: Reduviidae: Triatominae) en la región nororiental de los Andes de Colom. Biomédica. 2017; 37(Suppl. 2): 106-23.
35. Ayala S, Alvarado S, Cáceres D, Zulantay I, Canals M. Estimando el efecto del cambio climático sobre el riesgo de la enfermedad de Chagas en Chile por medio del número reproductiv. Rev Med Chil. 2019; 147(6): 683-92.
36. Shi Y, Wei Y, Feng X, Liu J, Jiang Z, Ou F, et al. Distribution, genetic characteristics and public health implications of Triatoma rubrofasciata, the vector of Chagas disease in Guangxi, China. Parasit Vectors. 2020; 13: 33.
37. Cecere MC, Rodríguez-Planes LI, Vazquez-Prokopec G, Kitron U, Gurtler RE. Community-based surveillance and control of Chagas disease vectors in remote rural areas of the Argentine Chaco: a five-year follow-up. Acta Trop. 2019; 191: 108-15.
38. Abrahan L, Cavallo MJ, Amelotti I. Impact of involving the community in entomological surveillance of Triatoma infestans (Klug, 1834) (Hemiptera, Triatominae) vectorial control. Parasit Vectors. 2021; 14: 98.
39. Pennington PM, Rivera EP, de Urioste-Stone SM, Aguilar T, Juárez JG. A successful community-based pilot programme to control insect vectors of Chagas disease in rural Guatemala. In: Hendrichs J, Pereira R, Vreysen MJB, editors. Area-wide integrated pest management: development and field application. Boca Raton: CRC Press; 2021. p. 709-27.
40. Larson ER, Graham BM, Achury R, Coon JJ, Daniels MK, Gambrell DK, et al. From eDNA to citizen science: emerging tools for the early detection of invasive species. Front Ecol Environ. 2020; 18(4): 194-202.
41. Ceccarelli S, Balsalobre A, Vicente ME, Curtis-Robles R, Hamer SA, Ayala Landa JM, et al. American triatomine species occurrences: updates and novelties in the DataTri database. GigaByte. 2022; 2022: gigabyte62. doi:10.46471/gigabyte.62.
42. GBIF.org. GBIF occurrence download. 2022 Jun 11. Available from: https://doi.org/10.15468/dl.3fjwjq.
43. Heinrich PL, Gilbert E, Cobb NS, Franz N. Symbiota collections of arthropods network (SCAN): a data portal built to visualize, manipulate, and export species occurrences. 2015. Available from: https://openknowledge.nau.edu/id/eprint/2258/.
44. Shirey V, Belitz MW, Barve V, Guralnick R. A complete inventory of North American butterfly occurrence data: narrowing data gaps, but increasing bias. Ecography. 2021; 44(4): 537-47.
45. Girardello M, Chapman A, Dennis R, Kaila L, Borges PA, Santangeli A. Gaps in butterfly inventory data: a global analysis. Biol Conserv. 2019; 236: 289-95.
46. Bowler DE, Callaghan CT, Bhandari N, Henle K, Barth MB, Koppitz C, et al. Temporal trends in the spatial bias of species occurrence records. Ecography. 2022; 8: e06219.
47. Valenca-Barbosa C, Lima MM, Sarquis O, Bezerra CM, Abad- Franch F. Modeling disease vector occurrence when detection is imperfect II: drivers of site-occupancy by synanthropic Triatoma brasiliensis in the Brazilian northeast. PLoS Negl Trop Dis. 2014; 8(5): e2861.
48. Ribeiro-Jr G, Abad-Franch F, de Sousa OMF, dos Santos CGS, Fonseca EOL, dos Santos RF, et al. TriatoScore: an entomological- -risk score for Chagas disease vector control-surveillance. Parasit Vectors. 2021; 14: 492.
49. Abad-Franch F, Gurgel-Gonçalves R. The ecology and natural history of wild Triatominae in the Americas. In: Guarneri A, Lorenzo M, editors. Triatominae ― The biology of Chagas disease vectors. Entomology in Focus. Vol. 5. Springer, Cham: 2021; p. 387-445.
50. Carcavallo RU, Curto de Casas SI, Sherlock IA, Galíndez Girón I, Jurberg J, Galvão C, et al. Geographical distribution and alti-latitudinal dispersion of Triatominae. In: Carcavallo RU, Galíndez Girón I, Jurberg J, Lent H, editors. Atlas of Chagas’ disease vectors in the Americas. Vol. III. Rio de Janeiro: Fiocruz; 1999. p. 747-92.
51. Páez-Rondón O, Otálora-Luna F, Aldana E. Revalidation of synonymy between Nesotriatoma flavida and N. bruneri (Hemiptera, Reduviidae, Triatominae). J Arthropod Borne Dis. 2017; 11(4): 446-52.
52. Monteiro FA, Weirauch C, Felix M, Lazoski C, Abad-Franch F. Evolution, systematics, and biogeography of the Triatominae, vectors of Chagas disease. Adv Parasitol. 2018; 99: 265-344.
53. Oliveira Correia JPS, Gil-Santana HR, Dale C, Galvão C. Triatoma guazu is a junior synonym of Triatoma williami. Insects. 2022; 13: 591.
54. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2022. Available from: https://www.R-project.org/.
55. Carlson CJ. Embarcadero: species distribution modeling with Bayesian additive regression trees in R. Methods Ecol Evol. 2020; 11(7): 850-8.
56. Chipman HA, George EI, McCulloch RE. BART: Bayesian additive regression trees. Ann Appl Stat. 2010; 4(1): 266-98.
57. Fielding AH, Bell JF. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv. 1997; 24(1): 38-49.
58. 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.
59. Barbet-Massin M, Jiguet F, Albert CH, Thuiller W. Selecting pseudo-absences for species distribution models: How, where and how many? Methods Ecol Evol. 2012; 3: 327-38.
60. Fick SE, Hijmans RJ. WorldClim2: new 1km spatial resolution climate surfaces for global land areas. Int J Climatol. 2017; 37: 4302-15.
61. Wheelwright S, Makridakis S, Hyndman RJ. Forecasting: methods and applications. 3rd ed. New York: John Wiley & Sons; 1998.
62. Shmueli G. To explain or predict? Stat Sci. 2010; 25(3): 289-310.
63. VanDerWal J, Shoo LP, Graham C, Williams SE. Selecting pseudo- absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol Model. 2009; 220: 589-94.
64. Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, et al. The crucial role of accessible area in ecological niche modeling and species distribution. Ecol Model. 2011; 222: 1810-9.
65. Mainali KP, Hefley TJ, Ries L, Fagan WF. Matching expert range maps with species distribution model predictions. Conserv Biol. 2020; 34: 1292-304.
66. Hijmans RJ. raster: geographic data analysis and modeling. R package version 3.6-23. 2023. Available from: https://CRAN.Rproject. org/package=raster.
67. Ceccarelli S, Justi SA, Rabinovich JE, Diniz Filho JAF, Villalobos F. Phylogenetic structure of geographical co-occurrence among New World Triatominae species, vectors of Chagas disease. J Biogeogr. 2020; 47(6): 1218-31.
68. Blomberg SP, Garland Jr T, Ives AR. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution. 2003; 57(4): 717-45.
69. Pagel M. Inferring the historical patterns of biological evolution. Nature. 1999; 401(6756): 877-84.
70. Keck F, Rimet F, Bouchez A, Franc A. phylosignal: an R package to measure, test, and explore the phylogenetic signal. Ecol Evol. 2016; 6(9): 2774-80.
71. Münkemüller T, Lavergne S, Bzeznik B, Dray S, Jombart T, Schiffers K, et al. How to measure and test phylogenetic signal. Methods Ecol Evol. 2012; 3(4): 743-56.
72. Weins J, Graham CH. Niche conservatism: integrating evolution, ecology, and conservation biology. Annu Rev Ecol Evol Syst. 2005; 36(1): 519-39.
73. Weins J, Ackerly DD, Allen AP, Buckley LB, et al. Niche conservatism as an emerging principle in ecology and conservation biology. Ecol Lett. 2010; 13(10): 1310-24.
74. Ibarra-Cerdeña CN, Zaldívar-Riverón A, Peterson AT, Sánchez- Cordero V, Ramsey JM. Phylogeny and niche conservatism in North and Central American triatomine bugs (Hemiptera: Reduviidae: Triatominae), vectors of Chagas’ disease. PLoS Negl Trop Dis. 2014; 8(10): e3266.
75. Gurgel-Gonçalves R, Abad-Franch F, Ferreira JBC, Santana DB, Cuba CAC. Is Rhodinius proxilus (Triatominae) invading houses in central Brazil? Acta Trop. 2008; 107(2): 90-8.
76. Reyes M, Esteban L, Torres FA, Flórez M, Agudelo JC, Angulo VM. Intrusión de Pastrongylus geniculatus y Rhodnius pallescens a viviendas y áreas sociales en un barrio de Bucaramanga, Santander, Colombia. Biomédica. 2011; 31(Suppl. 3): 54.
77. Da Costa Valente V. Potential for domestication of Panstrongylus geniculatus (Latreille, 1811) (Liemiptera, Reduviidae, Triatominae) in the municipality of Muaná, Marajó Island, State of Pará. Mem Inst Oswaldo Cruz. 1999; 94(Suppl. 1): 399-400.
78. Ceretti-Junior W, Vendrami DP, de Matos-Junior MO, Rimoldi- -Ribeiro A, Alvarez JV, Marques S, et al. Occurrences of triatomines (Hemiptera: Reduviidae) and first reports of Panstrongylus geniculatus in urban environments in the city of São Paulo, Brazil. Rev Inst Med Trop São Paulo. 2018; 60; e33.
79. Feliciangeli MD, Carrasco H, Patterson JS, Suarez B, Martínez C, Medina M. Mixed domestic infestation by Rhodnius prolixus Stäl, 1859 and Panstrongylus geniculatus Latreille, 1811, vector incrimination, and seroprevalence for Trypanosoma cruzi among inhabitants in El Guamito, Lara State, Venezuela. Am J Trop Med Hyg. 2004; 71(4): 501-5.
80. Catalá SS. The infra-red (IR) landscape of Triatoma infestans. An hypothesis about the role of IR radiation as a cue for Triatominae dispersal. Infect Genet Evol. 2011; 11: 1891-8.
81. Baines CB, Ferzoco IMC, McCauley SJ. Phenotype-by-environment interactions influence dispersal. J Anim Ecol. 2020; 88(8): 1263-74.
82. Simmons AD, Thomas CD. Changes in dispersal during species’ range expansions. Am Nat. 2004; 164(3): 378-95.
83. Almeida CE, Vinhaes MC, Silveira AC, de Almeide JR, Silveira AC, Costa J. Monitoring the domiciliary and peridomiciliary invasion process of Triatoma rubrovaria in the State of Rio Grande do Sul, Brazil. Mem Inst Oswaldo Cruz. 2000; 95(6): 761-8.
84. Berenger JM, Pages F. Les Triatominae: une domestication qui se généralise (Triatominae: Growing Trend to Domesticity). Med Trop (Mars). 2007; 67: 217-22.
85. Abrahan L, Gorla D, Catalá S. Active dispersal of Triatoma infestans and other triatomines in the Argentinean arid Chaco before and after vector control interventions. J Vector Ecol. 2016; 41(1): 90-6.
86. Khatchikian CE, Foley EA, Barbu CM, Hwang J, Ancca-Juárez J, Borrini-Mayori K, et al. Population structure of the Chagas disease vector Triatoma infestans in an urban environment. PLoS Negl Trop Dis. 2015; 9(2): e0003425.
87. Ribeiro Jr G, Araújo RF, Carvalho CMM, Cunha GM, Lanza FC, Miranda DLP, et al. Triatomine fauna in the state of Bahia, Brazil: what changed after 40 years of the vector-control program? Rev Soc Bras Med Trop. 2022; 55: 1-9.
88. Salvatella R, Rosa R. La interrupción en Uruguay de la transmisión vectorial de Trypanosoma cruzi, agente de la enfermedad de Chagas, por control de Triatoma infestans. Rev Patol Trop. 2000; 29(2): 213-31.
89. INCOSUR (Southern Cone Initiative). Evaluaciones nacionales en 2000 y 2001: Paraguay, Brasil, Argentina, Uruguay y Bolivia. In: INCOSUR Chagas 10th Meeting, Montevideo, Uruguay. 2001. p. 128.
90. Cortez MR, Monteiro FA, Noireau F. New insights on the spread of Triatoma infestans from Bolivia-implications for Chagas disease emergence in the Southern Cone. Infect Genet Evol. 2010; 10: 350-3.
91. Abrahan LB, Gorla DE, Catalá SS. Dispersal of Triatoma infestans and other Triatominae species in the arid Chaco of Argentina - Flying, walking or passive carriage? The importance of walking females. Mem Inst Oswaldo Cruz. 2011; 106(2): 232-9.
92. Gurgel-Gonçalves R, Cuba CAC. Predicting the potential geographical distribution of Rhodnius neglectus (Hemiptera, Reduviidae) based on ecological niche modeling. J Med Entomol. 2009; 46(4): 952-60.
93. Richer W, Kengne P, Rojas Cortez MR, Perrineau MM, Cohuet A, Fontenille D, et al. Active dispersal by wild Triatoma infestans in the Bolivian Andes. Trop Med Int Health. 2007; 12(6): 759-64.
94. Hernandez-Castro LE, Villacis AG, Jacobs A, Cheaib B, Day CC, Ocaña-Mayorga S, et al. Population genomics and geographic dispersal in Chagas disease vectors: Landscape drivers and evidence of possible adaptation to the domestic setting. PLoS Genet. 2022; 18(2): e1010019.
95. Panzera F, Ferreiro MJ, Pita S, Calleros L, Pérez R, Basmadjián Y, et al. Evolutionary and dispersal history of Triatoma infestans, main vector of Chagas disease, by chromosomal markers. Infect Genet Evol. 2014; 27: 105-13.
96. Rousseau JS, Betts MG. Factors influencing transferability in species distribution models. Ecography. 2022; 2022(7): e06060.
97. Weins J, Stralberg D, Jongsomjit D, Howell CA, Snyder MA. Niches, models, and climate change: assessing the assumptions and uncertainties. Proc Natl Acad Sci USA. 2009; 106(2): 19729-36.
98. Brown JL, Carnaval AC. A tale of two niches: methods, concepts, and evolution. Front Biogeogr. 2019; 11(4): e44158.
99. Veloz DS, Williams JW, Blois JS, He F, Otto-Bliesner B, Liu Z. No-analog climates and shifting realized niches during the late quaternary: implications for 21st-century predictions by species distribution models. Glob Chang Biol. 2011; 18(5): 1698-713.
100. Owens HL, Campbell LP, Dornak L, Saupe EE, Barve N, Soberónet J, al. Constraints on interpretation of ecological niche models by limited environmental range on calibration areas. Ecol Model. 2013; 263: 10-8.
101. Feng X, Park SD, Liang Y, Pandey R, Pape SM. Collinearity in ecological niche modeling: confusions and challenges. Ecol Evol. 2019; 9(18): 10365-76.
102. Curtis-Robles R, Wozniak EJ, Auckland LD, Hamer GL, Hamer SA. Combining public health education and disease ecology research: using citizen science to assess Chagas disease entomological risk in Texas. PLoS Negl Trop Dis. 2015; 9(12): e0004235.
103. Delgado-Noguera LA, Hernández-Pereira CE, Ramírez JD, Hernández C, Velasquez-Ortíz N, Clavijo J, et al. Tele-entomology and tele-parasitology: a citizen science-based approach for surveillance and control of Chagas disease in Venezuela. Parasite Epidemiol Control. 2022; 19: e00273.
104. Khalighifar A, Komp E, Ramsey JM, Gurgel-Gonçalves R, Peterson AT. Deep learning algorithms improve automated identification of Chagas disease vectors. J Med Entomol. 2019; 56(5): 1401-10.
105. Abdelghani BA, Banitaan S, Maleki M, Mazen A. Kissing bugs identification using convolutional neural network. IEEE Access. 2021; 9: 140539-48.
106. Cochero J, Pattori L, Balsalobre A, Ceccarelli S, Marti G. A convolutional neural network to recognize Chagas disease vectors using mobile phone images. Ecol Inform. 2022; 68: 101587.
107. de Miranda VL, de Souza EP, Bambil D, Khalighifar A, Peterson AT, de Oliveira Nascimento FA, et al. Cellphone picture-based, genus-level automated identification of Chagas disease vectors: effects of picture orientation on the performance of five machinelearning algorithms. Ecol Inform. 2024; 79: 102430.
108. Gurgel-Gonçalves R, de Miranda VL, Khalighifar A, Peterson AT. Shooting in the dark: automatic identification of disease vectors without taxonomic expert supervision. Ecol Inform. 2023; 75: 102029.
109. Ceballos LA, Cardinal MV, Vazquez-Prokopec GM, Lauricella MA, Orozco MM, Cortinas R, et al. Long-term reduction of Trypanosoma cruzi infection in sylvatic mammals following deforestation and sustained surveillance in northwestern Argentina. Acta Trop. 2006; 98(3): 286-96.