Skip to main content

Evaluation of the effectiveness of BG-Sentinel and CDC light traps in assessing the abundance, richness, and community composition of mosquitoes in rural and natural areas

Abstract

Background

Vector-borne diseases are a major burden to public health. Controlling mosquitoes is considered the most effective way to prevent vector-borne disease transmission. Mosquito surveillance is a core component of integrated vector management, as surveillance programs are often the cornerstone for the development of mosquito control operations. Two traps are the most commonly used for the surveillance of adult mosquitoes: Centers for Disease Control and Prevention miniature light trap (CDC light trap) and BG-Sentinel trap (BioGents, Regensburg, Germany). However, despite the importance of the BG-Sentinel trap in surveillance programs in the United States, especially in the Southern states, its effectiveness in consistently and reliably collecting mosquitoes in rural and natural areas is still unknown. We hypothesized that BG-Sentinel and CDC light traps would be more attractive to specific mosquito species present in rural and natural areas. Therefore, our objective was to compare the relative abundance, species richness, and community composition of mosquitoes collected in natural and rural areas by BG-Sentinel and CDC light traps.

Methods

Mosquitoes were collected from October 2020 to March 2021 using BG-Sentinel and CDC light traps baited with dry ice, totaling 105 trap-nights.

Results

The BG-Sentinel traps collected 195,115 mosquitoes comprising 23 species from eight genera, and the CDC light traps collected 188,594 mosquitoes comprising 23 species from eight genera. The results from the permutational multivariate analysis of variance (PERMANOVA) and generalized estimating equation model for repeated measures indicate the BG-Sentinel and CDC light traps had similar sampling power.

Conclusion

Even though BG-Sentinel traps had a slightly better performance, the difference was not statistically significant indicating that both traps are suitable to be used in mosquito surveillance in rural and natural areas.

Graphical Abstract

Background

Vector-borne diseases are a major burden to public health. Currently, half of the world's population is at risk of vector-borne pathogen infections, resulting in approximately 1 billion infections every year [1,2,3,4]. In 2019, approximately 3 million cases of dengue were reported in the Americas [5], and vector-borne pathogen transmission is being reported more frequently not only in endemic areas [6,7,8,9], but also in non-endemic countries such as Croatia, France, and Italy [10,11,12].

The availability of effective drugs and vaccines is limited to a few pathogens and has reduced effectiveness in decreasing the prevalence and incidence of vector-borne pathogens [13,14,15,16]. In this context, controlling mosquitoes is widely accepted as the most effective way to prevent vector-borne pathogen transmission to humans and animals [17,18,19]. However, many mosquito vector species are responsible for transmitting different arboviruses and other pathogens, and the timely and precise detection of mosquito vector species in a given area is key for the development of targeted and effective control strategies [20].

Mosquito surveillance is a core component of integrated vector management, as surveillance programs are often the cornerstone for the development of mosquito control operations [18]. Most mosquito control districts in the United States operate surveillance systems to inform their control operations and guide control efforts including source reduction, chemical interventions, and environmental management [21,22,23]. However, consistently, accurately, and reliably assessing the presence and relative abundance of mosquito species is not a simple task often relying upon multiple trap types and approaches [23,24,25,26]. Furthermore, invasive mosquito species are an increasing threat to public health, and vector species such as Aedes albopictus and Culex coronator have expanded their range and abundance considerably in the last decade [27,28,29,30,31,32]. A reliable mosquito surveillance system should be able to early detect invasive species allowing stakeholders to implement control efforts to curb their proliferation and avoid their establishment.

Therefore, a surveillance system should be able to inform stakeholders regarding the relative abundance, species richness, and community composition of mosquitoes. However, different traps have different levels of attractiveness for different mosquito species, and choosing the right trap for collecting adult mosquitoes in different areas is key to achieving reliable and actionable results. In this context, two traps are the most commonly used for the surveillance of adult mosquitoes, Centers for Disease Control and Prevention miniature light trap (CDC light trap) and BG-Sentinel trap (BioGents, Regensburg, Germany). BG-Sentinel traps are the current gold standard for collecting Aedes stegomyia mosquitoes [33]. On the other hand, CDC light traps are considered more of a generalist trap that will attract a wider range of mosquito species, including Anopheles and Culex [23, 34, 35].

However, despite the importance of the BG-Sentinel trap in surveillance programs in the United States, especially in the Southern states, its effectiveness in consistently and reliably collecting mosquitoes in rural and natural areas in the United States is yet to be determined. We hypothesized that BG-Sentinel and CDC light traps have different levels of attractiveness and will be more attractive to specific mosquito species present in rural and natural areas leading to different outcomes of the community composition assessment. Therefore, our objective was to compare the relative abundance, species richness, and community composition of mosquitoes collected in natural and rural areas by BG-Sentinel and CDC light traps.

Methods

Collection of mosquitoes

Mosquitoes were collected from October 2020 to March 2021 using battery-powered BG-Sentinel 2 and CDC light traps, totaling 105 trap-nights. Firstly, we set three CDC and three BG-Sentinel traps at no more than 50 m from each other once a week for 24 h for 7 weeks in a rural area in the southern region of Miami-Dade County, Florida known for having great richness and abundance of mosquitoes (25°24′19.9″N; 80°30′03.9″W). Secondly, to test if the mosquito species richness and relative abundance after 7 weeks of collections would be similar in other areas of the Miami-Dade County, we selected 11 different collection sites in rural and natural areas, in which one BG-Sentinel and one CDC light trap were deployed together for 24 h at no more than 50 m from each other (Fig. 1). An insulated cooler with approximately 2 kg of dry ice was placed next to the traps as bait [36]. The traps were placed under similar environmental conditions hidden in the vegetation in shaded areas to protect the traps from the elements and enhance mosquito collections. CDC traps were set in tree branches at a height of approximately 1 m above ground level and BG-Sentinel traps were set directly on the ground. The collected mosquitoes were transported to the Miami-Dade County Mosquito Control Laboratory and subsequently morphologically identified to species using taxonomic keys [37].

Fig. 1
figure 1

Map showing the location of the collection sites in Miami-Dade, Florida. The first set of 7 weeks of collection sites and trap locations are displayed in green, and the second set of experiments showing the 11 different collection sites in rural and natural areas and trap locations are displayed in red. The figure was produced using ArcGIS 10.2 (Esri, Redlands, CA) using freely available layers from the Miami-Dade County’s Open Data Hub—https://gis-mdc.opendata.arcgis.com/

Statistical analyses

Biodiversity analyses were carried out for each trap type based on the Shannon, dominance, and equitability indices. The Shannon index takes into consideration species abundance and richness, therefore, less diversity results in lower values, and more diversity results in higher values [38]. Dominance (1-Simpson) index estimates the association between species richness and abundance, values close to 1 indicate the presence of dominant species whereas values closer to 0 imply a more even distribution between species richness and abundance [39]. The equitability index is calculated using the Shannon diversity index divided by the logarithm of the number of species [40]. This measures the evenness with which specimens are divided among the species in a given mosquito community. Analyses were carried out with 10,000 randomizations where each randomization is done without replacement using a 95% confidence interval (CI). To compare the mosquito species composition collected by CDC light traps and BG-Sentinel traps, we performed a permutational multivariate analysis of variance (PERMANOVA) with 9999 permutations based on Bray–Curtis distances [41, 42]. The data were organized into two groups (Group 1 = CDC light traps; and Group 2 = BG-Sentinel traps) to compare the mosquito species composition collected by the two different traps. Then, we used the SIMPER (similarity percentage) method to assess the contribution of each mosquito species to the observed differences between trap types [43]. Analyses were done using PAST v3.2 [44].

We performed a generalized estimating equation (GEE) model for repeated measures to assess differences in the species richness (number of species) and relative abundance of mosquitoes collected by BG-Sentinel and CDC light traps [45]. Species richness and relative abundance were used as dependent variables, trap type (BG-Sentinel and CDC light trap) as units, and collection date as repeated measures (longitudinal model). The model was done in SPSS v.28 software.

Results

A total of 26 species from nine genera were collected by both BG-Sentinel and CDC light traps, totaling 383,709 specimens. The BG-Sentinel traps collected 195,115 mosquitoes comprising 23 species from eight genera. The CDC light traps collected 188,594 mosquitoes comprising 23 species from eight genera. Aedes triseriatus, Coquillettidia perturbans, and Culex quinquefasciatus were only collected by the BG-Sentinel traps, whereas Aedeomyia squamipennis, Aedes condolescens, and Wyeomyia mitchelli were only collected by the CDC light traps. The BG-Sentinel traps collected 6521 more mosquitoes than the CDC light traps. Culex nigripalpus was the most abundant species collected by both the BG-Sentinel (131,661) and CDC light traps (131,237), followed by Culex erraticus (BG-Sentinel = 16,127; CDC light trap = 13,442) and Anopheles crucians (BG-Sentinel = 15,515; CDC light trap = 13,878). Aedes triseriatus and Ae. condolescens were the least common species, being collected only once by a BG-Sentinel and a CDC light trap, respectively. Mansonia dyari was collected in larger numbers by BG-Sentinel traps (BG-Sentinel = 16,823; CDC light trap = 7984), on the other hand, Culex panocossa was collected in larger numbers by CDC light traps (BG-Sentinel = 2148; CDC light trap = 8387) (Table 1).

Table 1 Total number of mosquitoes collected by BG-Sentinel and CDC light traps in Miami-Dade County, Florida

The diversity indices yielded similar values for the mosquito community identified by both BG-Sentinel and CDC light traps. The mosquito community estimated by the BG-Sentinel traps yielded a dominance index of 0.47, whereas the CDC light traps yielded a dominance index of 0.5. The Shannon and equitability indices also yielded similar results for both traps 1.2 and 0.38, respectively (Table 2).

Table 2 Diversity indices values for the mosquito community identified by both BG-Sentinel and CDC light traps

The PERMANOVA did not yield significant results for the comparison between the mosquito community comprising the mosquitoes collected by the BG-Sentinel and CDC light traps (F = 1.54; P = 0.11). The subsequent SIMPER analysis of the mosquito community showed that Cx. nigripalpus, An. crucians, and Cx. erraticus contributed the most to the observed differences (Table 3).

Table 3 SIMPER (similarity percentage) analysis of which species contributed the most to the observed differences comparing BG-sentinel and CDC light traps

The results of the GEE models for repeated measures for species richness and relative abundance of mosquitoes collected by BG-Sentinel and CDC light traps showed no statistically significant differences between traps (Table 4). Even though the comparison of the species richness and relative abundance of the mosquitoes collected by the BG-Sentinel and CDC light traps were not significantly different, the BG-Sentinel traps collected more species per trap-night when compared to CDC light traps. Both the BG-Sentinel and CDC light traps had similar performances estimating the relative abundance of mosquitoes (Fig. 2).

Table 4 Results of the generalized estimating equation models for repeated measures for species richness and relative abundance of mosquitoes collected by BG-Sentinel and CDC light traps
Fig. 2
figure 2

Comparison of the effectiveness of BG-Sentinel and CDC light traps in assessing species richness and relative abundance of mosquitoes in rural and natural areas of Miami-Dade, Florida

Discussion

BG-Sentinel and CDC light traps have been extensively used to successfully assess the relative abundance, species richness, and community composition of vector mosquitoes [23, 46, 47], and are essential for mosquito control operations. Our results show that the BG-Sentinel and the CDC light traps performed equally in assessing the species richness and relative abundance of mosquitoes. The number of mosquitoes collected by the BG-Sentinel and the CDC light traps varied less than 4%. Both the BG-Sentinel and the CDC light traps collected 23 species from eight genera from a total of 26 species from nine genera detected in total. The diversity indices yielded virtually identical results and the GEE model for repeated measures showed no significant differences between the performance of the BG-Sentinel and CDC light traps. However, even though the difference was not statistically significant, the BG-Sentinel traps had slightly superior performance; they collected more mosquitoes in total and yielded higher species richness in more trap-nights.

Most species collected by the BG-Sentinel and the CDC light traps during this study were fairly evenly distributed between traps. However, Ma. dyari and Cx. panocossa were the exceptions. Twice as many Ma. dyari were collected by BG-Sentinel traps, and Cx. panocossa was collected approximately 4 times more by CDC light traps. Furthermore, BG-Sentinel traps failed to collect Ad. squamipennis, and CDC light traps failed to collect Cq. perturbans. These results indicate that even though the performance in collecting mosquitoes of the BG-Sentinel and the CDC light traps were not statistically significantly different, some species were more attracted by one trap instead of the other.

The results of the PERMANOVA showed no significant differences in the mosquito community collected by the BG-Sentinel and the CDC light traps, in agreement with the GEE model for repeated measures and the diversity indices. Furthermore, the subsequent SIMPER analysis showed that the most abundant mosquitoes contributed the most to the observed differences indicating that the performance of the traps in collecting mosquitoes was similar. These results indicate that both trap types had similar performances in collecting rare species or failing to collect specific species.

Studies done in Europe and China had similar results to the ones obtained in this study, in which BG-Sentinel traps performed equally or were slightly superior to CDC light traps [48, 49]. However, in another study done in South Africa CDC traps had a superior performance in comparison to BG-Sentinel traps [50]. Local environmental and climatic conditions have a major influence on the development and proliferation of mosquitoes and greatly affect their behavior and ecology [51,52,53,54]. Therefore, locally assessing the effectiveness of the traps used to investigate the mosquito community composition, species richness and relative abundance in rural and natural areas is essential to improve the reliability and usefulness of mosquito surveillance and early warning systems.

Our results showed the presence of mosquito vector species in the rural and natural areas surveyed in this study. Among them two primary vectors of pathogens were collected in large numbers, Anopheles quadrimaculatus (primary vector of malaria in the Americas) and Cx. nigripalpus (primary vector of West Nile virus) [53, 55]. Mosquito surveillance in rural and natural areas bordering urban areas is key to avoiding vector-borne pathogen transmission to human and animal populations [56,57,58]. Anthropogenic alterations in the environment such as deforestation and defaunation often lead to habitat fragmentation [59]. Such human-made environmental alterations have a substantial impact on the mosquito community composition, relative abundance, and species richness. The behavior and ecology of mosquito vector species that are anthropophilic but non-synanthropic are greatly affected by anthropogenic alterations in the environment [60,61,62,63]. As a consequence, these mosquito vector species will invade urban areas seeking resources and will increase their contact with humans leading to a higher risk of pathogen spillover to human populations [64,65,66]. Therefore, the correct identification of the relative abundance and species richness in rural and natural areas bordering urban areas is key to determining the risk of vector-borne pathogens to humans [67,68,69].

Reliable and effective mosquito surveillance systems are key for the early detection of invasive species and to help to prevent their establishment as well as to inform mosquito control operations and guide control efforts [70,71,72]. Even though BG-Sentinel and the CDC light traps have had similar performances and were able to assess the community composition of mosquitoes in rural and natural areas, other sampling methods should also be considered to improve the effectiveness of surveillance systems. Immature mosquito surveillance systems should also be considered as the information obtained from such surveillance systems is complementary to adult mosquito surveillance systems, providing important information on what aquatic habitats are being used by each species and where the highest relative abundance levels of immature mosquitoes are concentrated [24]. Gravid traps are also an important tool since they use a different approach than traps that mimic a host (e.g., BG-Sentinel and the CDC light traps), and thus potentially complementing the sampling power of the surveillance system [73,74,75].

Mosquito collections were done between October 2020 and March 2021, and therefore, we were unable to assess all weather and season variations that would have provided further insight into the population dynamics and the mosquito community composition. CDC light traps and BG-Sentinel traps were not rotated in the first set of experiments due to the need to tie CDC light traps to tree branches. For this reason, the traps were set in the same environment at no more than 50 m from each other to avoid biases and inconsistencies in the collections.

Conclusion

The results of the BG-Sentinel and CDC light traps in assessing the relative abundance and species richness of mosquitoes in rural and natural areas of Miami-Dade indicate that both traps performed equally, yielding similar results in all analyses. Therefore, we were able to reject the hypothesis that BG-Sentinel and CDC light traps would be more attractive to specific mosquito species present in rural and natural areas. Even though BG-Sentinel traps had a slightly better performance, the difference was not statistically significant indicating that both traps are suitable to be used in mosquito surveillance in rural and natural areas.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

References

  1. Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature. 2013;496:504–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Gaythorpe KAM, Hamlet A, Jean K, Garkauskas Ramos D, Cibrelus L, Garske T, et al. The global burden of yellow fever. Elife. 2021;10:1–22.

    Google Scholar 

  3. Breman JG, Egan A, Keusch GT. The intolerable burden of malaria: a new look at the numbers. Am J Trop Med Hyg. 2001;64:iv–vii.

    CAS  PubMed  Google Scholar 

  4. World Health Organization—WHO. Vector-borne diseases. https:// www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue

  5. Panamerican Health Organization—PAHO. Reported cases of dengue fever in The Americas, 2019. http://www.paho.org/data/index.php/en/mnu-topics/indicadores-dengue-en/dengue-nacional-en/252-dengue-pais-ano-en.htm

  6. Abdul-Ghani R, Mahdy MAK, Al-Eryani SMA, Fouque F, Lenhart AE, Alkwri A, et al. Impact of population displacement and forced movements on the transmission and outbreaks of Aedes-borne viral diseases: dengue as a model. Acta Trop. 2019;197:105066.

    PubMed  Google Scholar 

  7. Brady OJ, Hay SI. The global expansion of dengue: how Aedes aegypti mosquitoes enabled the first pandemic arbovirus. Annu Rev Entomol. 2020;65:191–208.

    CAS  PubMed  Google Scholar 

  8. Rosenberg R, Lindsey NP, Fischer M, Gregory CJ, Hinckley AF, Mead PS, et al. Vital signs: trends in reported vectorborne disease cases — United States and Territories, 2004–2016. MMWR Morb Mortal Wkly Rep. 2018;67:496–501.

    PubMed  PubMed Central  Google Scholar 

  9. Messina J, Brady O, Pigott D, Brownstein J, Hoen A, Hay S. A global compendium of human dengue virus occurrence. Sci Data. 2014;1:140004.

    PubMed  PubMed Central  Google Scholar 

  10. Poletti P, Messeri G, Ajelli M, Vallorani R, Rizzo C, Merler S. Transmission potential of chikungunya virus and control measures: the case of Italy. PLoS ONE. 2011;6:e18860.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Gould EA, Gallian P, De Lamballerie X, Charrel RN. First cases of autochthonous dengue fever and chikungunya fever in France: from bad dream to reality! Clin Microbiol Infect. 2010;16:1702–4.

    CAS  PubMed  Google Scholar 

  12. Gjenero-Margan I, Aleraj B, Krajcar D, Lesnikar V, Klobucar A, Pem-Novosel I, et al. Autochthonous dengue fever in Croatia, August-September 2010. Euro Surveill. 2011;16:1–4.

    Google Scholar 

  13. Beier JC, Wilke ABB, Benelli G. Newer approaches for malaria vector control and challenges of outdoor transmission. Towar Malar Elimin Leap Forw. 2018. https://doi.org/10.5772/intechopen.75513.

    Article  Google Scholar 

  14. Wilke ABB, Beier JC, Benelli G. Filariasis vector control down-played due to the belief the drugs will be enough—not true! Entomol Gen. 2020;40:15–24.

    Google Scholar 

  15. Kraemer MUG, Faria NR, Reiner RC, Golding N, Nikolay B, Stasse S, et al. Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study. Lancet Infect Dis. 2017;17:330–8.

    PubMed  PubMed Central  Google Scholar 

  16. Barrett ADT. Yellow Fever in Angola and Beyond—the problem of vaccine supply and demand. N Engl J Med. 2016;375:301–3.

    PubMed  Google Scholar 

  17. Lizzi KM, Qualls WA, Brown SC, Beier JC. Expanding integrated vector management to promote healthy environments. Trends Parasitol. 2014;30:394–400.

    PubMed  PubMed Central  Google Scholar 

  18. World Health Organization WHO. Handbook for integrated vector management 2012. Geneva: World Health Organization; 2012.

    Google Scholar 

  19. Roiz D, Wilson AL, Scott TW, Fonseca DM, Jourdain F, Müller P, et al. Integrated Aedes management for the control of Aedes-borne diseases. PLoS Negl Trop Dis. 2018;12:e0006845.

    PubMed  PubMed Central  Google Scholar 

  20. World Health Organization—WHO. Vector-borne diseases. 2021. https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases

  21. Stevens MCAA, Faulkner SC, Wilke ABB, Beier JC, Vasquez C, Petrie WD, et al. Spatially clustered count data provide more efficient search strategies in invasion biology and disease control. Ecol Appl. 2021;31:1–11.

    Google Scholar 

  22. Wilke ABB, Vasquez C, Carvajal A, Ramirez M, Cardenas G, Petrie WD, et al. Effectiveness of adulticide and larvicide in controlling high densities of Aedes aegypti in urban environments. PLoS ONE. 2021;16:e0246046.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Wilke ABB, Vasquez C, Medina J, Carvajal A, Petrie W, Beier JC. Community composition and year-round abundance of vector species of mosquitoes make Miami-Dade County, Florida a receptive gateway for arbovirus entry to the United States. Sci Rep. 2019;9:8732.

    PubMed  PubMed Central  Google Scholar 

  24. Wilke ABB, Chase C, Vasquez C, Carvajal A, Medina J, Petrie WD, et al. Urbanization creates diverse aquatic habitats for immature mosquitoes in urban areas. Sci Rep. 2019;9:15335.

    PubMed  PubMed Central  Google Scholar 

  25. Mackay AJ, Amador M, Barrera R. An improved autocidal gravid ovitrap for the control and surveillance of Aedes aegypti. Parasit Vectors. 2013;6:225.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Mcgregor BL, Connelly CR. A review of the control of Aedes aegypti (Diptera: Culicidae) in the Continental United States a review of the control of Aedes aegypti (Diptera: Culicidae) in the Continental United States. J Med Entomol. 2020;58:10–25.

    Google Scholar 

  27. Wilke ABB, Benelli G, Beier JC. Beyond frontiers: on invasive alien mosquito species in America and Europe. PLoS Negl Trop Dis. 2020;14:e0007864.

    PubMed  PubMed Central  Google Scholar 

  28. Wilke ABB, Vasquez C, Cardenas G, Carvajal A, Medina J, Petrie WD, et al. Invasion, establishment, and spread of invasive mosquitoes from the Culex coronator complex in urban areas of Miami-Dade County, Florida. Sci Rep. 2021;11:14620.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Benelli G, Wilke ABB, Beier JC. Aedes albopictus (Asian Tiger Mosquito). Trends Parasitol. 2020;36:942–3.

    PubMed  Google Scholar 

  30. Alto BW, Connelly CR, Meara GFO, Hickman D, Karr N. Reproductive biology and susceptibility of Florida Culex coronator to infection with West Nile Virus. Vector Borne Zoonotic Dis. 2014;14:606–14.

    PubMed  PubMed Central  Google Scholar 

  31. Roche B, Léger L, L’Ambert G, Lacour G, Foussadier R, Besnard G, et al. The spread of Aedes albopictus in Metropolitan France: contribution of environmental drivers and human activities and predictions for a near future. PLoS ONE. 2015;10:1–13.

    Google Scholar 

  32. Cebri S. A literature review of host feeding patterns of invasive Aedes mosquitoes in Europe. Insects. 2020;11:848.

    Google Scholar 

  33. Maciel-de-Freitas R, Eiras ÁE, Lourenço-de-Oliveira R. Field evaluation of effectiveness of the BG-Sentinel, a new trap for capturing adult Aedes aegypti (Diptera: Culicidae). Mem Inst Oswaldo Cruz. 2006;101:321–5.

    PubMed  Google Scholar 

  34. Medeiros-Sousa AR, Fernandes A, Ceretti-Junior W, Wilke ABB, Marrelli MT. Mosquitoes in urban green spaces: using an island biogeographic approach to identify drivers of species richness and composition. Sci Rep. 2017;7:17826.

    PubMed  PubMed Central  Google Scholar 

  35. Sudia WD. Battery-operated light trap, an improved model. Mosq News. 1962. p. 265.

  36. Wilke ABB, Carvajal A, Medina J, Anderson M, Nieves VJ, Ramirez M, et al. Assessment of the effectiveness of BG-Sentinel traps baited with CO2 and BG-Lure for the surveillance of vector mosquitoes in Miami-Dade County, Florida. Samy AM, editor. PLoS One 2019;14:e0212688. https://doi.org/10.1371/journal.pone.0212688

  37. Darsie RF Jr, Morris CD. Keys to the adult females and fourth-instar larvae of the mosquitoes of Florida (Diptera, Culicidae), vol. 1, 1st edn. Tech Bull Florida Mosq Cont Assoc; 2000.

    Google Scholar 

  38. Hutcheson K. A test for comparing diversities based on the Shannon formula. J Theor Biol. 1970;29:151–4.

    CAS  PubMed  Google Scholar 

  39. Colwell RK. Biodiversity: concepts, patterns, and measurement. Communities Ecosyst. 2009. p. 257–64.

  40. Sheldon AL. Equitability indices: dependence on the species count. Ecology. 1969;50:466–7.

    Google Scholar 

  41. Anderson MJ. Permutational multivariate analysis of variance (PERMANOVA). Wiley StatsRef: Statistics Reference Online. 1–15 (2017) Doi:https://doi.org/10.1002/9781118445112.stat07841.

  42. Alencar J, De Mello CF, Guimarães AÉ, Gil-Santana HR, Dos Santos SJ, Santos-Mallet JR, et al. Culicidae community composition and temporal dynamics in Guapiaçu ecological reserve, Cachoeiras de Macacu, Rio de Janeiro, Brazil. PLoS ONE. 2015;10:1–16.

    Google Scholar 

  43. Clarke KR. Non-parametric multivariate analyses of changes in community structure. Austral Ecol. 1993;18:117–43.

    Google Scholar 

  44. Hammer Ø, Harper DATT, Ryan PD. PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron. 2001;4:9.

    Google Scholar 

  45. Zeger SL, Liang K-Y, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics. 1988;44:1049.

    CAS  PubMed  Google Scholar 

  46. De Carvalho GC, Ceretti-Junior W, Barrio-Nuevo KM, Wilk-da-Silva R, Christe RO, De Paula MB, et al. Composition and diversity of mosquitoes (Diptera: Culicidae) in urban parks in the South region of the city of São Paulo. Brazil Biota Neotrop. 2017;17:e20160274.

    Google Scholar 

  47. Petruff TA, McMillan JR, Shepard JJ, Andreadis TG, Armstrong PM. Increased mosquito abundance and species richness in Connecticut, United States 2001–2019. Sci Rep. 2020;10:1–14.

    Google Scholar 

  48. Lühken R, Pfitzner WP, Börstler J, Garms R, Huber K, Schork N, et al. Field evaluation of four widely used mosquito traps in Central Europe. Parasit Vectors. 2014;7:1–11.

    Google Scholar 

  49. Li Y, Su X, Zhou G, Zhang H, Puthiyakunnon S, Shuai S, et al. Comparative evaluation of the efficiency of the BG-Sentinel trap, CDC light trap and Mosquito-oviposition trap for the surveillance of vector mosquitoes. Parasit Vectors. 2016;9:446.

    PubMed  PubMed Central  Google Scholar 

  50. Gorsich EE, Beechler BR, van Bodegom PM, Govender D, Guarido MM, Venter M, et al. A comparative assessment of adult mosquito trapping methods to estimate spatial patterns of abundance and community composition in southern Africa. Parasit Vectors. 2019;12:462.

    PubMed  PubMed Central  Google Scholar 

  51. Wilke ABB, Vasquez C, Carvajal A, Medina J, Chase C, Cardenas G, et al. Proliferation of Aedes aegypti in urban environments mediated by the availability of key aquatic habitats. Sci Rep. 2020;10:12925.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Wilke ABB, Wisinski BF, Benelli G, Vasquez C, Mutebi J, Petrie WD, et al. Local conditions favor dengue transmission in the contiguous United States. Entomol Gen. 2021;41:523–9.

    Google Scholar 

  53. Sinka ME, Bangs MJ, Manguin S, Rubio-palis Y, Chareonviriyaphap T, Coetzee M, et al. A global map of dominant malaria vectors. Parasit Vectors. 2012;5:69.

    PubMed  PubMed Central  Google Scholar 

  54. Ferraguti M, Martínez-de la Puente J, Roiz D, Ruiz S, Soriguer R, Figuerola J. Effects of landscape anthropization on mosquito community composition and abundance. Sci Rep. 2016;6:29002.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Centers for Disease Control and Prevention. Mosquito species in which West Nile virus has been detected. 2017. https://www.cdc.gov/westnile/resources/pdfs/MosquitoSpecies1999-2012.pdf

  56. Burkett-Cadena ND, Vittor AY. Deforestation and vector-borne disease: forest conversion favors important mosquito vectors of human pathogens. Basic Appl Ecol. 2018;26:101–10.

    PubMed  Google Scholar 

  57. Dunphy BM, Kovach KB, Gehrke EJ, Field EN, Rowley WA, Bartholomay LC, et al. Long-term surveillance defines spatial and temporal patterns implicating Culex tarsalis as the primary vector of West Nile virus. Sci Rep. 2019;9:1–10.

    Google Scholar 

  58. Weaver SC. Urbanization and geographic expansion of zoonotic arboviral diseases: mechanisms and potential strategies for prevention. Trends Microbiol. 2013;21:360–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Dirzo R, Young H, Galetti M, Ceballos G, Nick J, Collen B. Defaunation in the Anthropocene. Science. 2014;345:401.

    CAS  PubMed  Google Scholar 

  60. Multini LC, Wilke ABB, Marrelli MT. Neotropical Anopheles (Kerteszia) mosquitoes associated with bromeliad-malaria transmission in a changing world. Acta Trop. 2020;205:105413.

    PubMed  Google Scholar 

  61. Souza D, Goes D, Paula D, Road SP, Tropical M, Janeiro D, et al. Genomic surveillance of yellow fever virus epidemic waves in São Paulo, Brazil, 2017–2018. PLOS Pathog. 2020;16:e1008699.

    PubMed  PubMed Central  Google Scholar 

  62. Abreu FV, Ribeiro IP, Ferreira-de-Brito A, Santos AA, Miranda RM, Bonelly ID, et al. Haemagogus leucocelaenus and Haemagogus janthinomys are the primary vectors in the major yellow fever outbreak in Brazil, 2016–2018. Emerg Microbes Infect. 2019;8:218–31.

    PubMed  PubMed Central  Google Scholar 

  63. Lorenz C, Azevedo TS, Virginio F, Aguiar BS, Chiaravalloti-Neto F, Suesdek L. Impact of environmental factors on neglected emerging arboviral diseases. PLoS Negl Trop Dis. 2017;11:1–19.

    Google Scholar 

  64. Multini LC, de Souza AL, Marrelli MT, Wilke AB. The influence of anthropogenic habitat fragmentation on the genetic structure and diversity of the malaria vector Anopheles cruzii (Diptera: Culicidae). Sci Rep. 2020;10:18018.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Laporta GZ, de Prado PIKL, Kraenkel RA, Coutinho RM, Sallum MAM. Biodiversity can help prevent malaria outbreaks in tropical forests. PLoS Negl Trop Dis. 2013;7:e2139.

    PubMed  PubMed Central  Google Scholar 

  66. Zohdy S, Schwartz TS, Oaks JR. The coevolution effect as a driver of spillover. Trends Parasitol. 2019;35:399–408.

    PubMed  Google Scholar 

  67. Multini LC, Marrelli MT, Beier JC, Wilke ABB. Increasing complexity threatens the elimination of Extra-Amazonian malaria in Brazil. Trends Parasitol. 2019;35:383–7.

    PubMed  Google Scholar 

  68. Laporta GZ, Burattini MN, Levy D, Fukuya LA, de Oliveira TMP, Maselli LMF, et al. Plasmodium falciparum in the southeastern Atlantic forest: a challenge to the bromeliad-malaria paradigm? Malar J. 2015;14:181.

    PubMed  PubMed Central  Google Scholar 

  69. Taipe-Lagos CB, Natal D. Culicidae mosquito abundance in a preserved metropolitan area and its epidemiological implications. Rev Saude Publica. 2003;37:275–9.

    PubMed  Google Scholar 

  70. Scholte EJ, den Hartog W, Dik M, Schoelitsz B, Brooks M, Schaffner F, et al. Introduction and control of three invasive mosquito species in the Netherlands, July October 2010. Eurosurveillance. 2010;15:1–4.

    Google Scholar 

  71. Pagac BB, Spring AR, Stawicki JR, Dinh TL, Lura T, Kavanaugh MD, et al. Incursion and establishment of the Old World arbovirus vector Aedes (Fredwardsius) vittatus (Bigot, 1861) in the Americas. Acta Trop. 2021;213:105739.

    CAS  PubMed  Google Scholar 

  72. Ammar SE, Mclntyre M, Swan T, Kasper J, Derraik JGB, Baker MG, et al. Intercepted mosquitoes at New Zealand’s ports of entry, 2001 to 2018: current status and future concerns. Trop Med Infect Dis. 2019;4:101.

    PubMed Central  Google Scholar 

  73. Williams GM, Gingrich JB. Comparison of light traps, gravid traps, and resting boxes for West Nile virus surveillance. J Vector Ecol. 2007;32:285–91.

    PubMed  Google Scholar 

  74. Eiras AE, Buhagiar TS, Ritchie SA. Development of the gravid Aedes trap for the capture of adult female container-exploiting mosquitoes (Diptera: Culicidae). J Med Entomol. 2014;51:200–9.

    PubMed  Google Scholar 

  75. Irisha SR, Moorea SJ, Deruac YA, Brucea J, Camerona MM. Evaluation of gravid traps for the collection of Culex quinquefasciatus, a vector of lymphatic filariasis in Tanzania. Trans R Soc Trop Med Hyg. 2013;107:15–22.

    Google Scholar 

Download references

Acknowledgements

We thank the staff of the Miami-Dade County Mosquito Control Division for their help in the processing and identification of the mosquitoes.

Funding

This research was supported by the Miami-Dade Mosquito Control Division and by the CDC (https://www.cdc.gov/) grant 1U01CK000510-05: Southeastern Regional Center of Excellence in Vector-Borne Diseases: The Gateway Program. CDC had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

ABBW and CV conceived of and designed the study. ABBW, AC, and CV were responsible for the mosquito collection. MM was responsible for the taxonomic identification. ABBW and CV developed the study methodology and data analysis methodologies. ABBW analyzed the data and prepared the original figures. ABBW wrote the original draft of the paper. All authors contributed to reviewing and editing the paper. CV, WDP, and JCB were responsible for the project administration, funding acquisition, resources, supervision, and validation of this study.

Corresponding author

Correspondence to André B. B. Wilke.

Ethics declarations

Ethics approval and consent to participate

Since this study posed less than minimal risk to participants and did not involve endangered or protected species the Institutional Review Board at the University of Miami determined that the study was exempt from institutional review board assessment (IRB Protocol Number: 20161212).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wilke, A.B.B., Vasquez, C., Carvajal, A. et al. Evaluation of the effectiveness of BG-Sentinel and CDC light traps in assessing the abundance, richness, and community composition of mosquitoes in rural and natural areas. Parasites Vectors 15, 51 (2022). https://doi.org/10.1186/s13071-022-05172-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13071-022-05172-3

Keywords