Larval ecology of mosquitoes in sylvatic arbovirus foci in southeastern Senegal

BACKGROUND: Although adult mosquito vectors of sylvatic arbovirus [yellow fever (YFV), dengue-2 (DENV-2) and chikungunya (CHIKV)] have been studied for the past 40 years in southeastern Senegal, data are still lacking on the ecology of larval mosquitoes in this area. In this study, we investigated the larval habitats of mosquitoes and characterized their seasonal and spatial dynamics in arbovirus foci. METHODS: We searched for wet microhabitats, classified in 9 categories, in five land cover classes (agriculture, forest, savannah, barren and village) from June, 2010 to January, 2011. Mosquito immatures were sampled monthly in up to 30 microhabitats of each category per land cover and bred until adult stage for determination. RESULTS: No wet microhabitats were found in the agricultural sites; in the remaining land covers immature stages of 35 mosquito species in 7 genera were sampled from 9 microhabitats (tree holes, fresh fruit husks, decaying fruit husks, puddles, bamboo holes, discarded containers, tires, rock holes and storage containers). The most abundant species was Aedes aegypti formosus, representing 30.2% of the collections, followed by 12 species, representing each more than 1% of the total, among them the arbovirus vectors Ae. vittatus (7.9%), Ae. luteocephalus (5.7%), Ae. taylori (5.0%), and Ae. furcifer (1.3%). Aedes aegypti, Cx. nebulosus, Cx. perfuscus, Cx. tritaeniorhynchus, Er. chrysogster and Ae. vittatus were the only common species collected from all land covers. Aedes furcifer and Ae. taylori were collected in fresh fruit husks and tree holes. Species richness and dominance varied significantly in land covers and microhabitats. Positive associations were found mainly between Ae. furcifer, Ae. taylori and Ae. luteocephalus. A high proportion of potential enzootic vectors that are not anthropophilic were found in the larval mosquito fauna. CONCLUSIONS: In southeastern Senegal, Ae. furcifer and Ae. taylori larvae showed a more limited distribution among both land cover and microhabitat types than the other common species. Uniquely among vector species, Ae. aegypti formosus larvae occurred at the highest frequency in villages. Finally, a high proportion of the potential non-anthropophilic vectors were represented in the larval mosquito fauna, suggesting the existence of unidentified sylvatic arbovirus cycles in southeastern Senegal

Landscape Ecology of Sylvatic Chikungunya Virus and Mosquito Vectors in Southeastern Senegal

The risk of human infection with sylvatic chikungunya (CHIKV) virus was assessed in a focus of sylvatic arbovirus circulation in Senegal by investigating distribution and abundance of anthropophilic Aedes mosquitoes, as well as the abundance and distribution of CHIKV in these mosquitoes. A 1650 km2 area was classified into five land cover classes: forest, barren, savanna, agriculture and village. A total of 39,799 mosquitoes was sampled from all classes using human landing collections between June 2009 and January 2010. Mosquito diversity was extremely high, and overall vector abundance peaked at the start of the rainy season. CHIKV was detected in 42 mosquito pools. Our data suggest that Aedes furcifer, which occurred abundantly in all land cover classes and landed frequently on humans in villages outside of houses, is probably the major bridge vector responsible for the spillover of sylvatic CHIKV to humans

Errors in soil maps: The need for better on-site estimates and soil map predictions.

High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, SoilGrids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to the number, types, and spatial distribution of texture classes. The topsoil texture information provided by the maps is thus sufficiently inaccurate that it would result in significant errors in a number of applications, including irrigation system design and predictions of potential forage and crop productivity, water runoff, and soil erosion. Clearly, the use of these existing maps for policy- and decision-making is highly questionable and there is a critical need for better on-site estimates and soil map predictions. We propose that mobile apps, citizen science, and crowdsourcing can help meet this need

Errors in soil maps: The need for better on-site estimates and soil map predictions

High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, SoilGrids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to the number, types, and spatial distribution of texture classes. The topsoil texture information provided by the maps is thus sufficiently inaccurate that it would result in significant errors in a number of applications, including irrigation system design and predictions of potential forage and crop productivity, water runoff, and soil erosion. Clearly, the use of these existing maps for policy- and decision-making is highly questionable and there is a critical need for better on-site estimates and soil map predictions. We propose that mobile apps, citizen science, and crowdsourcing can help meet this need

Morphologic and Genetic Characterization of Ilheus Virus, a Potential Emergent Flavivirus in the Americas

Ilheus virus (ILHV) is a mosquito-borne flavivirus circulating throughout Central and South America and the Caribbean. It has been detected in several mosquito genera including Aedes and Culex, and birds are thought to be its primary amplifying and reservoir host. Here, we describe the genomic and morphologic characterization of ten ILHV strains. Our analyses revealed a high conservation of both the 5′- and 3′-untranslated regions but considerable divergence within the open reading frame. We also showed that ILHV displays a typical flavivirus structural and genomic organization. Our work lays the foundation for subsequent ILHV studies to better understand its transmission cycles, pathogenicity, and emergence potential

Identification of Mosquito Bloodmeals Collected in Diverse Habitats in Malaysian Borneo Using COI Barcoding

Land cover and land use change (LCLUC) acts as a catalyst for spillover of arthropod-borne pathogens into novel hosts by shifting host and vector diversity, abundance, and distribution, ultimately reshaping host-vector interactions. Identification of bloodmeals from wild-caught mosquitoes provides insight into host utilization of particular species in particular land cover types, and hence their potential role in pathogen maintenance and spillover. Here, we collected 134 blood-engorged mosquitoes comprising 10 taxa across 9 land cover types in Sarawak, Malaysian Borneo, a region experiencing intense LCLUC and concomitant spillover of arthropod-borne pathogens. Host sources of blood were successfully identified for 116 (87%) mosquitoes using cytochrome oxidase subunit I (COI) barcoding. A diverse range of hosts were identified, including reptiles, amphibians, birds, and mammals. Sixteen engorged Aedes albopictus, a major vector of dengue virus, were collected from seven land cover types and found to feed exclusively on humans (73%) and boar (27%). Culex tritaeniohynchus (n = 2), Cx. gelidus (n = 3), and Cx. quiquefasciatus (n = 3), vectors of Japanese encephalitis virus, fed on humans and pigs in the rural built-up land cover, creating potential transmission networks between these species. Our data support the use of COI barcoding to characterize mosquito-host networks in a biodiversity hotspot

Additional file 2: of Ecological niche modeling of Aedes mosquito vectors of chikungunya virus in southeastern Senegal

Maxent response curves for the mosquito habitat suitability models. A compilation of figures that show how the environmental variables affected the Maxent predictions. The red curve in each figure shows the mean response of the fifteen replicate Maxent runs; the blue shaded areas indicate the mean Âą one standard deviation. The x-axis represents the variable value; the y-axis represents the relative occurrence rate. (DOCX 810 kb

Additional file 2: of Ecological niche modeling of Aedes mosquito vectors of chikungunya virus in southeastern Senegal

Maxent response curves for the mosquito habitat suitability models. A compilation of figures that show how the environmental variables affected the Maxent predictions. The red curve in each figure shows the mean response of the fifteen replicate Maxent runs; the blue shaded areas indicate the mean Âą one standard deviation. The x-axis represents the variable value; the y-axis represents the relative occurrence rate. (DOCX 810 kb