Land use and cover changes and sand fly (Diptera: Psychodidae) assemblages in an emerging focus of leishmaniasis

The dataset documents the relationship between changes in land use and occupation and the diversity and abundance of phlebotomines, the vectors of leishmaniasis, in a rural area of the municipality of Codó, Maranhão. It integrates spatial and entomological information collected between 2012 and 2023, providing a comprehensive basis for environmental, epidemiological and vector management analyses. The land use and occupation data was obtained from Sentinel-2 satellite images, processed in QGIS software (version 3.10) and classified using the Orfeo Toolbox Processing (OTB) tool. The images represent the years 2012, 2014, 2021 and 2023 and include variables such as the density and fragmentation of vegetation cover, as well as the expansion of built-up areas. This information was made available in geospatial formats, such as Geotiff and shapefiles, allowing detailed analysis of temporal changes in the landscape. Entomological data was collected bimonthly between August 2022 and June 2023. Phlebotomines were captured using CDC and Shannon traps (white and black) installed in peri- and extra-domicile environments. The set includes information on species identification, number of individuals captured by trap type and environment, as well as data on total and relative abundance. This data is organized in CSV files and descriptive reports. A total of 3,375 phlebotomines were captured, of which Psychodopygus wellcomei was the most abundant species (78.19%), followed by Nyssomyia whitmani (7.53%). Ny. whitmani predominated in the peridomicile (84.97%), while Ps. wellcomei was more frequent in the extradomicile (96.51%). This set of data is of high scientific relevance, providing unprecedented support for studies on the relationship between habitat fragmentation and disease vector dynamics. It also has great potential for reuse and can be applied in various areas, such as public health planning, teaching and research. It can support the development of vector surveillance and control strategies, as well as serving as a basis for predictive models of leishmaniasis transmission. The data is made available in accordance with ethical and legal standards. It does not include sensitive or identifiable information about human beings, guaranteeing compliance with applicable legislation. The entomological collections were carried out with the authorization of regulatory bodies and in compliance with Brazilian environmental legislation. To access the data, it is necessary to request permission and ensure proper citation of the original study. This dataset is a valuable resource for understanding the interactions between anthropogenic changes in the landscape and vector dynamics. It offers insights into environmental impacts and enables more effective approaches to leishmaniasis control in tropical rural areas, contributing to the advancement of public health and environmental conservation policies.

Methods
Study area

This study was conducted in the Santana IV rural settlement, Codó (04°27′12.8″S 43°53′01.7″W), Eastern Mesoregion of Maranhão State, Brazil (Figure 1). The municipality has an area of 4364.5 km2 and an estimated population of 114,275 people, with a population density of 26.20 people/km2 (IBGE, 2022). Vegetation cover varies according to relief characteristics, proximity to watercourses, and the extent of anthropic transformations. The predominant vegetation type is open forest/babassu forest, occupying the entire valley of the Itapecuru River. The main tree species are babassu palm (Attalea speciosa Mart. ex Spreng.) and carnauba [Copernicia prunifera (Miller) H.E.Moore]. Another common type of vegetation cover is campo cerrado, found mainly in the east, northwest, and southwest parts of the municipality (Correia Filho et al., 2011a).

The climate is semi-humid, transitioning to semi-arid with precipitation. According to the Köppen classification, the climate is of the Aw type, with rainy summers and dry winters. The driest month has less than 60 mm rainfall. Temperatures in the coldest month remain above 18 °C. The annual average temperature is about 27 °C, and the maximum temperature is 36 °C. Air humidity reaches high values during the rainy season, indicating that this parameter is directly related to the rainfall regime of the region. The average annual rainfall is approximately 1200 mm, with the wettest quarter being January, February, and March (Lima, 1998; Correia Filho et al., 2011b).

Five sampling points were located in peridomestic environments (Animal shelter backyard): P1 (04°55′062″S 043°89′450″W), P2 (04°55′402″S 043°89′822″W), P3 (04°55′474″S 043°90′048″W), P4 (04°54′959″S 043°90′936″W), and P5 (04°55′161″S 043°91′053″W). These areas were close to houses, debris, and waste. The soil was moist, and various domestic animals were observed, such as dogs, cats, pigs, cattle, chickens, guineafowl (capotes), horses, and geese. Samplings were also performed in extradomestic environments located at least 500 m away from peridomestic sampling points. Extradomestic environments were characterized by primary forests (fragmented) with the presence of fruit trees (cashew and mango trees) and shaded soil, with the presence of wet litter and bogs in the vicinity of the Saco River.  

Sand fly collection and identification

Sand flies were captured at the five sampling points (P1 to P5) in the dry (August to December 2022) and wet (February to June 2023) periods. In alternate months, two Centers for Disease Control and Prevention (CDC) light traps were installed per sampling point, one in a peridomestic environment (near residences where animals are raised) and another in an extradomestic environment (within a fragment of closed vegetation), totaling 10 traps. Traps were kept in the field for two consecutive nights, being installed at 18:00 h and removed the next day at 6:00 h. Active sampling was performed from 18:00 to 21:00 h using white and black Shannon traps (Galati et al., 2001).

Captured sand flies were taken to the Medical Entomology Laboratory, Caxias Campus, Maranhão State University (UEMA), for sex discrimination, clearing, and dissection. All collected sand flies (males and females) were dissected by removing the head, thorax, and the last three segments of the abdomen and mounted on a glass slide with Canada balsam for species identification. The rest of the body was individually stored dry at −20 °C in a 1.5 mL tube for future molecular studies. Species were identified using the updated version of the classification system proposed by Galati (2024). Genus abbreviations follow Marcondes (2007).

Data analysis

For the time-scale analysis of land use and cover in the Santana IV settlement, Sentinel 2 satellite imagery was acquired over sand fly sampling points in 2012, 2014, 2021, and 2023. Images were downloaded from the LandView website. The analyzed years were chosen because they were within a 12-year time frame. Image treatment for visualization of landscape changes was carried out using Quantum GIS (QGIS) software free version 3.10 based on image features and characteristics. Shapefile files were obtained from the Brazilian Institute of Geography and Statistics (IBGE) database to delimit sampling points. Orfeo Toolbox, an open-source project native to QGIS for state-of-the-art remote sensing, was used to classify images. Developed by the open-source geospatial community, Orfeo Toolbox can process high-resolution optical, multispectral, and radar images on a terabyte scale.

For analysis of sand fly assemblages, relative abundance was estimated as a percentage of the total number of collected individuals. Species richness was estimated from the number of identified species. The abundance and diversity of sand flies captured in the dry and wet seasons in peridomestic and extradomestic environments were measured by the Shannon diversity index (SHDI). Cluster analysis was performed to compare the composition of sand fly populations between dry and wet periods based on the presence and absence of species, using Jaccard's similarity index (Ludwig & Reynolds, 1988). Rarefaction curves for the dry and wet periods were constructed by extrapolating the expected number of species to larger sample sizes. These curves were used to estimate species richness in each period and compare the completeness of samples. The effects of temperature, relative humidity, peridomestic conditions, and extradomestic conditions on abundance and diversity were examined using generalized linear mixed models (GLMMs) at the 5% significance level. Temperature (°C) and relative humidity (%) were measured by using a digital thermohygrometer (IHT-2200, Instrutemp) at each sampling point when traps were installed and removed. The constancy index (CI) was calculated by the equation CI = P × 100/N, where P is the number of collections in which the species was present and N is the total number of months in which the species was collected. CI values were used to group species into three categories: constant (CI ≥ 50%), accessory (25% < CI < 50%), and accidental (CI ≤ 25%) (Silveira Neto et al., 1976). Data analysis was conducted using specific packages for R software version 4.3.1 (2023).

REFERENCES

Correia Filho, F. L., Gomes, É. R., Nunes, O. O., & Lopes Filho, J. B. (2011a). Projeto cadastro de fontes de abastecimento por água subterrânea: estado do Maranhão: relatório diagnóstico do município de Codó. CPRM. Available from:  https://rigeo.sgb.gov.br/handle/doc/15443

Correia Filho, F. L., Gomes, É. R., Nunes, O. O., & Lopes Filho, J. B. (2011b). Projeto cadastro de fontes de abastecimento por água subterrânea: estado do Maranhão: relatório diagnóstico do município de Açailândia. CPRM. Available from: https://rigeo.sgb.gov.br/handle/doc/15303

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