Ecological niche models for sand fly species and predicted distribution of Lutzomyia longipalpis (Diptera: Psychodidae) and visceral leishmaniasis in Bahia state, Brazil
| Autor: | Luciana Lobato Cardim, John B. Malone, Moara de Santana Martins Rodgers, Deborah Daniela Madureira Trabuco Carneiro, Eduardo Oyama Lins Fonseca, Bruno Cova, Marta Mariana Nascimento Silva, Maria Emília Bavia |
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| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
Rain 010501 environmental sciences Management Monitoring Policy and Law 01 natural sciences Normalized Difference Vegetation Index medicine Animals Precipitation Psychodidae Geography Medical Ecosystem 0105 earth and related environmental sciences General Environmental Science Ecological niche biology Temperature General Medicine Enhanced vegetation index Vegetation Seasonality medicine.disease biology.organism_classification Pollution Insect Vectors Environmental niche modelling Geography Leishmaniasis Visceral Physical geography Brazil Environmental Monitoring |
| Zdroj: | Environmental Monitoring and Assessment. 191 |
| ISSN: | 1573-2959 0167-6369 |
| Popis: | Visceral leishmaniasis is a public health problem in Brazil. This disease is endemic in most of Bahia state, with increasing reports of cases in new areas. Ecological niche models (ENM) can be used as a tool for predicting potential distribution for disease, vectors, and to identify risk factors associated with their distribution. In this study, ecological niche models (ENMs) were developed for visceral leishmaniasis (VL) cases and 12 sand fly species captured in Bahia state. Sand fly data was collected monthly by CDC light traps from July 2009 to December 2012. MODIS satellite imagery was used to calculate NDVI, NDMI, and NDWI vegetation indices, MODIS day and night land surface temperature (LST), enhanced vegetation index (EVI), and 19 Bioclim variables were used to develop the ENM using the maximum entropy approach (Maxent). Mean diurnal range was the variable that most contributed to all the models for sand flies, followed by precipitation in wettest month. For Lutzomyia longipalpis (L. longipalpis), annual precipitation, precipitation in wettest quarter, precipitation in wettest month, and NDVI were the most contributing variables. For the VL model, the variables that contributed most were precipitation in wettest month, annual precipitation, LST day, and temperature seasonality. L. longipalpis was the species with the widest potential distribution in the state. The identification of risk areas and factors associated with this distribution is fundamental to prioritize resource allocation and to improve the efficacy of the state's program for surveillance and control of VL. |
| Databáze: | OpenAIRE |
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