Abstract
Epizootic hemorrhagic disease (EHD) is a viral arthropod-borne disease affecting wild and domestic ruminants, caused by infection with epizootic hemorrhagic disease virus (EHDV). EHDV is transmitted to vertebrate animal hosts by biting midges in the genus Culicoides Latreille (Diptera: Ceratopogonidae). Culicoides sonorensis Wirth and Jones is the only confirmed vector of EHDV in the United States but is considered rare in Florida and not sufficiently abundant to support EHDV transmission. This study used ecological niche modeling to map the potential geographical distributions and associated ecological variable space of four Culicoides species suspected of transmitting EHDV in Florida, including Culicoides insignis Lutz, Culicoides stellifer (Coquillett), Culicoides debilipalpis Hoffman and Culicoides venustus Lutz. Models were developed with the Genetic Algorithm for Rule Set Production in DesktopGARP v1.1.3 using species occurrence data from field sampling along with environmental variables from WorldClim and Trypanosomiasis and Land use in Africa. For three Culicoides species (C. insignis, C. stellifer and C. debilipalpis) 96–98% of the presence points were predicted across the Florida landscape (63.8% - 72.5%). For C. venustus, models predicted 98.00% of presence points across 27.4% of Florida. Geographic variations were detected between species. Culicoides insignis was predicted to be restricted to peninsular Florida, and in contrast, C. venustus was predicted to be primarily in north Florida and the panhandle region. Culicoides stellifer and C. debilipalpis were predicted nearly statewide. Environmental conditions also differed by species, with some species’ ranges predicted by more narrow ranges of variables than others. The Normalized Difference Vegetation Index (NDVI) was a major predictor of C. venustus and C. insignis presence. For C. stellifer, Land Surface Temperature, Middle Infrared were the most limiting predictors of presence. The limiting variables for C. debilipalpis were NDVI Bi-Annual Amplitude and NDVI Annual Amplitude at 22.5% and 28.1%, respectively. The model outputs, including maps and environmental variable range predictions generated from these experiments provide an important first pass at predicting species of veterinary importance in Florida. Because EHDV cannot exist in the environment without the vector, model outputs can be used to estimate the potential risk of disease for animal hosts across Florida. Results also provide distribution and habitat information useful for integrated pest management practices.
Highlights
Vector-borne pathogens can only exist in a permissive environment that supports appropriate vectors, such that the distribution of disease is linked to vector distribution
Ecological niche models (ENMs) are Species distribution models (SDMs) commonly used to predict the geographic distribution of a species by determining the most likely environmental conditions associated with collection locations of the target species [8]
Four final experiments were developed for C. insignis, C. stellifer, C. venustus, and C. debilipalpis (Fig 2)
Summary
Vector-borne pathogens can only exist in a permissive environment that supports appropriate vectors (and hosts), such that the distribution of disease is linked to vector distribution. SDMs are useful to determine the potential current and future geographic distribution of vector species as proxies for the pathogens they transmit [2,4,5,6]. Map predictions help researchers better understand where vector-borne disease risk is most likely and to target surveillance to areas of highest risk. Ecological niche models (ENMs) are SDMs commonly used to predict the geographic distribution of a species by determining the most likely environmental conditions associated with collection locations of the target species [8]. ENMs apply each unique set of ecological parameters allowing for a species to maintain a population without immigration [9,10], with a focus on abiotic and climatological conditions that support a species [11]. Used models include machine learning techniques such as MAXENT [13], Boosted Regression Trees [14], Random Forest [15], and the Genetic Algorithm for Rule-Set Production (GARP) [1,12]
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