Modeling the spatial distribution of Culicoides species (Diptera: Ceratopogonidae) as vectors of animal diseases in Ethiopia

Abstract

Culicoides biting midges (Diptera: Ceratopogonidae) are the major vectors of bluetongue, Schmallenberg, and African horse sickness viruses. This study was conducted to survey Culicoides species in different parts of Ethiopia and to develop habitat suitability for the major Culicoides species in Ethiopia. Culicoides traps were set in different parts of the country from December 2018 to April 2021 using UV light Onderstepoort traps and the collected Culicoides were sorted to species level. To develop the species distribution model for the two predominant Culicoides species, namely Culicoides imicola and C. kingi, an ensemble modeling technique was used with the Biomod2 package of R software. KAPPA True skill statistics (TSS) and ROC curve were used to evaluate the accuracy of species distribution models. In the ensemble modeling, models which score TSS values greater than 0.8 were considered. Negative binomialregression models were used to evaluate the relationship between C. imicola and C. kingi catch and various environmental and climatic factors. During the study period, a total of 9148 Culicoides were collected from 66 trapping sites. Of the total 9148, 8576 of them belongs to seven species and the remaining 572 Culicoides were unidentified. The predominant species was C. imicola (52.8%), followed by C. kingi (23.6%). The abundance of these two species was highly influenced by the agro-ecological zone of the capture sites and the proximity of the capture sites to livestock farms. Climatic variables such as mean annual minimum and maximum temperature and mean annual rainfall were found to influence the catch of C. imicola at the different study sites. The ensemble model performed very well for both species with KAPPA (0.9), TSS (0.98), and ROC (0.999) for C. imicola and KAPPA (0.889), TSS (0.999), and ROC (0.999) for C. kingi. Culicoides imicola has a larger suitability range compared to C. kingi. The Great Rift Valley in Ethiopia, the southern and eastern parts of the country, and the areas along the Blue Nile and Lake Tana basins in northern Ethiopia were particularly suitable for C. imicola. High suitability for C. kingi was found in central Ethiopia and the Southern Nations, Nationalities and Peoples Region (SNNPR). The habitat suitability model developed here could help researchers better understand where the above vector-borne diseases are likely to occur and target surveillance to high-risk areas.

Ecological Niche Modelling Approaches: Challenges and Applications in Vector-Borne Diseases

“…Thus, whenever possible, the databases to be used must be filtered and reinforced with a molecular identification (i.e., [69,[80][81][82]). Despite the aforementioned, the issue is usually neglected, and most of the studies rely solely on morphological identification (e.g., [83][84][85][86][87][88][89][90][91][92][93][94][95][96], but see [97] for a mixed approach), or do not even describe the identification method [70,82,[98][99][100][101][102][103][104][105][106][107][108].…”

“…As far as we know, this is what occurs with a large amount of the studies dealing with VBDs. As these kinds of studies are usually performed with public health purposes at regional or local scales, they often rely on regional/national datasets collected by administrations or NGOs whose range of action is defined by restricted geographical or political borders (i.e., country or even provinces' administrations) (e.g., [67][68][69]80,[83][84][85][86][87][88][89][90][91][92][93][94][95][96]98,99,101,[104][105][106][107][108][112][113][114][115][116][117]). The concern resides in the fact that ENM built with occurrence data restricted to artificial boundaries might consider only a subset of the environmental conditions experienced by a species across its entire range (i.e., "spatial niche truncation" [118]); therefore, providing an incomplete description of the environmental limits [109] and underestimating the environmental conditions that the species can withstand [111] (see an example in Figure 2).…”

“…Despite the sampling bias and the consequent non-independence of occurrences needing to be accounted for, a good number of the VBDs studies published in the last three years omitted declaring any procedure for dealing with the sampling bias [67,68,[80][81][82][83][84][85][86]88,89,91,[94][95][96]98,101,104,108,113,124,142].…”

“…Despite the choice of algorithms or estimation frameworks, model calibration is a key process in which variable selection and model settings are fine-tuned to obtain the best model performance [64,155]. Unfortunately, in spite of the recommendations for maximizing reproducibility of ENMs [64,156], the information concerning model calibration is lacking in a high number of studies dealing with VBDs [80,[82][83][84][85][86]88,89,91,92,96,98,107,112,113,124].…”

The ecological suitability area of Cirsium lineare (Thunb.) Sch.‐Bip. under future climate change in China based on MaxEnt modeling

“…In recent years, the maximum entropy model (MaxEnt model), Maxlike, Biomod2, and other modeling methods combined with geographic information spatial analysis technology have been widely used to assess the impact of climatic environmental variables on the potential distribution of species (Fetene et al., 2022; Merow & Silander, 2014; Pant et al., 2021) and further used to comprehensively analyze trends in these distributional changes under past or future climate change (Liu et al., 2021; Pan et al., 2022). MaxEnt model is commonly used for species distribution modeling, and its performance is excellent.…”