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Habitat suitability modeling of mosquito species in Faiyum Governorate, Egypt, using GIS
Beni-Suef University Journal of Basic and Applied Sciences volume 13, Article number: 35 (2024)
Abstract
Background
The life cycle and geographic range of mosquitoes are profoundly influenced by weather conditions. In Faiyum Governorate, Egypt, researchers looked at over 42 different mosquito breeding areas in the wild. Culex pipiens, Culex antennatus, Culex perexiguus, Culex theileri, Anopheles multicolor, Anopheles sergentii, Ochlerotatus caspius, Culiseta longiareolata, and Uranotaenia unguiculata, were all represented among the collected mosquito larvae. Using Maxent, we identified the most important bioclimatic factors affecting habitat appropriateness for each species. In order to determine the link between a species’ habitat appropriateness and bioclimatic factors, the computer builds response curves for each bioclimatic factor. All species risk maps, species richness maps, and individual species maps were generated with a Software that build interactive web maps (ArcGIS 10.3).
Results
Each mosquito species’ range was impacted by four bioclimatic factors. The most influential factors were those linked to temperature and precipitation. Culex antennatus, Culex perexiguus, Culex pipiens, and Ochlerotatus caspius responded best to temperature-related factors. Precipitation of wettest month (bio 13) was effective in four species (Anopheles multicolor, Culex antennatus, Culex perexiguus, and Culex pipiens).
Conclusion
Areas in the north and west of El-Fayoum district (Zawyat El-Karadsah, Beni-Saleh, and Monshat Abd-Allah), in Tamiya in Kafr Mahfouz, in the south of Abshway, especially in Tobhar, in the north of Atssa, in the south and center of Sinnuris have been identified as high-risk areas and more susceptible to mosquito-borne diseases.
1 Background
Diseases spread by vectors kill more than 17 percent of the world’s population every year, resulting in over 700,000 annual deaths [1]. Mosquitoes are carriers of a variety of parasites and viruses, including the Sindbis virus, which causes Ockelbo disease, chikungunya, dengue, Japanese encephalitis, malaria, lymphatic filariasis, dirofilariasis, West Nile virus, Zika virus, Usutu virus, Tahyna virus, Batai virus, and Japanese encephalitis [2,3,4]. Because they transmit deadly diseases to animals, mosquitoes threaten not only humans but also livestock. Equine alphaviruses, which can be lethal to horses, are spread by certain mosquito species [5]. Also, Japanese encephalitis virus (JEV) can infect dogs, cats, goats, buffaloes, chickens, and other domestic species [6]. There are five mosquito genera, Culex, Anopheles, Aedes, Culiseta, and Uranotaenia, which are the most prevalent genera in Egypt. Most species in these genera serve as significant disease vectors for both humans and animals [7, 8]. Throughout Egyptian territory, the fauna of mosquito species is very diverse, especially around the Nile Valley. Faiyum Governorate is located in western Egypt and is a significant agricultural region. The area’s water resources, such as the Nile River and Lake Qarun, make it an ideal mosquito breeding site. Faiyum Governorate is home to a variety of mosquito species, including Anopheles mosquitoes, which are the primary vectors of malaria in this region, and they breed in stagnant water bodies, such as irrigation canals and rice paddies. Culex mosquitoes are known to transmit diseases, such as West Nile virus and filariasis [9,10,11]. They may be found in sewage, drainage ditches, and other water sources where they can reproduce. Malaria, dengue fever, and the Zika virus are just a few of the illnesses spread by the Aedes mosquito. They procreate in manmade objects like empty cans, flower vases, and tires [12,13,14]. These mosquitoes can spread a variety of diseases, making their presence in Faiyum Governorate a serious public health concern. In terms of malaria, Faiyum Governorate is a significant epicenter because the illness has been present there since antiquity. Polymerase chain reaction (PCR) assays were used to confirm its presence in Egyptian mummies (El-Fayoum) [15]. Anopheles mosquitoes are the most important vectors of malaria transmission in the area. Malaria is a serious public health concern in Egypt, and Faiyum Governorate is one of the regions with the highest incidence of the disease [16,17,18]. Several public health initiatives, such as the use of insecticide-treated bed nets, repellents, and larvicides to target mosquito breeding grounds, are used to lessen the effects of disease spread by mosquitoes. The absence of clear distribution maps of these mosquito species in the study area reduces the effectiveness of the control strategy. Such maps can easily be generated using data science. Many biologists are motivated to solve environmental problems using data science [19]. Biodiversity biological records are the focus of informatics, a subfield of data science. Information on biodiversity may benefit from bioinformatics methods for display, discovery, exploration, and analysis of biological data [20]. It relies on digitally preserved taxonomic, biogeographic, and ecological information gathered in the contemporary era [21]. Biodiversity informatics in the final set usually produces species distribution models (SDMs). SDMs calculate the relationship between species records at sites and species spatial analyses [22, 23]. It makes predictions on where species will be found and how communities and ecosystems will change as a result of climate and other environmental shifts [24]. Spatial biodiversity patterns may be broken down into their ecological and evolutionary components with the use of species distribution models [25]. SDMs have been used in a variety of disciplines, including ecology, parasitology, biogeography, conservation ecology, and evolution, to evaluate species’ spatial distributions in recent years [26,27,28,29,30]. The maximum entropy model (Maxent) [31] is a crucial resource for modeling species distributions since it generates a jackknife, accommodates limited data, and provides reliable results [22, 31, 32]. Maxent is superior to other ecological niche models for forecasting species distribution when just a few sample places are provided [25, 33]. Replication is made easier since cross-validation and testing of the model’s performance are made possible by repeated subsampling [34]. A Geographical Information System (GIS) is a database and set of related software applications that allows users to see and manage geographic data, conduct geographical analysis, and simulate real-world events [35, 36]. The application of this new technique in mosquito ecology will help in their control and evaluation of their status in different landscapes. The present study aimed to apply SDMs tools to monitor mosquito species and their suitable habitats in El Fayoum by surveying the governorate and assessing the status of these vectors in this important area of Egypt.
2 Methods
2.1 Occurrence data and study area
El-Fayoum is a 6,068.7 Square kilometers (km2) agricultural governorate in Egypt located below sea level. (https://en.wikipedia.org/wiki/Faiyum_Governorate). It lies 90 km (km) southwest of Cairo and is connected to the Nile by the Bahr Youssef River, which enters the governorate from the east and branches into other rivers before emptying into Qaroun Lake. The Governorate, which resembles a depression, is situated in the western desert between the latitudes of 29° 02' and 29° 35' N and the longitudes of 30° 23' and 31° 05' E. Sinnuris, Tamiya, El-Fayoum, Atssa, Abshway, and Youssef El-Sedik are the six districts that make up this city. El-Fayoum has a hot, dry climate with infrequent winter rain and abundant sun throughout the year. In El-Fayoum, it is estimated that there are 3845.1 h of sunshine every year. In January, the average temperature is 13.5 Degree Celsius (°C), while in August, it is 30.6 °C. According to climate-data.org (https://en.climate-data.org/africa/egypt/faiyum-Governorate/faiyum-5569/#weather), May had the lowest relative humidity (31.46%) and December had the highest (55.31%). In October and November 2020, mosquito larvae were collected from locations where they might develop successfully throughout the governorate, as shown in Fig. 1. The 42 sites that made up the breeding habitats were canals, sewage tanks, agricultural puddles, stagnant water puddles, and wetlands. According to the World Health Organization (WHO)[37], The dipping technique was used to capture the larvae, and further laboratory identification followed Mattingly and Knight criteria [38], Harbach [39], Harbach [40], Savage and Strickman [41], Harbach [42], and Harbach [43] (Fig. 1).
3 Climatological data
Climate data with a spatial resolution of 30 arc seconds were downloaded from the WorldClim database (http://www.worldclim.org/) (1 km). The present-day weather was calculated using data from 19 different meteorological elements in this investigation (Table 1). We started by utilizing Maxent’s jackknifing function to estimate the minimum set of variables necessary for our purposes. Jackknife parameters with zero impact on species distribution were also discovered in this study. Strongly linked parameters with little biological impact on the species were eliminated using the Universal tool in ArcGIS 10.3 SDM Tools (Remove highly correlated variables) [44]. Therefore, we chose four important factors for each species to consider while establishing a suitable habitat in the study area. ArcGIS V.10.3 was used to clip and resize all bioclimatic layers to fit inside the boundaries of the Faiyum governorate, and the resulting ASCII grid files were imported into Maxent. Extract by mask in Arc Map was used to collect data for each bioclimatic variable [45]. These factors were included in models of species dispersion [46, 47].
4 Habitat suitability modeling
In this study, we used the maximum entropy model (Maxent version 3.3.3 k, available at http://www.cs.princeton.edu/wschapire/maxent/) since it performed better than other modeling techniques when dealing with limited data sets (see reference [48]). To determine the correlations between a species’ habitat appropriateness and bioclimatic parameters, the software assigns a value between 0 and 1 to each bioclimatic variable and creates response curves for each [49]. Maxent will automatically delete any entries that are entered twice in the same cell. To train our models, we used 75% of the occurrence data, whereas 25% were used for testing [50]. By running the model 10 times for each species and averaging the results, tenfold cross-validation was used to increase model performance. The area under the curve (AUC) of the receiver operating characteristics (ROC) was used to evaluate the performance of the models [51, 52]. The AUC may be anywhere from 0 (random discrimination) to 1 (perfect discrimination).
5 Species richness and risk map
The species richness map was obtained by submitting the presence/absence maps that were generated for each of the five mosquito species using ArcGIS 10.3. This process was performed using the Arc toolbox Map algebra. All recorded data of collected mosquito species (either the five selected species or the remaining species that were not included in the modeling study) were implemented in Maxent software using the 19 bioclimatic variables to produce a risk map that represents the most suitable area for all mosquitoes reported in the governorate [53].
6 Results
6.1 Model performance contribution of bioclimatic variables
With AUCs between 0.77 and 0.94, the models for all five mosquito species outperformed chance. These results indicate the perfection of the model for the five species: Anopheles multicolor (Cambouliu, 1902) 0.83; Culex antennatus (Becker, 1903) 0.94; Culex perexiguus (Theobald, 1901) 0.89; Culex pipiens (Linnaeus, 1758) 0.82; and Ochlerotatus caspius (Pallas, 1771) 0.77, where any results above 0.75 consider acceptable for the generated models. Species’ distributions are profoundly impacted by changes in both temperature and precipitation. Insect species such as Culex antennatus, Culex perexiguus, Culex pipiens, and Ochlerotatus caspius were best predicted by factors associated with temperature. Four species benefited from the heavy rains that fell in the wettest month (bio 13) (Anopheles multicolor, Culex antennatus, Culex perexiguus, and Culex pipiens). The effect of each bioclimatic variable used in the generation of the models was tabulated using the jackknife test (Table 2 and Fig. 2). The response curves for the most effective climatological parameters contributing to the distribution of each species are shown in Fig. 3. The curves show how the occurrence of each species is correlated with each variable. Table 2 shows percentages of bioclimatic variables used in modeling mosquito distribution throughout Faiyum Governorate.
By reading Fig. 2a, it showed that bio 9 (Mean Temperature of Driest Quarter) is the best parameter for Culex antennatus. Bio 16 (Wettest Quarter Precipitation) and bio 13 (Wettest Month Precipitation) tied for second and third place in terms of impact, respectively, while bio 7 (Annual Temperature Range) ranked last.
Biotype 9 (Mean Temperature of Driest Quarter), Biotype 12 (Annual Precipitation), and Biotype 13 (Day length) were the most important environmental factors for Culex perexiguus (Fig. 2b) (Precipitation of Wettest Month). The least productive biovariable in this study was bio 2 (Mean Diurnal Range).
In the case of Culex pipiens (Fig. 2c), bio 3 (isothermality) had the greatest impact, followed by bio 5. (Max Temperature of Warmest Month). These two factors explain 81.6% of the variation. Among the most useful metrics, bio 12 was the least useful, while bio 13 (Wet Month Precipitation) ranked third (Annual Precipitation).
Bio 13 (Precipitation of Wettest Month) and bio 16 (Day length of Day) were the two most useful parameters in controlling Anopheles multicolor (Fig. 2d) (Precipitation of Wettest Quarter). In addition, this species ranked third for bio 5 (Highest Temperature in Hottest Month) and fourth for bio 10 (Warmest Quarterly Average Temperature).
Figure 2 e shows that the most influential climatic factor for Ochlerotatus caspius was the mean temperature of the driest quarter (bio 9), followed by annual precipitation (bio 12), the maximum temperature of the hottest month (bio 5), and the minimum temperature of the coldest month (bio 3) (isothermal).
7 Mosquito species distribution modeling
The collected mosquito larvae were morphologically identified in the laboratory according to Mattingly and Knight (38), Harbach (39), Harbach (40), Savage and Strickman (41), and Harbach (42), Harbach (43) into nine mosquito species: Culex pipiens, Culex antennatus, Culex perexiguus, Culex theileri (Theobald, 1903), (Fig. 4), Anopheles multicolor, Anopheles sergentii (Theobald, 1907) (Fig. 5), Ochlerotatus caspius, Culiseta longiareolata (Macquart, 1838), and Uranotaenia unguiculata (Edwards, 1913) (Fig. 6). Five mosquito species were the most frequent: Culex antennatus, Culex perexiguus, Culex pipiens, Anopheles multicolor, and Ochlerotatus caspius. Of these five species, Culex pipiens was the most abundant.
8 Habitat suitability modeling
According to the acquired results of the selected species distribution modeling, their distribution patterns were similar to their real situation throughout the governorate (Fig. 7).
For Culex antennatus, the most current suitable habitats appeared in Atssa (Abo Gandeer, Garado, Menia El-Heet) and El-Fayoum (the center of the city, Manashi El-Khatib, Belal Bin Rabah) districts, while in Tamyia (Kafr Mahfouz, Dar El-Salam, Sersena), Sinnuris (the center of the city, Fedmeen, Tersa, Behmo), and Abshway (Senaro, El-Agmeen) districts habitats were less suitable.
For Culex perexiguus, the highest suitability marked in El-Fayoum, Tamyia (Dar El-Salam, Sersena) and Atssa (Garado) districts, while medium to low suitable habitats showed in Sinnuris (Sanhor, Monshaat Tantawy), Abshway, and Youssef El-Sedik districts.
For Culex pipiens, the Abshway district displayed the highest suitability, followed by Sinnuris (Fedmeen) and Youssef El-Sedik (Kahk, El-Nazlah). Also, the habitat around Wadi El-Rayan protected area, El-Fayoum (Beni-Saleh, Manashi El-Khatib, Belal Bin Rabah) and Atssa (Abo Gandeer, Garado) districts showed high suitability while medium to low suitableness marked in Youssef El-Sedik, Sinnuris (Kafr Fazara, Abheet, El-Zaweia El-Khadra), Tamiya (Kafr Mahfouz, Kasr Rashwan, Fanos), El-Fayoum (El-Nasria, Ellahon, Hawart Elmaktaa), Atssa (Tatoon, Kalmshah) districts.
For Anopheles multicolor, the most suitable habitats marked in Atssa (Menia El-Heet, Abo Gandeer, Garado), Youssef El-Sedik (El-Nazlah), El-Fayoum (Zaweit El-Karadsah, Beni-Saleh), Tamiya (Dar El-Salam, Sersena, El-Rawdah), Sinnuris (Behmo, Garrfs, Motrtars), Abshway (Abo-Gnash, Tobhar, El-Agmeen, Senro), and around Wadi El-Rayan protected area. Additionally, medium suitability was noticeable in El-Fayoum (the center of the city, Monsheiat Abd-Allah) and Atssa (Tatoon) districts. Habitats showed low suitability for Qarun Lake.
For Ochlerotatus caspius, habitat suitability was the highest around Qarun Lake, in Sinnuris, Abshway, Tamiya (Kafr Mahouz), El-Fayoum (Beni-Saleh, Belal Bin Rabah), Atssa (Garado, Abo Gandeer), Youssef El-Sedik (Kahk, El-Shawashna, El-Nazlah), and around Wadi El-Rayan protected area. Medium habitat suitability marked in Tamiya (Fanos, Monshaiet El-Gammal, El-Rawda), El-Fayoum (El-Nasria, Ellahon, Hawart Elmaktaa), south of Atssa (Taton, Kalmshah), and Youssef El-Sedik. Sites with low suitability appeared at the marginal sites around the governorate.
9 Species richness and risk map
Five mosquito species were unequally distributed across the six administrative districts of the governorate (Fig. 8). Culex antennatus was noticeable in Tamiya (Kafr Mahfouz), east of El- Fayoum (El-Nasriah), center of Sinnuris, south of Atssa (Tatoun), north of Youssef El-Sedik (Kahk), and peripheral sites around Qarun lake. Culex antennatus and Culex perexiguus marked in the center of El-Fayoum, Sinnuris (Sanhour), Abshway (Aboksaah), Youssef El-Sedik (El-Shawashna), Atssa (Menya Elhiet). Culex antennatus, Culex perexiguus, and Culex pipiens were abundant in the center of Atssa district, Youssef El-Sedik district (El-Nazlah, El-Hamoly), and around the Wadi El-Rayan protected area. Culex antennatus, Culex perexiguus, Culex pipiens and Anopheles multicolor were noticeable in Tamiya (Dar El-Salam), El-Fayoum (Zaweit El-Karadsah, Beni-Saleh), Sinnuris (Behmo), Abshway (Tobhar, Abo-Gnash), Atssa (Garado). Culex antennatus, Culex perexiguus, Culex pipiens, Anopheles multicolor, and Ochlerotatus caspius distributed in few sites in Sinnuris (Tersa, Ebheet, Nakalefa), El-Fayoum (Manashi El-Khatib, Belal Bin Rabah), and Atssa (Abo Gandeer) districts.
All recorded mosquito species were used to produce the risk map throughout the governorate (Fig. 9). El-Fayoum district north and west (Zawyat El-Karadsah, Beni-Saleh, and Monshat Abd-Allah), Kafr Mahfouz’s Tamiya, Abshway south (particularly Tobhar), Atssa north, and the south and center of Sinnuris are all sites with extremely high danger markings (Kafr Fazara, Tersa, and Elzaweia El khadra). Additionally, high-risk places located in Atssa (Abu-Seer and El-Sawafna), Youssef El-Sedik especially in El-Nazlah and Sinnuris district (Behmo). Other places were vulnerable to medium mosquito risk, such as Bandar El-Fayoum, the center of Abshway, and Atssa (Definno) districts.
10 Discussion
The governorate of El-Fayoum is geographically distinct. It is distinguished by its gentle inclination and proximity to sea level. In addition, most people are employed in agriculture and related industries [54]. The mild environment and high relative humidity [55] that favor mosquito breeding in this region causes the season of disease transmission to be extended to eight months each year, from roughly the end of March to the end of November [56].
Culex pipiens, Culex antennatus, Culex theileri, Culex perexiguus, Anopheles multicolor, Anopheles sergentii, Ochlerotatus caspius, Culiseta longiareolata, and Uranotaenia unguiculata were among the mosquito species discovered in this study. The five genera to which these species belong are Aedes, Anopheles, Culex, Culiseta, and Uranotaenia. These are the most prevalent genera in Egypt, according to numerous research [17, 40, 56, 57]. Most of these diseases are also important disease vectors for both humans and animals. Consider the genus Culex, which includes species such as Culex pipiens, which is thought to be the vector of numerous arboviruses, including the West Nile virus. Louis encephalitis, rift valley disease, and Sindbis viruses pose hazards to human life [58]. Wuchereria bancrofti, a filarial nematode that causes lymphatic filariasis, is transmitted by Cx. pipiens [59, 60]. According to Salamah et al. [61], the primary vector of lymphatic filariasis in Egypt is female Cx. pipiens. It can act as a hepatitis C virus (HCV) vector because an experimental study by El‐Kholy et al. [62] demonstrated that Cx. pipiens can contract the disease, the virus reproduces inside the insect body, and the infected insect bites humans.
The current predicted habitat suitability modeling for Culex pipiens was high in many regions, especially in the Abshway and Sinnuris districts, which is consistent with the results of larval collection, as these districts recorded a high abundance of Cx. pipiens larvae. This species was the most frequent type and was found in different breeding habitats, especially in agricultural canals [61, 63]. Moreover, Cx. pipiens tolerated high degrees of pollution, as it was found with high densities in stagnant water habitats consistent with the findings of Ibrahim et al. [64] (particularly those related to human activities). Generally, breeding sites with the highest larval density were closest to houses, which agrees with Madewell et al. [65], who stated that there was an inverse relationship between the distance to the nearest house and larval abundance. Adult female mosquitoes require a blood meal for oviposition. It may be fed on mammalian blood meal especially human [66] or mixed blood meal [67]. In this context, the female preferred to stay close to its host for blood feeding and then chose the nearest breeding site for oviposition; therefore, breeding sites that were close to humans and animals were characterized by high larval density.
Ochlerotatus caspius was predicted to be high in many regions, such as Qarun Lake, Tamiya, El-Fayoum (Beni-Saleh), and areas with high seepage and sewage water [64, 68]. On the other hand, Cx. antennatus showed high suitability in Atssa, in agreement with the collected samples, as this district had the highest larval density of this species. Oringanje et al. [69] found that polluted breeding habitats encouraged the spreading of more Culicine species but Anopheline species did not prefer this type of habitats. This agrees with our results, as the majority of Culicine breeding sites were polluted. Anopheles sergentii larvae were found at two sites characterized by shallow, clean, freshwater, but some Anopheles multicolor breeding sites were polluted, similar to the observations of El-Hefniet al. [56].
Mosquito species were unequally distributed throughout the governorate. Sites displaying mosquito richness are vulnerable to disease transmission El-Hefni et al. [57]. The risk map shows that the desert part of the governorate displays no risk. On the other hand, highly populated agricultural areas display a very high risk of diversity and variety in breeding sites.
For bioclimatic variables, precipitation-related variables and temperature-related variables had the highest effects on all species distributions. High summer temperatures likely have a role in the distribution of the three species evaluated (Culex antennatus, Culex perexiguus, and Ochlerotatus caspius), as shown by the driest quarter mean temperature (bio 9). Temperature plays an important role in mosquito development and density because it is a cooled-blooded animal. Development is accelerated by increasing temperature until a definite limit is reached, mortality begins to increase, and the appropriate temperature for development is approximately 25–27 °C [70]. In addition, Loetti et al. [71] found that temperature between 7 and 25 °C had an inverse relationship with the time needed for larval development. According to research conducted by Mamai et al. [72], when temperatures were elevated to 27 °C, the number of larvae rose but the size of the emerging adult dropped. This, in turn, demonstrates how climate change has affected the distribution of different species [24].
Mosquito-borne diseases are a significant public health concern throughout Faiyum Governorate and Egypt as a whole. It is imperative that measures be taken to reduce mosquito populations and stop disease spreading for the sake of public health and safety.
11 Conclusion
This study is a baby step toward a fuller understanding of the ecology of significant mosquito species in Egypt. Further surveillance is needed to better evaluate the entire mosquito fauna in the region. In addition, the investigation of GIS and its modeling tools to predict the pattern of such distribution and abundance under different climate change scenarios is required.
Availability of data and materials
The datasets (tables and figures) generated and analyzed during the current study are available from the corresponding author upon reasonable request. All data generated or analyzed during this study are included in the present article, and its supplementary information will be available from the corresponding author on reasonable request.
Abbreviations
- ArcGIS:
-
Software that build interactive web maps
- AUC:
-
Area under the curve
- °C:
-
Celsius, the most common temperature scale in the world
- GIS:
-
Geographic Information System
- HCV:
-
Hepatitis C Virus
- JEV:
-
Japanese encephalitis virus
- Km:
-
Kilometer
- PCR:
-
Polymerase chain reaction
- ROC:
-
Receiver operating characteristics
- SDMs:
-
Species distribution models
- WHO:
-
World Health Organization
References
World Health Organization (WHO) (2020) Vector-borne diseases. WHO, Geneva
Chala B, Hamde F (2021) Emerging and re-emerging vector-borne infectious diseases and the challenges for control: a review. Front Public Health 9:715759
Burkett-Cadena ND, Vittor AY (2018) Deforestation and vector-borne disease: forest conversion favors important mosquito vectors of human pathogens. Basic Appl Ecol 26:101–110
Govindarajan M, Rajeswary M, Muthukumaran U, Hoti SL, Khater HF, Benelli G (2016) Single-step biosynthesis and characterization of silver nanoparticles using Zornia diphylla leaves: a potent eco-friendly tool against malaria and arbovirus vectors. J Photochem Photobiol B 161:482–489. https://doi.org/10.1016/j.jphotobiol.2016.06.016
S. Nilsson, “Seroprevalence of Japanese encephalitis virus in pigs and dogs in the Mekong Delta,” (2013) http://stud.epsilon.slu.se/5793/.
Boukraa S, de La Grandiere MA, Bawin T, Raharimalala FN, Zimmer JY, Haubruge E et al (2016) Diversity and ecology survey of mosquitoes potential vectors in Belgian equestrian farms: a threat prevention of mosquito-borne equine arboviruses. Prev Vet Med 124:58–68. https://doi.org/10.1016/j.prevetmed.2015.12.013
Ward W, Hassan MI, Shehata AZ (2022) Spatial distribution and relative abundance of some mosquito species transmitted diseases in Beheira Governorate Egypt. Egypt J Hospi Med 89(2):7775–7785
Fang Y, Khater EI, Xue JB, Ghallab EH, Li YY, Jiang TG, Li SZ (2022) Epidemiology of mosquito-borne viruses in Egypt: a systematic review. Viruses 14(7):1577
Ragab SH, Khaled MA, Taha RH, El-Tabakh M (2023) Spatial distribution of appropriate aquatic mosquitos’ larval sites occurrence using integration of field data and GIS techniques. Egypt J Aquatic Biol Fisheries 27(4):355–371
Srividya, A., Subramanian, S., Jambulingam, P., Vijayakumar, B., & Dinesh Raja, J. (2019). Mapping and monitoring for a lymphatic filariasis elimination program: a systematic review. Res Rep Trop Med, pp 43–90.
Shehata A, Hammad K, Abdel-Samad M (2019) Toxicological and repellent effects of lantana camara (verbenaceae) and eucalyptus citriodora (Myrtaceae) extracts against rift valley fever vector, culex antennatus (Becker)(Diptera: Culicidae). Nuclear Technol Appl Sci 7:151–160. https://doi.org/10.21608/jntas.2019.54581
Metz HC, Miller AK, You J, Akorli J, Avila FW, Buckner EA, McBride CS (2023) Evolution of a mosquito’s hatching behavior to match its human-provided habitat. Am Nat 201(2):200–214
Heath CJ, Grossi-Soyster EN, Ndenga BA, Mutuku FM, Sahoo MK, Ngugi HN, LaBeaud AD (2020) Evidence of transovarial transmission of Chikungunya and Dengue viruses in field-caught mosquitoes in Kenya. PLoS Neglected Trop Dis 14(6):e0008362
Leslie TE, Carson M, Coeverden EV, De Klein K, Braks M, Krumeich A (2017) An analysis of community perceptions of mosquito-borne disease control and prevention in Sint Eustatius. Caribbean Netherlands Glob Health Action 10:1350394. https://doi.org/10.1080/16549716.2017.1350394
Lalremruata A, Ball M, Bianucci R, Welte B, Nerlich AG, Kun JF et al (2013) Molecular identification of falciparum malaria and human tuberculosis co-infections in mummies from the Fayum depression (Lower Egypt). PLoS ONE 8:e60307. https://doi.org/10.1371/journal.pone.0060307
Bassiouny HK (2001) Bioenvironmental and meteorological factors related to the persistence of malaria in Fayoum Governorate: a retrospective study. East Mediterr Health J 7:895–906
Dahesh SM, Mostafa HI (2015) Reevaluation of malaria parasites in El-Fayoum governorate, Egypt using rapid diagnostic tests (RDTS). J Egypt Soc Parasitol 45:617–628. https://doi.org/10.12816/0017929
Dahesh SM, Bassiouny HK, El-Masry SA (2009) Malariometric parasitological survey in El-Fayoum Governorate. Egypt J Egypt Soc Parasitol 39:213–225
Proença D, Borbinha J (2016) Maturity models for information systems-a state of the art. Proced Comp Sci 100:1042–1049. https://doi.org/10.1016/j.procs.2016.09.279
Soberón J, Peterson AT (2004) Biodiversity informatics: managing and applying primary biodiversity data. Philos Trans R Soc Lond B Biol Sci 359:689–698. https://doi.org/10.1098/rstb.2003.1439
Canhos VP, De Souza S, De Giovanni R, Canhos DAL (2004) Global Biodiversity Informatics: setting the scene for a “new world” of ecological forecasting. Biodiver Inform 1:1–13. https://doi.org/10.17161/bi.v1i0.3
Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
Miller J (2010) Species distribution modeling. Geogr Compass 4:490–509. https://doi.org/10.1111/j.1749-8198.2010.00351.x
Yi Y-j, Cheng X, Yang Z-F, Zhang S-H (2016) Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan. China Ecol Eng 92:260–269. https://doi.org/10.1016/j.ecoleng.2016.04.010
Elith J, Graham HC, Anderson PR, Dudík M, Ferrier S, Guisan A et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
Ganeshaiah K, Barve N, Chandrachekara K, Swamy M, Uma Shaanker R (2003) Predicting the potrntial geographical distribution of the sugarcane wooly aphid using GARP and DIVA-GIS. Curr Sci 85:1526–1528
Adler PB, HilleRisLambers J, Levine JM (2007) A niche for neutrality. Ecol Lett 10:95–104. https://doi.org/10.1111/j.1461-0248.2006.00996.x
Elith J, Graham CH (2009) Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography 32:66–77. https://doi.org/10.1111/j.1600-0527.2008.05505.x
Zimmermann NE, Edwards TC Jr, Graham CH, Pearman PB, Svenning JC (2010) New trends in species distribution modelling. Ecography 33:985–989. https://doi.org/10.1111/j.1600-0587.2010.06953.x
Hosni EM, Al-Khalaf AA, Nasser MG, Abou-Shaara HF, Radwan MH (2022) Modeling the potential global distribution of honeybee pest. Galleria Mellonella Under Changing Climate Insects 13:484. https://doi.org/10.3390/insects13050484
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Peterson LL, Davie BS (2007) Computer networks: a systems approach, 4th edn. Elsevier, San Francisco
Hernandez P, Franke I, Herzog S, Pacheco V, Paniagua L, Quintana H et al (2008) Predicting species distributions in poorly-studied landscapes. Biodivers Conserv 17:1353–1366. https://doi.org/10.1007/s10531-007-9314-z
Adhikari D, Barik S, Upadhaya K (2012) Habitat distribution modelling for reintroduction of Ilex khasiana Purk., a critically endangered tree species of northeastern India. Ecol Eng 40:37–43. https://doi.org/10.1016/j.ecoleng.2011.12.004
Clarke KC (1986) Advances in geographic information systems. Comp Environ Urban Syst 10:175–184. https://doi.org/10.1016/0198-9715(86)90006-2
Heywood DI, Cornelius SC, Carver SJ (2011) An Introduction to Geographical Information Systems, 4th edn. Pearson Prentice Hall, London
World Health Organization (WHO) (1975) Manual on practical entomology in malaria. WHO, Geneva
Mattingly PF, Knight KL (1956) The mosquito of Arabia I. Bull Br Museum 4:91–141
Harbach RE (1985) Pictorial keys to the genera of mosquitoes, subgenera of Culex and the species of Culex (Culex) occurring in southwestern Asia and Egypt, with a note on the subgeneric placement of Culex deserticola (Diptera: Culicidae). Mosquito Syst 17:83–107
Harbach RE (1988) The mosquitoes of the subgenus culex in Southwestern Asia and Egypt (Diptera: Culicidae). Cont Am Entomol Institute 24:247
avage HM, Strickman D, (2004) The genus and subgenus categories within Culicidae and placement of Ochlerotatus as a subgenus of Aedes. J Am Mosquito Cont Assoc 20:208–214
Harbach RE (2011) Classification within the cosmopolitan genus Culex (Diptera: Culicidae): The foundation for molecular systematics and phylogenetic research. Acta Ttropica 120:1–14. https://doi.org/10.1016/j.actatropica.2011.06.005
Harbach RE (2013) Mosquito taxonomic inventory. Available from: https://mosquito-taxonomic-inventory.myspecies.info/.
Alkishe AA, Peterson AT, Samy AM (2017) Climate change influences on the potential geographic distribution of the disease vector tick Ixodes ricinus. PLoS ONE 12:e0189092. https://doi.org/10.1371/journal.pone.0189092
Al Ahmed AM, Naeem M, Kheir SM, Sallam MF (2015) Ecological distribution modeling of two malaria mosquito vectors using geographical information system in Al-Baha Province, Kingdom of Saudi Arabia. Pakistan J Zool 47:1797–1806
Kumar S, Spaulding SA, Stohlgren TJ, Hermann KA, Schmidt TS, Bahls LL (2009) Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US. Front Ecol Environ 7:415–420. https://doi.org/10.1890/080054
Sanchez AC, Osborne PE, Haq N (2011) Climate change and the African baobab (Adansonia digitata L.): the need for better conservation strategies. Afr J Ecol 49:234–245. https://doi.org/10.1111/j.1365-2028.2011.01257.x
Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeograp 34:102–117. https://doi.org/10.1111/j.1365-2699.2006.01594.x
Khanum R, Mumtaz A, Kumar S (2013) Predicting impacts of climate change on medicinal asclepiads of Pakistan using Maxent modeling. Acta Oecologica 49:23–31
Phillips SJ (2008) Transferability, sample selection bias and background data in presence-only modelling: a response to Peterson et al. (2007). Ecography 31:272–278
Swets JA (1988) Measuring the accuracy of diagnostic systems. Science (New York, NY) 240:1285–1293. https://doi.org/10.1126/science.3287615
Peterson AT, Papeş M, Soberón J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Model 213:63–72. https://doi.org/10.1016/j.ecolmodel.2007.11.008
Nasser M, El-Hawagry M, Okely M (2019) Environmental niche modeling for some species of the genus Anthrax Scopoli (Diptera: Bombyliidae) in Egypt, with special notes on St. Catherine protected area as a suitable habitat. J Insect Conser 23:831–841. https://doi.org/10.1007/s10841-019-00174-6
Bassiouny HK (1996) Determination of epidemiological factors causing the persistence of malaria transmission in Fayoum goveronorate, final report Alexandria. WHO Reginal Office for the Eastern Mediteranean, Geneva.
Faiyum climate (Egypt) (2023) Data and graphs for weather, climate in Faiyum. Available from: https://en.climate-data.org/africa/egypt/faiyum-governorate/faiyum-5569/#weather, Egypt.
El-Hefni A, El-Zeiny AM, Sowilem M, Elshaier M, Atwa W (2020) Hyperspectral based assessment of mosquito breeding water in Suez Canal zone, Egypt. In: Elbeih SF, Negm AM, Kostianoy A (eds) Environmental Remote Sensing in Egypt. Springer Nature, Switzerland, pp 183–207
El-Hefni AM , El-Zeiny AM, Effat HA. (2020). Environmental sensitivity to mosquito transmitted diseases in El-Fayoum using spatial analyses. Paper presented at the 11th International Conference on Environmental Science and Development (ICESD).
Michel F, Fischer D, Eiden M, Fast C, Reuschel M, Müller K, Ziegler U (2018) West Nile virus and Usutu virus monitoring of wild birds in Germany. Int J Environ Res Public Health 15(1):171
Shahat MA, El-Sheikh TM, Hammad KM, Hasaballah AI, Shehata AZ (2020) Activity of Otostegia fruticosa (Lamiaceae) leaves extracts against lymphatic filariasis vector, Culex pipiens L. (Diptera: Culicidae). Egypt Acad J Biol Sci A, Entomol 13(4):175–186
Ramzy RMR, Kamal HA, Hassan MA, Haggag AA (2019) Elimination of lymphatic filariasis as a public health problem from the Arab Republic of Egypt. Acta Trop 199:105121. https://doi.org/10.1016/j.actatropica.2019.105121
Salamah MMI, Moustafa MA, Thabet HS, Tawfik RA, Hamdy DM (2016) A comparative ecological study between two Egyptian villages previously endemic for bancroftian filariasis. Ain Shams Med J 67:183–190
El-Kholy S, El-Husseiny I, Meshrif W, El-Azm AA, Salem M (2018) Does the mosquito Culex pipiens represent a potential vector of hepatitis C virus? Med Veter Entomol 32:155–161. https://doi.org/10.1111/mve.12288
Kenawy MA, Abdel-Hamid YM, Beier JC (2018) Rift Valley Fever in Egypt and other African countries: Historical review, recent outbreaks and possibility of disease occurrence in Egypt. Acta Trop 181:40–49
Ibrahim AEA, El-Monairy OM, El-Sayed YA, Baz MM (2011) Mosquito breeding sources in Qalyubiya Governorate. Egypt Egypt Acad J Biol Sci 3:25–39. https://doi.org/10.21608/eajbse.2011.16454
Madewell ZJ, Sosa S, Brouwer KC, Juárez JG, Romero C, Lenhart A et al (2019) Associations between household environmental factors and immature mosquito abundance in Quetzaltenango, Guatemala. BMC Public Health 19:1–11. https://doi.org/10.1186/s12889-019-8102-5
Asigau S, Salah S, Parker PG (2019) Assessing the blood meal hosts of Culex quinquefasciatus and Aedes taeniorhynchus in Isla Santa Cruz, Galápagos. Parasit Vectors 12:1–10. https://doi.org/10.1186/s13071-019-3835-7
Adugna T, Yewhelew D, Getu E (2021) Bloodmeal sources and feeding behavior of anopheline mosquitoes in Bure district, northwestern Ethiopia. Parasit Vectors 14:1–12. https://doi.org/10.1186/s13071-021-04669-7
Nagy A, El-Zeiny A, Elshaier M, Sowilem M, Atwa W (2021) Water quality assessment of mosquito breeding water localities in the Nile Valley of Giza Governorate. J Environ Sci Mansoura Univ 50:1–10. https://doi.org/10.21608/joese.2021.52428.1002
Nwana AO, Okoh HI, Oyeniyi AT, Adeogun AO (2021) Larval Habitat Characterization and Molecular Identification of Anopheles gambiae Complex in three Local Government Areas (LGAs) of Ekiti State, Nigeria. Pan African J Life Sci 5(3):312–332
World Health Organization (WHO) (1975) Manual on practical entomology in malaria. Part (I). Vector bionomics and organization of anti-malaria activities. WHO, Geneva.
Loetti V, Schweigmann N, Burroni N (2011) Temperature effects on the immature development time of Culex eduardoi Casal, García (Diptera: Culicidae). Neotr Entomol 40:138–142
Mamai W, Lobb LN, Bimbilé Somda NS, Maiga H, Yamada H, Lees RS et al (2018) Optimization of mass-rearing methods for Anopheles arabiensis larval stages: effects of rearing water temperature and larval density on mosquito life-history traits. J Econ Entomol 111:2383–2390. https://doi.org/10.1093/jee/toy213
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Adel. Abdel Hakeem Abo El-Ela and Azza Mostafa supervised and followed up the practical work and revised the manuscript, Eman Ali Ahmed carried out the practical work and wrote the manuscript, Mohamed Gamal ElDin Nasser and Sara Ahmed Al-Ashaal analyzed results, modeling analysis, and manuscript writing, and Abdelwahab Khalil designed the experiments, review the manuscript, and final editing.
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El-Ela, A.A.H.A., Mostafa, A., Ahmed, E.A. et al. Habitat suitability modeling of mosquito species in Faiyum Governorate, Egypt, using GIS. Beni-Suef Univ J Basic Appl Sci 13, 35 (2024). https://doi.org/10.1186/s43088-024-00488-8
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DOI: https://doi.org/10.1186/s43088-024-00488-8