Environmental Suitability of Anopheles Mosquito Species in the Maekel Region, Eritrea: Species Distribution Modeling Using Maximum Entropy

Environmental Suitability of Anopheles Mosquito Species in the Maekel Region, Eritrea: Species Distribution Modeling Using Maximum Entropy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Environmental Suitability of Anopheles Mosquito Species in the Maekel Region, Eritrea: Species Distribution Modeling Using Maximum Entropy Filmon Ghebreyesus Mebrahtu, Kibreab Tesfamichael Haile, Adam Mengesteab Teweldebirhan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6647593/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Introduction: Malaria, a significant global health challenge, is closely associated with Anopheles mosquitoes. The dependence of mosquito habitats on environmental conditions, coupled with the established link between malaria prevalence and habitat niches, highlights the dynamic interplay between mosquito ecology and environmental factors in malaria transmission. This study aims to determine the current and future environmental suitability of Anopheles mosquitoes in the Maekel region using species distribution modeling (SDM). Methodology: Species occurrence data, along with environmental and bioclimatic variables, were used to model habitat suitability using Maxent. Highly correlated variables, defined as those with a Pearson correlation coefficient exceeding 0.8, were excluded from the model. Future climate projections from the Coupled Model Intercomparison Project (CMIP-6) were used to project species distributions under two shared socioeconomic pathway scenarios: SSP370 and SSP585. Key environmental variables influencing species survival were identified through the jackknife test of variable importance. Model performance was evaluated via area under the curve (AUC) statistics. Results: A total of 94 samples were collected from 24 villages. The most prevalent species, Anopheles cinereus , was identified in 20 villages, followed by Anopheles demeilloni and Anopheles squamosus . Anopheles gambiae , the primary malaria vector in Eritrea and Africa, was identified at 5 sites in 4 villages. Maxent modeling indicated that large areas of the Maekel region are highly suitable for Anopheles species, with projected increases in suitability by 2030 and 2050 under both climate change scenarios. Conclusion: Anopheles mosquitoes are well established across significant portions of the Maekel region, and their distribution is projected to expand in the future. Comprehensive nationwide studies are necessary to map species distributions and bionomics, enabling the refinement of malaria control strategies in response to the rapid expansion of Anopheles mosquitoes driven by climate change. Figures Figure 1 Figure 2 Introduction Malaria remains a major global health burden and is transmitted by the bite of female Anopheles mosquitoes. In 2023, malaria resulted in an estimated 263 million cases and 597,000 deaths, with the majority occurring in sub-Saharan Africa and among children under five years of age. ( 1 ) Efforts to control and eradicate malaria have focused primarily on the control of Anopheles mosquito vectors. Control measures, including larviciding, insecticide residual spraying, and the use of insecticide-treated bed nets, have been effective in reducing malaria transmission. Globally, 2.2 billion cases and 12.7 million deaths were averted between 2000 and 2023. ( 1 ) Emerging trends, such as rapid vector mutation, evolution of Anopheles mosquito bionomics, and climate change, threaten to reverse progress in malaria control. An invasive vector, Anopheles stephensi , originating from Southeast Asia, has been spreading across Africa since its landfall in Djibouti in 2012. This has exposed 126 million people in urban areas to malaria risk. ( 2 ) Climate change is also facilitating the spread of malaria into previously unaffected areas, as evidenced by recent outbreaks. Mosquitoes are defeating aegis of altitude, ascending approximately 6.5 meters annually. ( 3 , 4 ) This has significant implications, as many urban settlements were historically established at relatively high altitudes to avoid mosquitoes. Consequently, millions of individuals with limited immunity to malaria in these areas may become vulnerable to malaria infection. Species distribution models (SDMs) and disease risk maps have become vital tools for predicting such scenarios. SDMs visualize areas suitable for mosquito reproduction and malaria transmission. Highly accurate SDMs can assist malaria control programs in implementing targeted interventions, thereby enhancing preparedness for future malaria trends. Among the available SDM techniques, maximum entropy (Maxent) has gained prominence because of its simplicity, robustness, and accuracy. ( 5 ) Maxent can produce reliable projections using only species presence data, even with limited sample sizes. In Eritrea, malaria transmission exhibits seasonal patterns, proceeding after the rainy season. Prevention efforts have achieved substantial reductions in malaria cases and deaths, with an 80% decrease since the 1998 peak outbreak. However, Eritrea's vulnerability to climate change poses a risk to these gains. Currently, there are limited data on the distribution of Anopheles mosquitoes, the factors influencing their distribution, and future projections of their spread in Eritrea. Analyzing areas of potential habitat suitability for Anopheles mosquitoes is crucial for guiding malaria prevention and control efforts. Therefore, this study aims to assess the environmental suitability, influential climatic and environmental variables, and future projected distribution of Anopheles mosquitoes using Shared Socioeconomic Pathway (SSP) scenarios. Methods Study Area The study was conducted in the Maekel region, one of the six administrative regions of Eritrea. The Maekel region is situated in the Eritrean highlands, with an average altitude of 2219m (range: 1307–2601 m) above sea level. The region is characterized by a humid‒arid subtropical climate with localized microclimates. The Maekel Region is the most densely populated region in Eritrea, with an average population density of 459 people per square kilometer (range: 46–5912). ( 6 ) Malaria is largely absent in the region, although a few indigenous cases are reported annually (image 1). Species occurrence and climatic data Larval collection was conducted by entomological experts at potential breeding sites from September 10th to 30th, 2023. Anopheles species larvae were collected from breeding habitats WHO standard 350 ml dipper. Larvae were transported to the Mendefera Entomological Laboratory for rearing and identification. The geographic positioning system (GPS) coordinates of each breeding site were recorded via Android phones with an open data kit (ODK) form. All the GPS coordinates were collected in the WGS1983 projection, with a minimum accuracy of 4 meters. Current bioclimatic data (1970–2010) were downloaded at a 30 arc-second (1 km²) resolution from WorldClim (V2.1). ( 7 ) Future bioclimatic data for two projection periods (2021–2040 and 2041–2060) and two shared socioeconomic pathway scenarios (SSP3-3.7 and SSP5-8.5) from the ACCESS-CM2 projection group ( 8 ) were obtained. ( 7 ) Elevation data were also downloaded from the same source. ( 7 ) Four additional variables, namely, aspect, slope, hill shade, and topographic wetness index, were derived from the elevation data. Pearson correlation tests were performed via the SDM toolbox v2.0 ( 9 ) in ArcMap v10.8 to assess correlations between variables. Variables with a correlation coefficient exceeding 0.7 (r > 0.7) were excluded from the modeling. Of the 24 initial variables (19 bioclimatic variables and 5 topographic variables), 14 were retained for the final model (Table 1 ). Table 1 Variables included in modeling Abbreviation Descriptions Bio1 Annual mean Temperature Bio2 Mean diurnal range (mean of monthly temperature max-min) Bio3 Isothermally (bio2/bio7) *100 Bio7 Temperature annual range (Bio5-Bio6) Bio8 Mean temperature of wettest Quarter Bio12 Annual precipitation Bio13 precipitation of the wettest month Bio14 precipitation of driest month Bio18 precipitation of warmest quarter Bio19 precipitation of coldest quarter Aspect Direction of the maximum gradient and relates to the degree of solar exposure. The aspect determines the effect of solar heating, air temperature, and moisture. Aspect ranges from 0 0 to 360 0 (0 0 &360 0 north, 90 0 east, 180 0 south and 270 0 west) Slope The angle of ascent and descent for each pixel on a hillside. The lower the slope value the flatter the terrain; the higher the slope value, the stepper the terrain. Slope strongly influences overland and subsurface flow velocity, drainage, and water accumulation. Hill shade Local areas with shadows thrown upon raised landscapes. The higher the shading values, the higher and warmer the surface (more exposure to sun rays); and the lower the hill shading values the darker and cooler the surface. Topographic wetness index TWI is an estimate of the predicted water accumulation and is calculated as the natural logarithm of the ratio between the total catchment area and slope. High TWI values indicate converging terrains and valleys, while low values are typical steep and diverging terrains. TWI is a proxy for the depth of groundwater, soil pH, vegetation species richness, and soil organic matter. Data Modeling All climatic data were clipped to the Maekel region boundaries and converted to ASCII raster format with the same grain size and WGS1983 projection. To enhance model performance, 10,000 randomly distributed background points were generated. To remove overrepresentation of the ecological area, rarefying of the presence data was performed at a 5 km distance. All Maxent feature types (linear, quadratic, product, threshold, hinge, and categorical) were evaluated during modeling. Regularization parameters of 1.5, 2, and 2.5 were used to minimize model overfitting. Model training and validation were performed via a cross-validation technique with 10 replicates. Jackknife tests and response curves were used to assess variable importance. Model performance was evaluated via the area under the curve (AUC) of the receiver operating characteristic (ROC). An AUC value of 1 indicates perfect discrimination. The final species distribution maps were categorized into five suitability classes: very high (> 0.6), high (0.4–0.6), moderate (0.2–0.4), low (0.1–0.2), and very low (< 0.1). ( 10 ) Data preparation was conducted via ESRI ArcMap version 10.8 ( 11 ), and modeling was performed using Maxent software version 3.4.4. ( 12 ) Results A total of 94 samples were collected from 24 villages and reared for identification. Seven Anopheles species were identified. Anopheles cinereus was the most common species, found in 20 villages, followed by Anopheles demeilloni (16 villages) and Anopheles squamosus (12 villages). Anopheles gambiae , the primary vector in Eritrea, was identified at 5 sites (Table 2 ). Table 2 Anopheles species Identified Anopheles Species Villages identified Sites Anopheles cinerus 20 28 Anopheles demeilloni 16 20 Anopheles squamosus 12 20 Anopheles dthali 7 11 Anopheles gambiae 4 5 Anopheles preptorensis 2 3 Anopheles salbi 1 2 Species distribution model Species distribution models were constructed for Anopheles cinereus , Anopheles demeilloni , Anopheles squamosus , Anopheles D’thali , and Anopheles gambiae . Models for Anopheles pretorensis and Anopheles salbi were not developed due to insufficient sample sizes after rarefying the presence points. Anopheles gambiae were identified at five sites. Under current climatic conditions, large portions of the study area exhibit moderate and high suitability for Anopheles gambiae , with small areas in the southeast showing very high suitability. Under the SSP3-3.7 projection, areas of moderate suitability transition to high and very high suitability by 2030, particularly in the northern parts. This trend is further pronounced by 2050. Similar patterns of increased suitability are observed under the SSP5-8.5 scenario. The model demonstrated good performance, with training and test AUC values of 0.76. Jackknife tests revealed that hill shade and the topographic wetness index were the most influential variables for Anopheles gambiae habitat suitability. Anopheles cinereus was the most widespread species and was identified at 28 sites. Under current climatic conditions, areas in the north, west, center, and southeast regions exhibit very high suitability, whereas the eastern region has very low potential. Under SSP3-3.7, areas of high suitability for Anopheles cinereus increase and expand westward by 2030, with further expansion and consolidation observed by 2050. A similar, but more pronounced, pattern of expansion is observed under SSP5-8.5. The model demonstrated good performance, with training and test AUC values of 0.843 and 0.695, respectively. The key influential variables included the topographic wetness index, precipitation in the driest month (Bio14), aspect, hill shade, mean diurnal temperature range (Bio2), and precipitation in the warmest month (Bio18). Anopheles demeilloni was the second most common Anopheles mosquito and was identified at 20 sites across 16 villages. Large areas of the region exhibit very high and high suitability for Anopheles demeilloni . Under the SSP3-3.7 scenario, very high suitability areas are limited primarily to the east and center of the region by 2030. By 2050, very high suitability areas in the east revert to high suitability areas, and high suitability areas are concentrated in the southeast. Under SSP5-8.5, very high suitability areas are projected in the east, southeast, and southern fringes by 2030. By 2050, these areas show slight expansion. The model demonstrated good predictive performance, with an AUC of 0.77. The key influential variables included hill shade, aspect, mean diurnal temperature range (Bio2), precipitation in the driest month (Bio14), topographic wetness index, and precipitation in the warmest month (Bio18). Anopheles squamosus was identified at 20 sites across 16 villages. Current climate projections show very high suitability in the northwest and in patches in the central and south of the region. Under SSP3-3.7, high suitability areas transition to moderate suitability areas by 2030, while very high suitability areas in the center shrink, and those in the south parts will expand slightly (Fig. 1). The model demonstrated good to excellent predictive performance, with training and test AUC values of 0.80 and 0.95, respectively. The key influential variables included the topographic wetness index, precipitation in the driest month (Bio14), aspect, hill shade, slope, mean diurnal temperature range (Bio2), and precipitation in the warmest month (Bio18). Anopheles d’thali were identified at 11 sites across 7 villages. Currently, areas in the central, southeastern, and northwestern parts of the regions are highly suitable. A large diagonal region between the southeast and northwest regions shows moderate suitability. Similar patterns are projected under future climate conditions. The high-suitability area in the center shrinks by 2050 under SSP3-3.7 and disappears under SSP5-8.5. The other high-suitability areas remain stable under both scenarios and projection periods. The model demonstrated excellent performance, with training and test AUC values of 0.88 and 0.95, respectively. The key influential variables included the topographic wetness index, precipitation in the driest month (Bio14), aspect, hill shade, slope, mean diurnal temperature range (Bio2), and precipitation in the warmest month (Bio18). Discussion This study aimed to analyze the potentially suitable areas and future expansion of Anopheles mosquitoes in the Maekel Region of Eritrea via Maxent. The results indicate that large areas of the Maekel region are suitable for Anopheles mosquitoes, specifically Anopheles gambiae, Anopheles cinereus, Anopheles squamosus , and Anopheles demeilloni . Further increases in suitability are projected under both the SSP3-3.7 and SSP5-8.5 scenarios. Anopheles gambiae is associated with small areas of very high suitability, which are projected to expand in the future under both scenarios. Anopheles gambiae is known for its high adaptability to topographic and ecological factors, making it the most efficient malaria vector in Africa. ( 13 , 14 ) In Eritrea, Anopheles gambiae is the primary malaria vector and is predominantly found in the malarious areas of the Gash Barka and Debub Regions. ( 15 ) Similar results have been reported by Olabimi et al., who concluded that Anopheles gambiae will continue to have large areas of high suitability. ( 16 ) The study area features large areas of very high and high suitability for Anopheles cinereus and Anopheles d’thali . A study conducted in Eritrea in 2001–2002 identified seven Anopheles species, three of which are potential malaria vectors. ( 15 ) The same study also revealed one Anopheles gambiae infected with Plasmodium falciparum at an altitude above 2000 meters. ( 15 ) The results of the present study indicate a broader expansion of Anopheles mosquitoes in the Maekel region, supporting the adaptive nature of these mosquitoes to diverse climatic and environmental conditions. Studies in Mount Kenya have identified mosquitoes in high-altitude areas previously free from malaria. ( 3 ) The observed 2°C increase in average temperature in the Kenyan highlands has been attributed to climate change. Similarly, the average temperature in the Eritrean highlands has increased by 1.72°C ( 17 ), suggesting that climate change has been a primary driver of Anopheles mosquito expansion in the Maekel region since the 2001–2002 observations. While the other Anopheles species identified in this study are less potent malaria vectors than Anopheles gambiae are , their extensive habitat suitability is concerning. Several Anopheles species have been shown to transition to primary malaria vectors in various countries. ( 18 – 20 ) A study in northwest Ethiopia implicated Anopheles cinereus as a major malaria vector,( 18 ) and Anopheles squamosus carrying Plasmodium falciparum sporozoites was identified in Zambia.( 20 ) The authors of the Zambian study concluded that the feeding behavior of Anopheles squamosus may help it evade current control measures, hindering malaria elimination. ( 20 ) Thus, identifying the specific Anopheles species responsible for indigenous malaria cases is crucial for malaria elimination efforts in the Maekel Region. The findings of this study indicate that large areas of the Maekel region are highly suitable for various Anopheles mosquitoes, demonstrating their potential to overcome altitudinal barriers. Climate change is projected to further drive the expansion of Anopheles mosquitoes across the Maekel Region under both SSP3-3.7 and SSP5-8.5. Conclusion This study indicates that many areas in the Maekel Region will be suitable habitats for Anopheles mosquitoes in the future. This finding poses a potential threat to the success of malaria control programs in the region. Therefore, enhanced surveillance and research are necessary to mitigate these risks. Additionally, a nationwide study is needed to map the distribution of Anopheles and Plasmodium species, enabling the development of a malaria control program to refine its strategies. Strengths and Limitations This study is the first of its kind in Eritrea to illustrate the potential risks of climate change and Anopheles adaptation and expansion via a robust analytical method. However, sample sizes for certain species are not optimal. While Maxent is robust with small sample sizes, additional samples enhance model accuracy. Declarations Author Contribution FGM and AMT: Conceptualized the StudyAMT and KTH: Supervised the data collection processFGM: Conducted the AnalysisFGM and MBT: Wrote the draft ManuscriptAB and SM :Supervised the ResearchFGM, AMT, MBT, KTH, AB, SM: read and approved the final manuscript References World Health Organization. World malaria report 2024: addressing inequity in the global malaria response. Generva: World Health Organization; 2024. Sinka ME, Pironon S, Massey NC, Longbottom J, Hemingway J, Moyes CL, et al. A new malaria vector in Africa: Predicting the expansion range of Anopheles stephensi and identifying the urban populations at risk. Proc Natl Acad Sci. 2020 Oct 6;117(40):24900–8. Williams N. Malaria climbs the mountain. Curr Biol. 2010 Jan 26;20(2):R37–8. Carlson CJ, Bannon E, Mendenhal E, Newfield T, Bansa S. Rapid range shifts in African Anopheles mosquitoes over the last century. Biol Lett. 2023;19(20220365). Phillips SJ, Anderson RP, Schapire RE. 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Predicting the probable distribution and threat of invasive Mimosa diplotricha Suavalle and Mikania micrantha Kunth in a protected tropical grassland. Ecol Eng. 2016;97:23–31. Environmental Systems research Institute. ArcMap [Internet]. U.S.A: ESRI; 2020. Available from: Https://desktop.arcgis.com/en/arcmap/ Phillips SJ, Dudik M, Schapire RE. Maxent software for modeling species niches and distribution [Internet]. [cited 2025 Mar 24]. Available from: biodiversityinformatics.amnh.org/open_source/maxent Coetzee M. Distribution of the African malaria vectors of the Anopheles gambiae complex. Am J Trop Med Hyg [Internet]. 2004 Feb [cited 2024 Jan 24];70(2). Available from: https://pubmed.ncbi.nlm.nih.gov/14993617/ Adeogun A, Babalola AS, Okoko OO, Oyeniyi T, Omotayo A, Izekor RT, et al. Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data. Sci Rep. 2023;13(13679). Shililu JI. Malaria Vector Studies in Eritrea. 2001. Olabimi IO, Ileke KD, Adu BW, Arotolu TE. Potential distribution of the primary malaria vector Anopheles gambiae Giles [Diptera: Culicidae] in Southwest Nigeria under current and future climatic conditions. J Basic Appl Zool. 2021;82(63). Binder L, Gleixner S, Gornot C, Lange S, Šedová B, Tomalk J. Climate Risk Profile for Eastern Africa [Internet]. 2023. Available from: weatheringrisk.org/sites/default/files/document/Climate-Risk-Profile_Eastern-Africa.pdf Lemma W. Anopheles cinereus implicated as a vector of malaria transmission in the highlands of north‒west Ethiopia. 2019; Fornadel CM, Norris LC, Franco V, Norris DE. Unexpected Anthropophily in the Potential Secondary Malaria Vectors Anopheles coustani s.l. and Anopheles squamosus in Macha, Zambia. VECTOR-BORNE ZOONOTIC Dis. 2011;11(8). Stevenson JC, Simubali L, Mbambara S, Musonda M, Mweetwa S, Mudenda T, et al. Detection of Plasmodium falciparum Infection in Anopheles squamosus (Diptera: Culicidae) in an Area Targeted for Malaria Elimination, Southern Zambia. J Med Entomol. 2016;1(6). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Nov, 2025 Reviews received at journal 30 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers invited by journal 10 Jun, 2025 Editor assigned by journal 16 May, 2025 Submission checks completed at journal 16 May, 2025 First submitted to journal 12 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6647593","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456092341,"identity":"df7cbc67-1ff2-48d5-be04-7ebe813b0074","order_by":0,"name":"Filmon Ghebreyesus 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Region\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6647593/v1/89010033d944aeb95485ab1c.png"},{"id":82768438,"identity":"85602316-5e33-4b09-b8ed-6bb7a4537b5f","added_by":"auto","created_at":"2025-05-15 05:33:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6447890,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies distribution models were constructed for \u003cem\u003eAnopheles cinereus\u003c/em\u003e, \u003cem\u003eAnopheles demeilloni\u003c/em\u003e, \u003cem\u003eAnopheles squamosus\u003c/em\u003e, \u003cem\u003eAnopheles D’thali\u003c/em\u003e, and \u003cem\u003eAnopheles gambiae\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6647593/v1/141851edbeee54bdd51a8520.png"},{"id":82770196,"identity":"f10f9265-f472-4531-970b-fff425349192","added_by":"auto","created_at":"2025-05-15 06:05:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9175402,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6647593/v1/14b0f226-a82a-47e6-9974-ce4f61b40175.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental Suitability of Anopheles Mosquito Species in the Maekel Region, Eritrea: Species Distribution Modeling Using Maximum Entropy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalaria remains a major global health burden and is transmitted by the bite of female Anopheles mosquitoes. In 2023, malaria resulted in an estimated 263\u0026nbsp;million cases and 597,000 deaths, with the majority occurring in sub-Saharan Africa and among children under five years of age. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Efforts to control and eradicate malaria have focused primarily on the control of Anopheles mosquito vectors. Control measures, including larviciding, insecticide residual spraying, and the use of insecticide-treated bed nets, have been effective in reducing malaria transmission. Globally, 2.2\u0026nbsp;billion cases and 12.7\u0026nbsp;million deaths were averted between 2000 and 2023. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eEmerging trends, such as rapid vector mutation, evolution of Anopheles mosquito bionomics, and climate change, threaten to reverse progress in malaria control. An invasive vector, \u003cem\u003eAnopheles stephensi\u003c/em\u003e, originating from Southeast Asia, has been spreading across Africa since its landfall in Djibouti in 2012. This has exposed 126\u0026nbsp;million people in urban areas to malaria risk. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Climate change is also facilitating the spread of malaria into previously unaffected areas, as evidenced by recent outbreaks. Mosquitoes are defeating aegis of altitude, ascending approximately 6.5 meters annually. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) This has significant implications, as many urban settlements were historically established at relatively high altitudes to avoid mosquitoes. Consequently, millions of individuals with limited immunity to malaria in these areas may become vulnerable to malaria infection.\u003c/p\u003e \u003cp\u003eSpecies distribution models (SDMs) and disease risk maps have become vital tools for predicting such scenarios. SDMs visualize areas suitable for mosquito reproduction and malaria transmission. Highly accurate SDMs can assist malaria control programs in implementing targeted interventions, thereby enhancing preparedness for future malaria trends. Among the available SDM techniques, maximum entropy (Maxent) has gained prominence because of its simplicity, robustness, and accuracy. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Maxent can produce reliable projections using only species presence data, even with limited sample sizes.\u003c/p\u003e \u003cp\u003eIn Eritrea, malaria transmission exhibits seasonal patterns, proceeding after the rainy season. Prevention efforts have achieved substantial reductions in malaria cases and deaths, with an 80% decrease since the 1998 peak outbreak. However, Eritrea's vulnerability to climate change poses a risk to these gains. Currently, there are limited data on the distribution of Anopheles mosquitoes, the factors influencing their distribution, and future projections of their spread in Eritrea. Analyzing areas of potential habitat suitability for Anopheles mosquitoes is crucial for guiding malaria prevention and control efforts. Therefore, this study aims to assess the environmental suitability, influential climatic and environmental variables, and future projected distribution of Anopheles mosquitoes using Shared Socioeconomic Pathway (SSP) scenarios.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003e The study was conducted in the Maekel region, one of the six administrative regions of Eritrea. The Maekel region is situated in the Eritrean highlands, with an average altitude of 2219m (range: 1307\u0026ndash;2601 m) above sea level. The region is characterized by a humid‒arid subtropical climate with localized microclimates. The Maekel Region is the most densely populated region in Eritrea, with an average population density of 459 people per square kilometer (range: 46\u0026ndash;5912). (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Malaria is largely absent in the region, although a few indigenous cases are reported annually (image 1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpecies occurrence and climatic data\u003c/h3\u003e\n\u003cp\u003eLarval collection was conducted by entomological experts at potential breeding sites from September 10th to 30th, 2023. Anopheles species larvae were collected from breeding habitats WHO standard 350 ml dipper. Larvae were transported to the Mendefera Entomological Laboratory for rearing and identification. The geographic positioning system (GPS) coordinates of each breeding site were recorded via Android phones with an open data kit (ODK) form. All the GPS coordinates were collected in the WGS1983 projection, with a minimum accuracy of 4 meters.\u003c/p\u003e \u003cp\u003eCurrent bioclimatic data (1970\u0026ndash;2010) were downloaded at a 30 arc-second (1 km\u0026sup2;) resolution from WorldClim (V2.1). (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Future bioclimatic data for two projection periods (2021\u0026ndash;2040 and 2041\u0026ndash;2060) and two shared socioeconomic pathway scenarios (SSP3-3.7 and SSP5-8.5) from the ACCESS-CM2 projection group (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) were obtained. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Elevation data were also downloaded from the same source. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Four additional variables, namely, aspect, slope, hill shade, and topographic wetness index, were derived from the elevation data. Pearson correlation tests were performed via the SDM toolbox v2.0 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) in ArcMap v10.8 to assess correlations between variables. Variables with a correlation coefficient exceeding 0.7 (r\u0026thinsp;\u0026gt;\u0026thinsp;0.7) were excluded from the modeling. Of the 24 initial variables (19 bioclimatic variables and 5 topographic variables), 14 were retained for the final model (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables included in modeling\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescriptions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBio1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual mean Temperature\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBio2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean diurnal range (mean of monthly temperature max-min)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBio3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsothermally (bio2/bio7) *100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBio7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature annual range (Bio5-Bio6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBio8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean temperature of wettest Quarter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBio12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual precipitation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBio13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprecipitation of the wettest month\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBio14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprecipitation of driest month\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBio18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprecipitation of warmest quarter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBio19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprecipitation of coldest quarter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAspect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirection of the maximum gradient and relates to the degree of solar exposure. The aspect determines the effect of solar heating, air temperature, and moisture. Aspect ranges from 0\u003csup\u003e0\u003c/sup\u003e to 360\u003csup\u003e0\u003c/sup\u003e (0\u003csup\u003e0\u003c/sup\u003e \u0026amp;360\u003csup\u003e0\u003c/sup\u003e north, 90\u003csup\u003e0\u003c/sup\u003e east, 180\u003csup\u003e0\u003c/sup\u003e south and 270\u003csup\u003e0\u003c/sup\u003e west)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSlope\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe angle of ascent and descent for each pixel on a hillside. The lower the slope value the flatter the terrain; the higher the slope value, the stepper the terrain. Slope strongly influences overland and subsurface flow velocity, drainage, and water accumulation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHill shade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal areas with shadows thrown upon raised landscapes. The higher the shading values, the higher and warmer the surface (more exposure to sun rays); and the lower the hill shading values the darker and cooler the surface.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTopographic wetness index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTWI is an estimate of the predicted water accumulation and is calculated as the natural logarithm of the ratio between the total catchment area and slope. High TWI values indicate converging terrains and valleys, while low values are typical steep and diverging terrains. TWI is a proxy for the depth of groundwater, soil pH, vegetation species richness, and soil organic matter.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eData Modeling\u003c/h3\u003e\n\u003cp\u003eAll climatic data were clipped to the Maekel region boundaries and converted to ASCII raster format with the same grain size and WGS1983 projection. To enhance model performance, 10,000 randomly distributed background points were generated. To remove overrepresentation of the ecological area, rarefying of the presence data was performed at a 5 km distance. All Maxent feature types (linear, quadratic, product, threshold, hinge, and categorical) were evaluated during modeling. Regularization parameters of 1.5, 2, and 2.5 were used to minimize model overfitting. Model training and validation were performed via a cross-validation technique with 10 replicates. Jackknife tests and response curves were used to assess variable importance.\u003c/p\u003e \u003cp\u003eModel performance was evaluated via the area under the curve (AUC) of the receiver operating characteristic (ROC). An AUC value of 1 indicates perfect discrimination. The final species distribution maps were categorized into five suitability classes: very high (\u0026gt;\u0026thinsp;0.6), high (0.4\u0026ndash;0.6), moderate (0.2\u0026ndash;0.4), low (0.1\u0026ndash;0.2), and very low (\u0026lt;\u0026thinsp;0.1). (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Data preparation was conducted via ESRI ArcMap version 10.8 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), and modeling was performed using Maxent software version 3.4.4. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 94 samples were collected from 24 villages and reared for identification. Seven Anopheles species were identified. \u003cem\u003eAnopheles cinereus\u003c/em\u003e was the most common species, found in 20 villages, followed by \u003cem\u003eAnopheles demeilloni\u003c/em\u003e (16 villages) and \u003cem\u003eAnopheles squamosus\u003c/em\u003e (12 villages). \u003cem\u003eAnopheles gambiae\u003c/em\u003e, the primary vector in Eritrea, was identified at 5 sites (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnopheles species Identified\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnopheles\u003c/em\u003e Species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVillages identified\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnopheles cinerus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnopheles demeilloni\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnopheles squamosus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnopheles dthali\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnopheles gambiae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnopheles preptorensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnopheles salbi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSpecies distribution model\u003c/h3\u003e\n\u003cp\u003eSpecies distribution models were constructed for \u003cem\u003eAnopheles cinereus\u003c/em\u003e, \u003cem\u003eAnopheles demeilloni\u003c/em\u003e, \u003cem\u003eAnopheles squamosus\u003c/em\u003e, \u003cem\u003eAnopheles D\u0026rsquo;thali\u003c/em\u003e, and \u003cem\u003eAnopheles gambiae\u003c/em\u003e. Models for \u003cem\u003eAnopheles pretorensis\u003c/em\u003e and \u003cem\u003eAnopheles salbi\u003c/em\u003e were not developed due to insufficient sample sizes after rarefying the presence points.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnopheles gambiae\u003c/em\u003e were identified at five sites. Under current climatic conditions, large portions of the study area exhibit moderate and high suitability for \u003cem\u003eAnopheles gambiae\u003c/em\u003e, with small areas in the southeast showing very high suitability. Under the SSP3-3.7 projection, areas of moderate suitability transition to high and very high suitability by 2030, particularly in the northern parts. This trend is further pronounced by 2050. Similar patterns of increased suitability are observed under the SSP5-8.5 scenario. The model demonstrated good performance, with training and test AUC values of 0.76. Jackknife tests revealed that hill shade and the topographic wetness index were the most influential variables for Anopheles gambiae habitat suitability.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnopheles cinereus\u003c/em\u003e was the most widespread species and was identified at 28 sites. Under current climatic conditions, areas in the north, west, center, and southeast regions exhibit very high suitability, whereas the eastern region has very low potential. Under SSP3-3.7, areas of high suitability for Anopheles cinereus increase and expand westward by 2030, with further expansion and consolidation observed by 2050. A similar, but more pronounced, pattern of expansion is observed under SSP5-8.5. The model demonstrated good performance, with training and test AUC values of 0.843 and 0.695, respectively. The key influential variables included the topographic wetness index, precipitation in the driest month (Bio14), aspect, hill shade, mean diurnal temperature range (Bio2), and precipitation in the warmest month (Bio18).\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnopheles demeilloni\u003c/em\u003e was the second most common Anopheles mosquito and was identified at 20 sites across 16 villages. Large areas of the region exhibit very high and high suitability for \u003cem\u003eAnopheles demeilloni\u003c/em\u003e. Under the SSP3-3.7 scenario, very high suitability areas are limited primarily to the east and center of the region by 2030. By 2050, very high suitability areas in the east revert to high suitability areas, and high suitability areas are concentrated in the southeast. Under SSP5-8.5, very high suitability areas are projected in the east, southeast, and southern fringes by 2030. By 2050, these areas show slight expansion. The model demonstrated good predictive performance, with an AUC of 0.77. The key influential variables included hill shade, aspect, mean diurnal temperature range (Bio2), precipitation in the driest month (Bio14), topographic wetness index, and precipitation in the warmest month (Bio18).\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnopheles squamosus\u003c/em\u003e was identified at 20 sites across 16 villages. Current climate projections show very high suitability in the northwest and in patches in the central and south of the region. Under SSP3-3.7, high suitability areas transition to moderate suitability areas by 2030, while very high suitability areas in the center shrink, and those in the south parts will expand slightly (Fig.\u0026nbsp;1). The model demonstrated good to excellent predictive performance, with training and test AUC values of 0.80 and 0.95, respectively. The key influential variables included the topographic wetness index, precipitation in the driest month (Bio14), aspect, hill shade, slope, mean diurnal temperature range (Bio2), and precipitation in the warmest month (Bio18).\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnopheles d\u0026rsquo;thali\u003c/em\u003e were identified at 11 sites across 7 villages. Currently, areas in the central, southeastern, and northwestern parts of the regions are highly suitable. A large diagonal region between the southeast and northwest regions shows moderate suitability. Similar patterns are projected under future climate conditions. The high-suitability area in the center shrinks by 2050 under SSP3-3.7 and disappears under SSP5-8.5. The other high-suitability areas remain stable under both scenarios and projection periods. The model demonstrated excellent performance, with training and test AUC values of 0.88 and 0.95, respectively. The key influential variables included the topographic wetness index, precipitation in the driest month (Bio14), aspect, hill shade, slope, mean diurnal temperature range (Bio2), and precipitation in the warmest month (Bio18).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to analyze the potentially suitable areas and future expansion of Anopheles mosquitoes in the Maekel Region of Eritrea via Maxent. The results indicate that large areas of the Maekel region are suitable for Anopheles mosquitoes, specifically \u003cem\u003eAnopheles gambiae, Anopheles cinereus, Anopheles squamosus\u003c/em\u003e, and \u003cem\u003eAnopheles demeilloni\u003c/em\u003e. Further increases in suitability are projected under both the SSP3-3.7 and SSP5-8.5 scenarios.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnopheles gambiae\u003c/em\u003e is associated with small areas of very high suitability, which are projected to expand in the future under both scenarios. Anopheles gambiae is known for its high adaptability to topographic and ecological factors, making it the most efficient malaria vector in Africa. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) In Eritrea, Anopheles gambiae is the primary malaria vector and is predominantly found in the malarious areas of the Gash Barka and Debub Regions. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) Similar results have been reported by Olabimi et al., who concluded that \u003cem\u003eAnopheles gambiae\u003c/em\u003e will continue to have large areas of high suitability. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe study area features large areas of very high and high suitability for \u003cem\u003eAnopheles cinereus\u003c/em\u003e and \u003cem\u003eAnopheles d\u0026rsquo;thali\u003c/em\u003e. A study conducted in Eritrea in 2001\u0026ndash;2002 identified seven Anopheles species, three of which are potential malaria vectors. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) The same study also revealed one \u003cem\u003eAnopheles gambiae\u003c/em\u003e infected with Plasmodium falciparum at an altitude above 2000 meters. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) The results of the present study indicate a broader expansion of Anopheles mosquitoes in the Maekel region, supporting the adaptive nature of these mosquitoes to diverse climatic and environmental conditions. Studies in Mount Kenya have identified mosquitoes in high-altitude areas previously free from malaria. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The observed 2\u0026deg;C increase in average temperature in the Kenyan highlands has been attributed to climate change. Similarly, the average temperature in the Eritrean highlands has increased by 1.72\u0026deg;C (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), suggesting that climate change has been a primary driver of Anopheles mosquito expansion in the Maekel region since the 2001\u0026ndash;2002 observations.\u003c/p\u003e \u003cp\u003eWhile the other Anopheles species identified in this study are less potent malaria vectors than \u003cem\u003eAnopheles gambiae are\u003c/em\u003e, their extensive habitat suitability is concerning. Several Anopheles species have been shown to transition to primary malaria vectors in various countries. (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) A study in northwest Ethiopia implicated \u003cem\u003eAnopheles cinereus\u003c/em\u003e as a major malaria vector,(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and \u003cem\u003eAnopheles squamosus\u003c/em\u003e carrying Plasmodium falciparum sporozoites was identified in Zambia.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) The authors of the Zambian study concluded that the feeding behavior of \u003cem\u003eAnopheles squamosus\u003c/em\u003e may help it evade current control measures, hindering malaria elimination. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Thus, identifying the specific Anopheles species responsible for indigenous malaria cases is crucial for malaria elimination efforts in the Maekel Region.\u003c/p\u003e \u003cp\u003eThe findings of this study indicate that large areas of the Maekel region are highly suitable for various Anopheles mosquitoes, demonstrating their potential to overcome altitudinal barriers. Climate change is projected to further drive the expansion of Anopheles mosquitoes across the Maekel Region under both SSP3-3.7 and SSP5-8.5.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study indicates that many areas in the Maekel Region will be suitable habitats for Anopheles mosquitoes in the future. This finding poses a potential threat to the success of malaria control programs in the region. Therefore, enhanced surveillance and research are necessary to mitigate these risks. Additionally, a nationwide study is needed to map the distribution of Anopheles and Plasmodium species, enabling the development of a malaria control program to refine its strategies.\u003c/p\u003e \u003cp\u003eStrengths and Limitations\u003c/p\u003e \u003cp\u003eThis study is the first of its kind in Eritrea to illustrate the potential risks of climate change and Anopheles adaptation and expansion via a robust analytical method. However, sample sizes for certain species are not optimal. While Maxent is robust with small sample sizes, additional samples enhance model accuracy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFGM and AMT: Conceptualized the StudyAMT and KTH: Supervised the data collection processFGM: Conducted the AnalysisFGM and MBT: Wrote the draft ManuscriptAB and SM :Supervised the ResearchFGM, AMT, MBT, KTH, AB, SM: read and approved the final manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. World malaria report 2024: addressing inequity in the global malaria response. Generva: World Health Organization; 2024.\u003c/li\u003e\n\u003cli\u003eSinka ME, Pironon S, Massey NC, Longbottom J, Hemingway J, Moyes CL, et al. A new malaria vector in Africa: Predicting the expansion range of Anopheles stephensi and identifying the urban populations at risk. Proc Natl Acad Sci. 2020 Oct 6;117(40):24900\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eWilliams N. Malaria climbs the mountain. Curr Biol. 2010 Jan 26;20(2):R37\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eCarlson CJ, Bannon E, Mendenhal E, Newfield T, Bansa S. Rapid range shifts in African Anopheles mosquitoes over the last century. Biol Lett. 2023;19(20220365).\u003c/li\u003e\n\u003cli\u003ePhillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Model [Internet]. 2006;190. Available from: www.elsevier.com/locate/ecolmodel\u003c/li\u003e\n\u003cli\u003eOpen Spatial Demographic Data and Research - WorldPop. [Internet]. [cited 2025 Feb 20]. Available from: https://www.worldpop.org/\u003c/li\u003e\n\u003cli\u003eWorldClim - Global Climate Data | Free climate data for ecological modeling and GIS. [Internet]. [cited 2025 Feb 20]. Available from: http://worldclim.com/\u003c/li\u003e\n\u003cli\u003eCSIRO. Australian Community Climate and Earth System Simulator (ACCESS) [Internet]. 2025. Available from: https://research.csiro.au/access/about/cm2\u003c/li\u003e\n\u003cli\u003eBrown J, Bennet J, French C. SDMtoolbox [Internet]. 2017 [cited 2025 Mar 25]. (The next generation python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses). Available from: sdmtoolbox.org\u003c/li\u003e\n\u003cli\u003eChoudhury MR, Deb P, Singha H, Chakdar B, Medhi M. Predicting the probable distribution and threat of invasive Mimosa diplotricha Suavalle and Mikania micrantha Kunth in a protected tropical grassland. Ecol Eng. 2016;97:23\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eEnvironmental Systems research Institute. ArcMap [Internet]. U.S.A: ESRI; 2020. Available from: Https://desktop.arcgis.com/en/arcmap/\u003c/li\u003e\n\u003cli\u003ePhillips SJ, Dudik M, Schapire RE. Maxent software for modeling species niches and distribution [Internet]. [cited 2025 Mar 24]. Available from: biodiversityinformatics.amnh.org/open_source/maxent\u003c/li\u003e\n\u003cli\u003eCoetzee M. Distribution of the African malaria vectors of the Anopheles gambiae complex. Am J Trop Med Hyg [Internet]. 2004 Feb [cited 2024 Jan 24];70(2). Available from: https://pubmed.ncbi.nlm.nih.gov/14993617/\u003c/li\u003e\n\u003cli\u003eAdeogun A, Babalola AS, Okoko OO, Oyeniyi T, Omotayo A, Izekor RT, et al. Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data. Sci Rep. 2023;13(13679).\u003c/li\u003e\n\u003cli\u003eShililu JI. Malaria Vector Studies in Eritrea. 2001.\u003c/li\u003e\n\u003cli\u003eOlabimi IO, Ileke KD, Adu BW, Arotolu TE. Potential distribution of the primary malaria vector Anopheles gambiae Giles [Diptera: Culicidae] in Southwest Nigeria under current and future climatic conditions. J Basic Appl Zool. 2021;82(63).\u003c/li\u003e\n\u003cli\u003eBinder L, Gleixner S, Gornot C, Lange S, \u0026Scaron;edov\u0026aacute; B, Tomalk J. Climate Risk Profile for Eastern Africa [Internet]. 2023. Available from: weatheringrisk.org/sites/default/files/document/Climate-Risk-Profile_Eastern-Africa.pdf\u003c/li\u003e\n\u003cli\u003eLemma W. Anopheles cinereus implicated as a vector of malaria transmission in the highlands of north‒west Ethiopia. 2019;\u003c/li\u003e\n\u003cli\u003eFornadel CM, Norris LC, Franco V, Norris DE. Unexpected Anthropophily in the Potential Secondary Malaria Vectors Anopheles coustani s.l. and Anopheles squamosus in Macha, Zambia. VECTOR-BORNE ZOONOTIC Dis. 2011;11(8).\u003c/li\u003e\n\u003cli\u003eStevenson JC, Simubali L, Mbambara S, Musonda M, Mweetwa S, Mudenda T, et al. Detection of Plasmodium falciparum Infection in Anopheles squamosus (Diptera: Culicidae) in an Area Targeted for Malaria Elimination, Southern Zambia. J Med Entomol. 2016;1(6).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6647593/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6647593/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction: Malaria, a significant global health challenge, is closely associated with Anopheles mosquitoes. The dependence of mosquito habitats on environmental conditions, coupled with the established link between malaria prevalence and habitat niches, highlights the dynamic interplay between mosquito ecology and environmental factors in malaria transmission. This study aims to determine the current and future environmental suitability of Anopheles mosquitoes in the Maekel region using species distribution modeling (SDM).\u003c/p\u003e \u003cp\u003eMethodology: Species occurrence data, along with environmental and bioclimatic variables, were used to model habitat suitability using Maxent. Highly correlated variables, defined as those with a Pearson correlation coefficient exceeding 0.8, were excluded from the model. Future climate projections from the Coupled Model Intercomparison Project (CMIP-6) were used to project species distributions under two shared socioeconomic pathway scenarios: SSP370 and SSP585. Key environmental variables influencing species survival were identified through the jackknife test of variable importance. Model performance was evaluated via area under the curve (AUC) statistics.\u003c/p\u003e \u003cp\u003eResults: A total of 94 samples were collected from 24 villages. The most prevalent species, \u003cem\u003eAnopheles cinereus\u003c/em\u003e, was identified in 20 villages, followed by \u003cem\u003eAnopheles demeilloni\u003c/em\u003e and \u003cem\u003eAnopheles squamosus\u003c/em\u003e. \u003cem\u003eAnopheles gambiae\u003c/em\u003e, the primary malaria vector in Eritrea and Africa, was identified at 5 sites in 4 villages. Maxent modeling indicated that large areas of the Maekel region are highly suitable for Anopheles species, with projected increases in suitability by 2030 and 2050 under both climate change scenarios.\u003c/p\u003e \u003cp\u003eConclusion: Anopheles mosquitoes are well established across significant portions of the Maekel region, and their distribution is projected to expand in the future. Comprehensive nationwide studies are necessary to map species distributions and bionomics, enabling the refinement of malaria control strategies in response to the rapid expansion of Anopheles mosquitoes driven by climate change.\u003c/p\u003e","manuscriptTitle":"Environmental Suitability of Anopheles Mosquito Species in the Maekel Region, Eritrea: Species Distribution Modeling Using Maximum Entropy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 05:33:11","doi":"10.21203/rs.3.rs-6647593/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-07T23:53:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-30T12:07:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T14:27:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218357422158226290034504060961571490465","date":"2025-10-08T08:39:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65075996860145867368913413441294987840","date":"2025-10-06T09:04:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284238232128736881234232959821327910967","date":"2025-08-17T22:11:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T17:27:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-16T12:22:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-16T12:19:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2025-05-12T14:21:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b22ee41-b192-40e1-a159-3e9135d6b8df","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T10:08:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-15 05:33:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6647593","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6647593","identity":"rs-6647593","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}