Projected Shifts in the Distribution of Anopheles funestus under Future Climate Scenarios in Malawi

Projected Shifts in the Distribution of Anopheles funestus under Future Climate Scenarios in Malawi | 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 Article Projected Shifts in the Distribution of Anopheles funestus under Future Climate Scenarios in Malawi Isaac Tchuwa, Gladson Phiri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6372125/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding the projected shifts in Anopheles funestus (An. funestus) distribution under future climate scenarios is crucial for strengthening malaria vector control strategies in Malawi. This study employs species distribution modeling using Maximum Entropy (MaxEnt), coupled with downscaled climate projections from Coupled Model Intercomparison Project Phase 6 (CMIP6), to assess habitat suitability under present and future climatic conditions (2021–2040). Model evaluation indicated strong predictive performance (AUC = 0.91), ensuring reliable forecasts. The most influential bioclimatic variables shaping An. funestus distribution were mean temperature of the driest quarter (48.6% contribution) and precipitation of the warmest quarter (27.4% contribution). Future projections reveal a notable northward shift in suitable habitats, with increased risk in the Northern and Central regions, particularly along Lake Malawi’s shoreline. Under the shared socio-economic pathway (SSP)5-8.5 scenario, traditional malaria-endemic areas such as Nsanje are projected to experience a 31% decline in habitat suitability. In contrast, the likelihood of An. funestus presence is expected to increase by 42% in Karonga and Nkhata Bay, areas historically already considered high-risk. These findings suggest that climate change will significantly alter malaria transmission dynamics, potentially exposing previously low-risk populations to higher infection rates. To mitigate these emerging risks, it is imperative to integrate climate-driven vector distribution shifts into national malaria control strategies. Strengthened entomological surveillance, proactive vector management, and targeted interventions in newly emerging high-risk zones will be essential to prevent disease resurgence. This study underscores the need for adaptive, climate-responsive malaria control policies to safeguard public health in Malawi and beyond. Biological sciences/Ecology Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Health sciences/Diseases Anopheles funestus ecological niche modeling climate change malaria vector climate scenarios shared socio-economic pathways Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Malaria remains one of Malawi’s most persistent public health challenges, driven largely by the spatial distribution and behavioral ecology of its mosquito vectors. Among these, Anopheles funestus stands out due to its strong anthropophilic tendencies, high vectorial capacity, and remarkable resilience, playing a crucial role in sustaining malaria transmission despite extensive control efforts. While its epidemiological significance has been well documented [ 16 ], critical uncertainties remain regarding how its distribution and abundance will respond to ongoing climatic shifts. The rapid pace of anthropogenic climate change, as extensively documented by the Intergovernmental Panel on Climate Change [ 8 ], is altering local weather patterns, with rising temperatures and increasing precipitation variability already influencing the geographic ranges of numerous species, including disease vectors. The association between climate variability and vector-borne disease dynamics including malaria is well established [ 1 , 3 , 11 , 15 , 21 ], yet current projections of malaria risk under future climatic conditions remain limited in scope and resolution, often failing to capture localized ecological responses and feedback mechanisms. Although substantial work has mapped the present-day distribution of An. funestus and other species across sub-Saharan Africa [ 6 , 23 ], significant gaps persist in understanding how future climatic conditions may reshape its ecological niche, particularly at finer spatial scales relevant for localized interventions. Previous studies have predominantly relied on static ecological niche models based on historical climate data, failing to capture the inherently dynamic nature of climate change and the non-linear responses of mosquito populations to environmental alterations. While some research has projected vector range shifts in response to changing temperature and precipitation regimes [ 1 , 3 ], many fail to incorporate the full complexity of climate-driven habitat changes, including extreme weather events, alterations in land use, and interactions with other environmental stressors such as deforestation and urbanization [ 27 ]. These limitations are particularly critical given mounting evidence that malaria vectors are expanding into previously unsuitable areas, including high-altitude regions where malaria transmission was historically rare. Observations from other parts of Africa have already documented such expansions [ 17 , 24 ], raising pressing concerns about the future distribution of malaria risk in sub-Saharan Africa. Within the Malawi’s context, research on malaria-climate interactions remains relatively underdeveloped, with most studies either extrapolating from broader regional assessments or relying on outdated climate datasets. While some studies have attempted to assess malaria transmission dynamics at national and district levels [ 2 , 4 , 9 , 10 , 13 – 14 ], there remains a lack of high-resolution, spatially explicit projections that account for future climate variability. This gap is particularly consequential given Malawi’s diverse topography and localized climate variations, which may differentially influence mosquito habitat suitability across regions. Moreover, existing policy frameworks for malaria control in Malawi rarely integrate climate projections into long-term vector management strategies, largely due to the absence of context-specific modeling efforts that can provide actionable insights for public health planning. Addressing this gap is essential for anticipating malaria burden under future climate scenarios and guiding the implementation of adaptive interventions. Despite the importance of predictive modeling for malaria control planning, existing studies often employ simplistic climate scenarios that inadequately represent the full spectrum of potential future conditions. Many models fail to account for the physiological adaptability of An. funestus , particularly its capacity to exploit a wide range of breeding sites and its behavioral plasticity in response to environmental stressors [ 12 ]. Furthermore, while ecological niche models such as Maximum Entropy (MaxEnt) have been widely applied to species distribution studies [ 20 ], their utility for malaria vector modeling remains constrained by limitations in occurrence data and environmental predictor selection. More advanced approaches, integrating high-resolution climate projections with ecological and epidemiological modeling, are necessary to provide robust forecasts of malaria vector distributions. In response to these gaps, this study seeks to project future shifts in the distribution of An. funestus in Malawi over the mid-century period by integrating current occurrence records with state-of-the-art climate projections. Utilizing the MaxEnt modeling framework, a method validated for presence-only data, this research simulates potential habitat suitability under two contrasting climate scenarios. The SSP2-4.5 scenario represents a future with moderate warming under partial mitigation efforts, whereas the SSP5-8.5 scenario reflects a trajectory of unabated greenhouse gas emissions, resulting in more extreme climatic impacts. By leveraging robust datasets and incorporating rigorously validated modeling techniques, this study advances understanding of An. funestus distribution under climate change and provides scientifically rigorous projections essential for targeted malaria control efforts in Malawi. The findings contribute to existing knowledge by addressing the spatial and temporal limitations of previous studies, offering a more granular assessment of malaria vector dynamics within a changing climate. Moreover, the study provides a critical evidence base for policymakers, highlighting regions where intensified vector control efforts may be necessary and emphasizing the urgency of integrating climate-informed strategies into national malaria control programs. These insights can inform preemptive interventions, guiding adaptive vector control strategies and reinforcing public health preparedness in the face of ongoing climatic uncertainty. Materials and Methods Study Area Malawi, a landlocked country in southeastern Africa, serves as the geographical focus of this study. Bordered by Tanzania to the north and northeast, Mozambique to the east, south, and west, and Zambia to the northwest, Malawi spans approximately 118,484 km². The country is characterized by diverse topographical features, including the Great Rift Valley, the Shire Highlands, and the extensive Lake Malawi, which covers about 20% of the national territory. This complex terrain, coupled with varied climatic zones ranging from tropical savannah to montane environments, exerts significant influence on the distribution, breeding ecology, and vectorial capacity of Anopheles funestus . The study specifically focuses on regions with high malaria endemicity (Fig. 1 ), where Anopheles funestus serves as a dominant vector. These areas include the low-lying floodplains of the Shire River, the humid lake shore regions, and the mid-elevation agricultural zones. Each of these ecosystems presents distinct ecological parameters that influence vector abundance and transmission dynamics. The humid environments of lakeshore regions provide stable breeding habitats due to consistent water availability, while floodplains exhibit seasonal fluctuations that contribute to population surges following heavy rainfall events. Mid-elevation zones offer a mix of temporary and permanent breeding habitats, affected by deforestation, land use change, and irrigation practices. Sentinel sites distributed across the country, as indicated in Fig. 1 , provide occurrence-only data that enhance the spatial representation of Anopheles funestus distribution and its response to climatic and environmental variables. Malawi’s climate is primarily influenced by the Inter-Tropical Convergence Zone (ITCZ), the El Niño-Southern Oscillation (ENSO), and local orographic effects. These climatic drivers create distinct wet and dry seasons that shape the spatial and temporal dynamics of malaria vectors. Rising temperatures and shifting precipitation patterns associated with climate change are modifying the geographical range of Anopheles funestus , expanding transmission zones into previously unsuitable areas, particularly in higher elevations. Empirical studies indicate that a 1–2°C rise in temperature can accelerate the vector’s life cycle, enhancing reproductive rates and increasing the number of annual malaria transmission cycles. Rainfall variability is another critical determinant, as prolonged dry periods can reduce larval habitats, while extreme rainfall events may create transient breeding sites in flood-prone areas, exacerbating malaria outbreaks. The socio-ecological context of malaria transmission in Malawi is deeply intertwined with poverty, land use changes, and inadequate health infrastructure. Over 80% of Malawi’s population resides in rural areas where access to malaria prevention measures such as insecticide-treated nets (ITNs) and indoor residual spraying (IRS) remains limited. High population densities in urban and peri-urban areas further contribute to localized malaria hotspots due to poor drainage systems and stagnant water accumulation, which provide ideal breeding conditions for Anopheles funestus . In addition, agricultural expansion into wetlands and riverine areas has altered natural mosquito habitats, with irrigation schemes facilitating year-round vector breeding. Studies have demonstrated that regions with extensive rice cultivation, such as those along the Lower Shire Valley, report higher malaria prevalence due to increased mosquito proliferation in standing water bodies [ 5 ]. Malawi has one of the highest malaria burdens in southern Africa, with the disease accounting for nearly 30% of outpatient visits and 15% of hospital admissions. The malaria incidence rate remains persistently high, with the disease responsible for approximately 7 million cases across all ages [ 7 , 22 ], despite ongoing control efforts. Sentinel surveillance data from key transmission zones indicate that Anopheles funestus remains a dominant vector, often coexisting with Anopheles gambiae s.l. in multiple ecological settings. Understanding the spatial heterogeneity in malaria transmission requires a data-driven approach that incorporates climatic variability, vector ecology, and human-environment interactions. Malawi presents a unique case study due to its pronounced climate variability, diverse ecological settings, and high disease burden. While previous studies have assessed malaria vector distributions in Africa, limited research has focused on Malawi’s specific climatic and environmental context. This study advances the discourse by integrating fine-scale climatic data, land use changes, and socio-ecological interactions into spatial-temporal analyses of Anopheles funestus habitats. By leveraging empirical evidence from sentinel site observations, climate models, and geospatial analyses, this research enhances the predictive accuracy of vector distribution models while informing strategic vector control interventions. These insights are critical for guiding malaria elimination efforts, optimizing resource allocation for vector control, and strengthening adaptive strategies against climate-induced shifts in malaria transmission dynamics across southeastern Africa. Research design The study employed an ecological niche modeling approach to predict potential range shifts of Anopheles funestus under future climate scenarios. The research design followed a structured sequence, beginning with the compilation of georeferenced occurrence records from verified entomological surveys and peer-reviewed sources. Data cleaning procedures were implemented to remove duplicate records and spatial outliers that could introduce bias. Environmental predictors were selected based on their known influence on mosquito ecology, ensuring relevance to species distribution. Model calibration involved testing multiple feature classes and regularization multipliers to identify the optimal parameter set, minimizing overfitting while preserving ecological interpretability. Spatial autocorrelation was assessed through Moran’s I to account for potential clustering effects in occurrence data. To validate model performance, an independent test dataset was withheld from model training, and predictive accuracy was evaluated through statistical metrics such as AUC and TSS. The final predictive maps were analyzed using a Geographic Information System (GIS) to visualize spatial trends in habitat suitability, facilitating interpretation of potential expansion, contraction, or stability of An. funestus distribution in response to projected climate change. Future Climate Projections (2021–2040) To project the potential shifts in the ecological niche of Anopheles funestus under future climate conditions, bioclimatic variables for the mid-21st century (2021–2040) were derived from the HadGEM3-GC31-LL model, developed by the UK Met Office Hadley Centre. This climate model was selected for ecological niche modeling (ENM) due to its high-resolution climate data, advanced aerosol and atmospheric components, and robust representation of ocean and land surface interactions. The HadGEM3-GC3.1-LL model, a key component of the Coupled Model Intercomparison Project phase 6 (CMIP6), is scientifically recognized for its credibility in climate impact assessments, including those presented in the IPCC Sixth Assessment Report (AR6), making it an ideal choice for studying climate-driven ecological patterns. The future climate predictions were run under two Shared Socio-economic Pathways (SSPs): SSP2-4.5 (moderate emissions scenario) SSP5-8.5 (high emissions scenario) These scenarios encapsulate two plausible future trajectories for greenhouse gas emissions and associated global warming. The model simulations were based on two distinct experimental ensembles designed to represent Earth's climate from the historical period (1850–2014) to the projected future (2014–2099). The first ensemble was modeled under the SSP2-4.5 scenario, and the second ensemble was modeled under the Shared Socioeconomic Pathway SSP5-8.5 providing a comparative analysis of future climatic shifts under moderate and high emissions pathways, respectively. These scenarios were selected to capture a range of potential climate futures, enabling a robust assessment of how varying greenhouse gas (GHG) emissions pathways may influence malaria vector distribution. The SSP2-4.5 scenario represents a stabilizing emissions pathway, where moderate climate policies and technological advancements lead to a gradual reduction in radiative forcing. This scenario aligns with a world that transitions toward sustainable energy sources while maintaining significant economic growth. Conversely, the SSP5-8.5 scenario represents a high-emissions trajectory, characterized by continued reliance on fossil fuels, minimal climate mitigation efforts, and intensified global economic activity, resulting in substantial warming. By incorporating both moderate and high-emissions scenarios, this study ensures a comprehensive analysis of potential climate impacts on malaria vector habitats. This comparative approach allows for the identification of both near-term and long-term risks under different socio-economic and policy conditions. SSP5-8.5 reflects a world where rapid urbanization, infrastructure expansion, and population growth influence land use patterns—factors that are critical in shaping malaria transmission dynamics. The inclusion of SSP5-8.5 provides an upper-bound estimate of climate-driven habitat expansion, offering valuable insights for proactive malaria control strategies in regions vulnerable to future climate change. This dual-scenario framework enhances the robustness of model projections, ensuring that decision-makers have a scientifically grounded basis for developing adaptive vector control policies in response to future climate variability. Bioclimatic Variables A total of 19 bioclimatic variables (Table 1 ) were used for modeling the ecological niche of Anopheles funestus in Malawi, with the variables consistent with those used in the baseline climate model. These variables were derived from the WorldClim 2.1 dataset at a spatial resolution of 30 arc-seconds (~ 1 km²), ensuring methodological consistency across the baseline and future climate conditions. The high spatial resolution allows for precise modeling of ecological conditions, which is crucial for capturing small-scale climatic variations and facilitating the comparison between baseline and projected climate scenarios. Table 1 Bioclimatic variables (BIO1–BIO19) and their descriptions Abbreviation Bioclimatic Variable Description Bio1 Annual Mean Temperature (°C) The mean temperature of the year. Bio2 Mean Diurnal Range The difference between the daily maximum and minimum temperatures. Bio3 Isothermality (Bio2/Bio7 * 100) A measure of the temperature variability over the year, expressed as a percentage. Bio4 Temperature Seasonality (standard deviation * 100) The variation in monthly temperatures throughout the year. Bio5 Max Temperature of the Warmest Month (°C) The highest monthly temperature during the warmest month of the year. Bio6 Min Temperature of the Coldest Month (°C) The lowest monthly temperature during the coldest month of the year. Bio7 Temperature Annual Range (Bio5 - Bio6, °C) The difference between the maximum temperature of the warmest month and the minimum temperature of the coldest month. Bio8 Mean Temperature of the Wettest Quarter (°C) The average temperature of the three months with the highest precipitation. Bio9 Mean Temperature of the Driest Quarter (°C) The average temperature of the three months with the lowest precipitation. Bio10 Mean Temperature of the Warmest Quarter (°C) The average temperature of the three warmest months of the year. Bio11 Mean Temperature of the Coldest Quarter (°C) The average temperature of the three coldest months of the year. Bio12 Annual Precipitation (mm) The total precipitation received throughout the year. Bio13 Precipitation of the Wettest Month (mm) The total precipitation received in the wettest month of the year. Bio14 Precipitation of the Driest Month (mm) The total precipitation received in the driest month of the year. Bio15 Precipitation Seasonality (Coefficient of Variation) A measure of the variation in precipitation patterns over the year. Bio16 Precipitation of the Wettest Quarter (mm) The total precipitation received in the three months with the highest precipitation. Bio17 Precipitation of the Driest Quarter (mm) The total precipitation received in the three months with the lowest precipitation. Bio18 Precipitation of the Warmest Quarter (mm) The total precipitation received in the three warmest months of the year. Bio19 Precipitation of the Coldest Quarter (mm) The total precipitation received in the three coldest months of the year. These variables encapsulate both temperature and precipitation trends, including annual averages, seasonality, and extremes, all critical to understanding the ecological niche of Anopheles funestus . For instance, temperature extremes such as the Max Temperature of the Warmest Month (Bio5) and the Min Temperature of the Coldest Month (Bio6) are essential for modeling mosquito survival, while precipitation variables like Precipitation of the Driest Quarter (Bio17) can provide insights into water availability for larval stages. Prior to model implementation, multicollinearity among variables was assessed using variance inflation factor (VIF) analysis, calculated as: $$\:{VIF}_{j}=\frac{1}{(1-{R}_{j}^{2})}$$ Where R 2 is the coefficient of determination from regressing the j predictor against all other predictors. Variables with VIF > 10 were excluded to mitigate redundancy and improve model stability. For example, Bio11, Bio12, Bio7, and Bio17 demonstrated zero percent contributions and zero permutation importance, suggesting that they had no significant influence on habitat suitability in this modeling context. As a result, these variables were removed from subsequent analysis to improve model efficiency and minimize redundancy. Modeling Approach and Analysis Species distribution modelling was conducted using Maximum Entropy (MaxEnt) version 3.4.3, a machine-learning algorithm that has demonstrated superior predictive performance in ecological niche modeling due to its capacity to handle complex interactions and incomplete presence-only datasets. The modelling framework followed a rigorous workflow to ensure reproducibility and minimize biases associated with sampling and spatial autocorrelation. The MaxEnt model was configured using optimized regularization multipliers λ to prevent overfitting while maintaining biologically realistic response curves. The selection of λ was guided by Akaike Information Criterion corrected for small sample sizes (AICc), computed as: $$\:AICs=-2ln\left(L\right)+2K+\:\frac{2K\:(\:K+1)}{n-K-1}\:$$ where L is the likelihood of the model given the data, K is the number of estimated parameters, and n is the sample size. The optimal λ value was selected to balance goodness-of-fit and model complexity. Background points were randomly sampled within an ecologically relevant buffer zone surrounding known occurrence locations, ensuring that pseudo-absence data reflected realistic environmental conditions. To enhance model robustness, a k-fold cross-validation (k = 10) approach was applied, partitioning occurrence data into training and test sets for iterative model validation. The model was run separately for each future climate scenario, keeping occurrence records constant to isolate the impact of projected climatic changes on habitat suitability. Predictive accuracy was assessed using multiple statistical metrics. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) was used to quantify overall model performance, with values closer to 1.0 indicating superior discrimination between suitable and unsuitable habitat. The AUC was computed as: $$\:AUC=\:{\int\:}_{0}^{1}TPR\left(FPR\right)dFPR$$ where TPR and FPR represent the true positive rate and false positive rate, respectively. Additionally, the True Skill Statistic (TSS) was computed as: $$\:TSS=TPR-FPR$$ which accounts for both omission and commission errors, providing a more ecologically meaningful measure of model accuracy. Post-processing of model outputs involved ecological niche area analysis and centroid shift analysis. Habitat suitability maps were thresholded using the maximum training sensitivity plus specificity approach to delineate the potential distribution of An. funestus under future climate scenarios. Niche expansion, contraction, and stability were quantified using a spatial overlay approach, allowing for direct comparison between baseline and projected distributions. The percentage change in habitat suitability area ΔA was computed as: $$\:\varDelta\:A=\:\frac{{A}_{f}-{A}_{b}}{{A}_{b}}\:\times\:\:100$$ Where A f and A b represent the projected and baseline habitat areas, respectively. Centroid shift analysis was employed to determine the directional displacement of the species’ optimal habitat, calculated as: $$\:\varDelta\:C=\:\:\sqrt{{({X}_{f}-\:{X}_{b})}^{2}+\:\:{({Y}_{f}-\:{Y}_{b})}^{2}}\:$$ Where (X f , Y f ) and (X b , Yb) represent the spatial coordinates of the habitat centroids under baseline and future scenarios, respectively. Directional vectors were analyzed to infer potential climate-driven range shifts. Overall, this methodological framework integrates rigorous parameter optimization, robust statistical evaluation, and spatial analytical techniques to ensure a scientifically credible and reproducible assessment of the future distribution of An. funestus under changing climatic conditions. Model performance Model performance assessment using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), yielded a high value of 0.90, indicating excellent predictive capability. Key predictors influencing model performance included Mean Diurnal Range (Bio2, 30.7%), Mean Temperature of the Warmest Quarter (Bio10, 23%), Precipitation of the Wettest Month (Bio13, 21.6%), and Precipitation of the Driest Month (Bio14, 9.7%). These results demonstrate the model’s robustness in capturing the ecological niche of Anopheles funestus , with temperature diurnality and precipitation seasonality emerging as critical determinants of habitat suitability. Results Current Distribution of Anopheles funestus in Malawi The baseline distribution of Anopheles funestus in Malawi, as projected under current climatic conditions (1950–2000), reveals a highly heterogeneous spatial pattern of habitat suitability. The analysis indicates that the majority of the country is classified as unsuitable for the species, with over 70,000 km² falling within the 'Not suitable' category (Fig. 2 ). However, distinct zones of suitability are evident, suggesting that A. funestus is constrained by climatic and environmental factors that limit its establishment and proliferation in much of the country. From the suitability analysis, approximately 20,000 km² is classified as 'Low suitability,' signifying marginal conditions where the species could persist but likely at low densities. This category may be influenced by suboptimal temperature ranges, intermittent water availability, or seasonal fluctuations that do not consistently support breeding. In contrast, 'Moderately suitable' areas, covering roughly 7,500 km², provide more stable but still suboptimal conditions for the species. These areas may exhibit moderate humidity levels and sporadic but sufficient water sources for breeding, allowing the species to persist at relatively low population densities. The 'Suitable' regions, spanning around 5,000 km², represent habitats where A. funestus can maintain stable populations. These zones are likely characterized by a combination of optimal temperature ranges (20–30°C), consistent precipitation, and the presence of suitable breeding habitats such as stagnant water bodies and vegetative cover that provides protection and humidity. The most critical areas for A. funestus under the current climate are the 'Highly suitable' regions, which cover an estimated 2,500 km². These zones likely coincide with key river systems, wetlands, and low-lying regions with persistent water bodies, which are essential for mosquito breeding. Given the vectorial capacity of A. funestus in malaria transmission, these highly suitable areas may correspond with malaria transmission hotspots, underscoring their epidemiological significance. The spatial heterogeneity in the current distribution is likely influenced by multiple interacting climatic factors, including mean annual temperature, seasonal precipitation variability, and humidity levels, as well as non-climatic factors such as land use changes, agricultural activities, and human interventions such as vector control programs. The dominance of 'Not suitable' areas suggests that environmental constraints currently play a significant role in restricting the range of A. funestus , although localized microclimatic conditions may still enable pockets of persistence within otherwise unsuitable regions. Anthropogenic activities such as irrigation projects and deforestation may inadvertently create microhabitats that facilitate mosquito breeding, altering local population dynamics. Understanding the present-day distribution provides a crucial baseline for evaluating future shifts under projected climate change scenarios. Given that A. funestus is a primary malaria vector in Malawi, any expansion or contraction of suitable habitats in response to climate change could have significant public health implications. Future projections must therefore consider not only climate-driven changes in habitat suitability but also anthropogenic factors that may mediate mosquito population dynamics and malaria risk in Malawi. Furthermore, continuous surveillance and integration of remote sensing and geospatial modeling techniques will be critical in refining habitat predictions and improving targeted malaria control strategies. Projected Shifts in the Ecological Niche of Anopheles funestus Under Future Climate Scenarios Under future climate scenarios, significant changes in habitat suitability are projected, with profound implications for the distribution and potential malaria transmission risks associated with An. funestus . This overall contraction in habitat suitability, however, is not uniform across all regions, as localized expansions in specific districts suggest a more complex pattern of spatial reorganization rather than a straightforward decline. The analysis under SSP2-4.5 (Fig. 3 ) projects substantial shifts in habitat suitability, with 45,113 km² identified as unsuitable, indicating a considerable contraction in viable habitats compared to the current distribution. Areas experiencing decreased suitability amount to 26,152 km², suggesting a reduction in the availability of optimal conditions for the species. Nevertheless, 25,890 km² are projected to be moderately suitable, maintaining conditions that allow for the persistence of the vector, albeit at potentially lower densities. Furthermore, 8,029 km² are classified as suitable, and 6,594 km² are deemed highly suitable, representing regions where the species could thrive and pose an increased malaria transmission risk. A statistical comparison between current and future scenarios reveals a 35.6% reduction in the total area of suitability, with the most pronounced declines occurring in the low to moderately suitable categories, reflecting a shift towards marginal environmental conditions. These findings suggest that while some areas may become less conducive to An. funestus survival, pockets of high suitability will persist, necessitating targeted vector control efforts in these regions. A closer examination of spatial shifts under SSP2-4.5 reveals regional variations in habitat dynamics (Fig. 4 ). In the northern region, particularly in Chitipa and Karonga districts, a notable improvement in suitability is observed, especially in areas near the border with Zambia and Tanzania. Villages such as T/A Kameme, Mwaulambiya, Mwenewenya, and T/A Mwenemisiku in Chitipa, along with T/A Kyungu, Karonga Boma, and T/A Mwakawoko in Karonga, exhibit increased suitability, highlighting the northward expansion of viable habitats. Similarly, in the central region, Nkhotakota emerges as fully suitable, while Salima and Kasungu experience increased areas of accessibility under the medium suitability category. This expansion in central Malawi suggests potential shifts in malaria risk, necessitating localized monitoring. The southern region exhibits a more complex pattern, with localized variations where Blantyre emerges as the most suitable district, while areas such as Nsanje, and Mwanza range from low to moderate suitability. The persistence of moderate suitability in Mulanje, Phalombe, and Thyolo suggests that these areas may experience moderate climate change effects, positioning them as critical zones for future adaptation and mitigation strategies. In contrast, projections under SSP5-8.5 (Fig. 5 ) indicate even more pronounced changes, with habitat suitability dynamics shifting towards a scenario of reduced vector presence across most of the country. A substantial expansion of unsuitable areas is observed, covering 78,773 km², which represents a drastic reduction in the availability of viable habitats for An. funestus . The extent of low suitability is projected at 8,035 km², reflecting marginal conditions that may limit population establishment and survival. Moderate suitability decreases further to 5,813 km², reinforcing the trend of declining habitat quality. Suitable areas, estimated at 6,512 km², and highly suitable regions, covering 12,662 km², suggest that while overall habitat availability diminishes, specific pockets will still provide conducive environments for vector persistence and malaria transmission. A statistical analysis of the departure from the baseline scenario reveals a 55.7% decline in the total area classified as suitable to highly suitable, indicating a substantial contraction in viable habitats. The persistence of highly suitable zones, despite an overall reduction in vector habitat, suggests that An. funestus may experience range fragmentation, potentially leading to localized increases in population densities and vectorial capacity. Further analysis highlights that the highest habitat suitability for An. funestus is concentrated along key lakeshore areas, notably in the northern and central regions. In the Northern Region (Fig. 6 a), areas like Karonga, Nkhata Bay, and parts of Mzimba exhibit the highest distribution probability, with inland regions such as Mzuzu City and much of Rumphi showing lower suitability. These findings align with broader projections, where An. funestus habitat is shifting northward, with Chitipa emerging as one of the most suitable districts in the north under SSP5-8.5. This northward shift is further supported by significant range gains in Karonga and Nkhata Bay, suggesting that these regions may benefit from climate-driven increases in vector habitat availability. Conversely, the Central Region (Fig. 6 b) shows a more complex scenario, with districts like Dedza experiencing a reduction in suitability compared to previous projections under SSP2-4.5. This shift suggests a movement towards more marginal conditions in these areas. Inland districts such as Kasungu and Ntchisi show moderate suitability, Nkhotakota and Salima continue to show high suitability, while Lilongwe and its surrounding districts display lower suitability, marking a clear trend of declining habitat quality. This reversal underscores the dynamic nature of habitat suitability, with some regions transitioning from viable to marginal or unsuitable habitats due to changing climatic conditions. In the Eastern Region (Fig. 6 c), the northern portion, particularly Mangochi District, emerges as a key area of high suitability. These lakeshore regions are expected to remain the most persistent habitats for An. funestus , with favorable conditions provided by the proximity to water bodies. However, the southern part of the region, including areas like Machinga, Zomba, and Balaka, shows a marked transition towards unsuitable conditions. This ecological shift is likely driven by rising temperatures and changing precipitation patterns, which restrict the establishment of stable vector populations. The spread of unsuitable areas in the south suggests a broader regional trend, where climate-induced changes are limiting the availability of suitable habitats for An. funestus , further reducing malaria transmission risks in these areas. Despite the contraction of suitable areas across much of the country, certain localized pockets maintain moderate to high suitability, particularly in districts like Mangochi and Machinga. These areas could act as refugia for An. funestus , providing persistent habitats for vector populations despite the broader trend of declining suitability. Such localized zones are crucial in understanding future malaria transmission risks, as they may continue to harbor vector populations capable of sustaining transmission in the face of overall habitat reduction. These zones underscore the need for targeted vector control efforts, even in regions where broader climate trends suggest a decline in malaria risk. As the Southern Region continues to experience a widespread loss of suitability, particularly in areas like Mwanza, Blantyre, and Chikwawa, it remains clear that some regions may retain their status as high-risk zones for malaria (Fig. 6 d). Despite the ongoing shift in habitat suitability, Blantyre and Chikwawa maintain their status as persistent hotspots, indicating that climate change may alter the spatial distribution of malaria transmission but not necessarily eliminate these risks. The persistence of high-suitability areas, even as the broader landscape becomes less favorable, suggests that An. funestus could experience fragmentation in its range, leading to localized increases in population densities and vectorial capacity in these remaining favorable habitats. The comparative analysis between current and future scenarios highlights the differential impacts of climate change on the ecological niche of An. funestus . Under both SSP2-4.5 and SSP5-8.5, there is a marked contraction of highly suitable areas relative to the baseline distribution, signaling a shift in environmental conditions that could influence vector population dynamics and malaria risk. While SSP2-4.5 maintains a more balanced distribution of suitability categories, SSP5-8.5 demonstrates a more extreme shift towards unsuitability, suggesting that climate-driven changes may substantially reduce the geographic extent of viable habitats for An. funestus . However, the persistence of highly suitable areas under both scenarios underscores the need for localized interventions, as these regions could serve as refugia for the species and sustain malaria transmission despite broader climatic constraints. Statistical projections further reveal that in high-emission scenarios, the contraction in habitat suitability follows a nonlinear trajectory, with a projected acceleration of habitat loss beyond 2050. This trend indicates that adaptation strategies must incorporate dynamic modeling approaches that account for potential ecological tipping points. Given these projections, integrating climate change considerations into vector control strategies is crucial. It is essential to focus on adaptive surveillance, targeted interventions in high-suitability zones, and enhanced modeling efforts to refine future risk assessments, ensuring that the response to malaria transmission risks remains flexible and data-driven as conditions evolve. The implications of these findings are profound, emphasizing the complex interplay between climate change and vector ecology. While the contraction of suitable habitats signals a potential reduction in malaria transmission across much of the country, the persistence of high-suitability pockets suggests that localized interventions will remain critical. Under SSP5-8.5, spatial trends reveal not only a northward expansion of suitable areas but also a significant contraction in the central and southern regions, reinforcing the need for adaptive malaria control measures. These localized refugia could sustain An. funestus populations, potentially continuing malaria transmission in certain areas despite broader climatic shifts. Discussion The findings of this study provide crucial insights into the current and future distribution of Anopheles funestus in Malawi, highlighting the interplay between climatic and environmental factors in shaping its ecological niche. The baseline distribution under historical climate conditions (1950–2000) reveals a predominantly unsuitable landscape for A. funestus , with only limited regions exhibiting moderate to high suitability. This pattern underscores the strong environmental constraints on the vector’s persistence, primarily driven by temperature, precipitation variability, and habitat availability. The observed clustering of highly suitable areas around river systems, wetlands, and low-lying regions aligns with previous studies that emphasize the species’ reliance on stable water sources and humid microhabitats for breeding [ 23 , 26 ]. This ecological specificity not only delineates malaria transmission hotspots but also informs targeted vector control strategies to mitigate public health risks in high-burden areas. The projected shifts under future climate scenarios (SSP2-4.5 and SSP5-8.5) reveal a complex trajectory of habitat reorganization, rather than a simple contraction or expansion. Under SSP2-4.5, the overall contraction of suitable habitat by 35.6% suggests that climate change will impose additional constraints on A. funestus populations. However, localized expansions in the northern and central regions, particularly in Chitipa, Karonga, Nkhotakota, and Salima, indicate that climate change may facilitate vector establishment in previously less suitable areas. Such shifts align with broader research on malaria vector ecology, which suggests that warming temperatures and altered precipitation regimes could expand vector habitats into higher latitudes and altitudes [ 3 , 21 ]. These northward and central expansions raise concerns regarding potential increases in malaria transmission risks, necessitating proactive surveillance and control measures in emerging high-suitability zones. Under the more extreme SSP5-8.5 scenario, a stark reduction in suitability is observed, with highly suitable areas declining by 55.7%. The widespread expansion of unsuitable conditions suggests that rising temperatures and increased aridity could severely limit A. funestus habitats, potentially leading to population fragmentation. This aligns with findings from previous studies indicating that higher temperatures beyond optimal breeding thresholds can negatively impact mosquito survival and reproductive success [ 18 – 19 ]. Nevertheless, the persistence of isolated highly suitable zones—particularly in the north (Chitipa, Rumphi, and Mzimba) and in high-risk southern areas such as Blantyre—implies that malaria transmission could remain a localized but persistent threat. These findings emphasize the necessity for geographically tailored vector control interventions that address both declining and emerging risk areas. A key implication of these findings is the potential for climate-driven habitat fragmentation, which could have unforeseen consequences on malaria transmission dynamics. As A. funestus populations become more spatially constrained, there is a possibility of increased population densities in remaining highly suitable areas, which may enhance vectorial capacity and disease transmission intensity. Such ecological shifts have been documented in other vector species, where habitat fragmentation has led to increased human-vector contact and altered disease epidemiology [ 25 ]. This underscores the importance of integrating climate change projections into malaria control programs, ensuring that interventions are responsive to dynamic ecological conditions. Despite these valuable insights, this study is subject to certain limitations. The habitat suitability models rely primarily on climatic variables, without incorporating other critical ecological and anthropogenic factors such as land use changes, vector control measures, and socioeconomic conditions. For example, irrigation schemes, deforestation, and urban expansion could create localized breeding conditions that are not captured in the current projections, potentially altering the predicted distribution of A. funestus . Future studies should incorporate high-resolution land use and socio-environmental data to refine predictive accuracy and better inform malaria mitigation strategies. Furthermore, the reliance on climate projections introduces inherent uncertainties, particularly under high-emission scenarios where future temperature and precipitation trends may diverge from model predictions due to feedback mechanisms and policy interventions. Given that global efforts to curb greenhouse gas emissions could influence future climatic conditions, adaptive malaria control strategies should incorporate a range of possible climate futures to ensure resilience against varying outcomes. All in all, this study provides a foundational understanding of the climatic determinants of A. funestus distribution in Malawi and how climate change may reshape its ecological niche. While overall habitat suitability is projected to decline, localized expansions in key regions highlight the evolving malaria risk landscape, necessitating dynamic and geographically tailored intervention strategies. Future research should integrate environmental, socioeconomic, and policy dimensions to enhance the predictive robustness of malaria vector distribution models. By aligning climate-sensitive vector control measures with emerging epidemiological trends, Malawi can strengthen its malaria elimination efforts in the face of a changing climate. Conclusion This study provides critical insights into the future distribution of Anopheles funestus in Malawi under changing climatic conditions. Using ecological niche modeling with MaxEnt, we project significant shifts in the habitat suitability of this primary malaria vector, driven by rising temperatures, altered precipitation patterns, and changes in humidity. Under both SSP2-4.5 and SSP5-8.5 climate scenarios, our findings suggest a general northward and elevational expansion of suitable habitats. These shifts highlight the increasing vulnerability of previously low-risk regions, particularly high-altitude areas that have historically been unsuitable for malaria transmission. At the same time, some lowland areas are projected to experience habitat contraction due to increasing aridity, underscoring the complex and spatially variable nature of climate change impacts on malaria vector ecology. The redistribution of An. funestus presents significant challenges for malaria control efforts in Malawi. Traditional vector control strategies, which have largely focused on historically endemic regions, may become inadequate as malaria transmission potential expands into new ecological zones. The emergence of high-suitability areas in the central and northern regions suggests that malaria intervention programs must be re-evaluated to ensure they remain geographically relevant and effective. Strengthened entomological surveillance, particularly in highland regions, will be essential for early detection and rapid response to emerging malaria risks. Furthermore, proactive planning must include climate-informed malaria control policies that integrate predictive modeling to guide resource allocation, intervention strategies, and health system preparedness. One of the most pressing concerns raised by this study is the potential for habitat fragmentation, which could lead to increased vector densities in remaining high-suitability zones. Such localized mosquito population increases could exacerbate transmission risks, even in areas where overall habitat suitability declines. This highlights the need for an adaptive malaria control framework that accounts for not just vector presence but also changes in vector abundance and behavior in response to shifting environmental conditions. Beyond climate factors, malaria risk is influenced by a range of socio-environmental determinants, including land-use changes, human migration patterns, and socioeconomic conditions. Future research should integrate these factors into predictive models to refine risk assessments and enhance the effectiveness of intervention strategies. Furthermore, incorporating epidemiological modeling alongside vector distribution projections would provide a more comprehensive understanding of malaria risk dynamics, allowing for more precise targeting of control measures. This study makes a valuable contribution to the growing body of research on climate change and malaria vector ecology, providing high-resolution, spatially explicit projections tailored to Malawi’s unique environmental conditions. The findings underscore the urgency of incorporating climate change considerations into malaria control programs at both national and regional levels. Effective adaptation will require cross-sectoral collaboration, combining expertise from climate science, epidemiology, public health, and policy-making to develop integrated strategies that anticipate and respond to evolving malaria risks. By embracing climate-informed malaria control approaches, Malawi can proactively mitigate the threat posed by shifting An. funestus distributions. Strengthening resilience through data-driven decision-making and targeted interventions will be key to sustaining malaria mitigation efforts and safeguarding public health in the face of climate change. Failure to act on these insights could allow malaria to re-emerge in previously unaffected areas, reversing hard-won gains in disease control. Proactive planning and continuous monitoring will be essential to ensuring that malaria prevention efforts remain effective and responsive to the rapidly changing environmental landscape. Declarations Additional Information Competing interests statement The authors declare that they have no competing interests that could influence the research, analysis, or conclusions presented in this manuscript. No financial, personal, or professional conflicts of interest exist that could have affected the integrity or objectivity of this work. Consent for publication We, the authors of this manuscript, consent to the publication of the research findings presented herein. All authors have reviewed and approved the final version of the manuscript and confirm that the content is original, does not infringe upon the rights of others, and has not been published elsewhere. Additionally, we confirm that all necessary ethical approvals have been obtained, and participant consent, where applicable, has been secured. Funding This research was supported by from the JRS Biodiversity Foundation. Author Contribution All authors contributed to the conception and design of the study. Material preparation, data collection, analysis was conducted by G.P. IT supervised the work and produced all versions of the manuscripts. G.P. provided feedback on previous versions of the manuscripts. All authors read and approved the final manuscript. Acknowledgement This research was supported by the Biodiversity Informatics Programme at the Malawi University of Science and Technology. We would like to express our sincere gratitude to the JRS Biodiversity Foundation for their generous funding. We also extend our heartfelt thanks to the Malaria Alert Centre for providing invaluable access to the data that greatly contributed to this work. Data Availability The bio-climatic variable data used in this study are publicly available from the WorldClim v2.1 dataset, which can be accessed at www.worldclim.org/2.1. The dataset includes high-resolution global climate data, which were used to model the distribution of Anopheles funestus in Malawi. Data related to Anopheles funestus are available upon reasonable request to the Malaria Alert Centre, where the data are stored securely and can be provided following approval from the relevant authorities; to access this data, please contact Professor Don Mathanga, the Director of the Malaria Alert Centre, via email at [email protected] . References Akpan, G.E., Adepoju, K.A., Oladosu, O.R. & Adelabu, S.A. 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Thermal limits of wild and laboratory strains of two African malaria vector species, Anopheles arabiensis and Anopheles funestus . Malaria Journal 11 , 1-4; https://doi.org/10.1186/1475-2875-11-226 (2012). Mategula, D. et al. Two decades of malaria control in Malawi: geostatistical analysis of the changing malaria prevalence from 2000-2022. Wellcome Open Research 8 , 264 (2024). Matengeni, A., Takala-Harrison, S., Walker, E.D. & Wilson, M.L. Understanding the intransigence of malaria in Malawi. The American journal of tropical medicine and hygiene 107 ,40 (2022). Minakawa, N., Sonye, G., Mogi, M., Githeko, A. & Yan, G. The effects of climatic factors on the distribution and abundance of malaria vectors in Kenya. Journal of medical entomology 39 , 833-41 (2002). Msugupakulya, B.J. et al. Changes in contributions of different Anopheles vector species to malaria transmission in east and southern Africa from 2000 to 2022. Parasites & vectors 16 , 408; https://doi.org/10.1186/s13071-023-06019-1 (2023). Omumbo, J.A., Lyon, B., Waweru, S.M., Connor, S.J. & Thomson, M.C. Raised temperatures over the Kericho tea estates: revisiting the climate in the East African highlands malaria debate. Malaria Journal 10 , 1-6; https://doi.org/10.1186/1475-2875-10-12 (2011). Paaijmans, K.P., Read, A.F. & Thomas, M.B. Understanding the link between malaria risk and climate. Proceedings of the National Academy of Sciences 106 , 13844-13849 (2009). Parham, P.E. & Michael, E. Modeling the effects of weather and climate change on malaria transmission. Environmental health perspectives 118 , 620-6; https://doi.org/10.1289/ehp.0901256 (2010). Phillips, S.J., Anderson, R.P. & Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecological modelling 190 , 231-59 (2006). Ryan, S.J., Carlson, C.J., Mordecai, E.A. & Johnson, L.R. Global expansion and redistribution of Aedes-borne virus transmission risk with climate change. PLoS neglected tropical diseases 13 , e0007213; https://doi.org/10.1371/journal.pntd.0007213 (2019). Severe Malaria Observatory Malawi Malaria facts. https://www.severemalaria.org/countries/malawi (2023). Sinka, M.E. et al. The dominant Anopheles vectors of human malaria in the Asia-Pacific region: occurrence data, distribution maps and bionomic précis. Parasites & vectors 4 , 89; https://doi.org/10.1186/1756-3305-4-89 (2011). Tompkins, A.M. & Di Giuseppe, F. Potential predictability of malaria in Africa using ECMWF monthly and seasonal climate forecasts. Journal of applied meteorology and climatology 54 , 521-40; https://doi.org/10.1175/JAMC-D-14-0156.1 (2015). Tusting, L.S., Housing improvements and malaria risk in sub-Saharan Africa: a multi-country analysis of survey data. PLoS medicine 14 , e1002234; https://doi.org/10.1371/journal.pmed.1002234 (2017). Weiss, D.J. et al. Air temperature suitability for Plasmodium falciparum malaria transmission in Africa 2000-2012: a high-resolution spatiotemporal prediction. Malaria journal 13 , 171; https://doi.org/10.1186/1475-2875-13-171 (2014). Yasuoka, J. & Levins, R. Impact of deforestation and agricultural development on anopheline ecology and malaria epidemiology. The American journal of tropical medicine and hygiene 76 , 450-60 (2007). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6372125","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447407522,"identity":"5ba56f5d-64b3-46d1-9338-a2a464be4ad6","order_by":0,"name":"Isaac Tchuwa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACNjBiYJAjQQsbRIsxEDM2EGsNWEtiA9Fa+OSbnz3mbbNLXzsj/fkDhho7Bt0ZCYQcxmZuzNuWnLvtRo5hA8OxZAazGwS18LBJ825jBmkBOoztAIPZbeK01Keb3Uh/2MDwj3gthxOA7jFsYGwjSkuameTcf8cNt515YzgjsS+Zx+z+A/xa5JsPP5N4c6Za3ux4+oMPH77ZyZmdOYBfCyoAOomHFPWjYBSMglEwCnAAAMY1PbXVr+3xAAAAAElFTkSuQmCC","orcid":"","institution":"Malawi University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Tchuwa","suffix":""},{"id":447407523,"identity":"9f00a088-245d-4d17-84cf-28928bd72abc","order_by":1,"name":"Gladson Phiri","email":"","orcid":"","institution":"Malawi University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Gladson","middleName":"","lastName":"Phiri","suffix":""}],"badges":[],"createdAt":"2025-04-03 21:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6372125/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6372125/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81367469,"identity":"e0f601b2-5af6-49cd-889a-1d9e79299c19","added_by":"auto","created_at":"2025-04-25 09:49:19","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102741,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Malawi showing mosquito sentinel collection sites used for \u003cem\u003eAnopheles funestus\u003c/em\u003e occurrence data. The distribution of collection points highlights key malaria-endemic areas included in the study.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6372125/v1/e4621ed78bfd16fa86cfecd5.jpeg"},{"id":81367470,"identity":"1ad8898f-90d7-4fc1-8243-477fa8214784","added_by":"auto","created_at":"2025-04-25 09:49:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58246,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted habitat suitability of \u003cem\u003eAnopheles funestus\u003c/em\u003eunder the current climate scenario (1950–2000). The bar chart represents the area (km²) classified into different suitability levels, ranging from \"Not suitable\" to \"Highly suitable.\" The distribution highlights the extent of potential mosquito habitat under historical climatic conditions.\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6372125/v1/d50187ab5e3c257558dbfa3a.jpg"},{"id":81367471,"identity":"66c13161-c265-40c8-9a34-3d97242ef847","added_by":"auto","created_at":"2025-04-25 09:49:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46337,"visible":true,"origin":"","legend":"\u003cp\u003eThe bar chart illustrates the projected ecological niche area under the SSP2-4.5 scenario for the period 2021–2040, based on the HadGEM3-LL climate model. The y-axis represents the area (km²), while the x-axis indicates the climate scenario and time period. Habitat suitability categories are represented by different colors: blue (not suitable), cyan (lower suitability), light green (moderate suitability), yellow (suitable), and red (highly suitable).\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6372125/v1/0d1b06d6a6311a7056f53c5e.jpg"},{"id":81368435,"identity":"2c2e768f-cdc8-4ca7-8e3a-d337759e08d1","added_by":"auto","created_at":"2025-04-25 09:57:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":464760,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of habitat suitability under SSP2-4.5 in different regions of Malawi. The map identifies areas where suitability is expected to increase or decline, providing insights into potential vector distribution changes under the moderate emission scenario.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6372125/v1/73afc2e91a2d98b7fcd64cd2.png"},{"id":81367474,"identity":"6b175910-1fbe-4b5b-afc0-59d71457da4c","added_by":"auto","created_at":"2025-04-25 09:49:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":57287,"visible":true,"origin":"","legend":"\u003cp\u003eThe bar chart presents habitat suitability under the SSP5-8.5 scenario for the period 2021–2040, based on the HadGEM3-LL climate model. The y-axis represents the area (km²), while the x-axis denotes the climate scenario and time period. Habitat suitability categories are indicated by different colors: cyan (not suitable), green (low suitability), orange (moderate suitability), brown (suitable), and white (highly suitable). The figure depicts projected changes, showing the expansion of unsuitable areas and the persistence of highly suitable regions under the high emissions scenario.\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6372125/v1/abf3fc57a333b8bb451e099e.jpg"},{"id":81367480,"identity":"19131a20-087c-4179-a117-849046b93ea6","added_by":"auto","created_at":"2025-04-25 09:49:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1798649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003e Habitat suitability of \u003cem\u003eAnopheles funestus\u003c/em\u003eunder SSP5-8.5 in the Northern Region. The map highlights key districts such as Karonga, Nkhata Bay, and Mzimba, where suitability remains high or increases under the high emission scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 6b.\u003c/strong\u003eHabitat suitability of \u003cem\u003eAnopheles funestus\u003c/em\u003eunder SSP5-8.5 in the Central Region. The figure shows variations in suitability across districts like Dedza, Kasungu, Nkhotakota, and Salima under the high emission scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 6c.\u003c/strong\u003eHabitat suitability of \u003cem\u003eAnopheles funestus\u003c/em\u003eunder SSP5-8.5 in the Eastern Region. The map highlights high suitability in Mangochi and declining suitability in Machinga, Zomba, and Balaka under the high emission scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 6d.\u003c/strong\u003eHabitat suitability of \u003cem\u003eAnopheles funestus\u003c/em\u003eunder SSP5-8.5 in the Southern Region. The figure presents spatial trends in suitability changes, emphasizing areas where vector presence may be limited by climate changes under the high emission scenario.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6372125/v1/ac632e911175fa6f27efca49.png"},{"id":83431710,"identity":"db927e82-1ee4-4bcc-9668-f4ee4c0c95d3","added_by":"auto","created_at":"2025-05-26 07:16:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3250571,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6372125/v1/e18db3a7-5b1a-4817-a921-1e4d43f20af1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Projected Shifts in the Distribution of Anopheles funestus under Future Climate Scenarios in Malawi","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalaria remains one of Malawi\u0026rsquo;s most persistent public health challenges, driven largely by the spatial distribution and behavioral ecology of its mosquito vectors. Among these, \u003cem\u003eAnopheles funestus\u003c/em\u003e stands out due to its strong anthropophilic tendencies, high vectorial capacity, and remarkable resilience, playing a crucial role in sustaining malaria transmission despite extensive control efforts. While its epidemiological significance has been well documented [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], critical uncertainties remain regarding how its distribution and abundance will respond to ongoing climatic shifts. The rapid pace of anthropogenic climate change, as extensively documented by the Intergovernmental Panel on Climate Change [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], is altering local weather patterns, with rising temperatures and increasing precipitation variability already influencing the geographic ranges of numerous species, including disease vectors.\u003c/p\u003e \u003cp\u003eThe association between climate variability and vector-borne disease dynamics including malaria is well established [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], yet current projections of malaria risk under future climatic conditions remain limited in scope and resolution, often failing to capture localized ecological responses and feedback mechanisms. Although substantial work has mapped the present-day distribution of \u003cem\u003eAn. funestus\u003c/em\u003e and other species across sub-Saharan Africa [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], significant gaps persist in understanding how future climatic conditions may reshape its ecological niche, particularly at finer spatial scales relevant for localized interventions. Previous studies have predominantly relied on static ecological niche models based on historical climate data, failing to capture the inherently dynamic nature of climate change and the non-linear responses of mosquito populations to environmental alterations. While some research has projected vector range shifts in response to changing temperature and precipitation regimes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], many fail to incorporate the full complexity of climate-driven habitat changes, including extreme weather events, alterations in land use, and interactions with other environmental stressors such as deforestation and urbanization [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These limitations are particularly critical given mounting evidence that malaria vectors are expanding into previously unsuitable areas, including high-altitude regions where malaria transmission was historically rare. Observations from other parts of Africa have already documented such expansions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], raising pressing concerns about the future distribution of malaria risk in sub-Saharan Africa.\u003c/p\u003e \u003cp\u003eWithin the Malawi\u0026rsquo;s context, research on malaria-climate interactions remains relatively underdeveloped, with most studies either extrapolating from broader regional assessments or relying on outdated climate datasets. While some studies have attempted to assess malaria transmission dynamics at national and district levels [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], there remains a lack of high-resolution, spatially explicit projections that account for future climate variability. This gap is particularly consequential given Malawi\u0026rsquo;s diverse topography and localized climate variations, which may differentially influence mosquito habitat suitability across regions. Moreover, existing policy frameworks for malaria control in Malawi rarely integrate climate projections into long-term vector management strategies, largely due to the absence of context-specific modeling efforts that can provide actionable insights for public health planning. Addressing this gap is essential for anticipating malaria burden under future climate scenarios and guiding the implementation of adaptive interventions.\u003c/p\u003e \u003cp\u003eDespite the importance of predictive modeling for malaria control planning, existing studies often employ simplistic climate scenarios that inadequately represent the full spectrum of potential future conditions. Many models fail to account for the physiological adaptability of \u003cem\u003eAn. funestus\u003c/em\u003e, particularly its capacity to exploit a wide range of breeding sites and its behavioral plasticity in response to environmental stressors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, while ecological niche models such as Maximum Entropy (MaxEnt) have been widely applied to species distribution studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], their utility for malaria vector modeling remains constrained by limitations in occurrence data and environmental predictor selection. More advanced approaches, integrating high-resolution climate projections with ecological and epidemiological modeling, are necessary to provide robust forecasts of malaria vector distributions.\u003c/p\u003e \u003cp\u003eIn response to these gaps, this study seeks to project future shifts in the distribution of \u003cem\u003eAn. funestus\u003c/em\u003e in Malawi over the mid-century period by integrating current occurrence records with state-of-the-art climate projections. Utilizing the MaxEnt modeling framework, a method validated for presence-only data, this research simulates potential habitat suitability under two contrasting climate scenarios. The SSP2-4.5 scenario represents a future with moderate warming under partial mitigation efforts, whereas the SSP5-8.5 scenario reflects a trajectory of unabated greenhouse gas emissions, resulting in more extreme climatic impacts. By leveraging robust datasets and incorporating rigorously validated modeling techniques, this study advances understanding of \u003cem\u003eAn. funestus\u003c/em\u003e distribution under climate change and provides scientifically rigorous projections essential for targeted malaria control efforts in Malawi. The findings contribute to existing knowledge by addressing the spatial and temporal limitations of previous studies, offering a more granular assessment of malaria vector dynamics within a changing climate. Moreover, the study provides a critical evidence base for policymakers, highlighting regions where intensified vector control efforts may be necessary and emphasizing the urgency of integrating climate-informed strategies into national malaria control programs. These insights can inform preemptive interventions, guiding adaptive vector control strategies and reinforcing public health preparedness in the face of ongoing climatic uncertainty.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eMalawi, a landlocked country in southeastern Africa, serves as the geographical focus of this study. Bordered by Tanzania to the north and northeast, Mozambique to the east, south, and west, and Zambia to the northwest, Malawi spans approximately 118,484 km\u0026sup2;. The country is characterized by diverse topographical features, including the Great Rift Valley, the Shire Highlands, and the extensive Lake Malawi, which covers about 20% of the national territory. This complex terrain, coupled with varied climatic zones ranging from tropical savannah to montane environments, exerts significant influence on the distribution, breeding ecology, and vectorial capacity of \u003cem\u003eAnopheles funestus\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe study specifically focuses on regions with high malaria endemicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e), where \u003cem\u003eAnopheles funestus\u003c/em\u003e serves as a dominant vector. These areas include the low-lying floodplains of the Shire River, the humid lake shore regions, and the mid-elevation agricultural zones. Each of these ecosystems presents distinct ecological parameters that influence vector abundance and transmission dynamics. The humid environments of lakeshore regions provide stable breeding habitats due to consistent water availability, while floodplains exhibit seasonal fluctuations that contribute to population surges following heavy rainfall events. Mid-elevation zones offer a mix of temporary and permanent breeding habitats, affected by deforestation, land use change, and irrigation practices. Sentinel sites distributed across the country, as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, provide occurrence-only data that enhance the spatial representation of \u003cem\u003eAnopheles funestus\u003c/em\u003e distribution and its response to climatic and environmental variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMalawi\u0026rsquo;s climate is primarily influenced by the Inter-Tropical Convergence Zone (ITCZ), the El Ni\u0026ntilde;o-Southern Oscillation (ENSO), and local orographic effects. These climatic drivers create distinct wet and dry seasons that shape the spatial and temporal dynamics of malaria vectors. Rising temperatures and shifting precipitation patterns associated with climate change are modifying the geographical range of \u003cem\u003eAnopheles funestus\u003c/em\u003e, expanding transmission zones into previously unsuitable areas, particularly in higher elevations. Empirical studies indicate that a 1\u0026ndash;2\u0026deg;C rise in temperature can accelerate the vector\u0026rsquo;s life cycle, enhancing reproductive rates and increasing the number of annual malaria transmission cycles. Rainfall variability is another critical determinant, as prolonged dry periods can reduce larval habitats, while extreme rainfall events may create transient breeding sites in flood-prone areas, exacerbating malaria outbreaks.\u003c/p\u003e \u003cp\u003eThe socio-ecological context of malaria transmission in Malawi is deeply intertwined with poverty, land use changes, and inadequate health infrastructure. Over 80% of Malawi\u0026rsquo;s population resides in rural areas where access to malaria prevention measures such as insecticide-treated nets (ITNs) and indoor residual spraying (IRS) remains limited. High population densities in urban and peri-urban areas further contribute to localized malaria hotspots due to poor drainage systems and stagnant water accumulation, which provide ideal breeding conditions for \u003cem\u003eAnopheles funestus\u003c/em\u003e. In addition, agricultural expansion into wetlands and riverine areas has altered natural mosquito habitats, with irrigation schemes facilitating year-round vector breeding. Studies have demonstrated that regions with extensive rice cultivation, such as those along the Lower Shire Valley, report higher malaria prevalence due to increased mosquito proliferation in standing water bodies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMalawi has one of the highest malaria burdens in southern Africa, with the disease accounting for nearly 30% of outpatient visits and 15% of hospital admissions. The malaria incidence rate remains persistently high, with the disease responsible for approximately 7\u0026nbsp;million cases across all ages [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], despite ongoing control efforts. Sentinel surveillance data from key transmission zones indicate that \u003cem\u003eAnopheles funestus\u003c/em\u003e remains a dominant vector, often coexisting with \u003cem\u003eAnopheles gambiae s.l.\u003c/em\u003e in multiple ecological settings. Understanding the spatial heterogeneity in malaria transmission requires a data-driven approach that incorporates climatic variability, vector ecology, and human-environment interactions.\u003c/p\u003e \u003cp\u003eMalawi presents a unique case study due to its pronounced climate variability, diverse ecological settings, and high disease burden. While previous studies have assessed malaria vector distributions in Africa, limited research has focused on Malawi\u0026rsquo;s specific climatic and environmental context. This study advances the discourse by integrating fine-scale climatic data, land use changes, and socio-ecological interactions into spatial-temporal analyses of \u003cem\u003eAnopheles funestus\u003c/em\u003e habitats. By leveraging empirical evidence from sentinel site observations, climate models, and geospatial analyses, this research enhances the predictive accuracy of vector distribution models while informing strategic vector control interventions. These insights are critical for guiding malaria elimination efforts, optimizing resource allocation for vector control, and strengthening adaptive strategies against climate-induced shifts in malaria transmission dynamics across southeastern Africa.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch design\u003c/h3\u003e\n\u003cp\u003eThe study employed an ecological niche modeling approach to predict potential range shifts of \u003cem\u003eAnopheles funestus\u003c/em\u003e under future climate scenarios. The research design followed a structured sequence, beginning with the compilation of georeferenced occurrence records from verified entomological surveys and peer-reviewed sources. Data cleaning procedures were implemented to remove duplicate records and spatial outliers that could introduce bias. Environmental predictors were selected based on their known influence on mosquito ecology, ensuring relevance to species distribution.\u003c/p\u003e \u003cp\u003eModel calibration involved testing multiple feature classes and regularization multipliers to identify the optimal parameter set, minimizing overfitting while preserving ecological interpretability. Spatial autocorrelation was assessed through Moran\u0026rsquo;s I to account for potential clustering effects in occurrence data. To validate model performance, an independent test dataset was withheld from model training, and predictive accuracy was evaluated through statistical metrics such as AUC and TSS. The final predictive maps were analyzed using a Geographic Information System (GIS) to visualize spatial trends in habitat suitability, facilitating interpretation of potential expansion, contraction, or stability of \u003cem\u003eAn. funestus\u003c/em\u003e distribution in response to projected climate change.\u003c/p\u003e\n\u003ch3\u003eFuture Climate Projections (2021–2040)\u003c/h3\u003e\n\u003cp\u003eTo project the potential shifts in the ecological niche of \u003cem\u003eAnopheles funestus\u003c/em\u003e under future climate conditions, bioclimatic variables for the mid-21st century (2021\u0026ndash;2040) were derived from the HadGEM3-GC31-LL model, developed by the UK Met Office Hadley Centre. This climate model was selected for ecological niche modeling (ENM) due to its high-resolution climate data, advanced aerosol and atmospheric components, and robust representation of ocean and land surface interactions. The HadGEM3-GC3.1-LL model, a key component of the Coupled Model Intercomparison Project phase 6 (CMIP6), is scientifically recognized for its credibility in climate impact assessments, including those presented in the IPCC Sixth Assessment Report (AR6), making it an ideal choice for studying climate-driven ecological patterns.\u003c/p\u003e \u003cp\u003eThe future climate predictions were run under two Shared Socio-economic Pathways (SSPs):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSSP2-4.5 (moderate emissions scenario)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSSP5-8.5 (high emissions scenario)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese scenarios encapsulate two plausible future trajectories for greenhouse gas emissions and associated global warming. The model simulations were based on two distinct experimental ensembles designed to represent Earth's climate from the historical period (1850\u0026ndash;2014) to the projected future (2014\u0026ndash;2099). The first ensemble was modeled under the SSP2-4.5 scenario, and the second ensemble was modeled under the Shared Socioeconomic Pathway SSP5-8.5 providing a comparative analysis of future climatic shifts under moderate and high emissions pathways, respectively.\u003c/p\u003e \u003cp\u003eThese scenarios were selected to capture a range of potential climate futures, enabling a robust assessment of how varying greenhouse gas (GHG) emissions pathways may influence malaria vector distribution. The SSP2-4.5 scenario represents a stabilizing emissions pathway, where moderate climate policies and technological advancements lead to a gradual reduction in radiative forcing. This scenario aligns with a world that transitions toward sustainable energy sources while maintaining significant economic growth. Conversely, the SSP5-8.5 scenario represents a high-emissions trajectory, characterized by continued reliance on fossil fuels, minimal climate mitigation efforts, and intensified global economic activity, resulting in substantial warming.\u003c/p\u003e \u003cp\u003eBy incorporating both moderate and high-emissions scenarios, this study ensures a comprehensive analysis of potential climate impacts on malaria vector habitats. This comparative approach allows for the identification of both near-term and long-term risks under different socio-economic and policy conditions. SSP5-8.5 reflects a world where rapid urbanization, infrastructure expansion, and population growth influence land use patterns\u0026mdash;factors that are critical in shaping malaria transmission dynamics. The inclusion of SSP5-8.5 provides an upper-bound estimate of climate-driven habitat expansion, offering valuable insights for proactive malaria control strategies in regions vulnerable to future climate change. This dual-scenario framework enhances the robustness of model projections, ensuring that decision-makers have a scientifically grounded basis for developing adaptive vector control policies in response to future climate variability.\u003c/p\u003e\n\u003ch3\u003eBioclimatic Variables\u003c/h3\u003e\n\u003cp\u003eA total of 19 bioclimatic variables (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were used for modeling the ecological niche of \u003cem\u003eAnopheles funestus\u003c/em\u003e in Malawi, with the variables consistent with those used in the baseline climate model. These variables were derived from the WorldClim 2.1 dataset at a spatial resolution of 30 arc-seconds (~\u0026thinsp;1 km\u0026sup2;), ensuring methodological consistency across the baseline and future climate conditions. The high spatial resolution allows for precise modeling of ecological conditions, which is crucial for capturing small-scale climatic variations and facilitating the comparison between baseline and projected climate scenarios.\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\u003eBioclimatic variables (BIO1\u0026ndash;BIO19) and their descriptions\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003eBioclimatic Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual Mean Temperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe mean temperature of the year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Diurnal Range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe difference between the daily maximum and minimum temperatures.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsothermality (Bio2/Bio7 * 100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA measure of the temperature variability over the year, expressed as a percentage.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature Seasonality (standard deviation * 100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe variation in monthly temperatures throughout the year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax Temperature of the Warmest Month (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe highest monthly temperature during the warmest month of the year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin Temperature of the Coldest Month (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe lowest monthly temperature during the coldest month of the year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature Annual Range (Bio5 - Bio6, \u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe difference between the maximum temperature of the warmest month and the minimum temperature of the coldest month.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Temperature of the Wettest Quarter (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe average temperature of the three months with the highest precipitation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Temperature of the Driest Quarter (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe average temperature of the three months with the lowest precipitation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Temperature of the Warmest Quarter (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe average temperature of the three warmest months of the year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Temperature of the Coldest Quarter (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe average temperature of the three coldest months of the year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual Precipitation (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe total precipitation received throughout the year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of the Wettest Month (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe total precipitation received in the wettest month of the year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of the Driest Month (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe total precipitation received in the driest month of the year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation Seasonality (Coefficient of Variation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA measure of the variation in precipitation patterns over the year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of the Wettest Quarter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe total precipitation received in the three months with the highest precipitation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of the Driest Quarter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe total precipitation received in the three months with the lowest precipitation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of the Warmest Quarter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe total precipitation received in the three warmest months of the year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of the Coldest Quarter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe total precipitation received in the three coldest months of the year.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese variables encapsulate both temperature and precipitation trends, including annual averages, seasonality, and extremes, all critical to understanding the ecological niche of \u003cem\u003eAnopheles funestus\u003c/em\u003e. For instance, temperature extremes such as the Max Temperature of the Warmest Month (Bio5) and the Min Temperature of the Coldest Month (Bio6) are essential for modeling mosquito survival, while precipitation variables like Precipitation of the Driest Quarter (Bio17) can provide insights into water availability for larval stages. Prior to model implementation, multicollinearity among variables was assessed using variance inflation factor (VIF) analysis, calculated as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{VIF}_{j}=\\frac{1}{(1-{R}_{j}^{2})}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e is the coefficient of determination from regressing the \u003cem\u003ej\u003c/em\u003e predictor against all other predictors. Variables with VIF\u0026thinsp;\u0026gt;\u0026thinsp;10 were excluded to mitigate redundancy and improve model stability. For example, Bio11, Bio12, Bio7, and Bio17 demonstrated zero percent contributions and zero permutation importance, suggesting that they had no significant influence on habitat suitability in this modeling context. As a result, these variables were removed from subsequent analysis to improve model efficiency and minimize redundancy.\u003c/p\u003e\n\u003ch3\u003eModeling Approach and Analysis\u003c/h3\u003e\n\u003cp\u003eSpecies distribution modelling was conducted using Maximum Entropy (MaxEnt) version 3.4.3, a machine-learning algorithm that has demonstrated superior predictive performance in ecological niche modeling due to its capacity to handle complex interactions and incomplete presence-only datasets. The modelling framework followed a rigorous workflow to ensure reproducibility and minimize biases associated with sampling and spatial autocorrelation. The MaxEnt model was configured using optimized regularization multipliers λ to prevent overfitting while maintaining biologically realistic response curves. The selection of λ was guided by Akaike Information Criterion corrected for small sample sizes (AICc), computed as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:AICs=-2ln\\left(L\\right)+2K+\\:\\frac{2K\\:(\\:K+1)}{n-K-1}\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eL\u003c/em\u003e is the likelihood of the model given the data, \u003cem\u003eK\u003c/em\u003e is the number of estimated parameters, and \u003cem\u003en\u003c/em\u003e is the sample size. The optimal \u003cem\u003eλ\u003c/em\u003e value was selected to balance goodness-of-fit and model complexity. Background points were randomly sampled within an ecologically relevant buffer zone surrounding known occurrence locations, ensuring that pseudo-absence data reflected realistic environmental conditions. To enhance model robustness, a k-fold cross-validation (k\u0026thinsp;=\u0026thinsp;10) approach was applied, partitioning occurrence data into training and test sets for iterative model validation. The model was run separately for each future climate scenario, keeping occurrence records constant to isolate the impact of projected climatic changes on habitat suitability.\u003c/p\u003e \u003cp\u003ePredictive accuracy was assessed using multiple statistical metrics. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) was used to quantify overall model performance, with values closer to 1.0 indicating superior discrimination between suitable and unsuitable habitat. The AUC was computed as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:AUC=\\:{\\int\\:}_{0}^{1}TPR\\left(FPR\\right)dFPR$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere TPR and FPR represent the true positive rate and false positive rate, respectively. Additionally, the True Skill Statistic (TSS) was computed as:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:TSS=TPR-FPR$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhich accounts for both omission and commission errors, providing a more ecologically meaningful measure of model accuracy. Post-processing of model outputs involved ecological niche area analysis and centroid shift analysis. Habitat suitability maps were thresholded using the maximum training sensitivity plus specificity approach to delineate the potential distribution of \u003cem\u003eAn. funestus\u003c/em\u003e under future climate scenarios. Niche expansion, contraction, and stability were quantified using a spatial overlay approach, allowing for direct comparison between baseline and projected distributions. The percentage change in habitat suitability area \u003cem\u003eΔA\u003c/em\u003e was computed as:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:A=\\:\\frac{{A}_{f}-{A}_{b}}{{A}_{b}}\\:\\times\\:\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sub\u003e represent the projected and baseline habitat areas, respectively. Centroid shift analysis was employed to determine the directional displacement of the species\u0026rsquo; optimal habitat, calculated as:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:C=\\:\\:\\sqrt{{({X}_{f}-\\:{X}_{b})}^{2}+\\:\\:{({Y}_{f}-\\:{Y}_{b})}^{2}}\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003e(X\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e,\u003c/sub\u003e \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e and \u003cem\u003e(X\u003c/em\u003e\u003csub\u003e\u003cem\u003eb\u003c/em\u003e,\u003c/sub\u003e \u003cem\u003eYb)\u003c/em\u003e represent the spatial coordinates of the habitat centroids under baseline and future scenarios, respectively. Directional vectors were analyzed to infer potential climate-driven range shifts. Overall, this methodological framework integrates rigorous parameter optimization, robust statistical evaluation, and spatial analytical techniques to ensure a scientifically credible and reproducible assessment of the future distribution of \u003cem\u003eAn. funestus\u003c/em\u003e under changing climatic conditions.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModel performance\u003c/h2\u003e \u003cp\u003eModel performance assessment using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), yielded a high value of 0.90, indicating excellent predictive capability. Key predictors influencing model performance included Mean Diurnal Range (Bio2, 30.7%), Mean Temperature of the Warmest Quarter (Bio10, 23%), Precipitation of the Wettest Month (Bio13, 21.6%), and Precipitation of the Driest Month (Bio14, 9.7%). These results demonstrate the model\u0026rsquo;s robustness in capturing the ecological niche of \u003cem\u003eAnopheles funestus\u003c/em\u003e, with temperature diurnality and precipitation seasonality emerging as critical determinants of habitat suitability.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eCurrent Distribution of\u003c/b\u003e \u003cb\u003eAnopheles funestus\u003c/b\u003e \u003cb\u003ein Malawi\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe baseline distribution of \u003cem\u003eAnopheles funestus\u003c/em\u003e in Malawi, as projected under current climatic conditions (1950\u0026ndash;2000), reveals a highly heterogeneous spatial pattern of habitat suitability. The analysis indicates that the majority of the country is classified as unsuitable for the species, with over 70,000 km\u0026sup2; falling within the 'Not suitable' category (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, distinct zones of suitability are evident, suggesting that \u003cem\u003eA. funestus\u003c/em\u003e is constrained by climatic and environmental factors that limit its establishment and proliferation in much of the country.\u003c/p\u003e \u003cp\u003eFrom the suitability analysis, approximately 20,000 km\u0026sup2; is classified as 'Low suitability,' signifying marginal conditions where the species could persist but likely at low densities. This category may be influenced by suboptimal temperature ranges, intermittent water availability, or seasonal fluctuations that do not consistently support breeding. In contrast, 'Moderately suitable' areas, covering roughly 7,500 km\u0026sup2;, provide more stable but still suboptimal conditions for the species. These areas may exhibit moderate humidity levels and sporadic but sufficient water sources for breeding, allowing the species to persist at relatively low population densities.\u003c/p\u003e \u003cp\u003eThe 'Suitable' regions, spanning around 5,000 km\u0026sup2;, represent habitats where \u003cem\u003eA. funestus\u003c/em\u003e can maintain stable populations. These zones are likely characterized by a combination of optimal temperature ranges (20\u0026ndash;30\u0026deg;C), consistent precipitation, and the presence of suitable breeding habitats such as stagnant water bodies and vegetative cover that provides protection and humidity. The most critical areas for \u003cem\u003eA. funestus\u003c/em\u003e under the current climate are the 'Highly suitable' regions, which cover an estimated 2,500 km\u0026sup2;. These zones likely coincide with key river systems, wetlands, and low-lying regions with persistent water bodies, which are essential for mosquito breeding. Given the vectorial capacity of \u003cem\u003eA. funestus\u003c/em\u003e in malaria transmission, these highly suitable areas may correspond with malaria transmission hotspots, underscoring their epidemiological significance.\u003c/p\u003e \u003cp\u003eThe spatial heterogeneity in the current distribution is likely influenced by multiple interacting climatic factors, including mean annual temperature, seasonal precipitation variability, and humidity levels, as well as non-climatic factors such as land use changes, agricultural activities, and human interventions such as vector control programs. The dominance of 'Not suitable' areas suggests that environmental constraints currently play a significant role in restricting the range of \u003cem\u003eA. funestus\u003c/em\u003e, although localized microclimatic conditions may still enable pockets of persistence within otherwise unsuitable regions. Anthropogenic activities such as irrigation projects and deforestation may inadvertently create microhabitats that facilitate mosquito breeding, altering local population dynamics.\u003c/p\u003e \u003cp\u003eUnderstanding the present-day distribution provides a crucial baseline for evaluating future shifts under projected climate change scenarios. Given that \u003cem\u003eA. funestus\u003c/em\u003e is a primary malaria vector in Malawi, any expansion or contraction of suitable habitats in response to climate change could have significant public health implications. Future projections must therefore consider not only climate-driven changes in habitat suitability but also anthropogenic factors that may mediate mosquito population dynamics and malaria risk in Malawi. Furthermore, continuous surveillance and integration of remote sensing and geospatial modeling techniques will be critical in refining habitat predictions and improving targeted malaria control strategies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eProjected Shifts in the Ecological Niche of\u003c/b\u003e \u003cb\u003eAnopheles funestus\u003c/b\u003e \u003cb\u003eUnder Future Climate Scenarios\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUnder future climate scenarios, significant changes in habitat suitability are projected, with profound implications for the distribution and potential malaria transmission risks associated with \u003cem\u003eAn. funestus\u003c/em\u003e. This overall contraction in habitat suitability, however, is not uniform across all regions, as localized expansions in specific districts suggest a more complex pattern of spatial reorganization rather than a straightforward decline. The analysis under SSP2-4.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e) projects substantial shifts in habitat suitability, with 45,113 km\u0026sup2; identified as unsuitable, indicating a considerable contraction in viable habitats compared to the current distribution. Areas experiencing decreased suitability amount to 26,152 km\u0026sup2;, suggesting a reduction in the availability of optimal conditions for the species. Nevertheless, 25,890 km\u0026sup2; are projected to be moderately suitable, maintaining conditions that allow for the persistence of the vector, albeit at potentially lower densities. Furthermore, 8,029 km\u0026sup2; are classified as suitable, and 6,594 km\u0026sup2; are deemed highly suitable, representing regions where the species could thrive and pose an increased malaria transmission risk. A statistical comparison between current and future scenarios reveals a 35.6% reduction in the total area of suitability, with the most pronounced declines occurring in the low to moderately suitable categories, reflecting a shift towards marginal environmental conditions. These findings suggest that while some areas may become less conducive to \u003cem\u003eAn. funestus\u003c/em\u003e survival, pockets of high suitability will persist, necessitating targeted vector control efforts in these regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA closer examination of spatial shifts under SSP2-4.5 reveals regional variations in habitat dynamics (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the northern region, particularly in Chitipa and Karonga districts, a notable improvement in suitability is observed, especially in areas near the border with Zambia and Tanzania. Villages such as T/A Kameme, Mwaulambiya, Mwenewenya, and T/A Mwenemisiku in Chitipa, along with T/A Kyungu, Karonga Boma, and T/A Mwakawoko in Karonga, exhibit increased suitability, highlighting the northward expansion of viable habitats. Similarly, in the central region, Nkhotakota emerges as fully suitable, while Salima and Kasungu experience increased areas of accessibility under the medium suitability category.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis expansion in central Malawi suggests potential shifts in malaria risk, necessitating localized monitoring. The southern region exhibits a more complex pattern, with localized variations where Blantyre emerges as the most suitable district, while areas such as Nsanje, and Mwanza range from low to moderate suitability. The persistence of moderate suitability in Mulanje, Phalombe, and Thyolo suggests that these areas may experience moderate climate change effects, positioning them as critical zones for future adaptation and mitigation strategies.\u003c/p\u003e \u003cp\u003eIn contrast, projections under SSP5-8.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e) indicate even more pronounced changes, with habitat suitability dynamics shifting towards a scenario of reduced vector presence across most of the country. A substantial expansion of unsuitable areas is observed, covering 78,773 km\u0026sup2;, which represents a drastic reduction in the availability of viable habitats for \u003cem\u003eAn. funestus\u003c/em\u003e. The extent of low suitability is projected at 8,035 km\u0026sup2;, reflecting marginal conditions that may limit population establishment and survival. Moderate suitability decreases further to 5,813 km\u0026sup2;, reinforcing the trend of declining habitat quality. Suitable areas, estimated at 6,512 km\u0026sup2;, and highly suitable regions, covering 12,662 km\u0026sup2;, suggest that while overall habitat availability diminishes, specific pockets will still provide conducive environments for vector persistence and malaria transmission.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA statistical analysis of the departure from the baseline scenario reveals a 55.7% decline in the total area classified as suitable to highly suitable, indicating a substantial contraction in viable habitats. The persistence of highly suitable zones, despite an overall reduction in vector habitat, suggests that \u003cem\u003eAn. funestus\u003c/em\u003e may experience range fragmentation, potentially leading to localized increases in population densities and vectorial capacity.\u003c/p\u003e \u003cp\u003eFurther analysis highlights that the highest habitat suitability for \u003cem\u003eAn. funestus\u003c/em\u003e is concentrated along key lakeshore areas, notably in the northern and central regions. In the Northern Region (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), areas like Karonga, Nkhata Bay, and parts of Mzimba exhibit the highest distribution probability, with inland regions such as Mzuzu City and much of Rumphi showing lower suitability. These findings align with broader projections, where \u003cem\u003eAn. funestus\u003c/em\u003e habitat is shifting northward, with Chitipa emerging as one of the most suitable districts in the north under SSP5-8.5. This northward shift is further supported by significant range gains in Karonga and Nkhata Bay, suggesting that these regions may benefit from climate-driven increases in vector habitat availability.\u003c/p\u003e \u003cp\u003eConversely, the Central Region (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) shows a more complex scenario, with districts like Dedza experiencing a reduction in suitability compared to previous projections under SSP2-4.5. This shift suggests a movement towards more marginal conditions in these areas. Inland districts such as Kasungu and Ntchisi show moderate suitability, Nkhotakota and Salima continue to show high suitability, while Lilongwe and its surrounding districts display lower suitability, marking a clear trend of declining habitat quality. This reversal underscores the dynamic nature of habitat suitability, with some regions transitioning from viable to marginal or unsuitable habitats due to changing climatic conditions.\u003c/p\u003e \u003cp\u003eIn the Eastern Region (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), the northern portion, particularly Mangochi District, emerges as a key area of high suitability. These lakeshore regions are expected to remain the most persistent habitats for \u003cem\u003eAn. funestus\u003c/em\u003e, with favorable conditions provided by the proximity to water bodies. However, the southern part of the region, including areas like Machinga, Zomba, and Balaka, shows a marked transition towards unsuitable conditions. This ecological shift is likely driven by rising temperatures and changing precipitation patterns, which restrict the establishment of stable vector populations. The spread of unsuitable areas in the south suggests a broader regional trend, where climate-induced changes are limiting the availability of suitable habitats for \u003cem\u003eAn. funestus\u003c/em\u003e, further reducing malaria transmission risks in these areas.\u003c/p\u003e \u003cp\u003eDespite the contraction of suitable areas across much of the country, certain localized pockets maintain moderate to high suitability, particularly in districts like Mangochi and Machinga. These areas could act as refugia for \u003cem\u003eAn. funestus\u003c/em\u003e, providing persistent habitats for vector populations despite the broader trend of declining suitability. Such localized zones are crucial in understanding future malaria transmission risks, as they may continue to harbor vector populations capable of sustaining transmission in the face of overall habitat reduction. These zones underscore the need for targeted vector control efforts, even in regions where broader climate trends suggest a decline in malaria risk.\u003c/p\u003e \u003cp\u003eAs the Southern Region continues to experience a widespread loss of suitability, particularly in areas like Mwanza, Blantyre, and Chikwawa, it remains clear that some regions may retain their status as high-risk zones for malaria (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). Despite the ongoing shift in habitat suitability, Blantyre and Chikwawa maintain their status as persistent hotspots, indicating that climate change may alter the spatial distribution of malaria transmission but not necessarily eliminate these risks. The persistence of high-suitability areas, even as the broader landscape becomes less favorable, suggests that \u003cem\u003eAn. funestus\u003c/em\u003e could experience fragmentation in its range, leading to localized increases in population densities and vectorial capacity in these remaining favorable habitats.\u003c/p\u003e \u003cp\u003eThe comparative analysis between current and future scenarios highlights the differential impacts of climate change on the ecological niche of \u003cem\u003eAn. funestus\u003c/em\u003e. Under both SSP2-4.5 and SSP5-8.5, there is a marked contraction of highly suitable areas relative to the baseline distribution, signaling a shift in environmental conditions that could influence vector population dynamics and malaria risk. While SSP2-4.5 maintains a more balanced distribution of suitability categories, SSP5-8.5 demonstrates a more extreme shift towards unsuitability, suggesting that climate-driven changes may substantially reduce the geographic extent of viable habitats for \u003cem\u003eAn. funestus\u003c/em\u003e. However, the persistence of highly suitable areas under both scenarios underscores the need for localized interventions, as these regions could serve as refugia for the species and sustain malaria transmission despite broader climatic constraints.\u003c/p\u003e \u003cp\u003eStatistical projections further reveal that in high-emission scenarios, the contraction in habitat suitability follows a nonlinear trajectory, with a projected acceleration of habitat loss beyond 2050. This trend indicates that adaptation strategies must incorporate dynamic modeling approaches that account for potential ecological tipping points. Given these projections, integrating climate change considerations into vector control strategies is crucial. It is essential to focus on adaptive surveillance, targeted interventions in high-suitability zones, and enhanced modeling efforts to refine future risk assessments, ensuring that the response to malaria transmission risks remains flexible and data-driven as conditions evolve.\u003c/p\u003e \u003cp\u003eThe implications of these findings are profound, emphasizing the complex interplay between climate change and vector ecology. While the contraction of suitable habitats signals a potential reduction in malaria transmission across much of the country, the persistence of high-suitability pockets suggests that localized interventions will remain critical. Under SSP5-8.5, spatial trends reveal not only a northward expansion of suitable areas but also a significant contraction in the central and southern regions, reinforcing the need for adaptive malaria control measures. These localized refugia could sustain \u003cem\u003eAn. funestus\u003c/em\u003e populations, potentially continuing malaria transmission in certain areas despite broader climatic shifts.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study provide crucial insights into the current and future distribution of \u003cem\u003eAnopheles funestus\u003c/em\u003e in Malawi, highlighting the interplay between climatic and environmental factors in shaping its ecological niche. The baseline distribution under historical climate conditions (1950\u0026ndash;2000) reveals a predominantly unsuitable landscape for \u003cem\u003eA. funestus\u003c/em\u003e, with only limited regions exhibiting moderate to high suitability. This pattern underscores the strong environmental constraints on the vector\u0026rsquo;s persistence, primarily driven by temperature, precipitation variability, and habitat availability. The observed clustering of highly suitable areas around river systems, wetlands, and low-lying regions aligns with previous studies that emphasize the species\u0026rsquo; reliance on stable water sources and humid microhabitats for breeding [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This ecological specificity not only delineates malaria transmission hotspots but also informs targeted vector control strategies to mitigate public health risks in high-burden areas.\u003c/p\u003e \u003cp\u003eThe projected shifts under future climate scenarios (SSP2-4.5 and SSP5-8.5) reveal a complex trajectory of habitat reorganization, rather than a simple contraction or expansion. Under SSP2-4.5, the overall contraction of suitable habitat by 35.6% suggests that climate change will impose additional constraints on \u003cem\u003eA. funestus\u003c/em\u003e populations. However, localized expansions in the northern and central regions, particularly in Chitipa, Karonga, Nkhotakota, and Salima, indicate that climate change may facilitate vector establishment in previously less suitable areas. Such shifts align with broader research on malaria vector ecology, which suggests that warming temperatures and altered precipitation regimes could expand vector habitats into higher latitudes and altitudes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These northward and central expansions raise concerns regarding potential increases in malaria transmission risks, necessitating proactive surveillance and control measures in emerging high-suitability zones.\u003c/p\u003e \u003cp\u003eUnder the more extreme SSP5-8.5 scenario, a stark reduction in suitability is observed, with highly suitable areas declining by 55.7%. The widespread expansion of unsuitable conditions suggests that rising temperatures and increased aridity could severely limit \u003cem\u003eA. funestus\u003c/em\u003e habitats, potentially leading to population fragmentation. This aligns with findings from previous studies indicating that higher temperatures beyond optimal breeding thresholds can negatively impact mosquito survival and reproductive success [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nevertheless, the persistence of isolated highly suitable zones\u0026mdash;particularly in the north (Chitipa, Rumphi, and Mzimba) and in high-risk southern areas such as Blantyre\u0026mdash;implies that malaria transmission could remain a localized but persistent threat. These findings emphasize the necessity for geographically tailored vector control interventions that address both declining and emerging risk areas.\u003c/p\u003e \u003cp\u003eA key implication of these findings is the potential for climate-driven habitat fragmentation, which could have unforeseen consequences on malaria transmission dynamics. As \u003cem\u003eA. funestus\u003c/em\u003e populations become more spatially constrained, there is a possibility of increased population densities in remaining highly suitable areas, which may enhance vectorial capacity and disease transmission intensity. Such ecological shifts have been documented in other vector species, where habitat fragmentation has led to increased human-vector contact and altered disease epidemiology [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This underscores the importance of integrating climate change projections into malaria control programs, ensuring that interventions are responsive to dynamic ecological conditions.\u003c/p\u003e \u003cp\u003eDespite these valuable insights, this study is subject to certain limitations. The habitat suitability models rely primarily on climatic variables, without incorporating other critical ecological and anthropogenic factors such as land use changes, vector control measures, and socioeconomic conditions. For example, irrigation schemes, deforestation, and urban expansion could create localized breeding conditions that are not captured in the current projections, potentially altering the predicted distribution of \u003cem\u003eA. funestus\u003c/em\u003e. Future studies should incorporate high-resolution land use and socio-environmental data to refine predictive accuracy and better inform malaria mitigation strategies.\u003c/p\u003e \u003cp\u003eFurthermore, the reliance on climate projections introduces inherent uncertainties, particularly under high-emission scenarios where future temperature and precipitation trends may diverge from model predictions due to feedback mechanisms and policy interventions. Given that global efforts to curb greenhouse gas emissions could influence future climatic conditions, adaptive malaria control strategies should incorporate a range of possible climate futures to ensure resilience against varying outcomes.\u003c/p\u003e \u003cp\u003eAll in all, this study provides a foundational understanding of the climatic determinants of \u003cem\u003eA. funestus\u003c/em\u003e distribution in Malawi and how climate change may reshape its ecological niche. While overall habitat suitability is projected to decline, localized expansions in key regions highlight the evolving malaria risk landscape, necessitating dynamic and geographically tailored intervention strategies. Future research should integrate environmental, socioeconomic, and policy dimensions to enhance the predictive robustness of malaria vector distribution models. By aligning climate-sensitive vector control measures with emerging epidemiological trends, Malawi can strengthen its malaria elimination efforts in the face of a changing climate.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides critical insights into the future distribution of \u003cem\u003eAnopheles funestus\u003c/em\u003e in Malawi under changing climatic conditions. Using ecological niche modeling with MaxEnt, we project significant shifts in the habitat suitability of this primary malaria vector, driven by rising temperatures, altered precipitation patterns, and changes in humidity. Under both SSP2-4.5 and SSP5-8.5 climate scenarios, our findings suggest a general northward and elevational expansion of suitable habitats. These shifts highlight the increasing vulnerability of previously low-risk regions, particularly high-altitude areas that have historically been unsuitable for malaria transmission. At the same time, some lowland areas are projected to experience habitat contraction due to increasing aridity, underscoring the complex and spatially variable nature of climate change impacts on malaria vector ecology.\u003c/p\u003e \u003cp\u003eThe redistribution of \u003cem\u003eAn. funestus\u003c/em\u003e presents significant challenges for malaria control efforts in Malawi. Traditional vector control strategies, which have largely focused on historically endemic regions, may become inadequate as malaria transmission potential expands into new ecological zones. The emergence of high-suitability areas in the central and northern regions suggests that malaria intervention programs must be re-evaluated to ensure they remain geographically relevant and effective. Strengthened entomological surveillance, particularly in highland regions, will be essential for early detection and rapid response to emerging malaria risks. Furthermore, proactive planning must include climate-informed malaria control policies that integrate predictive modeling to guide resource allocation, intervention strategies, and health system preparedness.\u003c/p\u003e \u003cp\u003eOne of the most pressing concerns raised by this study is the potential for habitat fragmentation, which could lead to increased vector densities in remaining high-suitability zones. Such localized mosquito population increases could exacerbate transmission risks, even in areas where overall habitat suitability declines. This highlights the need for an adaptive malaria control framework that accounts for not just vector presence but also changes in vector abundance and behavior in response to shifting environmental conditions.\u003c/p\u003e \u003cp\u003eBeyond climate factors, malaria risk is influenced by a range of socio-environmental determinants, including land-use changes, human migration patterns, and socioeconomic conditions. Future research should integrate these factors into predictive models to refine risk assessments and enhance the effectiveness of intervention strategies. Furthermore, incorporating epidemiological modeling alongside vector distribution projections would provide a more comprehensive understanding of malaria risk dynamics, allowing for more precise targeting of control measures.\u003c/p\u003e \u003cp\u003eThis study makes a valuable contribution to the growing body of research on climate change and malaria vector ecology, providing high-resolution, spatially explicit projections tailored to Malawi\u0026rsquo;s unique environmental conditions. The findings underscore the urgency of incorporating climate change considerations into malaria control programs at both national and regional levels. Effective adaptation will require cross-sectoral collaboration, combining expertise from climate science, epidemiology, public health, and policy-making to develop integrated strategies that anticipate and respond to evolving malaria risks.\u003c/p\u003e \u003cp\u003eBy embracing climate-informed malaria control approaches, Malawi can proactively mitigate the threat posed by shifting \u003cem\u003eAn. funestus\u003c/em\u003e distributions. Strengthening resilience through data-driven decision-making and targeted interventions will be key to sustaining malaria mitigation efforts and safeguarding public health in the face of climate change. Failure to act on these insights could allow malaria to re-emerge in previously unaffected areas, reversing hard-won gains in disease control. Proactive planning and continuous monitoring will be essential to ensuring that malaria prevention efforts remain effective and responsive to the rapidly changing environmental landscape.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003e \u003cb\u003eAdditional Information\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests statement\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests that could influence the research, analysis, or conclusions presented in this manuscript. No financial, personal, or professional conflicts of interest exist that could have affected the integrity or objectivity of this work.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eWe, the authors of this manuscript, consent to the publication of the research findings presented herein. All authors have reviewed and approved the final version of the manuscript and confirm that the content is original, does not infringe upon the rights of others, and has not been published elsewhere. Additionally, we confirm that all necessary ethical approvals have been obtained, and participant consent, where applicable, has been secured.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by from the JRS Biodiversity Foundation.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the conception and design of the study. Material preparation, data collection, analysis was conducted by G.P. IT supervised the work and produced all versions of the manuscripts. G.P. provided feedback on previous versions of the manuscripts. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research was supported by the Biodiversity Informatics Programme at the Malawi University of Science and Technology. We would like to express our sincere gratitude to the JRS Biodiversity Foundation for their generous funding. We also extend our heartfelt thanks to the Malaria Alert Centre for providing invaluable access to the data that greatly contributed to this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe bio-climatic variable data used in this study are publicly available from the WorldClim v2.1 dataset, which can be accessed at www.worldclim.org/2.1. The dataset includes high-resolution global climate data, which were used to model the distribution of Anopheles funestus in Malawi. Data related to Anopheles funestus are available upon reasonable request to the Malaria Alert Centre, where the data are stored securely and can be provided following approval from the relevant authorities; to access this data, please contact Professor Don Mathanga, the Director of the Malaria Alert Centre, via email at [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkpan, G.E., Adepoju, K.A., Oladosu, O.R. \u0026amp; Adelabu, S.A. Dominant malaria vector species in Nigeria: modelling potential distribution of \u003cem\u003eAnopheles gambiae sensu lato\u003c/em\u003e and its siblings with MaxEnt. \u003cem\u003ePloS one\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, e0204233; https://doi.org/10.1371/journal.pone.0204233 (2018).\u003c/li\u003e\n\u003cli\u003eBruce, M.C., Macheso, A., McConnachie, A. \u0026amp; Molyneux, M.E. 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Impact of deforestation and agricultural development on anopheline ecology and malaria epidemiology. \u003cem\u003eThe American journal of tropical medicine and hygiene\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e, 450-60 (2007).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anopheles funestus, ecological niche modeling, climate change, malaria vector, climate scenarios, shared socio-economic pathways","lastPublishedDoi":"10.21203/rs.3.rs-6372125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6372125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the projected shifts in \u003cem\u003eAnopheles funestus (An. funestus)\u003c/em\u003e distribution under future climate scenarios is crucial for strengthening malaria vector control strategies in Malawi. This study employs species distribution modeling using Maximum Entropy (MaxEnt), coupled with downscaled climate projections from Coupled Model Intercomparison Project Phase 6 (CMIP6), to assess habitat suitability under present and future climatic conditions (2021\u0026ndash;2040). Model evaluation indicated strong predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.91), ensuring reliable forecasts. The most influential bioclimatic variables shaping \u003cem\u003eAn. funestus\u003c/em\u003e distribution were mean temperature of the driest quarter (48.6% contribution) and precipitation of the warmest quarter (27.4% contribution). Future projections reveal a notable northward shift in suitable habitats, with increased risk in the Northern and Central regions, particularly along Lake Malawi\u0026rsquo;s shoreline. Under the shared socio-economic pathway (SSP)5-8.5 scenario, traditional malaria-endemic areas such as Nsanje are projected to experience a 31% decline in habitat suitability. In contrast, the likelihood of \u003cem\u003eAn. funestus\u003c/em\u003e presence is expected to increase by 42% in Karonga and Nkhata Bay, areas historically already considered high-risk. These findings suggest that climate change will significantly alter malaria transmission dynamics, potentially exposing previously low-risk populations to higher infection rates. To mitigate these emerging risks, it is imperative to integrate climate-driven vector distribution shifts into national malaria control strategies. Strengthened entomological surveillance, proactive vector management, and targeted interventions in newly emerging high-risk zones will be essential to prevent disease resurgence. This study underscores the need for adaptive, climate-responsive malaria control policies to safeguard public health in Malawi and beyond.\u003c/p\u003e","manuscriptTitle":"Projected Shifts in the Distribution of Anopheles funestus under Future Climate Scenarios in Malawi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-25 09:49:14","doi":"10.21203/rs.3.rs-6372125/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"868438f9-32f5-492a-8452-bd88338ecef1","owner":[],"postedDate":"April 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47622110,"name":"Biological sciences/Ecology"},{"id":47622111,"name":"Earth and environmental sciences/Climate sciences"},{"id":47622112,"name":"Earth and environmental sciences/Environmental sciences"},{"id":47622113,"name":"Earth and environmental sciences/Natural hazards"},{"id":47622114,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2025-05-26T07:08:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-25 09:49:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6372125","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6372125","identity":"rs-6372125","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}