Environmental determinants and potential suitability for Phlebotomus mascittii sand flies in central-western Europe under future Shared Socioeconomic Pathways climate change projections

Environmental determinants and potential suitability for Phlebotomus mascittii sand flies in central-western Europe under future Shared Socioeconomic Pathways climate change projections | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Environmental determinants and potential suitability for Phlebotomus mascittii sand flies in central-western Europe under future Shared Socioeconomic Pathways climate change projections Pedro Pérez-Cutillas, José Risueño, Elena Verdú-Serrano, Francis Schaffner, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8718479/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 Background Climate warming is expected to drive the northwards expansion of sand flies in Europe, increasing the risk of infection by sand fly-borne pathogens such as Leishmania spp. This study assessed the probability of sand fly presence in central-western Europe under various climate change scenarios for the periods 2041–2060 and 2081–2100. Methods We used summer climatic data from 10 global climate models and CORINE Land Cover variables to develop a parsimonious mixed-effects logistic regression model for sand fly presence on the basis of findings from 2023 and 2024 surveys across France, Germany, Luxembourg, Belgium, and the Netherlands—covering the northernmost extent of the known sand fly distribution—where the dominant species was the Leishmania infantum vector, Phlebotomus mascittii . The logistic function derived from this model was applied to project future sand fly distributions under the Shared Socioeconomic Pathways (SSPs), SSP2-4.5, SSP3-7.0, and SSP5-8.5, depicting scenarios of socioeconomic development and their impact on greenhouse gas emissions as outlined in the Sixth Climate Change Assessment Report (AR6). Results The spatially distributed probability model, which incorporates average temperature and solar radiation, predicts a 20–30% increase in the likelihood of Ph. mascittii presence across Luxembourg, Belgium, western Germany, and the southern Netherlands from 2041–2060 under all three SSP scenarios. By 2081–2100, the projected expansion in these northern regions intensified, reaching 35% under SSP2-4.5, 60% under SSP3-7.0, and 80% under SSP5-8.5. There is considerable variability in the predicted probability both between and within countries, influenced by country topography and latitudinal range. Conclusions It is highly probable that Ph. mascittii , will expand its natural distribution in central-western Europe into areas that are presently too cold and have insufficient solar radiation, to an extent that will depend on how global society, demographics, and economics might evolve over the 21st century. These predictions emphasize the need for adaptive surveillance and proactive measures to mitigate the risks of sand fly vector expansion and leishmaniosis transmission, as outlined in the CLIMOS project's goal to develop an early warning system. climate change Phlebotomus mascittii sand flies central-western Europe predictions Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Climate change is a critical factor in the emergence and distribution of infectious diseases, particularly those caused by pathogens that complete part of their life cycle outside the host. Among these diseases, vector-borne diseases such as leishmaniosis, caused by the protozoan parasites Leishmania spp., are of particular concern because of their significant health impact on both humans and animals [ 1 ]. Leishmania spp. are transmitted by phlebotomine sand flies (Diptera, Psychodidae), which are abundant in tropical and subtropical areas such as the Mediterranean Basin, and their distribution is strongly influenced by environmental and climatic factors [ 2 ]. Changes in temperature, humidity, and precipitation patterns directly affect their habitat, survival, and reproduction, thereby affecting leishmaniasis prevalence and geographical distribution [ 3 ]. It is highly probable that future climate change will lead to the emergence of sand flies and leishmaniosis in regions that were previously unaffected, such as many areas of Central Europe [ 4 ]. In Europe, Leishmania infantum causes canine and human leishmaniosis and is most prevalent in southern Mediterranean countries. However, the distribution of its sand fly vectors extends further north, with low-lying regions of northern France, Germany, Luxembourg, and Belgium representing the current limits of sand fly presence in central-western Europe [ 5 ]. Vector species in this region include Phlebotomus perniciosus and Ph. mascittii [ 6 ], the latter being the most northerly distributed species. Assessing the climate-driven expansion of Ph. mascittii into currently unsuitable latitudes and altitudes is therefore essential for evaluating the risk of L. infantum becoming endemic and for informing evidence-based strategies to mitigate long-term climate-related risks. Central Europe offers a unique setting for studying the effects of climate change, given the complex interactions among geographical, climatic, and socioeconomic factors [ 7 ]. The region is particularly susceptible to rising temperatures, with significant shifts in climatic patterns projected over the coming decades, leading to more frequent and prolonged heatwaves and changes in precipitation regimes [ 8 ]. These climatic transformations pose considerable risks to ecosystems, agriculture, and public health systems, exacerbating climate-sensitive challenges such as the spread of vector-borne diseases [ 9 , 10 ]. Furthermore, the high population density and interconnected urban infrastructure of Central Europe increase its vulnerability to the cascading effects of climate change, amplifying socioenvironmental impacts [ 11 ]. Spatial analysis of environmental and climatic data, combined with ecological niche modelling (ENM), is a key tool for understanding the distribution and potential expansion of species, particularly vectors of infectious diseases [ 12 , 13 ]. The ENM is used to predict the geographic range of species by correlating their known occurrences with environmental variables, thereby identifying the bioclimatic conditions that are suitable for their survival and reproduction. Spatial models have been developed to predict areas in Europe that may become suitable habitats for sand flies because of climate change, facilitating the identification of high-risk zones for the emergence of leishmaniasis [ 13 – 16 ]. Recent studies integrate vector climatic and habitat suitability with parasite development constraints and host or case data, enabling explicit infection-risk mapping [ 2 , 17 ]. Moreover, the incorporation of global climate models (GCMs) significantly enhances the predictive capacity of these niche models [ 2 ]. GCMs use representative concentration pathways (RCPs), which are greenhouse gas (GHG) concentration trajectories, to predict possible future climate conditions. The RCPs represent different levels of radiative forcing (the change in energy balance in Earth's atmosphere between the incoming energy from the sun and the outgoing energy from Earth back into space) by the year 2100. They were developed for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) to explore how human activities, such as fossil fuel use and deforestation, could influence global temperatures and climate patterns [ 18 ]. A significant methodological advancement, aligning climate scenario modelling with real-world demographic and socioeconomic trends and policy decisions, was the introduction by the Sixth Assessment Report (AR6) of the Shared Socioeconomic Pathways (SSPs) [ 19 , 20 ]. The SSPs consider five distinct scenarios that incorporate a broader range of factors beyond just greenhouse gas concentrations, including society's capacity to adapt to and mitigate climate change. This approach provides a more comprehensive perspective on future emissions and their impact on climate change. The aim of this work was to integrate SSP climate change projections into spatial and ecological modelling frameworks to identify potential future areas of Ph. mascittii presence in central-western Europe. This analysis employed data from sand fly surveys conducted in 2023 [ 21 ] and 2024 in France, Germany, Luxembourg, Belgium, and the Netherlands, by the research team of the present manuscript. The ultimate goal of this study is to contribute to the design of an early warning system (EWS) for sand flies and sand fly-borne pathogens to facilitate targeted public health interventions and sand fly control strategies, as proposed in the ongoing CLIMOS project ( https://climos-project.eu ) under the HORIZON-HLTH-2021-ENVHLTH-02 initiative. Methods Study area Sand fly modelling projections for Ph. mascittii focused on central-western Europe, which includes the area of sand fly surveys performed in the summers of 2023 [ 21 ] and 2024 (Schaffner, 2025). The area spans a narrow transect from south to north, extending from Savoie (Lat: 45.8219°N, Lon: 5.8492°E) in France to Zuid-Limburg (Lat: 50.8421°N, Lon: 5.7690°E) in the Netherlands, also encompassing sites in Belgium, Germany and Luxemburg. It is a key region in Central Europe and is characterised by a high degree of geographical, climatic, and demographic diversity (Fig. 1 ). This area is located at the transition point between the North European plains and the mountainous chains of the Alps and Jura, significantly influencing the ecological patterns and species distributions [ 5 ]. From a topographical perspective, it includes a variety of landscapes ranging from coastal lowlands in the Netherlands to the mountainous regions of Switzerland and France. The climate in this region varies from a temperate oceanic climate in the northwest to a humid continental climate in the interior and a Mediterranean climate in the south. The sand fly survey included 507 sampling sites (with unique geographical coordinates) in 140 localities in 26 NUTS3 (Nomenclature of Territorial Units for Statistics level 3) subdivissions (Fig. 1 ). They comprised most NUTS3 not surveyed before, according to ECDC´s February 2023 Ph. mascittii distribution map (Supplementary Figure S1 ; https://www.ecdc.europa.eu/en/publications-data/phlebotomus-mascittii-current-known-distribution-february-2023 ). The sampling locations were selected through a two-step approach. Initially, two or three locations within (NUTS3) along our transect were selected on the basis of a run of environmental suitability modelling for Ph. mascittii (updated from [ 22 ]). High-resolution images (Google Earth™) were then used to locate villages with suitable sand fly habitats, such as old houses/barns or cliffs. Sites where phlebotomines had been previously detected were also included [ 23 ]. Once in the field, specific sites for sand fly sampling were selected on the basis of environmental characteristics deemed favourable for phlebotomines [ 24 , 25 ]. These included sheltered gardens, stone walls, abandoned houses, basements, old barns with potential animal presence inside or nearby, rock piles, cliff bases, and caves. Sand fly trapping methods included 487 miniature CDC light traps-nights, 28 mouth and mechanical aspirating events (21 on resting spots and 7 on human-landings) and 6 sticky trapsnights series. Sand fly data A total of 153 sand flies were collected (55 in 2023 and 98 in 2024), including 108 Phlebotomus mascittii and 45 P. perniciosus . Specimens were obtained from 80 sampling sites across 51 localities in 19 NUTS3 subdivisions (Fig. 1 ). Phlebotomus mascittii was recorded at 69 sites in 46 localities and was present in all 19 NUTS3 subdivisions, whereas P. perniciosus was detected at 15 sites in 11 localities (6 together with Ph. mascittii) within five NUTS3 subdivisions. The latitudinal and altitudinal range of Ph. mascittii were 45.83°N–49.78°N and 142–715m. Likewise, for P. perniciosus these values were 45.82°N–48.02°N and 151–715m. Climatic and environmental data The summer climatic data were sourced from the latest WorldClim database version 2.1 [ 26 ], which provides data at a 30-second spatial resolution (approximately 1 km per pixel). It included the monthly averages of the 1970–2020 time series of temperature (maximum, minimum and average), solar radiation, precipitation, vapour (water) pressure, and wind speed within a 500-meter radius of each trapping site (buffer zone). Furthermore, land cover information in buffer zones was obtained from the georeferenced CORINE Land Cover 2018 dataset (EEA-CLC 2018) [ 27 ], with a spatial resolution of 250 m. To simplify the analysis, CLC codes were grouped into ecologically relevant classes and the percentage of each land use type in buffer zones was calculated. Climatic change scenarios Climate projections based on three SPPs (SPP2, SPP3 and SPP5) were incorporated into the modelling of future scenarios. SSP2 ("middle-of-the-road") projects a moderate, middle-ground future, with moderate efforts to mitigate climate change and relatively steady socioeconomic development and population growth. SSP3 ("regional rivalry") involves a fragmented world, where regional rivalries hinder international cooperation, leading to slow development, high emissions, and climate vulnerability. SSP5 ("fossil-fuelled development") anticipates rapid economic growth driven by fossil fuels, with high emissions and environmental degradation, leading to severe climate impacts, with the population peaking before declining [ 28 ]. Figure 2 illustrates the nonlinear relationships among the five SSPs and socioeconomic challenges for adaptation and mitigation (left), and radiative forcings for the different SSP scenarios and traditional RCP scenarios, reflecting the disparity between them (right). To represent realistic future climate change scenarios, we considered climatic data from 10 global climate models (GCMs) [ 29 – 38 ] (Table 1 ), under SSP2-4.5, SSP3-7.0, and SSP5-8.5, and for two 20-year periods: 2041–2060 and 2081–2100. The estimations produced by GCMs exhibit a significant degree of uncertainty that arises primarily from the variability observed among the different GCMs, internal climate variability and the challenges associated with downscaling climate model outputs to specific spatial and temporal scales [ 39 ]. To lessen this variability, an ensemble model was constructed that represented the average values of the multiple GCMs employed in the study. Table 1 Set of global climate models employed for the ensemble model. Global Climate Models Institution/Country Reference ACCESS-CM2 CSIRO-ARCCSS/Australia (Bi et al., 2020) CMCC-ESM2 CMCC/Italy (Lovato et al., 2022) EC-Earth3-Veg EC-Earth-Consortium/European consortium (Döscher et al., 2021) GISS-E2-1-G NASA-GISS/USA (Kelley et al., 2020) INM-CM5-0 INM/Russia (Volodin and Gritsun, 2018) IPSL-CM6A-LR IPSL/France (Boucher et al., 2020) MIROC6 MIROC/Japan (Tatebe et al., 2019) MPI-ESM1-2-HR MPI-M, DWD, DKRZ/Germany (Müller et al., 2018) MRI-ESM2-0 MRI/Japan (Yukimoto et al., 2019) UKESM1-0-LL MOHC/United Kingdom (Sellar et al., 2020) Note: Australian Community Climate and Earth System Simulator (ACCESS); Euro-Mediterranean Center on Climate Change (CMCC); Earth-Consortium (EC); Goddard Institute for Space Studies (GISS); Institute of Research for the Management of Natural Resources (INM); Institut Pierre-Simon Laplace (IPSL); Model for Interdisciplinary Research on Climate (MIROC); Max Planck Institute (MPI); Meteorological Research Institute (MRI); UK Earth System Modelling (UKESM). Statistical analysis and geo-modelling To minimise detection bias, only data from CDC light traps were included, comprising 487 trap-night collections from 476 sites across 132 localities. Phlebotomus mascittii was detected at 64 sites in 14 localities. The associations between the proportion of traps with Ph. mascittii (positive traps) and the climatic and land cover variables in buffer zones - categorised into three or four groups on the basis of variable distribution - were evaluated using Yates' chi-square test or Fisher's exact test, as appropriate [ 40 ]. Mixed-effects logistic regression models using maximum likelihood estimation [ 41 ], were then used to examine the multivariate relationship between Ph. mascittii presence in a trap (outcome binary variable), climatic, and land cover factors significantly associated in the bivariate analysis (explanatory variables). Locality was included as a random intercept to account for non-independence of traps placed in the same locality and to quantify residual spatial heterogeneity. Model selection was performed using a backward elimination approach starting from a saturated model. Predictors with high collinearity were sequentially removed based on variance inflation factor (VIF) values > 3 [ 42 ]. The final model was selected using the Akaike information criterion (AIC), retaining the combination of non-collinear variables with the lowest AIC. The intraclass correlation coefficient (ICC) was calculated on the logit scale from the random-effect variance to quantify the proportion of variance attributable to locality. Model explanatory power was assessed using marginal R² (fixed effects only) and conditional R² (fixed + random effects). Statistical significance was set at p < 0.05 for a two-sided test. All analyses were conducted in R [ 43 ]. To project potential sand fly presence under future climate scenarios, the logistic model was applied to CMIP6 climate data for SSP2-4.5, SSP3-7.0, and SSP5-8.5 for the periods 2041–2060 and 2081–2100 using QGIS (2024) [ 44 ]. The probability of sand fly presence, Y(s), was estimated using the logistic function( \(\:Y\left(s\right)=\:1/(1+{e}^{-({b}_{0}\text{+}{b}_{1}{X}_{1}\text{+}{\dots\:+b}_{n}{X}_{n}\text{)}})\) , where \(\:{b}_{1}\dots\:{b}_{n}\) are the estimated model coefficients and \(\:{X}_{1}\dots\:{X}_{n}\:\) are the corresponding continuous explanatory variables. Seven models were developed, one using current climatic data (historical data) and the remaining six corresponding to the previously described SSP x time period scenarios. To determine the evolution of the distribution of Ph. mascittii presence due to the effects of climate change, the difference between each of the six scenarios and the current model was calculated, and the percentage increase in the probability of Ph. mascittii presence was obtained. The regional boundaries of each country were used as the limits of these calculations, and they included 140 NUTS level 3 regions (Supplementary Table 1: Table S1 ). Results Relationships between Ph. mascittii presence and summer climate and land cover variables According to the bivariate analysis, the proportion of Ph. mascittii positive traps was significantly positively associated with the average total summer solar radiation, mean, maximum and minimum temperature and mean vapour pressure, negatively associated with the average mean wind speed (p < 0.05) (Table S2). Among the land cover variables considered, the proportion of positive traps was significantly associated, with areas containing pastures, green urban areas and sport and leisure facilities (p < 0.05), but the relationship was not linear, so the proportion of positive traps was 15% in areas where this land cover was not present and 7%, 21% and 14% in areas where it represented 1–33%, 34–66% and 67–100% of the land cover, respectively (Table S3). Also, the proportion of positive traps was marginally, associated to discontinuous urban fabric being lowest in sites with highest areas of this land cover (p < 0.10). The mixed-effects logistic regression model identified summer precipitation, mean minimum temperature, and solar radiation as important predictors of Ph. mascittii presence (Table 2 ). The marginal R² indicated that these climatic variables explained 19.3% of the variance in occurrence probability. Including locality as a random effect, raised the conditional R² to 31.8%, indicating that locality-level heterogeneity accounted for an additional proportion of variance beyond that explained by fixed effects alone. The unadjusted intraclass correlation coefficient (ICC) showed that 12.5% of the variance in Ph. mascittii presence was attributable to differences between localities. After adjusting for climatic predictors, the ICC increased to 15.4%, suggesting that summer precipitation, mean minimum temperature and solar radiation primarily explained variation among traps within localities, while residual heterogeneity between localities remained important. Table 2 Mixed-effects logistic regression analysis examining the relationship between sand fly presence and summer climate variables. Fixed explanatory variables are included as categorical (a) or continuous (b) variables. Variables Level Estimate Std. Error P value a) Categoriced Fixed effects Intercept -3.8204 0.7432 < 0.0001 Precipiation (mm) 170–215 0 216–260 -0.12633 0.43028 0.76906 262–306 -2.25923 1.15664 0.05079 Mean solar radiation (W/m²) 14467–15809 0 15817–17151 2.02778 0.8111 0.01242 17177–18514 2.46144 0.86011 0.00421 Minimum temperature ( 0 C) 10.2–11.3 0 11.4–12.4 -0.04581 0.65549 0.94429 12.5–13.5 1.35125 0.81005 0.0953 Random effect: variance estimate Locality 0.4455 b) Continuous Fixed effects Intercept -22.0500 3.7740 < 0.0001 Precipiation (mm) -0.0159 0.0083 0.0567 Mean solar radiation (W/m²) 0.0008 0.0003 0.0056 Minimum temperature ( 0 C) 0.8247 0.3656 0.0241 Random effect: variance estimate Locality 0.5348 Current and future large-scale spatial scenarios of sand fly presence The spatially distributed probability model, based on average mean temperature and total solar radiation, illustrates both the estimated current and projected future changes in the probability of sand fly presence under different climate scenarios (Fig. 3 ). The current model depicts a latitudinal gradient of sand fly presence, with the highest probabilities (> 90%) concentrated in inland and pre-littoral plains of southern France and northern Italy (purple areas in Fig. 3 ). The probability of sand fly presence decreased northwards, although it was greater than 35% in areas of the French Jura region and the upper Rhone River basin, as well as in parts of the Centre-Val de Loire and Nouvelle-Aquitaine regions. (orange areas in Fig. 3 ). Future scenario maps indicate increases in most of the study area (illustrated as a blue-to-red gradient, with blue denoting minimal or no change and red indicating the greatest increase in sand fly presence) (Fig. 3 ). The short-term projections (2041–2060) show a relatively uniform pattern across the three SSPs, with moderate increases (20–30%) in sand fly probability across the northernmost regions of the study area, particularly in Northern France, Luxembourg, Western Germany and Belgium. By the end of the 21st century (2081–2100), the predicted expansion intensifies significantly (Fig. 3 ). The projected average increases in sand fly probability reach 28% under SSP2-4.5, 45% under SSP3-7.0, and 57% under SSP5-8.5 for the area under study. Notably, the magnitude of change varies depending on the initial probability of presence. In areas where the current probability is already high (> 90%), such as parts of southeast France and northern Italy, the potential for further increase is naturally limited owing to the probabilistic ceiling (maximum 100%) (Fig. 3 ). Conversely, areas with historically low probabilities, particularly in Belgium, Luxembourg, Northern Germany and the Netherlands, demonstrate the most pronounced increases. Geographic expansion is particularly evident under SSP5-8.5, which represents a high-emission, fossil fuel-driven development pathway, indicating that stronger climate warming scenarios could accelerate the northwards spread of the vector (Fig. 3 ). Country-wide variability in the predicted probability of sand fly presence Figure 4 depicts the probability of sand fly presence in the current scenario (green horizontal bars, right Y axis), and the average values of the difference in the probability between the future and current scenarios (box plots, left Y axis), which are calculated for NUTS 3 geographical subdivisions (regions) for each country in the study area. The probability of sand fly presence is predicted to increase in every country and all SSP scenarios except in Monaco, a small and already sand fly endemic country. However, the impact of climate change on the probability of sand fly presence elsewhere differed substantially between and within countries. Compared with Austria, sand fly presence in Belgium, Liechtenstein and Luxembourg, Switzerland, Germany, France, and the Netherlands presented a wider range of probability increases in all the scenarios. Discussion Limitations of Ecological Niche Modelling and Climate Projections Several limitations should be considered when interpreting our projections. First, the model is based exclusively on climatic predictors and assumes that the relationships between sand fly presence and climatic variables remain constant under future conditions. As such, it does not explicitly account for dispersal constraints, host distribution, land-use changes, or other non-climatic factors known to influence sand fly occurrence [ 2 , 14 , 24 , 25 , 45 , 46 ]. Although climatically suitable conditions may emerge in northern Europe, natural dispersal processes may delay or prevent colonisation unless facilitated by anthropogenic factors such as passive transport through trade and travel. Nonetheless, adult sand flies are fragile insects with a limited capacity to survive long-distance passive dispersal [ 47 ]. Second, the quality and representativeness of Ph. mascittii occurrence data may be incomplete or geographically biased [ 48 ]. Sampling was limited to a restricted number of sites, although these covered a broad latitudinal gradient. Trapping sites were selected within comparable habitats to ensure consistency and to minimise known sampling biases associated with sand fly surveys. Third, while the mixed-effects modelling framework accounts for locality-level heterogeneity, future projections assume that random effects remain constant over time. This assumption may not fully capture temporal changes in local ecological conditions, land use, or anthropogenic influences that could modify sand fly suitability at the locality scale. Finally, uncertainties inherent to climate projections and Shared Socioeconomic Pathways (SSPs) may affect the precision of predicted suitability patterns [ 49 ]. SSP-based projections reflect different socioeconomic and greenhouse gas emission trajectories, making long-term forecasts inherently uncertain [ 28 ]. The transition from Representative Concentration Pathways (RCPs; CMIP5) to SSPs (CMIP6) has introduced methodological advances but has also altered model sensitivity to emissions, which may partly explain discrepancies with earlier studies [ 11 ]. In addition, general circulation models typically operate at coarse spatial resolutions that may not capture fine-scale climatic variability relevant to species distributions. While downscaling can improve spatial resolution, it may introduce additional uncertainty. Therefore, our projections should be interpreted as indicating potential areas of climatic suitability rather than definitive predictions of future sand fly distribution. Environmental Determinants of Sand Fly Distribution Predicting sand fly distributions using climatic models is challenging due to strong correlations among environmental variables and the limited understanding of how these factors influence population dynamics across seasons. Our analysis focused on summer, when sand fly activity peaks and coincides with the survey period. We evaluated climatic variables essential for larval development, including temperature, vapor pressure, and precipitation (used as a proxy for humidity) [ 50 ], as well as wind speed, which influences adult activity [ 51 ], and solar radiation. Although land use can influence sand fly occurrence - European populations typically prefer rural and peri-urban green areas over highly urbanised habitats [ 3 , 45 ] - microhabitat adaptability allows sand flies to persist in otherwise marginal climates [ 52 ]. In our study, the deliberate selection of sampling sites within suitable habitats likely explains why broader land-cover characteristics surrounding the sites were not associated with sand fly presence. Consequently, the discussion that follows focuses exclusively on climatic determinants of sand fly distribution. Summer temperature has been highlighted as a key predictor of sand fly distributions across Europe [ 16 ], while minimum summer temperature and solar radiation were identified as the best predictors of sand fly presence and abundance in Spain in the ongoing CLIMOS survey [ 5 ]. The importance of solar radiation is less intuitive, given that immature stages breed in sheltered habitats and adults are primarily nocturnal [ 51 ]. However, solar radiation may indirectly enhance adult activity by raising daytime temperatures, accelerating evaporation, and reducing humidity levels, thereby creating more favorable microclimates for nocturnal flight, as observed in studies across southern Europe and the United States [ 3 , 25 , 53 – 55 ]. The negative association between Ph. mascittii presence and high mean precipitation is biologically plausible. Heavy rainfall reduces adult activity and survival and can disrupt the terrestrial microhabitats where immature stages develop [ 51 ]. At broader spatial scales, large-scale modelling of European sand fly distributions has shown that climatic moisture indices – which integrate precipitation with temperature and evapotranspiration – are stronger predictors of occurrence than precipitation alone, suggesting that sand flies respond primarily to net moisture availability rather than rainfall totals [ 56 ]. Species-specific evidence from Austria supports this view: relative humidity was significantly associated with Ph. mascittii abundance, with peak activity at intermediate humidity and reduced activity at both higher and lower extremes, reflecting a non-linear and species-specific response to moisture [ 4 ]. Despite these moisture-related constraints, Ph. mascittii exhibits the widest climatic activity niches among European sand fly species, and recent analyses of meteorological limits suggest that its broad ecological tolerance may facilitate northward dispersal under ongoing climate change [ 57 ]. Unlike previous studies that employed the RCP scenarios of the IPCC, our study integrates the newer SSP scenarios from CMIP6. This methodological shift represents an improvement. The results obtained under SSP2-4.5, SSP3-7.0 and SSP5-8.5 reveal a northwards expansion of climatically suitable areas driven by rising temperatures, solar exposure and changes in seasonal climatic patterns, which is largely consistent with the projections of Fisher et al. (2011), Trájer et al. (2013), Koch et al. (2017) and Chalghaf et al. (2018) [ 13 – 16 ]. By the end of the 21st century (2081–2100), the predicted expansion intensifies significantly, with areas with historically zero or low probability, particularly in Belgium, Luxembourg, Northern Germany and the Netherlands, becoming new or more suitable habitats. The variation in the predicted probability of sand fly presence is wide in some countries. In Switzerland and northern Italy this would be associated with the country's topography, with the regions with the highest altitudes being least affected by climate change. In the cases of France and Germany, the effect is related to the wide latitudinal and altitudinal ranges of these countries. The variation predicted in the Netherlands under the three scenarios could be attributed to this country’s southern regions being at the limit of sand fly distribution under those scenarios. Sand fly species and subspecies may differ in their ideal ecological needs [ 3 ] and response to climate change [ 14 – 16 ]. The models do not forecast recession areas for Ph. mascittii in the study area due to climate change, which differs from the projection by Koch et al. (2017) [ 16 ], who predict that Ph. mascitti will be restricted to northern Europe between 2061 and 2080 under the worst-case RCP8.5 climate change scenario. Similarly, Fisher et al. (2011) [ 14 ] projected that Ph. mascittii in Germany would be primarily distributed in the centre and north of the country, with a low probability of occurrence in the south as early as 2011–2040. The differences between studies may be attributed to the consideration of socioeconomic factors in SSP scenarios, as well as the updated temperature projections for Central Europe in the present study. Currently, Ph. mascittii latitudinal range in Western Europe extends from southern Italy to southern Belgium and Germany, although at low densities [ 23 , 53 , 58 , 59 ]. Implications for Public Health, Vector Surveillance, and Future Research At present, the spatial overlap between autochthonous leishmaniosis and its vectors in the study area remains limited, largely because sand fly distributions extend further north than reported human and canine leishmaniosis cases [ 5 ]. However, the risk of introducing leishmaniosis into vector peripheral areas through frequent movement and importation of infected dogs – the domestic reservoir of L. infantum – is considered high [ 60 ]. The projected northwards expansion of sand fly vectors would naturally be associated to a parallel expansion of the parasite to areas where it has historically been absent. These findings support the need for coordinated, cross-sectoral surveillance strategies in line with EU One Health frameworks, integrating entomological surveillance, veterinary monitoring, and human health data. Early-warning systems combining ecological niche modelling with further vector surveillance will contribute to risk-based preparedness and targeted prevention, as advocated by the European Centre for Disease Prevention and Control (ECDC) and European Food Safety Authority (EFSA) for emerging vector-borne diseases. From a policy perspective, such approaches may inform harmonised surveillance priorities, guide resource allocation, and support evidence-based adaptation strategies under climate change. Future research should prioritise the integration of land-use, host density, and socioeconomic drivers into quantitative modelling frameworks to better reflect transmission risk. In parallel, improving the spatial resolution of climate projections through downscaling techniques would reduce uncertainty in local-scale habitat suitability assessments. Finally, systematic validation of model predictions through longitudinal field studies using standardised methodologies is essential to support robust risk assessment and to strengthen preparedness and response capacity across regions. Conclusions Climate change is expected to substantially reshape the distribution of phlebotomine sand flies in central-western Europe, enabling their expansion into regions that are currently constrained by low temperatures and limited solar radiation. This shift is likely to increase the risk of leishmaniosis transmission in areas where the disease has so far been absent. Our findings underscore the need for adaptive, risk-based surveillance and proactive mitigation strategies to anticipate and manage the expansion of sand fly vectors and associated pathogens. In this context, our projections directly support the objectives of the CLIMOS project, contributing to the development of early-warning systems aimed at strengthening preparedness and reducing climate-driven public health risks. Declarations Acknowledgements We are grateful to all co-authors of Risueño et al. (2024) and the VectorNet project and its contracting bodies, ECDC and EFSA, for the sand fly surveys that provided the data used in this study. Funding This article was financed by the CLIMOS Project (http://www.climos-project.eu) co-funded by European Commission grant 101057690 and UKRI grants 10038150 and 10039289, and the manuscript is catalogued by the CLIMOS Scientific Committee as CLIMOS number …. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission, the Health and Digital Executive Agency, or the UKRI. Neither the European Union nor the granting authority or the UKRI can be held responsible for them. Neither the European Commission nor the UKRI had roles in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. For the purposes of open access, the authors have applied a CC BY (2) public copyright licence to any Author Accepted Manuscript version arising from this submission. The six Horizon Europe projects, BlueAdapt, CATALYSE, CLIMOS, HIGH Horizons, IDAlert, and TRIGGER, form the Climate Change and Health Cluster. Availability of data and materials The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request. Author contributions PPC and EB contributed to the study conception, data analysis, interpretation of the results and writing of the article. JR, EV and FS contributed to the acquisition, organisation and analysis of the data, the review and acquisition of the reviewed literature and to the interpretation of the results. All the authors reviewed and approved the submitted version, taking personal responsibility for their contributions. They also commit to addressing any questions regarding the accuracy or integrity of the work, ensuring appropriate investigation, resolution, and documentation in the literature. Ethics and consent to participate This study was based on publicly available information, did not involve human or animal participants or materials, and did not require approval by an ethical committee. Consent for publication This manuscript does not include details, images, or videos related to an individual person, requiring consent for publication. Competing interests The authors declare that they have no competing interests. References Cosma C, Maia C, Khan N, Infantino M, Del Riccio M. Leishmaniasis in Humans and Animals: A One Health Approach for Surveillance, Prevention and Control in a Changing World. Trop Med Infect Dis. 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Supplementary Files PerezCutillasSFClimateChange2Suppltables.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8718479","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591277816,"identity":"c84d5823-86db-4e85-99ab-461272f33719","order_by":0,"name":"Pedro Pérez-Cutillas","email":"","orcid":"","institution":"University of Murcia","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Pérez-Cutillas","suffix":""},{"id":591277817,"identity":"0b8906fe-1709-4e5a-8374-48fa7fd9852a","order_by":1,"name":"José Risueño","email":"","orcid":"","institution":"University of Murcia","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"","lastName":"Risueño","suffix":""},{"id":591277822,"identity":"6f6ee2df-d4ee-4035-9c5a-ce22182f40e2","order_by":2,"name":"Elena Verdú-Serrano","email":"","orcid":"","institution":"University of Murcia","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Verdú-Serrano","suffix":""},{"id":591277823,"identity":"6d511ca0-8805-4b6b-b592-2fedf9aa99b7","order_by":3,"name":"Francis Schaffner","email":"","orcid":"","institution":"BioSys","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"","lastName":"Schaffner","suffix":""},{"id":591277824,"identity":"8b769b9b-8ffd-4ff0-b366-1b1c48dea162","order_by":4,"name":"Eduardo Berriatua","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYBACAxCRAMT8pGoxYJBsIEkLiDI4QKwWc/beZx8e1PxJ3HwjO4Hhwx8itFj2HDeekXDMIHHbjdwNjDPbiHHYjTRmhgQ2iBZm3gZitNx/BtTyzyBx8wyglj/EOMzgBhszQ2KbQeIGCaAWBjYitFj2AB2W2GdsPOPM2w0He4nxizn7MWbGH9/kZPvbczc++EGMw1DAAVI1jIJRMApGwSjAAQAeczdzaejHbwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Murcia","correspondingAuthor":true,"prefix":"","firstName":"Eduardo","middleName":"","lastName":"Berriatua","suffix":""}],"badges":[],"createdAt":"2026-01-28 09:35:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8718479/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8718479/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102789801,"identity":"9ac8e57d-fb1f-4b11-a261-f476c42afd0f","added_by":"auto","created_at":"2026-02-16 16:56:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":803528,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and sampling sites with \u003cem\u003ePhlebotomus\u003c/em\u003e spp. sand flies’ distribution.\u003c/p\u003e\n\u003cp\u003eNote: The black lines represent territorial delimitations, with thick lines representingcountries (AT: Austria; BE: Belgium; CH: Switzerland; DE: Germany; FR: France; IT: Italy;LI: Liechtenstein; LU: Luxembourg; MC: Monaco; NL: the Netherlands). Thin lines representregions defined by the Nomenclature of Territorial Units for Statistics level 3 (NUTS3). The background colour gradient shows the topography expressed in meters above sea level (m.a.s.l.).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8718479/v1/bbcb5c9ae66735221d5563ac.png"},{"id":102789805,"identity":"a1ed3f0c-1c1a-4523-8fda-781ac1e98969","added_by":"auto","created_at":"2026-02-16 16:56:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":377645,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of shared socioeconomic pathways (SSPs) (left) and radiative forcing of SPP and representative concentration pathways (RCPs) over time (right).\u003c/p\u003e\n\u003cp\u003eNote: RCPs are scenarios that describe different possible greenhouse gas concentration trajectories and their impact on radiative forcing (the change in energy balance in Earth's atmosphere, measured in watts per square meter, W/m²). SSPs complement RCPs describing scenarios according to different levels of socioeconomic development and their impact on greenhouse gas emissions. SSPs represent combinations of challenges to mitigation and adaptation with numbers in brackets indicating radiative forcing. Figures based on O'Neill et al., 2017 (left) and Riahi et al., 2017 (right).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8718479/v1/b07272285cd1a4f3224e6885.png"},{"id":102789799,"identity":"45c49c2a-dbb9-4b5f-a5dc-359afb1d1ba0","added_by":"auto","created_at":"2026-02-16 16:56:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1157706,"visible":true,"origin":"","legend":"\u003cp\u003eSpatially distributed model representation of the current and projected increased probability of phlebotomine sand fly presence.\u003c/p\u003e\n\u003cp\u003eNote: The colour gradient shows purple as the maximum probability and yellow as the lowest probability. The mapping of the differences in the probability of sand fly presence between the current model and the six models developed for the shared socioeconomic pathways (SSPs) revealsa range of red and blue colours, representing a high increase and a low increase in the probability of phlebotomine sand fly presence, respectively.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8718479/v1/7c434cc5846bca7a9c3776b3.png"},{"id":102789800,"identity":"796131c1-4a26-4a4b-bc90-f1db32913afa","added_by":"auto","created_at":"2026-02-16 16:56:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":294697,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot diagram illustrating the projected country-wide variability in the increased probability of sand fly presence for the SSP scenarios analysed.\u003c/p\u003e\n\u003cp\u003eNote: The values are obtained from the average probability data for each Nomenclature of Territorial Units for Statistics (NUTS) at level 2, in the study area. The number of NUTS-2 analysed by country was AT (Austria): 1; BE (Belgium): 11; CH (Switzerland): 7; DE (Germany): 25; FR (France): 56; IT (northern Italy): 25; LI (Liechtenstein): 1; LU (Luxembourg): 1; MC (Monaco): 1; NL (The Netherlands): 12.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8718479/v1/06610471f5978273fcd697a5.png"},{"id":107145403,"identity":"6dcf922a-5b09-4237-9059-cc39f8618dc8","added_by":"auto","created_at":"2026-04-17 09:42:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3129969,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8718479/v1/6b4d11a4-87a1-40b4-991a-6d485f7aa996.pdf"},{"id":102962473,"identity":"0910a75a-a2e3-4ea2-802c-b564db8acbb6","added_by":"auto","created_at":"2026-02-19 04:09:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":71493,"visible":true,"origin":"","legend":"","description":"","filename":"PerezCutillasSFClimateChange2Suppltables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8718479/v1/30d62cea3fc882871fcd7f79.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental determinants and potential suitability for Phlebotomus mascittii sand flies in central-western Europe under future Shared Socioeconomic Pathways climate change projections","fulltext":[{"header":"Background","content":"\u003cp\u003eClimate change is a critical factor in the emergence and distribution of infectious diseases, particularly those caused by pathogens that complete part of their life cycle outside the host. Among these diseases, vector-borne diseases such as leishmaniosis, caused by the protozoan parasites \u003cem\u003eLeishmania\u003c/em\u003e spp., are of particular concern because of their significant health impact on both humans and animals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. \u003cem\u003eLeishmania\u003c/em\u003e spp. are transmitted by phlebotomine sand flies (Diptera, Psychodidae), which are abundant in tropical and subtropical areas such as the Mediterranean Basin, and their distribution is strongly influenced by environmental and climatic factors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Changes in temperature, humidity, and precipitation patterns directly affect their habitat, survival, and reproduction, thereby affecting leishmaniasis prevalence and geographical distribution [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is highly probable that future climate change will lead to the emergence of sand flies and leishmaniosis in regions that were previously unaffected, such as many areas of Central Europe [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In Europe, \u003cem\u003eLeishmania infantum\u003c/em\u003e causes canine and human leishmaniosis and is most prevalent in southern Mediterranean countries. However, the distribution of its sand fly vectors extends further north, with low-lying regions of northern France, Germany, Luxembourg, and Belgium representing the current limits of sand fly presence in central-western Europe [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Vector species in this region include \u003cem\u003ePhlebotomus perniciosus\u003c/em\u003e and \u003cem\u003ePh. mascittii\u003c/em\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], the latter being the most northerly distributed species. Assessing the climate-driven expansion of \u003cem\u003ePh. mascittii\u003c/em\u003e into currently unsuitable latitudes and altitudes is therefore essential for evaluating the risk of \u003cem\u003eL. infantum\u003c/em\u003e becoming endemic and for informing evidence-based strategies to mitigate long-term climate-related risks.\u003c/p\u003e \u003cp\u003eCentral Europe offers a unique setting for studying the effects of climate change, given the complex interactions among geographical, climatic, and socioeconomic factors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The region is particularly susceptible to rising temperatures, with significant shifts in climatic patterns projected over the coming decades, leading to more frequent and prolonged heatwaves and changes in precipitation regimes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These climatic transformations pose considerable risks to ecosystems, agriculture, and public health systems, exacerbating climate-sensitive challenges such as the spread of vector-borne diseases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, the high population density and interconnected urban infrastructure of Central Europe increase its vulnerability to the cascading effects of climate change, amplifying socioenvironmental impacts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpatial analysis of environmental and climatic data, combined with ecological niche modelling (ENM), is a key tool for understanding the distribution and potential expansion of species, particularly vectors of infectious diseases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The ENM is used to predict the geographic range of species by correlating their known occurrences with environmental variables, thereby identifying the bioclimatic conditions that are suitable for their survival and reproduction. Spatial models have been developed to predict areas in Europe that may become suitable habitats for sand flies because of climate change, facilitating the identification of high-risk zones for the emergence of leishmaniasis [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Recent studies integrate vector climatic and habitat suitability with parasite development constraints and host or case data, enabling explicit infection-risk mapping [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, the incorporation of global climate models (GCMs) significantly enhances the predictive capacity of these niche models [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. GCMs use representative concentration pathways (RCPs), which are greenhouse gas (GHG) concentration trajectories, to predict possible future climate conditions. The RCPs represent different levels of radiative forcing (the change in energy balance in Earth's atmosphere between the incoming energy from the sun and the outgoing energy from Earth back into space) by the year 2100. They were developed for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) to explore how human activities, such as fossil fuel use and deforestation, could influence global temperatures and climate patterns [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A significant methodological advancement, aligning climate scenario modelling with real-world demographic and socioeconomic trends and policy decisions, was the introduction by the Sixth Assessment Report (AR6) of the Shared Socioeconomic Pathways (SSPs) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The SSPs consider five distinct scenarios that incorporate a broader range of factors beyond just greenhouse gas concentrations, including society's capacity to adapt to and mitigate climate change. This approach provides a more comprehensive perspective on future emissions and their impact on climate change.\u003c/p\u003e \u003cp\u003eThe aim of this work was to integrate SSP climate change projections into spatial and ecological modelling frameworks to identify potential future areas of \u003cem\u003ePh. mascittii\u003c/em\u003e presence in central-western Europe. This analysis employed data from sand fly surveys conducted in 2023 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and 2024 in France, Germany, Luxembourg, Belgium, and the Netherlands, by the research team of the present manuscript. The ultimate goal of this study is to contribute to the design of an early warning system (EWS) for sand flies and sand fly-borne pathogens to facilitate targeted public health interventions and sand fly control strategies, as proposed in the ongoing CLIMOS project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://climos-project.eu\u003c/span\u003e\u003cspan address=\"https://climos-project.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) under the HORIZON-HLTH-2021-ENVHLTH-02 initiative.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eSand fly modelling projections for \u003cem\u003ePh. mascittii\u003c/em\u003e focused on central-western Europe, which includes the area of sand fly surveys performed in the summers of 2023 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and 2024 (Schaffner, 2025). The area spans a narrow transect from south to north, extending from Savoie (Lat: 45.8219\u0026deg;N, Lon: 5.8492\u0026deg;E) in France to Zuid-Limburg (Lat: 50.8421\u0026deg;N, Lon: 5.7690\u0026deg;E) in the Netherlands, also encompassing sites in Belgium, Germany and Luxemburg. It is a key region in Central Europe and is characterised by a high degree of geographical, climatic, and demographic diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis area is located at the transition point between the North European plains and the mountainous chains of the Alps and Jura, significantly influencing the ecological patterns and species distributions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. From a topographical perspective, it includes a variety of landscapes ranging from coastal lowlands in the Netherlands to the mountainous regions of Switzerland and France. The climate in this region varies from a temperate oceanic climate in the northwest to a humid continental climate in the interior and a Mediterranean climate in the south.\u003c/p\u003e \u003cp\u003eThe sand fly survey included 507 sampling sites (with unique geographical coordinates) in 140 localities in 26 NUTS3 (Nomenclature of Territorial Units for Statistics level 3) subdivissions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). They comprised most NUTS3 not surveyed before, according to ECDC\u0026acute;s February 2023 \u003cem\u003ePh. mascittii\u003c/em\u003e distribution map (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ecdc.europa.eu/en/publications-data/phlebotomus-mascittii-current-known-distribution-february-2023\u003c/span\u003e\u003cspan address=\"https://www.ecdc.europa.eu/en/publications-data/phlebotomus-mascittii-current-known-distribution-february-2023\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The sampling locations were selected through a two-step approach. Initially, two or three locations within (NUTS3) along our transect were selected on the basis of a run of environmental suitability modelling for \u003cem\u003ePh. mascittii\u003c/em\u003e (updated from [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]). High-resolution images (Google Earth\u0026trade;) were then used to locate villages with suitable sand fly habitats, such as old houses/barns or cliffs. Sites where phlebotomines had been previously detected were also included [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Once in the field, specific sites for sand fly sampling were selected on the basis of environmental characteristics deemed favourable for phlebotomines [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These included sheltered gardens, stone walls, abandoned houses, basements, old barns with potential animal presence inside or nearby, rock piles, cliff bases, and caves. Sand fly trapping methods included 487 miniature CDC light traps-nights, 28 mouth and mechanical aspirating events (21 on resting spots and 7 on human-landings) and 6 sticky trapsnights series.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSand fly data\u003c/h3\u003e\n\u003cp\u003eA total of 153 sand flies were collected (55 in 2023 and 98 in 2024), including 108 \u003cem\u003ePhlebotomus mascittii\u003c/em\u003e and 45 \u003cem\u003eP. perniciosus\u003c/em\u003e. Specimens were obtained from 80 sampling sites across 51 localities in 19 NUTS3 subdivisions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003ePhlebotomus mascittii\u003c/em\u003e was recorded at 69 sites in 46 localities and was present in all 19 NUTS3 subdivisions, whereas \u003cem\u003eP. perniciosus\u003c/em\u003e was detected at 15 sites in 11 localities (6 together with \u003cem\u003ePh. mascittii)\u003c/em\u003e within five NUTS3 subdivisions. The latitudinal and altitudinal range of \u003cem\u003ePh. mascittii\u003c/em\u003e were 45.83\u0026deg;N\u0026ndash;49.78\u0026deg;N and 142\u0026ndash;715m. Likewise, for \u003cem\u003eP. perniciosus\u003c/em\u003e these values were 45.82\u0026deg;N\u0026ndash;48.02\u0026deg;N and 151\u0026ndash;715m.\u003c/p\u003e\n\u003ch3\u003eClimatic and environmental data\u003c/h3\u003e\n\u003cp\u003eThe summer climatic data were sourced from the latest WorldClim database version 2.1 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which provides data at a 30-second spatial resolution (approximately 1 km per pixel). It included the monthly averages of the 1970\u0026ndash;2020 time series of temperature (maximum, minimum and average), solar radiation, precipitation, vapour (water) pressure, and wind speed within a 500-meter radius of each trapping site (buffer zone). Furthermore, land cover information in buffer zones was obtained from the georeferenced CORINE Land Cover 2018 dataset (EEA-CLC 2018) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], with a spatial resolution of 250 m. To simplify the analysis, CLC codes were grouped into ecologically relevant classes and the percentage of each land use type in buffer zones was calculated.\u003c/p\u003e\n\u003ch3\u003eClimatic change scenarios\u003c/h3\u003e\n\u003cp\u003eClimate projections based on three SPPs (SPP2, SPP3 and SPP5) were incorporated into the modelling of future scenarios. SSP2 (\"middle-of-the-road\") projects a moderate, middle-ground future, with moderate efforts to mitigate climate change and relatively steady socioeconomic development and population growth. SSP3 (\"regional rivalry\") involves a fragmented world, where regional rivalries hinder international cooperation, leading to slow development, high emissions, and climate vulnerability. SSP5 (\"fossil-fuelled development\") anticipates rapid economic growth driven by fossil fuels, with high emissions and environmental degradation, leading to severe climate impacts, with the population peaking before declining [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the nonlinear relationships among the five SSPs and socioeconomic challenges for adaptation and mitigation (left), and radiative forcings for the different SSP scenarios and traditional RCP scenarios, reflecting the disparity between them (right).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo represent realistic future climate change scenarios, we considered climatic data from 10 global climate models (GCMs) [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), under SSP2-4.5, SSP3-7.0, and SSP5-8.5, and for two 20-year periods: 2041\u0026ndash;2060 and 2081\u0026ndash;2100. The estimations produced by GCMs exhibit a significant degree of uncertainty that arises primarily from the variability observed among the different GCMs, internal climate variability and the challenges associated with downscaling climate model outputs to specific spatial and temporal scales [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. To lessen this variability, an ensemble model was constructed that represented the average values of the multiple GCMs employed in the study.\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\u003eSet of global climate models employed for the ensemble model.\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\u003eGlobal Climate Models\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitution/Country\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACCESS-CM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSIRO-ARCCSS/Australia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Bi et al., 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMCC-ESM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMCC/Italy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Lovato et al., 2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC-Earth3-Veg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth-Consortium/European consortium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(D\u0026ouml;scher et al., 2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGISS-E2-1-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNASA-GISS/USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Kelley et al., 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINM-CM5-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINM/Russia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Volodin and Gritsun, 2018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPSL/France\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Boucher et al., 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIROC/Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Tatebe et al., 2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-M, DWD, DKRZ/Germany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(M\u0026uuml;ller et al., 2018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI-ESM2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRI/Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Yukimoto et al., 2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUKESM1-0-LL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOHC/United Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Sellar et al., 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Australian Community Climate and Earth System Simulator (ACCESS); Euro-Mediterranean Center on Climate Change (CMCC); Earth-Consortium (EC); Goddard Institute for Space Studies (GISS); Institute of Research for the Management of Natural Resources (INM); Institut Pierre-Simon Laplace (IPSL); Model for Interdisciplinary Research on Climate (MIROC); Max Planck Institute (MPI); Meteorological Research Institute (MRI); UK Earth System Modelling (UKESM).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eStatistical analysis and geo-modelling\u003c/h3\u003e\n\u003cp\u003eTo minimise detection bias, only data from CDC light traps were included, comprising 487 trap-night collections from 476 sites across 132 localities. \u003cem\u003ePhlebotomus mascittii\u003c/em\u003e was detected at 64 sites in 14 localities. The associations between the proportion of traps with \u003cem\u003ePh. mascittii\u003c/em\u003e (positive traps) and the climatic and land cover variables in buffer zones - categorised into three or four groups on the basis of variable distribution - were evaluated using Yates' chi-square test or Fisher's exact test, as appropriate [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMixed-effects logistic regression models using maximum likelihood estimation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], were then used to examine the multivariate relationship between \u003cem\u003ePh. mascittii\u003c/em\u003e presence in a trap (outcome binary variable), climatic, and land cover factors significantly associated in the bivariate analysis (explanatory variables). Locality was included as a random intercept to account for non-independence of traps placed in the same locality and to quantify residual spatial heterogeneity. Model selection was performed using a backward elimination approach starting from a saturated model. Predictors with high collinearity were sequentially removed based on variance inflation factor (VIF) values\u0026thinsp;\u0026gt;\u0026thinsp;3 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The final model was selected using the Akaike information criterion (AIC), retaining the combination of non-collinear variables with the lowest AIC. The intraclass correlation coefficient (ICC) was calculated on the logit scale from the random-effect variance to quantify the proportion of variance attributable to locality. Model explanatory power was assessed using marginal R\u0026sup2; (fixed effects only) and conditional R\u0026sup2; (fixed\u0026thinsp;+\u0026thinsp;random effects). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for a two-sided test. All analyses were conducted in R [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo project potential sand fly presence under future climate scenarios, the logistic model was applied to CMIP6 climate data for SSP2-4.5, SSP3-7.0, and SSP5-8.5 for the periods 2041\u0026ndash;2060 and 2081\u0026ndash;2100 using QGIS (2024) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The probability of sand fly presence, Y(s), was estimated using the logistic function(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Y\\left(s\\right)=\\:1/(1+{e}^{-({b}_{0}\\text{+}{b}_{1}{X}_{1}\\text{+}{\\dots\\:+b}_{n}{X}_{n}\\text{)}})\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{b}_{1}\\dots\\:{b}_{n}\\)\u003c/span\u003e\u003c/span\u003eare the estimated model coefficients and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{1}\\dots\\:{X}_{n}\\:\\)\u003c/span\u003e\u003c/span\u003eare the corresponding continuous explanatory variables.\u003c/p\u003e \u003cp\u003eSeven models were developed, one using current climatic data (historical data) and the remaining six corresponding to the previously described SSP x time period scenarios. To determine the evolution of the distribution of \u003cem\u003ePh. mascittii\u003c/em\u003e presence due to the effects of climate change, the difference between each of the six scenarios and the current model was calculated, and the percentage increase in the probability of \u003cem\u003ePh. mascittii\u003c/em\u003e presence was obtained. The regional boundaries of each country were used as the limits of these calculations, and they included 140 NUTS level 3 regions (Supplementary Table\u0026nbsp;1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eRelationships between\u003c/b\u003e \u003cb\u003ePh. mascittii\u003c/b\u003e \u003cb\u003epresence and summer climate and land cover variables\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAccording to the bivariate analysis, the proportion of \u003cem\u003ePh. mascittii\u003c/em\u003e positive traps was significantly positively associated with the average total summer solar radiation, mean, maximum and minimum temperature and mean vapour pressure, negatively associated with the average mean wind speed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table S2). Among the land cover variables considered, the proportion of positive traps was significantly associated, with areas containing pastures, green urban areas and sport and leisure facilities (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but the relationship was not linear, so the proportion of positive traps was 15% in areas where this land cover was not present and 7%, 21% and 14% in areas where it represented 1\u0026ndash;33%, 34\u0026ndash;66% and 67\u0026ndash;100% of the land cover, respectively (Table S3). Also, the proportion of positive traps was marginally, associated to discontinuous urban fabric being lowest in sites with highest areas of this land cover (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10).\u003c/p\u003e \u003cp\u003eThe mixed-effects logistic regression model identified summer precipitation, mean minimum temperature, and solar radiation as important predictors of \u003cem\u003ePh. mascittii\u003c/em\u003e presence (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The marginal R\u0026sup2; indicated that these climatic variables explained 19.3% of the variance in occurrence probability. Including locality as a random effect, raised the conditional R\u0026sup2; to 31.8%, indicating that locality-level heterogeneity accounted for an additional proportion of variance beyond that explained by fixed effects alone. The unadjusted intraclass correlation coefficient (ICC) showed that 12.5% of the variance in \u003cem\u003ePh. mascittii\u003c/em\u003e presence was attributable to differences between localities. After adjusting for climatic predictors, the ICC increased to 15.4%, suggesting that summer precipitation, mean minimum temperature and solar radiation primarily explained variation among traps within localities, while residual heterogeneity between localities remained important.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMixed-effects logistic regression analysis examining the relationship between sand fly presence and summer climate variables. Fixed explanatory variables are included as categorical (a) or continuous (b) variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ea) Categoriced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.8204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipiation (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170\u0026ndash;215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216\u0026ndash;260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.12633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262\u0026ndash;306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.25923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean solar radiation (W/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14467\u0026ndash;15809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15817\u0026ndash;17151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17177\u0026ndash;18514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum temperature (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.2\u0026ndash;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4\u0026ndash;12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.5\u0026ndash;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRandom effect: variance estimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb) Continuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-22.0500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.7740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipiation (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean solar radiation (W/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum temperature (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRandom effect: variance estimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eCurrent and future large-scale spatial scenarios of sand fly presence\u003c/h3\u003e\n\u003cp\u003eThe spatially distributed probability model, based on average mean temperature and total solar radiation, illustrates both the estimated current and projected future changes in the probability of sand fly presence under different climate scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The current model depicts a latitudinal gradient of sand fly presence, with the highest probabilities (\u0026gt;\u0026thinsp;90%) concentrated in inland and pre-littoral plains of southern France and northern Italy (purple areas in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The probability of sand fly presence decreased northwards, although it was greater than 35% in areas of the French Jura region and the upper Rhone River basin, as well as in parts of the Centre-Val de Loire and Nouvelle-Aquitaine regions. (orange areas in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Future scenario maps indicate increases in most of the study area (illustrated as a blue-to-red gradient, with blue denoting minimal or no change and red indicating the greatest increase in sand fly presence) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe short-term projections (2041\u0026ndash;2060) show a relatively uniform pattern across the three SSPs, with moderate increases (20\u0026ndash;30%) in sand fly probability across the northernmost regions of the study area, particularly in Northern France, Luxembourg, Western Germany and Belgium. By the end of the 21st century (2081\u0026ndash;2100), the predicted expansion intensifies significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The projected average increases in sand fly probability reach 28% under SSP2-4.5, 45% under SSP3-7.0, and 57% under SSP5-8.5 for the area under study.\u003c/p\u003e \u003cp\u003eNotably, the magnitude of change varies depending on the initial probability of presence. In areas where the current probability is already high (\u0026gt;\u0026thinsp;90%), such as parts of southeast France and northern Italy, the potential for further increase is naturally limited owing to the probabilistic ceiling (maximum 100%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Conversely, areas with historically low probabilities, particularly in Belgium, Luxembourg, Northern Germany and the Netherlands, demonstrate the most pronounced increases. Geographic expansion is particularly evident under SSP5-8.5, which represents a high-emission, fossil fuel-driven development pathway, indicating that stronger climate warming scenarios could accelerate the northwards spread of the vector (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCountry-wide variability in the predicted probability of sand fly presence\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the probability of sand fly presence in the current scenario (green horizontal bars, right Y axis), and the average values of the difference in the probability between the future and current scenarios (box plots, left Y axis), which are calculated for NUTS 3 geographical subdivisions (regions) for each country in the study area. The probability of sand fly presence is predicted to increase in every country and all SSP scenarios except in Monaco, a small and already sand fly endemic country. However, the impact of climate change on the probability of sand fly presence elsewhere differed substantially between and within countries. Compared with Austria, sand fly presence in Belgium, Liechtenstein and Luxembourg, Switzerland, Germany, France, and the Netherlands presented a wider range of probability increases in all the scenarios.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of Ecological Niche Modelling and Climate Projections\u003c/h2\u003e \u003cp\u003eSeveral limitations should be considered when interpreting our projections. First, the model is based exclusively on climatic predictors and assumes that the relationships between sand fly presence and climatic variables remain constant under future conditions. As such, it does not explicitly account for dispersal constraints, host distribution, land-use changes, or other non-climatic factors known to influence sand fly occurrence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough climatically suitable conditions may emerge in northern Europe, natural dispersal processes may delay or prevent colonisation unless facilitated by anthropogenic factors such as passive transport through trade and travel. Nonetheless, adult sand flies are fragile insects with a limited capacity to survive long-distance passive dispersal [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Second, the quality and representativeness of \u003cem\u003ePh. mascittii\u003c/em\u003e occurrence data may be incomplete or geographically biased [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Sampling was limited to a restricted number of sites, although these covered a broad latitudinal gradient. Trapping sites were selected within comparable habitats to ensure consistency and to minimise known sampling biases associated with sand fly surveys. Third, while the mixed-effects modelling framework accounts for locality-level heterogeneity, future projections assume that random effects remain constant over time. This assumption may not fully capture temporal changes in local ecological conditions, land use, or anthropogenic influences that could modify sand fly suitability at the locality scale. Finally, uncertainties inherent to climate projections and Shared Socioeconomic Pathways (SSPs) may affect the precision of predicted suitability patterns [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. SSP-based projections reflect different socioeconomic and greenhouse gas emission trajectories, making long-term forecasts inherently uncertain [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The transition from Representative Concentration Pathways (RCPs; CMIP5) to SSPs (CMIP6) has introduced methodological advances but has also altered model sensitivity to emissions, which may partly explain discrepancies with earlier studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, general circulation models typically operate at coarse spatial resolutions that may not capture fine-scale climatic variability relevant to species distributions. While downscaling can improve spatial resolution, it may introduce additional uncertainty. Therefore, our projections should be interpreted as indicating potential areas of climatic suitability rather than definitive predictions of future sand fly distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental Determinants of Sand Fly Distribution\u003c/h2\u003e \u003cp\u003ePredicting sand fly distributions using climatic models is challenging due to strong correlations among environmental variables and the limited understanding of how these factors influence population dynamics across seasons. Our analysis focused on summer, when sand fly activity peaks and coincides with the survey period. We evaluated climatic variables essential for larval development, including temperature, vapor pressure, and precipitation (used as a proxy for humidity) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], as well as wind speed, which influences adult activity [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and solar radiation.\u003c/p\u003e \u003cp\u003eAlthough land use can influence sand fly occurrence - European populations typically prefer rural and peri-urban green areas over highly urbanised habitats [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] - microhabitat adaptability allows sand flies to persist in otherwise marginal climates [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In our study, the deliberate selection of sampling sites within suitable habitats likely explains why broader land-cover characteristics surrounding the sites were not associated with sand fly presence. Consequently, the discussion that follows focuses exclusively on climatic determinants of sand fly distribution.\u003c/p\u003e \u003cp\u003eSummer temperature has been highlighted as a key predictor of sand fly distributions across Europe [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], while minimum summer temperature and solar radiation were identified as the best predictors of sand fly presence and abundance in Spain in the ongoing CLIMOS survey [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The importance of solar radiation is less intuitive, given that immature stages breed in sheltered habitats and adults are primarily nocturnal [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. However, solar radiation may indirectly enhance adult activity by raising daytime temperatures, accelerating evaporation, and reducing humidity levels, thereby creating more favorable microclimates for nocturnal flight, as observed in studies across southern Europe and the United States [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe negative association between \u003cem\u003ePh. mascittii\u003c/em\u003e presence and high mean precipitation is biologically plausible. Heavy rainfall reduces adult activity and survival and can disrupt the terrestrial microhabitats where immature stages develop [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. At broader spatial scales, large-scale modelling of European sand fly distributions has shown that climatic moisture indices \u0026ndash; which integrate precipitation with temperature and evapotranspiration \u0026ndash; are stronger predictors of occurrence than precipitation alone, suggesting that sand flies respond primarily to net moisture availability rather than rainfall totals [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Species-specific evidence from Austria supports this view: relative humidity was significantly associated with \u003cem\u003ePh. mascittii\u003c/em\u003e abundance, with peak activity at intermediate humidity and reduced activity at both higher and lower extremes, reflecting a non-linear and species-specific response to moisture [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite these moisture-related constraints, \u003cem\u003ePh. mascittii\u003c/em\u003e exhibits the widest climatic activity niches among European sand fly species, and recent analyses of meteorological limits suggest that its broad ecological tolerance may facilitate northward dispersal under ongoing climate change [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnlike previous studies that employed the RCP scenarios of the IPCC, our study integrates the newer SSP scenarios from CMIP6. This methodological shift represents an improvement. The results obtained under SSP2-4.5, SSP3-7.0 and SSP5-8.5 reveal a northwards expansion of climatically suitable areas driven by rising temperatures, solar exposure and changes in seasonal climatic patterns, which is largely consistent with the projections of Fisher et al. (2011), Tr\u0026aacute;jer et al. (2013), Koch et al. (2017) and Chalghaf et al. (2018) [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. By the end of the 21st century (2081\u0026ndash;2100), the predicted expansion intensifies significantly, with areas with historically zero or low probability, particularly in Belgium, Luxembourg, Northern Germany and the Netherlands, becoming new or more suitable habitats. The variation in the predicted probability of sand fly presence is wide in some countries. In Switzerland and northern Italy this would be associated with the country's topography, with the regions with the highest altitudes being least affected by climate change. In the cases of France and Germany, the effect is related to the wide latitudinal and altitudinal ranges of these countries. The variation predicted in the Netherlands under the three scenarios could be attributed to this country\u0026rsquo;s southern regions being at the limit of sand fly distribution under those scenarios.\u003c/p\u003e \u003cp\u003eSand fly species and subspecies may differ in their ideal ecological needs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and response to climate change [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The models do not forecast recession areas for \u003cem\u003ePh. mascittii\u003c/em\u003e in the study area due to climate change, which differs from the projection by Koch et al. (2017) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], who predict that \u003cem\u003ePh. mascitti\u003c/em\u003e will be restricted to northern Europe between 2061 and 2080 under the worst-case RCP8.5 climate change scenario. Similarly, Fisher et al. (2011) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] projected that \u003cem\u003ePh. mascittii\u003c/em\u003e in Germany would be primarily distributed in the centre and north of the country, with a low probability of occurrence in the south as early as 2011\u0026ndash;2040. The differences between studies may be attributed to the consideration of socioeconomic factors in SSP scenarios, as well as the updated temperature projections for Central Europe in the present study. Currently, \u003cem\u003ePh. mascittii\u003c/em\u003e latitudinal range in Western Europe extends from southern Italy to southern Belgium and Germany, although at low densities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Public Health, Vector Surveillance, and Future Research\u003c/h2\u003e \u003cp\u003eAt present, the spatial overlap between autochthonous leishmaniosis and its vectors in the study area remains limited, largely because sand fly distributions extend further north than reported human and canine leishmaniosis cases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the risk of introducing leishmaniosis into vector peripheral areas through frequent movement and importation of infected dogs \u0026ndash; the domestic reservoir of \u003cem\u003eL. infantum\u003c/em\u003e \u0026ndash; is considered high [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The projected northwards expansion of sand fly vectors would naturally be associated to a parallel expansion of the parasite to areas where it has historically been absent. These findings support the need for coordinated, cross-sectoral surveillance strategies in line with EU One Health frameworks, integrating entomological surveillance, veterinary monitoring, and human health data. Early-warning systems combining ecological niche modelling with further vector surveillance will contribute to risk-based preparedness and targeted prevention, as advocated by the European Centre for Disease Prevention and Control (ECDC) and European Food Safety Authority (EFSA) for emerging vector-borne diseases. From a policy perspective, such approaches may inform harmonised surveillance priorities, guide resource allocation, and support evidence-based adaptation strategies under climate change.\u003c/p\u003e \u003cp\u003eFuture research should prioritise the integration of land-use, host density, and socioeconomic drivers into quantitative modelling frameworks to better reflect transmission risk. In parallel, improving the spatial resolution of climate projections through downscaling techniques would reduce uncertainty in local-scale habitat suitability assessments. Finally, systematic validation of model predictions through longitudinal field studies using standardised methodologies is essential to support robust risk assessment and to strengthen preparedness and response capacity across regions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eClimate change is expected to substantially reshape the distribution of phlebotomine sand flies in central-western Europe, enabling their expansion into regions that are currently constrained by low temperatures and limited solar radiation. This shift is likely to increase the risk of leishmaniosis transmission in areas where the disease has so far been absent. Our findings underscore the need for adaptive, risk-based surveillance and proactive mitigation strategies to anticipate and manage the expansion of sand fly vectors and associated pathogens. In this context, our projections directly support the objectives of the CLIMOS project, contributing to the development of early-warning systems aimed at strengthening preparedness and reducing climate-driven public health risks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all co-authors of Risue\u0026ntilde;o et al. (2024) and the VectorNet project and its contracting bodies, ECDC and EFSA, for the sand fly surveys that provided the data used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article was financed by the CLIMOS Project (http://www.climos-project.eu) co-funded by European Commission grant 101057690 and UKRI grants 10038150 and 10039289, and the manuscript is catalogued by the CLIMOS Scientific Committee as CLIMOS number \u0026hellip;. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission, the Health and Digital Executive Agency, or the UKRI. Neither the European Union nor the granting authority or the UKRI can be held responsible for them. Neither the European Commission nor the UKRI had roles in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. For the purposes of open access, the authors have applied a CC BY (2) public copyright licence to any Author Accepted Manuscript version arising from this submission. The six Horizon Europe projects, BlueAdapt, CATALYSE, CLIMOS, HIGH Horizons, IDAlert, and TRIGGER, form the Climate Change and Health Cluster.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePPC and EB contributed to the study conception, data analysis, interpretation of the results and writing of the article. JR, EV and FS contributed to the acquisition, organisation and analysis of the data, the review and acquisition of the reviewed literature and to the interpretation of the results. All the authors reviewed and approved the submitted version, taking personal responsibility for their contributions. They also commit to addressing any questions regarding the accuracy or integrity of the work, ensuring appropriate investigation, resolution, and documentation in the literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on publicly available information, did not involve human or animal participants or materials, and did not require approval by an ethical committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript does not include details, images, or videos related to an individual person, requiring consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCosma C, Maia C, Khan N, Infantino M, Del Riccio M. 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Scientific Opinion on canine leishmaniosis. EFSA Journal. Wiley-Blackwell Publishing Ltd; 2015;13:1\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2903/j.efsa.2015.4075\u003c/span\u003e\u003cspan address=\"10.2903/j.efsa.2015.4075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"climate change, Phlebotomus mascittii, sand flies, central-western Europe, predictions","lastPublishedDoi":"10.21203/rs.3.rs-8718479/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8718479/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eClimate warming is expected to drive the northwards expansion of sand flies in Europe, increasing the risk of infection by sand fly-borne pathogens such as \u003cem\u003eLeishmania\u003c/em\u003e spp. This study assessed the probability of sand fly presence in central-western Europe under various climate change scenarios for the periods 2041\u0026ndash;2060 and 2081\u0026ndash;2100.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used summer climatic data from 10 global climate models and CORINE Land Cover variables to develop a parsimonious mixed-effects logistic regression model for sand fly presence on the basis of findings from 2023 and 2024 surveys across France, Germany, Luxembourg, Belgium, and the Netherlands\u0026mdash;covering the northernmost extent of the known sand fly distribution\u0026mdash;where the dominant species was the \u003cem\u003eLeishmania infantum\u003c/em\u003e vector, \u003cem\u003ePhlebotomus mascittii\u003c/em\u003e. The logistic function derived from this model was applied to project future sand fly distributions under the Shared Socioeconomic Pathways (SSPs), SSP2-4.5, SSP3-7.0, and SSP5-8.5, depicting scenarios of socioeconomic development and their impact on greenhouse gas emissions as outlined in the Sixth Climate Change Assessment Report (AR6).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe spatially distributed probability model, which incorporates average temperature and solar radiation, predicts a 20\u0026ndash;30% increase in the likelihood of \u003cem\u003ePh. mascittii\u003c/em\u003e presence across Luxembourg, Belgium, western Germany, and the southern Netherlands from 2041\u0026ndash;2060 under all three SSP scenarios. By 2081\u0026ndash;2100, the projected expansion in these northern regions intensified, reaching 35% under SSP2-4.5, 60% under SSP3-7.0, and 80% under SSP5-8.5. There is considerable variability in the predicted probability both between and within countries, influenced by country topography and latitudinal range.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIt is highly probable that \u003cem\u003ePh. mascittii\u003c/em\u003e, will expand its natural distribution in central-western Europe into areas that are presently too cold and have insufficient solar radiation, to an extent that will depend on how global society, demographics, and economics might evolve over the 21st century. These predictions emphasize the need for adaptive surveillance and proactive measures to mitigate the risks of sand fly vector expansion and leishmaniosis transmission, as outlined in the CLIMOS project's goal to develop an early warning system.\u003c/p\u003e","manuscriptTitle":"Environmental determinants and potential suitability for Phlebotomus mascittii sand flies in central-western Europe under future Shared Socioeconomic Pathways climate change projections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 16:56:26","doi":"10.21203/rs.3.rs-8718479/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":"6480b428-8db9-4e6a-9bde-0f095ab4aa2d","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T09:41:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 16:56:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8718479","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8718479","identity":"rs-8718479","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}