Trait based assessment of the invasion potential of disease vector mosquitoes

19 Mosquito-borne diseases pose a growing global health threat, largely driven by the human -20 mediated spread of vector species beyond their native regions. Although only a few mosquito 21 species historically established populations outside their native ranges, many have expanded 22 rapidly in recent decades. Once established, these invaders are notoriously difficult to control, 23 emphasizing the need for proactive identification before human-mediated spread occurs. Here, we 24 present a framework to anticipate invasion potential for 184 mosquito species of medical 25 importance based on their ecological, life-history, and macroecological traits. We first compiled a 26 comprehensive dataset of 26 traits characterizing each species. We then used random forest models 27 to relate these traits with the probability of species being introduced in new regions (before and 28 after 1950, marking the onset of widespread trade globalization), and of establishment following 29 introduction. Models achieved moderate to good predictive performance (AUC = 0.78 -0.85) and 30 revealed that species native to Asia and Australia, adapted to human -made breeding sites, and 31 tolerant of climatic extremes are consistently more likely to be introduced and to establish in non-32 native regions. Among species with no known invasion history, we identified 24 with higher 33 potential to become future spreaders, of which 1 7 also exhibit high establishment probabilities 34 (‘high-risk species’). These results show that invasion potential can be inferred, to some extent, 35 from intrinsic species traits and provide a quantitative basis for proactive surveillance, enabling 36 prioritization of species most likely to become introduced in the future. 37 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 3 Author Summary 38 Mosquito-borne diseases threaten more than half of the world’s population and cause over 700,000 39 deaths each year. Only a small share of mosquito species can spread these diseases, but some of 40 them are moving into new regions where they have never been seen before . The spread of these 41 mosquitoes has led to increasing numbers of locally transmitted outbreaks in regions that 42 previously, or in recent times, had no mosquito -borne diseases. Human activities like trade and 43 travel help mosquitoes spread, and once they arrive, they are extremely difficult to eradicate . 44 Therefore, it is crucial to understand which species may spread in the future and to identify those 45 that should be closely monitored to prevent their introduction and establishment. In this work, we 46 linked species characteristics with their known invasion history to identify the factors driving their 47

and establishment in new regions. We found that species from Asia and Australia, 48 capable of using human-made breeding sites, and tolerant of climatic extremes are most likely to 49 become invaders. We then used these findings to predict which species might spread next . We 50 identified 24 species with high invasion potential, including 1 7 that also have high chances of 51 establishing once introduced. These results demonstrate that invasion risk can be predicted from 52 measurable species traits, providing a framework to guide early -warning surveillance and 53 prioritize species for monitoring before they begin spreading and become widespread vectors of 54 human disease. 55 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 4

56 Mosquito-borne diseases are a major threat to global public health, causing more than 700,000 57 deaths annually and placing over half of the world’s population at risk of infection (1). This risk 58 is largely attributed to a small group of competent vector species capable of transmitting pathogens 59 such as dengue, Zika, chikungunya, malaria, and West Nile virus (2,3). Historically, many of these 60 species maintained relatively restricted distributions. However, in recent decades, the range of 61 several mosquito vectors has expanded at an unprecedented pace. Of the approximately 184 62 mosquito species, from which human pathogens have been isolated from wild-caught females (4), 63 by now 46 have already been introduced into regions outside their native ranges, with 28 confirmed 64 as having established populations (5). These introductions have expanded the species’ ranges, 65 sometimes into new continents and other fairway regions and enabled local disease transmission 66 in areas considered unsuitable or free of certain diseases, a reminder that while mosquitoes can 67 exist without transmitting pathogens, such pathogens cannot be without mosquitoes (6,7). 68 Despite receiving increasing attention, the drivers of mosquito introductions and 69 establishment in non -native regions remain poorly understood. Some species, such as Aedes 70 aegypti, have expanded globally since the 15th century (8), while others, like Aedes albopictus, 71 Aedes japonicus and Anopheles stephensi, emerged as invasive non-native species (i.e., introduced 72 and established populations outside their native range; cf. ,9) only in recent decades (10–12). For 73 many other mosquito species, however, there is no evidence that they were transported by humans 74 or successfully established themselves following introductions. This is intriguing because, in 75 several cases, such species have ecological traits, habitat use, or associations with humans that are 76 broadly similar to those of invasive species (13). Hence, a key question in vector ecology and 77 prevention remains: why are some mosquito species being introduced and becoming established 78 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 5 in non -native regions while others remain restricted to their native ranges? Shedding light on 79 drivers of these differences would be of key relevance for supporting surveillance and 80 introduction-prevention efforts, allowing to help identify species more likely to become introduced 81 in the future. Similarly, in a global context where resources for surveillance are limited and the 82 pool of known invasive species is likely incomplete (14), understanding these factors may also 83 help identify introduced or invasive species that remain undetected. To address this question, it is 84 necessary to consider multiple factors shaping the propensity of mosquito species to be introduced 85 and established in non-native regions (15). The disposition to be transported is expected to depend 86 strongly on intrinsic traits that mediate associations with human -traded commodities or transport 87 vectors. Classic examples include oviposition in human-made breeding sites such as used tires and 88 living plant pots, which constitute major pathways for the spread of widespread species such as 89 Aedes aegypti and Ae. albopictus (16). The breadth of a species’ geographic distribution is also 90 likely to be relevant, with taxa occupying wide native ranges, particularly those overlapping 91 regions of high trade volume and openness, being more frequently exposed to transport 92 opportunities (17). Beyond introduction, establishment success likewise depends on species traits. 93 Water requirements and desiccation resistance influence survival during transit and the prevalence 94 of viable propagules upon arrival (18), while broader environmental tolerances increase the 95 likelihood of encountering suitable conditions for colonization in non-native regions (e.g., salinity 96 19). Species with higher heat tolerances may also have a competitive advantage under increasingly 97 extreme temperatures (20). In order to provide warning lists to guide preventive biosecurity 98 policies, trait-based approaches have recently emerged as valuable tools to identify species more 99 likely to become introduced and invasive (e.g., 21 –23). By integrating variables representing 100 ecological and life-history traits and macroecological patterns into predictive frameworks, these 101 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 6 approaches assess which species are more or less likely to enter the global invasion pathway, while 102 also highlighting the traits mediating interspecific variation in invasion potential. For example, Pili 103 et al. (22) developed a global trait-based model and showed that to predict species establishment, 104 both life -history and macroecological traits were important predictors and show that invasion 105 success concentrates in species combining ecological flexibility with high probability of human 106 mediated transport. Similar trait-based assessments for disease vector mosquitoes, would provide 107 critical advances in anticipating future biological invasions and the emergence of vector -borne 108 diseases to protect naïve human populations. Revealing which mosquito species are predisposed 109 to introduction and establishment, would support surveillance planning, refine monitoring 110 priorities and advance our understanding of how ecological specialization, climate tolerance, and 111 human-mediated transport interact to shape global mosquito invasion risk. However, to date, such 112 an assessment has been constrained by the lack of comprehensive trait data, with most 113 compilations restricted to a subset of species of known medical importance (24,25). Here, we take 114 advantage of a newly compiled, extensive database covering 184 mosquito species capable of 115 natural infection with human pathogens to perform a trait-based assessment of species introduction 116 and invasion potential. The trait dataset integrates species ecological, life -history, and 117 macroecological data and to our knowledge is by far, the most taxonomically comprehensive 118 available. Thus, this dataset provides a unique opportunity to test which characteristics of species 119 are associated with a higher or lower invasion potential. Specifically, we combined these data with 120 data from a recent global assessment of mosquitos introduction records and non -native ranges 121 (Pabst et al., 2025) within a machine learning framework to: (i) identify species traits that predict 122 their propensity for introduction and establishment in non -native regions and, (ii) assess which 123 currently unintroduced or non-established species may pose future invasion risks. 124 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 7

125 2.1 Trait compilation 126 Our aim was to estimate the probability of introduction and establishment of mosquito species 127 vectors for human diseases. Species included in the analysis followed Pabst et al. (5) and were 128 limited to mosquito vectors from which human pathogens were isolated from field-caught females, 129 verified by at least two literature sources (4) or by one source plus a disease association recorded 130 in Wilkerson et al. (26). For that purpose, we assembled a database of variables describing 131 ecological and life -history, as well as macroecological traits of these mosquitoes. Traits were 132 selected based on their potential relevance on human-mediated transport of species, their survival 133 during transit, and their establishment in new environments (Table 1). We focused on variables 134 that are relatively stable across environmental gradients and for which information was available 135 for most species, thereby minimizing data gaps and ensuring general applicability. Most ecological 136 and life-history traits (e.g., oviposition behavior, desiccation resistance, salinity tolerance , flight 137 range) were collected through literature research, and each species -trait combination was 138 referenced to a specific literature source (Appendix S1). Macroecological variables (e.g., climatic 139 limits) were derived from species distribution data and spatial environmental layers (CHELSA; 140 27,28) or obtained from specialized sources like species distribution from Wilkerson et al. (26) 141 and blood meal hosts from Soghigian et al. (29). The rationale for including these derived traits 142 and details on the procedures for their collection are described below. 143 Table 1. List of species traits collected for the 184 mosquito species of medical importance. 144 Trait Definition Information recorded in variable .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 8 Oviposition in non-human- made breeding sites Whether a species lays eggs in natural meaning non- human-made breeding sites. Examples include tree holes, swamps, rivers. Classes: yes / no Oviposition in human- made breeding sites Whether a species lays eggs in human-made breeding sites. Examples include plastic container, vase, man-made cut bamboo that collects water, drainage ditch, rice field. Classes: yes / no Oviposition in small breeding sites Whether a species lays eggs in breeding sites smaller than 1 m2. Examples include tree holes, coconut shells, plastic containers, rockpools. Classes: yes / no Oviposition in large breeding sites Whether a species lays eggs in breeding sites larger than 1 m2. Examples include swamps, river margins, swimming pools. Classes: yes / no Fresh water Whether a species can develop in fresh water. Classes: yes / no Brackish water Whether a species can develop in brackish water. Classes: yes / no Salt water Whether a species can develop in salt water. Classes: yes / no Egg survives without water The ability of eggs to survive without water for long periods of time. Short-time means that eggs from dry soil samples or under experimental conditions could still be incubated in some cases after up to 14 days. Classes: yes / no / short- time Desiccation resistance Ability of eggs to resist desiccation (i.e. survive after drying). Classes: yes / no Oviposition strategy The way in which a species lays its eggs. Classes: In rafts / individually / individually but .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 9 clustered / clustered underwater Minimum mean annual temperatures of its native range Minimum mean annual minimum temperature at which the species was observed. The extracted value corresponds to the 97.5% percentile of mean annual minimum temperatures at observation sites within countries where the species occurred naturally (in its native range). Continuous value (°C) Maximum mean annual temperatures of its native range Maximum mean annual maximum temperature at which the species was observed. The extracted value corresponds to the 97.5% percentile of mean annual maximum temperature at observation sites within countries where the species occurred naturally (in its native range). Continuous value (°C) Minimum sum of annual precipitation of its native range Minimum sum of annual precipitation at which the species was observed. The extracted value corresponds to the 97.5% percentile of minimum sum of annual precipitation at observation sites within countries where the species occurred naturally (in its native range). Continuous value (mm) Maximum sum of annual precipitation of its native range Maximum sum of annual precipitation at which the species was observed. The extracted value corresponds to the 97.5% percentile of maximum sum of annual precipitation at observation sites within countries where the species occurred naturally (in its native range). Continuous value (mm) Native distribution range (whole country area) Distribution range of species based on the total area of countries in which the species occurred in its native range. Continuous value (km2) Native to Africa Whether a species is native to the African continent. Classes: yes / no .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 10 Native to Asia Whether a species is native to the Asian continent. Classes: yes / no Native to Australia Whether a species is native to the Australian continent. Classes: yes / no Native to Europe Whether a species is native to the European continent. Classes: yes / no Native to North America Whether a species is native to the North American continent. Classes: yes / no Native to South America Whether a species is native to the South American continent. Classes: yes / no Proportion amphibian blood meal host Proportion of bloodmeals derived from amphibian hosts. Together, the amphibian, avian, mammalian, and reptilian values sum to 1. Proportion, between 0 and 1. Proportion avian blood meal host Proportion of bloodmeals derived from avian hosts. Together, the amphibian, avian, mammalian, and reptilian values sum to 1. Proportion, between 0 and 1. Proportion mammalian blood meal host Proportion of bloodmeals derived from mammalian hosts. Together, the amphibian, avian, mammalian, and reptilian values sum to 1. Proportion, between 0 and 1. Proportion reptilian blood meal host Proportion of bloodmeals derived from reptilian hosts. Together, the amphibian, avian, mammalian, and reptilian values sum to 1. Proportion, between 0 and 1. 145 2.1.1 Country wide distribution data and native range delineation 146 Native ranges were delineated to capture each species’ pre -modern trade-driven distributions and 147 natural ecological context and to avoid temporal overlap between predictors and invasion 148 outcomes. Furthermore, natural distribution determines invasion outcomes due to regional 149 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 11 differences in historical trade relations. Country -level data were obtained from Wilkerson et al. 150 (26) and the Walter Reed Biosystematics Unit (30). Each distribution was visually inspected, and 151 only countries within the confirmed native range were retained. Calculated metrics included 152 native-range area (sum of constituent country areas in km²) and native continent(s) as binary 153 variables. Including the latter accounts for the species’ origins. 154 155 2.1.2 Climate data 156 Our trait-based predictive framework requires understanding the climatic tolerances that determine 157 a species’ survival, development, and reproductive cycles. Temperature constrains processes, such 158 as gonotrophic cycle duration, body size, and fecundity (31,32), while precipitation shapes larval 159 habitat availability by influencing standing water body formation and habitat persistence (33). To 160 characterize each species’ climatic niche, we extracted native-range climate values from CHELSA 161 v2.1 layers (27,28) at 30 arc-second resolution (~1 km at the equator). We assembled occurrence 162 records (1980–2024) from GBIF (34) (via the ‘rgbif’ package; Chamberlain et al. 2012) and from 163 VectorMap (30), two global sources of mosquito data. We then cleaned the records for coordinate 164 accuracy, duplicates, and outliers using the ‘CoordinateCleaner’ package (Zizka 2017). Points 165 were restricted to one per grid cell within the species’ native range and overlaid with monthly 166 maximum and minimum temperature, and precipitation layers, to calculate annual averages 167 (temperature) and totals (precipitation). Climatic tolerance thresholds were defined by the 2.5th 168 and 97.5th percentiles to minimize errors from anomalous records caused by errors in 169 georeferencing or species identification. 170 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 12 2.1.3 Host preference 171 Mosquitoes’ dispersal potential and habitat use are shaped by the availability of their preferred 172 blood meal hosts. Generalist, anthropophilic and mammal-feeding species often occupy urban or 173 peri-urban environments (35), facilitating unintentional human transport, whereas specialised 174 ornithophilic species are typically associated with forested or wetland settings (36). To include 175 this in our model we obtained blood meal host data from Soghigian et al. (29), who compiled 176 molecular blood meal analyses quantifying feeding proportions on mammals, birds, reptiles, and 177 amphibians, providing a standardized host use metric across taxa. 178 179 2.2 Imputation of missing data 180 Despite our efforts of data compilation, several species still had incomplete trait information. To 181 ensure the reliability of model estimates, we only included species with at least 75% data 182 completeness and at least 3 unique occurrence points in our modelling (n=1 69 out of 184). For 183 kept species, the data gaps were imputed using the ‘missForest’ algorithm (37), which iteratively 184 predicts missing values via random forest. To account for imputation uncertainty, we repeated the 185 imputation 100 times, producing 100 complete datasets. Imputation accuracy was evaluated using 186 out-of-bag (OOB) error estimates (normalized root mean square error (NRMSE) for continuous 187 variables; proportion falsely classified (PFC) for categorical variables). 188 189 2.3 Modeling framework and response variables 190 We used a random forest (RF) modeling framework to estimate the probability of introduction and 191 establishment of mosquitoes that transmit diseases to humans, based on the fully impute d trait 192 datasets. RF is a nonparametric ensemble learning method that handles continuous, categorical, 193 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 13 and partially redundant predictors with high robustness to overfitting (38,39). The algorithm builds 194 multiple decision trees from bootstrap samples and aggregates their predictions, resulting in stable, 195 nonlinear models with strong predictive performance. RF models perform well with sparse or 196 noisy ecological data and offer interpretable measures such as variable importance and partial 197 dependence plots (38,40). These strengths have led to their widespread application in ecological 198 and epidemiological studies (41–44). Our models require two sets of input components: A binary 199 response variable representing a species' invasion status, and a set of predictor variables describing 200 mosquito ecological, life -history, and macroecological traits. We developed four RF models to 201 assess the invasion potential of mosquito species. The first model estimated the probability of a 202 species being introduced outside its native range, and the second the probability of the species 203 being introduced after 1950. The third and fourth models evaluated the probability of species 204 establishing themselves outside their native range (i.e., forming self -sustaining populations after 205 their introduction), considering either all records, or only those establishments occurring after 1950 206 respectively. The year 1950 was used as a threshold because it marks the onset of the major 207 acceleration of biological invasions associated with the post -World War II globalization of trade 208 (45). In each model the response variable was binary, coded as “Yes” when species met the 209 criterion and “No” otherwise. Introduction and establishment dates were derived from Pabst et al. 210 (5). Each of these four models was repeated independently 100 times, based on the 100 separate 211 imputation datasets, to generate ensemble predictions that capture prediction uncertainty. 212 213 2.4 Variable selection 214 To minimize redundancy and collinearity among predictors, pairwise Pearson correlations were 215 calculated for numerical variables, removing one of each pair with |r| > 0.7 (46). Multicollinearity 216 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 14 was further evaluated using the variance inflation factor (VIF) with the ‘vifstep’ function from the 217 ‘usdm’ R package (47), keeping only variables with a VIF < 5 (48). As a result, we excluded the 218 variables: Oviposition in human -made breeding sites and proportion of avian blood meal hosts. 219 Following this process, in each model we used the same non-redundant combination of continuous, 220 binary, and categorical predictors. The correlation structure of the final numeric variables is shown 221 in Figure 1. In addition to models that included the full set of ecological, life -history, and 222 macroecological traits, we also fitted models using only ecological and life -history variables to 223 assess the stability and consistency of species intrinsic traits as drivers of introduction and 224 establishment. In the main manuscript, we present only the results of the full -variable models, as 225 these performed better and reflect current real-world scenarios; results from the reduced variable 226 set are provided in the Appendix S2. 227 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 15 228 Figure 1. Pairwise Pearson correlation matrix of all numerical predictor variables kept after correlation analysis. 229 230 2.5 Model fitting and cross-validation 231 For each response variable, the 100 imputed datasets produced in section 2.2 were used to train 232 100 separate RF models having the same parameters, implemented in the ‘ ranger’ package (49). 233 Following standard practice, each model was built with 1,000 trees to ensure prediction stability. 234 Each tree used four randomly selected predictors at each split, following the common rule of using 235 the rounded square root of the total number of predictor variables in classification models. A 236 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 16 minimum node size of five was specified to reduce overfitting and to ensure that each terminal 237 node contained a sufficient number of observations for stable mean estimates (50). 238 Model performance was evaluated through leave -one-out cross -validation (LOOCV) (51), in 239 which each species was iteratively withheld from the training set, the model is fitted to the 240 remaining species, and predictions are generated for the excluded species. This was repeated until 241 every species had been left out once, resulting in a full set of out -of-sample predictions. Model 242 performance was evaluated by comparing predicted probabilities with observed outcomes across 243 all species, using the area under the receiver operating characteristic curve (AUC; 52) as a measure 244 of discrimination ability. This approach is robust and mimics a real -world situation where the 245 invasion potential of a species is assessed based on what has been observed for other species. 246 While AUC is a robust, threshold -independent metric for model evaluation and comparison, it 247 does not indicate the probability cutoff at which classification performance is maximized. In our 248 context, identifying such a threshold is important because it allows us to identify which species 249 were correctly or incorrectly classified by the models. To address this, we used the True Skill 250 Statistic (TSS; 53) , which provides the probability threshold that maximizes classification 251 accuracy by balancing sensitivity and specificity. The optimal threshold of each RF model was 252 identified by maximizing TSS with the function ‘ecospat.max.tss()’ from the ‘ecospat’ package 253 (54). Predicted probabilities were then averaged across all 100 RF model repetitions to obtain 254 stable ensemble estimates of introduction and establishment probabilities. 255 2.6 Variable importance and trait effects 256 For each of the four response variables, we additionally ran 100 full RF models with all species to 257 calculate contributions of the predictors using permutation -based importance scores, which 258 represent the decrease in model accuracy after shuffling each variable (38). These values were then 259 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 17 averaged to obtain stable estimates of variable importance at the ensemble -level. Variables that 260 showed consistent influence (importance ≥ 0.01 across models) were further explored (50) using 261 partial dependency plots generated from the averaged ensemble predictions, using the 262 FeatureEffect$new() function in the ‘iml’ package (55). 263 2.7 Identifying species with high invasion potential 264 Using predictions from the LOOCV procedure, we identified species with high potential for future 265 invasion. We first selected taxa not currently introduced but predicted to have a probability of 266

exceeding the TSS -defined threshold. These were considered as potential future 267 spreaders. We then cross-checked these species against predictions from the establishment model 268 and retained those exhibiting simultaneously high probabilities of introduction and establishment, 269 as species of highest invasion concern. All analyses were performed in R (56,57), with scripts and 270 reproducible code provided in the Appendix S3. 271

272 3.1 Model performance 273 A comprehensive dataset was compiled with sufficient data for 169 mosquito species characterized 274 by 24 ecological, life -history and macroecological variables, including eight continuous, three 275 categorical and thirteen one -hot encoded traits. Missing values were imputed with low error, 276 averaging <0.01 % for continuous traits and 3.3 % for categorical traits. Random forest models 277 applied to this dataset yielded cross-validated performances with AUC values indicating moderate 278 (0.78) to good (0.81 and 0.85) performance (Table 2). The introduction models exhibited higher 279 specificity than sensitivity, suggesting stronger performance in identifying non‐introduced species, 280 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 18 while the establishment models displayed the opposite pattern, reflecting improved recognition of 281 species capable of successful establishment. 282 Overall, the establishment models slightly outperformed the introduction models, reflecting 283 greater consistency in identifying traits linked to persistence after arrival. The models correctly 284 identified 35.3 of 46 species known to be introduced beyond their native ranges and 2 4.1 of 29 285 established species. For the post -1950 subset, 2 9.8 of 41 introduced and 2 1.6 of 26 established 286 species were accurately predicted (Figure 2). 287 Table 2. LOOCV random forest model performance metrics (mean ± SD) based on 100 replicates predicting species 288

and establishment probabilities. 289 Response OOB error Threshold tss Accuracy Sensitivit y Specificity Precision F1 AUC Introduced 0.14±0 0.30±0.01 0.80±0.01 0.78±0.02 0.80±0.02 0.60±0.02 0.65±0.01 0.82±0 Introduced after 1950 0.15±0 0.26±0.03 0.76±0.03 0.76±0.06 0.76±0.05 0.51±0.04 0.58±0.01 0.78±0 Established 0.11±0 0.17±0.01 0.77±0.01 0.85±0.02 0.75±0.02 0.42±0.01 0.56±0.01 0.85±0 Established after 1950 0.11±0 0.16±0.01 0.74±0.01 0.84±0.01 0.73±0.02 0.36±0.01 0.50±0.01 0.81±0 290 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 19 291 Figure 2. LOOCV random forest model performance. (a) Introduction model (AUC = 0. 82); (b) Introduced species 292 after 1950 (AUC = 0.78); (c) Establishment model (AUC = 0.8 5); (d) Established species after 1950 (AUC = 0.8 1). 293 Panels show, from left to right, mean predicted probability distributions, mean ± SD confusion matrices, and ROC 294 curves from 100 replicates with the average shown in black. 295 296 3.2 Variable importance and partial dependency plots 297 Permutation importance analysis identified native range in Asia as the strongest predictors of 298 mosquito introduction risk, with mean decrease in accuracy of 0.02 7±0.001 (Figure 3A). 299 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 20 Additional influential variables included oviposition in human-made breeding sites (0.018±0.001), 300 native range in Australia (0.016±0.001), native range in North America (0.011±0.001), maximum 301 and minimum precipitation within native ranges (0.015±0.001 and 0.00 3±0.001), thermal 302 tolerance limits (maximum and minimum temperatures at 0.0 10±0.001 and 0.01 1±0.001), and 303 overall native distribution area (0.015±0.001). 304 In models restricted to species introduced since 1950, origin in Asia remained the predominant 305 predictor (0.021±0.001), along with increased importance of climatic variables such as maximum 306 precipitation, maximum and minimum temperature (0.015±0.001, 0.011±0.001 and 0.011±0.001), 307 consistent with tropical origin of recent introductions, originating in Australia (0.01 5±0.001) and 308 oviposition in human-made breeding sites (0.011±0.001), (Figure 3B). 309 For establishment probability in both models, maximum precipitation emerged as the top predictor 310 (0.023±0.001 and 0.02 1±0.001), followed by Asian origin (0.020±0.001 and 0.014±0.001) 311 (Figures 3C-D). Results based only on ecological and life -history traits were similar in terms of 312 identified and ranking of important variables and the predicted probabilities for the introduction 313 and establishment of species. Major changes were general lower model performance and the 314 emergence of several species predicted as potential spreaders that were not identified by the full 315 models based on all variables (Appendix S2). 316 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 21 317 Figure 3. Variable importance rankings from the full random forest models, shown as mean decrease in accuracy 318 (points) ± standard deviation, across 100 model replications. Higher values indicate greater influence in predicting (a) 319 introduction, (b) introduction after 1950, (c) establishment, and (d) establishment after 1950. 320 321

probabilities increased for species native to Asia and Australia, those using 322 human-made breeding sites, those from regions with higher maximum precipitation in their native 323 range and those with wider distribution in original range. The probability decreased for species 324 native to North America. The relationship with minimum temperature was bimodal, with elevated 325 probabilities for species from both cold and warm minimum temperature regimes, whereas species 326 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 22 from intermediate temperature ranges were less likely to be introduced (Figure 4A). Although for 327 species introduced after 1950, the maximum temperature of the regions of origin is more important 328 than the distribution area, the patterns remain largely consistent. (Figure 4B). 329 Establishment probability is largely driven by maximum precipitation in their native range 330 and Asian origin (Figures 4C-D). 331 332 Figure 4. Partial dependence plots illustrating the marginal effects of predictors with importance ≥ 0.01 on predicted 333 probabilities, averaged (green) across 100 random forest model replications (grey). Panels show (a) introduction, (b) 334

after 1950, (c) establishment, and (d) establishment after 1950. 335 336 3.3 Predicted spreaders and discrepancies 337 The introduction model identified 2 4 mosquito species without known invasion history to have 338 traits consistent with unintentionally transported and introduced species (Table 3). These species 339 share ecological profiles of known invaders, including human altered environments, geographic 340 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 23 origin, and climatic characterization of their native range, underscoring their potential as emerging 341 invaders warranting targeted surveillance. 342 Among them 1 7 “high-risk” species have attributes consistent with species that also 343 established non-native populations. These include Culex vishnui, Anopheles culicifacies, Culex 344 univittatus, Aedes lineatopennis, Anopheles amictus, Culex theileri, Mansonia septempunctata, 345 Culex torrentium, Anopheles hyrcanus, Culex perexiguus, Culex poicilipes, Anopheles claviger, 346 Culex nigripalpus, Anopheles plumbeus, Culiseta inornata, Aedes geniculatus, and Anopheles 347 pseudopunctipennis. 348 On the other hand, seven species that have attributes consistent with accidentally 349 introduced species do not share characteristics with species that were established. Such species 350 may survive transport but fail to overcome ecological or climatic constraints necessary for 351 sustained establishment. These species include Anopheles rufipes, Anopheles fluviatilis, 352 Coquillettidia linealis, Coquillettidia richiardii, Anopheles sergentii, Anopheles pulcherrimus, and 353 Anopheles quadrimaculatus. 354 Eleven species with known invasion history were not captured by our introduction model 355 (predicted probabilities < 0.3). The same applies to five species in the establishment model. Most 356 of these species exhibited transient detection or failure to establish self -sustaining populations, 357 suggesting stochastic introduction events or influences from drivers beyond trait-based predictors, 358 such as chance transport or climatic anomalies. 359 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 24 Table 3. Predicted potential spreaders. Mosquito species with a model -predicted introduction probability above the 360 average TSS threshold (≥ 0.3) but no confirmed introduction outside their native range. Species in bold also show 361 high predicted establishment probabilities, indicating elevated overall invasion risk. 362 Species True Label Predicted Probability ± SD Predicted Label Culex vishnui No 0.89 ± 0.01 Yes Anopheles culicifacies No 0.70 ± 0.01 Yes Aedes lineatopennis No 0.57 ± 0.01 Yes Culex univittatus No 0.57 ± 0.02 Yes Anopheles amictus No 0.56 ± 0.01 Yes Culex theileri No 0.55 ± 0.01 Yes Mansonia septempunctata No 0.50 ± 0.01 Yes Culex torrentium No 0.46 ± 0.01 Yes Anopheles hyrcanus No 0.45 ± 0.01 Yes Culex perexiguus No 0.45 ± 0.02 Yes Culex poicilipes No 0.45 ± 0.01 Yes Anopheles rufipes No 0.43 ± 0.02 Yes Anopheles claviger No 0.42 ± 0.01 Yes Culex nigripalpus No 0.42 ± 0.02 Yes Anopheles plumbeus No 0.42 ± 0.01 Yes Culiseta inornata No 0.40 ± 0.01 Yes Anopheles fluviatilis No 0.40 ± 0.01 Yes Aedes geniculatus No 0.40 ± 0.01 Yes Anopheles pseudopunctipennis No 0.37 ± 0.01 Yes Coquillettidia linealis No 0.36 ± 0.01 Yes Coquillettidia richiardii No 0.32 ± 0.01 Yes Anopheles sergentii No 0.32 ± 0.01 Yes Anopheles pulcherrimus No 0.31 ± 0.01 Yes Anopheles quadrimaculatus No 0.31 ± 0.01 Yes 363 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 25 3.4 Occurrence of possible spreaders 364 Below we show the current distributions of species that have been identified as high -risk species 365 obtained from Wilkerson et al. (26), meaning species not yet detected outside their native ranges 366 with high probability of introduction and establishment (Figure 5). 367 368 369 Figure 5. Current distribution of species with high invasion potential, meaning high probability to be introduced as 370 well as to become established. Green shading shows countries where the species are currently reported (26). 371 372 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 26

373 Our findings suggest that the introduction and establishment of non-native mosquitoes are driven, 374 to some extent, by some ecological, life -history, and macroecological characteristics. Once 375 established, vector mosquitoes can profoundly alter disease transmission dynamics, or introduce 376 pathogens into previously unaffected regions (3). Responding to their global spread requires 377 predictive tools that go beyond local surveillance and include proactive prevention and risk 378 assessment strategies. Our modelling approach achieved moderate to good predictive ability, stable 379 across model repetitions, which indicates that the invasion potential of species can be inferred to 380 some extent from trait data alone. Unlike previous efforts that modelled the potential distribution 381 of species already introduced (58,59), our framework indicates potential invaders before they 382 spread. Specifically, of the 169 species analyzed, 24 species with no prior invasion history received 383

probabilities equal to or higher than for known introduced species, including 1 7 384 species with a high probability of also becoming established. These species are predominantly 385 native to Asia, originated in regions with high precipitation, tend to have broad climatic tolerances, 386 have a wide distribution and are adapted to human-modified environments. 387 A main result of our analysis is the consistent importance of native biogeographic origin 388 in shaping invasion potential. Species native to Asia and Australia consistently ranked as the most 389 likely to be introduced and to establish outside their native ranges, whereas species from Africa, 390 the Americas or Europe showed substantial lower importance and probabilities. This pattern 391 reflects Asia’s role as principal source region for invasive species overall (60), including invasive 392 insects (61), and recently introduced mosquitoes (5). In Asia, high human population density, 393 intensive containerized trade, and rapid economic expansion create both the propagule pressure 394 and the disturbed habitats that favor human-commensal vectors. Following World War II, used 395 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 27 aircraft tires were shipped from Asia and the Pacific region back to the United States as part of 396 postwar recovery and logistics operations because rubber remained a valuable commodity (62), 397 inadvertently facilitating the long-distance transport of mosquito eggs and larvae from that region. 398 Today, the globalization of plant and materials trade continues to provide suitable pathways. For 399 instance, China is by far the leading bamboo exporter (63), and ornamental -plant producers 400 increasingly outsource cultivation to tropical developing countries such as Thailand to reduce 401 labor, land, and infrastructure costs (64). According to the World Bank, China alone accounted for 402 roughly 32% of global container port traffic in 2022 (65). Ongoing large-scale initiatives such as 403 China’s Belt and Road Initiative (BRI) further expand trade and transport networks involving more 404 than 120 countries (66), and the thawing of the Northern Sea Route through the Arctic Ocean (67), 405 potentially opens opportunities for the spread of non-native mosquito species from Asia to Europe. 406 Australia's significance arises mainly from the fact that most of its native mosquito species were 407 introduced to the Pacific region and New Zealand. Yet, there are several examples of long-distance 408 introductions of mosquitoes native to Australia, including Aedes vexans in Hawaii (68), Anopheles 409 subpictus in the Netherlands (69), and Aedes notoscriptus in California (70). The latter, first 410 detected in Los Angeles County in 2014, shows how integration into global trade networks, 411 combined with climatic similarities between native and introduced regions, can lower 412 environmental barriers and facilitate rapid establishment. In that sense, native continent acts as an 413 integrative factor, capturing evolutionary history, trade exportation intensity, and environmental 414 matching simultaneously. However, global trade networks continuously evolve, potentially 415 reshaping introduction pathways and altering the relative importance of certain origins, routes, and 416 goods (17,45). Nevertheless, our results suggest that the intrinsic traits of a species remain 417 relatively stable predictors, independent of macroecological factors (Appendix S2). 418 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 28 Concerning probability of species introduction, another main predictor identified is the use 419 of human-made breeding sites. Species that oviposit in artificial habitats, plastic vessels, plant pots, 420 rice fields, drainage systems or discarded tires, showed markedly higher probabilities of being 421 transported and introduced than species restricted to natural water bodies. This finding aligns with 422 classic invasion ecology, in which propagule pressure and human association are primary 423 determinants of transport success (15,71). Container breeding has the advantage that eggs and 424 larvae persist in environments closely associated with human activity, trade and travel, from cargo 425 holds to used -tire shipments (72). This pattern, documented e.g., for Ae. albopictus and Ae. 426 japonicus (10,72,73), is generalizable: nearly all species predicted with high probabilities of both 427

and establishment use human -made breeding sites. This underscores that invasion 428 potential is closely coupled with the degree of adaptation to human -modified landscapes. Global 429 urban expansion and the proliferation of disposable containers continue to multiply these breeding 430 sites, providing abundant breeding opportunities for species with suitable ecological strategies 431 (74,75). 432 Climatic predictors had a complementary influence, particularly on establishment success. 433 The maximum annual precipitation of the native range was by far the strongest predictor of 434 probability of species establishment, with probabilities rising sharply for species native from areas 435 receiving approximately 1,500 mm yr⁻¹. The relationship between minimum temperature and 436

probability was bimodal: species from both cold and warm native ranges were more 437 likely to be introduced than those from mild climates. This dual pattern might just again stand as 438 a proxy for the species native region, or it suggests that both cold-adapted and tropical mosquitoes 439 possess distinct mechanisms enabling survival during transport, either tolerance to cold or 440 resistance to dehydration. Species from thermally extreme regions on the other hand were more 441 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 29 likely to establish, with over half of the successfully established species originating from areas 442 exceeding 34 °C in average maximum temperature. Again, the climate data used here reflect the 443 conditions within the species’ native ranges . H owever, there are several species were able to 444 establish under environmental conditions that differ from those in their native ranges. This applies, 445 for example, to Ae. albopictus, which was initially thought unlikely to establish beyond its native 446 range in tropical and subtropical regions of Southeast Asia, requiring a lengthy adaptation to new 447 ecological conditions and to be constrained by competition with local mosquito species (76). 448 Nevertheless, it rapidly adapted to colder conditions (77,78), outperformed presumed competitors 449 (79,80) and expanded into temperate regions worldwide. Hence, predictions of invasion and 450 establishment potential based solely on native-range climates should be interpreted with caution. 451 Finally, we also found that widely distributed species are also more likely to be introduced 452 into new regions. This may translate into increased propagule pressure as greater distribution 453 increases the chances of introduction. This was however not observed for established species, 454 which could indicate a filter for species establishment that is based more on intrinsic traits or 455 adaptation to new environments (20,81). 456 Overall, our results indicate that mosquito invasion potential is best explained by the 457 interaction between life -history flexibility and macroecological breadth. Adaptation to human 458 environments and occurrence in regions that are export hubs for commodities associated with 459 human-made oviposition sites that promote dispersal and introduction. In turn, broad climatic 460 tolerance, particularly to extremes of rainfall and temperature, determines whether colonization 461

in successful establishment. Together, these traits define a functional profile of likely 462 invaders: human -adapted species, often native to Asia and/or Australia, capable of exploiting 463 human-made breeding sites , tolerant of precipitation and thermal extremes and with a wide 464 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 30 distribution. These findings extend previous qualitative insights (18) to a quantitative global 465 framework, go beyond the few medically very important vectors (e.g., 59,72,82,83), and provide 466 insights into the invasi ve potential of lesser -known species. The results also demonstrate that 467 invasion potential in mosquitoes can be forecasted to some extent from measurable ecological, 468 life-history, and macroecological traits. 469 Beyond identifying traits of importance, our models identified 24 species with no recorded 470 invasion history but trait profiles closely matching those of known invaders. Of these, 1 7 species 471 were also predicted with high probabilities of establishment, marking them as priority candidates 472 for early surveillance. For example, Culex vishnui, the species with the highest predicted 473

probability and likely to establish is native to South and East Asia, where it thrives in 474 rice fields, ground pools, and small artificial containers (26). It has been found naturally infected 475 with multiple arboviruses, including Japanese encephalitis and West Nile virus (84,85). 476 Anopheles culicifacies, ranking second in our high risk species list, is an important malaria 477 vector, particularly in South and Southwest Asia. (86). Predominantly anthropophilic but 478 occasionally zoophilic, it can take multiple blood meals per gonotrophic cycle (26) increasing its 479 potential for pathogen transmission. 480 Aedes lineatopennis, native to the Australasian and Oriental regions, was first described 481 from the Philippines (26,87). It has been found naturally infected with Japanese encephalitis (88) 482 and Middelburg virus (89), and is considered a potential vector for Ross River and Murray Valley 483 encephalitis viruses (90,91). 484 Our results provide valuable insights, but their interpretation should also take certain 485 considerations into account. First, trait data for some mosquitoes remains partly incomplete. 486 Similar to other studies (21,22,92), we used a trait imputation approach to overcome this limitation. 487 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 31 To minimize bias, we included only species with at least 75% trait completeness, and only 488 variables that were known for at least 75% of the species. Nonetheless, the imputation process may 489 obscure subtle ecological differences that influence invasion potential. Second, because our 490 approach is species trait–centric, it does not account for the local conditions species encounter at 491

sites, such as climate, biotic interactions, or vector control measures that can influence 492 invasion outcomes (93,94). Furthermore, environmental change can modify the current 493 distribution of suitable habitats, impairing species establishment success. Thus, our framework 494 should be regarded as an initial profiling tool for species with invasion potential, to be 495 complemented by geographically explicit assessments of establishment risk. Finally, it is important 496 to recognize that the predictors used in our models are not static. Global trade networks may 497 continue to evolve and other regions of origin may become sources for species exports. However, 498

based solely on ecological and life -history traits gave similar predicted probabilities of 499 species introduction and establishment showing the resilience of our findings (Appendix S2) . 500 Thus, although our results may be temporally bounded, our framework provides a robust baseline 501 for identifying species with elevated invasion potential under present -day ecological and trade 502 conditions and are most relevant for near-term invasion assessments. 503 5 Conclusion 504 Overall, our results indicate that the invasion potential of mosquitoes can be partially predicted 505 from intrinsic biological and ecological traits alone. Species displaying traits like being native to 506 Asia and Australia, using human-made breeding sites, from regions with climate extremes can be 507 expected to be those posing higher risk of invasion in non -native regions in the near future. By 508 identifying these species and their characteristic profiles, our approach underscores the potential 509 for proactive, trait-based surveillance strategies that extend beyond the currently recognized vector 510 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint 32 species. Trait -based frameworks such as the one presented here can support early -warning 511 systems, guide allocation of surveillance resources, and ultimately reduce the risk of novel vector-512 borne disease emergence. Once invasive mosquitoes succeed in establishing populations, their 513 control and eradication become exceedingly challenging (95,96). In this context, preventing 514 introductions remains the most efficient and economically viable strategy. 515 Acknowledgments 516 RP and CAS gratefully acknowledge the support of the Portuguese Foundation for Science and 517 Technology (FCT) for funds to the R&D Unit Global Health and Tropical Medicine 518 (UIDB/04413/2025) and the Associated Laboratory in Translation and Innovation Towards Global 519 Health REAL (LA/P/0117/2020). RP acknowledges funding from FCT (PRT/BD/153694/2021; 520 https://doi.org/10.54499/PRT/BD/153694/2021) and thanks the AIR Center for their support. CC 521 acknowledges funding from FCT through InvaSTOP grant 522 (https://doi.org/10.54499/2023.12533.PEX) and the support from FCT through funds to 523 CEG/IGOT Research Unit (https://doi.org/10.54499/UID/00295/2025). 524

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The original global mosquito introduction dataset can be accessed at: 791 https://doi.org/10.5281/zenodo.15731141. 792 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted January 5, 2026. ; https://doi.org/10.64898/2026.01.05.697723doi: bioRxiv preprint .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 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