Research Article |
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Corresponding author: Zoltán Soltész ( soltesz.zoltan@ecolres.hu ) Academic editor: Ross Cuthbert
© 2025 Zoltán Soltész, Zoltán Kenyeres, Gábor Markó, Gergely Nagy, László Zsolt Garamszegi.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Soltész Z, Kenyeres Z, Markó G, Nagy G, Garamszegi LZ (2025) The co-existence patterns between native and an invasive mosquito species in Hungary based on a field survey. NeoBiota 103: 165-185. https://doi.org/10.3897/neobiota.103.165442
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Biological invasions by non-native mosquito species pose significant global economic and public health threats due to their high vector competence. Still, their ecological relationships with the native fauna are rarely considered. This field study investigates the association between Aedes albopictus and the native mosquito community in Hungary. We conducted field sampling from 2020 to 2023, identified 104,612 mosquitoes from 29 species captured in traps across 532 locations, and characterized species diversity as well as the abundance of native species at each trapping event. We found that, where Ae. albopictus is already present, its abundance was positively associated with the species richness and diversity of native mosquitoes even after controlling for spatial and seasonal effects. We identified several significant pair-wise associations between Ae. albopictus and native species, both in positive and negative directions. These correlative patterns may indicate either direct interaction between species (e.g., competition) or patterns of co-existence that are driven by a third variable in the background. We performed path analyses to uncover the causal relationship among environmental traits (temperature, precipitation, urbanization and distance from water surfaces), the abundance of Ae. albopictus and that of four native species depicting co-existence patterns with the invasive one. These analyses revealed that the co-occurrences of Ae. albopictus with Anopheles plumbeus and Coquillettidia richiardii were mediated by seasonal activity, indicating that invasive mosquitoes can thrive in ecological conditions that disfavor native species. However, path models for Ochlerotatus dorsalis and Oc. geniculatus suggested a more direct causal relationship with Ae. albopictus. Although we cannot exclude that interspecific competition may partly shape the distribution of invasive and native mosquitoes, environmental filtering via shared ecological preferences appears to play a dominant role in structuring co-occurrence patterns.
Aedes albopictus, BG-Sentinel mosquito trap, competitive exclusion, ecological niche, emerging infectious diseases
Biological invasion refers to the process by which non-native species spread into new environments and establish populations, and often has significant ecological, economic, and social impacts (
An increasing number of invasive mosquito species have appeared in Europe, posing substantial and immediate risks to public health and ecosystems, with Aedes albopictus being the most abundant and threatening (
Theories about the ecological interactions of invasive species often emphasize that competition plays a pivotal role in shaping species assemblages, particularly when invasive species exploit similar resources and habitats as native species. One classic outcome of such competition is the competitive exclusion principle, which states that two species competing for the same limiting resources cannot stably coexist in the same ecological niche (
One of the most common approaches to infer ecological interactions between invasive and native species from field studies is the analysis of correlations in their prevalence or abundance across space and time. These co-occurrence patterns—whether positive or negative—are often used as proxies for making inferences about biotic relationships such as competition, facilitation, or neutral coexistence (
Studies, using field surveillance data, such as trap-based sampling across broad geographic scales, have shown that invasive Aedes albopictus often exhibits negative spatial associations with native species, such as Culex pipiens, Ochlerotatus triseriatus, and Anopheles quadrimaculatus, in parts of North America, suggesting the possibility of competitive exclusion (
In this study, we investigate the ecological relationships between Ae. albopictus and native mosquito species using a large-scale field dataset collected across Hungary based on samples from adult traps. Ae. albopictus has been introduced in Hungary in the last 15 years (
We conducted direct field sampling, collecting mosquitoes from 192 settlements and 532 locations during 897 trapping days between 2020 and 2023 (Fig.
The localities that were selected to sample mosquito communities by using adult mosquito traps in the field in Hungary. The traps were operating at least for one night. The filled dots represent the permanent traps (operating continuously for several months), while the open dots indicate the short-term traps (operating temporarily for a few days only).
BG-Sentinel mosquito traps were used with CO2 and lure as attractants for mosquito sampling. The content of the traps was collected and stored in a freezer (-20 °C) until further examination, upon which all specimens were identified at the species level under a microscope by the same expert taxonomist based on morphological characteristics (
The number of individuals of mosquito species trapped between 2020 and 2023, and the number of settlements and traps where each species was captured within the short-term trapping campaign. For the long-term trapping campaign performed at four locations, only the total number of individuals captured for each species is shown. The scientific names of mosquitoes are written based on
| SHORT-TERM TRAPS | PERMANENT TRAPS | |||||
|---|---|---|---|---|---|---|
| specimens | settlement | settlement % | trap | trap% | specimens | |
| Aedes albopictus | 853 | 26 | 13.54 | 96 | 18.05 | 9040 |
| Aedes cinereus | 0 | 0 | 0 | 0 | 0 | 2 |
| Aedes japonicus* | 38 | 16 | 8.33 | 19 | 3.57 | 10 |
| Aedes koreicus* | 325 | 58 | 30.21 | 95 | 17.86 | 324 |
| Aedes rossicus | 0 | 0 | 0 | 0 | 0 | 2 |
| Aedes vexans | 3860 | 104 | 54.17 | 196 | 36.84 | 1097 |
| Anopheles claviger | 4 | 4 | 2.08 | 4 | 0.75 | 25 |
| Anopheles hyrcanus | 34 | 11 | 5.73 | 11 | 2.07 | 0 |
| Anopheles maculipennis s.l. | 62 | 16 | 8.33 | 21 | 3.95 | 22 |
| Anopheles plumbeus | 102 | 29 | 15.1 | 38 | 7.14 | 506 |
| Coquillettidia buxtoni | 1 | 1 | 0.52 | 1 | 0.19 | 5 |
| Coquillettidia richiardii | 476 | 25 | 13.02 | 39 | 7.33 | 289 |
| Culex hortensis | 0 | 0 | 1.56 | 0 | 0 | 14 |
| Culex martinii | 1 | 1 | 0.52 | 1 | 0.19 | 0 |
| Culex modestus | 80 | 21 | 10.94 | 26 | 4.89 | 75 |
| Culex pipiens s.l. | 42140 | 188 | 97.92 | 498 | 93.61 | 43437 |
| Culiseta annulata | 199 | 39 | 20.31 | 49 | 9.21 | 20 |
| Culiseta longiareolata | 27 | 10 | 5.21 | 15 | 2.82 | 0 |
| Ochlerotatus annulipes | 2 | 2 | 1.04 | 15 | 2.82 | 189 |
| Ochlerotatus caspius | 679 | 54 | 28.13 | 72 | 13.53 | 110 |
| Ochlerotatus cataphylla | 0 | 0 | 0 | 0 | 0 | 3 |
| Ochlerotatus detritus | 0 | 0 | 0 | 0 | 0 | 1 |
| Ochlerotatus dorsalis | 2 | 1 | 0.52 | 2 | 0.38 | 50 |
| Ochlerotatus geniculatus | 18 | 13 | 6.77 | 15 | 2.82 | 269 |
| Ochlerotatus pulcritarsis | 4 | 4 | 2.08 | 2 | 0.38 | 8 |
| Ochlerotatus pullatus | 0 | 0 | 0 | 0 | 0 | 28 |
| Ochlerotatus sticticus | 0 | 0 | 0 | 0 | 0 | 172 |
| Ochlerotatus punctor | 0 | 0 | 0 | 0 | 0 | 2 |
| Uranotaenia unguiculata | 4 | 4 | 2.08 | 4 | 0.75 | 1 |
| TOTAL | 48911 | 192 | 532 | 55701 | ||
Georeferenced climatic data were gathered for each sampling location using the Hungarian Meteorological Service (HungaroMet) open Meteorological Database (odp.met.hu). The meteorological data were generated by MISH interpolation methods from the measured and quality-controlled, homogenized, and completed data (grid resolution: 10 × 10 km; see for more details:
Based on the geographic coordinates of each sampling site, we calculated the urbanization index within the surrounding area (i.e., 1 × 1 km square). For this purpose, we used the ‘UrbanizationScore’ software (
Based on the geographical coordinates, we calculated the distance between each trap location and the nearest surface water body (river, stream, lake, or pond) using QGIS 3.28. Distances were measured as straight-line (Euclidean) distances based on a hydrographic layer, and were later used as environmental variables in the analysis. The distance from the closest water surface was estimated in meters and was log10-transformed for the statistical analyses.
From the trap samples, we calculated the following variables reflecting the composition of the mosquito fauna for each trap originating from the direct fieldwork, if at least one individual for any native species were caught: i) species richness of the native mosquito fauna, ii) Shannon index to characterize the diversity of the native mosquito fauna by considering their relative abundance. We also calculated the abundance of Ae. albopictus and each native species that was captured at least once in the traps. Abundance values were log10-transformed for the statistical analyses.
First, we tested for the relationship between the abundance of Ae. albopictus and estimates that reflect the composition of the native mosquito community. This was done by testing for the correlation of these variables, and also by constructing mixed-effects models with spatial correlation structure, in which species richness or diversity index was entered as the response variable. In each model, the focal predictor variable was the abundance of Ae. albopictus (log10-transformed), and we also included detection date (circular variable using the sine and cosine of the radians of the actual date to define seasonal rhythms) to account for temporal effects. Given that the data were geographically distributed, ecological conditions as well as the composition of the mosquito community could be more similar between closely situated sampling locations than between distantly positioned traps. Therefore, we defined an exponential spatial correlation structure for the residuals based on the geographical coordinates of the sampling sites. In this way, the pseudo-replication caused by the traps operating at the same locations between collection events was also considered. To control for temporal non-independence of data due to year effects (data from the same year may be more similar than from different years), we set the random effect term based on year as a categorical variable as a random effect. The spatial mixed-effects models were performed using the statistical package “nlme” (
Second, we investigated if any relationships between particular pairs of species can be observed in the data. For this, we examined if detected co-existence patterns between pairs of species followed a random expectation or shifted towards a positive or negative direction. Accordingly, we counted the traps that successfully captured Ae. albopictus or the given native species being compared (i.e., we identified at least one individual in the content of the trap) – independently of each other. Based on these observed incidences, we separately generated random scenarios for the presence of the two species along the sampling localities. We repeated these simulations 1,000 times, and in each run, we recorded how often the two species appeared together in the same traps purely by chance. These estimates define the null distribution of their expected co-occurrence, which was used as a statistical reference against the observed number of instances when both species were captured in the same trap. An observed number of co-occurrences lower than the 2.5th percentile of the generated random distribution indicates a negative association between the two species, whereas co-occurrences exceeding the 97.5th percentile of the null distribution suggest a positive association.
Third, to investigate if the significant relationships between the presence of species are mediated by a third variable that reflects common environmental conditions or by direct interspecific interactions, we fitted path analysis models. More specifically, by using the R package “piecewiseSEM,” (
To fit models for the above hypotheses, we relied on linear mixed-effects models with gaussian error terms available in the package “nlme” (
In the raw data, there was no significant association between the abundance of Ae. albopictus and species richness of the native mosquito fauna (r = 0.009, N = 1517, P = 0.738). A similar result was found for Shannon diversity index (r = 0.023, N = 1517, P = 0.367). However, when we only included sites where the invasive species was present, these relationships were significant and positive (species richness: r = 0.281, N = 350, P < 0.001; diversity index: r = 0.263, N = 350, P < 0.001; Fig.
The results of mixed-effects models with spatially correlated random effects testing for the association between the abundance of Aedes albopictus and estimates of the composition of the native mosquito fauna, while holding spatial and temporal effects constant.
| Species richness of the native mosquito fauna | ||||
| Fixed effects | Estimate | S.E. | t-value | P |
| Intercept | 1.768 | 0.252 | 7.028 | < 0.001 |
| Abundance of Ae. albopictus | 0.088 | 0.062 | 1.425 | 0.154 |
| sin(date) | -0.372 | 0.059 | -6.267 | < 0.001 |
| cos(date) | 0.188 | 0.128 | 1.470 | 0.142 |
| Random effects | ||||
| Variance parameters | ||||
| Year | 0.192 | |||
| Residual | 1.442 | |||
| Dimensions | ||||
| # of obs. | 1517 | |||
| # of groups | 4 | |||
| Shannon diversity index of the native mosquito fauna | ||||
| Fixed effects | Estimate | S.E. | t-value | P |
| Intercept | 0.175 | 0.052 | 3.352 | < 0.001 |
| Abundance of Ae. albopictus | 0.077 | 0.020 | 3.813 | < 0.001 |
| sin(date) | -0.032 | 0.015 | -2.089 | 0.037 |
| cos(date) | 0.054 | 0.034 | 1.596 | 0.111 |
| Random effects | ||||
| Variance parameters | ||||
| Year | 0.006 | |||
| Residual | 0.146 | |||
| Dimensions | ||||
| # of obs. | 1517 | |||
| # of groups | 4 | |||
The relationship between the abundance of Aedes albopictus and species richness (A), species diversity (B) based on the content of traps originating from a direct field survey in Hungary. Colors (green and light brown) separate the data based on the presence or absence of the invasive species. Data are jittered for better visualization at zero prevalence of Ae. albopictus. The diamonds show the summary statistics (mean ± standard deviation) for the response variable when the focal invasive species is present or absent. The light brown lines show the regression lines that are fitted on the non-zero prevalence data (see Table
The co-occurrence of Ae. albopictus and Anopheles plumbeus was detected in 12 traps only. Ae. albopictus was present in 350 trap samples, while An. plumbeus was captured in 180 instances. We can generate a null-hypothesis from these observations in which we expect that both species will be present in 41–53 samples simply by chance with 95% confidence. The detected co-occurrence (12) of the two species was significantly lower than this random expectation (Table
The pairwise association between the presence of Ae. albopictus and the presence of native mosquitoes that were present at least in one trap during the course of the study. The results are based on simulations that estimate the expected number of co-occurrences of the two mosquitoes in the same trap given the number of instances when these species are caught. The significant associations are in bold, * indicate native species that were included in the path analysis.
| Native species | |||
|---|---|---|---|
| # observed co-occurrence | The 95% CI range of the expected co-occurrences | P | |
| Aedes cinereus | 0 | 0–2 | 0.004 |
| Aedes rossicus | 0 | 0–2 | 0.004 |
| Aedes vexans | 132 | 123–138 | 0.260 |
| Anopheles claviger | 3 | 5–9 | 0.422 |
| Anopheles hyrcanus | 1 | 2–5 | 0.486 |
| Anopheles maculipennis s.l. | 5 | 9–15 | 0.150 |
| Anopheles plumbeus * | 12 | 41–53 | < 0.001 |
| Coquillettidia buxtoni | 0 | 1–3 | 0.506 |
| Coquillettidia richiardii * | 14 | 33–43 | < 0.001 |
| Culex hortensis | 1 | 1–3 | 0.250 |
| Culex martinii | 0 | 0–1 | 0.250 |
| Culex modestus | 11 | 15–22 | 0.250 |
| Culex pipiens s.l. | 338 | 334–341 | 0.330 |
| Culiseta annulata | 13 | 16–23 | 0.484 |
| Culiseta longiareolata | 2 | 3–7 | 0.588 |
| Ochlerotatus annulipes | 0 | 13–19 | < 0.001 |
| Ochlerotatus caspius | 35 | 29–38 | 0.208 |
| Ochlerotatus cataphylla | 0 | 1–2 | 0.894 |
| Ochlerotatus detritus | 0 | 0–1 | < 0.001 |
| Ochlerotatus dorsalis * | 19 | 7–12 | < 0.001 |
| Ochlerotatus geniculatus * | 53 | 36–46 | 0.004 |
| Ochlerotatus pulcritarsis | 7 | 2–5 | 0.004 |
| Ochlerotatus pullatus | 0 | 5–9 | 0.012 |
| Ochlerotatus punctor | 0 | 0–1 | 0.894 |
| Ochlerotatus sticticus | 4 | 12–19 | 0.004 |
| Uranotaenia unguiculata | 0 | 1–3 | 0.534 |
The positive relationships between Ae. albopictus and native species may be mediated by environmental effects that render species pairs to be captured at the same time or location. In addition, such environmental effects may also raise negative associations between species, if they prefer very different ecological conditions, which results in rare co-occurrence patterns. We discriminated the direct and indirect causal effects of temporal fluctuations, environmental effects on the abundance of invasive and native mosquito species by using path analyses (Fig.
Path analyses unraveling the direct and indirect relationships between environmental variables and the abundance of Ae. albopictus and that of four native mosquito species with detected co-occurrence patterns (A. An. plumbeus; B. Cq. richiardii; C. Oc. dorsalis; D. Oc. geniculatus). The models included pathways that are defined by the underlying hypotheses (see main text). The final model includes all pathways after backward model simplification based on significance values. Variables describing within-year temporal fluctuations are in white boxes, grey boxes represent environmental variables, and measures of abundances are in blue boxes. Marginal (conditional) R-squared values for individual models are given with the corresponding response variables. Black arrows show positive effects, and red arrows show negative effects with a thickness that is proportional to the standardized path coefficient (which are also presented as values on the arrows). Node of two arrows indicates an interaction effect between the corresponding variables. Significance levels: *: P < 0.05; **: P < 0.01; ***: P < 0.001.
Overall, the path models were statistically supported (An. plumbeus: Fisher’s C10 = 8.500, P = 0.580; Cq. richiardii: Fisher’s C10 = 8.149, P = 0.614; Oc. dorsalis: Fisher’s C2 = 1.186, P = 0.553; Oc. geniculatus: Fisher’s C10 = 4.143, P = 0.941), and suggested the following general patterns: i) fine-scale variation in temperature is strongly determined by components of seasonality, ii) the abundance of Ae. albopictus primarily depends on the interaction between temperature and precipitation and urbanization (Fig.
Effect of different environmental variables (A. Seasonality; B. The interacting effect of temperature and precipitation; C. Urbanization) on the abundance of Ae. albopictus. Dots represent trap samples, lines are regression lines. To demonstrate the interacting effect of temperature and precipitation (B), sites with different degree of rainfall are shown with different colors (green: precipitation during 30 days prior to trapping < 50 mm, brown: precipitation during 30 days prior to trapping >= 50 mm, as the 50 mm threshold is defined as the median of data distribution).
The main results of this study were that i) the abundance of Ae. albopictus was positively associated with mosquito species richness and the diversity of the native mosquito fauna; ii) based on patterns of observed presence/absence of species in the traps, we detected evidence for significant positive co-occurrences for 3 species pairs and for significant negative co-occurrences for 8 species; iii) the abundance of Ae. albopictus was significantly associated with environmental variables (urbanization and the interaction between temperature and precipitation); iv) when we controlled for the potentially confounding effect of environmental variables, the positive relationship between the abundance of Ae. albopictus and that of 2 native mosquito species (Oc. dorsalis and Oc. geniculatus) were still significant.
The positive associations between the abundance of Ae. albopictus and species richness and diversity of the native mosquito fauna likely reflect shared environmental preferences rather than indicating facilitative biotic interactions. Most hypotheses about the effect of invasive species on native species emphasize the role of competition. Accordingly, competitive interactions among species should cause a negative relationship between the abundance of the invasive species and the diversity indices of the native community (e.g.,
The co-occurrence analysis presented in this study demonstrates how diversity patterns of mosquito assemblages can be potentially influenced by interactions with particular species. By testing all possible invasive–native species pairs against null expectations, we were able to identify which particular associations may be responsible for the detected positive associations between the abundance of the invasive species and the diversity of the native mosquito community. Out of the 26 native species assessed, 11 showed statistically significant associations with Ae. albopictus, including both positive and negative relationships. This relatively high proportion suggests that co-occurrence with Ae. albopictus is not random and warrants further ecological interpretation. Patterns of co-occurrences have been reported in other regions as well. For instance, Ae. albopictus was found to co-occur with native Ochlerotatus triseriatus in the eastern United States, though laboratory studies indicated a competitive advantage of the invader (
To disentangle these potential mechanisms, we applied path analyses, aiming to separate the direct associations between the abundance of Ae. albopictus and that of native mosquito species from indirect effects that act through environmental traits. First, we identified those abiotic ecological factors that shape the abundance of Ae. albopictus. Specifically, we found that the species was more abundant in more urbanized sites, while there was also a significant interactive effect of temperature and precipitation on abundance. The latter pattern suggests that the positive association with temperature is more emphasized in rainy periods than in dry periods. This can be explained by a mechanism in which, for the enhancing effect of temperature, some optimal levels of water are required to provide suitable conditions for breeding. Similar relationships have been repeatedly shown for the same species in other studies relying on different populations or methodologies (
Given that Ae. albopictus is apparently responsive to certain environmental conditions, it is plausible that the detected co-occurrence patterns with native mosquitoes are casually driven by the similar or dissimilar ecological requirements of the species being compared. Accordingly, we found that the initially observed negative associations between Ae. albopictus and two native species. An. plumbeus and Cq. richiardii, disappeared when environmental variables were included in the models, and the path analyses revealed no direct relationship between Ae. albopictus and these native species. An. plumbeus is a native forest-associated mosquito that traditionally breeds in tree holes, but has recently expanded into suburban environments in parts of Europe, utilizing artificial breeding sites, such as tires (
A similar argument can be made for the indirect relationship between Ae. albopictus and Cq. richiardii. Cq. richiardii is a widespread Palearctic mosquito that typically breeds in permanent aquatic habitats rich in emergent vegetation, such as reed beds, lakeshores, marshes, and old riverbeds (
The path analyses implied a more direct (positive) relationship between the abundance of Ae. albopictus and that of Oc. dorsalis and Oc. geniculatus. However, it is challenging to find a biological explanation for Ae. albopictus favoring large population sizes for these two native mosquitoes. Therefore, we suggest that the casual links between species occur due to some unmeasured environmental variables that would actually mediate the observed causal relationships. It is plausible that, for example, other components of habitat characteristics, additional climatic factors (such as winter temperature, wind) or microhabitat-level interactions, and predation pressure result these species being captured together more frequently than could be expected by chance. Accordingly, Oc. geniculatus is known to develop primarily in stable breeding sites such as tree holes in forested areas (
While our study relies on a large, standardized dataset, we acknowledge that BG-Sentinel traps baited with CO2 and synthetic lure may exhibit species-specific attractiveness, which could affect the catching probabilities of some mosquito taxa. Several studies have reported such biases, showing that trap efficacy can vary substantially across species due to differences in physiology, behavior, or olfactory sensitivity (
Taken together, we have shown that Ae. albopictus shows remarkable co-occurrence patterns with several native mosquitoes in Hungary, and that its local abundance can display positive relationships with estimates of the diversity of the endemic mosquito fauna. However, by considering some key ecological factors and by applying a path analysis, we were able to demonstrate – at least for two species – that the detected negative associations are unlikely to reflect direct interaction effects between populations (e.g., competition), and that environmental filtering explains species co-occurrences. These insights emphasize the importance of using environmentally informed models to avoid misattributing ecological effects and to better distinguish environmental associations from potential competitive processes. Our study also highlights that the laboratory results suggesting competition between the larvae of different species do not necessarily translate into population patterns detected in the field for reproducing generations. Overall, our results imply that shared ecological preferences can facilitate long-term coexistence even in the presence of potential competitive interactions. Such dynamics may ultimately promote the establishment and persistence of invasive mosquito species in new habitats, offering important implications for invasion biology and vector surveillance strategies. To better understand the ecological mechanisms behind co-occurrence, future studies should adopt a more comprehensive approach that focuses on a broader range of environmental variables and also integrates species-specific morphological, behavioral, and life-history traits into the analytical framework. Such integrative models would help to disentangle the relative importance of environmental filtering, niche differentiation, and biotic interactions in structuring mosquito assemblages. Considering the complexity of invasion processes, incorporating these functional attributes may provide critical insights into the mechanisms that facilitate coexistence or promote displacement among native and invasive mosquito species.
We would like to thank all colleagues of the Mosquito Monitor team (www.mosquitosurveillance.hu) who participated in the field work and assisted during taxonomic identification, namely Ágnes Klein, Tamara Szentiványi, Beáta Újvárosi. Two anonymous reviewers provided valuable comments on a previous version of the manuscript. The study was supported by funds from the Hungary’s National Research, Development and Innovation Office (K135841, PD-135143, RRF-2.3.1-21-2022-00006) and the Sustainable Development and Technologies National Programme of the Hungarian Academy of Sciences (NP2022-II-5/2022).
The authors have declared that no competing interests exist.
No ethical statement was reported.
No use of AI was reported.
Authors were supported by funds from the Hungary’s National Research, Development and Innovation Office (K135841, PD-135143, RRF-2.3.1-21-2022-00006) and the Sustainable Development and Technologies National Programme of the Hungarian Academy of Sciences (NP2022-II-5/2022).
ZS and LZG conceived the main ideas, ZS performed literature review, ZS and LZG designed methodology for sampling, ZS, GM, NG, ZK collected the data, ZS performed mosquito identification, LZG analyzed the data, ZS and LZG wrote the first draft of the manuscript. All authors contributed critically to subsequent versions and gave final approval for publication.
Zoltán Soltész https://orcid.org/0000-0003-3423-9258
Zoltán Kenyeres https://orcid.org/0000-0002-0941-7254
Gábor Markó https://orcid.org/0000-0003-1351-4070
Gergely Nagy https://orcid.org/0000-0002-0943-2876
László Zsolt Garamszegi https://orcid.org/0000-0001-8920-2183
All of the data that support the findings of this study are available in the main text.
Basic data forming the basis of the analysis
Data type: xlsx