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Habitat characterization and breeding preferences of mosquito larvae in northwestern Spain: abundance, diversity, and species composition

Parasites & Vectors volume 18, Article number: 165 (2025) Cite this article

Abstract

Background

Understanding how environmental variables determine the presence, abundance, and diversity of mosquitoes (Diptera: Culicidae) in their larval habitats is crucial to establish appropriate preventive and control measures against these disease vectors. Although the autonomous community of Galicia (northwestern Spain) is an optimal area for the development of mosquitoes, little is known about their larval ecology.

Methods

The study was performed in 333 sampling points evenly distributed throughout Galicia. Different habitat characteristics (climatic zone, hydroregime, water body type, substrate, surface, depth, degree of insolation, environment type, and land use) and physicochemical parameters of the water (temperature, pH, electrical conductivity, dissolved oxygen, turbidity, salinity, and total dissolved solids) were recorded in each water body. Mosquitoes were collected using the standardized dipping technique between May and October in 2021 and 2022. The relationship between environmental variables with diversity, abundance, and species composition was assessed throughout the Kruskal–Wallis test (K-W), redundancy analysis (RDA), and generalized linear models (GLM). The affinity index between species that shared breeding sites was also analyzed.

Results

A total of 14 mosquito species belonging to the genus Culex (88.1%), Anopheles (7.5%), and Culiseta (4.4%) were identified, with Culex pipiens s.l. being the most abundant in the region (48.1%). The frequency, abundance, and diversity of mosquitoes varied significantly among climatic zones, hydroregime, water body types, substrates, and seasons according to the K-W results (P < 0.05). RDA indicated that water body type, temperature, pH, and the conductivity of the water accounted for the main part of the variation in species composition. GLM revealed that water conductivity, hydroregime, land use, and degree of insolation affect Cx. pipiens s.l. larval abundance. Many species shared breeding sites, but Cx. pipiens s.l. and Cx. torrentium had the highest affinity index (2.58). Both species are competent vectors of West Nile virus (WNV), so their wide presence in Galicia is of interest to public health.

Conclusions

Several environmental variables determine the diversity, abundance, and species composition of mosquitoes at breeding sites. The information presented in this study provides valuable insights into mosquito larval ecology, especially useful for the identification of epidemiological risk areas and the design of vector surveillance and control programs.

Graphical Abstract

Background

Mosquitoes (Diptera: Culicidae) are important vectors of diseases (malaria, dengue, West Nile virus, etc.) whose immature stages develop in a wide range of freshwater ecosystems [1]. Characterizing larval habitats and knowing the breeding preferences of the different species, as well as the environmental variables that determine their abundance, diversity, and distribution, is crucial for designing effective vector control strategies [2]. This is particularly relevant considering that the most practical way to reduce local mosquito populations is to eliminate their breeding sites [3]. Although studies about mosquito larval ecology have increased in recent years [2, 4,5,6], this knowledge remains insufficient, especially in those regions where epidemiological risk is less apparent. In Spain, the available data in this regard are very limited, focusing on the Mediterranean basin [7,8,9] and occasionally in the north [10]. Despite the fact that Galicia, in the northwest of the country, has a great abundance and diversity of aquatic ecosystems, only a few natural hydric enclaves have been studied to date [11], while most of the efforts have been limited to monitoring adult populations [12,13,14]. Therefore, the main objective of the present study is to remedy the lack of information available on the larval ecology of the mosquitoes present in the region. For this purpose, the relationship between different habitat characteristics (climatic zone, hydroregime, water body type, substrate, surface, depth, degree of insolation, environment type, and land use) and physicochemical parameters of the water (temperature, pH, electrical conductivity, dissolved oxygen, turbidity, salinity, and total dissolved solids) with larval abundance, diversity, and species composition of mosquitoes in larval habitats of northwestern Spain is analyzed. The data obtained can be used in favor of public health by supporting vector management programs, and hence, help in the prevention of epidemiological risks.

Methods

Study area

The autonomous community of Galicia, located in the most northwestern region of Spain, covers an area of 29,574 km2 and is divided into four provinces (A Coruña, Lugo, Ourense, and Pontevedra). Its geography ranges from coastal to mountainous landscapes and is characterized by the presence of numerous rivers, forests, and crops (Fig. 1). The overall climatology of Galicia is defined by high rainfall (annual average precipitation over 1000 mm) that mellow from the coast inland. According to the Köppen–Geiger climate classification (KCC) [15], the territory is divided into three temperate climates (temperature averaging above 0 °C in the coldest month and below 22 °C in all months): the temperate oceanic climate (Cfb), the warm-summer Mediterranean climate (Csb), and the hot-summer Mediterranean climate (Csa) (Fig. 1).The Cfb climate has cold winters, cool summers, and a uniform rainfall between seasons; the Csb climate has cold or mild winters, dry and cool summers, and seasonal rainfall; and the Csa climate has mild winters, dry and hot summers, and seasonal rainfall.

Fig. 1
figure 1

Study area showing the sampling point distribution (Galicia, northwestern Spain). A Land use map (SIOSE 2011) extracted and modified from Instituto Geográfico Nacional de España – Xunta de Galicia. B Köppen climate classification extracted from Instituto Geográfico Nacional (IGN España)

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Habitat characterization

The field research was conducted in 333 hydric ecosystems evenly distributed throughout Galicia, so that at least one sampling point fell within a 10 km × 10 km Universal Transverse Mercator (UTM) quadrant (considering for sampling only those with more than half of its surface over the Galician peninsular territory) (Fig. 1). The selection of sampling points was done through a first step of identification and location of aquatic ecosystems present in each quadrant using geographic information systems (GIS) such as Google Earth and Google Maps; and a second step of evaluation and inspection in the field to choose the most optimal water body for sampling. Priority was given to those with the easiest and safest access (requesting permission for private properties), as well as those less common water body types in order to achieve an equal surveillance of larval habitats. However, relatively few rockpools and artificial containers were found to be sampled compared with ponds, rivers, and lagoons (no tree holes nor other similar cavities filled with water were found).

The sampling point altitudes ranged from 0 to 1182 m and were characterized according to different environmental variables (hydroregime, water body type, substrate, surface, depth, degree of insolation, environment type, and land use) by in situ observation and the use of Quantum GIS (3.8 QGIS version) [16]. In terms of hydroregime, the aquatic ecosystem was defined as temporary (with temporal desiccation in dry seasons) or permanent (with a continuous layer of water throughout the year). Water bodies were classified into five main types: lagoons (including lakes, marshes, and swamps), ponds (including puddles), rockpools (pools occurring in rocky substrates at fluvial margins), rivers (including streams and ditches), and containers (artificial containers of all shapes and sizes) (Fig. 2). The predominant substrate at the bottom of each water body was identified as sandy, muddy, rocky (or stony), or plastic (including other materials of artificial origin such as metal and porcelain). Following previous criteria [7], the water surface and depth were typified into large (≥ 1 m2) and small (< 1 m2), and deep (≥ 40 cm) and shallow (< 40 cm), respectively. The degree of insolation at each sampling point was categorized as open sun (completely exposed to sunshine), half shade (partially shaded throughout the day), and shade (completely shaded). Matching other studies [17], the environment type was classified into four categories based on human activities and population density (data collected from the regional repository at “Instituto Galego de Estadística”) [18]: natural (< 40 inhabitants/km2, natural areas with limited or absent human activity), rural (< 40 inhabitants/km2, areas with farming activities), suburban (≥ 40 inhabitants/km2, city surroundings with human activity), and urban (≥ 40 inhabitants/km2, urban center with high human activity). Land use was categorized into five main typologies based on data extracted from “Sistema de Información sobre Ocupación del Suelo de España (SIOSE)” [19]: wetlands, forests, heathlands, crops and grasslands, and urban and industrial areas (Fig. 1).

Fig. 2
figure 2

Photographs of the types of water bodies studied (A: lagoon, B: pond, C: rockpool, D: river, E: container) and the dipping technique use to capture mosquito larvae and pupae (F)

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Physicochemical parameters of the water were measured in situ with a multiparameter water quality meter (HI 9829-HANNA Instruments) (Leighton Buzzard, Bedfordshire, England) at 313 sampling points (those with sufficient volume of water to introduce the device). The study area was characterized by the following mean ± standard deviation values (along with the maximum and minimum values) for each parameter: temperature of 18.1 ± 4.3 (10.8–41.2) °C, pH of 6.8 ± 0.9 (3.9–10.4), electrical conductivity of 534.6 ± 3716.7 (0–47,560) µS/cm, dissolved oxygen of 8.3 ± 4.7 (0.1–36.9) mg/L, turbidity of 5.2 ± 8 (0–82) formazin nephelometric unit (FNU), salinity of 0.3 ± 2.4 (0–31) practical salinity unit (PSU), and total dissolved solids of 120.1 ± 564.8 (0–8766) mg/L.

Mosquito sampling and processing

Sampling was performed once per sampling point, during daylight hours (09:00–21:00 h), between May and October of 2021 and 2022. Mosquitoes were collected using the standardized dipping technique [20], introducing a 500 mL dipper in those areas of the water body where mosquitoes are more likely to be present (edges, shallow spots, and surroundings of aquatic vegetation) (Fig. 2). Whenever possible, ten dips were taken per sampling point [21], with pipetting in those areas with low water volume [7]. Mosquito larvae and pupae were transported to the laboratory in plastic jars with water from their breeding grounds (containing organic material and remains of aquatic vegetation) and were reared at room temperature until reaching the IV instar larvae and adult stages, respectively. The jars were checked daily and those specimens that did not survive were removed and identified immediately (included in the final data). Both adults and larvae were fixed in 70% ethanol and identified to species level under a binocular magnifier and an optical microscope according to the morphological criteria of Becker et al. [1]. The samples are currently stored in the scientific collection of the Aquatic Entomology Laboratory in the Faculty of Biology at Universidade de Vigo (Vigo, Galicia, Spain).

Data analysis

Statistical analyses were performed using the BiodiversityR, car, MASS, spded and vegan packages of version 4.2.0 of the R software [22].

Diversity index comparison

Frequency (percentage of the number of samples with mosquitoes divided by the total number of samples), abundance (total number of mosquitoes) (N), species richness (S), Shannon–Wiener’s (H0) and Simpson’s (DS) diversity indexes were calculated for each group of habitat characteristics based on the complete database (consisting of information collected at 333 sampling points). The normality of the data was analyzed using the Shapiro–Wilk test and, since none of the variables followed a normal distribution, the Kruskal–Wallis test was applied to determine differences between groups at a significance level of 0.05 [23].

Species affinity analysis

In order to determine the relationship between pairs of species in their larval habitats, the occurrence percentage and the index of affinity [24] were calculated. The occurrence percentage is defined by (J/nA + nB) × 100, while the affinity index is driven by [J/(nA + nB)1/2]−1/2(nB1/2); where J = the number of joint occurrences of both species, nA = total number of occurrences of species A, and nB = total number of occurrences of species B, so that nA ≤ nB. Pairs of species with index results equal or higher than 0.5 are considered to show affinity [24].

Constrained ordination analysis

Constrained ordination analysis was applied to determine the influence of environmental factors on species composition and larval abundance of mosquitoes, as this method can simultaneously relate biological and environmental variables. The database compiled information obtained from 90 mosquito breeding sites where the set of water physicochemical parameters were successfully measured. The biological data, composed of the larval abundance of each species (the two least abundant species were omitted due to lack of data), were log (x + 1) transformed (where x is the number of larvae) to avoid dominance of the highest values [25]. The environmental data is composed of both quantitative (geographic and physicochemical parameters) and qualitative (habitat characteristics) environmental variables. Quantitative parameters were log transformed (except for altitude) in order to improve normality, and standardized when variables presented large measurement units (latitude, longitude, and altitude) to reduce dominance biases in the model. Depending on whether the data follows a linear or unimodal response, a redundancy analysis (RDA) or a canonical correspondence analysis (CCA) should be used, respectively. A detrended correspondence analysis (DCA) was applied to determine the data response by means of model axes length (measured in standard deviation, SD), indicating whether the data follow a linear (< 3 SD) or a unimodal (> 3 SD) distribution [25]. Since the length of the first axis of the DCA model was 1 SD, a RDA was chosen for the analyses. Owing to the large amount of zeros in the database (absence of species), the Bray–Curtis dissimilarity was selected to develop the RDA [26]. Pairwise correlations between quantitative variables and their variance inflation factor (VIF) were examined to avoid multicollinearity in the model, resulting in the removal of salinity and total dissolved solids (TDS) due to their high correlation with conductivity (> 0.5) and high VIF (≤ 5) [6]. Statistical significance of ordination axes, environmental variables, and the global model was analyzed by analysis of variance (ANOVA) permutation tests for redundancy analyses (“anova.cca” function from vegan package) with 999 permutations under the reduced model [27]. The global RDA model included all the quantitative variables under study as well as qualitative variables that contributed with statistically significant information to the model according to the ANOVA test results (P < 0.05).

Generalized linear model (GLM)

In order to complement RDA analysis, a generalized linear model (GLM) was performed for the most abundant mosquito in the study area. The association between the larval abundance of Culex pipiens s.l. and environmental variables was analyzed. A negative binomial generalized linear model (NBGLM) (logit link function) was applied on the previously untransformed database (data from 90 sampling points) as a suitable method for processing over-dispersed data (uneven abundance values) [28]. The best model was selected by a manual forward stepwise approach according to the results of the ANOVA tests for model comparison (P < 0.05). The final model was determined by the lowest Akaike information criterion (AIC) and the highest percentage of explained deviance (DE), which is representative of the “full” model significance calculated by contrasting it with the null model by ANOVA (“deviancepercentage” function from BiodiversityR package) [26]. In addtion, a Moran’s I test was performed (applying a maximum distance radius of 20,000 m to define neighboring points) to determine the robustness of the model by analyzing the spatial autocorrelation of the residuals, so that the absence of a significant correlation between them would imply a good fit of the model (I ≈ 0, P > 0.05) [29].

Results

Abundance and distribution of mosquito species

A total of 5308 mosquitoes belonging to 14 species of the genus Culex (88.1%), Anopheles (7.5%), and Culiseta (4.4%) were identified, confirming the presence of culicids in most of the sampling points (64.3%) (Table 1; Fig. 1). The most abundant species was by far Cx. pipiens s.l. (48.1%), followed by Cx. territans (13%), Cx. torrentium (12.1%), and Cx. hortensis (10.9%) (Table 1). Similarly, the most widely distributed species were Cx. pipiens s.l. (28.5%), Cx. territans (21.9%), Anopheles petragnani (17.1%), Cx. torrentium (13.2%), and Cx. hortensis (10.8%) (Table 1). Although altitude and water physicochemical variables of larval habitats differed depending on each species, mosquitoes were found more frequently above 400 m and in fresh (≈ 0.1 PSU), cold (≈18 °C), neutral (≈ pH 7), well oxygenated (≈ 8 mg/L) and slightly clear waters (≈ 5 FNU) with low levels of conductivity and total dissolved solids (< 80 mg/L) (Table 1).

Table 1 Sampling results for each mosquito species and overall results
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Mosquito frequency, abundance, and diversity regarding habitat characteristics

Among the habitat characteristics analyzed in terms of frequency, abundance, and diversity of mosquitoes, statistically significant differences were detected between climatic zones (KCC), hydroregime, water body types, substrates, and seasons (Table 2). Abundance, richness, and Shannon’s diversity index (H0) were highest in Csa climates, while the lowest mosquito frequency and highest Simpson’s dominance index (DS) occurred in Cfb climates (Table 2). Among water bodies, artificial containers and rockpools recorded the highest abundance, richness, and Shannon diversity; as did temporary water ecosystems and plastic substrates (Table 2). Regarding seasons, statistically significant differences were only observed in Simpson’s index, which was higher in spring (Table 2).

Table 2 Sampling points information and mosquito frequency, abundance, and diversity regarding habitat characteristics
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Occurrence and affinity between mosquito species at breeding sites

High affinity was observed between different pairs of species (Table 3). Culex pipiens s.l. recorded some of the highest values of occurrence percentages and affinity indexes (> 0.5) with Cx. torrentium (22.3%, 2.58), Cx. territans (14.9%, 1.88), Cx. hortensis (16.8%, 1.87), Cs. longiareolata (13.8%, 1.43), An. petragnani (9.2%, 1.08), Cx. impudicus (10.1%, 1.05), and An. maculipennis s.l. (7.1%, 0.75). Culex territans also showed affinity with An. petragnani (21.5%, 2.40), Cx. impudicus (18.6%, 1.77), An. maculipennis s.l. (10.6%, 1.02), Cx. hortensis (8.3%, 0.80), An. claviger s.s. (7.1%, 0.59), and Cx. torrentium (6%, 0.59); while the latter was also affine to Cx. hortensis (18.8%, 1.60), Cs. longiareolata (12.3%, 0.92), and An. petragnani (5.9%, 0.53). Anopheles maculipennis s.l. and Cx. hortensis exhibited affinity (10.4%, 0.77), as did An. claviger s.s. and An. petragnani (8.7%, 0.66).

Table 3 Occurrence percentage (right diagonal) and affinity indexes (left diagonal) for each pair of species
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Association of environmental variables with mosquito species composition

A RDA-triplot of larval habitats, abundance of mosquito species, and environmental variables based on the first two axes explained 71% of the variance in the fitted biological data (55% is explained by the first axis and 16% by the second axis) (Table 4; Fig. 3). The first axis and the global model were statistically significant (Table 5). Although all the measured environmental variables provide information to the global model, the variation in mosquito species composition is accounted primarily by water body type, conductivity, temperature, and pH according to their relationship with the first axis (Table 4) and their statistical significance in the RDA model (Table 5). Culex pipiens s.l. larvae are found mainly in artificial containers and ponds (Fig. 4), as well as in waters with high conductivity, temperature, pH, and turbidity values (Fig. 3; Table 4). Culex territans shows a preference for lagoons as breeding sites, while An. petragnani is mainly present in waters with high levels of dissolved oxygen, such as rivers (Figs. 3 and 4). Anopheles maculipennis s.l., Cx. theileri, and Cx. impudicus are weakly linked to ponds and lagoons, while Cs. longiareolata, Cx. torrentium, and Cx. hortensis are more related to both ponds and artificial containers (Figs. 3 and 4). The lack of distribution data for An. claviger s.s., An. plumbeus, and Cs. annulata does not allow conclusions about their breeding preferences.

Table 4 Results of redundancy analyses (RDA)
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Fig. 3
figure 3

Redundancy analysis ordination graphic. RDA triplot with sampling points according to water body type (symbols), mosquito species (red text), and environmental variables (blue text and arrows). The mosquito species are: An. claviger s.s. (cla), An. maculipenniss.l. (mac), An. petragnani (pet), An. plumbeus (plu), Cx. hortensis (hor), Cx. impudicus (imp), Cx. pipiens s.l. (pip), Cx. territans (ter), Cx. theileri (Cthe), Cx. torrentium (tor), Cs. annulata (ann), and Cs. longiareolata (lon). The environmental variables are: longitude (X), latitude (Y), altitude (Alt), temperature (T°), pH, electrical conductivity (EC), dissolved oxygen (DO), and turbidity (Tur)

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Table 5 Results of ANOVA permutation tests for redundancy analyses (RDA)
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Fig. 4
figure 4

Relative abundance of mosquito species in each of the water body types

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Environmental variables determining Culex pipiens s.l. larval abundance

The best-fitting model of Cx. pipiens s.l. larval abundance explains 62.5% of the variance and includes four environmental variables: conductivity, hydroregime, land use, and degree of insolation (Table 6). Model parameter estimates indicate a very slight positive effect of conductivity on larval abundance, whereas this effect is considerably greater in terms of temporal hydroregime (Table 6). Regarding land use, the abundance of Cx. pipiens s.l. is favored by anthropized lands such as crops and grasslands, and urban and industrial areas, compared with more natural landscapes such as forests and heathlands (Table 6). In addition, an intermediate degree of insolation (half shade) promotes larval proliferation of this species, while it decreases in shaded areas (Table 6). The results obtained by the Moran’s I test (I = 0.001, P = 0.12) confirmed the reliability of the model, as no significant spatial autocorrelation between the residuals was detected (P > 0.05), indicating that the model is correctly representing the spatial structure of the data.

Table 6 Negative binomial regression (NBGLM) and ANOVA test results for the Culex pipiens s.l. larval abundance model
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Discussion

Specialized sampling for the study of culicids in different types of habitats throughout the entire autonomous community of Galicia has allowed the detection of breeding sites in most of the territory (64.3%), confirming that the region meets the optimal environmental requirements for the proliferation of different species of mosquitoes. The 14 species analyzed in the present study represent more than half of those officially recorded in Galicia [12, 14]. Consistent with literature [13, 14], the genus Culex was the most diverse and abundant in the region, with Cx. pipiens s.l. being the most widely distributed species followed by Cx. territans. Anopheles petragnani was the third most frequent mosquito in the study area and the best represented of its genus, which is surprising given its recent detection in the territory [30] and the known dominance of An. maculipennis s.l. in natural ponds and lagoons of Galicia [11]. This could indicate a recent introduction or a population explosion, but most likely the absence of specialized studies in its usual breeding sites, such as rockpools and rivers [1], has prevented its earlier detection [30]. Although the genus Culiseta was the least abundant, Cs. longiareolata was relatively frequent, as noted in previous studies [13, 14]. There are records of some species of Coquillettidia and Aedes in Galicia [11,12,13,14], but no specimens were captured in the present study. This suggests that these groups occur in low population densities and have a limited dispersal distribution [6], but biases in the sampling methodology must also be considered. The presence of Coquillettidia could have been underestimated due to the exclusive use of the dipping technique for mosquito capture [20]; being more advisable to remove the substrate with an entomological net [12] to detach these larvae from their anchorage to aquatic plants [1]. Similarly, limited sampling in water bodies particularly suitable for Aedes spp., such as artificial containers (mainly confined to private properties) and phytotelmata (no tree holes or similar cavities filled with water were found), must have reduced the probability of capturing this mosquito genus [31]. Rather than the dipping technique, the use of ovitraps is a more advisable method for detecting invasive Aedes species such as Aedes albopictus and Aedes japonicus [32, 33]. In any case, and given the results obtained during the 2021 and 2022 surveys, there is no evidence that the tiger mosquito Ae. albopictus reached Galicia prior to its detection in 2023 [34]. Aedes japonicus is spreading across northern Spain and is very close geographically to Galicia [35], but no specimens have yet been detected in the region [14].

Even though the median values of abundance (< 20), species richness (< 3), and Shannon–Weiner diversity index (< 1) were low in all cases, statistically significant differences were found between climatic zones, hydroregime, type of water bodies, and substrates. These values were significantly higher in the Csa climate, defined by mild winters and dry and hot summers, compared with the Cfb climate, defined by cold winters and mild summers. The opposite has been observed in the Spanish Mediterranean region, where areas with more rainfall recorded greater species richness by favoring the appearance of different larval biotopes [7]. This suggests that while in southeastern Spain rainfall is a limiting factor in the formation of breeding sites, in the northwest of the country environmental temperature would be a more relevant parameter in the larval proliferation of mosquitoes. Water bodies of temporary hydroregime and plastic substrates, such as rockpools and artificial containers, also registered the highest values of mosquito abundance and diversity. Matching results were observed regarding other temporary water bodies (drinking water pools, plastic containers, puddles, etc.) in similar studies [5, 10, 36]. Temporary waters have a high risk of desiccation but a lower probability of being colonized by predators, favoring oviposition selection by gravid females and a greater proliferation of mosquito larvae [37,38,39]. Some studies relate high anthropogenic pressure to low mosquito diversity [7] and abundance [10], but no statistically significant differences between environments and land uses were detected in this research. This suggests that mosquito diversity is not so much related to the type of environment (urban, suburban, rural, and natural) as to the variety of breeding sites available in each of these environments. As for Simpson’s dominance index, significantly higher values have been observed in the Cfb climate and spring season, where opportunistic species such as Cx. pipiens s.l. and Cx. torrentium [1] have been predominant.

The coexistence of species in the same larval habitats is indicative of similar ecological requirements and breeding preferences [7, 40]. Culex pipiens s.l. and Cx. torrentium not only share morphological but also ecological similarities, as they usually breed together [1, 10]. These two species were the ones with the highest percentage of occurrence (> 20%) and affinity index (> 2.5), typically occurring in similar conditions of altitude, temperature, pH, dissolved oxygen, and salinity. The affinity of both species with others such as Cx. territans, Cx. hortensis, Cs. longiareolata, and An. petragnani (> 0.5) reflects their adaptation to breed in different habitats [1]. While Cx. torrentium has not been found breeding in lagoons, Cx. pipiens s.l. occurred in all types of water bodies and in a wider range of physicochemical parameters that allowed it to also appear in association with Cx. impudicus and An. maculipennis s.l. (> 0.5). Culex territans also showed a great adaptation to breed in different water bodies (with the exception of artificial containers) and affinity to a large number of species such as An. petragnani (> 2), Cx impudicus (> 1.5), An. claviger s.s., An. maculipennis s.l., and Cx. hortensis (> 0.5). The latter two species, just as An. claviger s.s. and its sibling An. petragnani, also exhibited a paired larval affinity (> 0.5). These findings expand and update the knowledge about the larval association of different mosquito species in their breeding sites [1, 7,8,9], and raise questions about the criteria of oviposition site selection by females. Although predation and competitive exclusion for limited resources (food, space, and oxygen) among species at breeding sites has been documented [41], the presence of larvae may induce other mosquitoes to oviposit in the same habitat as a good sign of its suitability for breeding (available food, lack of predators, and appropriate abiotic conditions) [42, 43]. Gravid females preferentially choose habitats with a higher presence of first instar larvae of mosquitoes rather than those with stages more developed (IV instar) that may act as predators or strong competitors [43]. Therefore, the affinity between certain mosquito species at breeding sites would not only be explained by sharing ecological requirements, but also to similar criteria for oviposition site selection and a compatible phenological cycle.

The presence and distribution of mosquito species in larval habitats depends on different environmental characteristics such as landscape and water physicochemical conditions [2, 4, 39, 44]. The main environmental factors that determined the larval abundance and species composition of mosquitoes in the study area were temperature, pH, electrical conductivity, and type of water body. Warm water temperatures favor the development of mosquito larvae [45] and microbes that provide food sources [46]. Anopheles larvae may be tolerant to high water temperatures [4, 45] but above 30 °C an enzyme-catalyzed reaction occurs that affects their survival [47]. This is consistent with the data obtained as no Anopheles species has been found above 29 °C, whereas Cx. hortensis and Cx. torrentium have been observed above 31 °C and Cx. pipiens s.l. even close to 35 °C. Larvae of most mosquito species can tolerate pH values ranging between 3 and 11 [48], increasing their abundance when pH levels are between 6 and 8 [49]. Effectively, mosquitoes were most frequently found in this pH range, but some species such as Cx. hortensis and An. maculipennis s.l. were also found breeding in moderately acidic waters (pH < 4) and in moderately alkaline ones (pH > 10), respectively. Elevated levels of conductivity have been associated with decreased water quality [50] and increased abundance of mosquitoes [49]. The occurrence of different Anopheles species in both slightly acidic (pH < 6.5) and high conductivity waters (> 2000 µS/cm) strengthens the hypothesis of their growing adaptation to breed in polluted waters [5, 51]. Although in this case no relationship between mosquito abundance and dissolved oxygen has been observed, other studies indicate that well oxygenated waters favor the proliferation of Aedes and Anopheles, while Culex and Culiseta mosquitoes seems to be unaffected [49]. In fact, some species such as Cx. pipiens s.l., Cx. territans, Cx. impudicus, and Cs. annulata have been found breeding even in anoxic waters (< 1 mg/L). This ecological characteristic may allow them to compete with other aquatic species for ecosystem resources and avoid predation by larvivorous fish that unsuccessfully develop in poorly oxygenated waters [49]. Turbidity has been positively related to the presence of mosquito larvae [49] by limiting the visibility of predators [52], but no significant effects have been observed in the larval habitats studied. As previously observed in other regions of Spain [7, 10], the type of water body influences the species composition and larval abundance of mosquitoes. While Cx. pipiens s.l., Cx. torrentium, Cx. hortensis, Cs. longiareolata, An. petragnani, Cx. impudicus, and An. plumbeus are adapted to breed in artificial containers; Cx. territans and An. maculipennis s.l. show a preference for breeding in lagoons and ponds (Figs. 3 and 4).

Regarding the epidemiological risk in the region, at least seven mosquito species of sanitary interest have been captured: An. claviger s.s, An. maculipennis s.l., An. plumbeus, Cx. pipiens s.l., Cx. theileri, Cx. torrentium, and Cs. annulata. These Anopheles mosquitoes are potential malaria vectors whose preferred host are mammals, including humans [1]. Anopheles maculipennis s.l. is a complex of species that in Spain is represented primarily by An. atroparvus, the main malaria vector in Europe [53]; while An. claviger s.s. and An. plumbeus are secondary vectors [1]. In Galicia, these last two species are of minor importance owing to their small populations, although the emerging tendency of An. plumbeus to breed in artificial containers may increase its vector relevance in the future [1, 54]. Anopheles maculipennis s.l. is more abundant and frequent in the region and, although it shows a predilection for inhabiting rural areas [11], its growing adaptation to breed in polluted waters could increase its occurrence in more urban environments. In any case, the current risk associated with these species in the territory is low due to the absence of endemic circulation of the malaria parasite (Plasmodium spp.) in Spain [53]. The species known as the common mosquito, Cx. pipiens s.l., is actually considered an assemblage of morphologically similar species (Pipiens assemblage) that in Europe includes Cx. quinquefasciatus, typical of the tropics and subtropics, and Cx. pipiens, present in the Holarctic region [1]. This mosquito feeds both on avian and mammalian hosts, playing a major role in the transmission of different arboviruses such as West Nile virus (WNV), Usutu virus (USUV), and Sindbis virus [1, 55]. Given that WNV is already endemic in Spain [56] and that Cx. pipiens s.l. is by far the most abundant and widely distributed mosquito in Galicia, there is a growing concern about its health implications in the region. Culex theileri can occasionally bite humans and be carrier of WNV, Sindbis, and Rift Valley fever viruses [1], but its reduced populations in the territory determines its low epidemiological interest. Culex torrentium feeds on birds and mammals and is a highly competent vector for WNV and Sindbis virus [57], so its regular presence in the region requires us to not underestimate its sanitary significance. Culiseta annulata preferentially feeds on mammals (occasionally on birds) and can transmit the Tahyna virus [1], but its small population and the apparent absence of the virus circulation in the study area minimizes its health interest.

Therefore, the risk associated with autochthonous mosquito borne-disease transmission in Galicia is mainly related to the role played by Cx. pipiens s.l. and Cx. torrentium in the circulation of WNV. At the moment, this risk is considered remote in northwestern Spain as the endemism of the disease is limited to the southeast, where more than 200 human cases have already been reported in the last 4 years [56, 58]. However, it is expected that with climate change and increasing temperatures, the range of virus circulation linked to greater migratory dispersal of host birds will increase, as well as the vectorial capacity of these species [56,57,58]. In this context, knowing the distribution and ecology of vector populations is crucial to apply the most effective prevention and control measures. The high abundance of Cx. pipiens s.l. in the region has allowed the characterization of its main breeding sites. Supporting and expanding available knowledge [2, 4], its preferred larval biotopes have been identified as those with high water conductivity, temporal hydroregime, anthropized land use, and partially shaded areas. Other studies also relate greater larval abundance to higher turbidity and pH levels [36]. Generally, these parameters are associated with polluted waters, coinciding with in situ observations of organic discharges in several water bodies of the study area. In fact, waters contaminated by combined sewer overflows (CSOs) are beneficial for Cx. pipiens s.l. proliferation, as they are more attractive for oviposition, reducing the risk of mortality and favoring larval development [59,60,61]. The high affinity of Cx. torrentium with Cx. pipiens allows us to infer its usual occurrence in the same larval habitats, registering the highest abundances in artificial containers.

On the basis of the information gathered, the area of Galicia with the highest health risk would be located on the west coast, where urban zones and the greatest population densities are concentrated [11]. In these places Cx. pipiens s.l. not only find more available preferred breeding sites, such as artificial containers and polluted waters, but also a large number of hosts. Artificial containers also favor the proliferation of Cx. torrentium and Ae. albopictus, the invasive mosquito recently detected in the region and capable of transmitting tropical diseases such as dengue, Zika, and chikungunya [34]. Given that most of these breeding sites are located in private properties, raising public awareness about their proper elimination and treatment is essential. Likewise, the administration and responsible entities should dedicate more efforts to the proper management and sanitation of wastewaters, prioritizing the care of stagnant and temporary wetlands close to human settlements.

Conclusions

This study represents the most detailed characterization of mosquito larval habitats in northwestern Spain and leads to new contributions to the knowledge of the larval ecology of fourteen species. Larval abundance and diversity of mosquitoes are affected by habitat characteristics such as climate type, hydroregime, and water body type, as these significantly increase in warmer climatic zones and in temporary waters such as artificial containers and rockpools. Water body type, temperature, pH, and conductivity of the water determine larval density and species composition. While Cx. pipiens s.l., Cx. torrentium, Cx. hortensis, Cs. longiareolata, An. petragnani, Cx. impudicus, and An. plumbeus are adapted to breed in artificial containers, Cx. territans and An. maculipennis s.l. show a preference for breeding in lagoons and ponds. Currently, the epidemiological risk related to mosquito-borne diseases in Galicia is remote and mainly linked to Cx. pipiens and Cx. torrentium, as both are wide distributed in the region and competent in the transmission of WNV. The larval abundance of Cx. pipiens s.l. increase significantly in water bodies with high water conductivity, a temporal hydroregime, anthropized land use, and an intermediate degree of insolation (half shade). The high larval affinity of Cx. torrentium with Cx. pipiens indicates that they share breeding preferences, being common in artificial containers and polluted waters. Therefore, the elimination of potential breeding sites in artificial containers and the proper management of residual waters are basic measures in the prevention of mosquito-borne diseases in Galicia. Integrated vector management should always be supported by an updated knowledge of the abundance, diversity, distribution, and ecology of mosquitoes in a region, so it is vital to keep an active surveillance system with a varied methodology capable of monitoring species with differing biology. The information provided in the present study remains at the disposal of the public health authorities for promoting and preserving a good quality of life.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

AIC:

Akaike information criterion

DE:

Explained deviance

DO:

Dissolved oxygen

DS:

Simpson’s diversity index

EC:

Electrical conductivity

FNU:

Formazin nephelometric unit

GLM:

Generalized linear model

H0 :

Shannon–Wiener’s diversity index

KCC:

Köppen–Geiger climate classification

K-W:

Kruskal–Wallis test

NBGLM:

Negative binomial regression

PSU:

Practical salinity unit

RDA:

Redundancy analysis

S:

Species richness

SD:

Standard deviation

TDS:

Total dissolved solids

VIF:

Variance inflation factor

WNV:

West Nile virus

References

  1. Becker N, Petric D, Zgomba M, Boase C, Madon MB, Dahl C, et al. Mosquitoes: identification, ecology and control. 3rd ed. Switzerland: Springer; 2020.

    Book  Google Scholar 

  2. Amini M, Hanafi-Bojd AA, Aghapour AA, Chavshin AR. Larval habitats and species diversity of mosquitoes (Diptera: Culicidae) in West Azerbaijan Province Northwestern Iran. BMC Ecol. 2020;20:60. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12898-020-00328-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Muzari MO, Devine G, Davis J, Crunkhorn B, van den Hurk A, Whelan P, et al. Holding back the tiger: successful control program protects Australia from Aedes albopictus expansion. PLoS Negl Trop Dis. 2017;11(2):1-17. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pntd.0005286.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bashar K, Rahman MdS, Nodi IJ, Howlader AJ. Species composition and habitat characterization of mosquito (Diptera: Culicidae) larvae in semi-urban areas of Dhaka, Bangladesh. Pathog Glob Health. 2016;110:48–61. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/20477724.2016.1179862.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Emidi B, Kisinza WN, Mmbando BP, Malima R, Mosha FW. Effect of physicochemical parameters on Anopheles and Culex mosquito larvae abundance in different breeding sites in a rural setting of Muheza, Tanzania. Parasit Vect. 2017;10:304. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13071-017-2238-x.

    Article  Google Scholar 

  6. Grech MG, Manzo LM, Epele LB, Laurito M, Claverie AÑ, Ludueña-Almeida FF, et al. Mosquito (Dipera: Culicidae) larval ecology in natural habitats in the cold temperate Patagonia region of Argentina. Parasit Vect. 2019;12:214. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13071-019-3459-y.

    Article  Google Scholar 

  7. Bueno-Marí R. Bioecología, diversidad e interés epidemiológico de los culícidos mediterráneos (Diptera: Culicidae). Valencia: Universitat de València; 2010.

    Google Scholar 

  8. Bernués-Bañeres A. Mosquitos (Diptera: Culicidae) de los Parques Naturales de la Comunidad Valenciana. Valencia: Universitat de València; 2013.

    Google Scholar 

  9. Chordá-Olmos FA. Biología de mosquitos (Diptera: Culicidae) en enclaves representativos de la Comunidad Valenciana. Valencia: Universitat de València; 2014.

    Google Scholar 

  10. González MA, Cevidanes A, Goiri F, Barandika JF, García-Pérez AL. Diversity and distribution of larval habitats of mosquitoes (Diptera: Culicidae) in northern Spain: from urban to natural areas. J Vector Ecol. 2021;46:173–85. https://doiorg.publicaciones.saludcastillayleon.es/10.5270/1081-1710-46.2.173.

    Article  PubMed  Google Scholar 

  11. Martínez-Barciela Y, Polina A, Garrido J. Ecology and diversity of mosquito larvae in ponds and lagoons of northwestern Spain. Med Vet Entomol. 2023;38:38–47. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/mve.12693.

    Article  CAS  PubMed  Google Scholar 

  12. Martínez-Barciela Y, Polina A, Gutiérrez D, Garrido J. First records of five species of mosquitoes (Diptera: Culicidae) in Galicia, including the first evidence of the genus Coquillettidia in northwestern Spain. J Vector Ecol. 2021;46:96–102. https://doiorg.publicaciones.saludcastillayleon.es/10.5270/1081-1710-46.1.96.

    Article  PubMed  Google Scholar 

  13. Silva-Torres MI. Epidemiología de dípteros que actúan como vectores de zoonosis en Galicia. In: Diseño de una red de vigilancia de vectores. Santiago de Compostela: University of Santiago de Compostela; 2021.

    Google Scholar 

  14. Polina A, Pereira JM, Martínez-Barciela Y, Cobo F, Garrido J. Vixilancia de dípteros vectores de enfermidades humanas e animais: Informe 2023. Rede Galega de Vixilancia de Vectores (ReGaViVec). Santiago de Compostela: Xunta de Galicia; 2024.

  15. Köppen W. Versuch einer Klassifikation der Klimate, vorzugsweise nach ihren Beziehungen zur Pflanzenwelt. Geogr Z. 1900;6:657–79.

    Google Scholar 

  16. QGIS Development Team. Geographic information system. Open Source Geospacial Foundation. 2009. http://qgis.org. Accessed 23 Oct 2019.

  17. Bravo-Barriga D, Gomes B, Almeida APG, Serrano-Aguilera FJ, Pérez-Martín JE, Calero-Bernal R, et al. The mosquito fauna of the western region of Spain with emphasis on ecological factors and the characterization of Culex pipiens forms. J Vector Ecol. 2017;42:136–47. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/jvec.12248.

    Article  PubMed  Google Scholar 

  18. Instituto Galego de Estadística (IGE). Distribución espacial das características da poboación de Galicia por cuadrícula de 1km2 (ano 2022). Xunta de Galicia. 2023. https://www.ige.gal. Accessed 7 May 2023.

  19. Instituto Geográfico Nacional – Xunta de Galicia (IGN-XG). Sistema de Información sobre Ocupación del Suelo de España (SIOSE): Información Xeográfica de Galicia. Instituto Geográfico Nacional en colaboración con el Instituto de Estudos do Territorio da Consellería de Medio Ambiente, Territorio e Infraestructuras da Xunta de Galicia. 2011. https://mapas.xunta.gal/. Accessed 13 May 2024.

  20. Service MW. Mosquito ecology field sampling methods. United Kingdom: Elsevier Science Publishers; 1993.

    Book  Google Scholar 

  21. Becker N, Pfitzner WP, Czajka C, Kaiser A, Weitzel T. Anopheles (Anopheles) petragnani Del Vecchio 1939: a new mosquito species for Germany. Parasitol Res. 2016;115:2671–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00436-016-5014-5.

    Article  PubMed  PubMed Central  Google Scholar 

  22. R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. 2022. https://www.r-project.org/. Accessed 2 May 2022.

  23. Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. JASA. 1952;47:583–621.

    Article  Google Scholar 

  24. Fager EW, McGowan JA. Zooplankton species groups in the North Pacific. Science. 1963;140:453–60.

    Article  CAS  PubMed  Google Scholar 

  25. Lepš J, Šmilauer P. Multivariate analysis of ecological data using CANOCO. New York: Cambridge University Press; 2003.

    Book  Google Scholar 

  26. Kindt R, Coe R. Tree diversity analysis. In: A manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre: Kenya; 2005.

    Google Scholar 

  27. Legendre P, Oksanen J, ter Braak CJF. Testing the significance of canonical axes in redundancy analysis. Methods Ecol Evol. 2011;2:269–77.

    Article  Google Scholar 

  28. González MA, Goiri F, Cevidanes A, Hernández-Triana LM, Barandika JF, García-Pérez AL. Mosquito community composition in two major stopover aquatic ecosystems used by migratory birds in northern Spain. Med Vet Entomol. 2023;37(3):616–629. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/mve.12661.

    Article  PubMed  Google Scholar 

  29. Cliff AD, Ord JK. Spatial processes: models and applications. London: Pion Limited; 1981.

    Google Scholar 

  30. Martínez-Barciela Y, Polina A, Garrido J. New contributions to the knowledge of two riparian mosquitoes in northwestern Spain: Anopheles petragnani and Culex mimeticus (Diptera: Culicidae). Environ Entomol. 2024;53:619–28. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/ee/nvae061.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Sota T, Mogi M, Hayamizu E. Habitat stability and the larval mosquito community in treeholes and other containers on a temperate Island. Res Popul Ecol. 1994;36:93–104. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/BF02515090.

    Article  Google Scholar 

  32. Chaves LF, Moji K. Density dependence, landscape, and weather impacts on aquatic Aedes japonicus japonicus (Diptera: Culicidae) abundance along an urban altitudinal gradient. J Med Entomol. 2018;55:329–41. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/jme/tjx200.

    Article  PubMed  Google Scholar 

  33. Chaves LF, Friberg MD. Aedes albopictus and Aedes flavopictus (Diptera: Culicidae) pre-imaginal abundance patterns are associated with different environmental factors along an altitudinal gradient. Curr Res Insect Sci. 2020;15:100001. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.cris.2020.100001.

    Article  Google Scholar 

  34. Martínez-Barciela Y, Polina A, Pereira JM, Cobo F, Garrido J, Abalo X, et al. Primera cita de Aedes albopictus para Galicia, obtenida mediante ciencia ciudadana por Mosquito Alert. Gac Sanit. 2024;38:1–4. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.gaceta.2024.102374.

    Article  Google Scholar 

  35. Eritja R, Ruiz-Arrondo I, Delacour-Estrella S, Schaffner F, Álvarez-Cachero J, Bengoa M, et al. First detection of Aedes japonicus in Spain: an unexpected finding triggered by citizen science. Parasit Vect. 2019;12:53. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13071-019-3317-y.

    Article  Google Scholar 

  36. Alkhayat FA, Ahmad AH, Rahim J, Dieng H, Ismail BA, Imran M, et al. Characterization of mosquito larval habitats in Qatar. Saudi J Biol Sci. 2020;27:2358–65. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.sjbs.2020.07.006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Arav D, Blaustein L. Effects of pool depth and risk of predation on oviposition habitat selection by temporary pool dipterans. J Med Entomol. 2006;43:493–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/jmedent/43.3.493.

    Article  PubMed  Google Scholar 

  38. Mereta ST, Yewhalaw D, Boets P, Ahmed A, Duchateau L, Speybroeck N, et al. Physico-chemical and biological characterization of anopheline mosquito larval habitats (Diptera: Culicidae): implications for malaria control. Parasit Vectors. 2013;6:320. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/1756-3305-6-320.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Roiz D, Ruiz A, Soriguer R, Figuerola J. Landscape effects on the presence, abundance and diversity of mosquitoes in Mediterranean wetlands. PLoS ONE. 2015;10:1–17. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0128112.

    Article  CAS  Google Scholar 

  40. Pemola Devi N, Jauhari RK. Mosquito species associated within some western Himalayas phytogeographic zones in the Garhwal region of India. J Insect Sci. 2007;7:1–10.

    Article  CAS  PubMed  Google Scholar 

  41. Lushasi SC, Mwalugelo YA, Swai JK, Mmbando AS, Muyaga LL, et al. The interspecific competition between larvae of Aedes aegypti and major African malaria vectors in a semi-field system in Tanzania. Insects. 2025;16:34. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/insects16010034.

    Article  Google Scholar 

  42. Juliano SA. Species interactions among larval mosquitoes: context dependence across habitat gradients. Annu Rev Entomol. 2009;54:37–56. https://doiorg.publicaciones.saludcastillayleon.es/10.1146/annurev.ento.54.110807.090611.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Mwingira VS, Spitzen J, Mboera LEG, Torres-Estrada JL, Takken W. The influence of larval stage and density on oviposition site-selection behavior of the afrotropical malaria mosquito Anopheles coluzzii (Diptera: Culicidae). J Med Entomol. 2020;57:657–66. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/jme/tjz172.

    Article  CAS  PubMed  Google Scholar 

  44. Shililu J, Tewolde G, Fessahaye S, Mengistu S, Fekadu H, Mehari Z, et al. Larval habitat diversity and ecology of anopheline larvae in Eritrea. J Med Entomol. 2003;40:921–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1603/0022-2585-40.6.921.

    Article  PubMed  Google Scholar 

  45. McCrae AW. Oviposition by African malaria vector mosquitoes.I. Temporal activity patterns of caged, wild-caught freshwater Anopheles gambiae Giles sensu lato. Ann Trop Med Parasitol. 1983;77(6):615–625. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/00034983.1983.11811761.

    Article  PubMed  Google Scholar 

  46. Minakawa N, Mutero C, Githure J, Beier J, Guiyun Y. Spatial distribution and habitat characterization of anopheline mosquito larvae in western Kenya. Am J Trop Med Hyg. 1999;61:1010–6. https://doiorg.publicaciones.saludcastillayleon.es/10.4269/ajtmh.1999.61.1010.

    Article  CAS  PubMed  Google Scholar 

  47. Imam AA, Deeni Y. Common types of Anopheles gambiae breeding habitats in north western Nigeria. J Innov Res Eng Sci. 2014;4:496–504.

    Google Scholar 

  48. Clements AN. The biology of mosquitoes development, nutrition and reproduction, vol. 1. London: Chapman & Hall; 1992.

    Book  Google Scholar 

  49. Avramov M, Thaivalappi A, Ludwig A, Miner L, Cullingham CI, Waddell L, et al. Relationship between water quality and mosquito presence and abundance: a systematic review and meta-analysis. J Med Entomol. 2024;61:1–33. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/jme/tjad139.

    Article  CAS  PubMed  Google Scholar 

  50. Banna MH, Najjaran H, Sadiq R, Imran SA, Rodriguez MJ, Hoorfar M. Miniaturized water quality monitoring pH and conductivity sensors. Sens Actua B. 2014;193:434–41. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.snb.2013.12.002.

    Article  CAS  Google Scholar 

  51. Amawulu E, Commander T, Amaebi A. Effect of physicochemical parameters on mosquito larva population in the Niger Delta University campuses, Bayelsa State Nigeria. Int J Zool. 2020;16:63–8.

    Article  Google Scholar 

  52. Kweka EJ, Zhou G, Gilbreath TM, Afrane Y, Nyindo M, Githeko AK, et al. Predation efficiency of Anopheles gambiae larvae by aquatic predators in western Kenya highlands. Parasit Vect. 2011;4:128. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/1756-3305-4-128.

    Article  Google Scholar 

  53. Taheri S, González MA, Ruiz-López MJ, Magallanes S, Delacour-Estrella S, Lucientes J, et al. Modelling the spatial risk of malaria through probability distribution of Anopheles maculipennis sl and imported cases. Emerg Microbes Infect. 2024;13:2343911. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/22221751.2024.2343911.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Martínez-Barciela Y, Pereira JM, Silva-Torres MI, Pousa Á, Otero JC, Garrido J. First records of Anopheles (Anopheles) plumbeus Stephens, 1828 and Culex (Culex) torrentium Martini, 1925 (Diptera: Culicidae) in Galicia (NW Spain). J Vector Ecol. 2020;45:306–11. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/jvec.12401.

    Article  PubMed  Google Scholar 

  55. Blom R, Krol L, Langezaal M, Schrama M, Trimbos KB, Wasenaar D, et al. Blood-feeding patterns of Culex pipiens biotype pipiens and pipiens/molestus hybrids in relation to avian community composition in urban habitats. Parasit Vect. 2024;95(17):1–12. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13071-024-06186-9.

    Article  Google Scholar 

  56. Magallanes S, Llorente F, Ruiz-López MJ, Puente JM, Ferraguti M, Gutiérrez-López R, et al. Warm winters are associated to more intense West Nile virus circulation in southern Spain. Emerg Microbes Infect. 2024;13:1–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/22221751.2024.2348510.

    Article  Google Scholar 

  57. Jansen S, Heitmann A, Lühken R, Leggewie M, Helms M, Badusche M, et al. Culex torrentium: a potent vector for the transmission of West Nile Virus in Central Europe. Viruses. 2019;11:492. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/v11060492.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Cuervo PF, Artigas P, Mas-Coma S, Bargues MD. West Nile virus in Spain: forecasting the geographical distribution of risky areas with an ecological niche modelling approach. Transbound Emerg Dis. 2022;69:e1113–29. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/tbed.14398.

    Article  PubMed  Google Scholar 

  59. Chaves LF, Keogh CL, Vazquez-Prokopec GM, Kitron UD. Combined sewage overflow enhances oviposition of Culex quinquefasciatus (Diptera: Culicidae) in urban areas. J Med Entomol. 2009;46:220–6. https://doiorg.publicaciones.saludcastillayleon.es/10.1603/033.046.0206.

    Article  PubMed  Google Scholar 

  60. Chaves LF, Keogh CL, Nguyen AM, Decker GM, Vazquez-Prokopec GM, Kitron UD. Combined sewage overflow accelerates immature development and increases body size in the urban mosquito Culex quinquefasciatus. J Appl Entomol. 2011;135:611–20. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/j.1439-0418.2010.01580.x.

    Article  Google Scholar 

  61. Nguyen AT, Williams-Newkirk AJ, Kitron UD, Chaves LF. Seasonal weather, nutrients, and conspecific presence impacts on the southern house mosquito oviposition dynamics in combined sewage overflows. J Med Entomol. 2012;49:1328–38. https://doiorg.publicaciones.saludcastillayleon.es/10.1603/ME12090.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors are grateful to all the people who collaborated in this study by allowing access to their properties for data collection, as well to the three reviewers for all the contributions that have greatly helped to improve this work.

Funding

This study was financially supported by the “Programa de axudas á etapa predoutoral da Consellería de Cultura, Educación e Universidades da Xunta de Galicia” through a PhD fellowship awarded to the first author Y. Martínez-Barciela.

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Authors and Affiliations

  1. Departamento de Ecoloxía e Bioloxía Animal, Universidade de Vigo, Vigo, Galicia, Spain

    Yasmina Martínez-Barciela, Alejandro Polina & Josefina Garrido

Authors
  1. Yasmina Martínez-Barciela
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  2. Alejandro Polina
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  3. Josefina Garrido
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Contributions

Y.M.B. designed and performed the study, participated in the field and data collection, developed the laboratory tasks and statistical analyses, drafted the manuscript and applied the revisions. A.P. participated in the field and data collection, contributed to interpretation of results, and critically reviewed the manuscript. J.G. provided support in funding acquisition, supervised the study development, and critically reviewed the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yasmina Martínez-Barciela.

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Martínez-Barciela, Y., Polina, A. & Garrido, J. Habitat characterization and breeding preferences of mosquito larvae in northwestern Spain: abundance, diversity, and species composition. Parasites Vectors 18, 165 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13071-025-06803-1

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13071-025-06803-1

Keywords

Parasites & Vectors

ISSN: 1756-3305

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