Abstract
Understanding the ecological characteristics of mosquito larval habitats is essential for effective vector surveillance and control. This study investigated larval habitat characteristics, water physicochemical properties, and culicine mosquito assemblages in Hormozgan Province, southern Iran. A cross-sectional survey was conducted from September 2016 to April 2017 across four counties representing diverse ecological conditions. Larvae were collected from natural and artificial aquatic habitats and identified morphologically using standard taxonomic keys. Water samples were analyzed for physicochemical parameters, including temperature, pH, electrical conductivity (EC), total dissolved solids (TDS), total suspended solids (TSS), and major ionic constituents.
A total of 3236 larvae belonging to 16 species from three genera (Aedes, Culex, and Culiseta) were collected. Culiseta longiareolata (25.65%), Culex pipiens (16.62%), and Cx. quinquefasciatus (16.16%) were the dominant species. Most larvae were collected from artificial stagnant habitats characterized by sandy substrates, limited vegetation, and full sunlight exposure. Larval abundance was highest in habitats with water temperatures of 20–30 °C and pH values of 6.5–7.5. Several physicochemical variables, including EC, TDS, TSS, and ionic composition, were associated with species occurrence patterns.
These findings provide baseline ecological evidence regarding environmental conditions associated with culicine mosquito breeding habitats in Hormozgan Province. Within a One Health framework, integrating environmental habitat assessment with vector surveillance may improve identification of productive larval habitats and support targeted larval source management strategies.
Keywords: Culicinae, Vector ecology, Larval habitat, Physicochemical water parameters, Iran
Graphical abstract
Highlights
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3236 larvae from 16 culicine mosquito species were identified across southern Iran.
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Culiseta longiareolata, Culex pipiens, and Cx. quinquefasciatus were the dominant species.
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92.1% of larvae occurred in artificial stagnant habitats with sandy substrates and full sunlight.
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Larval abundance peaked at 20–30 °C and pH 6.5–7.5, indicating optimal breeding conditions.
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Water physicochemical factors and ion composition significantly influenced species distribution and abundance.
1. Introduction
The mosquitoes (Diptera: Culicidae) are a major group of Diptera with medical and public health significance [1]. The family Culicidae comprises more than 3700 recognized species worldwide, classified into two subfamilies, Anophelinae and Culicinae, with Culicinae representing the largest and most diverse subfamily [2]. Mosquitoes play a crucial role in the transmission of numerous infectious diseases, including malaria, encephalitis, filariasis, and several globally important arboviral diseases such as dengue, yellow fever, Zika, and chikungunya. These mosquito-borne diseases affect millions of people worldwide each year and represent major public health challenges, particularly due to the expanding geographic distribution and increasing risk of outbreaks of several arboviral diseases. Overall, vector-borne diseases account for approximately 17% of the global burden of infectious and parasitic diseases [3]. Mosquito ecology represents a critical component of the One Health framework because mosquito populations are influenced by environmental conditions while simultaneously affecting human, animal, and ecosystem health through pathogen transmission [4], [5].
According to the most recent checklist, the family Culicidae in Iran comprises two subfamilies, eight genera, and 73 species [6]. Based on previous studies, 27 species from four genera, Aedes, Culex, Culiseta and Uranotaenia, within the subfamily Culicinae have been reported from Hormozgan Province, including its islands [7]. Aedes chelli (Edwards) was first reported by McIntosh in 1973 from Jask County [8]. Since then, no further records of this species have been documented in Iran, and its name does not appear in official checklists [6].
In Iran, seven arboviral diseases (chikungunya fever, West Nile fever, Rift Valley fever, dengue fever, Sindbis fever, ephemeral bovine fever, and varicella), two bacterial diseases (anthrax and tularemia), four helminthic diseases (Dirofilariasis, Setariasis, Diptalonemiasis and one imported case of lymphatic filariasis), and two protozoan diseases (human and avian malaria) have been reported. The pathogens responsible for these diseases are transmitted biologically or mechanically by mosquitoes [9]. Other culicine-borne pathogens pose threats in Hormozgan Province, including infections such as Malaria, Dirofilariasis and West Nile fever [10]. Additionally, viruses transmitted by Aedes mosquitoes, such as chikungunya, dengue and Zika, represent further threats [11], [12].
Existing studies indicate that ecological information on culicine mosquitoes in Iran remains limited and scattered [13]. Most published documents are restricted to faunistic reports [7], [14]. Previous Iranian studies have investigated ecological characteristics and physicochemical properties of mosquito larval habitats, including water temperature, pH, electrical conductivity, dissolved solids, and ionic composition [15]. In more recent decades, Yaghoubi (1986) studied larval habitat characteristics of mosquitoes in Minab [16], and Zaim (1987) conducted a comprehensive review of the geographical distribution and habitat features of culicine mosquitoes [17]. Previous studies in Iran have demonstrated that both physicochemical and environmental characteristics of larval habitats influence mosquito larval occurrence, survival, development, and habitat suitability. For example, physicochemical parameters of larval habitat waters, including temperature, pH, electrical conductivity, dissolved solids, and ionic composition, have been associated with mosquito larval occurrence and habitat suitability [15]. Similarly, Jaberhashemi et al. (2022), in a comprehensive survey conducted from September 2016 to April 2017 in Bandar Abbas, Bandar Khamir, Bashagard, and Jask counties, collected and identified 3236 larvae and 1901 adult mosquitoes belonging to 16 culicine species [7]. Their findings, together with those of other studies, indicate that larval survival and development are influenced by a combination of habitat characteristics, including substrate type, vegetation cover, light and shade conditions, and various physicochemical properties of the water [18], [19].
Understanding the physicochemical characteristics of larval habitats, such as water temperature, salinity, pH, organic matter, and pollution, is crucial, as these factors influence the growth, survival and distribution of mosquito larvae [15]. Furthermore, assessing species diversity reveals which mosquito species are present, their relative abundance and their habitat preferences. This information is essential for identifying potential disease vectors and for designing effective control measures [15], [20].
Hormozgan Province occupies a strategic geographic position along the northern Persian Gulf and the Gulf of Oman, where extensive maritime trade, livestock movement, migratory bird flyways, rapid urban expansion, and climatic conditions favorable for mosquito development converge. These interacting environmental and socioecological factors may facilitate the introduction, establishment, and spread of mosquito-borne pathogens, including emerging arboviruses, highlighting the importance of identifying environmental determinants of mosquito breeding habitats for integrated surveillance and early detection of ecological conditions conducive to disease emergence within a One Health framework [21], [22], [23]. Moreover, as a malaria-endemic region with documented insecticide resistance among mosquito vectors, Hormozgan requires comprehensive knowledge of mosquito species diversity and larval habitat characteristics to optimize vector control strategies and mitigate the risk of disease transmission. Therefore, the present study aimed to investigate the characteristics of larval habitats and species diversity of culicine mosquitoes in Hormozgan Province.
Despite increasing concern regarding mosquito-borne diseases in the Middle East, quantitative evidence linking physicochemical characteristics of larval habitats with culicine community composition remains scarce, particularly in arid coastal ecosystems. Most previous Iranian investigations have focused on species inventories or isolated ecological observations without integrating detailed hydrochemical analyses across multiple ecological zones. However, quantitative evidence linking hydrochemical characteristics of larval habitats with culicine mosquito assemblage patterns remains limited, particularly in arid coastal ecosystems such as Hormozgan Province.
The novelty of this study lies in integrating mosquito community ecology with detailed hydrochemical characterization across multiple ecological settings within a region facing increasing risks of arbovirus emergence. Unlike previous regional surveys that primarily described mosquito fauna and habitat characteristics, the present study integrates species assemblage data with a detailed assessment of the physicochemical characteristics of larval habitats across multiple ecological settings in Hormozgan Province and examines their implications for environmentally based surveillance within a One Health framework.
2. Material and methods
2.1. Study area
This cross-sectional study was conducted in four counties of Hormozgan Province, including Bandar Abbas, Bandar Khamir, Bashagard, and Jask (Fig. 1). Hormozgan Province covers an approximate area of 68,475 km2, located between 25°23′–28°57′ N latitude and 52°41′–59°15′ E longitude, and officially comprises 13 counties. The province lies in southern Iran and is characterized by a dry climate according to various climatic classifications [24].
Fig. 1.
Map of Iran, highlighting the position of Hormozgan Province, southern Iran, and its four selected counties for sampling during September 2016–April 2017.
High temperatures are among the most prominent climatic features of the province. In coastal areas, summers are extremely hot and humid, with air temperatures occasionally exceeding 52 °C. The annual mean temperature of the region is approximately 27 °C.
2.2. Larval and pupal collection
A cross-sectional survey was conducted from September 2016 to April 2017 in four purposively selected counties of Hormozgan Province: Bandar Abbas, Bandar Khamir, Bashagard, and Jask. These counties were selected to represent diverse ecological and topographical conditions, including coastal, plain, foothill, and mountainous environments. Sampling stations were selected to capture the diversity of mosquito larval habitats across these ecological settings. Detailed information on the geographical coordinates, altitude, and topography of the sampling stations is presented in Table 1.
Table 1.
Collection of data for culicine catches at sites in Hormozgan Province, southern Iran, September 2016–April 2017.
| Locality (city/village) | Topography | County | Coordinates | Altitude (m) |
|---|---|---|---|---|
| Gachin | Plain | Bandar Abbas | 27.09297 N, 55.88667 E | 5 |
| Shamil | Foothill | Bandar Abbas | 27.50979 N, 56.84582 E | 67 |
| Fin | Plain | Bandar Abbas | 27.63330 N, 55.88380 E | 325 |
| Siahoo | Mountainous | Bandar Abbas | 27.76041 N, 56.33421 E | 532 |
| Hormoodar | Plain | Bandar Abbas | 27.31580 N, 56.31445 E | 25 |
| Takht | Plain | Bandar Abbas | 27.50140 N, 56.63494 E | 36 |
| Khorgoo | Mountainous | Bandar Abbas | 27.54360 N, 56.39923 E | 625 |
| Geno | Foothill | Bandar Abbas | 27.45280 N, 56.29312 E | 289 |
| Dasht-e-Imam | Plain | Bandar Abbas | 27.43759 N, 56.32410 E | 622 |
| Al-e-darvish | Plain | Bandar Khamir | 27.16768 N, 55.64246 E | 33 |
| Kahoorestan | Plain | Bandar Khamir | 27.19591 N, 55.60494 E | 12 |
| Kishi | Foothill | Bandar Khamir | 27.32444 N, 55.36645 E | 389 |
| Nakhl-e-kandal | Coastal | Bandar Khamir | 26.95857 N, 55.60681 E | 3 |
| Bandar Khamir | Coastal | Bandar Khamir | 26.94668 N, 55.57938 E | 8 |
| Bandar-e-pol | Coastal | Bandar Khamir | 27.01075 N, 55.73830 E | 12 |
| Pahtek | Foothill | Bashagard | 26.58692 N, 57.87107 E | 966 |
| Sardasht | Plain | Bashagard | 26.45419 N, 55.90515 E | 720 |
| Goharan | Mountainous | Bashagard | 26.59394 N, 55.89763 E | 918 |
| Chokhoon | Foothill | Bashagard | 26.30229 N, 55.66917 E | 456 |
| Tidar | Mountainous | Bashagard | 26.75496 N, 55.83851 E | 1274 |
| Erar | Mountainous | Bashagard | 26.67747 N, 57.90502 E | 1109 |
| Kolahoo | Mountainous | Bashagard | 26.73619 N, 57.92905 E | 1238 |
| Gorichi | Mountainous | Bashagard | 26.45622 N, 57.75109 E | 980 |
| Tisoor | Foothill | Bashagard | 26.49170 N, 58.24791 E | 833 |
| Jangal | Mountainous | Bashagard | 26.28619 N, 58.44887 E | 516 |
| Dastgerd dargaz | Foothill | Bashagard | 26.85750 N, 57.81740 E | 1344 |
| Ermish | Foothill | Bashagard | 26.74820 N, 57.95922 E | 1146 |
| Shahbabak dargazan | Mountainous | Bashagard | 26.47860 N, 57.77878 E | 1031 |
| Panhan | Plain | Jask | 25.72153 N, 58.19773 E | 20 |
| Hangestan | Foothill | Jask | 25.82767 N, 58.17112 E | 32 |
| Hasar | Foothill | Jask | 25.79284 N, 58.17249 E | 27 |
| Jask | Coastal | Jask | 25.67275 N, 57.81480 E | 4 |
| Alimarja | Foothill | Jask | 25.95130 N, 58.08813 E | 70 |
| Noroozkar | Plain | Jask | 25.75133 N, 58.44484 E | 16 |
| Kiaei | Foothill | Jask | 25.85177 N, 58.15058 E | 38 |
| Gazdan | Plain | Jask | 25.76559 N, 57.79750 E | 29 |
| Gangan | Plain | Jask | 25.86419 N, 57.46091 E | 28 |
| Quik | Plain | Jask | 25.79239 N, 57.77696 E | 49 |
| Ganjag | Foothill | Jask | 25.78323 N, 58.47322 E | 24 |
Mosquito larvae and pupae were collected from natural and artificial aquatic habitats using standard dipping and pipette methods. A 350-mL dipper was used for accessible habitats, while pipettes were used for small breeding sites and buckets were used for wells. From each habitat, 10 dips were taken from different locations within the water body following standard larval sampling procedures [25]. Information regarding habitat characteristics, including habitat status (temporary or permanent), habitat type (natural or artificial), substrate type, vegetation cover, water condition (stagnant or running), sunlight exposure, and other environmental characteristics, was recorded during sampling.
Collected specimens were preserved in vials containing lactophenol, labeled, and transferred to the Medical Entomology Laboratory, School of Health, Hormozgan University of Medical Sciences. Third- and fourth-instar larvae were mounted on microscopic slides using Berlese's medium for morphological identification. Pupae were transferred into 250-mL plastic containers covered with mesh and maintained under controlled laboratory conditions (27 ± 5 °C and 75 ± 5% relative humidity). Emerged adults were identified to species level using valid taxonomic keys [1].
The geographical coordinates and altitude of sampling stations were recorded using a Garmin eTrex 20 GPS device (Garmin Ltd., Olathe, KS, USA). The proximity of larval habitats to human settlements was recorded during field surveys. The distance from jetty areas to the nearest settlements was categorized and included among habitat characteristics.
2.3. Water sampling and physicochemical analysis
Simultaneously with larval collection, water samples were taken to assess physicochemical parameters of larval habitats including temperature, pH, taste (saline or fresh), turbidity measured in Nephelometric Turbidity Units (NTU), electrical conductivity (EC) expressed in μmhos/cm, water hardness, total dissolved solids (TDS, mg/L), total suspended solids (TSS, mg/L), and all ionic compositions (mg/L) such as fluoride, chloride, sulfate, carbonate, bicarbonate, nitrite, nitrate, phosphate, calcium, magnesium, sodium, potassium, iron, manganese, and total alkalinity. From larval habitats in the study areas, 2 Liters of water were randomly sampled and transported under cold chain conditions to the Water Laboratory, Department of Environmental Health, School of Health, Hormozgan University of Medical Sciences, for analysis [15].
2.4. Statistical analysis
Data were entered into Microsoft Excel and analyzed using SPSS version 22 (IBM Corp., Armonk, NY, USA). Descriptive statistics were calculated for mosquito larval abundance, habitat characteristics, and physicochemical parameters of breeding-site waters. Prior to analysis, the normality of continuous variables was assessed using the Shapiro–Wilk test and graphical examination of histograms. Pearson's correlation coefficient (r) was used as an exploratory analysis to assess the strength and direction of linear associations between the larval abundance of individual mosquito species and measured physicochemical variables. Results are presented as correlation coefficients (r) with corresponding (P)-values, and statistical significance was set at (P < 0.05). Significant correlations were subsequently summarized through an integrated species–environment association framework to facilitate ecological interpretation of mosquito species responses to physicochemical gradients. Given that ecological community data may involve complex interactions among multiple environmental factors, these bivariate analyses were intended to identify potential associations with individual physicochemical variables; future studies should consider multivariate approaches, such as ordination methods or generalized linear models, to account for the joint effects of environmental factors.
2.5. Integrated species–environment association analysis
To provide an integrated ecological interpretation of the relationships between mosquito assemblages and larval habitat characteristics, significant associations between mosquito species occurrence and physicochemical variables of breeding-site waters were further synthesized. Pearson's correlation coefficients obtained from the correlation analysis were used to summarize species-specific relationships with measured chemical parameters, including fluoride, chloride, sulfate, calcium, magnesium, sodium, and potassium. Only statistically significant associations (P < 0.05) were considered for interpretation.
A species–environment association matrix was constructed based on significant correlation coefficients, and the direction and strength of associations were visualized using a heatmap. Positive and negative correlations were represented separately to facilitate interpretation of potential ecological relationships between culicine mosquito species and water chemistry variables. This analysis was intended as an exploratory ecological synthesis of observed associations and was not interpreted as evidence of causal relationships between environmental variables and mosquito occurrence [26].
3. Results
In this study, a total of 3236 mosquito larvae belonging to three genera Aedes, Culex and Culiseta and 16 species were collected during eight sampling rounds from 39 active larval habitats across four counties of Hormozgan Province (Table 2). Culiseta longiareolata (25.65%), Culex pipiens (16.62%) and Cx. quinquefasciatus (16.16%) represented the most abundant species, while Cx. hortensis (0.09%) was the least abundant. The highest number of larvae was collected in November 2016 (20.55%), whereas the lowest was recorded in December 2016 (6.83%) (Table 3).
Table 2.
The distribution and abundance of culicine larvae collected in four counties of Hormozgan Province, southern Iran, September 2016–April 2017.
| County |
|||||
|---|---|---|---|---|---|
| Species | Bandar Abbas | Bandar Khamir | Bashagard | Jask | Total |
| Ae. caballus | 36 | – | – | – | 36 |
| Ae. caspius | 29 | 68 | – | – | 97 |
| Ae. vexans | 160 | 61 | – | – | 221 |
| Ae. vittatus | 38 | 106 | – | – | 144 |
| Cx. arbieeni | 31 | 31 | – | – | 62 |
| Cx. bitaeniorhynchus | 27 | 18 | – | – | 45 |
| Cx. hortensis | – | – | 3 | – | 3 |
| Cx. laticinctus | 71 | 13 | 115 | 20 | 219 |
| Cx. mimeticus | 120 | 42 | 21 | – | 183 |
| Cx. perexiguus | 46 | 9 | 11 | – | 66 |
| Cx. pipiens | 345 | 193 | – | – | 538 |
| Cx. pusillus | – | – | 9 | 35 | 44 |
| Cx. quinquefasciatus | 323 | 160 | – | 40 | 523 |
| Cx. sinaiticus | 60 | 25 | 16 | 9 | 110 |
| Cx. theileri | 68 | 32 | 15 | – | 115 |
| Cs. longiareolata | 45 | 58 | 673 | 54 | 830 |
| Total | 1399 | 816 | 863 | 158 | 3236 |
Table 3.
The abundance of culicine larvae in different sampling months, Hormozgan Province, southern Iran, September 2016–April 2017.
| Sampling years and months |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 2016 |
2017 |
|||||||||
| Larval collected species | September | October | November | December | January | Febuary | March | April | Total | (%) |
| Ae. caballus | 14 | – | 22 | – | – | – | – | – | 36 | 1.11 |
| Ae. caspius | – | – | 11 | 14 | 13 | 28 | 31 | – | 97 | 3.00 |
| Ae. vexans | 21 | – | – | – | 119 | – | 41 | 40 | 221 | 6.83 |
| Ae. vittatus | – | 11 | – | 17 | 50 | 29 | 29 | 8 | 144 | 4.45 |
| Cx. arbieeni | – | 12 | – | 13 | – | – | 28 | 9 | 62 | 1.92 |
| Cx. bitaeniorhynchus | 11 | – | 28 | – | – | 6 | – | – | 45 | 1.39 |
| Cx. hortensis | – | – | 3 | – | – | – | – | – | 3 | 0.09 |
| Cx. laticinctus | 62 | 14 | 121 | – | – | 8 | – | 14 | 219 | 6.77 |
| Cx. mimeticus | – | 52 | 32 | 44 | 12 | 7 | – | 36 | 183 | 5.66 |
| Cx. perexiguus | 9 | 11 | – | 14 | – | 16 | – | 16 | 66 | 2.04 |
| Cx. pipiens | 62 | 63 | 53 | 49 | 107 | 59 | 83 | 62 | 538 | 16.62 |
| Cx. pusillus | – | 3 | 6 | – | – | 32 | – | 3 | 44 | 1.36 |
| Cx. quinquefasciatus | 78 | 66 | 59 | 33 | 88 | 86 | 60 | 53 | 523 | 16.16 |
| Cx. sinaiticus | 22 | 3 | – | 16 | 44 | 25 | – | – | 110 | 3.40 |
| Cx. theileri | 16 | 48 | 12 | – | – | – | 32 | 7 | 115 | 3.55 |
| Cs. longiareolata | 8 | 42 | 318 | 21 | 63 | 92 | 258 | 28 | 830 | 25.65 |
| Total | 303 | 325 | 665 | 221 | 496 | 388 | 562 | 276 | 3236 | 100.0 |
| Percentage (%) | 9.36 | 10.04 | 20.55 | 6.83 | 15.33 | 11.99 | 17.37 | 8.53 | 100.0 | |
3.1. Larval habitat characteristics
With respect to habitat type, 92.1% (2980 larvae) were collected from artificial habitats and 7.9% (256 larvae) from natural habitats. All larvae (100%) were observed in stagnant waters. Regarding water depth, the highest larval abundance was recorded at depths of 0.5–1 m (31.2%) and 1–2.9 m (32.4%). Aedes species were predominantly found (88.7%) at depths less than 0.5 m. In terms of vegetation cover, 85.4% (2763 larvae) were collected from habitats without vegetation. Sandy substrates hosted 67.2% (2175 larvae), making them the most common substrate type. Water temperature in larval habitats ranged between 20 and 30 °C (83.5%), and pH values were between 6.5 and 7.5 (97.1%). Moreover, 87.4% of larvae were collected from habitats fully exposed to sunlight (Table 4, Fig. 2). Aquatic organisms associated with larval habitats were recorded as habitat indicators. The presence of frogs, spiders, and beetles varied among mosquito species-associated habitats (Fig. 2). (See Fig. 3.)
Table 4.
Larval habitat characteristics of different culicine mosquito species in four counties of Hormozgan Province, southern Iran, September 2016 – April 2017.
| Ae. vexans | Ae. vittatus | Cx. bitaeniorhynchus | Cx. laticinctus | Cs. longiareolata | Cx. mimeticus | Cx. perexiguus | Cx. quinquefasciatus | Cx. sinaiticus | Cx. theileri | Ae. caballus | Ae. caspius | Cx. pusillus | Cx. hortensis | Cx. arbieeni | Cx. pipiens | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Habitat type | ||||||||||||||||
| Artificial | 6.6 | 4.3 | 1.39 | 6.77 | 17.6 | 4.9 | 1.7 | 16.16 | 3 | 3 | 1.11 | 3 | 0.9 | 0.09 | 1.92 | 16.62 |
| Natural | 0.23 | 0.15 | 0 | 0 | 8.05 | 0.76 | 0.34 | 0 | 0.4 | 0.55 | 0 | 0 | 0.46 | 0 | 0 | 0 |
| Habitat status | ||||||||||||||||
| Temporary | 6.6 | 4.3 | 1.4 | 3.2 | 13.1 | 5.5 | 2 | 15.7 | 3.1 | 3.5 | 1.1 | 2.9 | 1.3 | 0 | 1.9 | 16.62 |
| Permanent | 0 | 0 | 0 | 3.4 | 11.9 | 0 | 0 | 0 | 0.2 | 0 | 0 | 0 | 0 | 0.1 | 0 | 0 |
| Resident | 6.6 | 4.3 | 1.4 | 6.6 | 24.9 | 5.5 | 2 | 15.7 | 3.3 | 3.5 | 1.1 | 2.9 | 1.3 | 0.1 | 1.9 | 16.62 |
| Current | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Depth 0.05–0.1 m | 0 | 0 | 0 | 0 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 0 | 0 | 0 |
| Depth 0.1–0.5 | 1.4 | 0.2 | 0 | 4.1 | 19.4 | 0.6 | 0.5 | 1.8 | 0.8 | 0.6 | 0.3 | 0.2 | 0.4 | 0.1 | 0.1 | 0.5 |
| Depth 0.5-1 m | 1.4 | 0.7 | 0.5 | 0.9 | 2.8 | 1.8 | 1.1 | 5.6 | 1.3 | 1.6 | 0.3 | 0.5 | 0.9 | 0 | 0.5 | 7.1 |
| Depth 1–2.9 m | 3.9 | 3.5 | 0.8 | 1.6 | 1.9 | 3 | 0.4 | 8.4 | 1.2 | 1.3 | 0.5 | 2.2 | 0 | 0 | 1.3 | 8.5 |
| Area 0.0002–0.1 m2 | 6.6 | 4.3 | 1.4 | 2.5 | 3.1 | 4.9 | 1.7 | 14.5 | 3 | 3 | 1.1 | 2.9 | 0.1 | 0 | 1.9 | 16.2 |
| Area 0.1-1 m2 | 0 | 0 | 0 | 0 | 3.6 | 0.6 | 0 | 0 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 |
| Area 1-10 m2 | 0 | 0 | 0 | 3.1 | 13.6 | 0 | 0.3 | 0 | 0 | 0 | 0 | 0 | 1.1 | 0 | 0 | 0 |
| Area 10–180 m2 | 0 | 0 | 0 | 1 | 4.8 | 0 | 0 | 1.2 | 0.2 | 0 | 0 | 0.2 | 0.1 | 0 | 0 | 0 |
| DJS* 0-50 m | 6.6 | 4.3 | 1.4 | 2.5 | 3.1 | 4.9 | 1.7 | 14.5 | 2.6 | 3 | 1.1 | 2.9 | 0 | 0 | 1.9 | 16.2 |
| DJS* 50–100 m | 0 | 0 | 0 | 0 | 0.8 | 0 | 0 | 0 | 0 | 0.5 | 0 | 0 | 0.9 | 0 | 0 | 0 |
| DJS*100–1000 | 0 | 0 | 0 | 4.1 | 20.2 | 0.6 | 0.3 | 1.2 | 0.5 | 0.5 | 0 | 0 | 0.4 | 0.1 | 0 | 0 |
| DJS*1000–100,000 m | 0 | 0 | 0 | 0 | 0.8 | 0 | 0 | 0.2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Water condition | ||||||||||||||||
| Transparent | 6.6 | 4.3 | 1.4 | 6.6 | 24.9 | 5.5 | 1.7 | 15.7 | 2.8 | 3.5 | 1.1 | 2.9 | 1.1 | 0.1 | 1.9 | 16.62 |
| Muddy | 0 | 0 | 0 | 0 | 0 | 0 | 0.3 | 0 | 0.5 | 0 | 0 | 0 | 0.2 | 0 | 0 | 0 |
| WT⁎⁎ 2–10 °C | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| WT 10–20 °C | 0 | 0 | 0 | 3.7 | 16.8 | 0 | 0 | 1.2 | 0 | 0 | 0 | 0 | 1.1 | 0.1 | 0 | 0 |
| WT 20–30 °C | 6.6 | 4.3 | 1.4 | 2.5 | 8.1 | 5.5 | 2.0 | 14.5 | 2.8 | 3.5 | 1.1 | 2.9 | 0.2 | 0 | 1.9 | 16.2 |
| WT 30–38 °C | 0 | 0 | 0 | 0.4 | 0 | 0 | 0 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| pH = 6–6.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| pH = 6.5–7.5 | 6.6 | 4.3 | 1.4 | 6.6 | 24.8 | 4.9 | 2 | 15.7 | 3.1 | 3.5 | 1.1 | 2.9 | 1.3 | 0.1 | 1.9 | 16.2 |
| pH = 7.5–9 | 0 | 0 | 0 | 0 | 0.1 | 0.6 | 0 | 0 | 0.2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Sunlight condition | ||||||||||||||||
| Sunny | 4.7 | 2.4 | 1.2 | 1.9 | 7.9 | 3.6 | 1.2 | 9.7 | 1.7 | 2.1 | 0.8 | 1.6 | 0.3 | 0 | 1.2 | 11.2 |
| Penumbral | 0 | 0 | 0 | 4.1 | 13.2 | 0 | 0.1 | 1.4 | 0.6 | 0 | 0.1 | 0.3 | 0.9 | 0.1 | 0.1 | 0.1 |
| Shade | 0.4 | 1.6 | 0 | 0.1 | 3.6 | 1 | 0.3 | 1.1 | 0.5 | 0.1 | 0 | 0.6 | 0.1 | 0 | 0.1 | 1.7 |
| Shade-light | 1.6 | 0.4 | 0.1 | 0.5 | 0.3 | 0.9 | 0.3 | 3.5 | 0.5 | 1.2 | 0.2 | 0.5 | 0 | 0 | 0.5 | 3.2 |
| Vegetation condition | ||||||||||||||||
| No vegetation | 6.6 | 4.3 | 1.4 | 5.6 | 19.3 | 4.9 | 2 | 14.5 | 2.9 | 3.5 | 1.1 | 2.9 | 0.2 | 0 | 1.9 | 16.62 |
| With vegetation | 0 | 0 | 0 | 0.9 | 5.6 | 0.6 | 0 | 1.2 | 0.5 | 0 | 0 | 0 | 1.2 | 0.1 | 0 | 0 |
* DJS: Distance from jetty to settlement, ** WT: Water temperature.
Fig. 2.
Larval habitat characteristics of different culicine mosquito species based on substrate type and aquatic organisms in Hormozgan Province.
Fig. 3.
Heatmap of significant associations between culicine mosquito species occurrence and physicochemical characteristics of larval habitat waters in Hormozgan Province, southern Iran
3.2. Larval species distribution by water parameters
Culex pipiens (91.0%) and Cx. quinquefasciatus (83.6%) were predominantly found in waters with EC greater than 2000 μS/cm. In contrast, Ae. vexans and Ae. vittatus were observed only in habitats with EC above 2000 μS/cm. Similarly, Cx. pipiens (91.0%) and Cx. quinquefasciatus (83.6%) showed the highest abundance in waters with TDS above 1200 mg/L. Regarding TSS, Cx. pipiens (100%) and Cx. quinquefasciatus (95.2%) were most frequently found in habitats with TSS levels exceeding 600 mg/L (Table 5).
Table 5.
Percentage distribution of different culicine mosquito larval species according to water parameters in four counties of Hormozgan Province, southern Iran, September 2016 to April 2017.
| Water parameters |
NTU |
EC |
TDS |
TSS |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Larval species | 0–1 | 1–5 | 5–10 | 10 − + | 0–500 | 500–1000 | 1000–2000 | 2000 − + | 0–300 | 300–600 | 600–1200 | 1200 − + | 0–100 | 100–300 | 300–600 | 600 − + |
| Ae. vxans | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 0.0 | 0.0 | 0.0 | 100.0 | 0.0 | 0.0 | 0.0 | 100.0 |
| Ae. vittatus | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 0.0 | 0.0 | 0.0 | 100.0 | 0.0 | 0.0 | 0.0 | 100.0 |
| Cx. bitaeniorhynchus | 92.86 | 7.14 | 0.0 | 0.0 | 0.0 | 0.0 | 7.14 | 92.86 | 0.0 | 0.0 | 0.0 | 100.0 | 0.0 | 0.0 | 0.0 | 100.0 |
| Cx. laticinctus | 65.02 | 34.98 | 0.0 | 0.0 | 7.98 | 49.43 | 37.64 | 4.94 | 0.0 | 57.41 | 37.64 | 4.94 | 0.0 | 7.98 | 84.41 | 7.6 |
| Cs. longiareolata | 80.13 | 19.87 | 0.0 | 0.0 | 1.3 | 59.45 | 37.3 | 1.95 | 0.0 | 59.93 | 38.11 | 1.95 | 0.0 | 1.3 | 81.92 | 16.78 |
| Cx. mimeticus | 78.38 | 21.62 | 0.0 | 0.0 | 0.0 | 56.76 | 0.0 | 43.24 | 0.0 | 56.76 | 0.0 | 43.24 | 0.0 | 0.0 | 56.76 | 43.24 |
| Cx. perexiguus | 100.0 | 0.0 | 0.0 | 0.0 | 45.45 | 54.55 | 0.0 | 0.0 | 0.0 | 100.0 | 0.0 | 0.0 | 0.0 | 45.45 | 54.55 | 0.0 |
| Cx. quinquefasciatus | 84.93 | 15.07 | 0.0 | 0.0 | 0.0 | 4.79 | 11.64 | 83.56 | 0.0 | 4.79 | 11.64 | 83.56 | 0.0 | 0.0 | 4.79 | 95.21 |
| Cx. sinaiticus | 11.11 | 88.89 | 0.0 | 0.0 | 0.0 | 11.11 | 88.89 | 0.0 | 0.0 | 11.11 | 88.89 | 0.0 | 0.0 | 0.0 | 11.11 | 88.89 |
| Cx. theileri | 89.47 | 10.53 | 0.0 | 0.0 | 0.0 | 27.63 | 13.16 | 59.21 | 0.0 | 27.63 | 13.16 | 59.21 | 0.0 | 0.0 | 27.63 | 72.37 |
| Ae. caballus | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 27.78 | 72.22 | 0.0 | 0.0 | 27.78 | 72.22 | 0.0 | 0.0 | 0.0 | 100.0 |
| Ae. caspius | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.83 | 0.0 | 54.17 | 0.0 | 45.83 | 0.0 | 54.17 | 0.0 | 0.0 | 45.83 | 54.17 |
| Cx. pusillus | 79.55 | 15.91 | 4.55 | 0.0 | 0.0 | 27.27 | 70.45 | 2.27 | 0.0 | 18.18 | 79.55 | 2.27 | 0.0 | 0.0 | 27.27 | 70.45 |
| Cx. hortensis | 66.67 | 33.33 | 0.0 | 0.0 | 0.0 | 66.67 | 33.33 | 0.0 | 0.0 | 66.67 | 33.33 | 0.0 | 0.0 | 0.0 | 100.0 | 0.0 |
| Cx. arbieeni | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 | 0.0 | 0.0 | 0.0 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 100.0 |
| Cx. pipiens | 85.25 | 14.75 | 0.0 | 0.0 | 0.0 | 0.0 | 9.02 | 90.98 | 0.0 | 0.0 | 9.02 | 90.98 | 0.0 | 0.0 | 0.0 | 100.0 |
3.3. Chemical characteristics of larval habitat water and culicine larval species
The most abundant ions in larval habitat waters were SO₄2−, NO₂−, HCO₃−, CaCO₃, and Cl−. Significant associations were observed between the larval species Cs. longiareolata, Cx. mimeticus, and Cx. theileri with F−, K+, and SO₄2− ions in larval habitats.
The presence of Cx. theileri larvae showed positive correlations with chloride (P < 0.01, r = 0.404), sodium (P < 0.01, r = 0.404), and magnesium (P < 0.01, r = 0.410) concentrations. Cx. bitaeniorhynchus larvae exhibited positive correlations with chloride (P < 0.01, r = 0.337) and calcium (P < 0.05, r = 0.284). In contrast, Cx. perexiguus larvae demonstrated a negative correlation with chloride concentration (P < 0.05, r = −0.289).
Furthermore, Cs. longiareolata larvae showed negative correlations with fluoride (P < 0.05, r = −0.308), sulfate (P < 0.05, r = −0.308), sodium (P < 0.05, r = −0.288), and potassium (P < 0.05, r = −0.287) concentrations. Finally, a significant positive correlation was observed between sodium concentration and the total number of larvae collected (P < 0.05) (Table 6, Table 7).
Table 6.
Mean and standard deviation of chemical elements in larval habitat waters across four counties of Hormozgan Province, southern Iran, September 2016 to April 2017.
| Chemical parameters | Mean | SD | Max | Min |
|---|---|---|---|---|
| F− | 0.25 | 0.68 | 2.80 | 0 |
| CL− | 735.30 | 3361.07 | 25,900 | 12.60 |
| SO4−− | 105.28 | 338.21 | 1638 | 0 |
| CO3 −− | 0 | 0 | 0 | 0 |
| HCO3− | 192.63 | 89.98 | 411 | 0 |
| NO2− | 192.63 | 17.32 | 134.20 | 0 |
| NO3- | 55 | 2.01 | 10.20 | 0 |
| PO4−−− | 0 | 0 | 0 | 0 |
| Caco3 | 161.39 | 73.27 | 337.00 | 50.00 |
| CO3 −− | 0 | 0 | 0 | 0 |
| Mn | 0 | 0 | 0 | 0 |
| Ca++ | 93.89 | 98.82 | 516. | 5.40 |
| Mg++ | 65.05 | 210.91 | 1613.00 | 3.00 |
| Na+ | 23.19 | 66.22 | 252.50 | 0 |
| K+ | 1.13 | 3.11 | 11.90 | 0 |
| Fe | 0 | 0 | 0 | 0 |
Table 7.
Analysis of the relationship between chemical characteristics of larval habitat waters and culicine mosquito larval species in four counties of Hormozgan Province, southern Iran, September 2016 to April 2017.
| Species |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Chemical characteristics | Cx. hortensis | Cx. pusills | Cx. theileri | Cx. sinaitis | Cx. perexiguus | Cx. mimeticus | Cs. longiareolata | Cx. laticinctus | Cx. bitaeniorhynchus | |
| F− | Correlation | −0.078 | −0.186 | 0.336⁎⁎ | 0.168 | −0.078 | 0.341⁎⁎ | −0.308⁎ | 0.150 | −0.112 |
| Sig | 0.555 | 0.155 | 0.009 | 0.200 | 0.555 | 0.008 | 0.017 | 0.252 | 0.395 | |
| −CL | Correlation | −0.089 | −0.048 | 0.404⁎⁎ | −0.052 | −0.289⁎ | 0.104 | −0.245 | −0.063 | 0.337⁎⁎ |
| Sig | 0.499 | 0.716 | 0.001 | 0.693 | 0.025 | 0.430 | 0.059 | 0.632 | 0.008 | |
| −−SO4 | Correlation | −0.078 | −0.186 | 0.329⁎ | 0.177 | −0.078 | 0.323⁎ | −0.308⁎ | 0.152 | −0.112 |
| Sig | 0.555 | 0.155 | 0.010 | 0.177 | 0.555 | 0.012 | 0.017 | 0.245 | 0.395 | |
| HCO3− | Correlation | 0.251 | −0.185 | 0.075 | −0.115 | 0.108 | 0.110 | 0.122 | 0.086 | 0.215 |
| Sig | 0.053 | 0.157 | 0.569 | 0.381 | 0.411 | 0.402 | 0.353 | 0.514 | 0.100 | |
| NO2− | Correlation | −0.024 | −0.058 | 0.319⁎ | −0.024 | −0.024 | −0.030 | −0.096 | −0.047 | −0.035 |
| Sig | 0.855 | 0.660 | 0.013 | 0.855 | 0.855 | 0.821 | 0.467 | 0.720 | 0.792 | |
| NO3- | Correlation | −0.056 | −0.134 | 0.358⁎⁎ | 0.297⁎ | −0.056 | 0.447⁎⁎ | −0.221 | −0.109 | −0.080 |
| Sig | 0.671 | 0.307 | 0.005 | 0.021 | 0.671 | 0.000 | 0.089 | 0.406 | 0.541 | |
| Ca++ | Correlation | −0.121 | 0.080 | 0.151 | −0.133 | −0.138 | −0.025 | −0.056 | 0.048 | 0.284⁎ |
| Sig | 0.358 | 0.544 | 0.249 | 0.311 | 0.292 | 0.851 | 0.673 | 0.713 | 0.028 | |
| Mg++ | Correlation | −0.060 | −0.079 | 0.410⁎⁎ | −0.034 | −0.225 | 0.121 | −0.265⁎ | 0.041 | 0.325⁎ |
| Sig | 0.649 | 0.547 | 0.001 | 0.795 | 0.084 | 0.356 | 0.040 | 0.756 | 0.011 | |
| Na+ | Correlation | −0.073 | −0.174 | 0.404⁎⁎ | 0.190 | −0.073 | 0.395⁎⁎ | −0.288⁎ | 0.144 | −0.104 |
| Sig | 0.582 | 0.184 | 0.001 | 0.145 | 0.582 | 0.002 | 0.026 | 0.271 | 0.427 | |
| K+ | Correlation | −0.073 | −0.174 | 0.367⁎⁎ | 0.209 | −0.073 | 0.351⁎⁎ | −0.287⁎ | 0.173 | −0.104 |
| Sig | 0.582 | 0.184 | 0.004 | 0.109 | 0.582 | 0.006 | 0.026 | 0.186 | 0.427 | |
| Caco3 | Correlation | 0.246 | −0.207 | 0.141 | −0.121 | 0.103 | 0.103 | 0.096 | 0.075 | 0.264⁎ |
| Sig | 0.058 | 0.113 | 0.282 | 0.359 | 0.435 | 0.431 | 0.466 | 0.567 | 0.042 | |
3.4. Integrated species–environment association analysis
To provide an integrated interpretation of the relationships between mosquito assemblages and habitat water characteristics, significant associations between mosquito species occurrence and physicochemical variables were summarized (Table 8). Several mosquito species showed species-specific relationships with ionic characteristics of larval habitat waters.
Table 8.
Summary of significant associations between culicine mosquito species occurrence and physicochemical characteristics of larval habitat waters in Hormozgan Province, southern Iran.
| Mosquito species | Environmental variable | Correlation coefficient (r) | Direction of association | P-value |
|---|---|---|---|---|
| Culex theileri | Chloride (Cl−) | 0.404 | Positive | <0.01 |
| Culex theileri | Sodium (Na+) | 0.404 | Positive | <0.01 |
| Culex theileri | Magnesium (Mg2+) | 0.410 | Positive | <0.01 |
| Culex bitaeniorhynchus | Chloride (Cl−) | 0.337 | Positive | <0.01 |
| Culex bitaeniorhynchus | Calcium (Ca2+) | 0.284 | Positive | <0.05 |
| Culex perexiguus | Chloride (Cl−) | −0.289 | Negative | <0.05 |
| Culiseta longiareolata | Fluoride (F−) | −0.308 | Negative | <0.05 |
| Culiseta longiareolata | Sulfate (SO₄2−) | −0.308 | Negative | <0.05 |
| Culiseta longiareolata | Sodium (Na+) | −0.288 | Negative | <0.05 |
| Culiseta longiareolata | Potassium (K+) | −0.287 | Negative | <0.05 |
| Total larvae | Sodium (Na+) | Positive correlation reported | Positive | <0.05 |
Culex theileri showed significant positive associations with chloride (r = 0.404, P < 0.01), sodium (r = 0.404, P < 0.01), and magnesium (r = 0.410, P < 0.01). Similarly, Cx. bitaeniorhynchus showed positive associations with chloride (r = 0.337, P < 0.01) and calcium (r = 0.284, P < 0.05). In contrast, Cx. perexiguus exhibited a negative association with chloride concentration (r = −0.289, P < 0.05).
Culiseta longiareolata, the most abundant species collected in this study, showed significant negative associations with fluoride (r = −0.308, P < 0.05), sulfate (r = −0.308, P < 0.05), sodium (r = −0.288, P < 0.05), and potassium (r = −0.287, P < 0.05). These findings indicate that different culicine mosquito species may occupy habitats characterized by distinct ionic profiles.
4. Discussion
The present study investigated the ecological characteristics of larval habitats and physicochemical factors associated with culicine mosquito assemblages in Hormozgan Province, southern Iran. A total of 3236 larvae belonging to 16 species were collected, with Cs. longiareolata, Cx. pipiens, and Cx. quinquefasciatus being the dominant species. Most larvae were collected from artificial stagnant water habitats characterized by sandy substrates, limited vegetation cover, temperatures of 20–30 °C, and pH values of 6.5–7.5. Variations in EC, TDS, TSS, and selected ionic constituents were associated with differences in species occurrence, suggesting that water characteristics may influence culicine mosquito assemblages in the study area [4], [5]. Associated aquatic organisms may reflect ecological characteristics of breeding habitats and potentially influence mosquito larval survival through predator–prey interactions. However, predator abundance was not quantitatively assessed in this study.
Although proximity to human settlements represents an important factor influencing vector–human contact, the present study primarily focused on ecological characteristics of larval habitats and did not evaluate human exposure or disease transmission directly.
The observed temperature and pH ranges are consistent with previous reports indicating that these factors influence mosquito larval development and habitat suitability [27], [28]. Similarly, the predominance of larvae in artificial stagnant habitats with sandy substrates agrees with previous ecological studies showing that such habitats provide suitable conditions for oviposition and immature development [29], [30]. Sandy substrates may influence larval habitat suitability by affecting water retention, organic matter accumulation, and microhabitat stability. However, because this study was observational, these relationships should be interpreted as ecological associations rather than direct evidence of habitat preference.
Seasonal variation in larval abundance was observed, with higher abundance recorded during November and lower abundance during December. This pattern agrees with previous studies showing that mosquito population dynamics are influenced by seasonal environmental changes, particularly temperature and rainfall [31], [32], [33]. The observed pattern suggests that seasonal conditions may affect larval habitat suitability in the study area.
The dominance of Cs. longiareolata, Cx. pipiens, and Cx. quinquefasciatus is consistent with previous studies reporting these species as common components of mosquito communities in diverse aquatic habitats [34], [35], [36]. In the present study, Cx. pipiens and Cx. quinquefasciatus were frequently detected in habitats with elevated EC, TDS, and TSS values. Similar associations have been reported by Zidan et al. [37], suggesting that these species can occur in aquatic habitats with different levels of dissolved materials and organic content.
From a One Health perspective, the observed associations between mosquito occurrence and physicochemical characteristics of breeding habitats extend beyond species ecology. Environmental variables such as EC, TDS, and TSS may serve as practical indicators for identifying habitats that are more likely to support medically important mosquito species. Integrating these environmental indicators into routine surveillance programs could improve early identification of productive larval habitats and facilitate environmentally based interventions before increases in vector populations translate into greater risks of pathogen transmission. Such approaches are consistent with integrated vector management strategies that emphasize environmental monitoring alongside entomological and epidemiological surveillance [38], [39].
The occurrence of Ae. vexans and Ae. vittatus in habitats with comparatively high EC values may indicate an association with particular environmental conditions present in the study area [27]. Likewise, their presence in habitats with elevated TSS levels suggests that these species are capable of utilizing a range of aquatic environments. However, the ecological significance of these observations remains uncertain because the study was not designed to evaluate species-specific physiological responses to water quality variables [27], [37]. Additional laboratory and field investigations would be necessary to clarify the mechanisms underlying these patterns.
The present findings also have broader implications within a One Health framework. Mosquito larval habitats represent the environmental interface where ecological conditions influence the abundance and distribution of vectors capable of transmitting pathogens among humans, domestic animals, and wildlife. Identifying physicochemical characteristics associated with productive breeding habitats may therefore improve integrated vector surveillance by enabling earlier recognition of environments that favor medically important mosquito species. In Hormozgan Province, where rapid urban development, agricultural activities, livestock production, and coastal ecosystems coexist, environmentally based surveillance may complement human and veterinary disease monitoring and support more targeted vector control interventions [5], [38].
Several significant correlations were observed between mosquito species and individual water ions, including chloride, sodium, magnesium, fluoride, sulfate, calcium, and potassium. These associations indicate that differences in water chemistry may contribute to variation in mosquito species composition among larval habitats. For example, positive correlations between Cx. theileri and chloride, sodium, and magnesium concentrations may suggest that this species is more frequently encountered in habitats with relatively elevated concentrations of these ions. Similarly, the negative associations observed for Cs. longiareolata with several ionic parameters may indicate differing habitat preferences among species [40]. However, these relationships should be interpreted cautiously because correlation does not establish causation, and other unmeasured environmental factors may also influence species distribution.
When considered collectively, the species-specific associations suggest that culicine mosquito assemblages in Hormozgan Province are structured by complex environmental gradients rather than by a single physicochemical factor. Species belonging to the genus Culex demonstrated different relationships with ionic characteristics, indicating variation in ecological tolerance and habitat utilization strategies. For example, the positive associations observed for Cx. theileri with chloride, sodium, and magnesium suggest its frequent occurrence in habitats with relatively higher ionic concentrations, whereas the negative associations observed for Cs. longiareolata indicate that this dominant species may respond differently to variations in water chemistry.
Although these associations provide ecological indicators of habitat characteristics, they should not be interpreted as direct physiological responses because the observational design of the study does not allow causal inference. Nevertheless, integrating multiple environmental indicators provides a more comprehensive understanding of mosquito habitat suitability and can support targeted environmental surveillance within a One Health framework.
The observed relationships are consistent with previous studies emphasizing the importance of water chemistry in shaping mosquito larval ecology. Avramov et al. highlighted the influence of water-quality characteristics on mosquito occurrence and abundance across different habitat types [27]. Likewise, Sivabalakrishnan et al. demonstrated that mosquito species may differ in their responses to saline environments and ionic composition [41]. Taken together, these studies support the view that physicochemical conditions can influence habitat suitability, although species-specific responses may vary across geographic regions and ecological settings.
4.1. Study novelty and one health implications
A major contribution of the present study is the integration of detailed physicochemical characterization of larval habitats with culicine mosquito assemblage data across multiple ecological settings in southern Iran. Although previous investigations in Iran have primarily focused on mosquito species inventories, geographical distribution, or general habitat descriptions, few have simultaneously examined a broad spectrum of hydrochemical variables together with species-specific larval occurrence across diverse ecological zones [27]. By combining ecological observations with quantitative measurements of water quality, including electrical conductivity, total dissolved solids, total suspended solids, and major ionic constituents, this study provides a more comprehensive understanding of environmental factors associated with mosquito breeding habitats in an arid coastal ecosystem.
Beyond its regional relevance, the study contributes to the growing recognition that environmental surveillance represents a fundamental component of the One Health approach to vector-borne disease prevention. Mosquito populations develop at the interface of environmental, animal, and human health, and changes in aquatic habitat quality can influence vector abundance long before human or animal infections become apparent [39]. The identification of physicochemical characteristics associated with dominant vector species therefore offers practical environmental indicators that may support proactive surveillance and targeted larval source management. Such environmentally informed surveillance complements conventional entomological and epidemiological monitoring and aligns with the integrated vector management principles recommended by the World Health Organization [38].
The ecological characteristics of Hormozgan Province further emphasize the relevance of these findings within a One Health framework. The province includes extensive coastal ecosystems, active maritime transportation routes, rapid urban expansion, livestock production systems, and migratory bird pathways, creating interacting environmental and anthropogenic conditions that may influence mosquito ecology and the circulation of mosquito-borne pathogens. Therefore, integrating environmental surveillance of larval habitats with human and veterinary health monitoring may strengthen early warning systems and support targeted vector control strategies. Although this study does not establish causal relationships between environmental variables and disease transmission, it provides ecological evidence that can assist risk assessment and prioritization of surveillance activities [23].
From an applied perspective, the present findings suggest that physicochemical monitoring of larval habitats could complement conventional mosquito surveillance by identifying environmental conditions associated with increased suitability for medically important culicine species. Artificial stagnant water bodies exhibiting favorable physicochemical characteristics represent practical targets for integrated larval source management because they are often accessible for environmental modification. Within a One Health framework, integrating ecological surveillance with human and veterinary disease surveillance may strengthen early warning systems for mosquito-borne diseases, particularly in regions undergoing rapid environmental and climatic change. Although the present study does not establish causal relationships between environmental conditions and disease transmission, it provides ecological evidence that can support risk assessment and prioritization of surveillance and vector control activities [22], [38], [39]. Future investigations incorporating longitudinal sampling, climatic variables, pathogen detection in mosquito populations, and multivariate ecological modeling would further improve understanding of environmental drivers of mosquito distribution and their implications for disease emergence [27], [37].
4.2. Study limitations and future perspectives
The main limitation of this study is that sampling was conducted during 2016–2017 and therefore does not represent recent temporal changes in mosquito populations. Environmental changes, including urban expansion, land-use modification, and climate variability, may influence mosquito distribution over time. Future longitudinal studies incorporating repeated sampling, climatic variables, and pathogen detection would provide additional insight into temporal changes in vector ecology.
Although distributional assumptions were evaluated before analysis, count-based ecological data may require specialized modeling approaches. Therefore, the observed correlations should be interpreted cautiously.
Because multiple environmental variables were examined simultaneously, the possibility of false-positive associations cannot be excluded. Therefore, the identified relationships should be considered hypothesis-generating and require confirmation through multivariate analyses and independent datasets.
The present findings should be interpreted as associations rather than causal ecological drivers. Future studies using multivariate ecological models and longitudinal sampling would improve understanding of community-level responses to environmental gradients.
The present study integrated species-specific associations with multiple physicochemical parameters to provide a broader ecological interpretation of mosquito habitat characteristics. However, because the available dataset was based on aggregated observations rather than a complete habitat-by-species environmental matrix, advanced community-level ordination methods requiring site-level data could not be applied retrospectively. Future studies designed with repeated habitat-level measurements and standardized environmental sampling will allow application of multivariate ecological models to further clarify community-level responses.
5. Conclusion
This study provides baseline ecological information on culicine mosquito larval habitats and their associated physicochemical characteristics in Hormozgan Province, southern Iran. The results demonstrated that artificial stagnant habitats with specific environmental conditions, including moderate temperature, near-neutral pH, and particular water quality characteristics, supported diverse mosquito assemblages. Cs. longiareolata, Cx. pipiens, and Cx. quinquefasciatus were the dominant species, highlighting their ecological importance in the region.
Although the field surveys were conducted during 2016–2017, the dataset provides an important ecological baseline for understanding mosquito larval habitat characteristics in Hormozgan Province. Such baseline information is valuable for evaluating future changes associated with urban expansion, environmental modification, and climate variability [21].
Within a One Health framework, integrating environmental habitat assessment with mosquito surveillance may improve identification of productive breeding sites and support targeted larval source management strategies. Future studies incorporating long-term monitoring, climatic variables, and advanced ecological modeling are recommended to further clarify factors influencing mosquito distribution.
CRediT authorship contribution statement
Seyed Aghil Jaberhashemi: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Conceptualization. Aboozar Soltani: Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Funding acquisition, Formal analysis, Conceptualization. Shahyad Azari-Hamidian: Writing – review & editing, Visualization, Validation, Methodology, Investigation, Formal analysis. Kourosh Azizi: Writing – review & editing, Software, Methodology, Investigation. Hedayat Dorzaban: Visualization, Methodology, Investigation. Mojtaba Norouzi: Software, Methodology, Investigation. Seyed Mohsen Mohebbi Nodez: Methodology, Investigation, Data curation.
Ethical approval and consent to participate
All study procedures were conducted in compliance with the Declaration of Helsinki and were approved by the Iran National Committee for Ethics in Biomedical Research (Approval ID: IR.SUMS.REC. 1396.S129).
Declaration of generative AI and AI-assisted technologies in the writing process
During the preparation of this work the authors used ChatGPT in order to improve readability and language. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.
Funding
This research was supported by the Vice-chancellor for Research and Technology Affairs of Shiraz University of Medical Sciences (Grant number: 95-01-04-12469), Shiraz, Iran.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This article was developed from a thesis written by Seyed-Aghil Jaberhashemi for the degree of Master of Science at Shiraz University of Medical Sciences (Project No.: 95-01-04-12469). We thank the Vice Chancellor for Research Affairs of Shiraz University of Medical Sciences, as well as all individuals who contributed to the implementation of this project.
Data availability
All data supporting the findings of this study can be obtained from the corresponding authors upon reasonable request.
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Data Availability Statement
All data supporting the findings of this study can be obtained from the corresponding authors upon reasonable request.