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1
Review
Relationships between water quality and mosquito
presence and abundance: a systematic review and
meta-analysis
MarcAvramov1,2,3,, AbhinandThaivalappil4, AntoinetteLudwig2,, LaurenMiner1,
Catherine I.Cullingham1, LisaWaddell5, David R.Lapen3,*
1Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada, 2National Microbiology Laboratory
Branch, Public Health Agency of Canada, 3200 rue Sicotte, C.P. 5000, St. Hyacinthe, QC J2S 2M2, Canada, 3Ottawa Research
and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada, 4Department of
Population Medicine, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada, 5National Microbiology Laboratory
Branch, Public Health Agency of Canada, 370 Speedvale Avenue West, Guelph, ON N1H 7M7, Canada *Corresponding author, mail:
david.lapen@agr.gc.ca
Subject Editor: KristenHealy
Received on 27 April 2023; revised on 31 August 2023; accepted on 18 September 2023
Mosquito-borne diseases (MBDs) are emerging in response to climate and land use changes. As mosquito
(Diptera: Culicidae) habitat selection is often contingent on water availability for egg and larval development,
studies have recognized water quality also influences larval habitats. However, underlying species-, genera-,
and mosquito level preferences for water quality conditions are varied. This systematic review and meta-
analysis aimed to identify, characterize, appraise, and synthesize available global data on the relationships
between water quality and mosquito presence and abundance (MPA); with the goal to further our under-
standing of the geographic expansion of MBD risks. A systematic review was conducted to identify studies
investigating the relationships between water quality properties and MPA. Where appropriate, random-effects
meta-analyses were conducted to provide pooled estimates for the association between the most reported
water quality properties and MPA. The most reported water quality parameters were pH (87%), nitrogen
concentrations (56%), turbidity (56%), electrical conductivity (54%), dissolved oxygen (43%), phosphorus
concentrations (30%), and alkalinity (10%). Overall, pH (P = 0.05), turbidity (P < 0.0001), electrical conductivity
(P = 0.005), dissolved oxygen (P < 0.0001), nitrogen (P < 0.0001), and phosphorus (P < 0.0001) showed signifi-
cantly positive pooled correlations with MPA, while alkalinity showed a nonsignificant null pooled correlation
(P = 0.85). We observed high heterogeneity in most meta-analyses, and climate zonation was shown to influ-
ence the pooled estimates. Linkages between MPA and water quality properties will enhance our capacity to
predict MBD risks under changing environmental and land use changes.
Key words: mosquito, climate, water quality, systematic review, meta-analysis
© The Author(s) 2023. Published by Oxford University Press on behalf of Entomological Society of America.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.
org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or
transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Journal of Medical Entomology, 61(1), 2024, 1–33
https://doi.org/10.1093/jme/tjad139
Advance Access Publication Date: 13 October 2023
Review
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2Journal of Medical Entomology, 2024, Vol. 61, No. 1
Graphical Abstract
Introduction
Mosquito-borne diseases (MBDs), such as malaria, dengue,
chikungunya, and yellow fever, are the leading cause of approx-
imately 1 million deaths and over 700 million infections attributed
to vector-borne diseases annually (World Health Organisation 2020).
High MBD prevalence is most common in tropical and arid regions;
however, climate change is broadening the geographic distribution of
MBD globally (Reiter 2001, Parkinson et al. 2014, Villeneuve et al.
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3Journal of Medical Entomology, 2024, Vol. 61, No. 1
2021). In addition to climate factors, land use is a major contributor
to the dispersal of mosquito arboviruses (Norris 2004). Importantly,
poorly managed urban and agricultural water management systems
can increase mosquito (Diptera: Culicidae) presence and abundance
(MPA) by providing prolonged access to stagnant water, which is
a primary condition for the oviposition of most female mosquitoes
(Clements 1992, Leisnham et al. 2004, Dale et al. 2007, Gardner et al.
2013). Some species such as Aedes albopictus and Aedes aegypti, have
adapted to ovipositing and immature maturation (OIM) in surface
waters found in urban environments (Ferraguti et al. 2016, Loaiza
et al. 2019, Wilke et al. 2019, Perrin et al. 2022); consequentially
increasing the risk of MBD transmission in more densely populated
areas (Gardner et al. 2014, Madzokere et al. 2020). Thus, the iden-
tication of (OIM) sites conducive to mosquito propagation remains
pivotal in predicting mosquito expansion and associated disease risks.
While the availability of OIM habitats have a direct link to MBD
transmission, the water quality of these habitats is an important de-
terminant of mosquito development and survival (Clements 1992,
Gardner et al. 2014, Neff and Dharmarajan 2021). For example,
mosquito larvae and pupae are most successful around a neutral pH
(Clark et al. 2004, Okogun et al. 2005, Afolabi et al. 2019, Medeiros-
Sousa et al. 2020), with lower survivorship outside pH levels of 6–8
(Ukubuiwe et al. 2020). Furthermore, turbidity has been positively as-
sociated with MPA (Muturi et al. 2008, Mbuya et al. 2014, Alkhayat
et al. 2020, Villarreal-Treviño et al. 2020), although water clarity
necessities are more pronounced in articial OIM sources (Juliano et
al. 2004). In addition, ion content has been shown to inuence the
rate of larvae and pupae growth (Ukubuiwe et al. 2020, Mamai et al.
2021). Likewise, dissolved oxygen has been categorized as vital for
MPA in eutrophic OIM environments (Yamada et al. 2020), while
larvae survival remains negatively impacted by reduced dissolved
oxygen regardless of atmospheric oxygen availability (Reiter 1978,
Silberbush et al. 2015). Eutrophication from excess nutrients has not
deterred productive mosquito OIM, as nutrient-rich habitats have
been shown to favor mosquito development (Leisnham et al. 2004,
Darriet and Corbel 2008, Nikookar et al. 2017, Carvajal-Lago et al.
2021). Other water quality determinants, such as metal and sulfate
concentrations, have also been associated with increased MPA (Rao
et al. 2011, Nikookar et al. 2017, Djamouko-Djonkam et al. 2019,
Neff and Dharmarajan 2021).
The magnitude and direction of relationships between water
quality properties and MPA vary across studies (e.g., Ranjeeta et
al. 2008, Soumendranath et al. 2015, Alam et al. 2018, Aklilu et
al. 2020), suggesting MPA is inuenced by other context dependent
determinants (Leisnham et al. 2005, Mukhtar et al. 2006, Nikookar
et al. 2017). Water quality-mosquito relationships may be species-
dependent, adding to the complexities of predicting MPA broadly
(Mercer et al. 2005, Burroni et al. 2013, Gardner et al. 2013, Abai et
al. 2016, Cepeda-Palacios et al. 2017). The impact of water quality
on MBD persistence has been acknowledged in recent years (Yee
et al. 2019, Nagy et al. 2021, Neff and Dharmarajan 2021, Fazeli-
Dinan et al. 2022, Kinga et al. 2022); however, the extent of varia-
tion in the magnitude and direction of its effects on mosquitoes at
the species-, genus-, and family-level is less clear. Therefore, there is
a need to investigate water quality determinants for MPA to increase
the delity of MBD risk assessments.
Here, we conducted a systematic review and meta-analysis to ap-
praise, identify, characterize, and quantitatively synthesize currently
available studies across the globe, that investigate relationships be-
tween water quality and MPA; with the aim to further our under-
standing of the geographic expansion of MBD risks. Our primary
objectives were to determine the impact of water quality on MPA
and the degree to which it inuences MPA. Results from this review
will help to classify water quality properties that are linked to MPA,
identify research gaps, improve modeling approaches for MBD risk
assessments, and contribute to information that can be conveyed to
the public on OIM hotspots in urban and rural environments.
Materials and Methods
Questions and Expectations
The following 2 questions have driven this systematic review
and meta-analysis: (i) What is the evidence linking water quality
properties to MPA? (ii) To what extent do water quality properties
inuence MPA?
We expected that pH, alkalinity, turbidity, electrical conductivity,
dissolved oxygen, nitrogen, and phosphorus would be the most re-
ported properties in studies investigating linkages between water
quality and MPA. These expectations were based on previous reports
establishing the inuences of these water quality properties on the life
history traits of mosquitoes, compared to other properties that have
been less recognized to impact MPA. Therefore, we anticipated that
these 7 water quality properties would be sufciently reported on for
purposes of meta-analyses. Given the ecological plasticity of various
mosquito species, we expected that the magnitude and direction of the
effects would be dependent on species and the type of available habitat.
We hypothesized that pooled effects of most water quality properties
would be inuenced by the regional climate of the study settings, which
is in line with studies investigating the adaptations of vector species in
anthropized landscapes that have shown poor water quality regimes
in urban and agricultural settings play a role in the spread of MBDs
(Reiter 2001, Norris 2004, Brugueras et al. 2020, Perrin et al. 2022).
Review Approach and Search Strategy
This research was conducted using standard systematic review and
meta-analysis methodologies (Young et al. 2014, Higgins et al.
2019), and applying the Preferred Reporting Items for Systematic
Reviews and Meta-Analysis (PRISMA) statement (Moher 2009). A
comprehensive search strategy was developed and tested through an
iterative process with the assistance of a trained librarian. A list of
search terms was used for the following categories: mosquito genera
(e.g., Aedes, Anopheles, Culex), exposure (e.g., water quality, tur-
bidity, dissolved oxygen), and outcome (e.g., abundance, presence,
larval density). This was applied to search for relevant articles in the
following databases on 2 August 2022: Zoological Records, Scopus,
CAB Direct, and Biological Abstracts. Search results were veried
by screening references from 15 handpicked articles to ensure that
relevant articles were successfully captured by the search strategy
and to include potentially missed studies. Databases were searched
without date or language restrictions. Search documentation and
full search algorithms used for each database are included in Supp.
Dataset S1. Duplicates were identied and removed using EndNote
(version 20; Clarivate, Philadelphia, United States). Reviewing was
conducted using the online systematic review management software
DistillerSR (Evidence Partners, Ottawa, Canada). All steps of the
systematic review were conducted using pre-tested forms by 2 inde-
pendent reviewers. The relevance screening form was pre-tested on
50 abstracts until a kappa agreement of ≥0.80 was reached, while
the other forms were each pre-tested on 3–5 articles to ensure both
reviewers interpreted items clearly and consistently.
Eligibility Criteria
Studies selected for inclusion in the review were primary peer-
reviewed quantitative research studies investigating MPA and water
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4Journal of Medical Entomology, 2024, Vol. 61, No. 1
quality conditions of the reported habitats published in English,
French, or Spanish in nonpredatory journals as per Beall’s List of po-
tential predatory journals and publishers. All mosquito species and
water quality properties were considered for inclusion. Outcomes of
interest were the abundance and presence of mosquitoes in any stage
of their life cycle (i.e., eggs, early/late instar larval stage, pupal stage,
and adult stage). Any primary studies not directly reporting on water
quality and MPA were excluded.
Relevance Screening and Risk of Bias Assessment
The titles and abstracts of citations identied during the search were
assessed for their relevance using a structured screening form shown
in Supplementary Text S2. Full texts of relevant references were
obtained and then conrmed for relevance (Supplementary Text S2).
Relevant studies that met all eligibility criteria underwent a risk of
bias assessment to assess the internal validity of the studies and eval-
uate various biases (e.g., selection bias, detection bias, reporting bias,
and confounding bias). An overall risk of bias of low, unclear, or high
was determined for each outcome using a structured form shown
in Supplementary Text S2. The risk of bias tool was developed and
modied using previously established risk of bias tools for obser-
vational studies (Sterne et al. 2016, Higgins et al. 2019). The form
contained 7 criteria that evaluated various biases (e.g., selection
bias, confounding bias) and allowed multiple outcome assessments
per study. In this review, overall unclear or high risk of bias was
attributed to studies with unclear, unexplained, or insufcient in-
formation for the following elements: (i) quality of outcome and ex-
posure measurement methodology, (ii) whether sites were selected in
a way that makes them comparable across groups and/or unlikely
to inuence the outcome, (iii) possible inuences of confounding
elements on outcomes measured, (iv) complete assessment of all in-
tended outcomes by authors, or (v) reported exclusions from nal
analyses. All relevance and risk of bias assessments were done by
2 independent reviewers, and conicts were resolved by discussing
until a consensus was met.
Data Extraction and Effect Size Conversion
A data characterization form (Supplementary Text S2) was used to
extract the study characteristics from relevant articles including pub-
lication year, language, study design, location, climate, land cover,
mosquito species, water quality properties, data collection methods
(e.g., instruments used, sampling frequency), natural habitat drivers,
and anthropogenic drivers. Studies sufciently reporting outcome
measurements for meta-analysis underwent an additional data ex-
traction to collect association measures between water quality and
MPA (Supplementary Text S2). Relevant data included correlation
outcomes, continuous outcomes (e.g., mean differences), dichot-
omous outcomes (e.g., odds ratios), and contingency tables. Data
were descriptively summarized using the following elements: mos-
quito species, stage of the life cycle, and water quality property for
which the outcomes were measured.
Since the effect size metrics used in different studies were not
consistent, it was not feasible to compare them directly. Therefore,
odds ratios and continuous data were converted to standardized
mean differences (Cohen’s d), where odds ratios were rst collapsed
into 2 categories while ensuring the same direction of effect was
used across effect sizes. We then utilized conversion methods from
Borenstein et al. (2009) to derive a common metric of effect size, the
Pearson coefcient r. Studies that reported Spearman and Kendall
correlation coefcients were converted to Pearson r coefcients
using previously established methods (Gilpin 1993). To satisfy the
conditions of meta-analytical tests, such as the normality of effect
size distribution, we applied Fisher’s z transformation (Cooper et al.
2019). After conducting the analyses, the Fisher z estimate means
were back-transformed to r means for interpretation purposes.
Meta-analyses and Potential Publication Bias
Although all water quality properties were eligible for inclusion
in our review, we only synthesized those reporting sufcient data
for meta-analyses. To draw meaningful conclusions and reduce the
likelihood of spurious pooled estimates, we set a conservative min-
imum of >25 data points to be eligible for meta-analysis. This also
ensured that analyses were performed with sufcient mosquito spe-
cies to elucidate relationships at the mosquito level. We conducted
a random-effects meta-analysis to estimate the overall mean of the
distribution of effect sizes of the relationships between each of the
most reported water quality properties and MPA. Estimates of het-
erogeneity in effect sizes between studies were measured by I2, which
measures the portion of the variance in effect sizes that is unrelated
to sampling error, and the impact of heterogeneity was estimated
using 95% prediction intervals (95% PI). As variability in effect size
was expected, thus the random-effects meta-analysis incorporates
variability between studies by assuming the true effect size is a dis-
tribution. The variance of each effect size was calculated using 1/
(n − 3) (Borenstein et al. 2009), where n was the determined sample
size of each effect size based on the number of mosquito sampling
sites and/or the duration of sampling. To determine if overall mean
effect sizes varied between climates, we further analyzed effect sizes
of each of the most reported water quality properties by stratifying
the effect sizes per climatic subgroups based on its Köppen classi-
cation (i.e., arid, tropical, temperate, and continental zones) when >1
effect sizes were present within each subgroup (Sterne et al. 2011).
For each analysis, we reported the following: pooled correlation (r),
95% condence intervals (95% CI), P value, number of effect sizes
(n), median sample size (Mdn), I2, and 95% PI.
To evaluate the possibility of publication bias, we conducted
an Egger’s regression to test the symmetry of the funnel plots for
each of the meta-analyses (Egger et al. 1997, Rothstein et al. 2005,
). Publication bias can occur when the decision to publish a study
is inuenced by its results, which can lead to overestimation or
underestimation of the true effect size (Rothstein et al. 2005). By
examining the results of Egger’s regressions for each meta-analysis,
we determined if there was an asymmetry that indicated poten-
tial publication bias. In line with the suggestion of Nakagawa and
Santos (Nakagawa and Santos 2012) for biological meta-analyses,
we also performed a trim-and-ll analysis (Duval and Tweedie 2000)
to address the potential nonindependence issue that can arise when
multiple effect sizes are derived from the same study or species.
All analyses were performed using the metafor (Viechtbauer 2010)
packages available in R software (version 4.2.1; R Core Team, 2022).
Results
Study Characteristics and Risk of Bias Assessment
Our comprehensive search strategy identied 4058 unique
references; from these, 79 relevant articles met the inclusion criteria
(Fig. 1). Most were cross-sectional studies (n = 44), followed by lon-
gitudinal (n = 37), controlled before-and-after experimental designs
(n = 2), and a case-control study (n = 1). All germane articles were
found to be published in English between 1986 and 2022 (Fig. 2).
Thirty-four of the included articles (n = 34, 43%) were based in
Africa; followed by studies set in tropical and arid regions of Asia
(n = 30, 38%); and a small proportion was set in warm, mild, and
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5Journal of Medical Entomology, 2024, Vol. 61, No. 1
continental regions of North America (n = 7, 9%). The rest were set
in South America (n = 5, 6%), Australia (n = 2, 3%), and Europe (n
= 1, 1%). The most reported mosquito genera were Anopheles (n =
52, 66%), Culex (n = 33, 42%), and Aedes (n = 28, 35%) (Table 1).
Mosquito abundance was reported in most studies (n = 67, 85%)
compared to mosquito presence/absence, which was investigated less
frequently (n = 27, 34%). Most studies reported only on outcomes
regarding the immature life stage of mosquitoes (n = 77, 97%), while
only 2 studies investigated adults (n = 2, 3%). Some studies made
multiple measurements of the same outcome due to periodic sam-
pling. The most reported water quality properties were pH (n = 69,
87%), nitrogen (n = 52, 56%), turbidity (n = 52, 56%), electrical
conductivity (n = 43, 54%), dissolved oxygen (n = 34, 43%), phos-
phorus concentrations (n = 24, 30%), and alkalinity (n = 8, 10%).
These 7 properties were the only ones sufciently reported for meta-
analyses (>25 effect sizes). Reported nitrogen concentrations include
ammonium (NH4), ammonia (NH3), nitrate (NO3), nitrite (NO2),
and total nitrogen (TN), while reported phosphorus concentrations
include total phosphorus (TP), dissolved phosphorus (DP), phos-
phate (PO4
3-) and organophosphate. Other water quality properties
were less frequently reported, such as biochemical oxygen demand
(BOD), chemical oxygen demand (COD), total organic carbon
(TOC), oxidation-reduction potential (ORP), sulfate, and metal
concentrations (e.g., copper, iron, magnesium, and calcium). Some
Fig. 1. PRISMA flow (Moher et al. 2009) of references through the systematic review process of studies captured by the keyword strategy (see Supplementary
Dataset S1).
Fig. 2. Distribution of publications, including (counts and study designs), of the 79 included primary research publications relevant to relationships between
water quality and MPA.
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6Journal of Medical Entomology, 2024, Vol. 61, No. 1
Table 1. General characteristics of the 79 included primary research publications
Category No. of studies (n) %
Continent
Africa 34 43
Asia 30 38
North America 7 9
South America 5 6
Oceania 2 3
Europe 1 1
Mosquito generaa
Anopheles 52 66
Culex 33 42
Aedes 28 35
Ochlerotatus 5 6
Culiseta 4 5
Otherb19 24
Outcomesa
Mosquito abundance 67 85
Mosquito presence/absence 27 34
Mosquito life stage
Immature 77 97
Adult 2 3
Water quality propertiesa
pH 69 87
Nitrogenc52 56
Turbidity 52 56
Conductivity 43 54
Dissolved oxygen (DO) 34 43
Phosphorusd24 30
Othere39 49
Type of mosquito samplinga
Larval dips 57 72
Pipetting 7 9
Centre for Disease Control (CDC) Miniature Light trap 2 3
Otherf16 20
Not reported 8 10
Type of water quality testinga
In situ sonde measurement 53 67
Chemical testing 48 60
Physical testing 43 54
Biotopea
Rural 50 63
Urban 32 40
Natural 28 35
Not reported/ Not applicable 2 3
Land covera g
Articial surfaces (including urban and associated areas) 41 52
Inland water bodies 33 42
Shrub/herbaceous vegetation, aquatic or regularly ooded area 18 23
Tree-covered areas 10 13
Herbaceous crops 9 11
Multiple/layered crops 6 8
Coastal water bodies and intertidal areas 6 8
Sparsely natural vegetated areas 5 6
Grassland 4 5
Terrestrial barren land 3 4
Shrub-covered areas 3 4
Mangroves 2 3
Woody crops 1 1
Not reported/ Not applicable 6 8
Climate (Köppen classication)a
Equatorial (tropical) zone 34 43
Arid (dry) zone 27 34
Warm/mild temperate zone 14 18
Continental zone 3 4
Not reported/ Not applicable 1 1
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7Journal of Medical Entomology, 2024, Vol. 61, No. 1
of their reported relationships with MPA are summarized in Table 2.
The most common generalized habitat environments in the included
articles were rural regions (n = 50, 63%), followed by urban areas (n =
32, 40%), and natural landscapes (n = 28, 28%). The 2 experimental
studies did not report biotopes as the study areas were articially
designed. As most studies were conducted in Africa and Asia, most
were equatorial (n = 34, 43%) and arid (n = 27, 34%), followed by
temperate climate zonations (n = 14, 18%). Many studies reported
the specic land covers of their study areas, and most frequently re-
ported, as dened by the Food and Agriculture Organization of the
United Nations (Food and Agriculture Organization of the United
Nations 2000), articial surfaces (n = 41, 52%), inland water bodies
(n = 33, 42%), and vegetated, aquatic, and regularly ooded areas
(n = 18, 23%). Numerous natural and anthropogenic drivers were
reported across studies. Specically, drivers included consideration
of air/water temperature (n = 58, 73%); qualitatively typied water
containment systems housing mosquito communities (n = 47, 59%);
vegetation diversity/density (n = 44, 56%); water depth (n = 39,
49%); variables related to shade/sunlight exposure of habitats and
potential habitats (n = 38, 48%); and amount of precipitation during
the study period (n = 30, 38%) (Table 1). Anthropogenic effects were
less frequently reported (vs. natural drivers) with most not discussing
any anthropogenic driver (n = 49, 62%). However, some did con-
sider the distance of OIM sites to the closest building in the area
(n = 23, 29%). Other drivers, although rarely reported (n = 10,
13%), included point source pollution, sediment runoff, livestock,
farm waste, human-made oods, and chemical treatments. A full list
of study characteristics is available in Supplementary Dataset S1.
A summary risk of bias assessment is shown in Table 3. Most
studies had an overall high risk of bias (n = 48, 61%) and these
deciencies were commonly attributed to possible confounders (i.e.,
insufcient control for external elements affecting MPA). Several
studies also had an unclear risk of bias rating (n = 27, 34%), due
mainly to insufcient reporting of water sampling methods, and
possible selection bias in the site and sample selection. Only a few
studies (n = 8, 10%) received an overall low risk of bias rating.
Details on the risk of bias ratings and relevant outcomes for each
study are available in Supplementary Dataset S1.
Effects of pH and Alkalinity on MPA
There was a signicant positive pooled correlation of pH on MPA
(r = 0.10, 95% CI: 0–0.20, P = 0.05, n = 132) (Fig. 3) with a me-
dian number of sampling sites of 56 (Mdn = 56). We found sub-
stantial heterogeneity between studies not caused by sampling error
(I2 = 97%) and a large prediction interval (95% PI: −0.73 to 0.92).
Species-specic correlations (Fig. 3) within the Aedes genus revealed
signicant positive correlations for 2 species with pH (Ae. aegypti
and Ae. albopictus), while 1 species exhibited a signicant negative
correlation (Ae. camptorhynchus). Correlations observed within the
Category No. of studies (n) %
Natural drivers (confounding factors)a
Air/water temperature 58 73
Water-holding containment types 47 59
Vegetation (diversity and/or density) 44 56
Water depth 39 49
Sunlight/shade exposure 38 48
Precipitation 30 38
Size of water surface 25 32
Dissolved solids 25 32
Algae presence 23 29
Salinity 21 27
Water source elevation 18 23
Water velocity 19 24
Detritus content 16 20
Humidity 16 20
Substrate type 13 16
Bacterial abundance 9 11
Ion content 7 9
Otherh27 34
Not reported/ Not applicable 0 0
Anthropogenic drivers (confounding factors)a
Distance to buildings 23 29
Point source pollution 3 4
Sediment runoff 1 1
Otheri6 8
Not reported/ Not applicable 49 62
aTotal numbers may exceed 79 when more than 1 option has been selected within a category.
bOther lesser reported mosquito genera including Mansonia, Armigeres, Lutzia, and Toxorhynchites.
cIncludes all forms of nitrogen such as ammonium, ammonia, nitrate, nitrite, and total nitrogen.
dIncludes all forms of phosphorous such as total and dissolved phosphorus, phosphate, and organophosphate.
eIncludes all other properties such as alkalinity, BOD, COD, ORP, sulfate, metal concentration, etc.
fIncludes all other forms of sampling such as area samples, nets, ladles, and hand collections.
gLand cover categories dened by the Food and Agriculture Organization of the United Nations (FAO).
hIncludes all other natural drivers such as altitude, wind speed, habitat permanence, and suspended solids.
iIncludes all other anthropogenic drivers such as livestock, farm waste, man-made oods, and chemical treatments.
Table 1. Continued
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8Journal of Medical Entomology, 2024, Vol. 61, No. 1
Table 2. Summary of outcomes of the 79 included primary research publications relevant to relationships between water quality and mosquito presence/abundance
Reference
Country
(Climate)a
Sampling
year(s)b
Water source
typecBiotopedOutcome Water quality propertyeMain ndings
Abai et al. (2016) Iran (Arid/Dry,
Temperate)
2008 -
2009
Ponds
Streams
Natural Abundance Conductivity
Turbidity
Nitrate
Nitrite
Phosphate
Metals
Other
Water quality parameters did not show any signicant differences among dif-
ferent mosquito species. There was no signicant correlation between the
abundance of larvae and any of the water quality parameters.
Abdel-Meguid
(2022)
Egypt (Arid/
Dry)
2020 Articial
Ponds
Urban
Rural
Abundance pH
Conductivity
Turbidity
Nitrate
Phosphate
Other
The density of Cx. pipiens s.l. L. was signicantly negatively correlated with pH,
turbidity, phosphates, sulfates, and nitrates, while there was no signicant cor-
relation between larval density and conductivity.
Aklilu et al. (2020) Ethiopia (Arid/
Dry)
2012–
2013
Ponds
Streams
Puddles
Rural Abundance pH
Turbidity
An. pretoriensis Theobald larval density was signicantly negatively associated
with turbid habitats.
Alam et al. (2018) Bangladesh
(Tropical)
2011–
2012
Articial
Ponds
Streams
Rural
Natural
Abundance pH
Turbidity
The abundance of An. peditaeniatus Leicester decreased signicantly with
increased pH. The abundance of An. vagus increased with a rise in pH.
Alkhayat et al.
(2020)
Quatar (Arid/
Dry)
2015–
2016
Articial
Ditches
Urban
Rural
Natural
Abundance
Presence/
absence
pH
Turbidity
DO
Cx. pipiens s.l. was positively associated with turbidity and pH.
Cx. quinquefasciatusSay was negatively associated with dissolved oxygen.
Bashar et al.
(2016)
Bangladesh
(Tropical)
2012 Articial
Ponds
Urban
Natural
Abundance DO
pH
Turbidity
Ammonia
Other
Dissolved oxygen is found to be one of the main predictors for the abundance
of all species’ larvae (except Ae. aegypti L.). Some associations were found
between the abundance of Culex spp. and chemical oxygen demand.
Burroni et al.
(2013)
Argentina
(Temperate)
2002 Ponds Natural Abundance Turbidity There was a signicant negative relationship between the abundance of Oc.
albifasciatus Macquart and turbidity.
Carver et al.
(2011)
Australia
(Temperate)
2009 Saltmarshes Urban
Natural
Abundance DO
pH
Conductivity
Turbidity
Nitrate
Nitrite
Total phosphorus
Total nitrogen
Metals
Other
During dry season, Ae. camptorhynchus Thomson mosquito abundance was sig-
nicantly negatively associated with pH, turbidity and dissolved magnesium
content. No other water quality parameter showed signicant association
with the specie’s abundance.
Cepeda-Palacios et
al. (2017)
Mexico (Arid/
Dry)
2015–
2016
Articial Rural
Urban
Rural
Abundance
Presence/
absence
DO
pH
Turbidity
Water samples from troughs (peridomestic water containers) where Ae. aegypti
larvae were present had signicantly greater turbidity and DO compared to
samples without the presence of Ae. aegypti larvae.
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9Journal of Medical Entomology, 2024, Vol. 61, No. 1
Reference
Country
(Climate)a
Sampling
year(s)b
Water source
typecBiotopedOutcome Water quality propertyeMain ndings
Chaiphongpachara
et al. (2018)
Thailand
(Tropical)
2016 Ponds
Streams
Rivers
Rural
Natural
Abundance DO
pH
Turbidity
Abundance of 2 malaria vector species An. subpictus Grassi and An. barbirostris
s.l. van der Wulpwas found to be signicantly positively associated to DO
and signicantly negatively associated with pH. Turbidity had no signicant
associations.
Chirebvu and
Chimbari (2015)
Botswana
(Arid/Dry)
2013 Ponds
Streams
Rural Abundance DO
pH
Conductivity
Turbidity
Abundance of larvae was signicantly negatively correlated to conductivity, and
positively associated with turbidity. There was no statistically signicant cor-
relation between abundance and DO/pH.
David et al. (2021) Brazil (Trop-
ical)
2010 Articial Urban Abundance pH
Conductivity
DOC
Total phosphorus Total nitrogen
Higher levels of dissolved organic carbon was the best predictor for the abun-
dance of Ae. albopictus Skuse.
Djamouko-
Djonkam et al.
(2019)
Cameroon
(Tropical)
2017–
2018
Articial Urban Presence/
absence
pH
Conductivity
Turbidity
Organophosphate
Metals
Other
Conductivity, turbidity, and organophosphate were found to be at signicantly
higher levels in Anopheles Meigen positive samples compared to negative
samples.
El-Naggar et al.
(2013)
Egypt (Arid/
Dry)
2012 Articial Urban Abundance pH
Turbidity
Cx. pipiens s.l.,Cx. perexiguus Theobald, and Cx. antennatus Becker
abundances were signicantly positively correlated with turbidity. Cx.
perexiguus, Cx. antennatus, Cx. pusillus Macquart, and Oc. detritus Haliday
abundances were signicantly positively associated with pH.
Emidi et al. (2017) Tanzania
(Tropical)
2015–
2016
Articial
Ponds
Streams
Puddles
Rural Abundance
Presence/
absence
pH
Conductivity
Upper percentiles of conductivity (OR) was signicantly associated with the
presence and the observed increase of abundance of Anopheles mosquitoes.
Fillinger et al.
(2009)
Gambia (Arid/
Dry)
2005 Ponds
Rice paddies
Puddles
Rivers
Natural Abundance
Presence/
absence
DO
pH
Conductivity
Turbidity
Case-control study: An. gambiae s.l. Giles larval density was found to be higher
in areas where conductivity level was over 2,000 μS/cm. (statistically signi-
cant)
Gadiaga et al.
(2011)
Senegal (Arid/
Dry)
2007–
2010
Articial
Puddles
Ponds
Rivers
Marshes
Lake
Urban
Rural
Abundance
Presence/
absence
pH
Conductivity
Turbidity
Water pH >= 8.0 was signicantly positively associated with Anopheles larvae
presence and abundance.
Gadzama et al.
(2018)
Nigeria (Arid/
Dry)
Not re-
ported
Articial Urban Abundance
Presence/
absence
DO
pH
Conductivity
Turbidity
Nitrate
Nitrite
Ammonium
Phosphate
Metals
Turbidity, pH, conductivity, sulfate, calcium content, and magnesium content
were all signicantly positively associated to An. gambiae s.l. presence.
Table 2. Continued
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10 Journal of Medical Entomology, 2024, Vol. 61, No. 1
Reference
Country
(Climate)a
Sampling
year(s)b
Water source
typecBiotopedOutcome Water quality propertyeMain ndings
Gardner et al.
(2013)
U.S.A. (Conti-
nental)
2009 Articial Urban Abundance pH
Ammonia
Nitrate
Phosphate
Ammonia and nitrate were signicantly positively associated to larval abun-
dance, whereas pH was negatively associated to larval abundance.
Getachew et al.
(2020)
Ethiopia (Arid/
Dry)
2014–2016 Puddles
Streams
Swamps
Rural Abundance pH
Turbidity
No statistically signicant associations between Anopheles larval abundance and
WQP.
Ghosh et al.
(2020)
India (Trop-
ical)
2017–2018 Articial
Puddles
Swamps
Urban
Rural
Abundance DO
pH
Conductivity, Ammonia
Nitrate
Other
Positive correlations were found between all WQP and the density of Cx.
vishnui Theobald and Ae. albopictus, except for chemical oxygen demand and
alkalinity. Hardness showed a positive correlation with An. stephensi Liston
and Cx. vishnui but showed a negative correlation with Ae. albopictus density.
Gouagna et al.
(2012)
Reunion Island
(Republic
of France)
(Tropical)
2010–2011 Articial
Puddles
Ponds
Streams
Natural Abundance
Presence/
absence
pH
Conductivity
Turbidity
Within all aquatic habitat sampled, turbidity was signicantly positively associ-
ated with An. arabiensis Patton larval presence. Conductivity was associated
with larval abundance.
Gowelo et al.
(2020)
Malawi (Trop-
ical)
2017–2018 Articial
Streams
Puddles
Rural Abundance pH
Turbidity
Turbidity was signicantly negatively associated with larval abundance.
Hafeez et al.
(2022)
Pakistan (Arid/
Dry)
Not re-
ported
Articial
Puddles
Ponds
Streams
Urban
Rural
Natural
Presence/
absence
DO
pH
Conductivity
Turbidity
The most remote sites had highest Ae. albopictus presence in conductivity
conditions of ≤1,000 µS/m and DO conditions of 2–5 mg/liter.
Hawaria et al.
(2020)
Ethiopia (Tem-
perate)
2017–2018 Articial Rural Abundance
Presence/
absence
Turbidity Turbidity was signicantly positively associated with larval abundance.
Imai and Panjaitan
(1990)
Indonesia
(Tropical)
1982 Saltmarshes
Lagoons
Ponds
Rural Abundance pH
Turbidity
Within the Anopheles group, turbidity was responsible for the variation of hab-
itat preferences for each species (at a statistically signicant level).
Kindu et al. (2018) Ethiopia (Arid/
Dry)
2011–2012 Articial
Puddles
Ponds
Streams
Rural Abundance
Presence/
absence
pH
Turbidity
Using multiple regression analysis, An. gambiae s.l. abundance was signicantly
positively associated to low turbidity, and signicantly negatively associated
to pH.
Keno et al. (2022) Ethiopia
(Tropical)
2020 Articial
Puddles
Swamps
Rural Abundance DO
Conductivity
Turbidity
Anopheles abundance was signicantly negatively correlated to conductivity, but
no signicance was attributed to DO.
Laboudi et al.
(2012)
Morocco
(Arid/Dry)
2009 Swamps
Rivers
Rice paddies
Rural Abundance DO
pH
Conductivity
Turbidity
An. labranchiae Falleroni abundance was signicantly negatively associated to
pH and turbidity. No signicant ndings were found of the relationships of
abundance and DO/conductivity.
Table 2. Continued
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11Journal of Medical Entomology, 2024, Vol. 61, No. 1
Reference
Country
(Climate)a
Sampling
year(s)b
Water source
typecBiotopedOutcome Water quality propertyeMain ndings
Leisnham et al.
(2004)
New Zealand
(Temperate)
2003 Articial
Swamps
Urban
Rural
Natural
Abundance pH
Conductivity
DOC
Ammonia
Nitrate
Nitrite
Phosphate
Other
Study of relationships between environmental variables in various landuses and
mosquito abundance. Dissolved organic carbon was the only WQP that was
signicantly positively associated with mosquito abundance.
Liu et al. (2012) China (Tem-
perate)
2010 Articial Rural Abundance
Presence/
absence
pH
Ammonia
Other
The majority of An. sinensis Wiedemann larvae were found in chemical oxgyen
demand conditions of < 2 mg/liter, ammonia of < 0.4 mg/liter, and sulfate <
150 mg/liter.
Low et al. (2016) Ethiopia (Arid/
Dry)
Not
specied
Articial
Ponds
Streams
Natural Presence/
absence
pH There were some signicant positive associations between pH and mosquito
larvae abundance.
Ma et al. (2016) China (Tem-
perate)
2013 Rivers Urban Abundance DO
pH
Ammonium
Nitrate
Nitrite
Total phosphorus
Dissolved phosphorus
Total nitrogen
Other
In urban rivers, larval density was signicantly positively correlated to am-
monium, TP, and DP, whereas larval density was signicantly negatively
correlated to DO, pH, and nitrate.
Mala and Irungu
(2011)
Kenya (Arid/
Dry)
2005 Streams Rural Abundance pH
Conductivity
Turbidity
Total phosphorus
Total nitrogen
An. gambiae s.l. larvae density was signicantly positively correlated with tur-
bidity and TN, whereas there was a signicant negative correlation with pH.
Mala et al. (2011) Kenya (Arid/
Dry)
2008–2010 Articial
Ponds
Streams
Rural Abundance pH
Conductivity
Turbidity
There was a weak signicant positive correlation between mosquito abundance
and conductivity.
Mbuya et al.
(2014)
Kenya (Arid/
Dry)
2006 Natural swamps
vs. cow-
dung treated
swamps
N/A Abundance DO
pH
Conductivity
Turbidity
Ammonium
Nitrate
Phosphate
Controlled before-and after study: Both anopheline and culicine larvae abun-
dance was signicantly positively correlated to DO, conductivity, and tur-
bidity, whereas there was a signicant negative correlation with pH.
Table 2. Continued
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12 Journal of Medical Entomology, 2024, Vol. 61, No. 1
Reference
Country
(Climate)a
Sampling
year(s)b
Water source
typecBiotopedOutcome Water quality propertyeMain ndings
Mercer et al.
(2005)
U.S.A. (Conti-
nental)
2002–2003 Wetland Natural Abundance DO
pH
Conductivity
Turbidity
Ammonia
Nitrate
Nitrite
Phosphate
In 2002, mosquito abundance was signicantly positively correlated with nitrate
and phosphate. In 2003, mosquito abundance was signicantly positively
correlated with nitrate, phosphate, and turbidity.
Mukhtar et al.
(2006)
Pakistan (Arid/
Dry)
2001–2002 Articial Rural Abundance pH
Conductivity
Turbidity
Ammonium
Total phosphorus
Other
Anopheles and Culex abundance was signicantly negatively associated to con-
ductivity at levels over 1.5 dS/m, and also signicantly positively associated
to TP.
Muturi et al.
(2009)
Kenya (Arid/
Dry)
2006 Rice paddies Rural Abundance Turbidity Cx. quinquefasciatus signicantly positively associated to turbidity.
Muturi et al.
(2008)
Kenya (Arid/
Dry)
2006 Rice paddies Rural
Natural
Abundance DO
pH
Conductivity
Turbidity
An. arabiensis and Cx. quinquefasciatus were signicantly positively correlated
(weakly and strongly, respectively) to DO.
Mwangangi et al.
(2007)
Kenya (Arid/
Dry)
1999 Rice paddies Rural Abundance pH
Conductivity
Turbidity
No signicant associations were found between mosquito abundance and WQP.
Mwangangi et al.
(2010)
Kenya (Arid/
Dry)
2004–2005 Articial
Ponds
Swamps
Rural Abundance Turbidity Anopheles abundance was signicantly negatively associated to turbidity.
Nabar et al.
(2011)
India (Arid/
Dry)
Not
specied
Articial
Rice paddies
Urban Abundance pH
Other
Anopheles, Aedes Meigen, and Culex abundances were positively correlated to
pH.
Nambunga et al.
(2020)
Tanzania
(Arid/Dry)
2018–2019 Ponds
Streams
Rural Presence/
absence
pH
Conductivity
Turbidity
Nitrate
An. funestus Giles larvae presence had no signicant association with WQP
Ndenga et al.
(2012)
Kenya (Arid/
Dry)
2008–2009 Articial
Swamps
Ponds
Rivers
Natural Abundance
Presence/
absence
pH
Ammonium
Nitrate
Nitrite
Metals
Anopheles late instar larvae abundance was signicantly negatively associated
to iron content. Mean nitrate content was higher in mosquito present habitats
(statistically signicant).
Nihad et al. (2022) India (Trop-
ical)
2019–2020 Articial
Ponds
Urban
Rural
Presence/
absence
pH Ae. albopictus presence was negatively associated to pH.
Nikookar et al.
(2017)
Iran (Tem-
perate)
2014 Articial
Ponds
Streams
Urban
Rural
Natural
Abundance pH
Conductivity
Turbidity
Nitrate
Phosphate
Cx. pipiens s.l. had a signicant positive correlation with conductivity. No other
signicant correlations were found between all species sampled and WQP.
Table 2. Continued
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13Journal of Medical Entomology, 2024, Vol. 61, No. 1
Reference
Country
(Climate)a
Sampling
year(s)b
Water source
typecBiotopedOutcome Water quality propertyeMain ndings
Noori et al. (2015) U.S.A. (N/A) N/A Articial
Streams
N/A Abundance Ammonium
Nitrate
Phosphate
Controlled before-and after study: Higher levels of nitrate and phosphate was
signicantly positively associated to Culex spp. survival rate in articial
breeding sites.
Obi et al. (2019) Nigeria (Trop-
ical)
2013 Rock pools Natural Abundance DO
pH
Conductivity
Turbidity
Nitrate
Phosphate
Other
Mosquito abundance was signicantly positively correlated to conductivity,
whereas DO was weakly negatively associated to abundance.
Okanga et al.
(2013)
South Africa
(Arid/Dry,
Temperate)
Not
specied
Articial
Wetlands
Natural Abundance DO
pH
Abundance of malaria prevalent mosquitoes was signicantly positively
correlated to DO.
Onchuru et al.
(2016)
Kenya (Arid/
Dry)
2012 Ponds Natural Abundance DO
pH
Conductivity
ORP
Ammonium
Nitrate
Phosphate
Metals
Other
Ae. aegypti and some other Aedes species larval abundances were signicantly
positively correlated to conductivity, ammonium, and phosphate. Culex spe-
cies larval abundance was signicantly positively correlated to ORP and free
copper content. Total Anopheles larval abundance was signicantly positively
associated to DO.
Oussad et al.
(2021)
Algeria (tem-
perate)
2018–2019 Articial
Ponds
Urban
Rural
Abundance DO
Ph
Conductivity
DO negatively correlated with Cx. hortensis Ficalbi,conductivity posi-
tively correlated with Cs. longiareolata Macquart and Cx. pipiens s.l.,Cx.
perexiguus negatively correlated with pH, An. labranchiae strongly negatively
correlated with pH
Overgaard et al.
(2017)
Columbia
(Tropical)
2011 Articial Urban
Rural
Abundance
Presence/
absence
DO
pH
Conductivity
Ammonia
Nitrate
Phosphate
Ae. aegypti presence was signicantly positively associated to DO, and nega-
tively associated to pH. Ae. aegypti abundance was signicantly negatively
associated to DO.
Pinault and
Hunter (2012)
Ecuador
(Tropical)
2008–2010 Articial
Streams
Swamps
Rural
Natural
Presence/
absence
DO
pH
Conductivity
An. punctimacula Dyar & Knab larval presence was signicantly positively asso-
ciated to DO.
Piyaratne et al.
(2005)
Sri Lanka
(Tropical)
1997–1998 Stream Rural
Natural
Abundance DO
pH
Conductivity
Turbidity
Ammonia
Nitrate
Phosphate
Metals
Other
Only 1 signicant association was found for both Anopheles species
investigated, where An. varuna Lyengar was positively correlated to calcium
content.
Table 2. Continued
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14 Journal of Medical Entomology, 2024, Vol. 61, No. 1
Reference
Country
(Climate)a
Sampling
year(s)b
Water source
typecBiotopedOutcome Water quality propertyeMain ndings
Rajavel (1992) India (Trop-
ical)
1998 Articial Urban
Rural
Abundance DO
pH
Conductivity
Ammonia
Nitrate
Other
Ar. subalbatus Coquillett larval abundance was signicantly positively correlated
only to ammonia.
Ranathunge et al.
(2020)
Sri Lanka
(Tropical)
2013–2015 Articial
Ponds
Swamps
Streams
Urban
Rural
Abundance DO
pH
Conductivity
Turbidity
The abundance of Anopheles spp. larvae showed a signicant positive correla-
tion with DO and turbidity.
Ranjeeta et al.
(2008)
India (Trop-
ical)
2005–2006 Articial Urban Abundance pH
Conductivity
Metals
Other
Conductivity, pH, and calcium content were signicantly positively associated to
Anopheles and Culex larvae abundances.
Rao et al. (2011) India (Trop-
ical)
Not re-
ported
Articial Urban Abundance pH
Conductivity
Turbidity
Nitrate
Phosphate
Metals
Other
Ae. albopictus larval density showed signicant positive moderate to strong
correlations with all WQP except for pH which showed a signicant moderate
correlation with larval abundance.
Ratnasari et al.
(2020)
Indonesia
(Tropical)
2019 Articial
Ponds
Streams
Rural
Natural
Abundance pH Ae. albopictus and Ae. aegypti larvae abundance were signicantly positively
correlated to pH.
Reiskind and
Hopperstad
(2017)
U.S.A. (Tem-
perate)
2016 Articial Urban Presence/
absence
Turbidity Aedes albopictus presence was signicantly negatively associated to turbidity.
Reji et al. (2013) India (Trop-
ical)
2010–2011 Articial Urban
Rural
Abundance DO
pH
Conductivity
Turbidity
Larval abundance was signicantly negatively associated to conductivity. No
other signicant associations were found.
Rejmankova et al.
(1993)
Belize (Trop-
ical)
1990–1991 Articial
Puddles
Ponds
Swamps
Streams
Rural
Natural
Presence/
absence
DO
pH
Conductivity
Ammonia
Nitrate
Phosphate
Metals
During the dry season, conductivity was signicantly positively associated
to An. albimanus Wiedemann presence, and negatively associated to An.
pseudopunctipennis and An. argyritarsis Robineau-Devoidy presence. During
the wet season, pH was signicantly positively associated to An. albimanus
presence, and negatively associated to An. crucians Wiedemann presence.
Rosmanida et al.
(2020)
Indonesia
(Tropical)
2017 Articial
Puddles
Ponds
Swamps
Streams
Urban Abundance DO
pH
Turbidity
Ammonia
Nitrate
Other
Only DO showed a signicant correlation (weak positive) with total larval
abundance.
Table 2. Continued
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15Journal of Medical Entomology, 2024, Vol. 61, No. 1
Reference
Country
(Climate)a
Sampling
year(s)b
Water source
typecBiotopedOutcome Water quality propertyeMain ndings
Sasikumar et al.
(1986)
India (Trop-
ical)
1984–1985 Articial Rural Abundance pH
Metals
Larval abundance was signicantly positively associated to pH.
Seal et al. (2019) India (Trop-
ical)
2015–2016 Articial
Ponds
Swamps
Rural Abundance
Presence/
absence
DO
pH
Turbidity
Larval abundance was signicantly positively associated to DO. No other signi-
cant associations were found.
Sérandour et al.
(2010)
France (Trop-
ical)
2003–2007 Wetlands Natural Presence/
absence
DO
pH
Conductivity
Nitrate
Nitrite
Coquillettidia sp. Dyar presence/absence was signicantly positively associated
to conductivity, and negatively associated to nitrate.
Soares Gil et al.
(2021)
Brazil (Trop-
ical)
2016–2018 Rivers Rural
Natural
Abundance DO
pH
Conductivity
Data was collected from aquatic plant species and associated water. Associations
between Mansonia Blanchard species and WQP varied for each plant species.
Soleimani-Ahmadi
et al. (2014)
Iran (Arid/Dry) 2009–2010 Ponds
Rivers
Rural
Natural
Abundance
Presence/
absence
pH
Conductivity
Turbidity
Metals
Other
Anopheles abundance was signicantly negatively associated to turbidity, and
was positively correlated to pH, conductivity, and sulfate.
Soumendranath et
al. (2015)
India (Trop-
ical)
2012–2014 Articial Urban
Rural
Abundance pH
Conductivity
Metals
Other
Aedes abundance was signicantly positively associated to conductivity.
Suryadi et al.
(2019)
Indonesia
(Tropical)
Not
specied
Articial Rural Abundance pH
Turbidity
No WQP had any signicant associations with mosquito abundance.
Tarekegn et al.
(2022)
Ethiopia
(Tropical)
2018–2019 Articial
Puddles
Rural Abundance
Presence/
absence
pH
Conductivity
Turbidity
Mildly turbid habitats were associated to the presence of Anopheles larvae.
Tedjou et al.
(2020)
Cameroon
(Tropical)
2018 Articial Urban Abundance
Presence/
absence
Turbidity Ae. aegypti and Ae. albopictus abundance was positively associated to turbid
waters.
Thomas et al.
(2016)
India (Trop-
ical)
2013–2014 Articial Urban Abundance DO
pH
Turbidity
Nitrate
Nitrite
Phosphate
Other
General larval abundance was signicantly positively associated to conduc-
tivity, sulfate, uoride, and total hardness. When Anopheles abundance was
investigated alone, it was also signicantly positively associated to nitrate.
Villarreal-Treviño
et al. (2020)
Mexico (Arid/
Dry)
2012–2016 Articial
Puddles
Streams
Urban
Rural
Natural
Abundance
Presence/
absence
Turbidity An. pseudopunctipennis Theobald larval abundance and presence was signi-
cantly positively associated to turbidity, whereas An. albimanus was signi-
cantly negatively associated to turbidity.
Vong et al. (2021) Thailand
(Tropical)
2016–2017 Pitcher plants Natural Abundance pH
Conductivity
Total mosquito larvae abundance was signicantly positively correlated to pH.
Table 2. Continued
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16 Journal of Medical Entomology, 2024, Vol. 61, No. 1
Reference
Country
(Climate)a
Sampling
year(s)b
Water source
typecBiotopedOutcome Water quality propertyeMain ndings
Wang et al. (2020) China (Tem-
perate)
2018 Articial
Puddles
Rice paddies
Urban
Rural
Abundance DO
pH
Ammonia
An. sinensis larval abundance was signicantly positively associated to DO,
whereas Cx. p. pallens was signicantly positively associated to ammonia.
Wang et al. (2021) China (Conti-
nental)
2019 Articial
Rice paddies
Urban
Rural
Abundance DO
pH
Conductivity
Turbidity
Ammonia
Other
Six different WQP were investigated, and their association to 6 species were
assessed. The directions and strength of associations varied across species.
Zogo et al. (2019) Côte d’Ivoire
(Tropical)
2016–
2017
Articial
Streams
Rivers
Rice paddies
Rural Abundance
Presence/
absence
Turbidity Anopheles abundance and presence was signicantly positively associated to
turbidity.
WQP, Water quality property; Ae, Aedes; An, Anopheles; Ar, Armigeres; Coq, Coquillettidia; Cx, Culex; Cs, Culiseta; Mn, Mansonia; Oc, Ochlerotatus; Tr, Tripteroides Giles;Tx, Toxorhynchites Theobald.
aSome studies have reported more than 1 climate, as sampling has occurred in different climatic regions of their respective countries.
bTime period of the study’s sampling. The years shown are the intervals in which mosquito and water sampling has occurred.
c“Articial” water type includes throughs, human-made containers, canals, drains, animal hoof-prints, potholes, water reservoirs, catch basins, dams, irrigation canals, drainage ditch, water-treatment ponds, septic tanks.
dUrban – any region in city bounds; Rural – any region outside of urban areas, usually where agriculture and farming occur; Natural – any landform with no man-made infrastructure (e.g., uvial, aeolian, coastal landforms).
eDO – Dissolved Oxygen; Metals – any metal content that has been quantied from water samples; Other – all other parameters that are not DO, pH, conductivity, turbidity, nitrogen (of any form) and phosphorus (of any
form). Some examples are alkalinity, total hardness, biochemical oxygen demand, chemical oxygen demand, etc.
Table 2. Continued
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17Journal of Medical Entomology, 2024, Vol. 61, No. 1
Anopheles genus demonstrated signicant positive correlations for 4
species (An. albimanus, An. culicifacies Giles, An. funestus, and An.
gambiae), and 4 species displayed signicant negative correlations
(An. barbirostris van der Wulp, An. crucians, An. labranchiae, and
An. peditaeniatus). Within the Culex genus, 2 species displayed sig-
nicant positive correlations (Cx. bitaeniorhynchus Giles and Cx.
vishnui), while no species showed signicant negative correlations.
The impact of pH on other species, including both direction and
magnitude, is illustrated in Fig. 3.
Subgroup analysis by climate showed highly heterogenous
nonsignicant near-null overall correlations for pH and MPA in arid
regions (r = 0.03, 95% CI: −0.21 to 0.26, P = 0.79, n = 33, Mdn = 40,
I2 = 98%, 95% PI: −0.86 to 0.88). Whereas the equatorial subgroup
showed a highly heterogenous signicant positive pooled correla-
tion (r = 0.25, 95% CI: 0.08–0.40, P = 0.004, n = 59, Mdn = 84, I2
= 98%, 95% PI: −0.77 to 0.91). The temperate subgroup showed a
moderately heterogenous signicant negative pooled correlation (r =
−0.10, 95% CI: −0.19 to −0.015, P = 0.02, n = 38, Mdn = 30, I2 =
71%, 95% PI: −0.48 to 0.31). The continental subgroup showed a
highly heterogenous nonsignicant near-null pooled correlation (r =
−0.01, 95% CI: −0.19 to 0.24, P = 0.91, n = 7, Mdn = 230, I2 = 93%,
95% PI: −0.50 to 0.52).
There was a nonsignicant near-null pooled correlation of al-
kalinity on MPA (r = 0.02, 95% CI: −0.20 to 0.24, P = 0.85, n =
36, Mdn = 30) (Fig. 4). We found substantial heterogeneity between
studies not caused by sampling error (I2 = 95%) and a large predic-
tion interval (95% PI: −0.85 to 0.87). Species-specic correlations
(Fig. 4) within the Aedes genus revealed a signicant negative cor-
relation for 1 species with alkalinity (Ae. aegypti), while no species
exhibited signicant positive correlations. Correlations observed
within the Anopheles genus demonstrated signicant positive
correlations for 3 species (An. barbirostris, An. peditaeniatus, and
An. vagus), and 1 species displayed a signicant negative correlation
(An. stephensi). Within the Culex genus, 2 species displayed signif-
icant positive correlations (Cx. gelidus Theobald and Cx. pipiens),
and 1 species showed a signicant negative correlation (Cx. vishnui).
The impact of alkalinity on other species, including both direction
and magnitude, is illustrated in Fig. 4.
Subgroup analysis by climate showed highly heterogenous
nonsignicant near-null overall correlations for alkalinity and MPA
in arid regions (r = 0.07, 95% CI: −0.43 to 0.53, P = 0.80, n = 2,
Mdn = 10, I2 = 0%, 95% PI: −0.43 to 0.53). The equatorial sub-
group showed a highly heterogenous nonsignicant near-null pooled
correlation (r = −0.04, 95% CI: −0.43 to 0.17, P = 0.87, n = 19,
Mdn = 84, I2 = 98%, 95% PI: −0.95 to 0.95). Whereas the temperate
subgroup showed a homogeneous signicant positive pooled corre-
lation (r = 0.12, 95% CI: 0.01–0.23, P = 0.03, n = 15, Mdn = 30, I2
= 22%, 95% PI: −0.11 to 0.34). The continental subgroup showed
a highly heterogenous nonsignicant near-null pooled correlation (r
= −0.01, 95% CI: −0.19 to 0.24, P = 0.91, n = 230, Mdn = 84, I2 =
93%, 95% PI: −0.50 to 0.52). The continental subgroup (n = 0) was
not meta-analyzed.
Effects of Turbidity on MPA
From the turbidity dataset, there was a signicant positive pooled
correlation on MPA (r = 0.26, 95% CI: 0.13–0.37, P < 0.0001, n
= 92, Mdn = 77) (Fig. 5). We found substantial heterogeneity be-
tween studies not caused by sampling error (I2 = 99%) and a
large prediction interval (95% PI: −0.73 to 0.90). Species-specic
correlations (Fig. 5) within the Aedes genus revealed signicant
positive correlations for 2 species with turbidity (Ae. aegypti and
Ae. albopictus), while 1 species exhibited a signicant negative cor-
relation (Ae. camptorhynchus). Correlations observed within the
Anopheles genus demonstrated signicant positive correlations for
5 species (An. albimanus, An. arabiensis, An. cinereus Theobald,An.
culicifacies, and An. stephensi), and 2 species displayed signicant
negative correlations (An. labranchiae and An. sinensis). Within the
Culex genus, 1 species displayed a signicant positive correlation
(Cx. bitaeniorhynchus), while no species showed signicant negative
correlations. The impact of turbidity on other species, including both
direction and magnitude, is illustrated in Fig. 5.
Subgroup analysis by climate showed highly heterogenous
nonsignicant near-null overall correlations for turbidity and MPA
in arid regions (r = 0.44, 95% CI: 0.17–0.64, P = 0.002, n = 26, Mdn
= 47, I2 = 99%, 95% PI: −0.75 to 0.96). Whereas the equatorial
subgroup showed a highly heterogenous signicant positive pooled
Table 3. Summary of risk of bias assessments of the outcomes within the 79 included studies
Criteria
No. of unique outcome
assessmentsa
No. (%)a,b
Yes (Low risk) Not clear (Unclear risk) No (High risk)
Were sites selected in a way which makes them comparable
across groups and/or unlikely to inuence the outcome?
79 44 (56%) 33 (42%) 2 (3%)
Were confounders appropriately identied and accounted for? 79 30 (38%) 5 (6%) 38 (48%)
Was exposure measurement conducted in a valid and reliable
manner?
79 20 (25%) 56 (71%) 3 (4%)
Was outcome assessment conducted in a valid and reliable
manner?
79 52 (66%) 26 (33%) 1 (1%)
Were exclusions from analysis reported? 79 72 (91%) 3 (4%) 4 (5%)
Did the authors report all intended outcomes? 79 62 (78%) 2 (3%) 15 (19%)
Was the study free of other problems that could put it at a high
risk of bias?
79 78 (99%) 0 (%) 1 (1%)
Overall risk-of-bias for each outcome (within-study summary
assessment)
83 8 (10%) 27 (34%) 48 (61%)
aAll percentages were calculated using the total number of included relevant studies (n = 79), thus percentages and sum of counts can exceed 100% and 79,
respectively, when more than 1 unique outcome assessment is attributed to a study.
bRisk of bias denitions: Low = bias unlikely to modify reported outcomes; Unclear = unclear if bias will modify reported outcomes; High = bias signicantly
reduces condence in results of outcomes.
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18 Journal of Medical Entomology, 2024, Vol. 61, No. 1
correlation (r = 0.33, 95% CI: 0.11–0.52, P = 0.004, n = 34, Mdn
= 84, I2 = 99%, 95% PI: −0.76 to 0.93). The temperate subgroup
showed a highly heterogenous nonsignicant near-null pooled cor-
relation (r = −0.02, 95% CI: −0.15 to 0.010, P = 0.73, n = 27, Mdn
= 30, I2 = 76%, 95% PI: −0.52 to 0.49). The continental subgroup
showed a highly heterogenous nonsignicant positive pooled corre-
lation (r = 0.11, 95% CI: −0.17 to 0.38, P = 0.43, n = 7, Mdn = 230,
I2 = 97%, 95% PI: −0.59 to 0.72).
Effects of Conductivity on MPA
There was a signicant positive pooled correlation of conductivity
on MPA (r = 0.18, 95% CI: 0.06–0.30, P = 0.005, n = 74, Mdn =
40) (Fig. 6). We found substantial heterogeneity between studies not
caused by sampling error (I2 = 98%) and a large prediction interval
(95% PI: −0.70 to 0.84). Species-specic correlations (Fig. 6) within
the Aedes genus revealed signicant positive correlations for 2 spe-
cies with conductivity (Ae. aegypti and Ae. albopictus), while no spe-
cies exhibited signicant negative correlations. Correlations observed
within the Anopheles genus demonstrated a signicant positive cor-
relation for 1 species (An. funestus), while 3 species displayed signi-
cant negative correlations (An. argyritarsis, An. pseudopunctipennis,
and An. sinensis). Within the Culex genus, 1 species displayed a sig-
nicant positive correlation (Cx. vishnui), and 1 species showed a
signicant negative correlation (Cx. quinquefasciatus). The impact
of conductivity on other species, including both direction and mag-
nitude, is illustrated in Fig. 6.
Subgroup analysis by climate showed highly heterogenous
nonsignicant near-null overall correlations for conductivity and
MPA in arid regions (r = 0.12, 95% CI: −0.12 to 0.35, P = 0.32,
n = 17, Mdn = 40, I2 = 99%, 95% PI: −0.68 to 0.79). Whereas the
equatorial subgroup showed a highly heterogenous signicant pos-
itive pooled correlation (r = 0.40, 95% CI: 0.13–0.62, P = 0.005, n
= 28, Mdn = 44, I2 = 98%, 95% PI: −0.81 to 0.96). The temperate
subgroup showed a moderately heterogenous nonsignicant near-
null pooled correlation (r = 0.04, 95% CI: −0.03 to 0.12, P = 0.26, n
= 25, Mdn = 30, I2 = 54%, 95% PI: −0.22 to 0.30). The continental
subgroup showed a highly heterogenous nonsignicant near-null
pooled correlation (r = −0.06, 95% CI: −0.22 to 0.09, P = 0.42, n =
6, Mdn = 230, I2 = 89%, 95% PI: −0.43 to 0.32).
Effects of Dissolved Oxygen on MPA
There was a signicant positive pooled correlation of dissolved ox-
ygen on MPA (r = 0.32, 95% CI: 0.18 to 0.44, P < 0.0001, n =
79, Mdn = 72) (Fig. 7). We found substantial heterogeneity between
studies not caused by sampling error (I2 = 98%) and a large predic-
tion interval (95% PI: −0.72 to 0.92). Species-specic correlations
(Fig. 7) within the Aedes genus revealed a signicant positive cor-
relation for 1 species with dissolved oxygen (Ae. aegypti), while
no species exhibited signicant negative correlations. Correlations
observed within the Anopheles genus demonstrated signicant posi-
tive correlations for 7 species (An. barbirostris, An. culicifacies, An.
oswaldoi Peryassú, An. peditaeniatus, An. pseudopunctipennis, An.
punctimacula, and An. subpictus), while no species displayed signif-
icant negative correlations. Within the Culex genus, 2 species dis-
played signicant positive correlations (Cx. gelidus and Cx. vishnui),
and 2 species showed signicant negative correlations (Cx. hortensis
and Cx. hutchinsoni Barraud). The impact of dissolved oxygen on
other species, including both direction and magnitude, is illustrated
in Fig. 7.
Subgroup analysis by climate showed highly heterogenous
nonsignicant near-null overall correlations for dissolved oxygen
and MPA in arid regions (r = 0.18, 95% CI: −0.02 to 0.37, P = 0.08,
Fig. 3. Meta-analytic means per mosquito species and estimated pooled correlation
based on the Pearson r coefficient (±95% CI) in a random-effects meta-analysis for
pH effects on MPA. The number of effect sizes is denoted by n. Confidence intervals
positioned to the right (without crossing the dotted line), to the left (without crossing
the dotted line), or overlapping with the dotted line, indicatewhether mosquito species
were positively, negatively, or not affected by pH. “All sampled”, studies including all
sampled mosquito for analyses; Ae, Aedes; An, Anopheles; Ar, Armigeres; Cx, Culex;
Cs, Culiseta; Mn, Mansonia; Oc, Ochlerotatus; Tr, Tripteroides; Tx, Toxorhynchites.
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19Journal of Medical Entomology, 2024, Vol. 61, No. 1
Fig. 4. Meta-analytic means per mosquito species and estimated pooled correlation based on the Pearson r coefficient (±95% CI) in a random-effects meta-
analysis for alkalinity effects on MPA. The number of effect sizes is denoted by n. Confidence intervals positioned to the right (without crossing the dotted
line), to the left (without crossing the dotted line), or overlapping with the dotted line, indicate whether mosquito species were positively, negatively, or not
affected by alkalinity. “All sampled”, studies including all sampled mosquito for analyses; Ae, Aedes; An, Anopheles; Cx, Culex; Cs, Culiseta; Mn, Mansonia; Tx,
Toxorhynchites.
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20 Journal of Medical Entomology, 2024, Vol. 61, No. 1
n = 16, Mdn = 143.5, I2 = 97%, 95% PI: −0.69 to 0.97). Whereas the
equatorial subgroup showed a highly heterogenous signicant posi-
tive pooled correlation (r = 0.54, 95% CI: 0.34 to 0.70, P < 0.0001,
n = 34, Mdn = 72, I2 = 97%, 95% PI: −0.69 to 0.97). The temperate
subgroup showed a highly heterogenous nonsignicant near-null
pooled correlation (r = −0.05, 95% CI: −0.21 to 0.60, P = 0.53, n =
24, Mdn = 33, I2 = 90%, 95% PI: −0.65 to 0.58). The continental
subgroup showed a highly heterogenous signicant positive pooled
correlation (r = 0.17, 95% CI: 0.02–0.32, P = 0.03, n = 6, Mdn =
230, I2 = 89%, 95% PI: −0.22 to 0.52).
Effects of Nutrients on MPA
First, from the studies investigating nitrogen, there was a signicant
positive pooled correlation on MPA (r = 0.21, 95% CI: 0.11–0.32, P
< 0.0001, n = 97, Mdn = 33) (Fig. 8). We found substantial heteroge-
neity between studies not caused by sampling error (I2 = 97%) and
a large prediction interval (95% PI: −0.66 to 0.84). Species-specic
correlations (Fig. 8) within the Aedes genus revealed no signicant
positive or negative correlations. Correlations observed within the
Anopheles genus demonstrated signicant positive correlations
for 4 species (An. culicifacies, An. funestus, An. stephensi, and
An. subpictus), while no species displayed signicant negative
correlations. Within the Culex genus, 3 species displayed signicant
positive correlations (Cx. quinquefasciatus, Cx. tritaeniorhynchus
Giles, and Cx. vishnui), while 1 species showed a signicant nega-
tive correlation (Cx. bitaeniorhynchus). The impact of nitrogen on
other species, including both direction and magnitude, is illustrated
in Fig. 8.
Subgroup analysis by climate showed highly heterogenous
nonsignicant near-null overall correlations for nitrogen and MPA
in arid regions (r = 0.39, 95% CI: 0.10–0.61, P = 0.009, n = 11, Mdn
= 176, I2 = 99%, 95% PI: −0.51 to 0.88). Whereas the equatorial
subgroup showed a highly heterogenous signicant positive pooled
correlation (r = 0.47, 95% CI: 0.23 to 0.65, P = 0.003, n = 26, Mdn
= 84, I2 = 97%, 95% PI: −0.71 to 0.96). The temperate subgroup
showed a moderately heterogenous nonsignicant positive pooled
correlation (r = 0.08, 95% CI: −0.01 to 0.017, P = 0.09, n = 40, Mdn
= 30, I2 = 68%, 95% PI: −0.41 to 0.53). The continental subgroup
showed a highly heterogenous nonsignicant negative pooled corre-
lation (r = −0.08, 95% CI: −0.40 to 0.26, P = 0.64, n = 9, Mdn = 230,
I2 = 98%, 95% PI: −0.82 to 0.76).
As for the phosphorus dataset, there was a signicant positive
pooled correlation on MPA (r = 0.24, 95% CI: 0.16–0.32, P <
0.0001, n = 43, Mdn = 30) (Fig. 9). We found substantial heteroge-
neity between studies not caused by sampling error (I2 = 90%) and
a large prediction interval (95% PI: −0.19 to 0.59). Species-specic
correlations with phosphorus (Fig. 9) showed signicant positive
correlations for 2 Aedes species (Ae. aegypti and Ae. albopictus), 4
Anopheles species (An. culicifacies, An. gambiae, An. stephensi, and
An. subpictus), and 1 Culex species (Cx. quinquefasciatus). No spe-
cies displayed signicant negative correlations. The impact of phos-
phorus on other species, including both direction and magnitude, is
illustrated in Fig. 9.
Subgroup analysis by climate showed highly heterogenous
nonsignicant near-null overall correlations for phosphorus and
MPA in arid regions (r = 0.42, 95% CI: 0.28–0.54, P = <0.0001, n =
7, Mdn = 1063.5, I2 = 97%, 95% PI: 0.04–0.69). The equatorial sub-
group showed a highly heterogenous nonsignicant positive pooled
correlation (r = 0.30, 95% CI: −0.05 to 0.58, P = 0.09, n = 4, Mdn =
140, I2 = 93%, 95% PI: −0.43 to 0.79). Whereas the temperate sub-
group showed a moderately heterogenous signicant positive pooled
Fig. 5. Meta-analytic means per mosquito species and estimated pooled
correlation based on the Pearson r coefficient (±95% CI) in a random-effects
meta-analysis for turbidity effects on MPA. The number of effect sizes is
denoted by n. whether mosquito species were positively, negatively, or not
affected by turbidity. “All sampled”, studies including all sampled mosquito
for analyses; Ae, Aedes; An, Anopheles; Cx, Culex; Cs, Culiseta; Mn,
Mansonia; Oc, Ochlerotatus; Tx, Toxorhynchites.
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21Journal of Medical Entomology, 2024, Vol. 61, No. 1
Fig. 6. Meta-analytic means per mosquito species and estimated pooled correlation based on the Pearson r coefficient (±95% CI) in a random-effects meta-
analysis for conductivity effects on MPA. The number of effect sizes is denoted by n. whether mosquito species were positively, negatively, or not affected by
conductivity. “All sampled”, studies including all sampled mosquito for analyses; Ae, Aedes; An, Anopheles; Cx, Culex; Cs, Culiseta; Coq, Coquillettidia; Mn,
Mansonia; Tr, Tripteroides; Tx, Toxorhynchites.
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22 Journal of Medical Entomology, 2024, Vol. 61, No. 1
Fig. 7. Meta-analytic means per mosquito species and estimated pooled correlation based on the Pearson r coefficient (±95% CI) in a random-effects meta-
analysis for dissolved oxygen effects on MPA. The number of effect sizes is denoted by n. whether mosquito species were positively, negatively, or not affected
by dissolved oxygen. “All sampled”, studies including all sampled mosquito for analyses; Ae, Aedes; An, Anopheles; Ar, Armigeres; Cx, Culex; Cs, Culiseta; Coq,
Coquillettidia; Mn, Mansonia; Tx, Toxorhynchites.
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23Journal of Medical Entomology, 2024, Vol. 61, No. 1
Fig. 8. Meta-analytic means per mosquito species and estimated pooled correlation based on the Pearson r coefficient (±95% CI) in a random-effects meta-
analysis for nitrogen effects on MPA. The number of effect sizes is denoted by n. whether mosquito species were positively, negatively, or not affected by
nitrogen. “All sampled”, studies including all sampled mosquito for analyses; Ae, Aedes; An, Anopheles; Ar, Armigeres; Cx, Culex; Cs, Culiseta; Mn, Mansonia;
Tx, Toxorhynchites.
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24 Journal of Medical Entomology, 2024, Vol. 61, No. 1
correlation (r = 0.14, 95% CI: 0.05–0.23, P = 0.002, n = 29, Mdn =
18, I2 = 31%, 95% PI: −0.14 to 0.40). The continental subgroup was
not meta-analyzed (n = 1).
Publication Bias
Based on Egger’s regression, only the pooled correlations of turbidity
(intercept = 0.46, 95% CI: 0.25–0.67, P = 0.02) and phosphorus
Fig. 9. Meta-analytic means per mosquito species and estimated pooled correlation based on the Pearson r coefficient (±95% CI) in a random-effects meta-
analysis for phosphorus effects on MPA. The number of effect sizes is denoted by n. mosquito species were positively, negatively, or not affected by phosphorus.
“All sampled”, studies including all sampled mosquito for analyses; Ae, Aedes; An, Anopheles; Cx, Culex; Cs, Culiseta.
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25Journal of Medical Entomology, 2024, Vol. 61, No. 1
(intercept = 0.49, 95% CI: 0.35–0.63, P = 0.001) showed statistical
signicance and potential publication bias, while the trim-and-nal
analysis estimated 22 and 16 effect sizes missing on the right side
of the distribution of their effect sizes, respectively (Supplementary
Table S3). This analysis increased the pooled correlation of both
properties (Supplementary Table S3). However, the high hetero-
geneity between effect sizes and the lack of evidence of signicant
publication bias in the other meta-analyses indicates the potential
skewing of the newly estimated correlations for the overall turbidity
and phosphorus meta-analyses (Peters et al. 2007). This suggests
there was no evidence of important publication bias in the overall
set of included studies. Other sources for the newly estimated
correlations (Supplementary Table S3) and high heterogeneity be-
tween effect sizes must be investigated, such as the quality of studies,
differences in study designs, and the biological implications of the
observed pooled correlations.
Other Water Quality Properties
All other properties, including sulfate and hardness, for example,
were not sufciently reported in our nal list of articles for potential
meta-analysis. These other water quality properties had < 25 total
effect sizes reported. Nevertheless, as observed in the 7 properties
meta-analyzed, the strength and direction of relationships varied
between studies and species for all other captured water quality
properties. A full list of effect sizes for all properties is available in
Supplementary Dataset S1.
Discussion
The effects of physicochemical characteristics of mosquito OIM sites
on MPA have been studied in most parts of the world, with a signif-
icant focus on areas with important potential for MBD outbreaks.
However, no study to-date has applied meta-analytical approaches
to investigate and synthesize species-specic tendencies at a global
scale. Encompassing all mosquito species and several generalized ge-
ographical and climatic settings, this systematic review identied an
overall positive link between MPA and several easy-to-characterize
water quality properties. However, these effects varied when ac-
counting for climate zones and mosquito species. Considering the
high levels of heterogeneity observed in our pooled estimates, this
suggests the observed overall correlation of some water quality
properties may be species-specic, climate-specic, and/or proxy of
other factors inuencing MPA.
As expected, the most reported water quality properties were pH,
alkalinity, turbidity, electrical conductivity, dissolved oxygen, and
nutrient concentrations. To attempt to uncover the ecological and
biological signicance of our results, we considered the role of these
water quality properties on the ecology, development, OIM, and sur-
vivorship characteristics of the reported mosquito species.
The Role of pH and Alkalinity on MPA
Water pH was a frequently investigated property, likely because of
its signicance in assessing suitable conditions for aquatic life and
its ease of measurement (Bell 1971, Thurston et al. 1981, Wiseman
et al. 2010). A circumneutral level of pH has been shown to be cru-
cial for bioregulatory processes in aquatic species (Gensemer et al.
2018). Laboratory assays for mosquito pH tolerance have shown
that aedine larvae from various genera can have successful larval and
pupal development when pH levels range between 4 and 11, while
pH levels below 3 and above 11 negatively affected the survival of
these species (Clark et al. 2004). In addition, the post-emergence
longevity of Cx. quinquefasciatus, an important vector for Zika
and West Nile viruses, has been shown to be most successful in pH
ranging between 6 and 8, whereas larvae growth rate signicantly
reduced in pH levels outside of 7 (Ukubuiwe et al. 2020); although
this species maintained survivorship in pH values ranging from 5
to 9. These controlled laboratory experiments suggest a triangular
distribution for the effects of pH on MPA, where MPA is positively
correlated with pH levels between 6 and 8, while values outside that
range result in negative relationships. Considering the variability
of correlations at the species-level, we posit that the implications
of correlations captured across studies may be spurious, and that
controlled laboratory assays may be better suited for determining
pH preferences of mosquito species. Furthermore, the high heter-
ogeneity between effect sizes, including in the climatic subgroups,
indicates other factors may have been at play for the resulting MPA
measures. These may include the type of OIM habitat, where con-
tainer species, such as Ae. aegypti and Ae. albopictus, were sampled
from various articial habitats (e.g., discarded tires, owerpots, and
sewers) which can have more dynamic pH conditions compared to
permanent water sources. Also, considering that pH is known to uc-
tuate diurnally, studies that have measured the pH of larval habitats
rarely controlled for the time of sampling. Other confounding
elements such as climate, pollution, temperature, and altitude are
known drivers for the variation of pH in rainwater, which constitute
a large fraction of larvae habitats (Ruiz et al. 2010, Mohammed
and Chadee 2011). These environmental factors have demonstrated
themselves as primary drivers of MPA independently of pH (Bai et al.
2013, Brugueras et al. 2020, Nambunga et al. 2020). Consequently,
it becomes imperative to recognize the concurrent inuence of these
environmental factors on both pH and MPA, potentially introducing
confounding bias when elucidating the direct impacts of pH on
MPA. It is also key to acknowledge that the pH levels of water
sources also exert an inuence on the presence and abundance of
microorganisms upon which mosquito larvae and immatures rely
for sustenance (Baker et al. 1982). This observation suggests a po-
tential connection between pH variations and the dietary availability
for mosquitoes, rather than a direct inuence on their bioregulatory
demands. Altogether, if studies are investigating habitat preferences,
results from direct measures of association between pH and MPA
must be carefully assessed. Our meta-analyses reinforce the no-
tion that suitable pH conditions vary by species, while the hetero-
geneity in effects sizes between climatic subgroups suggest that pH
preferences may be affected by the type of available water habitats
and their permanence, as well as precipitation patterns. Specically,
the pooled correlation was near-null in the arid subgroup (r = 0.03)
and the pooled correlation for the temperate subgroup was weakly
negative (r = −0.10). As these regions receive signicantly less rainfall
than tropical localities, this may in turn reduce the impact of rainfall
related pH instabilities on MPA.
Some research attention was attributed to the relationships
between alkalinity and MPA (i.e., water’s resistance to acidica-
tion), however our meta-analysis showed that pooled correlations
yielded no signicant direction of association with MPA (Fig. 4).
When comparing the species-specic pooled correlations of pH
and alkalinity, Ae. aegypti and Cx. vishnui had signicantly posi-
tive pooled correlations with pH and signicantly negative pooled
correlations with alkalinity, while we observed the opposite for An.
barbirostris and An. peditaeniatus. Studies have shown that many
mosquito species have mechanisms to acclimate to extreme levels of
both pH and alkalinity (Clark et al. 2007, Multini et al. 2021), but
our results further hint that some species may not be as adaptable.
We also observed a signicantly positive and homogenous pooled
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26 Journal of Medical Entomology, 2024, Vol. 61, No. 1
correlation for the temperate subgroup. This could be indicative of
the heavy rainfall patterns of these regions decreasing overall alka-
linity over time (Zeng et al. 2020), limiting certain mosquito species
to OIM habitats with milder levels of alkalinity, such as puddles and
ponds (Torreias et al. 2010). However, our analyses showed high
heterogeneity for all other alkalinity meta-analyses, which signals
that the same proxies discussed for pH are most likely affecting the
relationships between alkalinity and MPA.
The Role of Turbidity on MPA
Turbidity refers to the degree of cloudiness of water, which in turn
affects the amount and distance of light that can traverse the column.
For some species, access to sunlight for immature development has
shown to be an important ecological characteristic for oviposition
preferences. For example, species such as Ae. albopictus and Ae.
aegypti opt for oviposition in container habitats in part due to the
clarity of water emanating from recent precipitation events (Juliano
et al. 2004), while An. arabiensis and An. gambiae s.l. larvae are
especially prevalent on the surface of pools and paddy elds where
access to direct sunlight is uninterrupted (Gimnig et al. 2001).
However, except for An. gambiae s.l. which had a large interval of
correlations, the meta-analytical means of these clear-water species
showed signicantly positive correlations. This does not necessarily
contradict the typical OIM characteristics of these species, as higher
levels of turbidity can inuence MPA positively by limiting the vis-
ibility of larvae to predators (Kweka et al. 2011) and by increasing
larval development time via an increase in water temperature (i.e.,
decreased albedo) (Leisnham et al. 2004). Therefore, these signi-
cant positive relationships may be reective of confounding albedo
modications. Additionally, turbidity can also stem from natural
processes such as algae blooms, which have been shown to be part of
the dietary regimes of some mosquitoes. Although species outside of
the Toxorhynchites, Psorophora Fabricius, and Uranotaenia Lynch
Arribálzaga genera do not rely on algae as primary food sources,
some habitats may be lacking in bacterial and protozoan food
sources that are preferred by most mosquito larvae which may pro-
mote their consumption of algae for development (Clements 1992).
The increased pooled correlation captured in the arid subgroup anal-
ysis could be explained by lack of rainfall and surface water limiting
the amount of organic matter that can be introduced into the aquatic
environment. This can lead to fewer microorganisms for larvae con-
sumption, and therefore, a reliability on algae for feed (Pointing and
Belnap 2012, De Senerpont Domis et al. 2013, Carvajal-Lago et al.
2021). In contrast, it is also essential to consider that the impact of
turbidity on MPA might be intertwined with visibility factors that
inuence predatorial feeding on eggs and immatures (Ortega et al.
2020). As such, turbidity conditions may not directly inuence mos-
quito OIM, but rather manifest as an indirect outcome stemming
from reduced predatorial activity.
The Role of Conductivity on MPA
Electrical conductivity, also know as specic conductance, is meas-
ured to assess the ability of water to conduct electricity; it is typically
used as a proxy for salt concentrations. Conductance capabilities are
linked to the concentration of ions present in the water column and
may be induced by drivers such as hardness, salinity, and metal con-
centration. Consequently, elevated levels of conductivity have been
associated with decreased water quality (Banna et al. 2014). Like
pH, the variability of pooled estimates per species for the effects of
conductivity on MPA may be justied by a triangular distribution.
To help corroborate this, we considered an experiment by Mamai et
al. (2021) where the authors tested the effects of conductivity levels
on the rearing productivity of Aedes mosquitoes. They observed that
conductivity levels above 368 µS/cm negatively impacted pupal de-
velopment, yet the rate of larvae to pupae development increased
in parallel to the rise in conductivity. Similar outcomes have been
observed with Cx. quinquefasciatus (Ukubuiwe et al. 2020). The
authors of both experiments suggested that the increase in con-
ductivity appeared to inuence the molting and metabolic rate of
larvae, but once at the pupal stage, the mosquitoes were exposed
to an excess in biolm which resulted in overfeeding and subse-
quent mortality. They proposed that ion concentrations could im-
pact the microbial and bacterial community composition in both
the larval diet and water environment over time. In fact, Goller &
Romeo (2008) have shown that higher levels of conductivity can
drive biolm development. This, in turn, may affect larval growth
by limiting nutrient availability as a result of bacterial competition.
Hence, we suspect that these trade-offs explain the signicant pos-
itive and negative pooled correlations of the meta-analyses (Fig. 6),
while the nonsignicant relationships may stem from confounding
factors elucidated earlier. These factors encompass modications in
land use, which have been demonstrated to induce changes in sedi-
ment runoff, as well as variations in temperature and rainfall within
OIM habitats, all of which have exhibited direct inuences on both
conductivity and MPA independently (Mainuri and Owino 2013,
Rakotoarinia et al. 2022).
The Role of Dissolved Oxygen on MPA
As reviewed by Clements et al. (Clements 1992), mosquito larvae
generally consume atmospheric oxygen for development and sur-
vival, but they can rely on dissolved oxygen in certain low-oxygen
habitats. The pooled correlation for dissolved oxygen effects on
MPA was signicantly positive, possibly due to the large number of
positive pooled correlations captured in the Anopheles genus (Fig.
7). Specically, we observed a pattern of positive correlations for
anopheline and aedine species whereas culicines were relatively un-
affected by dissolved oxygen. Although dissolved oxygen has shown
to be an important water quality property for the presence and
abundance of multiple genera, its impact varies when considering
the species-specic habitat niches. Container species like Ae. aegypti
will colonize stagnant OIM sources in which oxygen resources are
largely derived from aquatic plants and algae (Suryadi et al. 2019),
while Anopheles species, such as An. arabiensis and An. subpictus,
are prevalent in ponds, swamps, and irrigation ditches with similar
oxygen sources (Muturi et al. 2008, Ratnasari et al. 2020). This
corroborates with the results of an experiment by Yamada et al.
(2020) where they quantied the role of oxygen depletion on an
Ae. aegypti, Ae. albopictus, and An. arabiensis pupae and reported
that all species depleted the dissolved oxygen under 0.5% in less
than 30 min in articial containers. In contrast, culicine species, in-
cluding Cx. pipiens s.l., Cx. tarsalis, and Cx. quinquefasciatus, have
been observed to colonize water bodies with reduced dissolved ox-
ygen levels (Vinogradova 2000, Muturi et al. 2009). This ecolog-
ical characteristic allows them to outcompete other aquatic species
for ecosystem resources, such as common water eas, while also
evading predation from larvivorous sh, which have both shown to
unsuccessfully develop in less oxygenated waters (Cech et al. 1985,
Nebeker et al. 1992). However, a study measured the isolated effects
of dissolved oxygen on the survival and development time of Cx.
pipiens and found that lower levels of dissolved oxygen in water
led to decreased larval survival rates and increased development
time even when provided with atmospheric oxygen (Silberbush et al.
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27Journal of Medical Entomology, 2024, Vol. 61, No. 1
2015), suggesting that this property remains crucial in some culicine
species. While outcomes from the dissolved oxygen meta-analysis
can further substantiate previously reported niche characteristics of
mosquito species, the high heterogeneity between effect sizes must
be considered. This may be in part due to dissolved oxygen levels
being associated with the other water quality properties explored in
this study as well as other MPA drivers such as temperature and pre-
cipitation. Nevertheless, we underscore the signicance of assessing
dissolved oxygen in larval habitats to assess potential productive col-
onization, particularly in arid and tropical regions where dissolved
oxygen can often serve as the primary source of oxygen.
The Role of Nutrients on MPA
One of the primary hypotheses proposed for the effects of nutrients
on MPA is that increased nutrients provide additional access to
thriving algae and microorganisms for developing mosquitoes, while
promoting aquatic species growth for added habitat provision, and
therefore, shelter from predation (Clements 1992, Carvajal-Lago et
al. 2021). Pooled correlations revealed a signicant positive asso-
ciation between both nitrogen and phosphorus and MPA. Pooled
correlations by species showed once again that the strength and sig-
nicance of correlations were species-specic, however only a single
signicant negative association was found in both meta-analyses,
where Cx. bitaeniorhynchus was negatively impacted by nitrogen
concentrations (Fig. 8). Interestingly, the meta-analytical means for
the effects of phosphorus on MPA showed that no species had a sig-
nicant negative relationship with the various forms of phosphorus
(Fig. 9). It is important to note that the high heterogeneity found
within the meta-analyses may be due to various factors inuencing
nutrient necessities, including immature feeding habits, habitat char-
acteristics, and life history traits. This suggests that different mos-
quito species have varying nutrient type and abundance necessities
for their development and reproduction, and the effect of nutrient
availability on MPA may depend on the mosquito species present in
the habitat. For example, some mosquito species require high levels
of phosphorus for optimal development, such as Ae. aegypti, which
has been shown to benet from phosphorus enrichment in articial
containers (Clements 1992, Darriet and Corbel 2008, Carvajal-Lago
et al. 2021). Leisnham et al. (2004) noted that other species may
require higher levels of nitrogen, such as Culex mosquitoes, which
have shown to prosper in sources with high nitrogen levels due to
their ability to feed on blooming bacteria and algae, although the
authors noted that larvae could not successfully develop in waters
with extreme detrital loads; perhaps as a response to the organisms
being damaged physically by moving material in the water course.
Factors such as temperature and humidity may also play a role in
shaping the nutrient requirements of different mosquito species and
their response to nutrient availability (Clements 1992, Brugueras et
al. 2020). In addition, nutrient concentrations may be inuenced
by agricultural runoff and rainfall patterns, leading to higher nu-
trient levels in some areas more than others (Zanon et al. 2020).
Furthermore, the climatic subgroup analyses also revealed that the
pooled correlations for nitrogen were signicantly increased in both
the arid and tropical climate subgroup analyses. It can be inferred
that the effects of nitrogen on MPA may be more pronounced in
environments with limited water resources, such as arid regions,
where mosquito larvae and pupae may be more reliant on nutrients
from the water body due to a lack of alternative food sources (e.g.,
algae). These results highlight the importance of considering species-
specic nutrient necessities based on life stage, climate, and land-
scape. This proposed stratication would provide a more robust
framework for understanding the underlying processes driving the
association between nutrient availability and MPA, while providing
evidence for the use of effective water management systems to limit
the proliferation of MBD vectors.
Recommendations and Limitations
We identied several biases and shortcomings associated with the
identied studies which may have affected the overall conclusions
of this meta-analysis. Firstly, the geographical distribution of the in-
cluded studies was largely focused in Asian and African countries (n
= 64, 81%). This focused distribution is engendered by the amplied
risks of MBD outbreaks in these regions (Norris 2004, Tolle 2009,
Bai et al. 2013, Okanga et al. 2013). However, we feel the small
number of relevant studies in North America, South America and
Europe is not proportional to these regions’ concern surrounding
MBD outbreaks (Lanciotti et al. 2007, Waits et al. 2018, Brugueras
et al. 2020). Furthermore, within-study biases were identied in a
few areas. Some studies did not clearly state the rationale behind
selection or allocation of sampling sites as well as methods for
quantifying water quality properties. We recommend future studies
in this area to improve transparency by reporting on the following:
reasoning and justication behind the selection of study sites;
instruments and analytical approaches used throughout the study
and across groups with multiple readings or continuous monitoring;
whether trained personnel were recruited for measurements using
calibrated and pre-tested instruments as well as for data collec-
tion and tabulation (Millsap and Everson 1993, Fitzpatrick et al.
2009). Also, many studies in this review did not account for ex-
ternal factors affecting MPA. Meta-analytical results for the effects
of each water quality property on MPA has suggested a need for
multi-factorial assessments to further understand the preferences
for oviposition as many properties likely interactively impact MPA.
Moreover, the independent analysis of water quality properties
on MPA veils the autocorrelative nature of relationships between
these indicators, hindering on establishing the strongest predictors
for MPA. Some examples include autocorrelations between pH and
alkalinity (Saalidong et al. 2022) as well as dissolved oxygen and
organic nutrient loading (Dodds 2006). We recommend the identi-
cation and control of such inuences through matching, stratica-
tion, multivariable analysis, or other approaches (Skelly et al. 2012).
Lastly, we suggest the inclusion of temporal lags in any multivariate
modeling as the effect of MPA drivers can vary based on mosquito
life history traits (Rakotoarinia et al. 2022).
Some limitations were present in our review. For instance, lan-
guage bias was present as only publications in English, French, and
Spanish were included for review, which ultimately excluded 14 po-
tentially relevant articles (Fig. 1). There was also potential bias in
excluding predatory journals per Beall’s List (2020), since this list
may be biased as well (Kimotho 2019). Additionally, we recognize
that our search strategy focused on MPA which limited the inclusion
of studies exploring drivers for mosquito development traits (e.g.,
molting period and survival thresholds). However, these traits were
investigated in our discussion to uncover the underlying mechanisms
for the effects of water quality on MPA. Finally, we acknowledge
that the high heterogeneity among correlations could be indicative of
external factors impacting MPA, and these must be considered when
interpreting our ndings.
Conclusion
Globally, studies aggregating information on the ecological char-
acterization of vector species habitats help to provide evidence on
the effect of climatic and environmental variables related to habitat
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28 Journal of Medical Entomology, 2024, Vol. 61, No. 1
preferences of disease vectors, and therefore, provide insights on
factors promoting their expansion and persistence (Servadio et al.
2018, Brugueras et al. 2020, Perrin et al. 2022). We synthesized
global data and determined whether the water quality of OIM
habitats in various climates played a role in mosquito occurrence and
density, while considering the preferable range of the investigated
water quality properties at the species, genera, and mosquito level.
Based on our synthesis, we have the following conclusions and
observations:
• There was a signicant positive pooled correlation between MPA
and pH, turbidity, electrical conductivity, dissolved oxygen, and
nutrients.
• Correlations per species revealed that suitable ranges for pH, al-
kalinity, turbidity, electrical conductivity, and dissolved oxygen
are species- and/or genus-specic.
• The high heterogeneity between effect sizes suggests that other
abiotic and biotic factors may be inuencing the impact of these
properties on MPA.
• Climate regime has shown to inuence the strength and direc-
tion of pH, alkalinity, turbidity, electrical conductivity, dissolved
oxygen, and nutrient effects on MPA. Yet climate zonation must
be interpreted in the context of standard of living of urban
populations living within them. Countries with a high Human
Development Index (HDI) may be able to provide critical re-
sources that directly or indirectly manage MPA and associated
disease risks, in relation to countries with a lower HDI, and
many of the countries with a high HDI occur in the more tem-
perate regions of the world.
• Urban vector species have shown to be most adaptable to a wider
range of values for water quality properties.
By considering the key factors highlighted in this review, future
research can strengthen existing models of vector species expansion
in diverse landscapes and serve as a fundamental basis for further
investigations into the effects of water quality properties on the spread
of vectors. Such insights could help support urban and rural water
quality management, encourage improved agricultural practices and
waste production to prevent vector OIM, and advocate for stronger
water management policies to control the spread of MBDs.
Acknowledgments
We would like to give our most sincere thanks to Mark Sunohara
and Emilia Craiovan for their support in conceptualization. We
are grateful for the help from Susan Young who helped structure
our search strategy. We would also like to thank all authors whose
studies were included in this review for their publications.
Funding
Funding for this project was possible thanks to Agriculture and Agri-
Food Canada and Carleton University.
Data Availability
All data supporting the ndings of this study are available within the
paper and its Supplementary Information.
Supplementary Material
Supplementary material is available at Journal of Medical
Entomology online.
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