A Multi-Species Simulation of Mosquito Disease Vector Development in Temperate Australian Tidal Wetlands Using Publicly Available Data

Abstract

Worldwide, mosquito monitoring and control programs consume large amounts of resources in the effort to minimise mosquito-borne disease incidence. On-site larval monitoring is highly effective but time consuming. A number of mechanistic models of mosquito development have been developed to reduce the reliance on larval monitoring, but none for Ross River virus, the most commonly occurring mosquito-borne disease in Australia. This research modifies existing mechanistic models for malaria vectors and applies it to a wetland field site in Southwest, Western Australia. Environmental monitoring data were applied to an enzyme kinetic model of larval mosquito development to simulate timing of adult emergence and relative population abundance of three mosquito vectors of the Ross River virus for the period of 2018–2020. The model results were compared with field measured adult mosquitoes trapped using carbon dioxide light traps. The model showed different patterns of emergence for the three mosquito species, capturing inter-seasonal and inter-year variation, and correlated well with field adult trapping data. The model provides a useful tool to investigate the effects of different weather and environmental variables on larval and adult mosquito development and can be used to investigate the possible effects of changes to short-term and long-term sea level and climate changes.

Full text

Citation: Staples, K.; Richardson, S.;
Neville, P.J.; Oosthuizen, J. A
Multi-Species Simulation of
Mosquito Disease Vector
Development in Temperate
Australian Tidal Wetlands Using
Publicly Available Data. Trop. Med.
Infect. Dis. 2023,8, 215. https://
doi.org/10.3390/tropicalmed8040215
Academic Editor: John Frean
Received: 21 February 2023
Revised: 15 March 2023
Accepted: 29 March 2023
Published: 3 April 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Tropical Medicine and
Infectious Disease
Article
A Multi-Species Simulation of Mosquito Disease Vector
Development in Temperate Australian Tidal Wetlands Using
Publicly Available Data
Kerry Staples 1, * , Steven Richardson 2, Peter J. Neville 1,3 and Jacques Oosthuizen 1
1Occupational and Environmental Health, School of Medical and Health Sciences, Edith Cowan University,
Joondalup 6027, Australia
2School of Science, Edith Cowan University, Joondalup 6027, Australia
3Biological and Applied Environmental Health, Environmental Health Directorate, Department of Health,
Perth 6849, Australia
*Correspondence: k.staples@ecu.edu.au
Abstract:
Worldwide, mosquito monitoring and control programs consume large amounts of re-
sources in the effort to minimise mosquito-borne disease incidence. On-site larval monitoring is
highly effective but time consuming. A number of mechanistic models of mosquito development
have been developed to reduce the reliance on larval monitoring, but none for Ross River virus,
the most commonly occurring mosquito-borne disease in Australia. This research modifies existing
mechanistic models for malaria vectors and applies it to a wetland field site in Southwest, West-
ern Australia. Environmental monitoring data were applied to an enzyme kinetic model of larval
mosquito development to simulate timing of adult emergence and relative population abundance of
three mosquito vectors of the Ross River virus for the period of 2018–2020. The model results were
compared with field measured adult mosquitoes trapped using carbon dioxide light traps. The model
showed different patterns of emergence for the three mosquito species, capturing inter-seasonal and
inter-year variation, and correlated well with field adult trapping data. The model provides a useful
tool to investigate the effects of different weather and environmental variables on larval and adult
mosquito development and can be used to investigate the possible effects of changes to short-term
and long-term sea level and climate changes.
Keywords:
mathematical modelling; larval mosquito development; vector modelling;
temperature-dependent development; Aedes camptorhynchus;Aedes vigilax;Culex annulirostris
1. Introduction
Mosquitoes are present in environmental habitats ranging from the Arctic to the forests,
deserts, and the tropics [
1
,
2
], and mosquito-borne disease is a global concern. The Ross
River virus (RRV), present in Australia and the Western Pacific region, is the most commonly
reported vector-borne disease in Australia, with 1451 to 9553 cases each year [
3
,
4
]. From
the 1950s until 1987 when it was legally banned, Dichloro-Diphenyl-Trichloroethane (DDT)
was used to control adult mosquitoes via broad spraying [
5
]. Current programs focus on
controlling larval stages of mosquitoes, before they emerge as adults, using highly selective
compounds such as s-methoprene and Bacillus thuringiensis subspecies israelensis (BTI).
This involves larval monitoring, commonly conducted using a dipper at the larval habitat
site [
6
]. To be effective and targeted, this type of monitoring requires routine observation of
known larval habitats, which is time intensive and can only predict adult emergence a few
days to a week in advance. Research into alternative ways of predicting adult mosquito
populations and resultant disease, such as mathematical models, is needed.
Since 2001 a range of statistical methods have been used to assess the potential envi-
ronmental triggers that stimulate mosquito breeding cycles [
7
,
8
]. This has culminated in the
Trop. Med. Infect. Dis. 2023,8, 215. https://doi.org/10.3390/tropicalmed8040215 https://www.mdpi.com/journal/tropicalmed

Trop. Med. Infect. Dis. 2023,8, 215 2 of 23
identification of several important mosquito vectors including Culex annulirostris (Skuse),
Aedes vigilax (Skuse), and Aedes camptorhynchus (Thomson) that are influenced by a range of
environmental triggers including tides, river height, humidity, rainfall, sea-surface temper-
ature, and air temperature. Subsequently models have been developed to predict epidemic
outbreaks at local to regional levels but not at the larger state or national level [8–19].
To reduce the reliance of environmental health practitioners physically conducting
larval monitoring and adult trapping to quantify mosquito activity, models that could
be used to predict Ross River virus epidemics based on environmental variables were
developed [
14
,
19
], thus negating the need for larval or adult mosquito surveillance. While
this approach proved to be successful in many locations, the authors were not able to de-
velop a statistically significant model for all areas studied. Most research to date highlights
the need to incorporate mosquito abundance to increase the predictive power of these
models [17–23].
Explicit models of environmental conditions and mosquito abundance have shown
that adult Ae. camptorhynchus correlates with mean air temperature, Ae. vigilax correlates
with mean air temperature, day length, tide height and tide frequency, and Cx. annulirostris
correlates with site elevation and rainfall [
13
,
24
], but these environmental factors leave
a large proportion of the variability in temporal abundance unexplained. Deterministic
SEIR (Susceptible–Exposed–Infectious–Recovered) modelling has been used to investigate
the relationship between RRV and the life traits of its mosquito vectors and found useful
insights, but it did not encompass explicit larval components or the related real-time
environmental inputs [20,25].
Statistical models together with adult trapping can provide a comprehensive analysis
of disease threat under existing conditions but are unable to predict future events under
changing habitats or environmental conditions. To investigate further, future scenario
simulation models using known life traits of mosquito vectors would be useful for mosquito
and mosquito-borne disease surveillance. Simulation models have been developed for
malaria and dengue mosquito vectors [26–29] but not for Ross River virus vectors.
To develop a useful simulation, a good understanding of the mosquito species under
consideration is required. The mosquito lifecycle consists of four distinct phases: egg, larval,
pupal, and adult, with the larval phase encompassing four instars. Active development of
the sub-adult stages (egg, larval, and pupal) occurs in shallow water environments. The
speed of development through these stages is regulated by temperature. The temperature
response, or speed of development at a given temperature, varies across genera and
species [
30
]. At any of the life stages, except the pupal stage, mosquitoes can enter a stage
of dormancy (quiescence or diapause), although within-species dormancy mechanisms
usually only exist at a single stage [
31
]. This interspecies variation makes it necessary to
build species-specific representations to allow accurate estimation of timing and magnitude
of adult mosquito emergence.
Official description of Australian mosquitoes commenced in the late 19th century and
research into their habitats and physiology gathered pace in the late 20th century [
32
], so
while the total number of studies of RRV vectors is fewer than those focusing on malaria or
dengue, a significant body of research has developed [7,33].
Ross River virus is transmitted by a wide range of mosquito species, the four most
reported in Western Australia are Ae. vigilax,Ae. camptorhynchus,Cx. annulirostris, and
Ae. notoscriptus [
34
]. These species can be characterized by their preferred larval habitats:
saltwater and brackish tidal marshes (Ae. camptorhynchus and Ae. vigilax), permanent to
semi-permanent freshwater sites (Cx. annulirostris), and small volume containers in close
proximity to human populations (Ae. notoscriptus) [
32
,
35
]. This study investigates the
dynamics of larger scale sites, which are suitable for the first three species.
Aedes camptorhynchus and Ae. vigilax are believed to be responsible for a high pro-
portion of RRV transmission in Southwest, Western Australia [
20
,
33
,
36
]. Both emerge in
high numbers from the tidal waters of the Swan River and are capable of dispersing many
kilometers from the site of emergence [37,38].

Trop. Med. Infect. Dis. 2023,8, 215 3 of 23
Both species survive unfavourable environmental conditions via a diapausing egg
stage. Their oviposition site preferences overlap with both preferring Samphire dominant
vegetation habitats [
39
]. It has been theorized that Ae. camptorhynchus prefer sites in high
tidal zones that are recharged by rainfall and groundwater and occasional very high tides
in the cooler months, while Ae. vigilax prefer lower tidal areas, higher salinity levels, and
emerge in higher numbers in the warmest months of the year [
13
]. The relationship between
elevation and population density has resulted in mixed results for Ae. vigilax [
24
,
40
] and
with vegetation being a stronger driver for Ae. camptorhynchus [41].
Eggs of Ae. camptorhynchus can remain viable for well over 12 months [
42
]. One report
of the egg lifespan for Aedes vigilax is between 98 days at 17% relative humidity and
116 days at 65% [
43
]; however, the study methodology is unclear. Robust studies of other
Aedes species have shown egg lifespans exceeding 200 days [
44
,
45
]. There is evidence that
desiccation resistance is related to egg volume [
31
]. Aedes camptorhynchus, with a maximum
egg survival of over 15 months have a larger egg volume than Ae. vigilax [
46
,
47
]; however,
Ae. notoscriptus have an egg viability of over 1 year [
44
], despite a smaller egg volume [
48
].
It is possible the lifespan of Ae. vigilax eggs is significantly longer than previously reported.
Once laid, Ae. vigilax eggs take two days to complete embryonic development at
25
◦
C [
49
] while egg development time in Ae. camptorhynchus has not been studied. Other
Aedes species have development times that vary with temperature. Aedes aegypti take 2 to
20 days to complete egg development [
50
], Aedes albopictus (Skuse) take 4.6 to 42 days at
similar temperatures (Lee, 1994) as cited in [
51
], and Aedes taeniorhynchus, another wetland
species, take 3 to 10 days at 20 to 27
◦
C [
52
]. It is likely Ae. camptorhynchus take longer than
Ae. vigilax to complete egg development, in line with its other life traits, discussed below.
Aedes camptorhynchus hatch over a wide range of temperatures and hatch in installments [
53
].
Aedes vigilax eggs hatch once a minimum temperature threshold is reached, and at warmer
temperatures, all eggs hatch at once [
43
]. Once hatched, development and mortality are
temperature dependent. Aedes camptorhynchus larvae develop into adults within 12 to
37 days [
54
]. Aedes vigilax can complete development in 5 to 20 days in the field and 5 to
14 days when held at constant temperatures in the laboratory [49,55].
Culex annulirostris are found in vegetated freshwater pools, ephemeral or permanent,
including ponded streams, natural and constructed wetlands, and irrigation drains and
have been shown to demonstrate vegetation preferences for oviposition Laird (1988) in [
56
]
and elevation [
24
]. Cx. annulirostris have been found in the same larval habitats as Ae. vigilax,
where it replaces Ae. vigilax as rainfall becomes the predominant environmental driver
rather than brackish tides [
32
,
57
]. Cx. annulirostris can reproduce rapidly at temperatures
above 25
◦
C [
56
] and disperse up to 10 km from its larval habitat [
34
]. Temperature-
dependent development and mortality of all development stages of this species has been
studied [56,58,59] in both laboratory and field conditions. Egg development in this species
is very rapid and is completed within 1.25 to 5 days [
58
]. Adults can survive in laboratory
conditions for up to 70 days [
58
]; however, field studies have estimated a 25 to 30 percent
daily mortality rate, giving a mean survival time of 3–7 days [
60
]. At cooler temperatures
Cx. annulirostris have been shown to enter a state of quiescence and overwinter as adults [
61
],
and this period of dormancy and extended lifespan has been shown to be necessary to
enable RRV transmission in this species [25].
There is sufficient understanding of the biology of these three mosquito species to
enable a simulation model to be developed. A simulation model for mosquito devel-
opment has been developed which includes separate hydrological and sub-adult com-
partments [
27
]. The sub-adult compartment consists of a set of equations describing the
temperature-dependent development of malaria mosquito vectors. It includes a range
of larval characteristics, including the effects of density-dependent mortality, sub-adult
survival rates, and water temperature. Ross River virus mosquito vectors are different,
and some parameters used for malaria may not be relevant. For example, Cx. annulirostris
have been shown to be independent of density mortality [
62
]. Additionally, the literature
for RRV vectors is not as comprehensive, so modification is required. The outputs from

Trop. Med. Infect. Dis. 2023,8, 215 4 of 23
the sub-adult compartment of the malaria model include the number of adult mosquitoes.
This sub-adult compartment was validated by [
26
] using a different hydrology compart-
ment. The main driver of larval development is water temperature. Larval mosquito
development responds non-linearly to temperature, so capturing the entire daily range
of temperature fluctuation is important for accurately predicting mosquito development.
Most temperature-dependent developmental studies are conducted under constant tem-
peratures in the laboratory. It has been well established that mosquito larvae develop
differently under constant versus varying temperatures, but the difference can be higher or
lower depending on which part of the larval temperature range is encompassed [63–68].
The simulation model of [
27
] successfully predicts adult emergence but has some
limitations including estimation of thermal mortality rates and underestimation of water
temperature. The simulation model uses a thermal death point estimate to determine
mortality due to high temperature where 10%, 50%, and 100% of larval mosquitoes die
when temperatures of 1, 2, and 3
◦
C, respectively, above the thermal death point are reached.
This does not account for accumulated mortality at less than lethal temperatures. Thermal
mortality is thought to accumulate in larval mosquitoes. Anopheles quadrimaculatus larvae
reared mostly at 25
◦
C have been found to occasionally emerge successfully at 35.5
◦
C, but
larvae reared constantly at 35 ◦C would never emerge at that temperature [68].
The model described by [
27
] estimates water temperature using a model that assumes
water temperature is always below air temperature. It is well documented that water
temperature can be higher than air temperature for much of the day, due to the high
thermal mass of water, taking longer to heat up and longer to cool down [
69
–
71
]. This
small underestimate of temperature and therefore development, can accumulate over the
immature mosquito stage. A shallow water temperature model that more accurately tracks
water temperatures in a hot, humid region has been developed [
72
]. A modified version of
which has been developed for use in Australian temperature conditions [73].
The aim of this research is to see if the current body of physiological development
knowledge can be applied to simulate the local-scale pattern of emergence of vectors of
Ross River virus in tidal wetland habitats using easily accessed environmental parameters
such as tidal height, rainfall, temperature, and humidity. This simulation model should be
capable of accurately predicting adult mosquito population patterns and assist in improving
statistical models by inclusion of an entomological component without increasing the need
for onsite mosquito surveillance and identification. The information provided would
allow a more nuanced understanding of the dynamics of the mosquito populations within
this habitat and may provide a means to estimate what may happen to mosquito species
diversity and abundance under direct habitat modification, different mosquito control
regimens, or as the local topography and climate change over the coming decades.
The study site, field sampling methods, and sources of public environmental data
are outlined in the Materials and Methods section along with a diagram of the conceptual
model. This is followed by a detailed explanation of the model equations used to represent
the biological processes being simulated for temperature-dependent development and
mortality at the egg, larval/pupal, and adult stages, and how these are applied specifically
to each mosquito species. The number of adult female mosquitoes emerging each week
is given in Section 3and compared with on-site adult trapping data for validation. The
model output for each mosquito species and the Aedes species egg bank is considered in
the Discussion along with a reflection on the use and limitations of the model.
2. Materials and Methods
This research applies an enzyme kinetic model of larval mosquito development
from [
27
]. It incorporates hourly development and mortality estimates for sub-adult
stages of Cx. annulirostris,Ae. vigilax, and Ae. camptorhynchus using the environmental
variables river height, water depth, rainfall, humidity, windspeed, and air temperature.
The outputs are compared with adult trapping data provided by the Local Government
Authority and the Department of Health (WA) for model validation.

Trop. Med. Infect. Dis. 2023,8, 215 5 of 23
The mosquito development compartmentis a set of equations describing the temperature-
dependent development of mosquito vectors. It forms a loop of egg, larval/pupal, and
adult development stages. The models are a set of iterative equations. The presence and
temperature of water are the main drivers of mosquito development and mortality. Salinity,
predation, nutrient limitation, and population density impacts are not included in the current
model parameters.
Larval and pupal stages are confined to the water, so each water body is considered
as a point source of adult mosquito emergence. Hourly temperatures are used as larvae
and pupae develop in shallow water which is homogeneous in temperature [
73
]. Adult
mosquitoes can move to cooler or warmer sub-climates, such as under a shady tree, to
avoid unfavourable conditions, so the daily mean air temperature is used for this stage.
This is a closed-loop system in which all adults emerging and surviving to egg-laying stage
will lay within these same waterbodies. There is no immigration of adults from other water
sources for the Aedes species, but Culex annulirostris requires a stream of fecund adults as
this species has no egg bank.
The model runs for one year to cover the peak breeding season in the southern
hemisphere (from January to December for Ae. camptorhynchus, and from July to June for
the two remaining species) and loops generation by generation. Culex species were allowed
to continue to loop until no more adults emerged. Aedes species were run for four or five
generations until the pattern of emergence stabilized.
2.1. Study Site
The study location is the Ashfield Flats, a tidal wetland adjacent to the Swan River in
the suburb of Bassendean, located approximately 10 km east of Perth, Western Australia.
The ground at the site is flat, varying from 0 to 400 mm in height (Australian Height Datum)
over the main area of interest, Figure 1, and has an area of approximately 16 hectares. The
surrounding land use is predominantly suburban residential.
Trop.Med.Infect.Dis.2023,8,xFORPEERREVIEW6of24



Figure1.AshfieldFlatssitemapshowing100mmcontourbreaklevelsandthelocationofthetwo
waterbodiesstudied.
2.1.1.OnsiteMeasurements
Samplingoftheadultmosquitopopulationusingacarbondioxidelighttrap(CO
2

trap)[74]wasconductedon34occasionsbyofficersfromtheLocalGovernmentAuthor‐
ityasapartoftheirroutinemosquitomonitoringinthearea.Wheremorethanonetrap
wassetonthesameday,thehighesttrapcountwasusedresultingin32trappingevents
overthethreeyearsanalysed.
2.1.2.PublicDataSources
Long‐termtidalheightsforJanuary2018toJuly2021wereobtainedfortheBarrack
StreetJetty[75].Hourlyairtemperature,rainfall,humidity,windspeed,airpressure,and
dailyevapotranspirationandevaporationfortheperiodfromJanuary2018toJuly2021
wereobtainedfromthePerthAirportweatherstation,located2.5kmsoutheastofthe
studysite[76].
2.2.ModelDescription
ThemodelwascodedutilizingRStudioVersion2022.02.3,usingRversion4.2.0and
mainpackages;Matrixv1.4‐1,matrixStatsv0.62.0,dplyrv1.0.9,zoov1.8‐10,andlubridate
v1.8.0(RStudio,PBC,Boston,MA,USA).
2.2.1.HydrologyCompartment
Thehydrologicalcompartmentisamodificationof[27]asdetailedin[73].Waterlev‐
elsfromtheBarrackStreetJettywereusedasaproxyforriverheightatthestudysiteby
addingaverticaladjustmentandmeteorologicalvariablesfromthePerthAirportweather
stationasinputs.Theoutputofthehydrologycompartmentarewaterheightandwater
temperature.Theseareusedasinputstothemosquitodevelopmentcompartment.The
hydrologycompartmenthastwomainsections,waterheightandwatertemperature.
Thewaterheightiscalculatedincrementallyatone‐hourintervals.Atimevector𝑡
oflength𝑛isdefinedsuchthat𝑡󰇛1󰇜isthetimeatwhichthemodelisinitiated,𝑡󰇛𝑛󰇜is
thetimethatthemodelterminates,and𝑡󰇛𝑖1
󰇜𝑡󰇛𝑖󰇜equalsonehourforall𝑖
1,2…,𝑛1.Waterheight,𝑊󰇛𝑖󰇜,mm,denotesthewaterheightattime,t󰇛𝑖󰇜hours.Wa‐
terheightisdeterminedasthebalanceofflowsintoandoutofthesite.Inflowsinclude
site‐specificfixedinflows,𝑈,suchassteamsandpipelines,rainfall,𝑟,andriverheight
𝑅and𝑈thewaterlevelincreaseper1mmofrainfall.Wateroutflowsincludewater
Figure 1.
Ashfield Flats site map showing 100 mm contour break levels and the location of the two
water bodies studied.
The area is subject to routine monitoring of larval, pupal, and adult stages using larval
dippers and encephalitis virus surveillance carbon dioxide (CO
2
) light traps, respectively,
and mosquito control chemicals are applied in response to larval mosquito activity. The
primary chemical used in this area is the briquet formulation of s-methoprene, an insect
growth regulator. As the area is bounded by residences, halting chemical intervention for
the duration of this study would result in higher numbers of nuisance biting and increased

Trop. Med. Infect. Dis. 2023,8, 215 6 of 23
risk of vector-borne disease in the surrounding area, and for this reason, the usual treatment
and control practices were continued.
Two tidal waterbodies within the Ashfield Flats site were modelled. Water height was
measured at 15-min intervals, from August 2018 to November 2020, using staff gauges and
capacitance probes. Initial water depth measurements, conducted by the Department of
Biodiversity, Conservation and Attractions, were taken at 30-min intervals with a HOBO S-
TMB-M006 temperature sensor from 11 September 2019 to 5 November 2019. Supplemental
measurements were taken by the researcher using a LogTag UTRIX-16 temperature logger
contained in a waterproof wrapping from 7 to 11 December 2021.
2.1.1. Onsite Measurements
Sampling of the adult mosquito population using a carbon dioxide light trap (CO
2
trap) [
74
] was conducted on 34 occasions by officers from the Local Government Authority
as a part of their routine mosquito monitoring in the area. Where more than one trap was
set on the same day, the highest trap count was used resulting in 32 trapping events over
the three years analysed.
2.1.2. Public Data Sources
Long-term tidal heights for January 2018 to July 2021 were obtained for the Barrack
Street Jetty [
75
]. Hourly air temperature, rainfall, humidity, wind speed, air pressure, and
daily evapotranspiration and evaporation for the period from January 2018 to July 2021
were obtained from the Perth Airport weather station, located 2.5 km southeast of the study
site [76].
2.2. Model Description
The model was coded utilizing R Studio Version 2022.02.3, using R version 4.2.0 and
main packages; Matrix v 1.4-1, matrixStats v0.62.0, dplyr v1.0.9, zoo v1.8-10, and lubridate
v1.8.0 (RStudio, PBC, Boston, MA, USA).
2.2.1. Hydrology Compartment
The hydrological compartment is a modification of [
27
] as detailed in [
73
]. Water
levels from the Barrack Street Jetty were used as a proxy for river height at the study site by
adding a vertical adjustment and meteorological variables from the Perth Airport weather
station as inputs. The output of the hydrology compartment are water height and water
temperature. These are used as inputs to the mosquito development compartment. The
hydrology compartment has two main sections, water height and water temperature.
The water height is calculated incrementally at one-hour intervals. A time vector
t
of length
n
is defined such that
t(1)
is the time at which the model is initiated,
t(n)
is the
time that the model terminates, and
t(i+1)−t(i)
equals one hour for all
i=
1, 2
. . .
,
n−
1.
Water height,
WH(i)
, mm, denotes the water height at time, t
(i)
hours. Water height is
determined as the balance of flows into and out of the site. Inflows include site-specific
fixed inflows,
UIF
, such as steams and pipelines, rainfall,
ra
, and river height
RH
and
UIV
the water level increase per 1 mm of rainfall. Water outflows include water lost due to
soil infiltration,
UO
, and water lost due to evapotranspiration,
ET
which is scaled with
a user-defined scale factor,
ETO
. The river height impacts the water height only when
a minimum overflow threshold,
RT
, is exceeded, which represents the riverbank height
adjacent to the wetland. All water flow variables have units of mm h−1, Equation (1).
WH(i+1)=RH
WH(i)+UIF +UI V ra−UO−EToET,,RH≥RT
RH<RT(1)
The water temperature is modelled using a one-layer iterative heat balance equation for
shallow water pools developed by [
77
], with a modified calculation method for evaporative
flux,
LE
, as detailed in [
73
], and shown in Equation (3). The change in water temperature
is a function of incoming short wave,
Kin
, and long wave,
Lin
, radiation, and outgoing,

Trop. Med. Infect. Dis. 2023,8, 215 7 of 23
Lout
radiation, allowing for solar reflection,
αt
, and heat exchange via convection at the
water surface,
H
, heat exchange via evaporation at the water surface,
LE
, heat conduction
at the soil/water interface,
Gs
, the density of water,
ρw
, the heat capacity of the water,
cw
,
the depth of water pool,
WH
, and the water temperature at the previous time step,
Tw
,
Equation (2).
Tw(i+1)=Tw(i)+∆t(Kin(1−αt)+Lin −Lout −H−LE −Gs)
(ρwcwWH(i)) (2)
The hydrology compartment was run for 2018–2019, 2019–2020, and 2020–2021. The
outputs for water temperature and water height are shown in Figure 2.
Trop.Med.Infect.Dis.2023,8,xFORPEERREVIEW7of24
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
lostduetosoilinfiltration,𝑈,andwaterlostduetoevapotranspiration,𝐸𝑇whichis
scaledwithauser‐definedscalefactor,𝐸𝑇.Theriverheightimpactsthewaterheight
onlywhenaminimumoverflowthreshold,𝑅,isexceeded,whichrepresentsthe
riverbankheightadjacenttothewetland.Allwaterflowvariableshaveunitsofmmh−1,
Equation(1).
𝑊󰇛𝑖1
󰇜 𝑅
𝑊󰇛𝑖󰇜𝑈
 𝑈
𝑟𝑈
𝐸𝑇
𝐸𝑇,, 𝑅𝑅

𝑅𝑅
(1)
Thewatertemperatureismodelledusingaone‐layeriterativeheatbalanceequation
forshallowwaterpoolsdevelopedby[77],withamodifiedcalculationmethodforevap‐
orativeflux,𝐿𝐸,asdetailedin[73],andshowninEquation(3).Thechangeinwatertem‐
peratureisafunctionofincomingshortwave,𝐾,andlongwave,𝐿,radiation,and
outgoing,𝐿radiation,allowingforsolarreflection,𝛼,andheatexchangeviaconvec‐
tionatthewatersurface,𝐻,heatexchangeviaevaporationatthewatersurface,𝐿𝐸,heat
conductionatthesoil/waterinterface,𝐺,thedensityofwater,𝜌,theheatcapacityof
thewater,𝑐,thedepthofwaterpool,𝑊,andthewatertemperatureattheprevious
timestep,𝑇,Equation(2).
𝑇󰇛𝑖1
󰇜𝑇
󰇛𝑖󰇜∆𝑡󰇛𝐾󰇛1𝛼󰇜𝐿 𝐿 𝐻𝐿𝐸𝐺
󰇜
𝜌𝑐𝑊󰇛𝑖󰇜(2)
Thehydrologycompartmentwasrunfor2018–2019,2019–2020,and2020–2021.The
outputsforwatertemperatureandwaterheightareshowninFigure2.

Figure2.Waterdepthanddailymeanwatertemperatureforwaterbody1(top)andwaterbody2
(bottom)foreachyear.Theblacklineindicatesthemaximumdepthofthepooloncetherivertide
heightrecedes.
2.2.2.MosquitoDevelopmentModel
Parameterestimatesareusedtorepresenta“best‐case”survivalscenarioforeach
mosquitospecies,Figure3.Thiswillover‐estimatethemosquitopopulationbutwillre‐
vealtheoverallpatternsofpopulationgrowthandmortality.Duetothesmallnumberof
studiesofthephysiologyofthethreeRossRivervirusmosquitovectorsitwasnecessary
topoolthelarvalandpupalstagessothattheyaretreatedasasinglestage.Bothlarval
Figure 2.
Water depth and daily mean water temperature for waterbody 1 (
top
) and waterbody
2 (
bottom
) for each year. The black line indicates the maximum depth of the pool once the river tide
height recedes.
2.2.2. Mosquito Development Model
Parameter estimates are used to represent a “best-case” survival scenario for each
mosquito species, Figure 3. This will over-estimate the mosquito population but will reveal
the overall patterns of population growth and mortality. Due to the small number of studies
of the physiology of the three Ross River virus mosquito vectors it was necessary to pool
the larval and pupal stages so that they are treated as a single stage. Both larval and pupal
stages occur in the aquatic environment and are affected similarly by water temperature,
and this simplification reduces the computational complexity of the model while retaining
fidelity to biological processes.

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andpupalstagesoccurintheaquaticenvironmentandareaffectedsimilarlybywater
temperature,andthissimplificationreducesthecomputationalcomplexityofthemodel
whileretainingfidelitytobiologicalprocesses.

Figure3.MosquitodevelopmentforCulexannulirostris(right)andAedesspp.(left).𝑁denotesthe
stagenumber,andtheblueshadedareasshowstagesdependentonthepresenceofwaterforcom‐
pletion.Thedashedlinesindicateanoptionalstage,whichistriggeredbyunfavourableenviron‐
mentalconditions.
Temperature‐DependentDevelopment
Eggandlarval/pupalstagesofdevelopmentaredeterminedbycomparingthecalcu‐
latedcumulativedevelopmenttime,𝐶𝐷,ofthemosquitotoameanvalue,𝐶𝐷,Equation
(3).Whenthecalculatedcumulativedevelopmenttimeexceedsthemeanvalue,develop‐
mentisconsideredcompleteandtheindividualprogressestothenextdevelopmental
stage.Assomeindividualvariationoccursinthefieldthe𝐶𝐷followsanormaldistribu‐
tion,anddevelopmentiscompletewhen:
𝐶𝐷𝐶𝐷
𝑁0,0.1𝐶𝐷
(3)
Thecalculatedcumulativedevelopmenttimeisthesumofthedevelopment,𝑑,at
eachtimestep,𝑘,asshowninEquation(4).
𝐶𝐷∑𝑑

  (4)
Figure 3.
Mosquito development for Culex annulirostris (
right
) and Aedes spp. (
left
).
Ni
denotes
the stage number, and the blue shaded areas show stages dependent on the presence of water
for completion. The dashed lines indicate an optional stage, which is triggered by unfavourable
environmental conditions.
Temperature-Dependent Development
Egg and larval/pupal stages of development are determined by comparing the calculated
cumulative development time,
CDt
, of the mosquito to a mean value,
CD f
, Equation (3).
When the calculated cumulative development time exceeds the mean value, development is
considered complete and the individual progresses to the next developmental stage. As
some individual variation occurs in the field the
CD f
follows a normal distribution, and
development is complete when:
CDt>CDf+N0, 0.1CDf(3)
The calculated cumulative development time is the sum of the development,
dk
, at
each time step, k, as shown in Equation (4).
CDt=∑n
k=1dk(4)
The development at each time step, dk, for k=1, . . . , n, is determined over each time
step,
∆tk=t(k+1)−tk
, in hours, using the water temperature,
Tw
, and the development
rate per hour, r(Tw), as shown in Equation (5).
dk=r(Tw)∆tk(5)

Trop. Med. Infect. Dis. 2023,8, 215 9 of 23
The rate of development,
r(Tw)
, as shown in Equation (6), is governed by a temperature-
dependent enzyme. The calculation requires estimates of the enthalpy of activation of
the enzyme,
∆H6=
A
, and the change in enthalpy associated with low and high temperature
inactivation of the enzyme
∆HL
, and
∆HH
, respectively. These values are estimated using
curve fitting of data from observation of egg, larval, and pupal development.
R
is the
universal gas constant and
ρ(25◦C)
is the development rate per hour at 25
◦
C, assuming
no inactivation of the enzyme,
Tw
is water temperature, and
T(1/2H)
and
T(1/2L)
are the
temperatures, in Kelvin, at which 50% of the enzyme is inactivated at the high and low
temperatures, respectively, which were also determined using curve-fitting to experimental
development rates as described in [78].
r(Tw)=
ρ25◦CTw+273
298 ex p∆H6=
A
R1
298 −1
Tw+273 
1+ex ph∆HL
R1
T1/2L−1
Tw+273 i+exph∆HH
R1
T1/2H−1
Tw+273 i (6)
Temperature-Dependent Mortality
Larval/pupal mortality
M
is a cumulative sum of the hourly mortality at temperature,
Tw
, from the time the stage commences to the
ith
time step, Equation (7). Hourly mortality
is estimated using curve-fitting of data from previous studies [52,54,58,79,80].
M=∑i
tlM(Tw)(7)
Adult mosquito mortality,
MA
is a function of the time since emergence,
t
, and is
estimated using observed data for each species where possible, as shown in Equation (8).
MA=Aln(t)(8)
Not all mosquito species and developmental stages have sufficient data available to
develop consistent mortality and temperature-dependent development curves. Where
required, species- and stage-specific modifications and alternate data sources are used for
the egg and larval/pupal stages.
2.2.3. Egg Stage
Aedes species’ newly laid eggs,
N1
and mature eggs
N2
can accumulate at the site and
form an egg bank. To simulate this, the site is seeded with an equal number of mature
eggs for each species at each contour height (mm). The initial number is estimated for
both species using the density of Ae. vigilax eggs [
81
]. In contrast, Culex species lay eggs
directly on the water surface and commence maturation and hatching without a period
of dormancy. The model for this species commences with a population of adult females,
laying five egg rafts per day, normally distributed with a standard deviation of 1.
The three main characteristics of the egg stage are lifespan, development time, and
hatching triggers: each being species specific, as shown in Table 1.
Egg Survival
Survival for Cx. annulirostris eggs depend only on the presence of water, and they
survive for up to 24 h if water is not present. Eggs are only laid when water is present. The
egg survival proportion for Ae. camptorhynchus is dependent only on time, as shown in
Equation (9).
S(t)=∑i
tl0.9810.99982t(9)
Egg survival in Ae. vigilax depends on relative humidity, H. Two models were tested,
one linear, Equations (10) and (11), and one proportional, Equation (12). The first is an
additive model, determined by adding the mortality at each timestep,
Mi
, from the time
the egg is laid,
tl
. Mortality is equal to the inverse of the lifespan at that humidity,
LE(Hi)
.

Trop. Med. Infect. Dis. 2023,8, 215 10 of 23
Mortality is summed to give a survival proportion giving a maximum egg viability of
around 100 days. The second model takes the proportion surviving each day but multiplies
it, with an adjustment so that the median value is equal to that of the linear model. This
gives a longer maximum egg viability and better represents the real distribution of mosquito
egg survival times [
42
,
44
]. These two survival models are shown for two humidity values,
along with the value for Ae. camptorhynchus, in Figure 4.
Mi =1
LE(H)i
(10)
S(t)=1−∑i
tlMi(11)
S(t)=
i
∏
tl
2exp−2
Mi(12)
Table 1. Egg stage parameters.
Attribute Species Value Reference
Egg hatch mortality Ae. camptorhynchus 2% [42]
Ae. vigilax 17% [40]
Egg maximum lifespan Ae. camptorhynchus 15 months [42]
Ae. vigilax 116 days at 65% RH 98 days at 17% RH [43]
Egg density/m2(initial) Ae. camptorhynchus 0.24 SD 0.05 [81]
Embryonic development time Ae. vigilax 48–54 h [49]
Cx. annulirostris 1.25–5 days at 35 ◦C to 15 ◦C [58]
Instalment hatching rate Ae. camptorhynchus 43% [53]
Ae. vigilax from 0% at 8 ◦C to 98% at 11.5 ◦C [43]
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Ae.vigilax116daysat65%RH98daysat17%
RH[43]
Eggdensity/m2(initial)Ae.camptorhynchus0.24SD0.05[81]
Embryonicdevelopment
time
Ae.vigilax48–54h[49]
Cx.annulirostris1.25–5daysat35°Cto15°C[58]
InstalmenthatchingrateAe.camptorhynchus43%[53]
Ae.vigilaxfrom0%at8°Cto98%at11.5°C[43]
EggSurvival
SurvivalforCx.annulirostriseggsdependonlyonthepresenceofwater,andthey
surviveforupto24hifwaterisnotpresent.Eggsareonlylaidwhenwaterispresent.
TheeggsurvivalproportionforAe.camptorhynchusisdependentonlyontime,asshown
inEquation(9).
𝑆󰇛𝑡󰇜∑0.981

󰇛0.99982󰇜 (9)
EggsurvivalinAe.vigilaxdependsonrelativehumidity,H.Twomodelsweretested,
onelinear,Equations(10)and(11),andoneproportional,Equation(12).Thefirstisan
additivemodel,determinedbyaddingthemortalityateachtimestep,𝑀,fromthetime
theeggislaid,𝑡.Mortalityisequaltotheinverseofthelifespanatthathumidity,𝐿𝐸󰇛𝐻󰇜.
Mortalityissummedtogiveasurvivalproportiongivingamaximumeggviabilityof
around100days.Thesecondmodeltakestheproportionsurvivingeachdaybut
multipliesit,withanadjustmentsothatthemedianvalueisequaltothatofthelinear
model.Thisgivesalongermaximumeggviabilityandbetterrepresentsthereal
distributionofmosquitoeggsurvivaltimes[42,44].Thesetwosurvivalmodelsareshown
fortwohumidityvalues,alongwiththevalueforAe.camptorhynchus,inFigure4.
𝑀𝑖  1
𝐿𝐸󰇛𝐻󰇜(10)
𝑆󰇛𝑡󰇜1∑𝑀

(11)
𝑆󰇛𝑡󰇜  2exp2
𝑀


(12)

Figure4.EggsurvivalbydayforAe.camptorhynchusandtwomodelsforAe.vigilax.Model1—
linear,Model2—proportionalwithequivalentmedian.

Figure 4.
Egg survival by day for Ae. camptorhynchus and two models for Ae. vigilax. Model 1—linear,
Model 2—proportional with equivalent median.
Egg Development and Hatching
In Aedes species, egg development depends on the air temperature,
Tair
, and is mod-
elled using the rate of development equation given in Equation (6). After completing
development, eggs remain dormant until they die (mortality,
M
, equal to one) or are sub-
merged with water. If submerged, a proportion of eggs remaining viable hatch, and the
rest are removed from the model. For Cx. annulirostris, egg development depends on the
temperature of the water,
Tw
, as shown in Figure 5, [
58
] and is modelled using Equation (6).

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EggDevelopmentandHatching
InAedesspecies,eggdevelopmentdependsontheairtemperature,𝑇,andis
modelledusingtherateofdevelopmentequationgiveninEquation(6).Aftercompleting
development,eggsremaindormantuntiltheydie(mortality,𝑀,equaltoone)orare
submergedwithwater.Ifsubmerged,aproportionofeggsremainingviablehatch,and
therestareremovedfromthemodel.ForCx.annulirostris,eggdevelopmentdependson
thetemperatureofthewater,𝑇,asshowninFigure5,[58]andismodelledusing
Equation(6).

Figure5.ModelledandobservedeggdevelopmentperhourforCx.annulirostris,Ae.vigilax,and
Aedesalbopictusspecies.
Aedesvigilaxeggdevelopmentiscompletedin48to54hat25°C[49].Thisfigureis
consistentwitheggdevelopmentinCx.annulirostrisatthesametemperatureand,inthe
absenceoffurtherdata,thecurveforCx.annulirostriseggdevelopmentisusedforboth
species,asshowninFigure5.EggdevelopmenttimeforAe.camptorhynchushasnotbeen
studied,andeggdevelopmentforAedesalbopictusisusedasaproxy(Lee,1994)in[51].
Oncefullymature,eggscanhatchwithinthehour,subjecttothecorrecthatchingtriggers
(Standfast,1967bin[57]).
Aedesvigilaxeggsexhibitaminimumtemperaturethresholdtocommencehatching.
Estimatesofthisvary:maximumairtemperatureisabove20°C[82],ordailyminimum
temperaturesabove11.5°C[43].Temperaturesatthestudysitecaneasilyexceed20°Call
yearroundandhaveminimumtemperaturesabovethoseofSydney,wheretheprevious
studiesoccurred.Weeklymeantemperatureislessvariable.Severalhatchingthresholds
weretested.Ae.camptorhynchusdoesnothaveanexplicithatchingthresholdcitedinthe
literaturebutanumberweretestedforthisspecies.
TheinstalmenthatchingrateforAe.camptorhynchusis0.43[53],anduponhatching
thereisa17%hatchmortality[40].Aedesvigilaxexhibitsomeinstallmenthatching,with
98%ofmatureeggshatchingwhenthewatertemperatureexceeds11.5°Candnone
hatchingifitisbelow8°C[43].Alinearinstalmenthatchingproportionisappliedfor
watertemperaturesbetweenthesetwopoints.
Thenumberofnewlyhatchedlarvae,𝑁isafunctionofthenumberofmatureeggs,
𝑁,thesurvivalproportion,𝑆󰇛𝑡󰇜,theinstallmenthatchingproportion,𝐼,andthehatch
mortality,𝑀,asshowninEquation(13).
𝑁𝑁
𝑆󰇛𝑡󰇜𝐼󰇛1𝑀
󰇜(13)
NohatchmortalityratehasbeenestimatedforAe.vigilax,sothe𝐻termisomitted
fromtheequation.
Figure 5.
Modelled and observed egg development per hour for Cx. annulirostris,Ae. vigilax, and
Aedes albopictus species.
Aedes vigilax egg development is completed in 48 to 54 h at 25
◦
C [
49
]. This figure is
consistent with egg development in Cx. annulirostris at the same temperature and, in the
absence of further data, the curve for Cx. annulirostris egg development is used for both
species, as shown in Figure 5. Egg development time for Ae. camptorhynchus has not been
studied, and egg development for Aedes albopictus is used as a proxy (Lee, 1994) in [
51
].
Once fully mature, eggs can hatch within the hour, subject to the correct hatching triggers
(Standfast, 1967b in [57]).
Aedes vigilax eggs exhibit a minimum temperature threshold to commence hatching.
Estimates of this vary: maximum air temperature is above 20
◦
C [
82
], or daily minimum
temperatures above 11.5
◦
C [
43
]. Temperatures at the study site can easily exceed 20
◦
C all
year round and have minimum temperatures above those of Sydney, where the previous
studies occurred. Weekly mean temperature is less variable. Several hatching thresholds
were tested. Ae. camptorhynchus does not have an explicit hatching threshold cited in the
literature but a number were tested for this species.
The instalment hatching rate for Ae. camptorhynchus is 0.43 [
53
], and upon hatching
there is a 17% hatch mortality [
40
]. Aedes vigilax exhibit some installment hatching, with
98% of mature eggs hatching when the water temperature exceeds 11.5
◦
C and none
hatching if it is below 8
◦
C [
43
]. A linear instalment hatching proportion is applied for
water temperatures between these two points.
The number of newly hatched larvae,
N3
is a function of the number of mature eggs,
N2
, the survival proportion,
S(t)
, the installment hatching proportion,
IH
, and the hatch
mortality, Mh, as shown in Equation (13).
N3=N2S(t)IH(1−Mh)(13)
No hatch mortality rate has been estimated for Ae. vigilax, so the
HM
term is omitted
from the equation.
2.2.4. Larval/Pupal Development and Mortality
The larval/pupal population is modelled by using the existing population, plus the
number of newly hatched eggs, minus deaths due to thermal mortality. Larval/pupal
development for all species is modelled using Equation (6), as shown in Figure 6.

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2.2.4.Larval/PupalDevelopmentandMortality
Thelarval/pupalpopulationismodelledbyusingtheexistingpopulation,plusthe
numberofnewlyhatchedeggs,minusdeathsduetothermalmortality.Larval/pupal
developmentforallspeciesismodelledusingEquation(6),asshowninFigure6.

Figure6.ProportionofmortalityanddevelopmentperhourforAedescamptorhynchus,Aedesvigilax,
andCulexannulirostrislarvalandpupalstagesvs.watertemperature—observedvaluesvs.models.
DevelopmentandmortalityforCx.annulirostrisisbasedonthelaboratoryworkof
[58].ForAe.vigilaxfield‐basedvaluesaredeterminedfromapreviousstudyinwhichonly
theairtemperatureisreported[57].Toestimatewatertemperature,historical
meteorologicalrecordsforDeceptionBay[76]wereobtained,andameanvalueof4°C
abovetheairtemperatureasthewatertemperaturewasusedtoestimatedaily
developmentproportions.Lowandhightemperaturedevelopmentlimitsweresetat16
°Cand45.5°C,respectively.Thelowtemperaturethresholdissetaslarvaearenot
observedbelowthistemperature[43].Thehightemperaturedevelopmentthresholdisset
at45.5°Candisbasedonone‐hourThermalDeathPointsforlarvaeofCx.annulirostris
[80]andAe.aegypti[79,83]asitisaspeciesthatalsodevelopsathightemperatures.
Ae.camptorhynchusdevelopmenthasnotbeenstudiedathighorlowtemperature
limits.Alowtemperaturedevelopmentthresholdof7.3°Chasbeenpreviouslyestimated
[55],sothisisusedasthelowtemperaturelimit;thehightemperaturedevelopmentlimit
issetat40°C,asitslarval/pupaldevelopmentpeaksatalowertemperaturethanAe.
aegypti[84].
Mortalityforallthreespecieswasmodelledasaquadraticrelationshipfittothe
observeddataforAe.camptorhynchusandCx.annulirostris.Thisislikelytobean
overestimationofhightemperaturesurvival,especiallyforCx.annulirostris;however,the
increasedmortalityattemperaturesover35°Ccurtailssuccessfuldevelopment,seeFigure
6.NostudieshavebeenconductedonAe.vigilaxlarvalmortality,soobservationsforAe.
taenirohynchus[52]wereused,Figure6.
Figure 6.
Proportion of mortality and development per hour for Aedes camptorhynchus,Aedes vigilax,
and Culex annulirostris larval and pupal stages vs. water temperature—observed values vs. models.
Development and mortality for Cx. annulirostris is based on the laboratory work of [
58
].
For Ae. vigilax field-based values are determined from a previous study in which only the
air temperature is reported [
57
]. To estimate water temperature, historical meteorological
records for Deception Bay [
76
] were obtained, and a mean value of 4
◦
C above the air
temperature as the water temperature was used to estimate daily development proportions.
Low and high temperature development limits were set at 16
◦
C and 45.5
◦
C, respectively.
The low temperature threshold is set as larvae are not observed below this temperature [
43
].
The high temperature development threshold is set at 45.5
◦
C and is based on one-hour
Thermal Death Points for larvae of Cx. annulirostris [
80
] and Ae. aegypti [
79
,
83
] as it is a
species that also develops at high temperatures.
Ae. camptorhynchus development has not been studied at high or low temperature lim-
its. A low temperature development threshold of 7.3
◦
C has been previously estimated [
55
],
so this is used as the low temperature limit; the high temperature development limit is set at
40 ◦C, as its larval/pupal development peaks at a lower temperature than Ae. aegypti [84].
Mortality for all three species was modelled as a quadratic relationship fit to the observed
data for Ae. camptorhynchus and Cx. annulirostris. This is likely to be an overestimation of
high temperature survival, especially for Cx. annulirostris; however, the increased mortality
at temperatures over 35
◦
C curtails successful development, see Figure 6. No studies have
been conducted on Ae. vigilax larval mortality, so observations for Ae. taenirohynchus [
52
] were
used, Figure 6.
2.2.5. Adult Development and Mortality
The male to female emergence rate is 1:1 for all species. A 183/201 male to female
emergence rate for Cx. annulirostris has been reported [
53
]; however, this is not a statistically
significant difference (z-test, p= 0.359).

Trop. Med. Infect. Dis. 2023,8, 215 13 of 23
The longevity of Cx. annulirostris has been shown to vary both by the age of the
mosquito [
58
], as shown in Figure 7, and air temperature and is modelled as the mean
cumulative temperature,
Tculm
, since the time the adult emerged. The adult mortality
probability,
MA
, at time,
t
, in hours is given by Equation (14) where the functions
f(Tculm )
and g(Tculm )are determined by curve fitting.
MA=f(Tculm )+g(Tculm )×ln(t/24)(14)
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2.2.5.AdultDevelopmentandMortality
Themaletofemaleemergencerateis1:1forallspecies.A183/201maletofemale
emergencerateforCx.annulirostrishasbeenreported[53];however,thisisnota
statisticallysignificantdifference(z‐test,p=0.359).
ThelongevityofCx.annulirostrishasbeenshowntovarybothbytheageofthe
mosquito[58],asshowninFigure7,andairtemperatureandismodelledasthemean
cumulativetemperature,𝑇,sincethetimetheadultemerged.Theadultmortality
probability,𝑀,attime,𝑡,inhoursisgivenbyEquation(14)wherethefunctions
𝑓󰇛𝑇󰇜and𝑔󰇛𝑇󰇜aredeterminedbycurvefitting.
𝑀
𝑓
󰇛𝑇󰇜𝑔󰇛𝑇󰇜  𝑙𝑛󰇛𝑡/24󰇜(14)

Figure7.ObservedadultdailyandcumulativemortalityforCx.annulirostris(left).Modelled
cumulativemortalityforAedescamptorhynchus,Aedesvigilax,andCulexannulirostrisbytimesince
emergence(right).
Toaccountfortheabilityofadultfemalestoenterastateofquiescence,maximum
lifeexpectancyissetat70dayswhenthemeanweeklyairtemperatureisbelow17°C.
Thistemperatureischosenasitisgenerallythetemperatureatthestudysitebetween
MayandOctobereachyear,whenthespeciesisgenerallyfoundtobepresentbutinactive
insimilarclimatesinAustralia[61,85].
ThelongevityofAe.camptorhynchusisagedependent,Equation(15),andisestimated
usingtheobservationsforthisspecies’survivalinthelaboratoryat20°C[53],andupper
thermallimitsaresetbasedonobservationsforAe.albopictusof50%and90%mortalityat
35°Cand37.5°C,respectively[86].NoestimatesareavailableforAe.vigilaxlifespanso
theyaremodelledonthedailysurvivalestimateof0.178forAe.aegypti[86].Mortality
curvesforallthreespeciesareshowninFigure7.
𝑀 0.421ln󰇛𝑡󰇜 0.584(15)
GonadotrophicTime
Adultsareassumedtohaveunlimitedaccesstoblood‐mealhosts,sobloodfeeding
andgonadotrophicdevelopmentaremodelledasasinglestage,calledgonadotrophic
development,𝐷.
Gonadotrophicdevelopmentistemperaturedependent.Developmenttimesforeach
speciesaregiveninTable2.Thereislimitedinformationaboutthetemperature
relationshipwithgonadotrophicdevelopment,soalinearmodelwasfittedsuchthat
gonadotrophicdevelopmentperhour,𝐷,isafunctionofmeanairtemperature,
𝑇,fittedtospeciesobservations,asinEquation(16).
𝐷
𝐴
𝑒 𝑇 𝐵(16)
Figure 7.
Observed adult daily and cumulative mortality for Cx. annulirostris (
left
). Modelled
cumulative mortality for Aedes camptorhynchus,Aedes vigilax, and Culex annulirostris by time since
emergence (right).
To account for the ability of adult females to enter a state of quiescence, maximum life
expectancy is set at 70 days when the mean weekly air temperature is below 17
◦
C. This
temperature is chosen as it is generally the temperature at the study site between May and
October each year, when the species is generally found to be present but inactive in similar
climates in Australia [61,85].
The longevity of Ae. camptorhynchus is age dependent, Equation (15), and is estimated
using the observations for this species’ survival in the laboratory at 20
◦
C [
53
], and upper
thermal limits are set based on observations for Ae. albopictus of 50% and 90% mortality
at 35
◦
C and 37.5
◦
C, respectively [
86
]. No estimates are available for Ae. vigilax lifespan
so they are modelled on the daily survival estimate of 0.178 for Ae. aegypti [
86
]. Mortality
curves for all three species are shown in Figure 7.
MA=0.421ln(t)−0.584 (15)
Gonadotrophic Time
Adults are assumed to have unlimited access to blood-meal hosts, so blood feeding
and gonadotrophic development are modelled as a single stage, called gonadotrophic
development, DG.
Gonadotrophic development is temperature dependent. Development times for each
species are given in Table 2. There is limited information about the temperature relationship
with gonadotrophic development, so a linear model was fitted such that gonadotrophic
development per hour,
DG
, is a function of mean air temperature,
Tairmean
, fitted to species
observations, as in Equation (16).
DG=AeTairmean +B(16)

Trop. Med. Infect. Dis. 2023,8, 215 14 of 23
Table 2. Adult mosquito parameters.
Attribute Species Value Reference
Gonadotrophic time
Ae. vigilax 72–96 h [43,50]
Ae. camptorhynchus 5–21 days [54]
Cx. annulirostris 4–9 days [43]
Daily mortality
Ae. vigilax 0.178 (Ae. aegypti) [87]
Ae. camptorhynchus 17.4 days at 20 ◦C, 5–43 days [54]
Cx. annulirostris age & temperature dependent [43,60]
Egg batch size
Ae. vigilax N~(69.3, 19.8) [88]
Ae. camptorhynchus N~(64, 18) [54,55]
Cx. annulirostris 100–260 [58]
Egg Laying and Egg Batch Size
Ae. camptorhynchus lay eggs at the edge or on the water surface, which can then float
to the edge of the pool and lodge in the mud, or sink to the water bottom [
53
] within the
preferred vegetation complex [
41
]. Ae. vigilax have been observed to prefer damp areas
in the same habitat. In the model, eggs are laid at the current water height,
WH(i)
, plus a
random variable with SD 50 mm above the water level, Equation (17).
HL(i)=WH(i)+|N∼(0, 50)|(17)
If one pond is dry when a female is ready for oviposition, all eggs are laid around the
pond containing water. If both ponds are dry the eggs are considered to be either not laid,
or to not have sufficient moisture to be viable and so are removed from the model.
Cx. annulirostris females lay egg rafts on the water surface, if the water level is zero, they
do not survive. The egg batch size for Cx. annulirostris is modelled as a quadratic equation of
mean water temperature fitted to observed values which have a peak of around 260 eggs at
27.5
◦
C and decrease at lower and higher temperatures [
59
]. Egg batch sizes for the two Aedes
species are drawn from a normal distribution with no temperature dependence.
3. Results
The model was run for 2018–2019, 2019–2020 and 2020–2021 for Culex annulirostris and
Aedes vigilax, which are most active during summer, and 2019 and 2020 for
Aedes camptorhynchus which remains active in winter and early spring. Figure 8shows the
modelled number of adult females emerging for each species for each of the simulated years.
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Table2.Adultmosquitoparameters.
AttributeSpeciesValueReference
Gonadotrophictime
A
e.vigilax72–96h[43,50]
A
e.camptorhynchus5–21days[54]
Cx.annulirostris4–9days[43]
Dailymortality
A
e.vigilax0.178(Ae.aegypti)[87]
A
e.camptorhynchus17.4daysat20°C,5–43days[54]
Cx.annulirostrisage&temperaturedependent[43,60]
Eggbatchsize
A
e.vigilaxN~(69.3,19.8)[88]
A
e.camptorhynchusN~(64,18)[54,55]
Cx.annulirostris100–260[58]
EggLayingandEggBatchSize
Ae.camptorhynchuslayeggsattheedgeoronthewatersurface,whichcanthenfloat
totheedgeofthepoolandlodgeinthemud,orsinktothewaterbottom[53]withinthe
preferredvegetationcomplex[41].Ae.vigilaxhavebeenobservedtopreferdampareasin
thesamehabitat.Inthemodel,eggsarelaidatthecurrentwaterheight,𝑊󰇛𝑖󰇜,plusa
randomvariablewithSD50mmabovethewaterlevel,Equation(17).
𝐻󰇛𝑖󰇜  𝑊󰇛𝑖󰇜|𝑁~󰇛0,50󰇜|(17)
Ifonepondisdrywhenafemaleisreadyforoviposition,alleggsarelaidaroundthe
pondcontainingwater.Ifbothpondsaredrytheeggsareconsideredtobeeithernotlaid,
ortonothavesufficientmoisturetobeviableandsoareremovedfromthemodel.
Cx.annulirostrisfemaleslayeggraftsonthewatersurface,ifthewaterleveliszero,
theydonotsurvive.TheeggbatchsizeforCx.annulirostrisismodelledasaquadratic
equationofmeanwatertemperaturefittedtoobservedvalueswhichhaveapeakof
around260eggsat27.5°Canddecreaseatlowerandhighertemperatures[59].Eggbatch
sizesforthetwoAedesspeciesaredrawnfromanormaldistributionwithnotemperature
dependence.
3.Results
Themodelwasrunfor2018–2019,2019–2020and2020–2021forCulexannulirostris
andAedesvigilax,whicharemostactiveduringsummer,and2019and2020forAedes
camptorhynchuswhichremainsactiveinwinterandearlyspring.Figure8showsthe
modellednumberofadultfemalesemergingforeachspeciesforeachofthesimulated
years.

Figure 8.
Modelled number of adult females over time. CxA = Culex annulirostris, AeV = Aedes vigilax,
AeC = Aedes camptorhynchus.

Trop. Med. Infect. Dis. 2023,8, 215 15 of 23
3.1. Culex annulirostris
The predicted number of adult female Cx. annulirostris remained low during 2018–2019
and 2019–2020. Overall, 2020–2021 is a peak year for species numbers, and 2018–2019 and
2019–2020 were less productive. Figure 9compares the modelled number of adult females
with the number caught during adult trapping.
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Figure8.Modellednumberofadultfemalesovertime.CxA=Culexannulirostris,AeV=Aedesvigilax,
AeC=Aedescamptorhynchus.
3.1.Culexannulirostris
ThepredictednumberofadultfemaleCx.annulirostrisremainedlowduring2018–
2019and2019–2020.Overall,2020–2021isapeakyearforspeciesnumbers,and2018–2019
and2019–2020werelessproductive.Figure9comparesthemodellednumberofadult
femaleswiththenumbercaughtduringadulttrapping.

Figure9.AdultfemaleCulexannulirostrisnumbersfor2018–2019,2019–2020,and2020–2021;shown
withadulttrappingresultsplottedontheleft‐handsidey‐axis.CxA=Culexannulirostris.
3.2.Aedescamptorhynchus
Aedescamptorhynchusshowapatternoflargepeaksinspringandasmallerpeakin
lateautumneachyear,with2020–2021beingthemostproductive.Adulttrapping
numbersagreewellwithmodellednumbersofAe.camptorhynchus.Thebestmodelfitwas
whenahatchingthresholdtemperatureof15°Cwasapplied.Theyear2019showed
relativelylowlevelsofactivityand2020relativelyhigheractivity.Peakadultpopulations
occurinearlyandlatespringinbothyears,asshowninFigure10.

Figure 9.
Adult female Culex annulirostris numbers for 2018–2019, 2019–2020, and 2020–2021; shown
with adult trapping results plotted on the left-hand side y-axis. Cx A = Culex annulirostris.
3.2. Aedes camptorhynchus
Aedes camptorhynchus show a pattern of large peaks in spring and a smaller peak in
late autumn each year, with 2020–2021 being the most productive. Adult trapping numbers
agree well with modelled numbers of Ae. camptorhynchus. The best model fit was when a
hatching threshold temperature of 15
◦
C was applied. The year 2019 showed relatively low
levels of activity and 2020 relatively higher activity. Peak adult populations occur in early
and late spring in both years, as shown in Figure 10.
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
Figure8.Modellednumberofadultfemalesovertime.CxA=Culexannulirostris,AeV=Aedesvigilax,
AeC=Aedescamptorhynchus.
3.1.Culexannulirostris
ThepredictednumberofadultfemaleCx.annulirostrisremainedlowduring2018–
2019and2019–2020.Overall,2020–2021isapeakyearforspeciesnumbers,and2018–2019
and2019–2020werelessproductive.Figure9comparesthemodellednumberofadult
femaleswiththenumbercaughtduringadulttrapping.

Figure9.AdultfemaleCulexannulirostrisnumbersfor2018–2019,2019–2020,and2020–2021;shown
withadulttrappingresultsplottedontheleft‐handsidey‐axis.CxA=Culexannulirostris.
3.2.Aedescamptorhynchus
Aedescamptorhynchusshowapatternoflargepeaksinspringandasmallerpeakin
lateautumneachyear,with2020–2021beingthemostproductive.Adulttrapping
numbersagreewellwithmodellednumbersofAe.camptorhynchus.Thebestmodelfitwas
whenahatchingthresholdtemperatureof15°Cwasapplied.Theyear2019showed
relativelylowlevelsofactivityand2020relativelyhigheractivity.Peakadultpopulations
occurinearlyandlatespringinbothyears,asshowninFigure10.

Figure 10.
Adult female Aedes camptorhynchus numbers for 2019 and 2020; shown with adult trapping
results plotted on the left-hand side y-axis. Three different egg hatching threshold temperatures were
tested, bottom left.

Trop. Med. Infect. Dis. 2023,8, 215 16 of 23
3.3. Aedes vigilax
Aedes vigilax show a large summer peak in 2020–2021 and not much activity in 2018–2019
or 2019–2020. When using the linear model of egg mortality Ae. vigilax became extinct within
the first generation in 2018–2019 and 2020–2021. In 2019–2020 it ran for four generations
and the egg bank remained relatively stable, starting with 24,000 eggs and ending with
approximately 20,000. Using the proportional model of egg mortality no extinction occurred,
and the resulting adult numbers are shown in Figure 11. Overall, using the hatching threshold
temperature of 19
◦
C gave the most alignment with adult trapping records so was used for
the model outputs shown.
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Figure10.AdultfemaleAedescamptorhynchusnumbersfor2019and2020;shownwithadult
trappingresultsplottedontheleft‐handsidey‐axis.Threedifferentegghatchingthreshold
temperaturesweretested,bottomleft.
3.3.Aedesvigilax
Aedesvigilaxshowalargesummerpeakin2020–2021andnotmuchactivityin2018–
2019or2019–2020.WhenusingthelinearmodelofeggmortalityAe.vigilaxbecameextinct
withinthefirstgenerationin2018–2019and2020–2021.In2019–2020itranforfour
generationsandtheeggbankremainedrelativelystable,startingwith24,000eggsand
endingwithapproximately20,000.Usingtheproportionalmodelofeggmortalityno
extinctionoccurred,andtheresultingadultnumbersareshowninFigure11.Overall,
usingthehatchingthresholdtemperatureof19°Cgavethemostalignmentwithadult
trappingrecordssowasusedforthemodeloutputsshown.

Figure11.AdultfemaleAedesvigilaxnumbersfor2018–2019,2019–2020,and2020–2021seasons;
shownwithadulttrappingresultsplottedontheleft‐handsidey‐axis.Acomparisonofadultfemale
numberswiththreeegghatchingthresholdtemperaturesisalsoshown,bottomright.AeV=Ae.
vigilax.
EggBank
Themeanheightofeggslaidiswellabovethebaselevelofthewaterbodyandclose
totheoverflowriverbanklevelforbothmosquitospecies.Themeanheightofeggsthat
eventuallyhatchishigherthanthemeanheightofeggslaidforbothspecies,indicating
thateggslaidhigherinthelandscapebutwithinthereachoftidalinundationhavea
greatersurvivalprobability.Thedistributionofeggslaidandhatchedbyheightisgiven
inFigure12.Thisshowsthewaterbodywiththeloweroverflowthresholdproducesthe
highestnumberofhatchedeggsforbothspecies.Asummaryofthedistributionofthe
eggslaidandhatchedbyheightforeachwaterbodyisshowninTable3.
Table3.EggbankheightcharacteristicsforAe.vigilaxandAe.camptorhynchus.
BaseLevel(mm)OverflowHeight
(mm)𝒙
HeightLaid(mm)𝒙
HeightHatched
(mm)
MosquitoSpeciesPond1Pond2Pond1Pond2Pond1Pond2Pond1Pond2
Figure 11.
Adult female Aedes vigilax numbers for 2018–2019, 2019–2020, and 2020–2021 seasons; shown
with adult trapping results plotted on the left-hand side y-axis. A comparison of adult female numbers
with three egg hatching threshold temperatures is also shown, bottom right. AeV = Ae. vigilax.
Egg Bank
The mean height of eggs laid is well above the base level of the waterbody and close
to the overflow riverbank level for both mosquito species. The mean height of eggs that
eventually hatch is higher than the mean height of eggs laid for both species, indicating
that eggs laid higher in the landscape but within the reach of tidal inundation have a
greater survival probability. The distribution of eggs laid and hatched by height is given
in Figure 12. This shows the waterbody with the lower overflow threshold produces the
highest number of hatched eggs for both species. A summary of the distribution of the
eggs laid and hatched by height for each waterbody is shown in Table 3.

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
Ae.camptorhynchus230320400520497
SD77
506
SD73
464
SD40
578
SD39
Ae.vigilax230320400520449
SD84
501
SD81
486
SD54
573
SD49

Figure12.Distributionoftheheightsatwhicheggswerelaid(left)andhatched(right)forAe.
camptorhynchus(bottom)andAe.vigilax(top)eggs.
4.Discussion
Themodelrespondswelltodifferingenvironmentalparameters.Allmosquito
speciesshowedrelativelyhigherpopulationsinthefinalyearmodelledbutwithintra‐
speciesdifferences.Thisshowsthemodelissensitivetospecies‐specificparameters.
Overall,theseasonof2020–2021supportedhigherlevelsoffemaleemergence.Thisis
reflectedinthenumberoffieldsampledadults.Thatyearwascharacterizedby
significantlymorefrequenttidalinundation,especiallyinsummer,andhigherspring
rainfall,morethantwiceasmuchastheprevioustwospringseasons.Maximumand
minimumairtemperaturesweresimilaracrossallthreeyears.Bothwaterbodiesweredry
formorethan12hon8daysin2020–2021,ascomparedto33and54daysin2019–2020
and2018–2019,respectively.
4.1.Culexannulirostris
TheoutputshowsCx.annulirostrisdonotbreedprolificallyinthisenvironment
duringthepeaksummerperioddespitebeingconsideredasummerbreedingspecies.In
theAutumnof2020–2021theirnumbersaccumulatedtoacertainextent.Thisagreeswith
thepreviousobservationsby[57],andCooling,1923bin[32]thatCx.annulirostrisdisplace
Ae.vigilaxtowardstheendoftheseason.Aphysicalexplanationforthisliesinthehigh
mortalityproportionattemperaturesabove33°CthanforAedesvigilax,seeFigure6.The
highdailytemperaturefluctuationofanexposedshallowwaterenvironmentregularly
placesthemoutsideoftheirhighertemperaturedevelopmentlimit.Theirlarvalmortality
athighertemperatureswouldsuggestthattheywouldsurvivebetterinadeeperwater
environmentwhichdoesnothavesuchlargedailytemperaturefluctuations.Thisis
supportedbytheobservationsof[88]whoshowedthatwhengivenachoiceofthreewater
Figure 12.
Distribution of the heights at which eggs were laid (
left
) and hatched (
right
) for
Ae. camptorhynchus (bottom) and Ae. vigilax (top) eggs.
Table 3. Egg bank height characteristics for Ae. vigilax and Ae. camptorhynchus.
Base Level (mm) Overflow Height (mm) −
xHeight Laid (mm) −
xHeight Hatched (mm)
Mosquito Species Pond 1 Pond 2 Pond 1 Pond 2 Pond 1 Pond 2 Pond 1 Pond 2
Ae. camptorhynchus 230 320 400 520 497
SD 77
506
SD 73
464
SD 40
578
SD 39
Ae. vigilax 230 320 400 520 449
SD 84
501
SD 81
486
SD 54
573
SD 49
4. Discussion
The model responds well to differing environmental parameters. All mosquito species
showed relatively higher populations in the final year modelled but with intra-species
differences. This shows the model is sensitive to species-specific parameters. Overall, the
season of 2020–2021 supported higher levels of female emergence. This is reflected in the
number of field sampled adults. That year was characterized by significantly more frequent
tidal inundation, especially in summer, and higher spring rainfall, more than twice as much
as the previous two spring seasons. Maximum and minimum air temperatures were similar
across all three years. Both waterbodies were dry for more than 12 h on 8 days in 2020–2021,
as compared to 33 and 54 days in 2019–2020 and 2018–2019, respectively.
4.1. Culex annulirostris
The output shows Cx. annulirostris do not breed prolifically in this environment
during the peak summer period despite being considered a summer breeding species.
In the Autumn of 2020–2021 their numbers accumulated to a certain extent. This agrees
with the previous observations by [
57
], and Cooling, 1923b in [
32
] that Cx. annulirostris
displace Ae. vigilax towards the end of the season. A physical explanation for this lies
in the high mortality proportion at temperatures above 33
◦
C than for Aedes vigilax, see
Figure 6. The high daily temperature fluctuation of an exposed shallow water environment

Trop. Med. Infect. Dis. 2023,8, 215 18 of 23
regularly places them outside of their higher temperature development limit. Their larval
mortality at higher temperatures would suggest that they would survive better in a deeper
water environment which does not have such large daily temperature fluctuations. This
is supported by the observations of [
88
] who showed that when given a choice of three
water depths Cx. annulirostris lay egg rafts preferentially in deeper water, with over two
thirds of rafts being laid in water 100 mm deep. Culex in the field have also been shown
to prefer deeper water relative to Aedes species [
25
,
89
]. The modelled increase in Culex
adult numbers was not reflected in the adults trapped at the end of the 2020/2021 season.
This could be due to the effects of the long-term s-methoprene application that season,
which had been in place since September 2021. It is also likely that temperatures from April
onward are cooling and adults during this period become less active, possibly entering
quiescence or diapause, as they head into their overwintering period. CO
2
light trap counts
can be negatively affected by local environmental conditions such as excessive wind, rain,
or temperatures below their lower activity threshold. CO
2
traps attract females looking
for a blood meal and catch only a portion of those nearby. A previous study found only
13–16% of approaching females were captured by the trap [
90
]. Thus, this trap bias could
explain the reduction in the observed adult females in the surrounding area which would
be expected to be higher.
Overall Cx. annulirostris population numbers are limited at this site, but the site condi-
tions will change as climate change increases sea levels and temperatures [
88
], particularly
if the site becomes more frequently inundated and capable of supporting floating aquatic
macrophytes which can be an ovipositional preference for this species [
89
]. If water depth
increases under these scenarios, Cx annulirostris is likely to become a more dominant vector
species in this region.
4.2. Aedes camptorhynchus
It has been theorised that a cool, wet spring with occasional very high tides is more
likely to produce large numbers of Ae. camptorhynchus [
18
]; however, the results of this
study were that Aedes camptorhynchus had a relatively larger population in the spring of 2020
than in 2019. Overall, 2020 had more frequent and higher tidal inundations, so this does
not support this view, although longer-term analysis would be required to be definitive.
It was also proposed that Ae. camptorhynchus larval populations are driven significantly
by rainfall [
18
], although this was subsequently disproved for southern Western Australia
by [
90
]. The output of this model also shows that rainfall is not a significant driver of larval
population in the study area. This highlights the importance of applying models to local
conditions as small changes in soil type, rainfall, or elevation can result in large differences
in larval populations.
There is a lot that is yet unknown about this species including its development,
survival, hatching thresholds at lower temperatures, upper thermal mortality limit, if
there is a variation in egg batch size with temperature, how the adult lifespan may vary
with temperature, and whether installment hatching may vary with the environmental.
For example, eggs of Ae. albopictus laid under shorter photoperiods may hatch relatively
later in the following season [
91
], and Ae. albopictus may undergo diapause in the adult
stage and the egg stage in temperate environments [
84
]. Overall, the model is very useful
for predicting peaks in Ae. camptorhynchus abundance at the field site, and the relative
magnitude of those peaks across years.
4.3. Aedes vigilax
The model for Ae. vigilax showed strong agreement with the adult trapping record
overall, with limited breeding in 2018/2019 and 2019/2020, and a large population peak in
2020/2021. Being able to populate in high numbers in high summer temperatures and in a
shallow water environment show Ae. vigilax has an ability to exploit an ecological niche
in which other mosquito species struggle to survive. This species is very sensitive to the
lower egg hatching threshold. Determining what egg conditioning is required for hatching

Trop. Med. Infect. Dis. 2023,8, 215 19 of 23
is very important, and a few degrees can change the pattern of emergence for the entire
year. When the hatching threshold was set at 17
◦
C the number of females in early summer
was low. When the threshold was 21
◦
C the number of females in early summer was too
low and in late summer was too high. It was estimated that egg conditioning of a mean
weekly air temperature of approximately 19
◦
C may be what occurs in this species, but this
needs to be confirmed with further research.
Contrary to the idea that Ae. vigilax are more likely to occur in large numbers during
periods of high temperature and low tides [
13
], for 2020–2021 Figure 2shows that the
frequency and heights of the tides in summer were greater than the previous 2 years and
these proved conducive to large populations of Ae. vigilax. This species certainly requires
the high temperatures of summer to thrive in large numbers, but frequent inundation also
results in large populations. However, if inundation increased to the point of becoming
permanent, it is possible fish and other predators of this species may become established
and reduce the overall numbers of Ae. vigilax emerging from the site.
Further research into egg longevity at different temperatures is required for this species,
as this can have a marked impact on survival at specific sites. Under the linear model, this
species became locally extinct in the first half of 2018/2019. Extending the lifespan slightly
allowed the species to survive. It is possible that local extinction occurs, and repopulation
is from nearby areas with more favorable conditions; however, it would seem likely that
nearby surrounding areas experience similar conditions as they are flooded by the same
water source with a very similar frequency.
Egg Bank Height
Under the conditions of the model, both Aedes species lay eggs at around the water-
body overflow height, as shown in Table 3. This agrees with previous research finding
higher numbers of Ae. vigilax eggshells at the edges of ponds and depressions and relatively
fewer at the bottom of ponds [
81
,
92
]. It also supports the finding that Ae. camptorhynchus
distribution within saltmarsh environments was more related to vegetation than eleva-
tion [
41
]. This is consistent with the modelled egg distribution as samphire vegetation is
found on the edges of pools and surrounds, rather than the bottoms. The mean hatching
height for both species was higher and more dispersed, as indicated by the larger standard
deviation, indicating that higher egg laying height increases the probability of successful
hatching, with the higher elevation limit being the frequency of high tides being less than
the lifespan of the egg.
5. Conclusions
The model will be useful for examining the effect of different seasonal patterns and
other possible impacts on the abundance of these species but is limited by the relatively
small number of studies on the physiology of these species. Other major gaps in knowledge
are the egg hatching cues such as the minimum temperature thresholds, egg development
rate, gonadotrophic time and adult survival at different temperatures for the Aedes species,
and the larval development and mortality rates at the limits of their temperature range,
particularly the high-end limit as that will become more relevant as the climate changes.
After further testing and validation of this model across a range of sites, it could
be used to provide insight into different treatment regimens, predicting the impact of
treatment of different periods or over different areas. It could also be coupled with a
spatial heterogeneous adult dispersal model, to further explore disease transmission dy-
namics, or be coupled with remote water-height sensing in regional areas to assist in
mosquito control programs in locations with many waterbodies that require monitoring
but where there is limited resourcing to do so. This model can also be used to investigate
the changes to mosquito species diversity and abundance at a local scale under different
climate change scenarios.

Trop. Med. Infect. Dis. 2023,8, 215 20 of 23
Author Contributions:
Conceptualization, K.S.; methodology, K.S. and S.R.; software, K.S.; valida-
tion, K.S., S.R. and P.J.N.; formal analysis, K.S.; investigation, K.S.; resources, K.S., P.J.N. and J.O.;
data curation, K.S.; writing—original draft preparation, K.S.; writing—review and editing, K.S., S.R.,
P.J.N. and J.O.; visualization, K.S.; supervision, S.R., P.J.N. and J.O.; project administration, K.S. All
authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
Publicly available datasets were analyzed in this study. Tide height
data was obtained from the Department of Transport and are available from https://www.transport.
wa.gov.au/imarine/download-tide-wave-data.asp (accessed on 19 November 2021). River height
data was obtained from the Department of Transport and are available from https://www.transport.
wa.gov.au/imarine/download-tide-wave-data.asp (accessed on 19 November 2021). Restrictions
apply to the availability of weather data, which was obtained from the Bureau of Meteorology and
are available from http://www.bom.gov.au/climate/data-services/data-requests.shtml (accessed
on 21 November 2021) with the permission of the Bureau of Meteorology. Restrictions apply to
the availability of mosquito monitoring data, which were obtained from the Town of Bassendean
https://www.bassendean.wa.gov.au/your-town/work-with-us/contact-us.aspx (accessed on 21
March 2017). R code is available from the corresponding author upon request. All remaining relevant
data are within the paper.
Acknowledgments:
The Town of Bassendean provided historical mosquito monitoring data and
access to the monitoring site.
Conflicts of Interest: The authors declare no conflict of interest.
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