Quantitative Sampling and Determination of Prevalence of Culex Mosquitoes in Gatch Basins and Their Assoc¡Ation with Canopy Cover in Manitoba

QUANTITATIVE SAMPLING AND DETERMINATION OF PREVALENCE OF CULEX MOSQUITOES IN GATCH BASINS AND THEIR ASSOC¡ATION WITH CANOPY COVER IN MANITOBA

Andrea Thomson Mclean

A Thesis submitted to the Faculty of Graduate Studies of

The University of Manitoba

ln partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

Department of Entomology

University of Manitoba

Winnipeg, Manitoba

FACULTY OF GRÄDUATE STUDIES úgúgg COPYRIGHT PERMISSION

Quantitative Sampling and Determination of Prevalence of Culex Mosquitoes in Catch Basins and Their Association with Canopy Cover in Manitoba

Andrea Thomson Mclean

A ThesisÆracticum submitted to the Faculty of Graduate Studies of The University of

Manitoba in partial fulfillment of the requirement of the degree of

MASTER OF SCIENCE

Andrea Thomson Mclean O 2008

Permission has been granted to the University of Manitoba Libraries to lend a copy of this thesis/practicum, to Library and Archives Canada (LAC) to lend a copy of this thesis/practicum, and to LAC's agent (UMlÆroQuest) to microfilm, sell copies and to publish an abstract of this thesis/practicum.

This reproduction or copy of this thesis has been made available by authority of the copyright o\ryner solely for the purpose of private study and research, and may only be reproduced and copied as permitted by copyright laws or with express wriffen authorization from the copyright owner. Abstract ïhis research was conducted to determine the prevalence of Culex spp.

larvae and pupae in street catch basins in Winnipeg, Manitoba, Canada. ln the

laboratory, two mosquito species, Ae. aegypfi and Cx. restuans were used to test the effects of various water depths, developmental stage, and total number of mosquitoes in an artificially constructed catch basin to determine the efficacy of a net sampling technique. At least one mosquito was detected in g8.6% of the Ae. aegypti trials and in 98.3% of the Cx. restuans trials. The total number of mosquitoes had a significant effect on the number of mosquitoes collected with the net. Net sampling of larvae and pupae from catch basins is an effective method to determine the presence/absence of mosquitoes. ln the field, three tree canopy cover categories were designated to explore the relationship between the prevalence of mosquitoes and vegetative cover. Other variables measured included precipitation, egg rafVoviposition activity, water temperature, water depth, and per cent leaf litter in catch basins. Catch basins in Winnipeg contained predominantly Culex restuans. ln 2004 and 2005, significantly more mosquitoes were found in catch basins with high canopy cover. As precipitation increased, the number of mosquitoes in catch basins declined. Numbers of mosquitoes in catch basins were primarily influenced by rainfall over the course of the season. Acknowledgements

The Province of Manitoba deserves many thanks for providing funding, without which this research would not have been possible. Thanks also to the

City of Winnipeg lnsect Control Branch for providing support for this study in

2004, permitting the use of their catch basins as study sites in 2004 and 2005, and for ceasing control applications wiihin study basins for these years.

Andrew McMillan at the City of Winnipeg Department of Water and Waste

Management provided rainfall accumulations for 2003, 2004, and 2005. Bill

Waters also within this department, provided on expertise the function and dynamics of catch basin systems in Winnipeg, Manitoba.

I would like to express my gratitude and appreciation to my supervisors Dr.

A. R. Westwood and T. D. Galloway for their time, patience, support and guidance throughout this study, they were great mentors and a pleasure to work with. I would also like to thank my committee members Mr. R. Gadawski, for his support and expertise, and Dr. L. Graham for giving an important perspective from outside the entomological field.

Another thank-you goes to all the people who helped with running laboratory experiments and field surveys: Dave Holder, Natalie Parks, Jeff

Shaddock, and Alanna Westwood.

Finally, I would like to thank my husband, Devin, for creating an artificial catch basin grate (a first demonstration of his carpentry skills), for his support, and encouragement to 'Just get it done". Thanks to my daughter, Jane, for alleviating

iii a mother's guilt, and only screaming when you had to come home from daycare.

For her understanding, and unconditional support through this degree and my whole life, I would like to thank my mother, Lesley Thomson, and fufiher dedicate this thesis to my late father, lan Thomson.

iv Table of Contents Abstract ...... ii Acknowledgements...... iii List of Figures ...... vii List of Tables ...... x Chapter l: General lntroduction ...... 1 Chapter ll: Literature Review...... 5 West Nile virus...... 5 Mosquito Biology ...... 8 Artificial Container Species (Cross-over Species)...... 10 West Nile Virus in Manitoba: Cross-over Species...... 13 Catch Basins as Mosquito Habitats ...... 15 Chapter lll: A dip net for quantitative sampling of immature Aedes aegypti and Culex restuans in a catch basin...... 21 Abstract...... 21 lntroduction ...... 22 Materials and Methods...... 24 Data 4na1ysis...... 26 Results ...... 28 Aedes aegypti...... 28 Culex restuans...... 29 Discussion ...... 32 Net Sensitivity...... 32 Aedes aegypti...... 34 Culex restuans...... 36 Prediction Equation...... 38 Future Research...... 42 Conclusion ...... 45 Chapter lV: Catch basin mosquito monitoring: determination of abundance and prevalence o'f Culex mosquitoes in catch basins and their association with canopy cover in Winnipeg, Manitoba ...... 46 Abstract...... ,...... 46 lntroduction ...... 48 Materials and Methods...... 52 Statistical Analysis ...... 56 Results ...... 58 a) Canopy type and sample period - 2004...... 58 b) Experimental variable analysis by canopy type and sample period for 2005...... 60 c) Spearman's rank order correlation analysis 2004 and 2005...... 62 Discussion ...... 64 2004...... 64 2005...... 70 Spearman's correlations for experimental variables in 2004 and 2005...... 72 Future Research...... 74 Conclusions...... ,...... 76 Chapter V: General Discussion...... 78 Literature Cited: ...... 131 Appendix A ...... 141 Appendix B ...... 147 Appendix C...... 174

vi List of Figures

Figure 1: Schematic of a catch basin with curbside inlet commonly found in the drainage system in Winnipeg, Manitoba..__-_.__ 9.9 Figure 2: Schematic of a catch basin with boulevard inlet commonly found in the drainage system in Winnipeg, Manitoba. .._... ..9.4 Figure 3: Side view of an artificial catch basin constructed for quantitative sampling of immature Aedes aegypti and Culex restuans using the net sampling method.-..__ .._....9.1 Figure 4: Top view of artificial grate for an artificial catch basin constructed for quantitative sampling of immature Aedes aegyptiand Culex resfuans using the net sampling method...... 92 Figure 5: One millimeter mesh 6x8cm fish net sampling apparatus used for quantitative sampling of immature Aedes aegypti and Culex resfuans in a catch basin. 9q Figure 6: lllustration of the sample sweeping protocol for quantitative catch basin sampling for immature Aedes aegypti and Culex resfuans using the net sampling method. One sample would equal both patterns (Sweep 1 and 2) in one continuous motion with the direction of the second pattern oriented 90o to the first. 94 Figure 7: The estimated marginal means of logarithmic transformed Ae.aegypti numbers captured with the net in an artificial catch basin at various water depths and initial total mosquito numbers combinations. 9þ- Figure 8: The estimated marginal means of logarithmic transformed Cx. restuans numbers captured with the net in an artificial catch basin at various water depths and initial total mosquito numbers combinations._.______- ..gg Figure 9: lllustrates the locations of differing tree canopy cover treatment neighbourhoods for the field catch basin sampling study in Winnipeg, Manitoba, Canada, in 2004 and 2005. High canopy cover treatments were in Riverview and River Heights, moderate treatments in East Kildonan, Transcona (2004 only), Fort Richmond (2005 only), and low treatments in East St. Paul and South Tuxedo. _.._.....-.97_ Figure 10: lllustration of positioning of tree canopy density measurement at nine different spots (grey circles) within a 20m radius of the selected catch basin (black rectangle). .._.....______g.B_

Figure 1 1: Typical high canopy cover street with dense tree cover associated with the catch basin found in high canopy covered neighbourhoods, Riverview and River Heights, Winnipeg. Tree canopy is fully closed, with trees approximately 4m apart 99

vil Figure 12: Typical moderate canopy cover street with moderate tree cover associated with the catch basin found in moderate canopy covered neighbourhoods, East Kildonan, Transcona and Fort Richmond, Winnipeg. Tree canopy is not fully closed, with trees approximately 5m apart..___...... 19.0_ Figure 13: Typical low canopy cover street with minimal tree cover associated with the catch basin found in low canopy covered neighbourhoods, South Tuxedo and East St. Paul, Winnipeg. Tree canopy was not closed, with trees less than 10 years in age.______lQl

Figure 14: Photo of pupation chamber to raise pupae to adults for identification.______1_02_ Figure 15: Mean (tSE) rainfall accumulation for each tree canopy cover treatment (n=2) during the catch basin sampling period in 2004. Each (low, moderate, and high) is represented by the average rainfall accumulation recorded in two areas by tree canopy cover treatments (The average rainfall accumulation (n=6) was calculated and was significantly different over the six sample periods (ANOVA, df = 5,35 F = 111.626, p < 0.001). Letters above average rainfall accumulation represents significant differences between means (Tukey's test p<0.05). Precipitation data were obtained from the City of Winnipeg Water

Management Department. 1.q.3_ Figure 16: Mean number of Culex restuans egg rafts (tSE) collected during four sampling periods from two ovipools within each tree canopy cover treatment, low, moderate, and high (n=6) in 2004. Letters following average egg rafts represents significant differences between average means (Mann Whitney U test p<0.05). 104 Figure 17: Mean number of Culex restuans adult females collected for six sampling periods from two New Jersey Light Traps in each tree canopy cover, low, moderate and high (n=6) in 2004...... 1A.5 Figure 18: Mean (tSE) rainfall accumulation for each tree canopy cover treatments (n=2) during the catch basin sampling period in 2005. Each (low, moderate, and high) is represented by the average rainfall accumulation recorded in two areas by tree canopy cover treatments. (The average rainfall accumulation (n=6) was calculated and was significantly different over the five sample periods (ANOVA, df = 4,29, F = 40.924, p.0.001). Letters above average rainfall accumulation represent significant differences between means (Tukey's test p<0.05). Precipitation data obtained from the City of Winnipeg Water Management Department._.______-__. ., lQ6.. Figure 19: Mean number of Culex restuans egg rafts (tSE) collected for three catch basin sampling periods from six ovipools in each tree canopy cover type, low, moderate, and high (n=18) in 2005. ..--...... L97..

viii Figure 20: Mean number of Culex restuans adult females collected for five sampling periods from two New Jersey Light Traps in each tree canopy cover, low, moderate and high in 2005. Data obtained from the City of Winnipeg...... _.__.___".19-B_

IX List of Tables

Table 1: Total mosquitoes (Aedes aegypti or Culex resfuans) per litre of water (density) at differing combinations of water depths (0.10, 0.2,0.3,0.4, and 0.47 m) and number of mosquitoes introduced (250, 500, 750, 1000 and 1125") to the * artificial catch basin. Aedes aegyptionly...____.--...... 19.9-.

Table 2: Mean number of Aedes aegyptilarvae and pupae (tSE) captured in two sweeps of the aquarium net at water depths of 0.10, 0.20, 0.30, 0.40 and 0.47 m, and number of mosquitoes (250, 500, 750, 1000 and 1125) in the artificial catch basin (n=3)...... 110- Table 3: Univariate analysis of variances of total numbers of Aedes aegypti (250, 500, 750, 1000 and 1125) and water depths (0.10, 0.20, 0.30, 0.40, and 0.47 m) in an artificial catch basin and the interaction of these variables on the number of larvae and pupae captured by two sweeps of the aquarium net. 111

Table 4: Mean number of Aedes aegyptilarvae and pupae (lSE) captured in two sweeps of the aquarium net at total number of mosquitoes (250, 500, 750,1000, 1125) in the artificial catch basin and correlations with water depth (10, 20,30, 40, 47cm) in an artificial catch basin...... 112

Table 5: Mean number of Aedes aegypti larvae and pupae (tSE) captured in two sweeps of the aquarium net at varying water depths (10, 20,30, 40, 47cm) in the artificial catch basin and correlations with total numbers of mosquitoes (250, 500, 750,1000, 1125).______...... 11.3_

Table 6: Mean number of Aedes aegypti(tSE) larvae and pupae captured in two sweeps of the aquarium net and One-Way ANOVA at various water depths and total number of mosquitoes in the catch basin. Only significant relationships are shown. 114

Table 7: Linear regression of Aedes aegypti larvae and pupae captured by two sweeps of the aquarium net and total number of mosquitoes (250, 500, 750, 1000, 1125) in the artificial catch basin varying water depths (0.10, 0.20, 0.30, 0.40, 0.47m). ln the linear regression equations Basin represents the known number of mosquitoes in the basin and num represents the number of larvae and pupae sampled..__ . "...... 11.5-

Table B: Mean number of Culex restuans larvae and pupae (tSE) captured in two sweeps of the aquarium net at varying water depths (0.10, 0.20, 0.30, 0.40 0.47 m) and initial mosquito total (250, 500, 750, and 1000) in the artificial catch basin (n=3)...... 11.6,

Table 9: Analysis of variance of total numbers of Culex resfuans (250, 500, 750, 1000) and water depths (0.10, 0.20, 0.30, 0.40,0.47 m) in the artificial catch basin and the interaction on the number of mosquitoes captured by two sweeps of the aquarium net. ___...... _.....___..._.__ ..LL7.

Table 10: Mean number o'f Culex restuans (tSE) captured in two sweeps of the aquarium net at varying total mosquitoess (250, 500, 750, 1000) and correlations with water depths (0.10, 0.20, 0.30, 0.40, and 0.47m) in the artificial catch basin _... -1.18-

Table 11: Mean number of Culex restuans (iSE) captured in two sweeps of the aquarium net at varying water depths (0.10, 0.20, 0.30, 0.40, 0.47m) and correlations with total mosquitoes (250, 500, 750,1000) in the artificial catch basin. _._...... 1119- Table 12. Mean number of Culex resfuans instar larvae and pupae (tSE) captured in two sweeps of an aquarium net and one-way ANOVA at differing combinations of water depth (0.10, 0.20, 0.30, 0.40,0.47m) and total mosquitoes (250, 500, 750, 1000) in the artificial catch basin. Only significant relationships are shown within columns..._ .._L29.

Table 13:Linear regression of Culex resfuans larvae and pupae captured by two sweeps of an aquarium net and total mosquitoes (250, 500, 750, 1000) at varying water depths (m) in the artificial catch basin. ln the linear regression equation, Basin represents the density of larvae and pupae in the basin, and num represents the number of individuals sampled...... _,...... _....__.___L2L.

ïable 14:

Table 15: ïotal mosquito larvae and pupae collected by the aquarium net sampling method from catch basins in Winnipeg, Manitoba, and catch basin conditions in 2004 and 2005.

Table 16: Mean water temperature (oC), water depth (mm), number of mosquitoes collected per two sweeps of the aquarium net, and leaf litter ratio (out of 25) (t SE) by tree canopy cover for wet catch basins from 22 June to 27 September, 2004in Winnipeg, Manitoba...... 1?4

Table 17: Mean water temperature (oC), water depth (mm), number of mosquitoes collected per two sweeps of the aquarium net, and leaf litter ratio (out of 25) (t SE) by sample periods for wet catch basins in Winnipeg, Manitobain 2004.__ ...... L25

TablelS: Kruskall Wallis anaylsis of mean number of Culex resfuans egg rafts (tSE) collected during 5 July and 29 August from two ovipools within each tree xi canopy cover type (treatment), low, moderate, and high (n=6) in 2004, and mean number of Culex restuans egg rafts (tSE) collected during 7 July and 21 August from six ovipools within each canopy treatment (n=18) in 2005.. ---..... 1_29 Table 19: Mean water temperature (oC), water depth (mm), number of mosquitoes collected per two sweeps of the aquarium net, and leaf litter ratio (out of 25) (t SE) by tree canopy cover for wet catch basins from 12 June to 20 August, 2005 in

Winnipeg, Manitoba.__ ...... _.___1_ZI_.

ïable 20: Mean water temperature ("C), water depth (mm), number of mosquitoes collected per two sweeps of the aquarium net, and leaf litter ratio (out of 25) (t SE) by sample periods for wet catch basins in Winnipeg, Manitoba in 2005.__ ...... L29 Table 21: Matrix of Spearman's Correlation Coefficients of environmental variables (water depth (WD), leaf litter (LL), water temperature (WT), tree canopy density (CD)), and mean number of larvae and pupae (LP) sampled from catch basins in 2004...... ______....._.__.___1_Z_9.

Table 22: Matrix of Spearman's Correlation Coefficients of environmental variables (water depth (WD), leaf litter (LL), water temperature (WT), tree canopy density (CD)), and mean number of larvae and pupae (LP) sampled from catch basins in 2005...___ ...... ___._._.....j.9.0_

xil Chapter l: General lntroduction

Mosquitoes have the potential to transmit pathogens which may pose significant risks to human health. ln 1999, West Nile virus (WNv) was detected in

New York state (Centers for Disease Control & Prevention2007, Lanicotti et al.

1999) and since has spread across North America causing thousands of human illnesses. With this introduction, there has been an increase in control programs which specifically target WNv vectors. The success of any mosquito control program relies on thorough mosquito surveillance (Centers for Disease Control &

Prevention 2007). With the introduction of WNv into North American, mosquito surveillance has become increasingly important for assessing the risk of WNv to humans.

Effective mosquito control programs require a knowledge of population dynamics, habitat ecology and distribution of targeted mosquito species. A general trend has been to determine the diversity of WNv vector species larval habitats, e.g. Culex spp., since larval habitat reduction is one of the most effective control methods.

Culex spp. oviposit and develop in a number of habitat types. With increasing urbanization there have been many mosquito species that have been able to adapt to anthropologically created oviposition and larval developmental habitats such as flower pots, tires, bird baths, rain barrels, fish ponds, and curbside puddles (Bohart and Washino 1978). lt appears these species are able to colonize container habitats based on their oviposition biology and requirements for larval development. Catch basins are concrete reservoirs found along roadsides and

boulevards in urban areas beneath storm-water grates. They are designed to

collect water runoff from streets, usually from a precipitation event and channel it

into the city storm-water or sewer system. As catch basins are designed to facilitate settling of sediment before water enters the sewer system, there is

potential for catch basins to maintain standing water. Since Culex spp. females

require the presence of water in order to deposit a floating egg raft on the water surface, these sites have been examined as larval habitats.

The main species found in catch basins in North America are Culex

pipiens Linnaeus, and Culex resfuans Theobald (Munstermann and Craig 1976;

Geery and Holub 1989; McCarry 1996; Rey ef al. 2006). ln Manitoba, Cx. restuans was the primary species found in Winnipeg catch basins, and presence of this mosquito species may be correlated to the organic content of catch basins

(Thomson 2004). Thomson's work was considered the pilot study. ln the summer of 2003, Thomson surveyed catch basins in Winnipeg to determine the extent to which organic matter within catch basins influenced the presence of mosquitoes in these basins (Thomson 2004). The current study is an extension of this work.

Since Cx. restuans has been implicated in the amplification of WNv, catch basins should perhaps be considered important in control programs.

Furthermore, I attempted to determine an effective way of assessing general organic input into catch basins by examining the landscapes around catch basins.

Since control activities may need to focus on areas where catch basins have large amounts of organic material it would be beneficial to determine treatment areas without directly measuring organic matter, or sampling for mosquitoes in each

catch basin.

The objectives of the survey component of this study were as follows:

o Determine if environmental variables (water depth, water temperature, leaf litter, tree canopy density, number of egg rafts, and precipitation) influenced the number of mosquitoes in catch basrns.

Determine if environmental variables influenced the number of mosquitoes in catch basrns in relation to time of year.

Determine if environmental variables were correlated with each other or with the number of mosquito larvae and pupae within the catch basrns.

Since targeted larviciding and larval habitat reduction are the primary strategies in control programs, larval detection is important to successful mosquito management. There have been many sampling devices constructed to sample different habitats. The mosquito dipper, although efficient in other habitats, has been shown in studies to be ineffective in determining presence and absence of mosquitoes in artificial containers (Zhen and Kay'1993; Tun-Lin ef a/. 1994;

Thomson 2004). Furthermore in these studies, net sampling was effective in sampling many containers including catch basins; however, the accuracy of this method has not been tested. ln this study, calibration of the aquarium net method for sampling catch basins was examined in the laboratory.

The objectives of the laboratory component of this study were as follows:

o Determine if water depth has an effect on the number of larvae and pupae collected with the aquarium net. o Determine if the total number of mosquitoes has an effect on the numbers of mosquitoes collected with the net at a standard water depth.

o Determine if there is an interaction between water depth and number of mosquitoes in the basin on numbers of larvae and pupae collected with

the sample net.

o Determine the bias of the aquarium net sampling method in relation to numbers of different developmental stages caught.

o Estimate the number of mosquitoes in a catch basin based on the number of mosquitoes sampled using the aquarium net and the water depth.

This thesis is written in paper style. Chapter ll is a review of the literature relevant io the issues of WNv, catch basins, and mosquitoes. Chapter lll focuses on the laboratory experiment where I attempted to calibrate the aquarium net method of sampling catch basins for mosquitoes. Chapter lV provides the results of the survey of Winnipeg catch basins in 2004, and 2005. Chapter V is a general discussion which focuses on the entire thesis combining laboratory results and field results and their implications for WNv mosquito monitoring and control programs. For the purposes of this study, when larvae and/or pupae are discussed, I refer to them as mosquitoes, unless otherwise stated. Ghapter ll: Literature Review

West Nile virus West Nile virus (WNv), Genus Flavivirus, Family Flaviviridae, is a single- stranded RNA virus that naturally infects birds and humans (Smithburn ef a/.

1940). lt belongs to the Japanese encephalitis virus antigenic complex, which includes Murray Valley, Kunjin, Japanese encephalitis, and St. Louis encephalitis viruses (Centers for Disease Control & Prevention 2004; Douglas et al. 2007).

West Nile virus was first isolated in Africa in 1937 from a patient in the

West Nile province in Uganda, and is currently endemic throughout Africa, the

Middle East, parts of Europe, southwestern Asia, and more recently North

America (Centers for Disease Control & Prevention 2004). The first recorded outbreaks of WNv disease in humans were in South Africa in 1947 and lsrael from

1951-1954, after which, the disease gradually became more widespread (Centers for Disease Control & Prevention 2004). More recently, WNv outbreaks occurred in Algeria in 1994, Romania in 1996 and 1997, the Czech Republic in 1997, the

Democratic Republic of the Congo in 1998, Russia in 1999, the United States in

1999-2003, and lsrael in 2000 (Centers for Disease Control & Prevention 2004).

ln 1999, WNv was documented for the first time in North America and caused avian, equine and human deaths (Jia ef a/. 1999). lt is not known how the virus arrived in North America; however, it is suggested the strain found in the eastern United States outbreak in 1999 was of Middle East origin (Jia ef a/. 1999).

Some theoretical modes of introduction of this virus to the United States include introduction by infected migratory birds, importation of infected birds, introduction of infected mosquitoes, and viraemic human or animal cases via international travel (Peterson et a\.2003).

Since its introduction into the United States, WNv has moved steadily west and northward (Sampathkumar 2003). Surveillance of birds, horses, and mosquitoes has produced evidence of the spread of the virus throughout the continental United States and Canada. Since the 1999 outbreak, there have been morethan 32,000 reported cases,770 deaths, and an estimated 31,300 illnesses in North America (Peterson and Hayes 2004; Centers for Disease Control &

Preventicn 2007; Public Health Agency of Canada 2008). A large outbreak in

2002 caused 284 deaths in the Ohio and Mississippi River basins (Centers for

Disease Control & Prevention 2007). ln 2003, WNv caused 8,393 human infections and 184 deaths in the USA (Centers for Disease Control & Prevention

2007) and 1240 infections and 10 deaths in Canada (Public Health Agency of

Canada 2008).

West Nile virus is transmitted by mosquitoes among birds in a natural, enzootic cycle. A mosquito that feeds on an infected bird and lives for the 10-14 day extrinsic incubation period (the time from first infection in the vector until a pathogen can be transmitted) of WNv may then transmit virus to other birds or mammal hosts during blood-feeding attempts subsequent to becoming infectious.

The virus penetrates into the haemocoel of the adult female mosquito from the infective blood meal and then multiples within body tissues until virions cause secondary infections in the salivary glands and ovaries of the infected mosquito

(Vaidyanathan and Scott 2006). There is some evidence that once in the ovaries, vertical transmission (transmission to the infected mosquito's progeny) can occur (Miller et a\.2000; Turell et a\.2001; Dohm et a\.2002). Vertically infected female progeny may aid in virus amplification by over-wintering as an infected adult without ever having taken a blood meal. The following spring, providing the infected adult survived, it could serve to initiate the infection cycle for the new season (Dohm et al. 2002). The success of this mode of initiation of infection is still unclear.

The main source of amplification of the virus during the mosquito season is via the salivary glands of an infected mosquito. West Nile virus can then be transmitted when the mosquito takes another blood meal and injects infectious saliva into the new host (Vaidyanathan and Scott 2006). lf the infected mosquito takes another blood meal from a different bird, the virus then may be transmitted and cause subsequent infection. This process constitutes the amplification of virus from one to many hosts in nature (Sardelis et a\.2001; Komar et aI.2003).

At north temperate latitudes, the cycle begins with migration and therefore introduction of infected birds migrating from southern latitudes, and the emergence of mosquitoes in the spring. From mid to late summer, the virus continues to amplify within the bird reservoir and mosquito vectors (Henley 2003) and continues until the fall when the last vector mosquitoes die.

Biological transmission of arboviruses depends on a variety of factors.

The enzootic bird-mosquito-bird cycle of WNv depends on availability of infected hosts, probability of ingestion of the virus and the probability of subsequent establishment of the virus in the salivary glands of the mosquito (Goddard 2000).

Epidemics are normally initiated when there is an increase in prevalence of WNv in the enzootic cycle. This amplification within the vector population results in an increase where infected vectors more frequently come into contact with other

hosts, including humans (Turell et a\.2000).

Some species of mosquitoes have the potential to act as 'bridge vectors',

meaning that they feed on avian and mammalian hosts (Turell et al. 2000).

lnstead of taking all blood meals from birds, infected mosquitoes may feed on

mammals, including humans, resulting in a WNv infection (Henley 2003).

Typically with the amplification of the virus in late summer in Nofih America, the

occurrence of incidental infections (infection in non-amplifying hosts) in humans

increases as the summer season progresses. However, host seeking mosquito

populations decrease later in the summer.

West Nile virus can cause severe symptoms in susceptible people,

including high fever, headache, meningitis, encephalitis, disorientation, tremors,

convulsions, muscle weakness, vision loss, numbness and paralysis (Public

Health Agency of Canada 2008). About 19% of infected individuals display mild symptoms, which typically appear 3-14 days after infection; however, B07o of

people infected with WNv show no symptoms. Less than 1% of infected people show severe and potentially lethal symptoms of infection. For severe infections, there is no effective treatment and thus, only supportive care within a hospital setting is possible (Centers for Disease Control & Prevention 2007).

Mosquito Biology There are approximately 3,500 species of mosquitoes found worldwide,

150 of which arefound in North America, and 76 in Canada (Roberts and Janovy

2000). Mosquitoes are most abundant in Arctic regions and most diverse in tropical rainforests (Clements 1992). They undergo complete metamorphosis,

passing through four stages of development of which three are aquatic stages.

Female mosquitoes select certain habitats to deposit eggs; particular

habitats vary by species. All species of mosquitoes require aquatic habitats for

immature stages of development (Carpenter and LaCasse 1974). Mosquitoes lay

their eggs either on substrates of low-lying areas that have potential to

accumulate water, or directly on standing water.

Once an egg is in contact with water and experiences the appropriate

conditions (including the required temperature, dissolved oxygen content, and

photoperiodlday length) (Clements 1992; Toma et al. 2003), the larva will hatch.

The larva cuts itself out of the egg and is released into the aquatic environment

(Wood et al. 1979). Larvae mainly feed on organic matter or bacteria suspended within the water column or on the surface of substrates and, depending on the species, may feed at different depths. Most larvae must breathe air from above the surface of the water and therefore frequently rise to the surface to obtain oxygen (Carpenter and LaCasse 1974). Other species remain submerged and breathe atmospheric air by attaching the siphon to stems and roots of aquatic plants and exploiting the air conducting tissues of the plant (Batzer and Sjogren

1e86).

Larvae may develop in as little as seven days or take many months depending on species, water temperature and habitat type. Four progressively larger larval instars occur before pupation (Carpenter and LaCasse 1974). The last larval moult results in an aquatic, non-feeding pupal stage (Carpenter and

LaCasse 1974). The pupa is a tissue-reorganization-stage that allows the mosquito larvae to change into their adult form. This stage lasts about three to

four days depending on environmental temperatures (Carpenter and LaCasse

1974;Wood et a\.1979).

Adult emergence occurs when pupae extend their bodies along the

surface of the water and split the dorsum of the cephalothorax such that adults

emerge at the water/air interface. As adults, most species feed on nectar,

although females of most species also require a blood meal to obtain protein for

egg production. One blood meal may be enough to produce 75-500 eggs

depending on the species size, age and nutritional status of a female mosquito

(Clements 1992).

Artificial Gontainer Species (Cross-over Spec¡es) There are eight species of mosquitoes in Canada that have been shown to develop in artificial containers. These cross-over species, (species that have adapted to develop in both natural habitats and artificial containers) include

Ochlerotatus albopictus (Skuse) (GraIz 2004), Ochlerotatus atropalpus

(Coquillett) (Covell and Brownell 1979, Nawrocki and Craig 1989), Ochlerotatus japonicus (Theobald) (Anderadis et al. 2001), Ochlerotatus triseriatus (Say)

(Wilton 1968, Barker et a|.2003), Culiseta inornata (Williston) (Joy ef a\.2003),

Cx. pipiens (Mogi et. al. 1995), Cx. tarsalis Coquillett and Cx. restuans (Brust

1990, Joy et a|.2003). Oviposition habits and larval adaptation from their natural habitats have allowed these mosquitoes to oviposit and develop in artificial containers such as tires, flower pots, bird baths, rain barrels, fish ponds, catch basins, curb-side puddles and other man-made structures which retain water for certain periods of time (Bohart and Washino 1978). As a result, Cx. pipiens has

10 become so efficient at colonizing artifical containers that its abundance is

positively correlated with human populations (Anderadis et al. 2004) and it is the

most common mosquito species found in these types of habitats in many parts of the United States (Moore ef a/. 1990). lt appears that cross-over species are able to colonize container habitats based on their oviposition biology, more specifically the act of oviposition, site selection for oviposition by a gravid female, and

requirements for larval development.

Many artificial containers resemble natural larval and pupal developmental sites normally used by cross-over mosquito species. ln Canada, tree-hole species oviposit in water-retaining cavities in deciduous trees. The adult female attaches the eggs to the sides of these cavities just above the water surface where eggs hatch when submerged by subsequent rainfall (Wilton 1968). Larvae generally graze on any organic debris found along the sides or at the bottom of the tree hole (Wilton 1968; Clements 1992). Artificial containers offer similar opportunities to these species. Ochlerotatus albopictus, a known tree-hole species, will colonize artificial containers, including flower pots, water storage containers, and especially discarded tires (Hong et al. 1971; Rightor et al. 1987:

Foster 1989; Fontenille and Rodhain 1989).

For other species, including Culex spp., oviposition requires the presence of water in order to deposit a floating egg raft on the water surface. lf stagnant water is present in an artificial container, Cx. pipiens, Cx. restuans, and Cx. tarsalis will oviposit on the water surface allowing larvae to develop within these containers (Brust 1990; Lampman and Novak 1996; Anderson ef al. 2006).

11 Wilton (1968) discussed container characteristics which influence site

selection by gravid females including orientation of container opening and

presence of decaying organic matter. Wilton (1968) studied Ochlerotatus

triseriatus (Say) and showed.that open-top containers consistently had more eggs than other variations of openings. This is most likely attributed to the conditions

needed to stimulate egg hatch. ln natural tree-hole environments, eggs are dependent upon rainfall events to raise the water level above the eggs for larvae to hatch. Open{op containers allow for precipitation to enter easily and raise the

level of water within the artificial container.

The presence of decaying organic materials is also important in making artificial containers attractive to gravid females. The chemical by-products of decaying material are used as cues by mosquitoes to locate oviposition sites

(Kramer and Mulla 1979; Millar ef al. 1992). Therefore, if decaying material in an artificial container is providing similar chemical cues found in natural habitats, it is likely that the gravid female will utilize these containers as habitats for its progeny.

Nutrient availability seems to favour larval development of container species. Haramis (1984) showed that larvae of Oc. triseriatus were dependent on decaying leaf litter as a substrate for saprophytic fungi upon which larvae fed in tires and tree holes. Furthermore, oviposition by Ochlerotatus spp. in natural and artificial containers was a response to a blend of decaying leaf material in the water (Trexler ef. a/. 1998).

Trexler et al. (2003) showed that larval density and survival were facilitated by the presence of bacteria decomposing the litter and not the actual amount of litter itself. They showed that Oc. albopicfus laid significantly more

12 eggs in containers which contained microbially-contaminated cotton towels and

approximately 60% of all eggs were laid in containers having bacterial populations

than ones without.

West Nile Virus in Manitoba: Cross-over Species The first human case of WNv in Manitoba was reported in 2003, wilh 142

positive human cases occurring that year. From 2004 to 2006 the number of

positive human cases of WNv in Manitoba has flucuated (2004-3 cases, 2005-55 cases,2006-50 cases, 2007-572 cases) (Public Health Agency of Canada 2008).

Historically, mosquitoes have been a significant nuisance in many regions of Canada, but with the introduction of WNv, they have also become a significant threat to public health in areas where only nuisance control was practiced (Public

Health Agency of Canada 2008). ln Manitoba, there are 45 mosquito species

(Wood et al. 1979), 38 of which occur in Winnipeg (Gadawaki 2002). Larvae of these mosquitoes develop in various habitats including transient low-lying, flooded areas and semi-permanent and permanent standing water (Gadawski 2002).

Since its introduction, WNv has been detected in at least 43 species of mosquitoes in North America (Turell et al. 2005). However, for these species to be efficient vectors of WNv pathogens, various criteria must be met. These include recurring detection of virus from field-collected individuals, ability of the individual species to become infected and infectious (vector competence), and association with naturally infected vertebrate hosts (Turell et a\.2005). Culex spp. meet these criteria for WNv (Hubalek and Halouzka 1999).

13 It is thought that Culex species are efficient vectors of WNv because once

an infected blood meal has been taken, they have no salivary gland barrier to

prevent their salivary cells from becoming infected (Colton and Nasci 2006). A

second criterion which makes Culex mosquitoes competent vectors of WNv

includes host preferences and associations (Colton and Nasci 2006).

Culex farsalis is thought to be highly efficient in maintenance and

amplification of WNv and is considered the most significant enzootic vector

species in western Canada, including Manitoba. Although this species almost

exclusively feeds on birds in early summer, it has an opportunistic change in feeding habits in late summer which makes them competent bridge vectors (Turell

et al. 2005). ln western Canada, Cx. tarsalis is considered the most important species in the transmission of incidental infections of WNv to humans (Robbin

Lindsay, pers. comm.).

Culex restuans may also play an important role in the amplification and maintenance of WNv within the bird population because of its vector competence for Western Equine Encephalitis (Canada Biting Fly Centre 1990; Buth ef a/.

1990) and its ornithophilic host preference (Surgeoner and Ralley 1981; Reinhert

2001). Apperson et al. (2002) also stated that Cx. restuans has a feeding preference for birds and therefore could play a major role during enzootic amplification of WNv. Ebel ef al. (2005) determined that Cx. resfuans was a competent laboratory vector of WNv. Since this species feeds on a broad range of avian hosts and occasionally on locally available mammalian species, it can be considered an important vector of WN pathogens in North America (Apperson ef a|.2002).

14 Larvae o'f Cx. restuans are found in various artificial water containers, rock pools, tree cavities, and other temporary pools, especially when decaying vegetation is present (Wood et al. 1979). ln Canada, Cx. restuans populations tend to reach a peak earlier in the summer than Cx. farsalis and adults seem to have a greater tolerance for lower temperatures in spring and fall (Wood ef a/.

1e7e).

Culex tarsalis is also important in the transmission of WNv in bird populations in that it feeds preferentially on avian hosts, but to a lesser extent than Cx. restuans (Hubalek and Halouzka 1999). Unlike Cx. restuans, Cx. tarsalis tends to feed on mammalian hosts in late summer (Wood et al. 1979) and is therefore more likely to act as a bridge vector of WNv between bird populations and mammal populations in late summer (Hubalek and Halouzka 1999).

Culex tarsalis, like Cx. restuans, oviposits in permanent or semi- permanent pools and will utilize artificial containers as the population density increases over the summer. Typically, this species reaches its greatest numbers in late July or early August (Wood et al. 1979; Buth ef a/. 1990).

Female Cx. restuans and Cx. tarsalis lay egg rafts on the surface of standing water (Clements 1992). Egg rafts are a mass of eggs that are locked together through chorionic protrusions (Clements 1992). Egg rafts float on the water surface by virtue of surface tension, and can hatch within a couple of hours or days (Carpenter and LaCasse 1974).

Catch Basins as Mosquito Habitats Catch basins are concrete reservoirs found along roadsides and boulevards in urban areas beneath storm-water grates. They are designed to

15 collect water runoff from streets, usually from a precipitation event and channel it into the city storm-water or sewer system (Su ef al. 2003; Kronenwetter-Koepel ef a\.2005). Urban centres may have many thousands of catch basins depending on the size of the city or town (Ellis 1995).

There are several general designs of catch basins found in North America.

Some basins have direct drainage to the sewer or storm-water system, and others have an outlet pipe above a settling area or'sump'which may be several metres deep (Lau et al. 2001) (Figures 1 and 2). The most common, and most important consideration from a mosquito habitat standpoint, are those basins that have a settling area where there is potential for standing water (Munstermann and Craig

1976). This type of basin is commonly found within the Winnipeg drainage system (City of Winnipeg 1990).

As catch basins are designed to facilitate settling of sediment before water enters the sewer system, there is potential for catch basins to maintain standing water. ln a typical basin, a spillover hood is located approximately 60 cm above the bottom of the basin. Once the water reaches this point, it can drain off into the general sewer system. Providing water remains for the duration of mosquito development and is not flushed by excessive runoff, the settling area has potential to produce mosquitoes (Russell et a\.2001).

Most mosquito larvae in catch basins are potential pathogen vectors, including Culex species (Munstermann and Craig 1976; Geery and Holub 1989;

McCarry 1996; Rey ef al. 2006). Anderson et al. (2006) surveyed 22 catch basins in Stamford, Connecticut and collected large numbers of adult females, including

Cx. pipiens and Cx. restuans infected with WNv. Geery and Holub (1989)

16 demonstrated 63% of the catch basins in lllinois had immature stages of Cx.

pipiens and 37o/o had Cx. resfuans. However, they used a standard mosquito dipper to sample rather than a more effective aquatic net sample (Thomson 2004) and therefore numbers from the Geery and Holub (1989) study may be

underestimated. Moreover, although Cx. pipiens has been shown to be a principal vector of WNv, this species has not been collected in Manitoba, where

Cx. restuans is abundant.

It has been postulated that increased numbers of catch basins could result in an increased number of human infections. Although there has been no evidence to support this hypothesis, Hedberg ef a/. (1985) found a significant relationship between the number of LaCrosse encephalitis infections in humans and the presence of artificial containers within the areas occupied by humans.

McMahon (2006) suggested two reasons for this occurrence; there would be greater numbers of mosquitoes within an area given higher number of artificial container development sites, and with greater human numbers, there would be increased probability of pathogen transmission.

ln Winnipeg, Manitoba, catch basins are developmental sites for Cx.

restuans larvae and pupae. Thomson (200$ demonstrated that approximately

94% o'f the immature mosquitoes in catch basins were Cx. resfuans, a species that may contribute to the amplification of WNv in birds. The remaining 6% was composed of Cs. inornata, and to a lesser extent Ae. vexans.

Furthermore, tree canopy cover may be a significant indicator of the numbers of Cx. resfuans larvae and pupae in catch basins (Thomson 2004).

Where higher tree canopy cover occurred, the number of Cx. restuans sampled

17 from catch basins in Winnipeg also seemed to be higher. The number of

mosquitoes sampled was also correlated with water depth, water temperature and per cent leaf litter composition in catch basins (Thomson 2004).

There are several devices which are used to sample immature mosquitoes.

These include the standard mosquito dipper, turkey baster, siphoning tubes, funnel traps, and various pumping systems (Nagamine et al. 1979; Livdahl and

Willey 1991; Woodrow and Howard 1994). Although most of these sampling systems are economical, portable and easy to use, they are not ideal for the below ground natuie of sampling catch basins. Siphoning tubes are limited to sampling habitats which are at or above ground level and therefore cannot be used on catch basins. Turkey basters are too small to sample the potential volume of water within a catch basin. And, although pumping systems provide an accurate estimate of larval and pupal numbers within an artificial container

(Livdahl and Willey 1991), they may be cumbersome and labour intensive for large scale catch basin sampling. Goettel et al. (1981) sampled crab hole mosquito species using a modified garden sprayer; however, this unit would not be suitable for catch basins as the maximum volume of water that could be pumped is 4.5 litres.

Area and removal sampling have been tried in a variety of larval habitats; however, area sampling would include removal of the catch basin grate, which often proves time consuming and expensive, and removal sampling assumes that all larvae and pupae have an equal chance of being captured, and successive catches are fewer when more larvae are removed (Lesser 1977). Mosquito larvae are rarely evenly distributed throughout the water column and often

1B congregate. Therefore successive dips may not yield a lower capture rate in this

case.

ln a population study in southern Alberta, larvae were sampled using a

PVC pipe modified with a galvanized trapping vessel (Enfield and Pritchard 1977).

This method is efficient for estimating the number of larvae below the water surface area of the PVC pipe.

Munstermann and Craig (1976) and McCarry (1996) described a method of sampling catch basins for immature mosquitoes. They removed the grate and sampled the standing water using a standard mosquito dipper attached to a pole.

For deep basins, cans have been attached to ropes to collect water and sample mosquitoes (Ellis 1995). There have been problems with the efficiency and practicality of these methods of sampling. For example, the removal of the grates can cause vibrations and debris to fall into the basins. This causes larvae and pupae to dive to the bottom of the settling area (Kasap 1981 ; Tun-Lin et al. 1994), resulting in an inaccurate estimate of mosquitoes within the catch basins.

Resurfacing of roads may cause grates to be sealed to the roadway which requires removal with heavy equipment (Munstermann and Craig 1976; McCarry

1996). Furthermore, grate removal prior to dipping is time-consuming and labour intensive and is therefore ineffective for large scale catch basin sampling programs.

ln Warren County, Ohio, variations of catch basin dippers were constructed using, coffee scoops, translucent film cases, rope, duct tape and wooden doweling (Musa 2002). Sampling with these dippers could take place from the curb or boulevard which made sampling less hazardous as it was not

19 conducted in the middle of the road. This sampling method required many dips

within the water column to achieve the appropriate sample volume with great

potential for larval and pupal dispersion by disturbing the water surface.

Net sampling is more effective for larval and pupal sampling in various

artificial containers than the standard dipper (Zhen and Kay 1993; Tun-Lin ef a/.

1994; Thomson 2004). There has been limited published research on net

sampling within a catch basin environment.

Since the introduction of WNv into North America, there has been a surge

in control programs to target the WN pathogen vectors in catch basins (Su ef a/.

2003; Thomson 2004; Anderson et al. 2006). Mosquito catch basin control

programs have been established in the United States in Minnesota, California, and Ohio where catch basins have the potential to be a significant source of

Culex spp. including Cx. resfuans (Su ef al. 2003). More recently, cities in

Canada including Montreal, Toronto, and Hamilton have commenced control programs to focus on potential vectors of WNv species, including Cx. restuans, in catch basins.

20 Ghapter lll: A dip net for quantitative sampling of immature Aedes aegypti and Culex restuans in a catch basin.

Abstract Net sampling is an effective way to sample larvae and pupae in artificial containers. This research study was designed to quantify the influence of water depth and total mosquitoes on the number of larvae and pupae collected by a 6 x

I cm, '1mm-mesh aquarium net from a catch basin. Two species were examined,

Ae. aegypfi and Cx. restuans. Various water depths, developmental stages and total numbers of mosquitoes were combined within an artificially constructed catch basin. At least one mosquito was detected in 98.6% of the Ae. aegyptitrials and in 98.3% of the Cx. restuans trials. Water depth had a significant effect on the number of mosquitoes collected for Ae. aegypti. The initial total number of mosquitoes also had a significant effect on the number of mosquitoes collected for Ae. aegypti and Cx. restuans. The density of mosquitoes (total mosquitoes/volume of water) had a significant effect on the number of mosquitoes collected with the net for Ae. aegypfi and Cx. restuans. Aedes aegyptiand Cx. restuans larvae and pupae can be effectively sampled in catch basins using an aquarium net. As water depth increased, the number of larvae and pupae captured with the net decreased. As total mosquitoes increased, the numbers of mosquitoes captured with the net increased.

21 lntroduction Catch basins are concrete reservoirs found along roadsides and

boulevards in urban areas beneath storm water grates. They are designed to

collect water runoff from streets and to channel it into the city storm water or

sewer system (Su ef al. 2003: Kronenwetter-Koepel et al. 2005). Urban centres

may have thousands of catch basins depending on the size of the city or town

(Ellis 1ee5).

There are several general designs of catch basins found in North America.

Some basins have an outlet pipe above a settling area or'sump'which may be a cubic metres in volume (Lau et al. 2001) (Figure 1 and 2). The most common, and most important consideration from a mosquito habitat standpoint, are basins that have a settling area where there is potentialfor standing water (Munstermann and Craig 1976). This type of basin is commonly found within the Winnipeg drainage system (City of Winnipeg 1990).

Most mosquito larvae found in catch basins in North America are Culex species (Munstermann and Craig 1976; McCarry 1996). With the introduction of

West Nile virus (WNv), there has been greater need to survey and manage mosquitoes in catch basins (Su ef a\.2003).

There are several devices which are used to sample immature mosquitoes. These include the standard mosquito dipper, turkey baster, siphoning tubes, funnel traps, and various pumping systems (Nagamine ef a/.

1979; Livdahl and Willey 1991;Woodrow and Howard 1994). Although most of these sampling systems are economical, portable and easy to use, they are not ideal for sampling below ground catch basins.

22 Net sampling is a more effective way to sample larvae and pupae in

various artificial containers than the standard dipper (Zhen and Kay 1993; Tun-Lin

et al. 1994; Thomson 2004). There has been limited published research on net

sam.pling in catch basins. Therefore, the purpose of this study was to determine

the effect of water depth and the total number of mosquitoes in the catch basin,

on the number of individuals captured using an aquarium net. Furthermore, I wanted to determine if there was bias associated with sampling different larval

instars and to calibrate the net for estimating mosquito abundance in catch

basins.

The objectives of this study were as follows:

o To determine if water depth has an effect on the number of larvae and pupae collected with the aquarium net.

o To determine if total mosquitoes have an effect on the numbers of mosquitoes collected with the net at a standard water depth.

o To determine if there is an interactive effect of water depth and total mosquitoes on numbers of larvae and pupae collected with the sample net.

o To determine the bias of the aquarium net sampling method in relation to numbers of different developmental stages caught.

o To estimate the number of mosquitoes in a catch basin utilizing the number of mosquitoes sampled with the aquarium net and the water depth.

o To determine the relationship between numbers of mosquitoes collected using an aquarium net and total numbers of mosquitoes from a known volume of water in a catch basin.

23 Materials and Methods An artificial catch basin was constructed using a plastic mini bulk tank, four metal shelving rods and a piece of plywood (Figure 3). A 133L mini bulk tank

originally 0.74m in Jength was shortened to 0.65m and four opposing holes were

drilled 0.60m from the bottom. The mini bulk tank was 0.97m in diameter. To

reproduce the 2.3m typical distance from the top of a catch basin grate, to the

bottom of the catch basin, four 1.65m metal rods were attached to the holes on the bulk tank. A grate was traced and cut from two centimeter thick plywood, according to the City of Winnipeg (1990) catch basin specifications (Figure 4).

The grate was then fastened to the top of the four metal shelving rods, with one rod on each side of the simulated grate. After the basin was constructed, it was draped with 5mm black, opaque polyethylene. This allowed light to enter the basin only from the top grate mimicking the below ground position of a catch basin.

Two species of mosquitoes were used in this experiment. Aedes aegypti

(L.) was used as it is easily reared in laboratory. The second species used was

Cx. restuans as it is the most common species found in catch basins in Winnipeg

(Thomson 2004) and is considered an important vector of West Nile virus (WNv)

(Turell et al. 2001; 2005). A combined total of 3,625 Ae. aegypfi and 2,5O0 Cx. restuans larvae and pupae were raised from eggs at a density of approximately

600 eggs per 1,000 ml of de-chlorinated water. Aedes aegypti eggs were obtained from a laboratory colony and Cx. restuans egg rafts were collected from the field using 58.4 x 42.5 x 15.2cm black Rubbermaid@ containers as ovipools.

Aedes aegypti and Cx. restuans eggs were hatched in a controlled environment

24 chamber aI 23"C and 60% R.H (Gerberg 1970). Each species was housed separately for ease of collection. Larvae were fed three milliliters of a mixture of

1.5 g of bovine liver powder to 100 ml of water every other day to provide sufficient nutrients for growth (Gerberg 1970).

To obtain the various developmental stages of mosquitoes, larval development was intentionally staggered by adding eggs to separate containers of water at four day intervals for 16 days. This allowed grouping of mosquitoes into samples of 250, 500, 750, 1000, and 1125 mosquitoes each with equal numbers of the five developmental stages (four different larval instars and a pupal stage) at the beginning of each trial for each species. This was considered the total number of mosquitoes.

All combinations of the different water depths and different total numbers of mosquitoes were sampled with an aquarium fish net from the artificial catch basin. Water temperature was kept at 20oC. Mosquitoes were allowed to acclimate and disperse for one hour after introduction to the catch basin and 30 minutes when water depth was increased.

Mosquitoes were sampled using a 6 x Bcm, 1mm mesh aquarium fish net attached to a 2.5 m wooden pole (Figure 5). The net was submerged so that the top edge of the net (portion closest to the pole) was in level with the water surface. The net was swept in an s-shaped pattern across the water surface

(about 20sec. per sample), maintaining a forward motion. One sample consisted of two continuous s-shaped patterns across the entire diameter of the basin

(Figure 6) (Thomson 2004). Once sampled, the mosquitoes were washed into a container by pouring decholinated tap water through the inverted sample net. The

25 total number and stages of mosquitoes collected were recorded. Larvae and

pupae were returned to the basin and allowed to disperse for 30 minutes.

Water depths/volumes tested included 0.1m/0.074m3, 0.2m10.148m3,

0.30m10.222m3, 0.40m1O.296m3, and 0.47m10.347m3. Each depth was replicated

three times with 30 minutes of settling time between each trial which allowed the

mosquitoes to resume normal activity within the water column and to redistribute

themselves. Trials were run for Ae. aegyptiand Cx. restuans at all water depths

and repeated with 250, 500, 750, 1000 mosquitoes and in the case of Ae. aegypti

only, 1125 with equal numbers of each instar and pupae. Mosquito densities

were calculated for each water depth (Table 1).

Data Analysis Data sets for the number of individuals collected with the net were tested

for conformity to the normal distribution. Logarithmic transformation was required

to normalize the number of Ae. Aegypticollected with the sampling net as well as

the following; when collections were at 0.20 and 0.30 m water depth, and when

the initial total mosquitoes were 500, 750, 1000, or 1125. Logarithmic transformation was also required to normalize the number of Cx. restuans

collected as well as the following; when collections were made at water depths of

0.20, 0.30, or 0.40 m, and when the initial total number of mosquitoes was 1000.

The means, standard error of the mean and range of the number of

mosquitoes captured using the net were calculated. The number of samples in which mosquitoes were detected with the net when mosquitoes were present in the basin was determined (x). Sensitivity (s) of net sampling was calculated using s=x/n where n is the total number of samples taken.

26 ïotal number of mosquitoes collected with the net were compared to one

another to determine the effectiveness of the net in capturing individuals using

Analysis of Variance (ANOVA) and Pearson's Correlation (SPSS 2001). Water depth and number of mosquitoes caught with the net were compared using

ANOVA and Pearson's Correlation (SPSS 2001). The interaction of water depth and number of mosquitoes present in the basin on the efficiency of capturing mosquitoes using the net was analyzed using the General Linear Model

Univariate ANOVA (SPSS 2001).

The numbers of each of the five developmental stages of mosquitoes collected were compared to determine collection biases with the net using one- way ANOVA (SPSS 2001), Each water depth (0.10, 0.20, 0.30, 0.40 and 0.47m) was paired with total mosquitoes (250, 500, 750, 1000 and 1125). The one-way

ANOVA was then run on the numbers of each developmental stage captured with the net to identify any sampling differences between the five developmental stages. When the ANOVA was significant, Tukey's test was used to separate means.

Linear regression was used to calibrate the net for quantifying mosquito abundance in the catch basin. ïhis was intended to explore the relationship between total mosquitoes in the basin and the number collected based on a water depth and net capture result. For example, if five mosquitoes were collected using the net and the water depth was 0.3 m, could the total number of mosquitoes in the basin be estimated? Analysis of Variance was used to determine any differences in the number of individuals caught with the net between species. P values of <0.05 were accepted as significantly different.

27 Results

Aedes aegypti The mean number of Ae. aegypti larvae and pupae captured using the aquarium net varied according to depth and number of mosquitoes introduced.

The fewest mosquitoes were collected at shallow depths and lowest initial numbers with the net, while more mosquitoes were collected when initial total mosquitoes were increased in the catch basin (Table 2). ln terms of the sensitivity (the abiliiy of the net to detect the presence of mosquitoes in the catch basin) of the net for sampling Ae.aegypti, at least one mosquito was detected in

74 of lhe 75 samples taken.

Overall, water depth significantly effected the number of mosquitoes sampled (Table 3). The number of mosquitoes sampled with the net was also related to the total mosquitoes in the artificial catch basin (Table 3).

Both water depth and initial total number of mosquitoes affected the number of mosquitoes sampled with the net and there was also a significant interaction between the two variables (Table 3) (Figure 7). When Pearson's correlation test was carried out using mosquito density (total number of mosquitoes/water volume) and the number of mosquitoes collected, there was also a significant difference (r = 0.625, df = 75, p < 0.001).

As water depth increased, the number of mosquitoes caught decreased at the 500, 750, 1000, and 1125 abundance levels but collection numbers were not affected when the initial number of mosquitoes was 250 (Table 4). There was a consistent response across the various mosquito totals with the exception of 250, and generally when water depth decreased, more mosquitoes were caught (Table

28 4). Furthermore, as initial total number of mosquitoes increased, the numbers of mosquitoes captured using the net was significantly correlated at all water depths

(Table 5).

At most combinations of initial total mosquitoes added to the basin and water depth, the numbers of each developmental stages of mosquitoes collected were not significantly different (Table 6). When 1000 Ae. aegyptiwere introduced at water depths of 0.20 and 0.30m, the numbers of each developmental stage of mosquitoes collected were significantly different. At 0.20m water depth, fewer pupae were caught than other instars, and at 0.3m there were significantly more second than third instar larvae. A similar trend was observed with 1125 Ae. aegypti and water depths of 0.30 and 0.47m. Fewer pupae were caught than second instar larvae at the 0.3m water depth and there were significantly fewer first than second, third and fourth instar larvae collected aI0.47m depth.

Linear regression analysis at each water depth was significant for Ae. aegypti collected by the net, and total mosquitoes (Table 7). Therefore all equations were significant for estimating the total mosquitoes using water depth and numbers captured with the net for Ae. aegypti.

Culex restuans The mean number of Cx. restuans captured with the net ranged from 4.0 to 40.0 (Table B). Fewer mosquitoes were collected with shallow depths and low initial total numbers of mosquitoes, and more were collected at shallow depths and high initial total numbers of mosquitoes. Sensitivity of sampling was high. ln the 60 samples taken, at least one mosquito was collected in 59 samples.

29 Water depth did not have a significant effect on the number of Cx.

restuans sampled with the net from the catch basin (Table 9). The total number

of mosquitoes had a significant effect on the numbers captured, and there was a

significant interactive effect between water depth and total mosquitoes on the

number of mosquitoes captured using the net (Table 9) (Figure 8). When

Pearson's correlation test was carried out using mosquito density, (total

mosquitoes/water depth) and the number of mosquitoes collected, there was also a significant relationship (Pearson's correlation = 0.587, df = 60, p < 0.001).

With 500 Cx. restuans, as water depth increased, the number of mosquito caught with the net significantly decreased (Table 10). However, at all other initial mosquito totals, this relationship was not significant.

There was a positive correlation between the number of Cx. resfuans collected and initial mosquito totals at water depths of 0.10, 0.20,0.30 and 0.40m

(Table 11). At water depths of 0.47 m, this relationship was not significant.

At most combinations of mosquito totals and water depth, the number of mosquitoes collected was not significantly different (Table 12). AI the shallowest water depth and higher initial total number of Cx. restuans, the number of different stages of mosquitoes collected was significantly different. With an initial total of

500 mosquitoes and a 0.20m water depth, the number of stages sampled was also significantly different. Similarly, aI a 0.47m depth and an initial total number of 750 Cx. restuans, the number of each stage was significantly different (Table

12).

At water depths of 0.10 and 0.40m, linear regression analysis between Cx. restuans collected by the net and total mosquitoes was significant (Table 13).

30 Therefore, there were two significant regression equations using water depth, initial total mosquitoes and the number of mosquitoes sampled using the net for

Cx. restuans.

31 Discussion I focused on water depth and the initial total mosquitoes added to the

basin and the relationship to the number of mosquitoes sampled using the

aquarium net method. There has been little research conducted on water depth

and the number of mosquitoes in artificial containers, and the relationship to the

number of mosquitoes sampled, as well as the behavioural aspects of larvae and

pupae within an artificial container. ln this experiment, there were factors other

than those measured influencing the individual and group actions on the

probability of capture. The discussion on behaviour is restricted to water depth

and mosquito totals.

Net Sensitivity For both species, the aquarium net method for sampling in the artificial catch basins was 98% effective in detecting mosquitoes that were known to be

present. These results are consistent with Zhen and Kay (1993), who found a

100pm net to be 100% effective when sampling tires. Every sample taken had at least one mosquito, when mosquitoes were present within tires. ln subterranean service manholes, at introduced numbers ranging from 25 to 100 individuals, at least one larva was detected in 25% to 75% of samples (Kay ef al. 2000a). When the number of mosquitoes present in a manhole was increased to >200 in the same study, larvae were detected in every sample. ln sampling the artificial catch basin, no mosquitoes were collected in one sample out of 75 (Ae. aegyptî) and 60

(Cx. resfuans). Even though catch basins and tires are different environments, net sampling seems to be an effective device for determining the presence or absence of mosquitoes in a catch basin.

32 Thorough detection of mosquitoes in catch basins is needed to ensure

success of control programs. This is especially true early in the season when

mosquito numbers within the catch basins are low, and early control measures

may lead to better overall control throughout the season. Major focuses of control

programs are on larval detection, i.e. presence/absence, to determine if treatment

is necessary.

Several studies have been conducted on the presence of mosquitoes within catch basins. ln those studies, the numbers of mosquitoes sampled from catch basins have ranged from 1 to 168 (Russell et al.2001), 1 to 174 (Thomson

2004) and 1 to 600 (Stockwell et al. 2006) mosquitoes per catch basin sample.

Geery and Holub (1989) sampled forty catch basins over the course of two and a half months and had a range of sample means from 0.5 to 86.0 mosquitoes per sample. Since the focus of control programs is to detect the presence of mosquitoes in catch basins'the range of mosquito totals and water depths used in this experiment needed to mimic those found in nature. The numbers of mosquitoes that were introduced to the artificial catch basin in this experiment

(total mosquitoes), and the results obtained from sampling, seem to reflect results from these other studies.

Generally when mosquitoes are detected in samples, catch basin treatments are begun (R. M. Gadawski personal comm.). Since the aquarium nel method of sampling is 98% effective in detecting the presence of mosquitoes in an artificial catch basin, then it is likely a useful method of sampling catch basins in control programs.

33 Aedes aegypti Significantly fewer Ae. aegypti were captured with the aquarium sampling net when water depth increased. These results are consistent with Zhen and

Kay's (1993) findings with a sweep net method for sampling tires, where water depth in tires was the third most important factor in affecting the number of mosquitoes sampled with a net. A greater volume of water allows for greater space available for mosquitoes, therefore the lower the probability of the net encountering mosquitoes. ln this experiment, as the initial number of mosquitoes increased, the numbers sampled with the net increased and this effect was significant for almost all of the initial total mosquitoes in the catch basin (500, 750,

1000, and 1125). At an initial total of 250 mosquitoes in the basin there was no significant relationship between number of mosquitoes caught with the net and water depth.

Significantly more Ae. aegypfi were captured at higher initial numbers of mosquitoes which indicated that as total mosquito numbers increased, the number of mosquitoes caught in the net increased proportionally. Similar results were obtained when tires were sampled for mosquitoes using a mosquito dipper and 100pm net (Zhen and Kay 1993). These authors found positive correlations with the number of mosquitoes sampled and the number of mosquitoes introduced to the tires. ln North Queensland, Kay et al. (2000a) found no significant difference among mean collections of mosquitoes when the abundance of mosquitoes in manholes were greater than 200. However, manholes were covered when mosquitoes were introduced and sampling involved the removal of the grate which may have caused larvae to disperse.

34 When examining sampling methods for mosquito larvae, it is probable that

as the density of mosquitoes increases, the number of individuals sampled should

increase as well. Pernia et al. (2007) examined the effects of predacious cyclopoids on Anopheles larvae and showed that as larval density increased,

predation by cyclopoids increased too. ln this instance, Pernia et al. (2007) attributed the correlation to basic 'intrinsic density-dependent factors'. ln the case of the sampling net and mosquitoes in the artificial catch basin, the net is similar to the cyclopoids, although not "seeking" prey, the greater the mosquito numbers, the greater the probability of the net encountering mosquitoes, and thus greater numbers in the sample.

The results of this study support the general principle of density dependent relationships. When the number of individuals in a given space increases, the number of individuals sampled should also increase. Therefore as water depth increases, numbers of mosquitoes caught with the net should decrease, and as the total mosquito numbers increase, the number sampled should increase.

Water depth and the initial mosquito total both affected the number of Ae. aegypti sampled with the net, with these variables interacting with one another.

This may mean that as water depths increase, and the initial number of mosquitoes decrease, the number of Ae. aegyptisampled will decrease. The two variables, water depth, and total mosquitoes appear to be interacting and influencing the number of mosquitoes captured.

There was little bias in capturing different stages of Ae. aegyptilarvae and pupae. Out of a total of 25 different combinations of water depths and total initial

35 mosquitoes, only four combinations resulted in a bias toward one or more larval

instar and pupae using the aquarium net.

Out of these four combinations, more 2nd and to a lesser extent 3'd and 4th instar Ae. aegypti larvae were sampled than pupae and 1't instar larvae. These results are contrary to Tun-Lin et al's. (1994) findings in 220-litre drums and Zhen and Kay's (1993) findings in tires where more 3'd and 4th instar larvae were collected than 2nd instar larvae. These differences in results could be attributed to differences in artificial containers, sampling and environmental parameters. Tires are significantly different from catch basins in structure and volume. The 220L metal drums, although similar in dimensions and volumes of catch basins, have differing environmental variables such as lighting conditions (where catch basins were relatively dark) and temperature which could influence larval and pupal interactions and responses (Kasap 1978, 1981). ln the case of the metal drum experiment, samples were taken from the perimeter of the drums. Sampling in such a manner is different from my method of sampling the catch basins and therefore could account for the difference between results.

Culex restuans The number of Cx. resfuans captured with the aquarium sampling net was not significantly correlated to water depth. Unlike Ae. aegyptiwhich seemed more sensitive to water depth and appeared to be more randomly distributed in the artificial basin, Cx. restuans larvae and pupae may have been more aggregated in distribution. These results are similar to Tun-Lin et al. (1994) when collecting Ae. aegyptifrom sweep net sampling in 220 L metal drums. When water volumes

36 within a metal drum were increased, the per cent recovery per sweep was not significantly different. Furthermore, Tun-Lin et al. (1994) found that all but 1't instar larvae were concentrated towards the top 20 cm of the water column.

Changes in water depth greater than 20 cm in the metal drums would not affect the number of mosquitoes sampled with a net, as the volume of space the mosquitoes occupied had not changed significantly. ln the present study, mosquitoes were sampled from the top 10-15 cm of the catch basin water column.

Potentially Cx. restuans in the catch basin environments acted similarly lo Ae. aegyptiin the metal drum experiment in relation to water depth.

When the total numbers of mosquitoes were increased in the basin, the number o'f Cx. resfuans captured with the aquarium net was significantly different for most initial mosquito totals, consistent with an increase in the density of mosquitoes per litre. There was no correlation between the initial number of mosquitoes and number of mosquitoes captured at a water depth oT 0.47 m.

Therefore as the density increases there is a greater chance of the net encountering mosquitoes, and therefore a greater chance of capture. lt is likely that Cx. resfuans was distributed differently in the water column from Ae. aegypti in the present study. ln the case of Cx. restuans, aggregation may not show in results if the larvae were concentrated towards the top 20cm, because the density of those mosquitoes most likely to be captured with the net is still increasing.

Therefore there is still a greater chance of mosquitoes being captured by the net with an increase in the initial number of mosquitoes in the basin.

Although water depth did not affect the number of Cx. resfuans sampled, water depth and the initial number of mosquitoes in the basin were interacting

37 with one another. Like Ae. aegypti, as the initial number of mosquitoes increased

and the water depth decreased, the number of Cx. restuans sampled increased.

The results from the Cx. restuans trials suggest that the interaction may be a

result of density-dependent interactions, and as density increases (interaction of

depth and total mosquitoes) the number of mosquitoes captured with the net will

increase.

There was little apparent bias towards capturing different stages of Cx. resfuans mosquitoes. Out of a total of 20 different combinations of water depths

and initial number of mosquitoes, five combinations resulted in net sampling being

biased towards one or more instar larval stage or pupae.

At the lowest water depth and varying initial mosquito numbers, more 4th

instar Cx. restuans larvae were collected. These results support observations

made by Kasap (1978,1981), whofound a decrease in the responsiveness of 3'd and 4th instar Culex pipiens (L.), a species also commonlyfound in catch basins, to a moving shadow or a tap stimulus on their container. Even though stimulus of sampling was constant, neither light around the catch basin nor position in relation to the catch basin being sampled was controlled. Both factors could have influenced shadows cast by sampling and therefore affected the developmental stages being sampled.

Prediction Equation Another objective of this study was to explore a method of predicting absolute numbers of larvae and pupae within a catch basin given the actual water depth and number of mosquitoes captured with the aquarium net. Linear regression was used to predict mosquito abundance in catch basins. For

38 example, in a catch basin with a water depth of 0.40 m, and where three Ae. aegypti larvae were caught with the net, the number of larvae and pupae within the basin may approach 430 individuals (Table 7). ln the case of Cx. restuans, at a similar water depth with three mosquitoes captured, the estimate would be approximately 480 Cx. restuans larvae and pupae in the basin.

Although all the linear regression equations were significant for Ae. aegyptiand two were significant for Cx. restuans, there are a several problems in the application of these results to field situations. These problems include a wide range of water depths observed in field catch basins, and differences in prediction equations for different species that may limit the application of these results to field situations.

ln the field, catch basins may have a wide range of water depths.

Thomson (2004) found water depth to range from 0.003 to 0.890m; of that, 49.2%

(n=1034) had water depths greater than zero and less than 0.47 m. lonly examined water depths of 0.10, 0.20, 0.30, 0.40, and 0.47 m. Therefore for any water depths greater than 0.47 m, the number of mosquitoes in the catch basin could not be estimated using water depth and the number of mosquitoes sampled with the net results from this study. According to Thomson's (2004) observations,

50% of the catch basins had water depths greater than 0.47m. The estimation equations therefore, could only estimate 50% of the catch basins sampled in a field situation.

For quantative estimations of productivity in 220 L metal drums, water depth did not significantly affect per cent recovery by the net (Tun-Lin et a\.1994).

Thus, Tun-Lin et al. (1994) would not have to include water depth within their

39 estimation equations. Estimates of mosquito abundance in catch basins in the field require consideration of particular water depth and species, however. The objective of this study was to develop a method of predicting abundance in catch basins using a simple equation, using water depth and number of mosquitoes captured, to estimate the number of mosquitoes present in a catch basin. ln this case, a simple equation could not be calculated because water depth had a significant influence on the number of mosquitoes captured with the net.

Therefore separate equations were determined for each water depth, which would increase the time it takes to estimate numbers in catch basins and therefore limit its practicality in catch basin control programs.

The two different species exhibited different behaviours in relation to water depth. For example, total numbers of Cx. resfuans in a catch basin could only be predicted at depths of 0.10 and 0.40 m. ln field sampling programs, determining the species of larvae and pupae within the basin, at time of sampling is difficult without a microscope. Moreover some larval instars need to be reared, and pupae need to emerge as adults before they can be identified. Estimating standing crop of larvae and pupae within catch basins would be impractical if more than one species was present in the basin.

Most mosquito larvae present in catch basins, however, are Culex species

(Munstermann and Craig 1976; Geery and Holub 1989; McCarry 1996; Musa

2002; Rey ef al. 2006). Thomson (2004) found that approximately 94% of mosquitoes found in catch basins in Winnipeg, Manitoba, were Cx. restuans.

Since more than half of the predictive equations to determine number of mosquitoes in a basin calculated with Cx. restuans were not significant, this

40 method of determining mosquito abundance in catch basins in the field is not feasible at this time.

One could argue that these methods could be applied post survey in the laboratory; however, for catch basin control programs, time and cost are major considerations. Although the mosquito density could be estimated, the focus of control programs is generally presence or absence, therefore the cost associated with labour involved in sampling, identifying, and fitting models may not be consistent with mosquito control requirements. However, the net method of sampling in catch basins is effective in determining presence/absence.

41 Future Research Tun-Lin et al. (1994) recommended creating a vortex before sampling, thereby distributing the mosquitoes within the water column and increasing the ability to detect any mosquitoes. Leisnham et al. (2005) examined various methods of dipping and determined that destructive sampling, such as stirring the water before dipping, was more strongly corrleated with absolute counts and was more consistent and accurate. The estimation of the abundance of larvae and pupae within catch basins in the present study may have been influenced by the way in which the net was swept and samples taken, which was not examined in this experiment. Future research is recommended to determine the influence of destructive sampling on estimating abundance of larvae and pupae within catch basins.

Sampling efficacy in the present study was conducted without any suspended debris in the water column of the basin. Large amounts of leaf litter and other debris in catch basins interfere with net sampling (Kay ef al. 2000a).

Thomson (2004) determined that Cx. resfuans are more likely to be found in catch basins with higher levels of organic debris. Yee ef al. (200Q found leaves present on the water surface elicited greater browsing behaviour than when absent.

Since the effect of leaf litter on per cent recovery of mosquitoes in the net was not examined, effective sampling of a catch basin could be inhibited by the suspended debris. More research is needed to determine the effects of leaf litter and suspended debris on net sampling in catch basins.

The influence of suspended material on the diving behaviour of larvae and pupae was not examined. Workman and Walton (2003) found that Culex larvae in water with high food concentrations had a reduced dive frequency compared to 42 larvae in water with low food concentrations. This should also be examined in an artificial catch basin to determine the influence on the per cent recovery using the aquarium net.

When the water surface is disturbed, larvae and pupae disperse in many directions as a flight response (Carpenter and LaCasse 1974). Therefore, once the net was put into the water, the mosquitoes could have exhibited a similar reaction to this disturbance. Although the effects of lighting conditions around the catch basin were limited, shadow casting and vibrations created when sampling were not controlled. ïherefore any variations from the general trends which were evident in this experiment are more likely due to these uncontrolled environmental parameters and future research should include these variables.

Furthermore, because the catch basin was dark, other than the minimal light entering from the overhead grate, the movement of larvae and pupae within the water column was not observed. Movement of larvae and pupae throughout the water column might differ from movement within a water column with greater exposure to light. Workman and Walton (2003) found aggregation behaviour in larvae of various Culex spp. larvae to be significantly different. Therefore future experiments should include observations on larvae and pupae within similar catch basin lighting conditions to determine the level of aggregation of Cx. restuans.

The majority of the larvae and pupae may be caught within the first two sweeps of the net(Tun-Lin et al. 1994; Thomson 2004). Therefore the larval and pupal behaviours which most likely influenced my findings would have occurred from the time the net was passed through the grate (causing vibrations or

43 shadows) to the time when the second sweep of the net was complete, and future research in catch basins should examine this element.

ln this study, the behavioural aspect of the larvae and pupae was not observed, which could influence the number of larvae and pupae captured with the net. Since this is the first experiment of its kind to estimate abundance of mosquitoes in catch basins, further research is needed to examine the behaviour of larvae and pupae when sampled, greater ranges of water depths and total mosquitoes, and differences in methods of net sampling.

44 Conclusion Aedes aegypti and Cx. restuans larvae and pupae can be effectively

sampled in catch basins using an aquarium net. Water depth and initial numbers

of mosquitoes influence the number of larvae and pupae captured with the net.

As water depth increased, the number of Ae. aegyptilarvae and pupae captured with the net decreased, while there was no significant difference for Cx. resfuans.

As initial mosquito numbers increased, the numbers of mosquitoes captured with the net increased for both species.

The aquarium net sampling method for mosquitoes is effective in determining the presence/absence of mosquitoes in a catch basin. Significantly fewer Ae. aegypti larvae and pupae were captured with the net as water depth increased. As mosquito density increased, the number of Ae. aegypti and Cx.

restuans sampled with the net also increased. There was an interaction between water depth and total mosquitoes for the numbers of Ae. aegyptiand Cx. restuans collected with the net. There was little bias towards capturing different stages of

Ae. aegyptiand Cx. resfuans larvae and pupae.

It was possible to estimate the number of Ae. aegyptiwithin a catch basin, based on water depth and net capture counts but not particularly accurate for Cx. restuans. The influence of suspended debris and a larger range of water depths on captures of Cx. restuans need to be studied further.

45 Chapter lV: Gatch basin mosquito monitoring: determination of abundance and prevalence of Culex mosquitoes in catch basins and their association with canopy cover in Winnipeg, Manitoba

Abstract Catch basins in Winnipeg, Manitoba were surveyed, in 2004 and 2005, to determine their potential as sites for Culex spp. mosquito development. Six areas were selected based on tree canopy density to explore the relationship between the prevalence of immature mosquitoes and vegetative cover. Other environmental variables measured including water temperature, water depth, and per cent leaf litter. Precipitation and female oviposition rates were also determined. Catch basins in Winnipeg contained predominantly Culex resfuans.

Significantly more mosquitoes were found in catch basins within areas with high tree canopy cover than in catch basins with reduced or no tree canopy cover in

2004 and 2005. The number of mosquitoes collected from catch basins in 2004 was positively correlated with amount of leaf litter and water depth, but not in

2005. As precipitation increased, the number of mosquitoes in catch basins declined. Catch basins with greater leaf litter input appear to support more mosquitoes and these sites can be easily determined by visual assessments of canopy cover. With increasing tree canopy cover, water temperatures decreased, and water depth, leaf litter and numbers of mosquitoes increased. The number of mosquitoes sampled from catch basins was primarily influenced by rainfall over the course of the season. lndividual rainfall events should be carefully monitored

46 in mosquito control programs where catch basins are prevalent as large rainfalls may flush mosquitoes out of catch basins.

47 lntroduction ln 1999, West Nile virus was documented for the first time in North

America and caused avian, equine and human deaths (Jia ef a/. 1999). Since its

introduction into the United States, WNv has moved steadily west and northward

(Sampathkumar 2003). Since the 1999 outbreak, there have been more than

20,000 reported cases,770 deaths, and an estimated 215,000 illnesses in North

America (Peterson and Hayes 2004; Public Health Agency of Canada 2008;

Centers for Disease Control & Prevention 2007).

The first human case of WNv in Manitoba was reported in 2003. ln 2003 there were 142 human cases. From 2004 to 2006 the number of human cases positive for WNv in Manitoba dropped (2004-3 cases, 2005-55 cases, 2006-50 cases) (Public Health Agency of Canada 2008); however, in 2007 they rose significantly to a record high of 572 cases (Public Health Agency of Canada

2007).

1990) and its ornithophilic host preference (Surgeoner and Ralley 1981; Reinhert

2001). Ebel et al. (2005) determined that Cx. restuans was a competent laboratory vector of WNv and since it feeds on a broad range of avian hosts and occasionally on locally available mammalian species, can be considered an important vector of the WN pathogen in North America (Apperson et al.2002).

Catch basins are concrete reservoirs found along roadsides and on boulevards in urban areas beneath storm-water grates. They are designed to

4B collect water runoff from streets, usually from a precipitation event and channel it

into the city storm-water or sewer system (Su ef al. 2003; Kronenwetter-Koepel ef al. 2005). Urban centres may have thousands of catch basins depending on the size of the city or town (Ellis 1995).

There are several general designs of catch basins found in North America.

Some basins have direct drainage to the sewer or storm-water system, and others have an outlet pipe above a settling area or'sump'which may be several metres deep (Lau et al. 2001) (Figure 1 and 2). The most common, and most important consideration from a mosquito habitat standpoint, are those basíns that have a settling area where there is potential for standing water (Munstermann and Craíg

1976). This type of basin is commonly found within the City of Winnipeg,

Manitoba, Canada drainage system (City of Winnipeg 1990).

Most mosquitoes in catch basins are potential vectors for WNv, including

Culex species (Munstermann and Craig 1976; Geery and Holub 1989; McCarry

1996; Musa 2002, Rey ef a\.2006). Anderson et al. (2006) surveyed 22 calch basins in Stamford, Connecticut and collected large numbers of adult females, including both Cx. pipiens and Cx. resfuans infected with WNv. Geery and Holub

(1989) found 63% of the catch basins in lllinois had immature stages of Cx. pipiens and 37o/o contained Cx. restuans. Although Cx. pipiens has been shown to be a principal vector of WNv, this species has not been collected in Manitoba, where as Cx. restuans is abundant.

Hedberg et al. (1985) found a significant relationship between the number of LaCrosse encephalitis infections in humans and the presence of artificial containers within the area where infected humans inhabited. Therefore increased

49 numbers of catch basins may have potential to result in an increased number of

human infections. Although there is no evidence to prove this hypothesis,

ln Winnipeg, catch basins are developmental sites for Cx. restuans larvae

and pupae. Thomson (2004) demonstrated that approximately 94% of the

immature mosquitoes in catch basins were Cx. resfuans, a species that has been

shown to contribute to the amplification of WNv in birds.

Furthermore, tree canopy cover may be a significant indicator of the

number of Cx. restuans larvae and pupae in catch basins (Thomson 2004). With higher amounts of tree canopy cover, the number of immature Cx. restuans increased in catch basins in Winnipeg. The number of immature mosquitoes was also correlated with water depth, water temperature and per cent leaf litter composition in catch basins (Thomson 2004).

Since the introduction of WNv into North America, there has been a surge in control programs to target WNv vectors in catch basins (Su ef al. 2003;

ïhomson 2004; Anderson et al. 2006). Mosquito control programs have become established in the United States in Minnesota, California, and Ohio where catch basins have the potential to be a significant source of Culex spp. including Cx.

restuans (Su ef al. 2003). More recently, cities in Canada including Montreal,

Toronto, and Hamilton have commenced control programs to focus on potential vectors of WNv species, including Cx. restuans, in catch basins (personal communication R. Gadawski, Canadian Centre for Mosquito Management).

The primary purpose of this study was to explore population dynamics of mosquito larvae and pupae inhabiting stagnant water within catch basins in

Winnipeg, and to determine if there is a correlation between the abundance of

50 mosqu¡toes in catch basins and, nutrient availability, water depth, water

temperature, and number of gravid females. Additionally, I wanted to determine if

tree canopy cover could be used to predict the presence or absence of larvae and

pupae within catch basins.

It is important to determine if larval and pupal habitats in catch basins are more abundant in certain communitíes such that control measures can be targeted where they are needed. From a wNv perspective, there is also a need to clarify factors that influence abundance and species composition of mosquitoes in catch basins and how catch basin mosquito management should be integrated with WNv control programs in Winnipeg.

The objectives of this study were as follows:

o To determine the extent to which environmental variables (water depth, water temperature, leaf litter, tree canopy density, the number of egg rafts collected, and precipitation) influence the number of mosquitoes in catch basrns.

o To determine if environmental variables and mosquito numbers were influenced by sample period.

51 Materials and Methods The field study was located in Winnipeg, Manitoba. Mean monthly temperatures in Winnipeg range from 28.5"C in July (the warmest month) and ,

22.8"C in January (the coldest month) with mean annual precipitation of 513.7 mm (Environment Canada 2004). ln 2004, six neighbourhoods in Winnipeg were selected including, East St. Paul, South Tuxedo, Transcona, East Kildonan, River

Heights and Riverview for sampling catch basins (Figure g). ln 2005, Fort

Richmond was selected to replace Transcona. The dominant species of trees along streets over the catch basin in neighborhoods included ash (Fraxinus spp.),

American elm (Ulmus americana L.), and cherry (Prunus spp.). The neighbourhood was consider to be the replicate and neighbourhoods were considered independent of each other. The catch basins was considered the sampling unit.

The neighbourhoods were chosen on the basis of tree cover. A survey of tree canopy density, measured once in late July 2004 or 2005, at full canopy flush, was conducted to compliment the leaf litter measurements. Tree canopy density was measured at nine different locations within a 20m radius of the selected catch basin (Figure 10). Measurements were taken with a spherical densitometer and per cent canopy density was calculated to the nearest per cent.

Riverview and River Heights had high tree cover density with the main species being mature elm and ash trees on the street boulevards, over the catch basins (Figure 11). These neighbourhoods were considered the high canopy treatment. East Kildonan, Fort Richmond (2005) and Transcona (2004) contained moderate tree canopy cover (moderate tree canopy category) with younger and

52 more widely spaced trees compared to the high density canopy cover category

(Figure 12). South Tuxedo and East St. Paul were considered to have low tree canopy cover with young trees, as these residential developments were less than

15 years of age (Figure 13). These neighbourhoods were considered the low tree canopy cover category. Tree cover was the chosen as a significant indicator of nutrient availability within the catch basins.

Catch basins within each neighbourhood were further classified by position of catch basin in relation to the roadway, and the relative number and species of trees near or over the catch basin. Catch basins were positioned below grates with either curb inlets or boulevard inlets (City of Winnipeg 1990).

Catch basins were separated by up to 100m in each neighbourhood depending on configuration of streets, while others were only separated by the width of the street. (Several catch basins in the study were located adjacent to one another where well separated catch basins with adequate canopy cover were not available in the neighbourhood selected.) One catch basin was eliminated from the study in 2004, and four in 2005, because the City of Winnipeg had replaced them with a new catch basin or construction on surrounding roadway prohibited sampling access. ln 2004, the numbers of Cx. resfuans and other species of mosquitoes sampled from catch basins and female oviposition collections, were extremely low in Transcona. Therefore it was replaced with another moderate canopy cover area, Fort Richmond, for the sampling season of 2005.

Fifty catch basins were randomly selected within each of the seven neighbourhoods and sampled at two week intervals from 22 June to 13

September 2004, and 13 June Io 20 August 2005 (Tables 14 and 1b). Water

53 depth and temperature were measured on each sample date to the nearest 0.1"C and 0.1 cm, respectively (McCarry 1996). A 6x8cm, 1mm mesh aquarium fish net attached to a 2.5 m wooden pole was used to sample basins for mosquitoes

(Thomson 2004) (Figure 5). The net was swept twice under the surface of the water so that it was almost entirely submerged. One sweep consisted of an s- shaped pattern along the entire diameter of the basin (Figure 6). One sample consisted of two sweeps of the net in the basin. The mosquitoes and any accompanying leaf litter were washed into the sample collection container by pouring water through the inverted net.

ïhomson (2004) found Cx. restuans was the main species in catch basins, and the mean water temperature in catch basins in the low canopy treatment was

1B.8oC. Due to these results and results from Buth et al. (1990) which showed that the mean larval development time of Cx.restuans was 13.2 days at a temperature of 20"C, collections were made at two week intervals. Collections for low, moderate, and high canopy category were made six and five times in 2004 and 2005, respectively. Collections ceased when the number of mosquitoes collected reached zero in the fall of 2004 or resulted in fewer than one mosquito per wet catch basin in 2005.

Mosquitoes caught in the net were transferred to 50 ml plastic vials containing 40ml of water, labeled with their corresponding catch basin number and street address, and brought to the laboratory for species identification.

Pupae were placed in pupation chambers (Figure 14) and emerged adult females were identified, males were counted as unidentified (Thomson 2004). Third and fourth instar larvae were killed in boiling water and preserved in a 70% EIIOH.

54 First and second instar larvae were raised to fourth instars in the laboratory in 200 ml containers with 150 ml de-chlorínated water. Field-collected larvae were fed and maintained in the same manner described for Aedes aegypti and Culex restuans larvae reared in the laboratory in the pervious chapter (Gerberg 1g7O).

Adults and larvae were identified to species using a dissecting microscope and various taxonomic keys (Wood et al. 1gT9; Darsie and Ward 2005).

To measure organic content in catch basins, the amount of leaf litter was assessed by washing leaf litter obtained from the net sample into a 25x25cm clear glass collection pan. A five by five square grid was placed beneath the pan and the area occupied by leaf litter was given as a ratio out of 25 squares (Su ef a/.

2003).

Precipitation data were obtained from the City of Winnipeg Department of

Water prior to and after significant rainfall events. The City of Winnipeg rainfall gauges located within or closest to the selected neighbourhoods were used to collect the precipitation data.

ln 2004, female oviposition activity was measured using a 1.0x1.0x0.5m, polyethylene lined oviposition pool, set up in the six neighbourhoods (Brust 1976). ovipools were changed in 2005 so egg raft data would be more manageable, and to increase sample size within each treatment. ln 2005, three smaller,

0.45x0.60x0.15cm black Rubbermaid@ containers were used. Three containers were established in three different locaiions within each neighbourhood, giving a total of 18 containers.

55 Kentucky blue grass (Poa pratensis Hiitonen) sod was cut to line the

bottom of each ovipool and was replaced with new sod every four weeks.

ovipools were flooded and water was changed every two weeks. ln both years,

ovipools were monitored on a daily basis for July and August. Egg rafts were

collected, separated into 5 ml glass vials containing water, allowed to hatch, and first instar larvae were identified to species in the laboratory.

ln addition to egg raft data, the City of Winnipeg collected adult females using New Jersey Light Traps. Traps were run for 24 hours on a weekly basis.

Adult females were identified by the City of Winnipeg. All female mosquitoes were identifíed to species, to a maximum of 100 per sample.

Statistical Analysis Data for measured variables were first tested for fit to the normal distribution using a one-sample Kolmogorov-smirnov test (sPSS 2001).

Logarithmic transformation was required to normalize the Cx. restuans egg raft data. Where data met the assumptions of the normal distribution, parametric analysis was employed; where data could not be normalized, non-parametric analysis was used.

Dry catch basins were not included in the analysis. The proportion of wet catch basins and the mean number of mosquitoes present in wet catch basins was calculated for all sample days. A positive catch basin was considered a basin where at least one mosquito was collected, at least once over the season.

ïhe level of tree canopy cover (low, medium, high) was considered the category.

Thus, the catch basin was considered the sample unit within each replicate of canopy cover type.

56 The mean and standard error of the mean were calculated for all

experimental variables (i.e. water depth; per cent leaf litter; water temperature;

and number of immature mosquitoes). For per cent tree canopy cover, the mean

and standard error of the mean were calculated based on the one set of

observations made each year.

The mean and standard error of the mean were calculated for rainfall

accumulation and numbers of egg rafts within treatment areas and over the

sample periods. Since Cx. restuans predominated in the catch basins, only data from Cx. resfuans egg rafts were used.

Data analysis was divided into three sections: a) The experimental variables were separated by category (low, moderate and high canopy levels) for each year. Where data were normally distributed,

ANOVA was used to determine if there was a treatment effect. lf the ANOVA was significant Tukey's HSD test was used to separate the means. The Kruskal-

Wallis non-parametric ANOVA followed by a Mann-Whitney U test (where necessary) was used when data were not normally distributed and variances unequal (SPSS 2001). b) Experimental variables were separated by sample period and sample periods were compared statistically for each year. ln 2005, sample three (sample taken beiween 10-23 July) was ignored as it was incomplete due to the lack of mosquitoes sampled from high canopy cover catch basins. Within each sample period, normally distributed experimental variables were compared to assess changes over the sampling season using ANOVA. When means were significantly different, Tukey's HSD test was used to separate the means (SPSS

57 2001). The Kruskal Wallis non-parametric ANOVA test followed by the Mann-

Whitney U test (where necessary) was used to determine differences between

means when data could not successfully be transformed to the normal distribution and when variances were unequal (SPSS 2001). Results from analysis sections of a) and b) are discussed together for each experimental variable. c) Experimental variables were examined separately for the two years to determine trends and associations within each year. Spearman's rank order correlation was used to determine correlations between variables, as the data were not normally distributed (SPSS 2001).

Results a) Canopy type and sample period - 2004. Approximately 1,000 mosquitoes were collected over the summer in 2004

(Table 15). The mean number of mosquitoes collected was 18.1 per positive catch basin. The numbers of mosquitoes collected in positive catch basins ranged from one to 90. Of the total number of mosquitoes collected, 97.6% were

Cx. restuans, the remaining 2.4o/o consisting of Aedes vexans (Meigen) and

C ul i seta i nornata (Williston).

Per cent canopy cover was significantly different between categories

(H2,6ss=227.017, p<0.001). The low canopy cover neighbourhoods had a mean canopy density of 4.6x0.76%, followed by moderate and high canopy cover neighbourhoods at 29.9t1.83o/o, and 68.2t1 .79o/o, respectively. Since there was only one measurement taken at full leaf flush, variations in per cent canopy cover over differing sample periods were not considered in the analysis.

5B Mean water temperatures in basins were significantly different between

high tree canopy density, moderate and low canopy density areas in 2004 (Table

16). Mean leaf litter content significantly increased with increased canopy cover

(Table 16). Mean water depth in catch basins also significantly increased with

increased tree canopy density, and was different in all canopy cover types (Table

16). There were significantly more mosquitoes collected in high canopy cover catch basins (Table 16).

ln 2004, catch basins in high canopy cover neighbourhoods had lower temperatures than moderate canopy catch basins, which had lower temperatures than low canopy covered basins. Over the summer, mean water temperatures increased at each progressive sample period, and leveled out during lhe 1-14

August sample period. Mean water temperatures remained between 12"C and under 15"C over most of the summer (Table 17). Leaf litter content significantly decreased after mid August (Table 17). over the summer, mean water depths fluctuated, but all levels were within 100mm of one another over the summer

(Table 17). The maximum water depth measured was 1 .22 m and over the whole sampling season 315 catch basins had water depths which exceeded the 0.60 m spill over hood. of those 315 catch basins,230 were in high canopy cover treatments.

There was no significant difference in mosquito collections in moderate and low canopy cover catch basins. The mean number of mosquitoes collected fluctuated over the sampling periods during the summer (Table 17). The maximum number of mosquitoes collected peaked during 18-31 July at which time there were on average, two mosquitoes per sample per wet catch basin.

59 There was no significant difference in mean precipitation between

neighbourhoods in 2004. There was also no significant difference in precipitation

between treatments over the course of the sample period (Figure 15).

There was a significant difference between the number of Cx. resfuans egg rafts collected between canopy cover treatments in 2004 (Table 1B).

Ovipools under moderate canopy cover had a lower mean number of egg rafts collected versus both low and high canopy cover areas (Figure 16). Numbers of

Cx. restuans egg rafts significantly increased over the sampling period where it peaked during 1-14 August(Chi-square=31.237df = 3, n = 336 p<0.001). After this period, the numbers of Cx. restuans egg rafts decreased.

A total of 336 and 334 Cx. tarsalis and Cs. inornata egg rafts were collected, repectively. However, neither these species was collected from catch basins in this study.

Numbers of adult female Cx. restuans increased over the sampling period and peaked 3-30 August in the City of Winnipeg lnsect Control Branch's NJLT catches (Figure 17). There was a significant difference between the number of

Cx. restuans adult females between canopy cover areas (Chi-square = 9.008, df =

2, p = 0.011). b) Experimental variable analysis by canopy type and sample period for 2005. ln 2005, fewer mosquitoes were collected. ln total, there were 49 larvae and pupae collected. The mean number of mosquitoes found per positive catch basin also decreased from the previous year, where 2.3 mosquitoes were collected per positive basin (Table 15). The number of mosquitoes found in positive catch basins ranged from 1 to 10. Of the total number of mosquitoes 60 collected in 2005, the majority were Cx. restuans (79.4o/o) and Ae. vexans

(20.4%). The remaining were unidentifiable (0.2%).

A tree canopy cover density measurement was only taken once in either

2004 or 2005; therefore the per cent canopy cover densities for 2005 were the same for all categories except for the moderate category. ln 2005, Fort Richmond was substituted for Transcona. The moderate canopy cover mean for Fort

Richmond was 31 .97t1.90% compared to 29.87x1.83o/o for Transcona. All three canopy categories were still signíficantly different from one another

(H 2,6s6=223. 931, p<0. 00 1 ).

As in 2004, the mean water temperature in high tree canopy cover catch basins was lower than moderate canopy catch basins and low canopy catch basins. Water temperatures were significantly different between all canopy cover types (Table 19). Leaf litter counts in the low canopy cover catch basins were significantly different from other canopy treatments (Table 19). Water depths of catch basins were significantly different in all canopy cover types (Table 19). The lowest mean water depth was in the moderate canopy treatment, followed by low canopy and finally high canopy basins. Numbers of mosquitoes collected were significantly different between the low canopy catch basins and high canopy basins (Table 19).

All water temperatures were significantly different over the summer with exception of the periods 10 to 23 July and 24 July to 6 August in 2005 (Table 20).

During these sample periods, water temperatures peaked. Leaf litter counts initially dropped, but then increased towards the end of July and into August

(Table 20). Over the summer, the number of mosquitoes collected varied

61 significantly and peaked during the last sampling period of 7 - 20 August (Table 20). Water depths varied and peaked during the 10 - 23 July sample period

(Table 20). The maximum water depth measured was 1.9 m and over the whole

sampling season 216 catch basins had waterdepthswhich exceeded the 0.60 m

spill over hood. Of those 216 catch basins, 169 were in the high canopy cover

treatment.

There was no significant difference in precipitation among tree canopy

cover types in 2005. There was a significant difference in rainfall accumulation

over the course of the of the sample period (ANOVA, df = 2,9, F = 40.924, p

<0.001) (Figure 1B).

Numbers of Cx. restuans egg rafts increased over the summer; however,

there was no significant difference among different canopy cover types (Table

18). The number of Cx. restuans egg rafts significantly increased over the

sampling period and peaked during B-21 August(Hz,taz=28.207 p<0.001) (Figure

1e).

A total of 180 Cx. tarsalis and Cs. inornata egg rafts were collected.

However, neither of these species was collected from catch basins in this study.

Numbers of adult female Cx. restuans remained low over the sampling period in the City of Winnipeg lnsect Control Branch's NJLT catches (Figure 20).

There was no significant difference between the number of Cx. resfuans adult females between canopy cover areas. c) Spearman's rank order correlation analysis 2004 and 2005. ln 2004, canopy cover was positively correlated with water depth, leaf litter, and number of mosquitoes (Table 2'1). Canopy cover was negatively

62 correlated with water temperature (Table 21). Mean numbers of mosquitoes were

positively correlated with water depth and leaf litter; however, there was no

significant correlation between water temperature and number of mosquitoes

(Table 21).

As in 2004, canopy cover in 2005 was positively correlated with water depth and leaf litter, but negatively correlated with water temperature (Table 22).

Water temperature was negatively correlated with water depth and leaf litter counts. There was no correlation between any of the experimental variables and the number of mosquitoes collected (Table 22).

63 Discussion

2004 Since both Cx. pipiens and Cx. restuans have both been implicated as vectors of WNv and those are the dominant species found in catch basin environments (Munstermann and Craig 1976; Geery and Holub 1989; McCarry

1996; Musa 2002; Thomson 2004; Kronenwetter-Koepel et al. 2005; Rey ef a/.

2006), catch basins are a potential source of WNv vector development sites.

Geery and Holub (1989) demonstrated 63% of the catch basins in lllinois had Cx.

pipiens and 37o/o had Cx. restuans. Although Cx. pipiens has been shown to be a principal vector of WNv, this species has not been collected in Manitoba where

Cx. restuans is abundant.

ln Winnipeg, Thomson (2004) demonstrated that approximately 94% of mosquitoes in catch basins were Cx. restuans, a species that may contribute to the transmission of WNv in birds. ln 2004, the main species removed from catch basins was Cx. resfuans. This species composed 97% of the total number of mosquitoes sampled similar to results of previous studies.

The three treatment zones were based on different tree canopy cover densities. A main objective of this study was to determine if a visual indicator of catch basin nutrient input of catch basins (such as per cent canopy cover), could be correlated with presence of mosquitoes within those catch basins. Differences in tree canopy cover densities among treatments were significant, providing a range of tree canopy cover densities.

Leaf litter contents measured in 2004 were also significantly different and increased with increasing canopy cover. Catch basins with considerable tree

64 canopy cover should receive higher coarse particulate organic material. As tree canopy cover increases the amount of organic matter available from the surrounding area should increase, therefore the organic matter accumulation on the ground and in the catch basins should also increase. Since the category and replicate areas were chosen based on visual differences in canopy cover, it can be postulated that using visual differences in canopy cover types can be useful in estimating differences in coarse particulate organic material inputs which might influence the presence of mosquitoes in catch basins.

Therefore based on canopy cover and leaf litter counts, neighbourhoods with high levels of tree canopy cover over streets and boulevards will have correspondingly high numbers of mosquitoes in catch basins perhaps because of increased coarse organic input to catch basins. Higher canopy cover may lead to more nutrient availability which resulted in larger Cx. restuans populations.

The leaf litter counts were not significantly different during earlier sample periods when Cx. restuans numbers in catch basins were significantly increasing.

Culex restuans populations increase over the course of the summer. Lampman and Novak (1996) showed that adult populations of Cx. restuans peaked in mid-

July in lllinois, and in mid-July to early August in New York City (Ebel ef al. 2005). ln Mantioba, approximately 20o/o of the adult Cx. restuans population enters diapause within the first two weeks of August, and the number of diapausing adults continues to increase during the fall often reaching 80% prior to hibernation

(Buth ef a/. 1990).

Furthermore there was a decrease in the number of mosquitoes sampled from catch basins during 8 August and 29 August. lt is likely the decrease in the

65 numbers sampled during this time is also due to a decline in oviposition caused by the populations entering the diapauses phase.

ïhe variation in water depths in catch basins could be attributed to the higher amounts of leaf litter. At catch basins under high canopy cover and high counis of leaf litter, water depths often surpassed the depth at which the water should spill-over into the drainage system. lt was observed that at these sites, increased leaf litter occasionally blocked the spill-over hood, preventing or decreasing the amount of water spilling over, and allowing water levels to surpass the 0.60 m capacity, thus causing increased water depths.

Another explanation for these greater water depths could be the age of the high canopy cover treatment neighbourhoods. These neighbourhoods are closer to the city centre and are relatively old compared to the other neighbourhoods. lt may be that these catch basins had different dimensions than those in the newer neighbourhoods, and the distance between the spillover hood and the bottom was greater than 0.60 m.

Although larval survival is generally more influenced by available surface area than water depth, water depth along with shade may have caused the significantly lower temperatures in higher canopy treatments. As water volume increases, the amount of energy needed to heat the water also increases.

Therefore, given two different volumes of water and assuming there are similar energy inputs to each volume, the lower volume of water should reach an elevated temperature before the greater volume. The increased canopy cover could have shaded the basins, thus lowering the thermal energy inputs and

66 therefore temperatures in comparison to basins in areas of lower tree canopy cover.

Culex resfuans prefers to oviposit in shaded oviposition sites and prefers cooler water for larval development (Wood et al. 1979, Brust 1990). ln other studies, there was no preference to shaded or non-shaded habitats (Beier ef a/.

1983; Anderadis 1988) or mosquitoes preferred non-shaded habitats

(Baumgartner 1987, 19BB; Joy and Sullivan 2005; Gingrich et al.2006; McMahon

2006). ln these studies, mosquitoes were sampled from habitats in either direct sunlight or shade. Catch basin grates could influence the degree of shading. All water within a below ground catch basin, at some point in the day, may be shaded. ln the case of the present study, there is a lower chance for the direct sunlight to reach the water in catch basins located in higher canopy treatments, which resulted in lower water temperatures in those basins. Lower temperatures in basins with high canopy treatments could have caused the increased numbers of Cx. restuans sampled with potentially more mosquitoes within these catch basins because of increased shade.

Water temperatures peaked between 18 July and 14 August, which coincided with the peak of the number of mosquitoes sampled from catch basins during the 1B-31 Julysample period. ln a catch basin study in Australia, water temperature had no influence on the presence or absence of mosquitoes larvae

(Russell et al. 2001). However, this study was conducted during May and June,

Australia's fall, where water temperatures in catch basins were significantly warmer than surface water temperatures.

67 Culex restuans prefers lower water temperatures (Siverly 1972). Even

though temperatures in catch basins could be considered cool in this study, the

peak temperatures in basins seem to be closest to the optimal temperatures for

larval development of Cx. resfuans. This may have influenced the number of Cx.

resfuans sampled from the catch basins. Temperatures prior to this peak were

cooler for development, and catch basins were not as likely to be optimal

developmental sites for larvae, therefore influencing site selection by adult

females for oviposition. One could also argue that these lower temperatures

deterred gravid females from venturing below ground for oviposition.

Mosquitoes can be flushed from catch basins by rainfall events (Russell ef

al. 2001; Rey ef al. 2006; Stockwell et al. 2006). The degree to which mosquitoes

are flushed from catch basins by rainfall depends on the amount of rain. Geery

and Holub (1989) found a significant number of mosquitoes remain in catch

basins following precipitation events less than 25mm (normal precipitation event),

but rainfall which was classified as flooding (>100mm) significantly decreased the number of mosquitoes in catch basins.

Cumulative rainfall in 2004 did not seem to influence the difference in numbers of Cx. resfuans sampled from catch basins in different canopy types.

Over the summer, the numbers of mosquitoes collected significantly dropped between the sample periods 15 - 28 August and 29 August to 11 September, which coincided with the largest rainfall event of the summer (Figure 15). During the later sample period, the mean cumulative rainfall was 113mm, of which 96mm fell from 24 - 27 August. Therefore it is likely that the decrease in numbers of mosquitoes sampled from catch basins during this period was a result of

6B significant rainfall which flushed them out of the catch basins, though numbers

should be declining then anyway.

Previous studies have observed adults in catch basins (Geery and Holub

1989; Stockwell et a|.2006). However in both these studies, adults were sampled

using mechanical aspiration, and no difference was made between emerging females and gravid females. Russell et al. (2001) used stícky entry traps to measure number of gravid females entering the catch basins; however, this method may not give an accurate estimate of numbers of mosquitoes in catch basins in Winnipeg because in this study there was no distinction between gravid females and emerging females when trapping.

ln the present study, egg rafts were collected from ovipools to determine relative abundance of gravid females within the neighbourhoods. These data were further supported by NJLT data obtained from the City of Winnipeg, which provided an overall picture of species in the area, and female Cx. restuans population levels.

ln 2004, significantly more egg rafts and adult female Cx. resfuans were collected in neighbourhoods with low and high tree canopy cover than in the moderate canopy cover neighbourhoods. These results seem to be influenced by the Transcona neighbourhood where the mean number of egg rafts collected was significantly lower than all other areas. Since there was no significant difference between the number of egg rafts and adult females collected in low and high canopy cover, one could conclude differences between the number of mosquitoes collected from catch basins are likely the result of variables other than the abundance of gravid females.

69 2005 The summer of 2005 was different from 2004, in terms of temperature and

rainfall accumulation. There were however, some similar trends observed in experimental variables for both years. As each year presents different environmental conditions, and too few mosquitoes were present in catch basins in

2005, direct statistical comparisons of data between years could not be made.

For this reason, in this section describing 2005 environmental variables observed from catch basins, similarities and differences from the general trends observed in

2004 are discussed.

Only 49 mosquitoes were collected from the catch basins in 2005

(n=1200). During this season, the major species collected was still Cx. restuans

Q9.a %); however, a greater proportion of a second species, Ae. vexans (20.4%), was detected versus 2004. Although Ae. vexans has been found previously in catch basin studies (Thomson 2004), these mosquitoes seem to be in catch basins incidentally, rather than a species which intentionally oviposits within a catch basin environment (Geery and Holub 1989). During the 2004 sampling season, some Winnipeg street-side puddles contained mosquito larvae, the main species of which was Ae. vexans (Person. Comm. R. Gadawski, City of

Winnipeg). lt is postulated that Ae. vexans in basins may have been washed into the catch basin by rainfallfrom neighbouring habitats (Geery and Holub 1gB9).

As observed in 2004, 2005 differences in canopy densities among categories were significant. lt appears the visual assessment of canopy cover to choose various cover densities was quite accurate. Statistical analysis confirmed the visual separation of neighbourhoods. Thus the treatments were significantly

70 different based on canopy density measurements, even when Transcona was substituted with catch basins in Fort Richmond.

The major influence on the number of mosquitoes within catch basins in

Winnipeg in 2005 appeared to be rainfall accumulation. From late June until early

August, Winnipeg had several significant rainfall events. During the sample period of 26 June to 9 July, the average rainfall for the treatment areas was 134 mm. Of that,84.5 mm fell from29 -30 June. These events could have acted as

'flood rains'(approximately 100mm), as described by Geery and Holub (1989).

Coinciding with this, the number of mosquitoes collected from catch basins significantly decreased and did not increase until two weeks after last 'flood rain' event. Thus significant rainfall may have influenced the numbers of mosquitoes in catch basins, similar to 2004 observations.

Leaf litter also seemed to be affected by rainfall events. Between 12 -25

June, and 26 June to 9 July sample periods, leaf litter in catch basins significantly decreased. At this same time, the number of mosquitoes collected from catch basins also decreased. Leaf litter within the catch basin should increase with significant rainfall events, as water collects the debris from the street and deposits it in the catch basins, the main function of its design. However, it is likely that large amounts of rainfall in short periods of time, which can flush mosquito larvae, could also flush leaf litter from catch basins.

After the 'flood rains' stopped and rainfall events were less than 30 mm at any time, leaf litter counts increased. During the 24 July to 6 August and T - 20

August sampling periods, leaf litter was added from smaller rainfall events. The number of mosquitoes collected from catch basins significantly increased during

71 this time as well. Oviposition by Cx. restuans may be influenced by the presence of organic material. Reiskind and Wilson (2004) found adult female mosquitoes laid more than ten times the number of egg rafts in containers with organic debris than containers with water only. Furthermore, there was no difference between the number of egg rafts in containers with low nutrient levels and containers with hígh nutrient levels (Reiskind and Wilson 2004; Reiskind et al. 2004). Therefore it is likely that the increase in mosquitoes in catch basins in 2005 was influenced by the increase in leaf litter during the sample period of 7 - 20 August.

ln 2005, the number of mosquitoes collected was very low. lt is likely that rainfall events played a significant role in flushing mosquitoes from catch basins or deterríng adult female oviposition by decreasing the amounts of leaf litter in the basins. Therefore rainfall monitoring should play a major role in any catch basin control program. Another contributing factor may have been the relatively low population of Cx. resfuans as evident in both egg raft and NJLT data.

Spearman's correlations for experimental variables in 2004 and 2005. ln 2004, there were positive correlations between the number of mosquitoes sampled from catch basins and, leaf litter, and canopy density. lt is likely the number of mosquitoes in catch basins is more influenced by the amount of leaf litter in catch basins, and leaf litter is influenced by the canopy density around the catch basin. This would be consistent with findings by Geery and

Holub (1989), where numbers of mosquitoes present in catch basins were greatly influenced by organic material.

The strongest correlation for all comparisons was with leaf litter and canopy density (r" = 0.499). Since canopy densities were significantly different

72 between all three treatments, which could be confirmed visually, using canopy

cover as a visual indicator of organic input could determine which catch basins contain higher amounts of organic material. Moreover, canopy cover could

indirectly influence which catch basins are more likely to contain mosquitoes.

Water depth and the number of mosquitoes were also positively correlated. The r2 value (0.069) was quite low suggesting that this is potentially a product of correlation between water depth and all other environmental variables.

Because so few mosquitoes were collected in 2005, it is difficult to determine if the environmental factors which influenced the number of mosquitoes in catch basins in 2004 were operating in 2005. There was no significant relationship found between the variables that significantly influenced mosquito collection numbers in 2004. Lack of correlations observed in 2005 is likely a result of low numbers of mosquitoes collected. Thus one can conclude that environmental variables influenced the number of mosquitoes present in the catch basins other than, water depth, water temperature, canopy density, and leaf litter.

73 Future Research The presence of mosquitoes in catch basins varied with rainfall. lt has

been postulated that significant rainfall events can 'flush' mosquitoes out of the

catch basins. The degree to which rainfall affects the numbers of mosquitoes in

catch basins has not been determined. lt has been estimated that low rainfalls (7

to 17mm) result in 22-34% reduction in larvae, a moderate rainfall (22mm) has a

45o/o reduction and a strong rain (102-127mm) caused an 85-91% reduction of

larvae in catch basins in lllinois (Geery and Holub 19Bg). Trends observed in

Geery and Holub's (1989) experiments were similar to findings in 2004 and 2005.

Therefore while rainfall events could reduce mosquitoes in catch basins, it is not

known to what degree this occurs. lt is important to know if Winnipeg's catch

basins respond in a similar manner to catch basins in lllinois. lt seems monitoring

rainfall events which are likely to flush mosquitoes from basins need to be part of

mosquito catch basin control programs.

Research should also include examination of the effect of rainfall and leaf litter on mosquitoes in catch basins. lt has not been determined if excessive rainfall results in flushing of leaf litter or organic material from catch basins, or if leaf litter would affect the number of mosquitoes flushed from the basins or both.

As larval habitat abundance increases with increased rainfall, the number of Cx. restuans egg rafts deposited per habitat unit decreases (Reiskind and

Wilson 2004). Therefore any decrease in the number of mosquitoes sampled from catch basins originally thought to be caused by rainfall, could also be caused by an increase in habitat abundance. More rainfall could lead to a great number

74 of habitats to which Cx. restuans could oviposit, therefore decreasing the number of females ovipositing in catch basins.

Furthermore, it has been shown that Culex spp. preferentially oviposit in water which contained larvae (Dadd and Kleinjan 1973). Thus if basins in high canopy cover areas become colonized, then does this increase the likelihood that those basins will contain larvae again? Conversely, Reiskind and Wilson (2004) found gravid female Cx. restuans avoided habitats with conspecific larvae based on the likelihood of future competition. Therefore would Cx. restuans avoid catch basins which contained larvae?

There are few studies in which the survival rates of mosquitoes in catch basins have been examined (Kay ef al. 2000b; Russell et al. 2001). Actual numbers of mosquitoes emerging from catch basins ate a product of larval and pupal survival. Therefore determining actual numbers of mosquitoes emerging from catch basins could be important in determining if catch basin control programs are actually necessary within a specific area.

75 Gonclusions Catch basins may only be a significant source of mosquitoes during summers with fewer substantial single rainfall events (<100mm) or in the case of

2003 (Thomson 2004), drought years. Rainfall events should be monitored if catch basin control programs are implemented. Catch basins with greater leaf litter input appear to support more mosquitoes and these sites can be determined by visual assessments of canopy cover.

Water temperature, water depth, and the leaf litter were significantly different for all tree canopy types. With increasing tree canopy cover, water temperatures decreased, and leaf litter and numbers of mosquitoes increased.

Water depth may be affected by leaf litter content in or on top of basins which cause blockages rather than an influence on the number of mosquitoes in catch basins.

Environmental variables influenced the number of mosquitoes in catch basins in 2004 and 2005 in relation to sample periods. ln both years, rainfall had the most significant role in influencing the number of mosquitoes in catch basins.

After "flood rains" (>100mm) the number of mosquitoes was significantly decreased. The number of gravid females present seemed to influence the number of mosquitoes in catch basins over the course of the seasons. When numbers of egg rafts peaked in the ovíposition pools, the number of mosquitoes in catch basins also peaked. So it was assumed that the number of adult gravid females within an area could influence the number of mosquitoes in catch basins.

ln 2004, the number of mosquitoes sampled was significantly positively correlated with leaf litter, and canopy density. ln 2005, there was no correlation

76 between the number of mosquitoes sample and the catch basin environmental variables. This is most likely a result of the low numbers of mosquitoes collected from catch basins in that year.

The number of mosquitoes in catch basins was primarily influenced by rainfall over the course of the season. The number of mosquitoes in catch basins may also be related to the number of gravid adult female mosquitoes in the area assuming they are using catch basin as oviposition sites. During the early season, when numbers of gravid females are low, catch basin control may be unnecessary, but as numbers increase, there is greater need for control of mosquitoes within catch basins. lndividual rainfall events should be carefully monitored in any mosquito control program involving catch basins. lf 'flood' rains occur, control applications would be unnecessary directly after those events as rains would have decreased number of mosquitoes in catch basins.

It is possible to determine habitats which are more suitable for larval development within catch basins by using visual measurements of tree canopy cover_

77 Chapter V: General Discussion

The success of any mosquito control program relies on thorough mosquito surveillance (Centers for Disease Control & Prevention 2007).. Mosquito control programs require knowledge of population dynamics, habitat ecology and distribution of targeted mosquito species (Kronenwetter-Koepel et a|.2005).

Mosquito control can be directed on two levels: adulticiding and targeted larviciding. Larviciding has many advantages. ln the larval stage, mosquitoes are confined to specific areas. Within these confined habitats, a variety of control methods are available including chemical insecticides, insect growth regulators and applications of biologically toxic bacteria. Control can specifically iarget these habitats and prevent mosquito emergence thereby reducing emerging adult populations. By reducing larval populations, there will be fewer adult mosquitoes present to bite humans and fewer to transmit pathogens.

Adulticiding is less specific due to the ability of adults to disperse readily.

This method of control is usually used when the numbers of nuisance mosquitoes or the number of vector mosquitoes reach a pre-determined unacceptable level, thus increasing the risk of human and animal infection. Adulticiding can be costly both financially and environmentally, as large areas need treatment and chemicals used in adulticiding are less specific. Furthermore it's application is limited to very specific weather conditions. Adulticiding deals with the problem only after it has been recognized as a problem. However, when faced with a dangerous population of vector mosquitoes, it may be the only option in reducing vector populations.

7B Mosquito detection is one of the primary and most important roles of a mosquito control program. Mosquito control measures are wasted by applying chemicals in the absence of mosquitoes. Therefore thorough detection of mosquitoes in catch basins is essential. Prior to 2003, the City of Winnipeg's lnsect Control Branch used mosquito dippers to survey catch basins, and rarely detected mosquitoes in them. The main species they did collect were Culex spp.

Therefore catch basins were never considered within the scope of their program for two reasons, they didn't have an accurate measure of the mosquitoes in catch basins, and Culex species were not controlled for as it was prior to the threat of

WNv. I used net sampling and detected mosquitoes in catch basins in Winnipeg, which resulted in the City of Winnipeg using net sampling for detection of mosquitoes in catch basins and city wide catch basin treatments in 2003 and

2004 when WNv was present (Thomson 2004).

Since the aquarium net method of sampling was 98% effective in detecting the presence of Cx. resfuans in a catch basin environment, only two catch basins out of 100 basins which contain mosquitoes resulted in a negative sample.

Therefore this sampling method is highly efficient in detecting mosquitoes in catch basins and should be used to sample catch basins especially in the early stages of mosquito development when detection is essential.

Detection of vector mosquitoes further enhances the effectiveness of any vector control program. The sampling method used for WNv programs should be effective for the targeted vector species. Vector mosquito control measures could be wasted by applying chemicals or biological insecticides to habitats where only non-vector species are present. Even though catch basins did not provide habitat

79 'for Cx. tarsalis in Winnipeg, Cx. resfuans did utilize catch basins. Since these species are involved in the amplification of WNv in nature and are suggested to be primary vectors of WNv, catch basins in Winnipeg should be monitored with the aquarium net method of sampling (Kilpatrick et a|.2005).

Catch basins are significant developmental sites for WNv vectors specifically, Cx. pipiens and Cx. restuans. ln 1987, Geery and Holub (1989) surveyed catch basins in lllinois which contained both of these species. Out of

100 catch basins surveyed, they found over 8,000 Cx. pipiens and over 1,500 Cx. resfuans. lf one extrapolates, the DesPlaines Valley Mosquito Abatement District in the western Chicago suburbs has over 30,000 street catch basins, and Geery and Holub (1989) found over 90 larvae of a potential WNv vector species per catch basin. Assuming catch basins have equal numbers of larvae, catch basins in this area have the capability to produce over 2.7 million larvae of a WNv vector species. These authors sampled catch basins by siphoning entire contents of the basin to determine larval numbers; however, on a large scale catch basin program, where detection is important, the siphon method is relatively time consuming. Therefore the aquarium net method should be used when detection of mosquitoes in catch basins is the primary objective for sampling.

Once the detection and determination of species present in an area has been completed, the next step to mosquito control is determining the extent to which these habitats contribute to the overall vector populations. Using

Thomson's (2004) findings in Winnipeg catch basins, approximately seven larvae were collected per basin, in high tree canopy cover areas. When data were selected for water depths > 350 mm but < 420 mm, (to obtain a water depth

80 average of approximately 400mm) the mean number of mosquitoes sampled was

4.32 mosquitoes per catch basin. Using the significant linear regression analysis from the laboratory experiment for Cx. restLtans, where Basin =

452.52+9.90(num) (Table 13), one can extrapolate the potential number of

mosquitoes in those catch basins. lf there were 4.32 mosquitoes per catch basin

(num) in high canopy cover areas with a mean water depth of 399 mm, then Basin

= 452.52+9.90(4.32). Based on the artificial catch basin study, there could potentially be 495 Cx. restuans present per basin, in high canopy cover areas, assuming even distribution.

Although, I did not examine the effects of leaf litter on obstructing net samples, it is likely that leaf litter would inhibit sampling rather than enhance it.

Therefore the number of mosquitoes collected from field catch basins is likely an underestimate of the actual number of mosquitoes in catch basins with high leaf litter. Therefore from the data from the artificial catch basin experiment, combined with the 2003 findings, catch basins play a significant role in providing habitat to

Cx. restuans.

With the introduction of WNv into North America, there has been a surge in control programs to target vectors in catch basins (Su ef al. 2003; Thomson

2004; Anderson ef a/. 2006.) Control in catch basins has been less specific and treatment programs usually involve treatments of all catch basins within program area boundaries (person. comm. P. Curry). This is the case in many areas in

Canada including Hamilton, Regina, Moose Jaw, and the outer lying regions of

Toronto (person. comm.. R. M. Gadawski and P. Curry). Some control programs

81 in Canada do not target catch basins at all (Saskatoon), while others have discontinued catch basin control programs (Winnipeg, since 2004).

There are many products for mosquito control which are effective in catch basins. Some methods include introductions of predators such as Mesocyclops longisetus (Thiebaund) or varying the cleaning schedules of catch basins

(Suarez-Rubio and Suarez 2004; Stockwell et al. 2006) The main methods for mosquito control in catch basins in North America include insect growth regulators such as methorprene, or biological products such as Bacillus sphaericus (Neide)

(Siegel and Novak 1999; Stockwell ef al.2006).

lntroductions of copepods are very effective in controlling Ae. aegypti larvae in catch basins in Colombia (Suarez-Rubio and Suarez 2004). During this eight month study, the number of catch basins which contained larvae significantly decreased with introductions of M. longisetus, and size of the introduced populations of copepods did not significantly differ over the course of this period. Although this method seems quite effective for Ae. aegypfi, these copepods prey on more benthic feeding larvae rather than surface feeding larvae like Cx. resfuans (Rivere et al. 1987).

The amount and timing when catch basins are cleaned can also reduce numbers of mosquitoes. This method, is quite costly at $28.51 per catch basin, and is therefore not commonly employed in control programs as it puts budget restraints on the number of catch basins which can be treated (Stockwell ef a/.

2006).

Most mosquito control companies and municipal programs in Canada that treat catch basins use a combination of methoprene, and B. sphaericus (person.

82 comm. R. M. Gadawski). Altosid@ pellets and briquets are most commonly used.

These two formulations are of methoprene, a juvenile hormone mimic which prevents adult emergence (Knepper et al. 1992, McCarry 1996; Siegel and Novak

1999). Bacillus sphaericus is a bacterial larvicide that is toxic to mosquitoes when ingested (Groves and Meish 1996). This bacterium differs from other bacterial larvicides as it has a residual effect of approximately 35-45 days in catch basins

(Siegel and Novak 1999).

ln Ontario, catch basins are treated for control of Cx. pipiens and Cx. restuans. Regions such as Peel, Hamilton, York, and Barrie, apply Altosid pellets to roadside catch basins with applications commencing soon after larvae are first detected (person. comm. R. M. Gadawski). Applications occur three to four times per season with the season beginning in early June. ln these programs, all roadside catch basins are treated; however, some areas, which have been noted to have more mosquitoes, will be treated first. Costs associated with this method of larviciding in catch basins are approximately $1.25 per catch basin per treatment (person. comm. R. M. Gadawski).

Altosid briquets, which have an extended residual, can be used to treat catch basins where one treatment is required over the whole season. The manufacturer suggests the briquets provide sustained coverage for 120-150 days. ln the same regions in Ontario, treatment of catch basins which are difficult to access are commonly treated with these briquets. However, the costs for these are approximately $4.20 per treatment, and the products do not always have sustained treatment for 120-150 days as the manufacturer suggests (Stockwell ef a|.2006}

83 Environmentally sensitive catch basins (a catch basin that is in and

Environmentally Sensitive Area or outflows directly into an environmently sensitive area, i.e. river, lakes) are either left untreated (Hamilton and Barrie), or are treated with B. sphaericus pouches (Peel and York regions). These pouches are effective for approximately 35-45 days (Siegel and Novak 1999). Therefore, in a typical catch basin treatment program in Winnipeg, these catch basins would require treatment two or three times in a summer, at a treatment cost of approximately $3.00 per catch basin (person. comm. R. M. Gadawski), and up to

$9.00 per basin annually.

Since these regions treat all catch basins, and there are approximately

38,000,90,000,85,000, and 13,000 catch basins in Hamilton, Peel, York and

Barrie respectively, catch basin control programs are expensive. For example, assuming all catch basins in Peel region are treated with Altosid pellets, four times in a season, then annual budget for mosquito control in catch basins alone would be approximately $450,000. There are many variables which can influence the presence and absence of mosquitoes, and may provide information which could aid in more effective catch basin mosquito management programs. By targeting certain catch basins which are more likely to contain mosquitoes, and by decreasing the number of catch basins that need to be treated, it would make the program more affordable, assuming that Winnipeg data can be extrapolated to

Ontario circumstances.

Based upon the findings of Thomson (200Q in Winnipeg, and this study, control efforts may be wasted in catch basins with low organic inputs. ln many regions in Ontario, all catch basins are treated. ln this study, I suggest that

B4 because there were significantly greater numbers of mosquitoes in higher tree canopy cover treatments, then catch basin controls should be focused on high canopy cover areas. The main species found in catch basin in these Ontario regions, are Cx. pipiens and Cx. resfuans (person. comm. R. M. Gadawski).

Although Cx. pipiens was not detected in this study (as it has not yet been found in Manitoba), Cx. pipiens is a more urban species than Cx. resfuans, relying on water with high organic inputs for larval development (Ebel et al. 2005). Findings from the current study could mean that catch basins with higher organic inputs may also influence presence and absence of Cx. pipiens.

The number of mosquitoes in catch basins was also influenced by precipitation. ln 2003, ïhomson (2004), collected 4001 mosquitoes from catch basins in Riverview and South Tuxedo in Winnipeg. During 2003, there was one significant rainfall event of 57.8 mm in 24 hours. All other rainfall events were limited to < 20 mm in 24 hours and a total of 358.2 mm of rain fell during the summer. The pilot study (Thomson 2004) was expanded to the current study to include more replicates, the six zones and the three canopy treatments. ln 2004,

1054 mosquitoes were collected. The cumulative rainfall of this summer was

560.3 mm with the most significant rainfall event between 26 - 27 August where the city average was 63.3 mm. During this same year, there were seven rainfall events which exceeded 20 mm within 24 hours. ln 2005, a total of 49 mosquitoes were collected and a total rainfall of 503.2 mm was recorded. Of that, six rainfall events were greater than 20 mm and two significant rainfall events occurred on 29

June (80.9 mm) and 16 - 17 July (63.0 mm).

85 Habitat ovedlow has been shown to be a source of larval mortality

(Washburn and Anderson 1993). Furthermore, mosquitoes can be flushed from

catch basins due to rainfall events (Russell et al.2001; Rey ef a\.2006; Stockwell

et al. 2006). The degree to which mosquitoes are flushed by rainfall events depends on the amount of rain. Geery and Holub (1989) found a 17-22o/olarval

reduction after low rainfalls (7-17mm), a 45o/o reduction with moderate rains (22

mm), and strong rains (102-127 mm) resulted in 85-91o/olarval reduction in catch basins. When comparing this to the data collected from Winnipeg catch basins in

2003, 2004, and 2005, it is likely that rainfall events may contribute to mosquito reductions in catch basins.

Rainfall intensity should be monitored in mosquito catch basin programs.

ln drier years, where rainfall events are less than 20 mm, more mosquitoes are likely to be in catch basin, provided there is sufficient rainfall to maintain some water in the catch basins. ln years with larger rainfall events, mosquitoes are likely flushed from catch basins, and therefore controls may be unnecessary.

Shaman et al. (2005) used logistic regression to examine the relationship between cases of WNv in sentinel chickens and water table depths. When droughts were observed several months prior to WNv case detection, the probability of cases of WNv increased. Therefore in years where rainfall was low, a greater number of WNv cases were observed (Shaman ef al. 2005). The greatest number of human cases in Manitoba occurred in 2003 and 2007, where

142 and 576 cases were reported respectively (Public Health Agency of Canada

2007,2008). ln both years, total rainfall accumulations were 305.5 mm and 398.2 mm, respectively (Environment Canada 2004).

86 Periods of low rainfall may have two effects on the number of mosquitoes

in catch basins. Significant rainfall events can flush mosquitoes from basins.

Above ground mosquito habitat availability may be limited. Culex resfuans egg

rafts laid per day increased, as the number of oviposition habitats available

increased, but egg density per container decreased (Reiskind and Wilson 2004).

It is postulated that in wetter years, when there should be more above ground

habitats available, Cx. restuans may lay more eggs in these habitats, and in drier years when fewer surface habitats are available, they will oviposit in other habitats including catch basins. Therefore in drier years, catch basins may play a greater indirect role in WNv amplification.

Certain Winnipeg roadside catch basins were treated with Altosid pellets in

2003 and 2004 (person comm. R. Gadawski, Canadian Center for Mosquito

Management & T. Stuart, City of Winnipeg). Application commenced with the first detection of larvae in basins; however, only one application was made at the beginning of the season, as efforts were focused on controlling mosquito populations in other habitats. Since 2004, catch basin control programs have been limited in the City of Winnipeg Mosquito Control Program. Treatment occurs in areas with higher canopy cover due to results from Thomson (2004) and data from this study showing the association of higher numbers of mosquitoes in catch basins in high canopy cover areas (person. comm. T. Stuart, City of Winnipeg).

These catch basins were treated once over the mosquito season with a granular formulation of B. sphaericus, called Vectolex@. The City of Winnipeg could potentially make the program more effective by monitoring rainfall events and

87 treating catch basins after significant rainfall events, or by using treatments with

residuals which may still be effective after heavy rains.

The purpose of this study was to collect and examine data on mosquitoes in catch basins in Winnipeg, Manitoba, and to determine if certain catch basins in different areas were more likely to contain mosquitoes than others. Other objectives were to calibrate the net method of sampling catch basins and provide recommendations for mosquito control programs involving catch basins.

The Province of Manitoba focuses their WNv efforts on control of Cx. tarsalis. Culex restuans is not believed to play a significant enough role in viral transmission, as it does not directly transmit the WNv pathogen to humans, and is therefore not included in control measures (person. comm. P. Curry, Province of

Manitoba). Since this is the stance the province, catch basin control in Manitoba is not necessary (person. comm. P. Curry, Province of Manitoba).

lf Cx. resfuans was recognized as a significant vector in WNv control programs, catch basin treatments would need to be included within this program, depending on the extent to which catch basins contribute to the overall population

(person. comm. P. Curry). Therefore a catch basin treatment program in

Winnipeg should involve monitoring rainfall accumulation and specific rainfall events, be focused on catch basins with associated high canopy cover or high organic inputs, in order to have effective control with minimal costs associated and monitor using the aquarium net method of sampling.

88 clTv ûF tlrhb¡lÞËË EAPRIEE çUEB ¡AS¡D GUllEN IIiLET FRAMEANÐ COIH.- '

l'#Fl8'*'ff ,H¡ffIÎHrffie'

çEcTrûH å"A B.È,ÊE

ÞtÀ1EN5for{s lN l¡til, tÀtETÉ-E5

(Courtesy of the City of Winnipeg)

Figure 1: Schematic of a catch basin with curbside inlet commonly found in the drainage system in Winnipeg, Manitoba.

B9 CrTyCFt¡þlffic ßt?-¡,,ìËB FR¡,\,|E Ér,¡D Égl/;R

DIA CÀÎçH EÁSIN ÊÄHEA

GqTCs sn-91¡r 11É{Ð

- i$!Ì,{rÂtu¡.r ?-r0 FiA c,\ÎCH ÊÀctlt [EÀo Yo ãËvt'Fft oR ÀtÂHp,niË'

tl[lËl{stCf{S ¡H i/.lulùr,Ë¡Þ?S

(Courtesy of the City of Winnipeg)

Figure 2: Schematic of a catch basin with boulevard inlet commonly found in the drainage system in Winnipeg, Manitoba.

90 Figure 3: Side view of an artificial catch basin constructed for quantitative sampling of immature Aedes aegypti and Culex restuans using the net sampling method.

91 Figure 4: Top view of artificial grate for an artificial catch basin constructed for quantitative sampling of immature Aedes aegypti and Culex restuans using the net sampling method.

92 (Photo by Roy Ellis)

Figure 5: One millimeter mesh 6x8cm fish net sampling apparatus used for quantitative sampling of immature Aedes aegypti and Culex restuans in a catch basin.

93 SWEEP 1

SWEEP 2

Figure 6: lllustration of the sample sweeping protocol for quantitative catch basin sampling for immature Aedes aegypti and Culex restuans using the net sampling method. One sample would equal both patterns (Sweep 1 and 2) in one continuous motion with the direction of the second pattern oriented 90o to the first. 1.8

'1.6

1.4 Water Depth

1.2 \a ù) (!o 1 m o "..... 0.20 m q 0.8 -0.10 ct m o ----0.30 0.6 0.40 m

0.47 m 0.4

0.2

0 500 750 1000 1125 lnitial Number of Ae. aegypti

Figure 7: The estimated marginal means of logarithmic transformed Ae.aegyptinumbers captured with the net in an artificial catch basin at various water depths and initial total mosquito numbers combinations.

95 1.8

1.6

1.4 Water Depth

v, 1.2 (!

t¡, 0.10 m o 1 * ""'.. 0.20 m C) 0.8 ct, ----0.30 m o 0.6 0.40 m m 0.4 - -0.47 0.2

500 750 1 000 lntitial Number of Cx.restuans

Figure B: The estimated marginal means of logarithmic transformed Cx. restuans numbers captured with the net in an artificial catch basin at various water depths and initial total mosquito numbers combinations.

96 Figure 9: lllustrates the locations of differing tree canopy cover treatment neighbourhoods for the field catch basin sampling study in Winnipeg, Manitoba, Canada, in 2004 and 2005. High canopy cover treatments were in Riverview and River Heights, moderate treatments in East Kildonan, Transcona (2004 only), Fort Richmond (2005 only), and low treatments in East St. Paul and South Tuxedo.

97 o @ Sidewalk @

c Street @ Ø

@ @ Sidewalk Ø

Figure 10: lllustration of positioning of tree canopy density measurement at nine different spots (grey circles) within a 20m radius of the selected catch basin (black rectangle).

98 Figure 11: Typical high canopy cover street with dense tree cover associated with the catch basin found in high canopy covered neighbourhoods, Riverview and River Heights, Winnipeg. Tree canopy is fully closed, with trees approximately 4m apart.

99 Figure 12: Typical moderate canopy cover street with moderate tree cover associated with the catch basin found in moderate canopy covered neighbourhoods, East Kildonan, Transcona and Fort Richmond, Winnipeg. Tree canopy is not fully closed, wíth trees approximately 5m apart.

100 W +,ro 'ff,ã:lii,i,,¡!Ã

Figure 13: Typical low canopy cover street with minimal tree cover associated with the catch basin found in low canopy covered neighbourhoods, South Tuxedo and East St. Paul, Winnipeg. Tree canopy was not closed, with trees less than 10 years in age.

101 Figure 14: Photo of pupation chamber to raise pupae to adults for identification,

102 140.0

120.0

100.0

80.0 E E 'DLow N l\,4oderate õ ia Hign É, 60.0 ß Average

40.0

20.0

0.0

06/20-07/03 07t04-07t17 07t18-07t31 08/01-08/14 08t15-08r28 08/29-09/1 1 Sample Period

Figure 15: Mean (lSE) rainfall accumulation for each tree canopy cover treatment (n=2) during the catch basin sampling period in 2QQ4. Each (low, moderate, and high) is represented by the average rainfall accumulation recorded in two areas by tree canopy cover treatments (The average rainfall accumulation (n=6) was calculated and was significantlydifferentoverthe sixsample periods (ANOVA, df = 5,35 F = 111.626, p < 0.001). Letters above average rainfall accumulation represents significant differences between means (Tukey's test p<0.05). Precipitation data were obtained from the City of Winnipeg Water Management Department.

103 4b

,I L"' 35 t"t

t!, 25 l---+- Low G É, l"f--Moderatel o) lt) - {- High l¡¡ 20 -_. --_Totat ]

"t t-

07105 - 07118 07t19 - 08t01 08/02 - 08/15 08/16 - 08/29 Date

Figure 16: Mean number of Culex resfuans egg rafts (tSE)collected during four sampling periods from two ovipools within each tree canopy cover treatment, low, moderate, and high (n=6) in 2004. Letters following average egg rafts represents significant differences between average means (Mann Whitney U test p<0.0b).

104 /1 '\ , , t, ,t\

.t Low Canopy t/ ...... Moderate Canopy , High Canopy I , .t t lrt t , \- l, ----.-a¿-----.-:. .. ;i / ----)/

22106 -05107 06107 - 19107 20107 -02t08 03/08 - 16/08 17108 -30/08 3'1108 - 13i09

Figure 17: Mean number of Culex restuans adult females collected for six sampling periods from two New Jersey Light Traps in each tree canopy cover, low, moderate and high (n=6) in 2004.

105 180.0 .ì I I

160.0 l l l l

Ì 140.0 ì i

I 120.0 I

1l -l E 100.0 -i itra"*

iNModerate l =õi tr High i 1õ Bo.o -l v. i94vçqsc r

l l

i 60.0 ì

L I 40.0 -l

' i 20.0 j

o.ol- 06112-07125 06/26-07/09 07 t10-07 t23 07t24-08t06 08107-08120 Sample Period

Figure 18: Mean (tSE) rainfall accumulation for each tree canopy cover treatments (n=2) during the catch basin sampling period in 2005. Each (low, moderate, and high) is represented by the average rainfall accumulation recorded in two areas by tree canopy cover treatments. (The average rainfall accumulation (n=6) was calculated and was significantly different over the five sample periods (ANOVA, df = 4,29, F = 40.924, p < 0.00'1 ). Letters above average rainfall accumulation represent significant differences between means (Tukey's test p<0.05). Precipitation data obtained from the City of Winnipeg Water Management Department.

106 2.5

1.5 -- Ø t-+- Low # --l--Moderat u) E) 1- {- High uJ - a - Total

0.5 -.-2

07/11 - 07t24 07125 - O8/O7 08/08 - 08/21 Date

Figure 19: Mean number of Culex resfuans egg rafts (tSE)collected for three catch basin sampling periods from six ovipools in each tree canopy covertype, low, moderate, and high (n=18) in 200b.

107 t4 s 1.5 t4 o { Low Canopy o '...... Moderate Canopy o .'1 High Canopy zo'

\ ,-'1.- - 7! - :\ :\

\./

13106-25t06 26106-0st07 08107-23t07 24t07-06t08 07/08-20/08

Figure 20: Mean number o'f Culex resfuans adult females collected for five sampling periods from two New Jersey Light Traps in each tree canopy cover, low, moderate and high in 2005. Data obtained from the City of Winnipeg.

108 Table 1: Total mosquitoes (Aedes aegypti or Culex resfuans) per litre of water (density) at differing combinations of water depths (0.10, 0.2, 0.3, 0.4, and 0.47 m) and numberof mosquitoes introduced (250,500,750, 1000 and 1125.) to the artificial catch basin. * Aedes aegyptionly.

Artificial Catch Basin Number of Mosquitoes

Water Water 250 1000 1125 Volume Depth 750

63.6 L 0.10m 3.9 7.9 1'1.8 15.7 17.7

127.3 L 0.20m 2.0 3.9 5.9 7.9 8.8

190.9 L 0.30m 1.3 2.6 3.9 5.2 5.9

254.5 L 0.40m 1.0 2.0 2.9 3.9 4.4

299.1 L 0.47m 0.8 1.7 2.5 3.3 3.8 *Equal numbers of each instar larvae and pupae were included

109 Table 2: Mean number of Aedes aegyptilarvae and pupae (tSE) captured in two sweeps of the aquarium net at water depths of 0.10, 0.20, 0.30, 0.40 and 0.47 m, the number of mosquitoes (250, 500, 750, 1000 and 1125) and the percent of the total mosquitoes collected from the artificial catch basin (n=3).

Water Depth (m)

No. of 0.10 0.20 0.30 0.40 0.47 Mosquitoes

1.7t1.2 5.3!2.6 4.010.6 6.011.7 5.0r0.6 250 (0.7%) (2.0%) (1.6%) (2.4%) (2.0%)

17.013.6 11.0r1.5 6.7!1.9 5.711.5 6.7t2.0 500 (3.4%) (2.0%) (1.3%) (1.1%) (1.3%)

33.3110.6 27.0t7.6 13.3t3.9 20.0t4.2 10.3310.9 750 (3.e%) (3.6%) (1.7%) (2.6%) (1.3%)

39.018.7 17.7!0.3 19.613.2 14.3r0.9 12.3t1.9 1 000 (3.e%) (1.8%) (1.e%) (1.4%) (1.2%)

35.3112.9 32.0!10.1 39.312.3 17.3r3.3 17.0t1.5 1125 (3.1o/") (3.2%) (3.e%) (1.7o/o) (1.7%)

110 Table 3: Univariate analysis of variances of total numbers of Aedes aegypti (250, 500, 750, 1000 and 1125) and water depths (0.10, 0.20, 0.30, 0.40, and 0.47 m) in an artificial catch basin and the interaction of these variables on the number of larvae and pupae captured by two sweeps of the aquarium net.

Source df Mean Square F p

Depth 4 0.110 39.751 0.021

Total Mosquitoes 4 1.378 3.185 <0.001

Depth*Total Mosquitoes 16 0.0964 2.780 0.003

111 Table 4: Mean number of Aedes aegyptilarvae and pupae (tSE) captured in two sweeps of the aquarium net at total number of mosquitoes (250, 500, 750,1000, 1125) in the artificial catch basin and correlations with water depth (10,20,30, 40, 47cm) in an artificial catch basin.

Mean No. of Total Numbers Pearson's mosquitoes N p of Mosquitoes Correlation caught (tSE)

250 4.4.0.7 15 0.458 0.086

500 9.4!1.4 15 -0.688 0.005

750 20.8r3.3 15 -0.586 0.022

1 000 20.613.0 15 -0.802 <0.001

1125 28.2!3.8 15 -0.533 0.041

Pearson's Correlation, p<0.05

112 Table 5: Mean number of Aedes aegyptilarvae and pupae (tsE) captured in two sweeps of the aquarium net at varying water depths (10,20,30, 40, 47cm) in the artificial catch basin and correlations with total numbers of mosquitoes (250, 500, 750,1000,1125).

Water Depth Pearson's Mean (tSE) (m) Correlation

0.10 25.7!4.9 15 0.710 0.0031

0.20 18.613.4 15 0.754 0.001

0.30 16.613.5 15 0.920 <0.001

0.40 12.7t1.8 15 0.654 0.008

o.47 10.3r1.3 15 0.867 <0.001

Pearson's Correlation test, significant at p<0.05.

113 Table 6: Mean number of Aedes aegypti(tSE) larvae and pupae captured in two sweeps of the aquarium net and One-Way ANOVA at various water depths and total number of mosquitoes in the catch basin. Only significant relationships are shown.

1 125 mosquitoes / 1 125 mosquitoes / 1000 mosquitoes / 1000 mosquitoes / Life Stage 0.30 m depth 0.47 m depth 0.20 m depth 0.30 m depth

1ab First lnstar 15 6.7+0.9 0.7t0.3 a 2.7!0.6 b 4.0r0.3 ab

Second lnstar 15 10.7!1.2 b 4.010.6 b 5.010.6 b 7.0t0.6 b

ïhird lnstar 15 8.3t2.2 ab 4.310.3 b 5.010.6 b 2.3+0.3 a

Fourth lnstar 15 10,0t0.6 ab 5.711.3 b 4.0r0.6 b 3.0t1.2 ab

Pupae 15 4.0t1.5 a 2.3t0.3 ab 1.0r0.3 a 3.311.5 ab

F4 3.75 11.47 8.52 3.95

p 0.041 0,001 0.003 0.036

Means within columns followed by different letters are sign t, ANOVA, Tukey's test (p<0.05).

114 Table 7: Linear regression of Aedes aegypti larvae and pupae captured by two sweeps of the aquarium net and total number of mosquitoes (250, 500, 750, 1000, 1125) in the artificial catch basin varying water depths (0.10, 0.20, 0.30, 0.40, 0.47m). ln the linear regression equations Basin represents the known number of mosquitoes in the basin and num represents the number of larvae and pupae sampled.

Water Depth Equation 12 Ft,ts p (m)

0.10 Basin= 4 1 4.86+ 1 2.27 (num) 0.505 13.249 =0.003

+ 0.20 Basin=424.17 1 6.1 7( num ) 0.421 9.434 =0.009

0.30 Basin=377.67+20.92(num) 0.718 36.605 <0.001

0.40 Basin=342.01 +30.241nur, 0.428 9.717 =0.008

0.47 Basin=1 23.96+58.54(num ) 0.732 39.222 <0.001

115 Table 8: Mean number of Culex restuans larvae and pupae (tSE) and per cent of total mosquitoes captured Ìn two sweeps of the aquarium net at varying water depths (0.10, 0.20, 0.30, 0.40 0.47 m) and initial mosquito total (250, 500, 750, and 1000) in the artificial catch basin (n=3).

Water Depth (m) lmmature 0.10 0.20 0.30 0.40 0.47 Density

4.3!2.6 4.0r.1.5 4.0r1.5 5.7t1.9 5.011.1 (1.7%) (1.6%) (1.6%) (2.3%) (2.0%)

14.7!2.3 11.3t1.2 6.311.3 6.3!1.2 6.0r1.5 500 (2.e%) (2.3o/o) (1.3%) (1.3%) (1.3%)

30.0t2.1 25.7!8.9 21.7t2.4 28.0!14.0 13.0r0.6 750 (4.0o/o) (3.4%) (2.e%) (3.7%) (1.7%)

40.0111.5 17.3!7.9 9.7r0.9 29.7!11.3 6.7t1.5 1 000 (4.0%) (1.7%) (1.0%) (2.e%) (0.7%)

116 Table 9: Analysis of variance of total numbers of Culex restuans (250, 500, 750, 1000) and water depths (0.10, 0.20, 0.30, 0.40,0.47 m) in the artificial catch basin and the interaction on the number of mosquitoes captured by two sweeps of the aquarium net.

Source df Mean Square F p

Depth 4 0.0842 2.306 0.075

Total Mosquitoes 3 1.294 35.469 <0.001

Depth*Total Mosquitoes 12 0.111 3.049 0.004

117 Table 10: Mean number of Culex resfuans (tSE) captured in two sweeps of the aquarium net at varying total mosquitoess (250, 500, 750,1000) and correlations with water depths (0.10, 0.20, 0.30, 0.40, and 0.47m) in the artificial catch basin.

Mosquitoes Pearson's Mean (rSE) lntroduced Correlation

250 4.6!0.7 15 =0.558 = 0.165

500 8.9r1.1 15 =0.0011 = -0.778

750 23.7!3.3 15 =0.204 = -0.347

1 000 20.7L4.5 15 =0.072 = -0.477

'Pearson's Correlation test, pcO.05.

118 Table 1 1: Mean number of culex resfuans (tsE) captured in two sweeps of the aquarium net at varying water depths (0.10, 0.20, 0.30, 0.40, 0.47m) and correlations with total mosquitoes (250, 500, 750,1000) in the artificial catch basin.

Pearson's Water Depth Mean (tSE) Correlation

10 22.3!4.9 12 0.843 <0.001'

20 14.613.5 12 0.598 0.040

30 10.4!2.2 12 0.626 0.029

40 17.4!5.2 12 0.740 0.006

47 7.7+1.1 12 0.231 0.374

Pearson's Correlation test, p<0.05.

119 Table 12: Mean number of Culex resfuans instar larvae and pupae (tSE) captured in two sweeps of an aquarium net and one-way ANOVA at differing combinations of water depth (0.10, 0.20, 0.30, 0.40,0.47m) and total mosquitoes (250, 500, 750,1000) in the artificial catch basin. Only significant relationships are shown within columns.

500 750 I 000 500 750 mosquitoes/ Life Stage N mosquitoesi mosquitoes/ mosquitoes/ mosquitoes/

0.10m depth 0.10m depth 0.10m depth 0.20m depth 0.47m depth

First lnstar 15 1.3t0.9 a1 8.7r0.3 c 0.710.3 a 0.7t0.7 a 1.010.6 a

Second lnstar 15 2.0t0.6 ab 8.7t0.9 c 5.010.6 ab 0.710.3 a 1.0r0.6 a

Third lnstar 15 5.010.6 b 6.010.6 bc 7.0t1.2 ab 4.7!0.9 b 5.010.6 b

Fourth lnstar 15 5.0r1.0 b 4.311.3 ab 15.7J5.9 b 5.011.2 b 4.7t0.7 b

Pupae 15 1.310.3 a 2.3t0.3 a 11.7!5.4 ab 0.3r0.3 a 1.3t0.3 ab

F 5.30 13.1 0 3.78 9.94 7.27

p 0.015 0.00'1 0.040 0.002 0.005

Means followed by different letters are significantly different ANOVA (Tukey's test pcOOS¡

120 Table 13: Linear regression of Culex restuans larvae and pupae captured by two sweeps of an aquarium net and total mosquitoes (250, 500, 750, 1000) at varying water depths (m) in the artificial catch basin. ln the linear regression equation, Basin represents the density of larvae and pupae in the basin, and num represents the number of individuals sampled.

Water Depth Equation Ft,to (m)

0.10 Basin=301 .81 +1 4.53(num) 0.711 24.576 0.001

0.20 Bas i n =443 .26+ 1 2. 46(num) 0.271 3.715 0.083

0.30 Basin=420.93+1 9.59(num) 0.253 3.394 0.095

0.40 Basin=452. 52+9.90(num) 0.371 5.899 0.036

0.47 Basin=401 .94+29.90(num) o.140 1.623 0.231

Significant values Pearson's Correlation test, p<0.05.

121 Table 14: 2004 and 2005 Sampling days and corresponding dates of sampling periods.

Sampling Period Dates

2005

22June -5July 13 June - 26 June 6 July - 19 July 27 June - 1OJuly 20July-2August 11 July - 24 July 3 August - 16 August 25July-TAugust

1 7 August - 30 August I August - 2lAugust 31 August - 13 September 22August-SSeptember 14 September - 27 September

122 Table 15: Total mosquito larvae and pupae collected by the aquarium net sampling method from catch basins in Winnipeg, Manitoba, and catch basin conditions in 2004 and 2005.

2004 2005

Total number of mosquitoes 1054 50 collected

TotalWet Catch Basins 1726 1 163

Total Catch Basins Positive 116 21 for lmmature Mosquitoes

Culex restuans 1029 39

Aedes vexans 14 11

Culiseta inornata 11 0

123 Table 16: Mean water temperature (oC), water depth (mm), number of mosquitoes collected per two sweeps of the aquarium net, and leaf litter ratio (out of 25) (t SE) by tree canopy cover for wet catch basins from 22 June to 27 September, 2004 in Winnipeg, Manitoba.

Water Temperature Water Depth (mm) t Mean Number of Canopy Type N Leaf Litter t SE ("c)t sE sE Mosquitoes t(SE)

Low Canopy Cover 578 15.310.'1 c 343.0!7.1 a 0.4+0.2 a 0.310.0 a

Moderate Canopy Cover 576 14.2!0.1 b 369.417.0 b 0.410.1 a 2.1!0.1 b

High Canopy Cover 570 12.8t0.1 a 522.1t9.5 c 1.1!0.2 b 4.0!0.2 c

H z, nz¿ 305.793 247.237 62.026 503.1 98

p < 0.001 < 0.001 < 0.001 < 0.001

Means in columns followed by di t letters are significantly different (Mann-Whitney U test p<0.05).

124 Ïable 17: Mean water temperature (oC), water depth (mm), number of mosquitoes collected per two sweeps of the aquarium net, and leaf litter ratio (out of 25) (t SE) by sample periods for wet catch basins in Winnipeg, Manitoba in 2004.

Water Temperature Water Depth (mm) t Mean Number of Sample Period N Leaf Litter t SE (oc)r sE SE Mosquitoes t(SE)

20 June to 3 July 289 11.310.1 a 389.5113.5 a 0.010.0 a 2.3!0.2 b

4 to 17 July 290 14.2!0.2 b 436.6112.9 bc 0.0410.0 b 2.310.3 b

18 to 31 July 291 '15.1r0.1 c 399.51'1 1.8 ab 2.0 10.5 e 2.2!0.2 b

1 to 14 August 286 14.9t0.1 c 406.8112.4 abc 1.010,2 d 2.810.3 b

15 to 28 August 282 14.1!0.1 b 395.811 1.2 ab 0.510.1 c 1.5t0.2 a

29 August to l lSeptember 286 15.1t0.1 c 438.0r10.6 c 0.03t0.0 ab 1.7!0.2 a

H s, tzz¿ 543.638 14.191 116.523 23.086

p < 0.001 0.014 < 0.001 < 0.001

Means in columns lowed by different letters are significantly different (Mann-Whitney test p<0.05).

125 Table 18: Kruskall Wallis anaylsis of mean number of Culex resfuans egg rafts (tSE) collected during 5 July and 29 August from two ovipools within each tree canopy cover type (treatment), low, moderate, and high (n=6) in 2004, and mean number of Culex restuans egg rafts (tSE) collected during 7 July and 2l August from six ovipools within each canopy treatment (n=18) in 2005.

Canopy Type 2004 2005

Low canopy 22.8!2.2b 1.0!0.2

Moderate canopy 9.4!2.0a 0.8r0.2

High canopy 26.6t2.4b 0.910.2

N 336 180

df 2 2

Chi-square 47.634 0.668

p <0.001 0.716

'Means in columns followed by different letters are significantly different (Mann-Whitney U test p<0.05).

126 Table 19: Mean water temperature (oC), water depth (mm), number of mosquitoes collected per two sweeps of the aquarium net, and leaf litter ratio (out of 25) (t SE) by tree canopy cover for wet catch basins from 12 June to 20 August, 2005 in Winnipeg, Manitoba.

Water Temperature Water Depth (mm) Mean Number of Canopy Cover Type Leaf Litter t SE ("c)t sE tSE Mosquitoes t(SE)

Low Canopy Cover 351 1 7.810.1 c 373.0r9.7 b 0.03t0.0a 0.6t0.1 a

Moderate Canopy Cover 375 17.4!0.1 h 292.6x8.7 a 0.0310.0a 3.010.2 b

High Canopy Cover 368 15.Sr0.1 a b44.6t12.5 c 0.0610.0b 3.1J0.2 b

H z, tosq 213.191 208.389 7,336 213.191

p < 0.001 < 0.001 0.026 < 0.001 MeansfollowedbydifferentlettersaresignificantlydifferentKruskalWalli

127 Table 20: Mean water temperature (oC), water depth (mm), number of mosquitoes collected per two sweeps of the aquarium net, and leaf litter ratio (out of 25) (t SE) by sample periods for wet catch basins in Winnipeg, Manitoba in 2005.

Water Temperature Water Depth (mm) Mean Number of Sample Period Leaf Litter t SE ("c)1sE r sE Mosquitoes t(SE)

12 to 25 June 283 14.5t0.2 a 417.2!14.1 b 0.0210.0 b 3.310.3 c

26 June to 9 July 286 16.510.1 b 411.2!12.6 b 0.0'110.0 a 1.6!0.2 a

24 July to 6 August 267 18.210.1 d 394.4113.8 b 0.01r0.0 a 2.0!0.2 b

7 to 20 August 258 17.810.1 c 353.5t1 1.9 a 0.14J0.1 c 2.8r0.3 b

H s,rosq 285.639 12.959 15.289 66.664

p < 0.001 =0.005 0.002 < 0.001

Means in columns followed by different letters are sig cantly different (Mann-Whitney U test p<0.05).

128 Table 21: Matrix of Spearman's Correlation Coefficients of environmental variables (water depth (wD), leaf litter (LL), water temperature (wT), tree canopy density (cD)), and mean number of larvae and pupae (LP) sampled from catch basins in 2004.

wT("c) %CD LP

n = 1724 n = 1724 n = 1724 n = 1724

WD (mm) r" = 0.142 r, = -0'305 rr = 0.280 rs = 0.069

p < 0.0011 p < 0.001 p < 0.001 p = 0.004

n = 1724 n = 1724 n = 1724

LL r' = -0.208 rr = 0.499 r. = 0.195

p < 0.001 p < 0.001 p < 0.001

n = 1724 n = 1724

wT ("c) rs = -0.372 r. = -0'002

p < 0.001 p = 0.947

n = 1724

%CD rr = 0.159

p < 0.001

'Values are significant by Spearman's Correlation Coefficient p<0.05

129 Table 22: Matrix of Spearman's Correlation Coefficients of environmental variables (water depth (WD), leaf litter (LL), water temperature (WT), tree canopy density (CD)), and mean number of larvae and pupae (LP) sampled from catch basins in 2005.

LL wr('c) %cD

n = 1633 n = 1633 n = 1633 n = 1633

WD (mm) r" = -0-180 rs= -0'282 rs = 0.249 rs =0-047

p = 0.539 p < 0.001r p < 0.001 p = 0.107

n = 1633 n = 1633 n = 1633

LL rs = -0'247 rs = 0'340 rs =0'003

p < 0.001 p < 0.001 p = 0.917

n = 1633 n = 1633

wT ("c) r" = -0'256 r" =-0.014

p < 0.001 p = 0.627

n = 1633

o/o CD r" =0.046

p = 0.119

Values are significant by Spearman's Correlation Coefficient p<0.05

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140 Appendix A

Characterization Data Catch Basin Year Number Area Canopy Cover Type Canopy Dens¡W (%l 2004/zoos l- South Tuxedo Low 11.13 2004/200s 2 South Tuxedo Low 0 2004/2OOs 3 South Tuxedo Low 0 20041200s 4 South Tuxedo Low 0 zoo4lzoos 5 South Tuxedo Low 3.97 2004/200s 6 South Tuxedo Low 3.97 2004/2O0s 7 South Tuxedo Low 1,5.16 2004/200s 8 South Tuxedo Low 32.77 2004/zOOs 9 South Tuxedo Low 2.O7 2004/200s 1.0 South Tuxedo Low 1.08 2OO4lzj1s 11 South Tuxedo Low 10.56 2004/200s 12 South Tuxedo Low 1.32 z0o4/zojs 13 South Tuxedo Low 10.68 2004/200s 'J-4 South Tuxedo Low 24.3r 2004/2OO5 15 South Tuxedo Low 33.s6 2004/200s 16 South Tuxedo Low 33.56 2O04/zOOs l7 South Tuxedo Low 24.66 2004/2005 18 South Tuxedo Low 2.47 2004/20os 19 South Tuxedo Low 0 2OO4/200s 20 South Tuxedo Low 0 2004/zojs 21 South Tuxedo Low 1.08 2004/2OO5 22 South Tuxedo Low 28.82 2004/200s 23 South Tuxedo Low 10.79 2OO4l2OOs 24 South Tuxedo Low 1 2004/2005 25 South Tuxedo Low 2.93 2004/200s 26 South Tuxedo Low 0 2004/2005 27 South Tuxedo Low 9.87 2004/200s 28 South Tuxedo Low 0 2004l2OOs 29 South Tuxedo Low 9.4 2OO4/2005 30 South Tuxedo Low 6.05 2004/200s 31 South Tuxedo Low 0 2004/200s 32 South Tuxedo Low 72-52 2004/2005 33 South Tuxedo Low 7.78 2004/200s 34 South Tuxedo Low 1.08 2004/2OOs 35 South Tuxedo Low 0 2004/2OO5 36 South Tuxedo Low 2.47 2004/200s 37 South Tuxedo Low 0.85 2004/200s 38 South Tuxedo Low 4.2 2004/200s 39 South Tuxedo Low 19.11 2004/2O0s 40 South Tuxedo Low 0.62 2O04/2005 4L South Tuxedo Low 9.75 2004/200s 42 South Tuxedo Low ].7-25 2OO4l2OOs 43 South Tuxedo Low 0 2004/2005 44 South Tuxedo Low 0 2004/2O0s 45 South Tuxedo Low 0 200412005 46 South Tuxedo Low 0 2004/200s 47 South Tuxedo Low 0.39 2004/2005 48 South Tuxedo Low 0.39 2004/200s 49 South Tuxedo Low 0.28 2OO4/200s 50 South Tuxedo Low L4.49 2004/2005 51 East St. Paul Low 1.55 2004/zOOs 52 East 5t. Paul Low 0 2004/2OO5 53 East St. Paul Low 4.9 2004/200s 54 East St. Paul Low 0.28 2OO4/2OO5 55 East 5t. Paul Low 1.08 2004/200s 56 East St. Paul Low 2004/zoos 57 East St. Paul Low 1.08 2004/2005 58 East St. Paul Low 0.28 20o4/zOOs 59 East St. Paul Low 12.18 2004/2005 60 East St. Paul Low 2004/200s 61 East St. Paul Low 0 2OO4/200s 62 East 5t. Paul Low 0 2004/200s 63 East 51. Paul Low o.97

141 2004/2005 64 East 5t. Paul Low 0 2004/20os 65 East St. Paul Low L.7a 2O04/200s 66 East 5t. Paul Low 0 2004/200s 67 East St. Paul Low 1.43 2OO4l2OOs 68 East St. Paul Low 1.78 2004/200s 69 East St. Paul Low 0 zoo4l2oos 70 East St. Paul Low 0 2004/2005 7'J. East St. Paul Low 0.97 2OO4/ZO1s 72 East St. Paul Low 0 2004/2005 73 East St. Paul Low 0 2004/200s 74 East St. Paul Low 0 2004/2005 75 East St. Paul Low 3.51 2004/2oos 76 East St. Paul Low L.2 2OO4l2OOs 77 East 5t. Paul Low 3.4 2004/zOOs 78 East St. Paul Low 1.2 2004/2005 79 East St. Paul Low 0.85 2004/2O0s 80 East 5t. Paul Low 5.59 2O04/2005 81 East St. Paul Low 1.08 2004/2OOs AZ East St. Paul Low 0 2OO4/2005 83 East St. Paul Low 0 2004/2O0s 84 East St. Paul Iow 0 2004l2OOs 85 East 5t. Paul Low 2.82 2004/200s 86 East St. Paul Low 4.09 2004/2005 87 East St. Paul Low 71.72 2004/200s 88 East St. Paul Low 0 2OO4lzoos 89 East St. Paul Low 10.56 2004/200s 90 East St. Paul Low 0 2004/2O0s 91 East 5t. Paul Low 0 2OO4/2005 92 East St. Paul Low 1.08 2004/z}os 93 East St. Paul Low 2.93 2O04/2O0s 94 East St. Paul Low 1,.78 2004/200s 95 East St. Paul Low 0 2OO4l2OOs 96 East 5t. Paul Low 1.08 2004/2005 97 East St. Paul Low 1.08 2004/z1os 98 East St. Paul Low 0 2004/zjos 99 Eâst St. Paul Low 0 2004/2005 100 East St. Paul Low 0 2OO4l2OOs 101 East Kildonan Mod 46.27 2O04/2OOs 1O2 East K¡ldonan Mod 54.47 2004/2005 103 East Kildonan Mod 18.19 2004/zOOs 104 East K¡ldonan Mod 60.83 zoo4lzoos 105 East K¡ldonan Mod 48.46 2004/2005 106 East Kildonan Mod 40.03 2004/200s 7O7 East K¡ldonan Mod 7.32 2O04/zOOs 108 East K¡ldonan Mod 7.32 20041200s 109 East Kildonan Mod 29.74 2OO4l2OOs 110 East K¡ldonan Mod 60.02 2004/2005 111 East K¡ldonan Mod 37 -6 2004/zOOs 7I2 East Kildonan Mod 13.91 2004/2005 113 East K¡ldonan Mod 35.52 2004/200s tt4 East K¡ldonan Mod 35.52 2004lzÙOs 115 East Kildonan Mod 17.I5 2004/200s 116 East Kildonan Mod 2.93 2OO4l2OOs I77 East Kildonan Mod 81.16 2004/2005 118 East K¡ldonan Mod 81.16 2004/zoos 119 East K¡ldonan Mod 27.42 2004/2005 120 East K¡ldonan Mod 3.63 2004/2O0s tzL East K¡ldonan Mod 3.63 2OO4/zOOs 122 East K¡ldonan Mod 46.5 2004/2005 123 East K¡ldonan Mod 46.5 2004/200s I24 East Kildonan Mod 34.83 zo04/20os 125 East K¡ldonan Mod s6.09 2004/2005 726 East K¡ldonan Mod 55.09 2004/zOOs 127 East K¡ldonan Mod 44.42 2004/2005 L28 East Kildonan Mod 54.01 2004/2OOs 729 East Kildonan Mod 32.98 2OO4/2OO5 130 East Kìldonan Mod 8.13 2004/200s 131 East K¡ldonan Mod ó. r5 2004/zojs 132 East K¡ldonan Mod 42.68

142 2004/2OOs 133 East Klldonan Mod 43.96 200412005 734 East Klldonan Mod 36.21 2004/2OOs 135 East Kildonan Mod 5.24 2004/2005 136 East K¡ldonan Mod 27.89 2004/zOOs I37 East K¡ldonan Mod 69.72 2OO4/2005 138 East Kildonan Mod 28.59 2004/200s 139 East K¡ldonan Mod 19.46 2004/2005 L40 East Kildonan Mod 19.46 2004/20os 74I East Kildonan Mod 48 2004/zOOs L47 East Kildonan Mod 34.94 2004/200s 743 East Kildonan Mod 2OO4/2O0s t44 East K¡ldonan Mod 35 2004/20os 1,45 East K¡ldonan Mod 49.73 2OO4/200s L46 East Kildonan Mod 31,.24 2004/200s 1,47 Eâst K¡ldonan Mod 32.r7 2004/2OO5 148 East Kildonan Mod 34.94 2004/20os 749 East Kildonan Mod 38.87 2OO4/zOOs 150 East Kildonan Mod 53.32 2004 151 Transcona Mod 6.4 2004 1,52 Transcona Mod 6.4 2004 153 Transcona Mod 11.83 2004 154 Transcona Mod 11.83 2004 155 Transcona Mod 17.03 2004 156 Transcona Mod 7.44 2004 157 Transcona Mod 7.44 2004 158 Transcona Mod 35.4 2004 159 Transcona Mod 21.08 2004 160 Transcona Mod 63.6 2004 161 Transcona Mod 12.52 2004 1,62 Transcona Mod 45,92 2004 163 Transcona Mod 39.1 2004 764 franscona Mod 12.76 2004 165 Transcona Mod 22.46 2004 166 Transcona Mod 9.4 2004 767 Transcona Mod 70.44 2004 168 Transcona Mod 13.68 2004 169 Transcona Mod t.3z 2004 170 Transcona Mod 18.42 2004 771, Transcona Mod 15.41 2004 L72 Transcona Mod 28.7 2004 773 Transcona Mod 79,46 2004 174 Transcona Mod 15.99 2004 775 Transcona Mod 25.93 2004 176 Transcona Mod 36-79 2004 L77 Transcona Mod 36.56 2004 1.78 Transcona Mod 6.52 2004 179 Transcona Mod 69.72 2004 180 Transcona Mod 29.51 2004 181 Transcona Mod 38.29 2004 182 Transcona Mod 33.44 2004 183 Transcona Mod 74.84 2004 184 Transcona Mod 75.76 2004 185 Transcona Mod r.9.8 2004 186 Transcona Mod 24.89 2004 787 Transcona Mod 31.94 2004 188 Transcona Mod 16.92 zo04 1.89 Transcona Mod 19.92 2004 1.90 Transcona Mod 49.39 2004 191 Transcona Mod 25.t2 2004 I92 Transcona Mod 25.r2 2004 193 Transcona Mod 48.46 2004 794 Transcona Mod 27.77 7004 195 Transcona Mod 49.73 2004 196 Transcona Mod 16.34 2004 797 Transcona Mod 37 -r4 2004 198 Transcona Mod 34.6 2004 199 Transcona Mod 34.6 2004 200 Transcona Mod 15.99 zoo4/2005 20L River He¡ghts High 54.72

143 2004/2O0s ZO2 River Heights High 64.76 2004/2OO5 203 R¡ver Heights H¡gh 64.06 2004/200s 204 River Heights Hith 77.7 zoo4/200s 205 R¡ver Heights High 56.78 2004/2005 206 R¡ver He¡ghts H¡gh 87.97 2004/2O0s 207 R¡ver He¡thts H¡gh 88.79 2O04/2005 208 River Heights H¡th 80.82 2004/zOOs 2O9 R¡ver He¡ghts High 73.77 2004/2005 2f0 River He¡ghts H¡th 85.79 2004/20Os 2II River He¡thts High 49-96 2OO4/2005 zLZ R¡ver He¡ghts H¡th 52.74 2004/2oos 2I3 R¡ver He¡ghts H¡gh 54.7 2O04/2005 274 River Heights High 53.66 2004/zOOs 275 River Heithts High 54.12 2004/2005 216 River HeiBhts Hith 84.52 2004/200s 217 River He¡ghts H¡Bh 79.2 2004/2005 218 River He¡ghts H¡gh 62.44 2OO4/200s 279 R¡ver He¡ghts H¡gh 89.36 2004/200s 22O River Heights High 89.72 2004/zOOs 227 R¡ver Heights High 91.1 2004/2005 222 River He¡ghts H¡gh 89.14 2004l2OOs 223 R¡ver Heithts HiBh 86-94 2OO4/2005 224 R¡ver Heights H¡th 50.66 2004/zoos 225 R¡ver He¡ghts H¡gh 86.6 2O04/2005 226 River Heights H¡gh 86.6 2004/2O0s 227 River Heithts H¡th 86.O2 2004lzl1s 228 Rìver He¡Bhts High 86.02 2004/200s 229 R¡ver Heights H¡gh 82.32 2OO4/200s 230 R¡ver Heights H¡gh 85.32 2OO4/200s 23L R¡ver Heights H¡gh 86.25 2004/200s 232 R¡ver He¡ghts High 77.35 2004/2005 233 R¡ver Heights Hith 83.24 2004/200s 234 River He¡ghts High 2.O4 zo04lzoos 235 R¡ver Heights High 8s.9 zoo4/2005 236 R¡ver He¡ghts H¡gh 7L 2004/2O0s 237 R¡ver Heithts High 75.39 2004/zojs 238 R¡ver Heithts H¡gh 87.r7 2004/2005 239 River He¡ghts High 79.32 2004lzÙOs 24O River Heithts HiBh 86.25 2OO4/2O0s 241 River Heights H¡gh 79.43 2004/2005 242 River Helghts H¡gh 87.75 2004/2O0s 243 R¡ver He¡ghts High 91.33 2004/zols 244 River He¡ghts HiBh 84.t7 2004/2005 245 River He¡ghts H¡gh 84.98 2004l2OOs 246 R¡ver Heights High 69.15 2004/2005 247 River Heights High 88.91 2004/200s 248 R¡ver Hei8hts High 49.96 2OO4/2005 249 R¡ver Heights High 86.94 2004/200s 250 River He¡ghts High 85.32 2O04/2O0s 251, R¡veru¡ew H¡gh 56.9 2004/2005 252 R¡veruiew High 73.88 2OO4/20Os 253 Riverview H¡gh 75-76 zo04/2005 254 Riveru¡ew High 68.11 2004/200s 255 R¡veru¡ew HiBh 77 2O04/2005 256 Riveru¡ew High 68.68 2004/200s 257 R¡veru¡ew H¡Bh 74.35 2004l2OOs 258 Riveru¡ew H¡gh 65.45 2004/2005 259 Riveruiew High 74.23 2004/200s 260 R¡verview H¡Bh 73.54 7OO4/2005 261 Riveru¡ew High 44.42 2004/200s 262 Riveruiew H¡gh 47.77 2004lzD1s 263 R¡veruiew High 47.77 2004/200s 264 R¡veruiew H¡gh 74 2004/200s 265 R¡veru¡ew H¡gh 65.8 2OO4/2005 266 Rìveruiew High 6s.8 2004/200s 267 R¡veru¡ew H¡Bh 92.6 2004/zoos 268 R¡verview H¡th 57.48 2004/2005 269 R¡veru¡ew H¡gh 67.64 2004/2OOS 270 R¡verview High 68.68

144 2004/2005 27I Riveru¡ew High 57.36 20041200s 272 R¡veru¡ew High 49.85 2004/2005 273 R¡veru¡ew H¡gh 49.62 2004/200s 274 R¡veru¡ew High 43.61 2OO4/2OO5 275 Riveru¡ew High 46.96 2004/20os 276 R¡veruiew Híth ].2.52 2004/20os 277 R¡veruiew HiBh 12.52 2004/200s 278 R¡veruiew H¡th 89.02 2004/200s 279 Riveru¡ew H¡gh 60.7L 2004/200s 280 Riveru¡ew High 65.68 2OO4lz00s 28f Riveruiew High 42.68 2004/2005 282 R¡veruiew H¡gh 52.85 2004/zOOs 283 R¡veru¡ew H¡gh 69.15 2004/200s 284 R¡verview H¡th 64.76 2004/200s 285 R¡veru¡ew High 62.1, 2004/200s 286 R¡veru¡ew H¡gh 53.55 2OO4/2O0s 287 Rìveruiew High 57.Or 2004/200s 288 Riveru¡ew High 52.28 z0o4lzoos 289 Riverv¡ew H¡gh 55.4 2004/200s 290 R¡veruiew H¡th 73.88 2004/200s 29L R¡veruiew H¡gh 85.56 2004/200s 292 R¡veru¡ew H¡gh 91.68 2OO4/200s 293 R¡veru¡ew H¡gh s7.24 2004/200s 294 Riveruiew H¡gh 43.72 2004lzOOs 295 Riveru¡ew High 56.32 2004/200s 296 R¡veru¡ew Hith 52.74 2004/200s 297 R¡veruiew H¡gh 54.36 2004/2005 298 Riverview H¡gh 53.66 2004/200s 299 Riveruiew High 68.92 2O04/zOOs 300 R¡veru¡ew High 68.92 2005 151 Fort Richmond Mod 38.29 2005 152 Fort R¡chmond Mod 38.29 2005 153 Fort R¡chmond Mod 0.28 2005 154 Fort R¡chmond Mod 0.28 2005 155 Fort Richmond Mod 28.82 2005 156 Fort Richmond Mod 28.82 2005 157 Fort Richmond Mod 0.74 2005 158 Fort Rìchmond Mod 24.43 2005 159 Fort R¡chmond Mod 31.01 2005 160 Fort R¡chmond Mod 20.73 2005 161 Fort Richmond Mod 12.29 2005 162 Fort Richmond Mod 4.44 200s 163 Fort Richmond Mod 4.44 2005 L64 Fort Richmond Mod 52.04 2005 L65 Fort R¡chmond Mod 25.24 200s 166 Fort R¡chmond Mod 25.24

2005 f67 Fort R¡chmond Mod 68.1 1 2005 168 Fort R¡chmond Mod 21.77 2005 169 Fort Richmond Mod 21.77 2005 I70 Fort R¡chmond Mod 44.07 2005 171, Fort R¡chmond Mod 84.98 2005 772 Fort R¡chmond Mod 47.65 200s 773 Fort R¡chmond Mod 45.8 2005 174 Fort R¡chmond Mod 2.24 200s 175 Fort R¡chmond Mod 16.57 2005 L76 Fort Richmond Mod J4.b 2005 L77 Fort Richmond Mod 41.18 2005 178 Fort R¡chmond Mod 43.72 2005 179 Fort R¡chmond Mod 39.33

2005 180 Fort R¡chmond Mod T .JZ 2005 181 Fort R¡chmond Mod 37.37 2005 182 Fort Richmond Mod 14.03 2005 183 Fort Richmond Mod 6.52 2005 184 Fort Richmond Mod 34.71 2005 185 Fort R¡chmond Mod 37.48 2005 186 Fort R¡chmond Mod 4.9 200s ].87 Fort R¡chmond Mod 42.92 2005 188 Fort Richmond Mod 42-92 7005 189 Fort Richmond Mod 21.O8

145 2005 190 Fort Richmond Mod 40.72 2005 191 Fort Richmond Mod 31.48

2005 I92 tort R¡chmond Mod 19.1 1 200s 193 Fort Richmond Mod 36.33 2005 194 Fort Richmond Mod 36.33 2005 195 Fort R¡chmond Mod 51.7 2005 196 Fort Richmond Mod 30.67 200s 197 Fort Richmond Mod 15.88 2005 198 Fort Richmond Mod 41.18 2005 199 Fort R¡chmond Mod 23.04 2005 200 Forl R¡chmond Mod 20.5

146 Appendix B

2004 Raw Data

Catch Water Water Basin Sampling Depth Temper- Leaf Litter Total Lâruae Culex Aedes Cul¡seto Number Round (mm) ature ("C) /2s and Pupae restuons vexons ¡nornota 11 115 15.3 0 00 00 21 120 13.0 0 00 00 31 100 13.3 0 00 00 4I 90 13.6 0 00 00 51 45 13.9 1 00 00 61 726 13.7 0 00 00 7t 150 12.0 0 00 00 81 300 11.0 0 00 00 91 77 11.3 0 00 00 10r 136 13.0 0 00 00 11 I 89 17.7 0 00 00 72 1 167 11.0 0 00 00 13 1 L27 10.9 0 00 00 t4 1 300 9.8 1 00 00 15 1 330 9.7 L 00 00 tÞ1 105 13.3 '), 00 00 t7 1 707 12.7 1 00 00 18 1 116 12.7 0 00 00 19 1 200 12.7 0 00 00 20 1 20 15.0 0 00 00 2L 'J- 254 13.0 00 00 227 259 L2.9 L 00 00 23 I 287 r2.5 2 00 00 24 1 17 4 r.3.3 0 00 00 25 1 93 13.9 0 00 00 26 't- r28 13.6 0 00 00 27 1 130 13.3 0 00 00 28 1 189 13.4 0 00 00 29r 205 12.7 0 00 00 30 1 170 13.3 0 00 00 31 1 350 13.1 L 00 00 32 r. 1-45 16.5 0 00 00 33 r. 405 11.8 0 00 00 34 1 365 15.8 0 00 00 35 1 515 12.3 0 00 00 36 1 220 L2.9 0 00 00 37 1 340 13.5 0 00 00 38 1 340 'J.3.7 0 00 00 39 1 365 13.5 0 00 00 40 1 L20 14.0 0 00 00 4L1 506 L5.9 0 00 00 42 1 640 r7.2 0 00 00 43 1 515 13.8 0 00 00 44 1 280 13.0 0 00 00 45 1 75 15.6 0 00 00 46 1 270 15.2 0 00 00 47 1 180 16.9 0 00 00 487 150 14.0 0 00 00 49 1 770 13.5 0 00 00 50 1 230 74.4 0 00 00 51 7 610 72.3 1 00 00 52 1 529 1r..3 0 00 00 53 r. 550 12.3 0 00 00 54L 690 r2.1- 0 00 00 55 1 560 12.3 0 00 00 56 1 260 12.3 0 00 00 57 1 570 I2.5 0 00 00

147 58 1 5r.0 72.2 0 0 0 0 0 59 1, 270 11.4 0 0 0 0 0 60 1 730 12.6 0 0 0 0 0 Þ1 1 53s L2.5 0 0 0 0 0 62 1 580 t2.3 0 0 0 0 0 63 r 290 12.3 0 0 0 0 0 64 L 560 72.3 0 0 0 0 0 65 1 560 12.3 0 0 0 0 0 66 1 620 L2.3 0 0 0 0 0 67 1 410 1,2.7 0 0 0 0 0 68 1, 337 11.8 0 0 0 0 0 69 1 340 72.9 0 0 0 0 0 70 1 605 12-2 0 0 0 0 0 1 135 t7.4 0 0 0 0 0 72 1 240 \1,.7 0 0 0 0 0 73 1 280 9.8 0 0 0 0 0 74 1, 120 10.7 0 0 0 0 0 1 115 r0.2 0 0 0 0 0 76 1 0 1 277 9.6 0 0 0 0 0 78 1 260 L0.8 0 0 0 0 0 79 L 20 10.2 0 0 0 0 0 80 '1, 15 r.0.3 0 0 0 0 0 81 7 65 11.9 0 0 0 0 0 82 1 0 1 100 10.5 0 0 0 0 0 84 1 280 r.2.0 0 0 0 0 0 1 360 13.3 0 0 0 0 0 86 1 430 12.8 0 0 0 0 0 87 1 90 12.9 0 0 0 0 0 1 295 73.4 0 0 0 0 0 89 L 500 11,.4 0 0 0 0 0 90 1 20s 1,2.9 0 0 0 0 0 91 1 515 12.2 0 0 0 0 0 92 1 515 11.1 0 0 0 0 0 93 1 530 13.2 0 0 0 0 0 94 1 L0 14.0 0 0 0 0 0 95 ')- 236 1,2-3 0 0 0 0 0 96 1 LO7 12.O 0 0 0 0 0 97 1 300 11.9 0 0 0 0 0 98 L 305 11.0 0 0 0 0 0 99 I 85 12.3 0 0 0 0 0 100 1 76 12.o 0 0 0 0 0 101 1 243 10.9 3 0 n 0 0 102 1 0 103 1 546 9.8 6 0 0 0 0 r.04 1 550 10.0 L 0 0 0 0 105 1 450 72.6 11 0 0 0 0 106 1 260 10.2 0 0 0 0 0 107 7 395 1.0.1 0 0 0 0 0 108 1 400 \!-4 1 0 0 0 0 109 1 390 70.2 tz 0 0 0 0 110 1 53s 8.9 3 0 0 0 0 1l.t 1 410 9.1 2 0 0 0 0 rtz L 390 i.0.3 0 0 0 0 0 113 1 610 r.0.5 4 0 0 0 0 11,4 1 620 10.0 0 0 0 0 0 115 1 595 0 0 0 0 o 176 1 0 !L7 1 720 8.9 9 0 0 0 0 118 L 770 10.9 11 0 0 0 0 1r.9 1 470 10.9 2 0 0 0 0 1,20 1, 470 8.5 0 0 0 0 IzL 1 440 10.6 1 0 0 0 0 122 L 400 9.3 1 0 0 0 0 t23 1 380 11.3 6 0 0 0 0 r24 1 400 12.O 4 0 0 0 0 t25 1 300 9.4 9 0 0 0 0 126 1 360 9.7 3 0 0 0 0

148 127 1 410 10.3 0 0 0 0 r28 1. 335 9.3 0 0 0 0 729 1 460 11.3 0 0 0 0 130 1 330 10.3 0 0 0 0 131 1 90 11.6 15 n 0 0 0 \32 1 465 1-L.4 10 0 U 0 0 133 1 460 12.0 1 0 0 0 0 134 1 420 r.0.8 '1, 0 0 0 0 135 1 435 9.6 5 0 0 0 0 136 t 725 12.3 6 0 0 0 0 1,37 1, 590 72.r 7 0 0 0 0 138 1 0 139 1 539 Lt.4 5 0 0 0 0 140 1 570 9.9 6 0 0 0 0 741 ')- 400 11.9 0 0 0 0 0 ]42 1 500 11.5 1 0 0 0 0 143 1 440 10.8 0 0 0 0 0 1-44 1, 305 11.9 0 0 0 0 0 145 1 300 LO.4 0 0 0 0 746 1, 240 r.1.6 0 0 0 0 1,47 1 490 9.5 0 0 0 0 148 1 440 1,1,.4 0 0 0 0 'l- t49 480 11.4 1,2 0 0 0 0 150 1 770 11.6 9 0 0 0 0 151 1 210 14.4 0 0 0 0 0 752 1 110 11.6 1 0 0 0 0 153 1 200 13.5 1 0 0 0 0 154 1 90 13.5 1 0 0 0 0 155 1 45 13.8 L 0 0 0 0 156 1 170 12.0 1 0 0 0 0 157 1 50 13.0 1 0 0 0 0 158 1 0 159 1 150 11.8 4 0 0 0 0 160 L 115 10.1 0 0 0 0 0 161 1 150 11.8 L 0 0 0 0 762 1 175 12-5 0 0 0 0 0 163 1, 270 10.8 0 0 0 0 0 164 1 0 r.65 L c5 r.0.5 1, 0 0 0 0 166 1 400 r0.7 1 0 0 0 0 1,67 1 35 72.9 0 0 0 0 0 168 T 110 L0.8 0 0 0 0 0 169 1 225 17.2 1 0 0 0 0 770 1 230 12-1, 0 0 0 0 0 17'J. 7 355 12.O 2 0 0 0 0 172 1 590 10.3 0 0 0 0 0 173 I 490 12-3 L 0 0 0 0 L74 I 350 11.1 4 0 0 0 0 175 1 320 12.7 10 0 0 0 0 176 1 305 11.9 0 0 0 0 0 177 1 330 8.5 T4 0 0 0 0 778 1 340 r.1.8 7 0 0 0 0 179 1 680 72.8 1 0 0 0 0 180 1 280 L0.6 0 0 U 0 0 18r. L 500 8.8 3 0 0 0 0 1,82 1 720 1.1.8 0 0 0 0 0 183 1 320 L2.3 1 0 0 0 0 184 1 300 11.9 1 0 0 0 0 185 7 290 11.0 2 0 0 0 0 1ðb L 370 13.0 0 0 0 0 0 ra7 1 470 1,0.7 0 0 0 0 0 188 1, 450 10.5 1, 0 0 0 0 189 1 70 r.0.9 2 U 0 0 0 190 L 110 11.9 0 0 0 0 U 191 1 525 12.5 0 0 0 0 0 192 7 820 11.8 5 0 0 0 0 r.93 1 50 72.9 0 0 0 0 0 794 1 115 10-2 0 0 0 0 0 r.95 1 0

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164 31 5 Gravel overtop couldn't be d¡pped (conlruction zone) 5 305 13.9 0 0 0 0 0 33 5 410 13.6 0 0 0 0 0 a4 5 510 14.0 0 0 0 0 0 35 335 14.7 2 0 0 0 0 36 340 1,4.6 1 0 0 0 0 410 14.0 0 0 0 0 0 38 235 74.6 0 0 0 0 0 39 115 15.2 0 0 0 0 0 40 245 14.8 0 0 0 0 0 4t 280 14.8 0 0 0 0 0 42 310 1,4.8 0 0 0 0 0 43 355 1,4.6 0 0 0 0 0 44 4L0 14.0 0 0 0 0 0 565 L3.7 0 0 0 0 0 4b 110 1,5.2 0 0 0 0 0 47 135 15.1 0 0 0 0 0 48 95 15.3 0 0 0 0 0 49 L0 15.9 0 0 0 0 0 50 55 15.8 1 0 0 0 0 51 422 1,4.6 0 0 0 0 0 52 14.8 0 0 0 0 0 53 533 74.2 0 0 0 0 0 54 578 14.0 10 0 0 0 0 55 278 1,4.6 0 0 0 0 0 56 340 13.9 1 0 0 0 0 275 1.5.2 0 0 0 0 0 58 481 1,4.4 0 0 0 0 0 59 267 1,3-7 0 0 0 0 0 60 600 L4.4 0 0 0 0 0 61 459 15.0 0 0 0 0 0 62 48t 14.5 0 0 0 0 0 63 290 t4.o 0 0 0 0 0 64 310 14.9 0 0 0 0 0 65 446 1,4.4 0 0 0 0 0 66 215 14.3 0 0 0 0 0 67 377 1,4.6 0 0 0 0 0 68 273 t4.b L 0 0 0 0 69 260 14-2 0 0 0 0 0 70 593 15.0 0 0 0 0 0 7I 307 74.6 1 0 0 0 0 ,0 72 220 r.3.6 0 0 0 0 175 L2.7 1, 0 0 0 0 74 202 12.3 0 0 0 0 75 115 12.5 0 0 0 0 0 76 0 77 125 r.3.2 0 0 0 0 0 190 13.8 0 0 0 0 0 79 97 74.t 1 0 0 0 0 80 0 81 130 13.6 0

122 13.5 1. 0 0 0 0 84 249 14.6 2 0 0 0 0 85 332 14.4 0 0 0 0 0 86 374 t4.5 0 0 0 0 0 87 490 15.0 1 0 0 0 0 88 289 15.5 0 0 0 0 0 89 420 16.0 0 0 0 0 0 90 529 16.2 0 0 0 0 0 91 140 t4.o 1 0 0 0 0 92 408 L4.5 1 0 0 0 0 93 580 14.5 0 0 0 0 0 94 474 rt-2 1 0 0 0 0 95 403 13.9 0 0 0 0 0 96 551 74.9 0 0 0 0 0 97 532 1.5.2 0 0 0 0 0 98 549 15.4 1 0 0 0 0 99 550 r.5.5 0 0 0 0 0

165 100 336 14.8 0 0 0 0 0 101 510 13.7 10 0 0 0 0 L02 255 13-l 2 0 0 0 0 lUJ 420 1.4.2 2 0 0 0 0 r04 480 '14.6 1 0 0 0 0 105 330 14.6 0 0 0 0 0 106 220 r.3.8 0 0 0 0 0 707 365 L4.5 0 0 0 0 0 L08 360 L4.7 0 0 0 0 0 109 285 13.7 20 0 0 0 0 110 553 74.1, L 0 0 0 0 111 400 12.8 1 0 0 0 0 1r2 360 11.9 0 0 0 0 0 113 405 13.9 0 0 0 0 0 !1,4 620 14.8 0 1 1 0 0 115 420 l4.b 0 0 0 0 0 116 525 13.6 0 0 0 0 0 1r7 490 ),4-2 0 0 0 0 0 118 430 74.2 0 0 0 0 0 119 495 74.! 0 0 0 0 0 120 285 14.3 2 0 0 0 0 12I 455 L4.1, 0 0 0 0 0 722 320 73.7 4 0 0 0 0 tz3 335 13.7 0 0 0 0 0 L24 230 74.L 0 0 0 0 0 t25 400 r.3.6 5 0 0 0 0 126 400 13.4 0 0 0 0 0 127 425 13.5 0 0 0 0 0 128 335 13.7 0 0 0 0 0 129 400 13.6 0 0 0 0 0 130 405 13.5 0 0 0 0 0 131 445 13.9 0 0 0 0 0 132 400 14.6 0 0 0 0 0 133 460 14.3 0 0 0 0 0 134 625 12.0 0 0 0 0 0 135 725 13.9 0 0 0 0 0 136 645 13.4 0 0 0 0 0 137 750 13.3 0 0 0 0 0 138 325 L3.0 0 0 0 0 0 139 270 15.0 0 0 0 0 0 140 385 15.9 0 0 0 0 0 1,41, 4?O 13.8 4 0 0 0 0 142 490 1,4.1, 0 0 0 0 0 L43 525 34.9 0 0 0 0 0 1,44 475 1,4-0 0 0 0 0 0 145 470 t4.t 0 0 0 0 0 'J.46 475 1,4.3 0 0 0 0 0 ].47 0 i.48 500 t4.4 0 0 0 0 0 L49 480 13.6 0 0 0 0 0 150 525 13.7 0 0 0 0 0 151 27L 14.1, 1 0 0 0 0 !52 L35 14.6 1 0 0 0 0 153 268 15.4 L 0 0 0 0 754 198 L4.9 1 0 0 0 0 155 248 72.7 0 0 0 0 0 L56 280 r.3.6 0 0 0 0 0 757 0 158 408 14.2 0 0 0 0 0 159 338 13.8 0 0 0 0 0 160 376 r.4.3 2 0 0 0 0 16L 75 14.0 0 0 0 0 0 162 t3.7 0 0 0 0 0 163 521 13.7 0 0 0 0 0 764 378 14.9 0 0 0 0 0 165 370 74-2 0 0 0 0 0 166 324 L3.4 0 0 0 0 0 767 327 73.2 0 0 0 0 0 168 276 14.4 5 0 0 0 0

166 169 273 13.9 L 0 0 0 0 770 60 13.5 0 0 0 0 0 17L 350 15.8 0 0 0 0 0 772 97 L3.7 1 0 0 0 0 173 0 L74 0 775 177 t5.5 1 0 0 0 0 176 481 14.0 1 0 0 0 0 390 13.4 0 0 0 0 0 t78 392 1,3.2 0 0 0 0 0 179 233 13.7 0 0 0 0 180 427 L4.6 1 0 1 0 181 317 13.4 0 0 0 0 182 301 t2.4 0 0 0 0 183 446 12.8 0 0 0 0 184 530 13.6 0 0 0 0 0 185 289 12.1 0 0 0 0 0 186 830 13.8 1 0 0 0 0 187 475 13.2 1 0 0 0 0 188 384 14.4 0 0 0 0 0 189 315 14.9 0 0 0 0 0 190 0 191 0 192 499 13.3 1 0 0 0 0 193 284 14.8 1 0 0 0 0 194 155 12.9 1 0 0 0 0 195 0 196 0 197 15 14.7 0 0 0 0 0 198 299 1,4.4 1, 0 0 0 0 199 321 74.6 L 0 0 0 0 200 495 13.2 0 0 0 0 0 201 672 1,3.2 7 0 0 0 0 202 6r.0 12.7 0 0 0 0 0 203 535 12.8 0 0 0 0 0 204 550 12.1 L 0 0 0 0 20s 784 12.4 7 0 0 0 0 206 602 12.5 0 0 0 0 207 540 12.2 0 0 0 0 208 545 13.7 0 0 0 0 209 550 L3.7 0 0 0 0 2ro 95 14.9 0 0 0 0 21L 632 13.9 0 0 0 0 2r2 800 13-2 0 0 0 0 213 575 1,3.7 0 0 0 0 2L4 7r0 13.7 0 0 0 0 21,5 560 13.2 0 0 0 0 21,6 835 13.5 0 0 0 0 2r7 640 L3.7 0 0 0 0 218 560 13.5 0 0 0 0 279 660 13.0 0 0 0 0 220 570 0 0 0 0 227 650 12.2 0 0 0 0 222 652 12.3 1 1 0 0 223 807 13.1 6 6 0 0 224 653 14.0 0 0 0 0 0 22s 628 t3.7 5 13 13 0 0 226 119 13.6 9 0 0 0 0 227 369 13.6 2 0 0 0 0 22a 800 72.O 0 0 0 0 0 229 803 12.0 0 0 0 0 230 zo5 L2.7 6 0 0 0 0 23L 307 r.3.6 5 13 13 0 0 232 605 13.0 0 0 0 0 0 233 7r0 72.9 2 0 0 0 0 234 535 73.4 10 8 0 0 23s 543 13.7 22 1 1 0 0 236 57t 1,3.7 r.5 0 0 0 0 237 577 13.7 0 0 0 0 0

167 238 0 239 103 15.0 1 0 0 0 0 240 703 1,2.9 L 0 0 0 0 241 619 13.2 3 0 0 0 0 242 569 13.7 5 0 0 0 0 243 488 13.6 7 0 0 0 0 244 437 13.5 0 0 0 0 0 245 440 1,3-7 2 0 0 0 0 246 400 13.8 0 0 0 0 0 247 0 248 598 12.8 11 0 0 0 0 249 480 13.6 21 0 0 0 0 250 473 13.4 0 0 0 0 0 25r 249 74.1, 7 2 2 0 0 252 286 1,4.5 6 0 0 0 0 253 243 ].4.l 4 0 0 0 0 254 439 16.1 2 5 5 0 0 255 28s 1,4.8 0 0 0 0 0 256 180 !4.9 t 0 0 0 0 257 260 14.6 3 7 7 0 0 259 1,4.2 4 1 0 259 0 260 0 26L 32s 14.3 1 24 24 0 0 262 310 15.1 1 0 0 0 0 263 21,2 15.5 2 6 6 0 0 264 338 14.7 0 0 0 0 265 830 L4.2 6 0 0 0 0 266 aaE r.3.1 1 3 2 1 0 267 335 1,4.9 4 4 4 0 0 268 290 13.7 2 0 0 0 0 269 310 13.7 0 0 0 0 0 270 480 14.4 1 3 0 0 27t 310 L4.7 5 4 4 0 0 272 526 15.0 4 9 9 0 0 368 13.4 0 0 0 0 0 274 354 r.3.6 9 9 0 0 275 348 12.8 0 0 0 0 276 450 t3-4 17 0 0 670 13.5 0 0 0 0 278 572 13.8 0 0 0 0 0 279 40 15.1 4 0 0 0 0 280 548 1,4.6 L 0 0 0 0 281, 35 L6.4 2 0 0 0 0 282 700 12 0 0 0 0 102 648 15.9 4 4 4 0 0 284 180 12.5 1 0 0 0 0 285 460 16.4 2 0 0 0 0 286 600 t3.z T 0 0 0 0 247 5 730 r.5.1 2 6 6 0 0 288 5 546 17.L 0 0 0 0 0 289 5 640 13.7 1 0 0 0 0 290 5 768 10.9 8 0 0 0 0 ?91 5 592 14.7 1 0 0 0 0 292 5 310 \2-7 0 0 0 0 293 5 906 73.7 5 0 0 0 0 294 5 66s 12.3 1 0 0 0 0 295 5 485 12.8 0 2 2 0 0 296 5 545 12.9 2 0 0 0 0 297 5 r.3.5 0 0 0 0 0 298 5 270 72.3 2 L 1 0 0 299 5 355 12.9 2 0 0 0 0 300 5 3r7 13.1 1 0 0 0 0 1 6 470 L4.4 0 0 0 0 0 2 6 450 12.6 0 0 0 0 0 3 6 445 74,6 0 0 0 0 4 6 435 14.5 0 0 0 0 5 6 465 L4.9 0 0 0 0 6 6 470 16.2 0 0 0 0

168 7 6 100 15.5 0 0 0 0 8 6 100 16.5 0 0 0 0 9 6 36s 16.4 0 0 0 0 1U 6 470 16.8 0 0 0 0 11 6 0 0 0 0 T2 6 360 16.0 7 0 0 0 0 13 6 460 15.7 0 0 0 0 0 L4 6 375 16.8 0 0 0 0 0 15 6 245 1,6-7 1 0 0 0 0 16 6 275 16.6 2 0 0 0 0 I7 6 425 16.6 1 0 0 0 0 18 6 430 1,5.2 0 0 0 0 0 19 6 325 17.0 0 0 0 0 0 20 6 180 L6.2 0 0 0 0 0 2I 6 640 17.8 0 0 0 0 0 22 6 560 16.8 0 0 0 0 0 23 6 635 16.8 0 0 0 0 0 24 6 565 16.0 0 0 0 0 0 6 215 15.7 0 0 0 0 0 26 6 285 16.6 0 0 0 0 0 6 245 75.7 I 0 0 0 0 28 6 275 !7.7 0 0 0 0 0 29 6 350 L7.3 0 0 0 0 0 30 6 325 16.4 0 0 0 0 o 31 6 Covered w¡th Bravel could not d¡p (ConLruction zone) 6 L75 16.9 0 0 0 0 0 33 6 539 t7.4 0 0 0 0 0 34 6 570 17.0 0 0 0 0 0 35 6 365 16.8 0 0 0 0 0 36 6 605 14.0 0 0 0 0 0 6 365 L5.1 0 0 0 0 0 6 5r.5 L6.6 0 0 0 0 0 39 6 450 17-4 L 0 0 0 0 40 6 325 17.0 1 0 0 0 0 41- 6 265 76.7 1 0 0 0 0 42 6 335 18.3 0 0 0 0 0 43 6 685 16.5 0 0 0 0 0 44 6 490 16.6 0 0 0 0 0 45 6 370 16.2 0 0 0 0 0 46 6 405 t6.7 1 0 0 0 0 47 6 315 76.4 0 0 0 0 0 48 6 295 16.3 1 0 0 0 0 49 6 410 t6.4 0 0 0 0 0 50 6 385 16.0 L 0 0 0 0 51 6 465 14.0 0 0 0 0 0 52 6 420 14.r. 0 0 0 0 0 53 6 570 L4.t 0 0 0 0 0 54 6 455 L4.7 0 0 0 0 0 55 6 260 15.9 0 0 0 0 0 56 6 230 14.8 0 0 0 0 0 57 6 285 14.8 0 0 0 0 0 6 440 13.8 0 0 0 0 0 59 6 270 r3.7 0 0 0 0 0 6 670 r4.2 0 0 0 0 0 61 6 490 74.7 0 0 0 0 0 62 6 320 75.2 0 0 0 0 0 63 6 505 74.4 0 0 0 0 0 64 6 320 r4.7 0 0 0 0 0 65 6 425 14.9 0 0 0 0 0 66 6 290 75.2 0 0 0 0 0 67 6 415 'J,4.4 1 0 0 0 0 68 6 115 75.7 0 0 0 0 0 69 6 425 16.8 0 0 0 0 0 70 6 415 15.0 0 0 0 0 0 71 6 115 15.5 0 U 0 0 0 6 2rO 15.0 0 0 0 0 0 73 6 270 15.4 0 0 0 0 0 74 6 185 15.1 0 0 0 0 0 75 6 190 1,4.O 0 0 0 0 0

169 60 77 6 140 14.8 0 0 0 0 0 78 6 2t5 14.3 0 0 0 0 0 79 6 115 15.9 0 0 0 0 0 80 60 81 6 125 15.3 82 60 6 120 15.3 0 0 0 0 0 84 6 320 14.5 0 0 0 0 0 85 60 86 6 350 14.7 0 0 0 0 0 87 6 360 13.4 0 0 0 0 0 88 6 505 1,4.9 0 0 0 0 0 89 6 370 15.3 0 0 0 0 0 90 5 500 L4.4 0 0 0 0 0 91 6 130 15.4 0 0 0 0 0 92 360 13.4 0 0 0 0 0 93 6 600 74.7 2 0 0 0 0 94 6 515 13.7 0 0 0 0 0 95 6 555 14.1 0 0 0 0 0 96 6 505 1-4.7 0 0 0 0 0 97 6 555 14.8 0 0 0 0 0 98 6 555 L4.7 0 0 0 0 0 99 6 555 74.6 0 0 0 0 0 100 6 600 14.3 0 0 0 0 0 101 6 340 15.3 5 0 0 0 0 ro2 6 325 1-4.2 2 0 0 0 0 103 6 470 14.9 7 0 0 0 0 104 6 465 1 5.3 7 0 0 0 0 105 6 385 t7.7 z 0 0 0 0 106 6 335 15.5 1 0 0 0 0 707 6 4r5 1,5.2 2 0 0 0 0 108 6 375 1,.2 t 0 0 0 0 109 6 335 15.0 0 0 0 0 110 6 580 L4.6 2 0 0 0 0 111 6 430 73.7 2 0 0 0 0 II2 6 255 L6.3 1 0 0 0 0 113 6 530 16.3 4 0 0 0 0 L14 6 510 15.1 3 0 0 0 0 115 6 425 16.6 3 0 0 0 0 L1,6 6 310 15.9 1 0 0 0 0 t77 6 505 15.1 z 0 0 0 0 118 6 680 13.8 2 0 0 0 0 119 6 Car parked over grate 120 6 365 1s.5 5 0 0 0 0 121, 6 465 L5.2 1 0 0 0 0 r22 6 285 1s.9 2 0 0 0 0 t23 6 425 1,4-7 0 0 0 0 0 124 6 330 14.9 0 0 0 0 0 125 6 275 74.4 1 0 0 0 0 726 6 410 1,4.6 2 0 0 0 0 1,27 6 360 14.8 2 0 0 0 0 r28 6 320 15.0 3 0 0 0 0 129 6 305 15.1 4 0 0 0 0 130 6 325 16.0 1 0 0 0 0 't5.2 131 6 ?65 0 0 0 0 0 r32 6 440 L6.2 0 0 0 0 0 133 5 450 15.6 0 0 0 0 0 134 6 365 15.1 2 0 0 0 0 135 6 380 15.6 3 0 0 0 0 136 6 390 76.1 4 0 0 0 0 737 6 320 15.9 25 0 0 0 0 138 6 340 15.9 0 0 0 0 0 139 6 655 16.6 10 0 0 0 0 140 6 420 16.4 0 0 0 0 0 147 6 400 15.1 z 0 0 0 0 742 6 390 15.8 2 0 0 0 0 743 6 390 16.1 4 0 0 0 0 1,44 6 580 15.1 0 0 0 0 0

170 745 6 385 1,6.7 0 0 0 0 ].46 6 405 16.6 0 0 0 0 747 6 610 72.9 0 0 0 0 148 6 53s 15-4 0 0 0 0 749 6 410 15.1 0 0 0 0 150 6 710 15.8 0 0 0 0 151 6 230 1.5.1 0 0 0 0 0 1,52 6 300 0 0 0 0 0 153 6 250 15.5 0 0 0 0 0 754 6 275 17.7 0 0 0 0 0 155 6 350 17.3 1 0 0 0 0 r.56 6 345 L6.4 0 0 0 0 0 r57 6 300 1,6.4 0 0 0 0 0 158 6 L75 16.9 0 0 0 0 0 159 6 540 15.3 1 0 0 0 0 160 6 595 15.0 0 0 0 0 0 161 6 305 16.8 0 0 0 0 0 762 6 605 14.0 0 0 0 0 0 163 6 370 15.1 0 0 0 0 0 164 6 515 16.5 0 0 0 0 0 165 6 450 17.4 0 0 0 0 0 lbb 6 325 77.0 0 0 0 0 0 167 6 265 16.6 0 0 0 0 0 168 6 335 18.3 0 0 0 0 0 169 6 685 16.5 1 0 0 0 0 170 6 490 16.6 2 0 0 0 0 771 6 370 1,6-2 1 0 0 0 0 172 6 405 1,6.7 1 0 0 0 0 6 315 16.4 1 0 0 0 0 174 6 295 16.3 1 0 0 0 0 175 6 270 14.0 0 0 0 0 0 176 6 670 1,4.2 0 0 0 0 0 6 490 15.0 0 0 0 0 0 178 6 260 15.9 0 0 0 0 0 179 6 230 16.0 0 0 0 0 0 180 6 2A5 15.8 0 0 0 0 0 181 6 290 1,5.2 0 0 0 0 0 182 6 4L5 L4.4 0 0 0 0 0 183 6 115 75.7 4 0 0 0 0 184 6 41-0 1,6.4 0 0 0 0 0 185 6 385 16.0 0 0 0 0 0 186 6 465 14.0 1 0 0 0 0 187 6 420 1-4.J, 0 0 0 0 0 1.88 6 3ZO 75.2 0 0 0 0 0 189 6 505 L4.4 0 0 0 0 0 190 6 320 1,4.7 0 0 0 0 0 191 6 425 74.9 0 0 0 0 0 192 6 570 14.1- 0 0 0 0 0 193 6 455 L4.t 0 0 0 0 0 194 6 21-0 15.0 0 0 0 0 0 195 6 425 16.8 0 0 0 0 0 796 6 415 15.0 0 0 0 0 0 ].97 6 440 13.8 0 0 0 0 0 198 6 270 15.4 0 0 0 0 0 199 6 185 15.1 0 0 0 0 0 200 6 115 15.5 0 0 0 0 0 207 6 Car parked over grate 202 6 610 74.7 0 0 0 0 zo3 5 670 'J.4.4 0 0 0 0 204 6 575 13.5 0 0 0 0 20s 6 810 13.9 0 0 0 0 206 6 610 1,4-0 0 0 0 0 207 6 490 14.0 0 0 0 0 208 6 580 14.9 0 0 0 0 209 6 100 16.1 0 0 0 0 270 6 650 15.4 0 0 0 0 21'L 6 510 r.5.2 0 0 0 0 2LZ 6 7rO 13.9 0 0 0 0 213 15.1 0 0 0 0

171 2r4 6 59s 15.3 2 0 0 0 0 2r5 6 585 14.9 0 0 0 0 0 21,6 6 8s0 74-0 1 0 0 0 0 2t7 6 680 15.1 0 0 0 0 0 218 6 585 15.1 0 0 0 0 0 219 6 655 15.1 0 0 0 0 0 220 6 810 13.9 0 0 0 0 0 227 6 315 13.3 4 0 0 0 0 222 6 465 14.0 5 0 0 0 0 223 6 725 r.3.5 2 0 0 0 0 224 6 50 18.1 I 0 0 0 0 225 6 70 17.O 1, 0 0 0 0 226 6 0 227 6 7ro 15.1 0 0 0 0 0 228 6 685 1.4.5 0 0 0 0 0 229 6 650 L4.5 0 0 0 0 0 230 6 615 1,4.4 L 0 0 0 0 23L 6 535 15.0 4 0 0 0 0 232 6 610 t5.2 0 0 0 0 ?33 6 590 1,4.2 2 0 0 0 0 234 6 410 15.5 1 0 0 0 0 23s 6 750 L4.5 0 0 0 0 236 6 900 14.3 1- 0 0 0 0 ?37 6 810 15.6 2 0 0 0 0 238 6 0 239 6 505 r.5.9 1 0 0 0 0 240 6 30 16.6 4 0 0 0 0 24'J. 6 515 15.4 2 0 0 0 0 242 6 575 1,4.6 0 0 0 0 0 243 6 500 14.9 1 0 0 0 0 244 6 83s 15.4 5 0 0 0 0 245 6 610 1,4.7 3 0 0 0 0 246 6 820 15.4 2 0 0 0 0 247 6 0 248 6 565 15.0 5 0 0 0 0 249 6 55 18.9 4 0 0 0 0 250 6 0 25t 6 760 13.4 0 0 0 0 ,(t 6 750 r.3.6 0 0 0 0 253 6 645 13.4 22 0 0 0 0 254 6 750 L4.6 4 0 0 0 0 25s 6 630 13.7 5 0 0 0 0 256 6 660 L3.7 22 0 0 0 0 257 6 0 6 490 13.4 259 6 0 260 6 0 26L 6 350 13.4 0 0 0 0 262 6 520 13.5 0 0 0 0 263 5 290 r.3.9 0 0 0 0 264 6 420 13.3 0 0 0 0 265 6 1120 13.4 0 0 0 0 266 6 700 13.2 0 0 0 0 267 6 1010 r.3.3 0 0 0 0 268 6 785 L3.2 0 0 0 0 0 269 6 305 13.4 4 0 0 0 0 270 6 28s 13.5 5 0 0 0 0 z7r 6 740 L2.O 0 0 0 0 0 272 6 390 13.4 10 0 0 0 0 273 6 675 13.1 T2 0 0 0 0 274 6 555 13.2 0 0 0 0 0 275 6 505 !3.2 0 0 0 0 0 276 6 610 73.2 20 0 0 0 0 6 370 13.7 0 0 0 0 0 6 630 13.0 3 0 0 0 0 279 6 645 r.3.1 4 0 0 0 0 280 6 110 13.8 5 0 0 0 0 281 6 505 13.5 5 0 0 0 0 6 550 12.7 7 4 4 0 0

172 283 6 560 10.6 5 0 0 0 0 284 6 280 72.6 0 0 0 0 0 28s 6 490 t7.L 10 0 0 0 0 286 6 625 19.5 0 0 0 0 aaa 6 58s 14.3 4 4 0 0 288 6 300 11.9 0 0 0 0 289 6 650 r.5.5 0 0 0 0 290 6 585 L4.5 11 0 0 0 0 29L 6 365 r.4.8 0 0 0 0 0 292 6 595 13.6 1 0 0 0 0 293 6 810 13.3 0 0 0 0 0 294 6 565 74.7 1 0 0 0 0 295 6 450 r.6.5 1 0 0 0 0 296 6 335 L4.0 2 0 0 0 0 297 6 405 15.9 4 0 0 0 0 298 6 595 13.6 10 0 0 0 0 299 6 290 t2.6 6 0 0 0 0 300 6 305 11.9 9 U 0 0 0

173 Appendix G Raw Data 2005

Total Catch Water Leaf SamDlinE Laruâe Aedes Basin Water Depth (mm) Temperature Litter Culex nound and restuans vexans Number rc) l2s Pupae

1 1 750 10.1 0 00 2 1 2so 8 1 0 00 a 1 100 1.5 0 00 4 1 210 13 10 0 00 5 1 402 14.5 2 n 00 tl 1 12.6 10 0 00 7 1 62 21.1 0 0 00 8 1 20 20.9 4 0 00 9 1 360 15.3 15 0 00 10 1 435 1 0 00 11 1 280 tt.o 12 0 00 12 I 100 13.7 15 0 00 13 1 180 '18 4 0 00 14 1 380 3 0 00 lÂ 1 260 23.1 0 0 00 to 1 108 25.3 0 0 00 17 1 359 17.4 1 0 00 18 1 J¿Ó 14.6 t¿ 0 00 19 1 601 15.2 0 0 00 20 1 0 0 00 a4 1 649 13.9 0 0 00 22 1 414 16.4 0 0 00 ¿ó 1 631 21.6 0 0 00 24 1 573 19.s 0 0 00 25 1 220 16.9 0 0 00 26 1 212 t)a 0 0 00 27 1 149 21 .1 2 0 00 ¿Ò 1 20 25.6 0 0 00 29 1 331 22.1 0 0 00 '159 30 1 14.6 4 0 00 1 154 tb.J 2 0 00 32 1 170 21.5 2 0 00 a? 1 485 13.4 2 0 00 s 1 14.9 0 0 00 35 1 501 14.7 1 0 00 36 1 289 14.3 1 0 00 3T 1 469 15.1 0 0 00 JO I 301 13.5 0 00 39 1 205 22.1 2 0 00 40 1 252 17.6 1 0 00 41 1 215 17.3 J 0 00 42 1 658 20.4 1 00 43 1 270 16.7 0 0 00 44 1 529 10 0 00 45 1 100 17.3 0 00 4b 1 t3t 16.4 1 0 00 47 1 150 16.3 10 0 00 48 1 160 19.8 1 0 00 49 1 ?AO 15.5 1 0 00 50 1 291 17.7 1 0 00 51 1 434 tb 0 0 00 52 1 58s 16.1 0 0 00 53 1 612 13.5 0 4 04 1 600 14.1 0 0 00 55 1 610 17.9 0 0 00 56 1 s08 15.1 0 0 00 57 1 472 16.1 0 0 00 58 1 603 tb 0 0 00

174 59 1 340 IJ 0 0 0 0 60 1 710 17.1 0 0 0 0 OI 1 bvu 14.6 0 0 0 0 Ãôo 62 1 14.5 0 0 0 0 63 1 620 15.3 0 0 0 0 64 1 515 16.1 0 0 0 0 65 1 605 17 0 0 0 0 66 1 565 14.7 0 0 0 0

67 1 600 14.6 I 0 0 0 68 1 525 18.4 0 0 0 0 69 1 470 15.8 0 0 0 0 70 1 600 14.5 0 0 0 0 1 575 14.7 0 0 0 0 1 265 14.5 0 0 0 0 ¿J 1 190 13.8 0 0 0 0 74 1 309 14.4 0 0 0 0 1 230 14.3 0 0 0 0 tó 1 357 t4.J 0 0 0 0 1 0 0 0 0 78 1 425 15.9 0 0 0 0 79 1 430 14.6 0 0 0 0 80 1 J¿ó 20.1 0 0 0 0 81 1 295 17 .1 0 0 0 0 1 590 16.1 0 0 0 0 OJ 1 0 0 0 0 84 1 562 15.4 0 0 0 0 ôÃ 1 36s 18.5 0 0 0 0 86 1 5/U 14.6 1 0 0 0 ot 1 513 15.2 0 0 0 0 1 502 16.4 0 0 0 0 89 1 578 15.6 0 0 0 0 90 1 575 '1 6.1 0 0 0 0 91 1 120 15.5 1 0 0 0 92 1 628 11.6 0 0 0 0 93 1 600 15.4 0 0 0 0 94 1 590 0 0 0 0 95 1 520 2 0 0 0 96 1 605 14.6 0 U 0 0 97 1 " 580 13.2 1 0 0 0 98 1 600 15.2 1 0 0 0 99 1 435 15.8 0 0 0 0 100 1 555 17.2 0 0 0 0 101 1 307 12.1 0 0 0 102 1 47 11.4 J 0 0 0 'r 03 1 157 12.7 2 0 U 0 104 1 47 11.4 3 0 0 0 105 1 318 14.7 3 0 0 0 106 I 247 11.5 2 0 0 0 107 1 427 12.6 J 0 0 0 108 1 576 15.7 2 0 0 0

109 1 309 14.7 1 0 0 0 110 1 403 16.5 0 0 0 111 1 50 17 1 0 0 0 112 1 ot/ 11.3 2 0 0 0

113 1 519 11 .7 1 0 0 0 '114 1 bt/ 11 .1 2 0 0 0 115 1 107 15.9 J 0 0 0 116 1 58 17 2 0 0 0 117 1 390 15.2 1 0 0 0 118 1 329 15.8 I 0 0 0 119 1 u2 13.9 1 n 0 0 120 1 145 15 16 0 0 0 121 1 u4 14.4 1 0 0 0 122 1 88 13.1 22 0 0 0 123 1 197 t 4.o 4 0 0 0 '124 1 214 12.9 o 0 0 0 125 1 94 12.8 24 0 0 0 126 1 410 13.6 2 0 U 0 127 1 PARKED CAR 0 0 0

175 128 310 11.5 4 0 0 0 129 123 13.9 o 0 0 0 130 124 16.3 3 0 0 0 141 13 0 0 0 132 330 t4-b I 0 0 0 133 298 12.8 5 0 0 0 134 269 15.4 0 0 0 135 179 15.3 10 0 0 0 136 728 16 2 0 0 0 137 218 17.3 8 0 0 0 477 16.3 7 0 0 0

139 679 1 1.9 1 0 0 0 140 103 17.6 24 0 0 0 141 JJO 17.3 0 0 0 142 JJ¿ 15.9 7 0 0 0

143 194 16 1 0 0 0 144 396 5 0 0 0 145 478 14.3 7 0 0 0 t40 217 tb 3 0 0 0 147 599 12 11 0 0 0 148 514 16.3 7 0 0 0 149 505 10 0 0 0 150 657 16.3 4 0 0 0

151 21.7 1 0 0 0 152 170 16.2 2 0 0 0 r53 0 0 0 154 70 17.3 J 0 0 155 0 0 0

156 141 12.8 1 0 0 157 0 0 0 158 15.6 13 0 0 0 159 108 13.5 2 0 0 0 160 250 11.5 0 0 0

tot ôt I +.¿ 1 0 0 0

162 72 I t,.J 1 0 0 0 163 22 16.9 0 0 0 0 164 310 18.3 Ã 0 0 0 165 140 16.6 a 0 0 0 166 340 15.3 2 0 0 0 to/ 0 0 0 168 350 15.5 0 0 0 0 169 160 14.6 1 0 0 0 170 180 t4.o 0 0 0 171 180 16.3 4 0 0 0 310 12 2 0 0 0 173 47 14.2 0 0 0 0 174 10 ló-t 0 0 0 0 175 29 I 4.O 0 0 0 0 176 215 14.4 1 0 0 0 177 215 13.9 10 0 0 0 178 128 14.2 0 0 0 aao 240 ta-¿ 4 0 0 0 180 340 14. t I 0 0 0 181 z¿-ô 7 0 0 0 182 43 15.2 1 0 0 0 183 489 14.3 1 0 0 0 184 626 15.2 0 0 0 0 185 172 15.2 0 0 0 0

186 230 15.1 1 0 0 0 187 300 15.4 2 0 0 0 188 13.8 0 0 0 0 189 232 15.2 0 0 0 0 190 150 21.4 0 0 0 0

191 409 16.5 7 1 0 1

192 350 22.1 1 0 0 0 193 499 20.3 1 0 0 0 194 350 15.1 0 0 0 0 195 230 13.3 2 0 0 0 196 370 14.1 1 n 0 0

176 ,) 197 1 329 14 0 0 0 198 1 325 18 0 0 0 199 1 1 185 12.7 0 0 0 0 200 1 212 21.3 0 0 0 0 201 1 213 22 5 0 0 0 202 1 598 10.3 0 0 0 203 1 458 10.1 I 0 0 0 204 1 239 12.1 2 0 0 0

205 1 10.1 1 0 0 0

206 1. 564 12 I 0 0 0

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