Mansonia Titillans and Mansonia Dyari (Diptera: Culicidae) Seasonal Abundance and Host-Seeking Activity Patterns in Lee County, Florida

Mansonia titillans and Mansonia dyari (Diptera: Culicidae) seasonal abundance and host-seeking activity patterns in Lee County, Florida.

A Thesis Presented to

The Faculty of the College of Arts and Sciences Florida Gulf Coast University

In Partial Fulfillment of the Requirement for the Degree of Master of Science

Edward William Foley IV 2020

APPROVAL SHEET

This thesis is submitted in partial fulfillment of The requirements for the degree of Master of Science

______

Edward Foley

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Edwin M. Everham III Committee Chair

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Neil Wilkinson Committee Member

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Brian Bovard Committee Member

The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. ii

Acknowledgements The conclusion of this project is a time of immense excitement and great pride as

I get to see the countless hours of hard work come together to accomplish something as wonderful as science. I feel grateful for what I was a part of but by no means would I be here without the love, support and guidance of so many. Words almost seem bleak and abstract in comparison to the true impact these interactions have had on my life.

I would like to thank my family and my girlfriend for their never endless patience, understanding and support. I would like to thank my advisors Dr. Win Everham, Neil

Wilkinson, and Dr. Brian Bovard for being there to guide me throughout this journey. I would also like to thank Dr. Jonathan Hornby for his immense assistance in the early developmental stages of this project, for which I am eternally grateful.

I would like to give a giant thank you to Rachel Morreale for the blood, sweat and tears she had given to see this project, and myself, succeed.

In no particular order would I like to thank, with my greatest sincerity: Jessica

Cotter and Kara Tyler-Julian for their assistance with trap collections; Michael Thomas for his assistance with sample identification; Tom Miller for his assistance maintaining finicky traps; Katie Baker for her mentorship and support; Dr. David Hoel for his keen eye and immense help with editing this document; my many friends who have had to listen to the excuse ‘I’m working on my thesis’ for far too long; and the Florida Mosquito

Control Association for their scholarship support early on.

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Abstract Mansonia titillans (Walker) and Mansonia dyari (Belkin, Heinemann, and Page) are two mosquito species found throughout the southern United States. These species are aggressive biters and considered potential vectors for several debilitating diseases.

Understanding their flight activity as well as relevant environmental factors influencing this behavior is crucial to develop effective control strategies.

This study took place in Lee County, Florida, located along the Gulf coast of southwest Florida. Quarterly trapping was conducted using collection bottle rotator traps sampling one-hour increments between 5 pm and 8 am. An onsite weather station collected environmental data for wind speed, temperature, relative humidity, rain accumulation, and light levels (lux). Hourly capture data were evaluated using the

Wilcoxon nonparametric test with Steel-Dwass All-Pairs as a post-hoc test. A series of stepwise linear regressions were conducted to explore environmental factors.

The peak activity of Ma. titillans and Ma. dyari was determined to be between the hours of sunset and two hours post sunset. The environmental conditions light, temperature, relative humidity, and wind speed all had a significant impact on mosquito abundance over the course of the study. Mansonia appear to display an upper threshold limit to both humidity and temperature. Light appears to play an important role in activity but does not appear to be the environmental cue driving flight.

It is the goal of this study to aid public health managers in tailoring their nighttime spray operations around the flight activity of Ma. titillans and Ma. dyari. The increased precision of applications would allow for higher efficacy rates while potentially decreasing the unnecessary insecticidal load on the environment.

Table of Contents

Page Acknowledgements iii Abstract iv Table of Contents v List of Figures vii List of Tables ix Chapter 1: Introduction Literature Review 1 Taxonomy and Morphology 5 Flight Categories 8 Adult Feeding Behavior 9 Environmental Conditions Affecting Flight 11 Research Objectives 14 Chapter 2: Materials & Methods Study Site 15 Attractants 18 Trap Design 19 Experimental Design 21 Weather Data 23 Mosquito Identification/Proportional Analysis 25 Statistical Analysis 27 Chapter 3: Results Overview 29 Objective 1: Peak Host-Seeking Activity 30 Objective 2: Environmental Conditions Influencing Host-Seeking 35 Chapter 4: Discussion Limitations of the study 50 Peak Host-Seeking Activity 52

Environmental Conditions Influencing Host-Seeking Light 54 Humidity 55 Temperature 56 Wind 57 Future work 58 Management implications 58 Literature Cited 60 Appendix Appendix Table 1: Species count at secondary site 66 Appendix Table 2. Fall: Ma. titillans data from primary location 67 Appendix Table 3. Fall: Ma. dyari data from primary location 67 Appendix Table 4. Winter: Ma. titillans data from primary location 68 Appendix Table 5. Winter: Ma. dyari data from primary location 68 Appendix Table 6. Spring Ma. titillans data from primary location 69 Appendix Table 7. Spring: Ma. dyari data from primary location 69 Appendix Table 8. Summer: Ma. titillans data from primary location 70 Appendix Table 9. Summer: Ma. dyari data from primary location 70

List of Figures

Page 1.1 Mansonia larvae attached to aquatic vegetation 7 2.1 Study area map 16 2.2 Aerial imagery of study location 17 2.3 Aerial imagery of trap sites and weather station location 17 2.4 Collection bottle rotator (CBR) traps at study site 21 2.5 Onsite weather station 24 2.6 Moonlight photometer 24 3.1a Ma. titillans summer collection 31 3.1b Ma. titillans fall collection 31 3.1c Ma. titillans winter collection 31 3.1d Ma. titillans spring collection 31 3.2a Ma. dyari summer collection 32 3.2b Ma. dyari fall collection 32 3.2c Ma. dyari winter collection 32 3.2d Ma. dyari spring collection 32 3.3a Ma. titillans count data at sunset 33 3.3b Ma. titillans count data at sunrise 33 3.4a Ma. dyari count data at sunset 34 3.4b Ma. dyari count data at sunset 34 3.5a summer: Ma. titillans catch abundance by wind speed (mph) 36 3.5b summer: Ma. titillans catch abundance by temperature (Celsius) 36 3.5c. summer: Ma. dyari catch abundance by wind speed (mph) 37 3.5d summer: Ma. dyari catch abundance by temperature (Celsius) 37 3.6a fall: Ma. titillans catch abundance by light (lux) 39 3.6b fall: Ma. dyari catch abundance by temperature (Celsius) 39

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3.7 winter: Ma. titillans catch abundance by relative humidity 40 3.8a spring: Ma. titillans catch abundance by light (lux) 41 3.8b spring: Ma. titillans catch abundance by relative humidity 41 3.9a combo: Ma. titillans catch abundance by light (lux) 43 3.9b combo: Ma. titillans catch abundance by relative humidity 43 3.9c combo: Ma. dyari catch abundance by temperature (Celsius) 44 3.9d combo: Ma. dyari catch abundance by relative humidity 44 3.10a Ma. titillans counts by moon phase for summer collection period 45 3.10b Ma. dyari counts by moon phase for summer collection period 46 3.11a Ma. titillans counts by moon phase for fall collection period 46 3.11b. Ma. dyari counts by moon phase for fall collection period 47 3.12a Ma. titillans counts by moon phase for winter collection period 47 3.12b Ma. dyari counts by moon phase for winter collection period 48 3.13a Ma. titillans counts by moon phase for spring collection period 48 3.13b Ma. dyari counts by moon phase for spring collection period 49 4.1 30 year Climograph for Fort Myers, Florida 50

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List of Tables

Page 2.1 Trapping dates for Seasonality collections 22 2.2 Trapping event efficacy 28 3.1 Species count at primary site 29 3.2 Summer: Stepwise regression summary 35 3.3 Fall: Stepwise regression summary 38 3.4 Winter: Stepwise regression summary 39 3.5 Spring: Stepwise regression summary 40 3.6 Combo: Stepwise regression summary 42

Chapter 1

Introduction

Mosquitoes are regarded as some of the deadliest animals on the planet (Spielman

2001). Their ability to transmit disease agents responsible for malaria, yellow fever, West

Nile virus, dengue fever and Zika virus account for millions of deaths annually with countless cases of infection (WHO 2019a). In 2017 alone, more than 219 million cases of malaria were reported worldwide with an estimated 438,000 deaths (WHO 2019b).

Dengue fever, often referred to as break bone fever, is another extremely debilitating disease spread through the bite of a mosquito which impacts people across the globe. It’s estimated more than forty percent of the world’s population live in areas with at least some risk of dengue; this equates to more than 3 billion people worldwide at risk of contracting this disease (CDC 2019). The CDC (2019) estimates that up to 400 million people become infected with dengue annually, with 100 million symptomatic cases and

22,000 dying as a result.

Outbreaks of yellow fever, dengue fever, and endemic malaria prompted the formation of the Florida State Board of Health in 1889 (Patterson 2004). In 1922, the

Florida Anti-Mosquito Association was established followed three years later by the establishment of Florida’s first taxpayer-funded mosquito abatement program in Indian

River County (Connelly and Carlson 2009). By the early 1950s, the mosquito-borne diseases of malaria, yellow fever and dengue fever, once endemic to Florida, had been officially eradicated due in part to the formation of sanitation programs and organized mosquito control districts, the discovery and subsequent use of DDT and other organochlorine and organophosphate insecticides as mosquito control tools, and the

increased standards of living that occurred after WWII including air-conditioned housing with screened windows (Patterson 2004). Behavioral changes associated with increased nighttime television viewing vs. nighttime outdoor activity is also believed to contribute to diminishment of these endemic diseases as a result of decreased contact between humans and mosquitoes after sunset (Patterson 2004).

Today, public health officials continue to monitor for imported cases of arthropod-borne diseases as well as actively suppress nuisance mosquito populations

(Lloyd et al. 2018). An overabundance of biting mosquitoes comes with its own issues for humans and animals alike. Agriculture is an important part of Florida’s economy

(Main et al. 2004); cattle have shown symptoms of malnutrition and have experienced exsanguination to the point of death attributed to nuisance Aedes taeniorhynchus

(Wiedemann) populations in southwest Florida (Addison and Richie 1993). Florida’s economy also is heavily dependent on tourism with an estimated 131 million visitors to the state every year (Rockport Analytics 2019). Large swarms of mosquitoes and outbreaks of mosquito-borne disease could deter tourists who spend an estimated $88.6 billion dollars annually in the state of Florida (Rockport Analytics 2019). Since the

1950s Florida’s residential population has grown exponentially from 2.7 million to almost 22 million full time residents in 2018 (Census 2019). With so many people residing and visiting the state annually, promoting human health and comfort through mosquito abatement is a constant concern for public health officials.

The two most common methods of mosquito abatement are through larviciding and adulticiding. Larviciding is defined as the application of insecticides to a waterbody designed to target mosquitoes in their juvenile life stage (Lloyd et al. 2018). This method

often results in the greatest efficacy, as larval mosquitoes remain concentrated with little dispersal. Adulticiding is the broadcast application of insecticides for the control of adult mosquitoes (Llyod et al 2018). This method is usually the most visual method of mosquito control and is done to reduce the biting pressure on the local populace.

Although effective, this method is the least efficient as adult mosquitoes tend to disperse across the landscape. This method also has the greatest chance of adverse effects to non- target organisms because treatments are conducted over a wider area.

Ultra low volume (ULV) insecticide applications dispense a small amount of chemical into aerosol-sized droplets, which stay suspended in the air column for a period of time (Britch et al. 2010). In order for an application to be effective, these small droplets must come into contact with flying mosquitoes before the product deposits and degrades to a point where it’s ineffective (Britch et al. 2010). Understanding the flight activity pattern of a targeted mosquito species is crucial for the timing of spray events to ensure contact with those mosquitoes (Bonds 2012, Carney et al. 2008). Breidenbaugh et al. (2009) studied Aedes and Culex diel activity patterns and determined the two genera differed in their respective activities over a 24-hour period. For the Aedes mosquitoes collected (Ae. taeniorhynchus, Aedes sollicitans (Walker), Aedes vexans (Meigen), and

Aedes atlanticus (Dyar and Knab)), activity would peak suddenly with sunset and decline over a four-hour period where it would level off at low activity for the remainder of the night. However, the Culex mosquitoes collected (Culex salinarius (Coquillett) and Culex quinquefasciatus (Say)), would show a similar peak in activity associated with sunset but also experienced a secondary period of activity equal in magnitude throughout the nighttime hours (Breidenbaugh et al. 2009).

Although all of the Aedes sampled in the aforementioned study followed similar activity patterns, this is not always the case with all species of the same genera. Aedes

(Stegomyia) aegypti (Linneaus) is an example of an Aedes with a markedly different activity period than any of the species collected throughout the aforementioned study.

Where Breidenbaugh et al (2009) found the studied Aedes species had a peak activity at sunset followed by a decline in activity over a four-hour period, a study by Taylor and

Jones (1968) found Ae. aegypti followed a diurnal activity pattern under natural conditions. With such a wide variety in activity patterns among not only genera but among species, it is crucial to obtain accurate activity information for a particular species of concern that has not been proxied from other species.

Taxonomy and Morphology:

Mosquitoes are small soft-bodied insects belonging to the order Diptera and distinguished as having only one pair of functional wings for flight (Triplehorn and

Johnson 2005). They are characterized as having long and slender abdomens, narrow wings, and the absence of a v-shaped suture on the thorax (Matheson 1929). Mosquitoes belong to the family Culicidae and are perhaps the best-known group of biting insects, due to their importance to man as vectors of many human and animal diseases (Carpenter and LaCasse 1955) and their occurrence over most of the earth.

Mosquitoes undergo a complete metamorphosis throughout the course of their life cycle as they mature from egg, through four stages of larval development, a pupal resting stage, and emerge as adults (Matheson 1929). Egg development usually takes place approximately two to three days post blood meal and are found in various shapes and cluster arrangement depending on species. Eggs may be laid singularly or clustered either directly on the water’s surface or on a dry substrate (Woodbridge and Walker 2002).

Larval and pupal stages are aquatic and depend on specialized structures to extract air when they come to the serface to breath atmospheric oxygen. Larvae have a single breathing structure called the syphon while pupae have two breathing structures called trumpets (Matheson 1929).

Mansonia titillans and Mansonia dyari are two freshwater mosquito species commonly found across the state and targeted for control (Burkett-Cadena 2013). These species are described as medium sized with a stout body and speckled with dark brown and pale scales (Burkett-Cadena 2013). Mansonia titillans and Ma. Dyari are distinguished from one another by the presence or absence of a small row of spines

located at the base of the abdomen (Carpenter and LaCasse 1955). Ma. Titillans has a row of short dark spiniforms located at the base of abdominal tergum VII, while these features are absent on Ma. Dyari (Darsie and Ward 2005).

Both of these species are fierce biters and considered potential vectors for many diseases such as Rift Valley fever and dog heartworm (Dirofilaria immitis (Leidy))

(Tantely et al. 2015, Bemrick and Sandholm 1966). In other parts of the world, Mansonia are vectors for the seriously debilitating and incapacitating lymphatic filariasis,

Wuchereria bancrofti (Cobbold), (Rao 2019, Ughasi et al. 2012). Mansonia are also suspected vectors for transmission of dirofilariasis to humans in rare cases (Jayasinghe et al. 2015, Dissanaike et al. 1997).

Mosquitoes belonging to the tribe Mansoniini share a unique characteristic in the larval stage of having a shortened and modified siphon tube adapted for piercing aquatic vegetation (Carpenter and LaCasse 1955). With this modified siphon, larvae are able to derive oxygen directly from the root system of the host plants rather than needing to access the water surface (Figure 1.1). Mansonia genera share this adaptation, rarely needing to surface and typically only do so to emerge as adults. This unique behavior also provides protection from predators and makes surveillance difficult using traditional methods such as dip counts. This technique involves collecting a 400ml sample from the water surface and visibly inspecting for larvae presence along with species identification, instar size, and abundance per dip (Llyod et al. 2018). With proper sampling techniques quite labor intensive, often involving collection of the host plant and straining in collection pans, many mosquito abatement programs choose to primarily focus on controlling the adult mosquito instead.

Figure 1.1- Mansonia uniformis larvae attached to aquatic plant root. Photograph by S.L. Doggett, Department of Medical Entomology, NSW, Australia.

Mansonia adult females typically lay their eggs in a star like cluster pattern on the underside of floating aquatic vegetation, often invasive exotics such as water lettuce

(Pistia stratiotes (Linn)) or water hyacinth (Eichhornia crassipes (Mart) solms). The eggs are orientated at or near the waterline and held onto the leaf with a glue-like material

(Linley 1989). Once the eggs become fully submerged the larvae hatch head first and quickly attach to a nearby root of the host plant (Day 2016).

Larvae remain attached to the host plant’s root system where they derive oxygen and utilize their mouthparts for nourishment intake. Larvae only detach themselves from the plant to molt or in the event of a disturbance (Carpenter and LaCasse 1955). The dense network of vegetation and roots systems aid in larval survival from predation. Like the larvae, Mansonia pupae have modified trumpets designed to attach themselves to the root system of aquatic vegetation. Pupae do not rise to the water’s surface until it is time for emergence (Carpenter and LaCasse 1955).

Adult Mansonia mosquitoes are a medium sized, stout bodied species with speckles of dark and pale scales throughout (Burkett-Cadena 2013). They have a characteristically blunted abdomen and both species, Ma. titillans and Ma. dyari, are almost indistinguishable through field identification. These two species are distinguished in the adult stage by the presence or absence of a small row of spines at the base of the abdomen. Female Ma. titillans have a row of short dark spinoform spines at the apex of abdominal tergum VII, whereas Ma. dyari lack this feature (Burkett-Cadena 2013).

Flight Categories:

Mosquito flight can be classified into three categories: migratory, appetential and consumatory (Bidlingmayer 1985). Migratory flight is conducted by newly emerged adult mosquitoes and is defined as being a one-way flight lacking a clear objective. This flight activity serves as a way to disperse the species away from a terrain of stable biological needs in search of additional resources (Provost 1953). Migratory flight generally occurs within adults 6-10 hours post emergence and is typically less than 2 km in distance, although this distance can be greater, depending on species and wind speed

(Bidlingmayer 1985, Woodbridge and Walker 2002). The direction of a mosquito’s migratory flight is largely fortuitous as they tend to follow prevailing winds. Their ultimate destination is largely dependent on local wind conditions at time of a mosquito’s departure (Bidlingmayer 1985). The duration of migratory flight is limited only by the energy reserves of the individual and the immediate meteorological conditions at time of flight (Bidlingmayer 1985).

The majority of a mosquito’s flight activity throughout its lifetime can be classified as appetential flight. Appetential flight is a searching behavior in response to

physiological stimuli (Bidlingmayer 1985). When a resting female mosquito is in need of a blood meal, oviposition site, or a more suitable resting site, it will undergo appetential flight in search of its desired objective. Throughout the course of this flight, the mosquito may rest periodically but will continue until its objective is met. The mosquito uses the appropriate sensory organ (olfactory, visual, thermal, auditory, or humidity receptors) for environmental cues to indicate the presence of its objective and effectively end its flight

(Bidlingmayer 1985).

Consumatory flight, or target flight, is defined as flight towards an intended target once a mosquito receives a sensory cue indicating the presence of its objective

(Bidlingmayer 1985). This flight is recognized as short and direct due to visual and biochemical cues not effective over long distances. It is possible for a female mosquito to undergo consumatory flight without a preceding appetential flight. An example of such is a resting individual sensing a nearby host, which has wondered into the immediate area.

The resting female mosquito may choose to conduct consumatory flight for a targeted blood meal.

Adult Feeding Behavior:

Plant nectar is the primary energy source for adult male and female mosquitoes of most species (Clements 1963). Female mosquitoes alone feed on various hosts for a blood meal to aid in reproduction and supplement energy supplies (Woodbridge and

Walker 2002). Mosquitoes typically require the protein found within host blood for the production of their eggs. Although this is true for almost all species, female

Toxorhynchites feed primarily on plant nectar and do not require a blood meal for egg production (Clements 1963). Some mosquito species are capable of gathering enough

dietary protein in the larval stage to lay a first batch of eggs without first obtaining a blood meal, a survival strategy in situations where hosts are not readily available.

Subsequent egg batches are then laid only after a blood meal is obtained. This condition is known as autogeny, and is uncommon in Mansonia mosquitoes and not known to occur in Ma. dyari or Ma. titillans (Strickman and Fonseca 2012).

Host preference between mosquito genera is highly variable. Most mosquito species tend to feed on mammalian and avian hosts, however some species show a strong preference to feed on amphibians and reptiles (Clements 1963). According to Clements, although some species are found to feed exclusively on birds and others on mammals, it is doubtful any one species would be absolutely host specific. Rather, a species may show a strong preference towards a particular host, but if the desired host opportunity is limited, it may take advantage of a readily available host.

Mansonia titillans and Ma. dyari are thought to have a strong preference for mammalian and avian host species (Edman 1971) which is partially why they are considered so pestiferous towards humans. This affinity for large mammals is in part why they are thought to be vectors for Rift Valley fever, a disease that predominately affects domestic ruminants such as sheep and cattle in Africa (Logan et al. 1991, Sang et al.

2010). However, to Clements’ point regarding host preference, Mansonia have been shown to feed on reptiles when faced with limited avian and mammalian hosts. Rodrigues and Maruniak (2006) have isolated alligator (Alligator mississippiensis (Holbrook) DNA in the guts of both Ma. titillans and Ma. dyari collected in central Florida.

Environmental Conditions Affecting Flight:

Mosquito flight activity is heavily influenced by local meteorological conditions

(Bidlingmayer 1985, Grimstad and DeFoliart 1975, Clements, 1963). The most influential meteorological conditions affecting mosquito flight are wind, temperature, humidity, and illumination (Bidlingmayer 1985). Of these conditions, wind speed is the most spatially variable as it is largely a factor of the immediate physical conditions on the local terrain (Bidlingmayer 1985). Mosquito cruising speed during appetential flight is believed to average less than one meter per second (<2.2 mph) (Bidlingmayer 1974,

Snow 1980). When wind velocities are less than this speed, mosquitoes tend to fly upwind during appetential flights (Snow 1976). When wind velocity exceed their average cruising speeds, mosquitoes alter their activity by either flying downwind, flying at lower elevations, or may choose to stop flying all together (Grimstad and DeFoliart 1975).

Guptavanij et al. (1973) noted on windy evenings Mansonia host-seeking activity was substantially less in the outdoor catches than compared to the indoor activity samples.

This change in activity seems to indicate Mansonia react to moderate to high wind velocities by seeking shelter and foregoing host seeking.

It is well-documented that mosquito flight activity is heavily influenced by temperature (Clements, 1963, Bidlingmayer 1985). Temperature is often considered a limiting factor for mosquito flight activity. Taylor (1963) found mosquito species have minimum thresholds for temperatures, which must be met prior to an individual’s flight.

Once the minimum temperature threshold for flight is met, further increase in temperatures neither increase nor inhibit flight activity (Taylor 1963). Mosquitoes in various longitudinal climates have shown varying lower temperature thresholds for flight.

Mosquito populations recorded in Alaska have shown thresholds for flight as low as 5°C before becoming inactive (Gjullin et al. 1961). In Wisconsin, Grimstad and DeFoliart’s

(1975) work found mosquito nectar feeding activity occurring at temperatures as low as

10°C. In South Florida, Bidlingmayer (1974) found a substantial reduction in collections recorded around 18°C or below.

Like temperature, relative humidity plays a vital role in mosquito flight activity, particularly in areas where temperatures rarely drop below individual lower thresholds

(Provost 1973, Clements 1963). The optimal levels of relative humidity needed for mosquito flight seems to be quite variable, as mosquitoes have shown a diverse range of preferences to relative humidity. Wright and Knight (1966) found Ae. vexans (Meigen) demonstrated host-seeking activity between 40-90% relative humidity while Ae. trivittatus (Coquillett) were actively host-seeking between 32-98% relative humidity. In

Florida, Provost (1973) found substantial collections of Culex nigripalpus (Throbald) on nights when relative humidity surpassed 90%. Mansonia have shown similar preferences as Opayele et al. (2017) found a positive correlation between Mansonia abundance and relative humidity between 50-90% in Ibadan, Nigeria.

Illumination levels have a substantial impact on mosquito flight activity and are completely independent of other environmental factors (Bidlingmayer 1985, Nielsen

1963). Most mosquito species exhibit a crepuscular period of activity when undergoing appetential flight, with activity peaking between twilight periods and early evening

(Bidlingmayer 1985). According to Bidlingmayer (1985), twilight is defined as

“illumination due to sunlight reflected around the curvature of the earth by the earth’s atmosphere”. Civil twilight as defined by Neilsen (1963) is “the period between sunset

and the moment when it becomes too dark to work outdoors without artificial illumination”.

Twilight illumination ranges in intensity between 8.5 and 0.13 lux. Lux, the unit of measure for light intensity, is defined by Nielsen (1963) as “the amount of light energy on a surface receiving 1 lumen per m²”. Full moon illumination has shown to provide up to 0.2 lux, which falls between the range of typical twilight periods and has the potential to affect mosquito activity. New moons however, tend to have minimal effect on mosquito flight due to illumination levels being as little as one percent of full moon periods (Bidlingmayer 1985).

Bidlingmayer (1964) used a vehicle mounted non-attractant sampling technique to collect flying mosquitoes in Vero Beach, Florida. What he found was an increase in female mosquito catch on quarter moon and full moon nights as much as 55% and 122%, respectfully, over moonless evenings. His collections of Ae. taeniorhynchus females alone saw an increase of 95% for quarter moon and 546% for full moon nights. Similar to

Bidlingmayer’s findings, Kampango et al. (2011) reported an increase in host-seeking behavior in Anopheles funestus Giles on evenings of increased moonlight illumination.

Throughout this study, the researchers recorded moon phase with associated trapping evenings. The authors did theorize that use of a sensitive light meter would have more effectively accounted for environmental conditions affecting moonlight such as cloud cover. To their point, natural variations in nocturnal illumination, particularly due to cloud cover, was shown to affect a variety of species’ activity periods (Gaston et al.

2013).

Orlandin et al. (2017) studied mosquito activity during the crepuscular period in an Atlantic Forest in Southern Brazil and found that Ma. titillans was most active in the period 15 minutes to 45 minutes post sunset. This study did not look at nocturnal illumination levels associated with activity periods. No similar literature was found comparing the biting activity of Mansonia with nocturnal illumination levels regarding partial cloud cover, nor quantifying activity periods in Florida.

Research Objectives:

The objectives of this study are twofold: (1) determine the peak host-seeking activity time of Ma. titillans and Ma. dyari in southwest Florida, (2) determine the relevant environmental factors affecting this behavior. A more robust body of science available on the activity patterns of these species will help public health officials be more precise in targeting nighttime control operations and methods.

Chapter 2 Materials and Methods

Study Site:

This study was conducted in Lehigh Acres, Florida, located in eastern Lee County

(Figure 2.1). Lee County is located along Florida’s Gulf coast north of Collier County, south of Charlotte County, and west of Hendry County. This site was selected for its historical abundance of Ma. titillans and Ma. dyari collected annually in baited traps.

The study site is adjacent to a complex network of wet detention ponds, irrigation canals, and saturated furrow ditches, which typically hold water throughout the year

(Figure 2.2). Initial inspections of the area found an abundance of the exotics water lettuce (Pistia stratiotes) and water hyacinth (Eichhornia crassipes) throughout the property. With a stable body of water and an abundance of host plant species, this area is presumed to be the point source of Ma. titillans and Ma. dyari collected at the test site in eastern Lee County. With limited treatments of herbicides and pesticides, this site allows

Ma. titillans and Ma. dyari to reproduce naturally throughout the year.

Two trapping sites were selected roughly 150 meters apart to limit competition between traps while still providing similar trapping results (Figure 2.3). The primary trapping site (26.574356, -81.567189) is located within a thicket of Brazilian pepper trees

(Schinus terebinthifolia (Raddi)) to provide a shaded habitat. The secondary site

(26.574982, -81.568501) is located northwest of the primary site and is surrounded by a mixed habitat consisting mostly of live oak (Quercus virginiana (Mill)), cabbage palms

(Sabal palmetto (Walt.) Lodd), and Brazilian pepper. A third location (26.574207, -

81.567913) selected between the two trapping locations housed an onsite weather station.

Figure 2.1 – Map illustration of Lee County with relation to Florida. Yellow star indicates the research location chosen for this study along the easternmost boundary.

Figure 2.2 – Aerial imagery of study location in Lehigh Acres, Florida. This location is surrounded by canal networks, which support the host plant species, water lettuce (P. stratiotes) and water hyacinth (E. crassipes).

Figure 2.3 – Aerial imagery of trap sites and weather station location at study site in Lehigh Acres, Florida.

Attractants:

Four main attractants were utilized throughout the duration of this study to aid in trap collections. These attractants included carbon dioxide, 1-Octen-3-ol (octenol) lure, a proprietary BG Lure, and visual stimuli of the trap itself. Carbon dioxide (CO2) was supplied using a five-pound block of dry ice packaged into insulated shipping envelopes and loosely closed to allow multiple 2-3 cm openings along the seam (Caldwell et al.

2006). Carbon dioxide sublimation from dry ice has proven to substantially increase mosquito collections in nightly trappings (Newhouse et al. 1966). This packaging method allows for consistent sublimation of CO2 without the need for onsite storage tanks and allowed for convenient transport to the field.

Octenol lure pouches (Bioquip, Rancho Dominguez, CA, USA) and BG Lure cartridges (BioGents GmbH, Regensburg, Germany) were utilized to help mimic the natural attractants of mammalian hosts. BG lure cartridges are a proprietary blend of l-

(+)-lactic acid, ammonium hydrogen carbonate, and hexanoic acid designed to replicate the scent of human skin. Similar to BG lure cartridges, octenol pouches replicate the naturally occurring octenol found primarily in ruminant breath but also in human breath and sweat (Irish et al. 2008). Previous work by Kline et al. (1990) found a significant increase in Mansonia collections when carbon dioxide and octenol were used in synergy to trap Mansonia from phosphate mines in Polk County, Florida. The small packaging of these attractants allowed for convenient transport to the field and the ability to be conveniently set inside the trap body.

The traps’ polypropylene tarpaulin outer covering along with the breathable mesh top were constructed using the color white to aid in visual stimuli. The color scheme was

modeled after the first generation BG Sentinel trap. This light-colored outer covering acts as a visual cue for female mosquitoes involved with consumatory flight activity.

Although previous literature suggests Mansonia species have a visual affinity towards the colors blue and red (Bhuyan and Das 1985), the color white was used to stay consistent with previous work conducted with first generation BG Sentinel traps (Gama et al. 2013).

Trap Design:

BG Sentinel traps (BioGents GmbH, Regensburg, Germany) have proven to be useful tools in the sampling of mosquito populations (Krockel et al. 2006). Many researchers have successfully utilized these novel traps for the collection of a variety of mosquito genera including Aedes, Culex, Anopheles, and others (Gama et al. 2013, Irish et al. 2008). BG Sentinel traps have also shown to be useful tools for sampling for adult

Mansonia species. Gama et al. (2012) utilized the BG Sentinel traps to first document

Mansonia populations in a rural area of Brazil’s Amazon rainforest.

For this study, mosquitoes were collected using a Centers for Disease Control and

Prevention (CDC) miniature light trap attached to Collection Bottle Rotators (CBR)

(Model 1512, John Hock Company, Gainesville, FL, USA) and modified to give the functionality and outward appearance of the BG Sentinel trap (Jiang 2016, Gama et al.

2013, Williams et al. 2006) (Figure 2.4). This design offers the ability to segregate nighttime collections into eight individual collection jars over a predetermined time as set by the user. Collection bottles were modified by removing 1 cm from the bottom, and replacing the opening with a 12.7 cm x 12.7 cm piece of fiberglass screen mesh, secured in place with a 1.27 cm x 8.89 cm polyvinyl chloride piping band. This modification allows for airflow through the collection jar, limiting backpressure back out the top,

while also aiding in collection removal. The incandescent light bulb traditionally used with this trap type was removed throughout the duration of the study to replicate the BG

Sentinel traps’ lack of illumination.

Custom stands and outer covers were designed to lower the traps’ profile while also mimicking the outward appearance of the BG Sentinel trap. The custom stands were created to hold the trap 36 cm above ground level, rather than 1.5 m as traditionally used

(Jiang 2016). The outer covers were designed 61 cm in diameter by 61 cm tall and constructed out of a wire frame with a white non-porous polypropylene tarpaulin outer skin. A breathable mesh fabric top cover was constructed to enclose the trap while allowing attractant plums to exit as originally designed by Biogents for the BG Sentinel trap. Fabric tops were sewn with elastic edges to hold the top snug to the traps’ outer covering with an 8.9 cm opening cut to allow the intake funnel to pass through the covering for insect collection. Black plastic extensions attached to the intake funnel extended the height by 10.16 cm to ensure the fabric top did not interfere with functionality. A flexible draft guard placed firmly against the bottom circumference of the trap created an airtight seal limiting attractant escaping beneath the trap.

Figure 2.4 – Collection Bottle Rotator (CBR) traps at study site. Trap on left has outer covering in place while trap on the right demonstrates CBR trap height with attractants and battery setup.

Experimental Design:

Two collection bottle rotator traps were stationed at each of two collection sites and programmed to run consecutively between the hours of 1700 and 0800; for a total of four CBR traps. Each trap has eight individual collection jars which rotate on predetermined time intervals. For this study, one-hour collections were used to monitor the nightly flight activity of Mansonia. The traps were programmed to run in tandem with the first trap starting at 1700 and ending collections at 2400. The second trap at each location began collecting at 2400 and ran through 0800. Each trap was powered by an

individual 12-volt battery, which would run the collection fan continuously over its trapping cycle.

Traps were set by 1700 each trapping night and collected at 0800 the following morning. Attractants were placed inside the body of the trap with the 1-Octen-3-ol

(octenol) and BG Lure placed on top of the trap and the dry ice placed at the base of the stand. All traps were set with fresh batteries and lures prior to 1700.

In the morning, collection jars were unscrewed from the CBR trap body and a lid fastened to the jar to prevent escape of live mosquitoes. Once collections were secured at both sites, the traps were replenished with empty jars in preparation for the next trapping event. Batteries and attractants were collected from each location and returned to the shop for replenishing. Mosquito collections were brought back to the laboratory where they were stored at 0°C until further processing and identification was conducted.

Trapping was conducted over the course of one full moon cycle per quarter with three consecutive nightly collections per week (Jiang 2016). In the event of an adulticide spray event, trapping was conducted no sooner than three nights post-spray. Four series of collections were made corresponding with seasonality to track activity throughout the year (Table 2.1).

Table 2.1 – Trapping dates for seasonality collections.

Season Start Date End Date Summer 8-9-16 9-8-16 Fall 11-1-16 12-8-16 Winter 2-7-17 3-2-17 Spring 5-9-17 6-8-17

Weather Data:

Local weather data were collected via an onsite weather station (Figure 2.5).

Weather station data were downloaded onto a portable solid state drive and returned to the office for processing. Meteorological sensors for temperature (Delairco HMP45;

Frenchs Forest, NSW, Australia), relative humidity (Delairco HMP45; Frenchs Forest,

NSW, Australia), rainfall (Texas Electronics Inc. TR-525I; Dallas, TX, USA), and wind speed and wind direction (Vaisala WS425; Lake Villa, IL, USA) were wired to a

Campbell Scientific CR850 data logger (Logan, UT, USA). Ambient light was recorded using an OWL3pro data logger (EME Systems, Berkeley, CA, USA) set up to record low light readings from an ISL29033 lux sensor (Renesas Electronics Corp., Tokyo, Japan).

Although a separate unit, the moonlight photometer was mounted to the frame of the weather station for data consistency (Figure 2.6). Both the CR850 data logger and the

OWL3pro were programmed to take data every 60 seconds and record a 10-minute average. 10-minute averages were taken to limit inconsistencies in meteorological abnormalities such as brief cloud cover during time of recordings. Offsite weather data were acquired from a nearby weather tower located approximately 7.25 km to the north and downloaded via Weather Underground (2019). Moon phase data were acquired from the United States Naval Observatory (USNO) (2019). Official sunset and sunrise times were acquired from Time and Date (2019).

Figure 2.5 – Onsite weather station.

Figure 2.6 – Moonlight photometer. Unit attached to weather station frame.

Mosquito Identification and Proportional Analysis:

Mosquitoes were identified to the species taxonomic level with the exception of

Culex (Melanoconion) Theobald, which were classified to subgenus. Cx. (Melanoconion) are small dark brown mosquitoes that feed primarily on amphibians and are associated with stagnant water bodies. Although an important group of mosquitoes, their similar size and appearance makes for a slower identification process as additional body features must be analyzed. For the scope of this study, it was determined that identification of Cx.

(Melanoconion) to subgenus was appropriate and would aid in the identification of samples.

For hourly collections of moderate to low sample sizes, n <500, samples were identified in their entirety. Collections of high sample sizes (n >500) were identified using a proportional analysis to aid in the efficiency of sample processing. Proportional analysis is the process of identifying a subset from a larger collection and projecting its findings towards the larger collection as a whole based on weight of the whole sample to the weight of the subsample. Typically, subsamples are counted, weighted, and a correlation to the number of the whole sample is then made based on its weight. In this case, subsets of hourly collections were identified to species level and a ratio was calculated based on mass of subsamples to the mass of the whole samples from which they came, projected towards the collection as a whole.

To most accurately assess the collection, samples were first allowed to rest at room temperature until dried, then two subsets of 100 individuals were identified and averaged to establish a baseline for each collection. Although it is possible a collection would be under-represented using this technique, the average of two subsets should give

a realistic representation of the collection without manually identifying all samples. The protocol outlined below was followed for all proportional analysis throughout the duration of this study.

Protocol of proportional analysis used:

1. Identify two randomized subsets of 100 individuals from a high (≥500) sample size. 2. Weigh each sample independently from one another and of the remaining sample collection. 3. Establish the average mosquito collection for each species of subset 1 and subset 2. 4. Next, establish the multiplication factor to scale the sampling based on the total mass of mosquitoes collected. To do this, take the total mass of all mosquitoes collected and divide it by the average mass of subset 1 and subset 2.

Using the established multiplication factor, multiply this by the average number of mosquitoes collected between subsamples 1 and 2 for each species identified. The resulting product estimates the is the total mosquito counts for each species within a collection.

MF = TM/AMS

MF = multiplier factor TM = total mass of the samples AMS = (average mass) of the two subsamples

EMC = AMC×MF

EMC = estimated mosquito count by species AMC = average count for each species, average of the two subsamples MF = multiplier factor

Statistical Analysis:

Trapping was conducted at primary and secondary locations approximately 150 meters apart. The secondary site was established as a backup to the primary site in the event of a trap failure. Preliminary analysis indicated the trapping magnitude of the primary location was much higher than collections made at the secondary location. The determination to exclude collection dates without a complete data set was made to limit adding variation and error into the evaluation. When the total successful trap nights for each site was examined it was determined the primary site provided sufficient samples for all subsequent analysis and the secondary sites data would not be necessary (Table

2.2).

The hourly collections were analyzed using the Wilcoxon nonparametric analysis.

The Steel-Dwass post-hoc analysis was used to determine which hours were significantly higher. The Steel-Dwass All-Pairs post-hoc test was chosen to most appropriately analyze the data as it is considered the nonparametric version of the All-Pairs, Tukey Post-hoc analysis (Sall et al. 2017). Traditional parametric testing was initially evaluated, however the data failed to meet the assumptions required for the tests. The data were analyzed in the statistical program JMP 14.1.0 with a significance level set to p≤ 0.05. To determine if a secondary abundance peak was present, the nightly samples were separated into sunset and sunrise periods and analyzed using the same procedure.

Once peak activity was determined, the environmental data for wind speed, temperature, humidity, rainfall, and light were condensed into hourly averages during the peak activity period. The onsite local weather station data were unavailable during the summer collection period. Offsite weather was collected via

www.weatherunderground.com and used to evaluate the summer collection. The moonlight photometer was not available for the summer collection and thus not included in analysis.

Stepwise linear regressions were used to determine which environmental factors had significance impact on trap abundance during the established activity period. Once again JMP 14.1.0 was utilized with a significance level of p≤ 0.05. Statistical analysis was conducted on Ma. titillans and Ma. dyari individually for each of the four seasons.

The data for fall, winter and spring collections were condensed and similarly evaluated overall for both species throughout the year. The summer collection period lacked the onsite weather station and thus was not included in this evaluation.

Table 2.2 – Trapping event efficacy. Failed trapping event is classified as one or more trap failure, per site, over the course of a collection evening. Season Total Primary Primary Secondary Secondary trapping site site site site nights success failures success failures Summer 12 9 3 10 2 Fall 15 10 5 9 6 Winter 12 12 0 10 2 Spring 12 11 1 11 1

Chapter 3

Results

Over the course of 51 separate trapping events, 113,297 mosquitoes were collected between both locations. There were 75,635 mosquitoes were collected from the primary location representing seven different genera (Table 3.1). Mansonia represent 83 percent of the total catch for mosquitoes at the primary location. Ma. titillans was the dominant species with 56,635 individuals collected throughout the duration of the study, representing almost 75 percent of the total catch. Ma. dyari was the second highest species collected with 6,309 collected, representing eight percent of the total catch.

Table 3.1- Total count and percentage of total count of mosquito species collected at primary site. Mosquito Species Total Count Percent of Total Mansonia titillans 56635 74.88 Mansonia dyari 6309 8.34 Culex nigripalpus 3662 4.84 Psorophora columbiae 2029 2.68 Male Mansonia 1926 2.55 Anopheles crucians 1352 1.79 Culex melanoconion 1266 1.67 Aedes vexans 673 0.89 Anopheles quadrimaculatus 634 0.84 Aedes infirmatus 373 0.49 Aedes albopictus 195 0.26 Aedes taeniorhynchus 167 0.22 Coquillettidia peturbans 122 0.16 Psorophora ciliata 121 0.16 Anopheles atropos 51 0.07 Psorophora ferox 45 0.06 Male Psorophora ciliata 30 0.04 Culex quinquefasciatus 19 0.03 Aedes atlanticus 11 0.01 Male Psorophora ferox 9 0.06 Male Aedes albopictus 6 0.01 Total 75635

Objective 1: Peak host-seeking activity time

The data for the summer collection represent nine out of 12 collection dates due to gaps in the remaining data sets (Figure 3.1a, Figure 3.2a). The data for fall collection represent nine out of 14 collection dates due to gaps in the remaining data sets (Figure

3.1b, Figure 3.2b). The data for the winter collection represent 12 collection dates (Figure

3.1c, Figure 3.2c). The data for the spring collections represent 11 out of 12 collection dates due to gaps in the remaining collection (Figure 3.1d, Figure 3.2d).

The activity period around sunset was determined by arranging all data into the categories of -3 through +8 based on their relation to the average sunset time for the season, displayed as zero(Figure 3.3a, Figure 3.3a). Similarly, the activity period around sunrise was determined by arranging all data into the categories of -5 through 0 based on their relation to the average sunrise time, displayed as zero (Figure 3.3b, Figure 3.3b).

A B

C D Figure 3.1- Female Ma. titillans abundance trap count data. White background represents daylight hours while the grayed background represents hours of night collections after sunset. Data were analyzed with a Wilcoxon nonparametric test and using the Steel- Dwass All-Pairs as a post-hoc test. Hours connected with a common letter do not differ at a significance level of p<0.05 and bars not sharing any common letters are statistically significant at the p<0.05 level. Error bard display standard error between sample times. (A) Summer collection period consisting of nine of 12 collection dates, (B) fall collection period represents nine of 14 collection dates, (C) winter collection period represents 12 collection dates, (D) spring collection period represents 11 of 12 collection dates.

A B

C D Figure 3.2- Female Ma. dyari abundance trap count data. White background represents daylight hours while the grayed background represents hours of night collections after sunset. Data were analyzed with a Wilcoxon nonparametric test and using the Steel- Dwass All-Pairs as a post-hoc test. Hours connected with a common letter do not differ at a significance level of p<0.05 and bars not sharing any common letters are statistically significant at the p<0.05 level. Error bard display standard error between sample times. (A) Summer collection period consisting of nine of 12 collection dates, (B) fall collection period represents nine of 14 collection dates, (C) winter collection period represents 12 collection dates, (D) spring collection period represents 11 of 12 collection dates.

B Figure 3.3 – Ma. titillans trap count data. White background represents daylight hours while the grayed background represents hours of night collections after sunset. Data were analyzed with a Wilcoxon nonparametric test and using the Steel-Dwass All-Pairs as a post-hoc test. Hours connected with a common letter do not differ at a significance level of p<0.05 and bars not sharing any common letters are statistically significant at the p<0.05 level. Error bard display standard error between sample times. (A) Sunset collection hours with zero representing collection period which sunset occurs, (B) sunrise collection hours with zero representing collection period which sunrise occurs.

B Figure 3.4 – Ma. dyari trap count data. White background represents daylight hours while the grayed background represents hours of night collections after sunset. Data were analyzed with a Wilcoxon nonparametric test and using the Steel-Dwass All-Pairs as a post-hoc test. Hours connected with a common letter do not differ at a significance level of p<0.05 and bars not sharing any common letters are statistically significant at the p<0.05 level. Error bard display standard error between sample times. (A) Sunset collection hours with zero representing collection period which sunset occurs, (B) sunrise collection hours with zero representing collection period which sunrise occurs.

Objective 2: Environmental conditions influencing Host-Seeking.

A series of stepwise linear regressions were preformed to determine if there was a significant correlation between the environmental conditions sampled and catch abundance within the peak activity period of Ma. titillans and Ma. dyari.

For the summer collection, the activity period was determined to be the hours of

2000, 2100, and 2200. Four environmental conditions were evaluated: temperature, humidity, rainfall, and wind speed (Table 3.2). Temperature (p=0.0448) and wind speed

(p=0.0427) showed a significant effect on Ma. titillans flight activity (Figure 3.5a, Figure

3.5b). Temperature (p=0.0189) and wind speed (p=0.0070) also had a significant effect on Ma. dyari flight activity (Figure 3.5c, Figure 3.5d).

Table 3.2- Summer: Stepwise regression summary. Analyses where the F valve for the factor had a p ≤ 0.05 are indicated with “” symbol.

Season: Summer Ma titillans Ma. dyari

Temperature   Humidity Rain Wind Speed  

Figure 3.5a- Summer: Ma. titillans catch abundance by wind speed (mph).

Figure 3.5b- Summer: Ma. titillans catch abundance by temperature (Celsius).

Figure 3.5c- Summer: Ma. dyari catch abundance by wind speed (mph).

Figure 3.5d- Summer: Ma. dyari catch abundance by temperature (Celsius).

For the fall collection, the activity period was determined to be the hours of 1700,

1800, and 1900. Five environmental conditions were evaluated: light, temperature, humidity, rainfall and wind speed (Table 3.3). For Ma. titillans, light (p=0.0043) had a significant effect on flight activity (Figure 3.6a). For Ma. dyari, temperature (p=0.0023) had a significant effect on flight activity (Figure 3.6b).

Table 3.3- Fall: Stepwise regression summary. Analyses where the F valve for the factor had a p ≤ 0.05 are indicated with “” symbol.

Season: Fall Ma. titillans Ma. dyari

Light  Temperature  Humidity Rain Wind Speed

Figure 3.6a- Fall: Ma. titillans catch abundance by light (lux).

Figure 3.6b- Fall: Ma. dyari catch abundance by temperature (Celsius).

For the winter collection, the activity period was determined to be the hours of

1800, 1900, and 2000. Five environmental conditions were evaluated: light, temperature, humidity, rainfall and wind speed (Table 3.4). For Ma. titillans, humidity (p=0.0462) had a significant effect on flight activity (Figure 3.7).

Table 3.4- Winter: Stepwise regression summary. Analyses where the F valve for the factor had a p ≤ 0.05 are indicated with “” symbol.

Season: Winter Ma. titillans Ma. dyari

Light Temperature

Humidity  Rain Wind Speed

Figure 3.7- Winter: Ma. titillans catch abundance by relative humidity.

For the spring collection, the activity period was determined to be the hours of

2000, 2100, and 2200. Five environmental conditions were evaluated during this time: light, temperature, humidity, rainfall and wind speed (Table 3.5). For Ma. titillans, light

(p=0.0195) and humidity (p=0.0016) had a significant effect on flight activity (Figure

3.8a, Figure 3.8b).

Table 3.5- Spring: Stepwise regression summary. Analyses where the F valve for the factor had a p ≤ 0.05 are indicated with “” symbol.

Season: Spring Ma. titillans Ma. dyari

Light  Temperature Humidity  Rain Wind Speed

Figure 3.8a- Spring: Ma. titillans catch abundance by light (lux)

Figure 3.8b- Spring: Ma. titillans catch abundance by relative humidity.

The activity period for the fall, winter and spring collections were compiled using the time since sunset and evaluated for both target species. The summer collection period lacked the onsite weather station and was consequently not included. Five environmental conditions were evaluated in this analysis: light, temperature, humidity, rainfall and wind speed (Table 3.6). For Ma. titillans, light (p=0.0002) and humidity (p=0.0001) had a significant effect on flight activity (Figure 3.9a, Figure 3.9b). For Ma. dyari, temperature

(p=0.0010) and Humidity (p=0.0213) had a significant effect on flight activity (Figure

3.9c, Figure 3.9d).

Table 3.6- Combo: Stepwise regression summary. Results for the combination of the winter, spring, and fall seasons. Analyses where the F valve for the factor had a p ≤ 0.05 are indicated with “” symbol.

Season: combo Ma. titillans Ma. dyari

Light  Temperature  Humidity   Rain Wind Speed

Figure 3.9a- Combo: Ma. titillans catch abundance by light (lux).

Figure 3.9b- Combo: Ma. titillans catch abundance by relative humidity.

Figure 3.9c- Combo: Ma. dyari catch abundance by temperature (Celsius).

Figure 3.9d- Combo: Ma. dyari catch abundance by relative humidity.

Moon phase was monitored throughout the duration of this study. Moon phase was recorded as a percentage of full moon illumination between zero and 100. New moon is indicated as zero while full moon is indicated with 100. Preliminary analysis of moon phase was evaluated with mosquito counts for each trapping event. Mosquito counts for

Ma. titillans and Ma. dyari were compared separately for each season with the use of scatter diagrams. The summer collection did not seem to indicate a pattern for either Ma. titillans or Ma. dyari (Figure 3.10a, Figure 3.10b). The fall collection did seem to indicate increased collections for both Ma. titillans and Ma. dyari as the collection period was closer to a full moon (Figure 3.11a, Figure 3.11b). The winter collection showed an increased collection of Ma. titillans as the collection period neared full moon, however

Ma. dyari did not show as clear of a trend (Figure 3.12a, Figure 3.12b). The spring collection did not indicate a clear pattern for either Ma. titillans or Ma. dyari with possible outliers in the data (Figure 3.13a, Figure 3.13b).

Summer: Ma. titillans counts by full moon percentage 1400

1200

1000

800

600

400

200

0 0 20 40 60 80 100

Figure 3.10a- Ma. titillans counts by moon phase for summer collection period. The x- axis represents full moon percentage indicated from zero (new moon) to 100 (full moon).

Summer: Ma. dyari counts by full moon percentage 180

160

140

120

100

0 0 10 20 30 40 50 60 70 80 90 100

Figure 3.10b- Ma. dyari counts by moon phase for summer collection period. The x-axis represents full moon percentage indicated from zero (new moon) to 100 (full moon).

Fall: Ma. titillans counts by full moon percentage 2500

2000

1500

1000

500

0 0 10 20 30 40 50 60 70 80 90 100

Figure 3.11a- Ma. titillans counts by moon phase for fall collection period. The x-axis represents full moon percentage indicated from zero (new moon) to 100 (full moon).

Fall: Ma. dyari counts by full moon percentage 500 450 400 350 300 250 200 150 100 50 0 0 10 20 30 40 50 60 70 80 90 100

Figure 3.11b- Ma. dyari counts by moon phase for fall collection period. The x-axis represents full moon percentage indicated from zero (new moon) to 100 (full moon).

Winter: Ma. titillans counts by full moon percentage 1400

1200

1000

800

600

400

200

0 0 10 20 30 40 50 60 70 80 90 100

Figure 3.12a- Ma. titillans counts by moon phase for winter collection period. The x-axis represents full moon percentage indicated from zero (new moon) to 100 (full moon).

Winter: Ma. dyari counts by full moon percentage 50 45 40 35 30 25 20 15 10 5 0 0 10 20 30 40 50 60 70 80 90 100

Figure 3.12b- Ma. dyari counts by moon phase for winter collection period. The x-axis represents full moon percentage indicated from zero (new moon) to 100 (full moon).

Spring: Ma. titillans counts by full moon percentage 350

300

250

200

150

100

0 0 10 20 30 40 50 60 70 80 90 100

Figure 3.13a- Ma. titillans counts by moon phase for spring collection period. The x-axis represents full moon percentage indicated from zero (new moon) to 100 (full moon).

Spring: Ma. dyari counts by full moon percentage 90

0 0 10 20 30 40 50 60 70 80 90 100

Figure 3.13b- Ma. dyari counts by moon phase for spring collection period. The x-axis represents full moon percentage indicated from zero (new moon) to 100 (full moon).

Chapter 4

Discussion

Limitations of the study

This study was conducted over the course of a single year with quarterly trapping events capturing snapshots of typical mosquito activity patterns. It is reasonable to presume the unique weather conditions experienced at the time of collection were not truly representative of activity patterns as a whole. The weather conditions experienced throughout this study appeared to be in line with the 30 year average for Fort Myers,

Florida (Figure 4.1). Certainly, a more robust dataset consisting of multiple years would be preferred to more fully understand seasonal abundance across the species in question.

However, due to timeline constraints as well as resource limitations this was not feasible for this study. In addition, although two collection bottle rotator traps were used at each trapping location, a greater number of replicates would have further supported the analysis and conclusions.

Climograph: Fort Myers, Florida 30 year average 12.5 100

10 90

7.5 80

5 70

2.5 60 Temperature (F) Precipitation Precipitation (Inch) 0 50 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month

Precipitation Avg Low Avg High

Figure 4.1- 30 year climograph for Fort Myers, Florida. (Climate 2020)

The preliminary evaluation of moon phase data indicated possible trends of increased mosquito abundance towards periods of full moon in the fall and spring collection periods. Previous literature has indicated this is a known behavioral trend within mosquitoes of different genera (Bidlingmayer 1964, Bidlingmayer 1985).

Although the preliminary data seems to support this trend, the frequency of collection events left large gaps in the data set which may have skewed the results. Without sufficient data, the decision was made to not evaluate this trend further.

The use of dry ice to supply carbon dioxide was chosen for its convenience of purchase, portability to the field, and ability to fit into the body of the trap. Although the dry ice was all packaged into 2.3 kg blocks, it is possible their sublimation rates would vary throughout the evening as the blocks shrank in size. The use of CO2 tanks and programmable timers would have allowed for a more consistent flow of attractant throughout the collection period.

One challenge observed during the trap set up period was the congregation of mosquitoes around a trap not actively collecting. The use of two traps per site, running in tandem, was to ensure hourly collections throughout the evening hours. With both traps located approximately two meters apart, and filled with the same attractants, mosquitoes were observed on multiple occasions circling the trap not collecting. Although it is possible the use of competing sets of attractants could skew collection totals, the plume of attractants originating from the trap actively operating is larger than that of the nearby static trap. It is reasonable to assume the female mosquitoes are going to be more attracted to the trap currently operating.

Previous studies have documented rainfall to have a significant impact on mosquito flight activity (Bidlingmayer 1985). However, throughout the duration of this study rainfall was not captured during any of the trapping events. Typical rainfall patterns in this region take place prior to the trapping period as strong afternoon showers subsiding before sunset. This pattern was observed on several occasions where rainfall took place at the time of trap set up and stopped prior to the traps beginning collection period. It is likely rainfall during the Mansonia activity period would have a substantial effect on host-seeking behavior.

Host seeking behavior is conducted only by female mosquitoes, which have previously mated. Categorizing female mosquitoes as those which have shown signs of previously obtaining a blood meal, is a way to estimate the age of a group of mosquitoes.

This technique could be used for a variety of purposes including estimating seasonal abundance of overwintering populations, as well as estimating distance from emergence.

Although no individuals were observed as having previously taken a blood meal, it is possible this trait was under represented with proportional analysis.

Peak Host-Seeking Activity

The summer collection did not display a statistical peak around any one hourly collection for either Ma. titillans or Ma. dyari (Figure 3.1a and figure 3.2b). Rather, there appears to be a clear period of activity throughout the sunset hours with activity significantly reduced during the sunlight period.

The fall collection yielded the highest abundance of mosquitoes collected throughout the duration of this study. This observation mirrors that of work by Sabesan et al. (1991) who determined Mansonia uniformis were most abundant during the fall

months in southern India. The fall collection also has the most evident peak in activity around sunset for both species sampled (Figure 3.1b, Figure 3.2b). During the fall collection, Ma. titillans and Ma. dyari both displayed a period of activity between the sunset collection and +2 hours. Although a lower magnitude, the winter collection displayed a similar peak in activity between sunset and +2 hours (Figure 3.1c, Figure

3.2c).

The spring collection period yielded the lowest abundance of mosquitoes collected throughout the duration of this study. This finding somewhat contradicts the findings of Orlandin et al. (2017) who collected the highest abundance of Ma. titillans in the spring. One explanation to this contradiction is the geographical location of both study sites. Orlandin et al. conducted their work in southern Brazil, which is located in the southern hemisphere opposite of southwest Florida. With opposite fall and spring seasons the increased springtime collection seems to support the seasonality trends seen here.

The spring collection period, although lower in abundance, appears to show a similar activity trend around sunset (Figure 3.1d, Figure 3.2d) as seen in the fall collection. The spring collection also seemed to suggest an increase in flight activity prior to sunset for both Ma. titillans and Ma. dyari which was not apparent in other collections.

Although the seasonal abundance data did not statistically differentiate peak a single peak hour of mosquito activity, it did display interesting trends for further evaluation or use in timing of adulticide application. The collection period of one-hour post sunset consistently yielded the largest trap abundance with the exception of the Ma. dyari summer collection. The collection period of two hours post sunset appeared to be

the second highest collection period for Ma. titillans and Ma. dyari overall. Although noticeable in appearance, the Steel-Dwass All-Pairs post-hoc test did not differentiate these hours to be statistically different when compared to all the sampled hours. This is potentially due to an increase in variance around the sunset period driven by environmental factors.

When the sunset data was evaluated independently, the collection period one-hour post sunset and two-hours post sunset appeared to be statistically different than the other hours sampled (Figure 3.3a, Figure 3.4a). This finding is similar to that of Orlandin et al.

(2017) who determined Ma. titillans populations in southern Brazil were most active in the period 15 minutes to 45 minutes post sunset.

Initial exploration of the data also seemed to suggest a secondary peak in abundance near the sunrise timeframe as indicated in the spring and winter collections.

When the sunrise period was evaluated independently, Ma. titillans and Ma. dyari did not display a statistically significant increase in activity around sunrise (Figure 3.3b, Figure

3.3b). Although this secondary peak in abundance was not evident in this study, this behavioral pattern is well documented in Mansonia along with other species of mosquitoes (Guptavanij 1973, Carroll and Bourg 1977).

Environmental conditions influencing host-seeking

Light

Light was identified as having a significant effect on Ma. titillans activity on two separate collection periods, fall (p=0.0043) and spring (p=0.0195), as well as the combination period (p=0.0002). On all three accounts, the majority of abundance collections were made during the nighttime period of close to zero lux. The fall collection

period did indicate mosquito collections around 2000 to 3000 lux and another series around 8000 lux. The moonlight photometer used has a max reading of 8000 lux so any data points at this level are presumed to be above the sampling ability of this instrument.

The data collected at 2000 lux and higher are all indicative of activity during the sunlight hours. The spring collection saw a trend of mosquito activity around 100 to 300 lux and one data point at 500 lux. This is indicative of mosquito activity during the twilight period.

These findings are similar to those of Orlandin et al. (2017) who collected Ma. titillans of most abundance during the period of 15 minutes – 45 minutes post sunset, but also collected a smaller abundance in the pre-dusk period. The consistent activity prior to sunset seems to indicate light may not be the environmental cue which initially triggers the mosquito to enter appetential flight.

Humidity

Humidity was identified as having a significant effect on Ma. titillans activity on two separate collection periods, winter (p=0.0462) and spring (p=0.0016), as well as the combination period (p=0.0001). Humidity was also identified as having a significant effect on Ma. dyari flight during the combination period (p=0.0213).

Humidity is well known to have a substantial role in mosquito activity (Provost

1973, Clements 1963, Wright and Knight 1966) however, particular species thresholds are largely still unknown. On all four accounts Mansonia appear to have an activity window of 55-85 percent with collections highest between 65-75 percent. This finding appears to coincided with those of Opayele et al. (2017), who found a positive correlation between Mansonia catch abundance and 50-90 percent relative humidity in Ibadan,

Nigeria. Abundance collections were made in humidity ranges outside the activity window however appeared to be substantially reduced between 40-55 percent and 85-95 percent. Shelford (1931) initially described the Law of Toleration which suggests species may exist under suboptimal conditions while thriving once all limiting factors have been met. This law could be projected onto these findings to suggest Mansonia thrive under conditions of 65-75 percent humidity, tolerate humidities ranges of 55-65 as well as 75-

85 percent – particularly when other environmental factors are optimal, and preform suboptimal in all other humidity ranges.

Temperature

Temperature was identified as having a significant effect on Ma. titillans activity during the summer collection (p=0.0448), while also having a significant effect on Ma. dyari activity during the summer (p=0.0189), fall (p=0.0023), and combination period

(p=0.0010).

The summer collection recorded the warmest average temperatures with collections made between 24-29°C. During the summer collection, both Ma. titillans and

Ma. dyari displayed peak collections between 25-26°C and a decline in collections as temperatures increased. The fall collection displayed the widest range of temperatures for an individual sampling period, with temperatures stretching from 15-30°C. The fall collection shows an optimal range of abundance between the ranges of 15-23°C. Due to the wider range of sampling points, it is reasonable to assume the fall collection is more representative of the preferred temperature gradient of Mansonia.

All of the sampling periods seem to suggest temperatures above 26°C are suboptimal for Mansonia flight. Meanwhile, the collections made at the lower

temperature gradient do not seem to indicate a decline in activity. This is presumably due to lower temperatures still being within the species’ optimal threshold range.

Temperature is a well-known environmental condition limiting mosquito flight

(Clements 1963, Bidlingmayer 1985). Although much work has been conducted on the topic, the focus tends to be on the lower end of the tolerance range (Bidlingmayer 1974,

Taylor 1963). Taylor (1963) has suggested there may be an upper threshold to mosquito flight but this is generally not seen as a limiting factor in many cases. The findings of this study seems to suggest Mansonia display a more predominant upper temperature threshold which should be further explored if we are to fully understand the environmental conditions for optimal Mansonia flight.

Wind

Wind was identified as having a significant effect on Ma. titillans (p=0.0427) and

Ma. dyari (p=0.0070) during the summer collection. The summer collection recorded wind speed between zero mph and 2.0-2.5 mph. Although wind speed is considered a spatially variable and limiting factor in mosquito flight (Bidlingmayer 1985, Grimstad and DeFoliart 1975), it is presumed the maximum wind speeds recorded were less than the individual threshold for flight. We see the abundance collections of the 2.0-2.5 mph wind category being substantially higher than those made with zero wind. Although this seems contradictory, this is potentially due to the plume of attractant drifting further than those collections with no wind. As long as winds are not impeding the mosquito’s ability to fly, we see wind speed as playing a substantial influence to promote appetential flight.

Future Work

The focus of this study was primarily to identify the activity patterns of Ma. titillans and Ma. dyari over the course of the evening hours. To accomplish this, hourly trapping frequency was determined to be most appropriate in recognizing patterns over a wide period of time. The results of this study indicated Ma. titillans and Ma. dyari largely display crepuscular activity primarily around sunset. Future work to further refine this activity period would be largely beneficial in determining environmental cues which initially trigger flight. Quarter-hour collections are recommended to best identify these cues as it would identify more subtle trends around the twilight hours.

Future emergence studies would also be beneficial to understand the activity of the mosquito based on their biology. As previously indicated, there appears to be an increased catch abundance of both Ma. titillans and Ma. dyari towards full moon periods.

Future work to expand the trapping frequency would be able to more appropriately evaluate this trend. There also appears to be a strong seasonal abundance to both Ma. titillans and Ma. dyari with a large increase in population in the fall, and a substantial decrease in the summer. Further research into the seasonality of these species would be beneficial in understanding what drives these high emergence patterns. This work should take into account ground water levels, seasonal rainfall patterns, and host plant species abundance.

Management implications

Mosquitoes are thought to be the most well studied family of Diptera due to their importance to man as disease vectors. Their incredible resilience as a species coupled with their ability to vector diseases such as malaria, yellow fever, and West Nile Virus

ensure that they will continue to be targeted by public health officials. Understanding the activity patterns of any targeted insect species is crucial in developing effective control strategies. This study identified peak activity patterns of Ma. titillans and Ma. dyari, two important potential vector species found across the state of Florida, and linked their activity to the significance of light intensity, temperature, and relative humidity on their host-seeking activity.

As Florida’s population continues to increase, we are seeing once undeveloped areas becoming encroached by residential houses. New residents not accustomed to the native Florida fauna are exposed to levels of mosquito populations they are not comfortable with. In an effort to protect human health and comfort, areas once not managed for mosquito populations are becoming increasingly prioritized by public health officials. One would image as population growth continues to encroach the inland habitats of freshwater marshes, coupled with changing environmental conditions, Ma. titillans and Ma. dyari will continue to become an increased priority for mosquito abatement programs across the state.

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Appendix

Appendix Table 1: Species Count at Secondary Site Total count and percentage of total count for mosquito species collected at secondary site

Mosquito Species Total Count Percent of Total Mansonia titillans 27738 73.65 Culex nigripalpus 3706 9.84 Mansonia dyari 2931 7.78 Male Mansonia 750 1.99 Culex melanoconion 736 1.95 Psorophora columbiae 444 1.18 Anopheles crucians 390 1.04 Aedes vexans 270 0.72 Anopheles quadrimaculatus 175 0.46 Aedes infirmatus 170 0.45 Aedes albopictus 100 0.27 Aedes taeniorhynchus 72 0.19 Coquillettidia peturbans 64 0.17 Psorophora ciliata 41 0.11 Culex quinquefasciatus 20 0.05 Anopheles atropos 17 0.05 Psorophora ferox 12 0.03 Male Aedes albopictus 10 0.03 Aedes atlanticus 7 0.02 Male Psorophora ciliata 7 0.02 Total 37662

Appendix Table 2. Fall: Ma. titillans data from primary location.

11/1 11/2 11/3 11/8 11/9 11/14 11/15 11/16 11/29 11/30 TOTAL Avg St Dev 17:00 51 8 10 40 74 281 153 115 219 27 978 97.8 93.4 18:00 3 34 10 932 637 1393 1133 950 902 107 6101 610.1 527.6 19:00 48 1263 496 242 408 662 596 170 577 86 4548 454.8 359.4 20:00 4 624 247 116 166 432 266 48 214 62 2179 217.9 190.3 21:00 1 330 162 65 82 282 430 27 140 39 1558 155.8 145.2 22:00 4 203 141 78 110 269 243 13 79 18 1158 115.8 96.2 23:00 72 134 81 54 83 249 96 2 49 28 848 84.8 68.3 0:00 48 88 119 39 74 87 128 1 29 35 648 64.8 41.1 1:00 26 41 46 37 58 83 125 1 23 18 458 45.8 35.9 2:00 40 47 36 27 54 106 192 2 26 37 567 56.7 54.5 3:00 63 30 49 21 38 141 124 0 37 57 560 56 44.3 4:00 57 72 37 35 44 157 52 0 48 51 553 55.3 40.3 5:00 117 109 40 51 78 173 63 0 37 51 719 71.9 49.5 6:00 163 90 53 112 202 195 62 0 57 87 1021 102.1 66.1 7:00 58 59 14 6 0 6 0 0 0 4 147 14.7 23.5

Appendix Table 3. Fall: Ma. dyari date from primary location.

11/1 11/2 11/3 11/8 11/9 11/14 11/15 11/16 11/29 11/30 TOTAL Avg St Dev 17:00 21 0 1 8 1 32 0 12 2 0 77 7.7 11.0 18:00 1 0 2 259 105 260 293 244 23 9 1196 119.6 128.5 19:00 11 27 91 67 88 89 182 51 15 13 634 63.4 52.9 20:00 3 41 91 42 52 70 76 15 14 3 407 40.7 31.6 21:00 0 21 56 28 34 39 68 1 8 0 255 25.5 24.1 22:00 0 10 43 24 28 44 41 0 0 0 190 19 19.2 23:00 16 5 22 10 30 41 17 0 3 2 146 14.6 13.4 0:00 14 4 31 11 13 26 37 1 1 0 138 13.8 13.4 1:00 12 2 14 5 14 25 32 0 3 0 107 10.7 10.9 2:00 10 3 13 2 9 26 39 0 1 0 103 10.3 12.9 3:00 13 3 21 4 7 48 30 0 1 2 129 12.9 15.7 4:00 15 1 7 4 5 53 6 0 2 1 94 9.4 15.9 5:00 30 5 12 3 4 35 12 0 0 0 101 10.1 12.7 6:00 16 7 5 23 3 47 14 0 1 0 116 11.6 14.6 7:00 13 0 2 2 0 0 0 0 0 0 17 1.7 4.1

Appendix Table 4. Winter: Ma. titillans data from primary location.

2/7 2/8 2/9 2/14 2/15 2/16 2/21 2/22 2/23 2/28 3/1 3/2 TOTAL Avg St Dev 17:00 2 1 14 2 2 2 1 4 4 4 3 3 42 3.5 3.5 18:00 440 353 258 189 35 113 222 81 163 117 20 87 2078 173.2 127.5 19:00 544 555 399 143 25 95 279 101 106 326 52 133 2758 229.8 187.2 20:00 306 219 217 151 51 46 124 26 84 172 16 162 1574 131.2 89.9 21:00 144 221 176 83 29 11 72 11 46 143 12 101 1049 87.4 70.7 22:00 82 166 75 81 29 15 22 12 40 59 5 126 712 59.3 49.5 23:00 71 86 70 55 16 2 39 9 46 48 12 72 526 43.8 28.5 0:00 78 105 12 76 17 1 37 17 49 34 20 28 474 39.5 31.6 1:00 176 76 27 63 58 0 16 14 16 26 20 25 517 43.1 47.6 2:00 108 99 25 63 86 1 15 10 23 30 17 17 494 41.2 37.5 3:00 71 68 7 26 105 0 12 18 11 47 23 37 425 35.4 31.7 4:00 43 72 6 70 116 1 23 4 3 48 13 39 438 36.5 35.7 5:00 43 42 1 34 78 1 23 7 16 55 19 38 357 29.8 23.2 6:00 26 24 2 52 177 1 41 15 73 94 25 86 616 51.3 50.2 7:00 0 3 1 4 2 0 0 0 0 2 2 0 14 1.2 1.4

Appendix Table 5. Winter: Ma. dyari data from primary location.

2/7 2/8 2/9 2/14 2/15 2/16 2/21 2/22 2/23 2/28 3/1 3/2 TOTAL Avg St Dev 17:00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 18:00 2 6 3 4 0 19 5 3 5 0 0 3 50 4.2 5.1 19:00 9 27 15 8 2 21 5 1 11 5 3 9 116 9.7 7.9 20:00 4 1 0 7 2 5 1 1 3 7 0 9 40 3.3 3.1 21:00 4 6 0 3 2 3 3 1 3 3 1 6 35 2.9 1.8 22:00 2 3 4 1 1 3 0 0 4 1 0 6 25 2.1 1.9 23:00 0 1 2 4 1 0 0 0 2 0 0 5 15 1.3 1.7 0:00 0 1 0 1 0 0 0 0 2 0 0 2 6 0.5 0.8 1:00 1 1 0 0 3 0 0 0 0 0 0 1 6 0.5 0.9 2:00 3 3 0 2 2 0 0 0 0 0 0 0 10 0.8 1.3 3:00 2 2 0 0 2 0 0 0 0 1 0 0 7 0.6 0.9 4:00 0 0 0 1 1 0 0 0 1 0 0 2 5 0.4 0.7 5:00 0 0 0 4 1 0 0 0 0 0 0 3 8 0.7 1.4 6:00 0 0 0 0 0 1 0 0 2 0 0 2 5 0.4 0.8 7:00 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0

Appendix Table 6. Spring: Ma. titillans data from primary location.

6/8 6/7 6/6 5/25 5/24 5/23 5/18 5/17 5/16 5/10 5/9 TOTAL Avg St Dev 17:00 1 1 1 1 2 2 0 3 1 1 0 13 1.2 0.8 18:00 1 0 0 1 0 0 0 0 0 0 0 2 0.2 0.4 19:00 0 0 0 0 1 0 0 0 0 1 2 4 0.4 0.7 20:00 19 7 10 8 36 29 111 63 1 60 51 395 35.9 33.2 21:00 140 19 8 28 55 57 139 98 14 130 109 797 72.5 52.2 22:00 86 13 9 7 30 59 81 48 5 73 70 481 43.7 31.9 23:00 73 8 7 15 13 21 17 23 4 47 33 261 23.7 20.5 0:00 19 14 6 12 7 24 13 10 3 25 32 165 15.0 9.0 1:00 23 5 2 6 7 10 9 12 4 33 43 154 14.0 13.3 2:00 12 13 2 7 15 3 14 7 3 25 15 116 10.5 6.9 3:00 14 6 1 11 20 11 22 37 3 20 22 167 15.2 10.4 4:00 15 9 0 12 16 9 36 63 11 38 11 220 20.0 18.3 5:00 11 5 1 14 26 17 36 92 14 12 23 251 22.8 24.9 6:00 3 0 0 0 2 1 2 6 2 24 18 58 5.3 8.1 7:00 1 1 0 0 0 0 0 1 0 0 0 3 0.3 0.5

Appendix Table 7. Spring: Ma. dyari data from primary location.

6/8 6/7 6/6 5/25 5/24 5/23 5/18 5/17 5/16 5/10 5/9 TOTAL Avg St Dev 17:00 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0.2 18:00 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0.2 19:00 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0.2 20:00 4 2 7 2 12 1 2 1 0 7 6 44 3.7 3.6 21:00 40 5 6 5 5 4 4 3 0 16 13 101 8.5 10.9 22:00 36 7 7 5 10 6 7 5 0 5 3 91 7.7 9.3 23:00 30 3 5 2 2 4 5 3 0 2 4 60 5.1 8.0 0:00 18 0 1 4 6 4 5 4 0 3 4 49 4.2 4.8 1:00 12 2 5 2 0 1 0 2 0 1 2 27 2.3 3.3 2:00 5 2 1 1 2 2 1 1 0 2 1 18 1.6 1.2 3:00 10 1 1 1 5 4 3 3 0 0 1 29 2.5 2.8 4:00 17 4 5 1 6 4 1 1 0 0 1 40 3.4 4.7 5:00 3 0 0 4 3 1 0 5 0 0 5 21 1.9 2.0 6:00 1 1 1 0 0 0 0 0 1 2 0 6 0.6 0.7 7:00 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0.4

Appendix Table 8. Summer: Ma. titillans data from primary location.

8/11 8/16 8/17 8/18 8/23 8/24 8/25 9/6 9/8 TOTAL Avg St Dev 17:00 17 0 16 4 1 2 2 22 64 128 14.2 20.4 18:00 5 0 8 4 2 4 1 24 2 50 5.6 7.3 19:00 2 2 14 8 1 3 9 11 37 87 9.7 11.2 20:00 60 92 123 111 75 124 167 450 354 1556 172.9 135.7 21:00 158 181 178 96 115 129 255 457 86 1655 183.9 114.8 22:00 147 142 122 76 61 80 197 325 44 1194 132.7 87.0 23:00 101 141 55 33 58 95 129 200 50 862 95.8 53.8 0:00 67 150 11 39 128 342 158 105 218 1218 135.3 100.4 1:00 46 76 8 35 140 268 130 104 228 1035 115.0 87.5 2:00 33 79 16 34 136 283 91 188 150 1010 112.2 86.7 3:00 37 68 7 35 124 334 199 144 250 1198 133.1 110.4 4:00 57 121 13 26 191 365 242 187 204 1406 156.2 114.0 5:00 23 41 18 33 277 481 352 287 86 1598 177.6 173.8 6:00 20 7 10 18 80 247 164 170 237 953 105.9 99.6 7:00 5 1 4 2 6 21 15 23 0 77 8.6 8.8

Appendix Table 9. Summer: Ma. dyari data from primary location.

8/11 8/16 8/17 8/18 8/23 8/24 8/25 9/6 9/8 TOTAL Avg St Dev 17:00 1 0 0 0 0 0 0 1 3 5 0.6 1.0 18:00 3 0 0 0 0 0 0 2 0 5 0.6 1.1 19:00 1 0 0 0 0 0 0 0 6 7 0.8 2.0 20:00 3 0 6 14 6 7 7 42 50 135 15.0 18.1 21:00 16 2 18 12 11 7 21 57 10 154 17.1 16.0 22:00 5 3 9 7 9 10 15 67 10 135 15.0 19.8 23:00 9 9 2 6 10 8 22 53 8 127 14.1 15.5 0:00 4 6 0 4 14 28 18 48 28 150 16.7 15.7 1:00 8 6 0 5 22 28 24 18 9 120 13.3 9.8 2:00 7 5 0 1 12 19 22 27 6 99 11.0 9.6 3:00 3 2 0 6 5 16 14 43 10 99 11.0 13.2 4:00 4 2 0 2 12 26 6 18 15 85 9.4 8.9 5:00 5 3 0 0 13 21 18 32 8 100 11.1 10.9 6:00 0 1 0 1 10 9 4 19 16 60 6.7 7.2 7:00 0 0 0 0 0 0 0 2 5 7 0.8 1.7