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Modeling environmental factors that describe the number of active off-host Amblyomma 1

americanum (Lone star tick) adults 2

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BioHonors candidate: Matt Mangan 4

Collaborator: Nathan Wikle 5

Research mentors: Dr. Stephanie Foré and Dr. Hyun-Joo Kim 6

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Abstract 8

Amblyomma americanum (Lone star tick) is an important vector of pathogens in humans 9

and other animals throughout the Midwest and southeast United States. Our objective was to use 10

environmental variables to create a predictive statistical model that describes the number of 11

active off-host A. americanum adults in northeast Missouri from 2008 – 2013. Ticks were 12

collected every other week from February to December in two permanent sampling grids, 13

representing forested and old field habitats, as part of a long-term monitoring study in northeast 14

Missouri. Nine variables were incorporated into modeling: cumulative precipitation, day length, 15

the number of nymphs prior to sampling, cumulative degree days, saturation deficit, wind speed, 16

habitat, and extreme high and low temperatures. Negative binomial I modeling was chosen from 17

five regression types and a “best” model was unanimously selected from 511 generated models 18

across eight selection criteria. Variables strongly associated with number of active adults were 19

cumulative degree days, day length, wind speed, saturation deficit, extreme low temperatures, 20

and the number of nymphs prior to sampling. 21

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Introduction 22

Amblyomma americanum is a three-host tick of rising importance in disease transmission 23

throughout the Midwest and southeast United States, as evidenced by increases in Amblyomma-24

associated human ehrlichiosis (reviewed by Childs and Paddock 2003) and the emergence of 25

Heartland virus (HRTV), the first pathogenic phlebovirus discovered in the United States 26

(Savage et al. 2013). Apparent supplantation of sympatric ixodid ticks (Paddock and Telford 27

2011) amidst recent northward (Keirans and Lacombe 1998) and westward (Cortinas and 28

Spooner 2013) expansion suggests that A. americanum may become increasingly relevant to 29

disease transmission as its distribution grows. As A. americanum spends most of its life off-host, 30

elucidating how environmental variables influence development, survivorship and activity in 31

each of its three physiologically distinct life stages is essential in understanding A. americanum 32

vector potential. 33

While A. americanum can complete development in less than 22 weeks under laboratory 34

conditions (Troughton and Levin 2007), its life cycle extends multiple years in the wild (Hair 35

and Howell 1970, Bouzek et al. 2013). Ticks must search for a blood meal in order to progress 36

to the next life stage, a behavior called questing. After adults successfully feed, they mate to 37

produce a single clutch of eggs and die soon after. In contrast to nymphs and adults, successful 38

overwintering in unfed larvae is rare (Sonenshine and Levy 1971, Koch 1984), suggesting that 39

almost all larvae, primarily active July through September in Missouri (Kollars et al. 2000, 40

Bouzek et al 2013), are progeny of adults active earlier in the year (Bouzek et al. 2013). This, 41

and the fact that the number of active ticks in one stage can be used to predict future activity in 42

subsequent stages (Bouzek et al. 2013), may indicate that A. americanum progresses through its 43

life cycle in relatively discrete cohorts. 44

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Life stages differ in their response to environmental stimuli, possibly as a result of 45

distinct physiological characteristics. While high temperature and low humidity are associated 46

with decreased fecundity (Koch 1984) and larval survivorship (Patrick and Hair 1979), a positive 47

relationship between the number of active larvae and degree days (Kaizer et al 2015) likely 48

reflects that larvae hatch in late summer months when temperatures are high. Nymphs and adults 49

are generally tolerant of desiccating conditions in comparison (Schulze et al. 2002), questing 50

more actively as saturation deficit (the drying power of the environment) increases (Schulze and 51

Jordan 2003, 2005). Developmental rate is largely affected by seasonality, as longer 52

photoperiods resulted in faster molting rates (Pound and George 1988) in nymphs, and higher 53

ambient temperatures were associated with shorter molting times in all life stages (Koch 1984). 54

All A. americanum questing behavior ceases below approximately 13°C in nymphs and at 9.6°C 55

in adults (Clark 1995), so extreme low temperatures may also impact seasonal trends in activity. 56

This study will create a predictive statistical model to describe the number of active off-57

host A. americanum adults in northeast Missouri from 2007 – 2013. The model will determine 58

how the number of questing adults is associated with nine environmental factors: precipitation, 59

day length, the number of nymphs prior to sampling, degree days, saturation deficit, wind speed, 60

habitat, and extreme high and low temperatures. Kaizer et al. (2015) found that the number of 61

questing larvae in the same study areas was most associated with cumulative degree days, 62

number of adults prior to sampling, and the forested habitat. Developing predictive models of 63

activity in all three life stages should provide a more accurate characterization of A. americanum 64

population dynamics in response to changing environmental conditions, which can be used to 65

estimate potential for disease transmission. 66

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Methods 68

Tick Collection: Ticks were collected every other week in two permanent 70x120m 69

sampling grids approximately 300m apart in Adair County, Missouri – one representing an old 70

field habitat of primarily non-native grasses and the other a second-growth forest dominated by 71

hickory. Collection efficacy for larvae, nymphs, and adults differs between sampling methods 72

(Petry et al. 2010), so eight drag lines and eight dry ice bait stations were utilized for each grid. 73

In drag sampling, a 1m2 flannel cloth, cut into ten equal strips for ease of movement through 74

vegetation, was pinned to a wooden dowel and dragged along regular 30m transects. In dry-ice-75

baiting, approximately 200 g of dry ice was placed on a 1m2 flannel cloth at regular intervals and 76

allowed to sublimate for one hour. Cloths were sealed in bags and transported to the lab, where 77

ticks were picked from cloths and preserved in 95% ethanol for later identification by species 78

and life stage under a dissecting scope. The number of active ticks was assumed to be zero when 79

snow or ice was present, as adults cease questing activity at approximately 9.6˚C (Clark 1995). 80

Excluding dates that were assumed to have zero activity, subsequent analysis incorporated data 81

from February 2007 to September 2013. 82

Environmental variables: Nine environmental variables were incorporated into modeling: 83

precipitation, day length, the number of nymphs prior to sampling, degree days, saturation 84

deficit, wind speed, extreme high and low temperatures, and habitat. Temperature, average wind 85

speed (Wind), day length (DL) and precipitation, recorded at the Kirksville Regional Airport in 86

Adair County, were obtained from the National Weather Service (www.noaa.gov). Saturation 87

deficit (SD) was calculated according to Schulze et al. (2001), using average temperature and 88

relative humidity from 3 hours prior to 1 hour after sampling start time. The number of active 89

nymphs is associated with the number of active adults (44-52) weeks in the future for the forest 90

5

and (42-52) weeks in the future for the field (Bouzek et al. 2013), so average number of nymphs 91

collected per sampling session within these intervals (NymphLag) was incorporated into the 92

model. Habitat was defined as a binary variable with 0 and 1 representing the field and forested 93

site respectively. 94

Precipitation, degree days, extreme high temperatures, and extreme low temperatures 95

were calculated over a selected duration prior to sampling. Cumulative precipitation was 96

calculated by summing total precipitation of each day within an n-day period prior to sampling. 97

Cumulative average degree days over an n-day period prior to sampling was calculated as 98

described in Bouzek et al. (2013). Extreme high temperatures were defined as the proportion of 99

days over n-days prior to sampling where high temperature was more extreme than the 95% 100

confidence limit of the mean monthly high temperature over the last 40 years. Extreme low 101

temperatures were calculated in a similar manner. Optimal durations for cumulative degree 102

days, extreme highs and lows, and total precipitation were chosen from 10, 30, or 60 days. A 103

one-variable negative binomial II model that describes describe the number of active adults was 104

created for each potential duration, and durations with the lowest regression AIC value (Akaike 105

1973, 1974) were chosen for subsequent modeling (Table 1). 106

Statistical analyses: Modeling was conducted in R (version 3.1.3). Global models 107

containing all nine variables were fit to data using Poisson (POI), negative binomial I 108

(NEGBIN1), negative binomial II (NEGBIN2), zero-inflated Poisson (ZIP), and zero-inflated 109

negative binomial (ZINB) regression. While ZINB and NEGBIN2 had the lowest AIC values, 110

respective Pearson dispersion (ĉ) (Burnham and Anderson 2002) values below 1 indicated that 111

underdispersion was present (Table 2). Both POI and ZIP had high AIC and ĉ values, indicating 112

overdispersion (Table 2). NEGBIN1 displayed a ĉ closest to 1 (ĉ =1.05) with an AIC value only 113

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marginally higher than other negative binomial regression types (Table 2). As ĉ values between 114

one and four indicate adequate model structure and values close to one imply no overdispersion 115

(Burnham and Anderson 2002), NEGBIN1 was chosen for all subsequent modeling. 116

In total, 511 NEGBIN1 models were generated from all possible combinations of the nine 117

variables and compared relative to one another using eight model selection criteria: AIC, 118

Kullback information criterion (KIC) (Cavanaugh et al. 2003), “corrected” versions of AIC 119

(AICc) (Sigiura 1978, Hurvich and Tsai 1989) and KIC (KICc) (Cavanaugh et al. 2003), and 120

“quasi” versions of the four previous criteria which adjust for overdispersion (Lebreton et al. 121

1992, Hurvich and Tsai 1995, Burnham and Anderson 2002, Kim et al. 2014). 122

123

Results 124

From May 2007 to August 2013, more ticks were collected in the forest site (1133 adults, 125

4011 nymphs and 16922 larvae) than the field site (306 adults, 947 nymphs and 5285 larvae) 126

(Figure 1). Tick count was highest in 2007 for larvae and 2008 for adults and nymphs. Peak 127

activity for larvae always occurred later in the year than nymphs and adults. 128

All selection criteria selected the same model as optimal (Table 3), expressed below: 129

130

ln (mean expected count) = -10.4962 + 0.0157(DL) + 0.0098(NymphLag) - 0.0017(DD) + 131

0.0753(SD) + 0.0549(Wind) -2.0786(X60EL) 132

133

This indicates that the number of active adults is positively associated with saturation deficit 134

(SD), wind speed (Wind), day length (DL) and the number of nymphs prior to sampling 135

(NymphLag), and negatively associated with cumulative degree days over 60 days prior to 136

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sampling (DD) and extreme low temperatures over 60 days prior to sampling (X60EL). The 137

model performed well in predicting seasonal spikes of adult questing activity (Figure 2), but 138

sometimes failed to capture the magnitude of peak activity – the largest of these residuals 139

occurring in 2008 for the forested habitat (Figure 3a) and 2010 for the field habitat (Figure 3b) 140

141

Discussion 142

Our model demonstrated that saturation deficit, wind speed, day length, number of active 143

nymphs prior to sampling, cumulative degree days, and extreme low temperatures are all 144

important in describing the number of active off-host adult Amblyomma americanum. Timing of 145

seasonal trends in activity among A. americanum life stages in Missouri are relatively consistent 146

and predictable. As few larvae successfully overwinter (Sonenshine and Levy 1971, Koch 147

1984), most are born from spring adults and hatch in late summer, creating a discrete cohort that 148

moves through subsequent life stages. Nymphs that molt into adults usually do not resume 149

questing in the same season (Semtner et al. 1973, Robertson et al. 1975), and instead overwinter 150

to feed and mate with the rest of their cohort in the following spring. These seasonal patterns in 151

tick activity are influenced by environmental variables, all of which have distinct effects on 152

survivorship, development, and behavior in each of the three life stages. 153

Timing of seasonal patterns in activity is largely driven by metabolic function and 154

development. The number of nymphs 44-52 weeks prior to sampling (or 42-52 weeks in the 155

field habitat) was important in describing adult activity for our model, as these data likely 156

represent the same cohort of ticks at different life stages. Adults have higher overwintering 157

survivorship than nymphs (Koch 1984), so developmental rate of fed nymphs in the prior year 158

influences the number of adults that emerge in the spring. Environmental factors such as day 159

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length and ambient temperature, however, may moderate how successful nymphs are in feeding 160

and molting before winter. 161

Growing day length and low cumulative degree days may be indicators of optimal 162

seasonality, thereby increasing questing activity in adults. Longer photoperiods result in faster 163

molting time (Pound and George 1988) and shorter feeding time (attachment to detachment) in 164

nymphs (Barnard et al. 1985). Additionally, nymphs experience shorter molting times in 165

regimes where photoperiod is increasing and longer molting when photoperiod is decreasing 166

(Pound and George 1988). Adult behavioral diapause in response to decreasing day length would 167

increase clutch viability, as offspring of ticks which feed and mate later in the year are less likely 168

to find a blood meal before winter. Peaks in cumulative degree days occur later in the summer 169

than those of day length, so negative association of adult questing activity with cumulative 170

degree days may occur for similar reasons. Alternatively, this negative relationship may reflect 171

gradual exhaustion of energy stores in unfed adults as temperatures rise throughout late spring 172

and summer, lowering metabolic function and reducing activity. Higher ambient temperatures 173

are associated with faster molting rates in all life stages (Koch 1984), so cumulative degree days 174

during molting of fed nymphs in the previous year may also be important in describing adult 175

activity. 176

Extreme low temperatures likely lower metabolic function in ticks. This may reduce 177

winter survivorship or even delay seasonal spikes in questing activity. It also seems probable 178

that extreme lows would have negative effects on developmental rates, as cooler temperatures 179

interfere with molting in fed nymphs (Semtner and Hair 1973). 180

Although our model captured seasonal patterns of adult activity, it performs less well in 181

predicting the total number of ticks on a given sampling date. Daily variation in questing 182

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behavior is likely influenced by saturation deficit and wind speed on the day of sampling, both of 183

which contribute to the drying power of the environment. These factors are positively associated 184

with adult activity, which supports findings that A. americanum becomes more active in 185

desiccating conditions (Schulze and Jordan 2003, 2005). Differences in microenvironment 186

between habitats, however, may moderate how broad-scale meteorological variables affect 187

questing activity, as evidenced by large differences in number of active adults between the forest 188

and field sites. Large residuals in forest activity of adults during 2008 did not occur in the field 189

habitat, implying that an unusual event had differential effects across distinct habitats. These 190

effects also seem to have differed among life stages, as there were large positive residuals in our 191

adult model and large negative residuals in larval models (Kaizer et al. 2015) during 2008. 192

In addition to examining differences in questing activity between forest and field 193

populations, understanding how nymph behavior and development is influenced by 194

environmental variables may further explain subsequent trends in adult activity. Creating 195

predictive models describing all three life stages will provide a more comprehensive 196

understanding of A. americanum population dynamics and behavioral differences between life 197

stages. 198

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Figure 1 – Observed counts of A. americanum larvae (top), nymphs (middle) and adults (bottom) 199

in the forest (black) and field (red dotted) habitats from 2007 to 2013. Note: Scale of y-axis 200

differs in each panel. 201

Figure 2 – Adult A. americanum observed counts (red dotted) and expected values (black solid) 202

according to the best selected model for the forest (a) and field (b) habitats. 203

Figure 3 – Residuals for A. ameriacnum adult counts in the best selected model in the forest (a) 204

and field (b) habitats. Positive residuals indicate that the model underestimated observed counts, 205

and negative values indicate overestimation. 206

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2a)

2b)

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3a)

3b)