The Density of the Lyme Disease Vector Ixodes scapularis (Blacklegged Tick) Differs Between the Champlain Valley
and Green Mountains, Vermont
David Allen, Benjamin Borgmann-Winter, Laura Bashor, and Jeremy Ward
Northeastern Naturalist, Volume 26, Issue 3 (2019): 545–560
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2019 NORTHEASTERN NATURALIST 26(3):545–560
The Density of the Lyme Disease Vector Ixodes scapularis
(Blacklegged Tick) Differs Between the Champlain Valley
and Green Mountains, Vermont
David Allen1,*, Benjamin Borgmann-Winter1, Laura Bashor1, and Jeremy Ward1
Abstract - Lyme disease is an emerging infectious disease of public health concern in the
northeastern US. The disease’s vector, Ixodes scapularis (Blacklegged Tick), has increased
its range in the past 20 y. There have been few studies of the Blacklegged Tick’s habitat associations
in its newly endemic northern range. From 2016–2018, we sampled for nymphal
Blacklegged Ticks in the Champlain Valley and Green Mountains of Addison County, VT,
and tested them for Borrelia burgdorferi, the Lyme disease agent. We found 10 times more
ticks in the Champlain Valley than in the Green Mountains. Nymphal infection prevalence
was 0.21 and did not vary by year or region. The difference in tick density reported has public
health consequences, as Vermont has one of the highest rates of Lyme disease in the US.
Introduction
Lyme disease is an important emerging infectious disease found in many parts
of the temperate northern hemisphere and is now the most prevalent vector-borne
disease in both the US and western Europe (Kilpatrick et al. 2017, Rosenberg et al.
2018). It is caused by the spirochete bacterium Borrelia burgdorferi and transmitted
by ticks in the genus Ixodes.
In the last 20 y, the number of confirmed cases in the US has more than doubled
from 12,801 in 1997 to 29,513 in 2017 (CDC 2019). There has also been a
northward shift in disease incidence in eastern North America, with more cases
diagnosed in northern New England and southern Canada (Fig. 1; Kugeler et al.
2015, Ogden et al. 2009). In particular, Vermont has seen a dramatic rise, with 100
or fewer cases reported every year prior to 2008 and more than 1000 cases in 2017
(Vermont Department of Health 2018). A similar northward shift in the incidence
of the disease has been seen in Europe (Jore et al. 2011, Mysterud et al. 2017).
There has been a parallel northward shift in the distribution of the disease’s vectors,
Ixodes scapularis (Say) (Blacklegged Tick) in eastern North America and Ixodes
ricinus (L.) (Castor Bean Tick) in Europe (Eisen et al. 2016, Jore et al. 2011). It is
thought that this northward movement of Lyme disease vectors is due to climate
change (Simon et al. 2014).
Borrelia burgdorferi is maintained in an enzootic cycle by tick vectors and a
diverse community of vertebrate reservoirs. Tick infection rate depends on the
composition of this vertebrate community because each species has a different reservoir
competency for B. burgdorferi (LoGiudice et al. 2003). In the northeastern
1Department of Biology, Middlebury College, Middlebury, VT 05753. *Corresponding author
- dallen@middlebury.edu.
Manuscript Editor: David Orwig
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US, Peromyscus leucopus (Rafinesque) (White-footed Mouse) is thought to be the
most important reservoir, although other small mammal species are also reservoirs
(Bouchard et al. 2011, Brisson et al. 2008). Tick survival and behavior are sensitive
to temperature and leaf-litter relative humidity (Berger et al. 2014b, Ogden et al.
2004, Perret et al. 2004). This suite of biotic and abiotic factors makes it difficult
to predict and understand small-scale temporal and spatial variation in B. burgdorferi-
infected tick populations (Eisen et al. 2015). On the other hand, broad-scale
patterns of Blacklegged Tick distribution are successfully explained by climate,
terrain, and landcover variables (Diuk-Wasser et al. 2010, Hahn et al. 2016). Specifically,
Diuk-Wasser et al. (2010) and Hahn et al. (2016) both found decreasing
tick presence with elevation.
Within their original distribution in southern New England and the mid-Atlantic,
the ecology of the Blacklegged Tick and B. burgdorferi is well studied. But there
are fewer studies within the newly expanded northern range (some of the few
examples are Bouchard et al. 2011, Lubelczyk et al. 2004, Serra et al. 2013, Simon
Figure 1. County-level incidence map of Lyme disease cases in the US reported to the
Centers for Disease Control and Prevention (CDC). Each subgraph gives the average annual
number of cases per 10,000 people over the 4-y period. Lyme disease case numbers
from the CDC (2018). Annual county population estimates from the US Census Bureau
(2018a, 2018b).
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et al. 2014). There is reason to believe that Blacklegged Tick population and infection
dynamics are different in this northern emergent region (Burtis et al. 2016).
There is a gap in understanding of what controls Blacklegged Tick population
numbers and B. burgdorferi-infection rate in this region.
To address this gap, our study compared tick density and B. burgdorferi infection
across 2 ecoregions in Vermont—the Champlain Valley and the Green
Mountains—to provide a measure of how tick density changes in 2 geographically
close but ecologically and climatically different regions. Importantly, our study
also provides one of the few thorough measurements of tick density in Vermont
(along with Serra et al. 2013), a state with one of the highest incidences of Lyme
disease in the country (Fig. 1; CDC 2019).
Field-site Description
We used EPA ecoregions level III classifications to delineate the 2 regions in
this study (Omernik and Griffith 2014). The Champlain Valley is part of the Eastern
Great Lakes Lowlands ecoregion and the Green Mountains are part of the Northeastern
Highlands ecoregion. We used a shapefile with ecoregion boundaries to
delineate the areas (EPA 2018). We chose 11 sites based on the following criteria:
presence of a closed-canopy forest, deciduous trees predominant, leaf litter layer on
the forest floor, representation within both of the ecoregions, and locations along an
elevation gradient from the Champlain Valley to the spine of the Green Mountains
giving preference to locations on land owned by Middlebury College (Table 1,
Fig. 2). The elevation of sites varied from 126 m to 693 m above sea level, with a
mean (min–max) elevation of 156 m (126–210 m) for Champlain Valley sites and
471 m (254–693 m) for Green Mountains sites. The ruggedness and steepness did
Table 1. Density of questing nymphal ticks from 15 May to 15 July at each of 11 sampling sites across
the 3 y of the study. Three sites were added in 2017, so they do not have values for 2016. Ecoregion
is either CV for Champlain Valley (i.e., Eastern Great Lakes Lowlands) or GM for Green Mountains
(Northeastern Highlands), as determined from EPA (2018). Mean annual temperature and total precipitation
are 30-year normal, 1981–2010 (PRISM Climate Group 2019).
Mean
Mean annual
Elev. temp. precip. Nymphs per 200 m2 (± SE)
Site name Ecoregion (m) (°C) (mm) 2016 2017 2018
Major CV 126 7.7 968 NA 15.75 (± 3.22) 1.33 (± 0.86)
Lourie CV 134 7.6 938 NA 22.38 (± 3.60) 4.00 (± 2.20)
Foote CV 152 7.5 977 12.21 (± 4.12) 26.25 (± 7.67) 6.27 (± 2.34)
Chipman CV 210 7.2 966 4.38 (± 2.15) 16.27 (± 4.93) 3.27 (± 2.52)
Gorge GM 254 6.3 1095 3.63 (± 0.65) 9.50 (± 1.46) 0.75 (± 0.63)
BRF GM 382 6.2 1113 0.62 (± 0.44) 1.13 (± 0.60) 0.38 (± 0.53)
SPIN GM 405 5.7 1209 0.14 (± 0.27) 1.38 (± 0.83) 0.00 (± 0.00)
Frost GM 462 5.1 1216 0.21 (± 0.25) 0.58 (± 0.46) 0.33 (± 0.37)
Gilmore GM 532 5.0 1236 1.13 (± 0.88) 0.88 (± 0.59) 0.00 (± 0.00)
Crystal GM 569 4.8 1326 0.36 (± 0.43) 0.27 (± 0.37) 0.14 (± 0.22)
Snowbowl GM 693 4.3 1408 NA 0.00 (± 0.00) 0.13 (± 0.25)
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not differ greatly between sites in the 2 ecoregions because some Champlain Valley
sites were located on hills in the valley (Chipman Hill and Snake Mountain).
All sites were in northern hardwood forests dominated by Acer saccharum (Marshall)
(Sugar Maple), Acer rubrum (L.) (Red Maple), Fagus grandifolia (Ehrh.)
(American Beech), and Betula alleghaniensis (Britt.) (Yellow Birch). There were
smaller fractions of Fraxinus americana (L.) (White Ash), Tsuga canadensis (L.)
(Eastern Hemlock), and Pinus strobus (L.) (White Pine). Low-elevation sites had
more Quercus rubra (L.) (Red Oak), while high-elevation sites had more Picea rubens
(Sarg.) (Red Spruce). All sites had a thick leaf-litter layer on the forest floor.
The amount of herbaceous groundcover or shrub layer varied between the sites,
although without consistent patterns of denser cover in either ecoregion. Species
composition in the shrub layer differred between regions. Champlain Valley sites
had Lonicera spp. (Honeysuckle) and Berberis thunbergii (DC.) (Japanese Barberry).
Lubelczyk et al. (2004) found higher tick abundance in sites with these invasive
Figure 2. Map of the sampling sites in this study. The locations of the 11 sites are indicated
by the black dots; there were 2–3 plots at each site. The gray area in the map is forest (either
deciduous forest, evergreen forest, or mixed forest) and the white area is any other
landcover type. Landcover classification is from the National Land Cover Database 2011
(Homer et al. 2015). The thick black line represents the boundary between the Champlain
Valley and Green Mountains (EPA 2018). Contour lines show elevation above sea level in
meters. Inset map shows study area in regional context.
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shrubs. The shrub layer at the Green Mountain sites was dominated by Viburnum
lantanoides (Michx.) (Hobblebush). We collected all samples in forest interiors at
least 100 m away from the nearest forest edge.
Champlain Valley sites were generally in small forest fragments separated by
agricultural land—hay fields, corn fields, cow pasture, or apple orchards. Green
Mountain sites were largely contiguous with a section of the Green Mountain
National Forest. We delineated the boundary of the forest fragment at each site by
using leaf-on satellite imagery (Vermont Center for Geographic Information 2018).
We estimated the area of fragments using ArcGIS 10.3 (ESRI 2014). Mean (min–
max) forest fragment area was 721 ha (26–1378 ha) for Champlain Valley sites and
4348 ha (18–17,960 ha) for Green Mountain sites. PRISM modeled 30-y climate
normals showed differences between the ecoregions (Table 1; PRISM Climate
Group 2019). With increasing elevation, there was a decline in mean annual temperature
and an increase in total annual precipitation. The Champlain Valley sites
had similar climates, while the Green Mountain sites had larger, elevation-based
climate differences (Table 1).
Methods
Tick sampling
At each site we established 2 or 3, depending on the size of the forest sampled,
50 m x 50 m2 drag-sampling plots. To sample for ticks, we dragged a 1-m2 white
flannel cloth over the 200-m perimeter of the sampling plot. We stopped every 10 m
to check both sides of the drag cloth for ticks. Drag-cloth sampling is a standard
method to measure the density of questing ticks (Daniels et al. 2000). In the field,
we put ticks on ice in a cooler and stored them at -80 ºC upon returning to the lab
until identification and DNA extraction.
We drag-sampled for ticks from April to October in 2016, 2017, and 2018. We
sampled monthly in early spring and late summer to fall and every other week during
the period of peak nymphal activity, mid-May to early August. It is important to
sample regularly during the peak activity period to make comparisons across sites
(Dobson 2013). We focused on nymphal activity because nymphs are responsible for
the majority of human Lyme disease cases (Barbour and Fish 1993).
B. burgdorferi testing
We identified nymphs to species by dichotomous key (Durden and Keirans
1996, Lindquist et al. 2016). We followed the DNA extraction protocol of Wang
et al. (2014), except that for increased tissue lysis we initially bead-beat the ticks
(Ammazzalorso et al. 2015). We placed ticks in 180 μL of ATL buffer (Qiagen,
Hilden, DE) and homogenized the animals with a FastPrep-24 bead beater (MP
Biomedicals, Santa Ana, CA) in Lysis Matrix H (MP Biomedicals). We added 20
μL of proteinase K solution (600 mAU/ml), incubated the ticks overnight at 56 ºC,
and followed the standard DNeasy Blood and Tissue protocol (Qiagen).
We assessed whether each tick carried B. burgdorferi by amplifying a 600-bp
region of the ospC gene (Wang et al. 1999). We amplified this region using primers
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OC6+ 5'-AAAGAATACATTAAGTGCGATATT-3' and OC602- 5'-GGGCTTGTAAGCTCTTTAACTG-
3'. The 20-μL PCR reactions contained 10 μL Phusion
High-Fidelity Master Mix (New England BioLabs, Ipswich, MA), 0.7 μL 10-mM
OC6+, 0.7 μL 10-mM OC602-, 3 μL of sample, and 5.6 μL of nuclease-free water.
We used the following thermocycler conditions: an initial denaturing step at 98 ºC
for 2 min; then 32 cycles of 98 ºC for 10 sec, 52 ºC for 30 sec, and 72 ºC for 45
sec, and 72 ºC for 7 min for final elongation. We ran negative controls with DNA
extracted from uninfected ticks from the Oklahoma State University Tick-Rearing
Facility and positive controls with B. burgdorferi DNA strain B31 provided by R.
Cluss (Middlebury College, Middlebury, VT) and visualized the PCR product on a
1% agarose gel. We sequenced a subset of PCR products to confirm we were amplifying
B. burgdorferi sensu stricto.
Analysis
Tick density with ecoregion. We sampled for ticks from April to October. However,
to compare nymphal density, we looked at only the period of peak nymphal
activity, 15 May to 15 July. We compared questing tick counts per 200-m2 sample
during that time period using a generalized mixed effects model. We assumed the
tick count to be negative binomially distributed and set ecoregion and year as fixed
effects and sampling site as a random effect. We included site as a random effect
because tick collections were nested within sites, with each site having 2 or 3 sampling
plots. We used a log-link function (Bolker et al. 2009). The model was fit in
the lme4 package in the statistical computing language R (Bates et al. 2015, R Core
Team 2017).
Tick density with elevation. We also compared nymphal tick density with regards
to elevation, focusing only on nymphal density from 15 May to 15 July. We compared
the average tick density at each site across this time period to elevation. We performed
a simple linear regression between log + 1 tick density and elevation. We conducted
the analysis separately on each year’s data. We performed the log transformation
because of the wide range of tick densities observed between sites. We added 1 to density
because some sites had a density of zero, and thus, could not be log transformed.
Tick infection. We compared B. burgdorferi-infection rate in nymphs with a
generalized mixed effects model. Here, we took infection as a binomial variable,
ecoregion and year as fixed effects, and sampling site as a random effect. We used a
logit-link function (Bolker et al. 2009) and calculated the 95% binomial confidence
interval around estimated infection rate for each year–region combination using the
binom package (Dorai-Raj 2014).
Results
We found wide variation in the density of questing ticks across the 11 sites, from
as low as 0 ticks per 200-m2 sample for some sites in some years, to >25 ticks per
sample (Table 1). Relative rankings of tick density among sites remained largely
consistent across the 3 y of study; sites with a high density of ticks in one year
tended to have a high density of ticks in other years (Table 1, Fig. 3). Across all sites
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within a region, we found significantly more ticks per sample in the Champlain Valley
than in the Green Mountains (Table 2, Fig. 4). There was an average of about
10 times more ticks in Champlain Valley sites than in Green Mountain sites.
Table 2. Generalized linear mixed model of questing nymph density per 200-m2 sample between 15
May and 15 July. Ecoregion (Champlain Valley or Green Mountains) and year were fixed effects, while
sampling site was a random effect. Estimates compare samples to 2016 Champlain Valley values.
Green Mountain sites had fewer ticks than Champlain Valley sites, 2017 had more than 2016, and 2018
had fewer than 2016. An asterisk (*) indicates significant effects.
Estimate Std. error Z value P value
Intercept 1.762 0.383 4.601 4.2 x 10-6*
Region = GM -2.433 0.512 -4.756 2.0 x 10-6*
Year = 2017 1.032 0.138 7.469 8.1 x 10-14*
Year = 2018 -0.665 0.155 -4.280 1.9 x 10-5*
GM x 2017 inter. -0.329 0.241 -1.365 0.1722
GM x 2018 inter. -0.658 0.366 -1.797 0.0723
Figure 3. Density of questing nymphal ticks per 200-m2 drag sample between 15 May and
15 July as a function of site elevation. The error bars indicate the standard error around
each mean. For each year, we performed a simple linear regression between log + 1 tick
density and elevation. We took the inverse of the transformation to plot untransformed
tick density, so these lines became negative exponential curves. Each linear relationship
was significant: 2016: F1,6 = 16.4, P = 0.007; 2017: F1,8 = 92.0, P = 0.000005; and 2018:
F1,9 = 17.9 P = 0.002.
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We also found considerable year-to-year variation in tick densities at our sites.
This inter-annual variation was similar across sites, with most having the highest
density of ticks in 2017 and the lowest in 2018, with 2016 in the middle (Table 2,
Fig. 4). Overall, there was a significant effect of year on tick density (Table 2, Fig. 4).
There was a negative relationship between tick density and elevation. For each
year, the linear regression between log density and elevation was significant with
a negative slope (Fig. 3). Taking the inverse of this transformation results in a
negative exponential relationship between tick density and elevation, as seen in
Figure 3.
Our assay of B. burgdorferi-infection rate successfully identified positive controls
while giving negative results for our negative controls. All samples that were
sequenced matched the ospC gene for B. burgdorferi sensu stricto in GenBank.
Nymphal B. burgdorferi-infection rates are given in Table 3. Although the
Champlain Valley had a slightly higher rate than the Green Mountains, this difference
was not statistically significant (Table 4). There was also no difference in
infection rate across the 3 y of the study. Across all years and regions, the average
infection rate was 0.210 (95% binomial confidence interval: 0.180–0.242, n = 704).
Figure 4. Density of questing nymphal ticks per 200-m2 drag sample between 15 May and 15
July. The graph compares the Champlain Valley (white rectangles) to the Green Mountains
(gray rectangles) across the 3 y of the study. The box and whiskers show the mean (horizontal
line), 2 interquartiles (boxes), and full data range (whiskers). The raw data are plotted
over the box and whiskers with an x-axis jitter for clarity.
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Discussion
We found a dramatic difference in nymphal tick density between the Champlain
Valley and Green Mountains. This difference held up in the face of considerable
variation in tick density among sites within the 2 ecoregions. We also found large
inter-annual variation in tick density across the 3 y of this study. This inter-annual
variation was roughly consistent across all of our sampling sites. Therefore, although
tick density was very different from site to site, it might have been responding to
consistent region-wide annual changes, such as weather or host populations. The
high rate of spatiotemporal variation in Blacklegged Tick populations has been seen
in other studies (Ostfeld et al. 2006, Serra et al. 2013).
We found a negative relationship between tick density and elevation, which
might have been the driver for differences in tick density between the Champlain
Valley and Green Mountains. Diuk-Wasser et al. (2010) and Hahn et al. (2016) both
found negative correlations between Blacklegged Tick presence and elevation in
large-scale studies of tick habitat suitability. In Europe, studies along elevation gradients
found negative relationships between Castor Bean Tick density and elevation
(Gilbert 2010, Jouda et al. 2004, Materna et al. 2005, Qviller et al. 2014). Thus, it
is very possible that elevation could be responsible for part of the difference we
observed. We conducted this study near the northern range limit for the Blacklegged
Tick, which is understood to be determined by cold temperature as populations of
the tick require a minimum total degree-days to persist (Ogden et al. 2005). Thus,
the cold temperatures at higher elevation sites might limit tick population growth.
Another factor that could be responsible for the pattern is the difference in forest
fragment area between the 2 regions. Forest fragment area has a considerable effect
on vertebrate communities, and thus, potentially, tick host communities (Andrén
Table 4. Generalized linear mixed model of nymph infection rate. Ecoregion (Champlain Valley or
Green Mountains) and year were fixed effects, and sampling site was a random effect. Estimates
compare samples to 2016 Champlain Valley values. There was no significant effect of region or year
on infection rate. An asterisk (*) indicates significant effects.
Estimate Standard error Z value P value
Intercept -1.164 0.172 -6.756 1.4 x 10–11*
Region = GM -1.311 0.240 -1.295 0.195
Year = 2017 -0.180 0.204 -0.882 0.378
Year = 2018 0.112 0.350 0.321 0.749
Table 3. Nymphal Borrelia burgdorferi-infection rate compared between the 2 ecoregions and the 3 y
of the study. The table also gives the 95% binomial confidence interval and sample size for each yearregion
combination. See Table 4 for statistical comparison.
Champlain Valley Green Mountains Total
2016 0.23 (0.17–0.31, n = 159) 0.20 (0.11–0.23, n = 60) 0.22 (0.17–0.29, n = 219)
2017 0.21 (0.17–0.26, n = 348) 0.15 (0.08–0.24, n = 81) 0.20 (0.16–0.24, n = 429)
2018 0.26 (0.14–0.40, n = 47) 0.22 (0.03–0.60, n = 9) 0.25 (0.14–0.38, n = 56)
Total 0.22 (0.19–0.26, n = 554) 0.17 (0.12–0.24, n = 150) 0.21 (0.18–0.24, n = 704)
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1994). Ostfeld (2011) suggested that small forest fragments support species-poor
vertebrate communities dominated by the White-footed Mouse, which is a highly
permissive Blacklegged Tick host. There is evidence that smaller forest fragments
do, in fact, have higher densities of Blacklegged Ticks (Allan et al. 2003). However,
that study took place in a more fragmented landscape with much smaller forest
fragment sizes (less than 10 ha) than our Champlain Valley sites (average = 721 ha). It
is possible that the smaller Champlain Valley forest fragments support vertebrate
communities that are more suitable for the Blacklegged Tick than the larger Green
Mountain fragments, and that this feature is the driver of tick density differences.
On the other hand, other studies of Ixodes ticks found positive relationships between
fragment area and tick density (Ehrmann et al. 2017, Lawrence et al. 2018). The
data presented here cannot tease apart the roles of fragment area versus temperature
in determining tick density because fragment size increases with elevation in our
study area (Fig. 2). Further, we do not have information on the host communities at
our sites.
We found large variation in tick densities across the 3 y of the study. Inter-annual
variation in Ixodes density is common (Ostfeld et al. 2006, Perret et al. 2000).
This variation could result from annual differences in host populations or weather.
Ostfeld et al. (2006, 2018) found that nymphal Blacklegged Tick density was best
explained by mouse and chipmunk densities in the previous year which, in turn,
correlated with acorn density in the year previous to that. Other studies found that
dry spring conditions decreased the number of questing ticks in that year (Berger
et al. 2014a, Burtis et al. 2016, Rodgers et al. 2007). After just 3 y of study, we do
not have enough data to say what is responsible for the inter-annual variation in tick
density we found.
Overall, we found a nymphal infection rate of ~21%. This rate is considerably
higher than the 10% adult infection rate that Serra et al. (2013) reported previously
in Vermont. Adult Blacklegged Ticks are expected to have twice the infection rate
of nymphs because they have fed twice on potentially infected hosts versus once for
nymphs. That supposition makes even more striking the lower adult infection rate
reported by Serra et al. (2013) compared to the nymphal infection rate reported in
this study. This inconguity could reflect differences in the reservoir competency of
the host communities between the east and west sides of Vermont. The difference
could also be temporal, as Serra et al. (2013) collected ticks from 2011 to 2012
(4–7 y) earlier than this study. Lyme disease incidence has increased dramatically
in Vermont, and this increase might reflect an increase in tick infection rate. Finally,
the difference in results between the 2 studies could also reflect differences in the
sensitivities of our B. burgdorferi detection assays. Our nymphal infection rate,
however, is in line with other estimates (Diuk-Wasser et al. 2012, Feldman et al.
2015, States et al. 2014, Wang et al. 2003). Diuk-Wasser et al.’s (2012) sample of
over 5000 nymphal Blacklegged Ticks from across the eastern US had a 20% infection
rate—very close to our rate. Some studies have found other values. Horobik et
al. (2006) found an infection rate of 32% for nymphs collected in the Hudson Valley,
NY—an area with a longer history of B. burgdorferi than Vermont. Although
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Simon et al. (2014) found an infection rate of 10% in nymphs in southern Quebec,
this is an area with a more recent introduction of the disease than Vermont.
We expected to find a higher B. burgdorferi-infection rate in the Champlain Valley
than in the Green Mountains. Jouda et al. (2004) found a negative relationship
between tick B. burgdorferi-infection rate and elevation in Europe. The density of
ticks is higher in the Champlain Valley, and we expect regions with more vectors to
have a higher rate of a vector-borne disease. Finally, the small forest fragments in
the Champlain Valley may support a less-diverse, more reservoir-competent community
of hosts (Allan et al. 2003), which would also cause a higher infection rate
in the Champlain Valley. Unexpectedly, we found no statistically significant difference
in the infection rate between the 2 ecoregions. However, the non-significant
trend we observed was towards a higher rate in the Champlain Valley.
This work has clear public health implications. Lyme disease is a major human
health concern, with about 30,000 confirmed cases a year in the US reported to
the CDC (CDC 2018). Cases are likely under-reported to the CDC, so the actual
number may be closer to 300,000 (Hinckley et al. 2014, Nelson et al. 2015). Tick
control strategies are costly, and as such, must be targeted at areas of high tickborne
disease risk (Eisen et al. 2012). This study provides some guidance on areas
of high tick density to target for potential tick control strategies or tick preventative
messaging. However, the high level of variation in tick density even within
the Champlain Valley means that more information is needed before small-scale
targeting of the areas of highest disease risk can be carried out.
Finally, it is important to remember that patterns of Blacklegged Tick distribution
and density have changed considerably in the past 2 decades (Eisen et al. 2016).
Thus, the patterns of tick density reported here may not represent the equilibrium
state. Tick populations in the Green Mountains may be only starting to establish and
will increase to Champlain Valley levels in the future. Even if tick populations are
in equilibrium with current conditions, future changes in abiotic and biotic factors
could further alter tick distribution and density. It is important to continue to monitor
tick populations in their newly emergent northern range. Finally, future work
should aim to tease apart the role of climate versus the host community in explaining
the regional differences in tick density seen here.
Acknowledgments
Thanks to the Middlebury College students who helped sample for ticks and test them
for B. burgdorferi: Maisie Anrod, Harper Baldwin, Robert Cassidy, Evan Fedorov, Meaghan
Hickey, Nina Job, Sebastian Zavoico, and Grace Zhang. Thanks also to Bob Cluss for his
helpful discussions and providing B. burgdorferi positive control DNA. Research reported
in this publication was supported by an Institutional Development Award (IDeA) from the
National Institute of General Medical Sciences of the National Institutes of Health under
grant number P20GM103449. Its contents are solely the responsibility of the authors and
do not necessarily represent the official views of NIGMS or NIH. This research was also
supported by Middlebury College.
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2019 Vol. 26, No. 3
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