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DESCRIBING THE SPATIAL DISTRIBUTION OF PARASITES
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presented by

ERICA L. MIZE

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DESCRIBING THE SPATIAL DISTRIBUTION OF PARASITES ON PEROMYSC US
SPECIES IN SOUTHERN MICHIGAN

By

Erica L. Mize

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
MASTER OF SCIENCE
Fisheries and Wildlife

2009

ABSTRACT

DESCRIBING THE SPATIAL DISTRIBUTION OF PARASITES ON PEROMYSC US
SPECIES IN SOUTHERN MICHIGAN

By
Erica L. Mize

Ecto-parasites can be important vectors for many diseases affecting both humans
and wildlife. Thus, the ability to describe the distribution of these disease vectors could
have far-reaching applications in conservation and human health. The goal of this study
was to evaluate the role of habitat in ecto-parasite distribution. One hundred eighty-six
Peromyscus spp mice from 6 study sites in southern Michigan were collected and
examined for parasites during the summer of 2007. Sixty-nine hard ticks (46 Ixodes
scapularis and 23 Dermacentor variabilis), 98 fleas (95 Orchopeas leucopus, 2
Ctenophthalmus pseudagyrtes, and 1 unknown) and 91 lice (Hoplopleura hesperomydis)
were found across 66 study plots. Vegetation data were collected from the study plots as
well. The vegetation, mouse and parasite data were analyzed using principal component
and discriminate function analyses to distinguish the differences between plots without
Peromyscus, with non-parasitized Peromyscus and with parasitized Peromyscus. There
was significant separation of the three groups based on the vegetation for ticks, fleas and
lice. Mice parasitized by ticks were more likely to be found in areas having undergone a
recent disturbance and areas having species associated with dry soils. Mice parasitized
by fleas and lice were also more likely to be found in areas having tree species associated
with dry soils. The results of this study could be used to create risk assessment maps for

current or future diseases spread by these species of ticks, fleas and lice.

ACKNOWLEDGEMENTS

I would like to thank my advisor, Dr. Brian Maurer, for his patience and
understanding while I struggled through multivariate statistics and his support throughout
my thesis project. I would also like to thank my committee, Drs. Jean Tsao and Barbara
Lundrigan, for their time, expertise and most importantly their open doors.

This project would not be possible without funding from the Michigan
Department of Natural Resources, with special thanks going to Michael Donovan. I am
indebted to the diligent work of my field assistants Mike Cook and Dan Lipp and the long
hours they worked in the field.

I would like to thank Drs. Patrick Muzzall, Ned Walker, Mike Kaufman and Jen
Owen for the use of their lab space and equipment. I would like to specially thank Dr.
Muzzall who took the time to teach me how to mount my specimens onto slides and Dr.
Walker who taught me how to clear specimens. Special thanks, as well, to Gary Parsons
for helping me with the louse identification and Laura Abraczinskas for her help with the
Peromyscus specimens.

I would like to express my deepest gratitude towards my lab mates Anne Axel,
Andrea Dechner, Jason Karl and Jay Roberts for the many brainstorming sessions, laughs
and most of all their support. Sarah Hamer has my gratitude for all of her help with
citations, mouse wrangling techniques and of course, the opportunity to pluck ticks. I
would like to thank the many reviewers of this manuscript for helping me see the forest
for the trees. Additionally, I must thank all of my friends and family for their support and
encouragement. Finally, I would like to thank my wonderful partner Joe, for his patience

and sense of humor, both of which helped me finish this paper.

iii

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................... v
LIST OF FIGURES ........................................................................................................... vii
INTRODUCTION ............................................................................................................... 1
STUDY AREAS AND METHODS .................................................................................... 3
Study Areas .............................................................................................................. 3
Methods .................................................................................................................... 4
Parasite Species Identification ................................................................................. 6
Statistical Methods ................................................................................................... 7
RESULTS ............................................................................................................................ 9
Parasite-to-Vegetation Relationships ..................................................................... 10
Tick (Acari) ................................................................................................ 10
Flea (Siphonaptera) .................................................................................... 11
Louse (Phthiraptera) ................................................................................... 12
DISCUSSION .................................................................................................................... l3
Tick (Acari) ............................................................................................................ 13
Flea (Siphonaptera) ................................................................................................ 14
Louse (Phthiraptera) ............................................................................................... 15
Implications ............................................................................................................ 19
APPENDIX 1 — Record of Deposition of Voucher Specimens ............................... 39
APPENDIX 2 - Record of Deposition of Mammalian Vouchers ........................... 43
APPENDIX 3 - Estimate of Detection Error ................................................... 48
APPENDIX 4 - Comparison of Linear Versus Quadratic Discriminant Function Analysis
......................................................................................................... 50
LITERATURE CITED ...................................................................................................... 52

iv

LIST OF TABLES

Table 1.1 Total number of parasites collected, prevalence, average intensity of infestation
and the number of plots where each parasite was collected broken down by taxa and
species ................................................................................................................................ 21

Table 1.2 Eigen values, proportion of variation among groups, Wilk’s Lambda F
approximation and P value for the discriminant function analysis used to separate plots
into groups with parasitized mice, clean mice and no mice .................................... 22

Table 1.3 Standardized discriminant function coefficients from the discriminant function
analysis were used to separate plots into groups with ticks parasitizing mice, clean mice
and no mice. Analysis was conducted using the following variables: canopy basal area of
eleven tree species, height of ten subcanopy species and eight ground cover variables...23

Table 1.4 Contingency table for discriminant function analysis used to separate plots with
ticks parasitizing mice, clean mice and no mice. Top of the chart refers to the plot
classification derived from the posterior probabilities of the DFA, while the side of the
chart is the plot classification assigned from field observations .............................. 25

Table 1.5 Standardized discriminant function coefficients from the discriminant function
analysis were used to separate plots into groups with fleas parasitizing mice, clean mice
and no mice. Analysis was conducted using the following variables: canopy basal area of
eleven tree species, height of ten subcanopy species and eight ground cover variables...26

Table 1.6 Contingency table for discriminant function analysis used to separate plots with
fleas parasitizing mice, clean mice and no mice. Top of the chart refers to the plot
classification derived from the posterior probabilities of the DFA, while the side of the
chart is the plot classification assigned from field observations .............................. 28

Table 1.7 Standardized discriminant function coefficients from the discriminant function
analysis were used to separate plots into groups with lice parasitizing mice, clean mice
and no mice. Analysis was conducted using the following variables: canopy basal area of
eleven tree species, height of ten subcanopy species and eight ground cover variables...29

Table 1.8 Contingency table for discriminant function analysis used to separate plots with
lice parasitizing mice, clean mice and no mice. Top of the chart refers to the plot
classification derived from the posterior probabilities of the DFA, while the side of the
chart is the plot classification assigned from field observations .............................. 31

Table 1.9 Common and scientific names as well as the wetland indicator status (USDA
2009), habitat and colonizer indicator (Szafoni 1990, Barnes and Wagner 2002) of the
tree species included in the analysis .............................................................. 32

Table 1.10: Abundance, prevalence and average intensity of infection for two ecto-
parasite surveys from the nearby state of Indiana conducted by Whitaker (1982) and Ritzi
and Whitaker (2003) .............................................................................. 34

vi

LIST OF FIGURES
Figure 1.1 Distribution of State Game Areas across southern Michigan: 1) Sharonville
SGA, 2) Flat River SGA, 3) Three Rivers SGA, 4) Deford SGA, 5) Verona SGA and 6)
Barry and Yankee Springs SGA .................................................................. 35

Figure 1.2 Distribution of individual plots in discriminant function space into one of three
groups: plots with no mice, un-parasitized mice and mice parasitized by ticks ............. 36

Figure 1.3 Distribution of individual plots in discriminant function space into one of three
groups: plots with no mice, un-parasitized mice and mice parasitized by fleas ............ 37

Figure 1.4 Distribution of individual plots in discriminant function space into one of three
groups: plots with no mice, un-parasitized mice and mice parasitized by lice .............. 38

vii

INTRODUCTION

Rodents are reservoir hosts for many human diseases (Weber 1982). The
biological vectors and diseases associated with mice include ticks (Lyme disease, rocky
mountain spotted fever, and babesiosis), fleas (plague, sylvatic and murine typhus), and
lice (sylvatic typhus) (Center for Disease Control 2006b;2006a). White footed mice,
Peromyscus leucopus, are competent reservoir hosts for all these diseases. Abundant
species, such as mice, have higher abundance and species diversity of parasites than rare
species (Ameberg et a1. 1998). Lice, fleas, mites, ticks and botfly larvae are common
ecto-parasites of Peromyscus spp (Whitaker 1968). Peromyscus Ieucopus is more
sensitive to picking up tick presence than survey methods aimed at collecting parasites
directly from the environment such as dragging (Hamer et al. 2009a). These
characteristics make the white footed mouse a compelling study organism for examining
the association between ecto-parasite presence and the host’s habitat.

Ecto-parasite distributions among host populations are influenced by the
characteristics of the host organism, e.g. sex (Wilson et al. 2002), age (Anderson and
May 1991, Hudson and Dobson 1995), body condition (Wilson et al. 2002) and host
density (Tompkins et al. 2002). Until recently hosts were considered “biological islands”
for parasites, providing the habitat necessary to fulfill basic biological needs such as
food, shelter and opportunities for mating (Krasnov et a1. 1997, Krasnov et al. 2006).
However, ecto-parasites are also under the influence of the external environment
(Krasnov et al. 1997, Guerra et al. 2002, Krasnov et al. 2006) For instance, in Wisconsin,

black-legged ticks (Ixodes scapularis) were associated with abiotic factors such as soil

texture, soil order, forest type, land cover, and bedrock within its hosts’ range, possibly
restricting the distribution of black-legged ticks (Guerra et al. 2002).

External influences may explain ecto-parasite distribution across the landscape. If
presence of parasites is mainly a function of host characteristics, the expected distribution
of black-legged ticks in Michigan would coincide with the statewide Peromyscus
distribution. However, these ticks are limited in distribution to areas in Menominee
County in the Upper Peninsula and along the west coast of the Lower Peninsula in
Berrien, Van Buren, and Allegan counties (Walker et a1. 1998, Michigan Department of
Community Health et al. 2004, Hamer et al. 2007). One possible reason some mice have
few or no parasites may be because the host’s environment is inhospitable to potential
parasites. Another reason is that the parasite may not have the opportunity to feed from
mice because they are not yet present or have not yet invaded into the host’s
environment. Additionally, high ecto-parasite loads may be experienced in environments
that are conducive for ecto-parasite survival. Ecto-parasite presence and parasite species
assemblages are not just a fimction of host-parasite relationships but also host-habitat
relationships: a parasite’s distribution among its hosts is dependent on the right host in
the right habitat (Krasnov et al. 1997, Krasnov et al. 2006).

Parasites that spend a portion of their life or whole life stages off their host should
have stronger habitat associations than parasites whose life cycles are restricted solely to
the host. Ticks, fleas and lice represent three different modes of interaction with their
host: very little host-parasite interaction in the form of a few long term feeding
opportunities (tick), moderate amount of host-parasite interaction through repeated short

term feeding opportunities (flea), and permanent interaction where the parasite spends all

its life closely associated with the host (louse). By including species from different
taxonomic groups, this study examines the association of vegetation attributes to different
degrees of host interaction.

As Peromyscus abundance does not necessarily correspond to parasite presence
and abundance, mapping Peromyscus habitat and distribution is insufficient when
determining their parasite distribution. Parasite distribution and their potential habitat
may be correlated with abiotic factors such as land cover, vegetation presence and
distribution, soil and weather conditions. Parasite communities of Peromyscus may also
vary between different habitat types. The focus of this study was to associate parasite
occurrence to vegetation communities across the southern half of the lower peninsula of
Michigan. I examined the habitat associations of fleas, lice and ticks of P. leucopus to

determine the organisms’ level of association with their hosts’ environment.

STUDY AREAS AND METHODS
Study Areas

Six state game areas (SGAs) were studied (Figure 1.1). The SGAs surveyed
included Sharonville State Game Area (Jackson and Washtenaw Counties), Flat River
State Game Area (Ionia and Montcalm Counties), Three Rivers State Game Area (Cass
and St. Joseph Counties), Deford State Game Area (Tuscola County), Verona State Game
Area (Huron County), and Barry and Yankee Springs State Game Area (Barry County).
These areas were chosen because they span different habitats including forested, lowland
and agricultural land cover types, availability of GIS data and imagery, and IFMAP

stand-level surveys completed by MDNR personnel (MDNR 2005, Roberts 2009).

Methods

Twelve 50 m circular plots were chosen from each SGA, except Three Rivers and
Sharonville, which had 7 and 11 plots respectively for a total of 66 plots. Plots were
randomly selected and stratified based on the relative proportion of each land cover type
at each SGA projected to occur from satellite imagery (Roberts et al. 2006). Vegetation
data were collected at each plot following the guidelines established by MDNR (2005)
and conducted by Roberts et a1. (2006). The following vegetation attributes were
measured: tree species presence, percent canopy cover, average basal area, height of
subcanopy species, ground cover density and the IF MAP cover class. GPS coordinates
were taken at the center of each plot.

Mammals were collected from June 22 to August 5, 2007 across the 66 study
plots sampling each plot once over 36 hours by setting 30 Sherman live traps (H.B.
Sherman Traps, Tallahassee, FL) baited with rolled oats and placed 10 m apart in three
parallel 100 m transects at each plot. These traps were checked in the early morning and
evening at 10-12 hour intervals for 36 consecutive hours. All animals collected were
identified to genus and species when possible, sexed, weighed and marked to recognize
recaptures by removing a small tuft of fur from the rear thigh. Non-Peromyscus species
were then released.

Additional information recorded for Peromyscus species were age class (juvenile
or adult) as described by Baker (1983) and right ear and tail length to distinguish between
P. leucopus and P. maniculatus bairdii by assessing these lengths (Baker 1983). Each
Peromyscus was given a small dose of isoflurane (Isoflo, Abbott Laboratories, Chicago,

IL), an. inhaled anesthetic, by applying a prescribed amount to a cotton ball placed in a 1

gallon scalable plastic bag as advised by a veterinarian in order to incapacitate any fleas
on its body.

From June to August several parasite species may be collected from P. leucopus.
Black-legged tick larvae are active from May to September, peaking in mid-July, and the
nymphs from mid-April to October, peaking in June (Hamer 2009b). The common dog
tick (Dermacentor variabilis) is also active as larvae from mid-April to August and
nymphs from May to August, with both stages peaking in June (Hamer 2009a). Different
flea species may be active all year long or seasonally, either active during summer or
winter months (Krasnov et al. 2005a, Krasnov et al. 2005b). Lice breed throughout the
year (Marshall 1981) and are therefore active and can be collected during the summer
months.

Peromyscus caught on the first trap night (hours 12-24) were examined for
parasites and released. They received a dose of 0.2cc isoflurane to induce anesthesia,
which was maintained with a dose of 0.1cc isoflurane while monitering the breathing
continuously. Once anesthetized, the animal was removed from the chamber and
examined for fleas and ticks, which were collected using #5 watchmakers’ forceps.
Engorged ticks were carefully removed from the epidermis taking special care to remove
the mouth parts for identification. Fleas and unattached ticks were removed using
forceps or by brushing the mouse’s body with a hard bristle toothbrush over a white pan.
The collected ecto-parasites were placed in vials filled with 100% ethanol and labeled
with the animal identification number and SGA. Animals were allowed to fully recover,

were released, then traps were immediately reset.

A partial lethal take was conducted to assess louse burden as follows. Mice
caught on the second trap night (hour 36), including recaptured mice, were administered
0.3cc of isoflurane to induce a deep sleep and were euthanized by cervical dislocation.
After examination for ticks and fleas as above, each mouse was individually wrapped in
multiple layers of cheese cloth to prevent cross-contamination of parasites, as multiple
animals were stored in the same collection jar in 100% ethanol.

Louse specimens were collected post-mortem in the lab by examining each mouse
under a dissecting scope. The mouse and cheese cloth were then washed with dish
detergent and rinsed with water over a 1 gallon jar; the washings were strained in a
200mm opening 75 um mesh sieve (U .S.A. Standard Sieve Series, Newark Wire Cloth
Co., Newark, NJ ) for lice missed during initial inspection. Lice were collected using
forceps, and stored using the same method as described above for the fleas and ticks.

All procedures adhered to the Animal use guidelines established by Michigan
State University Institutional Animal Care & Use Committee (IACUC). This project was
authorized by the Animal Use Committee under Animal Use Form (AUF) number 04/07-
039-00.

Parasite Species Identification

Each parasite was prepared for identification according to taxon-specific
standards. Wet mount tick specimens were identified to species and appropriate life stage
by examination under a dissecting microscope using Sonenshine’s (1979) key. Fleas and
lice were cleared based on guidelines from Fox (1940), Kim et al. (1986) and Ferris and
Stojanovich (1951) in 10% KOH overnight to view informative internal anatomical

features for identification. After clearing, each organism was rinsed in deionized water

and allowed to soak for thirty minutes to end the clearing process before they were
dehydrated for mounting. Dehydration was achieved by running the specimens through
the following alcohol series: thirty minutes each in 70%, 90% and 100% ethanol and a
final soaking for 30 minutes in 100% ethanol. All specimens were then mounted on
slides in Canada balsam and allowed to dry on the bench top overnight before
examination. Each specimen was examined to determine the species, life stage and sex
when possible. Fleas were identified using Fox’s (1940) key and lice were identified
using the keys of Kim et al. (1986) and Ferris and Stojanovich (1951).

Flea voucher specimens were deposited at Michigan State University Entomology
Museum accession number MSU 2009-01, East Lansing, MI and United States National
Museum of Natural History, Washington, DC; louse voucher specimens were deposited
at Michigan State University Entomology Museum accession number MSU 2009-01,
East Lansing, MI and United States National Museum of Natural History, Washington,
DC; tick voucher specimens were deposited at United States National Museum of
Natural History, Washington, DC; and Peromyscus vouchers were deposited at the
Michigan State University Museum Mammal Research Collection accession numbers
MSU 37467- 37595, East Lansing, MI.

Statistical Methods

Field observations yielded 210 different vegetation variables at each plot, given I
had 66 plots I needed to reduce the number of variables to maintain degrees of freedom
and to meet the condition for discriminant function analysis that the number of variables
must be smaller then the number of observations. To reduce this number, I examined

correlations within the canopy variables and subcanopy variables (i.e. canopy basal area

versus canopy closure) to remove highly correlated variables and select a subset of the
vegetation variables. Variable reduction was further accomplished using principal
components analysis (PCA) to maximize the amount of variation explained in the data
while including the lowest number of variables possible (Johnson and Wichem 2002).
The resulting 29 variables retained were the basal area of 11 canopy tree species, height
of 10 subcanopy species and 8 ground cover types. Vegetation data were transformed to
meet the assumption of multivariate normality by square root transforming the canopy
and subcanopy variables and arcsine transforming the ground cover variables.
Discriminant function analysis (DFA) is a robust multivariate methodology often
used in ecological studies to assess how different two or more groups are based on a
consistent set of variables collected for each group (i.e. occupied versus unoccupied
habitats) (McGarigal et al. 2000, Mche and Grace 2002). Quadratic DFA was
conducted to assess the relationship between each parasite group (ticks, fleas, and lice)
and the environment. Linear DFA could not be used because the data violated the
assumption of equal variance/covariance matrices across groups. Each parasite group
was evaluated separately by dividing the 66 plots into 3 groups: 1) plots where no
Peromyscus were found, 2) plots with Peromyscus but no parasites, and finally 3) plots
with Peromyscus that had parasites. Afier the DFA was conducted, each plot was
classified using posterior probabilities as one of the 3 groups. Overall accuracy of the
classification routine and kappa coefficient of similarity were calculated as an assessment
of the model’s ability to separate the groups (Cohen 1968, Hudson and Ramm 1987,
McGarigal et al. 2000). I used kappa to determine the likelihood of the classification

routine randomly assigning plots into the groups. Kappa values close to 0 are considered

randomly assigned, and therefore, the discriminant function did not adequately
discriminate between the groups; values close to 1 are considered to be accurate and the
discriminate function was able to statistically distinguish between the groups. All
analyses were performed using R software (R Development Core Team 2008) with the
exception of the DF A, which was conducted using SAS software (Proc Discrim in

SASv9.1; SAS Institute, Cary, NC).

RESULTS

Three hundred four small mammals were captured in the field; 165 were
identified as Peromyscus leucopus and 21 juvenilles could only be identified to the genus
Peromyscus. These 186 mice were checked for ticks and fleas in the field, of which 105
mice, including the 23 recaptured animals, were euthanized and additionally inspected in
the lab for louse infestations. Parasites from three taxa were collected: 69 larval and
nymphal ticks (Acari), 98 adult fleas (Siphonaptera) and 91 adult lice (Phthiraptera)
(Table 1.1). Of the 69 ticks collected, 46 were Ixodes scapularis (black-legged tick) and
23 were Dermacentor variabilis (dog tick). Of the 98 fleas collected, 95 were Orchopeas
leucopus, 2 were Ctenophthalmus pseudagyrtes, and 1 was unknown; with males and
females collected from both species. All 91 lice collected were Hoplopleura
hesperomydis and both sexes were present. The average intensity of infestation across
taxa ranged from 1.8 to 4.1 parasites per infected mouse (Table 1.1). While fleas had the
lowest intensity of infestation, they were present on the most plots (28/66) and had the
highest prevalence of the taxa examined, where prevalence is the proportion of mice

infested with ecto-parasites of all examined mice (Margolis et al. 1982). Interestingly,

the intensity of infestation was different between the two species of ticks. The tick
species were combined for the analysis because the observations for both species were
too low to analyze separately. While there was only one case of co-infestation on a
mouse, there were three instances of co-infestation at the plot level (2 plots from Three
Rivers and 1 plot from Sharonville).

Parasite-to-Vegetation Relationships

Tick (Acari)

Vegetation characteristics were significantly different between the plots having
mice parasitized with ticks and the plots with clean mice or no mice as determined by the
separation of these three groups in the DFA (Table 1.2 and Figure 1.2). The first
discriminant axis (Table 1.3) had a strong positive association with primary seedling
ground cover, primary barren ground cover, secondary forb ground cover, black ash
(Fraxinus nigra) canopy basal area, black oak (Quercus velutina) canopy basal area, and
red pine (Pinus resinosa) canopy basal area and a strong negative association with
primary grass ground cover, black cherry (Prunus serotina) subcanopy height, and
secondary seedling ground cover; thus the first axis functionally represents a gradient
from unsuitable to suitable mouse habitat. The second discriminant axis (Table 1.3) had
a strong positive association with secondary leaf ground cover and secondary seedling
ground cover, quaking aspen (Populus tremuloides) subcanopy height, black ash
subcanopy height and white oak (Quercus alba) canopy basal area and a strong negative
association with red oak (Quercus rubrum) canopy basal area, big tooth aspen (Populus
grandidentata) canopy basal area, sassafras (Sassafias albidum) subcanopy height, elm

(Ulmus americana) subcanopy height, and primary forb ground cover. The second axis

10

represents a gradient fiom dry and disturbed to wet and undisturbed vegetation
associations.

The discriminant function accurately discriminated between plots with no mice,
mice and mice parasitized by ticks. Classification accuracy was 97% (64/66 correctly
classified), this represents a classification power roughly 95% better than random
assignment (kappa = 0.95) (Table 1.4). Not only were the three groups different, but the
model was able to discriminate between those groups with a high level of accuracy,
indicating the centroids (mean in multivariate space) of each group were distinctly
different. Therefore, habitat characteristics can be used to describe the presence of P.
leucopus and ticks on plots.

Flea (Siphonaptera)

Vegetation characteristics were significantly different between the plots having
mice parasitized with fleas and plots with clean mice or no mice as determined by the
separation of these three groups in the DFA (Table 1.2 and Figure 1.3). The first
discriminant axis (Table 1.5) had a strong positive association with big tooth aspen
canopy basal area, black cherry canopy basal area, red pine canopy basal area, red maple
canopy basal area, and secondary forb ground cover and a strong negative association
with primary grass ground cover, secondary seedling ground cover and black cherry
subcanopy height; thus functionally the first axis represents a gradient from unsuitable to
suitable mouse habitat. The second discriminant axis (Table 1.5) had a strong positive
association with dogwood (Cronus spp) subcanopy height, elm subcanopy height, black
ash subcanopy height, black ash canopy basal area, and white pine (Pinus strobus)

canopy basal area and a strong negative association with primary grass ground cover,

11

secondary leaf ground cover and quaking aspen subcanopy height. The second axis
represents a gradient from dry to wet vegetation associations.

The discriminant function accurately discriminated between plots with no mice,
mice and mice parasitized by fleas. Classification accuracy was 97% (64/66 correctly
classified), this represents a classification power roughly 95% better than random
assignment (kappa = 0.95) (Table 1.6). Not only were the three groups different, but the
model was able to discriminate between those groups with a high level of accuracy; this
indicates the centroids of each group were distinctly different. Therefore, habitat
characteristics can be used to describe the presence of P. leucopus and fleas on plots.
Louse (Phthiraptera)

Vegetation characteristics were significantly different between the plots having
mice parasitized with lice and plots with clean mice or no mice as determined by the
separation of these three groups in the DFA (Table 1.2 and Figure 1.4). The first
discriminant axis (Table 1.7) was strongly positively associate with white pine canopy
basal area, red oak canopy basal area, black oak canopy basal area, white pine subcanopy
height and primary forb ground cover and a strong negative association with white oak
canopy basal area, quaking aspen subcanopy height and primary grass ground cover; thus
functionally the first axis represents a gradient from unsuitable to suitable mouse habitat.
The second discriminant axis (Table 1.7) had a strong positive association with secondary
forb ground cover, red oak subcanopy height, dogwood subcanopy height, elm subcanopy
height and red pine canopy basal area and a strong negative association with black ash

canopy basal area, secondary leaf ground cover, secondary seedling ground cover and

12

black cherry subcanopy height. The second axis represents a gradient fi'om dry to wet
vegetation associations.

The discriminant fimction accurately discriminated between plots with no mice,
mice and mice parasitized by lice. Classification accuracy was 97% (64/66 correctly
classified), this represents a classification power roughly 95% better than random
assignment (kappa = 0.95) (Table 1.8). Not only were the three groups different, but the
model was able to discriminate between those groups with a high level of accuracy; this
indicates the centroids of each group were distinctly different. Therefore, habitat

characteristics can be used to describe the presence of .P. Ieucopus and lice on plots.

DISCUSSION

As indicated by the high kappa values, each taxon can be described by the
vegetation variables used in the DFA to separate the three groups (no mice, un-
parasitized mice and parasitized mice). Therefore, tick, flea and louse presence can be
described by vegetation characteristics distinctly different from those of un-parasitized
mice, indicating the preferred habitats of the parasites and hosts are distinct. However,
the mechanisms linking habitat to the presence of ticks, fleas and lice are unknown.

Tick (Acari)

Mice parasitized by ticks are more likely to be found in areas having undergone a
recent disturbance and having vegetation species that tolerate or thrive in dry soils. Plots
with ticks were characterized by colonizers such as black cherry, sassafras and elm which
are indicators of disturbance (Table 1.9). Plots without ticks were characterized by tree

species associated with wet soils such as silver maple, quaking aspen and big tooth aspen,

13

demonstrating a lack of water tolerant tree species may also be an indicator for tick
presence (Table 1.9). The results of this study provide further evidence that the presence
of tick species is associated with a subset of characteristics of their host’s habitat;
specifically tick presence is positively associated with the presence of early successional
tree species and negatively associated with tree species that indicate past or current flood
regimes.

The literature supports these findings. Lubelczyk et al. (2004) found tick
abundance increased when invasive shrub species were present, indicating a change from
the natural vegetation in Maine. The authors concluded disturbances leading to the
introduction and successful establishment of invasive species were positive indicators of
tick abundance. Guerra et al.(2002) found ticks to be present in forests characterized by
high densities of oak and maple species in the canopy. They felt this was due to the
influence of leaf litter on overwinter survival of black-legged ticks. They also found sites
without ticks were dominated by clay soils, which retain water and support wetland
vegetation species. Manangan et al.(2007) also found soil moisture played a role in tick
presence. They found the presence of tick borne pathogens Anaplasma phagocytophilum
and Ehrlichia chafieensis was negatively associated with indicators of flooding such as
high flood probability, low soil drainage, and wooded wetlands.

Flea (Siphonaptera)

Mice parasitized by fleas were more likely to be found in areas having tree
species able to tolerate dry soils. Primary grass ground cover was characteristic of both
plots without mice and plots with mice parasitized by fleas, suggesting less suitable

mouse habitat is an indicator for flea presence. However, plots with fleas were

14

characterized by fewer associated variables than plots without fleas. Plots without fleas
were characterized by tree species that thrive in wet soils such as dogwood, elm, and
black ash, demonstrating a lack of water tolerant species is an indicator for flea presence
(Table 1.9). Flea presence is negatively associated with tree species that indicate past or
current flood regimes.

This study is the first to look at the relationship between individual vegetation
species and the presence of fleas. Past studies have either looked at habitat types or
collected vegetation data and described habitat types based on those data, not focusing on
the potential effects of vegetation on flea presence, but rather on the effects of
microclimate (i.e. temperature and humidity) (Eskey 1938, Marshall 1981, Christie 1982,
Krasnov et al. 2001 , Adjemian et al. 2006) and host species assemblages (Krasnov et al.
2005a). For instance, Krasnov et al. (2004) has produced a large body of work on flea
species assemblages and various potential environmental influences such as vegetation
and soil attributes. Their findings indicate host body parameters influence flea species
richness far less than enviromnental parameters. Also, Krasnov et al. (1997) found the
relationship between soil, vegetation, relief patterns and percent cover of various ground
vegetation varied in strength depending on the flea species in question. Krasnov et al.
(2002) found substrate influenced both larval flea survival and the rate of development.
Finally, Krasnov et al. (2006) found the presence of flea species assemblages were based
on habitat types - mountain versus lowland areas.

Louse (Phthiraptera)
Mice parasitized by lice are more likely to be found in areas having tree species

that tolerate or thrive in dry soils or areas lacking colonizers, suggesting undisturbed sites

15

are also characteristic of louse presence. However, plots with lice were characterized by
fewer associated variables than plots without lice. Plots without lice were characterized
by tree species that thrive in wet tolerant soils such as dogwood, elm and aspen,
indicating a lack of water tolerant species is an indicator for louse presence (Table 1.9).
The presence of elm and aspen on plots without lice could also indicate a lack of early
successional species is a descriptive characteristic for louse presence on mice (Table 1.9).

Few studies looking into potential environmental influences on the presence of
lice have been conducted. Most studies have focused on the effects of the host’s
microclimate (i.e. temperature and humidity) on the presence of lice (Marshall 1981).
Calvete et al. (2003) found louse intensity on red legged partridges in Spain was
associated with mean environment temperature and Normalized Difference Vegetation
Index (N DVI), which is highly correlated with environmental humidity. They suggest
high temperature and humidity may increase the probability of transmission between
individuals fi'om communal resting or bathing areas. The most prolific studies conducted
concerning the effects of the environment on louse survival focus on unique lice of
several seal species able to survive while the host is at sea by withstanding extremely
cold temperatures and long periods of starvation (Kim 2006).

Despite the fact that parasites all appeared on plots with vegetation that thrive or
tolerate dry soils, there were subtle differences in the vegetation species composition
associated with each particular taxa. The presence of ticks, fleas and lice on mice were
characterized by completely different vegetation species. Though, the presence of
secondary leaf ground cover was characteristic for plots with mice parasitized by fleas as

well as plots with mice parasitized by lice.

16

Ecto-parasites collected and identified were found in similar abundance to two
ecto-parasite surveys conducted in Indiana (Figure 1.10). Whitaker (1982) conducted a
survey of the ecto-parasites of mammals in Indiana and Ritzi and Whitaker (2003)
conducted a survey of ecto-parasites of small mammals from the Newport Chemical
Depot in Verrnillion County, Indiana. I collected 23 D. variabilis with a prevalence of
7% which is similar to that recovered by Whitaker (1982), but far lower prevalence than
that collected by Ritzi and Whitaker (2003) (Figure 1.10). Neither of these studies
collected 1. scapularis from P. leucopus. I found a similar prevalence of Orchopeas
Ieucopus to Whitaker (1982) and to Ritzi and Whitaker (2003). Though the prevalence of
Ctenophthalmus pseudagyrtes was similar to Whitaker (1982), it was lower than that of
Ritzi and Whitaker (2003). The prevalence of Hoplopleura hesperomydis was higher
than Whitaker’s (1982) study, but lower than that of Ritzi and Whitaker’s (2003) study.
The intensity of infection is not comparable to Whitaker’s (1982) study, however the
intensity of infection was higher for each species recorded except for H. hesperomydis
which was lower for Ritzi and Whitaker’s (2003) study than those recorded across my
study.

Because mammal trapping only occurred during a short period of the summer
months, it is possible not all of the potential parasite species were collected, limiting the
implications of this study to those species of parasites found mid-June to August. While
larval black-legged ticks were in peak abundance during the time of the collections,
nymphal black-legged ticks and both larvae and nymphs of the dog tick had already
peaked (Hamer 2009b;2009a). Flea species collections were biased toward fur or body

fleas, as nidicolous (nest associated) fleas were not collected. Therefore, it is possible

17

both ticks and fleas’ spatial distributions and vegetation associations are incomplete. A
regular, year long trapping protocol would help discern any temporal relationships
between parasite presence and vegetation variables, in addition to any uncertainty
concerning the presence and distribution of parasites of P. leucopus.

Furthermore, it is not possible to fully describe the habitat associations of black-
legged ticks, as it is unknown if their absence was because the habitat was unsuitable or
they have not yet invaded those areas. Distribution of black-legged ticks in Michigan
may also be limited by opportunity, as this species is currently invading the southern
peninsula (Guerra et al. 2002, Hamer et al. 2007, Harner et al. 2009b). Not only does
host availability and movement impact tick distribution, but suitable habitat also affects
the ability of ticks to become established (Manangan et al. 2007). The vegetation
associations described in this study may be characteristic of invading tick populations and
not necessarily characteristic of established populations. Lastly, Orchopeas leucopus and
Hoplopleura hesperomydis are both specific to mice of the genus Peromyscus (Fox 1940,
Kim et al. 1986), whereas both black-legged and dog ticks are generalist species. As
generalists, the full extent of their habitat distributions cannot be fully discerned by
examining only one of several host species. However, mice are considered to be one of
the most important host species for Ixodes scapularis (Shaw et al. 2003 ).

Ixodes scapularis and Dermacentor variabilis were analyzed together because the
sample sizes for both species were very low and there were three plots (4% 3/66) where
both species were collected from parasitized mice. The literature suggests I. scapularis
and D. variabilis may not have very different vegetation associations. Sonenshine et al.

found that (1972) D. variabilis was associated with mesic deciduous plant species across

18

the eastern United States. Furthermore, adult, larval and nymphal D. variabilis were
collected from several habitat types in a study conducted in Nova Scotia, they found
adults and nymphs were in old field and Ecotones, and larvae were collected from a
variety of areas including woodlots, fields and ecotones (Campbell and MacKay 1979).
However, they hypothesized Peromyscus spp probably helped disperse engorged tick
larvae from the woodland to the old field and ecotone areas. Flea species were also
combined for analysis as there were only two observations of C. pseudagzrtes.
Implications

The results of this study could be used to create risk assessment maps for current
or future diseases spread by these species of ticks, fleas and lice. This is potentially
useful to wildlife managers and community health professionals as similar studies have
used environmental data for this purpose. Carbajal de la Fuentae et al. (2009) found
environmental information such as temperature, vapor pressure deficit, vegetation and
altitude provided by remote sensors could be used to predict the geographic distribution
of Chagas disease vectors T riatoma pseudomaculata and T. wygondzinskyi. Linard et al.
(2009) created a model to assess the risk of humans contracting malaria in southern
France if the malaria parasite were reintroduced in the area based on various types of land
use such as rice fields, vineyards, marshes and urban areas, while noting many statistical
models can predict the spatial distribution of Anopheles vectors based on environmental
variables.

The decisions of wildlife managers can have a lasting impact on disease risk as
demonstrated by the findings of Lubelczyk et al. (2004). They found the presence of

ticks was positively associated with the presence of several invasive species in the shrub

l9

layer and concluded landscape changes and alterations in species composition may create
favorable tick habitat. As these individuals make decisions on how to manage state lands
and resources, they can reduce disease risk by considering the impacts of management
actions on the populations of potential arthropod vectors of disease.

This study provides strong evidence non-host habitat associations exist across a
range of parasite taxa. These associations may be more important than previous research
has indicated. In the areas examined in this study, disturbance was an indicator for the
presence of ticks, warranting further investigation concerning, among other abiotic

factors, the impact of disturbance on other parasite species and different areas.

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Figure 1.1:

Distribution of State Game Areas across southern Michigan: 1) Sharonville SGA, 2) Flat
River SGA, 3) Three Rivers SGA, 4) Deford SGA, 5) Verona SGA and 6) Barry and
Yankee Springs SGA.

35

 

Second discn'minant function axis

    
 

0 No mice

A Mice w/o ticks
I Mice w/ticks I

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I I I
-4 -2 0 2 4

First discriminant function axis

Figure 1.2:

Distribution of individual plots in discriminant function space into one of three groups:
plots with no mice, un-parasitized mice and mice parasitized by ticks. Ellipses represent
95% confidence intervals around the population mean. Numbers refer to the SGA where
the plot occurred: 1 Sharonville, 2 Flat River, 3 Three Rivers, 4 Deford, 5 Verona, and 6
Barry. The first discriminant axis had a strong positive association with 1° seedling
ground cover, 1° barren ground cover and 2° forb ground cover and a strong negative
association with 1° grass ground cover, black cherry subcanopy height and 2° seedling
ground cover. The second discriminant axis had a strong positive association with 2° leaf
ground cover, 2° seedling ground cover and quaking aspen subcanopy height (Table 1.3).

36

 

 

Second discriminant function axis

 

A Mice w/o fleas
‘f - ' Mice wffleas

 

 

 

I I I I I
.4 -2 0 2 4

First discriminant function axis

Figure 1.3:

Distribution of individual plots in discriminant function space into one of three groups:
plots with no mice, un-parasitized mice and mice parasitized by fleas. Ellipses represent
95% confidence intervals around the population mean. Numbers refer to the SGA where
the plot occurred: 1 Sharonville, 2 Flat River, 3 Three Rivers, 4 Deford, 5 Verona, and 6
Barry. The first discriminant axis had a strong positive association with big tooth aspen,
black cherry, and red pine canopy basal area; and a strong negative association with 1°
grass ground cover, 2° seedling ground cover, and black cherry subcanopy height. The
second discriminant axis had a strong positive association with dogwood, elm, and black
ash subcanopy height; and a strong negative association 1° grass ground cover, 2° leaf
ground cover, and quaking aspen subcanopy height (Table 1.5).

37

 

Second discriminant function axis
0
l

     

0 No mice
A Mice w/o lice
Y - ' Mice w/Iice

I I I I I

-4 -2 0 2 4

 

 

 

First discriminant function axis

Figure 1.4:

Distribution of individual plots in discriminant function space into one of three groups:
plots with no mice, un-parasitized mice and mice parasitized by lice. Ellipses represent
95% confidence intervals around the population mean. Numbers refer to the SGA where
the plot occurred: 1 Sharonville, 2 Flat River, 3 Three Rivers, 4 Deford, 5 Verona, and 6
Barry. The first discriminant axis had a strong positive association with white pine, red
oak, and black oak canopy basal area; and a strong negative association with 1° grass
ground cover, quaking aspen subcanopy height and white oak canopy basal area. The
second discriminant axis had a strong positive association with 2° forb ground cover, red
oak and dogwood subcanopy height; and a strong negative association with black ash
canopy basal area, 2° leaf ground cover and 2° seedling ground cover (Table 1.7).

38

APPENDIX 1
Record of Deposition of Entomological Voucher Specimens

39

Appendix 1
Record of Deposition of Entomological Voucher Specimens*
The specimens listed on the following sheet(s) have been deposited in the named
museum(s) as samples of those species or other taxa, which were used in this research.
Voucher recognition labels bearing the Voucher No. have been attached or included in
fluid-preserved specimens.
Voucher No.: 2009-01
Title of thesis or dissertation (or other research projects):
DESCRIBING THE SPATIAL DISTRIBUTION OF PARASITES ON PEROMYSC US
SPECIES IN SOUTHERN MICHIGAN

Museum(s) where deposited and abbreviations for table on following sheets:

Entomology Museum, Michigan State University (MSU)
Other Museums:
United States National Museum of Natural History
Investigator’s Name(s):
Erica L. Mize
Date: May 08, 2009
*Reference: Yoshimoto, C. M. 1978. Voucher Specimens for Entomology in North
America.
Bull. Entomol. Soc. Amer. 24: 141-42.

Deposit as follows:
Original: Include as Appendix 1 in ribbon copy of thesis or dissertation.

Copies: Include as Appendix 1 in copies of thesis or dissertation.
Museum(s) files.

Research project files.

This form is available from and the Voucher No. is assigned by the Curator, Michigan
State University Entomology Museum.

40

Appendix 1.1

Voucher Specimen Data

of 2 Pages

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41

Appendix 1.1

Voucher Specimen Data

of 2 Pages

Page 2

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42

APPENDIX 2
Record of Deposition of Mammalian Vouchers

43

Appendix 2

Record of Deposition of Mammalian Vouchers

Accession and collector’s numbers of all mammals deposited at Michigan State

University Museum Mammal Research Collection. Non-Peromyscus species deposited
were the result of trap mortality.

 

Accession No. C011. No. Genus Species Subspecies
MSU 37491 73 Peromyscus leucopus noveboracensis
MSU 37492 109 Peromyscus leucopus noveboracensis
MSU 37493 106 Peromyscus leucopus noveboracensis
MSU 37494 108 Peromyscus leucopus noveboracensis
MSU 37495 110 Peromyscus leucopus noveboracensis
MSU 37496 112 Peromyscus leucopus noveboracensis
MSU 37497 128 Peromyscus leucopus noveboracensis
MSU 37498 129 Peromyscus leucopus noveboracensis
MSU 37499 130 Peromyscus leucopus noveboracensis
MSU 37500 131 Peromyscus leucopus noveboracensis
MSU 37501 132 Peromyscus leucopus noveboracensis
MSU 37502 136 Peromyscus leucopus noveboracensis
MSU 37503 151 Peromyscus leucopus noveboracensis
MSU 37504 152 Peromyscus leucopus noveboracensis
MSU 37505 154 Peromyscus leucopus noveboracensis
MSU 37506 155 Peromyscus leucopus noveboracensis
MSU 37507 160 Peromyscus leucopus noveboracensis
MSU 37508 161 Peromyscus leucopus noveboracensis
MSU 37509 164 Peromyscus leucopus noveboracensis
MSU 37510 166 Peromyscus leucopus noveboracensis
MSU 37511 - 167 Peromyscus leucopus noveboracensis
MSU 37512 179 Peromyscus leucopus noveboracensis
MSU 37513 177 Peromyscus leucopus noveboracensis
MSU 37514 178 Peromyscus leucopus noveboracensis
MSU 3 75 1 5 l 80 Peromyscus leucopus noveboracensis
MSU 3 7516 l 82 Peromyscus le ucopus noveboracensis
MSU 3 75 l 7 1 83 Peromyscus leucopus noveboracensis
MSU 3 751 8 215 Peromyscus leucopus noveboracensis
MSU 37519 216 Peromyscus leucopus noveboracensis
MSU 3 7520 21 7 Peromyscus 1e ucopus noveboracensis
MSU 37521 218 Peromyscus leucopus noveboracensis
MSU 37522 219 Peromyscus leucopus noveboracensis

44

Appendix 2 Cont.
MSU 37523
MSU 37524
MSU 37525
MSU 37526
MSU 37527
MSU 37528
MSU 37529
MSU 37530
MSU 37531
MSU 37532
MSU 37533
MSU 37534
MSU 37535
MSU 37536
MSU 37537
MSU 37538
MSU 37539
MSU 37540
MSU 37541
MSU 37542
MSU 37543
MSU 37544
MSU 37545
MSU 37546
MSU 37547
MSU 37548
MSU 37549
MSU 37550
MSU 37551
MSU 37552
MSU 37553
MSU 37554
MSU 37555
MSU 37556
MSU 37557
MSU 37558
MSU 37559
MSU 37560
MSU 37561
MSU 37562

220
221
222
223
225
226
227
229
232
233
234
235
236
237
238
265
266
267
268
270
271
272
273
274
275
276
277
278
280
281
282
323
329
338
342
343
345
346
347
371

Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus
Peromyscus

Peromyscus _

Peromyscus
Peromyscus
Peromyscus

45

leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus
leucopus

noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis
noveboracensis

Appendix 2 Cont.

MSU 37563 372 Peromyscus leucopus noveboracensis
MSU 37564 373 Peromyscus leucopus noveboracensis
MSU 37565 374 Peromyscus leucopus noveboracensis
MSU 37566 375 Peromyscus leucopus noveboracensis
MSU 37567 376 Peromyscus leucopus noveboracensis
MSU 37568 377 Peromyscus leucopus noveboracensis
MSU 37569 378 Peromyscus leucopus noveboracensis
MSU 37570 380 Peromyscus leucopus noveboracensis
MSU 37571 382 Peromyscus leucopus noveboracensis
MSU 37572 383 Peromyscus leucopus noveboracensis
MSU 37573 384 Peromyscus leucopus noveboracensis
MSU 37574 387 Peromyscus leucopus noveboracensis
MSU 37575 390 Peromyscus leucopus noveboracensis
MSU 37576 391 Peromyscus leucopus noveboracensis
MSU 37577 392 Peromyscus leucopus noveboracensis
MSU 37578 105 Peromyscus spp

MSU 37579 111 Peromyscus spp

MSU 37580 153 Peromyscus spp

MSU 37581 154 Peromyscus spp

MSU 37582 162 Peromyscus spp

MSU 37583 165 Peromyscus spp

MSU 37584 224 Peromyscus spp

MSU 37585 228 Peromyscus spp

MSU 37586 230 Peromyscus spp

MSU 37587 231 Peromyscus spp

MSU 37588 275 Peromyscus spp

MSU 37589 324 Peromyscus spp

MSU 37590 344 Peromyscus spp

MSU 37591 379 Peromyscus spp

MSU 37592 381 Peromyscus spp

MSU 37593 388 Peromyscus spp

MSU 37594 389 Peromyscus spp

MSU 37595 393 Peromyscus spp

MSU 37467 120 Blarina brevicauda kirtlandi

MSU 37468 124 Blarina brevicauda kirtlandi

MSU 37469 126 Blarina brevicauda kirtlandi

MSU 37470 127 Blarina brevicauda kirtlandi

MSU 37471 158 Blarina brevicauda kirtlandi

MSU 37472 163 Blarina brevicauda kirtlandi

MSU 37473 176 Blarina brevicauda kirtlandi

46

Appendix 2 Cont.

MSU 37474 181 Blarina brevicauda kirtlandi

MSU 37475 192 Blarina brevicauda kirtlandi

MSU 37476 214 Blarina brevicauda kirtlandi

MSU 37477 244 Blarina brevicauda kirtlandi

MSU 37478 254 Blarina brevicauda kirtlandi

MSU 37479 269 Blarina brevicauda kirtlandi

MSU 37480 284 Blarina brevicauda kirtlandi

MSU 37481 288 Blarina brevicauda kirtlandi

MSU 37482 293 Blarina brevicauda kirtlandi

MSU 37483 297 Blarina brevicauda kirtlandi

MSU 37484 204 Blarina brevicauda kirtlandi

MSU 37485 336 Blarina brevicauda kirtlandi

MSU 37486 250 Blarina brevicauda kirtlandi

MSU 37487 385 Blarina brevicauda kirtlandi

MSU 37488 94 Microtus pennsylvanicus pennsylvanicus
MSU 37489 103 Microtus pennsylvam'cus pennsylvanicus
MSU 37490 337 Microtus pennsylvanicus pennsylvanicus
MSU 37596 150 Sorex cinereus lesueurii

MSU 37597 213 Zapus hudsom'us americanus
MSU 37598 386 Zapus hudsom'us americanus

 

47

APPENDIX 3
Estimate of Detection Error

48

Appendix 3
Estimate of Detection Error
The number of each parasite group collected from mice in the field and in lab. The

estimate of detection error is calculated as the number of parasites collected in the lab
(missed in the field) out of the total number of parasites collected.

 

No. No. Estimated

recovered in recovered in detection

field lab error
Ticks 58 1 1 15.9%
Fleas 91 7 7.1%
Lice 8 83 91.2%

 

49

APPENDIX 4
Comparison of Linear Versus Quadratic Discriminant Function Analysis

50

Appendix 4

Comparison of Linear Versus Quadratic Discriminant Function Analysis

The classification accuracy, number of correctly classified plots, and kappa value for
each parasite taxa using both the quadratic and linear modes of discriminant function

 

analysis (DFA).

Quadratic

DFA Linear DFA

Classification Kappa Classification Kappa

accuracy Plots value accuracy Plots value
Tick 97% 64/66 0.95 92% 61/66 0.87
Flea 97% 64/66 0.95 88% 58/66 0.81
Louse 97% 64/66 0.95 94% 62/66 0.90

 

51

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