Download Temporal evolution of the ecological niche of the

Temporal evolution of the ecological niche of the white-footed mouse
(Peromyscus leucopus) and its relation with the emergence of Lyme disease in
Québec
Émilie Roy-Dufresne
Department of Geography
McGill University, Montreal
April 2013
A thesis submitted to McGill University in partial fulfilment of the
requirements of the degree of Masters of Science.
© Émilie Roy-Dufresne 2013
ABSTRACT
It is expected that climate change will influence the distribution of a
number of species. As some of these species are disease vectors, changes are also
expected to occur in the distribution of disease risk areas, which influence the
disease transmission risk to human populations. In this thesis I describe the
potential influence of climate change on the white-footed mouse distribution
(Peromscus leucopus), a range that is expanding within Québec. The white-footed
mouse is an important zoonotic reservoir for the Lyme disease pathogen (Borrelia
burgdorferi) and is a vector of the disease's principal host: the black-legged tick
(Ixodes scapularis). My results provide information to guide intervention
activities and to target sites for disease surveillance. I first characterize, based on
published literature, the environmental, geographical, and climatic determinants
of the current range of the mouse. I then use Ecological Niche Factor Analysis to
explore the influence of climate on the distribution of the white-footed mouse in
Québec. Finally, I model the mouse's current fundamental niche at its northeastern
range limit, using a combination of habitat-niche models from BIOMOD, and
estimate its future expansion along three scenarios of projected climate change. I
conclude by,predicting a potential expansion of the range of the white-footed
mouse into most of the province of Québec by 2050, as a result of warmer and
shorter winters.
ii
RÉSUMÉ
Il est estimé que les changements climatiques auront une incidence
profonde sur la distribution des espèces animales. Déjà, les impacts sont observés
chez certains vecteurs de maladies qui migrent vers de nouveaux territoires, ayant
des conséquences sur le risque de transmission des maladies chez les humains.
Dans cette thèse, j'étudie le cas de la souris à pattes blanches (Peromyscus
leucopus), dont le nombre de sites de présence augmente au sud de la province de
Québec depuis les dernières années. La souris à pattes blanches est un acteur
important dans le processus de transmission des pathogènes de la maladie de
Lyme (Borrelia burgdorferi) de la tique à pattes noires (Ixodes scapularis) à
l'humain.Les résultats obtenus fournissent donc des informations essentielles afin
de guider les activités de prévention de la maladie de Lyme, et cibler les cites de
surveillance. Je révise tout d'abord par une revue de la litterature, les facteurs
potentiels environnementaux, géographiques et climatiques qui influencent la
distribution de l’espèce à la limite nord de sa distribution. Je performe ensuite une
analyse factorielle de la niche afin de mieux comprendre l'influence potentielle de
variables climatiques sur la distribution de la souris à pattes blanches au Québec.
Finalement, je modélise la niche fondamentale de l'espèce pour le temps présent
en utilisant une combinaison de modèles de niche construite dans BIOMOD, et
estime son expansion future selon trois scénarios de projections de changements
climatiques. J'évalue, à partir de mes résultats, une expansion potentielle de la
distribution de la souris à pattes blanches dans l’ensemble de la province du
Québec d’ici 2050;un phénomène principalement lié à des hivers plus cléments et
plus courts.
iii
ACKNOWLEDGEMENTS
This work is the result of many hours of work, which would not have been
possible without the assistance of and encouragements from many individuals and
organizations to whom I am indebted. I would first like to thank my cosupervisors Dr. Virginie Millien and Dr. Gail L. Chmura for the opportunity they
provided to me to undertake this research in their labs. Their patience was
invaluable throughout this project, as well as their combined vision, support and
guidance. I would also like to thank my committee member Dr. Lea Berrang Ford
for her interesting and helpful statistical insights. I am deeply indebted to a
number of agencies and individuals for providing me the data to conduct this
research. I would like to thank Travis Logan from Ouranos who did a wonderful
work at providing me current and projected climatic conditions, as well as guiding
me while choosing proper climatic variables for my research. I am also very
grateful to Nathalie Desrosiers and Annie Paquet from the Ministère des
Ressources Naturelles et de la Faune du Québec, for their help and providing
access to the white-footed mouse presence dataset in Québec. I would also like to
thank Dr. Nicholas Ogden, Catherine Bouchard and Jules Konan Koffi from the
University of Montreal and Public Health Agency of Canada for stimulating
discussion opportunities and insights on the relationship between the mouse
distribution and the occurrence of Lyme disease. I would also like to thank and
acknowledge the time taken by Dr. Wilfried Thuiller from Grenoble University,
Cécile Albert from McGill University, and Catherine Paré from the Ministère des
Ressources Naturelles et de la Faune du Québec, for support and insights into the
world of species niche modelling and BIOMOD. I am grateful to the Quebec
Center for Biodiversity Science for their workshop organized on “BIOMODModéliser la biodiversité”, which was really helpful. I would also like to thank
Milka Radojevic and Alain Royer, members of the Global Environmental and
Climate Change Centre for their helpful advices and discussions opportunities on
climate modeling and snow data projection. I gratefully acknowledge funding
support from Ouranos, Global Environmental and Climate Change Centre, and the
Quebec Center for Biodiversity Science, without which this work would not have
iv
been possible. I would finally like to thank all people from Virginie’s lab,
graduate geography students, and field lab assistants for their support, jokes and
encouragements. And last but not least, I am most grateful for the loving
encouragement, patience and understanding provided by my family and close
friends.
v
CONTRIBUTIONS OF AUTHORS
The content, goals and objectives for the manuscript found in this thesis
(chapter 3) were conceptualized under the guidance of my co-supervisors,
Virginie Millien and Gail Chmura, and received addition editorial guidance from
Daniel Suter (acknowledged therein). All chapters were written by Émilie RoyDufresne but received intellectual and editorial comments from Gail Chmura and
Virginie Millien.
vi
TABLE OF CONTENTS
ABSTRACT
RÉSUMÉ
ACKNOWLEDGEMENTS
CONTRIBUTIONS OF AUTHORS
LIST OF TABLES
LIST OF FIGURES
ii
iii
iv
vi
ix
x
CHAPTER 1: GENERAL INTRODUCTION
Research Objectives
Thesis Outline
Literature Cited
1
1
2
3
CHAPTER 2: LITERATURE REVIEW
1. Climate Change and its Impact on Species Distribution
2. Modelling Species Distribution
3. Biogeographical Approach to the Study of Lyme Disease
Lyme Disease Occurrence
Lyme Disease Transmission Mechanism
White-footed Mouse as Zoonotic Reservoir
White-footed Mouse Distribution in Southern Québec
4. The White-footed Mouse and Environmental Factors Influencing its
Distribution
Shifting Distribution of the White-footed Mouse
Climate Conditions
Photoperiodic Regulation
Forest Type and Food Abundance
Habitat Fragmentation and Connectivity
White-Footed Mouse Co-Existence with the Deer Mouse
5. Conclusion
Literature Cited
Tables and Figures
5
5
6
9
10
11
12
12
CONNECTING STATEMENT
46
13
14
15
16
17
18
19
21
21
43
CHAPTER 3: NORTHERN RANGE EXPANSION OF THE WHITE-FOOTED
MOUSE (PEROMYSCUS LEUCOPUS) UNDER CLIMATE CHANGE, AND ITS
CONSEQUENCES ON THE EMERGENCE OF LYME DISEASE IN QUÉBEC 47
Abstract
48
Introduction
49
Material and Methods
52
Study Area
52
Species Data
53
Climate and Environmental Factors
54
Ecological-Niche Factor Analysis
56
BIOMOD Habitat Niche Modeling Analyses
58
Results
60
vii
Ecological-Niche Factor Analysis
BIOMOD Habitat Niche Modeling Analyses
Discussion
Acknowledgements
References
Tables
CHAPTER 4: GENERAL CONCLUSIONS
Main Findings
Limitations and Uncertainties
Directions for Further Research
Literature Cited
60
61
63
68
69
84
94
95
97
100
102
viii
LIST OF TABLES
Chapter 3
Table 1 Climatic variables for the regional climatic model and the sub-continental
habitat niche models.
84
Table 2 Evaluation of the predictive performance of the sub-continental habitat
niche models.
85
ix
LIST OF FIGURES
Chapter 2
Fig. 1 Conceptual model of the interactions between the factors that influence the
rate of disease occurrence.
43
Fig. 2 The life cycle of the black-legged tick (Ixodes scapularis).
44
Fig. 3 How food resources, vegetation, fragmentation, photoperiod, and climate
influence the distribution of the white-footed mouse.
45
Chapter 3
Fig. 1 White-footed mouse historical distribution through time in southern regions of
Québec. ............................................................................................................................. 86
Fig. 2 Study area for the sub-continental scale models BIOMOD.. ................................. 87
Fig. 3 Scatterplot of projected changes (∆) in annual average temperature (Tavg
annual) and annual total precipitation (PrTot annual) for the future horizon
2050 (2041-2070) over the study area.. ............................................................................ 88
Fig. 4 Environmental variable scores obtained from the second set of ENFAs .. ............ 89
Fig. 5 Variable importance scores from the different models used in the analysis. ......... 90
Fig. 6 Response curves of each variable considered in the seven models used. .............. 91
Fig. 7 White-footed mouse modeled distribution, including predicted distribution
andprojected distribution with a change in climate variables under the A1b,
A2, and B1 green gas emissions scenarios from the IPCC (2001) ................................... 92
Fig. 8 Spatial distribution of standard deviation values from the predictions. ................. 93
x
CHAPTER 1: GENERAL INTRODUCTION
Lyme disease is an inflammatory disease caused by the bacterium
pathogen Borrelia burgdorferi (Olsen et al., 1995), and is a major concern in
North American public health. The disease was first discovered in the United
States (Centers for Disease Control and Prevention, 2011). In southeastern
Canada, however, the occurrence of Lyme disease is recent, but preoccupying as
the number of diagnosed human cases increases each year (Ogden et al., 2009). In
2010, Lyme disease became a nationally reportable pathogen in Canada (Public
Health Agency of Canada, 2010).
Lyme disease is vector-borne, and its distribution reflects the geographical
occurrence of its vectors In this thesis, I focus on the distribution of one particular
vector in the province of Québec (Canada): the white-footed mouse (Peromyscus
leucopus). The white-footed mouse carries the main dispersal vector of the Lyme
disease: the black-legged tick (Ixodes scapularis). This mouse is also an important
reservoir of the Lyme bacterium, B. burgdorferi (Anderson, 1989; Thompson et
al., 2001; Tsao, 2009). Modelling of the current and future potential distribution
of the white-footed mouse will help elaborate geographical risk zones of the
Lyme disease pathogen to limit its transmission to humans. Identifying potential
risk zones of the disease is also critical to diagnose it on time. When diagnosed
early, treatments for Lyme disease are usually simple and successful. However, at
more advanced stages, treatment efficiency decreases, while costs increase
(Wormser et al., 2006). The projected distribution of the white-footed mouse can
thus guide intervention activities and target sites for surveillance of Lyme disease.
Research Objectives
The aim of my research is to describe the potential distribution of the
white-footed mouse in Québec, and to characterize how climate change may
influence the mouse distribution in 2050. My objectives are to:
1
1. Describe the factors influencing the distribution of the white-footed
mouse in Québec. I reviewed published works on the biology of the
white-footed mouse in North America, and I summarized our knowledge
of the factors that limit the mouse's geographic distribution. I focussed on
factors examined at broad scales that are likely to be impacted by climate
change. The identification of these environmental factors will help health
agencies predict the future occurrence of the white-footed mouse under
climate change.
2. Characterize the distribution of the white-footed mouse in Québec by
developing a set of ecological niche models to predict its current
potential distribution and to project that distribution in the future
under climate change. Using results obtained from the previous chapter, I
created a set of ecological niche models of the white-footed mouse
distribution. The models provide a geographical estimation of current
potential suitable habitat for the white-footed mouse and projections of
future suitable habitat based on climate change scenarios. The projection
of future habitat will enable scientists to create risk-maps for Lyme disease
probability of occurrence .
Thesis Outline
This dissertation consists of four chapters including this general
introduction. Chapter two consists of a literature review on the biogeographical
framework of studies on Lyme disease, the potential influences of climate change
of species distribution, modelling species distribution, and the ecology of whitefooted mouse in Québec and in the northeastern United States. This literature
review summarizes the necessary information to model the current and projected
distribution of the white-footed mouse in Québec. In Chapter three I develop a
set of ecological niche models of the white-footed mouse for the northeastern part
of its range. I use eight multivariate logistic regression models to maximize the
2
predictive and explanatory power of the analysis. The set of models is also used
to predict the current potential distribution of the species in Québec, and to project
the mouse's potential geographical distribution under the influence of climate
change. The potential influences of future climate change, in addition to model
limitations, are then discussed. In the general conclusion, I review the main
findings and discuss recommendations for further research.
Literature Cited
Anderson J. F. (1989) Epizootiology of Borrelia in Ixodes tick vectors and
reservoir hosts. Reviews of Infectious Diseases,11, S1451-S1459.
Centers for Disease Control and Prevention (2011) Summary of notifiable
diseases – United States, 2010. Morbidity and Mortality Weekly Report,
59, 1-111.
Ogden N. H., Lindsay L. R., Morshed M., Sockett P. N., Artsob H. (2009) The
emergence of Lyme disease in Canada. Canadian Medical Association
Journal, 180, 1221-1224.
Olsen B., Duffy D. C., Jaenson T. G. T., Bonnedahl Ǻ. G. J., Bergström S. (1995)
Transhemispheric exchange of Lyme disease spirochetes by seabirds.
Journal of Clinical Microbiology, 33, 3270-3274.
Public Health Agency of Canada (2010) Lyme disease fact sheet. Available:
http://www.phac-aspc.gc.ca/id-mi/lyme-fs-eng.php.
Thompson C., Spielman A., Krause P. J. (2001) Coinfecting deer-associated
zoonoses: Lyme disease, Babesiosis, and Ehrlichiosis. Clinical Infectious
Diseases, 33, 676-685.
Tsao J. I. (2009) Reviewing molecular adaptations of Lyme borreliosis
spirochetes in the context of reproductive fitness in natural transmission
cycles. Veterinary Research, 40, 1-42.
Wormser G. P., Dattwyler R. J., Shapiro E. D., Halperin J. J., Steere A. C.,
Klempner M. S., Krause P.J., Bakken J. S.,Strle F., Stanek G.,
Bockenstedt L., Fish D., Dumler J.S., Nadelman R. B. (2006) The clinical
3
assessment, treatment, and prevention of Lyme disease, human
Granulocytic anaplasmosis, and Babesiosis: clinical practice guidelines by
the Infectious Diseases Society of America. Clinical Infectious Diseases,
43, 1089-1134.
4
CHAPTER 2: LITERATURE REVIEW
1. Climate Change and its Impact on Species Distribution
A species’ distribution is influenced by various factors. Anthropogenic
barriers, such cities, and natural barriers, such as oceans and mountains, make a
species’ distribution discontinuous in space.. Another major factor constraining
the large scale geographical distribution of species is the climate (Venier, 1999;
Hansen et al., 2001). Climate has always constrainted the geographical occurrence
of species, as documented from paleontological records and recent observations
(Davis et al., 2001; Parmesan, 2006). Climate significantly influences the
geographic limits of species as recognized by biologists over the last century
(Grinnell, 1917). Climate has indirect effects on a species’ distribution: it
influences an organism’s physiological tolerance, phenology, genetics, behavior,
and sensitivity to habitat and food supply (Andrewartha & Birch, 1954;
MacArthur, 1972).
With recent global warming, climate has been fluctuating to a greater
extant and faster than before, with consequences on species distribution (IPCC,
2007). Although the magnitude of these consequences remains in constant
discussion (Bradley et al., 1999; Parmesan, 2002; Parmesan & Yohe, 2003; Root
et al., 2003; Shoo et al., 2006), biologists agree that the 20th century global
warming has already had dramatic effects on the Earth’s biota (Peñuelas & Filella,
2001). Climate change challenges the natural relationship between species
distribution and climatic conditions; it pushes species adaptation to its limits
(Davis, 2001). Many scientists expect that species will track the changing climatic
conditions and shift poleward and upward in elevation (Hugues, 2000; Petersonet
al., 2002; Parmesan & Yohe, 2003; Root et al., 2003; Peterson, 2004; Parmesan,
2007; Hellmann et al., 2008; Jackson et al., 2009; Pounds et al., 2006; Gilman et
al., 2010; Pereira et al., 2010; Dawson et al., 2011; Pellissier et al., 2012; Schloss
et al., 2012; Zipkin et al., 2012). Others suggest that climate change is likely to
5
increase opportunities for invasive species to cross-over historical climatic
barriers, establish and spread in new areas (Githeko et al., 2000; Epstein, 2001;
Ostfeld, 2009; Walther et al., 2009).
The rise of new powerful statistical techniques and GIS tools makes it
easier for biologists to evaluate the potential influence of climate change on
species’ distributions. The development of predictive niche distribution models is
rapidly increasing (e.g. Davis & Callahan, 1992; Thomas & Lennon, 1999; Venier
et al., 1999; Drake et al., 2004; Moore et al., 2005; Phillips et al., 2006; Ogden et
al., 2008a; Régnière et al., 2008; Sharma & Jackson, 2008; Bradley et al., 2009;
Jarema et al., 2009; Bradley et al., 2010; Dillon et al., 2010; Martin, 2010;
McCarty, 2001; McMahon et al., 2011; Barbet-Massin, et al. 2012; Illoldi-Rangel
et al., 2012; Peterson, 2012). Scientists use these models to estimate suitable
habitats for a given species and to project these habitats under new environmental
or climatic conditions. When the projections are made using estimated climatic
conditions from climate change scenarios, they are a powerful tool to inform
decision makers about the potential changes in the species distribution, especially
in the case of invasive infectious disease vectors, such as mosquitoes, ticks, and
other arthropod species (Confalonieri et al., 2007; Pereira et al., 2010; Bellard et
al., 2012; Smith et al., 2012).
2. Modelling Species Distribution
Biogeography is a science which grows out of the work of geologists and
biologists in the late 18th to early 19th century (Buffon, 1791; von Humboldt,
1833; Darwin, 1859; von Linné et al., 1964). Biogeography can be described as
the descriptive and explanatory study of species distributions. The methods used
in biogeography rely on the theory of “ecological niche”. Many definitions are
given for the ecological niche (Grinnell, 1917; Elton 1927), but the most
commonly applied was defined by Hutchinson (1957). The Hutchinsonian niche
theory states that patterns of species’ distribution across geographical areas, such
6
as biomes, can be explained through a combination of environmental conditions
and resources. There may be different types of organisms in one habitat, but every
species’ distribution is a result of a unique combination of physical factors (e.g.
Engelbrecht, 2007), cycles of natural perturbation (e.g. Gleason, 1913),
anthropogenic disturbances (e.g. Kolozsvary & Swihart, 1999), biotic interactions
(e.g. le Roux et al., 2012), and dispersal ability (e.g. Gause, 1934, Laube et al.,
2012). This combination defines the species’ living conditions required to survive
and reproduce. For statistical analysis, this combination can be quantified on a
continuous axis and studied at varied environmental levels, e.g. climatic,
geomorphologic, or hydrological levels (Hutchinson, 1957).
The ecological niche theory is the basis of most species’ distribution
models (Guisan & Zimmermann, 2000; Peterson, 2001; Guisan & Thuiller, 2005;
Austin, 2007). The aim of these models is to characterize the species’
fundamental niche, that is the set of environmental conditions in which the species
can form viable populations in the absence of biotic interactions (Soberón &
Peterson, 2005). Other models also describe the species’ realized niche which is
the part of its fundamental niche further constrained by biotic interactions and
dispersal ability of the species (Wisz, 2012).
Species distribution models aim to explain the relationship between the
species and their environment, predict the potential current species distribution,
and in some cases, project this distribution under various scenarios of climate
change (Rushton et al., 2004; Kearney & Porter, 2009). The process of modeling
a species’ potential distribution involves four major steps. First, data on species’
occurrence are used to calibrate the model. This dataset aggregates geographical
points representing either the species’ presence or abundance (Hirzel & Guisan,
2002; Stockwell & Peterson, 2002; Graham, 2004; Chefaoui & Lobo, 2008;
Phillips et al., 2009; Vanderwal et al., 2009; Barbet-Massin et al., 2012). The
model then uses statistics (Muñoz, 2004; Elith et al., 2006; Araújo & New, 2007)
to compare these data with grids of environmentalvariables for the same location
7
(Legendre, 1993; Turner et al., 2003; Thuiller et al., 2004; Austin, 2007;
Dormann et al., 2007). The results from this comparison are probabilities to
observe the species according to the environmental variables initially provided.
Finally, the predictive ability of the models is evaluated by testing the results
obtained with an independent set of presence points (Manel et al., 2001; Araújo et
al., 2005; Allouche et al., 2006). As an additional step, it is possible to extrapolate
the results to a new area, or project them in the future according to changing
climatic conditions.
Projections of species’ potential distributions are useful in a decisionmaking process (Anderson & Martínez-Meyer, 2004). Modeling species
distribution also helps to understand which environmental or anthropogenic
factors best describe the geographical patterns in a species’ abundance and
distribution (Elith & Leathwick, 2009; Sinclair et al., 2010). It also helps to
identify gaps and weaknesses in the scientific knowledge of wildlife, and to
generate hypothesis about a species or its ecosystem (Morrison et al., 1992;
Soberón & Peterson, 2005).
Predictions and projections ,made from models of species’ niches, provide
powerful information, but rely on a number of assumptions (Parmesan, 2002;
Wiens, 2002; Thuiller, 2004; Araújo & Guisan, 2006). First, the theory on which
species’ niche models relies assumes that species are in quasi-equilibrium with
their environment (Woodward & Beerling, 1997; Braunisch et al., 2008; Václavík
& Meentemeyer, 2012). In other words, the species only occupy sites
characterised by ecological conditions favorable to their survival, and do not
occur in unfavorable conditions. A second assumption is that all environmental
conditions and resources that potentially represent the species’ niche are included
in the data used to calibrate the species’ niche models. (Griffiths et al., 1999;
Soberón & Peterson, 2004). This condition is essential to correctly predict the
potential distribution of the species in areas where they were not previously
studied. It also relates with issues of geographic scale (MacKey & Lindenmayer,
8
2001; Gelfand et al., 2006; Wiens & Bachelet, 2010), model extrapolation
(Pearson & Dawson, 2003; Thuiller et al., 2004), data quality (Hirzel et al. 2001),
or species that are difficult to localize (Hernandez et al., 2006). A third
assumption of habitat niche models, is that climate change only affects species
survival. Changing climatic conditions are not considered to influence biotic
interactions between species, and altered species interactions are therefore ignored
(Araújo & Luoto, 2007; Van der Putten et al., 2010). Lastly, we do not consider
the dispersal capacity and limitations of species in models (Lawton, 2000).
According to the habitat niche models, if environmental conditions on a site allow
the survival and reproduction of the species, the species will disperse there.
Finally, it is assumed that for any model projections in time (i.e. past or future)
the relationship between the species’ distribution and its environment is constant;
no adaptation (i.e. plasticity or evolution) can occur (e.g. Bradshaw & Holzapfel,
2001; Parmesan, 2006; Salamin et al., 2010). In the case of climate change
projections, uncertainties and assumptions are also inherent to predicted scenarios
of future climate change (Allen et al., 2000). Climate niche modeling remains a
widely used and useful tool for predicting the future distribution of species and
better evaluating the impact of future species’ ranges.
3. Biogeographical Approach to the Study of Lyme Disease
In my thesis, I apply a biogeographical approach to the study of Lyme
disease, using species’ niche models (Peterson, 2008). Modeling species
distributions and their habitats helps us to understand and determine the abiotic
requirements, biotic constraints, and dispersal ability of a species (Guisan &
Zimmermann, 2000). Modeling also enables one to project the potential species’
distribution in the future, to identify gaps and weaknesses in our understanding of
a species, and to generate hypotheses about a species or its habitat (Morrison et
al., 1992).
9
The accessibility of the host that define the transmission cycle of a disease
may limit its distribution. Generally, several species play one or numerous roles in
the transmission system of a pathogen, serving as reservoirs and vectors. The
transmission system of pathogen can therefore be defined as a suite of species
interactions which lead to targeted or incidental hosts (Peterson, 2008). It is thus
possible to predict the geograph of a disease by analyzing its hosts’ distributions
and how they interact with each other along the disease’s transmission cycle
(Martens et al., 1995; Ostfeld et al., 2005; Soberón & Peterson, 2004) (Fig. 1). In
this thesis, I focus on the main reservoir host for Lyme disease: the white footedmouse. Studying the potential ecological niche of the white-footed mouse will
help us to better characterize and describe the pattern of Lyme disease emergence
and risk associated to its distribution in future years.
Lyme Disease Occurrence
Lyme disease was discovered in the United States in 1969 (Olsen et al.,
1995). It is an inflammatory disease caused by the bacterium Borrelia
burgdorferi. In the United States, this disease is common, with more than 30,000
cases declared annually by the Center for Disease Control (Centers for Disease
Control and Prevention, 2011). In Canada, however, its occurrence is more recent
and not until 2010 did Lyme disease become a nationally reportable disease in the
southern provinces of Canada. The number of cases reported annually and
voluntarily by the provinces and territories has increased since the late 1980s from
five cases in 1996 to 25 cases in 2006, but these numbers may be under-reported
(Ogden et al., 2008b; Public Health Agency of Canada, 2010). In Québec, the first
case was reported in 1984, but it is only since 2003 that Lyme disease figures
within the list of diseases that require attention from Quebec’s authorities
(Vallière & Beaudry, 1990; Santé et Services Sociaux Québec, 2012). In 20042005, one endemic (i.e. acquired in Québec) human case was reported in addition
to five more for which there was no previous evidence of travel outside Canada
10
(Ogden et al., 2008c). To date, 15 people have been diagnosed with the disease in
Québec (Santé et Services Sociaux Québec, 2012).
Lyme Disease Transmission Mechanism
Lyme disease is a vector-borne disease, with a pathogen occurrence in part
defined by the geographic distribution of its vectors (Tsao, 2009). Its main host
vector, from which humans incidentally acquire the pathogen is the black-legged
tick, Ixodes scapularis (Anderson, 1989; Ogden et al., 2006; Thompson et al.,
2001). In 2008, more than 3% of the ticks analysed as part of an epidemiological
active surveillance study in Southern Québec were positive for the presence of the
bacterium Borrelia burgdorferi (Nguon et al., 2008). The geographical range of
the black-legged tick is shifting northwards within Southern Québec (Gray et al.,
2009; Leighton et al., 2012; Ogden et al., 2006), as a result of increasing annual
mean temperatures (Ogden et al., 2006).
The tick’s presence in Québec depends mostly on the movement and
migration of its hosts, since the parasite cannot disperse more than few meters a
year (Daniels et al., 1996; Leger et al., 2012; Ostfeld, 2011). Throughout its two
year life cycle, the black-legged tick can be transported by multiple vertebrate
species (Fig. 2). These host species serve as source of food (i.e. blood) between
the four life stages of the black-legged tick and ensure its survival (Anderson,
1989; Baggs et al., 2011; Mather et al., 1989; Ostfeld & Keesing, 2001). The
most commonly known vector of the tick is the white-tailed deer (Odocoileus
virginianus). As part of their life cycle, adult ticks use deer as their terminal host
and also their mating ground (Ostfeld 1997, Ogden et al. 2011). Deer perpetuate
the tick population, but do not support the transmission of the tick-borne pathogen
(Perkins et al. 2006). On the other hand, small vertebrates, such as birds and
rodents, are the hosts for the larval and nymph life stages of the tick, and are also
reservoirs for the disease bacterium (Ostfeld & Keesing 2001). Olsen et al. (1995)
and Ogden et al. (2008a) suggested that the northward spread of the disease is due
11
primarily to migratory birds that can host infected ticks. However, another
important host of the tick is now getting more abundant in Southern Québec, as
well as moving poleward (Chapter 3, this thesis). This host is the white-footed
mouse.
White-footed Mouse as Zoonotic Reservoir
The importance of the white-footed mouse (Miller, 1893) as a zoonotic
reservoir in the United States is well documented (Levine et al., 1985; Mather et
al., 1989; Rand et al., 1993; Thompson et al., 2001; Tsao, 2009). According to
Thompson et al. (2001), more than 80% of the mouse population is infected by
Lyme disease in Northeastern United States (see also Anderson et al., 1985; 1986;
1987a; 1987b). The white-footed mouse is the principal vertebrate host
responsible for infecting the larval black-legged ticks with the Lyme disease
bacterium (Donahue et al., 1987). It is also so far the only host species that
transmits all eight identified genotypes of the disease to the ticks (Hanincová et
al., 2006). In Southern Québec, adult male white-footed mice were found to carry
larger numbers of both larval and nymphal black-legged ticks than all other
species and classes of small mammals, including the deer mouse (Peromyscus
maniculatus), short-tailed shrew (Blarina brevicauda), sorex shrew (Sorex sp.),
red-backed vole (Myodes gapperi), meadow/woodland jumping mouse (Zapus
hudsonicus/ N. insignis), chipmunk (Tamias striatus), red squirrel (Tamiasciurus
hudsonicus), stoat (Mustela erminea), and northern flying squirrel (Glaucomys
sabrinus) (Bouchard et al., 2011). The presence of the white-footed mouse is
therefore likely to play a critical role in the geographical distribution of Lyme
disease in Southern Québec.
White-footed Mouse Distribution in Southern Québec
To identify the factors promoting the expansion of the white-footed mouse
and to accurately project its potential present and future geographical ranges, it is
12
necessary to identify the variables limiting its niche (Václavik & Meentemeyer,
2012). Unfortunately, the information on range limiting factors for the species is
not always available for its entire range (Morrison et al., 1992). To date, most of
the information we have about the white-footed mouse comes from research
carried out in the northern United States (e.g. Myers, 2005; 2009, Martin, 2010).
As few studies have been conducted in Québec (Grant, 1976, Millien unpublished
data), it is necessary to determine if the environmental limiting factors identified
in the United States also apply to southern Québec (see Martin, 2010). To address
this issue, further analysis is needed on the white-footed mouse in Southern
Québec.
4. The White-footed Mouse and Environmental Factors Influencing its
Distribution
The white-footed mouse (Peromyscus leucopus) is a rodent species
widespread throughout eastern North America, and plays key roles in many
ecosystems (Marcello et al., 2008). For instance, it is often the most abundant
small rodent in eastern woodlands and bordering agricultural fields (Linzey et al.,
2008). Its high population densities make it an essential prey for many predators
including mammals, such as raccoons, fox, and mink (Fanson, 2010), but also
birds, such as owls and raptors (Myers et al., 2009). In addition, the white-footed
mouse plays an essential role as a predator and has a profound effect on forest
pests. It is an effective predator of the gypsy moth pupae (Lymantria dispar), an
exotic moth that defoliates forests during its outbreaks (Elkinton et al., 1996;
Ostfeld et al., 1996). It is also an effective seed predator influencing the seedling
growth at forest edges and in agricultural fields (Ostfeld et al., 1997; Clotfelter et
al., 2007). Lastly, the white-footed mouse is an excellent host for ticks that often
represent a notable threat to the public health (Bouchard et al., 2011). Ticks
within zoonotic cycles maintain diverse and multiple disease agents that interact
as vectors between zoonotic reservoirs and humans (Gage et al., 2008). In North
America, such diseases include Lyme disease, human babesiosis, and human
13
granulocytic ehrlichiosis (Thompson et al., 2001). The white-footed mouse thus
acts as a dispersal vector of several human-threatening diseases.
The white-footed mouse is widespread and occupies about two-thirds of
the United States extending up to southern Canada (Linzey et al., 2008). Recent
studies however, report that its distribution has substantially shifted poleward
(Myers et al., 2009; Chapter 3, this thesis). Given the role of the white-footed
mouse as an important vector for the black-legged ticks carrying Lyme disease, it
is important to better characterize its potential distribution in Québec over the
next few years. It would increase the chance for medical practitioners to make
timely diagnoses of the disease (Wormser et al., 2006). Many studies have
identified potential factors influencing the distribution and abundance of the
white-footed mouse (e.g. Grant, 1976; Myers et al., 2009), but there has yet to be
a synthesis published.
Shifting Distribution of the White-footed Mouse
The first research on the white-footed mouse dates back to the early 20th
century (e.g. Osgood, 1909). The white-footed mouse is widely used in studies on
species’ adaptation because it possesses generalist ecological characteristics and
successfully occupies a wide range of habitats (Blair, 1950; Bendell, 1959;
Gaertner et al., 1973, Macmillien & Garland, 1989; Wilder et al., 2005). Most
recently studies on the species have focused on its geographical distribution which
is presently shifting polewards (Myers et al., 2009). Recent research suggests that
the geographic distribution of the species is changing in regions around the Great
Lakes. The white-footed mouse has long been established on the Michigan
Peninsula. Its remains, including skulls dating to approximately 600 years B.P.,
are found in the region (Long, 1996). Yet, not until 1893, was its presence
documented by Miller (1893). Hooper (1942) first mentioned the changes in the
distribution of the white-footed mouse; observing that the species had become
more abundant over the entire Lower Peninsula in the state of Michigan. Later,
14
Long (1996) noted an increase in the white-footed mouse population size in
Wisconsin. More recently, Myers et al. (2009) documented rapid changes in the
ranges and relative abundance of nine small mammals, including the white-footed
mouse, in the northern Great Lakes region, where the distribution of the mouse is
supposed to reach its northern limit. Myers et al. (2009) note that the white-footed
mouse population had extended its range >225 km since 1980 at a rate of 15 km
per year. Most importantly, the species’ abundance is also increasing at its
northern range limit (Myers et al., 2009).
In the last 25 years, it has become evident that white-footed mouse range
shifts are not restricted to the Great Lakes regions, but are also occurring in
southern Québec. In 1976, Grant reported that most of the mice captured on Mont
Saint-Hilaire, in the Montérégie between 1966 and 1976, were deer mice
(Peromyscus maniculatus), which are native to the Province. Twenty years later,
however, the number of deer mice captured in the region has drastically
decreased, whereas the number of white-footed mouse has increased (Millien not
published; Rogic et al., 2013). A definitive explanation for this shifting
distribution and its possible drivers has yet to be provided. In this review I
consider a number of environmental factors that could influence and potentially
explain changes in the distribution of the white-footed mouse: climate and
photoperiodic regulation, forest type and food abundance, and habitat
fragmentation and connectivity (Fig. 3).
Climate Conditions
Some have noted changes in the species’ geographical occurrence
consistent with predictions of climate warming: changes in snow cover,
temperature, and precipitation (Long, 1996; Myers et al., 2005; Myers et al.,
2009; Martin, 2010) (Fig. 3). The white-footed mouse faces considerably greater
seasonal differences at its northern than southern range limits. Winter is the
hardest season for the species (Howard, 1951). When climatic conditions are
15
characterized by cold temperature, the mouse must use energy-saving
mechanisms to survive: fur density and thermoregulation, including the state of
torpor (Hill, 1983; Conley & Porter, 1986). Thermoregulation is possible under
extreme temperatures for calm and cloudy conditions, but when snow cover is
lacking and the wind is strong, the added heat loss could limit thermoregulation at
excessively low temperatures (Conley & Porter, 1986). During hard winters, the
white-footed mouse will experience significant loss of mass and/or heavy
mortality (Conley & Porter, 1986; Wolff & Durr, 1986).
The ability of the white-footed mouse to survive winter is thus dependant
on the presence of potential shelters or, simply avoiding hard winter conditions.
Snow cover provides an excellent insulated habitat for the white-footed mouse
that often forages under the snow cover during the winter (Conley & Porter, 1986;
Wolff & Durr, 1986). The white-footed mouse also prefers sites characterized by
late autumns and early springs. When winter ends late, the breeding activities of
the species begin before resources are available to sustain the breeding process,
and population numbers decrease (Long, 1973). On the other hand, when winter
ends early, mice breed successfully (Long, 1996). In general, winters were shorter
during the 20th century in Michigan’s Upper Peninsula (Myers et al., 2005),
which favoured the white-footed mouse range expansion.
Photoperiodic Regulation
The geographical characteristics of sites where the white-footed mouse is
established, such as latitude, can also influence its dispersal. According to Dark et
al. (1983), the white-footed mouse’s reproductive system and its survival success
in winter are regulated by the photoperiod which varies with latitude. The
photoperiod is a reliable indicator of many geophysical cycles. In the case of the
white-footed mouse, the photoperiod serves as a cue to adjust its physiology and
behavior in advance of critical changes in environmental conditions, such as
reduced food availability and ambient temperature (Heideman et al., 1999;
16
Bradshaw & Holzapfel, 2001; Bradshaw & Holzapfel, 2007; Bronson, 2009) (Fig.
3).
The critical day length of the white-footed mouse varies between 12 and 14
hours of light per day depending on its geographical location (Lynch & Gendler,
1980; Dark et al., 1983). Mice experimentally maintained for shorter days tend to
reduce the length of their breeding season, reduce migration movement, and
display enhanced immune functions under stress compared to long-day cycle
animals (Lynch et al., 1973; Lynch et al., 1978; Dark et al., 1983; Demas &
Nelson, 1998). They increase the amount of time spent for nesting, torpor and
molting behaviour, and are more active at building larger nests and hoard greater
quantities of food (Lynch et al., 1973; Lynch et al., 1978).
At its northern range limits the white-footed mouse distribution, growth and
reproduction must be achieved within a shorter time period, and in less favourable
conditions than at lower latitudes. There is a difference of up to two months in the
duration of the breeding season between the southern and northern ends of its
range (Health & Lynch, 1983). The mice thus need to adapt the timing of their
annual life cycle to the conditions of its habitat. It may avoid sites where the
timing is too short and conditions are too extreme for its growth or reproduction
to be completed. The photoperiod is thus an ecogeographic characteristic, which
in correlation with climate, may influence the northern distribution of the whitefooted mouse (Lynch, 1973; Bradshaw & Holzapfel, 2001; Bradshaw &
Holzapfel, 2007).
Forest Type and Food Abundance
The white-footed mouse is commonly found in a wide range of habitats:
from brushy prairies in Wisconsin (Long, 1996), to wet lowlands in Illinois
(Batzli, 1977), and dense upland hardwood forests in Michigan (Hooper, 1942)
(Fig. 3). The mouse also exploits a wide range of resources. It is a generalist in
17
terms of food selection, eating acorns, arthropods, beech nuts, maple, grass seeds,
and pits of chokecherries (Long, 1973). In northern regions, it selects sites
according to specific criteria that enhance its survival under harsh winter
conditions (Pierce & Vogt, 1993). The white-footed mouse chooses its habitat
among different land-covers (i.e. forest types) according to the availability of food
resources and proper material for insulating nests. The habitat must provide
enough food during the fall to be stored for the winter (Hopper, 1942; Long,
1973; Long, 1996; Clotfelter et al., 2007). In response to reduced food
availability, the white-footed mouse can suppress its reproductive and immune
function (Demas & Nelson, 1998), decrease its metabolism and reduce its body
temperature (Gaertner et al., 1973). In fact, studies show that white-footed mouse
populations are adjusted to food supply in critical times of the year through their
survival rates, timing of breeding, and possibly migration (Grant, 1976).
Habitat Fragmentation and Connectivity
The white-footed mouse probability of occurrence is sensitive to the
degree of fragmentation and connectivity between forest patches in pasture and
cultivated croplands (Fig. 3). In Southern Québec and especially in the
Montérégie region, the species faces habitat fragmentation due to agricultural
fields and urbanized areas. Habitat fragmentation can affect the white-footed
mouse’s population indirectly by modifying the level of available resources, or
directly by altering its dispersal rate (Nupp & Swihart, 1998).
The white-footed mouse is a generalist species and has a high tolerance for
marginal habitats. Even if the degree of habitat fragmentation influences the
spatial availability and variability of resources in its environment (Collinge, 1996;
Robinson et al., 1992; Cushman et al., 2010), the mouse is expected to thrive in
highly fragmented landscapes as long as its population number does not increase
dramatically in one habitat fragment (Linzey & Kesner, 1991; Wilder et al.,
2005). The white-footed mouse is territorial and thus a limited number of
18
individuals can live in a given habitat patches (Allan et al, 2003); new mature
individuals need to disperse (Anderson & Danielson, 1997; Hansson, 1987;
Krohne & Hoch, 1999). White-footed mouse females are known to move only as
far as required by competition to find a vacant home range (Keane, 1999). Males,
on the other hand, avoid intraspecific competition for habitat and resources and
reduce inbreeding by settling outside their natal home range. They thus need to
move farther within a fragmented landscape to find a suitable habitat (Keane,
1999).
The white-footed mouse dispersal within agricultural fields is minimal,
and if necessary it uses corridors to move between forest patches (Rizkalla &
Swihart, 2007). Such corridors are any form of woody understory and canopy
such as fence rows, hedgerows, and drainage ditches (Alder & Wilson, 1987;
Merriam & Lanoue, 1990; Krohne & Hock, 1999; Schweiger et al., 1999). The
levels of connectivity and quality of corridors between forest fragments influence
the rate at which the white-footed mouse uses the corridors and consequently the
level of its dispersal within the landscape (Anderson & Danielson, 1997; Krebs,
1996, Krohne & Burgin, 1987). Since dispersal is essential for individuals’
reproduction, the white-footed mouse is bound to use these corridors in a
fragmented landscape (Anderson & Danielson, 1997; Krohne & Burgin, 1987;
Krohne & Hock, 1999).Overall, the reduction of connectivity across the forest
patches will likely impede the species ‘ability to disperse.
White-Footed Mouse Co-Existence with the Deer Mouse
The white-footed mouse is closely related to the deer mouse (Peromyscus
maniculatus), which is morphologically similar and shares common ecological
requirements. From Darwin’s theory (1859), when two species have similar
ecological requirements, including foods and nest sites, one species’ geographical
range may be restricted by its related species. As mentionned previously, in
Southern Québec it was observed that the number of deer mice captured on the
19
sites diminished every years, while the number of white-footed mice increased
(Millien not published, Rogic et al. 2013). According to Long (1996), such
phenomenon may be explained by the superior niche exploitation of one species
over the other, in this case, the white-footed mouse over the deer mouse. The most
recent evidence on the coexisting ability of the white-footed mouse and the deer
mouse suggests that both species follow the competitive exclusion principle
(Anderson et al., 2002).
The deer mouse has a competitive advantage in winter only. For both
species, winter is a hard season to survive. The white-footed mouse and the deer
mouse have similar patterns of thermogenesis and accumulation of brown fat
(Lynch, 1973; Zegers & Merritt, 1988). The deer mouse has, however, a superior
ability for nest site selection, and at building larger hoards of food (Wolff & Durr,
1986; Tannenbaum & Pivorun, 1988; Pierce & Vogt, 1993; Dooley & Dueser,
1996). Stah (1980) found that both species prefer to nest in tree cavities and
hollows, but that the white-footed mouse shifts to ground nesting when
competiting with the deer mouse. When cold temperature persists into the spring,
the deer mouse thus has a higher change of survival and its abundance increases
(Rowland, 2003). Warmer winters and ealier springs, however, appear to be
advantageous for the white-footed mouse at the expense of the deer mouse. In
fact, Madison et al. (1984), found that the white-footed mouse is vulnerable to
thin snow and deep frost at the northern limits of its geographical range, because
it often dwells in its underground nest. In seasons other than winter, Stah (1980)
found the white-footed mouse to be more aggressive. The white-footed mouse
out-competes the deer mouse at getting food when available on the ground, and
even steals the caches of the other (Pierce & Vogt, 1993). The white-footed
mouse thus has an advantage over the deer mouse in summer and during times
when food is abundant.
20
5. Conclusion
The 20th century global warming has important effects on the Earth’s
biota. Scientists expect that species’ ranges will shift poleward and upward in
elevation tracking changing climatic conditions. Scientists study this phenomenon
using ecological niche models to explore the relationship between species and
their environment, predict potential species distribution, and project this
distribution under various scenarios of climate change. Ecological niche models
have the power to determine the potential occurrence of a disease, such as Lyme
disease. Lyme disease is a vector-born disease, whose occurrence is limited by the
geographic distribution of its vectors, including the white-footed mouse. While a
number of climatic factors and landscape features have been shown to influence
the patterns of dispersal of the white-footed mouse and its distribution, no one yet
has studied the impact of climate change on its distribution in Québec, the most
northern part of its range. Such results will be useful for government authorities
and public health agencies to better prioritize their management and surveillance
activities in the province of Québec in the context of Lyme disease emergence.
Literature Cited
Alder G. H., Wilson M. L. (1987) Demography of habitat generalist, the whitefooted mouse, in a heterogeneous environment. Ecology, 68, 1785-1796.
Allan B. F., Keesing F., Ostfeld R. S. (2003) Effect of forest fragmentation on
Lyme disease risk. Conservation Biology, 17, 267-272.
Allen M. R., Stott P. A., Mitchell J. F. B., Schnur R., Delworth T. L. (2000)
Quantifying the uncertainty in forecasts of anthropogenic climate change.
Nature, 407, 617-620.
Allouche O., Tsoar A., Kadmon R. (2006) Assessing the accuracy of species
distribution models: prevalence, kappa and the true skill statistic
(TSS).Journal of Applied Ecology, 43, 1223-1232.
Anderson J. F. (1989) Epizootiology of Borrelia in Ixodes tick vectors and
reservoir hosts. Reviews of Infectious Diseases, 11, S1451-S1459.
21
Anderson J. F., Duray P. H., Magnarelli L. A. (1987) Prevalence of Borrelia
burgdorferi in white footed mice and Ixodes dammini at Fort McCoy, Wis.
Journal of Clinical Microbiology,25, 1495-1497.
Anderson J. F., Johnson R. C., Magnarelli L. A. (1987) Seasonal prevalence of
Borrelia burgdorferi in natural populations of white-footed mice,
Peromyscus leucopus. Journal of Clinical Microbiology, 25, 1564-1566.
Anderson J. F., Johnson R. C., Magnarelli L. A., Hyde F. W. (1985) Identification
of endemic foci of Lyme disease: isolation of Borrelia burgdorferi from
feral rodents and ticks (Dermacentor variabilis). Journal of Clinical
Microbiology, 22, 36-38.
Anderson J. F., Johnson R. C., Magnarelli L. A., Hyde F. W., Myers J. E. (1986)
Peromyscus leucopus and Microtus pennsylvanicus simultaneously
infected with Borrelia burgdorferi and Babesia microti. Journal of
Clinical Microbiology, 23, 135-137.
Anderson G. S., Danielson B. J. (1997) The effects of landscape composition and
physiognomy on metapopulation size: the role of corridors. Landscape
Ecology, 12, 261-271.
Anderson R. P., Martínez-Meyer E. (2004) Modeling species' geographic
distributions for preliminary conservation assessments: an implementation
with the spiny pocket mice (Heteromys) of Ecuador. Biological
Conservation, 116, 167-179.
Anderson R. P., Peterson A. T., Gómez-Laverde M. (2002) Using niche-based
GIS modeling to test geographic predictions of competitive exclusion and
competitive release in South American pocket mice. OIKOS, 98, 3-16.
Araújo M. B., Guisan A. (2006) Five (or so) challenges for species distribution
modelling. Journal of Biogeography, 33, 1677-1688.
Araújo M. B., Luoto M. (2007) The importance of biotic interactions for
modelling species distributions under climate change. Global Ecology and
Biogeography, 16, 743-753.
Araújo M. B., New M. (2007) Ensemble forescasting of species distributions.
Trends in Ecology and Evolution, 22, 42-47.
22
Araújo M. B., Pearson R. G., Thuillers W., Erhard M. (2005) Validation of
species-climate impact models under climate change. Global Change
Biology, 11, 1504-1513.
Andrewartha H. G., Birch C. (1954) Distribution and abundance of animals,
Chicago, University of Chicago Press.
Austin M. (2007) Species distribution models and ecological theory: A critical
assessment and some possible new approaches. Ecological Modelling,
200, 1-19.
Baggs E. M., Stack S. H., Finney-Crawley J. R., Simon N. P. P. (2011)
Peromyscus maniculatus, a possible reservoir host of Borrelia garinii
from the Gannet Islands off Newfoundland and Labrador. Journal of
Parasitology, 97, 792-794.
Barbet-Massin M., Thuiller W., Jiguet F. (2012) The fate of European breeding
birds under climate, use and dispersal scenarios. Global Change Biology,
18, 881-890.
Batzli G. O. (1977) Population dynamics of the white-footed mouse in floodplain
and upland forests. American Midland Naturalist, 97, 18-32.
Bellard C., Bertelsmeier C., Leadley P., Thuiller W., Courchamp F. (2012)
Impacts of climate change on the future of biodiversity. Ecology Letters,
15, 365-377.
Bendell J. F. (1959) Food as a control of a population of white-footed mice,
Peromyscus leucopus novaboracensis (Fischer).Canadian Journal of
Zoology, 37, 173-209.
Blair W. F. (1950) Ecological factors in speciation of Peromyscus. Evolution, 4,
253-275.
Bouchard C., Beauchamp G., Nguon S., Trudel L., Milord F., Lindsay L. R.,
Bélanger, D., Ogden N. H. (2011) Associations between Ixodes scapularis
ticks and small mammal hosts in a newly endemic zone in southeastern
Canada: implications for Borrelia burgdorferi transmission. Ticks and
Tick-borne Diseases, 2, 183-190.
23
Bradley B. A. (2009) Regional analysis of the impacts of climate change on
cheatgrass invasion shows potential risk and opportunity. Global Change
Biology, 15, 196-208.
Bradley B. A., Wilcove D. S., Oppenheimer M. (2010) Climate change increases
risk of plant invasion in the Eastern United States. Biological Invasions,
12, 1855-1872.
Bradley N. L., Leopold A. C., Ross J., Huffaker W. (1999) Phenological changes
reflect climate change in Wisconsin. Proceedings of the National Academy
of Sciences ,96, 9701-9704.
Bradshaw W. E., Holzapfel C. M. (2001) Genetic shift in photoperiodic response
correlated with global warming. Proceedings of the National Academy of
Sciences, 98, 14509-14511.
Bradshaw W. E., Holzapfel C. M. (2007) Evolution of animal photoperiodism.
Annual Review of Ecology, Evolution, and Systematics, 38, 1-25.
Braunisch V., Bollmann K., Graf R. F., Hirzel A. H. (2008) Living on the edge modelling habitat suitability for species at the edge of their fundamental
niche. Ecological Modelling, 214, 153-167.
Bronson F. H. (2009) Climate change and seasonal reproduction in mammals.
Philosophical Transactions of the Royal Society B: Biological
Sciences,364, 3331-3340.
Centers for Disease Control and Prevention (2011) Summary of notifiable
diseases – United States, 2010. Morbidity and Mortality Weekly Report,
59, 1-111.
Chefaoui R. M., Lobo J. M. (2008) Assessing the effects of pseudo-absences on
predictive distribution model performance. Ecological Modelling, 210,
478-486.
Clotfelter E. D., Pedersen A. B., Cranford J. A. (2007) Acorn mast drives longterm dynamics of rodent and songbird populations. Oecologia, 154, 493503.
24
Collinge S. K. (1996) Ecological consequences of habitat fragmentation:
implications for landscape architecture and planning. Landscape and
Urban Planning, 36, 59-77.
Confalonieri U., Menne B., Akhatar R., Ebi K. L., Hauengue M., Kovats R. S.,
Revitch B., Woodward A. (2007) Human health. In: Climate change 2007:
Impacts, Adaptation and Vulnerability. Contribution of Working Group II
to the fourth Assessment Report of the Intergovernmental Panel of Climate
Change.(eds. M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. van der
Linden , C. E. Hanson) Cambridge, UK, Cambridge University Press.
Conley K. E., Porter W. P. (1986) Heat loss from Deer mice (Peromyscus)
evaluation of seasonal limits to thermoregulation. Journal of Experimental
Biology, 126, 249-269.
Cushman S. A., Littell J., Mcgarigal K. (2010) The problem of ecological scaling
in spatially complex, nonequilibrium ecological systems. In: Spatial
Complexity, Informatics, and Wildlife Conservation. (eds Cushman SA,
Huettmann F) New York, Springer.
Daniels T. J., Falco R. C., Curran K. L., Fish D. (1996) Timing of Ixodes
scapularis (Acari:Ixodidae) oviposition and larval activity in Southern
New York. Journal of Medical Entomology, 33, 140-147.
Dark J., Johnston P. G., Healy M., Zucker I. (1983) Latitude of origin influences
photoperiodic control of reproduction of Deer mice (Peromyscus
maniculatus).Biology of Reproduction,2 8, 213-220.
Darwin C. (1859) On the Origin of Species by Means of Natural Selection, or the
Preservation of Favoured Races in the Struggle for Life, London, John
Murray.
Davis R., Callahan J. R. (1992) Post-pleistocene dispersal in the Mexican vole
(Microtus mexicanus), an example of an apparent trend in the distribution
of southwestern mammals. Great Basin Naturalist, 52, 262-268.
Davis M. B., Shaw R. G. (2001) Range shits and adaptive responses to
Quaternary climate change. Science, 292, 673-679.
25
Dawson T. P., Jackson S. T., House J. I., Prentice I. C., Mace G. M. (2011)
Beyond predictions: biodiversity conservation in a changing climate.
Science, 332, 53-58.
Demas G. E., Nelson R. J. (1998) Photoperiod, ambient temperature, and food
availability interact to affect reproductive and immune function in adult
male deer mice (Peromyscus maniculatus). Journal of Biological Rhythms,
13, 253-262.
Dillon M. E., Wang G., Huey R. B. (2010) Global metabolic impacts of recent
climate warming. Nature,467, 704-707.
Donahue J. G., Piesman J., Spielman A. (1987) Reservoir competence of whitefooted mice for Lyme disease spirochetes. American Journal of Tropical
Medicine and Hygiene, 36, 92-96.
Dooley J. J. L., Dueser R. D. (1996) Experimental tests of nest site competition in
two Peromyscus species. Oecologia, 105, 81-86.
Dormann C. F., Mcpherson J. M., Araújo M. B., Bivand R., Bolliger J., Carl G.,
Davies R. G., Hirzel A., Jetz W., Kissling W. D., Kühn I., Ohlemüller R.,
Peres-Neto P. R., Reineking B., Schröder B., Schurr F. M., Wilson R.
(2007) Methods to account for spatial autocorrelation in the analysis of
species distribution data: a review. Ecography, 30, 609-628.
Drake J. M., Bossenbroek J. M. (2004) The potential distribution of zebra mussels
in the United States. Bioscience, 54, 931-941.
Elith J., Graham C. H., Anderson R. P., Dudík M., Ferrier S., Guisan A., Hijmans
R. J., Huettmann F., Leathwick J. R., Lehmann A., Li J., Lohmann L. G.,
Loiselle B. A., Manion G., Moritz C., Nakamura M., Nakazawa Y.,
Overton J. M., Peterson A. T., Phillips S. J., Richardson K., ScachettiPereira R., Schapire R. E., Soberón J., Williams S., Wisz M. S.,
Zimmermann N. E. (2006) Novel methods improve prediction of species’
distributions from occurence data. Econography, 29, 129-151.
Elith J., Leathwick J. R. (2009) Species distribution models: ecological
explanation and prediction across space and time. Annual Review of
Ecology, Evolution, and Systematics, 40, 677-697.
26
Elkinton J. S., Healy W. M., Buonaccorsi J. P., Boettner G. H., Hazzard A. M.,
Smith H. R., Liebhold A. M. (1996) Interactions among gypsy moths,
white-footed mice, and acorns. Ecology, 77, 2332-2342.
Elton C. (1927) Animal Ecology, London, Sidgewick and Jackson.
Engelbrecht B. M. J., Comita L. S., Condit R., Kursar T. A., Tyree M. T., Turner
B. L., Hubbell S. P. (2007) Drought sensitivity shapes species distribution
patterns in tropical forests. Nature, 447, 80-83.
Epstein P. R. (2001) Climate change and emerging infectious diseases. Microbes
and Infection, 3, 747-754.
Fanson B. G. (2010) Effects of direct and indirect cues or predation risk on the
foraging behavior of the white-footed mouse (Peromscus leucopus).
Northeastern Naturalist, 17, 19-28.
Gaertner R. A., Hart J. S., Roy O. Z. (1973) Seasonal spontaneous torpor in the
white-footed mouse, Peromyscus leucopus. Comparative Biochemistry
and Physiology, 45A, 169-181.
Gage K. L., Burkor T. R., Eisen R. J., Hayes E. B. (2008) Climate and
Vectorborne Diseases. American Journal of Preventive Medicine, 35, 436450.
Gause G. F. (1934) The Struggle for Existence, Baltimore, Maryland, USA,
Williams and Wilkins.
Gelfand A. E., Silander, J. A. Jr., Wu S., Latimer A., Lewis P. O., Rebelo A. G.,
Holder M. (2006) Explaining species distribution patterns through
hierarchical modeling. Bayesian Analysis, 1, 41-92.
Gilman S. E., Urban M. C., Tewksbury J., Gilchrist G. W., Holt R. D. (2010) A
framework for community interactions under climate change. Trends in
Ecology and Evolution, 25, 325-331.
Githeko A. K., Lindsay S. W., Confalonieri U. E., Patz J. A. (2000) Climate
change and vector-borne diseases: a regional analysis. Bulletin of the
World Health Organization, 78, 1136-1147.
Gleason H. A. (1913) The relation of forest distribution and prairie fires in the
middle west. Torreya, 13, 173-181.
27
Graham C. H., Ferrier S., Huettman F., Moritz C., Peterson A. T. (2004) New
developments in museum-based informatics and applications in
biodiversity analysis. Trends in Ecology and Evolution, 19, 497-503.
Grant P. R. (1976) An 11-year study of small mammal populations at Mont St.
Hilaire, Quebec. Canadian Journal of Zoology, 54, 2156-2173.
Gray J. S., Dautel H., Estrada-Peña A., Kahl O., Lindgren E. (2009) Effects of
climate change on ticks and tick-borne diseases in Europe.
Interdisciplinary Perspective on Infectious Diseases,.
Doi:10.1155/2009/593232
Griffiths G. H., Eversham B. C., Roy D. B. (1999) Integrating species and habitat
data for nature conservation in Great Britain: data sources and methods.
Global Ecology and Biogeography, 8, 329-345.
Grinnell J. (1917) Field tests of theories concerning distributional control. The
American Naturalist, 51, 115-128.
Guisan A., Thuiller W. (2005) Predicting species distribution: offering more than
simple habitat models.Ecology Letters, 8, 993-1009.
Guisan A., Zimmermann N. E. (2000) Predictive habitat distribution models in
ecology. Ecological Modelling, 135, 147-186.
Hanincová K., Kurtenback K., Diuk-Wasser M., Brei B., Fish D. (2006) Epidemic
spread of Lyme borreliosis, Northern United States. Emerging Infectious
Diseases, 12, 604-611.
Hansen A. J., Neilson R. P., Dale V. H., Flather C. H., Iverson L. R., Currie D. J.,
Shafer S., Cook R., Bartlein P. J. (2001) Global change in forests:
responses of species communities, and biomes. Bioscience, 51, 765-779.
Hansson L. (1987) Dispersal routes of small mammals at an abandoned field in
Central Sweden. Holarctic Ecology, 10,154-159.
Health H. W., Lynch G. R. (1983) Intraspecific differences in the use of
photoperiod and temperature as environmental cues in white-footed mice
Peromyscus leucopus. Physiological Zoology, 56, 506-512.
Heideman P. D., Bruno T. A., Singley J. W., Semdley J. V. (1999) Genetic
variation in photoperiodism in Peromyscus leucopus: geographic variation
28
in an alternative life-history strategy. Journal of Mammalogy, 80, 12321242.
Hellmann J. J., Byers J. E., Bierwagen B. G., Dukes J. S. (2008) Five potential
consequences of climate change for invasive species. Conservation
Biology, 22, 534-543.
Hernandez P. A., Graham C. H., Master L. L., Albert D. L. (2006) The effect of
sample size and species characteristics on performance of different species
distribution modeling methods. Ecography, 29, 773-785.
Hill R. W. (1983) Thermal physiology and energetics of Peromyscus: ontogeny,
body temperature, metabolism, insulation, and microclimatology. Journal
of Mammalogy, 64, 19-37.
Hirzel A. H., Guisan A. (2002) Which is the optimal sampling strategy for habitat
suitability modelling. Ecological Modelling, 157, 331-341.
Hirzel A. H., Helfer V., Metral F. (2001) Assessing habitat-suitability models
with a virtual species. Ecological Modelling, 145, 111-121.
Hooper E. T. (1942) An effect on the Peromyscus maniculatus rassenkreis of land
utilization in Michigan Journal of Mammalogy, 23, 193-196.
Howard W. E. (1951) Relationship between low temperature and available food to
survival of small rodents. Journal of Mammalogy, 32, 300-312.
Hugues L. (2000) Biological consequences of global warming: is the signal
already. Trends in Ecology and Evolution, 15, 56-61.
Hutchinson G. E. (1957) Concluding remarks. Cold Spring Harbor Symposia on
Quantitative Biology, 22, 415-427.
Illoldi-Rangel P., Rivaldi C.-L., Sissel B., Fryxell R. T., Gordillo-Pérez G.,
Rodríguez-Moreno A., Williamson P., Montiel-Parra G., Sánchez-Cordero
V., Sarkar S. (2012) Species distribution models and ecological suitability
analysis for potential tick vectors of Lyme disease in Mexico. Journal of
Tropical Medicine, 2012, 10 p. DOI: 10.1155/2012/959101.
IPCC (2007) Climate Change 2007: Synthesis Report. In: Contribution of
Working Groups I, II and III to the Fourth Assessment Report of the
29
Intergovernmental Panel on Climate Change (eds. R. K. Pachauri, A.
Reisinger) Geneva, Switzerland, IPCC.
Jackson S. T., Betancourt J. L., Booth R. K., Gray S. T. (2009) Ecology and the
ratchet of events: climate variability, niche dimensions, and species
distributions. Proceedings of the National Academy of Sciences, 106,
19685-19692.
Jarema S. I., Samson J., Mcgill B. J., Humphries M. M. (2009) Variation in
abundance across a species' range predicts climate change responses in the
range interior will exceed those at the edge: a case study with North
American beaver. Global Change Biology, 15, 508-522.
Keane B. (1990) Dispersal and inbreeding avoidance in the white-footed mouse,
Peromyscus leucopus.Animal Behaviour, 40, 143-152.
Kearney M., Porter W. (2009) Mechanistic niche modelling: combining
physiological and spatial data to predic species' ranges. Ecology Letters,
12, 334-350.
Kolozsvary M. B., Swihart R. K. (1999) Habitat fragmentation and the
distribution of amphibians: patch and landscape correlates in farmland.
Canadian Journal of Zoology, 77, 1288-1299.
Krebs C. J. (1996) Population cycles revisited. Journal of Mammalogy,77, 8-24.
Krohne D. T., Burgin A. B. (1987) Relative success of residents and immigrants
in Peromyscus leucopus. Holarctic Ecology, 10, 196-200.
Krohne D. T., Hoch G. A. (1999) Demography of Peromyscus leucopus
populations on habitat patches: the role of dispersal. Canadian Journal of
Zoology, 77, 1247-1253.
Laube I., Graham C. H., Böhning-Gaese K. (2012) Intra-generic species richness
and dispersal ability interact to determine geographic ranges of birds.
Global Ecology and Biogeography, 22, 223-232.
Lawton J. L. (2000) Concluding remarks: a review of some open questions. In:
Ecological consequences of heterogeneity. (ed. By M. J. Hutchings, E.
John, A. J. A. Stewart) Cambridge, Cambridge University Press.
30
Leclerc comte de Buffon G.-L. (1791) Natural History, General and Particular,
Edinburgh, William Creech.
Legendre P. (1993) Spatial autocorrelation: trouble or new paradigm? Ecology,
74, 1659-1673.
Léger, E., Vourc’h, G., Vial, L., Chevillon, C., McCoy, K. D. (2012) Changing
distributions of ticks: causes and consequences. Experimental and Applied
Acarology, 59, 219-244.
Leighton, P. A., Koffi, J. K., Pelcat, Y., Lindsay, R., Ogden, N. H. (2012)
Predicting the speed of tick invasion: an empirical model of range
expansion for the Lyme disease vector Ixodes scapularis in Canada.
Journal of Applied Ecology, 49, 457-464.
le Roux P. C., Virtanen R., Heikkinen R. K., Luoto M. (2012) Biotic interactions
affect the elevation ranges of high-latitude plant species. Ecography, 35,
001-009. Levine J. F., Wilson M. L., Spielman A. (1985) Mice as
reservoirs of the Lyme disease spirochete. American Journal of Tropical
Medicine and Hygiene, 34, 355-360.
Linzey A. V., Kesner M. H. (1991) Population regulation in white-footed mice
(Peromyscus leucopus) in a suboptimal habitat. Canadian Journal of
Zoology, 69,76-81.
Linzey A. V., Matson J., Timm R. (2008) Peromyscus leucopus. In: IUCN Red
List of Threatenend Species. Version 2012.2.
http://www.iucnredlist.org/apps/redlist/details/16669/0.
Long C. A. (1973) Reproduction in the white-footed mouse at the northern limits
of its geographical range.The Southwestern Naturalist, 18, 11-20.
Long C.A. (1996) Ecological replacement of the deer mouse, Peromyscus
maniculatus, by the white-footed mouse, P. leucopus, in the Great Lakes
region. Canadian Field-Naturalist, 110, 271-277.
Lynch G. R. (1973) Effect of simultaneous exposure to differences in photoperiod
and temperature on the seasonal molt and reproductive system of the
white-footed mouse, Peromyscus leucopus. Comparative Biochemistry
and Physiology, 44A, 1373-1376.
31
Lynch G. R., Gendler S. L. (1980) Multiple responses to different photoperiods
occur in the mouse, Peromyscus leucopus. Oecologia, 45, 318-321.
Lynch G. R., Lynch C. B., Dingle H. (1973) Photoperiodism and adaptive
behaviour in a small mammal. Nature, 244, 46-47.
Lynch G. R., White S. E., Grundel R., Berger M. S. (1978) Effects of
photoperiod, melatonin administration and thyroid block on spontaneous
daily torpor and temperature regulation in the white-footed mouse,
Peromyscus leucopus. Journal of Comparative Physiology, 125, 157-163.
MacArthur R. H. (1972) Geographical Ecology: Patterns in the Distribution of
Species, New York, Harper & Row.
MacKey B. G., Lindenmayer D. B. (2001) Towards a hierarchical framework for
modelling the spatial distribution of animals. Journal of Biogeography, 28,
1147-1166.
Macmillien R. E., Garland T. (1989) Adaptive physiology. In: Advances in the
Study of Peromyscus (Rodentia). (ed. by G. L. Kirland, J. N. Layne)
Texas, U.S., Texas Tech University Press.
Madison D. M., Hill J. P., Gleason P. E. (1984) Seasonality in the nesting
behavior of Peromyscus leucopus. American Midland Naturalist, 112,
201-204.
Manel S., Williams H. C., Ormerod S. J. (2001) Evaluating presence-absence
models in ecology: the need to account for prevalence. Journal of Applied
Ecology, 38, 921-931.
Marcello G. J., Wilder S. M., Meikle D. B. (2008) Population dynamics of a
generalist rodent in relation to variability in pulsed food resources in a
fragmented landscape. Journal of Animal Ecology, 77, 41-46.
Martens W. J., Jetten T. H., Rotmans J., Niesswn L. W. (1995) Climate change
and vector-born disease: A global modelling perspective. Global
Environmental Change, 5, 195-209.
Martin N. (2010) Effects of Climate Change on the Distribution of White-footed
Mouse (Peromyscus Leucopus), an Ecologically and Epidemiologically
32
Important Species. Unpublished Master of Science University of
Michigan, Michigan, 24 pp.
Mather T. N., Wilson M. L., Moore S. I., Ribeiro J. M. C., Spielman A. (1989)
Comparing the relative potential of rodents as reservoirs of the Lyme
disease spirochete (Borrelia burgdorferi). American Journal of
Epidemiology, 130, 143-150.
McCarty J. P. (2001) Ecological consequences of recent climate change.
Conservation Biology, 15, 320-331.
McMahon S. M., Harrison S. P., Armbruster W. S., Bartlein P. J., Beale C. M.,
Edwards M. E., Kattge J., Midgley G., Morin X., Prentice L. C. (2011)
Improving assessment and modelling of climate change impacts on global
terrestrial biodiversity. Trends in Ecology and Evolution, 26, 249-259.
Merriam G., Lanoue A. (1990) Corridor use by small mammals: field
measurement for three experimental types of Peromyscus leucopus.
Landscape Ecology, 4, 123-131.
Miller G. S. (1893) Description of a new white-footed mouse from the eastern
United States. Proceedings of the Biological Society of Washington, VIII,
55-70.
Moore R. D., Mckendry I. G., Stahl K., Kimmins H. P., Yueh-Hsien L. (2005)
Mountain pine beetle outbreaks in western Canada: coupled influences of
climate variability and stand development. In: Final Report for Climate
Change Action Fund Project, A675.Ottawa, ON, Natural Resources
Canada.
Morrison M. L., Marcot B. G., Mannan R. W. (1992) Modeling wildlife-habitat
relationships. In: Wildlife-habitat relationships: concepts & applications.
(eds. J. M. Scott, P. J. Heglund, M. L. Morrison, J. B. Haufler, M. G.
Raphael, W. A. Wall, F. B. Samson) Madison, Wisconsin, University of
Wisconsin.
Muñoz J., Felicísimo Á. M. (2004) Comparison of statistical methods commonly
used in predictive modelling. Journal of Vegeration Science, 15, 285-292.
33
Myers P., Lundrigan B. L., Hoffman S. M. G., Haraminac A. P., Seto S. H. (2009)
Climate-induced changes in the small mammal communities of the
Northern Great Lakes Region. Global Change Biology, 15, 1434-1454.
Myers P., Lundrigan B. L., Kopple R. V. (2005) Climate change and the
distribution of Peromyscus in Michigan: is global warming already having
an impact? In: Mammalian Diversification: From Chromosomes to
Phylogeography (ed. E. A. Lacey, P. Myers) Uiversity of California
Publications in Zoology.
Nguon S., Milord F., Ogden N., Trudel L., Lindsay R., Bouchard C. (2008) Étude
épidémiologique sur les zoonoses transmises par les tiques dans le sudouest du Québec - Rapport de l'année 2007.(ed. Québec INDSPD)
http://www.inspq.qc.ca/pdf/publications/1139_EtudeZooonoses2007.pdf,
Bibliothèque et archives nationales du Québec.
Nupp T. E., Swihart R. K. (1998) Effects of forest fragmentation on population
attributes of white-footed mice and eastern chipmunks. Journal of
Mammalogy, 79, 1234-1243.
Ogden N. H., Lindsay L. R., Hanincová K., Barker I. K., Bigras-Poulin M.,
Charron D. F., Heagy A., Francis C. M., O’Callaghan C. J., Schwartz I.,
Thompson R. A. (2008a) Role of migratory birds in introduction and range
expansion of Ixodes scapularis ticks and of Borrelia burgdorferi and
Anaplasma phagocytophilum in Canada. Applied and Environmental
Microbiology, 74, 1780-1790.
Ogden N. H., Lindsay L. R., Morshed M., Sockett P. N., Artsob H. (2008b) The
rising challenge of Lyme borreliosis in Canada. Canada Communicable
Disease Report, 34, 1-19.
Ogden N. H., Lindsay L. R., Morshed M., Sockett P. N., Artsob H. (2008c) La
borréliose de Lyme au Canada: un problème grandissant. In: Relevé des
maladies transmissibles au Canada. http://www.phacaspc.gc.ca/publicat/ccdr-rmtc/08vol34/dr-rm3401a-fra.php, Agence de la
santé publique du Canada.
34
Ogden N. H., Maarouf A., Barker I. K., Bigras-Poulin M., Lindsay L. R.,
Morshed M. G., O’Callaghan C. J., Ramay F., Waltner-Toews D., Charron
D. F. (2006) Climate change and the potential for range expansion of the
Lyme disease vector Ixodes scapularis in Canada. International Journal
for Parasitology, 36, 63-70.
Ogden N. H., Margos G., Aanensen D. M., Drebot M. A., Feil E. J., Hanincová
K., Schwartz I., Tyler S., Lindsay L. R. (2011) Investigation of genotypes
of Borrelia burgdorferi in Ixodes scapularis ticks collected in surveillance
in Canada. Applied and Environmental Microbiology, 10, 3244-3254.
Olsen B., Duffy D. C., Jaenson T. G. T., Bonnedahl Ǻ. G. J., Bergström S. (1995)
Transhemispheric exchange of Lyme disease spirochetes by seabirds.
Journal of Clinical Microbiology, 33, 3270-3274.
Osgood W. H. (1909) Revision of the mice of the American genus Peromyscus,
Washington, Govt. print. off.
Ostfeld R. S. (2009) Climate change and the distribution and intensity of
infectious diseases. Ecology, 90, 903-905.
Ostfeld R. S., Glass G. E., Keesing F. (2005) Spatial epidemiology: an emerging
(or re-emerging) discipline. Trends in Ecology and Evolution, 20, 328336.
Ostfeld R. S., Jones C. G., Wolff J. O. (1996) Of mice and mast. Bioscience, 46,
323-330.
Ostfeld R. S., Keesing F. (2001) Biodiversity and disease risk: the case of Lyme
disease. Conservation Biology, 14, 722-728.
Ostfeld R. S., Manson R. H., Canham C. D. (1997) Effects of rodents on survival
of tree seeds and seedlings invading old fields. Ecology, 78, 1531-1542.
Parmesan C. (2002) Detection of range shifts: general methodological issues and
case studies using butterflies. In: "Fingerprints" of Climate Change:
Adapted Behaviour and Shifting Species Ranges. (ed. By G.-R. Walther,
C. A. Burga, P. J. Edwards) New York, Kluwer Academic/Plenum
Publishers.
35
Parmesan C. (2006) Ecological and evolutionary responses to recent climate
change. Annual Review of Ecology, Evolution, and Systematics, 37, 637669.
Parmesan C. (2007) Influence of species, latitudes and methodologies on
estimates of phenological response to global warming. Global Change
Biology, 13, 1860-1872.
Parmesan C., Yohe G. (2003) A globally coherent fingerprint of climate change
impacts across natural systems. Nature, 421, 37-42.
Pearson R. G., Dawson T. P. (2003) Predicting the impacts of climate change on
the distribution of species: are bioclimate enveloppe models useful?
Global Ecology and Biogeography, 12, 361-371.
Pellissier L., Pradervand J.-N., Pottier J., Dupuis A., Maiorano L., Guisan A.
(2012) Climate-based empirical models show biased predictions of
butterfly communities along environmental gradients. Ecography, 35, 684692.
Peñuelas J., Filella I. (2001) Response to a warming world. Science, 294, 793795.
Pereira H. M., Leadley P. W., Proença V., Alkemade R., Scharlemann J. P. W.,
Fernandez-Manjarrés J. F., Araújo M. B., Balvanera P., Biggs R., Cheung
W. W. L., Chini L., Cooper H. D., Gilman E. L., Guénette S., Hurtt G. C.,
Huntington H. P., Mace G. M., Oberdorff T., Revenga C., Rodrigues P.,
Scholes R. J., Sumaila U. R., Walpole M. (2010) Scenarios of global
biodiversity in the 21st century. Science, 330, 1496-1501.
Perkins S. E., Cattadori I. M., Tagliapietra V., Rizzoli A. P., Hudson P. J. (2006)
Localized deer absence leads to tick amplification. Ecology, 87, 19811986.
Peterson A. T. (2001) Predicting species' geographic distributions based on
ecological niche modeling. The Condor, 103, 599-605.
Peterson A. T. (2008) Biogeography of diseases: a framework for analysis.
Naturwissenschaften, 95, 483-491.
36
Peterson A. T., Sánchez-Cordero V., Beard B., Ramsey J. M. (2002) Ecologic
niche modeling and potential reservoirs for Chagas disease, Mexico.
Emerging Infectious Diseases, 8, 662-667.
Peterson A. T., Bauer J. T., Mills J. N. (2004) Ecologic and geographic
distribution of Filovirus disease. Emerging Infectious Diseases, 10, 40-47.
Peterson M. J. (2012) Evidence of a climatic niche shift following North
American introductions of two crane flies (Diptera; genus Tipula).
Biological Invasions. DOI: 10.1007/s10530-012-0337-3
Phillips S. J., Anderson R. P., Schapire R. E. (2006) Maximum entropy modeling
of species geographic distributions. Ecological Modelling, 190, 231-259.
Phillips S. J., Dudík M., Elith J., Graham C. H., Lehmann A., Leathwick J.,
Ferrier S. (2009) Sample selection bias and presence-only distribution
models: implications for background and pseudo-absence data. Ecological
Applications, 19, 181-197.
Pierce S. S., Vogt F. D. (1993) Winter Acclimatization in Peromyscus
maniculatus gracilis, P. leucopus noveboracensis, and P. l. leucopus.
Journal of Mammalogy, 74, 665-677.
Pounds J. A., Bustamante M. R., Coloma L. A., Consuegra J. A., Fogden M. P. L.,
Foster P. N., La Marca, E., Masters K. L., Merino-Viteri A., Puschendorf
R., Ron S. R., Sánchez-Azofeifa G. A., Still C. J., Young B. E. (2006)
Widespread amphibian extinctions from epidemic disease driven by global
warming. Nature, 439, 161-167.
Public Health Agency of Canada (2010) Lyme disease fact sheet. Available:
http://www.phac-aspc.gc.ca/id-mi/lyme-fs-eng.php.
Rand P. W., Lacombe E. H., Smith Jr. R. P., Rich S. M., Kilpatrick C. W.,
Dragoni C. A., Caporale D. (1993) Competence of Peromyscus
maniculatus (Rodentia: Cricetidae) as a reservoir host for Borrelia
burgdorferi (Spirochaetares: Spirochaetaceae) in the wild. Journal of
Medical Entomology, 30, 614-618.
37
Régnière J., Nealis V., Porter K. (2008) Climate suitability and management of
the gypsy moth invasion into Canada. Journal Biological Invasions, 11,
135-148.
Rizkalla C. E., Swihart R. K. (2007) Explaining movement decisions of forest
rodents in fragmented landscapes. Biological Conservation, 140, 339-348.
Robinson G. R., Holt R. D., Gaines M. S., Hamburg S. P., Johnson M. L., Firtch
H. S., Martinko E. A. (1992) Diverse and contrasting effects of habitat
fragmentation. Science, 257, 524-526.
Rogic A., Tessier N., Lapointe F.-J., Millien V. (2013) Genetic structure of the
white-footed mouse in the context of the emergence of Lyme disease in
southern Québec. Accepted for publication in Ecology and Evolution.
Root T. L., Price J. T., Hall K. R., Schneider S. H., Rosenzweig C., Pounds A.
(2003) Fingerprints of global warming on wild animals and plants. Nature,
421, 57-60.
Rowland C. L. (2003) Relationship of Reproductive Timing and Climate Change
to the Displacement of Peromyscus maniculatus gracilis by Peromyscus
leucopus noveboracensis. Unpublished Master of Science University of
Miami, Oxford, 33 pp.
Rushton S. P., Ormerod S. J., Kerby G. (2004) New paradigms for modelling
species distributions? Journal of Applied Ecology, 41, 193-200.
Salamin N., Wüest R. O., Lavergne S., Thuiller W., Pearman P. B. (2010)
Assessing rapid evolution in a changing environment. Trends in Ecology
and Evolution, 25, 692-698.
Santé et Services Sociaux Québec (2012) Maladie de Lyme.
http://www.msss.gouv.qc.ca/sujets/santepub/maladie-lyme.php,
Gouvernement du Québec.
Schloss C. A., Nuñez T. A., Lawler J. J. (2012) Dispersal will limit ability of
mammals to track climate change in the western hemisphere. Proceedings
of the National Academy of Sciences, 109, 8606-8611.
Schweiger E. W., Diffendorfer J. E., Pierotti R., Holt R. D. (1999) The relative
importance of small-scale and landscape-level heterogeneity in structuring
38
small mammal distributions. In: Landscape Ecology of Small Mammals.
(ed. by G. W. Barrett, J. D. Peles) New York, Springer.
Sharma S., Jackson D. A. (2008) Predicting smallmouth bass (Micropterus
dolomieu) occurence across North America under climate change: a
comparison of statistical approaches. Canadian Journal of Fisheries and
Aquatic Sciences, 65, 471-481.
Shoo L. P., Williams S. E., Hero J.-M. (2006) Detecting climate change induced
range shifts: Where and how should we be looking? Austral Ecology, 31,
22-29.
Sinclair S. J., White M. D., Newell G. R. (2010) How useful are species
distribution models for managing biodiversity under future climates?
Ecology and Society, 15, 8-20.
Soberón J. M., Peterson A. T. (2004) Biodiversity informatics: managing and
applying primary biodiversity data. Philosophical Transactions of the
Royal Society B: Biological Sciences, 359, 689-698.
Soberón J. M., Peterson A. T. (2005) Interpretation of models of fundamental
ecological niches and species' distributional areas. Biodiversity
Informatics, 2, 1-10.
Smith A. L., Hewitt N., Klenk N., Bazely D. R., Yan N., Wood S., Henriques I.,
MacLellan J. I., Lipsig-Mummé C. (2012) Effects of climate change on
the distribution of invasive alien species in Canada: a knowledge synthesis
of range projections in a warming world. Environmental Reviews, 20, 116.
Stah C. D. (1980) Vertical nesting distribution of two species of Peromyscus
under experimental conditions Journal of Mammalogy, 61, 141-143.
Stockwell D. R. B., Peterson A. T. (2002) Effects of sample size on accuracy of
species distribution models. Ecological Modelling, 148, 1-13.
Tannenbaum M. G., Pivorun E. B. (1988) Seasonal study of daily torpor in
southeastern Peromyscus maniculatus and Peromyscus leucopus from
Mountains and Foothills. Physiological Zoology, 61, 10-16.
39
Thomas C. D., Lennon J. J. (1999) Birds extend their ranges northwards. Nature,
399, 213.
Thompson C., Spielman A., Krause P. J. (2001) Coinfecting deer-associated
zoonoses: Lyme disease, Babesiosis, and Ehrlichiosis. Clinical Infectious
Diseases, 33, 676-685.
Thuiller W. (2004) Patterns and uncertainties of species' range shifts under
climate change. Global Change Biology, 10, 2020-2027.
Thuiller W., Brotons L., Araújo M. B., Lavorel S. (2004) Effects of restricting
environmental range of data to project current and future species
distributions. Ecography, 27, 165-172.
Tsao J. I. (2009) Reviewing molecular adaptations of Lyme borreliosis
spirochetes in the context of reproductive fitness in natural transmission
cycles. Veterinary Research, 40, 1-42.
Turner W., Spector S., Gardiner N., Fladeland M., Sterling E., Steininger M.
(2003) Remote sensing for biodiversity science and conservation. Trends
in Ecology and Evolution, 18, 306-314.
Václavík T., Meentemeyer R. K. (2012) Equilibrium or not? Modelling potential
distribution of invasive species in different stages of invasion. Diversity
and Distributions, 18, 73-83.
Vallière L., Beaudry J. M. (1990) La maladie de Lyme un premier cas au Québec.
Le clinicien, 5, 75-81.
Van der Putten W. H., Macel M., Visser M. E. (2010) Predicting species
distribution and abundance responses to climate change: why it is essential
to include biotic interactions across trophic levels. Philosophical
Transactions of the Royal Society B: Biological Sciences, 365, 2025-2034.
Vanderwal J., Shoo L. P., Graham C., Williams S. E. (2009) Selecting pseudoabsence data for presence-only distribution modeling: How far should you
stray from what you know? Ecological Modelling, 220, 589-594.
Venier L. A., Mckenney D. W., Wang Y., Mckee J. (1999) Models of large-scale
breeding-bird distribution as a function of macro-climate in Ontario,
Canada. Journal of Biogeography, 26, 315-328.
40
von Humboldt A. (1833) Travels and Researches of Baron Humboldt, New York,
J. & J. Harper.
von Linné C., Engel-Ledeboer M. S. J., Engel H. (1964) Systema Naturae, 1735,
Nieuwkoop, Netherlands, B. de Graaf.
Walther G.-R., Post E., Convey P., Menzel A., Parmesan C., Beebee T. J. C.,
Fromentin J.-M., Hoegh-Guldberg O., Bairlein F. (2002) Ecological
responses to recent climate change.Nature, 416, 389-395.
Walther G.-R., Roques A., Hulme P. E., Sykes M. T., Pyšek P., Kühn I., Zobel
M., Bacher S., Botta-Dukát Z., Bugmann H., Czúcz B., Dauber J., Hickler
T., Jarošík V., Kenis M., Klotz S., Minchin D., Moora M., Nentwig W.,
Ott J., Panov V. E., Reineking B., Robinet C., Semenchenko V., Solarz
W., Thuiller W., Vilà M., Vohland K, Settele J. (2009) Alien species in a
warmer world: risks and opportunities. Trends in Ecology and Evolution,
24, 686-693.
Wiens J. A. (2002) Predicting species occurrences: progress, problems, and
prospects. In: Predicting Species Occurences: Issues of Accuracy and
Scale. (eds. J. M. Scott, P. J. Heglund, M. L. Morrison, J. B. Haufler, M.
G. Raphael, W. A. Wall, F. B. Samson) Covelo, CA, Island Press.
Wiens J. A., Bachelet D. (2010) Matching the multiple scales of conservation
with the multiple scales of climate change. Conservation Biology, 24, 5162.
Wiens J. A., Stralberg D., Jongsomjit D., Howell C. A., Snyder M. A. (2009)
Niches, models, and climate change: Assessing the assumptions and
uncertainties. Proceedings of the National Academy of Sciences, 106,
19729-19736.
Wilder S. M., Abtahi A. M., Meikle D. B. (2005) The effects of forest
fragmentation on densities of white-footed mice (Peromyscus leucopus)
during the winter. American Midland Naturalist, 153, 71-79.
Wisz M. S., Pottier J., Kissling W. D., Pellissier L., Lenoir J., Damgaard C. F.,
Dormann C. F., Forchhammer M. C., Grytnes J.-A., Guisan A., Heikkinen
R. K., Høye T. T., Kühn I., Luoto I., Maiorano L., Nilsson M.-C.,
41
Normand S., Öckinger E., Schmidt N. M., Termansen M., Timmermann
A., Wardle D. A., Aastrup P., Svenning J.-C. (2012) The role of biotic
interactions in shaping distributions and realised assemblages of species:
implications for species distribution modelling. Biological Reviews.
http://onlinelibrary.wiley.com/doi/10.1111/j.1469185X.2012.00235.x/pdf.
Wolff J. O., Durr D. S. (1986) Winter Nesting Behavior of Peromyscus leucopus
and Peromyscus maniculatus. Journal of Mammalogy, 67, 409-412.
Woodward F. I., Beerling D. J. (1997) The dynamics of vegetation change: health
warnings for equilibrium 'dodo' models. Global Ecology and
Biogeography Letters, 6, 413-418.
Wormser G. P., Dattwyler R. J., Shapiro E. D., Halperin J. J., Steere A. C.,
Klempner M. S., Krause P. J., Bakken J. S., Strle F., Stanek G.,
Bockenstedt L., Fish D., Dumler J. S., Nadelman R. B. (2006) The clinical
assessment, treatment, and prevention of Lyme disease, human
Granulocytic Anaplasmosis, and Babesiosis: Clinical Practice Guidelines
by the Infectious Diseases Society of America. Clinical Infectious
Diseases, 43, 1089-1134.
Zegers D. A., Merritt J. F. (1988) Adaptations of Peromyscus for winter survival
in an Appalachian montane forest. Journal of Mammalogy, 69, 516-523.
Zipkin E. F., Ries L., Reeves R., Regetz J., Oberhauser K. S. (2012) Tracking
climate impacts on the migratory monarch butterfly. Global Change
Biology, 18, 3039-3049.
42
Tables and Figures
White-tailed
deer
+
Temperature
Land use
+
+
+
Snow cover
Vector
occurrence
Rodents
+
Risk
probability to
humans
Distribution
of humans
Disease
incidences
Birds
Hosts
probability of
occurrence
Fig.1 Conceptual model of the interactions between factors influencing disease occurrence rate.. Environmental factors influence the
vectors’ and hosts’ distributions that are involved in the transmission cycle of the disease. These distributions then influence the
human risk of contacting the pathogen causing the disease, which determines the actual number of cases of the disease in humans
(Figure modified from Ostfeld et al. 2005).
43
Spring
Nymphs
Eggs
Winter
Larvae
Summer
Adults
Autumn
Fig. 2 The life cycle of the black-legged tick (Ixodes scapularis). The whitefooted mouse plays in important role for the larval and nymphs stages of the tick.
Dotted points highlight time of greater risk for human infections during late
spring and summer
44
Reduced
movement within
agricultural fields
Chooses habitat
according to available
food resources and
proper material for
insulating nests
Indicator of
geophysical cycle
and critical
changes in the
environment (e.g.
reduced food
availability and
ambient
temperature)
Photoperiod
Dispersal rate
of species
High tolerance to
numerous habitats (e.g.
brushy prairies, wet
lowlands, dense upland
hardwood forests)
Generalist species
(e.g. acorns, arthropods,
beech nuts, maple and
grass seeds, pits of
chokecherries)
Vegetation
Food resources
Critical day
length varies
between 12
and 14 hours
Uses corridors to
move between
forest patches (i.e.
woody understory
and canopy
corridors)
White-footed
mouse
distribution
Level of available
resources
Connectivity
and habitat
quality of
corridors
Fragmentation
Climate
Harsher winter
conditions in edge
habitat and in
small fragment
Winter
conditions
Use fur density and
thermoregulation
Survival capacity
limited in cold
temperature
High energymechanisms
Significant loss of
mass/ or heavy
mortality
Prefers late
autumns and
early springs
Food resources
available earlier
Survival
capacity
limited when
lack of snow
cover
Lower survival in
these areas relative
to interior and large
fragment
Potential shelters
foraging under
snow
Fig. 3 How food resources, vegetation, fragmentation, photoperiod, and climate influence the distribution of the whitefooted mouse. The lines indicate related processes.
45
CONNECTING STATEMENT
In the previous chapter, I reviewed the literature on the influence of
climate change on species distribution. I also explored how biologists use climate
niche modelling to study the relationship between a species’ niche and changing
environmental conditions, and how this approach can be useful for the study of
the emergence of Lyme disease in reltion with the white-footed mouse
occurrence. I highlighted the main environmental variables that potentially
influence the white-footed mouse niche at its northern range limit. The next
chapter builds on this review to construct a set of habitat-niche models for the
white-footed mouse potential current and projected distribution along three
scenarios of climate change. The results will help creating risk-maps for Lyme
disease occurrence for the studied area.
46
CHAPTER 3: NORTHERN RANGE EXPANSION OF THE WHITEFOOTED MOUSE (PEROMYSCUS LEUCOPUS) UNDER CLIMATE
CHANGE, AND ITS CONSEQUENCES ON THE EMERGENCE OF
LYME DISEASE IN QUÉBEC
Manuscript to be submited for publication.
47
Abstract
Lyme disease is common in the United States. In Québec, however, it is
emerging and the number of diagnosed cases increase each year. The white-footed
mouse is recognized as an important reservoir of the disease, and is an important
host of the black-legged tick, the main vector of the disease. This study aims to
understand how its distribution will evolve under climate change to better
anticipate the spread of the disease in the coming years in Québec. A primary
objective of the study is to characterize, using Ecological Niche Factor Analysis
(ENFA), how the white-footed mouse adapted to changing climatic conditions in
the last thirty years in Québec, and what were the main climatic factors
influencing its distribution. These results are then used, as part of the second
objective, to create a species’ niche model using the platform BIOMOD. This
model was used to predict the current distribution of the species and project it
under three climate change scenarios. According to our results, the main factors
influencing the distribution of white-footed mice in Québec are linked to the
winter conditions observed in the province. A mild winter, which starts later and
is followed by an early spring, favored the mouse’s immigration. I concluded that
the presence of the mouse, and consequently of Lyme disease, will be most
pronounced in southern Québec in future years. According to our model, by 2050,
almost the entire Québec territory, with the exception of mountainous regions,
will be suitable for the establishment of the mouse.
48
Introduction
Climate has always been defined as an important environmental condition
that delimits the niche under which species are able to survive and reproduce
(Venier, 1999; Hansen et al., 2001; Sinclair et al., 2010). It regulates a species’
geographical occurrence as documented from paleontological records or recent
observations (Davis & Shaw, 2001; Parmesan, 2006). Climate is dynamic and
recently, with global warming, is fluctuating more than before (IPCC, 2007).
Scientists try to understand the consequences of such fluctuations on species’
distributions. Many expect that species will track these changing climatic
conditions and shift poleward and upward in elevation (Peterson et al., 2002;
Parmesan & Yohe, 2003; Root et al., 2003; Parmesan, 2007; Jackson et al., 2009;
Pereira et al., 2010; Pellissier et al., 2012; Schloss et al., 2012; Zipkin et al.,
2012). Climate change is also likely to increase opportunities for invasive species
to cross historical climatic barriers, establish and spread into new areas (Githeko
et al., 2000; Epstein, 2001; Hellmann et al., 2008; Ostfeld, 2009; Walther et al.,
2009). For all these reasons, studies that aim to project the geographical
distribution of species under climate change have become essential (e.g. Davis &
Callahan, 1992; Thomas & Lennon, 1999; Drake et al,. 2004; Moore et al., 2005;
Phillips et al., 2006; Ogden et al., 2008b; Régnière et al., 2008; Sharma &
Jackson, 2008; Bradley, 2009; Jarema et al., 2009; Bradley et al., 2010; Dillon et
al., 2010; Martin, 2010; McMahon et al., 2011; Barbet-Massin et al., 2012b;
Peterson, 2012). Projecting species’ distributions under climate change plays an
important role in alerting scientists and decision makers about the potential risks
of changing species distributions, especially in the case of invasive infectious
disease vectors, such as mosquitoes, ticks, and other arthropods species
(Confalonieri et al., 2007; Pereira et al., 2010; Bellard et al., 2012; Illoldi-Rangel
et al., 2012; Smith et al., 2012).
Here I focus on such an example in which one species’ response to climate
change influences the geographical range of a disease and human health concerns.
49
Lyme disease is an inflammatory disease caused by the bacterium Borrelia
burgdorferi, and is common in the United States with more than 30,000 cases
reported annually (Thompson et al., 2001; Center for Diseases Control and
Prevention, 2011). Since 2005, researchers have noted the possibility that the
distribution of the disease in North America will change with climate change
(Brownstein et al, 2005; Ogden et al., 2006). Indeed, cases of Lyme disease
diagnosed in human population keeps increasing each year in southern regions of
the province of Québec, historically known as the disease northern periphery limit
(Ogden et al., 2006). In 2010, Lyme disease became a nationally reportable
disease in the Province and requires attention from the authorities.
Lyme disease is a vector-borne disease, which occurrence is in part
defined by the geographic distribution of its vectors (Tsao, 2009). Its main host
vector, from which humans incidentally acquire the pathogen is the black-legged
tick, Ixodes scapularis (Anderson, 1989; Thompson et al., 2001; Ogden et al.,
2006). The black-legged tick can be hosted by multiple vertebrate species
throughout its two year life cycle, one being the white-footed mouse, which is
also an important zoonotic reservoir for the Lyme bacterium (Levine et al., 1985;
Mather et al. 1989; Rand et al., 1993; Thompson et al., 2001; Ogden et al.,
2008b; Tsao, 2009; Bouchard et al., 2011). The white-footed mouse (Peromyscus
leucopus) is a successful rodent native to Eastern North America, with a northern
range periphery historically limited to the southern regions of Canada (Lackey et
al., 1985). It is a very abundant species in eastern woodlands and in
heterogeneous corridors bordering agricultural fields (Linzey et al., 2008). In the
recent decades, its distribution expanded at its northern range periphery (Ostfeld
et al., 1996; Long, 1996; Myers et al., 2009). For instance, in Michigan’s Upper
Peninsula, Myers et al. reported (2009) an expansion rate of 15 km per year since
1980 together, with an increase in abundance in the region. The phenomenon was
later linked with climate change specifically; an increasing minimum temperature
in April (Martin, 2010). Historical and recent records of the species’ presence
from the Ministère des Ressources Naturelles et de la Faune (MMACH, 1996)
50
illustrate a similar trend occurring in southern Québec, where the species is
captured further north every year (Fig. 1). Considering this trend, concerns have
arised about the potential importance of the mouse in the tick’s increasing
occurrence in Québec, and subsequently Lyme disease (Daniels et al., 1996;
Ostfeld, 2011).
In this study I used a modeling approach to determine the ecological niche
of the white-footed mouse to better characterize its distribution pattern and
subsequently the future geographical occurrence of the disease predicted under
global warming. Species’ niche models are based on the assumption that species’
niches are defined by a set of environmental variables that define suitable habitat
conditions limiting the species distribution. This approach traces back to G.
Evelyn Hutchinson’s definition of the niche (Hutchinson, 1957). Today, it
benefits from using the strength of geographic information systems, an important
tool for storing and manipulating species and environmental data including
interpolated climate data and remotely sensed surface characteristics of the
terrestrial habitat (Elith & Leathwick, 2009; Dawson et al., 2011). Species’ niche
models provide one of the few practical approaches to extrapolate and forecast
species distribution across various environmental and time spaces (Thuiller et al.,
2004; Guisan & Thuiller, 2005; Araújo & New, 2007; Botkin et al., 2007; Elith &
Leathwick, 2009; Broennimann et al., 2011). They are now widely used for plants
(Thuiller, 2004b; Hamann & Wang, 2006), mammals and birds (Anderson et al.,
2002; Tingley et al., 2009; Schloss et al., 2012), amphibians and reptiles (Araújo
& New, 2007), fishes (Perry et al., 2005) and butterflies (Beaumont & Hugues,
2002).
The intent of this study is to help government authorities and public health
agencies better prioritize their management and surveillance activities in the
province of Québec (Ogden et al., 2008b). It will add knowledge to the branch of
studies looking at the increasing occurrence of Lyme disease in southern Québec
(Ogden et al., 2008b; Bouchard et al., 2011; Ogden et al,. 2011; Koffi et al,.
51
2012). The results also will contribute to the body of literature that predicts
northward expansions of species distribution under ongoing climate change
(Brownstein et al., 2005; Parmesan, 2006; Ogden et al., 2008a; Martin, 2010).
Material and Methods
I used a two-scale modeling approach to determine the white-footed
mouse fundamental niche in Québec. I first used a climate niche model for
southern Québec to highlight climatic factors that constraint the species’ dispersal
at its northern range limit. Three different time periods were analyzed (19751984, 1985-1994, and 1995-2004) that correspond to successive stages of the
species’ range shift while containing the minimum number of species presence
data points necessary for statistical analysis (Hirzel et al., 2002; Václavík &
Meentemeyer, 2012). The study area for this first analysis was limited to ensure
that in the process of the model calibration, more weight was given to differences
in climatic conditions at the northern limit of the species’ distribution rather than
over its whole range. I then implemented these climatic factors in combination
with additional environmental and geographical variables into a set of habitat
niche models ran at the sub-continental scale. The additional variables included
were photoperiod (Dark et al., 1983), the forest type and the forest cover densities
(Howard, 1951; Pierce & Vogt, 1993; Long, 1996; Krohne & Hoch, 1999;
Schweiger et al., 1999). Using a sub-continental scale to determine the
environmental, climatic, and geographical conditions under which the species
occurs increases the predictive power of the models for the province of Québec
(Thuiller, 2004a; VanDerWal et al. 2009). The habitat niche models were then
used to predict the current fundamental niche of the white-footed mouse and
project its future range under climate change.
Study Area
Two approaches were used to model the habitat niche of the white-footed
mouse. The first was a climate niche model using presence data for southern
52
Québec. The region stretches from the southern border of the province (with the
United States) to 47.5°N, encompassing a total area of 227,011 km2 (Fig. 1).
The second approach used habitat niche models based on both climate and
habitat within the greatest part of the species’ known range (Linzey et al., 2008):
from southeast Québec down to Georgia and South Carolina in the United States
(Fig. 2). Although the actual species’ distribution extends to other Canadian
provinces, (Rich et al., 1996; Linzey et al., 2008), I limited our study to the
province of Québec for which I had obtained an extensive climatic and habitat
dataset.
Species Data
Occurence data for the white-footed mouse were obtained from the
Ministère des Ressources Naturelles et de la Faune of Québec (MMACH,1996). I
restricted our analyses to the period of 1975 to 2004, which corresponds to
available historical climate data in the province. The data were then split into
three time periods: 1975-1984, 1985-1994 and 1995-2004 (Fig. 1). Once a species
was trapped at one site, it was assumed to be present there for the following time
period. The total number of data points was 47 (n1975-1984= 13, n1975-1994=29, n19752004=47).
The habitat niche models were calibrated by both presence and pseudoabsence data points. Presence points were obtained from three different museum
and collection databases (Arctos, 2011; MMACH, 1996; Field Museum of
Natural History, 2011). Only data from 1990 to 2011 were used for the analysis to
reduce possible bias due to land cover changes over the years (Ramankutty &
Foley, 1999). Ninety occurrence points were selected using a randomly stratified
sampling method. Because species occurrences were obtained from museum
specimen databases, they were not accompanied by data indicating absence or
abundance at capture sites. Instead, I generated random pseudo-absences using the
surface range envelopes function in BIOMOD (Thuiller et al., 2009). Ten runs
53
with 100 records each were run with similar weighting for pseudo-absence and
presences records (Barbet-Massin et al., 2012a).
An independent set of species presence points was generated from
fieldwork conducted from June to September 2011. The white-footed mouse was
trapped at 27 sites in southern Québec (Fig. 2). Four of these sites have endemic
tick populations, which carry Lyme disease, four others will be confirmed in
future sampling, and the last 19 are sites where there is potential for Lyme disease
vectors, such as the white-footed mouse, to expand their range. These sites
therefore provide good sources of comparison to study correlating factors for the
establishment of the white-footed mouse and the establishment of the blacklegged tick populations. The trapping sites also go beyond the known northern
limit of the white-footed mouse distribution, and the St. Lawrence River, which
constitutes a barrier to the mouse’s dispersal (Fiset et al. in preparation ). At each
site, 112 ShermanTM live traps were baited with a mixture of oat and peanut butter
and placed at 3:00 p.m. in 4 grids of 4 parallel transects with 7 traps each for one
or two nights. The traps were checked at 9:00 a.m. the next morning. To confirm
the species’ absence, traps were installed for two consecutive nights if no
specimen was captured on the first night. I captured a total of 94 Peromyscus
leucopus that were identified to the species level using species-specific primers as
described in Rogic et al., (2013). All procedures were approved by the Ministère
des Ressources Naturelles et de la Faune of Québec (SEG Permit #2011-05-15014-00-S-F), and the McGill University Animal Care Committee (AUP#5420).
Climate and Environmental Factors
A georeferenced database of climatic, environmental, and geographical
components was generated withArcGIS10 (ESRI, Redlands, CA) to parameterize
the models (Table 1).
54
I identified 5 groups of climate variables from published studies,
represented by 21 variables, influencing the species' distribution. Temperature and
precipitation data were derived from the ANUSPLIN data (version 4.3;
Hutchinson, 2004) based on the Environment Canada's historical database
(McKenney et al. 2006; Meteorological Service of Canada, 2011).The length of
the winter season was defined as the period from when the daily average
temperature of a grid cell fell below 0°C after July 1 to the date when the average
temperature rose above 0°C in the following calendar year. Monthly snow depth
variables were interpolated from data from Environment Canada for weather
stations in Québec (Meteorological Service of Canada, 2011) and NOAA for
weather stations in the United States (National Oceanic and Atmospheric
Administration, 2011). Only weather stations with less than 30% missing data for
5 year block were used for the interpolations. The overall performance of the
interpolation was tested with an independent source of snow data for Québec
(Brown, 2010). The correlation coefficients between observed and interpolated
snow data were significant, ranging from 0.70 for the month of November to 0.84
for the months of February and March.
To calibrate the climate niche model, each variable was obtained at a 10
km resolution and averaged over each time period: 1975 to 1984, 1985 to 1994,
and 1995 to 2004. For the habitat niche models, I selected five climate variables
based on results obtained from the previous analysis: temporally averaged winter
total precipitation, winter average snow depth, winter average minimum
temperature, winter maximum temperature and winter length from 1961-2005.
Typically these climatic data cover the period of 1961-2005 whereas species’
presence data range from 1990-2011. Thus I compare climate monthly mean
values from 1985-2005, 1985-2010, 1990-2005, and 1990-2010 for 5 randomly
selected meteorological field stations (Meteorological Service of Canada, 2011)
using a Pearson’s chi-squared test and found no significant differences between
these four time periods (all p>0.05) indicating that the observations from the
55
ANUSPLIN dataset (from 1961-2005) are representative of the 1961-2011
meteorological conditions.
Photoperiod was used as a proxy for latitude, specifically, the average
maximum daily percentage of sunshine in winter, from December to April
(International Water Management Institute, 2011) and interpolated to a 10 km
resolution scale.
Canopy density, used to represent vegetation cover, was expressed as
percent canopy on the ground (at 1km resolution from the MODIS sensor of
Terra). Data were obtained from the International Steering Committee for Global
Mapping (2011) for Canada (last update: 2007-05-16) and the United States (last
update: 2008-06-03). Lastly, the land cover was obtained from the Global Land
Cover 2000 (version 2) for North America (Joint Research Centre, 2011;
Latifovic et al., 2004), which provides 35 land cover classes.
Ecological-Niche Factor Analysis
I constructed a climate niche model for the white-footed mouse using
ecological-niche factor analysis, ENFA (Hirzel et al., 2002), available in the
adehabitat package version 1.8.7 in R statistical software (Calenge, 2006; R.
Development Core Team, 2012). The ENFA approach was selected because it
generalizes species niche responses and uses algorithms designed for species at
the edge of their fundamental niche (Braunisch et al., 2008). In ENFA,
environmental predictors of a species' distribution from locations where it has
been captured are compared with predictors across the entire study area, based on
the assumption that species select sites in areas that offer suitable environmental
conditions for its survival (Braunisch et al., 2008). It then summarizes the
environmental predictors into a number of uncorrelated factors and produces two
indices. The marginality index is the difference between the average conditions at
the sites where the species was captured (species’ distribution) and those over the
entire study area (global distribution). Typically, if the marginality index is low
56
(close to 0) the species tend to live in average conditions throughout the study
area. If it is close to 1, it lives in the extreme conditions of the area. The species’
specialization index is the ratio of the variance of environmental predictors for the
global study area with the variance for the species distribution area (Hirzel et al.,
2002; Braunisch et al., 2008). A high specialization index indicates that the
species is a specialist and lives in a narrow range of conditions. A low value
indicates that the species uses a wide range of environmental conditions.
No transformation of the variables was necessary prior to the analyses
since the means of environmental conditions in the species range and the global
range were similar. The ENFA is robust enough to employ variables that deviated
from normality (Hirzel et al., 2002). I performed a comparative analysis using the
species’ presence data and climate factors during the three time periods to
determine which climate factors tend to promote the spread of the white-footed
mouse in southern Québec. For each period, the ENFA was performed using a
two-step procedure to determine the variables that best related the species'
distribution to its surrounding habitat. First, all the climatic variables were
grouped into classes, e.g. precipitation, temperature maximum, temperature
minimum, snow depth, and winter conditions and the ENFA was performed
separately for each group. Redundant pairs of variables were manually removed
to reduce colinearity within the dataset. Only the variables with the highest
coefficients for marginality and specialization factors were kept and used in a
final ENFA. Colinearity was controlled for using the same method as in the first
set of ENFAs.
The environmental conditions associated with the expansion of the whitefooted mouse in the southern regions of Québec were assessed by comparing the
ENFA results obtained for each time period. The climate variables that globally
(i.e. for the overall three periods) best characterized the presence sites and that
gave indications of the species’ climate tolerances were obtained. The mean,
57
maximum, and minimum values were calculated for these variables to
characterize the environmental conditions at the species presence locations.
BIOMOD Habitat Niche Modeling Analyses
Current distribution. The current distribution of the white-footed mouse
and projection of its future distribution under climate change in Québec were
investigated using BIOMOD (BIOdiversity MODelling), a computation platform
for species habitat niche modeling which combines a broad variety of statistical
modeling methods. I used the R statistical package BIOMOD Version 1.1-7.00 (R
Development Core Team, 2012; Thuiller, 2004a; Thuiller et al., 2009). BIOMOD
takes into account the variability and uncertainties between the model outcomes to
define the species’ distribution and maximize predictive accuracy (Morrison et
al., 1992; Araújo & New, 2007; Elith et al., 2006; Heikkinen et al., 2011). Models
were selected from the package based on two characteristics of their projected
results: realistic and generalist (Guisan & Zimmermann, 2000). The species
occurrence probability needed to be realistic to address public health concerns.
But, the models also needed to remain generalist since the mouse distribution was
studied at a coarse spatial resolution. Selected models were the artificial neural
networks (ANN), classification tree analyses (CTA), generalized boosting models
(GBM), generalized linear models (GLM), generalized additive models (GAM),
flexible discriminant analysis (FDA), multivariate adaptive regression splines
(MARS), and random forest (RF).
Models were evaluated using two different approaches. The first approach
was a multiple cross-validation technique. The original database was split into
two subsets with 70% for calibration and 30% for evaluation. The splitting
procedure was replicated 10 times, and the mean of the predictive performance of
the cross-validation was recorded. The second approach was to use an
independent presence dataset from the individuals I collected in the field to
evaluate the models’ performance.
58
Three statistical techniques available in BIOMOD were used to evaluate
each model for both approaches: the Relative Operating Characteristic Curve
(ROC Curve) (Swets, 1988), Cohen’s Kappa Statistic (Cohen, 1960), and True
Skill Statistic (TSS) (Allouche et al., 2006). Sensitivity (ratio of presence sites
correctly predicted over the number of presence sites in the sample) and
specificity (ratio of absence sites correctly predicted over the number of absence
sites in the sample) were also obtained for each model. Results were compared
using Swets (1988) and Cohen (1960) indices to qualify their predictive accuracy.
Standard deviations between the model outcomes were also calculated to
highlight regions where model outcomes varied the most. A cartographic
representation of errors and uncertainties was used to identify areas where
additional data sampling is needed to improve the models’ performance.
An analysis of spatial patterns observed in the model residuals was
performed to determine if any major ecological factor influencing the species’
distribution might have been omitted from the model calibration. Two indices
were calculated using the Anselin Local Morans I tool in ArcMap 10 (ESRI,
Redlands, CA): the Moran’s I index (Moran, 1948) and the Z-scores (Ebdon,
1985). The Moran’s I index gives a relative index of clustering in the observed
data, while the Z-scores describe the dispersion in the data.
Future projections. Future climate scenarios were derived using simulated
future climate data obtained from the Canadian Regional Climate Model, CRCM4
version 4.2.3 (Music & Caya, 2007), as well as an ensemble of global climate
simulations. Nine CRCM4 simulations were carried out over a large domain
covering North America (201 x 293 grid points) with a horizontal grid-size mesh
of 45 km (true at 60°N). Each run was driven by atmospheric fields simulated by
three different coupled global climate models (CGCM3, CNRM, and ECHAM5)
using the IPCC SRES A1b, A2, and B1 greenhouse gas and aerosol projected
evolution scenarios (Nakicenovic et al., 2000). Other future climate scenarios
59
were produced using output from an ensemble of global climate models (GCMs)
available from phase 3 of the Coupled Model Intercomparison Project (CMIP3)
(Meehl et al., 2007). A total of 37 of the available global simulations met our
study requirements in terms of time horizons and variables, and they were divided
approximately equally among three SRES emissions scenarios (12 A1b, 15 A2,
and 10 B1 simulations). Figure 3 shows that the range of variability provided by
the 37 scenarios covers reasonably well that of the ensemble as a whole.
Results
Ecological-Niche Factor Analysis
The ENFAs from 1975 to 2004 revealed that five climate variables were
likely related to the distribution of the white-footed mouse (Fig. 4). Three of
them, the maximum temperature (in December or January), minimum temperature
(in March or April), and length of winter had the greatest values on the
marginality axis. The species presence sites were located in the warmest regions
in early winter (i.e. December/January) and the coldest at the end of the winter
(i.e. March/April). The length of the winter also has high value on the
specialization axis. The species occurrence sites tended to be negatively related
with this variable. From 1995 to 2004, two additional variables influenced the
species distribution. The specialization coefficients, which maximize the variance
between sites, showed that the white-footed mouse distribution preferred locations
with greater snow depth in January and avoided sites with high snow depths in
March. Overall, marginality scores were low, indicating that while responding to
some climate factors, the white-footed mouse is a generalist species in terms of
site selection. Only one specialization axis was retained (out of the eight
computed) for each time period analyzed.
60
BIOMOD Habitat Niche Modeling Analyses
Variables Importance and Response Curves. The variable importance
values and response curves were used to determine the environmental, climatic, or
geographic factors that best predicted the white-footed mouse distribution.
Overall, eight climatic and habitat variables were selected in the analysis from
their relevance for the species distribution in the ENFAs and from the literature.
The comparison of the variables’ importance among the different models used
was assessed using a permutation procedure in BIOMOD. Importance scores
revealed that three variables characterized well the species’ habitat, two had lesser
importance, while another three were excluded as valuable variables by all the
models (Fig. 5).
The three variables that had the greatest predictive significance to the
white-footed mouse geographical distribution were in order of importance, the
winter length, the winter average maximum temperature, and the winter average
minimum temperature. All model predictions followed a similar trend as
illustrated by the response curves for the winter length average (Fig.6). The
probability of occurrence of the white-footed mouse decreased where winter
length spanned more than 125 to 175 days, depending on the model. The length of
the winter thus represented a defined boundary condition to the distribution of the
white-footed mouse, beyond which the probability of finding the species is
limited. The winter average maximum temperature was the second most
significant variable to the species distribution (Fig. 5). All models indicated that
the potential for the white-footed mouse to establish in a given area increased
considerably when the maximum average temperature in winter was warmer than
-5°C (Fig. 6). The response curves also showed that the probability of the species
occurrence decreased when the average maximum temperature in winter was
close to 0°C. This last result may nonetheless be an artefact due to the extent of
the data range which, by definition, does not go beyond 0°C. The third variable
with a high predictive accuracy was the average minimum winter temperature
61
(Fig. 5). The probability of occurrence of the white-footed mouse decreased as the
minimum average winter temperature became warmer than -15°C (Fig. 6).
The predictive value of snow depth and average winter precipitation were
intermediate to the three discussed above and the variables for photoperiod, land
cover and canopy (Fig. 5). When the snow depth was > 0.2 m, the probability of
occurrence of the white-footed mouse considerably reduced (Fig. 6). On the other
hand, the probability for the white-footed mouse to be observed at a site increased
when winter precipitation was >200 mm (Fig. 6).
Species Range Shift and Migration. The consensus model obtained from
the BIOMOD platform indicated that, under the 47ºN in Québec, the predicted
probability of the white-footed mouse current occurrence gradually decrease from
100% at the USA border down to less than 10% north of the 47°N parallel (Figure
7). Over the entire study area, less than 7.6% of the overall species’ niche had a
probability greater than 70%, while about 83.3% had a probability of 10%.
All projected SRES emission scenarios produced similar results (Figure 7).
For each, the probability to observe the species was ≥50% for the entire province,
except in mountainous areas along the northern shore of the St. Lawrence River
where the probability ranged from 0 to 40% for the different climate scenarios.
Under the B1 and A2 scenarios, the probability to observe the white-footed mouse
was 80% for 35% of the surface area, while under the A1b scenario, the
occurrence probability of the species was 70% for 55.5% of the surface area.
Autocorrelation in Residuals from Models Predictions. Results from the
cluster analysis on model residuals demonstrated that the residuals were randomly
dispersed (p<0.0001). Both cluster indices used in the analysis were low. The
Moran’s Index score ranged between 0.04 and 0.14, while the Z-scores ranged
between 40.02 and 132.22.
62
Assessing Error Propagation and Spatial Trends in Uncertainties. All
eight models successfully captured the fundamental niche of the white-footed
mouse. The range of Kappa, TSS, and ROC Curves scores were good to excellent
(Cohen, 1960; Swets, 1988) and the sensitivity values were generally high for the
three validation techniques used (Table 2).
Figure 8 illustrates the degree of agreement among model outcomes. The
standard deviations for the model outcomes ranged between 0 and 0.3 for 98% of
the study area. Sites with higher standard deviations, ranging from 0.3 to 0.5,
represented only 2% of the study area. They were concentrated in four areas
outside of Québec: northern Michigan, and the centers of Maine and New York
states. They accounted for 39%, 4%, and 4%, respectively of the surface area
where model predictions deviated the most. The remaining 52% were located
within Québec's Capitale-Nationale region.
Discussion
The white-footed mouse is an important vector in the complex
epidemiology of Lyme disease. Changes in its distribution will inevitably alter the
disease's geographical range (Bouchard et al., 2011). For instance, its new
occurrence in Québec coincided with the increase in human cases of Lyme disease
in the province. This relationship raises concern among medical practitioners, as it
is likely to impact the public health in the province (Ogden et al., 2008b). In this
study, I used spatial models to characterize the shift of the white-footed mouse
populations towards northern regions. Maps of current and expected distribution
of the white-footed mouse occurrence can be used to identify risk areas for Lyme
disease in Québec.
The first analysis highlighted the ability of the species to cope with
changing climatic conditions, while capturing the main climatic factors explaining
its distribution pattern, it demonstrated that, in general, the white-footed mouse
was sensitive to the winter length and temperature, and the snow cover on the
63
ground. However, its degree of sensitivity to these variables changed in time
during its different stages of expansion into Southern Québec. For instance, in
1975-1984, the white-footed mouse had a marked preference for locations with
reduced winter length. Ten years later, from 1975-1994, the trend observed was
similar, but new variables gained in importance; the white-footed mouse appeared
to get more specialized to early winter temperature (i.e. month of December and
January). From 1975-1984 to 1985-1994, the average winter length and winter
average minimum temperature stayed similar. Instead, temperature in December
and January played a greater role potentially to greater number of species
presences used to calibrate the model. The temperatures in December and January
likely act as a time limit for individuals’ dispersal before they settle down to the
nest for winter. Finally, in 1995-2004, early winter temperature was still an
important factor characterizing the white-footed mouse distribution, with the
addition of late winter temperature. The temperature records of the months of
March and April mark the return of warm temperature and the end of the hiding
period for the mouse, which can then disperse again and breed. In recent years,
therefore, warmer temperature in the early and late period of winter best described
the white-footed niche. The number of days below freezing, here defined as
winter length, still had importance to the species’ distribution, but to a lesser
extent since the average minimum temperature in winter decreased, allowing the
white-footed mouse to best survive in its habitat by the use of torpor and other
energy-saving mechanisms. Such other mechanisms include the use of snow as
insulation for the nest and travel corridors (Wolff et al., 1986). Snow provides
protection against difficult conditions in early winter, but is an obstacle to the
species' presence near the end of the winter season since it reduces the species’
potential dispersal. In 1995-2004, snow was another factor characterising the
species’ habitat in concordance with temperature in early and late winter. Such
results highlight the adaptive capacity of the white-footed mouse to new climatic
conditions. Although observed at a small scale here, climate appears to influence
the species' site preference, and indirectly, migratory dynamic, which is of great
interest with respect to the spread of of ticks and Lyme disease.
64
In our second analysis, I characterized the changes in the white-footed
mouse distribution under climate change scenarios, by comparing the mouse’s
current and projected distribution obtained from habitat niche modeling. The
models presented excellent prediction of the species’ distribution although they
slightly over-predicted it. The sensitivity scores were greater than the specificity
scores, suggesting that the model algorithms had difficulty at identifying pseudoabsence sites. In our model calibration approach, the species’ presence data points
were compared to background data. Widespread species often occupy a variety of
habitat types throughout their geographical ranges (Alder & Wilson, 1987;
Hernandez et al., 2006). Consequently, the pseudo-absences randomly selected by
the models may not differ markedly from the presence dataset, which can explain
the model over-prediction. Such issue can be improved in the future by increasing
the species presence sample size to best define its niche habitat or by using real
absence data (Hirzel & Guisan, 2002). Furthermore, widespread species often
show regional ecological adaptation and might not be limited by any of the
predictive factors at the scale the models were calibrated (Grenouillet et al.,
2011), which also may lead to overestimation of the actual species’ niche breadth.
The analyses of variations in model residuals, using Moran’s I, did not show that
residuals were correlated in their spatial distribution. One thus can assume that the
major factors influencing the species distribution at the scale studied were
included in the analyses (Gelfand et al., 2006).
Our models revealed that the white-footed mouse is sensitive to the winter
length, maximum average winter temperature, and minimum average winter
temperature. Winter snow density and winter average precipitation were also
significant factors for predicting the species’ distribution, although their
contribution to the models were lower. Finally, the photoperiod, land cover, and
canopy density variables showed no ability to predict the distribution pattern of
the white-footed mouse in Québec. I found that today the white-footed mouse’s
suitable habitat represents 10% of Québec territory. This percentage already
65
includes habitats close to areas with high human populations. As winter
conditions become more clement over the Québec territory, the northern regions
of Québec could be invaded more extensively in the future. Models were
consistent accross different climate scenarios. Under climate change, the potential
distribution of the white-footed mouse may include the majority of the province
of Québec by 2050s. The incidence of Lyme disease thus may increase
dramatically by then. Surveillance and prevention activities therefore need to be
considered seriously. As the white-footed mouse potential niche range increases
in northern territories, the prevalence of Lyme disease in human population may
do the same. Only mountainous regions located on the northern shore of St.
Lawrence River appear to be less suitable for the white-footed mouse under
climate change. According to the projected climatic data used in the models, the
precipitation regimes and snow depth of these regions will be greater there than in
the rest of the mouse’s habitat. I found that snow depth is only favorable to the
white-footed mouse when surrounding temperature conditions are very cold;
otherwise they restrict the individual movement, which may explain these
exceptions.
These results are consistent with observations from the northern United
States on white-footed mouse expansion into northern regions. Martin (2010)
found the average minimum temperature in April to be the most significant
environmental factor at predicting the white-footed mouse distribution in
Michigan. Her results supported previous observations by Myers et al. (2005) and
Rowland (2003) in the same region. Myers et al. (2005) stated that the whitefooted mouse would not be able to survive long winters, especially during years
when ice lasted late into the spring (late April and early May). Similar
observations were also made in the Wisconsin region, where temperature and
snow cover appeared to influence the species abundance (Long, 1996). The
similarities among results from studies in the northern United States and Québec
suggest that these trends are not site-dependent.
66
This study successfully demonstrated the dominant control of climate on
the white-footed mouse distribution, and the profound impacts it has on its range
expansion. Our results are only valid, however, if environment-organism
relationships remain unchanged in the future, and if no unknown constraint would
limit the species’ ability to track future climate change. At a fine geographical
scale, the species’ populations are likely to follow constant natural fluctuation
cycles and thus its distribution limits may also be constantly fluctuating (Pearson
& Dawson, 2003; Sinclair et al., 2010; Václavík & Meentemeyer, 2012). In
addition, the uncertainties stemming from both the climate change scenarios and
the modeling techniques need to be considered (Thuiller, 2004a). The CapitaleNationale was one of the regions found to have a high standard deviation between
the model predictions, which may be explained by local microclimate factors.
This region is characterized by areas of higher elevation (e.g. 800 m), which are
likely to influence local temperature and snow precipitation, thus lowering the
predictive ability of some of the models. More work will be needed in this region
to lower potential errors in model predictions and projections (McKenney et al.,
2006).
67
Acknowledgements
I thank the Ministère des Ressources Naturelles et de la Faune for data on
white-footed mouse distribution in Québec and Travis Logan from Ouranos for
assistance at obtaining climate models data. I thank Dr. Wilfried Thuiller and
Cécile Albert for their help with BIOMOD, and field work assistants for their help
during mouse captures. I thank Daniel Suter for help with editing. I finally
acknowledge Ouranos, the Quebec Center of Biodiversity Science, and the Global
Environmental and Climate Change Centre for funding.
68
References
Alder G. H., Wilson M. L. (1987). Demography of habitat generalist, the whitefooted mouse, in a heterogeneous environment. Ecology, 68(6), 17851796.
Allouche O., Tsoar A., Kadmon R. (2006). Assessing the accuracy of species
distribution models: prevalence, kappa and the true skill statistic (TSS).
Journal of Applied Ecology, 43, 1223-1232.
Anderson J. F. (1989) Epizootiology of Borrelia in Ixodes tick vectors and
reservoir hosts. Reviews of Infectious Diseases, 11, S1451-S1459.
Anderson R. P., Peterson A. T., Gómez-Laverde M. (2002). Using niche-based
GIS modeling to test geographic predictions of competitive exclusion and
competitive release in South American pocket mice. OIKOS, 98, 3-16.
Araújo M. B., New M. (2007). Ensemble forescasting of species distributions.
Trends in Ecology and Evolution, 22(1), 42-47.
Arctos. (2011). Multi-Institution, Multi-Collection Museum Database. In
Univeristy of Alaska Museum of the North, Museum of Southwestern
Biology, Museum of Vertebrate Zoology (Ed. by University of Alaska
Museum of the North, Museum of Southwestern Biology, Museum of
Vertebrate Zoology). http://arctos.database.museum/.
Barbet-Massin M., Jiguet F., Albert C. H., Thuiller W. (2012a) Selecting pseudoabsences for species distribution models: how, where and how many?
Methods in Ecology and Evolution, 3, 327-338.
Barbet-Massin M., Thuiller W., Jiguet F. (2012b) The fate of European breeding
birds under climate, use and dispersal scenarios. Global Change Biology,
18, 881-890.
Beaumont L. J., Hugues L. (2002). Potential changes in the distributions of
latitudinally restricted Australian butterfly species in response to climate
change. Global Change Biology, 8, 954-971.
69
Bellard C., Bertelsmeier C., Leadley P., Thuiller W., Courchamp F. (2012)
Impacts of climate change on the future of biodiversity. Ecology Letters,
15, 365-377.
Botkin D. B., Saxe H., Araújo M. B., Betts R., Bradshaw R. H. W., Cedhagen T.,
Chesson P., Dawson T. P., Etterson J. R., Faith D. P., Ferrier S., Guisan
A., Hansen A. S., Hilbert D. W., Loehle C., Margules C., New M., Sobel
M. J., Stockwell D. R. B. (2007). Forecasting the effects of global
warming on biodiversity. BioScience, 57(3), 227-236.
Bouchard C., Beauchamp G., Nguon S., Trudel L., Milord F., Lindsay L. R.,
Bélanger, D., Ogden N. H. (2011) Associations between Ixodes scapularis
ticks and small mammal hosts in a newly endemic zone in southeastern
Canada: implications for Borrelia burgdorferi transmission. Ticks and
Tick-borne Diseases, 2, 183-190.
Bradley B. A. (2009) Regional analysis of the impacts of climate change on
cheatgrass invasion shows potential risk and opportunity. Global Change
Biology, 15, 196-208.
Bradley B. A., Wilcove D. S., Oppenheimer M. (2010) Climate change increases
risk of plant invasion in the Eastern United States. Biological
Invasions,12, 1855-1872.
Braunisch V., Bollmann K., Graf R. F., Hirzel A. H. (2008). Living on the edge Modelling habitat suitability for species at the edge of their fundamental
niche. Ecological Modelling, 214, 153-167.
Broennimann O., Fitzpatrick M. C., Pearman P. B., Petitpierre B., Pellissier L.,
Yoccoz N. G., Thuiller W., Fortin M.-J., Randin C., Zimmermann N. E.,
Graham C. H., Guisan A. (2011). Measuring ecological niche overlap
from occurrence and spatial environmental data. Global Ecology and
Biogeography, 21(4), 481-497.
Brown R. D. (2010). Analysis of snow cover variability and change in Quebec,
1984-2005. Hydrological Processes, 24(4), 1929-1954.
70
Brownstein J. S., Skelly D. K., Holford T. R., Fish D. (2005) Forest fragmentation
predicts local scale heterogeneity of Lyme disease risk. Oecologia, 146,
469-475.
Calenge C. (2006). The package "adehabitat" for the R software: A tool for the
analysis of space and habitat use by animals Ecological Modelling, 197,
516-519
Centers for Disease Control and Prevention (2011) Summary of notifiable
diseases – United States, 2010. Morbidity and Mortality Weekly Report,
59, 1-111.
MMACH. (1996). Banque de données sur les micromammifères et les chiroptères
du Québec, active depuis 1996. Gouvernement du Québec, ministère des
Ressources naturelles et de la Faune, Direction de l’expertise sur la faune
et ses habitats, Québec, Québec. http://www.cdpnq.gouv.qc.ca/
Cohen J. (1960). A Coefficient of agreement for nominal scales. Educational and
Psychological Measurement, 20(1), 37-46.
Confalonieri U., Menne B., Akhatar R., Ebi K. L., Hauengue M., Kovats R. S.,
Revitch B., Woodward A. (2007) Human health. In: Climate change 2007:
Impacts, Adaptation and Vulnerability. Contribution of Working Group II
to the Fourth Assessment Report of the Intergovernmental Panel of
Climate Change.(Ed. by M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J.
van der Linden, C. E. Hanson) Cambridge, UK, Cambridge University
Press.
Conley K. E., Porter W. P. (1986) Heat loss from deer mice (Peromyscus)
evaluation of seasonal limits to thermoregulation. Journal of Experimental
Biology, 126, 249-269.
Daniels T. J., Falco R. C., Curran K. L., Fish D. (1996). Timing of Ixodes
scapularis (Acari:Ixodidae) oviposition and larval activity in Southern
New York. Journal of Medical Entomology, 33(1), 140-147.
Dark J., Johnston P. G., Healy M., Zucker I. (1983). Latitude of origin influences
photoperiodic control of reproduction of deer mice (Peromyscus
maniculatus). Biology of Reproduction, 28, 213-220.
71
Davis R., Callahan J. R. (1992) Post-Pleistocene dispersal in the Mexican vole
(Microtus mexicanus), an example of an apparent trend in the distribution
of southwestern mammals. Great Basin Naturalist, 52, 262-268.
Davis M. B., Shaw R. G. (2001). Range shits and adaptive responses to
Quaternary climate change. Science, 292, 673-679.
Dawson T. P., Jackson S. T., House J. I., Prentice I. C., Mace G. M. (2011)
Beyond predictions: biodiversity conservation in a changing climate.
Science, 332, 53-58.
Dillon M. E., Wang G., Huey R. B. (2010) Global metabolic impacts of recent
climate warming. Nature, 467, 704-707.
Drake J. M., Bossenbroek J. M. (2004) The potential distribution of zebra mussels
in the United States. Bioscience, 54, 931-941.
Ebdon D. (1985) Statistic in Geography, Oxford, Blackwell.
Elith J., Graham C. H., Anderson R. P., Dudík M., Ferrier S., Guisan A., Hijmans
R. J., Huettmann F., Leathwick J. R., Lehmann A., Li J., Lohmann L. G.,
Loiselle B. A., Manion G., Moritz C., Nakamura M., Nakazawa Y.,
Overton J. M., Peterson A. T., Phillips S. J., Richardson K., ScachettiPereira R., Schapire R. E., Soberón J., Williams S., Wisz M. S.,
Zimmermann N. E. (2006) Novel methods improve prediction of species’
distributions from occurence data. Econography, 29, 129-151.
Elith J., Leathwick J. R. (2009). Species distribution models: ecological
explanation and prediction across space and time. Annual Review of
Ecology, Evolution, and Systematics, 40, 677-697.
Epstein P. R. (2001) Climate change and emerging infectious diseases. Microbes
and Infection, 3, 747-754.
Field Museum of Natural History. (2011). The Collection of mammals database.
In Division of Mammals, Department of Zoology, Field Museum of
Natural History . Chicago.
http://fieldmuseum.org/explore/department/library
72
Fiset J., Tessier N., Millien V., Lapointe F.-J. Comparative phylogeography of the
white footed mouse and the deer mouse, two Lyme disease vectors in
Quebec. In preparation.
Gelfand A. E., Silander J. A. Jr., Latimer A., Lewis P. O., Rebelo A. G., Holder
M. (2006) Explaining species distribution patterns through hierarchical
modeling. Bayesian Analysis, 1, 41-92.
Githeko A. K., Lindsay S. W., Confalonieri U. E., Patz J. A. (2000) Climate
change and vector-borne diseases: a regional analysis. Bulletin of the
World Health Organization, 78, 1136-1147.
Grenouillet G., Buisson L., Casajus N., Lek S. (2011). Ensemble modelling of
species distribution: the effets of geographical and environmental ranges.
Ecography, 34, 9-17.
Guisan A., Thuiller W. (2005). Predicting species distribution: offering more than
simple habitat models. Ecology Letters, 8, 993-1009.
Guisan A., Zimmermann N. E. (2000). Predictive habitat distribution models in
ecology. Ecological Modelling, 135, 147-186.
Hamann A., Wang T. (2006). Potential effects of climate change on ecosystem
and tree species distribtion in British Columbia. Ecology, 87(11), 27732786.
Hansen A. J., Neilson R. P., Dale V. H., Flather C. H., Iverson L. R., Currie D. J.,
Shafer S., Cook R., Bartlein P. J. (2001) Global change in forests:
responses of species communities, and biomes. Bioscience, 51, 765-779.
Heikkinen R. K., Marmion M., Luoto M. (2011). Does the interpolation accuracy
of species distribution models come at the expense of transferability?
Ecography, 35, 276-288.
Hellmann J. J., Byers J. E., Bierwagen B. G., Dukes J. S. (2008) Five potential
consequences of climate change for invasive species. Conservation
Biology, 22, 534-543.
Hernandez P. A., Graham C. H., Master L. L., Albert D. L. (2006). The effect of
sample size and species characteristics on performance of different species
distribution modeling methods. Ecography, 29, 773-785.
73
Hill R. W. (1983) Thermal physiology and energetics of Peromyscus: ontogeny,
body temperature, metabolism, insulation, and microclimatology. Journal
of Mammalogy, 64, 19-37.
Hirzel A. H., Guisan A. (2002). Which is the optimal sampling strategy for habitat
suitability modelling. Ecological Modelling, 157, 331-341.
Hirzel A. H., Hausser J., Chessel D., Perrin N. (2002). Ecological-niche factor
analysis: how to compute habitat-suitability maps without absence data?
Ecology, 83(7), 2027-2036.
Hirzel A. H., Hausser J., Perrin N. (2002). Biomapper 3.1 - FAQ, from
http://www.unil.ch/biomapper
Howard W. E. (1951). Relationship between low temperature and available food
to survival of small rodents. Journal of Mammalogy, 32, 300-312.
Hutchinson G. E. (1957). Concluding remarks. Cold Spring Harbor Symposia on
Quantitative Biology, 22(2), 415-427.
Hutchinson M. F. (2004). ANUSPLIN Version 4.3 Retrieved November 23, 2005,
from http://cres.anu.edu.u/outputs/anusplin.php
Illoldi-Rangel P., Rivaldi C.-L., Sissel B., Fryxell R. T., Gordillo-Pérez G.,
Rodríguez-Moreno A., Williamson P., Montiel-Parra G., Sánchez-Cordero
V., Sarkar S. (2012) Species distribution models and ecological suitability
analysis for potential tick vectors of Lyme disease in Mexico. Journal of
Tropical Medicine,Article ID 959101, DOI: 10.1155/2012/959101.
International Steering Committee for Global Mapping. (2011). Vegetation.
Natural Resources Canada, United-States Geological Survey
http://www.iscgm.org/cgi-bin/fswiki/wiki.cgi.
International Water Management Institute. (2011). IWMI Online Climate
Summary Service Portal. In Consultative Group on Internatioal
Agricultural Research.http://www.iwmi.cgiar.org/WAtlas/Default.aspx.
IPCC (2007) Climate Change 2007: Synthesis Report. In: Contribution of
Working Groups I, II and III to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change (Ed. by R. K. Pachauri, A.
Reisinger) Geneva, Switzerland, IPCC.
74
Jackson S. T., Betancourt J. L., Booth R. K., Gray S. T. (2009) Ecology and the
ratchet of events: climate variability, niche dimensions, and species
distributions. Proceedings of the National Academy of Sciences, 106,
19685-19692.
Jarema S. I., Samson J., Mcgill B. J., Humphries M. M. (2009) Variation in
abundance across a species' range predicts climate change responses in the
range interior will exceed those at the edge: a case study with North
American beaver. Global Change Biology, 15, 508-522.
Joint Research Centre (2011). Global Land Cover 2000. In Natural Ressources
Canada-Canada Center for Remote Sensing, United States Geological
Survey. http://bioval.jrc.ec.europa.eu/products/glc2000/products.php.
Koffi J. K., Leighton P. A., Pelcat Y., Trudel L., Lindsay L. R., Milord F., Ogden
N. H. (2012) Passive surveillance for I. scapulatis ticks: enhanced analysis
for early detection of emerging Lyme disease risk. Journal of Medical
Entomology, 49, 400-409.
Krohne D. T., Hoch G. A. (1999). Demography of Peromyscus leucopus
populations on habitat patches: the role of dispersal. Canadian Journal of
Zoology, 77, 1247-1253.
Lackey J. A., Huckaby D. G., Ormiston B. G. (1985). Peromyscus leucopus.
Mammalian Species, 247, 1-10.
Latifovic R., Zhu Z.-L., Cihlar J., Giri C., Olthof I. (2004). Land cover mapping
of North and Central America - Global Land Cover 2000. Remote Sensing
of Environment, 89, 116-127.
Levine J. F., Wilson M. L., Spielman A. (1985) Mice as reservoirs of the Lyme
disease spirochete. American Journal of Tropical Medicine and Hygiene,
34, 355-360.
Linzey A. V., Matson J., Timm R. (2008) Peromyscus leucopus. In: IUCN Red
List of Threatenend Species. Version
2011.2.http://www.iucnredlist.org/apps/redlist/details/16669/0.
Long C. A. (1973) Reproduction in the white-footed mouse at the northern limits
of its geographical range. The Southwestern Naturalist, 18, 11-20.
75
Long C.A. (1996) Ecological Replacement of the Deer Mouse, Peromyscus
maniculatus, by the White-footed Mouse, P. leucopus, in the Great Lakes
Region. Canadian Field-Naturalist, 110, 271-277.
Martin N. (2010) Effects of climate change on the distribution of white-footed
mouse (Peromyscus leucopus), an ecologically and epidemiologically
important species. Unpublished Master of Science University of Michigan,
Michigan, 24 pp.
Mather T. N., Wilson M. L., Moore S. I., Ribeiro J. M. C., Spielman A. (1989).
Comparing the relative potential of rodents as reservoirs of the Lyme
disease spirochete (Borrelia burgdorferi). American Journal of
Epidemiology, 130(1), 143-150.
McKenney D. W., Pedlar J. H., Papadopol P., Hutchinson M. F. (2006). The
development of 1901-2000 historical monthly climate models for Canada
and the United States. Agricultural and Forest Meteorology, 138, 69-81.
McMahon S. M., Harrison S. P., Armbruster W. S., Bartlein P. J., Beale C. M.,
Edwards M. E., Kattge J., Midgley G., Morin X., Prentice L. C. (2011)
Improving assessment and modelling of climate change impacts on global
terrestrial biodiversity. Trends in Ecology and Evolution, 26, 249-259.
Meehl G. A., Covey C., Delworth T., Latif M., McAyaney B., Mitchell J. F. B.,
Stouffer R. J., Taylor K. E. (2007). The WCRP CMIP3 multimodel dataset
- A new era in climate change research. Bulletin of American
Meteorological Society, September 2007, 1383-1394.
Meteorological Service of Canada. (2011). National Climate data and Information
Archive. In Environment Canada
http://climate.weatheroffice.gc.ca/prods_servs/tables/attachment1_e.html.
Moore R. D., Mckendry I. G., Stahl K., Kimmins H. P., Yueh-Hsien L. (2005)
Mountain pine beetle outbreaks in western Canada: coupled influences of
climate variability and stand development. In: Final Report for Climate
Change Action Fund Project, A675.Ottawa, ON, Natural Resources
Canada.
76
Moran P. A. P. (1948). The interpretation of statistical maps. Journal of the Royal
Statistical Society. Series B (Methodological), 10(2), 243-251.
Morrison M. L., Marcot B. G., Mannan R. W. (1992). Modeling wildlife-habitat
relationships. In J. M. Scott, P. J. Heglund, M. L. Morrison, J. B. Haufler,
M. G. Raphael, W. A. Wall & F. B. Samson (Eds.), Wildlife-Habitat
Relationships: Concepts & Applications (pp. 309-349). Madison,
Wisconsin: University of Wisconsin.
Music B., Caya D. (2007). Evaluation of the hydrological cycle over the
Mississippi river basin as simulated by the Canadian Regional Climate
Model (CRCM). Journal of Hydrometeorology, 8(5), 969-988.
Myers P., Lundrigan B. L., Hoffman S. M. G., Haraminac A. P., Seto S. H. (2009)
Climate-induced changes in the small mammal communities of the
Northern Great Lakes Region. Global Change Biology, 15, 1434-1454.
Myers P., Lundrigan B. L., Kopple R. V. (2005). Climate change and the
distribution of Peromyscus in Michigan: is global warming already having
an impact? In E. A. Lacey,P. Myers (Eds.), Mammalian Diversification:
From Chromosomes to Phylogeography (pp. 101-125) University of
California Publications in Zoology.
Nakicenovic N., Alcamo J., Davis G., de Vries B., Fenhann J., Gaffin S., Gregory
K., Grübler A., Jung T. Y., Kram T., La Rovere E. L., Michaelis L., Moris
S., Morita T., Pepper W., Pitcher H., Price L., Raihi K., Roehrl A., Rogner
H.-H., Sankovski A., Schlesinger M., Shukla P., Smith S., Swart R., van
Rooijen S., Victor N., Dadi, Z. (2000). Special Repot on Emissions
Scenarios: A Special Report of Working Group III of the
Intergovernmental Panel on Climate Change (599 p.). Cambridge:
Cambridge University Press.
National Oceanic and Atmospheric Administration. (2011). National Weather
Service. In National Oceanic and Atmospheric Administration
http://www.noaa.gov/
Ogden N. H., Lindsay L. R., Hanincová K., Barker I. K., Bigras-Poulin M.,
Charron D. F., Heagy A., Francis C. M., O’Callaghan C. J., Schwartz I.,
77
Thompson R. A. (2008a) Role of migratory birds in introduction and range
expansion of Ixodes scapularis ticks and of Borrelia burgdorferi and
Anaplasma phagocytophilum in Canada. Applied and Environmental
Microbiology, 74, 1780-1790.
Ogden N. H., St-Onge L., Barker I. K., Brazeau S., Bigras-Poulin M., Charron D.
F., Francis C. M., Heagy A., Lindsay L. R., Maarouf A., Michel P., Milord
F., O’Callaghan C. J., Trudel L., Thompson R. A. (2008b). Risk maps for
range expansion of the Lyme disease vector, Ixodes scapularis, in Canada
now and with climate change. International Journal of Health
Geographics, 7(24), 1-15.
Ogden N. H., Maarouf A., Barker I. K., Bigras-Poulin M., Lindsay L. R.,
Morshed M. G., O’Callaghan C. J., Ramay F., Waltner-Toews D., Charron
D. F. (2006) Climate change and the potential for range expansion of the
Lyme disease vector Ixodes scapularis in Canada. International Journal
for Parasitology, 36, 63-70.
Ogden N. H., Margos G., Aanensen D. M., Drebot M. A., Feil E. J., Hanincová
K., Schwartz I., Tyler S., Lindsay L. R. (2011) Investigation of genotypes
of Borrelia burgdorferi in Ixodes scapularis ticks collected in surveillance
in Canada. Applied and Environmental Microbiology, 10, 3244-3254.
Ostfeld R. S. (2009) Climate change and the distribution and intensity of
infectious diseases. Ecology, 90, 903-905.
Ostfeld R. S. (2011). Lyme Disease: the Ecology of a Complex System. New
York: Oxford University Press.
Ostfeld R. S., Jones C. G., Wolff J. O. (1996) Of mice and mast. Bioscience, 46,
323-330.
Parmesan C. (2006). Ecological and evolutionary responses to recent climate
change. Annual Review of Ecology, Evolution, and Systematics, 37, 637669.
Pearson R. G., Dawson T. P. (2003). Predicting the impacts of climate change on
the distribution of species: are bioclimate envelope models useful? Global
Ecology and Biogeography, 12, 361-371.
78
Parmesan C. (2007) Influence of species, latitudes and methodologies on
estimates of phenological response to global warming.Global Change
Biology, 13, 1860-1872.
Parmesan C., Yohe G. (2003) A globally coherent fingerprint of climate change
impacts across natural systems. Nature, 421, 37-42.
Pellissier L., Pradervand J.-N., Pottier J., Dupuis A., Maiorano L., Guisan A.
(2012) Climate-based empirical models show biased predictions of
butterfly communities along environmental gradients. Ecography, 35, 684692.
Pereira H. M., Leadley P. W., Proença V., Alkemade R., Scharlemann J. P. W.,
Fernandez-Manjarrés J. F., Araújo M. B., Balvanera P., Biggs R., Cheung
W. W. L., Chini L., Cooper H. D., Gilman E. L., Guénette S., Hurtt G. C.,
Huntington H. P., Mace G. M., Oberdorff T., Revenga C., Rodrigues P.,
Scholes R. J., Sumaila U. R., Walpole M. (2010) Scenarios of global
biodiversity in the 21st century. Science, 330, 1496-1501.
Perry A. L., Low P. J., Ellis J. R., Reynolds J. D. (2005). Climate change and
distribution shifts in marine fishes. Science, 308, 1912-1915.
Peterson A. T., Bauer J. T., Mills J. N. (2004) Ecologic and geographic
distribution of Filovirus disease. Emerging Infectious Diseases, 10, 40-47.
Peterson A. T., Sánchez-Cordero V., Beard B., Ramsey J. M. (2002) Ecologic
niche modeling and potential reservoirs for Chagas disease, Mexico.
Emerging Infectious Diseases,8, 662-667.
Peterson M. J. (2012) Evidence of a climatic niche shift following North
American introductions of two crane flies (Diptera; genus Tipula). In:
Biological Invasions. DOI: 10.1007/s10530-012-0337-3
Phillips S. J., Anderson R. P., Schapire R. E. (2006) Maximum entropy modeling
of species geographic distributions. Ecological Modelling, 190, 231-259.
Pierce S. S., Vogt F. D. (1993). Winter accclimatization in Peromyscus
maniculatus gracilis, P. leucopus noveboracensis, and P. l. leucopus.
Journal of Mammalogy, 74(3), 665-677.
79
R Development Core Team. (2012). R: A language and environment for statistical
computing - Version 2.14.1. Vienna, Austria: R Foundation for Statistical
Computing.
Ramankutty N., Foley J. A. (1999). Estimating historical changes in global land
cover: Croplands from 1700 to 1992. Global Biogeochemical Cycles,
13(4), 997-1027.
Rand P. W., Lacombe E. H., Smith Jr. R. P., Rich S. M., Kilpatrick C. W.,
Dragoni C. A., Caporale D. (1993) Competence of Peromyscus
maniculatus (Rodentia: Cricetidae) as a reservoir host for Borrelia
burgdorferi (Spirochaetares: Spirochaetaceae) in the wild. Journal of
Medical Entomology, 30, 614-618.
Régnière J., Nealis V., Porter K. (2008) Climate suitability and management of
the gypsy moth invasion into Canada. Journal Biological Invasions, 11,
135-148.
Rich S. M., Kilpatrick C. W., Shippee,J. K., Crowell K. L. (1996). Morphological
differentiation and identification of Peromyscus leucopus and P.
maniculatus in Northeastern North America. Journal of Mammalogy,
77(4), 985-991.
Rogic A., Tessier N., Lapointe F.-J., Millien V. (2013) Genetic structure of the
white-footed mouse in the context of the emergence of Lyme disease in
southern Québec. Accepted for publication in Ecology and Evolution.
Root T. L., Price J. T., Hall K. R., Schneider S. H., Rosenzweig C., Pounds A.
(2003) Fingerprints of global warming on wild animals and plants. Nature,
421, 57-60.
Rowland C. L. (2003) Relationship of Reproductive Timing and Climate Change
to the Displacement of Peromyscus maniculatus gracilis by Peromyscus
leucopus noveboracensis. Unpublished Master of Science University of
Miami, Oxford, 33 pp.
Schloss C. A., Nuñez T. A., Lawler J. J. (2012) Dispersal will limit ability of
mammals to track climate change in the western hemisphere. Proceedings
of the National Academy of Sciences,109, 8606-8611.
80
Schweiger E. W., Diffendorfer J. E., Pierotti R., Holt R. D. (1999). The relative
importance of small-scale and landscape-level heterogeneity in structuring
small mammal distributions. In G. W. Barrett & J. D. Peles (Eds.),
Landscape Ecology of Small Mammals (pp. 175-207). New York:
Springer.
Sharma S., Jackson D. A. (2008) Predicting smallmouth bass (Micropterus
dolomieu) occurence across North America under climate change: a
comparison of statistical approaches. Canadian Journal of Fisheries and
Aquatic Sciences, 65, 471-481.
Sinclair S. J., White M. D., Newell G. R. (2010). How useful are species
distribution models for managing biodiversity under future climates?
Ecology and Society, 15(1) 8-20.
Smith A. L., Hewitt N., Klenk N., Bazely D. R., Yan N., Wood S., Henriques I.,
MacLellan J. I., Lipsig-Mummé C. (2012) Effects of climate change on
the distribution of invasive alien species in Canada: a knowledge synthesis
of range projections in a warming world. Environmental Reviews, 20, 116.
Swets J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240,
1285-1293.
Thomas C. D., Lennon J. J. (1999) Birds extend their ranges northwards. Nature,
399, 213.
Thompson C., Spielman A., Krause P. J. (2001) Coinfecting deer-associated
zoonoses: Lyme disease, Babesiosis, and Ehrlichiosis. Clinical Infectious
Diseases, 33, 676-685.
Thuiller W. (2004a). BIOMOD - optimizing predictions of species distributions
and projecting potential future shifts under global change. Global Change
Biology, 9, 1353-1362.
Thuiller W. (2004b). Patterns and uncertainties of species' range shifts under
climate change. Global Change Biology, 10, 2020-2027.
81
Thuiller W., Brotons L., Araújo M. B., Lavorel S. (2004). Effects of restricting
environmental range of data to project current and future species
distributions. Ecography, 27, 165-172.
Thuiller W., Lafourcade B., Engler R., Araújo M. B. (2009). BIOMOD - a
platform for ensemble forecasting of species distributions. Ecography, 32,
369-373
Tingley M. W., Monahan W. B., Beissinger S. R., Mortiz C. (2009). Birds track
their Grinnellian niche through a century of climate change. Proceedings
of the National Academy of Sciences, 106 (suppl. 2), 19637-19643.
Tsao J. I. (2009) Reviewing molecular adaptations of Lyme Borreliosis
spirochetes in the context of reproductive fitness in natural transmission
cycles. Veterinary Research, 40, 1-42.
Václavík T., Meentemeyer R. K. (2012). Equilibrium or not? Modelling potential
distribution of invasive species in different stages of invasion. Diversity
and Distributions, 18, 73-83.
VanDerWal J., Shoo L. P., Graham C., Williams S. E. (2009). Selecting pseudoabsence data for presence-only distribution modeling: How far should you
stray from what you know? Ecological Modelling, 220, 589-594.
Venier L. A., Mckenney D. W., Wang Y., Mckee J. (1999) Models of large-scale
breeding-bird distribution as a function of macro-climate in Ontario,
Canada. Journal of Biogeography, 26, 315-328.
Walther G.-R., Roques A., Hulme P. E., Sykes M. T., Pyšek P., Kühn I., Zobel
M., Bacher S., Botta-Dukát Z., Bugmann H., Czúcz B., Dauber J., Hickler
T., Jarošík V., Kenis M., Klotz S., Minchin D., Moora M., Nentwig W.,
Ott J., Panov V. E., Reineking B., Robinet C., Semenchenko V., Solarz
W., Thuiller W., Vilà M., Vohland K, Settele J. (2009) Alien species in a
warmer world: risks and opportunities. Trends in Ecology and Evolution,
24, 686-693.
Wolff J. O., Durr D. S. (1986) Winter nesting behavior of Peromyscus leucopus
and Peromyscus maniculatus. Journal of Mammalogy,67, 409-412.
82
Zipkin E. F., Ries L., Reeves R., Regetz J., Oberhauser K. S. (2012) Tracking
climate impacts on the migratory monarch butterfly. Global Change
Biology, 18, 3039-3049.
83
Tables
Table 1 Climatic variables for the regional climatic model and the sub-continental
habitat niche models. This table presents the list of variables used in predicting
and projecting the white-footed mouse distribution in Québec. Variables are
defined and selected references stating their importance relatively to the
distribution of the mouse are provided. Winter length was defined as the period
from when the daily average temperature of a grid cell fell below 0°C after July 1
to the date when the average temperature rose above 0°C in the following
calendar year.
Climate
Variables
Code
Description
Number
of
Variables
Regional scale – ENFA – Climate niche model
Tmin1
Monthly min. average temperature for each time series
(Dec., Jan., Feb., March, and April)
Monthly max. average temperature for each time series
(Dec., Jan., Feb., March, and April)
Monthly average quantity of precipitation on ground
for each time series (Dec., Jan., Feb., March, and
April)
Mean snow depth average per month for each time
series (Dec., Jan., Feb., March, and April)
Average winter length (days) for each time series
Tmax1
Prec1
SDM2
WLen2
5
5
5
5
1
Sub-continental scale BIOMOD – Habitat niche models
3
Allqcland
35 land cover categories
Recancover4 5 categories of tree cover in % (20-40-60-80-100)
PhotoAvg5
Average maximum daily % of sunshine from Dec. to
April
1
WtmxAvg
Averaged maximum temperature (°C) in winter (see
winter definition)
WtmnAvg1 Averaged minimum temperature (°C) in winter (see
winter definition)
WprAvg2
Winter total precipitation average (mm) (see winter
definition)
SdmAvg2
Mean snow depth average (m) (see winter definition)
2,3
WlenAvg
Winter average length (days) (see winter definition)
1
1
1
1
1
1
1
1
1
(Hill, 1983; Conley & Porter, 1986; Myers et al., 2005; Myers et al., 2009; Martin, 2010)
(Long, 1973; Wolff & Durr, 1986)
3
(Howard, 1951; Pierce & Vogt, 1993; Long, 1996)
4(Krohne & Hock, 1999; Schweiger et al., 1999
5
(Dark et al., 1983)
2
84
Table 2 Evaluation of the predictive performance of the sub-continental habitat
niche models. The third column is the average of the cross-validation of all the
repetitions, while the fourth is the average score when the models are evaluated
with independent data. The last three columns are results obtained from the final
model itself (i.e. consortium of all the repetitions). The sensitivity and specificity
are on a scale of 1000.
Statistical
Test
Model
Kappa
ANN
CTA
GAM
GBM
GLM
MARS
FDA
RF
Roc
ANN
CTA
GAM
GBM
GLM
MARS
FDA
RF
TSS
ANN
CTA
GAM
GBM
GLM
MARS
FDA
RF
Crossvalidation
0,78
0.77
0.79
0.81
0.78
0.80
0.80
0.86
0.87
0.86
0.90
0.92
0.89
0.89
0.90
0.95
0.73
0.71
0.75
0.76
0.73
0.75
0.74
0.84
Independent Total
data
score Sensitivity
0.01
0.80
484
0.00
0.85
134
0.13
0.80
370
0.05
0.91
545
0.04
0.81
282
0.01
0.85
444
0.00
0.82
507
0.02
1.00
390
0.79
0.91
900
0.72
0.935
810
0.96
0.93
678
0.90
0.99
632
0.93
0.94
669
0.82
0.93
927
0.86
0.93
937
0.91
1.00
867
0.70
0.78
530
0.52
0.83
188
0.87
0.76
551
0.75
0.92
638
0.84
0.77
472
0.64
0.80
731
0.63
0.78
883
0.73
1.00
390
Specificity
97
98
99
99
99
100
99
100
86
96
86
95
86
87
87
100
96
97
94
95
96
96
96
100
85
Fig.1 White-footed mouse historical distribution through time in southern regions
of Québec. The dots represent captured records for the white-footed mouse, coded
by symbols into three time series corresponding to the expansion in time of the
white-footed mouse.
86
Fig.2 Study area for the sub-continental scale models BIOMOD. United States
included are Alabama, Connecticut, Delaware, Georgia, Illinois, Indiana,
Kentucky, Maine, Maryland, Massachusetts, Michigan, New Hampshire, New
Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, South
Carolina, Tennessee, Vermont, Virginia, West Virginia, and Wisconsin. Black
dots are species occurrence points used to validate the models.
87
16
Selected
CMIP3 GCMs
14
12
∆ PrTot annual (%)
10
8
6
4
2
0
-2
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
∆ Tavg annual (%)
Fig.3 Scatterplot of projected changes (∆) in annual average temperature (Tavg
annual) and annual total precipitation (PrTot annual) for the future horizon 2050
(2041-2070) over the study area. The open circles represent the entire CMIP3
dataset (n=140), whereas the filled circles represent the projected changes for the
37 simulations.
88
Marginality Axis
0.85
(a)
0.85
Prec December
Snow April
Tmax December
Tmin April
Winter Length
1.5
(b)
Specialisation Axis
Tmax January
0.8
Prec January
Snow April
Winter Length
Tmin December
(c)
0.85 Snow January
Tmax January
0.83
Winter Length
Prec February
Snow March
Tmin March
Fig. 4 Environmental variable scores obtained from the second set of ENFAs. a:
1975-1984. b:1975-1994. c:1975-2004. The white dot is the centroid of the
distribution of utilization; the dark grey polygon represents the distribution of
utilization; and the light grey polygon represents position of the distribution of
available conditions.
89
1,4
1.4
Variable Importance Value
1,2
1.2
SRE
11
RF
GBM
0,8
0.8
MARS
0,6
0.6
FDA
GAM
0,4
0.4
GLM
ANN
0,2
0.2
CTA
0
SDMAvg
WLenAvg
WPrAvg
WTMnAvg
WTMxAvg
Variables Considered by Models
Fig.5 Variable importance scores from the different models used in the analysis.
The importance score of each variable is one minus the correlation score between
the original prediction and the prediction made by this variable. The importance
score is positively related with the importance of the variable. Scores larger than 1
indicate that the influence of the variable is very important.
90
Fig.6 Response curves of each variable considered in the seven models used
(variables defined in Table 1). These plots allow the visualization of the response
of the variable of interest while other variables are being held constant. The
abbreviation for each model is given in the legend and defined in the text.
91
Fig. 7 White-footed mouse modeled distribution, including predicted distribution (A) and projected distribution with
achange in climate variables under the B) A1b, C) A2, and D) B1 green gas emissions scenarios from the IPCC (2001).
92
Fig.8 Spatial distribution of standard deviation values from the predictions.
93
CHAPTER 4: GENERAL CONCLUSIONS
Climate change interests many scientists who study the patterns of species
distribution through time and space, because climate is a major determinant in
species’ distributions (Jeffree & Jeffree, 1994). Climate is an important parameter
that influences the geographical range of most species (Jeffree & Jeffree, 1994;
Martens et al., 1995). As climatic conditions are changing under global warming,
there is relatively high likelyhood that species’ habitats will also be altered. Range
shifts can occur in infectious disease vectors, such as mosquitoes, ticks, and other
arthropod species (Confalonieri et al., 2007).
In this thesis, I studied the current and projected distribution of the whitefooted mouse in Québec to explore the potential influence of global warming on
Lyme disease in Québec. Brownstein et al. (2005) and Ogden et al.(2006) both
have noted the possible changing distribution of Lyme disease vectors in North
America under changing climatic conditions due to global warming. The number
of diagnosed cases of Lyme disease increases each year in southern regions of
Québec (Ogden et al., 2006). Lyme disease is a vector-borne disease, and the
occurrence of this pathogen is, in part, defined by the geographic distribution of
its vectors (Tsao, 2009). While some have already recognized the impact of the
shift in the distribution of the pathogen’s main vector, the black-legged tick
(Ogden et al., 2006), other important vectors also need to be considered (Ostfeld
et al., 2005; Gage et al., 2008). The case studied here is the white-footed mouse, a
competent reservoir for the Lyme disease and an excellent host for the blacklegged tick (Donahue et al., 1987; Ogden et al., 2006; Tsao, 2009; Bouchard et
al., 2011). This chapter reviews the main findings of this thesis, discusses some of
their limitations, and suggestions for further research.
94
Main Findings
In this thesis, I provide maps of the white-footed mouse fundamental
distribution in southern Québec under current and projected climate changes. The
thesis was organized around two main research objectives, namely to:
1.
Describe the drivers influencing the white-footed mouse distribution in
Québec, and potential risk factors associated with the species’ occurrence.
2.
Understand the distribution change of the white-footed mouse in Québec
by developing a set of ecological niche models to predict its current potential
distribution and to project it in the future under climate change.
In Chapter 2, I addressed the first objective of the study. I summarized our
knowledge on the white-footed mouse’s adaptive capacity to various
environmental, climatic, and geographical factors that could limit its geographic
distribution. The literature review revealed that the white-footed mouse occupies a
variety of habitats throughout its extensive range in North America., Yet, the
northern limit of the species’ distribution is likely to be defined by specific
environmental conditions. The relative importance of such environmental factors
varies with the spatial scale, mediating both local and regional geographical
occurrence of the white-footed mouse. Consequently, the processes limiting the
species’ distribution cannot be generalized across different temporal and spatial
scales. Continued research is needed to understand how processes linking various
limiting factors influence the species distribution under different environmental
conditions.
In Chapter 3, I addressed the second research objective of the thesis. The
objective of this chapter was to determine how the white-footed mouse
populations will expand over the Québec territory, and to what extent climate
change will facilitate this expansion. This objective was addressed in the form of
two analyses built upon results obtained from the previous chapter. In the first
95
analysis, I examined the historical distribution patterns of the white-footed mouse
in southern Québec. Winter length, especially early springs and late autumns, act
as a primary climate factor influencing the white-footed mouse distribution, along
with maximum temperature in autumn, minimum temperature in spring, snow
depth and winter precipitation. Building on these results, the second analysis
performed was a modelisation of the current distribution of the white-footed
mouse in Québec in relation with a number of climatic, geographical and
environmental factors, and to project this distribution model into the future, under
climate change. In addition to the previous five climatic factors limiting the whitefooted mouse distribution, I also considered in these models the potential
influence of photoperiod, land cover, and canopy cover density on the species
niche range. I found that climate factors were the main drivers of the white-footed
mouse distribution. Models slightly over-predicted the known distribution of the
species, but this result was likely due to the fact that the fundamental niche of the
species, and not its realized niche, was modeled. The prediction of the niche
models showed that the white-footed mouse has already the potential to occupy
highly populated (human) areas in Québec. By 2050, its distribution could expand
over the whole Québec territory, with some exceptions from mountainous regions
north of the St. Lawrence River. These conclusions are thus pointing to the
potential future increase in areas of incidence of Lyme disease in Québec.
Overall, my thesis is a contribution to the body of literature that predicts
poleward expansions of species’ distributions under ongoing climate change (e.g.
Brownstein et al., 2005; Parmesan, 2006; Martin, 2010; Ogden et al., 2008). I also
contribute to the branch of studies on the increasing occurrence of Lyme disease
in southern Québec, and more generally on the influence of climate warming on
the emergence of vector-borne disease (e.g. Brownstein et al., 2005; Ogden et al.,
2008; Bouchard et al., 2011; Ogden et al., 2011; Koffi et al., 2012). Each of these
studies is part of the biogeographical framework for Lyme disease described in
the introduction. This framework considers the interactions amongst the factors
influencing the disease occurrence and the observed incidence of the disease in
96
humans. Results obtained in this thesis contribute to a better understanding of the
Lyme disease occurrence in Québec. Although my models predicted that the
majority of urban areas of Québec already lie within the current fundamental
niche of the white-footed mouse, the level of pathogen prevalence observed
among the mouse population may increase with climate change. Therefore, as the
white-footed mouse potential niche range increases in northern territories, so will
the prevalence of Lyme disease in the human population. It is important to keep
studying factors influencing the species’ distribution and their interactions to
detect the interacting factors determining the species’ actual range. Climate
change and landscape changes at various scales may favour some vectors over
others (Perkins et al., 2006), having further implications on the disease
transmission to humans (Parmesan. 2006).
Limitations and Uncertainties
There are three primary limitations to the analyses employed in this study.
These limitations relate to both the habitat niche models and the climate models
used to predict the current and project the future distributions of the white-footed
mouse.
First, the spatial scale at which habitat niche models are estimated is of
fundamental importance to accurately interpret the results (Robinson et al., 1992;
Pearson & Dawson, 2003).The habitat niche models used in this study aimed to
capture the bioclimatic envelopes that define the white-footed mouse niche. The
purpose was to better understand the potential impact of climate change on the
species distribution. However, as outlined in chapters1 and 2, there is a hierarchy
of factors influencing the distribution of the white-footed mouse. The relative
importance of these factors varies across different spatial scales (Gelfand et al.,
2006). The ecological niche model used here aimed to capture the factors relevant
to the white-footed mouse niche at the regional scale, but at a local scale it
became apparent that other factors limited the species’ distribution and dispersal.
97
For instance, the white-footed mouse is sensitive to the degree of connectivity of
its habitat (Myers et al., 2005). The St. Lawrence River represents a geographical
barrier to the species’ dispersal, which was not considered in our models. The
white-footed mouse is furthermore sensitive to the landscape geometry and
structure of its habitat, which is a mosaic of large forest patches, isolated forest
stands, and agricultural fields (Merriam et al., 1990; Krohne et al., 1999; Rizkalla
et al., 2007). The Montérégie region south of Montréal is highly fragmented with
patches of forest scattered among agricultural fields and urban areas (Beauregard,
1970). An important consequence is the reduction of habitat connectivity, which
could impede the species movement within the landscapes in response to climate
change (Hansson, 1987; Krohne & Hoch, 1999; Opdam et al., 2004). Habitat
fragmentation is likely to influence the individual dispersal rate and the species
distribution patterns (Hansson, 1987; Robinson et al., 1992; Nupp et al., 1998;
Schweiger et al., 1999). Therefore, to accurately predict and project the future
distribution of the white-footed mouse under a changing climate, it is necessary to
also acquire a detailed knowledge of the species ability to disperse through
heterogeneous landscapes that are fluctuating in time (Pearson &Dawson, 2003).
At large spatial scales and coarse resolution, species distribution is likely to be
controlled by climatic factors. At a finer resolution, however, micro-topography,
and habitat fragmentation, or connectivity, are more likely to constraint the
species distribution (Guisan et al., 2005). The proper selection of resolution and
spatial extent is thus critical to estimate the species niche and avoid mismatch and
biases in the results (MacKey & Lindemayer, 2001; Saura et al., 2001; Guisan et
al., 2005). Appropriate strategies, response plans, and enhanced surveillance
systems to prevent vector-borne diseases thus require considering the effect of
climate change locally (Wormser et al., 2006; Gage et al., 2008).
This scaling issue may also explain why my models over-predicted the
current species distribution. The statistical evaluations performed in Chapter 3
revealed the potential over-prediction of the models. Sensitivity scores were
greater than the specificity scores, suggesting that the model algorithms had a
98
harder time identifying pseudo-absence sites than presence sites. Such drawback
may result from the over-parameterization of the models on the presence dataset.
It is in accordance with the higher predictive accuracies obtained when the models
are evaluated using an independent sample prediction than when using a sub-set
of the dataset for calibration (cross-validation). It demonstrates the difficulty of
the models to define simple combinations of climatic, environmental, and
geographic factors delimiting the species distributions at the scale considered.
This may be due of the fact that the white-footed mouse is a generalist and
widespread species. The distributions of widespread species are harder to model
due to the methodology used, or the species’ biology itself (Hernandez et al.,
2006). In my model calibration, I compared the species presence data points to
background data. Widespread species often occupy a variety of habitat types
throughout their geographical ranges, and only specific local barriers may limit
their distribution (Alder & Wilson, 1987). Consequently, the pseudo-absences
randomly selected by the models here may not differ markedly from the presence
dataset, as local variations in the environment are not captured in the models’
calibration. The power of the models, or accuracy of their predictive evaluation, is
therefore reduced, but could be improved by increasing the sample size of species
presence points to better define its niche habitat, or by using real absence data
(Hirzel et al., 2002). Furthermore, widespread species often show regional
ecological adaptation, which might not be repesented by any of the predictive
factors at the scale the models were calibrated (Grenouillet et al., 2011). Modeling
these sub-populations at a finer spatial scale may lead to overestimation of the
niche breadth of the species (Lepers et al., 2005; Gelfand et al., 2006).
The second limitation of the models is the climate projection used in the
analysis. Any uncertainties in the projected climate data will affect the predicted
potential fundamental niche of the species. The climate data used here come from
a daily 10 km-gridded climate dataset for Canada (Hutchinson, 2004), and was
limited to regions south of 60°N, because there were few observations beyond this
latitude. Interpolations of climate conditions in these regions includes greater
99
uncertainty, reflected within the climate output, and consequently in projections
of the species’ distribution. The use of a variety of global climate models in the
analysis, taking into consideration the inter-variability in the models results, helps
reduce these types of errors.
A third limitation of the models lies in the quality of training data to
calibrate and validate the predictions. The models were calibrated using third
party data from the Ministère des Ressources Naturelles et de la Faune du
Québec, as well as museum and university collection databases from the United
States. Since these data were not collected with the intended purpose to calibrate
species distribution projections, they were often concentrated in specific regions,
which limited the range of conditions under which the species may be observed,
and that were used to calibrate the models. Lastly, the white-footed mouse is
difficult to distinguish from the deer mouse (Peromyscus maniculatus) by simple
visual inspection of the specimens, and there could be some misidentifications in
the data used here (Dooley & Dueser, 1990; Millien not published). This potential
issue is inherent to the use of large, heterogeneous datasets like the one I used
here, and is likely to impact the reliability of the models results (Hirzel &
Guissan, 2002; Morrison et al., 1992). It is difficult though to estimate how much
error is steming from the quality of the data in any such work relying on large
datasets.
Directions for Further Research
My thesis was written in the context of the emergence of Lyme disease in
the province of Québec. The goal of the study was to describe how the whitefooted mouse, an important vector of the disease pathogen and the main host of
the black-legged tick, could influence the disease spread over new territories.
Further work is needed, such as testing to which extent outputs from the whitefooted mouse niche models may provide the best predictors for the disease
occurrence. In addition, the contribution of the white-footed mouse to the disease
100
spread is important and well recognized (Bouchard et al., 2011), but other vectors
and species hosts, which may also influence the disease prevalence, need to be
considered. Further studies should investigate the role of the full range of hosts
involved in the disease transmission cycle. Other vectors and host species include
white-tailed deer, skunks, racoons, foxes, coyotes, birds, chipmunks, eastern grey
squirrels, and other small mammals (Schulze et al., 2005; LoGiudice et al., 2008;
Bouchard et al., 2011). In addition, the pathogen dynamic may evolve with time
and specialize for new host species as suggested for the deer mouse (Myers et al.,
2005). It is thus important to understand the ability of the pathogen to exploit new
vectors for its transmission and dispersal (Rosenthal, 2009), and how vector and
host species interact with each other in the context of seasonal host activity and
climate change (Gage et al., 2008). For example, some hypothesized that a greater
diversity of host species within the same habitat would result in a dilution effect
of the disease risk due to the availability of less competent hosts for the ticks,
(Ogden & Tsao, 2009; Bouchard et al., 2011; Ostfeld, 2011). Species interactions
within the community of hosts and vectors likely have important consequence on
the disease transmission efficiency to ticks, and subsequently to humans.
The spatial scaling issue recognized in the research reported here could be
addressed by integrating habitat niche models into a multiscale hierarchical
modelling framework (Pearson & Dawson, 2003; Guisan & Thuiller, 2005).
Variables used at a broad geographical scale may not necessarily provide the most
relevant information required at finer scales (MacKey & Lindenmayer, 2001;
Pearson & Dawson, 2003). This issue was illustrated when integrating land cover
into the set of habitat variables used in the analysis. Land cover was not
considered a significant limiting factor at a regional scale, while at a local scale
habitat fragmentation is known to affect the white-footed mouse distribution and
abundance (Nupp &Swihart, 1998). Furthermore, habitat fragmentation also plays
an important role in defining the spatial heterogeneity of Lyme disease’s
prevalence by influencing the density and diversity of its mammalian hosts
(Brownstein et al., 2005; LoGiudice et al., 2008). Considering a multiscale
101
environemental gradient would thus be useful to better characterize the response
of the white-footed mouse distribution in relation to mechanisms operating at
different geographic and temporal scales (Cushman et al., 2010), for which the
emergence of Lyme disease in southern Québec provides an excellent study case.
Literature Cited
Alder G. H., Wilson M. L. (1987) Demography of habitat generalist, the whitefooted mouse, in a heterogeneous environment. Ecology, 68, 1785-1796.
Beauregard L. (1970) Les étapes de la mise en valeur agricole de la vallée du
Richelieu. Cahiers de géographie du Québec, 14, 171-214.
Bouchard C., Beauchamp G., Nguon S., Trudel L., Milord F., Lindsay L. R.,
Bélanger, D., Ogden N. H. (2011) Associations between Ixodes scapularis
ticks and small mammal hosts in a newly endemic zone in southeastern
Canada: implications for Borrelia burgdorferi transmission. Ticks and
Tick-borne Diseases, 2, 183-190.
Brownstein J. S., Holford T. R., Fish D. (2005) Effect of climate change on Lyme
disease risk in North America. EcoHealth, 2, 38-46.
Confalonieri U., Menne B., Akhatar R., Ebi K. L., Hauengue M., Kovats R. S.,
Revich B., Woodward A. (2007) Human health. In: Climate change 2007:
Impacts, adaptation and vulnerability. Contribution of working group II to
the fourth assessment report of the intergovernmental panel of climate
change.(Ed. by M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. van der
Linden, C. E. Hanson) Cambridge, UK, Cambridge University Press.
Cushman S. A., Littell J., Mcgarigal K. (2010) The problem of ecological scaling
in spatially complex, nonequilibrium ecological systems. In: Spatial
Complexity, Informatics, and Wildlife Conservation. (Ed. by S. A.
Cushman, F. Huettmann) New York, Springer.
Donahue J. G., Piesman J., Spielman A. (1987) Reservoir competence of whitefooted mice for Lyme disease spirochetes. American Journal of Tropical
Medicine and Hygiene, 36, 92-96.
102
Dooley Jr. J. L., Dueser R. D. (1996) Experimental tests of nest site competition
in two Peromyscus species. Oecologia, 105, 81-86.
Gage K. L., Burkor T. R., Eisen R. J., Hayes E. B. (2008) Climate and
vectorborne diseases. American Journal of Preventive Medicine, 35, 436450.
Gelfand A. E., Silander J. A. Jr., Latimer A., Lewis P. O., Rebelo A. G., Holder
M. (2006) Explaining species distribution patterns through hierarchical
modeling. Bayesian Analysis, 1, 41-92.
Grenouillet G., Buisson L., Casajus N., Lek S. (2011) Ensemble modelling of
species distribution: the effects of geographical and environmental ranges.
Ecography, 34, 9-17.
Guisan A., Thuiller W. (2005) Predicting species distribution: offering more than
simple habitat models.Ecology Letters, 8, 993-1009.
Hansson L. (1987) Dispersal routes of small mammals at an abandoned field in
Central Sweden. Holarctic Ecology, 10 154-159.
Hernandez P. A., Graham C. H., Master L. L., Albert D. L. (2006) The effect of
sample size and species characteristics on performance of different species
distribution modeling methods. Ecography, 29, 773-785.
Hirzel A. H., Guisan A. (2002) Which is the optimal sampling strategy for habitat
suitability modelling. Ecological Modelling, 157, 331-341.
Hutchinson M. F. (2004) ANUSPLIN Version 4.3. Centre for Resource and
Environmental Studies, Australian National University
Jeffree E. P., Jeffree C. E. (1994) Temperature and the biogeograpical
distributions of species. Functional Ecology, 8, 640-650.
Krohne D. T., Hoch G. A. (1999) Demography of Peromyscus leucopus
populations on habitat patches: the role of dispersal. Canadian Journal of
Zoology, 77, 1247-1253.
Lepers E., Lambin E. F., Janetos A. C., Defries R., Achard F., Ramankutty N.,
Scholes R. J. (2005) A synthesis of information on rapid land-cover
change for the period 1981-2000. Bioscience,55, 115-124.
103
LoGiudice K., Duerr S. T. K., J.Newhouse M., Schmidt K. A., Killilea M. E.,
Ostfeld R. S. (2008) Impact of host community composition on Lyme
disease risk. Ecology, 89, 2841-2849.
MacKey B. G., Lindenmayer D. B. (2001) Towards a hierarchical framework for
modelling the spatial distribution of animals. Journal of Biogeography, 28,
1147-1166.
Martin N. (2010) Effects of climate change on the distribution of white-footed
mouse (Peromyscus leucopus), an ecologically and epidemiologically
important species. Unpublished Master of Science University of Michigan,
Michigan, 24 pp.
Merriam G., Lanoue A. (1990) Corridor use by small mammals: field
measurement for three experimental types of Peromyscus leucopus.
Landscape Ecology, 4, 123-131.
Morrison M. L., Marcot B. G., Mannan R. W. (1992) Modeling wildlife-habitat
relationships. In: Wildlife-habitat relationships: concepts & applications.
(Ed. by J. M. Scott, P. J. Heglund, M. L. Morrison, J. B. Haufler, M. G.
Raphael, W. A. Wall, F. B. Samson) Madion, Wisconsin, University of
Wisconsin.
Myers P., Lundrigan B. L., Kopple R. V. (2005) Climate change and the
distribution of Peromyscus in Michigan: is global warming already having
an impact? In: Mammalian Diversification: From Chromosomes to
Phylogeography (Ed. by E. A. Lacey, P. Myers) University of California
Publications in Zoology.
Nupp T. E., Swihart R. K. (1998) Effects of forest fragmentation on population
attributes of white-footed mice and eastern chipmunks. Journal of
Mammalogy, 79, 1234-1243.
Ogden N. H., Lindsay L. R., Hanincová K., Barker I. K., Bigras-Poulin M.,
Charron D. F., Heagy A., Francis C. M., O’Callaghan C. J., Schwartz I.,
Thompson R. A. (2008) Role of migratory birds in introduction and range
expansion of Ixodes scapularis ticks and of Borrelia burgdorferi and
104
Anaplasma phagocytophilum in Canada. Applied and Environmental
Microbiology, 74, 1780-1790.
Ogden N. H., Maarouf A., Barker I. K., Bigras-Poulin M., Lindsay L. R.,
Morshed M. G., O’Callaghan C. J., Ramay F., Waltner-Toews D., Charron
D. F. (2006) Climate change and the potential for range expansion of the
Lyme disease vector Ixodes scapularis in Canada. International Journal
for Parasitology, 36, 63-70.
Ogden N. H., Margos G., Aanensen D. M., Drebot M. A., Feil E. J., Hanincová
K., Schwartz I., Tyler S., Lindsay L. R. (2011) Investigation of genotypes
of Borrelia burgdorferi in Ixodes scapularis ticks collected in surveillance
in Canada. Applied and Environmental Microbiology, 10, 3244-3254.
Ogden N. H., Tsao J. L. (2009) Biodiversity and Lyme disease: dilution or
amplification? Epidemics, 1, 196-206.
Opdam P., Wascher D. (2004) Climate change meets habitat fragmentation:
linking landscape and biogeographical scale levels in research and
conservation. Biological Conservation,117, 285-297.
Ostfeld R. S. (2011) Lyme Disease: the Ecology of a Complex System, New York,
Oxford University Press.
Ostfeld R. S., Glass G. E., Keesing F. (2005) Spatial epidemiology: an emerging
(or re-emerging) discipline. Trends in Ecology and Evolution, 20, 328336.
Parmesan C. (2006) Ecological and evolutionary responses to recent climate
change. Annual Review of Ecology, Evolution, and Systematics, 37, 637669.
Pearson R. G., Dawson T. P. (2003) Predicting the impacts of climate change on
the distribution of species: are bioclimate enveloppe models useful?
Global Ecology and Biogeography, 12, 361-371.
Perkins S. E., Cattadori I. M., Tagliapietra V., Rizzoli A. P., Hudson P. J. (2006)
Localized deer absence leads to tick amplification. Ecology, 87, 19811986.
105
Rizkalla C. E., Swihart R. K. (2007) Explaining movement decisions of forest
rodents in fragmented landscapes. Biological Conservation, 140, 339-348.
Robinson G. R., Holt R. D., Gaines M. S., Hamburg S. P., Johnson M. L., Firtch
H. S., Martinko E. A. (1992) Diverse and contrasting effects of habitat
fragmentation. Science, 257, 524-526.
Rogic A., Tessier N., Lapointe F.-J., Millien V. (2013) Genetic structure of the
white-footed mouse in the context of the emergence of Lyme disease in
southern Québec. Accepted for publication in Ecology and Evolution.
Rosenthal J. (2009) Climate change and the geographic distribution of infectious
diseases. EcoHealth, 6, 489-495.
Saura S., Martinez-Millán J. (2001) Sensitivity of landscape pattern metrics to
map spatial extent. Photogrammetric Engineering & Remote Sensing, 67,
1027-1036.
Schulze T. L., Jordan R. A., Schulze C. J. (2005) Host sssociations of Ixodes
scapularis (Acari: Ixodidae) in residential and natural settings in a Lyme
disease-endemic area in New Jersey. Population and Community Ecology,
42, 966-973.
Schweiger E. W., Diffendorfer J. E., Pierotti R., Holt R. D. (1999) The relative
importance of small-scale and landscape-level heterogeneity in structuring
small mammal distributions. In: Landscape Ecology of Small Mammals.
(Ed. by G. W. Barrett, J. D. Peles) New York, Springer.
Tsao J. I. (2009) Reviewing molecular adaptations of Lyme borreliosis
spirochetes in the context of reproductive fitness in natural transmission
cycles. Veterinary Research, 40, 1-42.
Wiens J. A. (2002) Predicting species occurrences: progress, problems, and
prospects. In: Predicting Species Occurences: Issues of Accuracy and
Scale. (Ed. by J. M. Scott, P. J. Heglund, M. L. Morrison, J. B. Haufler, M.
G. Raphael, W. A. Wall, F. B. Samson) Covelo, CA, Island Press
Wormser G. P., Dattwyler R. J., Shapiro E. D., Halperin J. J., Steere A. C.,
Klempner M. S., Krause P. J., Bakken J. S., Strle F., Stanek G.,
Bockenstedt L., Fish D., Dumler J. S., Nadelman R. B. (2006) The clinical
106
assessment, treatment, and prevention of Lyme disease, human
Granulocytic anaplasmosis, and Babesiosis: clinical practice guidelines by
the Infectious Diseases Society of America. Clinical Infectious Diseases,
43, 1089-1134.
107