* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Temporal evolution of the ecological niche of the
Survey
Document related concepts
Solar radiation management wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Climate change and poverty wikipedia , lookup
Public opinion on global warming wikipedia , lookup
IPCC Fourth Assessment Report wikipedia , lookup
General circulation model wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Effects of global warming on Australia wikipedia , lookup
Transcript
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