Lyme borreliosis is rapidly emerging in Canada, and climate change is likely a key driver of the northern spread of the disease in North America. We used field and modeling approaches to predict the risk of occurrence of Borrelia burgdorferi, the bacteria causing Lyme disease in North America. We combined climatic and landscape variables to model the current and future (2050) potential distribution of the black-legged tick and the white-footed mouse at the northeastern range limit of Lyme disease and estimated a risk index for B. burgdorferi from these distributions. The risk index was mostly constrained by the distribution of the white-footed mouse, driven by winter climatic conditions. The next factor contributing to the risk index was the distribution of the black-legged tick, estimated from the temperature. Landscape variables such as forest habitat and connectivity contributed little to the risk index. We predict a further northern expansion of B. burgdorferi of approximately 250–500 km by 2050 – a rate of 3.5–11 km per year – and identify areas of rapid rise in the risk of occurrence of B. burgdorferi. Our results will improve understanding of the spread of Lyme disease and inform management strategies at the most northern limit of its distribution.

Least-cost modelling and circuit theory are common analogs used in ecology and evolution to model gene flow or animal movement across landscapes. Least-cost modelling estimates the least-cost distance, whereas circuit theory estimates resistance distance. The bias added in choosing one method over the other has not been well documented. We designed an experiment to test whether both methods were linearly related. We also tested the sensitivity of these metrics to variation in Euclidean distance, spatial autocorrelation, the number of pixels representing the landscape, and data aggregation. We found that least-cost and resistance distance were not linearly related unless a transformation was applied. Resistance distance was less sensitive to the number of pixels representing a landscape and was also less sensitive than least-cost distance to the Euclidean distance between nodes. Spatial autocorrelation did not affect either method or the relationship between methods. Resistance distance was more sensitive to aggregation in any form compared to least-cost distance. Therefore, the metric used to infer movement or gene flow and the manipulations applied to the data used to calculate these metrics may govern findings.

BackgroundHabitat fragmentation reduces genetic connectivity for multiple species, yet conservation efforts tend to rely heavily on single-species connectivity estimates to inform land-use planning. Such conservation activities may benefit from multi-species connectivity estimates, which provide a simple and practical means to mitigate the effects of habitat fragmentation for a larger number of species. To test the validity of a multi-species connectivity model, we used neutral microsatellite genetic datasets of Canada lynx (Lynx canadensis), American marten (Martes americana), fisher (Pekania pennanti), and southern flying squirrel (Glaucomys volans) to evaluate multi-species genetic connectivity across Ontario, Canada.ResultsWe used linear models to compare node-based estimates of genetic connectivity for each species to point-based estimates of landscape connectivity (current density) derived from circuit theory. To our knowledge, we are the first to evaluate current density as a measure of genetic connectivity. Our results depended on landscape context: habitat amount was more important than current density in explaining multi-species genetic connectivity in the northern part of our study area, where habitat was abundant and fragmentation was low. In the south however, where fragmentation was prevalent, genetic connectivity was correlated with current density. Contrary to our expectations however, locations with a high probability of movement as reflected by high current density were negatively associated with gene flow. Subsequent analyses of circuit theory outputs showed that high current density was also associated with high effective resistance, underscoring that the presence of pinch points is not necessarily indicative of gene flow.ConclusionsOverall, our study appears to provide support for the hypothesis that landscape pattern is important when habitat amount is low. We also conclude that while current density is proportional to the probability of movement per unit area, this does not imply increased gene flow, since high current density tends to be a result of neighbouring pixels with high cost of movement (e.g., low habitat amount). In other words, pinch points with high current density appear to constrict gene flow.Electronic supplementary materialThe online version of this article (10.1186/s40462-017-0112-2) contains supplementary material, which is available to authorized users.

We evaluated the effect of habitat and landscape characteristics on the population genetic structure of the white-footed mouse. We develop a new approach that uses numerical optimization to define a model that combines site differences and landscape resistance to explain the genetic differentiation between mouse populations inhabiting forest patches in southern Québec. We used ecological distance computed from resistance surfaces with Circuitscape to infer the effect of the landscape matrix on gene flow. We calculated site differences using a site index of habitat characteristics. A model that combined site differences and resistance distances explained a high proportion of the variance in genetic differentiation and outperformed models that used geographical distance alone. Urban and agriculture-related land uses were, respectively, the most and the least resistant landscape features influencing gene flow. Our method detected the effect of rivers and highways as highly resistant linear barriers. The density of grass and shrubs on the ground best explained the variation in the site index of habitat characteristics. Our model indicates that movement of white-footed mouse in this region is constrained along routes of low resistance. Our approach can generate models that may improve predictions of future northward range expansion of this small mammal.

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