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As humans and climate change alter the landscape, novel disease risk scenarios emerge. Understanding the complexities of pathogen emergence and subsequent spread as shaped by landscape heterogeneity is crucial to understanding disease emergence, pinpointing high-risk areas, and mitigating emerging disease threats in a dynamic environment. Tick-borne diseases present an important public health concern and incidence of many of these diseases are increasing in the United States. The complex epidemiology of tick-borne diseases includes strong ties with environmental factors that influence host availability, vector abundance, and pathogen transmission. Here, we used 16 years of case data from the Minnesota Department of Health to report spatial and temporal trends in Lyme disease (LD), human anaplasmosis, and babesiosis. We then used a spatial regression framework to evaluate the impact of landscape and climate factors on the spread of LD. Finally, we use the fitted model, and landscape and climate datasets projected under varying climate change scenarios, to predict future changes in tick-borne pathogen risk. Both forested habitat and temperature were important drivers of LD spread in Minnesota. Dramatic changes in future temperature regimes and forest communities predict rising risk of tick-borne disease.

The effects of herbivores on landscape patterns and ecosystem processes have generally been inferred only from small‐plot or exclosure experiments. However, it is important to directly determine the interactions between herbivores and landscape patterns, because herbivores range over large portions of the landscape to meet requirements for food and shelter. In two valleys on Isle Royale, Michigan, USA, soil nitrogen availability and its temporal variance decreased rapidly as consumption of browse by moose (Alces alces) increased up to 2 g·m−2·yr−1; with greater amounts of consumption, nitrogen availability was uniformly low and constant from year to year. We tested three geostatistical models of the spatial distribution of available browse, annual browse consumption, conifer basal area, and soil nitrogen availability across the landscape: (1) no spatial autocorrelation (random spatial distribution); (2) short‐range spatial autocorrelation within a patch, but random distribution of patches at larger scales (spherical model); and (3) both short‐range autocorrelation within a patch and regular arrangement of patches at larger scales (harmonic oscillator model). Conifer basal area and soil nitrogen availability fit the harmonic oscillator model in both valleys. Annual consumption and available browse showed oscillations in one of the valleys and only short‐range autocorrelation in the other. In both valleys, however, the spatial pattern of annual consumption followed that of available browse. The predominance of spatially oscillatory patterns suggests that the interactions of moose with the forest ecosystem cause the development of both local patches of vegetation and associated nitrogen cycling rates, as well as the development of higher order patterns across the larger landscape. We suggest a coupled diffusion model of herbivore foraging and plant seed dispersal that may account for these patterns.

Herbivores contend with spatial and temporal variation in the quality, quantity, and availability of their food resource. The energy that female moose (Alces alces) store during the summer as body fat is used to meet energy needs in winter. The foraging strategy used by an animal affects its daily and annual energy balance. Consequently, foraging strategies and the distribution of food in the landscape can affect individual fitness and population growth through their effects on the energy balance of reproductive females. Models that unify landscape structure, foraging theory, and animal energy metabolism can be used to investigate the effects of foraging strategies on survival and reproduction. We developed an energy and activity simulation environment (EASE) model that predicts the seasonal changes in energy requirements of a female moose foraging in a spatially explicit landscape with a resolution of 1 m 2 . We validated EASE for both moose and deer (Odocoileus spp.) with respect to body mass changes reported for feeding trials, activity times, and browse intake rates. Energy intake and body mass depended on forage abundance, its spatial distribution, and foraging strategy. In simulations with stochasticity imposed on model parameters, mean moose body mass decreased 4 kg in comparison to simulations that were deterministic. Simulated moose body mass was most sensitive to browse digestibility and least sensitive to maximum intake per day in 1-yr simulations. Moose using nonrandom foraging strategies had higher body mass and survival than moose using random foraging strategies. Differences between strategies increased as browse density decreased. Moose that selected foraging locations with higher food density or food items with higher digestibility deposited more fat and protein than less selective moose, leading to increased survival and reproduction.

Animal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is properly addressed. Historically, coarsely-sampled movement data have proved invaluable for understanding large scale processes (e.g., home range, habitat selection, etc.), but modern fine-scale data promise to unlock far more ecological information. While location error can often be ignored in coarsely sampled data, fine-scale data require much more care, and tools to do this have been lacking. Current approaches to dealing with location error largely fall into two categories—either discarding the least accurate location estimates prior to analysis or simultaneously fitting movement and error parameters in a hidden-state model. Unfortunately, both of these approaches have serious flaws. Here, we provide a general framework to account for location error in the analysis of animal tracking data, so that their potential can be unlocked. We apply our error-model-selection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices are used to track a wide range of animal species comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. Then, using empirical data on tracked individuals from multiple species, we provide an overview of modern, error-informed movement analyses, including continuous-time path reconstruction, home-range distribution, home-range overlap, speed and distance estimation. Adding to these techniques, we introduce new error-informed estimators for outlier detection and autocorrelation visualization. We furthermore demonstrate how error-informed analyses on calibrated tracking data can be necessary to ensure that estimates are accurate and insensitive to location error, and allow researchers to use all of their data. Because error-induced biases depend on so many factors—sampling schedule, movement characteristics, tracking device, habitat, etc.—differential bias can easily confound biological inference and lead researchers to draw false conclusions.

Understanding how moose (Alces alces (L., 1758)) are affected by temperature is critical for determining why populations have recently declined at the southern extent of their North American range. Warm-season heat-stress thresholds of 14 and 20 °C are commonly used to study moose, but the variable response of free-ranging moose to temperatures above these thresholds suggests that moose may be more tolerant to heat. We studied zoo-managed cow and bull moose to identify factors that influence warm-season heat stress. We found clear behavioral and physiological responses to thermal conditions. Moose selected shade, indicating solar radiation affects heat stress. Temperature and wind influenced respiration rates. Heat-stress thresholds for moose occurred at 17 °C when bedded under calm conditions and 24 °C when bedded under wind, demonstrating that the onset of heat stress is sensitive to wind and incorporating wind velocity into analyses would improve investigations of heat stress. Moose showing symptoms of gastrointestinal illness selected wind at lower temperatures than healthy moose, suggesting the effects of climate change will be compounded for health-compromised moose. Determining why moose are declining at the southern extent of their range may require understanding how temperature interacts with wind, moose health, and other factors.