Michael Egan

Anthropogenic land conversion is putting increasing pressure on wildlife populations around the world. To mitigate impacts, it is necessary to develop a detailed mechanistic understanding of how animals are affected by different types of human activity. A key challenge is to disentangle the effects of static infrastructure, like roads or buildings, and the presence of humans in the landscape. To address this question, we examined if terrestrial mammals altered their movement behaviour around buildings in response to reduced human mobility during COVID‐19 lockdowns. We compiled GPS tracking data from 35 study sites across five continents, for 10 carnivore species and 13 herbivore species, totalling >1 million location records from 586 individuals. For each study, we used integrated step selection analysis to test the extent to which animals changed their avoidance of buildings as lockdown took effect, leveraging the recently released Microsoft MLBuildings dataset of global building locations. Analysis of population‐level effects revealed that, in areas with high Human Footprint Index (HFI), animals tended to show a significant reduction in their avoidance of buildings during lockdown, but not in low HFI areas. No such trend was detected during equivalent periods in years other than 2020, indicating that behavioural changes were a result of reduced human mobility during lockdowns. Overall, our findings suggest that animals living alongside humans exhibit greater plasticity when people change their behaviour, likely indicating the combined effects of environmental filtering and habituation. More generally, our study provides a critical first step towards developing evidence‐based tools for forecasting how wildlife movement behaviour may change in response to different land‐use strategies, human activities, conservation interventions or environmental perturbations.

There has been extensive work on the predator–prey spatial game, with a focus on how prey spatially respond to predators and how predators respond spatially to the distribution of various prey. Central to this work is the distinction between actual risk of predation relative to landscape availability and determining what prey perceive as risky habitat and which areas predators perceive they might be successful relative to the amount of time they spend in these areas. Several models have been proposed to best quantify the predator–prey spatial game, with researchers are often focusing on where prey are at risk of encountering a predator, which is often treated as a representation of the landscape of fear. However, this viewpoint can oversimplify the predation process because it is difficult to integrate both predator and prey perspectives simultaneously. We leveraged two types of habitat selection functions (resource selection functions and latent selection difference functions) to create a multi‐perspective framework that simultaneously integrates data from both predator and prey, effectively capturing the predator–prey spatial game. The framework quantifies actual spatial risk (landscape perspective), spatial risk relative to prey use (prey perspective), resources where predators are particularly successful relative to their use (predator perspective), and the landscape of fear (predator resource selection). We applied this framework by relating neonatal white‐tailed deer ( Odocoileus virginianus ) kill site locations to GPS locations from bobcats ( Lynx rufus ), coyotes ( Canis latrans ), and female white‐tailed deer. Areas where fawns were at an elevated risk of predation relative to landscape availability were primarily driven by predator efficiency and sometimes by female deer selection of risky fawn habitats. We also found that female deer may be cognizant of increased risk of fawn predation in some habitats and may decrease their use of those risky areas accordingly. By considering these different perspectives of prey risk and predator success, these results shed light on the complexities that underlie predator–prey dynamics. Overall, our work highlights that predation risk can be assessed from several perspectives and that multiple habitat selection functions can generate a multi‐perspective assessment and provide a deeper understanding of the predator–prey spatial game.

Anthropogenic land conversion is increasingly affecting wildlife populations. To mitigate impacts, we must understand how animals are affected by different types of human activity. Here, we examine if terrestrial mammals altered their movements around buildings in response to reduced human mobility during COVID-19 lockdowns. Using GPS data from 35 study sites across five continents and 23 different species, we tested whether animals changed their avoidance of buildings during lockdowns. Meta-analysis of population-level effects revealed that, in areas with high (but not low) human footprint, animals tended to show a significant reduction in their avoidance of buildings during lockdown. No such effect was detected during equivalent periods in years other than 2020, indicating that behavioural changes were caused by reduced human mobility, independent of infrastructure. Our findings suggest that animals living alongside humans exhibit greater plasticity when people change their behaviour, likely reflecting the combined effects of environmental filtering and habituation.

Behavioral responses of prey to predation risk have ecological impacts that can be as great as the direct mortality. Theory often suggests that prey pair responses to risks based on the hunting mode of the predator (hunting mode hypothesis), but prey may ignore hunting mode to prioritize responding to the most lethal predators (lethality hypothesis). To test these hypotheses, we evaluated the behavioral responses of white-tailed deer (Odocoileus virginianus) to risks from two natural mesopredators and human sources of mortality. Specifically, we determined, for each source of risk, whether deer responded with behavioral state changes or spatial avoidance and whether this behavior changed with time (diurnally and annually). We collared and tracked 40 female and 29 male deer. To determine the response of deer to risk, we collected data on the distribution of coyotes (Canis latrans), bobcats (Lynx rufus), human modification, hunters, and roads. We used hidden Markov models (HMM) to determine whether each covariate impacted the probability of transitioning between behavioral states and selection functions (SSF) to determine whether deer spatially avoided each covariate.Generally, deer changed behavioral state in response to both mesopredators but avoided human modification. In response to mesopredators, deer consistently shifted to slower movement behavioral states. Spatial responses to human modification varied depending on the time of day. During daylight hours, deer selected for human modification, but during the crepuscular and nighttime period, deer avoided human modification. Space use was most strongly related to more lethal humans, providing support for the lethality hypothesis. Despite prioritizing humans, mesopredators impacted behavioral state, suggesting that mesopredators still have important impacts on prey behavior. Finally, temporal patterns of avoidance align with other studies that indicate avoidance of predators is time-dependent, but further highlight the complex push-pull relationship of human modified areas on wildlife.

The intensity of use of a location is one of the most studied properties of animal movement, yet movement analyses generally focus on the overall use of a location without much consideration of how patterns in intensity of use emerge. Extracting properties related to intensity of use, such as the number of visits, the average and variation in time spent and the average and variation in time between visits, could help provide a more mechanistic understanding of how animals use landscape. Combining and synthesizing these properties into a single spatial representation could inform the role that a location plays for an animal. We developed an R package named ‘UseScape’ that allows the extraction of these metrics and then clustered them using mixture modelling to create a spatial representation of the type of use an animal makes of the landscape. We illustrate applications of the approach using datasets of animal movement from four taxa and highlight species‐specific and cross‐species insights. Our framework highlights properties that functionally differ in how animals use them, contrasting, for example, heavily used locations that emerge because they are frequented for long durations, locations that are repeatedly and regularly visited for shorter durations of time or locations visited irregularly. We found that species generally had similar types of use, such as typical low, mid and high use, but there were also species‐specific clusters that would have been ignored when only focusing on the overall intensity of use. Our multi‐system comparison highlighted how the framework provided novel insights that would not have been directly obtainable by currently available approaches. By making the framework available as an R package, these analyses can be easily applicable to a myriad of systems where relocation data are available. Movement ecology as a field can strongly benefit from approaches that not just describe patterns in space use, but also highlight the behavioural mechanisms leading to these emerging patterns.

Host contact structure affects pathogen transmission in host populations, but many measures of host contact do not distinguish contacts that are relevant to pathogen transmission from those that are not. Scrapes are sites for chemical communication by white‐tailed deer (Odocoileus virginianus) during the breeding season and potential sites of transmission of prions, the causative agent of chronic wasting disease (CWD). Scrape‐related behaviors vary in their probability of transmitting prions to or from the environment, suggesting that behavior be combined with contact structure to better reflect potential heterogeneity in prion transmission at scrapes. We recorded visits and behaviors by deer at scrapes throughout DeSoto National Wildlife Refuge, Nebraska in 2005 and 2006. We recorded 2013 interactions by 169 unique identifiable males and 75 females. Adult males performed the most scrape‐related behaviors and spent the most time at scrapes, especially smelling the overhanging branch (70%), smelling the scrape (59%), licking/grasping the overhanging branch (44%), and scraping (36%). We used social network analysis to test the effect of behavior on indirect contact networks among deer at scrapes. By weighting edges based on the frequency and duration of behaviors, we produced networks representing sources of variation in scrape use and compared these networks to evaluate the effects of behavior on network contact structure. Social networks based on scrape‐related behavior were highly connected and dependent upon the frequency, duration, and type of behavior exhibited at scrapes (e.g., scraping, interacting with a scrape or overhanging branch, rub‐urinating, grazing) as well as the age of the deer. Accounting for contact frequency produced networks with lower variation in contact, but higher ability to facilitate contact among disparate groups. Including behavior when defining edges did not preserve the network properties of simpler measures (i.e., unweighted networks) suggesting that heterogeneity in behaviors that affect transmission probability is important for inferring transmission networks from contact networks. High connectivity through indirect contacts suggests that scrapes may be effective targets for management. Adult male deer had the highest connectivity, suggesting that management strategies focused on reducing their interaction with scrapes through density reduction or behavioral modification could reduce the connectivity of indirect contact networks.