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Spatiotemporal variation in predation risk arises from interactions between landscape heterogeneity, predator densities and predator hunting mode, generating landscapes of fear for prey species that can have important effects on prey behaviour and ecosystem dynamics.

As widespread apex predators, humans present a significant source of risk for hunted animal populations. Spatiotemporal patterns of risk from hunters can overlap or contrast with patterns of risk from other predators. Human infrastructure can also reshape spatial patterns of risk by facilitating or impeding hunter or predator movement, or deterring predators that are themselves wary of humans.

We examined how anthropogenic and natural landscape features interact with hunting modes of rifle hunters and mountain lionsPuma concolorto generate spatiotemporal patterns of risk for their primary prey. We explored the implications of human‐modified landscapes of fear for Columbian black‐tailed deerOdocoileus hemionus columbianusin Mendocino County, California. We used historical harvest records, hunter GPS trackers and camera trap records of mountain lions to model patterns of risk for deer. We then used camera traps to examine deer spatial and temporal activity patterns in response to this variation in risk.

Hunters and mountain lions exhibited distinct, contrasting patterns of spatiotemporal activity. Risk from rifle hunters, who rely on long lines of sight, was highest in open grasslands and near roads and was confined to the daytime. Risk from mountain lions, an ambush predator, was highest in dense shrubland habitat, farther from developed areas, and during the night and crepuscular periods. Areas of human settlement provided a refuge from both hunters and mountain lions. We found no evidence that deer avoided risk in space at the scale of our observations, but deer adjusted their temporal activity patterns to reduce the risk of encounters with humans and mountain lions in areas of higher risk.

Our study demonstrates that interactions between human infrastructure, habitat cover and predator hunting mode can result in distinct spatial patterns of predation risk from hunters and other predators that may lead to trade‐offs for prey species. However, distinct diel activity patterns of predators may create vacant hunting domains that reduce costly trade‐offs for prey. Our study highlights the importance of temporal partitioning as a mechanism of predation risk avoidance.

 

The implementation of intelligent software to identify and classify objects and individuals in visual fields is a technology of growing importance to operatives in many fields, including wildlife conservation and management. To non-experts, the methods can be abstruse and the results mystifying. Here, in the context of applying cutting edge methods to classify wildlife species from camera-trap data, we shed light on the methods themselves and types of features these methods extract to make efficient identifications and reliable classifications. The current state of the art is to employ convolutional neural networks (CNN) encoded within deep-learning algorithms. We outline these methods and present results obtained in training a CNN to classify 20 African wildlife species with an overall accuracy of 87.5% from a dataset containing 111,467 images. We demonstrate the application of a gradient-weighted class-activation-mapping (Grad-CAM) procedure to extract the most salient pixels in the final convolution layer. We show that these pixels highlight features in particular images that in some cases are similar to those used to train humans to identify these species. Further, we used mutual information methods to identify the neurons in the final convolution layer that consistently respond most strongly across a set of images of one particular species. We then interpret the features in the image where the strongest responses occur, and present dataset biases that were revealed by these extracted features. We also used hierarchical clustering of feature vectors (i.e., the state of the final fully-connected layer in the CNN) associated with each image to produce a visual similarity dendrogram of identified species. Finally, we evaluated the relative unfamiliarity of images that were not part of the training set when these images were one of the 20 species “known” to our CNN in contrast to images of the species that were “unknown” to our CNN.

 

Human activity and land use change impact every landscape on Earth, driving declines in many animal species while benefiting others. Species ecological and life history traits may predict success in human-dominated landscapes such that only species with “winning” combinations of traits will persist in disturbed environments. However, this link between species traits and successful coexistence with humans remains obscured by the complexity of anthropogenic disturbances and variability among study systems. We compiled detection data for 24 mammal species from 61 populations across North America to quantify the effects of (1) the direct presence of people and (2) the human footprint (landscape modification) on mammal occurrence and activity levels. Thirty-three percent of mammal species exhibited a net negative response (i.e., reduced occurrence or activity) to increasing human presence and/or footprint across populations, whereas 58% of species were positively associated with increasing disturbance. However, apparent benefits of human presence and footprint tended to decrease or disappear at higher disturbance levels, indicative of thresholds in mammal species’ capacity to tolerate disturbance or exploit human-dominated landscapes. Species ecological and life history traits were strong predictors of their responses to human footprint, with increasing footprint favoring smaller, less carnivorous, faster-reproducing species. The positive and negative effects of human presence were distributed more randomly with respect to species trait values, with apparent winners and losers across a range of body sizes and dietary guilds. Differential responses by some species to human presence and human footprint highlight the importance of considering these two forms of human disturbance separately when estimating anthropogenic impacts on wildlife. Our approach provides insights into the complex mechanisms through which human activities shape mammal communities globally, revealing the drivers of the loss of larger predators in human-modified landscapes. 

Conflict between livestock producers and wild predators is a central driver of large predator declines and simultaneously may imperil the lives and livelihoods of livestock producers. There is a growing recognition that livestock–predator conflict is a socio‐ecological problem, but few case studies exist to guide conflict research and management from this point of view. Here we present a case study of coyote‐sheep predation on a California ranch in which we combine methods from the rapidly growing field of predation risk modeling with participatory mapping of perceptions of predation risk. Our findings reveal an important selection bias that may occur when producer perceptions and decisions are excluded from ecological methods of studying conflict. We further demonstrate how producer inputs, participatory mapping, and ecological modeling of conflict can inform one another in understanding patterns, drivers, and management opportunities for livestock–predator conflict. Finally, we make recommendations for improving the interoperability of ecological and social data about predation risk. Collectively our methods offer a socio‐ecological approach that fills important research gaps and offers guidance to future research.

 

Carnivore predation on livestock is a complex management and policy challenge, yet it is also intrinsically an ecological interaction between predators and prey. Human–wildlife interactions occur in socioecological systems in which human and environmental processes are closely linked. However, underlying human–wildlife conflict and key to unpacking its complexity are concrete and identifiable ecological mechanisms that lead to predation events. To better understand how ecological theory accords with interactions between wild predators and domestic prey, we developed a framework to describe ecological drivers of predation on livestock. We based this framework on foundational ecological theory and current research on interactions between predators and domestic prey. We used this framework to examine ecological mechanisms (e.g., density‐mediated effects, behaviorally mediated effects, and optimal foraging theory) through which specific management interventions operate, and we analyzed the ecological determinants of failure and success of management interventions in 3 case studies: snow leopards (Panthera uncia), wolves (Canis lupus), and cougars (Puma concolor). The varied, context‐dependent successes and failures of the management interventions in these case studies demonstrated the utility of using an ecological framework to ground research and management of carnivore–livestock conflict. Mitigation of human–wildlife conflict appears to require an understanding of how fundamental ecological theories work within domestic predator–prey systems.