Hunting mode and habitat selection mediate the success of human hunters.

BACKGROUND: As a globally widespread apex predator, humans have unprecedented lethal and non-lethal effects on prey populations and ecosystems. Yet compared to non-human predators, little is known about the movement ecology of human hunters, including how hunting behavior interacts with the environment. METHODS: We characterized the hunting modes, habitat selection, and harvest success of 483 rifle hunters in California using high-resolution GPS data. We used Hidden Markov Models to characterize fine-scale movement behavior, and k-means clustering to group hunters by hunting mode, on the basis of their time spent in each behavioral state. Finally, we used Resource Selection Functions to quantify patterns of habitat selection for successful and unsuccessful hunters of each hunting mode. RESULTS: Hunters exhibited three distinct and successful hunting modes ("coursing", "stalking", and "sit-and-wait"), with coursings as the most successful strategy. Across hunting modes, there was variation in patterns of selection for roads, topography, and habitat cover, with differences in habitat use of successful and unsuccessful hunters across modes. CONCLUSIONS: Our study indicates that hunters can successfully employ a diversity of harvest strategies, and that hunting success is mediated by the interacting effects of hunting mode and landscape features. Such results highlight the breadth of human hunting modes, even within a single hunting technique, and lend insight into the varied ways that humans exert predation pressure on wildlife.

Contrasting patterns of risk from human and non-human predators shape temporal activity of prey.

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 lions Puma concolor to generate spatiotemporal patterns of risk for their primary prey. We explored the implications of human-modified landscapes of fear for Columbian black-tailed deer Odocoileus hemionus columbianus in 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.

Site fidelity and behavioral plasticity regulate an ungulate's response to extreme disturbance.

With rapid global change, the frequency and severity of extreme disturbance events are increasing worldwide. The ability of animal populations to survive these stochastic events depends on how individual animals respond to their altered environments, yet our understanding of the immediate and short-term behavioral responses of animals to acute disturbances remains poor. We focused on animal behavioral responses to the environmental disturbance created by megafire. Specifically, we explored the effects of the 2018 Mendocino Complex Fire in northern California, USA, on the behavior and body condition of black-tailed deer (Odocoileus hemionus columbianus). We predicted that deer would be displaced by the disturbance or experience high mortality post-fire if they stayed in the burn area. We used data from GPS collars on 18 individual deer to quantify patterns of home range use, movement, and habitat selection before and after the fire. We assessed changes in body condition using images from a camera trap grid. The fire burned through half of the study area, facilitating a comparison between deer in burned and unburned areas. Despite a dramatic reduction in vegetation in burned areas, deer showed high site fidelity to pre-fire home ranges, returning within hours of the fire. However, mean home range size doubled after the fire and corresponded to increased daily activity in a severely resource-depleted environment. Within their home ranges, deer also selected strongly for patches of surviving vegetation and woodland habitat, as these areas provided forage and cover in an otherwise desolate landscape. Deer body condition significantly decreased after the fire, likely as a result of a reduction in forage within their home ranges, but all collared deer survived for the duration of the study. Understanding the ways in which large mammals respond to disturbances such as wildfire is increasingly important as the extent and severity of such events increases across the world. While many animals are adapted to disturbance regimes, species that exhibit high site fidelity or otherwise fixed behavioral strategies may struggle to cope with increased climate instability and associated extreme disturbance events.

Disturbance type and species life history predict mammal responses to humans.

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.

An ecological framework for contextualizing carnivore-livestock conflict.

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.

Un Marco de Trabajo Ecológico para Contextualizar el Conflicto Carnívoro - Ganado Resumen La depredación del ganado por carnívoros es un reto complejo para el manejo y las políticas, a pesar de que es intrínsecamente una interacción ecológica entre depredadores y presas. Las interacciones entre humanos y la fauna ocurren en sistemas socio-ecológicos en los que los humanos y los procesos ambientales están conectados estrechamente. Sin embargo, el conflicto humano - fauna subyacente y la clave para desenredar su complejidad son mecanismos ecológicos complejos e identificables que resultan en eventos de depredación. Para tener un mejor entendimiento sobre cómo la teoría ecológica armoniza con las interacciones entre los depredadores silvestres y la presa doméstica, desarrollamos un marco de trabajo para describir las causantes ecológicas de la depredación del ganado. Basamos este marco de trabajo en las principales teorías ecológicas y las investigaciones actuales sobre las interacciones entre los depredadores y las presas domésticas. Usamos este marco de trabajo para examinar los mecanismos ecológicos (es decir, los efectos mediados por la densidad, los efectos mediados por el comportamiento, y la teoría del forrajeo óptimo) mediante los cuales operan ciertas intervenciones específicas de manejo y analizamos las determinantes ecológicas del fracaso y el éxito de las intervenciones de manejo en tres estudios de caso: el leopardo de las nieves (Panthera uncia), el lobo (Canis lupus), y el puma (Puma concolor). Los éxitos y fracasos variados y dependientes del contexto que sufrieron las intervenciones de manejo en estos estudios de caso demostraron la utilidad del uso de un marco de trabajo ecológico para aterrizar la investigación y el manejo del conflicto carnívoro - ganado. La mitigación del conflicto humano - fauna parece requerir de un entendimiento sobre cómo funcionan las teorías ecológicas fundamentales dentro del sistema doméstico depredador - presa.

Monitoring canid scent marking in space and time using a biologging and machine learning approach.

For canid species, scent marking plays a critical role in territoriality, social dynamics, and reproduction. However, due in part to human dependence on vision as our primary sensory modality, research on olfactory communication is hampered by a lack of tractable methods. In this study, we leverage a powerful biologging approach, using accelerometers in concert with GPS loggers to monitor and describe scent-marking events in time and space. We performed a validation experiment with domestic dogs, monitoring them by video concurrently with the novel biologging approach. We attached an accelerometer to the pelvis of 31 dogs (19 males and 12 females), detecting raised-leg and squat posture urinations by monitoring the change in device orientation. We then deployed this technique to describe the scent marking activity of 3 guardian dogs as they defend livestock from coyote depredation in California, providing an example use-case for the technique. During validation, the algorithm correctly classified 92% of accelerometer readings. High performance was partly due to the conspicuous signatures of archetypal raised-leg postures in the accelerometer data. Accuracy did not vary with the weight, age, and sex of the dogs, resulting in a method that is broadly applicable across canid species' morphologies. We also used models trained on each individual to detect scent marking of others to emulate the use of captive surrogates for model training. We observed no relationship between the similarity in body weight between the dog pairs and the overall accuracy of predictions, although models performed best when trained and tested on the same individual. We discuss how existing methods in the field of movement ecology can be extended to use this exciting new data type. This paper represents an important first step in opening new avenues of research by leveraging the power of modern-technologies and machine-learning to this field.

Insights and approaches using deep learning to classify wildlife.

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 infectious disease burdens decrease with urbanization but not with biodiversity.

Infectious disease burdens vary from country to country and year to year due to ecological and economic drivers. Recently, Murray et al. (Murray CJ et al 2012 Lancet380, 2197-2223. (doi:10.1016/S0140-6736(12)61689-4)) estimated country-level morbidity and mortality associated with a variety of factors, including infectious diseases, for the years 1990 and 2010. Unlike other databases that report disease prevalence or count outbreaks per country, Murray et al. report health impacts in per-person disability-adjusted life years (DALYs), allowing comparison across diseases with lethal and sublethal health effects. We investigated the spatial and temporal relationships between DALYs lost to infectious disease and potential demographic, economic, environmental and biotic drivers, for the 60 intermediate-sized countries where data were available and comparable. Most drivers had unique associations with each disease. For example, temperature was positively associated with some diseases and negatively associated with others, perhaps due to differences in disease agent thermal optima, transmission modes and host species identities. Biodiverse countries tended to have high disease burdens, consistent with the expectation that high diversity of potential hosts should support high disease transmission. Contrary to the dilution effect hypothesis, increases in biodiversity over time were not correlated with improvements in human health, and increases in forestation over time were actually associated with increased disease burden. Urbanization and wealth were associated with lower burdens for many diseases, a pattern that could arise from increased access to sanitation and healthcare in cities and increased investment in healthcare. The importance of urbanization and wealth helps to explain why most infectious diseases have become less burdensome over the past three decades, and points to possible levers for further progress in improving global public health.This article is part of the themed issue 'Conservation, biodiversity and infectious disease: scientific evidence and policy implications'.

Assessing the significance of global and local correlations under spatial autocorrelation: a nonparametric approach.

We propose a method to test the correlation of two random fields when they are both spatially autocorrelated. In this scenario, the assumption of independence for the pair of observations in the standard test does not hold, and as a result we reject in many cases where there is no effect (the precision of the null distribution is overestimated). Our method recovers the null distribution taking into account the autocorrelation. It uses Monte-Carlo methods, and focuses on permuting, and then smoothing and scaling one of the variables to destroy the correlation with the other, while maintaining at the same time the initial autocorrelation. With this simulation model, any test based on the independence of two (or more) random fields can be constructed. This research was motivated by a project in biodiversity and conservation in the Biology Department at Stanford University.