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Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.

 

Abstract Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network — a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. The network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted. 

Research on the ecology of fear has highlighted the importance of perceived risk from predators and humans in shaping animal behavior and physiology, with potential demographic and ecosystem-wide consequences. Despite recent conceptual advances and potential management implications of the ecology of fear, theory and conservation practices have rarely been linked. Many challenges in animal conservation may be alleviated by actively harnessing or compensating for risk perception and risk avoidance behavior in wild animal populations. Integration of the ecology of fear into conservation and management practice can contribute to the recovery of threatened populations, human–wildlife conflict mitigation, invasive species management, maintenance of sustainable harvest and species reintroduction plans. Here, we present an applied framework that links conservation interventions to desired outcomes by manipulating ecology of fear dynamics. We discuss how to reduce or amplify fear in wild animals by manipulating habitat structure, sensory stimuli, animal experience (previous exposure to risk) and food safety trade-offs to achieve management objectives. Changing the optimal decision-making of individuals in managed populations can then further conservation goals by shaping the spatiotemporal distribution of animals, changing predation rates and altering risk effects that scale up to demographic consequences. We also outline future directions for applied research on fear ecology that will better inform conservation practices. Our framework can help scientists and practitioners anticipate and mitigate unintended consequences of management decisions, and highlight new levers for multi-species conservation strategies that promote human–wildlife coexistence. 

While the functional response of predators is commonly measured, recent work has revealed that the age and sex composition of prey killed is often a better predictor of prey population dynamics because the reproductive value of adult females is usually higher than that of males or juveniles.

Climate is often an important mediating factor in determining the composition of predator kills, but we currently lack a mechanistic understanding of how the multiple facets of climate interact with prey abundance and demography to influence the composition of predator kills.

Over 20 winters, we monitored 17 wolf packs in Yellowstone National Park and recorded the sex, age and nutritional condition of kills of their dominant prey—elk—in both early and late winter periods when elk are in relatively good and relatively poor condition, respectively.

Nutritional condition (as indicated by per cent marrow fat) of wolf‐killed elk varied markedly with summer plant productivity, snow water equivalent (SWE) and winter period. Moreover, marrow was poorer for wolf‐killed bulls and especially for calves than it was for cows.

Wolf prey composition was influenced by a complex set of climatic and endogenous variables. In early winter, poor plant growth in either yeartort − 1, or relatively low elk abundance, increased the odds of wolves killing bulls relative to cows. Calves were most likely to get killed when elk abundance was high and when the forage productivity they experienced in utero was poor. In late winter, low SWE and a relatively large elk population increased the odds of wolves killing calves relative to cows, whereas low SWE and poor vegetation productivity 1 year prior together increased the likelihood of wolves killing a bull instead of a cow.

Since climate has a strong influence on whether wolves prey on cows (who, depending on their age, are the key reproductive components of the population) or lower reproductive value of calves and bulls, our results suggest that climate can drive wolf predation to be more or less additive from year to year.

 

Abstract The EXO-200 experiment searched for neutrinoless double-beta decay of 136 Xe with a single-phase liquid xenon detector. It used an active mass of 110 kg of 80.6%-enriched liquid xenon in an ultra-low background time projection chamber with ionization and scintillation detection and readout. This paper describes the design and performance of the various support systems necessary for detector operation, including cryogenics, xenon handling, and controls. Novel features of the system were driven by the need to protect the thin-walled detector chamber containing the liquid xenon, to achieve high chemical purity of the Xe, and to maintain thermal uniformity across the detector.