SignificanceMotion-sensor cameras in natural habitats offer the opportunity to inexpensively and unobtrusively gather vast amounts of data on animals in the wild. A key obstacle to harnessing their potential is the great cost of having humans analyze each image. Here, we demonstrate that a cutting-edge type of artificial intelligence called deep neural networks can automatically extract such invaluable information. For example, we show deep learning can automate animal identification for 99.3% of the 3.2 million-image Snapshot Serengeti dataset while performing at the same 96.6% accuracy of crowdsourced teams of human volunteers. Automatically, accurately, and inexpensively collecting such data could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences.

Ambiguous empirical support for 'landscapes of fear' in natural systems may stem from failure to consider dynamic temporal changes in predation risk. The lunar cycle dramatically alters night-time visibility, with low luminosity increasing hunting success of African lions. We used camera-trap data from Serengeti National Park to examine nocturnal anti-predator behaviours of four herbivore species. Interactions between predictable fluctuations in night-time luminosity and the underlying risk-resource landscape shaped herbivore distribution, herding propensity and the incidence of 'relaxed' behaviours. Buffalo responded least to temporal risk cues and minimised risk primarily through spatial redistribution. Gazelle and zebra made decisions based on current light levels and lunar phase, and wildebeest responded to lunar phase alone. These three species avoided areas where likelihood of encountering lions was high and changed their behaviours in risky areas to minimise predation threat. These patterns support the hypothesis that fear landscapes vary heterogeneously in both space and time.

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.

Herbivores play an important role in determining the structure and function of tropical savannahs. Here, we (i) outline a framework for how interactions among large mammalian herbivores, carnivores and environmental variation influence herbivore habitat occupancy in tropical savannahs. We then (ii) use a Bayesian hierarchical model to analyse camera trap data to quantify spatial patterns of habitat occupancy for lions and eight common ungulates of varying body size across an approximately 1100 km 2 landscape in the Serengeti ecosystem. Our results reveal strong positive associations among herbivores at the scale of the entire landscape. Lions were positively associated with migratory ungulates but negatively associated with residents. Herbivore habitat occupancy differed with body size and migratory strategy: large-bodied migrants, at less risk of predation and able to tolerate lower quality food, were associated with high NDVI, while smaller residents, constrained to higher quality forage, avoided these areas. Small herbivores were strongly associated with fires, likely due to the subsequent high-quality regrowth, while larger herbivores avoided burned areas. Body mass was strongly related to herbivore habitat use, with larger species more strongly associated with riverine and woodlands than smaller species. Large-bodied migrants displayed diffuse habitat occupancy, whereas smaller species demonstrated fine-scale occupancy reflecting use of smaller patches of high-quality habitat. Our results demonstrate the emergence of strong positive spatial associations among a diverse group of savannah herbivores, while highlighting species-specific habitat selection strongly determined by herbivore body size. This article is part of the themed issue 'Tropical grassy biomes: linking ecology, human use and conservation'.

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