Limited spatiotemporal niche partitioning among mesocarnivores in Gorongosa National Park, Mozambique.

Competition drives community composition and structure in many ecosystems. Spatial and temporal niche partitioning, in which competing species divide the environment in space or time, are mechanisms that may allow for coexistence among ecologically similar species. Such division of resources may be especially important for carnivores in African savannas, which support diverse carnivore assemblages. We used camera traps to explore patterns of spatial and temporal niche partitioning among four mesocarnivore species in Mozambique's Gorongosa National Park: large-spotted genet (Genetta maculata), African civet (Civettictis civetta), honey badger (Mellivora capensis) and marsh mongoose (Atilax paludinosus). We applied a multispecies occupancy model to evaluate spatial partitioning among mesocarnivores and to quantify the environmental factors that affect species-specific habitat use, including relative lion (Panthera leo) activity. We also analyzed the temporal activity overlap of the four focal species. We identified species-specific habitat covariates that influenced detection probabilities but found no evidence of spatial or temporal partitioning among mesocarnivores in the study system. Indeed, we found some evidence for spatial co-occurrence between two of our focal species: African civet and marsh mongoose. There may be limited competition among mesocarnivores in this system, perhaps due to niche and diet differentiation among these species and an abundance of resources. While we found limited evidence that lion activity impacts mesocarnivores, ongoing monitoring of intraguild interactions is vital as apex predator populations recover in the system. This study adds to a growing understanding of African mesocarnivore ecology and highlights the importance of understanding these dynamics for effective multispecies conservation and restoration.

Ecology: How mesopredators run with the big dogs.

Food webs are indispensable maps of community organization, but most are crudely drawn - an inconvenient truth that ecologists must confront. A new DNA metabarcoding study quantifies predator-prey interactions and ecological-network structure with long-overdue precision, supplying insight into how large and small carnivores coexist.

Using Live and Video Stimuli to Localize Face and Object Processing Regions of the Canine Brain.

Previous research to localize face areas in dogs' brains has generally relied on static images or videos. However, most dogs do not naturally engage with two-dimensional images, raising the question of whether dogs perceive such images as representations of real faces and objects. To measure the equivalency of live and two-dimensional stimuli in the dog's brain, during functional magnetic resonance imaging (fMRI) we presented dogs and humans with live-action stimuli (actors and objects) as well as videos of the same actors and objects. The dogs (n = 7) and humans (n = 5) were presented with 20 s blocks of faces and objects in random order. In dogs, we found significant areas of increased activation in the putative dog face area, and in humans, we found significant areas of increased activation in the fusiform face area to both live and video stimuli. In both dogs and humans, we found areas of significant activation in the posterior superior temporal sulcus (ectosylvian fissure in dogs) and the lateral occipital complex (entolateral gyrus in dogs) to both live and video stimuli. Of these regions of interest, only the area along the ectosylvian fissure in dogs showed significantly more activation to live faces than to video faces, whereas, in humans, both the fusiform face area and posterior superior temporal sulcus responded significantly more to live conditions than video conditions. However, using the video conditions alone, we were able to localize all regions of interest in both dogs and humans. Therefore, videos can be used to localize these regions of interest, though live conditions may be more salient.

Through a Dog's Eyes: fMRI Decoding of Naturalistic Videos from the Dog Cortex.

Recent advancements using machine learning and functional magnetic resonance imaging (fMRI) to decode visual stimuli from the human and nonhuman cortex have resulted in new insights into the nature of perception. However, this approach has yet to be applied substantially to animals other than primates, raising questions about the nature of such representations across the animal kingdom. Here, we used awake fMRI in two domestic dogs and two humans, obtained while each watched specially created dog-appropriate naturalistic videos. We then trained a neural net (Ivis) to classify the video content from a total of 90 min of recorded brain activity from each. We tested both an object-based classifier, attempting to discriminate categories such as dog, human, and car, and an action-based classifier, attempting to discriminate categories such as eating, sniffing, and talking. Compared to the two human subjects, for whom both types of classifier performed well above chance, only action-based classifiers were successful in decoding video content from the dogs. These results demonstrate the first known application of machine learning to decode naturalistic videos from the brain of a carnivore and suggest that the dog's-eye view of the world may be quite different from our own.