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The behavioural ecology of host species is likely to affect their microbial communities, because host sex, diet, physiology, and movement behaviour could all potentially influence their microbiota. We studied a wild population of barn owls (Tyto alba) and collected data on their microbiota, movement, diet, size, coloration, and reproduction. The composition of bacterial species differed by the sex of the host and female owls had more diverse bacterial communities than their male counterparts. The abundance of two families of bacteria, Actinomycetaceae and Lactobacillaceae, also varied between the sexes, potentially as a result of sex differences in hormones and immunological function, as has previously been found with Lactobacillaceae in the microbiota of mice. Male and female owls did not differ in the prey they brought to the nest, which suggests that dietary differences are unlikely to underlie the differences in their microbiota. The movement behaviour of the owls was associated with the host microbiota in both males and females because owls that moved further from their nest each day had more diverse bacterial communities than owls that stayed closer to their nests. This novel result suggests that the movement ecology of hosts can impact their microbiota, potentially on the basis of their differential encounters with new bacterial species as the hosts move and forage across the landscape. Overall, we found that many aspects of the microbial community are correlated with the behavioural ecology of the host and that data on the microbiota can aid in generating new hypotheses about host behaviour.

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.

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.

Uncertainties regarding food location and quality are among the greatest challenges faced by foragers and communal roosting may facilitate success through social foraging. The information centre hypothesis (ICH) suggests that uninformed individuals at shared roosts benefit from following informed individuals to previously visited resources. We tested several key prerequisites of the ICH in a social obligate scavenger, the Eurasian griffon vulture (Gyps fulvus), by tracking movements and behaviour of sympatric individuals over extended periods and across relatively large spatial scales, thereby precluding alternative explanations such as local enhancement. In agreement with the ICH, we found that 'informed' individuals returning to previously visited carcasses were followed by 'uninformed' vultures that consequently got access to these resources. When a dyad (two individuals that depart from the same roost within 2 min of each other) included an informed individual, they spent a higher proportion of the flight time close to each other at a shorter distance between them than otherwise. Although all individuals occasionally profited from following others, they differed in their tendencies to be informed or uninformed. This study provides evidence for 'following behaviour' in natural conditions and demonstrates differential roles and information states among foragers within a population. Moreover, demonstrating the possible reliance of vultures on following behaviour emphasizes that individuals in declining populations may suffer from reduced foraging efficiency.

According to the information centre hypothesis (ICH), colonial species use social information in roosts to locate ephemeral resources. Validating the ICH necessitates showing that uninformed individuals follow informed ones to the new resource. However, following behaviour may not be essential when individuals have a good memory of the resources' locations. For instance, Egyptian fruit bats forage on spatially predictable trees, but some bear fruit at unpredictable times. These circumstances suggest an alternative ICH pathway in which bats learn when fruits emerge from social cues in the roost but then use spatial memory to locate them without following conspecifics. Here, using an unique field manipulation and high-frequency tracking data, we test for this alternative pathway: we introduced bats smeared with the fruit odour of the unpredictably fruiting Ficus sycomorus trees to the roost, when they bore no fruits, and then tracked the movement of conspecifics exposed to the manipulated social cue. As predicted, bats visited the F. sycomorus trees with significantly higher probabilities than during routine foraging trips (of >200 bats). Our results show how the integration of spatial memory and social cues leads to efficient resource tracking and highlight the value of using large movement datasets and field experiments in behavioural ecology. This article is part of the theme issue 'The spatial–social interface: a theoretical and empirical integration'. [ABSTRACT FROM AUTHOR]

Natural selection theory suggests that mobile animals trade off time, energy and risk costs with food, safety and other pay-offs obtained by movement. We examined how birds make movement decisions by integrating aspects of flight biomechanics, movement ecology and behaviour in a hierarchical framework investigating flight track variation across several spatio-temporal scales. Using extensive global positioning system and accelerometer data from Eurasian griffon vultures (Gyps fulvus) in Israel and France, we examined soaring-gliding decision-making by comparing inbound versus outbound flights (to or from a central roost, respectively), and these (and other) home-range foraging movements (up to 300 km) versus long-range movements (longer than 300 km). We found that long-range movements and inbound flights have similar features compared with their counterparts: individuals reduced journey time by performing more efficient soaring-gliding flight, reduced energy expenditure by flapping less and were more risk-prone by gliding more steeply between thermals. Age, breeding status, wind conditions and flight altitude (but not sex) affected time and energy prioritization during flights. We therefore suggest that individuals facing time, energy and risk trade-offs during movements make similar decisions across a broad range of ecological contexts and spatial scales, presumably owing to similarity in the uncertainty about movement outcomes.This article is part of the themed issue 'Moving in a moving medium: new perspectives on flight'.

Animal movements exhibit an almost universal pattern of fat-tailed step-size distributions, mixing short and very long steps. The Lévy flight foraging hypothesis (LFFH) suggests a single optimal food search strategy to explain this pattern, yet mixed movement distributions are biologically more plausible and often convincingly fit movement data. To confront alternative explanations for these patterns, we tracked vultures of three species in two very different ecosystems using high-resolution global positioning system/accelerometer tags accompanied by behavioral, genetic, and morphological data. The Lévy distribution fitted the data sets reasonably well, matching expectations based on their sparsely distributed food resources; yet the fit of mixed models was considerably better, suggesting distinct movement modes operating at three different scales. Specifically, long-range forays (LRFs)—rare, short-term, large-scale circular journeys that greatly exceed the typical foraging range and contribute to the tail-fatness of the movement distribution in all three species— do not match an optimal foraging strategy suggested by the LFFH. We also found no support for preferred weather conditions or population genetic structure as alternative explanations, so the hypothesis that LRFs represent failed breeding dispersal attempts to find mates remains our most plausible explanation at this time. We conclude that inference about the mechanisms underlying animal movements should be confronted with complementary data, and suggest that mixed behavioral modes likely explain commonly observed fat-tailed movement distributions.

Abstract Background The need to obtain food is a critical proximate driver of an organism’s movement that shapes the foraging and survival of individual animals. Consequently, the relationship between hunger and foraging has received considerable attention, leading to the common conception that hunger primarily enhances a “food-intake maximization” (FIMax) strategy and intensive search. A complementary explanation, however, suggests a trade-off with precautions taken to reduce the risk of physiological collapse from starvation, under a strategy we denote as “energy-expenditure minimization” (EEMin). The FImax-EEmin trade-off may interact with the forager’s hunger level to shape a complex (non-monotonic) response pattern to increasing hunger. Yet, this important trade-off has rarely been investigated, particularly in free-ranging wild animals.We explored how hunger affects the movements of adult griffon vultures (Gyps fulvus) in southern Israel. Transmitters combining GPS and accelerometers provided high-resolution data on vultures’ movements and behavior, enabling the identification of feeding events and the estimation of food deprivation periods (FDPs, measured in days), which is used as a proxy for hunger. Results Data from 47 vultures, tracked for 339 ± 36 days, reveal high variability in FDPs. While flight speed, flight straightness and the proportion of active flights were invariant in relation to food deprivation, a clear hump-shaped response was found for daily flight distances, maximal displacements and flight elevation. These movement characteristics increased during the first five days of the FDP sequence and decreased during the following five days. These characteristics also differed between short FDPs of up to four days, and the first four days of longer FDP sequences.These results suggest a switch from FIMax to EEMin strategies along the FDP sequence. They also indicate that vultures’ response to hunger affected the eventual duration of the FDP. During winter (the vultures’ incubation period characterized by unfavorable soaring meteorological conditions), the vultures’ FIMax response was less intensive and resulted in longer starvation periods, while, in summer, more intensive FIMax response to hunger resulted in shorter FDPs. Conclusions Our results show a flexible, non-monotonic response of free-ranging wild animals to increasing hunger levels, reflecting a trade-off between increasing motivation to find food and the risk of starvation. The proposed trade-off offers a unifying perspective to apparently contradictory or case-specific empirical findings.