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Abstract Many animals living in anthropized landscapes try to avoid encountering people by being active at night. By doing so, however, they risk being disturbed while at rest during the day. To mitigate this risk, diurnally resting species may be highly selective about where they rest. Here, we used GPS and activity sensors to study how wild boars (Sus scrofa) might adjust their resting site selection and revisitation patterns to the risk of disturbance by people. We evaluated the probability of daytime relocation to assess the efficacy of wild boars' resting strategy in reducing the risk of human encounter while at rest. We attempted to identify the cause of some relocations using audio recordings. Generally, we found that wild boars did not specifically avoid resting near villages or roads, that is, where the risk of encountering people is higher, if they could find sites with suitable vegetation cover. The risk of disturbance by people was low, even near villages. Resting sites located close to villages were visited more repeatedly than those located further away, suggesting that focusing on a few familiar and quiet resting sites was a successful strategy for resting undisturbed in an anthropized landscape.

Biologging refers to the use of animal-borne recording devices to study wildlife behavior. In the case of audio recording, such devices generate large amounts of data over several months, and thus require some level of processing automation for the raw data collected. Academics have widely adopted offline deep-learning-classification algorithms to extract meaningful information from large datasets, mainly using time-frequency signal representations such as spectrograms. Because of the high deployment costs of animal-borne devices, the autonomy/weight ratio remains by far the fundamental concern. Basically, power consumption is addressed using onboard mass storage (no wireless transmission), yet the energy cost associated with data storage activity is far from negligible. In this paper, we evaluate various strategies to reduce the amount of stored data, making the fair assumption that audio will be categorized using a deep-learning classifier at some point of the process. This assumption opens up several scenarios, from straightforward raw audio storage paired with further offline classification on one side, to a fully embedded AI engine on the other side, with embedded audio compression or feature extraction in between. This paper investigates three approaches focusing on data-dimension reduction: (i) traditional inline audio compression, namely ADPCM and MP3, (ii) full deep-learning classification at the edge, and (iii) embedded pre-processing that only computes and stores spectrograms for later offline classification. We characterized each approach in terms of total (sensor + CPU + mass-storage) edge power consumption (i.e., recorder autonomy) and classification accuracy. Our results demonstrate that ADPCM encoding brings 17.6% energy savings compared to the baseline system (i.e., uncompressed raw audio samples). Using such compressed data, a state-of-the-art spectrogram-based classification model still achieves 91.25% accuracy on open speech datasets. Performing inline data-preparation can significantly reduce the amount of stored data allowing for a 19.8% energy saving compared to the baseline system, while still achieving 89% accuracy during classification. These results show that while massive data reduction can be achieved through the use of inline computation of spectrograms, it translates to little benefit on device autonomy when compared to ADPCM encoding, with the added downside of losing original audio information.

Abstract Fission–fusion dynamics allow animals to manage costs and benefits of group living by adjusting group size. The degree of intraspecific variation in fission–fusion dynamics across the geographical range is poorly known. During 2008–2016, 38 adult female Cape buffalo were equipped with GPS collars in three populations located in different protected areas (Gonarezhou National Park and Hwange National Park, Zimbabwe; Kruger National Park, South Africa) to investigate the patterns and environmental drivers of fission–fusion dynamics among populations. We estimated home range overlap and fission and fusion events between Cape buffalo dyads. We investigated the temporal dynamics of both events at daily and seasonal scales and examined the influence of habitat and distance to water on event location. Fission–fusion dynamics were generally consistent across populations: Fission and fusion periods lasted on average between less than one day and three days. However, we found seasonal differences in the underlying patterns of fission and fusion, which point out the likely influence of resource availability and distribution in time on group dynamics: During the wet season, Cape buffalo split and associated more frequently and were in the same or in a different subgroup for shorter periods. Cape buffalo subgroups were more likely to merge than to split in open areas located near water, but overall vegetation and distance to water were very poor predictors of where fission and fusion events occurred. This study is one of the first to quantify fission–fusion dynamics in a single species across several populations with a common methodology, thus robustly questioning the behavioral flexibility of fission–fusion dynamics among environments.

Movement of organisms plays a fundamental role in the evolution and diversity of life. Animals typically move at an irregular pace over time and space, alternating among movement states. Understanding movement decisions and developing mechanistic models of animal distribution dynamics can thus be contingent to adequate discrimination of behavioral phases. Existing methods to disentangle movement states typically require a follow-up analysis to identify state-dependent drivers of animal movement, which overlooks statistical uncertainty that comes with the state delineation process. Here, we developed population-level, multi-state step selection functions (HMM-SSF) that can identify simultaneously the different behavioral bouts and the specific underlying behavior-habitat relationship. Using simulated data and relocation data from mule deer (Odocoileus hemionus), plains bison (Bison bison bison) and plains zebra (Equus quagga), we illustrated the HMM-SSF robustness, versatility, and predictive ability for animals involved in distinct behavioral processes: foraging, migrating and avoiding a nearby predator. Individuals displayed different habitat selection pattern during the encamped and the travelling phase. Some landscape attributes switched from being selected to avoided, depending on the movement phase. We further showed that HMM-SSF can detect multi-modes of movement triggered by predators, with prey switching to the travelling phase when predators are in close vicinity. HMM-SSFs thus can be used to gain a mechanistic understanding of how animals use their environment in relation to the complex interplay between their needs to move, their knowledge of the environment and navigation capacity, their motion capacity and the external factors related to landscape heterogeneity.

In the past decades, biologging, i.e., the development and deployment of animal-borne loggers, has revolutionized ecology. Despite recent advances, power consumption and battery size however remain central issues and limiting factors, constraining the quantity of data that can be collected and the size of the animals that can be studied. Here, we present strategies to achieve ultra-low power in biologging, using the design of a lightweight audio-inertial logger as an example. It is designed with low-power MEMS sensors, and a firmware based on a small embedded RTOS. Both methodologies for power reduction and experimental results are detailed. With an average power consumption reduced to 5.3 mW, combined with a battery of 1800 mAh, the logger provides 900 h of continuous 8 kHz audio, 50 Hz accelerometer and 10 Hz magnetometer data.

Abstract The regulation of body temperature (thermoregulation) and of water balance (defined here as hydroregulation) are key processes underlying ecological and evolutionary responses to climate fluctuations in wild animal populations. In terrestrial (or semiterrestrial) ectotherms, thermoregulation and hydroregulation closely interact and combined temperature and water constraints should directly influence individual performances. Although comparative physiologists traditionally investigate jointly water and temperature regulation, the ecological and evolutionary implications of these coupled processes have so far mostly been studied independently. Here, we revisit the concept of thermo‐hydroregulation to address the functional integration of body temperature and water balance regulation in terrestrial ectotherms. We demonstrate how thermo‐hydroregulation provides a framework to investigate functional adaptations to joint environmental variation in temperature and water availability, and potential physiological and/or behavioral conflicts between thermoregulation and hydroregulation. We extend the classical cost–benefit model of thermoregulation in ectotherms to highlight the adaptive evolution of optimal thermo‐hydroregulation strategies. Critical gaps in the parameterization of this conceptual optimality model and guidelines for future empirical research are discussed. We show that studies of thermo‐hydroregulation refine our mechanistic understanding of physiological and behavioral plasticity, and of the fundamental niche of the species. This is illustrated with relevant and recent examples of space use and dispersal, resource‐based trade‐offs, and life‐history tactics in insects, amphibians, and nonavian reptiles.

Climate changes affect the distribution and abundance of organisms, often via changes in species interactions. Most animals experience predation, and a number of models have investigated how climate fluctuations can influence predator–prey dynamics by affecting prey abundance through changes in resource availability. However, field studies have shown that prey vulnerability is a key feature determining the outcome of predator–prey interactions, which also varies with climatic conditions, via changes in prey body condition or in habitat characteristics (e.g. vegetation cover). In this theoretical work, we explore, with large mammals of African savannas in mind, how the interplay between climate-induced changes in prey abundance and climate-induced changes in prey vulnerability affects the immediate and long-term responses of predator populations. We account for prey body condition and habitat effects on prey vulnerability to predation. We show that predictions on how predator abundance responds to climate fluctuations differ depending on how climate influences prey vulnerability (habitat characteristics vs. prey body condition). We discuss how species traits influence the relative importance of the different sources of vulnerability. For example, our results suggest that populations of cursorial predators (such as spotted hyaenas) are expected to fare better than populations of ambush predators (such as African lions) in African ecosystems that will be characterised by an aridification. This study highlights the importance of understanding, and accounting for, the vulnerability factors associated to a given predator–prey pair, and improves our comprehension of predator–prey relationships in a changing climate.

Abstract Background Our picture of behavioral management of risk by prey remains fragmentary. This partly stems from a lack of studies jointly analyzing different behavioral responses developed by prey, such as habitat use and fine-scale behavior, although they are expected to complement each other. We took advantage of a simple system on the Kerguelen archipelago, made of a prey species, European rabbit Oryctolagus cuniculus, a predator, feral cat Felis catus, and a mosaic of closed and open foraging patches, allowing reliable assessment of spatio-temporal change in predation risk. We investigated the way such a change triggered individual prey decisions on where, when and how to perform routine activities. Results Rabbit presence and behavior were recorded both day and night in patches with similar foraging characteristics, but contrasted in terms of openness. Cats, individually recognizable, were more active at night and in closed patches, in line with their expected higher hunting success in those conditions. Accordingly, rabbits avoided using closed patches at night and increased their vigilance if they did. Both day and night, rabbits increased their use of closed patches as compared to open patches in windy conditions, thereby probably reducing the thermoregulatory costs expected under such harsh environmental conditions. Conclusions Overall, our data map the landscape of fear in this study system and indicate that prey habitat use and vigilance complement each other. Solely focusing on one or the other tactic may lead to erroneous conclusions regarding the way predation risk triggers prey decisions. Finally, future studies should investigate inter-individual variability in the relative use of these different types of complementary behavioral responses to perceived risk, along with the determinants and outcomes of such tactics.

Interfaces between protected areas and their peripheries in southern Africa are subject to interactions between wildlife and livestock that vary in frequency and intensity. In these areas, the juxtaposition between production and conservation land uses in a context of increasing anthropisation can create issues associated with human-wildlife coexistence and raises concerns for biodiversity conservation, local development and livelihoods. This literature review aimed at addressing the need to consolidate and gather in one article current knowledge on potential uses of satellite remote sensing (SRS) products by movement ecologists to investigate the sympatry of wildlife/domestic ungulates in savanna interface environments. A keyword querying process of peer reviewed scientific paper, thesis and books has been implemented to identify references that (1) characterize the main environmental drivers impacting buffalo (Syncerus caffer caffer) and cattle (Bos taurus & Bos indicus) movements in southern Africa environments, (2) describe the SRS contribution to discriminate and characterize these drivers. In total, 327 references have been selected and analyzed. Surface water, precipitation, landcover and fire emerged as key drivers impacting the buffalo and cattle movements. These environmental drivers can be efficiently characterized by SRS, mainly through open-access SRS products and standard image processing methods. Applying SRS to better understand buffalo and cattle movements in semi-arid environments provides an operational framework that could be replicated in other type of interface where different wild and domestic species interact. There is, however, a need for animal movement ecologists to reinforce their knowledge of remote sensing and/or to increase pluridisciplinary collaborations.

Efforts to better understand patterns of animal behaviour have often been restricted by several environmental, human and experimental limitations associated with the collection of animal behavioural data. The introduction of new bio-logging technology has offered an alternative means of recording animal behaviour continuously and is being used in an increasing number of studies. Accurately calibrating these bio-loggers, however, still remains a challenge in many cases. Using lions as an example species, we test how audio recordings from animal-borne acoustic sensors can improve calibration and behaviour classification. Through a collaborative effort between computer scientists, engineers, and zoologists, custom designed acoustic bio-loggers were fitted to eight lions and recorded audio simultaneously with accelerometer and magnetometer data. Audio recordings were then used as the source of ground truth to train random forest classification models as well as to provide additional predictor variables for behaviour classification. We demonstrated near-perfect classification performance for five lion behaviour classes when all component variables were combined, with an average per-class precision of 98.5%. Using accelerometer features only, the audio-trained classifier predicted behaviours with an average per-class precision of 94.3%. On-animal audio recordings are therefore able to provide a valuable source of ground-truth for calibrating bio-loggers while also offering additional predictive features for increasing the accuracy of behaviour classification. This technological innovation has wide ranging application and provides a useful tool for behavioural ecologists wishing to collect fine scale behavioural data for animal research and conservation.

Vegetation mapping of protected areas is a cornerstone of conservation worldwide. Established in 1928 and covering over 1.4 million hectares, Hwange National Park (HNP) is the largest natural reserve in Zimbabwe. In 1993, the sole comprehensive map of its vegetation to date was produced and since then it has been used in numerous research and conservation endeavours. Over the last two decades, however, the park’s vegetation changed, safari areas and forest reserves were created at its edge and high-positional accuracy data on a suite of species were collected. To tend to contemporary mapping needs, in this article, we present the 2013–2014 vegetation structure map of HNP and its surroundings. It was produced by supervised classification of Landsat-8 Operational Land Imager (OLI) images, indices derived from these and the Landsat Tree Cover Continuous Field product. Its accuracy was assessed statistically using samples collected from high-resolution satellite imagery and basic ancillary field data. Of its total pixels, 83.2% were correctly classified. Mean omission and commission error were, respectively, 0.82 (0.74–0.90) and 0.82 (0.72–0.89), and this similarity held on a per class basis, indicating reliable area estimates. It was produced using only freely available imagery and software. Conservation implications: In addition to providing researchers and conservationists working within and around HNP with an updated vegetation map, aiming at an even broader audience, we provide a step-by-step approach for using modern freely available imagery and software for cost-effectively mapping HNP in future or other protected savannas across Africa.

Although proximity to cover has been routinely considered as an explanatory variable in studies investigating prey behavioral adjustments to predation pressure, the way it shapes risk perception still remains equivocal. This paradox arises from both the ambivalent nature of cover as potentially both obstructive and protective, making its impact on risk perception complex and context-dependent, and from the choice of the proxy used to measure proximity to cover in the field, which leads to an incomplete picture of the landscape of fear experienced by the prey. Here, we study a simple predator-prey-habitat system, i.e., rabbits Oryctolagus cuniculus facing feral cat Felis catus predation on Kerguelen archipelago. We assess how cover shapes risk perception in prey and develop an easily implementable field method to improve the estimation of proximity to cover. In contrast to protocols considering the “distance to nearest cover”, we focus on the overall “area to cover”. We show that fine-scale habitat use by rabbits is clearly related to our measure, in accordance with our hypothesis of higher risk in patches with smaller area to cover in this predator-prey-habitat system. In contrast, classical measures of proximity to cover are not retained in the best predictive models of habitat use. The use of this new approach, together with a more in-depth consideration of contrasting properties of cover, could help to better understand the role of this complex yet decisive parameter for predator-prey ecology.

The spatial structuring of populations or communities is an important driver of their functioning and their influence on ecosystems. Identifying the (in)stability of the spatial structure of populations is a first step towards understanding the underlying causes of these structures. Here we studied the relative importance of spatial vs. interannual variability in explaining the patterns of abundance of a large herbivore community (8 species) at waterholes in Hwange National Park (Zimbabwe). We analyzed census data collected over 13 years using multivariate methods. Our results showed that variability in the census data was mostly explained by the spatial structure of the community, as some waterholes had consistently greater herbivore abundance than others. Some temporal variability probably linked to Park-scale migration dependent on annual rainfall was noticeable, however. Once this was accounted for, little temporal variability remained to be explained, suggesting that other factors affecting herbivore abundance over time had a negligible effect at the scale of the study. The extent of spatial and temporal variability in census data was also measured for each species. This study could help in projecting the consequences of surface water management, and more generally presents a methodological framework to simultaneously address the relative importance of spatial vs. temporal effects in driving the distribution of organisms across landscapes.

Most organisms need to acquire various resources to survive and reproduce. Individuals should adjust their behavior to make optimal use of the landscape and limit the costs of trade-offs emerging from the use of these resources. Here we study how African elephants Loxodonta africana travel to foraging places between regular visits to waterholes. Elephant herds were tracked using GPS collars during two consecutive dry seasons in Hwange National Park, Zimbabwe. We segmented each individual movement track at each visit to water to define foraging trips, and then used trip-level statistics to build an understanding of movement strategies. Travel speed within these individually-consistent movement bouts was also analyzed to understand if speed was better linked to distance to water or progression in the trip over time. We found that elephants went further from water when drinking less often, which could result from a trade-off between drinking and foraging in less depleted, far from water, places. Speed increased towards the beginning and the end of the trips, and was also greater than observed during the wet season, suggesting that elephants were trying to save time. Numerous short trips traveled at greater speed, particularly when commuting to a different waterhole, was tentatively explained by the inability to drink at specific waterholes due to intra-specific interference. Unexpectedly elephants did not always minimize travel time by drinking at the closest waterhole, but the extra distance traveled remained never more than a few kilometers. Our results show how individuals may adjust movement behavior to deal with resource trade-offs at the landscape scale. We also highlight how behavioral context, here progression in the trip, may be more important than spatial context, here distance to water, in explaining animal movement patterns.