Bears habituate to the repeated exposure of a novel stimulus, unmanned aircraft systems

Drone Observation for the Quantitative Study of Complex Multilevel Societies

Unmanned aerial vehicles (drones) have recently been used in various behavioral ecology studies. However, their application has been limited to single groups, and most studies have not implemented individual identification. A multilevel society refers to a social structure in which small stable "core units" gather and make a larger, multiple-unit group. Here, we introduce recent applications of drone technology and individual identification to complex social structures involving multiple groups, such as multilevel societies. Drones made it possible to obtain the identification, accurate positioning, or movement of more than a hundred individuals in a multilevel social group. In addition, in multilevel social groups, drones facilitate the observation of heterogeneous spatial positioning patterns and mechanisms of behavioral propagation, which are different from those in a single-level group. Such findings may contribute to the quantitative definition and assessment of multilevel societies and enhance our understanding of mechanisms of multiple group aggregation. The application of drones to various species may resolve various questions related to multilevel societies.

Evaluation of physiological stress in free-ranging bears: current knowledge and future directions

Stress responses, which are mediated by the neurogenic system (NS) and hypothalamic-pituitary-adrenal (HPA) axis help vertebrates maintain physiological homeostasis. Fight-or-flight responses are activated by the NS, which releases norepinephrine/noradrenaline and epinephrine/adrenaline in response to immediate stressors, whilst the HPA axis releases glucocorticoid hormones (e.g. cortisol and corticosterone) to help mitigate allostatic load. There have been many studies on stress responses of captive animals, but they are not truly reflective of typical ranges or the types of stressors encountered by free-ranging wildlife, such as responses and adaptation to environmental change, which are particularly important from a conservation perspective. As stress can influence the composition of age and sex classes of free-ranging populations both directly and indirectly, ecological research must be prioritised towards more vulnerable taxa. Generally, large predators tend to be particularly at risk of anthropogenically driven population declines because they exhibit reduced behavioural plasticity required to adapt to changing landscapes and exist in reduced geographic ranges, have small population sizes, low fecundity rates, large spatial requirements and occupy high trophic positions. As a keystone species with a long history of coexistence with humans in highly anthropogenic landscapes, there has been growing concern about how humans influence bear behaviour and physiology, via numerous short- and long-term stressors. In this review, we synthesise research on the stress response in free-ranging bear populations and evaluate the effectiveness and limitations of current methodology in measuring stress in bears to identify the most effective metrics for future research. Particularly, we integrate research that utilised haematological variables, cardiac monitors and Global Positioning System (GPS) collars, serum/plasma and faecal glucocorticoid concentrations, hair cortisol levels, and morphological metrics (primarily skulls) to investigate the stress response in ursids in both short- and long-term contexts. We found that in free-ranging bears, food availability and consumption have the greatest influence on individual stress, with mixed responses to anthropogenic influences. Effects of sex and age on stress are also mixed, likely attributable to inconsistent methods. We recommend that methodology across all stress indicators used in free-ranging bears should be standardised to improve interpretation of results and that a wider range of species should be incorporated in future studies.

Towards the fully automated monitoring of ecological communities

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

Vision-Based Module for Herding with a Sheepdog Robot

Livestock farming is assisted more and more by technological solutions, such as robots. One of the main problems for shepherds is the control and care of livestock in areas difficult to access where grazing animals are attacked by predators such as the Iberian wolf in the northwest of the Iberian Peninsula. In this paper, we propose a system to automatically generate benchmarks of animal images of different species from iNaturalist API, which is coupled with a vision-based module that allows us to automatically detect predators and distinguish them from other animals. We tested multiple existing object detection models to determine the best one in terms of efficiency and speed, as it is conceived for real-time environments. YOLOv5m achieves the best performance as it can process 64 FPS, achieving an mAP (with IoU of 50%) of 99.49% for a dataset where wolves (predator) or dogs (prey) have to be detected and distinguished. This result meets the requirements of pasture-based livestock farms.

Robust Algorithms for Drone-Assisted Monitoring of Big Animals in Harsh Conditions of Siberian Winter Forests: Recovery of European elk (Alces alces) in Salair Mountains

Simple Summary

Forest animals can be used as a sensitive indicator of the real state of biodiversity. The research objective was to study the potential of drone planes equipped with thermal infrared imaging cameras for large animal monitoring in the conditions of Siberian winter forests with snow background at temperatures of −5 °C to −30 °C. The surveyed territory included the Salair State Nature Reserve in the Kemerovo Region, Russia. Drone planes were effective in covering large areas, while thermal infrared cameras provided accurate information in the harsh winter conditions of Siberia. The research featured the population of the European elk (Alces alces), which is gradually deteriorating due to poaching and deforestation. The designed technical methods and analytic algorithms are cost-efficient and they can be applied for monitoring large areas of Siberian, Canadian and Alaskan winter forests.

Abstract

There are two main reasons for monitoring the population of forest animals. First, regular surveys reveal the real state of biodiversity. Second, they guarantee a prompt response to any negative environmental factor that affects the animal population and make it possible to eliminate the threat before any permanent damage is done. The research objective was to study the potential of drone planes equipped with thermal infrared imaging cameras for large animal monitoring in the conditions of Siberian winter forests with snow background at temperatures −5 °C to −30 °C. The surveyed territory included the Salair State Nature Reserve in the Kemerovo Region, Russia. Drone planes were effective in covering large areas, while thermal infrared cameras provided accurate statistics in the harsh winter conditions of Siberia. The research featured the population of the European elk (Alces alces), which is gradually deteriorating due to poaching and deforestation. The authors developed an effective methodology for processing the data obtained from drone-mounted thermal infrared cameras. The research provided reliable results concerning the changes in the elk population on the territory in question. The use of drone planes proved an effective means of ungulate animal surveying in snow-covered winter forests. The designed technical methods and analytic algorithms are cost-efficient and they can be applied for monitoring large areas of Siberian and Canadian winter forests.

Sociability strongly affects the behavioural responses of wild guanacos to drones

Drones are being increasingly used in research and recreation but without an adequate assessment of their potential impacts on wildlife. Particularly, the effect of sociability on behavioural responses to drone-associated disturbance remains largely unknown. Using an ungulate with complex social behaviour, we (1) assessed how social aggregation and offspring presence, along with flight plan characteristics, influence the probability of behavioural reaction and the flight distance of wild guanacos (Lama guanicoe) to the drone's approach, and (2) estimated reaction thresholds and flight heights that minimise disturbance. Sociability significantly affected behavioural responses. Large groups showed higher reaction probability and greater flight distances than smaller groups and solitary individuals, regardless of the presence of offspring. This suggests greater detection abilities in large groups, but we cannot rule out the influence of other features inherent to each social unit (e.g., territoriality) that might be working simultaneously. Low flight heights increased the probability of reaction, although the effect of drone speed was less clear. Reaction thresholds ranged from 154 m (solitary individuals) to 344 m (mixed groups), revealing that the responsiveness of this guanaco population to the drone is the most dramatic reported so far for a wild species.

The behavioral responses of a nocturnal burrowing marsupial (Lasiorhinus latifrons) to drone flight

The use of drones in wildlife research and management is increasing. Recent evidence has demonstrated the impact of drones on animal behavior, but the response of nocturnal animals to drone flight remains unknown. Utilizing a lightweight commercial drone, the behavioral response of southern hairy-nosed wombats (Lasiorhinus latifrons) to drone flights was observed at Kooloola Station, Swan Reach, South Australia. All wombats flown over during both day and night flights responded behaviorally to the presence of drones. The response differed based on time of day. The most common night-time behavior elicited by drone flight was retreat, compared to stationary alertness behavior observed for daytime drone flights. The behavioral response of the wombats increased as flight altitude decreased. The marked difference of behavior between day and night indicates that this has implications for studies using drones. The behavior observed during flights was altered due to the presence of the drone, and therefore, shrewd study design is important (i.e., acclimation period to drone flight). Considering the sensory adaptations of the target species and how this may impact its behavioral response when flying at night is essential.

Future trends in measuring physiology in free-living animals

Thus far, ecophysiology research has predominantly been conducted within controlled laboratory-based environments, owing to a mismatch between the recording technologies available for physiological monitoring in wild animals and the suite of behaviours and environments they need to withstand, without unduly affecting subjects. While it is possible to record some physiological variables for free-living animals using animal-attached logging devices, including inertial-measurement, heart-rate and temperature loggers, the field is still in its infancy. In this opinion piece, we review the most important future research directions for advancing the field of 'physiologging' in wild animals, including the technological development that we anticipate will be required, and the fiscal and ethical challenges that must be overcome. Non-invasive, multi-sensor miniature devices are ubiquitous in the world of human health and fitness monitoring, creating invaluable opportunities for animal and human physiologging to drive synergistic advances. We argue that by capitalizing on the research efforts and advancements made in the development of human wearables, it will be possible to design the non-invasive loggers needed by ecophysiologists to collect accurate physiological data from free-ranging animals ethically and with an absolute minimum of impact. In turn, findings have the capacity to foster transformative advances in human health monitoring. Thus, we invite biomedical engineers and researchers to collaborate with the animal-tagging community to drive forward the advancements necessary to realize the full potential of both fields. This article is part of the theme issue 'Measuring physiology in free-living animals (Part II)'.

Inside out: heart rate monitoring to advance the welfare and conservation of maned wolves (Chrysocyon brachyurus)

Anthropogenic change is a major threat to individual species and biodiversity. Yet the behavioral and physiological responses of animals to these changes remain understudied. This is due to the technological challenges in assessing these effects in situ. Using captive maned wolves (Chrysocyon brachyurus, n = 6) as a model, we deployed implantable biologgers and collected physiological data on heart rate (HR) and heart rate variability (HRV) over a 1-year period. To test for links between HR and changes in the environment we analysed HR daily rhythms and responses to potential stressors (e.g. physical restraint, change in housing conditions, short-distance transportation and unfamiliar human presence). The 2-min HR averages ranged from 33 to 250 bpm, with an overall rest average of 73 bpm and a maximum of 296 bpm. On average, HRV was higher in females (227 ± 51 ms) than in males (151 ± 51 ms). As expected, HR increased at dusk and night when animals were more active and in response to stressors. Sudden decreases in HR were observed during transportation in three wolves, suggestive of fear bradycardia. We provide the first non-anesthetic HR values for the species and confirm that behaviour does not always reflect the shifts in autonomic tone in response to perceived threats. Because strong HR responses often were not revealed by observable changes in behaviour, our findings suggest that the number and variety of stressors in ex situ or in situ environments for maned wolves and most wildlife species may be underestimated. Our study also shows that integrating biologging with behavioral observations can provide vital information to guide captive management. Similar technology can be used to advance in situ research for developing more effective welfare, management and conservation plans for the species.

An engineering perspective on the development and evolution of implantable cardiac monitors in free-living animals

The latest technologies associated with implantable physiological monitoring devices can record multiple channels of data (including: heart rates and rhythms, activity, temperature, impedance and posture), and coupled with powerful software applications, have provided novel insights into the physiology of animals in the wild. This perspective details past challenges and lessons learned from the uses and developments of implanted biologgers designed for human clinical application in our research on free-ranging American black bears (Ursus americanus). In addition, we reference other research by colleagues and collaborators who have leveraged these devices in their work, including: brown bears (Ursus arctos), grey wolves (Canis lupus), moose (Alces alces), maned wolves (Chrysocyon brachyurus) and southern elephant seals (Mirounga leonina). We also discuss the potentials for applications of such devices across a range of other species. To date, the devices described have been used in fifteen different wild species, with publications pending in many instances. We have focused our physiological research on the analyses of heart rates and rhythms and thus special attention will be paid to this topic. We then discuss some major expected step changes such as improvements in sensing algorithms, data storage, and the incorporation of next-generation short-range wireless telemetry. The latter provides new avenues for data transfer, and when combined with cloud-based computing, it not only provides means for big data storage but also the ability to readily leverage high-performance computing platforms using artificial intelligence and machine learning algorithms. These advances will dramatically increase both data quantity and quality and will facilitate the development of automated recognition of extreme physiological events or key behaviours of interest in a broad array of environments, thus further aiding wildlife monitoring and management.

This article is part of the theme issue ‘Measuring physiology in free-living animals (Part I)’.

Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture

The opportunities for natural animal behaviours in pastures imply animal welfare benefits. Nevertheless, monitoring the animals can be challenging. The use of sensors, cameras, positioning equipment and unmanned aerial vehicles in large pastures has the potential to improve animal welfare surveillance. Directly or indirectly, sensors measure environmental factors together with the behaviour and physiological state of the animal, and deviations can trigger alarms for, e.g., disease, heat stress and imminent calving. Electronic positioning includes Radio Frequency Identification (RFID) for the recording of animals at fixed points. Positioning units (GPS) mounted on collars can determine animal movements over large areas, determine their habitat and, somewhat, health and welfare. In combination with other sensors, such units can give information that helps to evaluate the welfare of free-ranging animals. Drones equipped with cameras can also locate and count the animals, as well as herd them. Digitally defined virtual fences can keep animals within a predefined area without the use of physical barriers, relying on acoustic signals and weak electric shocks. Due to individual variations in learning ability, some individuals may be exposed to numerous electric shocks, which might compromise their welfare. More research and development are required, especially regarding the use of drones and virtual fences.

Trialling a real-time drone detection and validation protocol for the koala (Phascolarctos cinereus)

Remotely piloted aircraft system (RPAS), or drone, technology has emerged as a promising survey method for the cryptic koala. We demonstrate an in-field protocol for wild koala RPAS surveys which provides real-time validation of thermal signatures. During 15 trial flights using a quadcopter drone (DJI Matrice 200 v2) we successfully detected and validated koala thermal signatures (n=12) using two in-field approaches: validation by on-ground observer (n=10) and validation using 4K footage captured and reviewed directly after the survey (n=2). We also provide detectability considerations relative to survey time, temperature, wildlife–RPAS interactions and detection of non-target species, which can be used to further inform RPAS survey protocols.

Integrating satellite and unmanned aircraft system (UAS) imagery to model livestock population dynamics in the Longbao Wetland National Nature Reserve, China

The collection of field-based animal data is laborious, risky and costly in some areas, such as various nature reserves. Although multiple studies have used satellite imagery, aerial imagery, and field data individually for some animal species surveys, several technical issues still need to be addressed before full standardization of remote sensing methods for modeling animal population dynamics over large areas. This study is the first to model the population dynamics of livestock in the Longbao Wetland National Nature Reserve, China by utilizing yak estimations from Worldview-2 satellite imagery (0.5 m) collected in 2010 and yaks counted in a ground-based survey conducted in 2011 in combination with the animal population structure precisely extracted from UAS imagery captured in 2016. As a consequence, 5501, 5357, and 5510 yaks were estimated to appear in the reserve in 2010, 2011 and 2016, respectively. In total, 1092, 1062 and 1092 sheep were estimated to appear in the reserve in 2010, 2011 and 2016, respectively. The uncertainty of the presented method is also discussed. Primary experiments show that both the satellite imagery and UAS imagery are promising for use in yak censuses, but no sheep were observed in the satellite imagery because of the low resolution. Compared to the ground-based survey conducted in 2011, the UAS image estimate and satellite imagery count deviated in yak quantity by 2.69% and 2.86%, respectively. UASs are a reliable and low-budget alternative to animal surveys. No discernable changes in animal behaviors and animal distributions were observed as the UAS passed at a height of 700 m, and the accuracy of UAS imagery counts were not significantly affected by the short-distance animal movement and image mosaicking errors. The experimental results illustrate the advantages of the combination of satellite and UAS imagery in modeling animal population dynamics.

Long-term capture and handling effects on body condition, reproduction and survival in a semi-aquatic mammal

In long-term individual-based field studies, several parameters need to be assessed repeatedly to fully understand the potential fitness effects on individuals. Often studies only evaluate capture stress that appears in the immediate weeks or breeding season and even long-term studies fail to evaluate the long-term effects of their capture procedures. We investigated effects of long-term repeated capture and handling of individuals in a large semi-aquatic rodent using more than 20 years of monitoring data from a beaver population in Norway. To investigate the effects, we corrected for ecological factors and analysed the importance of total capture and handling events, years of monitoring and deployment of telemetry devices on measures related to body condition, reproduction and survival of individual beavers. Body mass of dominant individuals decreased considerably with number of capture events (107 g per capture), but we found no statistically clear short or long-term effects of capture and handling on survival or other body condition indices. Annual litter size decreased with increasing number of captures among older individuals. Number of captures furthermore negatively affected reproduction in the beginning of the monitoring, but the effect decreased over the years, indicating habituation to repeated capture and handling. By assessing potential impacts on several fitness-related parameters at multiple times, we can secure the welfare of wild animal populations when planning and executing future conservation studies as well as ensure ecologically reliable research data.

Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data

In the 4.5 decades since Altmann (1974) published her seminal paper on the methods for the observational study of behaviour, automated detection and analysis of social interaction networks have fundamentally transformed the ways that ecologists study social behaviour. Methodological developments for collecting data remotely on social behaviour involve indirect inference of associations, direct recordings of interactions and machine vision. These recent technological advances are improving the scale and resolution with which we can dissect interactions among animals. They are also revealing new intricacies of animal social interactions at spatial and temporal resolutions as well as in ecological contexts that have been hidden from humans, making the unwatchable seeable. We first outline how these technological applications are permitting researchers to collect exquisitely detailed information with little observer bias. We further recognize new emerging challenges from these new reality-mining approaches. While technological advances in automating data collection and its analysis are moving at an unprecedented rate, we urge ecologists to thoughtfully combine these new tools with classic behavioural and ecological monitoring methods to place our understanding of animal social networks within fundamental biological contexts.

Short-term exposure to unmanned aerial vehicles does not alter stress responses in breeding tree swallows

Recent studies suggest that visual and acoustic anthropogenic disturbances can cause physiological stress in animals. Human-induced stress may be particularly problematic for birds as new technologies, such as drones, increasingly invade their low-altitude air space. Although professional and recreational drone usage is increasing rapidly, there is little information on how drones affect avian behavior and physiology. We examined the effects of drone activity on behavior and physiology in adult, box-nesting tree swallows (Tachycineta bicolor). Specifically, we monitored bird behavior during drone flights and in response to a control object and measured telomere lengths and corticosterone levels as indicators of longer-term physiological stress. We predicted that drone-exposed tree swallows would habituate behaviorally after multiple flights, but that telomeres would shorten more quickly and that baseline corticosterone levels would be altered. One significant and two strong, non-significant trends in behavioral assays indicated that adult swallows acted more aggressively towards drone presence compared to a control object, but were slower to approach the drone initially. Swallows were also more reluctant to use nest boxes during drone activity. Tree swallows habituated to drone presence as expected, although the rate of habituation often did not differ between drone-exposed and control groups. Contrary to our prediction, drone activity did not affect telomere length, corticosterone levels, body mass or fledging rates. Overall, our results indicate that a small number of short, targeted, drone flights do not impact tree swallow health or productivity differently than a non-invasive control object. Minor behavioral differences suggest that increasing the frequency of drone use could impact this species. We provide some of the first results addressing how drone activity alters behavioral, physiological and molecular responses to stress in songbirds. A better understanding of these impacts will allow ecologists to make more informed decisions on the use and regulation of new drone technologies.

Thermal Imaging of Beach-Nesting Bird Habitat with Unmanned Aerial Vehicles: Considerations for Reducing Disturbance and Enhanced Image Accuracy

Knowledge of temperature variation within and across beach-nesting bird habitat, and how such variation may affect the nesting success and survival of these species, is currently lacking. This type of data is furthermore needed to refine predictions of population changes due to climate change, identify important breeding habitat, and guide habitat restoration efforts. Thermal imagery collected with unmanned aerial vehicles (UAVs) provides a potential approach to fill current knowledge gaps and accomplish these goals. Our research outlines a novel methodology for collecting and implementing active thermal ground control points (GCPs) and assess the accuracy of the resulting imagery using an off-the-shelf commercial fixed-wing UAV that allows for the reconstruction of thermal landscapes at high spatial, temporal, and radiometric resolutions. Additionally, we observed and documented the behavioral responses of beach-nesting birds to UAV flights and modifications made to flight plans or the physical appearance of the UAV to minimize disturbance. We found strong evidence that flying on cloudless days and using sky-blue camouflage greatly reduced disturbance to nesting birds. The incorporation of the novel active thermal GCPs into the processing workflow increased image spatial accuracy an average of 12 m horizontally (mean root mean square error of checkpoints in imagery with and without GCPs was 0.59 m and 23.75 m, respectively). The final thermal indices generated had a ground sampling distance of 25.10 cm and a thermal accuracy of less than 1 °C. This practical approach to collecting highly accurate thermal data for beach-nesting bird habitat while avoiding disturbance is a crucial step towards the continued monitoring and modeling of beach-nesting birds and their habitat.

An experimental approach to evaluate the potential of drones in terrestrial mammal research: a gregarious ungulate as a study model

Research on the use of unmanned aircraft systems (UAS) in wildlife has made remarkable progress recently. Few studies to date have experimentally evaluated the effect of UAS on animals and have usually focused primarily on aquatic fauna. In terrestrial open arid ecosystems, with relatively good visibility to detect animals but little environmental noise, there should be a trade-off between flying the UAS at high height above ground level (AGL) to limit the disturbance of animals and flying low enough to maintain count precision. In addition, body size or social aggregation of species can also affect the ability to detect animals from the air and their response to the UAS approach. To address this gap, we used a gregarious ungulate, the guanaco (Lama guanicoe), as a study model. Based on three types of experimental flights, we demonstrated that (i) the likelihood of miscounting guanacos in images increases with UAS height, but only for offspring and (ii) higher height AGL and lower UAS speed reduce disturbance, except for large groups, which always reacted. Our results call into question mostly indirect and observational previous evidence that terrestrial mammals are more tolerant to UAS than other species and highlight the need for experimental and species-specific studies before using UAS methods.