Mammalian energetics. Instantaneous energetics of puma kills reveal advantage of felid sneak attacks

Human procurement of meat from lion (Panthera leo) kills: Costs of disturbance and implications for carnivore conservation

In Africa, humans and large carnivores compete over access to resources, including prey. Disturbance by humans to kills made by carnivores, often for purposes of obtaining all or portions of the carcass, constitutes a form of human-wildlife conflict. However the occurrence of this practice, known as human kleptoparasitism, and its impact on carnivores has received little scientific attention. We obtained expert opinions from African lion researchers and stakeholders via a standardized questionnaire to characterize the geographic extent and frequency of human kleptoparasitism as it occurs in modern times. Our survey found modern human kleptoparasitism on kills made by lions, and possibly other large carnivores in Africa, to be geographically more widespread than previously reported. Meat lost to humans requires carnivores to hunt and kill additional prey thereby causing stress, increasing their energetic costs and risks of natural injury, and exposing them to risk of direct injury or death from human usurpers. Because of their conspicuous behaviors and tendency towards killing large-bodied prey, lions are particularly susceptible to humans detecting their kills. While human kleptoparasitism was geographically widespread, socio-economic factors influenced the frequency of occurrence. Prey type (wild game or domestic livestock) influenced human attitudes towards meat theft; ownership allows for legal recovery of livestock carcasses, while possessing wild game meat is mostly illegal and may incur penalties. Meat theft was associated with other illegal activities (i.e., illegal mining) and most prevalent among people of low income, including underpaid game scouts. Despite quantifiable costs to carnivores of human disturbance to their kills, the majority of experts surveyed reported a lack of knowledge on this practice. We propose that human disturbance at kills, especially loss of prey through human kleptoparasitism, constitutes an important anthropogenic threat that may seriously impact energy budgets of individual lions and other scavengers when meat and carcasses are removed from the ecosystem, and that the costs incurred by carnivores warrants further investigation.

Identifying animal behaviours from accelerometers: Improving predictive accuracy of machine learning by refining the variables selected, data frequency, and sample duration

Observing animals in the wild often poses extreme challenges, but animal-borne accelerometers are increasingly revealing unobservable behaviours. Automated machine learning streamlines behaviour identification from the substantial datasets generated during multi-animal, long-term studies; however, the accuracy of such models depends on the qualities of the training data. We examined how data processing influenced the predictive accuracy of random forest (RF) models, leveraging the easily observed domestic cat (Felis catus) as a model organism for terrestrial mammalian behaviours. Nine indoor domestic cats were equipped with collar-mounted tri-axial accelerometers, and behaviours were recorded alongside video footage. From this calibrated data, eight datasets were derived with (i) additional descriptive variables, (ii) altered frequencies of acceleration data (40 Hz vs. a mean over 1 s) and (iii) standardised durations of different behaviours. These training datasets were used to generate RF models that were validated against calibrated cat behaviours before identifying the behaviours of five free-ranging tag-equipped cats. These predictions were compared to those identified manually to validate the accuracy of the RF models for free-ranging animal behaviours. RF models accurately predicted the behaviours of indoor domestic cats (F-measure up to 0.96) with discernible improvements observed with post-data-collection processing. Additional variables, standardised durations of behaviours and higher recording frequencies improved model accuracy. However, prediction accuracy varied with different behaviours, where high-frequency models excelled in identifying fast-paced behaviours (e.g. locomotion), whereas lower-frequency models (1 Hz) more accurately identified slower, aperiodic behaviours such as grooming and feeding, particularly when examining free-ranging cat behaviours. While RF modelling offered a robust means of behaviour identification from accelerometer data, field validations were important to validate model accuracy for free-ranging individuals. Future studies may benefit from employing similar data processing methods that enhance RF behaviour identification accuracy, with extensive advantages for investigations into ecology, welfare and management of wild animals.

Predatory synapsid ecomorphology signals growing dynamism of late Palaeozoic terrestrial ecosystems

Terrestrial ecosystems evolved substantially through the Palaeozoic, especially the Permian, gaining much new complexity, especially among predators. Key among these predators were non-mammalian synapsids. Predator ecomorphology reflect interactions with prey and competitors, which are key controls on carnivore diversity and ecology. Therefore, carnivorous synapsids may offer insight on wider ecological evolution as the first complex, tetrapod-dominated, terrestrial ecosystems formed through the late Palaeozoic. Using morphometric and phylogenetic comparative methods, we chart carnivorous synapsid trophic morphology from the latest Carboniferous to the earliest Triassic (307-251.2 Ma). We find a major morphofunctional shift in synapsid carnivory between the early and middle Permian, via the addition of new feeding modes increasingly specialised for greater biting power or speed that captures the growing antagonism and dynamism of terrestrial tetrapod predator-prey interactions. The further evolution of new hypo- and hypercarnivorous synapsids highlight the nascent intrinsic pressures and complexification of terrestrial ecosystems across the mid-late Permian.

A Review of Cervidae Visual Ecology

This review examines the visual systems of cervids in relation to their ability to meet their ecological needs and how their visual systems are specialized for particular tasks. Cervidae encompasses a diverse group of mammals that serve as important ecological drivers within their ecosystems. Despite evidence of highly specialized visual systems, a large portion of cervid research ignores or fails to consider the realities of cervid vision as it relates to their ecology. Failure to account for an animal's visual ecology during research can lead to unintentional biases and uninformed conclusions regarding the decision making and behaviors for a species or population. Our review addresses core behaviors and their interrelationship with cervid visual characteristics. Historically, the study of cervid visual characteristics has been restricted to specific areas of inquiry such as color vision and contains limited integration into broader ecological and behavioral research. The purpose of our review is to bridge these gaps by offering a comprehensive review of cervid visual ecology that emphasizes the interplay between the visual adaptations of cervids and their interactions with habitats and other species. Ultimately, a better understanding of cervid visual ecology allows researchers to gain deeper insights into their behavior and ecology, providing critical information for conservation and management efforts.

Predator-mediated interactions through changes in predator home range size can lead to local prey exclusion

The strength of indirect biotic interactions is difficult to quantify in the wild and can alter community composition. To investigate whether the presence of a prey species affects the population growth rate of another prey species, we quantified predator-mediated interaction strength using a multi-prey mechanistic model of predation and a population matrix model. Models were parametrized using behavioural, demographic and experimental data from a vertebrate community that includes the arctic fox (Vulpes lagopus), a predator feeding on lemmings and eggs of various species such as sandpipers and geese. We show that the positive effects of the goose colony on sandpiper nesting success (due to reduction of search time for sandpiper nests) were outweighed by the negative effect of an increase in fox density. The fox numerical response was driven by changes in home range size. As a result, the net interaction from the presence of geese was negative and could lead to local exclusion of sandpipers. Our study provides a rare empirically based model that integrates mechanistic multi-species functional responses and behavioural processes underlying the predator numerical response. This is an important step forward in our ability to quantify the consequences of predation for community structure and dynamics.

Deep dives and high tissue density increase mean dive costs in California sea lions (Zalophus californianus)

Diving is central to the foraging strategies of many marine mammals and seabirds. Still, the effect of dive depth on foraging cost remains elusive because energy expenditure is difficult to measure at fine temporal scales in wild animals. We used depth and acceleration data from eight lactating California sea lions (Zalophus californianus) to model body density and investigate the effect of dive depth and tissue density on rates of energy expenditure. We calculated body density in 5 s intervals from the rate of gliding descent. We modeled body density across depth in each dive, revealing high tissue densities and diving lung volumes (DLVs). DLV increased with dive depth in four individuals. We used the buoyancy calculated from dive-specific body-density models and drag calculated from swim speed to estimate metabolic power and cost of transport in 5 s intervals during descents and ascents. Deeper dives required greater mean power for round-trip vertical transit, especially in individuals with higher tissue density. These trends likely follow from increased mean swim speed and buoyant hinderance that increasingly outweighs buoyant aid in deeper dives. This suggests that deep diving is either a 'high-cost, high-reward' strategy or an energetically expensive option to access prey when prey in shallow waters are limited, and that poor body condition may increase the energetic costs of deep diving. These results add to our mechanistic understanding of how foraging strategy and body condition affect energy expenditure in wild breath-hold divers.

Habitat fragmentation reduces survival and drives source-sink dynamics for a large carnivore

Rigorous understanding of how environmental conditions impact population dynamics is essential for species conservation, especially in mixed-use landscapes where source-sink dynamics may be at play. Conservation of large carnivore populations in fragmented, human-dominated landscapes is critical for their long-term persistence. However, living in human-dominated landscapes comes with myriad costs, including direct anthropogenic mortality and sublethal energetic costs. How these costs impact individual fitness and population dynamics are not fully understood, partly due to the difficulty in collecting long-term demographic data for these species. Here, we analyzed an 11-year dataset on puma (Puma concolor) space use, mortality, and reproduction in the Santa Cruz Mountains, California, USA, to quantify how living in a fragmented landscape impacts individual survival and population dynamics. Long-term exposure to housing density drove mortality risk for female pumas, resulting in an 18-percentage-point reduction in annual survival for females in exurban versus remote areas. While the overall population growth rate appeared stable, reduced female survival in more developed areas resulted in source-sink dynamics across the study area, with 42.1% of the Santa Cruz Mountains exhibiting estimated population growth rates <1. Since habitat selection is often used as a proxy for habitat quality, we also assessed whether puma habitat selection predicted source and sink areas. Patterns of daytime puma habitat selection predicted source areas, while time-of-day-independent habitat selection performed less well as a proxy. These results illuminate the individual- and population-level consequences of habitat fragmentation for large carnivores, illustrating that habitat fragmentation can produce source-sink dynamics that may not be apparent from other metrics of habitat quality. Locally, conserving high-quality source habitat within the Santa Cruz Mountains is necessary to support long-term puma population persistence. More broadly, source-sink dynamics may at play for other carnivore populations in similar fragmented systems, and linking landscape conditions to population dynamics is essential for effective conservation. Caution should be used in inferring habitat quality from habitat selection alone, but these results shed light on metrics of selection that may be better or worse proxies to identify source areas for large carnivores.

Combining accelerometry with allometry for estimating daily energy expenditure in joules when in-lab calibration is unavailable

BACKGROUND

All behaviour requires energy, and measuring energy expenditure in standard units (joules) is key to linking behaviour to ecological processes. Animal-borne accelerometers are commonly used to infer proxies of energy expenditure, termed 'dynamic body acceleration' (DBA). However, converting acceleration proxies (m/s2) to standard units (watts) involves costly in-lab respirometry measurements, and there is a lack of viable substitutes for empirical calibration relationships when these are unavailable.

METHODS

We used past allometric work quantifying energy expenditure during resting and locomotion as a function of body mass to calibrate DBA. We used the resulting 'power calibration equation' to estimate daily energy expenditure (DEE) using two models: (1) locomotion data-based linear calibration applied to the waking period, and Kleiber's law applied to the sleeping period (ACTIWAKE), and (2) locomotion and resting data-based linear calibration applied to the 24-h period (ACTIREST24). Since both models require locomotion speed information, we developed an algorithm to estimate speed from accelerometer, gyroscope, and behavioural annotation data. We applied these methods to estimate DEE in free-ranging meerkats (Suricata suricatta), and compared model estimates with published DEE measurements made using doubly labelled water (DLW) on the same meerkat population.

RESULTS

ACTIWAKE's DEE estimates did not differ significantly from DLW (t(19) = - 1.25; P = 0.22), while ACTIREST24's estimates did (t(19) = - 2.38; P = 0.028). Both models underestimated DEE compared to DLW: ACTIWAKE by 14% and ACTIREST by 26%. The inter-individual spread in model estimates of DEE (s.d. 1-2% of mean) was lower than that in DLW (s.d. 33% of mean).

CONCLUSIONS

We found that linear locomotion-based calibration applied to the waking period, and a 'flat' resting metabolic rate applied to the sleeping period can provide realistic joule estimates of DEE in terrestrial mammals. The underestimation and lower spread in model estimates compared to DLW likely arise because the accelerometer only captures movement-related energy expenditure, whereas DLW is an integrated measure. Our study offers new tools to incorporate body mass (through allometry), and changes in behavioural time budgets and intra-behaviour changes in intensity (through DBA) in acceleration-based field assessments of daily energy expenditure.

Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning

Abstract

Accelerometers capture rapid changes in animal motion, and the analysis of large quantities of such data using machine learning algorithms enables the inference of broad animal behaviour categories such as foraging, flying, and resting over long periods of time. We deployed GPS-GSM/GPRS trackers with tri-axial acceleration sensors on common woodpigeons (Columba palumbus) from Hesse, Germany (forest and urban birds) and from Lisbon, Portugal (urban park). We used three machine learning algorithms, Random Forest, Support Vector Machine, and Extreme Gradient Boosting, to classify the main behaviours of the birds, namely foraging, flying, and resting and calculated time budgets over the breeding and winter season. Woodpigeon time budgets varied between seasons, with more foraging time during the breeding season than in winter. Also, woodpigeons from different sites showed differences in the time invested in foraging. The proportion of time woodpigeons spent foraging was lowest in the forest habitat from Hesse, higher in the urban habitat of Hesse, and highest in the urban park in Lisbon. The time budgets we recorded contrast to previous findings in woodpigeons and reaffirm the importance of considering different populations to fully understand the behaviour and adaptation of a particular species to a particular environment. Furthermore, the differences in the time budgets of Woodpigeons from this study and previous ones might be related to environmental change and merit further attention and the future investigation of energy budgets.

Significance statement

In this study we took advantage of accelerometer technology and machine learning methods to investigate year-round behavioural time budgets of wild common woodpigeons (Columba palumbus). Our analysis focuses on identifying coarse-scale behaviours (foraging, flying, resting) using various machine learning algorithms. Woodpigeon time budgets varied between seasons and among sites. Particularly interesting is the result showing that urban woodpigeons spend more time foraging than forest conspecifics. Our study opens an opportunity to further investigate and understand how a successful bird species such as the woodpigeon copes with increasing environmental change and urbanisation. The increase in the proportion of time devoted to foraging might be one of the behavioural mechanisms involved but opens questions about the costs associated to such increase in terms of other important behaviours.

Telemetry and Accelerometer Tracking of Green Toads in an Urban Habitat: Methodological Notes and Preliminary Findings

Advancements in tracking technologies provide an increasingly important tool in animal monitoring and conservation that can describe animal spatial behavior in native habitats and uncover migratory routes that otherwise may be difficult or impossible to map. In addition, high-resolution accelerometer sensors provide powerful insights into animal activity patterns and can help to identify specific behaviors from accelerometer profiles alone. Previously, such accelerometers were restricted to larger animals due to size and mass constraints. However, recent advances make it possible to use such devices on smaller animals such as the European green toad (Bufotes viridis), the focus of our current study. We deploy custom made tracking devices, that consist of very-high-frequency transmitters and tri-axial accelerometers, to track toads in their native urban environment in Vienna (Austria). A total of nine toads were tracked, ranging from three to nine tracking days per individual during the post-breeding season period. We demonstrate that our devices could reliably monitor toad movement and activity during the observation period. Hence, we confirmed the predominantly nocturnal activity patterns and recorded low overall movement at this urban site. Accelerometer data revealed that toads exhibited brief but intense activity bursts between 10 pm and midnight, resting periods during the night and intermittent activity during the day. Positional tracking alone would have missed the major activity events as they rarely resulted in large positional displacements. This underscores the importance of and value in integrating multiple tracking sensors for studies of movement ecology. Our approach could be adapted for other amphibians or other animals with mass constraints and may become standard monitoring equipment in the near future.

Tag-based estimates of bottlenose dolphin swimming behavior and energetics

Current estimates of marine mammal hydrodynamic forces tend to be made using camera-based kinematic data for a limited number of fluke strokes during a prescribed swimming task. In contrast, biologging tag data yield kinematic measurements from thousands of strokes, enabling new insights into swimming behavior and mechanics. However, there have been limited tag-based estimates of mechanical work and power. In this work, we investigated bottlenose dolphin (Tursiops truncatus) swimming behavior using tag-measured kinematics and a hydrodynamic model to estimate propulsive power, work and cost of transport. Movement data were collected from six animals during prescribed straight-line swimming trials to investigate swimming mechanics over a range of sustained speeds (1.9-6.1 m s-1). Propulsive power ranged from 66 W to 3.8 kW over 282 total trials. During the lap trials, the dolphins swam at depths that mitigated wave drag, reducing overall drag throughout these mid- to high-speed tasks. Data were also collected from four individuals during undirected daytime (08:30-18:00 h) swimming to examine how self-selected movement strategies are used to modulate energetic efficiency and effort. Overall, self-selected swimming speeds (individual means ranging from 1.0 to 1.96 m s-1) tended to minimize cost of transport, and were on the lower range of animal-preferred speeds reported in literature. The results indicate that these dolphins moderate propulsive effort and efficiency through a combination of speed and depth regulation. This work provides new insights into dolphin swimming behavior in both prescribed tasks and self-selected swimming, and presents a path forward for continuous estimates of mechanical work and power from wild animals.

Increasingly detailed insights in animal behaviours using continuous on-board processing of accelerometer data

BACKGROUND

Studies of animal behaviour, ecology and physiology are continuously benefitting from progressing biologging techniques, including the collection of accelerometer data to infer animal behaviours and energy expenditure. In one of the most recent technological advances in this space, on-board processing of raw accelerometer data into animal behaviours proves highly energy-, weight- and cost-efficient allowing for continuous behavioural data collection in addition to regular positional data in a wide range of animal tracking studies.

METHODS

We implemented this latest development in collecting continuous behaviour records from 6 Pacific Black Ducks Anas superciliosa to evaluate some of this novel technique's potential advantages over tracking studies lacking behavioural data or recording accelerometer data intermittently only. We (i) compared the discrepancy of time-activity budgets between continuous records and behaviours sampled with different intervals, (ii) compared total daily distance flown using hourly GPS fixes with and without additional behavioural data and (iii) explored how behaviour records can provide additional insights for animal home range studies.

RESULTS

Using a total of 690 days of behaviour records across six individual ducks distinguishing eight different behaviours, we illustrated the improvement that is obtained in time-activity budget accuracy if continuous rather than interval-sampled accelerometer data is used. Notably, for rare behaviours such as flying and running, error ratios > 1 were common when sampling intervals exceeded 10 min. Using 72 days of hourly GPS fixes in combination with continuous behaviour records over the same period in one individual duck, we showed behaviour-based daily distance estimation is significantly higher (up to 540%) than the distance calculated from hourly sampled GPS fixes. Also, with the same 72 days of data for one individual duck, we showed how this individual used specific sites within its entire home range to satisfy specific needs (e.g. roosting and foraging).

CONCLUSION

We showed that by using trackers allowing for continuous recording of animal behaviour, substantial improvements in the estimation of time-activity budgets and daily traveling distances can be made. With integrating behaviour into home-range estimation we also highlight that this novel tracking technique may not only improve estimations but also open new avenues in animal behaviour research, importantly improving our knowledge of an animal's state while it is roaming the landscape.

Dietary patterns of a versatile large carnivore, the puma (Puma concolor)

Large carnivores play critical roles in terrestrial ecosystems but have suffered dramatic range contractions over the past two centuries. Developing an accurate understanding of large carnivore diets is an important first step towards an improved understanding of their ecological roles and addressing the conservation challenges faced by these species.The puma is one of seven large felid species in the world and the only one native to the non-tropical regions of the New World. We conducted a meta-analysis of puma diets across the species' range in the Americas and assessed the impact of varying environmental conditions, niche roles, and human activity on puma diets. Pumas displayed remarkable dietary flexibility, consuming at least 232 different prey species, including one Critically Endangered and five Endangered species.Our meta-analysis found clear patterns in puma diets with changing habitat and environmental conditions. Pumas consumed more larger-bodied prey species with increasing distance from the equator, but consumption of medium-sized species showed the opposite trend.Puma diets varied with their realized niche; however, contrary to our expectations, puma consumption of large species did not change with their trophic position, and pumas consumed more small prey and birds as apex predators. Consumption of domestic species was negatively correlated with consumption of medium-sized wild species, a finding which underscores the importance of maintaining intact native prey assemblages.The tremendous dietary flexibility displayed by pumas represents both an opportunity and a challenge for understanding the puma's role in ecosystems and for the species' management and conservation. Future studies should explore the linkages between availability and selection of primary and other wild prey, and consequent impacts on predation of domestic species, in order to guide conservation actions and reduce conflict between pumas and people.

A persistent behavioral state enables sustained predation of humans by mosquitoes

Predatory animals pursue prey in a noisy sensory landscape, deciding when to continue or abandon their chase. The mosquito Aedes aegypti is a micropredator that first detects humans at a distance through sensory cues such as carbon dioxide. As a mosquito nears its target, it senses more proximal cues such as body heat that guide it to a meal of blood. How long the search for blood continues after initial detection of a human is not known. Here, we show that a 5 s optogenetic pulse of fictive carbon dioxide induced a persistent behavioral state in female mosquitoes that lasted for more than 10 min. This state is highly specific to females searching for a blood meal and was not induced in recently blood-fed females or in males, who do not feed on blood. In males that lack the gene fruitless, which controls persistent social behaviors in other insects, fictive carbon dioxide induced a long-lasting behavior response resembling the predatory state of females. Finally, we show that the persistent state triggered by detection of fictive carbon dioxide enabled females to engorge on a blood meal mimic offered up to 14 min after the initial 5 s stimulus. Our results demonstrate that a persistent internal state allows female mosquitoes to integrate multiple human sensory cues over long timescales, an ability that is key to their success as an apex micropredator of humans.

Life in the slow lane: Field Metabolic Rate and Prey Consumption Rate of the Greenland Shark (Somniosus microcephalus) modeled using Archival Biologgers

Field metabolic rate (FMR) is a holistic measure of metabolism representing the routine energy utilization of a species living within a specific ecological context, thus providing insight into its ecology, fitness and resilience to environmental stressors. For animals which cannot be easily observed in the wild, FMR can also be used in concert with dietary data to quantitatively assess their role as consumers, improving understanding of the trophic linkages that structure food webs and allowing for informed management decisions. Here we modeled the FMR of Greenland sharks (Somniosus microcephalus) equipped with biologger packages or pop-up archival satellite tags (PSATs) in two coastal inlets of Baffin Island (Nunavut) using metabolic scaling relationships for mass, temperature and activity. We estimated that Greenland sharks had an overall mean FMR of 21.67±2.30 mgO2h-1kg-0.84 (n=30; 1-4 day accelerometer package deployments) while residing inside these cold-water fjord systems in the late summer, and 25.48±0.47 mgO2h-1kg-0.84 (n=6; PSATs) over an entire year. When considering prey consumption rate, an average shark in these systems (224kg) requires a maintenance ration of 61-193g of fish or marine mammal prey daily. As a lethargic polar species, these low FMR estimates, and corresponding prey consumption estimates suggest Greenland sharks require very little energy to sustain themselves under natural conditions. These data provide the first characterization of the energetics and consumer role of this vulnerable and understudied species in the wild, essential given growing pressures from climate change and expanding commercial fisheries in the Arctic.

Categorising cheetah behaviour using tri-axial accelerometer data loggers: a comparison of model resolution and data logger performance

BACKGROUND

Extinction is one of the greatest threats to the living world, endangering organisms globally, advancing conservation to the forefront of species research. To maximise the efficacy of conservation efforts, understanding the ecological, physiological, and behavioural requirements of vulnerable species is vital. Technological advances, particularly in remote sensing, enable researchers to continuously monitor movement and behaviours of multiple individuals simultaneously with minimal human intervention. Cheetahs, Acinonyx jubatus, constitute a "vulnerable" species for which only coarse behaviours have been elucidated. The aims of this study were to use animal-attached accelerometers to (1) determine fine-scale behaviours in cheetahs, (2) compare the performances of different devices in behaviour categorisation, and (3) provide a behavioural categorisation framework.

METHODS

Two different accelerometer devices (CEFAS, frequency: 30 Hz, maximum capacity: ~ 2 g; GCDC, frequency: 50 Hz, maximum capacity: ~ 8 g) were mounted onto collars, fitted to five individual captive cheetahs. The cheetahs chased a lure around a track, during which time their behaviours were videoed. Accelerometer data were temporally aligned with corresponding video footage and labelled with one of 17 behaviours. Six separate random forest models were run (three per device type) to determine the categorisation accuracy for behaviours at a fine, medium, and coarse resolution.

RESULTS

Fine- and medium-scale models had an overall categorisation accuracy of 83-86% and 84-88% respectively. Non-locomotory behaviours were best categorised on both loggers with GCDC outperforming CEFAS devices overall. On a coarse scale, both devices performed well when categorising activity (86.9% (CEFAS) vs. 89.3% (GCDC) accuracy) and inactivity (95.5% (CEFAS) vs. 95.0% (GCDC) accuracy). This study defined cheetah behaviour beyond three categories and accurately determined stalking behaviours by remote sensing. We also show that device specification and configuration may affect categorisation accuracy, so we recommend deploying several different loggers simultaneously on the same individual.

CONCLUSION

The results of this study will be useful in determining wild cheetah behaviour. The methods used here allowed broad-scale (active/inactive) as well as fine-scale (e.g. stalking) behaviours to be categorised remotely. These findings and methodological approaches will be useful in monitoring the behaviour of wild cheetahs and other species of conservation interest.

Activity, heart rate, and energy expenditure of a cold-climate mesocarnivore, the Canada lynx

The energetic consequences of body size, behaviour, and fine-scale environmental variation remain understudied, particularly among free-ranging carnivores, due to logistical and methodological challenges of studying them in the field. Here, we present novel activity, heart rate, and metabolic data on free-ranging Canada lynx (Lynx canadensis Kerr, 1792) to a) investigate intraspecific patterns of energy expenditure, particularly how they relate to body size, environmental conditions, and activity variation, and b) position lynx - a cold-climate, mesocarnivore - within interspecific allometries of carnivore energetics. Lynx demonstrated limited behavioural and metabolic responses to environmental conditions, despite extreme cold and moderate snow depths during our study, but marked body size patterns with larger lynx having higher activity and lower resting heart rate than smaller lynx. Compared to similar-sized carnivores, lynx were less active and had lower heart rate, likely due to their ambush hunting style, but higher energy expenditure, likely due to their cold-climate existence and access to abundant prey. Overall, lynx were more similar to other ambush hunters than to sympatric cold-climate species and mesocarnivores. Our data provide insight into the relative importance of abiotic and biotic drivers of carnivore energetics and the ways in which predators maintain energy balance in variable environments.

How fishing cats Prionailurus viverrinus Bennett, 1833 fish: describing a felid’s strategy to hunt aquatic prey

The fishing cat’s persistence in a ‘semi-aquatic niche’ suggests the evolution of a successful hunting strategy. We describe it for the first time by analysing 197 camera-trap video-clips, collected from a participatory-science initiative, within an ethogram framework. The cats spent ∼52% of the time sitting and waiting for prey (fishes) to come nearer and took limited attempts to hunt (3.89%) in deeper waters (in which the upper portions of the cat’s body were submerged), where its hunting success was found to be 42.86%. In shallow waters, it adopted a predominantly active mode of hunting (∼96%) to flush out prey.

Critical summer foraging tradeoffs in a subarctic ungulate

Summer diets are crucial for large herbivores in the subarctic and are affected by weather, harassment from insects and a variety of environmental changes linked to climate. Yet, understanding foraging behavior and diet of large herbivores is challenging in the subarctic because of their remote ranges. We used GPS video-camera collars to observe behaviors and summer diets of the migratory Fortymile Caribou Herd (Rangifer tarandus granti) across Alaska, USA and the Yukon, Canada. First, we characterized caribou behavior. Second, we tested if videos could be used to quantify changes in the probability of eating events. Third, we estimated summer diets at the finest taxonomic resolution possible through videos. Finally, we compared summer diet estimates from video collars to microhistological analysis of fecal pellets. We classified 18,134 videos from 30 female caribou over two summers (2018 and 2019). Caribou behaviors included eating (mean = 43.5%), ruminating (25.6%), travelling (14.0%), stationary awake (11.3%) and napping (5.1%). Eating was restricted by insect harassment. We classified forage(s) consumed in 5,549 videos where diet composition (monthly) highlighted a strong tradeoff between lichens and shrubs; shrubs dominated diets in June and July when lichen use declined. We identified 63 species, 70 genus and 33 family groups of summer forages from videos. After adjusting for digestibility, monthly estimates of diet composition were strongly correlated at the scale of the forage functional type (i.e., forage groups composed of forbs, graminoids, mosses, shrubs and lichens; r = 0.79, p < .01). Using video collars, we identified (1) a pronounced tradeoff in summer foraging between lichens and shrubs and (2) the costs of insect harassment on eating. Understanding caribou foraging ecology is needed to plan for their long-term conservation across the circumpolar north, and video collars can provide a powerful approach across remote regions.

Digging into the behaviour of an active hunting predator: arctic fox prey caching events revealed by accelerometry

BACKGROUND

Biologging now allows detailed recording of animal movement, thus informing behavioural ecology in ways unthinkable just a few years ago. In particular, combining GPS and accelerometry allows spatially explicit tracking of various behaviours, including predation events in large terrestrial mammalian predators. Specifically, identification of location clusters resulting from prey handling allows efficient location of killing events. For small predators with short prey handling times, however, identifying predation events through technology remains unresolved. We propose that a promising avenue emerges when specific foraging behaviours generate diagnostic acceleration patterns. One such example is the caching behaviour of the arctic fox (Vulpes lagopus), an active hunting predator strongly relying on food storage when living in proximity to bird colonies.

METHODS

We equipped 16 Arctic foxes from Bylot Island (Nunavut, Canada) with GPS and accelerometers, yielding 23 fox-summers of movement data. Accelerometers recorded tri-axial acceleration at 50 Hz while we obtained a sample of simultaneous video recordings of fox behaviour. Multiple supervised machine learning algorithms were tested to classify accelerometry data into 4 behaviours: motionless, running, walking and digging, the latter being associated with food caching. Finally, we assessed the spatio-temporal concordance of fox digging and greater snow goose (Anser caerulescens antlanticus) nesting, to test the ecological relevance of our behavioural classification in a well-known study system dominated by top-down trophic interactions.

RESULTS

The random forest model yielded the best behavioural classification, with accuracies for each behaviour over 96%. Overall, arctic foxes spent 49% of the time motionless, 34% running, 9% walking, and 8% digging. The probability of digging increased with goose nest density and this result held during both goose egg incubation and brooding periods.

CONCLUSIONS

Accelerometry combined with GPS allowed us to track across space and time a critical foraging behaviour from a small active hunting predator, informing on spatio-temporal distribution of predation risk in an Arctic vertebrate community. Our study opens new possibilities for assessing the foraging behaviour of terrestrial predators, a key step to disentangle the subtle mechanisms structuring many predator-prey interactions and trophic networks.

Mountain Lions (Puma concolor) Resist Long-Term Dietary Exposure To Chronic Wasting Disease

For nearly 18 yr, we evaluated susceptibility of captive mountain lions (Puma concolor) to chronic wasting disease (CWD) in the face of repeated exposure associated with consuming infected cervid carcasses. Three mountain lions with a monomorphic prion protein gene (PRNP) sequence identical to that described previously for the species had access to parts of ≥432 infected carcasses during ≥2,013 feeding occasions, conservatively representing >14,000 kg of infected feed material, during May 2002 to March 2020. The proportion of diet in infected carcass material averaged 43% overall but differed from year to year (minimally 11%-74%). Most infected carcasses were mule deer (Odocoileus hemionus; ∼75%). We observed no clinical signs suggestive of progressive encephalopathy or other neurologic disease over the ∼14.5-17.9 yr between first known exposure and eventual death. Histopathology revealed no spongiform changes or immunostaining suggestive of prion infection in multiple sections of nervous and lymphoid tissue. Similarly, none of 133 free-ranging mountain lion carcasses sampled opportunistically during 2004-2020 showed immunostaining consistent with prion infection in sections of brainstem or lymph node. These findings align with prior work suggesting that CWD-associated prions face strong barriers to natural transmission among species outside the family Cervidae.

Variable strategies to solve risk-reward tradeoffs in carnivore communities

Mesopredator release theory suggests that dominant predators suppress subordinate carnivores and ultimately shape community dynamics, but the assumption that subordinate species are only negatively affected ignores the possibility of facilitation through scavenging. We examined the interplay within a carnivore community consisting of cougars, coyotes, black bears, and bobcats using contemporaneous Global Positioning System telemetry data from 51 individuals; diet analysis from 972 DNA-metabarcoded scats; and data from 128 physical investigations of cougar kill sites, 28 of which were monitored with remote cameras. Resource provisioning from competitively dominant cougars to coyotes through scavenging was so prolific as to be an overwhelming determinant of coyote behavior, space use, and resource acquisition. This was evident via the strong attraction of coyotes to cougar kill sites, frequent scavenging of cougar-killed prey, and coyote diets that nearly matched cougars in the magnitude of ungulate consumption. Yet coyotes were often killed by cougars and used space to minimize encounters, complicating the fitness benefits gained from scavenging. We estimated that 23% (95% CI: 8 to 55%) of the coyote population in our study area was killed by cougars annually, suggesting that coyote interactions with cougars are a complex behavioral game of risk and reward. In contrast, we found no indication that bobcat space use or diet was influenced by cougars. Black bears avoided cougars, but there was no evidence of attraction to cougar kill sites and much lower levels of ungulate consumption and carcass visitation than for coyotes. Interspecific interactions among carnivores are multifaceted, encompassing both suppression and facilitation.

Locomotor compromises maintain group cohesion in baboon troops on the move

When members of a group differ in locomotor capacity, coordinating collective movement poses a challenge: some individuals may have to move faster (or slower) than their preferred speed to remain together. Such compromises have energetic repercussions, yet research in collective behaviour has largely neglected locomotor consensus costs. Here, we integrate high-resolution tracking of wild baboon locomotion and movement with simulations to demonstrate that size-based variation in locomotor capacity poses an obstacle to the collective movement. While all baboons modulate their gait and move-pause dynamics during collective movement, the costs of maintaining cohesion are disproportionately borne by smaller group members. Although consensus costs are not distributed equally, all group-mates do make locomotor compromises, suggesting a shared decision-making process drives the pace of collective movement in this highly despotic species. These results highlight the importance of considering how social dynamics and locomotor capacity interact to shape the movement ecology of group-living species.

Domestic cats (Felis catus) prefer freely available food over food that requires effort

Contrafreeloading is the willingness of animals to work for food when equivalent food is freely available. This behavior is observed in laboratory, domesticated, and captive animals. However, previous research found that six laboratory cats failed to contrafreeload. We hypothesized that cats would contrafreeload in the home environment when given a choice between a food puzzle and a tray of similar size and shape. We also hypothesized that more active cats would be more likely to contrafreeload. We assessed the behavior of 17 neutered, indoor domestic cats (Felis catus) when presented with both a food puzzle and a tray across ten 30-min trials. Each cat wore an activity tracker, and all sessions were video recorded. Cats ate more food from the free feed tray than the puzzle (t (16) = 6.77, p < 0.001). Cats made more first choices to approach and eat from the tray. There was no relationship between activity and contrafreeloading, and there was no effect of sex, age, or previous food puzzle experience on contrafreeloading. Our results suggest that cats do not show strong tendencies to contrafreeload in the home environment, although some cats (N = 4) ate most food offered in the puzzle or showed weak contrafreeloading tendencies (N = 5). Eight cats did not contrafreeload. Cats who consumed more food from the puzzle, consumed more food in general, suggesting a relationship between hunger and effort. Further research is required to understand why domestic cats, unlike other tested species, do not show a strong preference to work for food.

The Rapid Rise of Next-Generation Natural History

Many ecologists have lamented the demise of natural history and have attributed this decline to a misguided view that natural history is outdated and unscientific. Although there is a perception that the focus in ecology and conservation have shifted away from descriptive natural history research and training toward hypothetico-deductive research, we argue that natural history has entered a new phase that we call “next-generation natural history.” This renaissance of natural history is characterized by technological and statistical advances that aid in collecting detailed observations systematically over broad spatial and temporal extents. The technological advances that have increased exponentially in the last decade include electronic sensors such as camera-traps and acoustic recorders, aircraft- and satellite-based remote sensing, animal-borne biologgers, genetics and genomics methods, and community science programs. Advances in statistics and computation have aided in analyzing a growing quantity of observations to reveal patterns in nature. These robust next-generation natural history datasets have transformed the anecdotal perception of natural history observations into systematically collected observations that collectively constitute the foundation for hypothetico-deductive research and can be leveraged and applied to conservation and management. These advances are encouraging scientists to conduct and embrace detailed descriptions of nature that remain a critically important component of the scientific endeavor. Finally, these next-generation natural history observations are engaging scientists and non-scientists alike with new documentations of the wonders of nature. Thus, we celebrate next-generation natural history for encouraging people to experience nature directly.

A methodological roadmap to quantify animal-vectored spatial ecosystem subsidies

Energy, nutrients and organisms move over landscapes, connecting ecosystems across space and time. Meta-ecosystem theory investigates the emerging properties of local ecosystems coupled spatially by these movements of organisms and matter, by explicitly tracking exchanges of multiple substances across ecosystem borders. To date, meta-ecosystem research has focused mostly on abiotic flows-neglecting biotic nutrient flows. However, recent work has indicated animals act as spatial nutrient vectors when they transport nutrients across landscapes in the form of excreta, egesta and their own bodies. Partly due to its high level of abstraction, there are few empirical tests of meta-ecosystem theory. Furthermore, while animals may be viewed as important mediators of ecosystem functions, better integration of tools is needed to develop predictive insights of their relative roles and impacts on diverse ecosystems. We present a methodological roadmap that explains how to do such integration by discussing how to combine insights from movement, foraging and ecosystem ecology to develop a coherent understanding of animal-vectored nutrient transport on meta-ecosystems processes. We discuss how the slate of newly developed technologies and methods-tracking devices, mechanistic movement models, diet reconstruction techniques and remote sensing-that when integrated have the potential to advance the quantification of animal-vectored nutrient flows and increase the predictive power of meta-ecosystem theory. We demonstrate that by integrating novel and established tools of animal ecology, ecosystem ecology and remote sensing, we can begin to identify and quantify animal-mediated nutrient translocation by large animals. We also provide conceptual examples that show how our proposed integration of methodologies can help investigate ecosystem impacts of large animal movement. We conclude by describing practical advancements to understanding cross-ecosystem contributions of animals on the move. Understanding the mechanisms by which animals shape ecosystem dynamics is important for ongoing conservation, rewilding and restoration initiatives around the world, and for developing more accurate models of ecosystem nutrient budgets. Our roadmap will enable ecologists to better qualify and quantify animal-mediated nutrient translocation for animals on the move.

Variation in the sacroiliac joint in Felidae

Felidae species show a great diversity in their diet, foraging and hunting strategies, from small to large prey. Whether they belong to solitary or group hunters, the behavior of cats to subdue resisting small or large prey presents crucial differences. It is assumed that pack hunting reduces the per capita risk of each individual. We hypothesize that the sacroiliac articulation plays a key role in stabilizing the predator while subduing and killing prey. Using CT-scan from 59 felid coxal bones, we calculated the angle between both iliac articular surfaces. Correlation of this inter-iliac angle with body size was calculated and ecological stressors were evaluated on inter-iliac angle. Body size significantly influences inter-iliac angle with small cats having a wider angle than big cats. Arboreal species have a significantly larger angle compared to cursorial felids with the smallest value, and to scansorial and terrestrial species with intermediate angles. Felids hunting large prey have a smaller angle than felids hunting small and mixed prey. Within the Panthera lineage, pack hunters (lions) have a larger angle than all other species using solitary hunting strategy. According to the inter-iliac angle, two main groups of felids are determined: (i) predators with an angle of around 40° include small cats (i.e., Felis silvestris, Leopardus wiedii, Leptailurus serval, Lynx Canadensis, L. rufus; median = 43.45°), the only pack-hunting species (i.e., Panthera leo; median = 37.90°), and arboreal cats (i.e., L. wiedii, Neofelis nebulosa; median = 49.05°), (ii) predators with an angle of around 30° include solitary-hunting big cats (i.e., Acinonyx jubatus, P. onca, P. pardus, P. tigris, P. uncia; median = 31.80°). We suggest different pressures of selection to interpret these results. The tightening of the iliac wings around the sacrum probably enhances big cats’ ability for high speed and large prey control. In contrast, pack hunting in lions reduced the selective pressure for large prey.

An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers

BACKGROUND

Our understanding of movement patterns and behaviours of wildlife has advanced greatly through the use of improved tracking technologies, including application of accelerometry (ACC) across a wide range of taxa. However, most ACC studies either use intermittent sampling that hinders continuity or continuous data logging relying on tracker retrieval for data downloading which is not applicable for long term study. To allow long-term, fine-scale behavioural research, we evaluated a range of machine learning methods for their suitability for continuous on-board classification of ACC data into behaviour categories prior to data transmission.

METHODS

We tested six supervised machine learning methods, including linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and extreme gradient boosting (XGBoost) to classify behaviour using ACC data from three bird species (white stork Ciconia ciconia, griffon vulture Gyps fulvus and common crane Grus grus) and two mammals (dairy cow Bos taurus and roe deer Capreolus capreolus).

RESULTS

Using a range of quality criteria, SVM, ANN, RF and XGBoost performed well in determining behaviour from ACC data and their good performance appeared little affected when greatly reducing the number of input features for model training. On-board runtime and storage-requirement tests showed that notably ANN, RF and XGBoost would make suitable on-board classifiers.

CONCLUSIONS

Our identification of using feature reduction in combination with ANN, RF and XGBoost as suitable methods for on-board behavioural classification of continuous ACC data has considerable potential to benefit movement ecology and behavioural research, wildlife conservation and livestock husbandry.

Quantifying energetic costs and defining energy landscapes experienced by grizzly bears

Animal movements are major determinants of energy expenditure and ultimately the cost–benefit of landscape use. Thus, we sought to understand those costs and how grizzly bears (Ursus arctos) move in mountainous landscapes. We trained captive grizzly bears to walk on a horizontal treadmill and up and down 10% and 20% slopes. The cost of moving upslope increased linearly with speed and slope angle, and this was more costly than moving horizontally. The cost of downslope travel at slower speeds was greater than the cost of traveling horizontally but appeared to decrease at higher speeds. The most efficient walking speed that minimized cost per unit distance was 1.19±0.11 m s−1. However, grizzly bears fitted with GPS collars in the Greater Yellowstone Ecosystem moved at an average velocity of 0.61±0.28 m s−1 and preferred to travel on near-horizontal slopes at twice their occurrence. When traveling uphill or downhill, grizzly bears chose paths across all slopes that were ∼54% less steep and costly than the maximum available slope. The net costs (J kg−1 m−1) of moving horizontally and uphill were the same for grizzly bears, humans and digitigrade carnivores, but those costs were 46% higher than movement costs for ungulates. These movement costs and characteristics of landscape use determined using captive and wild grizzly bears were used to understand the strategies that grizzly bears use for preying on large ungulates and the similarities in travel between people and grizzly bears that might affect the risk of encountering each other on shared landscapes.

Physiological consequences of Arctic sea ice loss on large marine carnivores: unique responses by polar bears and narwhals

Rapid environmental changes in the Arctic are threatening the survival of marine species that rely on the predictable presence of the sea ice. Two Arctic marine mammal specialists, the polar bear (Ursus maritimus) and narwhal (Monodon monoceros), appear especially vulnerable to the speed and capriciousness of sea ice deterioration as a consequence of their unique hunting behaviors and diet, as well as their physiological adaptations for slow-aerobic exercise. These intrinsic characteristics limit the ability of these species to respond to extrinsic threats associated with environmental change and increased industrial activity in a warming Arctic. In assessing how sea ice loss may differentially affect polar bears that hunt on the ice surface and narwhals that hunt at extreme depths below, we found that major ice loss translated into elevated locomotor costs that range from 3- to 4-fold greater than expected for both species. For polar bears this instigates an energy imbalance from the combined effects of reduced caloric intake and increased energy expenditure. For narwhals, high locomotor costs during diving increase the risk of ice entrapment due to the unreliability of breathing holes. These species-specific physiological constraints and extreme reliance on the polar sea ice conspire to make these two marine mammal specialists sentinels of climate change within the Arctic marine ecosystem that may foreshadow rapid changes to the marine ecosystem.

Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species

BACKGROUND

Inertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective  classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors.

METHODS

We deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: 'flapping flight', 'soaring flight', and 'on-water'. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data.

RESULTS

HMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for 'flapping flight', 'soaring flight' and 'on-water', respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale.

CONCLUSIONS

The use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.

Wolves choose ambushing locations to counter and capitalize on the sensory abilities of their prey

Comprehensive knowledge of ambush behavior requires an understanding of where a predator expects prey to be, which is generally unknowable because ambush predators often hunt mobile prey that exhibit complex, irregular, or inconspicuous movements. Wolves (Canis lupus) are primarily cursorial predators, but they use ambush strategies to hunt beavers (Castor canadensis). Terrestrial beaver activity is predictable because beavers use well-defined, conspicuous habitat features repeatedly. Thus, studying where wolves wait-in-ambush for beavers provides a unique opportunity to understand how predators choose ambush locations in relation to prey activity. We searched 11 817 clusters of GPS locations from wolves in the Greater Voyageurs Ecosystem, International Falls, MN, and documented 748 ambushing sites and 214 instances where wolves killed beavers. Wolves chose ambush locations: 1) with olfactory concealment to avoid detection from the highly developed olfactory senses of beavers and 2) close (generally &lt;5 m) to beaver habitat features to take advantage of beavers’ inability to visually detect motionless predators. Our work describes in detail the ambush strategies wolves use to hunt beavers and continues to overturn the traditional notion that wolves rely solely on cursorial hunting strategies. We also demonstrate that ambush predators can anticipate the movements and behavior of their prey due to a fundamental understanding of their prey’s sensory abilities. Wolves, therefore, and likely ambush predators in general, appear capable of simultaneously accounting for abiotic and biotic factors when choosing ambush locations, ultimately allowing them to counter and capitalize on the sensory abilities of their prey.

Energetics and fear of humans constrain the spatial ecology of pumas

Energetic demands and fear of predators are considered primary factors shaping animal behavior, and both are likely drivers of movement decisions that ultimately determine the spatial ecology of wildlife. Yet energetic constraints on movement imposed by the physical landscape have only been considered separately from those imposed by risk avoidance, limiting our understanding of how short-term movement decisions scale up to affect long-term space use. Here, we integrate the costs of both physical terrain and predation risk into a common currency, energy, and then quantify their effects on the short-term movement and long-term spatial ecology of a large carnivore living in a human-dominated landscape. Using high-resolution GPS and accelerometer data from collared pumas (Puma concolor), we calculated the short-term (i.e., 5-min) energetic costs of navigating both rugged physical terrain and a landscape of risk from humans (major sources of both mortality and fear for our study population). Both the physical and risk landscapes affected puma short-term movement costs, with risk having a relatively greater impact by inducing high-energy but low-efficiency movement behavior. The cumulative effects of short-term movement costs led to reductions of 29% to 68% in daily travel distances and total home range area. For male pumas, long-term patterns of space use were predominantly driven by the energetic costs of human-induced risk. This work demonstrates that, along with physical terrain, predation risk plays a primary role in shaping an animal's "energy landscape" and suggests that fear of humans may be a major factor affecting wildlife movements worldwide.

Thermodynamics of Animal Locomotion

Muscles are biological actuators extensively studied in the frame of Hill's classic empirical model as isolated biomechanical entities, which hardly applies to a living organism subjected to physiological and environmental constraints. Here we elucidate the overarching principle of a living muscle action for locomotion, considering it from the thermodynamic viewpoint as an assembly of actuators (muscle units) connected in parallel, operating via chemical-to-mechanical energy conversion under mixed (potential and flux) boundary conditions. Introducing the energy cost of effort as the generalization of the well-known oxygen cost of transport in the frame of our compact locally linear nonequilibrium thermodynamics model, we analyze oxygen consumption measurement data from a documented experiment on energy cost management and optimization by horses moving at three different gaits. Horses adapt to a particular gait by mobilizing a nearly constant number of muscle units minimizing waste production per unit distance covered; this number significantly changes during transition between gaits. The mechanical function of the animal is therefore determined both by its own thermodynamic characteristics and by the metabolic operating point of the locomotor system.

Where and when to hunt? Decomposing predation success of an ambush carnivore

Predator-prey games emerge when predators and prey dynamically respond to the behavior of one another, driving the outcomes of predator-prey interactions. Predation success is a function of the combined probabilities of encountering and capturing prey, which are influenced by both prey behavior and environmental features. While the relative importance of encounter and capture probabilities have been evaluated in a spatial framework, temporal variation in prey behavior and intrinsic catchability are likely to also affect the distribution of predation events. Using a single-predator-single-prey (puma-vicuña) system, we evaluated which factors predict predation events across both temporal and spatial dimensions of the components of predation by testing the prey-abundance hypothesis (predators select for high encounter probability) and the prey-catchability hypothesis (predators select for high relative capture probability) in time and space. We found that for both temporal and spatial analyses, neither the prey-abundance hypothesis nor the prey-catchability hypothesis alone predicted kill frequency or distribution; puma kill frequency was static throughout the diel cycle and pumas consistently selected a single habitat type when hunting, despite temporal and spatial variation in encounter rates and intrinsic catchability. Our integrated spatiotemporal analysis revealed that an interaction between time of day and habitat influences kill probability, suggesting that trade-offs in the temporal and spatial components of predation drive the probability of predation events. These findings reinforce the importance of examining both the temporal and spatial patterns of the components of predation, rather than unidimensional measures of predator or prey behavior, to comprehensively describe the feedbacks between predator and prey in the predator-prey game.

Less is more: On-board lossy compression of accelerometer data increases biologging capacity

GPS‐tracking devices have been used in combination with a wide range of additional sensors to study animal behaviour, physiology and interaction with their environment. Tri‐axial accelerometers allow researchers to remotely infer the behaviour of individuals, at all places and times. Collection of accelerometer data is relatively cheap in terms of energy usage, but the amount of raw data collected generally requires much storage space and is particularly demanding in terms of energy needed for data transmission.

Here, we propose compressing the raw accelerometer (ACC) data into summary statistics within the tracking device (before transmission) to reduce data size, as a means to overcome limitations in storage and energy capacity.

We explored this type of lossy data compression in the accelerometer data of tagged Bewick's swans Cygnus columbianus bewickii collected in spring 2017. Using software settings in which bouts of 2 s of both raw ACC data and summary statistics were collected in parallel but with different bout intervals to keep total data size comparable, we created the opportunity for a direct comparison of time budgets derived by the two data collection methods.

We found that the data compression in our case yielded a six times reduction in data size per bout, and concurrent, similar decreases in storage and energy use of the device. We show that with the same accuracy of the behavioural classification, the freed memory and energy of the device can be used to increase the monitoring effort, resulting in a more detailed representation of the individuals’ time budget. Rare and/or short behaviours, such as daily roost flights, were picked up significantly more when collecting summary statistics instead of raw ACC data (but note differences in sampling rate). Such level of detail can be of essential importance, for instance to make a reliable estimate of the energy budgets of individuals.

In conclusion, we argue that this type of lossy data compression can be a well‐considered choice in study situations where limitations in energy and storage space of the device pose a problem. Ultimately, these developments can allow for long‐term and nearly continuous remote monitoring of the behaviour of free‐ranging animals.

Surviving in steep terrain: a lab-to-field assessment of locomotor costs for wild mountain lions (Puma concolor)

BACKGROUND

Under current scenarios of climate change and habitat loss, many wild animals, especially large predators, are moving into novel energetically challenging environments. Consequently, changes in terrain associated with such moves may heighten energetic costs and effect the decline of populations in new localities.

METHODS

To examine locomotor costs of a large carnivorous mammal moving in mountainous habitats, the oxygen consumption of captive pumas (Puma concolor) was measured during treadmill locomotion on level and incline (6.8°) surfaces. These data were used to predict energetic costs of locomotor behaviours of free-ranging pumas equipped with GPS/accelerometer collars in California's Santa Cruz Mountains.

RESULTS

Incline walking resulted in a 42.0% ± 7.2 SEM increase in the costs of transport compared to level performance. Pumas negotiated steep terrain by traversing across hillsides (mean hill incline 17.2° ± 0.3 SEM; mean path incline 7.3° ± 0.1 SEM). Pumas also walked more slowly up steeper paths, thereby minimizing the energetic impact of vertical terrains. Estimated daily energy expenditure (DEE) based on GPS-derived speeds of free-ranging pumas was 18.3 MJ day- 1 ± 0.2 SEM. Calculations show that a 20 degree increase in mean steepness of the terrain would increase puma DEE by less than 1% as they only spend a small proportion (10%) of their day travelling. They also avoided elevated costs by utilizing slower speeds and shallower path angles.

CONCLUSIONS

While many factors influence survival in novel habitats, we illustrate the importance of behaviours which reduce locomotor costs when traversing new, energetically challenging environments, and demonstrate that these behaviours are utilised by pumas in the wild.

Inter-individual differences in foraging tactics of a colonial raptor: consistency, weather effects, and fitness correlates

BACKGROUND

Consistent inter-individual differences in behavioural phenotypes may entail differences in energy efficiency and expenditure, with different fitness payoffs. In colonial-breeding species, inter-individual differences in foraging behaviour may evolve to reduce resource use overlap among conspecifics exploiting shared foraging areas. Furthermore, individual differences in foraging behaviour may covary with individual characteristics, such as sex or physiological conditions.

METHODS

We investigated individual differences in foraging tactics of a colonial raptor, the lesser kestrel (Falco naumanni). We tracked foraging trips of breeding individuals using miniaturized biologgers. We classified behaviours from GPS data and identified tactics at the foraging trip level by cluster analysis. We then estimated energy expenditure associated to each tactic from tri-axial accelerometer data.

RESULTS

We obtained 489 foraging trips by 36 individuals. Two clusters of trips were identified, one (SF) characterized by more static foraging behaviour and the other (DF) by more dynamic foraging behaviour, with a higher proportion of flying activity and a higher energy expenditure compared to SF. Lesser kestrels showed consistent inter-individual differences in foraging tactics across weather condition gradients, favouring DF trips as solar radiation and crosswind intensity increased. DF trips were more frequent during the nestling-rearing than during the egg incubation stage. Nestlings whose tracked parent was more prone to perform DF trips experienced higher daily mass increase, irrespective of nestling feeding rates.

CONCLUSIONS

Our study provided evidence that breeding lesser kestrels flexibly adopted different foraging tactics according to contingent weather landscapes, with birds showing consistent inter-individual differences in the tendency to adopt a given tactic. The positive correlation between the tendency to perform more energy-demanding DF trips and nestling growth suggests that individual differences in foraging behaviour may play a role in maintaining key life-history trade-offs between reproduction and self-maintenance.

Accounting for space and uncertainty in real-time location system-derived contact networks

Point data obtained from real-time location systems (RTLSs) can be processed into animal contact networks, describing instances of interaction between tracked individuals. Proximity-based definitions of interanimal "contact," however, may be inadequate for describing epidemiologically and sociologically relevant interactions involving body parts or other physical spaces relatively far from tracking devices. This weakness can be overcome by using polygons, rather than points, to represent tracked individuals and defining "contact" as polygon intersections.We present novel procedures for deriving polygons from RTLS point data while maintaining distances and orientations associated with individuals' relocation events. We demonstrate the versatility of this methodology for network modeling using two contact network creation examples, wherein we use this procedure to create (a) interanimal physical contact networks and (b) a visual contact network. Additionally, in creating our networks, we establish another procedure to adjust definitions of "contact" to account for RTLS positional accuracy, ensuring all true contacts are likely captured and represented in our networks.Using the methods described herein and the associated R package we have developed, called contact, researchers can derive polygons from RTLS points. Furthermore, we show that these polygons are highly versatile for contact network creation and can be used to answer a wide variety of epidemiological, ethological, and sociological research questions.By introducing these methodologies and providing the means to easily apply them through the contact R package, we hope to vastly improve network-model realism and researchers' ability to draw inferences from RTLS data.

Machine learning goes wild: Using data from captive individuals to infer wildlife behaviours

1. Remotely tracking distinct behaviours of animals using acceleration data and machine learning has been carried out successfully in several species in captive settings. In order to study the ecology of animals in natural habitats, such behaviour classification models need to be transferred to wild individuals. However, at present, the development of those models usually requires direct observation of the target animals. 2. The goal of this study was to infer the behaviour of wild, free-roaming animals from acceleration data by training behaviour classification models on captive individuals, without the necessity to observe their wild conspecifics. We further sought to develop methods to validate the credibility of the resulting behaviour extrapolations. 3. We trained two machine learning algorithms proposed by the literature, Random Forest (RF) and Support Vector Machine (SVM), on data from captive red foxes (Vulpes vulpes) and later applied them to data from wild foxes. We also tested a new advance for behaviour classification, by applying a moving window to an Artificial Neural Network (ANN). Finally, we investigated four strategies to validate our classification output. 4. While all three machine learning algorithms performed well under training conditions (Kappa values: RF (0.82), SVM (0.78), ANN (0.85)), the established methods, RF and SVM, failed in classifying distinct behaviours when transferred from captive to wild foxes. Behaviour classification with the ANN and a moving window, in contrast, inferred distinct behaviours and showed consistent results for most individuals. 5. Our approach is a substantial improvement over the methods previously proposed in the literature as it generated plausible results for wild fox behaviour. We were able to infer the behaviour of wild animals that have never been observed in the wild and to further illustrate the credibility of the output. This framework is not restricted to foxes but can be applied to infer the behaviour of many other species and thus empowers new advances in behavioural ecology.