Combined use of tri-axial accelerometers and GPS reveals the flexible foraging strategy of a bird in relation to weather conditions

Methodological Selection of Optimal Features for Object Classification Based on Stereovision System

With the expansion of green energy, more and more data show that wind turbines can pose a significant threat to some endangered bird species. The birds of prey are more frequently exposed to collision risk with the wind turbine blades due to their unique flight path patterns. This paper shows how data from a stereovision system can be used for an efficient classification of detected objects. A method for distinguishing endangered birds from common birds and other flying objects has been developed and tested. The research focused on the selection of a suitable feature extraction methodology. Both motion and visual features are extracted from the Bioseco BPS system and retested using a correlation-based and a wrapper-type approach with genetic algorithms (GAs). With optimal features and fine-tuned classifiers, birds can be distinguished from aeroplanes with a 98.6% recall and 97% accuracy, whereas endangered birds are delimited from common ones with 93.5% recall and 77.2% accuracy.

Supervised versus unsupervised approaches to classification of accelerometry data

Sophisticated animal-borne sensor systems are increasingly providing novel insight into how animals behave and move. Despite their widespread use in ecology, the diversity and expanding quality and quantity of data they produce have created a need for robust analytical methods for biological interpretation. Machine learning tools are often used to meet this need. However, their relative effectiveness is not well known and, in the case of unsupervised tools, given that they do not use validation data, their accuracy can be difficult to assess. We evaluated the effectiveness of supervised (n = 6), semi-supervised (n = 1), and unsupervised (n = 2) approaches to analyzing accelerometry data collected from critically endangered California condors (Gymnogyps californianus). Unsupervised K-means and EM (expectation-maximization) clustering approaches performed poorly, with adequate classification accuracies of <0.8 but very low values for kappa statistics (range: -0.02 to 0.06). The semi-supervised nearest mean classifier was moderately effective at classification, with an overall classification accuracy of 0.61 but effective classification only of two of the four behavioral classes. Supervised random forest (RF) and k-nearest neighbor (kNN) machine learning models were most effective at classification across all behavior types, with overall accuracies >0.81. Kappa statistics were also highest for RF and kNN, in most cases substantially greater than for other modeling approaches. Unsupervised modeling, which is commonly used for the classification of a priori-defined behaviors in telemetry data, can provide useful information but likely is instead better suited to post hoc definition of generalized behavioral states. This work also shows the potential for substantial variation in classification accuracy among different machine learning approaches and among different metrics of accuracy. As such, when analyzing biotelemetry data, best practices appear to call for the evaluation of several machine learning techniques and several measures of accuracy for each dataset under consideration.

Opportunistic soaring by birds suggests new opportunities for atmospheric energy harvesting by flying robots

The use of flying robots (drones) is increasing rapidly, but their utility is limited by high power demand, low specific energy storage and poor gust tolerance. By contrast, birds demonstrate long endurance, harvesting atmospheric energy in environments ranging from cluttered cityscapes to open landscapes, coasts and oceans. Here, we identify new opportunities for flying robots, drawing upon the soaring flight of birds. We evaluate mechanical energy transfer in soaring from first principles and review soaring strategies encompassing the use of updrafts (thermal or orographic) and wind gradients (spatial or temporal). We examine the extent to which state-of-the-art flying robots currently use each strategy and identify several untapped opportunities including slope soaring over built environments, thermal soaring over oceans and opportunistic gust soaring. In principle, the energetic benefits of soaring are accessible to flying robots of all kinds, given atmospherically aware sensor systems, guidance strategies and gust tolerance. Hence, while there is clear scope for specialist robots that soar like albatrosses, or which use persistent thermals like vultures, the greatest untapped potential may lie in non-specialist vehicles that make flexible use of atmospheric energy through path planning and flight control, as demonstrated by generalist flyers such as gulls, kites and crows.

Visitation rate, but not foraging range, responds to brood size manipulation in an aerial insectivore

Abstract

Life history theory predicts that increased investment in current offspring decreases future fecundity or survival. Avian parental investment decisions have been studied either via brood size manipulation or direct manipulation of parental energetic costs (also known as handicapping). However, we have limited experimental data on the potential interactive effects of these manipulations on parent behavior. Additionally, we know little about how these manipulations affect spatial foraging behavior away from the nest. We simultaneously manipulated brood size and parental costs (via added weight in the form of a GPS tag) in wild female barn swallows (Hirundo rustica). We measured multiple aspects of parent behavior at and away from the nest while controlling for measures of weather conditions. We found no significant interactive effects of manipulated brood size and parental costs. Both sexes increased their visitation rate with brood size, but nestlings in enlarged broods grew significantly less post-brood size manipulation than those in reduced broods. Foraging range area was highly variable among GPS-tagged females but was unaffected by brood size. As such, increased visitation rate in response to brood size may be more energetically costly for far-ranging females. GPS-tagged females did not alter their visitation rate relative to un-tagged birds, but their mates had higher visitation rates. This suggests that GPS tagging may affect some unmeasured aspect of female behavior, such as prey delivery. Our findings indicate that investigation of foraging tactics alongside visitation rate is critical to understanding parental investment and the benefits and costs of reproduction.

Significance statement

Avian parental investment decisions have been studied by either brood size manipulation or direct manipulation of parental costs, but rarely both simultaneously. We simultaneously manipulated brood size and parental costs (via addition of a GPS tag) in a wild avian system, allowing us to examine interactive effects of these manipulations. Additionally, studies of parental investment often examine behaviors at the nest, but measurements of parental care behavior away from the nest are rare. Our study is unique in that we measured multiple aspects of parental care, including spatial foraging behavior tracked with GPS tags. We found no interactive effects of manipulated brood size and parental costs on visitation rate or nestling growth, and spatial foraging behavior of females was individually variable. Documenting foraging tactics alongside visitation rate is critical to understanding parental investment because the same visitation rate might be more costly for far-ranging females.

Using Machine Learning to Identify Associations between the Environment, Occurrence, and Outcomes of Songbird Displacements at Supplemental Feeders

The context and outcome of aggressive interactions between individuals has important fitness consequences. Displacements—an aggressive interaction wherein one individual is chased from a location by another—also have implications for social hierarchy formation and geographic distribution in songbirds. Morphological correlates, like body size, and social correlates, such as dominance rank, have been shown to mediate displacements in songbirds. However, the role of the physical environment, namely temperature, humidity, and time of day, which may influence an individual’s energy needs and thus displacement motivation, has remained understudied. We monitored songbird feeding and displacement behaviors using computerized automated feeders. We observed asymmetric differences across species in displacement involvement. To identify the conditions of the social and physical environment that are associated with the occurrence and outcome of songbird displacements at supplemental feeders, we use the machine learning approach, random forest, which is a novel method to the fields of ornithology and animal behavior. From our random forest models, we found that the attributes of the physical environment (i.e., humidity and the time of day) are associated with the occurrence of a displacement event, whereas the attributes of the social environment (i.e., species of the displacer and displaced individuals) are associated with which species are involved. These results provide context to develop further observational and experimental hypotheses to tease apart the inner workings of these multifactorial behaviors on a larger scale and provide a proof of concept for our analytical methods in the study of avian behavior.

sspamlr: a toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic or light data in R

Light‐level geolocators have revolutionised the study of animal behaviour. However, lacking spatial precision, their usage has been primary targeted towards the analysis of large‐scale movements. Recent technological developments have allowed the integration of magnetometers and accelerometers into geolocator tags in addition to barometers and thermometers, offering new behavioural insights.

Here, we introduce an R toolbox for identifying behavioural patterns from multisensor geolocator tags, with functions specifically designed for data visualisation, calibration, classification and error estimation. More specifically, the package allows for the flexible analysis of any combination of sensor data using k‐means clustering, expectation maximisation binary clustering, hidden Markov models and changepoint analyses. Furthermore, the package integrates tailored algorithms for identifying periods of prolonged high activity (most commonly used for identifying migratory flapping flight), and pressure changes (most commonly used for identifying dive or flight events).

Finally, we highlight some of the limitations, implications and opportunities of using these methods.

Tri-axial accelerometry shows differences in energy expenditure and parental effort throughout the breeding season in long-lived raptors

Cutting-edge technologies are extremely useful to develop new workflows in studying ecological data, particularly to understand animal behavior and movement trajectories at the individual level. Although parental care is a well-studied phenomenon, most studies have been focused on direct observational or video recording data, as well as experimental manipulation. Therefore, what happens out of our sight still remains unknown. Using high-frequency GPS/GSM dataloggers and tri-axial accelerometers we monitored 25 Bonelli’s eagles Aquila fasciata during the breeding season to understand parental activities from a broader perspective. We used recursive data, measured as number of visits and residence time, to reveal nest attendance patterns of biparental care with role specialization between sexes. Accelerometry data interpreted as the overall dynamic body acceleration, a proxy of energy expenditure, showed strong differences in parental effort throughout the breeding season and between sexes. Thereby, males increased substantially their energetic requirements, due to the increased workload, while females spent most of the time on the nest. Furthermore, during critical phases of the breeding season, a low percentage of suitable hunting spots in eagles’ territories led them to increase their ranging behavior in order to find food, with important consequences in energy consumption and mortality risk. Our results highlight the crucial role of males in raptor species exhibiting biparental care. Finally, we exemplify how biologging technologies are an adequate and objective method to study parental care in raptors as well as to get deeper insight into breeding ecology of birds in general.

Long-distance natal dispersal is relatively frequent and correlated with environmental factors in a widespread raptor

Dispersal is a critical process influencing population dynamics and responses to global change. Long-distance dispersal (LDD) can be especially important for gene flow and adaptability, although little is known about the factors influencing LDD because studying large-scale movements is challenging and LDD tends to be observed less frequently than shorter-distance dispersal (SDD). We sought to understand patterns of natal dispersal at a large scale, specifically aiming to understand the relative frequency of LDD compared to SDD and correlates of dispersal distances. We used bird banding and encounter data for American kestrels (Falco sparverius) to investigate the effects of sex, migration strategy, population density, weather, year and agricultural land cover on LDD frequency, LDD distance and SDD distance in North America from 1961 to 2015. Nearly half of all natal dispersal (48.9%) was LDD (classified as >30 km), and the likelihood of LDD was positively associated with the proportion of agricultural land cover around natal sites. Correlates of distance differed between LDD and SDD movements. LDD distance was positively correlated with latitude, a proxy for migration strategy, suggesting that migratory individuals disperse farther than residents. Distance of LDD in males was positively associated with maximum summer temperature. We did not find sex-bias or an effect of population density in LDD distance or frequency. Within SDD, females tended to disperse farther than males, and distance was positively correlated with density. Sampling affected all responses, likely because local studies more frequently capture SDD within study areas. Our findings that LDD occurs at a relatively high frequency and is related to different proximate factors from SDD, including a lack of sex-bias in LDD, suggest that LDD may be more common than previously reported, and LDD and SDD may be distinct processes rather than two outcomes originating from a single dispersal distribution. To our knowledge, this is the first evidence that LDD and SDD may be separate processes in an avian species, and suggests that environmental change may have different outcomes on the two processes.

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.

Capture, immobilization, and Global Positioning System collaring of olive baboons (Papio anubis) and vervets (Chlorocebus pygerythrus): Lessons learned and suggested best practices

As the value of Global Positioning System (GPS) technology in addressing primatological questions becomes more obvious, more studies will include capturing and collaring primates, with concomitant increased risk of adverse consequences to primate subjects. Here we detail our experiences in capturing, immobilizing, and placing GPS collars on six olive baboons (Papio anubis) in four groups and 12 vervet monkeys (Chlorocebus pygerythrus) in five groups in Kenya. We captured baboons with cage traps and vervets with box traps, immobilized them, and attached GPS collars that were to be worn for 1 year. Adverse consequences from the trapping effort included incidental death of two nonsubjects (an adult female and her dependent infant), temporary rectal prolapse in one baboon, superficial wounds on the crown of the head in two vervets, and failure to recapture/remove collars from two baboons and two vervets. Obvious negative effects from wearing collars were limited to abrasions around the neck of one vervet. A possible, and if so, serious, adverse effect was greater mortality for collared adult female vervets compared with known uncollared adult female vervets, largely due to leopard (Panthera pardus) predation. Collared animals could be more vulnerable to predation because trapping favors bolder individuals, who may also be more vulnerable to predation, or because collars could slow them down or make them more noticeable to predators. Along with recommendations made by others, we suggest that future studies diversify trapping bait to minimize the risk of rectal prolapse, avoid capturing the first individuals to enter traps, test the movement speeds of collared versus noncollared animals, include a release system on the collars to avoid retrapping failure, and publish both positive and negative effects of capturing, immobilizing, and collaring.