Slowness and acceleration: a new method to quantify the activity budget of chelonians

Quantifying allo-grooming in wild chacma baboons (Papio ursinus) using tri-axial acceleration data and machine learning

Quantification of activity budgets is pivotal for understanding how animals respond to changes in their environment. Social grooming is a key activity that underpins various social processes with consequences for health and fitness. Traditional methods use direct (focal) observations to calculate grooming rates, providing systematic but sparse data. Accelerometers, in contrast, can quantify activity budgets continuously but have not been used to quantify social grooming. We test whether grooming can be accurately identified using machine learning (random forest model) trained on labelled acceleration data from wild chacma baboons (Papio ursinus). We successfully identified giving and receiving grooming with high precision (81% and 91%) and recall (87% and 79%). Giving grooming was associated with a distinct rhythmical signal along the surge axis. Receiving grooming had similar acceleration signals to resting, and thus was more difficult to assign. We applied our machine learning model to n = 680 collar data days from n = 12 baboons and found that grooming rates obtained from accelerometers were significantly and positively correlated with direct observation rates for giving but not receiving grooming. The ability to collect continuous grooming data in wild populations will allow researchers to re-examine and expand upon long-standing questions regarding the formation and function of grooming bonds.

Developing a classification system to assign activity states to two species of freshwater turtles

Research in ecology often requires robust assessment of animal behaviour, but classifying behavioural patterns in free-ranging animals and in natural environments can be especially challenging. New miniaturised bio-logging devices such as accelerometers are increasingly available to record animal behaviour remotely, and thereby address the gap in knowledge related to behaviour of free-ranging animals. However, validation of these data is rarely conducted and classification model transferability across closely-related species is often not tested. Here, we validated accelerometer and water sensor data to classify activity states in two free-ranging freshwater turtle species (Blanding's turtle, Emydoidea blandingii, and Painted turtle, Chrysemys picta). First, using only accelerometer data, we developed a decision tree to separate motion from motionless states, and second, we included water sensor data to classify the animal as being motionless or in-motion on land or in water. We found that accelerometers separated in-motion from motionless behaviour with > 83% accuracy, whereas models also including water sensor data predicted states in terrestrial and aquatic locations with > 77% accuracy. Despite differences in values separating activity states between the two species, we found high model transferability allowing cross-species application of classification models. Note that reducing sampling frequency did not affect predictive accuracy of our models up to a sampling frequency of 0.0625 Hz. We conclude that the use of accelerometers in animal research is promising, but requires prior data validation and development of robust classification models, and whenever possible cross-species assessment should be conducted to establish model generalisability.

Evaluation of locomotor activity in female Chelonoidis chilensis (Testudinidae, Gray 1870) in response to artificial photoperiod and temperature treatments

Turtles as many other reptiles are capable of orientating their bodies toward the sun. This conduct requires the presence of an internal biological chronometer in the organism that regulates this behavior. Thus, a description of the internal clock in these reptiles is of interest. The assessment of locomotor activity can be considered a reliable indicator of biological clock function. This study aimed to investigate the effect of different artificial photoperiod and ambient temperature schedules on total locomotor activity of female Chelonoidis chilensis and its rhythmicity. Six C. chilensis specimens were exposed to different artificial photoperiods and temperature regimes each fixed for seven days. It was observed that the activity period during the different experimental schedules was close to the 24 hours indicating a daily rhythmicity. Moreover, all tortoises showed a similar total locomotor activity pattern displaying the most of motion during light phase. Under the condition of constant light tortoises exhibited a self-sustaining rhythm not entrained to light and temperature zeitgebers, thus, suggesting its possible endogenous periodicity. Though this study deepens the knowledge on the rhythmic system of C. chilensis, further investigations are needed to achieve a more detailed understanding of tortoise biological clock.

Host-parasite association of Placobdella costata (Glossiphoniidae: Hirudinea) and Mauremys leprosa (Geoemydidae: Testudinoidea) in aquatic ecosystems of Morocco

Emys orbicularis (Linnaeus, 1758) was considered as a specific host of Placobdella costata (Fr. Mûller, 1846). However, since the parasite was recorded from outside the distribution area of its host, some authors suggested a possible relationship with other hosts. Although two accidental associations were found with another turtle, Mauremys leprosa (Schweigger, 1812), the obtained data remain insufficient to better understand this discovered host-parasite ecological system. In this context, the present study was carried out to evaluate the relationship between the Mediterranean pond turtle, M. leprosa, and the freshwater rhynchobdellid leech, P. costata (Hirudinida: Glossiphoniidae), in aquatic ecosystems of Morocco. During the period from April to June 2018, we found leeches attached to turtles in five out of 30 populations sampled with a prevalence of infection significantly higher in adult than that in juvenile turtles. Moreover, the males are the most infested with 51% of the total, followed by females (33.3%) and juveniles (15.7%). The obtained results indicated that 51 turtles were infested by 139 leeches with a mean intensity of infestation of 4.17 ± 0.47 leeches/turtle (up to 10 leeches/turtle). It was higher in males than that in females in almost all sites. The posterior limbs are the most preferred attachment site, and the body condition of turtles was not affected by the intensity of infestation but it is rather a function of altitude. Our findings proved that M. leprosa-P. costata association is more than accidental and that M. leprosa is rather the main host of P. costata in aquatic ecosystems of Morocco.

Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology

The identification of sea turtle behaviours is a prerequisite to predicting the activities and time-budget of these animals in their natural habitat over the long term. However, this is hampered by a lack of reliable methods that enable the detection and monitoring of certain key behaviours such as feeding. This study proposes a combined approach that automatically identifies the different behaviours of free-ranging sea turtles through the use of animal-borne multi-sensor recorders (accelerometer, gyroscope and time-depth recorder), validated by animal-borne video-recorder data. We show here that the combination of supervised learning algorithms and multi-signal analysis tools can provide accurate inferences of the behaviours expressed, including feeding and scratching behaviours that are of crucial ecological interest for sea turtles. Our procedure uses multi-sensor miniaturized loggers that can be deployed on free-ranging animals with minimal disturbance. It provides an easily adaptable and replicable approach for the long-term automatic identification of the different activities and determination of time-budgets in sea turtles. This approach should also be applicable to a broad range of other species and could significantly contribute to the conservation of endangered species by providing detailed knowledge of key animal activities such as feeding, travelling and resting.

Coping styles in capital breeders modulate behavioural trade-offs in time allocation: assessing fine-scale activity budgets in lactating grey seals (Halichoerus grypus) using accelerometry and heart rate variability

Abstract

Balancing time allocation among competing behaviours is an essential part of energy management for all animals. However, trade-offs in time allocation may vary according to the sex of the individual, their age, and even underlying physiology. During reproduction, higher energetic demands and constrained internal resources place greater demand on optimizing these trade-offs insofar that small adjustments in time-activity may lead to substantial effects on an individual’s limited energy budget. The most extreme case is found in animals that undergo capital breeding, where individuals fast for the duration of each reproductive episode. We investigated potential underlying drivers of time-activity and describe aspects of trade-offs in time-activity in a wild, capital breeding pinniped, the grey seal Halichoerus grypus, during the lactation period. For the first time, we were able to access full 24-h activity budgets across the core duration of lactation as well as characterize how aspects of stress-coping styles influence time allocation through the use of animal-borne accelerometers and heart rate monitors in situ. We found that there was a distinct trade-off in time activity between time spent Resting and Alert (vigilance). This trade-off varied with the pup’s development, date, and maternal stress-coping style as indicated by a measure of heart rate variability, rMSSD. In contrast, time spent Presenting/Nursing did not vary across the duration of lactation given the variables tested. We suggest that while mothers balance time spent conserving resources (Resting) against time expending energy (Alert), they are also influenced by the inherent physiological drivers of stress-coping styles.

Significance statement

How animals apportion their time among different behaviours is key to their success. These trade-offs should be finely balanced to avoid unnecessary energy expenditure. Here, we examine how grey seal mothers balance their activity patterns during the short, but energetically demanding, period of pup-rearing. Animal-borne accelerometers provided a uniquely detailed and continuous record of activity during pup-rearing for 38 mothers. We also used heart rate monitors to provide measures of each individual’s stress-coping style. We found that mothers balance time Resting against remaining Alert while time Presenting/Nursing was largely independent of all factors measured. Stress-coping styles were found to drive the balancing and variation of all behaviours. This novel indication that differences in personality-like traits may drive whole activity budgets should be considered when assessing trade-offs in time allocation across a much wider variety of species.

Method for Classifying Behavior of Livestock on Fenced Temperate Rangeland in Northern China

Different livestock behaviors have distinct effects on grassland degradation. However, because direct observation of livestock behavior is time- and labor-intensive, an automated methodology to classify livestock behavior according to animal position and posture is necessary. We applied the Random Forest algorithm to predict livestock behaviors in the Horqin Sand Land by using Global Positioning System (GPS) and tri-axis accelerometer data and then confirmed the results through field observations. The overall accuracy of GPS models was 85% to 90% when the time interval was greater than 300–800 s, which was approximated to the tri-axis model (96%) and GPS-tri models (96%). In the GPS model, the linear backward or forward distance were the most important determinants of behavior classification, and nongrazing was less than 30% when livestock travelled more than 30–50 m over a 5-min interval. For the tri-axis accelerometer model, the anteroposterior acceleration (–3 m/s²) of neck movement was the most accurate determinant of livestock behavior classification. Using instantaneous acceleration of livestock body movement more precisely classified livestock behaviors than did GPS location-based distance metrics. When a tri-axis model is unavailable, GPS models will yield sufficiently reliable classification accuracy when an appropriate time interval is defined.

Kinematic signatures of prey capture from archival tags reveal sex differences in killer whale foraging activity

Studies of odontocete foraging ecology have been limited by the challenges of observing prey capture events and outcomes underwater. We sought to determine whether subsurface movement behavior recorded from archival tags could accurately identify foraging events by fish-eating killer whales. We used multisensor bio-logging tags attached by suction cups to Southern Resident killer whales (Orcinus orca) to: (1) identify a stereotyped movement signature that co-occurred with visually confirmed prey capture dives; (2) construct a prey capture dive detector and validate it against acoustically confirmed prey capture dives; and (3) demonstrate the utility of the detector by testing hypotheses about foraging ecology. Predation events were significantly predicted by peaks in the rate of change of acceleration (‘jerk peak’), roll angle and heading variance. Detection of prey capture dives by movement signatures enabled substantially more dives to be included in subsequent analyses compared with previous surface or acoustic detection methods. Males made significantly more prey capture dives than females and more dives to the depth of their preferred prey, Chinook salmon. Additionally, only half of the tag deployments on females (5 out of 10) included a prey capture dive, whereas all tag deployments on males exhibited at least one prey capture dive (12 out of 12). This dual approach of kinematic detection of prey capture coupled with hypothesis testing can be applied across odontocetes and other marine predators to investigate the impacts of social, environmental and anthropogenic factors on foraging ecology.

Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data

Accelerometers are becoming ever more important sensors in animal-attached technology, providing data that allow determination of body posture and movement and thereby helping to elucidate behaviour in animals that are difficult to observe. We sought to validate the identification of sea turtle behaviours from accelerometer signals by deploying tags on the carapace of a juvenile loggerhead (Caretta caretta), an adult hawksbill (Eretmochelys imbricata) and an adult green turtle (Chelonia mydas) at Aquarium La Rochelle, France. We recorded tri-axial acceleration at 50 Hz for each species for a full day while two fixed cameras recorded their behaviours. We identified behaviours from the acceleration data using two different supervised learning algorithms, Random Forest and Classification And Regression Tree (CART), treating the data from the adult animals as separate from the juvenile data. We achieved a global accuracy of 81.30% for the adult hawksbill and green turtle CART model and 71.63% for the juvenile loggerhead, identifying 10 and 12 different behaviours, respectively. Equivalent figures were 86.96% for the adult hawksbill and green turtle Random Forest model and 79.49% for the juvenile loggerhead, for the same behaviours. The use of Random Forest combined with CART algorithms allowed us to understand the decision rules implicated in behaviour discrimination, and thus remove or group together some 'confused' or under--represented behaviours in order to get the most accurate models. This study is the first to validate accelerometer data to identify turtle behaviours and the approach can now be tested on other captive sea turtle species.

Sex-specific ecophysiological responses to environmental fluctuations of free-ranging Hermann's tortoises: implication for conservation

Physiological parameters provide indicators to evaluate how organisms respond to conservation actions. For example, individuals translocated during reinforcement programmes may not adapt to their novel host environment and may exhibit elevated chronic levels of stress hormones and/or decreasing body condition. Conversely, successful conservation actions should be associated with a lack of detrimental physiological perturbation. However, physiological references fluctuate over time and are influenced by various factors (e.g. sex, age, reproductive status). It is therefore necessary to determine the range of natural variations of the selected physiological metrics to establish useful baselines. This study focuses on endangered free-ranging Hermann's tortoises (Testudo hermanni hermanni), where conservation actions have been preconized to prevent extinction of French mainland populations. The influence of sex and of environmental factors (site, year and season) on eight physiological parameters (e.g. body condition, corticosterone concentrations) was assessed in 82 individuals from two populations living in different habitats. Daily displacements were monitored by radio-tracking. Most parameters varied between years and seasons and exhibited contrasting sex patterns but with no or limited effect of site. By combining behavioural and physiological traits, this study provides sex-specific seasonal baselines that can be used to monitor the health status of Hermann's tortoises facing environmental threats (e.g. habitat changes) or during conservation actions (e.g. translocation). These results might also assist in selection of the appropriate season for translocation.

Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales

The introduction of animal‐borne, multisensor tags has opened up many opportunities for ecological research, making previously inaccessible species and behaviors observable. The advancement of tag technology and the increasingly widespread use of bio‐logging tags are leading to large volumes of sometimes extremely detailed data. With the increasing quantity and duration of tag deployments, a set of tools needs to be developed to aid in facilitating and standardizing the analysis of movement sensor data. Here, we developed an observation‐based decision tree method to detect feeding events in data from multisensor movement tags attached to fin whales (Balaenoptera physalus). Fin whales exhibit an energetically costly and kinematically complex foraging behavior called lunge feeding, an intermittent ram filtration mechanism. Using this automated system, we identified feeding lunges in 19 fin whales tagged with multisensor tags, during a total of over 100 h of continuously sampled data. Using movement sensor and hydrophone data, the automated lunge detector correctly identified an average of 92.8% of all lunges, with a false‐positive rate of 9.5%. The strong performance of our automated feeding detector demonstrates an effective, straightforward method of activity identification in animal‐borne movement tag data. Our method employs a detection algorithm that utilizes a hierarchy of simple thresholds based on knowledge of observed features of feeding behavior, a technique that is readily modifiable to fit a variety of species and behaviors. Using automated methods to detect behavioral events in tag records will significantly decrease data analysis time and aid in standardizing analysis methods, crucial objectives with the rapidly increasing quantity and variety of on‐animal tag data. Furthermore, our results have implications for next‐generation tag design, especially long‐term tags that can be outfitted with on‐board processing algorithms that automatically detect kinematic events and transmit ethograms via acoustic or satellite telemetry.

Using accelerometers to remotely and automatically characterize behavior in small animals

Activity budgets in wild animals are challenging to measure via direct observation because data collection is time consuming and observer effects are potentially confounding. Although tri-axial accelerometers are increasingly employed for this purpose, their application in small-bodied animals has been limited by weight restrictions. Additionally, accelerometers engender novel complications, as a system is needed to reliably map acceleration to behaviors. In this study, we describe newly developed, tiny acceleration-logging devices (1.5-2.5 g) and use them to characterize behavior in two chipmunk species. We collected paired accelerometer readings and behavioral observations from captive individuals. We then employed techniques from machine learning to develop an automatic system for coding accelerometer readings into behavioral categories. Finally, we deployed and recovered accelerometers from free-living, wild chipmunks. This is the first time to our knowledge that accelerometers have been used to generate behavioral data for small-bodied (<100 g), free-living mammals.

The Use of Acceleration to Code for Animal Behaviours; A Case Study in Free-Ranging Eurasian Beavers Castor fiber

Recent technological innovations have led to the development of miniature, accelerometer-containing electronic loggers which can be attached to free-living animals. Accelerometers provide information on both body posture and dynamism which can be used as descriptors to define behaviour. We deployed tri-axial accelerometer loggers on 12 free-ranging Eurasian beavers Castor fiber in the county of Telemark, Norway, and on four captive beavers (two Eurasian beavers and two North American beavers C. canadensis) to corroborate acceleration signals with observed behaviours. By using random forests for classifying behavioural patterns of beavers from accelerometry data, we were able to distinguish seven behaviours; standing, walking, swimming, feeding, grooming, diving and sleeping. We show how to apply the use of acceleration to determine behaviour, and emphasise the ease with which this non-invasive method can be implemented. Furthermore, we discuss the strengths and weaknesses of this, and the implementation of accelerometry on animals, illustrating limitations, suggestions and solutions. Ultimately, this approach may also serve as a template facilitating studies on other animals with similar locomotor modes and deliver new insights into hitherto unknown aspects of behavioural ecology.

Supervised accelerometry analysis can identify prey capture by penguins at sea

Determining where, when and how much animals eat is fundamental to understanding their ecology. We developed a technique to identify a prey capture signature for little penguins from accelerometry, in order to quantify food intake remotely. We categorised behaviour of captive penguins from HD video and matched this to time-series data from back-mounted accelerometers. We then trained a support vector machine (SVM) to classify the penguins' behaviour at 0.3 s intervals as either 'prey handling' or 'swimming'. We applied this model to accelerometer data collected from foraging wild penguins to identify prey capture events. We compared prey capture and non-prey capture dives to test the model predictions against foraging theory. The SVM had an accuracy of 84.95±0.26% (mean ± s.e.) and a false positive rate of 9.82±0.24% when tested on unseen captive data. For wild data, we defined three independent, consecutive prey handling observations as representing true prey capture, with a false positive rate of 0.09%. Dives with prey captures had longer duration and bottom times, were deeper, had faster ascent rates, and had more 'wiggles' and 'dashes' (proxies for prey encounter used in other studies). The mean (±s.e.) number of prey captures per foraging trip was 446.6±66.28. By recording the behaviour of captive animals on HD video and using a supervised machine learning approach, we show that accelerometry signatures can classify the behaviour of wild animals at unprecedentedly fine scales.

Love thy neighbour: automatic animal behavioural classification of acceleration data using the K-nearest neighbour algorithm

Researchers hoping to elucidate the behaviour of species that aren't readily observed are able to do so using biotelemetry methods. Accelerometers in particular are proving particularly effective and have been used on terrestrial, aquatic and volant species with success. In the past, behavioural modes were detected in accelerometer data through manual inspection, but with developments in technology, modern accelerometers now record at frequencies that make this impractical. In light of this, some researchers have suggested the use of various machine learning approaches as a means to classify accelerometer data automatically. We feel uptake of this approach by the scientific community is inhibited for two reasons; 1) Most machine learning algorithms require selection of summary statistics which obscure the decision mechanisms by which classifications are arrived, and 2) they are difficult to implement without appreciable computational skill. We present a method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN). Raw acceleration data may be used in KNN without selection of summary statistics, and it is easily implemented using the freeware program R. The method is evaluated by detecting 5 behavioural modes in 8 species, with examples of quadrupedal, bipedal and volant species. Accuracy and Precision were found to be comparable with other, more complex methods. In order to assist in the application of this method, the script required to run KNN analysis in R is provided. We envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.

Optimizing acceleration-based ethograms: the use of variable-time versus fixed-time segmentation

BACKGROUND

Animal-borne accelerometers measure body orientation and movement and can thus be used to classify animal behaviour. To univocally and automatically analyse the large volume of data generated, we need classification models. An important step in the process of classification is the segmentation of acceleration data, i.e. the assignment of the boundaries between different behavioural classes in a time series. So far, analysts have worked with fixed-time segments, but this may weaken the strength of the derived classification models because transitions of behaviour do not necessarily coincide with boundaries of the segments. Here we develop random forest automated supervised classification models either built on variable-time segments generated with a so-called 'change-point model', or on fixed-time segments, and compare for eight behavioural classes the classification performance. The approach makes use of acceleration data measured in eight free-ranging crab plovers Dromas ardeola.

RESULTS

Useful classification was achieved by both the variable-time and fixed-time approach for flying (89% vs. 91%, respectively), walking (88% vs. 87%) and body care (68% vs. 72%). By using the variable-time segment approach, significant gains in classification performance were obtained for inactive behaviours (95% vs. 92%) and for two major foraging activities, i.e. handling (84% vs. 77%) and searching (78% vs. 67%). Attacking a prey and pecking were never accurately classified by either method.

CONCLUSION

Acceleration-based behavioural classification can be optimized using a variable-time segmentation approach. After implementing variable-time segments to our sample data, we achieved useful levels of classification performance for almost all behavioural classes. This enables behaviour, including motion, to be set in known spatial contexts, and the measurement of behavioural time-budgets of free-living birds with unprecedented coverage and precision. The methods developed here can be easily adopted in other studies, but we emphasize that for each species and set of questions, the presented string of work steps should be run through.

Body condition and habitat use by Hermann's tortoises in burnt and intact habitats

In Mediterranean regions, fires threaten terrestrial tortoises. Nevertheless, varying proportions of adults survive fire; these surviving individuals can play a central role for population recovery. The regions devastated by fire often include important habitat of Hermann's tortoises (Testudo hermanni hermanni), so assessing the ability of survivors to persist is essential for conserving the species. Body-condition indices provide an integrative estimate of how well individuals cope with environmental variations and impacts, including fires. Between 2002 and 2009, we monitored Hermann's tortoises in intact and burnt habitats in southeastern France. In summer 2003, a strong fire ravaged half of the surveyed zone, providing an opportunity to compare body condition of tortoises between intact and burnt areas over time. Six years later, the impact of fire on vegetation was still marked; large trees were abundant in the intact area, whereas open shrub vegetation prevailed in the burnt area. In both areas, the mean body condition of tortoises fluctuated over time; however, there were no differences between the two areas. A radio-tracking experiment demonstrated that individuals from each area were residents, and not vagrants commuting between areas. We also assessed changes in body condition and microhabitat use in radio-tracked individuals. We found no significant differences between the tortoises living in the burnt and intact areas, despite subtle differences in habitat use.

IN CONCLUSION

(i) surviving tortoises in an area ravaged by fire can maintain their body condition like individuals living in an intact area, and thus, individuals from burnt areas should not be translocated to supposedly better areas; and (ii) depopulated burnt areas are likely to be appropriate for population-augmentation programmes.

Do researchers impact their study populations? Assessing the effect of field procedures in a long term population monitoring of sea kraits

Long term population monitoring is essential to ecological studies; however, field procedures may disturb individuals. Assessing this topic is important in worldwide declining taxa such as reptiles. Previous studies focussed on animal welfare issues and examined short-term effects (e.g. increase of stress hormones due to handling). Long-term effects with possible consequences at the population level remain poorly investigated. In the present study, we evaluated the effects of widely used field procedures (e.g. handling, marking, forced regurgitation) both on short-term (hormonal stress response) and on long-term (changes in body condition, survival) scales in two intensively monitored populations of sea kraits (Laticauda spp.) in New Caledonia. Focusing on the most intensively monitored sites, from 2002 to 2012, we gathered approximately 11 200 captures/recaptures on 4500 individuals. Each snake was individually marked (scale clipping + branding) and subjected to various measurements (e.g. body size, head morphology, palpation). In addition, a subsample of more than 500 snakes was forced to regurgitate their prey for dietary analyses. Handling caused a significant stress hormonal response, however we found no detrimental long-term effect on body condition. Forced regurgitation did not cause any significant effect on both body condition one year later and survival. These results suggest that the strong short-term stress provoked by field procedures did not translate into negative effects on the population. Although similar analyses are required to test the validity of our conclusions in other species, our results suggest distinguishing welfare and population issues to evaluate the potential impact of population surveys.

Does testosterone influence activity budget in the male Greek tortoise (Testudo graeca graeca)?

The stimulatory effect of testosterone on male sexual activity is one of the clearest examples linking hormones and behaviors. However, this relationship is complex in Chelonians. We experimentally studied the influence of testosterone levels on the activity budget and space use in male Greek tortoises (Testudo graeca graeca) during the spring mating season. We first described the annual pattern of changes in plasma testosterone levels in free-ranging animals in Morocco. Two peaks, one in winter and one in summer, corresponded to periods of inactivity; whereas mating periods in spring and to a lesser extent in autumn were associated with low plasma testosterone levels. Second, we experimentally manipulated plasma testosterone levels in free-ranging males, and analyzed the behavioral consequences. The strong contrasts in plasma hormone levels induced by the experimental treatments did not result in changes in activity budget or space use, both in the short-term or more than one month after the beginning of the hormonal treatment. Our results suggest that testosterone levels did not influence directly behavioral activity in this species, either immediately or after a time delay of one month.

Resources and energetics determined dinosaur maximal size

Some dinosaurs reached masses that were approximately 8 times those of the largest, ecologically equivalent terrestrial mammals. The factors most responsible for setting the maximal body size of vertebrates are resource quality and quantity, as modified by the mobility of the consumer, and the vertebrate's rate of energy expenditure. If the food intake of the largest herbivorous mammals defines the maximal rate at which plant resources can be consumed in terrestrial environments and if that limit applied to dinosaurs, then the large size of sauropods occurred because they expended energy in the field at rates extrapolated from those of varanid lizards, which are approximately 22% of the rates in mammals and 3.6 times the rates of other lizards of equal size. Of 2 species having the same energy income, the species that uses the most energy for mass-independent maintenance of necessity has a smaller size. The larger mass found in some marine mammals reflects a greater resource abundance in marine environments. The presumptively low energy expenditures of dinosaurs potentially permitted Mesozoic communities to support dinosaur biomasses that were up to 5 times those found in mammalian herbivores in Africa today. The maximal size of predatory theropods was approximately 8 tons, which if it reflected the maximal capacity to consume vertebrates in terrestrial environments, corresponds in predatory mammals to a maximal mass less than a ton, which is what is observed. Some coelurosaurs may have evolved endothermy in association with the evolution of feathered insulation and a small mass.

Can ethograms be automatically generated using body acceleration data from free-ranging birds?

An ethogram is a catalogue of discrete behaviors typically employed by a species. Traditionally animal behavior has been recorded by observing study individuals directly. However, this approach is difficult, often impossible, in the case of behaviors which occur in remote areas and/or at great depth or altitude. The recent development of increasingly sophisticated, animal-borne data loggers, has started to overcome this problem. Accelerometers are particularly useful in this respect because they can record the dynamic motion of a body in e.g. flight, walking, or swimming. However, classifying behavior using body acceleration characteristics typically requires prior knowledge of the behavior of free-ranging animals. Here, we demonstrate an automated procedure to categorize behavior from body acceleration, together with the release of a user-friendly computer application, “Ethographer”. We evaluated its performance using longitudinal acceleration data collected from a foot-propelled diving seabird, the European shag, Phalacrocorax aristotelis. The time series data were converted into a spectrum by continuous wavelet transformation. Then, each second of the spectrum was categorized into one of 20 behavior groups by unsupervised cluster analysis, using k-means methods. The typical behaviors extracted were characterized by the periodicities of body acceleration. Each categorized behavior was assumed to correspond to when the bird was on land, in flight, on the sea surface, diving and so on. The behaviors classified by the procedures accorded well with those independently defined from depth profiles. Because our approach is performed by unsupervised computation of the data, it has the potential to detect previously unknown types of behavior and unknown sequences of some behaviors.