Movement ecology and the integration of ecology, behavior and biomechanicsRecent advances in mechanistic modeling and tracking technology have enriched our capacity to disentangle the key parameters affecting movement processes and to characterize movement patterns accurately. These advances set the stage for integrating the four existing paradigms for studying movement -the random, biomechanical, cognitive and optimality approaches ( Fig.1) -in the form of a new cohesive 'movement ecology' framework . The biomechanical paradigm elucidates the machineries that enable individuals or propagules to move, including their physical mechanics, energetics and physiology, and thus focuses on the study of the motion capacity of individual organisms. The cognitive paradigm explores the mechanisms of gathering, processing and responding to the environment in a way that produces nonrandom movement in time and space and thus focuses on the navigation capacity of the individual. The optimality paradigm examines the relative efficacy of different movement strategies in optimizing some particular fitness currencies (e.g. energy gain or survival) over ecological or evolutionary timescales, and thus focuses mostly on the external factors affecting the internal state of the individual. The random paradigm analyzes the fit of observed animal tracks to various random walk models to assess, for example, search efficiency and thus focuses exclusively on the movement patterns. The movement ecology framework explicitly combines these basic components of movement and the links among them ( Fig.1) , which can be identified across all movement types and taxonomic groups . Thus, this framework offers a template for transdisciplinary integration of the four existing movement research paradigms ( Fig.1) to create jointly the new paradigm of movement ecology, devoted to the comprehensive study of all biological (whole-organism) movement phenomena. Movement ecology thus aims at unifying organismal movement research and aiding the development of a general theory of wholeorganism movements . To facilitate this unification, we need tools that can provide simultaneous information about the movement, energy expenditure and behavior of the studied organisms, and the environmental conditions they SummaryIntegrating biomechanics, behavior and ecology requires a mechanistic understanding of the processes producing the movement of animals. This calls for contemporaneous biomechanical, behavioral and environmental data along movement pathways. A recently formulated unifying movement ecology paradigm facilitates the integration of existing biomechanics, optimality, cognitive and random paradigms for studying movement. We focus on the use of tri-axial acceleration (ACC) data to identify behavioral modes of GPS-tracked free-ranging wild animals and demonstrate its application to study the movements of griffon vultures (Gyps fulvus, Hablizl 1783). In particular, we explore a selection of nonlinear and decision tree methods that include support vector mach...

Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal “movement ecology” (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.

Due to the potentially detrimental consequences of low performance in basic functional tasks, individuals are expected to improve performance with age and show the most marked changes during early stages of life. Soaring-gliding birds use rising-air columns (thermals) to reduce energy expenditure allocated to flight. We offer a framework to evaluate thermal soaring performance, and use GPS-tracking to study movements of Eurasian griffon vultures (Gyps fulvus). Because the location and intensity of thermals are variable, we hypothesized that soaring performance would improve with experience and predicted that the performance of inexperienced individuals (<2 months) would be inferior to that of experienced ones (>5 years). No differences were found in body characteristics, climb rates under low wind shear, and thermal selection, presumably due to vultures’ tendency to forage in mixed-age groups. Adults, however, outperformed juveniles in their ability to adjust fine-scale movements under challenging conditions, as juveniles had lower climb rates under intermediate wind shear, particularly on the lee-side of thermal columns. Juveniles were also less efficient along the route both in terms of time and energy. The consequences of these handicaps are probably exacerbated if juveniles lag behind adults in finding and approaching food.

BackgroundThe study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement. Biologgers with Acceleration (ACC) recordings are becoming increasingly popular in the fields of animal behavior and movement ecology, for estimating energy expenditure and identifying behavior, with prospects for other potential uses as well. Supervised learning of behavioral modes from acceleration data has shown promising results in many species, and for a diverse range of behaviors. However, broad implementation of this technique in movement ecology research has been limited due to technical difficulties and complicated analysis, deterring many practitioners from applying this approach. This highlights the need to develop a broadly applicable tool for classifying behavior from acceleration data.DescriptionHere we present a free-access python-based web application called AcceleRater, for rapidly training, visualizing and using models for supervised learning of behavioral modes from ACC measurements. We introduce AcceleRater, and illustrate its successful application for classifying vulture behavioral modes from acceleration data obtained from free-ranging vultures. The seven models offered in the AcceleRater application achieved overall accuracy of between 77.68% (Decision Tree) and 84.84% (Artificial Neural Network), with a mean overall accuracy of 81.51% and standard deviation of 3.95%. Notably, variation in performance was larger between behavioral modes than between models.ConclusionsAcceleRater provides the means to identify animal behavior, offering a user-friendly tool for ACC-based behavioral annotation, which will be dynamically upgraded and maintained.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-014-0027-0) contains supplementary material, which is available to authorized users.

BackgroundThe need to obtain food is a critical proximate driver of an organism’s movement that shapes the foraging and survival of individual animals. Consequently, the relationship between hunger and foraging has received considerable attention, leading to the common conception that hunger primarily enhances a “food-intake maximization” (FIMax) strategy and intensive search. A complementary explanation, however, suggests a trade-off with precautions taken to reduce the risk of physiological collapse from starvation, under a strategy we denote as “energy-expenditure minimization” (EEMin). The FImax-EEmin trade-off may interact with the forager’s hunger level to shape a complex (non-monotonic) response pattern to increasing hunger. Yet, this important trade-off has rarely been investigated, particularly in free-ranging wild animals.We explored how hunger affects the movements of adult griffon vultures (Gyps fulvus) in southern Israel. Transmitters combining GPS and accelerometers provided high-resolution data on vultures’ movements and behavior, enabling the identification of feeding events and the estimation of food deprivation periods (FDPs, measured in days), which is used as a proxy for hunger.ResultsData from 47 vultures, tracked for 339 ± 36 days, reveal high variability in FDPs. While flight speed, flight straightness and the proportion of active flights were invariant in relation to food deprivation, a clear hump-shaped response was found for daily flight distances, maximal displacements and flight elevation. These movement characteristics increased during the first five days of the FDP sequence and decreased during the following five days. These characteristics also differed between short FDPs of up to four days, and the first four days of longer FDP sequences.These results suggest a switch from FIMax to EEMin strategies along the FDP sequence. They also indicate that vultures’ response to hunger affected the eventual duration of the FDP. During winter (the vultures’ incubation period characterized by unfavorable soaring meteorological conditions), the vultures’ FIMax response was less intensive and resulted in longer starvation periods, while, in summer, more intensive FIMax response to hunger resulted in shorter FDPs.ConclusionsOur results show a flexible, non-monotonic response of free-ranging wild animals to increasing hunger levels, reflecting a trade-off between increasing motivation to find food and the risk of starvation. The proposed trade-off offers a unifying perspective to apparently contradictory or case-specific empirical findings.Electronic supplementary materialThe online version of this article (doi:10.1186/2051-3933-1-5) contains supplementary material, which is available to authorized users.