49 research outputs found
Highlighting when animals expend excessive energy for travel using dynamic body acceleration
Travel represents a major cost for many animals so there should be selection pressure for it to be efficient â at minimum cost. However, animals sometimes exceed minimum travel costs for reasons that must be correspondingly important. We use Dynamic Body Acceleration (DBA), an acceleration-based metric, as a proxy for movement-based power, in tandem with vertical velocity (rate of change in depth) in a shark (Rhincodon typus) to derive the minimum estimated power required to swim at defined vertical velocities. We show how subtraction of measured DBA from the estimated minimum power for any given vertical velocity provides a âproxy for power above minimumâ metric (PPAmin), highlighting when these animals travel above minimum power. We suggest that the adoption of this metric across species has value in identifying where and when animals are subject to compelling conditions that lead them to deviate from ostensibly judicious energy expenditure
Making sense of ultrahigh-resolution movement data: A new algorithm for inferring sites of interest
Decomposing the life track of an animal into behavioral segments is a fundamental challenge for movement ecology. The proliferation of highâresolution data, often collected many times per second, offers much opportunity for understanding animal movement. However, the sheer size of modern data sets means there is an increasing need for rapid, novel computational techniques to make sense of these data. Most existing methods were designed with smaller data sets in mind and can thus be prohibitively slow. Here, we introduce a method for segmenting highâresolution movement trajectories into sites of interest and transitions between these sites. This builds on a previous algorithm of Benhamou and RiotteâLambert (2012). Adapting it for use with highâresolution data. The dataâs resolution removed the need to interpolate between successive locations, allowing us to increase the algorithmâs speed by approximately two orders of magnitude with essentially no drop in accuracy. Furthermore, we incorporate a color scheme for testing the level of confidence in the algorithm's inference (high = green, medium = amber, low = red). We demonstrate the speed and accuracy of our algorithm with application to both simulated and real data (Alpine cattle at 1 Hz resolution). On simulated data, our algorithm correctly identified the sites of interest for 99% of âhigh confidenceâ paths. For the cattle data, the algorithm identified the two known sites of interest: a watering hole and a milking station. It also identified several other sites which can be related to hypothesized environmental drivers (e.g., food). Our algorithm gives an efficient method for turning a long, highâresolution movement path into a schematic representation of broadscale decisions, allowing a direct link to existing pointâtoâpoint analysis techniques such as optimal foraging theory. It is encoded into an R package called SitesInterest, so should serve as a valuable tool for making sense of these increasingly large data streams
Why did the animal turn? Timeâvarying step selection analysis for inference between observed turningâpoints in high frequency data
1. Step selection analysis (SSA) is a fundamental technique for uncovering the drivers of animal movement decisions. Its typical use has been to view an animal as âselectingâ each measured location, given its current (and possibly previous) locations. Although an animal is unlikely to make decisions precisely at the times its locations are measured, if data are gathered at a relatively low frequency (every few minutes or hours) this is often the best that can be done. Nowadays, though, tracking data are increasingly gathered at very high frequencies, often â„1 Hz, so it may be possible to exploit these data to perform more behaviourallyâmeaningful step selection analysis.
2. Here, we present a technique to do this. We first use an existing algorithm to determine the turningâpoints in an animal's movement path. We define a âstepâ to be a straightâline movement between successive turningâpoints. We then construct a generalised version of integrated SSA (iSSA), called timeâvarying iSSA (tiSSA), which deals with the fact that turningâpoints are usually irregularly spaced in time. We demonstrate the efficacy of tiSSA by application to data on both simulated animals and freeâranging goats Capra aegagrus hircus, comparing our results to those of regular iSSA with locations that are separated by a constant timeâinterval.
3. Using (regular) iSSA with constant timeâsteps can give results that are misleading compared to using tiSSA with the actual turns made by the animals. Furthermore, tiSSA can be used to infer covariates that are dependent on the time between turns, which is not possible with regular iSSA. As an example, we show that our study animals tend to spend less time between successive turns when the ground is rockier and/or the temperature is hotter.
4. By constructing a step selection technique that works between observed turningâpoints of animals, we enable step selection to be used on highâfrequency movement data, which are becoming increasingly prevalent in modern biologging studies. Furthermore, since turningâpoints can be viewed as decisions, our method places step selection analysis on a more behaviourallyâmeaningful footing compared to previous techniques
Dead-reckoning animal movements in R: a reappraisal using Gundog.Tracks
BackgroundFine-scale data on animal position are increasingly enabling us to understand the details of animal movement ecology and dead-reckoning, a technique integrating motion sensor-derived information on heading and speed, can be used to reconstruct fine-scale movement paths at sub-second resolution, irrespective of the environment. On its own however, the dead-reckoning process is prone to cumulative errors, so that position estimates quickly become uncoupled from true location. Periodic ground-truthing with aligned location data (e.g., from global positioning technology) can correct for this drift between Verified Positions (VPs). We present step-by-step instructions for implementing Verified Position Correction (VPC) dead-reckoning in R using the tilt-compensated compass method, accompanied by the mathematical protocols underlying the code and improvements and extensions of this technique to reduce the trade-off between VPC rate and dead-reckoning accuracy. These protocols are all built into a user-friendly, fully annotated VPC dead-reckoning R function; Gundog.Tracks, with multi-functionality to reconstruct animal movement paths across terrestrial, aquatic, and aerial systems, provided within the Additional file 4 as well as online (GitHub).ResultsThe Gundog.Tracks function is demonstrated on three contrasting model species (the African lion Panthera leo, the Magellanic penguin Spheniscus magellanicus, and the Imperial cormorant Leucocarbo atriceps) moving on land, in water and in air. We show the effect of uncorrected errors in speed estimations, heading inaccuracies and infrequent VPC rate and demonstrate how these issues can be addressed.ConclusionsThe function provided will allow anyone familiar with R to dead-reckon animal tracks readily and accurately, as the key complex issues are dealt with by Gundog.Tracks. This will help the community to consider and implement a valuable, but often overlooked method of reconstructing high-resolution animal movement paths across diverse species and systems without requiring a bespoke application
A História da Alimentação: balizas historiogråficas
Os M. pretenderam traçar um quadro da HistĂłria da Alimentação, nĂŁo como um novo ramo epistemolĂłgico da disciplina, mas como um campo em desenvolvimento de prĂĄticas e atividades especializadas, incluindo pesquisa, formação, publicaçÔes, associaçÔes, encontros acadĂȘmicos, etc. Um breve relato das condiçÔes em que tal campo se assentou faz-se preceder de um panorama dos estudos de alimentação e temas correia tos, em geral, segundo cinco abardagens Ia biolĂłgica, a econĂŽmica, a social, a cultural e a filosĂłfica!, assim como da identificação das contribuiçÔes mais relevantes da Antropologia, Arqueologia, Sociologia e Geografia. A fim de comentar a multiforme e volumosa bibliografia histĂłrica, foi ela organizada segundo critĂ©rios morfolĂłgicos. A seguir, alguns tĂłpicos importantes mereceram tratamento Ă parte: a fome, o alimento e o domĂnio religioso, as descobertas europĂ©ias e a difusĂŁo mundial de alimentos, gosto e gastronomia. O artigo se encerra com um rĂĄpido balanço crĂtico da historiografia brasileira sobre o tema
Highlighting when animals expend excessive energy for travel using dynamic body acceleration
Travel represents a major cost for many animals so there should be selection pressure for it to be efficient â at minimum cost. However, animals sometimes exceed minimum travel costs for reasons that must be correspondingly important. We use Dynamic Body Acceleration (DBA), an acceleration-based metric, as a proxy for movement-based power, in tandem with vertical velocity (rate of change in depth) in a shark (Rhincodon typus) to derive the minimum estimated power required to swim at defined vertical velocities. We show how subtraction of measured DBA from the estimated minimum power for any given vertical velocity provides a âproxy for power above minimumâ metric (PPAmin), highlighting when these animals travel above minimum power. We suggest that the adoption of this metric across species has value in identifying where and when animals are subject to compelling conditions that lead them to deviate from ostensibly judicious energy expenditure