Search

AbstractWildlife tourism can benefit conservation of target species, however, it can have detrimental effects on animal behaviour and physiology. Whale shark Rhincodon typus tourism has seen recent rapid growth globally, but methods and regulations vary widely. Ningaloo Reef, Australia is considered “gold standard” whale shark tourism management due to legal regulation, strict enforcement, and high compliance. Rather than relying on observational data, we used biotelemetry to collect high-resolution data (20 Hz) on whale sharks’ movement behaviour in the presence or absence of tourists. Tourism encounters lasted an average of 62 min and swimming with tourists increased the activity levels of larger (> 7 m) but not smaller sharks. Given that activity levels positively correlate with energetic costs, it is likely the 18% increase seen in activity of large sharks would have incurred additional energetic costs. However, when considered as a proportion of daily energy requirements, these additional costs were only incurred for an average of 4% of a whale shark’s day. The tourism-induced impacts we found on the endangered whale sharks at this highly regulated tourism site would not have been apparent from purely observational studies, highlighting the utility of biotelemetry to quantify tourism-related impacts on wildlife. [ABSTRACT FROM AUTHOR]

The time that animals spend travelling at various speeds and the tortuosity of their movement paths are two of the many things that affect space‐use by animals. In this, high turn rates are predicted to be energetically costly, especially at high travel speeds, which implies that animals should modulate their speed according to path characteristics. When animals move so as to maximize distance and minimize metabolic energy expenditure, they travel most efficiently at the speed that gives them a minimum cost of transport (COTmin), a well‐defined point for animals that move entirely in fluid media. Theoretical considerations show though, that land animals should travel at their maximum speed to minimize COT, which they do not, instead travelling at walking pace. So, to what extent does COTmin depend on speed and turn rate and how might this relate to movement paths? We measured oxygen consumption in humans walking along paths with varied tortuosity at defined speeds to demonstrate that the energetic costs of negotiating these paths increase disproportionately with both speed and angular velocity. This resulted in the COTmin occurring at very low speeds, and these COTmin speeds reduced with increased path tortuosity and angular velocity. Logged movement data from six free‐ranging terrestrial species underpinned this because all individuals turned with greater angular velocity the slower their travel speeds across their full speed range. It seems, therefore, that land animals may strive to achieve minimum movement costs by reducing speed with increasing path variability, providing one of many possible explanations as to why speed is much lower than currently predicted based on lab measurements of mammalian locomotor performance. [ABSTRACT FROM AUTHOR]

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 'selecting' 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 ≥1 Hz, so it may be possible to exploit these data to perform more behaviourally‐meaningful step selection analysis.Here, we present a technique to do this. We first use an existing algorithm to determine the turning‐points in an animal's movement path. We define a 'step' to be a straight‐line movement between successive turning‐points. We then construct a generalised version of integrated SSA (iSSA), called time‐varying iSSA (tiSSA), which deals with the fact that turning‐points are usually irregularly spaced in time. We demonstrate the efficacy of tiSSA by application to data on both simulated animals and free‐ranging goats Capra aegagrus hircus, comparing our results to those of regular iSSA with locations that are separated by a constant time‐interval.Using (regular) iSSA with constant time‐steps 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.By constructing a step selection technique that works between observed turning‐points of animals, we enable step selection to be used on high‐frequency movement data, which are becoming increasingly prevalent in modern biologging studies. Furthermore, since turning‐points can be viewed as decisions, our method places step selection analysis on a more behaviourally‐meaningful footing compared to previous techniques. [ABSTRACT FROM AUTHOR]

Decomposing the life track of an animal into behavioral segments is a fundamental challenge for movement ecology. The proliferation of high‐resolution 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 high‐resolution movement trajectories into sites of interest and transitions between these sites. This builds on a previous algorithm of Benhamou and Riotte‐Lambert (2012). Adapting it for use with high‐resolution data. The data's resolution removed the need to interpolate between successive locations, allowing us to increase the algorithm's 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 "high confidence" 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, high‐resolution movement path into a schematic representation of broadscale decisions, allowing a direct link to existing point‐to‐point 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. We have constructed a new method for identifying sites of interest, tailored specifically for use with high resolution data. This allows for a complicated movement trajectory to be simplified into a "Markov chain‐like" description of behavioural choices, highlighting the animal's broadscale decisions. [ABSTRACT FROM AUTHOR]