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Abstract Background Understanding what animals do in time and space is important for a range of ecological questions, however accurate estimates of how animals use space is challenging. Within the use of animal-attached tags, radio telemetry (including the Global Positioning System, ‘GPS’) is typically used to verify an animal’s location periodically. Straight lines are typically drawn between these ‘Verified Positions’ (‘VPs’) so the interpolation of space-use is limited by the temporal and spatial resolution of the system’s measurement. As such, parameters such as route-taken and distance travelled can be poorly represented when using VP systems alone. Dead-reckoning has been suggested as a technique to improve the accuracy and resolution of reconstructed movement paths, whilst maximising battery life of VP systems. This typically involves deriving travel vectors from motion sensor systems and periodically correcting path dimensions for drift with simultaneously deployed VP systems. How often paths should be corrected for drift, however, has remained unclear. Methods and results Here, we review the utility of dead-reckoning across four contrasting model species using different forms of locomotion (the African lion Panthera leo, the red-tailed tropicbird Phaethon rubricauda, the Magellanic penguin Spheniscus magellanicus, and the imperial cormorant Leucocarbo atriceps). Simulations were performed to examine the extent of dead-reckoning error, relative to VPs, as a function of Verified Position correction (VP correction) rate and the effect of this on estimates of distance moved. Dead-reckoning error was greatest for animals travelling within air and water. We demonstrate how sources of measurement error can arise within VP-corrected dead-reckoned tracks and propose advancements to this procedure to maximise dead-reckoning accuracy. Conclusions We review the utility of VP-corrected dead-reckoning according to movement type and consider a range of ecological questions that would benefit from dead-reckoning, primarily concerning animal–barrier interactions and foraging strategies.

Abstract Background Fine-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). Results The 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. Conclusions The 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.

Abstract Background Recent advances in sensing technologies have enabled us to attach small loggers to animals in their natural habitat. It allows measurement of the animals’ behavior, along with associated environmental and physiological data and to unravel the adaptive significance of the behavior. However, because animal-borne loggers can now record multi-dimensional (here defined as multimodal) time series information from a variety of sensors, it is becoming increasingly difficult to identify biologically important patterns hidden in the high-dimensional long-term data. In particular, it is important to identify co-occurrences of several behavioral modes recorded by different sensors in order to understand an internal hidden state of an animal because the observed behavioral modes are reflected by the hidden state. This study proposed a method for automatically detecting co-occurrence of behavioral modes that differs between two groups (e.g., males vs. females) from multimodal time-series sensor data. The proposed method first extracted behavioral modes from time-series data (e.g., resting and cruising modes in GPS trajectories or relaxed and stressed modes in heart rates) and then identified two different behavioral modes that were frequently co-occur (e.g., co-occurrence of the cruising mode and relaxed mode). Finally, behavioral modes that differ between the two groups in terms of the frequency of co-occurrence were identified. Results We demonstrated the effectiveness of our method using animal-locomotion data collected from male and female Streaked Shearwaters by showing co-occurrences of locomotion modes and diving behavior recorded by GPS and water-depth sensors. For example, we found that the behavioral mode of high-speed locomotion and that of multiple dives into the sea were highly correlated in male seabirds. In addition, compared to the naive method, the proposed method reduced the computation costs by about 99.9%. Conclusion Because our method can automatically mine meaningful behavioral modes from multimodal time-series data, it can be potentially applied to analyzing co-occurrences of locomotion modes and behavioral modes from various environmental and physiological data.

Abstract Background Salmonids return to the river where they were born in a phenomenon known as mother-river migration. The underpinning of migration has been extensively examined, particularly regarding the behavioral correlations of external environmental cues such as the scent of the mother-river and geomagnetic compass. However, neuronal underpinning remains elusive, as there have been no biologging techniques suited to monitor neuronal activity in the brain of large free-swimming fish. In this study, we developed a wireless biologging system to record extracellular neuronal activity in the brains of free-swimming salmonids. Results Using this system, we recorded multiple neuronal activities from the telencephalon of trout swimming in a rectangular water tank. As proof of principle, we examined the activity statistics for extracellular spike waveforms and timing. We found cells firing maximally in response to a specific head direction, similar to the head direction cells found in the rodent brain. The results of our study suggest that the recorded signals originate from neurons. Conclusions We anticipate that our biologging system will facilitate a more detailed investigation into the neural underpinning of fish movement using internally generated information, including responses to external cues.