How often should dead-reckoned animal movement paths be corrected for drift?

Bridging the gap between movement data and connectivity analysis using the Time-Explicit Habitat Selection (TEHS) model

BACKGROUND

Understanding how to connect habitat remnants to facilitate the movement of species is a critical task in an increasingly fragmented world impacted by human activities. The identification of dispersal routes and corridors through connectivity analysis requires measures of landscape resistance but there has been no consensus on how to calculate resistance from habitat characteristics, potentially leading to very different connectivity outcomes.

METHODS

We propose a new model, called the Time-Explicit Habitat Selection (TEHS) model, that can be directly used for connectivity analysis. The TEHS model decomposes the movement process in a principled approach into a time and a selection component, providing complementary information regarding space use by separately assessing the drivers of time to traverse the landscape and the drivers of habitat selection. These models are illustrated using GPS-tracking data from giant anteaters (Myrmecophaga tridactyla) in the Pantanal wetlands of Brazil.

RESULTS

The time model revealed that the fastest movements tended to occur between 8 p.m. and 5 a.m., suggesting a crepuscular/nocturnal behavior. Giant anteaters moved faster over wetlands while moving much slower over forests and savannas, in comparison to grasslands. We also found that wetlands were consistently avoided whereas forest and savannas tended to be selected. Importantly, this model revealed that selection for forest increased with temperature, suggesting that forests may act as important thermal shelters when temperatures are high. Finally, using the spatial absorbing Markov chain framework, we show that the TEHS model results can be used to simulate movement and connectivity within a fragmented landscape, revealing that giant anteaters will often not use the shortest-distance path to the destination patch due to avoidance of certain habitats.

CONCLUSIONS

The proposed approach can be used to characterize how landscape features are perceived by individuals through the decomposition of movement patterns into a time and a habitat selection component. Additionally, this framework can help bridge the gap between movement-based models and connectivity analysis, enabling the generation of time-explicit connectivity results.

Worse sleep and increased energy expenditure yet no movement changes in sub-urban wild boar experiencing an influx of human visitors (anthropulse) during the COVID-19 pandemic

Expansion of urban areas, landscape transformation and increasing human outdoor activities strongly affect wildlife behaviour. The outbreak of the COVID-19 pandemic in particular led to drastic changes in human behaviour, exposing wildlife around the world to either reduced or increased human presence, potentially altering animal behaviour. Here, we investigate behavioural responses of wild boar (Sus scrofa) to changing numbers of human visitors to a suburban forest near Prague, Czech Republic, during the first 2.5 years of the COVID-19 epidemic (April 2019-November 2021). We used bio-logging and movement data of 63 GPS-collared wild boar and human visitation data based on an automatic counter installed in the field. We hypothesised that higher levels of human leisure activity will have a disturbing effect on wild boar behaviour manifested in increased movements and ranging, energy spent, and disrupted sleep patterns. Interestingly, whilst the number of people visiting the forest varied by two orders of magnitude (from 36 to 3431 people weekly), even high levels of human presence (>2000 visitors per week) did not affect weekly distance travelled, home range size, and maximum displacement of wild boar. Instead, individuals spent 41 % more energy at high levels of human presence (>2000 visitors per week), with more erratic sleep patterns, characterised by shorter and more frequent sleeping bouts. Our results highlight multifaceted effects of increased human activities ('anthropulses'), such as those related to COVID-19 countermeasures, on animal behaviour. High human pressure may not affect animal movements or habitat use, especially in highly adaptable species such as wild boar, but may disrupt animal activity rhythms, with potentially detrimental fitness consequences. Such subtle behavioural responses can be overlooked if using only standard tracking technology.

A novel method for identifying fine-scale bottom-use in a benthic-foraging pinniped

BACKGROUND

For diving, marine predators, accelerometer and magnetometer data provides critical information on sub-surface foraging behaviours that cannot be identified from location or time-depth data. By measuring head movement and body orientation, accelerometers and magnetometers can help identify broad shifts in foraging movements, fine-scale habitat use and energy expenditure of terrestrial and marine species. Here, we use accelerometer and magnetometer data from tagged Australian sea lions and provide a new method to identify key benthic foraging areas. As Australian sea lions are listed as endangered by the IUCN and Australian legislation, identifying key areas for the species is vital to support targeted management of populations.

METHODS

Firstly, tri-axial magnetometer and accelerometer data from adult female Australian sea lions is used in conjunction with GPS and dive data to dead-reckon their three-dimensional foraging paths. We then isolate all benthic phases from their foraging trips and calculate a range of dive metrics to characterise their bottom usage. Finally, k-means cluster analysis is used to identify core benthic areas utilised by sea lions. Backwards stepwise regressions are then iteratively performed to identify the most parsimonious model for describing bottom usage and its included predictor variables.

RESULTS

Our results show distinct spatial partitioning in benthic habitat-use by Australian sea lions. This method has also identified individual differences in benthic habitat-use. Here, the application of high-resolution magnetometer/accelerometer data has helped reveal the tortuous foraging movements Australian sea lions use to exploit key benthic marine habitats and features.

CONCLUSIONS

This study has illustrated how magnetometer and accelerometer data can provide a fine-scale description of the underwater movement of diving species, beyond GPS and depth data alone, For endangered species like Australian sea lions, management of populations must be spatially targeted. Here, this method demonstrates a fine-scale analysis of benthic habitat-use which can help identify key areas for both marine and terrestrial species. Future integration of this method with concurrent habitat and prey data would further augment its power as a tool for understanding the foraging behaviours of species.

Increasingly detailed insights in animal behaviours using continuous on-board processing of accelerometer data

BACKGROUND

Studies of animal behaviour, ecology and physiology are continuously benefitting from progressing biologging techniques, including the collection of accelerometer data to infer animal behaviours and energy expenditure. In one of the most recent technological advances in this space, on-board processing of raw accelerometer data into animal behaviours proves highly energy-, weight- and cost-efficient allowing for continuous behavioural data collection in addition to regular positional data in a wide range of animal tracking studies.

METHODS

We implemented this latest development in collecting continuous behaviour records from 6 Pacific Black Ducks Anas superciliosa to evaluate some of this novel technique's potential advantages over tracking studies lacking behavioural data or recording accelerometer data intermittently only. We (i) compared the discrepancy of time-activity budgets between continuous records and behaviours sampled with different intervals, (ii) compared total daily distance flown using hourly GPS fixes with and without additional behavioural data and (iii) explored how behaviour records can provide additional insights for animal home range studies.

RESULTS

Using a total of 690 days of behaviour records across six individual ducks distinguishing eight different behaviours, we illustrated the improvement that is obtained in time-activity budget accuracy if continuous rather than interval-sampled accelerometer data is used. Notably, for rare behaviours such as flying and running, error ratios > 1 were common when sampling intervals exceeded 10 min. Using 72 days of hourly GPS fixes in combination with continuous behaviour records over the same period in one individual duck, we showed behaviour-based daily distance estimation is significantly higher (up to 540%) than the distance calculated from hourly sampled GPS fixes. Also, with the same 72 days of data for one individual duck, we showed how this individual used specific sites within its entire home range to satisfy specific needs (e.g. roosting and foraging).

CONCLUSION

We showed that by using trackers allowing for continuous recording of animal behaviour, substantial improvements in the estimation of time-activity budgets and daily traveling distances can be made. With integrating behaviour into home-range estimation we also highlight that this novel tracking technique may not only improve estimations but also open new avenues in animal behaviour research, importantly improving our knowledge of an animal's state while it is roaming the landscape.

Long walk home: Magellanic penguins have strategies that lead them to areas where they can navigate most efficiently

Understanding how animals move in dense environments where vision is compromised is a major challenge. We used GPS and dead-reckoning to examine the movement of Magellanic penguins commuting through vegetation that precluded long-distance vision. Birds leaving the nest followed the shortest, quickest route to the sea (the 'ideal path', or 'I-path') but return tracks depended where the birds left the water. Penguins arriving at the beach departure spot mirrored the departure. Most of those landing at a distance from the departure spot travelled slowly, obliquely to the coast at a more acute angle than a beeline trajectory to the nest. On crossing their I-path, these birds then followed this route quickly to their nests. This movement strategy saves birds distance, time and energy compared to a route along the beach and the into the colony on the I-track and saves time and energy compared to a beeline trajectory which necessitates slow travel in unfamiliar areas. This suggests that some animals adopt tactics that take them to an area where their navigational capacities are enhanced for efficient travel in challenging environments.

Dead-reckoning elucidates fine-scale habitat use by European badgers Meles meles

Background

Recent developments in both hardware and software of animal-borne data loggers now enable large amounts of data to be collected on both animal movement and behaviour. In particular, the combined use of tri-axial accelerometers, tri-axial magnetometers and GPS loggers enables animal tracks to be elucidated using a procedure of 'dead-reckoning'. Although this approach was first suggested 30 years ago by Wilson et al. (1991), surprisingly few measurements have been made in free-ranging terrestrial animals. The current study examines movements, interactions with habitat features, and home-ranges calculated from just GPS data and also from dead-reckoned data in a model terrestrial mammal, the European badger (Meles meles).

Methods

Research was undertaken in farmland in Northern Ireland. Two badgers (one male, one female) were live-trapped and fitted with a GPS logger, a tri-axial accelerometer, and a tri-axial magnetometer. Thereafter, the badgers' movement paths over 2 weeks were elucidated using just GPS data and GPS-enabled dead-reckoned data, respectively.

Results

Badgers travelled further using data from dead-reckoned calculations than using the data from only GPS data. Whilst once-hourly GPS data could only be represented by straight-line movements between sequential points, the sub-second resolution dead-reckoned tracks were more tortuous. Although there were no differences in Minimum Convex Polygon determinations between GPS- and dead-reckoned data, Kernel Utilisation Distribution determinations of home-range size were larger using the former method. This was because dead-reckoned data more accurately described the particular parts of landscape constituting most-visited core areas, effectively narrowing the calculation of habitat use. Finally, the dead-reckoned data showed badgers spent more time near to field margins and hedges than simple GPS data would suggest.

Conclusion

Significant differences emerge when analyses of habitat use and movements are compared between calculations made using just GPS data or GPS-enabled dead-reckoned data. In particular, use of dead-reckoned data showed that animals moved 2.2 times farther, had better-defined use of the habitat (revealing clear core areas), and made more use of certain habitats (field margins, hedges). Use of dead-reckoning to provide detailed accounts of animal movement and highlight the minutiae of interactions with the environment should be considered an important technique in the ecologist's toolkit.