Changing measurements or changing movements? Sampling scale and movement model identifiability across generations of biologging technology

The efficiency of detecting seabird behaviour from movement patterns: the effect of sampling frequency on inferring movement metrics in Procellariiformes

BACKGROUND

Recent technological advances have resulted in low-cost GPS loggers that are small enough to be used on a range of seabirds, producing accurate location estimates (± 5 m) at sampling intervals as low as 1 s. However, tradeoffs between battery life and sampling frequency result in studies using GPS loggers on flying seabirds yielding locational data at a wide range of sampling intervals. Metrics derived from these data are known to be scale-sensitive, but quantification of these errors is rarely available. Very frequent sampling, coupled with limited movement, can result in measurement error, overestimating movement, but a much more pervasive problem results from sampling at long intervals, which grossly underestimates path lengths.

METHODS

We use fine-scale (1 Hz) GPS data from a range of albatrosses and petrels to study the effect of sampling interval on metrics derived from the data. The GPS paths were sub-sampled at increasing intervals to show the effect on path length (i.e. ground speed), turning angles, total distance travelled, as well as inferred behavioural states.

RESULTS

We show that distances (and per implication ground speeds) are overestimated (4% on average, but up to 20%) at the shortest sampling intervals (1-5 s) and underestimated at longer intervals. The latter bias is greater for more sinuous flights (underestimated by on average 40% when sampling > 1-min intervals) as opposed to straight flight (11%). Although sample sizes were modest, the effect of the bias seemingly varied with species, where species with more sinuous flight modes had larger bias. Sampling intervals also played a large role when inferring behavioural states from path length and turning angles.

CONCLUSIONS

Location estimates from low-cost GPS loggers are appropriate to study the large-scale movements of seabirds when using coarse sampling intervals, but actual flight distances are underestimated. When inferring behavioural states from path lengths and turning angles, moderate sampling intervals (10-30 min) may provide more stable models, but the accuracy of the inferred behavioural states will depend on the time period associated with specific behaviours. Sampling rates have to be considered when comparing behaviours derived using varying sampling intervals and the use of bias-informed analyses are encouraged.

Tracking random walks

In empirical studies, trajectories of animals or individuals are sampled in space and time. Yet, it is unclear how sampling procedures bias the recorded data. Here, we consider the important case of movements that consist of alternating rests and moves of random durations and study how the estimate of their statistical properties is affected by the way we measure them. We first discuss the ideal case of a constant sampling interval and short-tailed distributions of rest and move durations, and provide an exact analytical calculation of the fraction of correctly sampled trajectories. Further insights are obtained with simulations using more realistic long-tailed rest duration distributions showing that this fraction is dramatically reduced for real cases. We test our results for real human mobility with high-resolution GPS trajectories, where a constant sampling interval allows one to recover at best 18% of the movements, while over-evaluating the average trip length by a factor of 2. Using a sampling interval extracted from real communication data, we recover only 11% of the moves, a value that cannot be increased above 16% even with ideal algorithms. These figures call for a more cautious use of data in quantitative studies of individuals' movements.

Divergent foraging strategies during incubation of an unusually wide-ranging seabird, the Murphy's petrel

Divergent foraging strategies may emerge within a population due to a combination of physiological and environmental factors; yet to persist, neither strategy should offer a consistent selective advantage over the alternative in the long term. Murphy's petrels Pterodroma ultima from Henderson Island (24°20'S, 128°20'W) in the South Pacific Ocean are highly vagile, and exhibit two distinct foraging trip types during incubation; similar proportions of birds undertake either looping trips around the South Pacific Gyre to waters off Peru (hereafter "East") or trips south-west of the colony towards the Subtropical Front ("South") (mean maximum ranges of c. 3800 or 2000 km from the colony, respectively). However, the relative benefits of the distinct trip types remain unclear. Through tracking birds with GPS and salt-water immersion loggers in 2015, the fine-scale foraging behaviour was examined for East (trip durations: 14.1-19.8 days, maximum ranges 2387-4823 km) and South trips (12.9-25.8 days, 1565-1991 km). Data on behaviour classified from GPS tracks, the number of wet bouts per hour (a proxy for landing rates) and wind speeds, were used to distinguish two distinct foraging modes: birds on East trips spent more time in directed movement, whereas those on South trips spent a greater proportion of time in area-restricted search (ARS) behaviour. East trips were associated with higher overall mass gain, and wet bouts occurred in equal proportions during directed movement and ARS behaviour. This suggests that in unproductive marine environments, it may be more profitable to maximise area covered to increase the chances of encountering prey. Analysis of lower-resolution geolocator data (collected from 2011 to 2014) indicated that individuals were largely consistent in trip type between years. Since birds that conducted East trips were 19% lighter on departure from the colony and experienced more frequent tailwinds on foraging trips, we speculate that these birds may benefit from reduced movement costs, whilst also experiencing reduced competition for foraging opportunities.

Changing measurements or changing movements? Sampling scale and movement model identifiability across generations of biologging technology

Animal movement patterns contribute to our understanding of variation in breeding success and survival of individuals, and the implications for population dynamics. Over time, sensor technology for measuring movement patterns has improved. Although older technologies may be rendered obsolete, the existing data are still valuable, especially if new and old data can be compared to test whether a behavior has changed over time. We used simulated data to assess the ability to quantify and correctly identify patterns of seabird flight lengths under observational regimes used in successive generations of wet/dry logging technology. Care must be taken when comparing data collected at differing timescales, even when using inference procedures that incorporate the observational process, as model selection and parameter estimation may be biased. In practice, comparisons may only be valid when degrading all data to match the lowest resolution in a set. Changes in tracking technology, such as the wet/dry loggers explored here, that lead to aggregation of measurements at different temporal scales make comparisons challenging. We therefore urge ecologists to use synthetic data to assess whether accurate parameter estimation is possible for models comparing disparate data sets before planning experiments and conducting analyses such as responses to environmental changes or the assessment of management actions.