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

Abstract

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.

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

“…We considered the following activity metrics as indicators of foraging and flight behaviour: the proportion of time spent on the water (wet), the average duration of flight (dry) bouts in minutes and the number of wet bouts (succeeding dry events) as a proxy for the number of landings. We acknowledge that the low sampling interval is likely to underestimate landing rates (Johnson et al 2017). However, omission errors are likely to be similar across individuals and trip types.…”

Tracking random walks

“…This is particularly true for the study of trajectories from sampling movements in space. The choices taken for trajectory segmentation, together with the temporal and spatial granularity of the measures, influence all quantities associated with these trajectories [20,34].…”

“…We provided new analytical tools to evaluate the quality of a sampled trajectory for the study of both animal and human movements. Positions must be collected (or, when necessary for historical comparisons, down-sampled [34]) at least with a frequency commensurate with the underlying moving and resting dynamics ( D % 1:96 ffiffiffiffi t t p ). Alternatively, stay points can be reconstructed from high-frequency sampling (kDl ( t), but not when one has bursty inter-event times, because during the numerous extreme events constituting the long tail of the distribution P(D) the information on the movements is simply absent.…”

“…For example, in the case of periodic sampling, it has been shown that non-Lévy movements can be erroneously interpreted as Lévy flights when sampling time intervals are larger than the natural timescale of animals' movements [32,33]. The sampling rate is thus a crucial element that has to be taken into account when analysing empirical trajectories [20,34].…”