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

## TL;DR

This study evaluates how differences in sampling scale across generations of biologging technology affect the ability to accurately identify seabird movement patterns, emphasizing the need for synthetic data assessments.

## Contribution

It demonstrates the impact of varying observational regimes on movement model identifiability and provides guidelines for valid comparisons across different tracking technologies.

## Key findings

- Model selection and parameter estimation can be biased when comparing data at different temporal scales.
- Degrading data to the lowest resolution ensures more valid comparisons.
- Synthetic data assessments are recommended before analyzing disparate datasets.

## Abstract

1. Animal movement patterns contribute to our understanding of variation in breeding success and survival of individuals, and the implications for population dynamics. 2. 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 behaviour has changed over time. 3. 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 tracking technology. 4. Care must be taken when comparing data collected at differing time-scales, 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. 5. Changes in tracking technology 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 conducting analyses such as responses to environmental changes or the assessment of management actions.