Using at-sea tracking data to infer distributions of three Calonectris Shearwaters

Scopoli’s Shearwater, photograph by Georgios Karris

Virginia Morera-Pujol (Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Universitat de Barcelona, Spain) and colleagues have published in the open-access journal Diversity and Distributions on challenges using individual tracking data to infer higher-level distributions.  The authors used a data set of 1346 year-round migratory trips from 805 individuals of Cory’s Calonectris borealis, Cape Verde C. edwardsii and Scopoli’s C. diomedea Shearwaters breeding in 34 colonies in the Mediterranean Sea and the Atlantic Ocean, spanning 10 years, to address the issue.

The paper’s abstract follows:

"Aim

Over the last decades, the study of movement through tracking data has grown exceeding the expectations of movement ecologists. This has posed new challenges, specifically when using individual tracking data to infer higher-level distributions (e.g. population and species). Sources of variability such as individual site fidelity (ISF), environmental stochasticity over time, and space-use variability across species ranges must be considered, and their effects identified and corrected, to produce accurate estimates of spatial distribution using tracking data.

Innovation

We developed R functions to detect the effect of these sources of variability in the distribution of animal groups when inferred from individual tracking data. These procedures demonstrated our procedures with simulated datasets and showed their applicability on a real-world dataset containing 1346 year-round migratory trips from 805 individuals of three closely related seabird species breeding in 34 colonies in the Mediterranean Sea and the Atlantic Ocean, spanning 10 years. We detected an effect of ISF in one of the colonies, but no effect of the environmental stochasticity on the distribution of birds for any of the species. We also identified among-colony variability in nonbreeding space use for one species, with significant effects of population size and longitude.

Main conclusions

This work provides a useful, much-needed tool for researchers using animal tracking data to model species distributions or establish conservation measures. This methodology may be applied in studies using individual tracking data to accurately infer the distribution of a population or species and support the delineation of important areas for conservation based on tracking data. This step, designed to precede any analysis, has become increasingly relevant with the proliferation of studies using large tracking datasets that has accompanied the globalization process in science driving collaborations and tracking data sharing initiatives.”

With thanks to Georgios Karris.

Reference:

Morera-Pujol, V., Catry, P., Magalhãe, M., Péron, C., Reyes-González, J.M., Granadeiro, J.P., Militão, T., Dias, M.P., Oro, F., Dell'Omo, G., Müller, M., Paiva, V.H., Metzger, B., Neves, V., Navarro, J., Karris, G., Xirouchakis, S., Cecere, J.G., Zamora-López, A., Forero, M.G., Ouni, R., Romdhane, M.S., De Felipe, F., Zajková, Z., Cruz-Flores, M., Grémillet, D., González-Solís, J. & Ramos, R. 2022.  Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time.  Diversity and Distribution. doi.org/10.1111/ddi.13642.

John Cooper, ACAP News Correspondent, 27 December 2022