Publication Cover
Laterality
Asymmetries of Brain, Behaviour, and Cognition
Volume 29, 2024 - Issue 3
20
Views
0
CrossRef citations to date
0
Altmetric
Articles

Visual lateralization in the sky: Geese manifest visual lateralization when flying with pair mates

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon show all
Pages 313-330 | Received 13 Feb 2024, Accepted 11 Jun 2024, Published online: 09 Jul 2024
 

ABSTRACT

The brain’s sensory lateralization involves the processing of information from the sensory organs primarily in one hemisphere. This can improve brain efficiency by reducing interference and duplication of neural circuits. For species that rely on successful interaction among family partners, such as geese, lateralization can be advantageous. However, at the group level, one-sided biases in sensory lateralization can make individuals predictable to competitors and predators. We investigated lateral preferences in the positioning of pair mates of Greater white-fronted geese Anser albifrons albifrons. Using GPS-GSM trackers, we monitored individual geese in flight throughout the year. Our findings indicate that geese exhibit individual lateral biases when viewing their mate in flight, but the direction of these biases varies among individuals. We suggest that these patterns of visual lateralization could be an adaptive trait for the species with long-term social monogamy, high levels of interspecies communication and competition, and high levels of predator and hunting pressure.

Acknowledgements

We thank Wageningen Environmental Research (Alterra) and the Dutch Association of Goose Catchers for their assistance in catching and tagging the geese at the wintering grounds. We are grateful for the help of Berend Voslamber and Jan Vegelin with ringing the birds and of Bart A. Nolet with obtaining approval of the Dutch Commission for Animal Experiments (DEC). The equipment of geese with transmitters and GPS data collection for previous studies were conducted by GM and AK and supported by the German Aerospace Centre (DLR; EO-Move and ICARUS) and the DFG-funded project “Efficient algorithms to analyze movement in groups” to M. Buchin. The work was performed in full accordance with the Directive 2010/63/EU on the protection of animals used for scientific purposes and in accordance with ASAB/ABS guidelines. Approval for catching and tagging goose families was obtained from the Animal Welfare Committee of the Royal Netherlands Academy of Arts and Sciences (DEC NIOO13.14).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The individual tracking data is stored in the Movebank Database (www.movebank.org). Access to the data can be granted upon reasonable request to the author. The dataset of vectors can be found on https://figshare.com/ ( https://doi.org/10.6084/m9.figshare.24998501.v1

).

Author contributions

Conceptualization: Elmira Zaynagutdinova, Andrey Giljov, Karina Karenina; Methodology: Andrey Giljov, Karina Karenina, Andrea Kölzsch; Formal analysis and investigation: Elmira Zaynagutdinova, Alexandra Sinelshikova, Michael Vorotkov; Writing – original draft preparation: Elmira Zaynagutdinova; Writing – review and editing: all authors; Funding acquisition: Elmira Zaynagutdinova; Resources: Gerhard J. D. M. Müskens, Andrea Kölzsch; Supervision: Elmira Zaynagutdinova, Karina Karenina.

Additional information

Funding

This work was supported by Russian Science Foundation [grant number: 22-24-00346].

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 304.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.