Titelangaben
Schuster, Stefan:
The archerfish predictive C-start.
In: The Journal of Comparative Physiology A.
Bd. 209
(2023)
Heft 5
.
- S. 827-837.
ISSN 1432-1351
DOI: https://doi.org/10.1007/s00359-023-01658-2
Abstract
A very quick decision enables hunting archerfish to secure downed prey even when they are heavily outnumbered by competing other surface-feeding fish. Based exclusively on information that is taken briefly after the onset of prey motion, the fish select a rapid C-start that turns them right towards the later point of catch. Moreover, the C-start, and not later fin strokes, already lends the fish the speed needed to arrive at just the right time. The archerfish predictive C-starts are kinematically not distinguishable from escape C-starts made by the same individual and are among the fastest C-starts known in teleost fish. The start decisions allow the fish—for ballistically falling prey—to respond accurately to any combination of the initial variables of prey movement and for any position and orientation of the responding fish. The start decisions do not show a speed–accuracy tradeoff and their accuracy is buffered against substantial changes of environmental parameters. Here, I introduce key aspects of this high-speed decision that combines speed, complexity, and precision in an unusual way.
Weitere Angaben
Publikationsform: | Artikel in einer Zeitschrift |
---|---|
Begutachteter Beitrag: | Ja |
Keywords: | Decision-making; Neuroethology; C-start; Predator; Speed–accuracy |
Institutionen der Universität: | Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften > Fachgruppe Biologie > Lehrstuhl Tierphysiologie > Lehrstuhl Tierphysiologie - Univ.-Prof. Dr. Stefan Schuster Fakultäten Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften > Fachgruppe Biologie Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften > Fachgruppe Biologie > Lehrstuhl Tierphysiologie |
Titel an der UBT entstanden: | Ja |
Themengebiete aus DDC: | 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften; Biologie 500 Naturwissenschaften und Mathematik > 590 Tiere (Zoologie) |
Eingestellt am: | 16 Dec 2023 22:00 |
Letzte Änderung: | 18 Dec 2023 06:18 |
URI: | https://eref.uni-bayreuth.de/id/eprint/88067 |