Navigation by Extrapolation of Geomagnetic Cues in a Migratory Songbird
CURRENT BIOLOGY(2021)
Bangor Univ
Abstract
Displacement experiments have demonstrated that experienced migratory birds translocated thousands of kilometers away from their migratory corridor can orient toward and ultimately reach their intended destinations.1 This implies that they are capable of "true navigation," commonly defined2-4 as the ability to return to a known destination after displacement to an unknown location without relying on familiar surroundings, cues that emanate from the destination, or information collected during the outward journey.5-13 In birds, true navigation appears to require previous migratory experience5-7,14,15 (but see Kishkinev et al.16 and Piersma et al.17). It is generally assumed that, to correct for displacements outside the familiar area, birds initially gather information within their year-round distribution range, learn predictable spatial gradients of environmental cues within it, and extrapolate from those to unfamiliar magnitudes-the gradient hypothesis.6,9,18-22 However, the nature of the cues and evidence for actual extrapolation remain elusive. Geomagnetic cues (inclination, declination, and total intensity) provide predictable spatial gradients across large parts of the globe and could serve for navigation. We tested the orientation of long-distance migrants, Eurasian reed warblers, exposing them to geomagnetic cues of unfamiliar magnitude encountered beyond their natural distribution range. The birds demonstrated re-orientation toward their migratory corridor as if they were translocated to the corresponding location but only when all naturally occurring magnetic cues were presented, not when declination was changed alone. This result represents direct evidence for migratory birds' ability to navigate using geomagnetic cues extrapolated beyond their previous experience.
MoreTranslated text
Key words
magnetic sense,animal navigation,magnetic map,bird migration,magnetoreception,extrapolated map,true navigation,position determination,bicoordinate navigation
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined