Adaptation of Drosophila larva foraging in response to changes in food resources

  1. Marina E Wosniack
  2. Dylan Festa
  3. Nan Hu
  4. Julijana Gjorgjieva  Is a corresponding author
  5. Jimena Berni  Is a corresponding author
  1. Max Planck Institute for Brain Research, Germany
  2. Technical University of Munich, Germany
  3. University of Cambridge, United Kingdom
  4. University of Sussex, United Kingdom

Abstract

All animals face the challenge of finding nutritious resources in a changing environment. To maximize life-time fitness, the exploratory behavior has to be flexible, but which behavioral elements adapt and what triggers those changes remain elusive. Using experiments and modeling, we characterized extensively how Drosophila larvae foraging adapts to different food quality and distribution and how the foraging genetic background influences this adaptation. Our work shows that different food properties modulated specific motor programs. Food quality controls the travelled distance by modulating crawling speed and frequency of pauses and turns. Food distribution, and in particular the food-no food interphase, controls turning behavior, stimulating turns towards the food when reaching the patch border and increasing the proportion of time spent within patches of food. Finally, the polymorphism in the foraging gene (rover-sitter) of the larvae adjusts the magnitude of the behavioral response to different food conditions. This study defines several levels of control of foraging and provides the basis for the systematic identification of the neuronal circuits and mechanisms controlling each behavioral response.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files 1 and 2; Source Data files have been provided formal experimental data: Figures 1, 3, 4 and 6.

Article and author information

Author details

  1. Marina E Wosniack

    Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2175-9713
  2. Dylan Festa

    School of Life Sciences, Technical University of Munich, Munich, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Nan Hu

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Julijana Gjorgjieva

    Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt, Germany
    For correspondence
    gjorgjieva@brain.mpg.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7118-4079
  5. Jimena Berni

    Brighton and Sussex Medical School,, University of Sussex, Brighton, United Kingdom
    For correspondence
    j.berni@sussex.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5068-1372

Funding

Royal Society (105568/Z/14/Z)

  • Jimena Berni

Wellcome Trust (105568/Z/14/Z)

  • Jimena Berni

Max-Planck-Gesellschaft

  • Marina E Wosniack
  • Julijana Gjorgjieva

Alexander von Humboldt-Stiftung

  • Marina E Wosniack

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, Wosniack et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,894
    views
  • 257
    downloads
  • 10
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Marina E Wosniack
  2. Dylan Festa
  3. Nan Hu
  4. Julijana Gjorgjieva
  5. Jimena Berni
(2022)
Adaptation of Drosophila larva foraging in response to changes in food resources
eLife 11:e75826.
https://doi.org/10.7554/eLife.75826

Share this article

https://doi.org/10.7554/eLife.75826

Further reading

    1. Neuroscience
    Jacob A Miller
    Insight

    When navigating environments with changing rules, human brain circuits flexibly adapt how and where we retain information to help us achieve our immediate goals.

    1. Neuroscience
    Zhujun Shao, Mengya Zhang, Qing Yu
    Research Article

    When holding visual information temporarily in working memory (WM), the neural representation of the memorandum is distributed across various cortical regions, including visual and frontal cortices. However, the role of stimulus representation in visual and frontal cortices during WM has been controversial. Here, we tested the hypothesis that stimulus representation persists in the frontal cortex to facilitate flexible control demands in WM. During functional MRI, participants flexibly switched between simple WM maintenance of visual stimulus or more complex rule-based categorization of maintained stimulus on a trial-by-trial basis. Our results demonstrated enhanced stimulus representation in the frontal cortex that tracked demands for active WM control and enhanced stimulus representation in the visual cortex that tracked demands for precise WM maintenance. This differential frontal stimulus representation traded off with the newly-generated category representation with varying control demands. Simulation using multi-module recurrent neural networks replicated human neural patterns when stimulus information was preserved for network readout. Altogether, these findings help reconcile the long-standing debate in WM research, and provide empirical and computational evidence that flexible stimulus representation in the frontal cortex during WM serves as a potential neural coding scheme to accommodate the ever-changing environment.