IDEAS home Printed from https://ideas.repec.org/a/eee/thpobi/v156y2024icp12-21.html
   My bibliography  Save this article

Cultural transmission, competition for prey, and the evolution of cooperative hunting

Author

Listed:
  • Borofsky, Talia
  • Feldman, Marcus W.
  • Ram, Yoav

Abstract

Although cooperative hunting is widespread among animals, its benefits are unclear. At low frequencies, cooperative hunting may allow predators to escape competition and access bigger prey that could not be caught by a lone cooperative predator. Cooperative hunting is a more successful strategy when it is common, but its spread can result in overhunting big prey, which may have a lower per-capita growth rate than small prey. We construct a one-predator species, two-prey species model in which predators either learn to hunt small prey alone or learn to hunt big prey cooperatively. Predators first learn vertically from parents, then horizontally (i.e. socially) from random individuals or siblings. After horizontal transmission, they hunt with their learning partner if both are cooperative, and otherwise they hunt alone. Cooperative hunting cannot evolve when initially rare unless predators (a) interact with siblings, or (b) horizontally transmit the cooperative behavior to potential hunting partners. Whereas competition for small prey favors cooperative hunting when this cooperation is initially rare, the frequency of cooperative hunting cannot reach 100% unless big prey is abundant. Furthermore, a mutant that increases horizontal learning can invade if cooperative hunting is present, but not at 100%, because horizontal learning allows pairs of predators to have the same strategy. Our results reveal that the interactions between prey availability, social learning, and degree of cooperation among predators may have important effects on ecosystems.

Suggested Citation

  • Borofsky, Talia & Feldman, Marcus W. & Ram, Yoav, 2024. "Cultural transmission, competition for prey, and the evolution of cooperative hunting," Theoretical Population Biology, Elsevier, vol. 156(C), pages 12-21.
  • Handle: RePEc:eee:thpobi:v:156:y:2024:i:c:p:12-21
    DOI: 10.1016/j.tpb.2023.12.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040580924000017
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tpb.2023.12.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Steven J. Lade & Alessandro Tavoni & Simon A. Levin & Maja Schl�ter, 2013. "Regime shifts in a social-ecological system," GRI Working Papers 105, Grantham Research Institute on Climate Change and the Environment.
    2. Hauert, Christoph & Wakano, Joe Yuichiro & Doebeli, Michael, 2008. "Ecological public goods games: Cooperation and bifurcation," Theoretical Population Biology, Elsevier, vol. 73(2), pages 257-263.
    3. Borofsky, Talia & Feldman, Marcus W., 2022. "Success-biased social learning in a one-consumer, two-resource model," Theoretical Population Biology, Elsevier, vol. 146(C), pages 29-35.
    4. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    5. John M. Fryxell & Anna Mosser & Anthony R. E. Sinclair & Craig Packer, 2007. "Group formation stabilizes predator–prey dynamics," Nature, Nature, vol. 449(7165), pages 1041-1043, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    2. Jessica M. Vanslambrouck & Sean B. Wilson & Ker Sin Tan & Ella Groenewegen & Rajeev Rudraraju & Jessica Neil & Kynan T. Lawlor & Sophia Mah & Michelle Scurr & Sara E. Howden & Kanta Subbarao & Melissa, 2022. "Enhanced metanephric specification to functional proximal tubule enables toxicity screening and infectious disease modelling in kidney organoids," Nature Communications, Nature, vol. 13(1), pages 1-23, December.
    3. Lauren L. Porter & Allen K. Kim & Swechha Rimal & Loren L. Looger & Ananya Majumdar & Brett D. Mensh & Mary R. Starich & Marie-Paule Strub, 2022. "Many dissimilar NusG protein domains switch between α-helix and β-sheet folds," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Matthew Rosenblatt & Link Tejavibulya & Rongtao Jiang & Stephanie Noble & Dustin Scheinost, 2024. "Data leakage inflates prediction performance in connectome-based machine learning models," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. Sayedali Shetab Boushehri & Katharina Essig & Nikolaos-Kosmas Chlis & Sylvia Herter & Marina Bacac & Fabian J. Theis & Elke Glasmacher & Carsten Marr & Fabian Schmich, 2023. "Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Ivana Gudelj & Margie Kinnersley & Peter Rashkov & Karen Schmidt & Frank Rosenzweig, 2016. "Stability of Cross-Feeding Polymorphisms in Microbial Communities," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-17, December.
    7. Khaled Akkad & David He, 2023. "A dynamic mode decomposition based deep learning technique for prognostics," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2207-2224, June.
    8. Romain Fournier & Zoi Tsangalidou & David Reich & Pier Francesco Palamara, 2023. "Haplotype-based inference of recent effective population size in modern and ancient DNA samples," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    9. Laura Portell & Sergi Morera & Helena Ramalhinho, 2022. "Door-to-Door Transportation Services for Reduced Mobility Population: A Descriptive Analytics of the City of Barcelona," IJERPH, MDPI, vol. 19(8), pages 1-20, April.
    10. Caroline Haimerl & Douglas A. Ruff & Marlene R. Cohen & Cristina Savin & Eero P. Simoncelli, 2023. "Targeted V1 comodulation supports task-adaptive sensory decisions," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    11. Valentina Bosetti & Melanie Heugues & Alessandro Tavoni, 2017. "Luring others into climate action: coalition formation games with threshold and spillover effects," Oxford Economic Papers, Oxford University Press, vol. 69(2), pages 410-431.
    12. Matthias Wagener & Andriette Bekker & Mohammad Arashi, 2021. "Mastering the Body and Tail Shape of a Distribution," Mathematics, MDPI, vol. 9(21), pages 1-22, October.
    13. Gallo Cassarino, Tiziano & Barrett, Mark, 2022. "Meeting UK heat demands in zero emission renewable energy systems using storage and interconnectors," Applied Energy, Elsevier, vol. 306(PB).
    14. Maren Schnieder, 2023. "Ebike Sharing vs. Bike Sharing: Demand Prediction Using Deep Neural Networks and Random Forests," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    15. Mirza, M. Usman & Richter, Andries & van Nes, Egbert H. & Scheffer, Marten, 2019. "Technology driven inequality leads to poverty and resource depletion," Ecological Economics, Elsevier, vol. 160(C), pages 215-226.
    16. Gabriele Orlando & Daniele Raimondi & Ramon Duran-Romaña & Yves Moreau & Joost Schymkowitz & Frederic Rousseau, 2022. "PyUUL provides an interface between biological structures and deep learning algorithms," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    17. Mayuko Nakamaru & Akira Yokoyama, 2014. "The Effect of Ostracism and Optional Participation on the Evolution of Cooperation in the Voluntary Public Goods Game," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-9, September.
    18. Hazal Colak Oz & Çiçek Güven & Gonzalo Nápoles, 2023. "School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach," Journal of Computational Social Science, Springer, vol. 6(1), pages 245-287, April.
    19. Vincent Wagner & Nicole Erika Radde, 2021. "SiCaSMA: An Alternative Stochastic Description via Concatenation of Markov Processes for a Class of Catalytic Systems," Mathematics, MDPI, vol. 9(10), pages 1-13, May.
    20. Pal, Saheb & Ghosh, Indrajit, 2023. "Dynamics of a coupled socio-environmental model: An application to global CO2 emissions," Ecological Modelling, Elsevier, vol. 478(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:thpobi:v:156:y:2024:i:c:p:12-21. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.