IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0025937.html
   My bibliography  Save this article

The Allometry of Prey Preferences

Author

Listed:
  • Gregor Kalinkat
  • Björn Christian Rall
  • Olivera Vucic-Pestic
  • Ulrich Brose

Abstract

The distribution of weak and strong non-linear feeding interactions (i.e., functional responses) across the links of complex food webs is critically important for their stability. While empirical advances have unravelled constraints on single-prey functional responses, their validity in the context of complex food webs where most predators have multiple prey remain uncertain. In this study, we present conceptual evidence for the invalidity of strictly density-dependent consumption as the null model in multi-prey experiments. Instead, we employ two-prey functional responses parameterised with allometric scaling relationships of the functional response parameters that were derived from a previous single-prey functional response study as novel null models. Our experiments included predators of different sizes from two taxonomical groups (wolf spiders and ground beetles) simultaneously preying on one small and one large prey species. We define compliance with the null model predictions (based on two independent single-prey functional responses) as passive preferences or passive switching, and deviations from the null model as active preferences or active switching. Our results indicate active and passive preferences for the larger prey by predators that are at least twice the size of the larger prey. Moreover, our approach revealed that active preferences increased significantly with the predator-prey body-mass ratio. Together with prior allometric scaling relationships of functional response parameters, this preference allometry may allow estimating the distribution of functional response parameters across the myriads of interactions in natural ecosystems.

Suggested Citation

  • Gregor Kalinkat & Björn Christian Rall & Olivera Vucic-Pestic & Ulrich Brose, 2011. "The Allometry of Prey Preferences," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-11, October.
  • Handle: RePEc:plo:pone00:0025937
    DOI: 10.1371/journal.pone.0025937
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0025937
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0025937&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0025937?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
    ---><---

    References listed on IDEAS

    as
    1. Soetaert, Karline & Petzoldt, Thomas & Setzer, R. Woodrow, 2010. "Solving Differential Equations in R: Package deSolve," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i09).
    2. Chris Carbone & Georgina M. Mace & S. Craig Roberts & David W. Macdonald, 1999. "Energetic constraints on the diet of terrestrial carnivores," Nature, Nature, vol. 402(6759), pages 286-288, November.
    3. José M. Montoya & Stuart L. Pimm & Ricard V. Solé, 2006. "Ecological networks and their fragility," Nature, Nature, vol. 442(7100), pages 259-264, July.
    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. Zechen Wang & Zhenqin Shi & Jingeng Huo & Wenbo Zhu & Yanhui Yan & Na Ding, 2023. "Construction and Optimization of an Ecological Network in Funiu Mountain Area Based on MSPA and MCR Models, China," Land, MDPI, vol. 12(8), pages 1-13, August.
    2. Fatima-Zahra Jaouimaa & Daniel Dempsey & Suzanne Van Osch & Stephen Kinsella & Kevin Burke & Jason Wyse & James Sweeney, 2021. "An age-structured SEIR model for COVID-19 incidence in Dublin, Ireland with framework for evaluating health intervention cost," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-25, December.
    3. Xiaolong Lin & Zongmu Yao & Xinguang Wang & Shangqi Xu & Chunjie Tian & Lei Tian, 2021. "Water-Covered Depth with the Freeze–Thaw Cycle Influences Fungal Communities on Rice Straw Decomposition," Agriculture, MDPI, vol. 11(11), pages 1-16, November.
    4. Belém Barbosa & José Ramón Saura & Dag Bennett, 2024. "How do entrepreneurs perform digital marketing across the customer journey? A review and discussion of the main uses," The Journal of Technology Transfer, Springer, vol. 49(1), pages 69-103, February.
    5. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    6. Overstall, Antony M. & Woods, David C. & Martin, Kieran J., 2019. "Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 126-142.
    7. Dina in ‘t Zandt & Zuzana Kolaříková & Tomáš Cajthaml & Zuzana Münzbergová, 2023. "Plant community stability is associated with a decoupling of prokaryote and fungal soil networks," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    8. Serrouya, R. & Dickie, M. & DeMars, C. & Wittmann, M.J. & Boutin, S., 2020. "Predicting the effects of restoring linear features on woodland caribou populations," Ecological Modelling, Elsevier, vol. 416(C).
    9. Bellingeri, Michele & Cassi, Davide & Vincenzi, Simone, 2013. "Increasing the extinction risk of highly connected species causes a sharp robust-to-fragile transition in empirical food webs," Ecological Modelling, Elsevier, vol. 251(C), pages 1-8.
    10. Zhang, Yan & Wu, Tong & Song, Changsu & Hein, Lars & Shi, Faqi & Han, Mingchen & Ouyang, Zhiyun, 2022. "Influences of climate change and land use change on the interactions of ecosystem services in China’s Xijiang River Basin," Ecosystem Services, Elsevier, vol. 58(C).
    11. Zadoki Tabo & Chester Kalinda & Lutz Breuer & Christian Albrecht, 2023. "Adapting Strategies for Effective Schistosomiasis Prevention: A Mathematical Modeling Approach," Mathematics, MDPI, vol. 11(12), pages 1-18, June.
    12. Alexandra McQueen & Marcel Klaassen & Glenn J. Tattersall & Robyn Atkinson & Roz Jessop & Chris J. Hassell & Maureen Christie & Matthew R. E. Symonds, 2022. "Thermal adaptation best explains Bergmann’s and Allen’s Rules across ecologically diverse shorebirds," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    13. Matthew R Borths & Erik R Seiffert, 2017. "Craniodental and humeral morphology of a new species of Masrasector (Teratodontinae, Hyaenodonta, Placentalia) from the late Eocene of Egypt and locomotor diversity in hyaenodonts," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-60, April.
    14. Saucan, Emil & Sreejith, R.P. & Vivek-Ananth, R.P. & Jost, Jürgen & Samal, Areejit, 2019. "Discrete Ricci curvatures for directed networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 347-360.
    15. Moore, Christopher M. & Catella, Samantha A. & Abbott, Karen C., 2018. "Population dynamics of mutualism and intraspecific density dependence: How θ-logistic density dependence affects mutualistic positive feedback," Ecological Modelling, Elsevier, vol. 368(C), pages 191-197.
    16. Wang, Jin-Liang & Wu, Huai-Ning, 2011. "Stability analysis of impulsive parabolic complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 44(11), pages 1020-1034.
    17. Torres-Alruiz, Maria Daniela & Rodríguez, Diego J., 2013. "A topo-dynamical perspective to evaluate indirect interactions in trophic webs: New indexes," Ecological Modelling, Elsevier, vol. 250(C), pages 363-369.
    18. Shengnan Chen & Huiyan He & Rongrong Zong & Kaiwen Liu & Yutian Miao & Miaomiao Yan & Lei Xu, 2020. "Geographical Patterns of Algal Communities Associated with Different Urban Lakes in China," IJERPH, MDPI, vol. 17(3), pages 1-19, February.
    19. Yuxiang Zhao & Zishu Liu & Baofeng Zhang & Jingjie Cai & Xiangwu Yao & Meng Zhang & Ye Deng & Baolan Hu, 2023. "Inter-bacterial mutualism promoted by public goods in a system characterized by deterministic temperature variation," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    20. Hayato Goto & Hideki Takayasu & Misako Takayasu, 2017. "Estimating risk propagation between interacting firms on inter-firm complex network," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-12, October.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0025937. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.