IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v510y2025ics0304380025003308.html

Citizen scientists as butterfly predators: using foraging theory to understand individual recorder behaviour

Author

Listed:
  • Li, Mingrui
  • Boyd, Robin J.
  • Smith, Chloë
  • Fox, Richard
  • Roy, David
  • Bennie, Jonathan
  • ffrench-Constant, Richard H.

Abstract

Citizen science is increasingly important in the collection of biological data. However, to understand the broader utility of the growing number of citizen-derived records, we need to understand exactly how recorder behaviour affects the geographic distribution of records made. Here, we apply an optimal foraging model to citizen science data from the UK to determine how likely a recorder (predator) is to visit any given kilometre square and record a butterfly (prey). By defining the square with the highest density of an individual’s records as their ‘origin’, we show that the probability of visiting a given site depends on its distance from the origin and the rarity-weighted species richness of the species thought to be present. This pattern of behaviour differs between recorders visiting more than or fewer than five squares, termed broad and narrow-range foragers. The model shows that recorder behaviour is driven, in part, by a simple trade-off between distance travelled and the rarity-weighted species richness. This collective behaviour helps explain over-recording by broad-ranging foragers in protected areas at distance and under-recording, by narrow-range foragers, in the wider countryside. It also implies that estimating parameters describing rare species’ distributions (e.g. mean occupancy) will be challenging, since sample inclusion depends on occupancy itself. Mapping rare species’ distributions should be simpler, since the sites at which they can be found tend to be well-sampled, but the same is unlikely to be true of common species, which also occupy areas that are unlikely to be sampled. More work is needed to understand how widely our results can be generalised beyond the UK and the dataset considered.

Suggested Citation

  • Li, Mingrui & Boyd, Robin J. & Smith, Chloë & Fox, Richard & Roy, David & Bennie, Jonathan & ffrench-Constant, Richard H., 2025. "Citizen scientists as butterfly predators: using foraging theory to understand individual recorder behaviour," Ecological Modelling, Elsevier, vol. 510(C).
  • Handle: RePEc:eee:ecomod:v:510:y:2025:i:c:s0304380025003308
    DOI: 10.1016/j.ecolmodel.2025.111344
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380025003308
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2025.111344?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Corey T Callaghan & Jodi J L Rowley & William K Cornwell & Alistair G B Poore & Richard E Major, 2019. "Improving big citizen science data: Moving beyond haphazard sampling," PLOS Biology, Public Library of Science, vol. 17(6), pages 1-11, June.
    2. Politikos, Dimitrios V. & Huret, Martin & Petitgas, Pierre, 2015. "A coupled movement and bioenergetics model to explore the spawning migration of anchovy in the Bay of Biscay," Ecological Modelling, Elsevier, vol. 313(C), pages 212-222.
    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. Watson, Joseph W & Boyd, Robin & Dutta, Ritabrata & Vasdekis, Georgios & Walker, Nicola D. & Roy, Shovonlal & Everitt, Richard & Hyder, Kieran & Sibly, Richard M, 2022. "Incorporating environmental variability in a spatially-explicit individual-based model of European sea bass✰," Ecological Modelling, Elsevier, vol. 466(C).
    2. Adrien Guetté & Sébastien Caillault & Joséphine Pithon & Guillaume Pain & Hervé Daniel & Benoit Marchadour & Véronique Beaujouan, 2022. "Who and Where Are the Observers behind Biodiversity Citizen Science Data? Effect of Landscape Naturalness on the Spatial Distribution of French Birdwatching Records," Land, MDPI, vol. 11(11), pages 1-25, November.
    3. Beniamino Caputo & Mattia Manica & Federico Filipponi & Marta Blangiardo & Pietro Cobre & Luca Delucchi & Carlo Maria De Marco & Luca Iesu & Paola Morano & Valeria Petrella & Marco Salvemini & Cesare , 2020. "ZanzaMapp: A Scalable Citizen Science Tool to Monitor Perception of Mosquito Abundance and Nuisance in Italy and Beyond," IJERPH, MDPI, vol. 17(21), pages 1-19, October.
    4. Boyd, Robin & Roy, Shovonlal & Sibly, Richard & Thorpe, Robert & Hyder, Kieran, 2018. "A general approach to incorporating spatial and temporal variation in individual-based models of fish populations with application to Atlantic mackerel," Ecological Modelling, Elsevier, vol. 382(C), pages 9-17.
    5. M. Enenkel & M. E. Brown & J. V. Vogt & J. L. McCarty & A. Reid Bell & D. Guha-Sapir & W. Dorigo & K. Vasilaky & M. Svoboda & R. Bonifacio & M. Anderson & C. Funk & D. Osgood & C. Hain & P. Vinck, 2020. "Why predict climate hazards if we need to understand impacts? Putting humans back into the drought equation," Climatic Change, Springer, vol. 162(3), pages 1161-1176, October.
    6. Morrice, Katherine J. & Baptista, António M. & Burke, Brian J., 2020. "Environmental and behavioral controls on juvenile Chinook salmon migration pathways in the Columbia River estuary," Ecological Modelling, Elsevier, vol. 427(C).
    7. Ward-Paige, CA & White, Easton R & Madin, EMP & Bailes, LK & Bateman, RL & Belonje, E & Burns, KV & Cullain, N & de Waegh, R S & Eger, Aaron Matthius, 2020. "A framework for mapping and monitoring human-ocean interactions in near real-time during COVID-19 and beyond," OSF Preprints sxnu5, Center for Open Science.
    8. Coulon, Noémie & Elliott, Sophie & Barreau, Thomas & Lucas, Julie & Gousset, Emma & Feunteun, Eric & Carpentier, Alexandre, 2025. "Elasmobranch vulnerability to global warming: insights from bioenergetic modelling of catsharks under climate scenarios," Ecological Modelling, Elsevier, vol. 506(C).
    9. Nadja Pernat & Anika Kristin Gathof & Johann Herrmann & Birgit Seitz & Sascha Buchholz, 2023. "Citizen Science Apps in a Higher Education Botany Course: Data Quality and Learning Effects," Sustainability, MDPI, vol. 15(17), pages 1-15, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:eee:ecomod:v:510:y:2025:i:c:s0304380025003308. 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: http://www.journals.elsevier.com/ecological-modelling .

    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.