IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v320y2016icp322-333.html
   My bibliography  Save this article

Modelling small-scale foraging habitat use in breeding Eurasian oystercatchers (Haematopus ostralegus) in relation to prey distribution and environmental predictors

Author

Listed:
  • Schwemmer, Philipp
  • Güpner, Franziska
  • Adler, Sven
  • Klingbeil, Knut
  • Garthe, Stefan

Abstract

Detailed knowledge of species distributions at a fine spatial scale is an essential prerequisite for the understanding of ecosystems. However, relating species distributions to environmental variables is difficult, and distribution patterns of mobile benthic top predators can only be estimated at a rough spatial scale using visual cues. This is particularly problematic in systems with strong environmental gradients, such as intertidal habitats. Monitoring predators using GPS tags allows collecting precise spatial data over wide areas and during night time. We collected fine-scale data on prey abundance and quality, sediment composition, inundation time of tidal flats, and foraging distances from nest sites to develop a predictive distribution model for oystercatchers (Haematopus ostralegus) in the Wadden Sea, Germany. This shorebird species was able to identify the patches with high biomass and abundance of its endobenthic prey at a very fine spatial scale. Modelling suggested that prey abundance and biomass were essential for predicting oystercatcher occurrence: the probability of encountering a foraging oystercatcher was higher than expected in areas with >100 cockles per m2 and areas with 80g ash-free dry weight per m2. Our modelling approach also showed that habitat use by oystercatchers was very strongly dependent on abiotic factors, i.e., oystercatchers preferred muddy and low-lying tidal flats with short exposure times close to their breeding sites. Oystercatchers only used patches >4km away from their breeding territories if such patches were particularly prey-rich. This study demonstrates the importance of fine-scale models of predators and environmental predictors in patchy environments. These results have two conclusions with important management implications: (1) fine-scale models of distribution data for predators can provide a valuable indicator of the location of important sites worthy of protection; and (2) abiotic predictors alone are suitable to identify potential valuable feeding sites for oystercatchers without the need for time-consuming collection of prey-base data, even in a coastal zone with strong gradients.

Suggested Citation

  • Schwemmer, Philipp & Güpner, Franziska & Adler, Sven & Klingbeil, Knut & Garthe, Stefan, 2016. "Modelling small-scale foraging habitat use in breeding Eurasian oystercatchers (Haematopus ostralegus) in relation to prey distribution and environmental predictors," Ecological Modelling, Elsevier, vol. 320(C), pages 322-333.
  • Handle: RePEc:eee:ecomod:v:320:y:2016:i:c:p:322-333
    DOI: 10.1016/j.ecolmodel.2015.10.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380015005086
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2015.10.023?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. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    2. Gill Ward & Trevor Hastie & Simon Barry & Jane Elith & John R. Leathwick, 2009. "Presence-Only Data and the EM Algorithm," Biometrics, The International Biometric Society, vol. 65(2), pages 554-563, June.
    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. M.L. Nores & M.P. Díaz, 2016. "Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 810-826, April.
    2. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," Working Papers hal-02790523, HAL.
    3. Iñaki Galán & Lorena Simón & Elena Boldo & Cristina Ortiz & Rafael Fernández-Cuenca & Cristina Linares & María José Medrano & Roberto Pastor-Barriuso, 2017. "Changes in hospitalizations for chronic respiratory diseases after two successive smoking bans in Spain," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    4. Masahiro Kato & Shota Yasui, 2020. "Learning Classifiers under Delayed Feedback with a Time Window Assumption," Papers 2009.13092, arXiv.org, revised Jun 2022.
    5. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
    6. Strasak, Alexander M. & Umlauf, Nikolaus & Pfeiffer, Ruth M. & Lang, Stefan, 2011. "Comparing penalized splines and fractional polynomials for flexible modelling of the effects of continuous predictor variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1540-1551, April.
    7. Roberto Basile & Luigi Benfratello & Davide Castellani, 2012. "Geoadditive models for regional count data: an application to industrial location," ERSA conference papers ersa12p83, European Regional Science Association.
    8. Paolo Veneri, 2018. "Urban spatial structure in OECD cities: Is urban population decentralising or clustering?," Papers in Regional Science, Wiley Blackwell, vol. 97(4), pages 1355-1374, November.
    9. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    10. Soutik Ghosal & Timothy S. Lau & Jeremy Gaskins & Maiying Kong, 2020. "A hierarchical mixed effect hurdle model for spatiotemporal count data and its application to identifying factors impacting health professional shortages," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1121-1144, November.
    11. Damien Rousselière, 2019. "A Flexible Approach to Age Dependence in Organizational Mortality: Comparing the Life Duration for Cooperative and Non-Cooperative Enterprises Using a Bayesian Generalized Additive Discrete Time Survi," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(4), pages 829-855, December.
    12. Ferraccioli, Federico & Sangalli, Laura M. & Finos, Livio, 2022. "Some first inferential tools for spatial regression with differential regularization," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    13. Abdollah Jalilian, 2017. "Modelling and classification of species abundance: a case study in the Barro Colorado Island plot," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2401-2409, October.
    14. Carlo Fezzi & Ian Bateman, 2015. "The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 2(1), pages 57-92.
    15. ferrara, giancarlo & campagna, arianna & bucci, valeria & atella, vincenzo, 2021. "Presumptive taxation and firms’ efficiency: an integrated approach for tax compliance analysis," MPRA Paper 111516, University Library of Munich, Germany.
    16. Ronald E. Gangnon & Natasha K. Stout & Oguzhan Alagoz & John M. Hampton & Brian L. Sprague & Amy Trentham-Dietz, 2018. "Contribution of Breast Cancer to Overall Mortality for US Women," Medical Decision Making, , vol. 38(1_suppl), pages 24-31, April.
    17. Zhang, Zhaolin & Zhai, Guocong & Xie, Kun & Xiao, Feng, 2022. "Exploring the nonlinear effects of ridesharing on public transit usage: A case study of San Diego," Journal of Transport Geography, Elsevier, vol. 104(C).
    18. Yuting Xue & Ji Cong & Yi Bai & Pai Zheng & Guiping Hu & Yulin Kang & Yonghua Wu & Liyan Cui & Guang Jia & Tiancheng Wang, 2023. "Associations between Short-Term Air Pollution Exposure and the Peripheral Leukocyte Distribution in the Adult Male Population in Beijing, China," IJERPH, MDPI, vol. 20(6), pages 1-14, March.
    19. Florackis, Chrisostomos & Kostakis, Alexandros & Ozkan, Aydin, 2009. "Managerial ownership and performance," Journal of Business Research, Elsevier, vol. 62(12), pages 1350-1357, December.
    20. Marra, Giampiero & Wood, Simon N., 2011. "Practical variable selection for generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2372-2387, July.

    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:320:y:2016:i:c:p:322-333. 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.