IDEAS home Printed from https://ideas.repec.org/a/oup/beheco/v24y2013i1p162-168..html
   My bibliography  Save this article

Short-term but not long-term patch avoidance in an orchid-pollinating solitary wasp

Author

Listed:
  • Michael R. Whitehead
  • Rod Peakall

Abstract

The success of exploitative attraction of insect pollinators to rewardless flowers may depend on a constrained capacity for learning. In the case of sexually deceptive orchids, the extent to which pollinators can avoid dishonest signals through learning or adaptation is poorly known. We used field experiments with synthetic pheromone baits in concert with novel miniaturized marking techniques to investigate patterns of behavior and movement in Neozeleboria cryptoides, the wasp pollinator of the sexually deceptive orchid Chiloglottis trapeziformis. In trials of 4- and 60-min duration, visitation rates to synthetic sex pheromone declined rapidly after the first minute and remained low, suggesting short-term avoidance. Using spatially explicit capture–recapture models, we then assessed if wasps maintained this avoidance for more than 24h. Among our 4 competing behavioral models, the best supported model was one which showed an increase in detection probability at a location for wasps that had previously been caught at that location. Therefore, we found no evidence for long-term patch avoidance. If spatial learning underpins the short-term avoidance we observed, then this information appears not to be retained beyond 24h. The typical patterns of N. cryptoides movement (range = 0–161 m, median = 14.8) coupled with short-term patch avoidance likely promote outcrossing in the clonal, self-compatible orchid it pollinates.

Suggested Citation

  • Michael R. Whitehead & Rod Peakall, 2013. "Short-term but not long-term patch avoidance in an orchid-pollinating solitary wasp," Behavioral Ecology, International Society for Behavioral Ecology, vol. 24(1), pages 162-168.
  • Handle: RePEc:oup:beheco:v:24:y:2013:i:1:p:162-168.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/beheco/ars149
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. D. L. Borchers & M. G. Efford, 2008. "Spatially Explicit Maximum Likelihood Methods for Capture–Recapture Studies," Biometrics, The International Biometric Society, vol. 64(2), pages 377-385, 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. Mevin B. Hooten & Michael R. Schwob & Devin S. Johnson & Jacob S. Ivan, 2023. "Multistage hierarchical capture–recapture models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.
    2. Ben C. Stevenson & Rachel M. Fewster & Koustubh Sharma, 2022. "Spatial correlation structures for detections of individuals in spatial capture–recapture models," Biometrics, The International Biometric Society, vol. 78(3), pages 963-973, September.
    3. Tomáš Jůnek & Pavla Jůnková Vymyslická & Kateřina Hozdecká & Pavla Hejcmanová, 2015. "Application of Spatial and Closed Capture-Recapture Models on Known Population of the Western Derby Eland (Taurotragus derbianus derbianus) in Senegal," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-16, September.
    4. Jennifer B Smith & Bryan S Stevens & Dwayne R Etter & David M Williams, 2020. "Performance of spatial capture-recapture models with repurposed data: Assessing estimator robustness for retrospective applications," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.
    5. D. L. Borchers & B. C. Stevenson & D. Kidney & L. Thomas & T. A. Marques, 2015. "A Unifying Model for Capture-Recapture and Distance Sampling Surveys of Wildlife Populations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 195-204, March.
    6. Murray G. Efford & Christine M. Hunter, 2018. "Spatial capture–mark–resight estimation of animal population density," Biometrics, The International Biometric Society, vol. 74(2), pages 411-420, June.
    7. Felix T. Petersma & Len Thomas & Aaron M. Thode & Danielle Harris & Tiago A. Marques & Gisela V. Cheoo & Katherine H. Kim, 2024. "Accommodating False Positives Within Acoustic Spatial Capture–Recapture, with Variable Source Levels, Noisy Bearings and an Inhomogeneous Spatial Density," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(3), pages 471-490, September.
    8. Simone Tenan & Paolo Pedrini & Natalia Bragalanti & Claudio Groff & Chris Sutherland, 2017. "Data integration for inference about spatial processes: A model-based approach to test and account for data inconsistency," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-18, October.
    9. Russell, Robin E. & Walsh, Daniel P. & Samuel, Michael D. & Grunnill, Martin D. & Rocke, Tonie E., 2021. "Space matters: host spatial structure and the dynamics of plague transmission," Ecological Modelling, Elsevier, vol. 443(C).
    10. Soumen Dey & Mohan Delampady & Ravishankar Parameshwaran & N. Samba Kumar & Arjun Srivathsa & K. Ullas Karanth, 2017. "Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 111-139, June.
    11. Bart J Harmsen & Rebecca J Foster & Howard Quigley, 2020. "Spatially explicit capture recapture density estimates: Robustness, accuracy and precision in a long-term study of jaguars (Panthera onca)," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    12. Murray G. Efford & Matthew R. Schofield, 2020. "A spatial open‐population capture‐recapture model," Biometrics, The International Biometric Society, vol. 76(2), pages 392-402, June.
    13. David L. Borchers & Tiago A. Marques, 2017. "From distance sampling to spatial capture–recapture," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 475-494, October.
    14. Robert M Dorazio, 2013. "Bayes and Empirical Bayes Estimators of Abundance and Density from Spatial Capture-Recapture Data," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    15. Xinhai Li & Ning Li & Baidu Li & Yuehua Sun & Erhu Gao, 2022. "AbundanceR: A Novel Method for Estimating Wildlife Abundance Based on Distance Sampling and Species Distribution Models," Land, MDPI, vol. 11(5), pages 1-13, April.
    16. M. G. Efford, 2022. "Efficient Discretization of Movement Kernels for Spatiotemporal Capture–Recapture," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 641-651, December.
    17. Mehnaz Jahid & Holly N. Steeves & Jason T. Fisher & Simon J. Bonner & Saman Muthukumarana & Laura L. E. Cowen, 2023. "Shooting for abundance: Comparing integrated multi‐sampling models for camera trap and hair trap data," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    18. Dey, Soumen & Moqanaki, Ehsan & Milleret, Cyril & Dupont, Pierre & Tourani, Mahdieh & Bischof, Richard, 2023. "Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects," Ecological Modelling, Elsevier, vol. 479(C).
    19. Ben C. Stevenson & David L. Borchers & Rachel M. Fewster, 2019. "Cluster capture‐recapture to account for identification uncertainty on aerial surveys of animal populations," Biometrics, The International Biometric Society, vol. 75(1), pages 326-336, March.
    20. Simon J. Bonner & Wei Zhang & Jiaqi Mu, 2024. "On the identifiability of the trinomial model for mark‐recapture‐recovery studies," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.

    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:oup:beheco:v:24:y:2013:i:1:p:162-168.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/beheco .

    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.