IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v329y2016icp18-28.html
   My bibliography  Save this article

Hierarchical models for describing space-for-time variations in insect population size and sex-ratio along a primary succession

Author

Listed:
  • Tenan, S.
  • Maffioletti, C.
  • Caccianiga, M.
  • Compostella, C.
  • Seppi, R.
  • Gobbi, M.

Abstract

Chronosequences of glacier retreat are useful for investigating primary successions over time periods that are longer than direct observation would permit. In this context, space-for-time substitution studies have been applied to assess the effects of climate change on invertebrate assemblages. However, population dynamics of insect species following retreating glaciers has been under-investigated until now due to difficulty in applying capture-recapture methods and correctly identifying species in the field. Removal sampling methods are commonly used, but imperfect detectability is rarely accounted for in the analytical framework. In this paper we study the effects of environmental drivers of spatial, and indirectly temporal, variation in population size and sex-ratio of cold-adapted insects through a hierarchical framework for abundance. We show the importance of a metapopulation design, where samples are replicated in space and time, to model data from small and scattered populations, typically present in habitats with climate-mediated selective pressure like those along glacier forelands. This scattered distribution can influence the observation or sampling process and thus species detectability.

Suggested Citation

  • Tenan, S. & Maffioletti, C. & Caccianiga, M. & Compostella, C. & Seppi, R. & Gobbi, M., 2016. "Hierarchical models for describing space-for-time variations in insect population size and sex-ratio along a primary succession," Ecological Modelling, Elsevier, vol. 329(C), pages 18-28.
    • Handle: RePEc:eee:ecomod:v:329:y:2016:i:c:p:18-28
    DOI: 10.1016/j.ecolmodel.2016.02.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380016300229
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2016.02.006?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. Tenan, Simone & O’Hara, Robert B. & Hendriks, Iris & Tavecchia, Giacomo, 2014. "Bayesian model selection: The steepest mountain to climb," Ecological Modelling, Elsevier, vol. 283(C), pages 62-69.
    2. Losapio, Gianalberto & Jordán, Ferenc & Caccianiga, Marco & Gobbi, Mauro, 2015. "Structure-dynamic relationship of plant–insect networks along a primary succession gradient on a glacier foreland," Ecological Modelling, Elsevier, vol. 314(C), pages 73-79.
    3. Tenan, Simone & Rotger Vallespir, Andreu & Igual, José Manuel & Moya, Óscar & Royle, J. Andrew & Tavecchia, Giacomo, 2013. "Population abundance, size structure and sex-ratio in an insular lizard," Ecological Modelling, Elsevier, vol. 267(C), pages 39-47.
    4. Kenneth F Kellner & Robert K Swihart, 2014. "Accounting for Imperfect Detection in Ecology: A Quantitative Review," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-8, October.
    5. Robert M. Dorazio & Howard L. Jelks & Frank Jordan, 2005. "Improving Removal-Based Estimates of Abundance by Sampling a Population of Spatially Distinct Subpopulations," Biometrics, The International Biometric Society, vol. 61(4), pages 1093-1101, December.
    6. Sigmund Hagvar, 2012. "Primary Succession in Glacier Forelands: How Small Animals Conquer New Land Around Melting Glaciers," Chapters, in: Stephen Young & Steven Silvern (ed.), International Perspectives on Global Environmental Change, IntechOpen.
    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. De Cubber, Lola & Trenkel, Verena M. & Diez, Guzman & Gil-Herrera, Juan & Novoa Pabon, Ana Maria & Eme, David & Lorance, Pascal, 2023. "Robust identification of potential habitats of a rare demersal species (blackspot seabream) in the Northeast Atlantic," Ecological Modelling, Elsevier, vol. 477(C).
    2. Laplanche, Christophe & Leunda, Pedro M. & Boithias, Laurie & Ardaíz, José & Juanes, Francis, 2019. "Advantages and insights from a hierarchical Bayesian growth and dynamics model based on salmonid electrofishing removal data," Ecological Modelling, Elsevier, vol. 392(C), pages 8-21.
    3. Aubry, Philippe & Francesiaz, Charlotte & Guillemain, Matthieu, 2024. "On the impact of preferential sampling on ecological status and trend assessment," Ecological Modelling, Elsevier, vol. 492(C).
    4. Antonio Canale & Igor Prünster, 2017. "Robustifying Bayesian nonparametric mixtures for count data," Biometrics, The International Biometric Society, vol. 73(1), pages 174-184, March.
    5. Robert M. Dorazio & Bhramar Mukherjee & Li Zhang & Malay Ghosh & Howard L. Jelks & Frank Jordan, 2008. "Modeling Unobserved Sources of Heterogeneity in Animal Abundance Using a Dirichlet Process Prior," Biometrics, The International Biometric Society, vol. 64(2), pages 635-644, June.
    6. William A. Link & Sarah J. Converse & Amy A. Yackel Adams & Nathan J. Hostetter, 2018. "Analysis of Population Change and Movement Using Robust Design Removal Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(4), pages 463-477, December.
    7. 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.
    8. Ming Zhou & Rachel S. McCrea & Eleni Matechou & Diana J. Cole & Richard A. Griffiths, 2019. "Removal models accounting for temporary emigration," Biometrics, The International Biometric Society, vol. 75(1), pages 24-35, March.
    9. 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.
    10. Matt Higham & Jay Ver Hoef & Lisa Madsen & Andy Aderman, 2021. "Adjusting a finite population block kriging estimator for imperfect detection," Environmetrics, John Wiley & Sons, Ltd., vol. 32(1), February.
    11. Whitlock, Steven L. & Womble, Jamie N. & Peterson, James T., 2020. "Modelling pinniped abundance and distribution by combining counts at terrestrial sites and in-water sightings," Ecological Modelling, Elsevier, vol. 420(C).
    12. repec:jss:jstsof:43:i10 is not listed on IDEAS
    13. Palamara, Gian Marco & Dennis, Stuart R. & Haenggi, Corinne & Schuwirth, Nele & Reichert, Peter, 2022. "Investigating the effect of pesticides on Daphnia population dynamics by inferring structure and parameters of a stochastic model," Ecological Modelling, Elsevier, vol. 472(C).
    14. Adam Martin-Schwarze & Jarad Niemi & Philip Dixon, 2017. "Assessing the Impacts of Time-to-Detection Distribution Assumptions on Detection Probability Estimation," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 465-480, December.
    15. Linda M. Haines, 2020. "Multinomial N‐mixture models for removal sampling," Biometrics, The International Biometric Society, vol. 76(2), pages 540-548, June.
    16. Brun, Mélanie & Abraham, Christophe & Jarry, Marc & Dumas, Jacques & Lange, Frédéric & Prévost, Etienne, 2011. "Estimating an homogeneous series of a population abundance indicator despite changes in data collection procedure: A hierarchical Bayesian modelling approach," Ecological Modelling, Elsevier, vol. 222(5), pages 1069-1079.

    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:329:y:2016:i:c:p:18-28. 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.