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Caste-Specific Demography and Phenology in Bumblebees: Modelling BeeWalk Data

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  • Eleni Matechou

    (University of Kent)

  • Stephen N. Freeman

    (Centre for Ecology and Hydrology)

  • Richard Comont

    (Bumblebee Conservation Trust)

Abstract

We present novel dynamic mixture models for the monitoring of bumblebee populations on an unprecedented geographical scale, motivated by the UK citizen science scheme BeeWalk. The models allow us for the first time to estimate bumblebee phenology and within-season productivity, defined as the number of individuals in each caste per colony in the population in that year, from citizen science data. All of these parameters are estimated separately for each caste, giving a means of considerable ecological detail in examining temporal changes in the complex life cycle of a social insect in the wild. Due to the dynamic nature of the models, we are able to produce population trends for a number of UK bumblebee species using the available time-series. Via an additional simulation exercise, we show the extent to which useful information will increase if the survey continues, and expands in scale, as expected. Bumblebees are extraordinarily important components of the ecosystem, providing pollination services of vast economic impact and functioning as indicator species for changes in climate or land use. Our results demonstrate the changes in both phenology and productivity between years and provide an invaluable tool for monitoring bumblebee populations, many of which are in decline, in the UK and around the world. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Eleni Matechou & Stephen N. Freeman & Richard Comont, 2018. "Caste-Specific Demography and Phenology in Bumblebees: Modelling BeeWalk Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(4), pages 427-445, December.
    • Handle: RePEc:spr:jagbes:v:23:y:2018:i:4:d:10.1007_s13253-018-0332-y
    DOI: 10.1007/s13253-018-0332-y
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    References listed on IDEAS

    as
    1. Emily B. Dennis & Byron J. T. Morgan & Stephen N. Freeman & David B. Roy & Tom Brereton, 2016. "Dynamic Models for Longitudinal Butterfly Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(1), pages 1-21, March.
    2. Emily B. Dennis & Byron J. T. Morgan & Stephen N. Freeman & Tom M. Brereton & David B. Roy, 2016. "A generalized abundance index for seasonal invertebrates," Biometrics, The International Biometric Society, vol. 72(4), pages 1305-1314, December.
    3. Shirley Pledger, 2000. "Unified Maximum Likelihood Estimates for Closed Capture–Recapture Models Using Mixtures," Biometrics, The International Biometric Society, vol. 56(2), pages 434-442, June.
    4. Ben A. Woodcock & Nicholas J. B. Isaac & James M. Bullock & David B. Roy & David G. Garthwaite & Andrew Crowe & Richard F. Pywell, 2016. "Impacts of neonicotinoid use on long-term population changes in wild bees in England," Nature Communications, Nature, vol. 7(1), pages 1-8, November.
    5. Shirley Pledger & Kenneth H. Pollock & James L. Norris, 2003. "Open Capture-Recapture Models with Heterogeneity: I. Cormack-Jolly-Seber Model," Biometrics, The International Biometric Society, vol. 59(4), pages 786-794, December.
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    1. Dennis, Emily B. & Kéry, Marc & Morgan, Byron J.T. & Coray, Armin & Schaub, Michael & Baur, Bruno, 2021. "Integrated modelling of insect population dynamics at two temporal scales," Ecological Modelling, Elsevier, vol. 441(C).

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