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A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators Using Intermittent Data

Author

Listed:
  • Stephen N. Freeman

    (UK Centre for Ecology & Hydrology)

  • Nicholas J. B. Isaac

    (UK Centre for Ecology & Hydrology)

  • Panagiotis Besbeas

    (Athens University of Economics and Business
    University of Kent)

  • Emily B. Dennis

    (University of Kent
    Butterfly Conservation)

  • Byron J. T. Morgan

    (University of Kent)

Abstract

Biodiversity indicators summarise extensive, complex ecological data sets and are important in influencing government policy. Component data consist of time-varying indices for each of a number of different species. However, current biodiversity indicators suffer from multiple statistical shortcomings. We describe a state-space formulation for new multispecies biodiversity indicators, based on rates of change in the abundance or occupancy probability of the contributing individual species. The formulation is flexible and applicable to different taxa. It possesses several advantages, including the ability to accommodate the sporadic unavailability of data, incorporate variation in the estimation precision of the individual species’ indices when appropriate, and allow the direct incorporation of smoothing over time. Furthermore, model fitting is straightforward in Bayesian and classical implementations, the latter adopting either efficient Hidden Markov modelling or the Kalman filter. Conveniently, the same algorithms can be adopted for cases based on abundance or occupancy data—only the subsequent interpretation differs. The procedure removes the need for bootstrapping which can be prohibitive. We recommend which of two alternatives to use when taxa are fully or partially sampled. The performance of the new approach is demonstrated on simulated data, and through application to three diverse national UK data sets on butterflies, bats and dragonflies. We see that uncritical incorporation of index standard errors should be avoided. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Stephen N. Freeman & Nicholas J. B. Isaac & Panagiotis Besbeas & Emily B. Dennis & Byron J. T. Morgan, 2021. "A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators Using Intermittent Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(1), pages 71-89, March.
    • Handle: RePEc:spr:jagbes:v:26:y:2021:i:1:d:10.1007_s13253-020-00410-6
    DOI: 10.1007/s13253-020-00410-6
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    References listed on IDEAS

    as
    1. Crainiceanu, Ciprian M. & Ruppert, David & Wand, Matthew P., 2005. "Bayesian Analysis for Penalized Spline Regression Using WinBUGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i14).
    2. 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.
    3. 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.
    4. Panagiotis Besbeas & Byron J. T. Morgan, 2020. "A general framework for modeling population abundance data," Biometrics, The International Biometric Society, vol. 76(1), pages 281-292, March.
    5. O. Gimenez & C. Crainiceanu & C. Barbraud & S. Jenouvrier & B. J. T. Morgan, 2006. "Semiparametric Regression in Capture–Recapture Modeling," Biometrics, The International Biometric Society, vol. 62(3), pages 691-698, September.
    6. Panagiotis Besbeas & Byron J. T. Morgan, 2012. "Kalman filter initialization for integrated population modelling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(1), pages 151-162, January.
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