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A Spatial Modeling Framework for Monitoring Surveys with Different Sampling Protocols with a Case Study for Bird Abundance in Mid-Scandinavia

Author

Listed:
  • Jorge Sicacha-Parada

    (Norwegian University of Science and Technology (NTNU))

  • Diego Pavon-Jordan

    (Norwegian Institute for Nature Research (NINA))

  • Ingelin Steinsland

    (Norwegian University of Science and Technology (NTNU))

  • Roel May

    (Norwegian Institute for Nature Research (NINA))

  • Bård Stokke

    (Norwegian Institute for Nature Research (NINA))

  • Ingar Jostein Øien

    (Norwegian Ornithological Society-BirdLife Norway)

Abstract

Quantifying the total number of individuals (abundance) of species is the basis for spatial ecology and biodiversity conservation. Abundance data are mostly collected through professional surveys as part of monitoring programs, often at a national level. These surveys rarely follow exactly the same sampling protocol in different countries, which represents a challenge for producing biogeographical abundance maps based on the transboundary information available covering more than one country. Moreover, not all species are properly covered by a single monitoring scheme, and countries typically collect abundance data for target species through different monitoring schemes. We present a new methodology to model total abundance by merging count data information from surveys with different sampling protocols. The proposed methods are used for data from national breeding bird monitoring programs in Norway and Sweden. Each census collects abundance data following two different sampling protocols in each country, i.e., these protocols provide data from four different sampling processes. The modeling framework assumes a common Gaussian Random Field shared by both the observed and true abundance with either a linear or a relaxed linear association between them. The models account for particularities of each sampling protocol by including terms that affect each observation process, i.e., accounting for differences in observation units and detectability. Bayesian inference is performed using the Integrated Nested Laplace Approximation (INLA) and the Stochastic Partial Differential Equation (SPDE) approach for spatial modeling. We also present the results of a simulation study based on the empirical census data from mid-Scandinavia to assess the performance of the models under model misspecification. Finally, maps of the expected abundance of birds in our study region in mid-Scandinavia are presented with uncertainty estimates. We found that the framework allows for consistent integration of data from surveys with different sampling protocols. Further, the simulation study showed that models with a relaxed linear specification are less sensitive to misspecification, compared to the model that assumes linear association between counts. Relaxed linear specifications of total bird abundance in mid-Scandinavia improved both goodness of fit and the predictive performance of the models.

Suggested Citation

  • Jorge Sicacha-Parada & Diego Pavon-Jordan & Ingelin Steinsland & Roel May & Bård Stokke & Ingar Jostein Øien, 2022. "A Spatial Modeling Framework for Monitoring Surveys with Different Sampling Protocols with a Case Study for Bird Abundance in Mid-Scandinavia," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 562-591, September.
  • Handle: RePEc:spr:jagbes:v:27:y:2022:i:3:d:10.1007_s13253-022-00498-y
    DOI: 10.1007/s13253-022-00498-y
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    References listed on IDEAS

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    1. D. Simpson & J. B. Illian & F. Lindgren & S. H. Sørbye & H. Rue, 2016. "Going off grid: computationally efficient inference for log-Gaussian Cox processes," Biometrika, Biometrika Trust, vol. 103(1), pages 49-70.
    2. Dario Massimino & Alison Johnston & Simon Gillings & Frédéric Jiguet & James W. Pearce-Higgins, 2017. "Projected reductions in climatic suitability for vulnerable British birds," Climatic Change, Springer, vol. 145(1), pages 117-130, November.
    3. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    5. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    6. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    7. Yu, Hao & Cooper, Arthur R. & Infante, Dana M., 2020. "Improving species distribution model predictive accuracy using species abundance: Application with boosted regression trees," Ecological Modelling, Elsevier, vol. 432(C).
    8. Geir-Arne Fuglstad & Daniel Simpson & Finn Lindgren & Håvard Rue, 2019. "Constructing Priors that Penalize the Complexity of Gaussian Random Fields," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 445-452, January.
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