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Capitalizing on opportunistic data for monitoring relative abundances of species

Author

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  • Christophe Giraud
  • Clément Calenge
  • Camille Coron
  • Romain Julliard

Abstract

type="main" xml:lang="en"> With the internet, a massive amount of information on species abundance can be collected by citizen science programs. However, these data are often difficult to use directly in statistical inference, as their collection is generally opportunistic, and the distribution of the sampling effort is often not known. In this article, we develop a general statistical framework to combine such “opportunistic data” with data collected using schemes characterized by a known sampling effort. Under some structural assumptions regarding the sampling effort and detectability, our approach makes it possible to estimate the relative abundance of several species in different sites. It can be implemented through a simple generalized linear model. We illustrate the framework with typical bird datasets from the Aquitaine region in south-western France. We show that, under some assumptions, our approach provides estimates that are more precise than the ones obtained from the dataset with a known sampling effort alone. When the opportunistic data are abundant, the gain in precision may be considerable, especially for rare species. We also show that estimates can be obtained even for species recorded only in the opportunistic scheme. Opportunistic data combined with a relatively small amount of data collected with a known effort may thus provide access to accurate and precise estimates of quantitative changes in relative abundance over space and/or time.

Suggested Citation

  • Christophe Giraud & Clément Calenge & Camille Coron & Romain Julliard, 2016. "Capitalizing on opportunistic data for monitoring relative abundances of species," Biometrics, The International Biometric Society, vol. 72(2), pages 649-658, June.
  • Handle: RePEc:bla:biomet:v:72:y:2016:i:2:p:649-658
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    Cited by:

    1. Corey T Callaghan & Jodi J L Rowley & William K Cornwell & Alistair G B Poore & Richard E Major, 2019. "Improving big citizen science data: Moving beyond haphazard sampling," PLOS Biology, Public Library of Science, vol. 17(6), pages 1-11, June.
    2. Van Eupen, Camille & Maes, Dirk & Herremans, Marc & Swinnen, Kristijn R.R. & Somers, Ben & Luca, Stijn, 2021. "The impact of data quality filtering of opportunistic citizen science data on species distribution model performance," Ecological Modelling, Elsevier, vol. 444(C).
    3. Scott, E. Marian, 2018. "The role of Statistics in the era of big data: Crucial, critical and under-valued," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 20-24.

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