Biodiversity and Conservation

, Volume 22, Issue 3, pp 673–686 | Cite as

Occupancy modelling as a new approach to assess supranational trends using opportunistic data: a pilot study for the damselfly Calopteryx splendens

  • Arco J. van Strien
  • Tim Termaat
  • Vincent Kalkman
  • Marijn Prins
  • Geert De Knijf
  • Anne-Laure Gourmand
  • Xavier Houard
  • Brian Nelson
  • Calijn Plate
  • Stephen Prentice
  • Eugenie Regan
  • David Smallshire
  • Cédric Vanappelghem
  • Wouter Vanreusel
Original Paper

Abstract

There is limited information available on changes in biodiversity at the European scale, because there is a lack of data from standardised monitoring for most species groups. However, a great number of observations made without a standardised field protocol is available in many countries for many species. Such opportunistic data offer an alternative source of information, but unfortunately such data suffer from non-standardised observation effort and geographical bias. Here we describe a new approach to compiling supranational trends using opportunistic data which adjusts for these two major imperfections. The non-standardised observation effort is dealt with by occupancy modelling, and the unequal geographical distribution of sites by a weighting procedure. The damselfly Calopteryx splendens was chosen as our test species. The data were collected from five countries (Ireland, Great Britain, the Netherlands, Belgium and France), covering the period 1990–2008. We used occupancy models to estimate the annual number of occupied 1 × 1 km sites per country. Occupancy models use presence-absence data, account for imperfect detection of species, and thereby correct for between-year variability in observation effort. The occupancy models were run per country in a Bayesian mode of inference using JAGS. The occupancy estimates per country were then aggregated to assess the supranational trend in the number of occupied 1 × 1 km2. To adjust for the unequal geographical distribution of surveyed sites, we weighted the countries according to the number of sites surveyed and the range of the species per country. The distribution of C.splendens has increased significantly in the combined five countries. Our trial demonstrated that a supranational trend in distribution can be derived from opportunistic data, while adjusting for observation effort and geographical bias. This opens new perspectives for international monitoring of biodiversity.

Keywords

Detection Monitoring Distribution Citizen science data Odonata JAGS 

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Arco J. van Strien
    • 1
    • 2
  • Tim Termaat
    • 3
  • Vincent Kalkman
    • 4
  • Marijn Prins
    • 3
  • Geert De Knijf
    • 5
  • Anne-Laure Gourmand
    • 6
  • Xavier Houard
    • 7
  • Brian Nelson
    • 8
  • Calijn Plate
    • 1
  • Stephen Prentice
    • 9
  • Eugenie Regan
    • 10
  • David Smallshire
    • 11
  • Cédric Vanappelghem
    • 12
  • Wouter Vanreusel
    • 13
  1. 1.Statistics NetherlandsThe HagueThe Netherlands
  2. 2.Institute for Biodiversity and Ecosystem Dynamics, University of AmsterdamAmsterdamThe Netherlands
  3. 3.Dutch Butterfly ConservationWageningenThe Netherlands
  4. 4.European Invertebrate Survey—the Netherlands, Nationaal Natuurhistorisch Museum, NaturalisLeidenThe Netherlands
  5. 5.Research Institute for Nature and ForestBrusselsBelgium
  6. 6.Muséum National d’Histoire Naturelle, UMR 7204, CERSP ‘Conservation des Espèces, Restauration et Suivi des Populations’ParisFrance
  7. 7.Centre Entomologique de Ressources pour la Conservation, Office pour les insectes et leur environnement (Opie)Guyancourt CedexFrance
  8. 8.National Parks and Wildlife ServiceDublin 2Ireland
  9. 9.British Dragonfly Society, c/o Natural England, Parkside CourtTelfordUK
  10. 10.National Biodiversity Data CentreCo. WaterfordIreland
  11. 11.British Dragonfly SocietyNewton AbbotUK
  12. 12.Université de Lille 1, Laboratoire GEPV UMR CNRS 8198Villeneuve d’AscqFrance
  13. 13.NatuurpuntMechelenBelgium

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