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Plant Ecology

, 203:303 | Cite as

Can we reliably estimate species richness with large plots? an assessment through calibration training

  • Frédéric ArchauxEmail author
  • Sylvaine Camaret
  • Jean-Luc Dupouey
  • Erwin Ulrich
  • Emmanuel Corcket
  • Laurence Bourjot
  • Alain Brêthes
  • Richard Chevalier
  • Jean-Francois Dobremez
  • Yann Dumas
  • Gérard Dumé
  • Marie Forêt
  • Françoise Forgeard
  • Myriam Lebret Gallet
  • Jean-François Picard
  • Franck Richard
  • Jean-Marie Savoie
  • Laurent Seytre
  • Jean Timbal
  • Jean Touffet
Article

Abstract

The number of species (species richness) is certainly the most widely used descriptor of plant diversity. However, estimating richness is a difficult task because plant censuses are prone to overlooking and identification errors that may lead to spurious interpretations. We used calibration data from the French ICP-level II plots (RENECOFOR) to assess the magnitude of the two kinds of errors in large forest plots. Eleven teams of professional botanists recorded all plants on the same eight 100-m² plots in 2004 (four plots, eights teams) and 2005 (four plots, nine teams including six from 2004), first independently and then consensually. On average, 15.5% of the shrubs and trees above 2 m were overlooked and 2.3% not identified at the species level or misidentified. On average, 19.2% of the plant species below 2 m in height were overlooked and 5.3% were misidentified and 1.3% were misidentified at the genus level (especially bryophytes). The overlooking rate also varied with plant species, morphological type, plot and team. It was higher when only one botanist made the census. It rapidly decreased with species cover and increased with plot species richness, the recording time of the census in the tree layer and the number of the censuses carried out during the day in the ground layer. Familiarity of the team with the local flora reduced the risk of overlooking and identification errors, whereas training had little impact. Differences in species richness (over space or time) in large plots should be cautiously interpreted, especially when several botanists participate in the survey. In particular, the quality of the data needs to be evaluated using calibration training and, if necessary, may be improved by involving more experienced botanists working in teams and by fixing a minimum recording time.

Keywords

Calibration Data quality Long-term monitoring Observer effect Plant survey 

Notes

Acknowledgements

We thank Frédéric Gosselin for help in data analysis, Victoria Moore for carefully re-reading the manuscript and Leos Klimeš and two reviewers for constructive comments on the manuscript.

Supplementary material

11258_2008_9551_MOESM1_ESM.doc (196 kb)
MOESM1 [INSERT CAPTION HERE] (DOC 196 kb)

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Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Frédéric Archaux
    • 1
    Email author
  • Sylvaine Camaret
    • 2
  • Jean-Luc Dupouey
    • 3
  • Erwin Ulrich
    • 4
  • Emmanuel Corcket
    • 5
  • Laurence Bourjot
    • 6
  • Alain Brêthes
    • 7
  • Richard Chevalier
    • 8
  • Jean-Francois Dobremez
    • 9
  • Yann Dumas
    • 8
  • Gérard Dumé
    • 10
  • Marie Forêt
    • 10
  • Françoise Forgeard
    • 11
  • Myriam Lebret Gallet
    • 11
  • Jean-François Picard
    • 12
  • Franck Richard
    • 13
  • Jean-Marie Savoie
    • 14
  • Laurent Seytre
    • 15
  • Jean Timbal
    • 16
  • Jean Touffet
    • 11
  1. 1.CEMAGREF Domaine des BarresNogent sur VernissonFrance
  2. 2.Laboratoire d’Ecologie Alpine (LECA)Université de Savoie, UFR CISMLe Bourget-du-lac CedexFrance
  3. 3.INRA Nancy, Forest Ecology and Ecophysiology UnitChampenouxFrance
  4. 4.Département RechercheOffice National des ForêtsFontainebleauFrance
  5. 5.UMR1202 BioGeCo, Université Bordeaux 1TalenceFrance
  6. 6.Bourjot EnvironnementLe Bouget du LacFrance
  7. 7.Direction Territoriale de l’Office Nationale des ForêtsParc Technologique Orléans-CharbonnièreBoigny sur BionneFrance
  8. 8.CemagrefNogent sur VernissonFrance
  9. 9.Université de SavoieLe Bourget du Lac CedexFrance
  10. 10.Inventaire Forestier NationalChâteau des BarresNogent sur VernissonFrance
  11. 11.Laboratoire d’Ecologie VégétaleUniversité de Rennes IRennes CedexFrance
  12. 12.Institut National de la Recherche AgronomiqueEquipe phytoécologieChampenouxFrance
  13. 13.Office National des ForêtsCorsicaFrance
  14. 14.Ecole Supérieure d’Agriculture de PurpanToulouse Cedex 3France
  15. 15.Conservatoire botanique national du Massif central, Le BourgChavaniac-LafayetteFrance
  16. 16.Institut National de la Recherche Agronomique, Centre de Bordeaux-CestasEquipe Ecophysiologie et Nutrition, Unité de Recherche ForestièreCestasFrance

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