Vegetation History and Archaeobotany

, Volume 17, Issue 5, pp 479–495 | Cite as

Pollen productivity estimates and relevant source area of pollen for selected plant taxa in a pasture woodland landscape of the Jura Mountains (Switzerland)

  • Florence MazierEmail author
  • Anna Broström
  • Marie-José Gaillard
  • Shinya Sugita
  • Pascal Vittoz
  • Alexandre Buttler
Original Article


Relevant source area of pollen (RSAP) and pollen productivity for 11 key taxa characteristic of the pasture woodland landscape of the Jura Mountains, Switzerland, were estimated using pollen assemblages from moss polsters at 20 sites. To obtain robust pollen productivity estimates (PPEs), we used vegetation survey data at a fine spatial-resolution (1 × 1 m2) and randomized locations for sampling sites, techniques rarely used in palynology. Three Extended R value (ERV) submodels and three distance-weighting methods for plant abundance calculation were applied. Different combinations of the submodels and distance-weighting methods provide slightly different estimates of RSAP and PPEs. Although ERV submodel 1 using 1/d (d = distance in meters) best fits the dataset, PPE values for heavy pollen types (e.g. Abies) were sensitive to the method used for distance-weighting. Taxon-specific distance-weighting methods, such as Prentice’s model, emphasize the intertaxonomic differences in pollen dispersal and deposition, and are thus theoretically sound. For the dataset obtained in this project, Prentice’s model was more appropriate than other distance-weighting methods to estimate PPEs. Most of the taxa have PPEs equal to (Fagus, Plantago media and Potentilla-type), or higher (Abies, Picea, Rubiaceae and Trollius europaeus) than Poaceae (PPE = 1). Acer, Cyperaceae, and Plantago montana-type are low pollen producers. This set of PPEs will be useful for reconstructing heterogeneous, mountainous pasture woodland landscapes from fossil pollen records. The RSAP for moss polsters in this semi-open landscape region is ca. 300 m.


Pasture woodland landscape Relevant source area of pollen (RSAP) Pollen productivity estimates (PPE) Extended R value submodels Distance-weighting methods Pollen-vegetation relationship Moss polsters 



The study has been made possible with the help of a number of people to whom we are profoundly grateful: Zuzu Gadallah for supervision in the interpretation of vegetation from CIR aerial photos and for organizing the agreement between WSL and Swisstopo for the use of aerial photographs; Jesse Kalwij for sending CIR aerial photos; Sylvain Meier and Patrick Fouvy (Service des Forêts, de la Faune et de la Nature, canton de Vaud) for providing forest inventories; François Gillet for his continuous advice and help in the use of Phytobase software; Anne Vignot for her guidance in the use of GIS software; Florencia Oberli for preparing the pollen samples; Jacqueline van Leuwen for pollen analysis; Thomas Hickler and Jean-Daniel Tissot for spending long hours on computer programming. Thanks to François and his students team, Sylvie, Mireille, Olivier and Cécile for their precious and encouraging assistance during the fieldwork. The manuscript was improved thanks to the helpful comments and suggestions from the two referees, Sheila Hicks and André F. Lotter. This paper is a contribution to the POLLANDCAL (POLlen-LANDscape CALibration) network ( sponsored by Nordforsk and co-ordinated by M.-J. Gaillard (University of Kalmar, Sweden). We are very thankful to all POLLANDCAL members for useful and inspiring discussions during the numerous network workshops (2001–2005). Specific thanks are addressed to Anne-Brigitte Nielsen and Per Sjögren for all the discussions we had on the subject of PPEs. This research was funded by the National Centre of Competence in Research (NCCR) Plant survival of the Swiss National Science Foundation.


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Florence Mazier
    • 1
    • 2
    Email author
  • Anna Broström
    • 3
  • Marie-José Gaillard
    • 2
  • Shinya Sugita
    • 4
  • Pascal Vittoz
    • 5
  • Alexandre Buttler
    • 1
    • 6
  1. 1.Laboratoire de Chrono-EcologieUMR 6565 CNRS, Université de Franche-ComtéBesançon CedexFrance
  2. 2.School of Pure and Applied Natural SciencesKalmarSweden
  3. 3.Quaternary Science, GeoBiosphere Science CentreLund UniversityLundSweden
  4. 4.Department of Ecology, Evolution and BehaviourUniversity of MinnesotaSt PaulUSA
  5. 5.Département d’écologie et évolutionUniversité de Lausanne, Faculté des géosciences et de l’environnement (FGSE)LausanneSwitzerland
  6. 6.Laboratoire des Systèmes écologiques – ECOS, Ecole polytechnique fédérale de Lausanne (EPFL) et Institut fédéral de recherches WSLLausanneSwitzerland

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