Details zur Publikation

Referenztyp Zeitschriften
DOI / URL Link
Titel (primär) Relating geographical variation in pollination types to environmental and spatial factors using novel statistical methods
Autor Kühn, I.; Bierman, S.M.; Durka, W.; Klotz, S.;
Journal / Serie New Phytologist
Erscheinungsjahr 2006
Department BZF;
Band/Volume 172
Heft 1
Sprache englisch;
Keywords Bayesian methods; central Europe; compositional data; environmental correlates; plant functional types; pollination types; spatial autocorrelation
Abstract
  • The relative frequencies of functional traits of plant species show notable spatial variation, which is often related to environmental factors. Pollination type (insect-, wind- or self-pollination) is a critical trait for plant reproduction and provision of ecosystem services.

  • • 

    Here, we mapped the distribution of pollination types across Germany by combining databases on plant distribution and plant pollination types. Applying a new method, we modelled the composition of pollination types using a set of 12 environmental variables as predictors within a Bayesian framework which allows for the analysis of compositional data in the presence of spatial autocorrelation.

  • • 

    A clear biogeographical pattern in the distribution of pollination types was revealed which was adequately captured by our model. The most striking relationship was a relative increase in insect-pollination and a corresponding decrease of selfing with increasing altitude. Further important factors were wind speed, geology and land use.

  • • 

    We present a powerful tool to analyse the distribution patterns of plant functional types such as pollination types and their relationship with environmental parameters in a spatially explicit framework.

ID 2805
dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=2805
Kühn, I., Bierman, S.M., Durka, W., Klotz, S. (2006):
Relating geographical variation in pollination types to environmental and spatial factors using novel statistical methods
New Phytol. 172 (1), 127 - 139