Scientific article
Open access

The prediction of macrophyte species occurrence in Swiss ponds

Published inHydrobiologia, vol. 570, no. 1, p. 175-182
  • Open Access - Licence nationale Springer
Publication date2006

The study attempted to model the abundance of aquatic plant species recorded in a range of ponds in Switzerland. A stratified sample of 80 ponds, distributed all over the country, provided input data for model development. Of the 154 species recorded, 45 were selected for modelling. A total of 14 environmental parameters were preselected as candidate explanatory variables. Two types of statistical tools were used to explore the data and to develop the predictive models: linear regression (LR) and generalized additive models (GAMs). Six LR species models had a reasonable predictive ability (30-50% of variance explained by the selected predictors). There was a gradient in the quality of the 45 GAM models. Ten species models exhibited both a good fit and statistical robustness: Lemna minor, Phragmites australis, Lysimachia vulgaris, Galium palustre, Lysimachia nummularia, Iris pseudacorus, Lythrum salicaria, Lycopus europaeus, Phalaris arundinacea, Alisma plantago-aquatica, Schoenoplectus lacustris, Carex nigra. Altitude appeared to be a key explanatory variable in most of the species models. In some cases, the degree to which the shore was shaded, connectivity between water bodies, pond area, mineral nitrogen levels, pond age, pond depth, and the extent of agriculture or pasture in the catchment were selected as additional explanatory variables. The species models demonstrated that it is possible to predict species abundance of aquatic macrophytes and that each species responded individually to distinct environmental variables.

  • Aquatic plants
  • Environmental variables
  • Predictive models
  • Small waterbodies
  • Switzerland
Citation (ISO format)
AUDERSET JOYE, Dominique et al. The prediction of macrophyte species occurrence in Swiss ponds. In: Hydrobiologia, 2006, vol. 570, n° 1, p. 175–182. doi: 10.1007/s10750-006-0178-0
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Article (Published version)
ISSN of the journal0018-8158

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