Biodiversity & Conservation

, Volume 14, Issue 6, pp 1345–1364

Species richness coincidence: conservation strategies based on predictive modelling

Authors

    • Institute of Nature Conservation
  • Dirk Bauwens
    • Institute of Nature Conservation
  • Luc De Bruyn
    • Institute of Nature Conservation
    • Department of BiologyUniversity of Antwerp
  • Anny Anselin
    • Institute of Nature Conservation
  • Glenn Vermeersch
    • Institute of Nature Conservation
  • Wouter Van Landuyt
    • Institute of Nature Conservation
  • Geert De Knijf
    • Institute of Nature Conservation
  • Marius Gilbert
    • Laboratoire de Biologie Animale et Cellulaire, CP 160/12Université Libre de Bruxelles
    • Fonds National de la Recherche Scientifique
Article

DOI: 10.1007/s10531-004-9662-x

Cite this article as:
Maes, D., Bauwens, D., De Bruyn, L. et al. Biodivers Conserv (2005) 14: 1345. doi:10.1007/s10531-004-9662-x

Abstract

The present-day geographic distribution of individual species of five taxonomic groups (plants, dragonflies, butterflies, herpetofauna and breeding birds) is relatively well-known on a small scale (5 × 5 km squares) in Flanders (north Belgium). These data allow identification of areas with a high diversity within each of the species groups. However, differences in mapping intensity and coverage hamper straightforward comparisons of species-rich areas among the taxonomic groups. To overcome this problem, we modelled the species richness of each taxonomic group separately using various environmental characteristics as predictor variables (area of different land use types, biotope diversity, topographic and climatic features). We applied forward stepwise multiple regression to build the models, using a subset of well-surveyed squares. A separate set of equally well-surveyed squares was used to test the predictions of the models. The coincidence of geographic areas with high predicted species richness was remarkably high among the four faunal groups, but much lower between plants and each of the four faunal groups. Thus, the four investigated faunal groups can be used as relatively good indicator taxa for one another in Flanders, at least for their within-group species diversity. A mean predicted species diversity per mapping square was also estimated by averaging the standardised predicted species richness over the five taxonomic groups, to locate the regions that were predicted as being the most species-rich for all five investigated taxonomic groups together. Finally, the applicability of predictive modelling in nature conservation policy both in Flanders and in other regions is discussed.

Keywords

Breeding birdsButterfliesConservation prioritiesDragonfliesFlandersHerpetofaunaHotspotsPlantsPredictive modellingSpecies richness coincidence

Copyright information

© Springer 2005