Biodiversity & Conservation

, Volume 14, Issue 6, pp 1345-1364

First online:

Species richness coincidence: conservation strategies based on predictive modelling

  • Dirk MaesAffiliated withInstitute of Nature Conservation Email author 
  • , Dirk BauwensAffiliated withInstitute of Nature Conservation
  • , Luc De BruynAffiliated withInstitute of Nature ConservationDepartment of Biology, University of Antwerp
  • , Anny AnselinAffiliated withInstitute of Nature Conservation
  • , Glenn VermeerschAffiliated withInstitute of Nature Conservation
  • , Wouter Van LanduytAffiliated withInstitute of Nature Conservation
  • , Geert De KnijfAffiliated withInstitute of Nature Conservation
  • , Marius GilbertAffiliated withLaboratoire de Biologie Animale et Cellulaire, CP 160/12, Université Libre de BruxellesFonds National de la Recherche Scientifique

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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.


Breeding birds Butterflies Conservation priorities Dragonflies Flanders Herpetofauna Hotspots Plants Predictive modelling Species richness coincidence