Species richness coincidence: Conservation strategies based on predictive modelling

Onderzoeksoutput: Bijdrage aan tijdschriftA1: Web of Science-artikel



Originele taal-2Engels
TijdschriftBiodiversity and Conservation
Tijschrift nummer6
Pagina's (van-tot)1345-1364
Aantal pagina's20
StatusGepubliceerd - 2005

Bibliografische nota

Publication Authorstring : Maes, D.; Bauwens, D.; De Bruyn, L.; Anselin, A.; Vermeersch, G.; Van Landuyt, W.; De Knijf, G.; Gilbert, M.
Publication RefStringPartII : <i>Biodiversity and Conservation 14(6)</i>: 1345-1364. <a href="" target="_blank"></a>


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 (55 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.
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