Species richness coincidence: conservation strategies based on predictive modelling

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 Â 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.

Determinants of species richness patterns in the Netherlands across multiple taxonomic groups

“…Furthermore, they found a high degree of congruence among the patterns of the different groups. The major difference between the results of Maes et al (2005) and our study is that they did not find any of the incorporated climatic variables to be of much importance. However, we found that for the herpetofauna the duration of sunshine was strongly negatively correlated with the number of species.…”

“…Only for herpetofauna diversity the environmental heterogeneity variables (heterogeneity of habitat or soil types and range in altitude) were not entered at an early stage in the stepwise modelling procedure. In general, these findings agree very well with the work of Maes et al (2005), who studied the species richness patterns of partly the same taxonomic groups in Belgium. They found that for all studied groups (plants, dragonflies, herpetofauna, butterflies and birds) the number of species was positively correlated with biotope diversity.…”

“…We evaluated the models' accuracy by fitting it to the evaluation set and compared the predicted and observed species richness using Spearman's rank correlation (Heikkinen and Neuvonen 1997;Maes et al 2005).…”

Using crisp and fuzzy modelling to identify favourability hotspots useful to perform gap analysis

“…Although species richness has been considered a good surrogate of biodiversity in many studies (Real et al 1993;Araújo 1999;Maes et al 2005), there are some authors who have shown that species richness is more related with the distribution of generalist species than to the distribution of rare ones (Vázquez and Gaston 2004). However, the majority of the fuzzy favourability hotspots for mammals obtained in this study coincide with those of fuzzy mammal rarity in Andalusia (Real et al 2006b).…”

“…Maes et al (2005) recommended that policy makers should make more use of modelling techniques as a proactive conservation tool, because it allows to better target sites predisposed to be included to a natural reserve network. In the same way, Ferrier (2002) proposed integrating biological and environmental data through predictive modelling as a strategy that may help alleviate some of the problems associated with using remotely mapped surrogates in conservation planning.…”