How we improved a landscape study of species richness of beetles in woodland key habitats, and how model output can be improved

In an earlier study (Franc et al., 2007), local species richness of saproxylic oak beetles (including red-listed beetles) in forests was predicted mainly by the landscape (area of woodland key habitat within 1 km of plots). Such results are important for conservation work, but need to be backed up well, for reliable advice. We tested a two-stage method that improved our earlier models and our advice for conservation planning. We studied temperate mixed forest, rich in oaks Quercus robur/Quercus petraea, in a large landscape in Sweden. Franc et al. (2007) analysed 21 forests. Here we selected the significant explanatory variables (predictors) and other biologically relevant predictors, used the earlier 21 forests and sampled 11 new forests such that we expanded the range on the axes of the predictors. We collected in total 320 species of saproxylic oak beetles (23,137 individuals) of which 65 and 38 were red-listed (IUCN criteria, Swedish list 2000 and 2005, respectively). We partly confirmed our original results, but the results also changed in important ways: local species richness is now predicted by a combination of local, landscape and regional factors. Moreover, a local variable (dead wood) was the main predictor of saproxylic oak beetles (all species included), while for red-listed saproxylic oak beetles the landscape (woodland key habitat within 1 km of plots) was the main predictor, of local species richness. Thus, species richness of red-listed saproxylic oak beetles seems to depend mainly on landscapes factors, while total species richness of saproxylic oak beetles seems to depend more on local stand factors. We conclude that a two-stage research design can be useful in landscape and conservation studies, especially for species-rich taxa that require large samples per site.

► Landscape studies of local species richness are useful for conservation planning.
► We tested a two-stage method; in the second stage with extended predictors in models.
► First stage, effect of landscape; second stage, effects of local factor, landscape and region.
► Total species richness of beetles depended more on local factor (dead wood).
► Species richness of red-listed beetles depended more on landscape (woodland key habitats).