A cross-taxon analysis of the impact of climate change on abundance trends in central Europe. (July 2015) - Bowler, D.E.; Haase, P.; Kröncke, I.; Tackenberg, O.; Bauer, H.G.; Brendel, C.; Brooker, R.W.; Gerisch, M.; Henle, K.; Hickler, T.; Hof, C.; Klotz, S.; et al.
Highlights: Impact of climate change is predicted to depend on species temperature niche. Local population trends of birds, butterflies and beetles reveal fingerprints. Plants, springtails and bats show weaker signals of climate change. Abstract: Advances in phenology and pole- and up-ward shifts in geographic ranges are well-documented signs that species are responding to climate change. A deeper understanding of such responses across ecologically different species groups will help to assess future consequences for entire ecosystems. A less well-studied pattern linked with climate change is increases in abundances of warm-adapted species compared with cold-adapted species. To compare how recent climate change has affected the abundances of species across different taxonomic groups, we analyzed long-term local population trends and related them to the species temperature niche, as inferred from geographic distributions. We used population data sets collected in different regions of Central Europe, primarily Germany, for bats, birds, butterflies, ground beetles, springtails and dry grassland plants. We found that temperature niche was positively associated with long-term population trends in some of the taxonomic groups (birds, butterflies, ground beetles) but was less important in others (bats, springtails, and grassland plants). This variation in the importance of temperature niche suggested that some populations have been affected more than others by climate change, which may be explained by differences in species attributes, such as generation time and microhabitat preference. Our findings indicate that relating temperature niches of species to population trends is a useful method to quantify the impact of climate change on local population abundances. We show that this widely applicable approach is particularly suited for comparative cross-system analyses to identify which types of organisms, in which habitats, are responding the most to climate change.