Local snow and fluvial conditions drive taxonomic, functional and phylogenetic plant diversity in tundra

Abstract

To understand, how the diversity and hence functioning of tundra ecosystems might respond to altering environmental conditions, fine-scale studies are needed as local conditions may buffer broad-scale environmental changes. Furthermore, species functional traits and phylogeny may provide complementary insights to taxonomic diversity patterns as they link plant communities to ecosystem processes often more closely than species count. Here, we examined taxonomic, functional and phylogenetic plant diversity in relation to fundamental environmental factors, namely, growing degree days, snow persistence, soil moisture, pH and fluvial disturbance in northern Norway. The relationships between eight diversity metrics and environmental predictors were investigated using hierarchical generalised additive models. Our results indicated that taxonomic, functional and phylogenetic plant diversity in tundra are all strongly linked to local snow and fluvial conditions, with average variable importance of 0.19 and 0.14, respectively, whereas the importance of other predictors was low (average variable importance < 0.06). The average explained deviance by the models was 0.23. Predicted hotspots of different diversity metrics overlapped notably and were mostly located along the streams. However, when the effect of taxonomic richness was removed from the phylogenetic and functional diversities their connections with environmental predictors were weaker but indicated strongest relationships with snow and soil pH showing distinct diversity hotspots in areas with low species richness. Our study demonstrates that investigating multiple facets of biodiversity enhances understanding on community patterns and their drivers. Furthermore, our results highlight the importance of addressing local hydrological conditions that represent both resources and disturbances for vegetation. As arctic and alpine areas are probably shifting from snow to rain dominated, incorporating snow and fluvial information into the models might be particularly important to better understand tundra ecosystems under global change.