Co-occurrence patterns of trees along macro-climatic gradients and their potential influence on the present and future distribution of Fagus sylvatica L.

Abstract

Aim During recent and future climate change, shifts in large-scale species ranges are expected due to the hypothesized major role of climatic factors in regulating species distributions. The stress-gradient hypothesis suggests that biotic interactions may act as major constraints on species distributions under more favourable growing conditions, while climatic constraints may dominate under unfavourable conditions. We tested this hypothesis for one focal tree species having three major competitors using broad-scale environmental data. We evaluated the variation of species co-occurrence patterns in climate space and estimated the influence of these patterns on the distribution of the focal species for current and projected future climates.Location Europe.Methods We used ICP Forest Level 1 data as well as climatic, topographic and edaphic variables. First, correlations between the relative abundance of European beech (Fagus sylvatica) and three major competitor species (Picea abies, Pinus sylvestris and Quercus robur) were analysed in environmental space, and then projected to geographic space. Second, a sensitivity analysis was performed using generalized additive models (GAM) to evaluate where and how much the predicted F. sylvatica distribution varied under current and future climates if potential competitor species were included or excluded. We evaluated if these areas coincide with current species co-occurrence patterns.Results Correlation analyses supported the stress-gradient hypothesis: towards favourable growing conditions of F. sylvatica, its abundance was strongly linked to the abundance of its competitors, while this link weakened towards unfavourable growing conditions, with stronger correlations in the south and at low elevations than in the north and at high elevations. The sensitivity analysis showed a potential spatial segregation of species with changing climate and a pronounced shift of zones where co-occurrence patterns may play a major role.Main conclusions Our results demonstrate the importance of species co-occurrence patterns for calibrating improved species distribution models for use in projections of climate effects. The correlation approach is able to localize European areas where inclusion of biotic predictors is effective. The climateinduced spatial segregation of the major tree species could have ecological and economic consequences.

Available Climate Regimes Drive Niche Diversification during Range Expansion

“…However, our ability to approximate fundamental niche limits using occurrence data may vary along environmental gradients if the importance of abiotic conditions and biotic interactions varies (e.g., Grime 1979;Bertness and Callaway 1994). Empirical studies suggest that fundamental, abiotic niche limits in plants are best approximated by occurrence data in stressful environmental conditions (Normand et al 2009;Meier et al 2011).…”

Challenges in modelling the abundance of 105 tree species in eastern North America using climate, edaphic, and topographic variables

“…One approach to refine SAM for climate change research could be to infer future relative importance patterns from reliable SDM predicting the cooccurrence of species, coupled with more mechanistic knowledge of how abundance is expected to vary with species interactions in communities and landscapes. This could be based on species traits and a better knowledge of the individual response of species to climate (Clark et al, 2011;Gunderson et al, 2012;Meier et al, 2011). Some species, for instance, never reach dominance across their range, but it is still largely unclear how species will reassemble into communities under novel climate conditions.…”

Test the relative importance of biotic and abiotic factors on species distribution – A case study in the Yellow River Delta

“…Biotic interactions may change with spatial scale [31]. Hence, 3 Contributions of the biotic, abiotic factors set and the joint of biotic and abiotic factors set to explained deviance.…”

“…Because biotic interactions are very complex, it is difficult to quantify and incorporate them into SDMs. Nowadays, the popular approach is "using the co-occurring species of the target species as biotic factors in the SDMs" [30,31]. Some studies proved the effects of this approach [12,30,32].…”