Major tree species of Central European forests differ in their proportion of positive, negative, and nonstationary growth trends.

Temperate forests are undergoing significant transformations due to the influence of climate change, including varying responses of different tree species to increasing temperature and drought severity. To comprehensively understand the full range of growth responses, representative datasets spanning extensive site and climatic gradients are essential. This study utilizes tree-ring data from 550 sites from the temperate forests of Czechia to assess growth trends of six dominant Central European tree species (European beech, Norway spruce, Scots pine, silver fir, sessile and pedunculate oak) over 1990-2014. By modeling mean growth series for each species and site, and employing principal component analysis, we identified the predominant growth trends. Over the study period, linear growth trends were evident across most sites (56% increasing, 32% decreasing, and 10% neutral). The proportion of sites with stationary positive trends increased from low toward high elevations, whereas the opposite was true for the stationary negative trends. Notably, within the middle range of their distribution (between 500 and 700 m a.s.l.), Norway spruce and European beech exhibited a mix of positive and negative growth trends. While Scots pine growth trends showed no clear elevation-based pattern, silver fir and oaks displayed consistent positive growth trends regardless of site elevation, indicating resilience to the ongoing warming. We demonstrate divergent growth trajectories across space and among species. These findings are particularly important as recent warming has triggered a gradual shift in the elevation range of optimal growth conditions for most tree species and has also led to a decoupling of growth trends between lowlands and mountain areas. As a result, further future shifts in the elevation range and changes in species diversity of European temperate forests can be expected.

Tree differences in primary and secondary growth drive convergent scaling in leaf area to sapwood area across Europe.

Trees scale leaf (AL ) and xylem (AX ) areas to couple leaf transpiration and carbon gain with xylem water transport. Some species are known to acclimate in AL  : AX balance in response to climate conditions, but whether trees of different species acclimate in AL  : AX in similar ways over their entire (continental) distributions is unknown. We analyzed the species and climate effects on the scaling of AL vs AX in branches of conifers (Pinus sylvestris, Picea abies) and broadleaved (Betula pendula, Populus tremula) sampled across a continental wide transect in Europe. Along the branch axis, AL and AX change in equal proportion (isometric scaling: b Ëœ 1) as for trees. Branches of similar length converged in the scaling of AL vs AX with an exponent of b = 0.58 across European climates irrespective of species. Branches of slow-growing trees from Northern and Southern regions preferentially allocated into new leaf rather than xylem area, with older xylem rings contributing to maintaining total xylem conductivity. In conclusion, trees in contrasting climates adjust their functional balance between water transport and leaf transpiration by maintaining biomass allocation to leaves, and adjusting their growth rate and xylem production to maintain xylem conductance.

Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe.

To enhance our understanding of forest carbon sequestration, climate change mitigation and drought impact on forest ecosystems, the availability of high-resolution annual forest growth maps based on tree-ring width (TRW) would provide a significant advancement to the field. Site-specific characteristics, which can be approximated by high-resolution Earth observation by satellites (EOS), emerge as crucial drivers of forest growth, influencing how climate translates into tree growth. EOS provides information on surface reflectance related to forest characteristics and thus can potentially improve the accuracy of forest growth models based on TRW. Through the modelling of TRW using EOS, climate and topography data, we showed that species-specific models can explain up to 52 % of model variance (Quercus petraea), while combining different species results in relatively poor model performance (R2 = 13 %). The integration of EOS into models based solely on climate and elevation data improved the explained variance by 6 % on average. Leveraging these insights, we successfully generated a map of annual TRW for the year 2021. We employed the area of applicability (AOA) approach to delineate the range in which our models are deemed valid. The calculated AOA for the established forest-type models was 73 % of the study region, indicating robust spatial applicability. Notably, unreliable predictions predominantly occurred in the climate margins of our dataset. In conclusion, our large-scale assessment underscores the efficacy of combining climate, EOS and topographic data to develop robust models for mapping annual TRW. This research not only fills a critical void in the current understanding of forest growth dynamics but also highlights the potential of integrated data sources for comprehensive ecosystem assessments.