• Energy Research

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.

AbstractTree‐ring records provide global high‐resolution information on tree‐species responses to global change, forest carbon and water dynamics, and past climate variability and extremes. The underlying assumption is a stationary (time‐stable), quasi‐linear relationship between tree growth and environment, which however conflicts with basic ecological and evolutionary theory. Indeed, our global assessment of the relevant tree‐ring literature demonstrates non‐stationarity in the majority of tested cases, not limited to specific proxies, environmental parameters, regions or species. Non‐stationarity likely represents the general nature of the relationship between tree‐growth proxies and environment. Studies assuming stationarity however score two times more citations influencing other fields of science and the science–policy interface. To reconcile ecological reality with the application of tree‐ring proxies for climate or environmental estimates, we provide a clarification of the stationarity concept, propose a simple confidence framework for the re‐evaluation of existing studies and recommend the use of a new statistical tool to detect non‐stationarity in tree‐ring proxies. Our contribution is meant to stimulate and facilitate discussion in light of our results to help increase confidence in tree‐ring‐based climate and environmental estimates for science, the public and policymakers.

AbstractTemperate 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.

AbstractThe majority of variation in six traits critical to the growth, survival and reproduction of plant species is thought to be organised along just two dimensions, corresponding to strategies of plant size and resource acquisition. However, it is unknown whether global plant trait relationships extend to climatic extremes, and if these interspecific relationships are confounded by trait variation within species. We test whether trait relationships extend to the cold extremes of life on Earth using the largest database of tundra plant traits yet compiled. We show that tundra plants demonstrate remarkably similar resource economic traits, but not size traits, compared to global distributions, and exhibit the same two dimensions of trait variation. Three quarters of trait variation occurs among species, mirroring global estimates of interspecific trait variation. Plant trait relationships are thus generalizable to the edge of global trait-space, informing prediction of plant community change in a warming world.