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.

Empirical and process-based models predict enhanced beech growth in European mountains under climate change scenarios: A multimodel approach.

Process-based models and empirical modelling techniques are frequently used to (i) explore the sensitivity of tree growth to environmental variables, and (ii) predict the future growth of trees and forest stands under climate change scenarios. However, modelling approaches substantially influence predictions of the sensitivity of trees to environmental factors. Here, we used tree-ring width (TRW) data from 1630 beech trees from a network of 70 plots established across European mountains to build empirical predictive growth models using various modelling approaches. In addition, we used 3-PG and Biome-BGCMuSo process-based models to compare growth predictions with derived empirical models. Results revealed similar prediction errors (RMSE) across models ranging between 3.71 and 7.54 cm2 of basal area increment (BAI). The models explained most of the variability in BAI ranging from 54 % to 87 %. Selected explanatory variables (despite being statistically highly significant) and the pattern of the growth sensitivity differed between models substantially. We identified only five factors with the same effect and the same sensitivity pattern in all empirical models: tree DBH, competition index, elevation, Gini index of DBH, and soil silt content. However, the sensitivity to most of the climate variables was low and inconsistent among the empirical models. Both empirical and process-based models suggest that beech in European mountains will, on average, likely experience better growth conditions under both 4.5 and 8.5 RCP scenarios. The process-based models indicated that beech may grow better across European mountains by 1.05 to 1.4 times in warmer conditions. The empirical models identified several drivers of tree growth that are not included in the current process-based models (e.g., different nutrients) but may have a substantial effect on final results, particularly if they are limiting factors. Hence, future development of process-based models may build upon our findings to increase their ability to correctly capture ecosystem dynamics.

Climate-change-driven growth decline of European beech forests.

The growth of past, present, and future forests was, is and will be affected by climate variability. This multifaceted relationship has been assessed in several regional studies, but spatially resolved, large-scale analyses are largely missing so far. Here we estimate recent changes in growth of 5800 beech trees (Fagus sylvatica L.) from 324 sites, representing the full geographic and climatic range of species. Future growth trends were predicted considering state-of-the-art climate scenarios. The validated models indicate growth declines across large region of the distribution in recent decades, and project severe future growth declines ranging from -20% to more than -50% by 2090, depending on the region and climate change scenario (i.e. CMIP6 SSP1-2.6 and SSP5-8.5). Forecasted forest productivity losses are most striking towards the southern distribution limit of Fagus sylvatica, in regions where persisting atmospheric high-pressure systems are expected to increase drought severity. The projected 21st century growth changes across Europe indicate serious ecological and economic consequences that require immediate forest adaptation.

Frontiers of protected areas versus forest exploitation: Assessing habitat network functionality in 16 case study regions globally.

Exploitation of natural forests forms expanding frontiers. Simultaneously, protected area frontiers aim at maintaining functional habitat networks. To assess net effects of these frontiers, we examined 16 case study areas on five continents. We (1) mapped protected area instruments, (2) assessed their effectiveness, (3) mapped policy implementation tools, and (4) effects on protected areas originating from their surroundings. Results are given as follows: (1) conservation instruments covered 3-77%, (2) effectiveness of habitat networks depended on representativeness, habitat quality, functional connectivity, resource extraction in protected areas, time for landscape restoration, "paper parks", "fortress conservation", and data access, (3) regulatory policy instruments dominated over economic and informational, (4) negative matrix effects dominated over positive ones (protective forests, buffer zones, inaccessibility), which were restricted to former USSR and Costa Rica. Despite evidence-based knowledge about conservation targets, the importance of spatial segregation of conservation and use, and traditional knowledge, the trajectories for biodiversity conservation were generally negative.

European primary forest database v2.0.

Primary forests, defined here as forests where the signs of human impacts, if any, are strongly blurred due to decades without forest management, are scarce in Europe and continue to disappear. Despite these losses, we know little about where these forests occur. Here, we present a comprehensive geodatabase and map of Europe's known primary forests. Our geodatabase harmonizes 48 different, mostly field-based datasets of primary forests, and contains 18,411 individual patches (41.1 Mha) spread across 33 countries. When available, we provide information on each patch (name, location, naturalness, extent and dominant tree species) and the surrounding landscape (biogeographical regions, protection status, potential natural vegetation, current forest extent). Using Landsat satellite-image time series (1985-2018) we checked each patch for possible disturbance events since primary forests were identified, resulting in 94% of patches free of significant disturbances in the last 30 years. Although knowledge gaps remain, ours is the most comprehensive dataset on primary forests in Europe, and will be useful for ecological studies, and conservation planning to safeguard these unique forests.

Different tree-ring width sensitivities to satellite-based soil moisture from dry, moderate and wet pedunculate oak (Quercus robur L.) stands across a southeastern distribution margin.

Associations of pedunculate oak (Quercus robur L.) radial growth with satellite-based soil moisture (SM) during the intensive tree growth period over a 30-year time span (1980-2010) were analyzed. This study included tree-ring width (TRW) chronologies from 22 stands located in four southeastern (SE) European countries (Slovenia, Croatia, Serbia and Bulgaria), which were grouped into three wetness groups (WGs): dry (<650 mm), moderate (650-750 mm), and wet (>750 mm), following the annual sum of precipitation. High correlation strengths during the intensive growth period-late spring and early summer months (April to June) was noted, which was opposite to the trend in late summer months. Variations in detrended TRW (TRWi) sensitivity to SM were also observed among the WGs. Specifically, the TRWi chronologies from the dry and wet WGs provided a greater number of significant correlations (p < 0.01) than trees from the moderate WG did. In wetter stands, TRWi correlated more negatively in the wettest (spring) months, while the correlation was weaker in summer months; these trends were opposite to those of trees growing in drier conditions that had the strongest responses to SM. A generalized additive mixed model (GAMM) based on 38 variables indicated that the fit for SM and radial growth was as strong as the fits for other traditionally measured parameters (temperature, precipitation, and river water level) and calculated drought indices (standardized precipitation index and the Ellenberg index) and TRW. Additionally, radial growth chronologies from drier sites had stronger fits with surrounding environmental factors. In conclusion, our findings suggest that SM can potentially be used as a reliable remote sensing indicator of the soil wetness in oak forests, which affects tree productivity and radial growth patterns and provides a new opportunity in dendrochronology research on larger scales.

Geographical adaptation prevails over species-specific determinism in trees' vulnerability to climate change at Mediterranean rear-edge forests.

Climate change may reduce forest growth and increase forest mortality, which is connected to high carbon costs through reductions in gross primary production and net ecosystem exchange. Yet, the spatiotemporal patterns of vulnerability to both short-term extreme events and gradual environmental changes are quite uncertain across the species' limits of tolerance to dryness. Such information is fundamental for defining ecologically relevant upper limits of species tolerance to drought and, hence, to predict the risk of increased forest mortality and shifts in species composition. We investigate here to what extent the impact of short- and long-term environmental changes determines vulnerability to climate change of three evergreen conifers (Scots pine, silver fir, Norway spruce) and two deciduous hardwoods (European beech, sessile oak) tree species at their southernmost limits of distribution in the Mediterranean Basin. Finally, we simulated future forest growth under RCP 2.6 and 8.5 emission scenarios using a multispecies generalized linear mixed model. Our analysis provides four key insights into the patterns of species' vulnerability to climate change. First, site climatic marginality was significantly linked to the growth trends: increasing growth was related to less climatically limited sites. Second, estimated species-specific vulnerability did not match their a priori rank in drought tolerance: Scots pine and beech seem to be the most vulnerable species among those studied despite their contrasting physiologies. Third, adaptation to site conditions prevails over species-specific determinism in forest response to climate change. And fourth, regional differences in forests vulnerability to climate change across the Mediterranean Basin are linked to the influence of summer atmospheric circulation patterns, which are not correctly represented in global climate models. Thus, projections of forest performance should reconsider the traditional classification of tree species in functional types and critically evaluate the fine-scale limitations of the climate data generated by global climate models.