Invertebrate herbivory is a key ecological process shaping ecosystem multifunctionality, especially in forest habitats. The vertical stratification of forests is characterised by unique structural niches, microclimatic conditions, the availability of resources for organisms and biotic assemblages. Nevertheless, a comprehensive understanding of the processes driving plant-herbivore interactions across forests' complex three-dimensional systems remains poorly understood. We selected forest stands along physiological and structural axes and used high-resolution LiDAR data on stand structure, together with microclimate and biodiversity data, to investigate patterns of invertebrate herbivory on European beech (Fagus sylvatica) leaves and herbivore abundance across different spatial scales, in the canopy and understorey of a beech-dominated forest. Herbivore abundance in the canopy was 67% higher than in the understorey, yet relative herbivory did not differ between layers. This decoupling between herbivore abundance and herbivory suggests a resource dilution effect: greater foliage availability in canopies leads to herbivore aggregation but proportionally dilutes relative leaf damage. We found layer-specific drivers, with herbivore abundance responding negatively to vertical structure and positively to microclimate in both layers, while woody plant diversity had contrasting effects across layers. Relative herbivory was only influenced positively by woody plant diversity in the understorey. Stand-specific factors explained substantial variance in both metrics, suggesting that important drivers operate simultaneously across stands and vertical layers. Synthesis. This study shows that the relationship between herbivore abundance and herbivory can be decoupled across vertical forest layers through resource dilution, challenging the assumption that more herbivores result in more per-leaf damage. The effects of woody plant diversity and structure depend on whether a stratum functions as a resource-rich or resource-limited environment for beech leaf herbivores. These findings highlight that understanding the mechanisms of resource availability as drivers of herbivory in forests requires vertically resolved, multi-scale approaches.

Mountain forests provide essential ecosystem services, including protection against natural hazards, carbon storage, and biodiversity habitat. Climate change threatens the continuous provision of these services and is driving anticipated shifts in tree species ranges across elevation gradients. Understanding current regeneration dynamics is critical for predicting future forest composition and guiding adaptive management.We analyzed two decades (2004-2023) of tree regeneration data from 2,377 plots across seven forest types spanning 1.3 million ha and an elevation gradient from 200 to 2,300 m asl in the Swiss Jura mountains and Alps. Using data from the Swiss National Forest Inventory and statistical models accounting for repeated measurements (generalized estimating equations), we assessed trends in presence for 20 tree species in two height classes: small saplings (40-129 cm height) and tall saplings (≥130 cm, DBH

Spatio-temporal vegetation models are essential for simulating dynamics and shifts in species distributions over large areas, particularly under global change. One such model is the dynamic forest landscape model TreeMig. We have developed a framework that makes the model more user-friendly, improves its functionality, and expands its possibilities. The framework consists of the improved TreeMig model, the R package 'TreeMig-R' for pre-processing input data, model execution and visualization, a graphical user interface (GUI) for ease of use, and a set of sample input data. A suite of increasingly complex applications demonstrates its potential, including simulations of spatial forest dynamics under climate change in a highly fragmented landscape, the additional introduction and spread of an invasive tree species, and the coupling with an external model for the control of the invasive species.

ABSTRACT Despite the importance of soil for the distribution of forest organisms, the influence of edaphic properties on species range shifts under climate change remains poorly understood at the regional level. Current biodiversity forecasts frequently rely on climate‐only models, potentially overlooking the edaphic dimension of species niches and overestimating migration potential. Climate‐driven species shifts along elevational and latitudinal gradients may be enhanced or hindered by edaphic properties. Here, we focused on predicting the potential range shifts of 2403 temperate forest species across multiple kingdoms and phyla (grouped as Tracheophytes, Bryophytes, Fungi and Lichens) under rcp 4.5 and 8.5 scenarios, integrating high‐resolution digital soil maps. We employed a hierarchical modelling approach, coupling continental‐scale climate models across Europe with high‐resolution regional models in Switzerland to account for the full climate niche of the species using Nested Species Distribution Models (N‐SDM). Our results demonstrate that edaphic properties are among the most important predictors for regional forest species distributions. The inclusion of soil variables consistently reduced suitable habitat across all taxa. This suggests that soil heterogeneity governs species distributions and range shifts across taxonomic and functional groups in line with their edaphic preferences. These differences may, in turn, lead to divergent migration trajectories. Incorporating soil predictors constrained upslope shifts in tracheophytes and fungi, thereby delaying their migration, while facilitating climate tracking in bryophytes and lichens. Despite divergent migration patterns projected under climate warming, incorporating soil predictors consistently resulted in higher predicted habitat loss, except for tracheophytes under rcp8.5, which showed broadly wide responses. In conclusion, omitting high‐quality soil data from biodiversity forecasts leads to overly optimistic range shift predictions. Integrating soil variables is therefore essential for accurate ecological modelling and for prioritising conservation in areas where soil conditions may hinder climate tracking.

Predicting microbial community composition is essential for belowground biodiversity conservation and management yet remains methodologically challenging. Stacked species distribution models (S-SDM) offer a promising framework by allowing to model large numbers of individual microbial Amplicon Sequence Variants (ASVs) along environmental gradients and assembling them into community-level predictions. However, the consequences of key modelling choices on community-level predictions remain poorly understood, particularly for microbial data. Using bacterial sequence data from 250 topsoil samples collected in the western Swiss Alps through environmentally stratified random sampling, we evaluated how cross-validation and binarisation options influence S-SDM community-level predictions. We assessed prediction accuracy relative to observed communities, differences among predicted communities, and performance along environmental gradients. Stacked ASV model performance in predicting community composition were higher at both extremes of the pH gradient. Predicted communities tended to underestimate compositional dissimilarity but preserved the overall structure of observed beta-diversity. Cross-validation community constraints had negligible effects on predictions. In contrast, binarisation methods strongly influenced outcomes: better community composition predictions were obtained when the sum of predicted probabilities was used to determine threshold value between predicted presence and absence at ASV-level. These results demonstrate that S-SDM can be successfully applied to microbial communities and that the thresholding approach based on the ranking of predicted probabilities yields the most reliable community predictions. Our findings highlight the potential of S-SDMs to support the spatial predictions of microbial biodiversity and their integration into conservation assessments in line with recent IUCN initiatives.

Les vagues de chaleur et les sécheresses au cours de la saison de végétation deviennent de plus en plus fréquentes en raison du changement climatique. Afin d’évaluer l’impact de ces conditions sur la croissance de différentes essences d’avenir aux altitudes les plus basses en Suisse durant la phase de régénération, six essences ont été plantées en automne 2021 dans trois trouées forestières de basse altitude du Plateau et du Jura, chacune avec quatre provenances de graines. Une partie a été exposée à des conditions naturelles, l’autre à un réchauffement passif en serre, avec précipitations normales ou réduites (–50%). Les essences étudiées comprennent trois espèces indigènes (sapin blanc, hêtre, chêne sessile) ainsi que trois espèces non indigènes (douglas, cèdre de l’Atlas, chêne chevelu). Leur performance à l’âge de 5 ans sur des sols de hêtraie varie fortement selon la station. A Apples, sur un umbrisol profond, toutes les essences montrent une bonne croissance, notamment sous serre. A Pfeffingen, sur un sol calcaire peu profond (leptosol rendzic), leur croissance est restée faible, avec une mortalité élevée chez le hêtre, les deux chênes et le sapin blanc. Le douglas montre la meilleure croissance dans toutes les conditions, même sur le calcaire à Pfeffingen. Le cèdre de l’Atlas a survécu partout, mais présente une croissance variable selon les sites, tandis que la croissance du chêne chevelu est restée inférieure à celle du chêne sessile. Les différences entre les provenances des jeunes arbres étaient minimes. En conclusion, les effets potentiellement négatifs du réchauffement et de la sécheresse sur la régénération dépendent fortement du sol. Sur les sols profonds non limités en eau, les essences indigènes devraient pouvoir continuer à se rajeunir à l’avenir. En revanche, sur les stations pauvres à sol superficiel, des essences non indigènes comme le douglas pourraient présenter une alternative.

The onset of leaf discoloration in deciduous trees is primarily triggered by seasonal declines in temperature and photoperiod. However, severe drought conditions can induce early leaf discoloration and shedding. Early discolored European beech (Fagus sylvatica L.) trees may be more susceptible to crown dieback and mortality following a drought. These persistent legacy effects of drought underlie efforts to understand the environmental drivers of early leaf discoloration but also complicate ongoing monitoring. Here, field observations of early leaf discoloration in Switzerland's European beech forests and intra-annually normalized Sentinel-2 vegetation index time series were used to model and isolate the signal of discoloration from drought legacy effects in both drought and post-drought years. To demonstrate model efficacy and examine empirical inter-and intra-annual trends in early leaf discoloration, the trained model was applied to predict May to August early leaf discoloration across Switzerland's European beech dominated forests at a 10 m resolution from 2017 to 2023. Results reveal an order of magnitude greater proportion of early leaf discoloration at the end of the drought-affected months of August 2018 (5% discolored) and August 2023 (2% discolored) as compared to the non-drought affected month of August 2019 (0.4% discolored). In 2018 and 2023, discoloration hotspots formed as early as June in Switzerland's most severely affected northwestern regions and spread rapidly in the following weeks. Efforts here establish a robust framework for disentangling seasonal drought related discoloration from legacy effects of drought and forest management forming a baseline for further investigations of biotic and abiotic early leaf discoloration drivers.

Increasingly frequent droughts in Central Europe underscore the need for regular forest health monitoring. For decades, vegetation indices like Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) have been used as an indicator of forest condition and these indices are the focus of next-generation near real-time forest water stress monitoring which aims to update stress information coincident with satellite revisit times. However, few studies have robustly correlated vegetation indices with continuous ground-based monitoring tracking the water stress of individual trees. This study examines the relationship between 7 years of Sentinel-2 derived vegetation index time series (NDWI, NDVI, Chlorophyll Red Edge: CIRE, Chlorophyll Carotenoid Index: CCI, and Enhanced Vegetation Index: EVI) and continuous stem-level tree water deficit measurements from 41 European Beech (Fagus sylvatica L.) and 61 Norway Spruce (Picea abies (L.) H. Karst.) trees in Switzerland's temperate forests. Results show both an inter-and intra-seasonal correlation between tree water deficit and vegetation indices at a monthly time step (linear fit R2 values reaching 0.62 for Norway Spruce and 0.58 for European Beech), but it is more challenging to resolve recoverable tree water deficit at a finer temporal resolution. Common satellite-derived vegetation indices are unlikely to fully replace the sensitivity of in-situ tree water stress measurements but some indicators like EVI for Norway Spruce or NDWI for European Beech show potential for estimating broad tree water stress trends during the vegetated period. Monthly, or ongoing rolling average estimates could offer a scalable approach for regular drought stress assessment.

Over the past decade, extreme temperature and drought have resulted in widespread early leaf discoloration in European Beech (Fagus sylvatica) forests across central Europe. Discoloration during the particularly hot and dry summer of 2018 was ultimately associated with increased rates of crown dieback and tree mortality. Given the trend towards hotter and drier growing seasons under a changing climate, there is an increasing demand for site-specific recommendations on drought-resilient forest management practices in Switzerland. Making these recommendations requires a robust understanding of empirical forest disturbance and estimates of future forest health under a range of climatic and management conditions. To that end, using 2018 field observations, manual mapping of forest discoloration in aerial imagery, and multispectral Sentinel-2 imagery, we are developing 10 m/pixel estimates of European Beech discoloration across Switzerland during the 2018 to 2023 foliated periods. To date, we have 1) developed a robust interpolated Sentinel-2 time series from 2018 to 2023 for all of Switzerland, 2) trained a random forest model using 2018 ground control data and several vegetation indices from the Sentinel-2 time series to predict 2018 early leaf discoloration across Switzerland’s European Beech forests with c. 90% accuracy and, 3) used the Chlorophyll Red-Edge Index derived from the Sentinel-2 time series to approximate tree phenology and the length of the foliated period. We estimate that the 2018 foliated period was, on average, 45±19 days shorter for discolored sites as compared to sites without discoloration. Our results generally align well with previous studies of the 2018 drought in Switzerland and additional observational data is being compiled to validate the application of 2018 ground truth data across the foliated periods from 2018 to 2023. In combination with high-resolution soil maps, meteorological data, topographic derivatives, and information on Swiss forest structure, we will use empirical discoloration estimates to train ensemble models of site-specific susceptibility to drought. By artificially varying the meteorological and forest structure variables in these models we will have the unique opportunity to better understand European Beech susceptibility to drought and test the influence of a range of future climate scenarios and forest management strategies on Swiss forest health at a high spatial resolution.

Forests provide essential ecosystem services, from carbon sequestration and biodiversity conservation to soil protection and socio-economic benefits. Understanding forest regeneration patterns is crucial for predicting future forest composition and ensuring the continued provision of these services. While long-term forest monitoring is well-established in Europe and in particular Switzerland through the National Forest Inventories (NFI) comprehensive analyses of regeneration trends across different forest communities remain limited. This study analyzes 20 years of regeneration data from the Swiss NFI's presence plots, spanning three inventory periods (NFI3: 2004-2006, NFI4: 2009-2017, NFI5: 2018-ongoing). We examine regeneration patterns across major forest communities, including beech, fir-beech, and fir-spruce forests, focusing on presence data for key tree species in two height categories: 40-130 cm and above 130 cm to 11.9 cm DBH. The presence plot methodology, implemented since NFI3, surveys 200 m² sampling areas, providing standardized data on species occurrence in the regeneration layer. Our analysis reveals significant temporal trends in species presence across different forest communities, identifying both increasing and decreasing patterns in regeneration success. Preliminary results for beech and fir-beech communities show distinct regeneration patterns: while most conifer species display stable or slightly increasing trends, we observe a notable expansion in deciduous tree presence, particularly beech and maple species. A concerning pattern emerges for European ash, showing a consistent decline across different forest communities. These findings provide crucial insights into the dynamics of Swiss forest regeneration and potential future forest composition. This comprehensive assessment of regeneration trends across Switzerland's diverse forest ecosystems offers valuable information for forest managers and policymakers, supporting evidence-based decisions in forest management and conservation strategies. The results contribute to our understanding of forest ecosystem resilience and adaptation potential in the face of environmental change.

Accelerated global biodiversity loss critically threatens forest ecosystem multifunctionality and service provision. Understanding environmental drivers across taxonomic (TD), functional (FD), and phylogenetic (PD) biodiversity facets is essential for effective conservation. However, the multi-dimensional nature of biodiversity is difficult to assess, and many studies overlook the interplay between functional traits and evolutionary history within a community. Here, we use Hill numbers to integrate TD, FD, and PD across five ecologically distinct taxa (birds, butterflies, snails, vascular plants, and mosses) to better understand environmental factors driving forest biodiversity in Switzerland. We included micro- and macroclimatic conditions, soil properties, topography, and vegetation structure and diversity. Our results highlight the intricate, taxon-specific nature of environmental effects on biodiversity. Across taxa, vegetation structure and diversity, and climatic factors emerged as key drivers of biodiversity facets, while soil characteristics mostly influenced less-mobile taxa. Vegetation structure and diversity acted as strong ecological filters shaping species richness and traits, reflecting responsiveness to short-term dynamics like disturbance or management, but were weak predictors of PD. Conversely, more temporally stable abiotic factors such as climate and soil conditions were consistent drivers across all facets, highlighting their broad impact on biodiversity. We show that FD and PD metrics complement TD by revealing additional insights into ecosystem functionality and evolutionary history. Given the differential responses of biodiversity indicators to environmental drivers, especially climate, maintaining ecosystem functionality and resilience under climate change requires assessments that go beyond taxonomic diversity and include the functional and phylogenetic dimensions.

Climate change is expected to significantly alter forest ecosystems, reducing the suitability of the key economic tree species Norway spruce (Picea abies) and European beech (Fagus sylvatica) in low- and mid-elevation forests of Central Europe. As these species face increasing pressures from drought, storms, and pests, it is crucial to identify alternative tree species that are economically viable and capable of maintaining primary ecosystem services. This study investigated the potential of Douglas fir (Pseudotsuga menziesii), a non-native conifer, to establish from seed and compete with native broadleaf and conifer species during the early regeneration stage under differing resource availabilities. We assessed the growth performance and phenotypic plasticity of Douglas fir seedlings over three years in a controlled common-garden experiment. Seedlings of Douglas fir, along with seven native species — Norway spruce, silver fir (Abies alba), Scots pine (Pinus sylvestris), European beech, pedunculate oak (Quercus robur), sessile oak (Q. petraea), and sycamore (Acer pseudoplatanus) — were grown for three years under factorial combinations of high and low availabilities of light, nutrients, and water. Seedling height, biomass allocation to shoots and roots and phenotypic plasticity of these traits were measured to evaluate the competitive ability of individual species and their potential to adapt to changing environmental conditions. While Douglas fir seedlings exhibited strong growth performance compared to the conifers Norway spruce and silver fir, their biomass production and height growth was considerably lower than that of the broadleaved sycamore and beech. However, Douglas fir’s height growth rate in the third year exceeded all species except sycamore. This was particularly pronounced under dry and/or nutrient-poor conditions, indicating a potential competitive advantage under expected future climatic conditions. In agreement with field studies, our results indicate that non-native Douglas fir may sustainably establish in dry, nutrient poor European lowland forests due to its superior early growth performance under these conditions and the high phenotypic plasticity, of its root system. This holds especially in situations where the species competes with other conifers, while its ability to successfully compete with broadleaves appears to be largely restricted to nutrient-poor sites.

Soil properties influence plant physiology and growth, playing a fundamental role in shaping species niches in forest ecosystems. Here, we investigated the impact of soil data quality on the performance of climate-topography species distribution models (SDMs) of temperate forest woody plants. We compared models based on measured soil properties with those based on digitally mapped soil properties at different spatial resolutions (25m and 250m). We first calibrated SDMs with measured soil data and plant species presences and absences from plots in mature temperate forest stands. Then, we developed models using the same soil predictors, but extracted from digital soil maps at the nearest neighbouring plots of the Swiss National Forestry Inventory. Our approach enabled a comprehensive assessment of the significance of soil data quality for 41 Swiss forest woody plant species. The predictive power of SDMs without soil information compared to those with soil information, as well as those with measured vs digitally mapped soil information at different spatial resolutions was evaluated with metrics of model performance and variable contribution. On average, performance of models with measured and digitally mapped soil properties was significantly improved over those without soil information. SDMs based on measured and high-resolution soil maps showed a higher performance, especially for species with an ‘extreme’ niche position (e.g. preference for high or low pH), compared to those using coarse-resolution (250m) soil information. Nevertheless, globally available soil maps can provide important predictors if no high-resolution soil maps are available. Moreover, among the tested soil predictors, pH and clay content of the topsoil layers improved the predictive power of SDMs for forest woody plants the most. Such improved model performance informs biodiversity modelling about the relevance of soil data quality in SDMs for species of temperate forest ecosystems. In conclusion, the incorporation of accurate soil information into SDMs becomes indispensable for making well-informed forecasts for guiding decisions in forest management, also when addressing the potential distribution shifts of woody plant species due to climate change.

Aim: This study aims to (1) test whether mapped soil properties can improve the performance of species distribution models (SDMs) for 162 terricolous macrofungi at a regional level, (2) identify relevant soil predictors for macrofungal regional distribution and (3) quantify the relative importance of soil properties as compared to climate and topography in explaining macrofungal regional distribution. Location: The forested area (similar to 12,000 km(2)) in Switzerland. Taxon: Terricolous Macrofungi. Methods: We collected occurrences (presence-only) for 162 species of terricolous macrofungi, including 111 ectomycorrhizal and 51 saprotrophic species, from the SwissFungi database. We used soil property maps, generated through digital soil mapping at a 25 m resolution, to enhance macrofungal SDMs. For each species, we selected two climate, two topography and two soil predictors by an automated variable selection procedure. We built SDMs with randomised soil properties for performance comparison. We quantified the importance of soil properties based on permutation and variance partitioning. Finally, we projected the SDMs for three representative species at 25 m resolution with and without soil properties to assess the role of soil properties in shaping their biogeographical distributions. Results: Soil properties significantly improved the median performance of the SDMs across the 162 species. Ectomycorrhizal fungi showed a significantly greater improvement than saprotrophic fungi. On average, our models were able to explain two-thirds of the variance in macrofungal distribution, of which 11% could be independently explained by soil properties. Air temperature and topographic slope were identified as additional important factors controlling macrofungal distribution. Evident changes in geographical distribution were observed for the three representative species after adding soil properties. Main Conclusions: High-resolution digital soil maps significantly improve the predictive accuracy of macrofungal regional distribution. They should therefore be taken into account when modelling the geographical distribution of macrofungi.

Species distribution models (SDMs) relate species observations to mapped environmental variables to estimate the realized niche of species and predict their distribution. SDMs are key tools for projecting the impact of climate change on species and have been used in many biodiversity assessments. However, when fitted within spatial extents that do not encompass the whole species range (i.e. subrange), the estimated realized environmental niche can be truncated, which can lead to wrong or inaccurate predictions. A simple solution to this niche truncation consists in fitting SDMs at a spatial extent that encompasses the whole species range, but this often implies using a spatial resolution too coarse for local conservation assessments. To keep a fine resolution, a solution is to fit spatially nested SDMs (N‐SDMs), where a whole range, coarse‐grain SDM is combined with a subrange, fine‐grain SDM. N‐SDMs have demonstrated superior performance to subrange (truncated) SDMs in projecting species distributions under climate change and have accordingly regained considerable interest. Here, we review developments, applications and effectiveness of N‐SDMs. We present and discuss existing methods and tools to fit N‐SDMs, and assess when N‐SDMs are not needed. We highlight strengths and weaknesses of N‐SDMs, underline their importance in reducing niche truncation, and identify remaining challenges and future perspectives. Our review highlights that subrange SDMs most often lead to niche truncation and thus to incorrect spatial projections, a problem that can be overcome by using N‐SDMs. We show that the various N‐SDM methods come with their strengths and weaknesses and should be selected depending on the intended goal of the study. Synthesis. N‐SDMs are key tools to develop untruncated regional climate change forecasts of species distributions at fine resolution over restricted extent. While several N‐SDM approaches were proposed, there is currently no universal solution suggesting that further developments and testing are crucial if we are to derive robust future projections of species distributions, at least until SDMs can be applied for most species at high resolution over large geographic extents.

Over the past century, climate warming, nitrogen deposition, acidic rain, widespread disturbance events, adaptive management, and natural stand maturation have substantially altered the composition and structure of forests. However, the absence of historical data on the environment makes it difficult to prove these long-term effects. Ecological indicator values based on plant species characteristics offer a promising approach to detect presumed changes at relevant spatial scales (i.e., site level). In this study, we discerned the shifts in taxa composition of principal Central European beech forests and their associated environmental drivers by integrating three data sources: (1) 254 historical (1937 – 1948) and resurveyed (2019 – 2022) vegetation relevés of beech-dominated forests sites in the Swiss Jura mountains, (2) derived ecological indicator values for vascular plants regarding temperature, soil moisture, micro-climatic conditions, soil nutrients, pH, humus layer, and light availability, and (3) related data on disturbance/management intensity in these forest sites over the past 75 years, categorized as ‘undisturbed’ and ‘disturbed’ sites. We found a significant decrease in alpha diversity (taxa richness per site) on ‘undisturbed’ sites (loss of 7.0 ± 1.48 taxa) but stability on ‘disturbed’ sites (gain of 2.1 ± 5.71 taxa). Both beta (Jaccard dissimilarity) and gamma diversity (overall taxa richness) increased over the study period. The average ecological indicator values per site (EIV¯ s) consistently indicated climate warming impacts on all sites ranging from 390 to 1480 m a.s.l., while effects of acidic rain on soil pH and nitrogen deposition on soil fertility were negligible, except in densely populated lowland forest areas. The EIV¯ s for light and micro-climatic conditions differed between ‘undisturbed’ and ‘disturbed’ sites, indicating darker and more oceanic, and more open and continental conditions, respectively. The decrease in alpha diversity on ‘undisturbed’ sites corresponded primarily with reduced light availability, suggesting that stand maturation is the main driver of taxa loss while climate warming and pollution mainly affected composition rather than taxa richness. In contrast, the rise in beta and gamma diversity likely stems from changes in forest management and disturbance regimes, which have increased structural diversity with various micro-climatic niches enriching the taxa pool. In conclusion, our study demonstrates the ability to differentiate the profound effects of climate warming and pollution agents on forest composition and underscores the critical role of adaptive management in mitigating these impacts. By fostering structural diversity and biodiversity, adaptive management enhances the resilience of forests, enabling them to better adapt to evolving environmental conditions.

Forests are undergoing increasing risks of drought -induced tree mortality. Species replacement patterns following mortality may have a significant impact on the global carbon cycle. Among major hardwoods, deciduous oaks ( Quercus spp.) are increasingly reported as replacing dying conifers across the Northern Hemisphere. Yet, our knowledge on the growth responses of these oaks to drought is incomplete, especially regarding post -drought legacy effects. The objectives of this study were to determine the occurrence, duration, and magnitude of legacy effects of extreme droughts and how that vary across species, sites, and drought characteristics. The legacy effects were quantified by the deviation of observed from expected radial growth indices in the period 1940 -2016. We used stand -level chronologies from 458 sites and 21 oak species primarily from Europe, north-eastern America, and eastern Asia. We found that legacy effects of droughts could last from 1 to 5 years after the drought and were more prolonged in dry sites. Negative legacy effects (i.e., lower growth than expected) were more prevalent after repetitive droughts in dry sites. The effect of repetitive drought was stronger in Mediterranean oaks especially in Quercus faginea . Species -specific analyses revealed that Q. petraea and Q. macrocarpa from dry sites were more negatively affected by the droughts while growth of several oak species from mesic sites increased during post -drought years. Sites showing positive correlations to winter temperature showed little to no growth depression after drought, whereas sites with a positive correlation to previous summer water balance showed decreased growth. This may indicate that although winter warming favors tree growth during droughts, previous -year summer precipitation may predispose oak trees to current -year extreme droughts. Our results revealed a massive role of repetitive droughts in determining legacy effects and highlighted how growth sensitivity to climate, drought seasonality and species -specific traits drive the legacy effects in deciduous oak species.

AbstractTree regeneration is a key process in forest dynamics, particularly in the context of forest resilience and climate change. Models are pivotal for assessing long‐term forest dynamics, and they have been in use for more than 50 years. However, there is a need to evaluate their capacity to accurately represent tree regeneration. We assess how well current models capture the overall abundance, species composition, and mortality of tree regeneration. Using 15 models built to capture long‐term forest dynamics at the stand, landscape, and global levels, we simulate tree regeneration at 200 sites representing large environmental gradients across Central Europe. The results are evaluated against extensive data from unmanaged forests. Most of the models overestimate recruitment levels, which is compensated only in some models by high simulated mortality rates in the early stages of individual‐tree dynamics. Simulated species diversity of recruitment generally matches observed ranges. Models simulating higher stand‐level species diversity do not feature higher species diversity in the recruitment layer. The effect of light availability on recruitment levels is captured better than the effects of temperature and soil moisture, but patterns are not consistent across models. Increasing complexity in the tree regeneration modules is not related to higher accuracy of simulated tree recruitment. Furthermore, individual model design is more important than scale (stand, landscape, and global) and approach (empirical and process‐based) for accurately capturing tree regeneration. Despite the mismatches between simulation results and data, it is remarkable that most models capture the essential features of the highly complex process of tree regeneration, while not having been parameterized with such data. We conclude that much can be gained by evaluating and refining the modeling of tree regeneration processes. This has the potential to render long‐term projections of forest dynamics under changing environmental conditions much more robust.

Soil properties influence plant physiology and growth, playing a fundamental role in shaping species niches in temperate forest ecosystems. Here, we investigated the impact of soil data quality on the performance of species distribution models (SDMs) of 41 woody plant species in Swiss forests. We compared models based on measured soil properties with those based on digitally mapped soil properties on regional (Swiss Forest Soil Maps) and global scales (SoilGrids). We first calibrated topo-climatic SDMs with measured soil data and plant species presences and absences from mature temperate forest stand plots. We developed further models using the same soil predictors, but with values extracted from digital soil maps at the nearest neighbouring plots of the Swiss National Forestry Inventory. The predictive power of SDMs without soil information compared to those with soil information, as well as measured soil information vs digitally mapped, was evaluated with metrics of model performance and variable contribution. On average, models with measured and digitally mapped soil properties performed significantly better than those without soil information. SDMs based on measured and Swiss Forest Soil Maps showed higher performance, especially for species with an 'extreme' niche position (e.g., preference for high or low pH), compared to those using SoilGrids. Nevertheless, if no regional soil maps are available, SoilGrids should be tested for their potential to improve SDMs. Moreover, among the tested soil predictors, pH, and clay content of the topsoil layers most improved the predictive power of SDMs for forest woody plants. In conclusion, we demonstrate the value of regional soil maps for predicting the distribution of woody species across strong environmental gradients in temperate forests. The improved accuracy of SDMs and insights into drivers of distribution may support forest managers in strategies supporting e.g. biodiversity conservation, or climate adaptation planning.