Dr. Andri Baltensweiler

Although many tree species frequently hybridize and backcross, management decisions in forestry and nature conservation are usually concentrated on pure species. Therefore, understanding which environmental factors drive the distribution and admixture of tree species on a local stand scale is of great interest to support decision-making in the establishment and management of resilient forests. Here, we extensively sampled a mixed stand of hybridizing white oaks (Quercus petraea and Q. pubescens) near Lake Neuchâtel (Switzerland), where limestone and glacier moraine geologies coexist in proximity, to test whether micro-environmental conditions can predict taxonomic distribution and genetic admixture. We collected DNA from bud tissue, individual soil samples, and extracted high-resolution topographic data for 385 oak trees. We used 50 species-discriminatory single nucleotide polymorphism (SNP) markers to determine the taxonomic composition and admixture levels of individual trees and tested their association with micro-environmental conditions. We show that the trees' taxonomic distribution can be explained mainly by geographic position, soil pH, and potential rooting depth, a proxy for soil water availability. We found that admixed individuals tend to grow in habitats that are characteristic of the more drought-tolerant species Q. pubescens rather than in intermediate habitats. Using in situ measurements, we are the first to show that fine-scale variation in soil properties related to pH and water availability potentially drives the distribution of hybridizing tree species in a mixed stand. Microenvironmental variation therefore promotes local taxonomic diversity, facilitates admixture and adaptive introgression, and contributes to the resilience of forests under environmental change. Consequently, species such as white oaks should be managed and protected as a species complex rather than as pure species.

Forests provide essential ecosystem services that range from the production of timber to the mitigation of natural hazards. Rapid environmental changes, such as climate warming or the intensification of disturbance regimes, threaten forests and endanger forest ecosystem services. In light of these challenges, it is essential to understand forests' demographic processes of regeneration, growth, and mortality and their relationship with environmental conditions. Specifically, understanding the regeneration process in present-day forests is crucial since it lays the foundation for the structure of future forests and their tree species composition. We used Swiss National Forest Inventory (NFI) data covering vast bio-geographic gradients over four decades to achieve this understanding. Trees that reached a diameter at breast height of 12 cm between two consecutive NFI campaigns were used to determine regeneration and were referred to as ingrowth. Employing three independent statistical models, we investigated the number, species, and diameter of these ingrowth trees. The models were subsequently implemented into a forest simulator to project the development of Swiss forests until the mid-21st century. The simulation results showed an ingrowth decrease and a shift in its species composition, marked by a significant reduction in Norway spruce Picea abies and concurrent increases in broadleaves. Nevertheless, the pace of this change towards climatically better adapted species composition is relatively slow and is likely to slow down even further as ingrowth declines in the future, in contrast to the fast-changing climatic conditions. Hence, support through adaptive planting strategies should be tested in case ingrowth does not ensure the resilience of forests in the future. We conclude that since the regeneration of forests is becoming increasingly challenging, the current level at which ecosystem services are provided might not be ensured in the coming decades.

Aim: Forest disturbances are increasing around the globe due to changes in climate and management, deteriorating forests' carbon sink strength. Estimates of global forest carbon budgets account for losses of plant biomass but often neglect the effects of disturbances on soil organic carbon (SOC). Here, we aimed to quantify and conceptualize SOC losses in response to different disturbance agents on a global scale.
Location: Global.
Time Period: 1983–2022.
Major Taxa Studied: Forest soils.
Methods: We conducted a comprehensive global analysis of the effects of harvesting, wildfires, windstorms and insect infestations on forest SOC stocks in the surface organic layer and top mineral soil, synthesizing 927 paired observations from 151 existing field studies worldwide. We further used global mapping to assess potential SOC losses upon disturbance. Results: We found that both natural and anthropogenic forest disturbances can cause large SOC losses up to 60 Mg ha⁻¹. On average, the largest SOC losses were found after wildfires, followed by disturbances from windstorms, harvests and insects. However, initial carbon stock size, rather than disturbance agent, had the strongest influence on the magnitude of SOC losses. SOC losses were greatest in cold-climate forests (boreal and mountainous regions) with large accumulations of organic matter on or near the soil surface. Negative effects are present for at least four decades post-disturbance. In contrast, forests with small initial SOC stocks experienced quantitatively lower carbon losses, and their stocks returned to pre-disturbance levels more quickly.
Main Conclusions: Our results indicate that the more carbon is stored in the forest's organic layers and top mineral soils, the more carbon will be lost after disturbance. Robust estimates of forest carbon budgets must therefore consider disturbance-induced SOC losses, which strongly depend on site-specific stocks. Particularly in cold-climate forests, these disturbance-related losses may challenge forest management efforts to sequester CO₂.

The record-breaking drought in 2018 caused premature leaf discoloration and shedding (early browning) in many beech (Fagus sylvatica L.) dominated forests in Central Europe. However, a high degree of variability in drought response among individual beech trees was observed. While some trees were severely impacted by the prolonged water deficits and high temperatures, others remained vital with no or only minor signs of crown vitality loss. Why some beech trees were more susceptible to drought-induced crown damage than others and whether growth recovery is possible are poorly understood. Here, we aimed to identify growth characteristics associated with the variability in drought response between individual beech trees based on a sample of 470 trees in northern Switzerland. By combining tree growth measurements and crown condition assessments, we also investigated the possible link between crown dieback and growth recovery after drought. Beech trees with early browning exhibited an overall lower growth vigor before the 2018 drought than co-occurring vital beech trees. This lower vigor is mainly indicated by lower overall growth rates, stronger growth declines in the past decades, and higher growth-climate sensitivity. Particularly, warm previous year summer conditions negatively affected current growth of the early-browning trees. These findings suggest that the affected trees had less access to critical resources and were physiologically limited in their growth predisposing them to early browning. Following the 2018 drought, observed growth recovery potential corresponded to the amount of crown dieback and the local climatic water balance. Overall, our findings emphasize that beech-dominated forests in Central Europe are under increasing pressure from severe droughts, ultimately reducing the competitive ability of this species, especially on lowland sites with shallow soils and low water holding capacity.

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.

Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate change impacts on biodiversity and ecosystem functioning. Here we combine a network of microclimate temperature measurements across different habitats and vertical heights with a novel radiative transfer model to map daily temperatures during the vegetation period at 10 m spatial resolution across Switzerland. Our results reveal strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 ^∘C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 ^∘C, respectively. We also found that, in open grasslands, maximum temperatures at 5 cm above ground are, on average, 3.4 ^∘C warmer than those of the macroclimate, suggesting that, in such habitats, heat exposure close to the ground is often underestimated when using macroclimatic data. Spatial interpolation was achieved by using a hybrid approach based on linear mixed-effect models with input from detailed radiation estimates from radiative transfer models that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography, and vegetation height. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by root mean squared errors ranging from 1.18 to 3.43 ^∘C, with minimum temperatures being predicted more accurately overall than maximum temperatures. The microclimate-mapping methodology presented here enables a biologically relevant perspective when analysing climate–species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.

European grasslands face strong declines in extent and quality. Many grassland types are priority habitats for national and European conservation strategies. Countrywide, high spatial resolution maps of their distribution are often lacking. Here, we modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances across Switzerland at 10x10 m resolution. First, we applied ensemble models to provide distribution maps of the individual habitat types, using training data from various sources. Copernicus Sentinel satellite imagery and variables describing climate, soil and topography were used as predictors. The performance of these models was assessed based on the true skill statistics with a split-sampling of the data. Second, the individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert-based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat-type proportions with extrapolations from field surveys. Most individual maps had useful to excellent predictive performance (TSS ≥ 0.6). For most grid cells in the combined maps, the most and second most likely habitat types were either ecologically closely related or representing two grassland types along a nutrient gradient. The same was true for omission errors. We found good agreement between the predicted and estimated proportions from field surveys. The area of raised bogs appears to be underestimated, while dry grasslands showed highest agreement. This work highlights the potential of earth observation data at fine spatial and temporal resolution to map habitats at broad scales, thereby providing the foundation for diverse conservation applications. A particular challenge remains in capturing the transition from nutrient-poor to nutrient-rich grasslands, which is highly important for biodiversity conservation.

Storms represent a major disturbance factor in forest ecosystems, but the effects of windthrows on soil organic carbon (SOC) stocks are poorly quantified. Here, we assessed the SOC stocks of windthrown forests at 19 sites across Switzerland spanning an elevation gradient from 420 to 1550 m, encompassing a strong climatic gradient. Results show that the effect size of disturbance on SOC stocks increases with the size of the initial SOC stocks. The largest windthrow-induced SOC losses of up to 29 t C ha^-1 occurred in high-elevation forests with a harsh climate developing thick organic layers. In contrast, SOC stocks of low-elevation forests with thin organic layers were hardly affected. A mineralization study further revealed high elevation forests to store higher amounts of easily mineralizable C in thick organic layers that got lost following windthrow. These findings are supported by a meta-analysis of available windthrow studies, showing an increase of storm-induced SOC losses with the size of the initial SOC stocks. Modelling simulations further indicate longer-lasting SOC losses and a slower recovery of SOC stocks after windthrow at high compared to low elevations, due to a slower regeneration of mountain forests and associated lower C inputs into soils in a harsh climate. Upscaling the experimental findings/observed patterns by linking them to a data base of Swiss forest soils shows a total SOC loss of ∼0.4 Mt. C for the whole forested area of Switzerland after two major storm events, counteracting the forest net carbon sink of decades. Our study provides strong evidence that the vulnerability of SOC stocks to windthrow is particularly high in forests featuring thick and slowly forming organic layers, such as mountain soils. Thus, the risk of losing SOC to more frequent windthrows in mountain forests strongly limits their potential to mitigate climate change.

Maps of soil pH are an important tool for making decisions in sustainable forest management. Accurate pH mapping, therefore, is crucial to support decisions by authorities or forest companies. Soil pH values typically exhibit a distinct distribution characterized by two frequently encountered pH ranges, wherein aluminium oxides (AlO) and carbonates (CaCO) act as the primary buffer agents. Soil samples with moderately acid pH values (pH CaCl₂ of 4.5-6) are less commonly observed due to their weaker buffering capacity. The different strength of buffer agents results in a distinct bimodal distribution of soil pH values with peaks at pH of around 4 and 7.5. Commonly used approaches for spatial mapping neglect this often observed characteristic of soil pH and predict unimodal distributions with too many moderately acid pH values. For ecological map applications this might result in misleading interpretations.
This article presents a novel approach to produce pH maps that are able to reproduce pedogenic processes. The procedure is suitable for bimodal responses where the response distribution is naturally inherent and needs to be reproduced for the predictions. It is model-agnostic, namely independent from the used statistical prediction method. Calibration data is optimally split into two parts corresponding each to a data culmination, i.e. for soil pH values belonging to the ranges of the two principal buffer agents (Al₂O₃ and CaCO₃). For each subset a separate model is then built. In addition, a binary model is fitted to assign every new prediction location a probability to belong either to Al₂O₃ or CaCO₃ buffer range. Predictions are combined by weighted mean. Weights are derived from probabilities predicted by the binary model. Degree of smoothness is chosen by sigmoid transform which allows for optimal continuous transition of the pH values between Al₂O₃ and CaCO₃ buffer ranges. For each location uncertainty distributions may be combined by using the same weights.
We illustrated application of the new approach to a medium and strong bimodal distributed response (1) pH in 0-5 cm and (2) pH in 60-100 cm of forest soils in Switzerland (2 530 calibration sites). While model performance measured at 354 validation sites slightly dropped compared to a common modelling approach (drop of R² of 0.02-0.03) distributional properties of the predictions are much more meaningful from a pedogenic point of view. We were able to demonstrate the benefits of considering specific distributional properties of responses within the prediction process and expanded model assessment by comparing observed and predicted distributions.

Knowing which drivers affect the spatial distribution of hybridizing species and their admixed individuals on local or regional scale can leverage our understanding about processes that shape taxonomic diversity. Hybridizing white oak species (Quercus sect. Quercus) represent an ideal study system to elucidate which environmental factors determine their relative abundance and admixture levels within admixed forest stands. To elaborate these relationships, we used 58 species-diagnostic single-nucleotide polymorphism (SNP) markers and high-resolution topographic and soil data to identify the environmental factors associated with taxonomic composition of individuals and populations in 15 mixed stands of Q. petraea and Q. pubescens in the Valais, an inner-Alpine valley in Switzerland. At the individual tree level, generalized linear models (GLMs) explained a relatively small part of variation (R² = 0.32). At the population level, GLMs often explained a large part of variation (R² = 0.54-0.69) of the taxonomic indices. Mean taxonomic composition of the sites depended mainly on altitude and geographic position. Moreover, the more within-site variation we found in predictors related to topographic position, the higher was the average genetic admixture of single trees. Our results show that a multitude of topographic and edaphic factors affect the taxonomic composition and admixture levels of these two hybridizing oak species on local scale and that regional heterogeneity of these factors promote taxonomic diversity and admixture. Overall, our study highlights the prospects of using tailored genetic resources and high-resolution environmental data to understand and predict taxonomic composition in response to changing environments.

The tree species composition of Swiss forests is influenced by both environmental conditions and centuries of forest management, resulting in varying degrees of naturalness. Here, we estimate the naturalness of Swiss forests by comparing the tree species composition (i.e., dominance and presence/absence of tree species) recorded by the national forest inventory with the idealised species composition of the potential natural forests. We aimed to (1) evaluate the naturalness of different forest types, (2) identify the main drivers of low naturalness and (3) investigate the influence of naturalness on the susceptibility to disturbances. Based on our analysis, 45 % of Swiss forests had a tree species composition classified as ‘not natural’ while 42 % were classified as ‘natural’ or ‘close to natural’. The vast majority (65 %) of the forests classified as ‘not natural’ were potential European beech (Fagus sylvatica L.) and other broadleaf forests which are currently dominated by conifers. In addition, at higher elevations, forests often showed a higher proportion of European larch (Larix decidua Mill.) than expected, based on the potential natural forest. Overall, forests classified as ‘natural’ had a significantly lower risk of being affected by stand-level disturbance events. Potential natural European beech and other broadleaf forests were significantly less affected by disturbances, namely insect outbreaks and wind throw, when comparing ‘natural’ to ‘not natural’ (i.e., afforested with conifers). The observed dieback and associated loss of ecosystem function of conifer afforestations (mainly Norway spruce; Picea abies (L.) H. Karst.) in the Swiss plateau is likely a combination of climatic stress and higher pathogen/parasite pressure. These Norway spruce afforestations already experience a significantly elevated temperature of about 2.5 °C compared to their climatic optimum (i.e., primary natural distribution within Switzerland), representing a large-scale, long-term transplantation experiment. We discuss the question of whether similar diebacks and problems of maladaptation are to be expected for many other species, including European beech, due to accelerated climate warming during the 21st century and the slow transformation of long-lived systems such as forests. Consequently, an adaptation of current management practices might be needed to allow a faster transition of forests minimizing the risk of large-scale diebacks.

Bei der Beurteilung von Standortsbedingungen im Schweizer Wald spielt der Boden neben Klima und Relief eine entscheidende Rolle. Flächige Bodeninformationen für den Schweizer Wald sind jedoch nur in grober Form verfügbar. Basierend auf rund 2000 Waldbodenprofilen haben wir mittels maschinellen Lernens verschiedene Bodeneigenschaften (z.B. pH- und Sandgehalt) von sechs Bodentiefen für die gesamte Waldfläche der Schweiz mit einer Auflösung von 25 m × 25 m vorausgesagt. Während die Güte der Modellierung für die Bodendichte zufriedenstellend war, waren der Gehalt an organischem Kohlenstoff und die Bodengründigkeit schwieriger vorauszusagen. Zu jeder Bodenkarte berechneten wir zudem eine Unsicherheitskarte, sodass die Qualität der Bodenkarten auch räumlich beurteilt werden kann.

Wälder sind zunehmend von Sturmschäden betroffen. In einem Projekt der Eidgenössischen Forschungsanstalt WSL wurde untersucht, wie sich Windwürfe auf die Kohlenstoffspeicherung im Waldboden auswirken. Hoch gelegene Nadelwälder sind besonders anfällig für Kohlenstoffverluste aus dem Boden. Im Unterschied zu Wäldern tieferer Lagen werden dort grössere Mengen an Kohlenstoff in rein organischen, mächtigen Humusauflagen gespeichert. Eine Stabilisierung durch mineralische Interaktionen fehlt weitgehend, was die Humusauflagen empfindlich macht für Kohlenstoffverluste durch störungsbedingte Veränderungen des Bestandesklimas. Die langsame Baumverjüngung und die geringen Streueinträge in den Boden bremsen den Humusaufbau nach einer Störung zusätzlich. Eine weitere Zunahme von Windwürfen könnte die Bodenkohlenstoffvorräte in Bergwäldern drastisch reduzieren, was negative Auswirkungen auf das Klima hätte. Darüber hinaus könnten störungsbedingte Humusverluste die Bodenqualität nachhaltig verschlechtern.

Storms represent a major disturbance factor in forest ecosystems, but the effects of windthrows on soil organic carbon stocks are poorly known. The Swiss Federal Institute WSL assessed the soil organic carbon stocks of windthrown forests and neighbouring unaffected stands across Switzerland. The largest soil carbon losses occurred in high-elevation forests with thick organic layers. In contrast, soil organic carbon stocks of low-elevation forests with thin organic layers were hardly affected. The likely reason for this pattern is the high amount of easily mineralizable carbon in thick organic layers which got lost following windthrow. At low elevations, on the other hand, a greater carbon fraction may be stabilized by mineral interactions preventing fast decomposition. Modelling simulations further show longer-lasting soil carbon losses and a slower recovery of carbon stocks after windthrow at high elevations compared to low elevations, due to a slower regeneration of mountain forests and associated lower carbon inputs into soils. The study provides strong empirical evidence that the vulnerability of soil organic carbon stocks to windthrow is particularly high in mountain forests featuring thick and slowly forming organic layers.

Central Europe has been experiencing unprecedented droughts during the last decades, stressing the decrease in tree water availability. However, the assessment of physiological drought stress is challenging, and feedback between soil and vegetation is often omitted because of scarce belowground data. Here we aimed to model Swiss forests' water availability during the 2015 and 2018 droughts by implementing the mechanistic soil-vegetation-atmosphere-transport (SVAT) model LWF-Brook90 taking advantage of regionalized depth-resolved soil information. We calibrated the model against soil matric potential data measured from 2014 to 2018 at 44 sites along a Swiss climatic and edaphic drought gradient. Swiss forest soils' storage capacity of plant-available water ranged from 53 mm to 341 mm, with a median of 137 ± 42 mm down to the mean potential rooting depth of 1.2 m. Topsoil was the primary water source. However, trees switched to deeper soil water sources during drought. This effect was less pronounced for coniferous trees with a shallower rooting system than for deciduous trees, which resulted in a higher reduction of actual transpiration (transpiration deficit) in coniferous trees. Across Switzerland, forest trees reduced the transpiration by 23% (compared to potential transpiration) in 2015 and 2018, maintaining annual actual transpiration comparable to other years. Together with lower evaporative fluxes, the Swiss forests did not amplify the blue water deficit. The 2018 drought, characterized by a higher and more persistent transpiration deficit than in 2015, triggered widespread early wilting across Swiss forests that was better predicted by the SVAT-derived mean soil matric potential in the rooting zone than by climatic predictors. Such feedback-driven quantification of ecosystem water fluxes in the soil–plant-atmosphere continuum will be crucial to predicting physiological drought stress under future climate extremes.

Forest soils fulfil numerous important functions for humans and the environment by filtering water, protecting against floods, regulating nutrient balance, sequestering carbon, providing habitat for numerous organisms and being an important basis for biomass production. Knowledge on the spatial distribution of soil properties in forested areas is fundamental for biodiversity assessment and conservation planning, and improves sustainable forest management.
This study has the overall objective to improve spatial information of soil properties in the Swiss forest. For this purpose, we investigated for flysch areas in Switzerland whether a relationship between the spatial distribution of pH and very high-resolution terrain attributes exists.
The results showed that more than 50 % of the pH variations can be explained and that the accuracy of digital elevation models is crucial to describe microtopography. The optimal spatial resolution for inferring terrain attributes lies in the sub-meter range. By extrapolating the relationship between pH and terrain attributes across different flysch sites in Switzerland, we showed that small-scale topographic variation is the main environmental factor controlling topsoil pH through its influence on soil forming processes. The results help to better understand and predict ecological processes and biodiversity patterns in forests.
To improve soil information for the entire Swiss forest we applied machine learning methods to predict chemical and physical soil properties. Based on the approach of digital soil mapping we established a relationship between 2071 forest profile and spatially continuous soil forming factors with a resolution of 25 meters. Prediction performances showed large differences for the individual soil properties. While for soil density and pH the performance were satisfying, soil organic carbon content and soil thickness were more difficult predict. Along with the soil maps, we published accompanying uncertainty maps, which are important for determining the limits of the prediction and improving future mapping campaigns. Despite the large uncertainties in some regions, the soil maps will make a valuable contribution to numerous research projects, as they represent a significant improvement over existing soil information.

Spatial soil information in forests is crucial to assess ecosystem services such as carbon storage, water purification or biodiversity. However, spatially continuous information on soil properties at adequate resolution is rare in forested areas, especially in mountain regions. Therefore, we aimed to build high-resolution soil property maps for pH, soil organic carbon, clay, sand, gravel and soil density for six depth intervals as well as for soil thickness for the entire forested area of Switzerland. We used legacy data from 2071 soil profiles and evaluated six different modelling approaches of digital soil mapping, namely lasso, robust external-drift kriging, geoadditive modelling, quantile regression forest (QRF), cubist, and support vector machines. Moreover, we combined the predictions of the individual models by applying a weighted model averaging approach. All models were built from a large set of potential covariates which included e.g. multi-scale terrain attributes and remote sensing data characterizing vegetation cover.
Model performances, evaluated against an independent dataset were similar for all methods. However, QRF achieved the best prediction performance in most cases (18 out of 37 models), while model averaging outperformed the individual models in five cases. For the final soil property maps we therefore used the QRF predictions. Prediction performance showed large differences for the individual soil properties. While for fine earth density the R² of QRF varied between 0.51 and 0.64 across all depth intervals, soil organic carbon content was more difficult to predict (R² = 0.19-0.32). Since QRF was used for map prediction, we assessed the 90% prediction intervals from which we derived uncertainty maps. The latter are valuable to better interpret the predictions and provide guidance for future mapping campaigns to improve the soil maps.

Im Rahmen eines Projektes des Landes-forstinventars (LFI) wurden mit statisti-schen Methoden die potenziellen Vor-kommen der häufigsten Waldgehölzarten flächendeckend modelliert. Die hoch auf-gelösten Karten stehen auf der Website des LFI als interaktive Anwendung zur Verfügung.

Aim: Red wood ants (Formica rufa group) mitigate invertebrate pest outbreaks, alter invertebrate communities, and contribute to nutrient cycling. The IUCN lists these insects as near threatened. We investigated whether disturbances of Red Wood Ant (RWA) forest habitats related to recreation, infrastructure, and forest management were associated with RWA occurrence. We also investigated the habitat associations of this group.
Location: Switzerland.
Methods: We trained random forest models to predict RWA occurrence using data from 6,341 plots distributed throughout Swiss forests. Our explanatory variables included descriptions of vegetation, terrain, climate and human disturbance of the forest. A model trained using all of these variables (Model 1) was compared to another trained using all of these variables except those describing human disturbance (Model 2). We compared the abilities of these models to differentiate between RWA presences and absences using areas under receiver operator curves (AUC). The nature of the associations between the probability of RWA occurrence predicted by Model 1 and the explanatory variables that made the greatest contributions to the AUC of this model were investigated using individual conditional expectation plots.
Results: No significant difference in AUC was detected between Models 1 and 2. RWA occurrence was positively associated with elevation, conifers, canopy gaps, ground cover vegetation and solar insolation while negatively associated with air temperature and a soil wetness index.
Main conclusions: The distribution of RWA within Swiss forests appeared unassociated with the human disturbances we investigated. To conserve RWA in Switzerland, we recommend the conservation of forests with high proportions of conifers, particularly those at high elevations. We also recommend forest management that promotes ground cover vegetation and open canopy structures. The negative associations between RWA occurrence and temperatures raise concerns for the prospects of RWA in the context of predicted climate warming.

Die Waldstandortkunde ist eine wichtige Grundlage für die waldbauliche Planung, indem sie Standorttypen definiert und für diese Baumartenempfehlungen formuliert. In der Schweiz wurde im Rahmen von Projekten «Nachhaltigkeit und Erfolgskontrolle im Schutzwald» (NaiS) in den letzten Jahren ein Standard von Standorttypen erarbeitet, der landesweite Bedeutung erlangte. Für eine Mehrheit der Kantone existieren Kartierungen von unterschiedlich definierten Waldstandorteinheiten, jedoch fehlt eine flächendeckende, konsistente Kartierung von Standorttypen. Basierend auf der Zuordnung von 240 Standorttypen gemäss NaiS zu allen 6357 Probeflächen des Schweizerischen Landeforstinventars (LFI; Projekt «NaiS-LFI»), kantonalen Standortkartierungen, einer Vielzahl von flächendeckend verfügbaren Umweltdaten und «Machine Learning»-Techniken modellierten wir die schweizweite Verteilung der NaiS-Standorttypen. Unsere Modelle erreichen eine Vorhersagegenauigkeit von 57% bezüglich der drei wahrscheinlichsten Standorttypen auf dem jeweiligen Waldpixel von 25 × 25 m. Wenn die Standorttypen mit gleichem Mischung- und Verjüngungsziel betrachtet werden (Aggregation zu 42 Waldeinheiten), dann steigt die Vorhersagegenauigkeit auf 75%. Angesichts des Umstands, dass 80% der Standorttypen auf weniger als 1% der LFI-Probeflächen kartiert wurden, ist dieses Resultat zufriedenstellend. Die Modellierung liefert damit nützliche Übersichten auf nationaler oder regionaler Ebene. Zwei Fallbeispiele zeigen potenzielle Anwendungen des Modells in der Praxis: Im ersten stellen wir dar, zu welchen Anteilen die NaiSStandorttypen in der Schweiz sich als Folge des Klimawandels verschieben könnten, im zweiten, wo die aktuelle Bestockung im Kanton Zürich stark von der potenziellen natürlichen Baumartenzusammensetzung abweicht.

Forest community ecology and site evaluation is an important basis for forestry planning that comprises the definition of site types and the recommendation of suitable tree species. During the last years, a national standard of forest site types has been established in the frame of NaiS-projects (Nachhaltigkeit und Erfolgskontrolle im Schutzwald). While for a majority of cantons, forest site maps regarding different classification methods are available, a nationwide and consistent map of such site types is lacking. Here, we modelled the nationwide distribution of site types based on the assignment of 240 forest site types to the 6357 plots of the Swiss National forest Inventory, (NFI; Project “NaiS-NFI”), cantonal maps of forest site types, a large range of spatially explicit environmental variables and machine learning methods. The prediction accuracy of our models was 57% regarding the three most likely site types per 25 × 25 m forest pixel. When focusing on site types aggregated to units with identical mixture and recruitment targets (42 forest units), the prediction accuracy increased to 75%. Keeping in mind that 80% of all site types were present on less than 1% of the NFI sample plots, this result is encouraging. The model provides useful overviews at both national and regional scales. Two case studies are given to demonstrate potential applications of the model in forestry practice: firstly, we show the expected shifts of NaiS site types in Switzerland under projected climate change, and secondly, we illustrate where the current tree species composition in forests of the Canton of Zürich deviates strongly from the potential natural species composition.

Um der Waldbewirtschaftung und -planung sowie der Forschung und dem Naturschutz eine neue Grundlage zur Verfügung zu stellen, wurde im Projekt MoGLI (Modellierung Gehölzarten LFI) die potenzielle Verbreitung der häufigsten Gehölzarten für den Schweizer Wald modelliert. Zur räumlichen Modellierung standen Daten des Schweizerischen Landesforstinventars (LFI) zur Verfügung. Für die Verbreitungskarten wurde das bekannte Vorkommen von Arten aus dem LFI mit flächendeckenden Umweltvariablen als Prädiktoren in eine statistische Beziehung gesetzt. Für eine bessere Genauigkeit der Vorhersagen verwendeten wir neben häufig benutzten Prädiktoren wie Klima und Topografie auch eine Boden-pH-Karte und entwickelten neue Prädiktoren, welche die Waldstruktur mithilfe von luftgestütztem LiDAR (Light Detection and Ranging; Laserscanning) beschreiben. Auf diese Weise konnte das potenzielle Verbreitungsgebiet von 56 Gehölzarten mit hoher Genauigkeit modelliert werden. Die Verbreitungskarten für diese Arten stehen in einer Auflösung von 25 m × 25 m mit dazugehörigen Metadaten und Qualitätskriterien zur Verfügung und können in einer WebGIS-Applikation interaktiv betrachtet werden.

By means of modeling the potential distribution of the most common woody species for the Swiss forests, we wanted to provide an important basis for forest management and planning as well as for research and nature conservation. Data from the Swiss National Forest Inventory (LFI) were available for the spatial modeling of woody species. Potential distribution maps were created by statistically relating the known occurrence of species from the LFI with areal environmental variables as predictors. We used a soil pH map in addition to commonly used predictors such as climate and topography, and developed new predictors describing forest structure using airborne LiDAR (Light Detection and Ranging; laser scanning) in order to maximize the accuracy of spatial predictions. Overall, we were able to model the potential distribution range of 56 woody species with good accuracy. The distribution maps of these species are available at a resolution of 25 m × 25 m with associated metadata and quality indicators and can be viewed interactively in a WebGIS application.