Roman Flury

Arthropods drive essential ecosystem processes such as pollination, decomposition, and nutrient cycling and are widely used as indicators of ecosystem condition and function. Among various arthropod-derived metrics, body weight is a key variable in functional ecology and frequently assessed as dry body weight. However, drying arthropod specimens limits the samples’ future potential for research, as it prevents further processing such as trait measurements or species identification. Here, we present AWET (Arthropod Weight Estimation Tool), an open-source application for automated estimation of individual fresh body weight and extraction of morphometric measurements from standardized images of pre-sorted arthropod samples. AWET combines automated image analysis with taxon-specific allometric regression models to estimate fresh body weight while simultaneously quantifying body length, width, area, and specimen abundance. The software operates with standard imaging equipment, requires no machine-learning-based classification or segmentation, and allows users to define taxonomic groupings according to their objectives. By preserving specimens for downstream analyses while processing large numbers of individuals within milliseconds, AWET provides an efficient, non-destructive, and cost-effective workflow for high-throughput arthropod phenotyping. The software is a practical and expandable tool for biodiversity monitoring projects investigating changes in arthropod biomass, abundance and individual morphometric measures.

Predicting soil organic carbon (SOC) stocks in forest ecosystems is crucial for assessing forest C balance, but the relative importance of key drivers, including soil mineral properties, litter inputs, and climate, remains uncertain. Here, we linked SOC stocks measured to 100 cm depth at 556 old-growth Swiss forest sites (350 to 2000 m a.s.l.) to soil properties, net primary production (NPP), climate (mean annual precipitation, MAP: from 700 to 2100 mm; mean annual temperature, MAT from 0 to 12°C), and forest type. We compared measured SOC stocks with stocks simulated by Yasso20 model, commonly used for reporting SOC stock changes in greenhouse gas inventories. Since Yasso20 accounts only for litter inputs and climatic conditions, deviations between modelled and measured stocks can reveal the significance of organo-mineral interactions that we hypothesize to be crucial for SOC stocks.Total SOC stocks exhibited distinct regional patterns, with highest values in the Southern Alps, characterized by soils rich in Fe and Al oxides. On average, SOC stocks simulated by Yasso20 aligned with measured SOC stocks (13.7 vs 13.2 kg C m-2). In soils with pH ≤ 5, SOC stocks and model deviations were driven by exchangeable Fe, while in soil with pH > 5, exchangeable Ca was the dominant controlling factor. Beyond soil mineral properties, MAP emerged as an important driver of SOC stocks, while NPP remained unrelated to SOC stocks.Our study demonstrates that soil mineral properties play a dominant role for SOC stocks across Swiss forest soils. Incorporating mineral-driven SOC stabilization into models can thus improve our ability to predict SOC stocks and its long-term dynamics.

Forest management across Europe is confronted with a broad range of uncertainties, including the ecological and economic implications of silvicultural adaptation strategies. Especially in regions with limited forest accessibility, silvicultural constraints, and challenging topographic conditions, the economically viable potential for multifunctional management of forest ecosystem services is determined by its costs. In Swiss mountain forests, for example, costs for timber harvesting and extraction regularly exceed timber revenues; nevertheless, forest management is considered essential to sustain the forests’ protective function against gravitational hazards. Especially under future forest dynamics, it remains unexplored to what extent timber production alone is sufficient to cover forest management costs. Thus, for long-term assessments of management costs across various biogeographic conditions, structured frameworks that integrate both dynamic forest modelling and operational considerations are required to assess potential economic barriers for future forest management.To close this gap, we present a comprehensive framework to assess socio-economically best suitable timber harvesting methods (BEST) and corresponding harvesting costs in response to long-term forest dynamics. Across the Swiss National Forest Inventory (NFI), we integrated (i) dynamic simulations of alternative management strategies under climate change using the forest model MASSIMO, (ii) technical assessments of state-of-the-art harvesting methods, and (iii) timber harvesting productivity models allowing the estimation of associated harvesting costs.Our results revealed considerable temporal shifts in BEST portfolios that were mediated by an interplay of varying topographic conditions, differences in forest accessibility, as well as current forest composition and corresponding forest trajectories – for example, with higher shares of air- and cable-based harvesting methods being assigned within the Alpine regions. Further, considerable shifts in harvesting costs in response to long-term forest dynamics were observed. For example, in the Jura, the proportion of managed NFI plots with harvesting costs of < 50 CHF m-3 decreased from approx. 80 % (year 2023) to 50 % (year 2113) under a management strategy aiming for constant growing stocks. Over the simulation period, mean timber harvesting costs remained comparatively stable in the Swiss Prealps and Alps, whereas long-term increases were modelled for both the Jura and Plateau. Notably, harvesting costs under BEST were consistently lower than those estimated under the continuation of currently applied methods (i.e. as documented within the NFI), highlighting the potential for increased cost efficiency through shifts in harvesting methods.We conclude that climate- and management-induced shifts in forest dynamics may affect the economically viable potential for forest ecosystem service provision. Especially in regions where management costs outweigh timber revenues, economic assessments and decision-support tools need to adopt a supply-cost perspective by accounting for shifts in harvesting methods and associated costs. Further, the development of strategies aiming for forests’ adaptation to climate change needs to consider their long-term economic and technical implications to proactively identify real-world barriers to successful implementation.

Climate change and biodiversity loss are tightly linked and among the most urgent global challenges. Switzerland aims for net-zero CO2 emissions by 2050 while conserving biodiversity, which accelerates the transition to renewable energy. With the Federal Act on the Secure Supply of Electricity from Renewable Energy Sources, understanding and minimizing the ecological impacts of solar parks has become urgent. In its Biodiversity Strategy and Action Plan, the Federal Office for the Environment calls for integrating biodiversity into energy planning. Despite political incentives, major knowledge gaps remain on how ground-mounted photovoltaic (PV) parks affect biodiversity, ecosystem functioning, and ecosystem services, especially in temperate regions. High-alpine PV parks are particularly controversial: their production profile could help mitigate the winter electricity shortfall, but they may also increase pressure on sensitive alpine ecosystems. Empirical evidence is limited and rarely comparable across sites and elevations. Key unknowns include which taxa and ecological functions are most sensitive, whether responses differ along elevational gradients, and how impacts relate to the surrounding landscapes and legacy.To close these gaps, we propose a standardized research scheme for nationwide implementation. The scheme integrates assessments of solar panel impacts on biodiversity and ecosystem functioning below- and aboveground, and on ecosystem services such as soil formation, biomass production (e.g. for fodder), pollination, and regulation of the microclimate. Microclimatic variables are measured to relate local conditions to broader climate patterns, while vegetation dynamics are analyzed in the context of surrounding landscapes and land-use legacies. Standardized surveys of vegetation, soil microbes, and arthropods provide comprehensive insights to biodiversity responses. These are complemented by assessments of polarized-light effects on insects and birds and by evaluating habitat provisioning through increased structural complexity. Implemented nationwide, the scheme would generate the evidence base needed to guide a biodiversity-friendly expansion of solar energy and support an energy transition which aligns with Switzerland's Biodiversity Goals.

A bstract: Broad evidence suggests a positive relationship between local species richness and ecosystem functioning ^1,2 . However, Earth’s terrestrial surface is formed by a mosaic of different habitats, which constitutes a higher level of diversity known as landscape diversity. Recent studies indicate that landscape diversity can also promote ecosystem functions, including primary productivity, but the underlying mechanisms and relevant spatial scales remain poorly understood ^3,4 . Here, we combine satellite remote sensing of vegetation productivity with high-resolution land cover data to investigate the effects of landscape diversity on ecosystem functioning across different spatial scales. Using a plot network covering North America, we find that the observed landscape-wide effects cannot be explained by local-scale interactions along ecosystem interfaces. In a subsequent, global-scale analysis, we instead demonstrate that landscape diversity effects are most strongly associated to landscape diversity at a spatial scale of a few kilometers. This association holds across continents and climatic zones, indicating that this pattern is universal. Our investigation therefore suggests that effective land management and biodiversity conservation strategies must integrate both local species richness and higher-level organizational diversity, such as landscape diversity, to ensure robust ecosystem functioning.

Predicting soil organic carbon (SOC) stocks and their dynamics in forest ecosystems is crucial for assessing forest C balance, but the relative importance of key controls – litter inputs, climate, and soil properties – remains uncertain. Here, we linked SOC stocks at 556 old-growth Swiss forest sites from 350 to 2000 m a.s.l. to a comprehensive set of environmental variables, encompassing climate (mean annual precipitation, MAP: 700–2100 mm, mean annual temperature, MAT: 0–12 °C), soil properties, and forest types. In addition, we compared measured SOC stocks with stocks simulated by the Yasso20 model, which is widely used for reporting SOC stock changes. Since Yasso20 is driven solely by litter inputs and climate, deviations between modeled and measured stocks can reveal the significance of additional factors such as organo-mineral interactions that we hypothesized to be crucial for SOC stocks. Total SOC stocks exhibited distinct regional patterns, with the highest values in the Southern Alps, where soils are rich in Fe and Al oxides and receive high MAP. On average, total SOC stocks simulated by Yasso20 aligned well with measured SOC stocks (13.7 vs. 13.2 kg C m⁻²). However, the model did not capture regional SOC variability, underestimating SOC stocks by up to 7 kg C m⁻² in the Southern Alps. The underestimation was primarily explained by soil mineral properties, with their influence depending on soil pH. In soils with pH ≤ 5, exchangeable Fe had the strongest effect on Yasso20 deviations from measured stocks, while in soils with pH > 5, exchangeable Ca had the strongest effect on model deviations. Beyond Fe and Ca, MAP emerged as an important driver of total SOC stocks, with SOC stocks increasing with MAP. At higher elevations, this coincided with low MAT and a high share of conifers. While Yasso20 accounted for MAT, Yasso20 underestimated SOC stocks for MAP > 1400 mm. Overall, our results indicate that mineral-driven SOC stabilization and climate are the primary drivers of Yasso20 deviations from measured SOC stocks. Incorporating mineral-driven soil organic matter stabilization and coupling to a soil water model can improve the modeling of SOC stocks. However, further studies are needed to verify how C stabilization mechanisms and soil moisture can be included in model-based estimates of SOC stock changes, which is the primary application of Yasso in greenhouse gas inventories.

Conventional geostatistical methods often assume a single process across spatial scales, potentially masking scale-dependent patterns that originate from distinct underlying processes. Particularly, nearby locations exhibit similar values and thereby form connected structures—features—that vary across scales. While scale-space analysis aims to disentangle such overlapping structures and reveal scale-dependent features, there is no method available to detect statistically credible features in geostatistical data. Here, we introduce a scale-space decomposition method for identifying features in Gaussian process-modeled geostatistical data, which also enables the estimation of scale-dependent effects of predictor variables. Features are defined as statistically credible, scale-dependent structures identified by significant deviations from zero between differences of successive smooths of the data. To demonstrate these capabilities, we applied the approach to Finnish forest inventory data from the 1920s. We identified two essential spatial scales in basal area of common tree species: plot-to-plot variation and regional scale. Our scale-dependent analysis reveals that edaphic factors consistently influence all species across scales, while anthropogenic drivers show contrasting scale-specific effects: slash-and-burn agriculture negatively affects spruce at both scales but shows opposite effects on birch at different scales. These insights advance the understanding of historical forest ecology and demonstrate the utility of our approach.

Purpose MRI plays an important role in the evaluation of rotator cuff tears. Our study aimed to determine if specific MRI criteria can be used to distinguish between acute traumatic and pre-existing degenerative lesions. Materials and methods The study included 40 MRIs or MR-arthrograms with rotator cuff tears. Twenty tears were traumatic and 20 were non-traumatic. Two radiologists specialized in musculoskeletal imaging, an experienced shoulder surgeon and a registrar trained in shoulder surgery assessed on each MRI the presence or absence of the following 11 parameters: tendon oedema, oedema in the muscles of the rotator cuff and the deltoid, edema and cysts in the greater tuberosity, tendon stump at the insertion site, tangent sign, tendon kinking, cobra sign, superior and posterior decentering of the humeral head. All investigators were blinded to the age and sex of the patients, the cause of the tendon lesion, the duration of the symptoms and the proportions of traumatic and non-traumatic tears. They were also asked for their assessment regarding the cause of each patient’s tear, traumatic or degenerative. Results All features could be observed with varying percentages in both groups. It was therefore impossible to assign a specific MRI to one group or the other, even with a combination of two or three features. One observer categorized 25 of 40 cases correctly (62.5%), two observers assigned 22 cases (55%), and one observer 19 cases (47.5%) correctly. Conclusion In individual cases, a partial or full-thickness lesion of the supraspinatus tendon or a postero-superior rotator cuff tear without advanced fatty infiltration cannot be correctly assigned to an acute traumatic or a preexisting lesion based on MRI.