Swiss National Park

Large wood (LW) is vital for river ecosystems, influencing hydraulics, sediment dynamics, and habitat complexity. Quantifying LW storage is crucial for river management, habitat restoration, and flood mitigation but is challenging due to its spatial and temporal variability. Traditional field surveys are labour-intensive and limited in scope, while manual mapping of aerial imagery, though detailed, is equally time-consuming. This study presents an automated approach to detect and measure LW using high-resolution imagery and convolutional neural networks (CNNs). Two models, YOLOv10 for wood detection and YOLOv8 for wood segmentation, were trained using data from eight rivers in the Swiss Alps and Argentinean Andes. A separate river dataset was used for independent testing. The detection model achieved 90 % accuracy in estimating wood volume compared to field data and detected 97 % of large wood pieces at a 0.3 confidence threshold. The segmentation model achieved a mean Average Precision of 70 %. Although wood diameters were underestimated, the method reliably captured spatial distributions of wood. Expanding the training dataset with more diverse examples could improve performance. This automated approach offers a scalable and efficient tool for monitoring riverine wood, overcoming the limitations of traditional field-based surveys.

Data on past debris flows is normally scarce and biased towards large events or catchments close to settlements. Reconstructing past process activity with dendrogeomorphic approaches can help develop unbiased and spatially comprehensive timeseries, typically at annual resolution. Likewise, hydrometeorological data are often limited by low spatial or temporal resolutions, especially as one goes back in time. In the Swiss National Park (Swiss Alps), we rely on a comprehensive dataset of precisely dated debris flows covering the last >35years and on annually-dated dendrogeomorphic timeseries of past activity at some of these sites. The completeness of records allows evaluation of different hydrometeorological datasets to assess triggers of debris flows: (i) a highly resolved, spatially explicit (1×1 km, hourly records) dataset extending back to 2005; (ii) daily-resolved station records available since 1917; and (iii) spatially resolved (1×1 km) daily temperature and precipitation fields reconstructed back to 1763. All datasets have some skills to capture debris-flow triggering storms but also limitations: While the meteorological station lacks spatial resolution and does not capture localized, convective storms well, the highly-resolved, spatially-explicit timeseries remains excessively short for the time being. As for the long-term precipitation fields, the dataset has limited skills in capturing localized, convective precipitation. We also show that debris flows only rarely occurred on the day for which maximum one-day summer rainfall was recorded. This has strong implications for the identification of hydrometeorological triggers in tree-ring studies in which reconstructed debris flows are available at annual resolution and the detection of triggering is typically based on maximum precipitation sums of a given debris-flow season. Statistical assessment of possible triggers will lead to spurious correlations with such an approach and wrong results. Dendrogeomorphic studies should refrain from venturing into triggering analysis based on annually resolved debris-flow timeseries, unless dating is possible at intra-seasonal or monthly resolutions.

Fiber is essential for rumen health, microbial fermentation, and the energy supply of herbivores. Even though the study of fecal fiber contents (neutral detergent fiber NDF, acid detergent fiber ADF, and acid detergent lignin ADL) using near-infrared reflectance spectroscopy (NIRS) has allowed investigating nutritional ecology of different herbivore species, NIRS calibrations are species-specific and require a large number of samples for predictions. A multispecies calibration would be an advantage since samples from different herbivores could be used to calibrate a model capable of predicting the fecal fiber content of other herbivores. To date, however, multispecies models have not been developed to predict fiber contents in the feces of herbivores. Here, we fill this gap by calibrating three fiber multispecies models (NDF, ADF and ADL) using fecal samples from domestic and wild herbivore species. We also evaluated the effect of incorporating sodium sulfite in fiber determination protocol. The initial dataset consisting of 445 samples of six herbivore species was used to calibrate (80% of the samples) and validate (20% of the samples) the models. Subsequently, 63 samples of five herbivores not included in the calibration set were used for the external validation of the model. Since sodium sulfite did not significantly improve fecal fiber prediction, our model was developed without this compound. The multispecies models obtained were highly accurate determining NDF, ADF and ADL (R2CAL, coefficient of determination in calibration, ≥ 0.93, R2VAL, coefficient of determination in validation, ≥ 0.91) and independent of external confounders. For external validation, the accuracy in predicting fecal samples in other herbivore species was also satisfactory, with consistently better values for NDF (R2VAL, 0.86-0.94) and ADF (R2VAL, 0.80-0.95) than for ADL (R2VAL, 0.66-0.89). We show that multispecies NIRS calibrations can be used with high accuracy to assess fecal fiber contents across diverse herbivore species. This finding represents a significant advance in the study of the nutritional ecology of herbivores with contrasting foraging patterns. In the future, widening the data range (e.g., species and locations) of the initial dataset could further improve the accuracy of these models.

BackgroundThe habitat use of wild ungulates is determined by forage availability, but also the avoidance of predation and human disturbance. They should apply foraging strategies that provide the most energy at the lowest cost. However, due to data limitations at the scale of movement trajectories, it is not clear to what extent even well-studied species such as red deer (Cervus elaphus) trade-off between forage quality and quantity, especially in heterogeneous alpine habitats characterized by short vegetation periods.MethodsWe used remote sensing data to derive spatially continuous forage quality and quantity information. To predict relative nitrogen (i.e. forage quality) and biomass (i.e. forage quantity), we related field data to predictor variables derived from Sentinel-2 satellite data. In particular, our approach employed random forest regression algorithms, integrating various remote sensing variables such as reflectance values, vegetation indices and optical traits derived from a radiative transfer model. We combined these forage characteristics with variables representing human activity, and applied integrated step selection functions to estimate sex-specific summer habitat selection of red deer in open habitats within and around the Swiss National Park, an alpine Strict Nature Reserve.ResultsThe combination of vegetation indices and optical traits greatly improved predictive power in both the biomass (R2 = 0.60, Root mean square error (RMSE) = 88.55 g/m2) and relative nitrogen models (R2 = 0.34, RMSE = 0.28%). Both female and male red deer selected more strongly for biomass (estimate = 0.672 ± 0.059 SE for normalised values for females, and 0.507 ± 0.061 for males) than relative nitrogen (estimate = 0.124 ± 0.062 for females, and 0.161 ± 0.061 for males, respectively). Females showed higher levels of use of the Swiss National Park.ConclusionsRed deer in summer habitats select forage quantity over quality with little difference between sexes. Females respond more strongly to human activities and thus prefer the Swiss National Park. Our results demonstrate the capability of satellite data to estimate forage quality and quantity separately for movement ecology studies, going beyond the exclusive use of conventional vegetation indices.

Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The spectral variation hypothesis (SVH) proposes that spatial variation in reflectance data of an area is positively associated with plant diversity. While the SVH has exhibited validity in dense forests, it performs poorly in highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed to the mosaic-like spatial structure of human-dominated landscapes with fields in varying phenological and management stages. Therefore, we argued for re-evaluating SVH’s flawed window-based spatial analysis and underutilized temporal component. In particular, we captured the spatial and temporal variation in reflectance and assessed the relationships between spatial and temporal components of spectral diversity and plant diversity at the parcel level as a unit that relates to management patterns. Our investigation spanned three grasslands on two continents covering a wide spectrum of agricultural usage intensities. To calculate different components of spectral diversity, we used multi-temporal spaceborne Sentinel-2 data. We showed that plant diversity was negatively associated with the temporal component of spectral diversity across all sites. In contrast, the spatial component of spectral diversity was related to plant diversity in sites with larger parcels. Our findings highlighted that in agricultural landscapes, the temporal component of spectral diversity drives the spectral diversity-plant diversity associations. Consequently, our results offer a novel perspective for remote sensing of plant diversity globally.

Abstract AimsWe introduce ReSurveyEurope — a new data source of resurveyed vegetation plots in Europe, compiled by a collaborative network of vegetation scientists. We describe the scope of this initiative, provide an overview of currently available data, governance, data contribution rules, and accessibility. In addition, we outline further steps, including potential research questions.ResultsReSurveyEurope includes resurveyed vegetation plots from all habitats. Version 1.0 of ReSurveyEurope contains 283,135 observations (i.e., individual surveys of each plot) from 79,190 plots sampled in 449 independent resurvey projects. Of these, 62,139 (78%) are permanent plots, that is, marked in situ, or located with GPS, which allow for high spatial accuracy in resurvey. The remaining 17,051 (22%) plots are from studies in which plots from the initial survey could not be exactly relocated. Four data sets, which together account for 28,470 (36%) plots, provide only presence/absence information on plant species, while the remaining 50,720 (64%) plots contain abundance information (e.g., percentage cover or cover–abundance classes such as variants of the Braun‐Blanquet scale). The oldest plots were sampled in 1911 in the Swiss Alps, while most plots were sampled between 1950 and 2020.ConclusionsReSurveyEurope is a new resource to address a wide range of research questions on fine‐scale changes in European vegetation. The initiative is devoted to an inclusive and transparent governance and data usage approach, based on slightly adapted rules of the well‐established European Vegetation Archive (EVA). ReSurveyEurope data are ready for use, and proposals for analyses of the data set can be submitted at any time to the coordinators. Still, further data contributions are highly welcome.

Mountains are among the most biodiverse places on Earth, and plant lineages that inhabit them have some of the highest speciation rates ever recorded. Plant diversity within the alpine zone - the elevation above which trees cannot grow—contributes significantly to overall diversity within mountain systems, but the origins of alpine plant diversity are poorly understood. Here, we quantify the processes that generate alpine plant diversity and their changing dynamics through time in Saxifraga (Saxifragaceae), an angiosperm genus that occurs predominantly in mountain systems. We present a time-calibrated molecular phylogenetic tree for the genus that is inferred from 329 low-copy nuclear loci and incorporates 73% (407) of known species. We show that upslope biome shifts into the alpine zone are considerably more prevalent than dispersal of alpine specialists between regions, and that the rate of upslope biome shifts increased markedly in the last 5 Myr, a timeframe concordant with a cooling and fluctuating climate that is likely to have increased the extent of the alpine zone. Furthermore, alpine zone specialists have lower speciation rates than generalists that occur inside and outside the alpine zone, and major speciation rate increases within Saxifraga significantly pre-date increased rates of upslope biome shifts. Specialisation to the alpine zone is not therefore associated with speciation rate increases. Taken together, this study presents a quantified and broad scale perspective of processes underpinning alpine plant diversity.

Abstract The rock glaciers of the Swiss National Park were the first in the world where in-situ measurements of surface displacement were undertaken, starting with the pioneering work of Chaix in 1918. This globally unique long-term record is a main reason why these rock glaciers have recently been ordained by UNESCO as one of the top 100 IUGS geological world heritage sites. With our work we have filled the gap previously existing between the measurements from the early 20th century and the most recent measurements made over the last two decades. Through the application of photogrammetric techniques, we use this unique record spanning more than a century to geomorphologically analyze the spatiotemporal evolution of rock glaciers in the region, and to disentangle the interplay with their contributing glaciers. We show that these rock glaciers are in different phases of degradation with a general trend of deceleration and loss of volume. We observe how changes in kinematics seem to be historically controlled by the hydrological contribution of their nearby glaciers, with which they have been in close contact since the end of the Little Ice Age, but which are now severely degraded.