Landschaftsdynamik ¶

Information regarding the spatial arrangement and extent of past habitats is important for understanding present biodiversity, restoration potential, and fighting extinction-debt effects. European landscapes have changed profoundly over recent decades, with the trend accelerating following World War 2. We develop a proof of concept for mapping historic habitat distribution for Switzerland from black and white aerial imagery compatible with the present-day habitat map. Recently available orthorectified 1946 aerial imagery (1 m resolution) was segmented based on spectral and shape characteristics for training areas (320–508 km²) representing the main biogeographical regions of Switzerland. Initial training data was derived by manual aerial orthoimage interpretation differentiating 15 habitat classes. A random forest model was trained to classify the segments using variables describing spectral information, image texture, segment shape, topography, climate, and anthropogenic influence. Classification accuracy was improved with additional training samples derived in a stepwise approach, applying three different sampling techniques. Highest class accuracies (producer's and user's accuracies ≥ 0.75) were achieved for the habitats ‘Standing water’, ‘Flowing water’, ‘Glaciers, permanent ice and snow’, and ‘Forests and other wooded land’. Particularly low user's accuracies were found for ‘Wetlands’, ‘Hedges and tree rows’ and ‘Buildings’. The comparison to independent data further revealed minor differences in overall accuracy for the three different sampling strategies. Yet, map predictions sometimes varied substantially, indicating that the sampling strategies address different classification issues. Hence, we conclude that combining different sampling strategies for training data collection has the potential to improve the mapping, particularly in the case of multi-class classifications.

Weather radars detect more than weather, they also continuously register the movements of billions of animals aloft in the lower atmosphere. This makes archived, unfiltered weather radar data a goldmine for biological monitoring purposes, providing coverage of the aerial habitat in a way no other method can. Here we present two datasets of biological data extracted from European weather radar data, obtained through a collaboration with the Operational Programme for the Exchange of Weather Radar Information (OPERA) and three national meteorological services. The datasets were created by processing weather radar data with methods optimized for extracting bird targets, resulting in vertical profiles of biological targets. The datasets collectively cover 141 radar stations in 18 countries, from 2008 to 2023. Data quality and coverage differs between years, countries, and radar stations, so care must be taken when evaluating data for each specific use case. Despite these challenges the datasets are currently the most comprehensive of their kind in Europe and open new avenues in understanding continental scale movements of aerial animals.

Citizen science has become key to biodiversity monitoring but critically depends on accurate quality control that is scalable and tailored to the focal region. We developed FlorID, a free-to-use identification service for all native and many non-native plants of Switzerland. FlorID can identify >3000 species, using vision transformers trained on 1.5M photos, and ecological predictions from multilayer perceptrons, trained on 6.7M occurrence observations and 20 high-resolution environmental variables. Embedded in a free-to-use application programming interface, FlorID can be accessed directly, via webservice, and via FlorApp smartphone application. If multiple images and spatiotemporal location are available, FlorID correctly identifies 93% of field observations and has a top-5 accuracy of 99%. Ecological predictions boost identification success especially for native species with distinct distributions. By evaluating information on appearance and fine-grained ecology, FlorID is a blueprint for similar solutions targeting different taxa or regions, and a basis for developments like automated community inventories.

Close-range remote sensing technologies show great potential to support national forest inventories (NFIs). Mobile laser scanning (MLS) has performed especially well in recent studies, as it is capable of fast data acquisition while still delivering accurate information on NFI-relevant variables (e.g. tree position, diameter at breast height [DBH]). However, the performance of MLS acquisition under challenging conditions (e.g. steep terrain, dense understorey vegetation) has rarely been addressed. Here, on 29 NFI plots distributed over the entire country of Switzerland and representing various terrain and forest conditions, we evaluated the use and performance of handheld MLS acquisition, using a GeoSLAM ZebHorizon laser scanner, to deliver analysis-ready (with minimal occlusion) point clouds and to estimate primary tree variables such as tree position and DBH. Data acquisition was successful for all tested Swiss NFI plots. The average time needed for point cloud acquisition of a 50 × 50 m sample plot was 13.13 min. Over all tested NFI plots, we achieved a tree detection rate of 82.6%, with a relative root mean square error (RMSE) of 7.25%, a mean absolute error of 2.0 cm and a systematic prediction error (SPE) of 1.0 cm for DBH estimation. The terrain gradient had an especially strong influence on the accuracy of DBH estimation. Occlusion, as a metric for point cloud capacity to hold forest structural information for the entire canopy volume, was minimal when the suggested acquisition pattern was followed, at 9% on average for the entire canopy volume. However, the level of occlusion increased in the upper part of the canopy (> 10 m above ground), to 15% on average.
Overall, handheld MLS effectively produced analysis-ready point clouds within the 15-minute acquisition time limitation for a 50 x 50 m area, even under challenging conditions, showing promise for future use in the Swiss NFI. However, the current accuracy of MLS-estimated forest variables like DBH and tree detection is insufficient to replace conventional measurements within an established NFI. Despite this, estimating NFI-relevant variables, even with lower accuracy, and expanding the area of measured tree variables could improve information on forest functions and services (e.g., protection and biodiversity), currently not possible to derive from NFI data due to the limited plot size. In the future, MLS-acquired point clouds could (1) shift from expert-assessed to more quantitative and reproducible variable estimation, and (2) enhance NFI with variables difficult to measure conventionally. This could open new pathways for assessing and monitoring forest structural diversity nationally and provide valuable calibration and validation data for large-scale forest assessments from airborne or space-borne acquisitions.

While acquiring age information is crucial for efficient stock management and biodiversity conservation, traditional aging methods fail to offer a universal, non-invasive, and precise way of estimating a wild animal's age. DNA methylation from tissue DNA (tDNA) was recently proposed as a method to overcome these issues and showed more accurate results than telomere-length-based age assessments. Here, we used environmental DNA (eDNA) for the first time as a template for age estimation, focusing on the larval phase (10–24 days post-hatch) of cultured Dicentrarchus labrax (seabass), a species of major economic and conservation interest. Using third-generation sequencing, we were able to directly detect various modification types (e.g., cytosine and adenosine methylation in all contexts) across the whole genome using amplification-free nanopore sequencing. However, aging sites were only present in the mitogenome, which could be a specific feature of eDNA methylation or the consequence of better DNA protection within mitochondria. By considering qualitative and quantitative information about aging sites according to an objective model selection framework, our epigenetic clock reached a cross-validated accuracy of 2.6 days (Median Absolute Error). Such performances are higher than those of previous clocks, notably for adult seabass even when scaling MAE to the age range, which could be linked to a more dynamic epigenome during early life stages. Overall, our pilot study proposes new methods to determine the potential of eDNA for simultaneous age and biodiversity assessments, although robust validation of our preliminary results along with methodological developments are needed before field applications can be envisaged.