Search

The entry of microplastics (MPs) into marine food webs is a major environmental concern. We investigated how the behavior of planktonic copepods influences the risk of MPs to enter marine food webs by applying a trait-based approach and by combining experiments (bottle incubations and video observations) with biogeographical analyses. We aimed to evaluate which type of feeding behavior is most risky in terms of MP ingestion and which marine geographical areas are more susceptible to MP ingestion by planktonic copepods. We used different species as models of the main foraging behaviors in planktonic copepods: feeding-current, cruising, ambush, and mixed behavior feeding. All behaviors showed a similarly low risk of MP ingestion, up to 1 order of magnitude lower than for similar-sized microalgae. We did not observe any influence of the prey type or MP size (8 and 20 μm) on MP ingestion for any of the behaviors. By mapping the global distribution of feeding behaviors, we showed that feeding-current feeding is the most common behavior, but the risk of MP ingestion remains equally low across the global ocean, independently of the predominant behavior. Overall, our results suggest a low risk of MP ingestion by planktonic copepods and therefore a minimal risk of trophic transfer of MPs via marine pelagic copepods in marine ecosystems.

In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. We show that recasting multispecies distribution modeling as a ranking problem allows analyzing ubiquitous citizen-science observations with unprecedented efficiency. Based on 6.7M observations, we jointly modeled the distributions of 2477 plant species and species aggregates across Switzerland, using deep neural networks (DNNs). Compared to commonly-used approaches, multispecies DNNs predicted species distributions and especially community composition more accurately. Moreover, their setup allowed investigating understudied aspects of ecology: including seasonal variations of observation probability explicitly allowed approximating flowering phenology, especially for small, herbaceous species; reweighting predictions to mirror cover-abundance allowed mapping potentially canopy-dominant tree species nationwide; and projecting DNNs into the future allowed assessing how distributions, phenology, and dominance may change. Given their skill and their versatility, multispecies DNNs can refine our understanding of the distribution of plants and well-sampled taxa in general.

Planktonic copepods are the most abundant animals in the ocean and key players in global biochemical processes. Recent modeling suggests that zooplankton ingestion of microplastics (MPs) can disrupt the biological carbon pump and accelerate a global loss of oceanic oxygen. Here we investigate the behavioral responses and ingestion rates of a model feeding-current generating copepod when exposed to microplastics of different characteristics by small-scale video observations and bottle incubations. We found that copepods rejected 80% of the microplastics after touching them with their mouth parts, in essence exhibiting a kind of taste discrimination. High rejection rates of microplastics were independent of polymer type, shape, presence of biofilms, or sorbed pollutant (pyrene), indicating that microplastics are unpalatable for feeding-current feeding copepods and that post-capture taste discrimination is a main sensorial mechanism in the rejection of microplastics. In an ecological context, taking into account the behaviors of planktonic copepods and the concentrations of microplastics found in marine waters, our results suggest a low risk of microplastic ingestion by zooplankton and a low impact of microplastics on the vertical exportation of fecal pellets.

Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts. However, classifying plant specimens based on image data alone is challenging: some species exhibit large variations in visual appearance, while at the same time different species are often visually similar; additionally, species observations follow a highly imbalanced, long-tailed distribution due to differences in abundance as well as observer biases. On the other hand, most species observations are accompanied by side information about the spatial, temporal and ecological context. Moreover, biological species are not an unordered list of classes but embedded in a hierarchical taxonomic structure. We propose a multimodal deep learning model that takes into account these additional cues in a unified framework. Our Digital Taxonomist is able to identify plant species in photographs better than a classifier trained on the image content alone, the performance gained is over 6 percent points in terms of accuracy., ISPRS Journal of Photogrammetry and Remote Sensing, 182, ISSN:0924-2716

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., Global Change Biology, 28 (20), ISSN:1354-1013, ISSN:1365-2486

Re-analyzing data from our study, Bruun & Ejrnaes (2022) show that key species to productivity are more abundant than species threatened by extinction. They therefore conclude that biodiversity loss hardly hampers ecosystem processes. Acknowledging the validity of the findings, we clarify why we believe their conclusions are drawn too far.

Predictions from species distribution models (SDMs) that rely on presence-only data are strongly influenced by how pseudo-absences are derived. However, which strategies to generate pseudo-absences give rise to faithful SDMs in complex mountainous terrain, and whether species-specific or generic strategies perform better remain open questions.Here, across 500 plant species, we investigated comprehensively how predictions of SDMs at a 93 m spatial resolution are influenced by pseudo-absence strategies, using the complex topography of the Swiss mountains as a model system. We used five generic (random, equal-stratified, proportional-stratified, target, density) and three species-specific (target specific, density specific and geographic specific) approaches to derive pseudo-absence data. We conducted performance tests for each of our eight strategies in combination with (a) spatial bias, generated within our occurrence dataset on sites with highest sampling density, to investigate how this common bias problem influences the performance of pseudo-absence sampling strategies, and (b) a new approach to reduce model extrapolation in environmental space by including background data from all environmental conditions of the study area. SDMs were evaluated against an independent and well-sampled dataset of true presences and absences.The random, the density (generic), and the geographic specific (species-specific) strategies consistently performed best, even in cases of strong spatial sampling bias in the occurrence data. Including a background of environmentally stratified pseudo-absences improved predictions of species distributions towards environmental extremes, and significantly reduced spatial extrapolations of model predictions in environmental space.Our results indicate that both generic and species-specific pseudo-absence strategies allow estimating robust SDMs and we provide clear recommendations which strategies to choose in complex terrain and when presence data are prone to high sampling bias. In datasets with strong sampling bias, most pseudo-absence strategies produce extrapolation problems and we additionally recommend environmentally stratified pseudo-absences in these cases. Overall, in species rich datasets the use of complex and computationally demanding, species-specific pseudo-absence strategies may not always be justified compared to simpler generic approaches.

Räumliche Analyse von Trockenheitssymptomen im Schweizer Wald mit Sentinel-2-Satellitendaten Andri Baltensweiler, Philipp Brun, Joanna Pranga, Achilleas Psomas, Niklaus E. Zimmermann, Christian Ginzler Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft, WSL (CH)*, Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft, WSL (CH) *Zürcherstrasse 111, CH-8903 Birmensdorf, E-Mail andri.baltensweiler@wsl.ch, Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft, WSL (CH), Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft, WSL (CH), Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft, WSL (CH), Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft, WSL (CH) Multispektrale Satellitenbilder in hoher zeitlicher Wiederholung wurden genutzt, um die Gebiete in der Schweiz zu ermitteln, in denen die Buchen, aber auch die Nadelbäume besonders stark von der im Sommer 2018 herrschenden Trockenheit betroffen waren. Hierfür wurden aus den Satellitenbildern Vegetationsindizes berechnet und Schwellenwerte für Trockenheitssymptome wie die vorzeitige Verbraunung abgeleitet. Durch die freie Verfügbarkeit der Sentinel-2-Daten des Copernicus-Programms und von Cloud-Plattformen wie Google Earth Engine konnten die Analysen rasch durchgeführt werden.