Biology subjects

Scherber, C.

Publications and source records attributed to Scherber, C..

5 recordsLinked to original sources

bioRxiv2025

Edge computer vision produces microarthropod-based high-throughput biodiversity metrics

Soil fauna is crucial for carbon cycling, controlling organic matter decomposition and contributing to ecosystem services1-7. Soil microarthropods are top-down regulators in the decomposer food web and serve as fundamental indicators of soil health. Yet, routine field-level monitoring remains restricted to resource-intensive research, leaving a critical gap in arable land management and policy. To address this, we developed Edapholog(R) extractor, a fully automated laboratory device that detects and identifies live soil microarthropods via real-time, AI-based image analysis, enabling taxonomic identification. Here, we demonstrate its accuracy across 319 arable fields spanning ten European countries. We show that computer vision can support ecological interpretation in arable systems and long-term studies on conservation tillage and cover cropping, offering a scalable tool for integrating soil biodiversity metrics into regenerative agriculture and carbon farming. We validated the system against classical taxonomy and found that across [~]35,000 microarthropod individuals, the device achieved an overall accuracy of 86%, sensitivity of 75%, and specificity of 99% compared to manual identifications. Community composition analyses revealed high similarities (83%), with minimal richness differences (7%) and low species replacement (13%) across countries, indicating that the AI does not introduce taxonomic bias. When applied in a long-term field experiment, the system detected significant taxon-specific responses to conservation tillage, with effect sizes ranging from 0.5 to 4. Total abundance, richness, and a soil biological health index were 39%, 47%, and 150% higher, respectively, under conservation tillage compared to conventional ploughing. These effects were statistically consistent between the automated and classical methods. However, while manual microscopy required several hours per sample, the AI-based system delivered immediate results without the need for taxonomic expertise. Edapholog(R) extractor offers exciting opportunities for rapid, scalable soil biodiversity monitoring for future sustainable land management.

ecology↗

bioRxiv2024

Diversification mitigates pesticide but not microplastic effects on bees without compromising rapeseed yield in China

Humanity depends on agriculture for food, fiber and energy provisioning, but input-intensive agricultural production is impacting ecosystem services such as pollination. Pollution effects from neonicotinoid insecticides on pollinators receive much attention, but nothing is known on the synergistic effects with emerging plastic contaminants and the mitigation potential of agricultural diversification. Here, we conduct the first large-scale and full-factorial mesocosm study to understand two-generation effects of diversified floral resources (diversification treatment), neonicotinoid and microplastic pollution (pollution treatments) on Osmia cornifrons bees in 72 mesocosms. In our three-year experiment, we found that diversification can mitigate individual neonicotinoid effects. We did not find any individual or synergistic effects of microplastic on reproductive performance of solitary bees. None of our treatments affected rapeseed yield. Our results confirm the benefits of diversified flower resources to mitigate pesticide effects on bees in China and suggest that microplastics have no acute individual or interaction toxicity in semi-natural environment at realistic exposure levels. Diversified flower resources in Chinese agricultural landscapes to mitigate pesticide pollution effects on pollinators is an important policy argument for pollinator protection with downstream implications for food security.

ecology↗

bioRxiv2024

The FAIR-Device - a non-lethal and generalist semi-automatic Malaise trap for insect biodiversity monitoring: Proof of concept

Field monitoring plays a crucial role in understanding insect dynamics within ecosystems. It facilitates pest distribution assessment, control measure evaluation, and prediction of pest outbreaks. Additionally, it provides important information on bioindicators with which the state of biodiversity and ecological integrity in specific habitats and ecosystems can be accurately assessed. However, traditional monitoring systems can present various difficulties, leading to a limited temporal and spatial resolution of the obtained information. Despite recent advancements in automatic insect monitoring traps, also called e-traps, most of these systems focus exclusively on studying agricultural pests, rendering them unsuitable for monitoring diverse insect populations. To address this issue, we introduce the Field Automatic Insect Recognition (FAIR)-Device, a novel non-lethal field tool that relies on semi-automatic image capture and species identification using artificial intelligence via the iNaturalist platform. Our objective was to develop an automatic, cost-effective, and non-specific monitoring solution capable of providing high-resolution data for assessing insect diversity. During a 26-day proof-of-concept evaluation, the FAIR-Device recorded 24.8 GB of video, identifying 431 individuals from 9 orders, 50 families, and 69 genera. While improvements are possible, our device demonstrated potential as a cost-effective, non-lethal tool for monitoring insect biodiversity. Looking ahead, we envision new monitoring systems such as e-traps as valuable tools for real-time insect monitoring, offering unprecedented insights for ecological research and agricultural practices.

zoology↗

bioRxiv2022

A universal insect trait tool (ITT, v1.0) for statistical analysis and evaluation of biodiversity research data

We present a unique data set of trait information for 586 insect families in Central Europe, covering the largest known part of described species (over 34,000 species). Life history information and major functional traits were evaluated with fuzzy coding and weighted according to the number of known species in Germany. An overall analysis of the German insect fauna is given and the data set is exemplarily applied to metabarcoding results of malaise trap samples. Due to the high functional and taxonomic diversity in insects, further developments and refinements of traits to be included will be an ongoing process with advancements of upcoming database versions to be subsequently published.

ecology↗

bioRxiv2019

Plant traits are poor predictors of long-term ecosystem functioning

Earth is home to over 350,000 vascular plant species1 that differ in their traits in innumerable ways. Yet, a handful of functional traits can help explaining major differences among species in photosynthetic rate, growth rate, reproductive output and other aspects of plant performance2-6. A key challenge, coined "the Holy Grail" in ecology, is to upscale this understanding in order to predict how natural or anthropogenically driven changes in the identity and diversity of co-occurring plant species drive the functioning of ecosystems7, 8. Here, we analyze the extent to which 42 different ecosystem functions can be predicted by 41 plant traits in 78 experimentally manipulated grassland plots over 10 years. Despite the unprecedented number of traits analyzed, the average percentage of variation in ecosystem functioning that they jointly explained was only moderate (32.6%) within individual years, and even much lower (12.7%) across years. Most other studies linking ecosystem functioning to plant traits analyzed no more than six traits, and when including either only six random or the six most frequently studied traits in our analysis, the average percentage of explained variation in across-year ecosystem functioning dropped to 4.8%. Furthermore, different ecosystem functions were driven by different traits, with on average only 12.2% overlap in significant predictors. Thus, we did not find evidence for the existence of a small set of key traits able to explain variation in multiple ecosystem functions across years. Our results therefore suggest that there are strong limits in the extent to which we can predict the long-term functional consequences of the ongoing, rapid changes in the composition and diversity of plant communities that humanity is currently facing.

ecology↗