Lejoly, J. | bioRxiv Science

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↗

bioRxiv2025

Functional diversity of soil microbial communities increases with ecosystem development

Land abandonment is the single largest process of land-use change in the Global North driving succession and afforestation at continental scales, but assessing its impacts on soil microbial communities remains a challenge. Here, we established a nationwide successional gradient of paired grassland and forest sites to track developments in microbial structure and functioning following land abandonment and gradually changing plant communities. We show that microbes generally respond through threshold dynamics, leading to increasing functional but decreasing taxonomic diversity. Succession also increased the specialization of microbial nutrient (C-N-P) cycling genes while decreasing genetic redundancy, highlighting a putative trade-off between two desirable ecosystem properties: functional diversity and functional redundancy. Increasing fungal functional diversity underpinned higher microbial C-cycling capacity, underscoring the causal link between functional traits and ecosystem processes. Changing litter quality similarly provided a mechanistic link between plant and microbial communities despite otherwise largely decoupled successional developments. Land abandonment is frequently touted as an opportunity to increase biodiversity and carbon storage. Our results show that deeper knowledge about the multifaceted development of soil microbial communities and its links to plant communities during succession may be needed to fully grasp the impacts of global land abandonment processes.

ecology↗