Although DNA barcodes-based operational taxonomic units (OTUs) are increasingly used in earthworm research, the relative efficiency of the different methods available to delimit them has not yet been tested on a comprehensive dataset. For this study, we used three datasets containing 651, 2304 and 4773 COI barcodes of earthworms from French Guiana, respectively, to compare five of these methods: two phylogenetic methods—namely Poisson Tree Processes (PTP) and General Mixed Yule Coalescence (GMYC)—and three distance matrix methods—namely Refined Single Linkage (RESL, used for assigning Barcode Index Numbers in the Barcode of Life Data systems), Automatic Barcode Gap Discovery (ABGD), and Assemble Species by Automatic Partitioning (ASAP). We found that phylogenetic approaches are less suitable for delineating OTUs from DNA barcodes in earthworms, especially for large sets of sequences. The computation times are unreasonable, they often fail to converge, and they also show a strong tendency to oversplit species. Among distance-based methods, RESL also has a clear tendency to oversplitting, while ABGD and ASAP are less prone to mismatches and have short computation times. ASAP requires less a priori knowledge for model parameterisation than AGBD, provides efficient graphical outputs, and has a much lower tendency to generate mismatches.

Soil organic matter (SOM) transformation processes are regulated by the activities of plants, microbes, and fauna. Compared with plants and microbes, effects of soil fauna are less understood because of their high taxonomic and functional diversity, and mix of direct and indirect effect mechanisms. Trait‐based approaches offer a generic perspective to quantify mechanistic relationships between soil fauna and SOM transformations, including decomposition, translocation, and stabilisation of organic carbon. Yet, at present, we lack a consensus concerning relevant key effect traits of soil fauna (i.e. those affecting ecosystem functioning). Here, we address this knowledge gap by focusing on relationships between soil fauna effect traits and SOM transformations. Based on existing literature, we identify key processes linked to SOM transformations, and fauna effect traits universally applicable across taxa and soil types, and discuss the process‐trait links. We define eight SOM transformation processes that are directly affected by soil fauna: (i) litter mass loss, (ii) litter fragmentation, (iii) SOM aggregation in faeces, (iv) SOM aggregation in soil mineral particles, (v) decomposition of faeces, (vi) SOM and mineral translocation, (vii) pore space creation and maintenance and (viii) SOM stabilisation. We link these processes to general effect traits classified into four categories: (a) food selection and ingestion, (b), digestion and excretion, (c) mobility, and (d) body mass and metabolic rate. We also propose proxies when effect trait measurements are laborious. The proposed links between effect traits and SOM transformation processes need to be validated in targeted experiments. We urge researchers to obtain quantitative experimental data, together with metabolic approaches, to integratively quantify soil fauna contributions to soil functioning. Read the free Plain Language Summary for this article on the Journal blog.

Understanding the causes of biodiversity change is essential for addressing environmental challenges. While causal attribution has advanced in other fields, ecologists remain cautious about causal claims or misinterpret predictive models as causal. With growing spatio‐temporal data, computational power and cross‐disciplinary collaboration, discussions on improving attribution methods in ecology are gaining momentum. However, practical guidance remains limited for non‐experts. Here, we identify the challenges and decisions involved in detecting and attributing biodiversity change and provide an overview of suitable methods based on available data and specific research questions. The first challenge we address pertains to biodiversity and driver data. Unlike controlled experimental data in other disciplines, ecological data often stem from monitoring programs or field samplings with varying degrees of rigour, which complicates the analysis due to sampling biases, interacting drivers, measurement error or spatio‐temporal variations. We specifically outline how data structure (e.g. structured vs. opportunistic data) and data coverage along the spatial and temporal scale impact detection and attribution. The second challenge involves the ability to detect directional change in the system of interest, which is associated with numerous hurdles. We provide an overview of the most relevant approaches to deal with sampling variability, gaps and biases in the data, non‐linearity in the temporal trends and to identify the most appropriate spatio‐temporal resolution. For the third challenge, causal attribution, we focus on data‐driven approaches. We review recent frameworks that draw on methodologies from other disciplines, offering analytical roadmaps and step‐by‐step guidance for causal inference. These include constructing theoretical causal models a priori, full causal models based on data and theory and posterior causal interpretation tailored to specific data and research questions. Moving forward, it is essential to foster interdisciplinary collaboration to adapt and refine methodologies from other fields, ensure robust data collection and sharing practices, promote the integration of advanced computational tools and improve the link between data‐driven and theory‐driven approaches. This approach will enhance our ability to make robust causal inferences; thereby improving our understanding of biodiversity changes and informing effective conservation strategies.

The increasing severity and frequency of natural disturbances requires a better understanding of their effects on all compartments of biodiversity. In Northern Fennoscandia, recent large-scale moth outbreaks have led to an abrupt change in plant communities from birch forests dominated by dwarf shrubs to grass-dominated systems. However, the indirect effects on the belowground compartment remained unclear. Here, we combined eDNA surveys of multiple trophic groups with network analyses to demonstrate that moth defoliation has far-reaching consequences on soil food webs. Following this disturbance, diversity and relative abundance of certain trophic groups declined (e.g., ectomycorrhizal fungi), while many others expanded (e.g., bacterivores and omnivores) making soil food webs more diverse and structurally different. Overall, the direct and indirect consequences of moth outbreaks increased belowground diversity at different trophic levels. Our results highlight that a holistic view of ecosystems improves our understanding of cascading effects of major disturbances on soil food webs.