Mediterranean agroecosystems increasingly face prolonged summer drought, extended bare-soil periods and heat extremes, all of which constrain soil biota and threaten long-term soil functioning. Management practices maintaining plant cover during summer, such as cover crops, may alleviate these constraints but generally require irrigation in dry regions. However, the ecological consequences of combining summer cover crops with irrigation remain poorly quantified for soil fauna, particularly in heterogeneous agroforestry systems. We monitored earthworm communities for six years (2019-2024) at the DIAMS experimental platform, a replicated Mediterranean alley-cropping agroforestry trial where an irrigated summer cover crop was implemented factorially. Earthworms were surveyed each winter across contrasted habitat types to assess delayed (legacy) effects of summer management. To isolate irrigation effects from spatial heterogeneity and interannual climatic variability, we applied a Difference-in-Differences framework complemented by event-study analyses. Summer crop irrigation did not affect earthworm biomass or species richness but induced habitat-specific changes in abundance. Abundance increased in irrigated agroforestry crop alleys (& times;3.5 in AF_C and & times;2.5 in AF_C1m), while no detectable effects occurred in monoculture or tree-dominated habitats. Communities were largely dominated by Microscolex dubius, suggesting that irrigation primarily enhanced the survival or recruitment of this disturbance-tolerant species. Irrigation effects were stronger following dry autumns, indicating climate-dependent legacy responses. Overall, our results show that irrigation benefits for soil fauna are strongly context-dependent, emerging primarily in exposed crop habitats and shaped by habitat heterogeneity, seasonal legacy effects and interannual climatic variability.

ABSTRACT Aim To quantify the relative influence of climate, soil and land‐use on the distributions of terrestrial invertebrate species and to assess whether their importance is consistent among taxonomic/trophic groups, biogeographical regions and land‐use‐intensity gradients. Location Europe. Time Period Contemporary. Major Taxa Studied 6844 species in 13 invertebrate groups spanning decomposers (earthworms, springtails), herbivores (gastropods, grasshoppers, moths), predators (spiders, ground beetles, dragonflies), omnivores (ants), pollinators (bees, hoverflies, butterflies), and parasitoids (parasitoid wasps). Methods We aggregated > 18 million presence records to a 1‐km grid and fitted ensemble species‐distribution models with target‐group background sampling and spatial block cross‐validation. SHapley Additive exPlanations (SHAP) quantified the contribution of climatic, soil, land‐cover, and land‐use‐intensity variables to occurrence probabilities. Predictor importance, direction of effect, and land‐use‐mediated effects were compared across taxa, European biogeographical regions, and species niche characteristics. Results Climatic variables had the highest predictive importance for species distributions (mean ± SD = 44% ± 5% of weighted summed SHAP importance), followed by soil (30% ± 6%), with land cover and land‐use intensity each contributing ~13%. Although average variable hierarchies were similar among invertebrate groups, intra‐group variation was high, and the importance of soil increased with niche specialization. Limiting environmental variables shifted geographically: snow cover governed Arctic‐Alpine ranges, whereas precipitation seasonality and the number of growing degree days constrained species in the Mediterranean region. High land‐use intensity was negatively associated with species occurrence, especially in grasslands. Main Conclusions Continental‐scale invertebrate distribution patterns are mainly associated with climate and modulated by soil properties, but species‐specific niches and regional contexts generate diverse responses to land‐use change. Instead of relying on single‐taxon surrogates or uniform prescriptions, conservation strategies should better integrate soil management and habitat restoration in sensitive areas, such as the central Danube basin, and for sensitive species, such as grassland butterflies and hoverflies.

Soil biodiversity indicators are increasingly used to inform environmental assessment and policy, yet their robustness is often constrained by heterogeneous sampling designs and imperfect species detection. Surface-active arthropods, such as ground beetles (Carabidae), are widely monitored using pitfall-based methods, but differences among trapping protocols and sampling effort complicate comparisons across sites and programmes. Using data from the French Soil Quality Monitoring Network (RMQS-Biodiversity), we analysed how sampling protocol and trapping intensity shape species detectability and how these effects propagate to inventory completeness and taxonomic diversity estimates. We applied species-level occupancy models to explicitly estimate detection probabilities as functions of sampling protocol, effort and species traits, and propagated these estimates to community-level diversity metrics using coverage-based standardisation. Detection probability increased strongly and monotonically with sampling effort, approaching saturation under the 10-pitfall traps configuration. GPDs exhibited detectability comparable to intermediate pitfall configurations (6 pitfall traps), but remained below 8 and 10 pitfall traps. Body size showed a consistent positive effect on detectability across protocols, whereas wing development effects were weak. Coverage-based standardisation substantially reduced protocol effects on diversity estimates, but species richness (Hill q = 0) remained more sensitive to protocol identity than Shannon or Simpson diversity. Together, our results show that harmonising sampling effort alone is insufficient to ensure comparability among biodiversity monitoring protocols. Instead, combining detection-aware modelling with coverage based standardisation provides a robust framework for improving the comparability and policy relevance of biodiversity indicators under heterogeneous monitoring schemes. More broadly, this study highlights the importance of explicitly integrating observation processes into large-scale soil biodiversity monitoring frameworks.

Soil unsealing, which involves removing impermeable surfaces to restore soil functions, is increasingly being integrated into urban planning policies. However, its role in conserving biodiversity, particularly soil fauna, remains largely unexplored. This study provides one of the first assessments of early earthworm colonization in recently unsealed urban soils using environmental DNA (eDNA) metabarcoding. Twenty schoolyards in the French Mediterranean region were selected to encompass variation in local and landscape factors, and temporal conditions.A total of 117 Molecular Operational Taxonomic Units (MOTUs) were detected at a 90% similarity threshold, identifying 10 predominantly generalist and cosmopolitan earthworm species. Eukerria saltensis DNA was recorded for the first time in mainland France. MOTU richness increased significantly year-on-year, reaching levels comparable to lawns established at the same time as the schoolyards within two years. Community assembly in these newly unsealed soils appears to be primarily driven by non-random processes. Local variables, such as soil properties, had the strongest influence (about 11% of explained variance), followed by landscape-scale factors (6%).While long-term studies are needed to fully understand the mechanisms shaping earthworm communities in unsealed urban spaces, this study provides valuable insights into the early stages of colonization. The findings suggest that, depending on soil physicochemical properties and ground cover type, recently unsealed areas, can serve as habitats for soil biodiversity from their inception. These results highlight their importance when designing urban soil restoration projects. Finally, the use of eDNA metabarcoding raises important questions about the relevance of MOTUs as a proxy for richness index.

Understanding how climate shapes ecosystem productivity through both energetic constraints and biodiversity‑mediated pathways remains a major challenge in global change ecology, particularly in mountain grasslands where rapid warming and strong environmental gradients interact. Here, we disentangle and map direct climatic controls on productivity from indirect effects mediated by canopy functional structure across the European Alps. Using Sentinel‑2 time series (2017–2024), we quantified canopy functional structure from spectral proxies of pigment investment and water status, functional richness, and vegetation productivity using near‑infrared reflectance of vegetation (NIRv). We combined causal inference with piecewise structural equation modelling to partition total climate effects into direct and trait‑mediated components and used varying‑coefficient models to evaluate how these pathways vary along environmental gradients. We identified two concurrent pathways linking climate to productivity. Warmer growing seasons directly increased productivity but simultaneously reduced canopy pigment investment and water status, generating negative indirect effects that partly offset direct gains. Greater winter snow accumulation reduced productivity both directly, by shortening the effective growing season, and indirectly, through increases in canopy water content associated with lower productivity. Climatic water deficit produced opposing effects, with weakly positive direct impacts counteracted by negative indirect effects mediated by pigment‑related traits, resulting in minimal net productivity change. Trait diversity showed relatively weak climate responses and modest contributions to indirect effects. Environmental context strongly modulated climate–vegetation relationships. Baseline temperature and moisture availability most strongly shaped climate effects on canopy traits and productivity, whereas biodiversity–functioning relationships varied little across space. Strongest positive productivity responses to warming occurred in moist, mid‑elevation regions, while responses weakened or became neutral at high elevations, snow‑rich areas, and dry sites.

Agroforestry is promoted as a sustainable agricultural practice that enhances biodiversity and ecosystem services, including natural pest control. However, its effects on arthropod communities, particularly across different trophic groups and seasonal dynamics, remain poorly understood. In this study, we assessed the impact of a Mediterranean alley-cropping agroforestry system on the abundance, diversity, and community composition of eight arthropod trophic groups, in Southern France. Using pan traps and pitfall traps, we sampled arthropods in agroforestry alleys and tree rows at three dates in spring 2023 (April, March and May), comparing them to monocultures and tree plantations. After identification, invertebrate taxa were classified into eight trophic groups based on current ecological knowledge. Agroforestry influenced arthropod abundance and diversity, though responses varied among trophic groups. Community composition, as reflected through a Principal Coordinates Analysis, was primarily structured by phenology rather than habitat type, with pronounced seasonal shifts across most trophic groups. Effect size analysis showed that tree rows supported a higher abundance of certain beneficial arthropods, emphasizing their role in agroforestry system function. Further research on multi-trophic interactions and long-term dynamics is needed to optimize agroforestry as a strategy for ecological intensification.

Soil biodiversity indicators are increasingly used to assess soil health and support environmental monitoring frameworks. Their interpretation typically relies on reference distributions from which indicator thresholds are derived, implicitly assuming that these thresholds reflect underlying ecological conditions and are transferable across contexts. However, the extent to which sampling strategy shapes these reference systems remains poorly understood.Here, we investigate how contrasting sampling designs influence the construction and performance of soil biodiversity indicators using Collembola communities as a model system. We analysed multiple French datasets collected under comparable field and laboratory protocols but differing in spatial scale, site selection rules and sampling design. For each dataset, we constructed reference distributions for species richness and density, derived quantile-based scoring schemes, and applied them to independent test sites. We further evaluated the discriminant power of each reference framework by quantifying its ability to detect known local contrasts between treatments and controls.We show that sampling strategy is a primary driver of reference distribution shape, leading to substantial differences in indicator thresholds. These differences propagate directly into soil health classifications, with identical sites receiving contrasting scores depending on the reference framework used. Importantly, reference frameworks also differ in their discriminant power, with some consistently amplifying ecological contrasts while others attenuate them.Our results demonstrate that soil health thresholds are not intrinsic ecological properties but outcomes of methodological choices embedded in sampling design. Consequently, indicator interpretation is contingent on the reference framework employed, challenging the assumption of universal and transferable thresholds. We argue that evaluating discriminant power, alongside distributional robustness, should become a central criterion in the development of soil biodiversity indicators. More broadly, our findings highlight the need to explicitly integrate sampling design into indicator science to ensure robust, comparable and policy-relevant soil health assessments.

Preserving soil health is essential for sustaining ecosystem functions and human well-being, yet it is increasingly challenged by intensive agricultural practices. In Nonthaburi Province, Thailand, smallholder farmers produce premium-quality Monthong durian (Durio zibethinus L.) in raised-bed orchards using diverse management strategies whose impacts on soil health remain poorly understood. Therefore, this study assessed how contrasting management practices affect soil physicochemical properties, biodiversity and functioning. Thirty-two farmers were interviewed to characterize management diversity, with their orchards grouped into three clusters based on fertilizer and irrigation regimes: C1 (high organic fertilizer and tap water), C2 (high canal water use), and C3 (high mineral fertilizer use). Soil analyses conducted in 15 orchards showed predominantly clay textures and relatively high soil organic carbon levels (3.45–4.31%). Significant differences emerged for available phosphorus and extractable calcium, with Cluster C2 presenting the lowest concentrations. While earthworm biomass did not differ significantly among clusters, bait-lamina testing indicated higher soil biological activity in C1 than in C2 or C3. Overall, the management practices influenced nutrient availability and biological functioning, with the fertilization regimes shaping P, K, and Ca status, while organic amendments enhanced soil biological activity. Analysis of the results highlighted the complementarity of various methods to assess soil health. Furthermore, these findings should contribute to the development of more sustainable soil management strategies to maintain fertility and support long-term durian orchard productivity.

Soil biodiversity is fundamental to ecosystem functioning but remains underrepresented in conservation policies and large-scale monitoring. Here, we present RMQS-Biodiversity, a nationwide soil biodiversity survey integrated into the French Soil Quality Monitoring Network (RMQS), and illustrate its potential for soil ecology research. In this pioneer study, we examine three major ecological aspects: (i) how systematic grid-based sampling captures micro-food web patterns using nematode communities, (ii) the spatial turnover of detritivore communities (Collembola, Isopoda, Diplopoda) in response to environmental and geographic gradients, and (iii) the influence of macroecological drivers on predator (Carabidae) morphological traits. Across 69 sites, we identified a few widespread species coexisting with numerous rare taxa, underscoring the value of large-scale surveys for detecting cryptic biodiversity. Nematode indicators revealed high variability in food web structure across land uses, with increased facultative phytophagous nematodes in forests. Isopods and diplopods were strongly structured by dispersal constraints, while springtails exhibited weaker environmental responses, likely due to their higher dispersal capacity. Additionally, sexual size dimorphism in Carabidae varied by habitat, with female-biased dimorphism in closed habitats but no dimorphism in open environments, highlighting habitat stability's role in shaping morphological traits. This study demonstrates the value of multi-taxon, multi-trophic biodiversity assessments in long-term soil monitoring. RMQS-Biodiversity provides a robust framework for soil biodiversity monitoring and conservation, refining bioindicators of soil quality and informing policies such as the EU Soil Monitoring Law.

1 - Automated in situ sensors - e.g., buried scanners - are transforming biodiversity monitoring by generating data at spatio-temporal resolutions unattainable through traditional sampling, including in cryptic environments such as soil that have remained largely inaccessible to existing methods. However, extracting ecologically meaningful information from these data streams requires substantial image processing effort that currently constitutes a critical bottleneck, particularly when the signal-to-noise ratio is low and annotated training data are scarce. 2 - Standard end-to-end deep learning detection pipelines offer unsatisfactory results due to the lack of training data and heterogeneity of the taxa of interest. We explore the potential of combining traditional computer vision algorithms with state-of-the-art deep learning models to build an efficient raw data processing pipeline from limited annotation effort. Specifically, based on the observation that the background barely changes, we focus on the differences between two consecutive images to turn the initial detection problem (with very low signal) into a simpler classification problem, which we solve by fine-tuning foundation models on limited annotated data. 3 - Our approach significantly reduces the annotation effort, allowing us to release an open dataset with about 600 soil scans and more than 8 000 labeled invertebrate occurrences across nine taxa. Using this dataset to train our models, we obtained population count estimates with relative errors ranging from 10%to 61% across taxa over a three-month period. Ecological validation through a land-use stability analysis showed full directional congruence between automated and expert-annotated classifications across all nine taxa examined, with effect-size discrepancies proportional to per-taxon classification accuracy. 4 - These results demonstrate that combining domain-specific heuristics with fine-tuned foundation models provides an effective and data-efficient strategy for automating ecological image processing workflows in low-signal, data-scarce contexts. The validated pipeline removes the manual annotation bottleneck that has historically limited scanner-based soil monitoring to short observational windows and restricted taxonomic scope, opening the way for continuous, large-scale tracking of soil invertebrate community dynamics at resolutions previously unachievable.

Earthworms are key drivers of soil function, influencing organic matter turnover, nutrient cycling, and soil structure. Understanding the environmental controls on their distribution is essential for predicting the impacts of land use and climate change on soil ecosystems. While local studies have identified abiotic drivers of earthworm communities, broad-scale spatial patterns remain underexplored. We developed a multi-species, multi-task deep learning model to jointly predict the distribution of 77 earthworm species across metropolitan France, using historical (1960-1970) and contemporary (1990-2020) records. The model integrates climate, soil, and land cover variables to estimate habitat suitability. We applied SHapley Additive exPlanations (SHAP) to identify key environmental drivers and used species clustering to reveal ecological response groups. The joint model achieved high predictive performance (TSS >= 0.7) and improved predictions for rare species compared to traditional species distribution models. Shared feature extraction across species allowed for more robust identification of common and contrasting environmental responses. Precipitation variability, temperature seasonality, and land cover emerged as dominant predictors of earthworm distribution. Species clustering revealed distinct ecological strategies tied to climatic and land use gradients. Our study advances both the methodological and ecological understanding of soil biodiversity. We demonstrate the utility of interpretable deep learning approaches for large-scale soil fauna modeling and provide new insights into earthworm habitat specialization. These findings support improved soil biodiversity monitoring and conservation planning in the face of global environmental change.

Soil harbours immense biodiversity that plays crucial roles in ecosystem functioning. Although our current understanding of soil fauna relies on traditional sampling as a high-resolution benchmark, these methods are often destructive, labour-intensive and limited in temporal resolution. Recent technological advancements offer promising alternatives for real-time, low-invasive monitoring of soil meso- and macrofauna activity, overcoming key methodological constraints. This paper explores the possible integration of novel sensor-based technologies into soil ecology, drawing inspiration from successful applications in terrestrial and aquatic ecosystems. Optical imaging enables in situ visual observation of soil organisms at the millimetre scale, while bioacoustics captures vibrational signals produced by invertebrates, providing insights into their activity dynamics. These methods enhance spatial and temporal resolution and coverage in biodiversity assessments, even at lower taxonomic resolution, opening up possibilities for deeper understanding of interactions, phenology, diel or seasonal activity patterns, their functional implications and responses to environmental change. Challenges remain in enhancing detection accuracy, automating data processing, and extracting relevant biological information from this big data. The integration of sensors into global networks and/or their coupling with measurements of abiotic parameters are promising avenues to transform these tools into truly integrative systems. Such networks could revolutionise our understanding of soil biodiversity, unveiling previously invisible ecological processes and extend the predictive capabilities of models of ecosystem functioning.

Diversification within farming systems is a central lever to increase farm sustainability and general resilience. While the potential benefits of diversified farming systems are rather well known, the design process of such systems, especially with the objective of increasing their general resilience (i.e., buffering, adaptive, and transformative capacities), has received little attention. In this article, we review the key challenges faced when looking for general resilience in the design of diversified farming systems. These challenges both relate to the structure of the system to be designed (e.g., the nature and spatio-temporal arrangement of the system’s components) and to the design process (articulating de novo and step-by-step design). Addressing these challenges requires moving beyond traditional reductionist approaches (that sought to make farming systems as deterministic as possible through standardization) to relational, adaptive methods that acknowledge the uncertainties inherent in translating abstract system representations into real-world transformations. Based on a literature review and examples from three French open on-station system experiments, we show how designing a farming system for its buffering, adaptive, and transformative capacities benefits from a combination of de-novo and step-by-step approaches, while acknowledging for the need for constant evolution of the system in response to changing objectives and environmental conditions. Key solutions include shifting to holistic perspectives and leveraging empirical knowledge by valuing visualization and prototyping, as well as integrating previously “invisible” factors, notably working conditions and work distribution across the year.

Understanding the relative importance of biotic interactions, multiple environmental drivers, and neutral processes in shaping community diversity and composition is a central question for both theoretical and applied ecology. We analysed a dataset describing 125 earthworm communities sampled in 10 localities in French Guiana. DNA barcodes were used to delimit operational taxonomic units (OTUs) that we considered as species surrogates to avoid the taxonomic deficit and calculate community-scale species richness and pair-wise Sørensen beta-diversity. We used log-ratio and generalised linear models to highlight the effects of biotic interactions and environment as drivers of alpha diversity, and generalised dissimilarity models to figure out the relative contribution of space and environment to beta-diversity at different spatial extents. Community-scale alpha diversity was mainly explained by habitat filtering (soil texture) and interspecific competition that limit the number of locally co-existing species. Beta diversity between pairs of communities was mainly explained by distance when comparing communities in similar habitats, by topography and available soil phosphorus when comparing communities in different habitats, and by distance, elevation and climate when comparing all possible pairs of communities. While community composition is determined locally by neutral processes and environmental filtering, biogeographic processes linked to dispersal limitation and adaptation to local environment are the most influential on a regional scale. This highlights the complex interplay of dispersal limitation, biotic interactions and environmental filtering during the process of community assembly.

Plant-parasitic nematodes are known to impair plant development and can cause severe crop loss. Agroforestry is a promising land use management system for the preservation of soil fertility and biodiversity conservation, but very few studies have focused on the regulation of plant-parasitic nematodes with the inclusion of tree alleys in cropping systems. In this study, the capacity of the soil to suppress plant-parasitic nematodes was assessed in Southern France. Fresh and heated soils from different combinations of land use (monospecific crop, agroforestry or tree plantation) and plant cover (crop or tree) were tested for their capacity to suppress Meloidogyne javanica in a laboratory assay. In the samples taken under tree cover, the suppressive capacity of fresh soils was improved compared to that of the monospecific crop samples. As the suppressive capacity of the heated soils remained low, we considered that soil fauna was responsible for part of the soil suppressiveness. The characterization of nematode communities revealed no Meloidogyne spp. on the experimental site, but other plant-parasitic nematodes were found. The total density of Pratylenchus spp. was lower while the relative density of Helicotylenchus spp. was higher under tree cover, compared with the monospecific crop soils. In agroforestry, the relative density of herbivores was ca 200 % higher under tree cover compared to under crop cover but the parasitic pressure (assessed by the Plant-Parasitic Index/Maturity Index ratio) was ca 30 % lower. Moreover, the crop soils had the highest Enrichment Index while the tree row soils in agroforestry had the highest Structural Index. The suppressive capacity in agroforestry was associated with a specific nematofauna, including more predatory taxa than in the monospecific crop. This study showed that including trees in a cropping system in a Mediterranean area created a favorable niche for potential herbivore regulators among the soil fauna. Further investigations are required to validate the regulation of plant-parasitic nematodes, and to disentangle the complex interactions explaining it in agroforestry.

Tropical rainforests are among the most emblematic ecosystems in terms of biodiversity. However, our understanding of the structure of tropical biodiversity is still incomplete, particularly for certain groups of soil organisms such as earthworms, whose importance for ecosystem functioning is widely recognised. This study aims at determining the relative contribution of alpha and beta components to earthworm regional diversity at a hierarchy of nested spatial scales in natural ecosystems of French Guiana. For this, we performed a hierarchical diversity partitioning of a large dataset on earthworm communities, in which DNA barcode-based operational taxonomic units (OTUs) were used as species surrogates. Observed regional diversity comprised 256 OTUs. We found that alpha diversity was lower than predicted by chance, regardless of the scale considered. Community-scale alpha diversity was on average 7 OTUs. Beta diversity among remote landscapes was higher than expected by chance, explaining as much as 87% of regional diversity. This points to regional mechanisms as the main driver of species diversity distribution in this group of organisms with low dispersal capacity. At more local scales, multiplicative beta diversity was higher than expected by chance between habitats, while it was lower than expected by chance between communities in the same habitat. This highlights the local effect of environmental filters on the species composition of communities. The calculation of a Chao 2 index predicts that as many as 1700 species could be present in French Guiana, which represents a spectacular increase compared with available checklists, and calls into question the commonly accepted estimates of global number of earthworm species.