Journal of Applied Ecology

1. Grassland biodiversity restoration efforts have largely focused on re-establishing diverse plant communities, with less attention paid to higher trophic levels such as invertebrates. 2. We established a controlled, field-scale restoration experiment across twelve lowland grassland sites in Switzerland in close collaboration with farmers. We manipulated seed addition (hay transfer, directly harvested seeds, or multiplied seed mixtures) and soil disturbance (shallow or deep tillage), with treatments applied to meadows covering, on average, 0.5 ha. Four to six years after restoration, we quantified the components of species richness change (lost, persisting, gained from site surroundings, and gained from donors), and estimated dark diversity (species that are absent from a location but could potentially occur there) for plants, Auchenorrhyncha and Orthoptera. 3. Compared to plants, invertebrates exhibited greater species turnover, with higher proportions of species lost and gained from the surroundings, but much lower contributions from the donor meadows. However, species persistence was high across taxa. Restoration operations strongly influenced components of species richness change in plants but had weaker effects on invertebrates. In particular, multiplied seed mixtures + deep tillage had the highest proportion of plant species not transferred, suggesting that many species in the seed mixture were poorly suited to local conditions. In both plants and Auchenorrhyncha, components of species richness change were strongly mediated by site characteristics, with initial species richness in both receiver and donor emerging as the strongest predictors. 4. Within each taxonomic group, 41% of species not transferred from the donor belonged to the dark diversity, suggesting substantial unrealized restoration potential. Restoration operations impacted dark diversity for plants and Auchenorrhyncha but not for Orthoptera. In plants, dark diversity was substantially lower under multiplied seed mixtures + deep tillage than under other treatments. The probability of a species belonging to dark diversity was associated with specific leaf area in plants and initial relative abundance in Auchenorrhyncha. 5. Synthesis and applications. Components of species richness change were influenced by both restoration operations and site characteristics, with their relative importance varying among taxa, underscoring the need for multi-taxon restoration strategies.

Introduction: Despite decades of investment, stream restoration often fails to recover diverse benthic macroinvertebrate communities, as restored reaches tend to remain dominated by pollution-tolerant taxa. This shortfall in recovery is commonly attributed to either poor habitat quality or dispersal limitation of aquatic macroinvertebrate taxa, yet these hypotheses are rarely tested directly. Objectives: We conducted a manipulative field experiment in the eastern Piedmont region (Maryland, USA) within the Chesapeake Bay Watershed to evaluate whether restored streams can support pollution-sensitive taxa when dispersal barriers are removed. Methods: We translocated whole macroinvertebrate communities from three nearby reference streams into three restored streams using standardized habitat substrates and quantified macroinvertebrate survival after 28 days. We also assessed how effective leaf vs rock substrates were for accumulating diverse and abundant macroinvertebrate communities for translocation. Results: Across all sites, 77% of translocated individuals and 16 of 22 taxa sensitive and moderately sensitive to pollution persisted under restored stream conditions, demonstrating that restored reaches can support short-term survival of pollution-sensitive taxa that typically fail to recover. Substrate comparisons revealed that leaf packs accumulated higher macroinvertebrate abundances, whereas both substrates accumulated macroinvertebrate communities with similar taxa richness, EPT richness, and Shannon Diversity. Conclusions: This work suggests that dispersal limitation, rather than unsuitable habitat, may be an underappreciated constraint limiting macroinvertebrate recovery in restored streams. These findings challenge the assumption that habitat restoration alone is sufficient for biological recovery and highlight the potential for whole-community translocations to accelerate recovery and ecological uplift as part of stream restoration practices. Implication for Practice: Our study highlights the need to consider habitat connectivity and dispersal pathways during stream restoration designs. Restoring streams to meet habitat requirements of pollution-sensitive macroinvertebrate taxa may not be sufficient for their populations to recover if these taxa are unable to reach the restored stream. In scenarios where restoring connectivity to source populations is infeasible, whole community translocations may offer a novel restoration tool to overcome dispersal barriers and accelerate biological recovery in restored streams.

Agricultural intensification is a major cause of population declines in farmland birds and insects worldwide. While these declines are expected to disrupt trophic interactions through density-dependent cascading effects, examples outside of crop pest control or involving more than two trophic levels remain poorly documented. Here we quantify such cascading effects across three trophic levels involving a declining aerial insectivore, the Tree Swallow (Tachycineta bicolor), which breeding success decreases with agricultural intensity, Protocalliphora bird blowflies (Diptera: Calliphoridae) that parasitize its nestlings, and Nasonia parasitoid jewel wasps (Hymenoptera: Pteromalidae) that feed upon blowfly pupae within swallow nests. Agriculture intensity may affect each of these actors either directly via, for instance, toxicological effects from pesticide use, or indirectly through density-dependent trophic interactions, which can exacerbate or mitigate one another. Using Bayesian models within a causal inference framework, we quantified how nestling host density, meteorological conditions, and agricultural landscape context affect Nasonia parasitoid infestation of Protocalliphora pupae, and estimated Protocalliphora emergence success. Analyses were based on 3,340 Protocalliphora puparia collected from 248 Tree Swallow nests over a 16-year period (2004-2019) across a 10,200-km2 agricultural intensity gradient in Southern Quebec, Canada. Model predictions indicated that Protocalliphora emergence probability increased steeply with nestling host availability (i.e., nestling-days). Whereas Nasonia parasitoid infestation occurrence at the nest level increased with Protocalliphora host availability, the proportion of infested Protocalliphora pupae within nest decreased and led to a dilution effect at the individual level. Although Protocalliphora abundance within nests decreased with agricultural intensity, their emergence probability peaked in agrointensive landscapes. This increase was associated with reduced Nasonia infestation rates, suggesting that agricultural intensity indirectly mitigated parasitoid pressure on Protocalliphora host populations. Our results highlight how agricultural intensification can shape multitrophic interactions and life-history traits (emergence success and infestation rates) by simultaneously reducing host availability and weakening top-down control by parasitoids.

The potential of alternative agricultural systems, such as organic or conservation agriculture, for biodiversity restoration has been widely investigated. However, variability of practices within each of these two categories of agricultural system has rarely been accounted for. Furthermore, these systems have most often been studied separately, isolating either organic or conservation agriculture (itself often reduced to a soil tillage reduction), and rarely analyzing these systems side by side. This study attempts to disentangle these multiple interrelationships by sampling carabid beetles in a network of 48 commercial Belgian farms including organic agriculture (OA), conservation agriculture (CA), farms combining the two approaches (OCA) and farms applying conventional practices (CVA). In each farm, we sampled carabids using pitfall and emergence traps in one winter cereal field and collected a detailed description of crop management practices. We found that community composition was primarily structured by seasonality early in the season (March, April) followed by convergence (May, June) driven by the aggregation of the most dominant species. Based on agroecosystem classification, organic certification (i.e., the absence of synthetic pesticide use) exerted a twofold stronger effect than application of conservation agriculture principles (i.e., reduced or no soil tillage) on carabid beetle community composition. Notably, OCA supported carabid assemblages that were structurally distinct from those of CA and CVA. Furthermore, carabid communities associated with OA differed significantly in composition from those of CVA. Observed and estimated species richness were also significantly higher in OCA than in CA and CVA, while OA differed from CVA only for observed richness. Beyond differences among agricultural systems, community responses were associated with specific management practices, which explained nearly a quarter of the compositional variation with significant influence of the duration since organic conversion and the frequency of synthetic pesticide application. Carabid abundances were negatively affected by synthetic pesticides and moldboard ploughing, whereas richness was more strongly related to the time since conversion to OA, cover crops, and organic amendments. Overall, these results position synthetic inputs and time since conversion as the main filters over carabid communities. Our study highlights the importance of jointly considering system level analyses and detailed management practices to better understand the complex relationships between agricultural practices and carabid communities.

Global biodiversity recovery demands restoration at landscape scales, where ecological outcomes are shaped not only by habitat amount but also spatial arrangement. Alternative landscape restoration strategies can increase habitat amount, improve quality, and alter configuration in fundamentally different ways and may therefore vary in their efficiency. Here, we conceptualise five landscape restoration strategies and use a spatially explicit metapopulation framework to compare their ability to increase predicted species persistence across heterogeneous landscapes and trait space defined by dispersal ability and specialisation. We find that restoration outcomes are strongly trait-dependent, with restoration of existing degraded habitats yielding the highest benefits - particularly for specialists. Improvement of the landscape matrix to create low-quality habitat primarily benefits generalists, while strategies creating new habitat patches or enlarging patches provide more consistent but modest gains. These results reveal fundamental trade-offs among restoration strategies and highlight the utility of trait-explicit approaches to inform their implementation and evaluation.

Climate change is reshaping weed population dynamics, creating non-stationary management challenges for which static decision rules are insufficient. This chapter presents and evaluates an open surveillance toolkit of artificial intelligence tools for recognizing and mapping Ambrosia artemisiifolia (common ragweed) at complementary spatial scales, tested across two phenological phases (seedling, September 2024; adult, December 2024) in a lentil paddock in central Chile. At the field scale, a YOLOv11 detection model coupled with Slicing Aided Hyper Inference achieved mAP50 of 0.886. Cross-domain experiments revealed that single-site models collapse catastrophically when deployed in new geographies (mAP50 dropping to 0.108), but multi-domain training recovers performance to 0.874. At the satellite scale, PRESTO foundation model embeddings correlated strongly with ground-truth weed density (r = 0.739); geographically weighted regression explained up to 91% of local density variance. All methods are released as the ragweed-ai-toolkit (https://github.com/agroia-lab/ragweed-ai-toolkit), a modular open-source Python package enabling adaptation to new species and geographies.

Automated monitoring camera systems offer an efficient approach for quantifying pollinator biodiversity and plant-pollinator interactions, but financial and logistical constraints limit the number of flowering plant species that can be monitored. Using a large European database of plant-pollinator networks, we evaluated whether plant subsampling can capture key metrics of interest (i.e. pollinator richness and network structure). We compared abundance-based, flower-shape-informed, phylogenetically informed and random plant sampling. Abundance-based sampling consistently outperformed random sampling, while adding flower shape provided little additional benefit and phylogenetic selection performed similarly to random sampling. Overall, monitoring 12-15 flowering species was sufficient to characterize key network properties across varying community sizes. Performance of abundance-based plant sampling declined in species-rich communities and when rare but highly attractive plants were present in the community, while a specific subset of pollinator species was consistently missed even by the best sampling strategy. Based on their relative contribution to interactions within each network, only 12.6% of pollinator species accounted for 95% of interactions, suggesting that AI classifiers could prioritize a relatively small subset of species. Our results provide practical guidance for designing efficient camera-based pollinator monitoring schemes at large spatial scales.

Streambank erosion degrades both terrestrial and aquatic ecosystems, and riparian buffers are among the most widely used tools to control it. Yet most of what is known about how riparian vegetation controls erosion comes from one or two-species systems, which overlook the diversity of riparian communities and species interactions (competition and facilitation) that may shape the root systems that bind soil. We used a streambank mesocosm experiment to test whether plant species richness alters root traits, and whether root traits and species interactions reduce erosion. We established 85 mesocosms ranging from unvegetated controls to six-species polycultures, measured root biomass, length, volume, and diameter, quantified species interactions with relative yield totals (RYT), and measured erosion rate with a jet erosion test. Vegetated mesocosms eroded significantly less than unvegetated controls (t = 4.05, p = 0.023). Richness did not affect most root traits, although mean root diameter decreased as richness increased ({beta} = -0.0225, p = 0.047). RYT ranged from competition (RYT < 1) to facilitation (RYT >1), yet neither interactions nor any root trait was directly associated with erosion rate. However, RYT was negatively associated with soil moisture ({beta} = -0.196, p = 0.010). Piecewise structural equation models showed that erosion was best explained by a pathway linking species presence, root length, and soil moisture (AIC = 39.27; Fisher's C = 11.27, df = 12, p = 0.506). Panicum virgatum increased root length, whereas Lolium multiflorum and Echinochloa crus-galli reduced it; longer roots were associated with drier soil (standardized {beta} = -0.31, p = 0.005), and wetter soils eroded more slowly (standardized {beta} = -0.39, p < 0.001). Which species were present mattered more than how many, and plants were linked to erosion through soil moisture rather than root traits alone. Species selection for riparian buffers may need to consider effects on soil moisture as well as rooting extent, particularly as droughts followed by intense storms become more common under a changing climate.

Land use change is widely recognized as a major driver of emerging zoonotic diseases. Yet, while forest plantations are expanding worldwide, their consequences on the circulation of infectious agents in wildlife remain poorly understood. We investigated how native pine plantations and broadleaved habitats shape rodent-associated infectious hazard in the generalist wood mouse (Apodemus sylvaticus), in southwestern France. Using a multi-season survey, we screened 424 wood mice for a broad range of infectious taxa and compared infection probability and infected-host abundance among pine plantation edges, broadleaved hedgerows embedded within pine plantations and broadleaved forests. Using a combination of linear mixed model and joint species distribution modelling, we tested whether infectious hazard differed between pine plantations and broadleaved forests, whether broadleaved hedgerows shifted these patterns towards those observed in broadleaved forests, and how these habitat-related patterns varied across seasons. We detected 22 potentially infectious taxa, including nine recognized zoonotic bacteria. Season strongly influenced infectious patterns, while habitat effects were taxa and season dependent. In spring, pine plantation edges increased infection probability for Bartonella grahamii and Neoerhlichia mikurensis compared to broadleaved forest, but their strong negative effect on wood mouse abundance resulted in consistently lower infected-host abundance for most infectious taxa. Broadleaved hedgerows showed distinct and season-dependent patterns with infectious profiles resembling those of pine edges in spring, but with higher infectious hazard than both other habitats for several taxa in autumn. Our results highlight that habitat effects on infectious hazard cannot be inferred from infection probability alone. Pine plantations may reduce rodent-associated zoonotic hazard through their strong negative effect on host abundance, despite increasing infection probability for some agents. Forest diversification and restoration initiatives, such as broadleaved hedgerows here, may have unforeseen epidemiological consequences, underscoring the need to integrate disease ecology into forest management and restoration to anticipate variation in zoonotic risks, while maximizing ecological benefits.

Management of marine protected areas (MPAs) require long-term monitoring and evaluation that is often limited by the cost and effort of conventional visual based surveys. We evaluated a community science-based eDNA approach to MPA monitoring over two years in the Point Dume State Marine Reserve, the Point Dume State Marine Conservation Area, and two paired unprotected reference sites. Across the sampling period, we detected 119 species of fish and marine mammals, including 19 of the 22 regional management-priority species for Southern CA MPAs. Protected and unprotected comparison sites were broadly similar in overall assemblage with 93 species shared across all sites. Multivariate analyses revealed site-level differences, although season and year explained more variation than protection status. Species presence also shifted during a La-Nina transition, highlighting the importance of long-term monitoring under different climatological contexts. Community scientists greatly expanded the temporal and spatial scope of eDNA sampling and generated high-quality data. Subsampling analysis showed that quarterly sampling, with three sample replicates and sequencing depths between 700k-990k, recovered 95% of observed biodiversity of the full time series. These results demonstrate that a community science-based eDNA program could provide a standardized, scalable, low-cost, and management-relevant approach for long-term monitoring across California's MPA network.

Remote and vast ecosystems present a challenge for biodiversity monitoring. The Central Kalahari Game Reserve in Botswana is one such ecosystem spanning 52,800 km2, a mosaic of fossilized rivers, clay pans, semi-arid sand dunes, scrub, and woodland. It is one of the last remaining untouched wildernesses in the world and hosts unique biodiversity, including populations of threatened mammals. Due to its extreme arid environment, size, and inaccessibility, surveying biodiversity across the reserve is both logistically and technically difficult. Here we demonstrate that a low-effort approach of sampling soil across the reserve is an efficient way to rapidly assess the terrestrial mammal biodiversity using eDNA metabarcoding. We found that eDNA from soil near waterholes performed better at capturing local biodiversity than soil from roads. eDNA metabarcoding detected 38 mammal species, 15 bird species and two amphibians and a simultaneously conducted camera trapping study detected 29 mammal species. eDNA was particularly powerful for detecting small-bodied taxa, including murids, mongooses and bats, which were largely missed by camera traps, while camera trapping was aimed at and performed well for detecting felids. Mammal species that had a higher activity nearby eDNA sampling points (i.e., a higher number of camera trapping recordings nearby) had a higher probability of being detected via eDNA. We propose that simple soil collection for eDNA metabarcoding, particularly close to animal congregation points such as savanna waterholes, offers a practical, scalable and cost-effective strategy for DNA-based biomonitoring in large, remote areas such those in Southern Africa.

BACKGROUND: The Argentine ant (Linepithema humile) is among the world's worst invasive species. In the Donana Biological Reserve (Spain), it is associated with human structures that provide the mild, moist conditions it needs within the hot, dry scrubland. We evaluated a habitat-modification treatment, predicting that removing this suitable habitat would eradicate the invader locally. Over an 18-year period, we monitored ant communities at a treated and an untreated site, at different distances from the human structures, before and after treatment, and analysed how temperature and worker numbers affected encounters at baits between the Argentine ant and native ants. RESULTS: Distance to the human structures shaped ant communities at both sites, but only the treated site changed after treatment. There, the Argentine ant decreased close to the human structure and almost disappeared farther from it, whereas the endemic Cataglyphis tartessica and C. floricola recovered, especially close to the structure. At baits, the outcome of encounters depended mainly on how temperature changes favoured each species relative to its maximum activity temperature: Cataglyphis spp. displaced the Argentine ant in the morning, as temperature increased, and the Argentine ant recovered the baits in the late afternoon. Numerical advantage favoured only the Argentine ant. CONCLUSION: Habitat modification reduced the Argentine ant and allowed endemic ants to recover, but did not eradicate the invader, which persisted at low abundance 13 years after treatment. In protected areas, habitat management should be combined with eradication, monitoring and prevention of spread.

Recent work on the design of spatial capture-recapture (SCR) studies has largely focused on settings with a single set of movement and detection parameters. In many applied contexts, however, populations are naturally stratified-for example, when different sexes exhibit markedly different movement scales and encounter processes, or when more than one focal species is monitored. Designs that perform well for one group may be inefficient or suboptimal for others, despite the substantial logistical effort typically required to implement an SCR survey. We describe several potential SCR design strategies for surveying multiple groups, and compare these using simulation. Designs considered include regular and optimised grids, clustered and lacework designs, and designs generated using genetic algorithms, including new approaches that optimise information from both groups and a recently published two-stage design. Using proximity detectors such as camera traps, we evaluate design performance in terms of precision, bias, and coverage, and examine how performance changes with the number of available resources. Our results show that multi-group SCR surveys can perform well even when focal groups differ greatly in movement scale and encounter rate, provided that some simple guidelines are followed. Systematic layouts accommodating more than one characteristic spacing e.g. cluster designs, performed consistently well and are a reliable default option. Regular grid designs with a single spacing between detectors are feasible when differences between groups are moderate but performed poorly as differences increased. Establishing when these designs become problematic adds unnecessary risk to the design process. Algorithmic designs did not outperform systematic designs even in highly-skewed scenarios expected to favour them. In practice, when there is more than one focal group we recommend identifying the smallest and largest plausible movement scales and rejecting potentially pathological designs, primarily those that cannot generate recaptures at the smaller scale or cover a substantial area at the larger scale.

1. Common dolphins (Delphinus delphis) are the most abundant cetaceans off mainland Portugal and a species of conservation and bycatch concern, so reliable abundance estimates are a management priority. Yet no single survey provides adequate spatiotemporal coverage: dedicated programmes offer sparse snapshots, while opportunistic programmes are spatially constrained by their routes. Integrating them is methodologically demanding, and the existing methods that do so in a single model estimate the density of animal groups, not of the individual animals that management often requires. 2. We develop a spatiotemporal marked log-Gaussian Cox process that integrates four structurally distinct surveys through a shared intensity surface, comprising environmental covariates, a latent spatial field, and survey-specific detection functions. Group size enters as a zero-truncated negative binomial mark with its own spatiotemporal field. We apply it to a dedicated aerial survey and three opportunistic ship-based programmes off mainland Portugal, comprising 162,514 km of on-effort transects and 1,756 sightings of 17,000 animals. We fit the model in a Bayesian framework with integrated nested Laplace approximations, yielding seasonally resolved group and animal density surfaces across the Portuguese Exclusive Economic Zone, and abundance estimates with fully propagated uncertainty. 3. Predicted animal density concentrated over the shelf and upper slope in every season, peaking northwest, and increased in shallower, cooler, more productive waters and over steeper seabed. Abundance nearly doubled from summer (77,000 [95% credible interval: 60,000, 98,000]) to winter (146,000 [99,000, 208,000]). Seasons shared 56% of the spatial field's variance, so effort from any programme in any season informed all others, and the seasonal contrast remained estimable despite uneven coverage. 4. Synthesis and applications. Our framework allows managers to combine dedicated surveys with existing platforms of opportunity to estimate animal density by season. Because group size is modelled explicitly, densities are estimated in animals rather than groups, as required for bycatch risk assessment and marine spatial planning. Because each survey enters through its own observation model, the approach accommodates programmes that differ in platform and protocol and transfers to any species or region where several surveys each cover part of a population.

1. Soil invertebrate biomass is closely linked to ecosystem processes, yet its routine quantification remains constrained by the effort required to weigh large numbers of small individuals. Length-mass relationships provide an indirect alternative, but body length can be difficult to standardise and extract automatically from images of curved, coiled or contracted organisms. Projected body area may provide a more tractable image-derived predictor, but its performance across diverse soil-faunal taxa and at the community scale remains insufficiently evaluated. 2. Here, we developed a framework using projected body area and contour length to estimate fresh and dry mass from measurements of 1,792 soil invertebrates representing 15 higher-level taxa. We fitted taxon-specific models for six well-represented groups and general models across taxa, and evaluated predictive performance using leave-one-out cross-validation. We then assessed cross-dataset transferability by comparing our data with those of Lemoine et al. (2026) and pooled the two datasets to derive combined models. Finally, we tested whether individual predictions could be scaled to independently measured community biomass. 3. Taxon-specific models generally reduced prediction error relative to general models, with mean absolute percentage error (MAPE) values of 6.9-37.3% for fresh mass and 7.6-30.2% for dry mass. General models retained MAPEs of 27.2% and 22.4%, respectively. Comparisons of our dataset with that of Lemoine et al. (2026) showed no detectable differences in six of eight taxon-by-mass slopes. Although intercepts frequently differed, no overall dataset effect was detected after accounting for taxa. Pooling both datasets broadened model development, while taxon-specific models generally retained lower error than the combined general model. Finally, community-level predictions tracked measured biomass across all model sets (R2 = 0.87-0.99), but absolute error varied among models and faunal groups (MAPE = 9.6-52.5%). 4. Body area and contour length extracted from images therefore provide a practical basis for scaling soil invertebrate biomass estimation from individuals to communities. Integration with automated imaging and segmentation tools such as flatbug, BIODISCOVER and CollembolAI could extend soil invertebrate monitoring at larger scales beyond abundance and taxonomic composition to biomass, while providing scalable inputs for analyses of food webs and general ecosystem ecology.

Ecoacoustic monitoring is enabling scientists and land managers to monitor and manage biodiversity more effectively and cost-efficiently in the face of human pressures and rapidly changing climates. Currently, most ecoacoustic surveys use manually deployed static sensors to record data, limiting the scale and reach of surveying efforts. Here we present a proof-of-concept autonomous drone platform that can use intermittent locomotion to conduct ecoacoustic surveys using an onboard sensor. Our custom prototype is able to fly, navigate, and avoid obstacles autonomously, land at a pre-determined location, record audio from an onboard microphone whilst static, before taking off and moving to the next sampling site. Autonomous navigation and operation enable greater sampling flexibility, reach, and scalability. Furthermore, by recording audio only whilst landed, noise from the drone's rotors does not mask signals or disturb animals, simplifying signal processing and downstream ecological analyses. We conducted trials in a scrubland habitat at the Knepp Estate in West Sussex, where our prototype demonstrated successful autonomous navigation and obstacle avoidance. Furthermore, we found that avian biodiversity data collected from the drone platform was comparable to that from traditional static acoustic sensor deployments, and that vocalisation patterns were not significantly impacted by the noise of the drone arriving or leaving a site. While scaled deployments of our technology would require further technical and regulatory challenges to be solved, our first demonstration of autonomous intermittent robotics-assisted ecoacoustic surveys lays the foundations for more cost-effective and far-reaching biodiversity surveys, with transformative potential for conservation, agricultural management, biosecurity, and more.

1. Grassland ecosystems are pivotal for global biodiversity, carbon (C) storage and food security. This biome, however, together with the ecosystem services it provides, is threatened by global change perturbations. These perturbations rarely occur in isolation and most often affect environments simultaneously, which is a pressing issue in ecology. Nevertheless, most of the research conducted so far has focussed on single simultaneous perturbations or pairs thereof. The few examples considering the effect of more than two simultaneous perturbations focussed only on plants or soil separately, which ignores the potential buffering or feedback effects on ecosystem functioning induced by plant-soils interactions. 2. To tackle this knowledge gap, we used an experimental setup involving mesocosms (soil and plants intact) collected in an extensively managed permanent grassland. Mesocosms were arranged in a greenhouse experiment where we applied individual and combined global change perturbations (up to ten in total) including application of fertilisers, pesticides and antibiotics, simulated trampling and grazing, heat stress and drought. Measured ecosystem functions and properties included a large set of biological, physical and chemical soil and plant associated variables. 3. Results showed that multiple perturbations in most cases had negative effects irrespective of the direction found for individual perturbations. However, in contrast to previous bare soil experiments, we only found scarce evidence for substantial synergistic effects of simultaneous perturbations in our experiment conducted with mesocosms that contained vegetation. This suggests a mitigating impact of plants on soil responses to global change. Moreover, we found a strong dominative effect of drought on ecosystem functioning, driving most of the effects observed. 4. Synthesis: Our results highlight the importance of evaluating global change impacts under more realistic settings, i.e., applying multiple perturbations simultaneously and including plant-soil interactions. Mitigation of global change in terrestrial ecosystems will only be possible once we holistically understand how ecosystems respond to altered conditions.

Anticipating and managing the impact of herbivorous pests requires evidence-based knowledge about the interactions between pests and their natural enemies. Knowledge of species' ecologies and biological interactions are typically disseminated in unstructured text across hundreds of thousands of scientific articles spanning a rapidly growing scientific literature, but manually screening this literature is time-consuming and impractical at scale. Taxonomic and geographic coverage thus remains sparse, and structured data on interactions between individual species are lacking, particularly for invertebrates. Large language models (LLMs) have recently emerged as a novel and powerful text-mining tool for knowledge extraction and evidence synthesis in a wide range of scientific disciplines, including ecology and conservation. In this study, we use an open-weight, pre-trained LLM to analyse abstracts of a corpus of published material on biological pest control to obtain a global Database of Pest Herbivores and their Natural Enemies (DAPHNE). This method resulted in the extraction of 175,404 species interactions from 112,830 published pest control studies, containing16,755 unique animal taxa resolved at the species level. Interactions include herbivory (granivory, frugivory and gall-formation), predation, parasitism and hyperparasitism, and comprise a range of additional information, such as species' taxonomy, pest status, pest importance, natural enemy importance, provision of biocontrol, associated plants and industries, invasiveness and the vectoring of pathogens and diseases. Validation of the results shows that most data columns were extracted with high recall and precision (>90%) and comparison of DAPHNE with other sources shows a high degree of agreement, but also highlights that the dataset contains information that may have been missed by other sources. In addition to the identification of important crop pests and their natural enemies, DAPHNE may be used for a variety of use-cases, including the identification of invasive species and pathogen vectors, trophic network analysis and literature search and discovery.

Freshwater biodiversity is declining at alarming rates globally, yet our capacity to monitor it depends heavily on the methods used for biodiversity assessment. Stream benthic macroinvertebrates are widely used as bioindicators, but traditional morphological identification is labor-intensive and limited by taxonomic expertise, while DNA metabarcoding offers a promising but incompletely evaluated alternative. Here, we compared biodiversity patterns detected by four macroinvertebrate sampling and identification methods (morphological identification of benthic samples, morphological identification of combined benthic and stream-edge samples, eDNA metabarcoding of bulk benthic samples, and eDNA metabarcoding of streamwater) across 62 sites in seven watersheds spanning the southern continental United States. We compared methods across three facets of biodiversity (gamma diversity, alpha diversity, and taxonomic composition) and three taxonomic levels (family, genus, and species). We found that which method detected more taxa shifted with taxonomic resolution: morphological methods detected more families and genera, while metabarcoding methods detected more species. Bulk benthic metabarcoding detected more families and genera than water eDNA metabarcoding, consistent with the "watered-down biodiversity" effect of DNA dilution in water samples, but the two metabarcoding methods did not differ significantly in species richness. All four methods detected largely distinct suites of taxa, with metabarcoding methods yielding more unique taxa than morphological methods. Notably, taxa uniquely detected by water eDNA were dominated by soft-bodied organisms (oligochaetes, leeches, earthworms) and water-column-associated taxa, while benthic eDNA uniquely detected hard-bodied EPT insects. We attribute this pattern to fundamental differences in eDNA shedding, transport, and deposition dynamics across organism types. These results demonstrate that no single method captures a complete picture of stream macroinvertebrate biodiversity, and that method choice should be guided by the taxonomic resolution and community components most relevant to the goals of the bioassessment program.

Soil is one of the most biodiverse habitats on the planet, yet the extent of soil biodiversity knowledge in Canada is unknown. To better understand our knowledge, we systematically mapped soil invertebrate biodiversity knowledge from peer-reviewed literature to identify spatial and taxonomic gaps and to assess whether areas projected to experience different magnitudes of climate change are adequately sampled. A total of 1,176 Canadian sampling locations were identified from 427 articles. We found biases in sampling coverage spatially and in relation to current and projected climatic conditions. The geographic region above 60 degrees North is sparsely sampled. Colder regions (< -0.12 C) and regions receiving the least (< 646.47 mm), moderately high (2268.67 to 2809.40 mm), and high (2809.40 to 3350.14 mm) amounts of precipitation were under sampled. Regions projected to have the smallest (< 0.86 C) and greatest (> 1.59 C) magnitudes in temperature change and regions projected to have moderate (-2.06 to +2.69 mm) to large changes (+2.69 to +7.44 mm) in precipitation were underrepresented. The sampled locations also suffer from taxonomic biases which were skewed toward Nematoda (19%), Annelida (18%), Acari (15%), and Collembola (14%). Our results suggest that methods such as stratified sampling is essential to capture underrepresented regions, taxonomic groups, and an array of current and projected climatic conditions. Additionally, shifting to a multispecies sampling approach is essential for broader soil ecosystem knowledge. Importantly, data from well-sampled areas should be examined to determine how it can be leveraged to advance conservation management of soil invertebrates.