Ecological Indicators

Primary and old-growth forests are globally valued for their biodiversity, ecosystem services, and cultural significance. The EU Biodiversity Strategy and EU Forest Strategy for 2030 require strict protection of remaining primary and old-growth forests, yet they cover only about 3% of EU forest area and remain highly threatened. The European Commissions guidelines define old-growth forests using three main indicators--native tree species, deadwood, and large/old trees--supported by five complementary indicators. Implementing these indicators for boreal and hemiboreal old-growth forests in northern Europe currently lack science-based operational criteria that meet EU legal standards. We provide recommendations for implementing European Commissions indicators with science-based operational criteria and thresholds to minimize misclassification and ensure cost-effective conservation. Key thresholds include native species dominance, [≥]5% deadwood of the total wood volume, and [≥]20 large/old trees per hectare. Additional guidance is offered for regeneration patterns, structural complexity, microhabitats, and indicator species, emphasizing that all indicators should be applied collectively.

Convolutional Neural Networks (CNNs) have become increasingly prominent in biodiversity monitoring due to their strong performance in accurately detecting species from sound recordings, overcoming some limitations of traditional methods such as point-counts. Yet, their use in urban ecosystems remains limited, highlighting the need for frameworks that identify modelling strategies to optimize their performance in these complex soundscapes. Here, we evaluated how preprocessing and labelling strategies, detection thresholds, sample size, and architecture affect the performance of CNNs for bird identification in urban tropical ecosystems. We also assessed its potential by comparing CNN-derived biodiversity estimates with those from point-counts and acoustic indices. For this, we used one week of recordings collected along urbanization gradients in five Colombian Andes cities to developed 11 multiclass CNN models varying in spectral representation, labelling strategies, training data source and backbone architecture. The best-performing model, evaluated with F1-scores, combined Log-Mel spectrograms, multispecies labels, ecosystem-specific recordings, a probability threshold of 0.3 and a ConvNeXt backbone with its performance generally improving with sample size. Although CNNs and point counts detected partially distinct assemblages, CNN-derived species richness was comparable to that estimated from point-counts. In addition, the Normalized Difference Soundscape Index (NDSI) was positively associated with richness, suggesting its potential as a biodiversity proxy in tropical urban soundscapes. Overall, by identifying effective modelling designs and monitoring strategies, our study advances the development of robust biodiversity assessment frameworks in urbanized ecosystems in the Neotropics whilst also providing methodological guidance for future research and practical insights for wildlife monitoring and conservation.

Fire has acted as a major eco-evolutionary force since the evolutionary appearance of plants, shaping plant-traits, diversity, vegetation assembly, and ecosystem functioning. Its ecological role depends on long-term fire regimes. Anthropogenic land-use change and climate warming are disrupting these regimes, particularly in densely populated regions such as the Mediterranean Basin. In the Italian peninsula (Mediterranean region) fire activity peaks during the dry summer months and is projected to intensify under climate change scenarios. Recent methodological developments - based on emerging satellite data, ground-based observations combined with Random Forest (RF) habitat classification, and spectral indices such as the NDVI provide a robust framework for monitoring post-fire land-cover dynamics over time. In this study, we applied RF modelling to classify vegetation cover using a 2017-2024 satellite imagery time series of the Monte Pisano area (central Italy) to assess pre- and post-fire vegetation trajectories. Evergreen shrubs and trees exhibited rapid post-fire regrowth, whereas coniferous stands showed slower recovery rates. NDVI trends revealed an expected sharp decline immediately after the fire, followed by gradual recovery of broadleaf forests and shrubland communities. Moreover, our results indicated a progressive increase in the cover of native deciduous and evergreen species of high conservation value (listed under the Habitat Directive). The framework delivers spatially explicit insights into post-fire recovery, supporting targeted management, restoration under European Nature Restoration Regulation, and long-term monitoring in Mediterranean ecosystems. Incorporating fine-scale environmental variables may further improve classification accuracy and enhance assessments of vegetation resilience and ecosystem recovery following fire events. HighlightsO_LIRecurring fires strongly affect ecosystem structure and function in Mediterranean landscapes. C_LIO_LIIntegrating remote sensing with Random Forest models enables effective monitoring of post-fire vegetation recovery over time. C_LIO_LINDVI time series provide reliable proxies for tracking vegetation vigor and land-cover change. C_LIO_LIPost-fire recovery trajectories are shaped by fire severity, vegetation physiognomy, plant functional types, and soil conditions. C_LIO_LITargeted restoration and management interventions informed by spatial-temporal vegetation patterns are urgently needed. C_LIO_LIThe proposed framework aligns with objectives of the EU Nature Restoration Regulation for ecosystem and habitat recovery. C_LI

BackgroundMangrove forests are crucial ecosystems which support biodiversity, protect coastlines and store vast amounts of carbon. Mangrove conservation and protection rely on accurate carbon accounting to unlock investment. However, the allometric equations underpinning these carbon estimates remain poorly constrained, particularly for the large trees. MethodsWe used terrestrial laser scanning (TLS) to estimate the biomass of 187 mangrove stems across Suriname, Panama, Colombia and Jamaica, including 84 stems >20 cm DBH. TLS-derived biomass estimates were used to evaluate local, regional and pantropical allometric equations. ResultsMost diameter-based allometric equations underestimated biomass by 8-65%. Equations additionally incorporating tree height performed better, but still underestimated biomass by 12-16% on average. Applying alternative allometries to a representative mangrove inventory from Panama produced biomass estimates ranging from 80 to 200 Mg ha-{superscript 1}, demonstrating that allometric uncertainty alone can generate more than a two-fold difference in estimated carbon stocks. ConclusionsCurrent allometric equations systematically underestimate the biomass of large mangrove trees and are therefore likely to underestimate mangrove carbon stocks. TLS provides a practical, non-destructive approach for expanding biomass datasets and improving allometric equations. Reducing allometric uncertainty should be a priority for strengthening blue carbon accounting and mangrove conservation.

Environmental DNA (eDNA) metabarcoding has become a cornerstone of marine biodiversity monitoring, yet it recovers genetic material irrespective of organism viability and may therefore conflate historical and contemporary community signals. Environmental RNA (eRNA), derived from less stable ribonucleic acid, is hypothesized to be biased toward metabolically active organisms and may provide a more temporally resolved snapshot of living communities. Here we present a paired eDNA/eRNA metabarcoding comparison across a tropical marine seascape, analyzing 19 co-sampled sites spanning coral reefs, mangroves, a seagrass bed, shipwrecks, a cenote, and coastal infrastructure around San Andres Island, Colombia. To our knowledge this is the first in situ, ecosystem-scale paired eDNA/eRNA survey of the broad eukaryotic community across multiple natural habitat types in a tropical marine system, extending mesocosm and freshwater work (e.g., Giroux et al., 2022) to a field setting. Using COI-region amplicon sequencing processed by NatureMetrics, we recovered 1,944 operational taxonomic units (OTUs) across the 19 paired sites. Of these, 1,015 (52.2%) were detected by both approaches, 305 (15.7%) were unique to eDNA, and 624 (32.1%) were unique to eRNA. The eRNA-unique fraction was taxonomically enriched for groups including diatoms (class Bacillariophyceae, phylum Ochrophyta), ciliates, and other protists. Paired Wilcoxon signed-rank tests showed that eRNA recovered significantly higher OTU richness (median 239 vs. 207; W = 36, p = 0.016) and Shannon diversity (median 3.64 vs. 3.38; W = 40, p = 0.026) than eDNA. The mean per-site Jaccard similarity between paired samples was 0.40, indicating substantial turnover in the rare-taxon composition recovered by each method. Principal coordinates analysis of Bray-Curtis dissimilarity showed that habitat type structured abundance-weighted community composition (PERMANOVA F = 2.49, p = 0.001) whereas molecular method did not (F = 1.37, p = 0.107). A PERMDISP test found homogeneous multivariate dispersion between methods (F = 0.01, p = 0.92), reinforcing the absence of a method effect, but significant dispersion heterogeneity among habitats (F = 24.0, p < 0.01), so the habitat result is interpreted with caution. Indicator species analysis identified 73 OTUs significantly associated with one template: eDNA indicators were dominated by dinoflagellates (Dinophyceae) and eRNA indicators by diatoms (Bacillariophyceae) and fungi, consistent with an eRNA bias toward metabolically active microbial eukaryotes. A read-weighted overlap analysis showed that although eRNA-unique OTUs outnumbered eDNA-unique OTUs roughly two to one, the large majority of reads (>95%) fell in shared OTUs, so method-unique detections are predominantly rare taxa. We discuss the complementary value of eRNA for marine monitoring, with the seagrass habitat -- where eRNA reduced masking by terrestrial plant material -- as the clearest use case, and propose, rather than prescribe, the integration of eRNA into routine programs.

Governments, civil society organizations and businesses often lack the biodiversity data they need for decision-making and adaptive management, impacting their planning, reporting and performance. We explored the biodiversity data needs of such actors in Colombia, Ghana and Switzerland to identify factors affecting data availability and use. Responses to questionnaire surveys showed that the data types with the biggest gaps between user needs and access were progress on conservation or sustainability actions, species populations, habitat state and ecological risk. The most frequent data blockages related to inadequate resources and organizational capacity. Obstacles significantly associated with a lack of primary data included an absence of organizational biodiversity goals and monitoring systems. Problems accessing habitat quality and species abundance data were associated with data collection methods being unknown or unavailable. Businesses were more likely than other groups to need data on threats, perhaps reflecting the increasing importance of environmental risk to the corporate sector. Businesses are less likely to collect primary data or use secondary data and are significantly more likely to be unclear on what biodiversity indicators to use. Non-business organizations are significantly more likely to be unable to access data because of a lack of funding for data collection, analysis, and use. Our results highlight the need for stakeholders across sectors to work together to find common solutions to build and invest in monitoring capacity that unblocks the flow of biodiversity data.

AimA comprehensive understanding of the spatial distribution of biodiversity is hindered by fragmented datasets, sampling biases, and inconsistent observation protocols. Here, we present a workflow that integrates disparate datasets to produce large scale maps of biodiversity metrics as a basis for management-relevant information tools. We use integrated species distribution modeling (iSDM) to account for sampling biases and disparate data collection techniques, taking advantage of the vast numbers of open datasets available in data aggregators like GBIF. LocationNorway (excluding Svalbard and Jan Mayen) TaxonVascular plants MethodsThe workflow consists of four main steps: data acquisition, data integration, integrated species distribution modelling (iSDM), and the production of derived outputs. Input data include structured surveys, opportunistic observations, and environmental covariates. These are standardised and integrated into a point-processed based iSDM framework to produce species richness maps, associated uncertainties, and sampling effort maps. The outputs are further processed to identify biodiversity hotspots or to summarise species-environment relationships. The workflow used vascular plant data from Norway, combining occurrence-only and presence-absence datasets with environmental covariates. Outputs were generated at a spatial resolution of 500 x 500 meters, balancing accuracy, computational feasibility and relevance for management decisions. High-performance computing resources were utilized for model fitting and predictions. A subset of available data was used to validate the species richness maps. ResultsWe produced detailed maps of species richness, uncertainties and sampling intensity across Norways heterogeneous landscape, incorporating 1218 species in our final results. The species richness patterns highlight patterns consistent with previous mapping efforts. Validation showed an increase in model accuracy when compared to models which did not use an iSDM framework. The workflow highlights limitations in the infrastructure of the currently openly accessible data, particularly the need for more structured presence-absence datasets and standardized metadata. Main conclusionsThis study underscores the potential of workflows that integrate disparate datasets for biodiversity modeling. To maximize accuracy and utility, future efforts should focus on improving data standardization, the publication and collection of more structured data, and fostering data-sharing collaborations. Advances in the workflow itself, including optimising modelling covariates and integrating more comprehensive spatio-temporal aspects, will also increase the relevance of the outputs. These advances will increase our ability to estimate species richness with a precision and accuracy that can reliably inform conservation and management decisions.

Environmental DNA (eDNA) metabarcoding has transformed biodiversity monitoring, yet most analyses rely on taxonomic metrics that are sensitive to methodological variation and limit cross-study comparability. We propose a "phylogenetic turn" in eDNA analysis through the integration of phylogenetic diversity (PD) metrics. By incorporating evolutionary relationships, PD reduces dependence on species-level resolution, increases robustness to detection biases, and better captures the evolutionary "option value" of biodiversity. We synthesize key PD metrics across richness, divergence, and regularity, emphasizing the use of standardized effect sizes (SES) for ecological interpretation while addressing challenges in metric selection. We apply this framework to five marine eDNA datasets (2021-2025) spanning ecologically and geographically contrasting ecosystems, from tropical to Arctic regions, and encompassing a wide gradient of anthropogenic pressure. Across datasets, we identify consistent patterns: anthropized ecosystems exhibit high taxonomic richness but reduced phylogenetic diversity, indicating phylogenetic clustering, whereas less disturbed systems show lower richness but greater evolutionary breadth. These findings demonstrate that PD reveals ecological structure not captured by taxonomic metrics, including signatures of environmental filtering and community assembly processes. By providing a reproducible analytical workflow based on standardized eDNA datasets, we position phylogenetic diversity as a critical bridge between eDNA data and conservation frameworks. Ultimately, eDNA-based phylogenetic approaches open new avenues for decoding global biodiversity patterns across heterogeneous ecosystems.

Passive acoustic monitoring poses an immense potential to assess avian diversity in many habitats, including agricultural landscapes. At the same time, automated recorders generate large datasets which present a challenge for processing and effectively assessing biodiversity. Methods such as manual listening by experts, automated detection algorithms like BirdNET and calculating acoustic indices all present different trade-offs in assessment of biodiversity through passive acoustic monitoring. In the present study we recorded soundscapes in a low-intensity agricultural landscape in western Turkiye in all four seasons. Two expert ornithologists listened to a subset of these recordings identifying bird species from the recordings. We also ran the same sample of recordings on BirdNET to compare BirdNET detections with expert detections and calculated acoustic indices for each recording. The results showed that BirdNET detected more species than experts, although some may not be reliable detections. Two acoustic indices (bioacoustic index and acoustic complexity index) were correlated positively with number of species detected by experts and one (normalized difference soundscape index) with number of species detected by BirdNET but the correlations were modest. The results show that acoustic indices may have limited value in detecting biodiversity and automated detection algorithms may do a better job, although these may need to be trained with local data to improve detection and classification.

Animal COI (mitochondrial cytochrome oxidase I) metabarcoding of environmental DNA (eDNA) is increasingly used to assess biodiversity in complex substrates such as soil. However, due to read-length constraints of second-generation sequencing platforms, mini-barcodes have been used instead of the full barcode region. Long-read sequencing technologies now enable the recovery of full-length barcode sequences, and are more commonly applied for studying microbes, but their use for metabarcoding the full-length standard COI barcoding region in animals remains limited. In this study, we compared three COI amplicon sets -- 313 bp, 660 bp, and 1,256 bp -- amplified from soil eDNA samples and sequenced using Illumina and PacBio platforms to evaluate their overall concurrence, the effectiveness of identifying nuclear mitochondrial DNA segments (NUMTs) and chimeras, as well as their respective taxonomic resolution. The long-read datasets exhibited a higher identification rate of NUMTs and true chimeras, suggesting that longer sequences improve the detection of noise in COI metabarcoding data, thereby reducing the occurrence of spurious taxa. Taxonomy assignment confidence was similar between the 313 bp and 660 bp datasets, whereas extending the amplicon beyond the standard COI barcode region (1,256 bp) reduced confidence, likely because longer reads extend into regions poorly represented in barcode reference databases. Despite substantially lower sequencing depth in the 660 bp dataset, per-sample OTU richness did not differ significantly from that recovered with the Illumina 313 bp amplicon set. Similarly, the relationships between samples were strongly correlated across the detected OTU communities, indicating consistent ecological interpretations between short and long amplicons. We conclude that the standard ~658 bp COI barcode is an optimal marker for soil animal metabarcoding from eDNA, balancing target recovery, artifact detection, taxonomic assignment and ecological interpretability. As COI eDNA metabarcoding becomes increasingly used in biodiversity assessment and is increasingly adopted in large-scale monitoring initiatives, this study provides methodological guidance for improving the robustness of soil animal community biomonitoring.

The choice of appropriate methods to detect species is crucial for biodiversity monitoring. Camera trapping is currently one of the most widely used methods for characterizing mammal communities, although it requires substantial investment in equipment and personnel. In contrast, questionnaires administered to local populations provide a faster and more cost-effective alternative for assessing community composition, but may be influenced by respondent-related biases that compromise data reliability. This study evaluates the concordance between these approaches for characterizing the carnivore community in the Sierra de Segura (Jaen, southern Spain), using Cohens kappa coefficient, while also examining the individual and social factors shaping Local Ecological Knowledge (LEK). We deployed 24 camera-trap stations (144 trap nights) across a 25 km2 area to record carnivore presence. In parallel, we conducted two types of surveys with local residents (n = 103): (i) free-listing and (ii) image-based species recognition, while recording individual and social characteristics of respondents. Free-listing surveys tended to underreport species, whereas image-based surveys showed higher agreement with camera-trap data, although occasionally overestimating species presence. Higher concordance was associated with social factors indicative of closer and prolonged contact with the environment, such as permanent residence and ownership of agricultural land. Mammal communities differed between methods; however, agreement improved when respondents had higher LEK, while species-specific behavioral traits could also influence perception. Our findings demonstrate that image-based questionnaires can provide results comparable to camera trapping when respondents have strong connections to their natural surroundings. These results highlight the importance of both survey design and respondent selection in improving the accuracy of biodiversity monitoring, offering a transferable framework for integrating LEK into conservation protocols across diverse ecosystems. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/720805v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@d55c34org.highwire.dtl.DTLVardef@1985c66org.highwire.dtl.DTLVardef@1da576aorg.highwire.dtl.DTLVardef@1a10ccb_HPS_FORMAT_FIGEXP M_FIG C_FIG

Freshwater monitoring programmes like the European Unions Water Framework Directive (WFD) provide a wealth of data on European lake status, including water quality and macrophytes (aquatic plants) as critical habitat features that support health of humans and wildlife. Easier WFD data access can enable external management and research to better safeguard human and natural freshwater use. We demonstrate a replicable workflow to easily download and process multi-year (2007-2024) observations of lake macrophytes (425 sites) and complementary water quality variables (202 sites) from Swedish WFD data. Then, we illustrate the value of improved data access to address ecological questions that drive conservation, investigating how spatial scales influence macrophyte richness and associated water quality relationships using a spatial random intercept model. Decomposing the spatial intercept links small scales (<10 km) to site-level gradients and large scales (>100 km) to biogeographical drivers. Stochastic and environmentally-structured processes coexisted at intermediate scales (10-100 km). Adding water quality rarely improved overall predictive performance of macrophyte diversity models but consistently influences the role of different spatial scales. Water quality variables showed consistent spatially structured variation at intermediate scales and unique spatial patterns in tandem, overlapping with large-scale biogeographical influences. Altogether, we show context-dependencies for spatial model interpretation and provide guidance in accounting for spatial confounding to improve inferential and predictive performance. Our workflow and results show a clear way forward for accessing high-quality macrophyte and water quality data sets and their utility for addressing ecological questions that guide macrophyte protection under the WFD. HighlightsO_LIyears Swedish of macrophyte and water quality monitoring data were extracted. C_LIO_LIrichness showed scale-specific patterns linked to geographic gradients. C_LIO_LIbest predictive models for richness had no water quality at all. C_LIO_LIoverlap in their spatial scales and must be carefully separated. C_LIO_LIpen access data and multiscale analysis can apply to many ecological questions. C_LI

iNaturalist has rapidly grown to become one of the largest contributors of global biodiversity data, being widely used in academic research, to support and inform applied conservation, and to help develop policy indicators for decision makers. However, the availability of iNaturalist data varies based on the digital platform it is accessed from. Here, we assess whether the pathway used to access iNaturalist data from three commonly available biodiversity data sources: iNaturalist, the Global Biodiversity Information Facility (GBIF) and ESRIs ArcGIS Online, alters occurrence record availability and downstream analyses for ecological inference. First, we investigate iNaturalist data availability on ArcGIS Online and find that the reduced field metadata for record location and obscuration information can lead to biased assumptions in the spatial ranges of sensitive species. Second, when assessing iNaturalist records available through GBIF, we find that restrictive Creative Commons observation licenses prevent an average of 26.1% of iNaturalist Research Grade records from being exported to GBIF, and this can lead to differences in environmental niche analysis when compared to datasets including all available research grade records. Understanding differences in platform licensing when integrating across biodiversity data repositories is another consideration for researchers and practitioners when conducting biodiversity assessments. Our results show that the pathway through which iNaturalist data are accessed can function as a methodological filter, potentially altering spatial coverage, the climatic conditions represented by occurrence datasets, and downstream ecological analysis.

1. Detecting environmental DNA (eDNA) from rare or low-abundance aquatic species remains a major challenge, particularly when it is highly degraded, present at low concentrations, and dominated by DNA from non-target taxa. These challenges are further amplified in sedimentary ancient DNA (sedaDNA) studies, where thousands of years can degrade eDNA further, making the detection and quantitative interpretation of weak biological signals difficult. 2. Metabarcoding is commonly used to produce high-throughput community-level data from eDNA but is inherently compositional and influenced by amplification biases. Nonetheless, metabarcoding read abundance or PCR replicate detection frequency are increasingly used as proxies for relative DNA concentration, but their quantitative interpretation has rarely been evaluated against independent measures of absolute DNA abundance. 3. We used droplet digital PCR (ddPCR) to quantify mitochondrial DNA from Atlantic cod (Gadus morhua) and Atlantic herring (Clupea harengus) in 136 ancient eDNA extracts from Icelandic marine sediment cores spanning the last three millennia. We compared ddPCR copy number estimates with metabarcoding (18S) derived relative abundance and detection frequency, and evaluated whether temporal DNA trends corresponded with proxy reconstructed sea surface temperature (SST) variability. 4. We found that ddPCR-measured fish sedaDNA abundance was positively correlated with the proportion of metabarcoding PCR replicates for both Atlantic cod and Atlantic herring. Moreover, temporal trends in Atlantic herring DNA abundance were consistent with proxy reconstructed SST variability, supporting the ecological relevance of the molecular signal. 5. Overall, our results show that ddPCR-derived DNA concentrations and metabarcoding PCR replicate detection frequency capture consistent patterns in low-abundance fish sedaDNA from marine sediments. The observed agreement between approaches supports the use of PCR replicate detection frequency as a semi-quantitative proxy for low-abundance sedaDNA.

Early detection and monitoring of non-indigenous species (NIS) is crucial to prevent their establishment and to reduce ecological and economic impacts in coastal ecosystems. Traditional monitoring approaches, which rely largely on morphological identification of collected organisms, are often time-consuming and may fail to detect species that occur at low abundance, are morphologically cryptic, or are present in the form of inconspicuous life stages. DNA-based approaches, particularly those resorting to environmental DNA, have demonstrated high aptitude for biodiversity monitoring and biosecurity surveillance. By examining the genetic material from bulk community samples or released into the environment, DNA-based approaches enable the detection of species without the need for direct observation, thereby increasing detection sensitivity and expanding the scope of monitoring programs. Despite the rapid growth of its employment in marine monitoring, a global synthesis of the status and trends of DNA-based approaches for detecting NIS in this environment has been lacking. Here, we present such synthesis, based on 146 published studies employing DNA for NIS detections in coastal environments. Two main methodological approaches were used across the reviewed studies, namely DNA metabarcoding which was applied in 49% of studies, closely followed by targeted single-species PCR assays, used in 42% of the studies. A smaller proportion of studies (10%) combined both approaches, integrating broad community screening with targeted detection to improve surveillance efficiency. Globally, 752 NIS were detected across disparate taxonomic groups, with metazoans representing the largest proportion of detections (464 species), followed by Chromista (210 species) and Plantae (77 species). Among these, the most frequently detected taxonomic groups included Dinophyceae (Dinoflagellata), Teleostei (Chordata), Florideophyceae (Rodophyta), Polychaeta (Annelida), Copepoda and Malacostraca (Arthropoda), and Ascidiacea (Chordata). At the species level, several well-known marine invaders were recurrently reported, including Bugula neritina (Linnaeus, 1758), Styela plicata (Lesueur, 1823), Acartia (Acanthacartia) tonsa Dana, 1849-1852, and Botryllus schlosseri (Pallas, 1766), highlighting the ability of DNA approaches to detect widespread and established invaders across different regions. The mitochondrial cytochrome c oxidase subunit I (COI) gene was the most widely used genetic marker, reflecting its broad taxonomic coverage and extensive representation in reference databases, particularly for targeting Metazoa. Ribosomal RNA genes, particularly 18S and 16S rRNA gene markers, were also frequently employed to target a wider range of eukaryotic taxa. Regarding sampled substrates, water was by far the most analyzed substrate, followed by zooplankton and biofouling communities collected from man-made structures. Notably, approximately 31% of all NIS detections reported in the reviewed studies constituted new regional records. These results highlight the potential of eDNA for coastal monitoring but also underline important limitations. Persistent geographical, taxonomic, and methodological biases can affect detection outcomes, and reliance on single sample types or markers may increase false negatives - particularly critical for NIS early detection. Therefore, multi-marker and multi-substrate approaches are essential to improve detection reliability and support effective biosecurity strategies. As reference databases continue to expand and methodological protocols become increasingly standardized, DNA-based monitoring is likely to play a central role in future management and surveillance of biological invasions in coastal ecosystems. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/722998v1_ufig1.gif" ALT="Figure 1"> View larger version (75K): org.highwire.dtl.DTLVardef@17948b1org.highwire.dtl.DTLVardef@193832dorg.highwire.dtl.DTLVardef@189033dorg.highwire.dtl.DTLVardef@33cddf_HPS_FORMAT_FIGEXP M_FIG C_FIG

Tropical forest succession can reorganize biodiversity not only by changing species richness, but also by filtering the functional traits that represent ecological strategies. Harvestmen are highly sensitive to microclimatic and structural changes in Neotropical forests, yet their functional diversity remains poorly explored. We investigated taxonomic and functional diversity of harvestmen in a local scale, an Atlantic Forest remnant in southeastern Brazil containing forest patches at different successional stages. Standardized nocturnal active searches and leaf-litter sampling yielded 384 individuals belonging to 14 morphospecies. Functional diversity was quantified from four morphological traits using Hill numbers (q = 0, 1 and 2), morphofunctional ordination, beta-diversity partitioning and environmental models based on forest-structure variables and PCA-derived gradients. Functional diversity was highest when rare species were weighted equally and declined strongly from q = 0 to q = 2, indicating that uncommon species carried much of the regional morphofunctional variation. Functional alpha diversity was positively associated with taxonomic alpha diversity, and Mantel tests showed that taxonomic and functional dissimilarities among sampling points were significantly correlated. However, formal beta-diversity partitioning refined this interpretation: functional beta diversity was dominated by nestedness-resultant dissimilarity rather than turnover, suggesting that functionally poorer assemblages represented contracted subsets of the regional trait space. Morphofunctional analyses identified compact, robust and long-legged species groups, and environmental models showed that lower vegetation structure, litter depth and forest-maturity gradients significantly influenced functional diversity. These findings indicate that mature, structurally complex Atlantic Forest patches help maintain the full spectrum of harvestman morphofunctional strategies and highlight harvestmen as promising models for trait-based conservation ecology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/731358v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@1f91597org.highwire.dtl.DTLVardef@1f89aa6org.highwire.dtl.DTLVardef@7141b8org.highwire.dtl.DTLVardef@191accc_HPS_FORMAT_FIGEXP M_FIG C_FIG

Quantifying biodiversity patterns in remote Amazonian ecosystems remains constrained by the limitations of traditional field surveys. We combined passive acoustic monitoring (PAM), machine learning, and ecoacoustic metrics to assess the taxonomic and functional structure of bird communities along a riparian gradient in the Kapawi River, Ecuadorian Amazon. A total of 2,030 recording hours were acquired using 16 Autonomous Recording Units (ARUs) deployed along a river-to-interior forest gradient (0-800 m from the riverbank). Automated detection with BirdNET yielded 92,137 records corresponding to 379 bird species. Species richness was highest at the river edge (325 species), which also harboured the greatest number of unique taxa (71 species), while interior sites showed lower but more consistent local richness. Multivariate analyses showed clear spatial segregation between riparian and interior communities. Despite this turnover, the trophic structure remained highly homogeneous (>90% similarity), dominated by insectivorous and frugivorous guilds. Generalized linear models (GLMs) indicated strong positive associations between avian species richness and key ecoacoustic metrics, with particularly pronounced effects for the Acoustic Diversity Index (ADI) and the Bioacoustic Index (BI). Spatially explicit analyses further demonstrated marked heterogeneity in acoustic structure along the fluvial gradient, reflecting fine-scale variation in soundscape composition. Together, these findings show that riparian habitats structure avian communities primarily at the taxonomic level, while functional organization remains largely conserved across the gradient. This mismatch indicates that biodiversity components respond unevenly to environmental variation, with taxonomic richness being more sensitive than functional composition. Our results underscore the potential of ecoacoustic approaches as scalable, non-invasive tools for detecting spatial patterns in biodiversity and habitat-driven community assembly in tropical systems.

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 pipelines 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.

Subalpine forests in the Alps are fragile ecosystems that play a crucial role in regional water resources and the local climate. These ecosystems are ecologically significant due to their unique biodiversity and vulnerability to climate change. While several components of the hydrological balance have been studied, the interplay between catchment-scale processes and plot-scale drivers such as fog presence and forest age remains insufficiently understood. To address this, we investigated the hydrological balance of a subalpine coniferous forest catchment at the Renon site in the Italian Alps, integrating observations across spatial scales. The study area includes a mosaic of mature and younger regrowth forest, where both interannual and seasonal variability in precipitation and fog presence are pronounced. At the catchment scale, we quantified above-canopy precipitation, evapotranspiration (ET, measured via eddy covariance at the ICOS tower), stream discharge, and soil moisture dynamics. Within the catchment, we characterised water partitioning using sap flow sensors for tree transpiration, throughfall and stemflow collectors with rain gauges above and below the canopy and epiphyte sampling. Mixed fog-rain events frequently coincided with higher throughfall. However, these changes had a minor effect on soil water storage and catchment discharge in the annual water balance, which was nearly closed. At the plot scale, our results show that tree transpiration was higher in the younger forest structure, while canopy interception is a dominant process in water partitioning in the older forest structure, where lichen abundance likely enhances interception. This study highlights the importance of multi-scale monitoring in temperate mountain forests, where forest age influences water partitioning, and fog presence, though not directly quantified, can still contribute to reducing evaporative processes. Such contributions may gain importance under changing climate conditions, albeit less prominently than in tropical or subtropical cloud forests.

BackgroundUnderstanding ecosystem dynamics is essential for assessing ecosystem health, yet remains challenging due to complex biotic and abiotic interactions. Microbial communities are valuable indicators of environmental change, but the high dimensionality of microbiome data requires advanced analytical methods. This study explores the use of topic modeling (TM), an unsupervised machine learning approach initially designed for text analysis, to analyze microbiome data from the dynamic Warnow Estuary on the southern Baltic Sea coast. ResultsWe applied TM to estuarine microbiome data and compared its performance to traditional dimensionality reduction methods, Principal Component Analysis (PCA) and Principal Coordinate Analysis (PCoA). Quantitative results indicate that TM performs comparably to conventional approaches in preserving ecological and functional information, and in certain aspects even superior. In addition, we show qualitatively that NNMF, a TM method, captures latent patterns in the data providing an interpretable perspective on the microbiome. In this exploratory framework, NNMF suggested five distinct sub-communities within the estuary that appear to follow a seasonal succession influenced by freshwater inflow. These sub-communities were associated with specific ranges of salinity and temperature and showed distinct taxonomic profiles, with shared characteristics across the estuarine system. ConclusionsOur findings suggest that TM is a useful tool for exploring complex environmental microbiome datasets, offering a complementary perspective that can provide additional ecological insights. TMs ability to highlight coherent microbial community patterns indicates its promise for supporting environmental monitoring and informing targeted ecosystem management in dynamic habitats, though further studies are needed to fully assess its applicability.