Ecological Indicators

Soil nematodes play key roles in regulating organic matter decomposition, nutrient cycling and trophic interactions in the soil ecosystem, and are very useful bioindicators of soil condition and soil health. But to characterise them, we need reliable measurements of nematode biodiversity in soils. DNA-based techniques such as metabarcoding have recently explored their biodiversity, with successful results. However, there are many methodological steps that can modify the outcome considerably and require informed decisions. In this study, we tested the effect on nematode alpha and beta diversity metrics of three methodological steps, namely i) type of extraction of genetic material from the matrix, either from soil directly with different starting amount of material or extracting nematodes first via the Baermann or Oostenbrink methods, ii) the choice of taxonomically-relevant DNA segment for amplification, with a nematode specific primer (NemF/18Sr2b) and a universal eukaryotic primer (3NDf/1132rmod), both within 18S rDNA, and iii) the selection of appropriate reference library for taxonomic assignment (comparing three nematode specialised databases and three broad taxonomic databases). Observed differences in alpha diversity were more pronounced when comparing the nematode extraction vs soil extractions. In particular, direct soil extractions co-extracted many non-nematode taxa, especially if amplified with a universal primer, which can significantly reduce the number of nematode reads obtained and therefore the accuracy of nematode community indexes and other nematode-based metrics. The choice of the reference database strongly affected the taxonomic identification, which calls for extreme caution when making this selection. Notably, specialised databases tended to over assign reads to nematodes that were detected as non-nematodes by broad taxonomic databases. However, beta diversity metrics were consistent across methods, highlighting these metrics as robust against methodological choices. Our study clearly demonstrates that the choices of methods for nematode biodiversity studies using molecular tools can have a big impact on the results, and decisions should be made consciously and carefully.

Bottom trawling is the most widespread human disturbance on the seafloor, known to affect seabed structure and benthic biota. Yet, its impact on benthic communities remains documented mainly for taxa identifiable through morphology, leaving microscopic benthic communities largely overlooked. The Gulf of Lion continental shelf has been trawled for decades by French and Spanish fleets, to the point that encountering untrawled reference areas became elusive. Here, we examined prokaryote, protist and Animalia benthic communities at eight stations, using eDNA metabarcoding of core sediment samples, with five primer-pairs. Benthic communities were cross-referenced with contrasted trawling impacts, integrated across three periods: over one month, three years or ten years preceding the sampling. The community composition was primarily structured by ecological habitats, yet, trawling pressure showed association with prokaryotes and Animalia diversity. Diversity was higher at sites trawled more intensively in the preceding month, whereas long-term trawling was associated with a significant erosion of Animalia alpha diversity. Unexpectedly, communities did not display consistent vertical structure and the relative contributions of trawling and hydrodynamic reworking remain unresolved due to the absence of untrawled sites. Overall, our results show that trawling effects extend to prokaryotic compartments rarely considered in impact assessments, providing a proof of concept for eDNA-based assessment, and underline a pervasive long-term effect on Animalia communities. This study also highlights the importance of the timescale used to quantify fishing pressure and underscores the complexity of impact assessment when monitoring begins too late to establish a clear baseline, particularly when no reference site remains.

This paper presents a comparative analysis of the floristic composition and site characteristics of four peat bogs in Bucovina, Romania: Poiana Stampei, Romanesti, Saru Dornei, and Gaina-Lucina. The research was based on phytosociological releves on 25 msq plots and direct field phenological observations, on six field visits from May to August 2026. Vegetation was characterised using the Braun Blanquet method, and floristic similarity between sites was assessed with the Sorensen and Bray Curtis indices. All four plots shared a common core of taxa characteristic of peatland vegetation: Sphagnum spp., Carex rostrata, Drosera rotundifolia, Eriophorum vaginatum, and Vaccinium species. Species richness was 13 taxa at Poiana Stampei, Romanesti, and Saru Dornei, and 12 at Gaina-Lucina. Romanesti and Saru Dornei showed the highest floristic similarity (descriptive values, not statistically tested, given a single releve per site), while Gaina-Lucina differed most markedly, not through species richness, which was similar across sites, but through species identity and through the presence of Betula nana, a glacial relict absent from the other sites. The results provide a descriptive basis for future research on the floristic composition and conservation of these habitats.

International wildlife trade represents one of the major drivers of biodiversity exploitation worldwide. However, the true taxonomic diversity embedded within commercial wildlife products often remains unknown because processing removes diagnostic morphological characteristics, preventing reliable species identification. Consequently, biodiversity assessments based solely on product labels may substantially underestimate the diversity of species involved in trade networks. To investigate hidden biodiversity within wildlife trade products, we applied DNA barcoding based on the mitochondrial cytochrome c oxidase subunit I (COI) gene to 77 products commercialized as fish swim bladders and seized at Guarulhos International Airport, Brazil. Molecular analyses successfully identified all samples and revealed the presence of four species: Plagioscion auratus (n = 38), Cynoscion acoupa (n = 7), Melanosuchus niger (n = 17), and Caiman crocodilus (n = 15). Fish species accounted for 71.4% of all samples, whereas crocodilians represented 28.6%, demonstrating that products marketed under a single commercial category may conceal substantial taxonomic diversity. Notably, the occurrence of two Amazonian crocodilian species within a trade chain traditionally associated with fish products reveals a previously undocumented component of the international wildlife trade. Our findings demonstrate that DNA barcoding is an effective tool for uncovering hidden biodiversity within processed wildlife products and provide evidence that wildlife trade networks may involve a broader spectrum of species than suggested by commercial labels. These results highlight the importance of molecular surveillance for biodiversity monitoring, wildlife trade regulation, and conservation planning.

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.

Aims In forest ecosystems, tree species composition and diversity are fundamental drivers of biodiversity and many species are closely associated to specific tree species as hosts. Saproxylic diversity across tree species may be shaped by interactions between ecological and environmental factors, such as resource availability. However, a throughout and unbiased quantification of diversity across tree species is missing. We investigated patterns an drivers of saproxylic beetle diversity across European tree genera while accounting for differences in sampling effort. Location Europe Methods We used a large dataset, of 25 studies, comprising 500,000 deadwood-dependent beetles, representing 1041 saproxylic beetle species, sampled from 29 tree genera. We accounted for sampling effort by estimating diversity using coverage-based standardization across Hill numbers. We than used linear models to identify drivers of saproxylic beetle diversity across tree genera and assessed differences in community composition using Nonmetric Multidimensional Scaling (NMDS). Additionally, the most abundant beetle families were identified for a subset of 14 genera. Results Quercus supported the highest richness of infrequent, frequent, and highly frequent species, while Fagus exhibited the highest observed (unstandardized) species richness. Beetle communities were less differentiated when focusing on infrequent species, whereas tree genera were separated when focusing on frequent and highly frequent species. In addition, we found that resource availability and maximum tree height best predicted infrequent beetle diversity, whereas resource availability was the main driver of frequent species. In contrast, the same analyses for unstandardized data indicated postglacial occurrence as an important factor for infrequent species. Main conclusions Although resource availability supported the species area hypothesis, standardized data revealed higher richness on smaller trees, partly contradicting this pattern. Our results highlight the importance of accounting for sampling bias and promoting tree diversity, particularly oaks, to sustain saproxylic beetle diversity.

Plant phenology influences biotic interactions and the cycling of water, carbon, and nutrients, ultimately affecting global climate. Accurately quantifying phenological dynamics is therefore essential for ecological forecasting, yet estimates vary widely depending on data sources, smoothing approaches, and date-extraction techniques. For this reason, the selection of an appropriate processing pipeline requires benchmarking under controlled conditions. In this study, we use a Land Surface Model (LSM) to simulate ecosystem dynamics at four temperate forest sites under five lesser studied climate perturbations. The resulting synthetic dataset allows us to control for environmental conditions while comparing commonly used phenological detection methods (relative thresholds at 10%, 20% and 50%, highest rate of change, detection of inflection points in the curvature angle, detection of recovery and senescence points). Amongst tested methods, we find general agreement in responses to climate alterations, where inflection and low thresholds (10%) methods provide the closest match to the model predicted start of season, with a maximum discrepancy of 4 days. Higher thresholds (20%, 50%) are closer to the model predicted end of season, though discrepancies reach up to 10 days. High thresholds and rate-of-change methods also reliably detect the peak of season. We further show that some methods detect phenological responses missed by temperature-only type growing season, supporting the need for more realistic phenological representations in LSMs. Beyond evaluating extraction methods within a common framework, our study expands on the potential of using LSMs as sandboxes for ecological testing rather than solely predictive tools.

Global biodiversity loss is a consequence of habitat degradation and fragmentation caused by the expansion of the Anthropocene and the overexploitation of natural resources. Passive acoustic monitoring enables longitudinal assessment of biodiversity but generates massive datasets. Advancements in machine learning have enabled the development of convolutional neural networks capable of automating this process. We developed a lightweight 2D convolutional neural network capable of detecting echolocation calls of the greater sac-winged bat, Saccopteryx bilineata, an insectivorous aerial edge-foraging bat, across a tropical restoration gradient in Para, Brazil. The 2D-CNN model achieved a 97.84% accuracy and 100% precision. Deploying this model across 157,335 field recordings across 29 sampling points resampled over 3 years (2023-2025) yielded 2,098 positive detections of S. bilineata. Generalised linear mixed models revealed that time since reforestation significantly increased the odds of detecting S. bilineata ({beta} = 0.22, p < 0.001), and that detection probability was 2.14 times higher in restoration plots than in forest habitats ({beta} = 0.76, p = 0.0701). Acoustic hardware had a significant covariate influence in both fitted models ({beta} = 0.52, {beta} = 0.72, both p < 0.001). These findings demonstrate the practical applications of deep learning and bioacoustics to quantify habitat recovery and the value of restoration in regenerating functional ecological niches for aerial insectivores during early successional stages.

Habitat destruction leads to a severe decline in biodiversity. Ascertaining effects of habitat loss afterwards is regularly done, whereas predicting the effects of different habitat loss scenarios on biodiversity beforehand is much preferred but as of yet rarely possible. Such predictions would help inform management decisions to prevent biodiversity loss in the first place, or at least mitigate effects if necessary, by providing handles to set priorities in making trade-offs. To this end, we developed and applied an adapted version of the information-theoretical framework of Maximum Entropy Theory in Ecology (METE) to predict species-area relationships in tropical forest landscapes undergoing habitat loss and fragmentation. Species-area relationships provide information about biodiversity and can, in undisturbed systems, be predicted using METE, based on state variables such as the total number of individuals, the total number of species, and the total area size. In disturbed systems, however, it is established that METEs predictions are off. Using an individual-based model, we explore how METEs state variables should be adjusted, based on the landscape characteristics habitat cover and degree of habitat fragmentation, to restore its accuracy of species-area relationship predictions in disturbed environments. We validate our adjusted METE predictions with empirical data. Our adjusted METE predictions demonstrate the severity of fragmentations impact on biodiversity, relative to what could be expected in a pristine, continuous environment. With this new method, we can estimate the immediate effects of habitat loss on biodiversity, as well as its long-term effects.

Mediterranean forests are emerging as one of the clearest hotspots of climate-change impacts, where rapid warming and increasingly severe droughts and heatwaves are pushing forest ecosystems toward their physiological and functional limits. These pressures threaten productivity, carbon dynamics, ecosystem stability, and the services forests provide. In this context, a process-based forest ecosystem model was used to assess climate-driven changes in carbon dynamics and productivity stability across Italian forests within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework. 5135 plots were simulated, corresponding to ~80% of the field plots included in the second Italian National Forest Inventory (INFI). Model simulations were first evaluated against independent observations of gross primary production (GPP) from satellite-based products and forest carbon variables from the third INFI. Forest dynamics from 2015 to 2100 were simulated under three CMIP6-based emission scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) using two climate forcings (GFDL-ESM4 and UKESM1-0-LL). Future responses were assessed for GPP, aboveground biomass, growing stock volume, and carbon stocks, while variability in GPP was quantified using the coefficient of variation as an indicator of forest stability. Results show that the model reproduced the main spatial and temporal patterns of forest productivity and carbon stocks. Future projections revealed strong spatial heterogeneity and increasing divergence among scenarios after mid-century. GPP showed the earliest and strongest response to climate change, with mountain forests generally maintaining or increasing productivity and exhibiting greater stability, whereas coastal forests showed pronounced productivity declines and increasing variability, particularly under higher-emission scenarios and more extreme climate forcing. In contrast, carbon stocks generally continued to increase throughout the century, although carbon accumulation progressively slowed toward the end of the century, particularly under higher-emission scenarios.

Recent years have witnessed the discovery of an immense diversity of protists in almost all ecosystems on Earth. However, a major challenge consists in identifying diversity patterns and the drivers that explain the distribution of this diversity. While several works have focused on local parameters, only a few studies have addressed this question at regional scales. In this study, we characterize the diversity and distribution of Arcellinida (testate amoebae) at regional scale of a climatically contrasted country, Bulgaria. We collected 75 samples geographically evenly distributed and representative of the country's natural ecosystems and used metabarcoding to determine their diversity. Communities composition differed markedly among habitats, revealing sharp beta diversity patterns. "Mesophilic" environments such as forests and ponds were most diverse, while acidic and oligotrophic Sphagnum peatlands were poorest and hosted the highest proportion of unique taxa. Our analyses of richness-environment based on a Random Forest approach applied to all soil systems indicated that stable humidity (low silt content, low precipitation seasonality) and intermediate soil carbon stocks had a positive effect on richness. The predicted increase in continentality in the next decades in Bulgaria calls for testing climatic models to evaluate the potential risk of declining Arcellinida biodiversity.

Environmental DNA (eDNA) has emerged as a transformative tool for biodiversity monitoring, offering a non-invasive means to detect and identify species across various ecosystems. However, despite its potential, current eDNA methodologies have significant limitations, including limited sensitivity, restriction to a specific taxonomic group, lengthy processing times, the need for specialized laboratory conditions and equipment, as well as computational bottlenecks in equipment, connectivity, and algorithms. Overcoming these limitations is crucial for the real-time monitoring of a high number of taxa in field-based scenarios. Here we present an integrated eDNA workflow from extraction to data analysis that mitigates several of these challenges through several technical innovations. Our approach adapts the Tagsteady protocol to Nanopore sequencing, integrates multiplex PCR for simultaneous plant and animal detection, and introduces RTBioScan, a real-time analysis pipeline employing hierarchical basecalling and OTU-level consensus generation to achieve high-accuracy taxonomic assignments during sequencing. Using a single set of universal primers, we integrate metabarcoding and barcoding into a cohesive protocol, utilizing universal markers for both animals and plants (cytochrome oxidase 1 region, COI, and Internal Transcribed Spacer 2, ITS2). This comprehensive approach can detect and identify a broad spectrum of species, from individual organisms to entire communities in a single workflow.

Environmental concerns and the need to mitigate the effects of climate change have driven the adoption of renewable energy sources, such as offshore wind farms (OWFs), making it essential to assess their overlap with seabird habitats to estimate potential impacts. This study is the first to estimate the vulnerability of coastal and seabirds to OWFs at a national scale by integrating biological trait data from the literature, spatial occurrence records from citizen science platforms, and expert knowledge (elicited from 23 specialists) into established quantitative risk frameworks. Collision risk and susceptibility scores were calculated at the family and subfamily levels, as well as disturbance and displacement indices by species and susceptibility by state. The groups with the highest collision risk and susceptibility were Fregatidae (0.354; 0.486), Sulidae (0.199; 0.419), Sterninae (0.190; 0.384) and Diomedeidae (0.190; 0.416). The extreme risk for Fregatidae is driven by their specific flight characteristics, with impacts concentrated in the highly frequented southeast and northeast regions. Regarding disturbance and displacement, Limnodromus griseus exhibited the highest national score (MMA: 0.098), while Diomedea dabbenena had the highest global score (IUCN: 0.092), highlighting the critical need to integrate both regional and global conservation statuses. Spatially, the state exhibiting the highest overall vulnerability to OWF impacts was Rio Grande do Sul (23.78; 10.69), a critical hotspot characterized by high species richness and oceanographic productivity. These findings establish an indispensable national baseline for Marine Spatial Planning, reinforcing the need for targeted pre-installation assessments, the identification of sentinel species, and spatially differentiated management during environmental licensing processes.

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.

1. Species monitoring programmes regularly encounter missing data, which complicates tasks such as estimating population size or detecting temporal trends. Selecting an imputation method suited to the properties of the data is therefore an important practical challenge, particularly for species exhibiting overdispersion and zero-inflation. 2. We compare seventeen imputation methods, comprising thirteen Poisson-based statistical models (accounting for overdispersion and zero-inflation, with fixed, random and multivariate structures) and four contrast-based approaches (LORI, MICE, missForest, correspondence analysis). Using four complete monitoring datasets of waterbird species surveyed across France and Italy over 21 years, illustrating a variety of abundance distributions, we introduced missing observations under a realistic Missing At Random mechanism, at rates from 5% to 70%. We evaluated all methods on computational burden, prediction accuracy for positive and null counts, quantification of uncertainty, and accuracy of population-size estimates. 3. No single method dominates across all criteria. Statistical models and contrast-based approaches yield similar point predictions, but only statistical models provide a genuine measure of uncertainty. Models that jointly account for overdispersion and zero-inflation perform best at predicting zero counts and achieving reliable prediction intervals, and the most suitable method ultimately depends on the abundance distribution of the target species. 4. These results provide practical guidance for ecologists selecting an imputation strategy for incomplete count data, highlighting the trade-offs between predictive accuracy, computational cost, and the ability to propagate uncertainty through subsequent ecological analyses such as population-trend estimation.

Trail cameras have become ubiquitous tools for ecological data collection over recent decades. Despite progress in the development of automated algorithms and artificial intelligence for image classification, our ability to process large volumes of data remain limited by the need for trained human observers to make refined judgements. We provide guidance on placement of trail cameras for observing Asian elephants (Elephas maximus) and outline a protocol for training and testing naive human observers in performing image classifications (age/sex class and group composition) that cannot yet be automated. This process can be used to develop a high-throughput workflow capable of extracting useful data from large volumes of images. Our training material consisted of 14,007 images collected from 6 trail cameras around Udawalawe National Park in Sri Lanka from 2017-2019. In the first stage, expert observers (n=3) trained a group of inexperienced participants (n=4), who engaged in an iterative process to develop a protocol document. The document was then tested on a second set of subjects (n=6) each of whom classified 350 test images in four separate sequential batches using quantitative measures of precision and accuracy. The test set was sampled from 54,435 images from an additional 25 cameras. When compared to expert observers, they achieved a fair level of precision (Fleiss' kappa = 0.247) and 82.6% accuracy. Our approach can usefully be extended to other species and contexts.

The Peruvian Andes, and especially its slopes towards the Amazonian Basin are well-known biodiversity hotspots for plants and animals, which represent the visible part of biodiversity. However, not much is known about microeukaryotic diversity, despite the significant role protists play in the ecosystem. Based on a molecular survey of the diversity of Arcellinida (a group of amoebae well known for its environmental sensitivity and restricted geographical distributions), we estimated its diversity along an elevation gradient spanning more than 3000 meters. Surprisingly, Neotropical forest Arcellinida were less abundant and diverse than in most other extratropical biomes; nevertheless, communities differed strongly in their taxonomic composition as compared with forests in higher latitudes. Notably, sequences related to Trigonopyxis were most abundant, whereas this group occurs only marginally in extratropical settings. Elevation influenced beta diversity, showing different assembly processes occurring at different elevation belts. Canopy cover positively influenced both phylogenetic and taxonomic diversity, showing the importance of above and below ground integration in tropical forests.

1. Re-identifying individual animals (re-ID) from their calls with machine learning (ML) could extend individual-based monitoring to species and areas hard to survey by capture and marking, and published studies often report re-ID accuracy above 90%. Two methodological practices can inflate these values: (1) attributing calls to individuals by territory, nest, tag or station, tying each animal to its own recording conditions, and (2) randomly splitting individual clips into training and test sets, allowing recording context to be shared between them. Each effect has so far been documented only in single-species studies. 2. We implement four diagnostics of how methodological choices affect re-ID accuracy: (1) splitting clips by recording session; (2) background tests using ambient recordings without the animal; (3) within-recording permutation and paired-clip tests where animals share recordings; and (4) open-set tests with thresholds set on other individuals. We applied them to 13 public datasets from nine species (eight birds, one bat), using BirdNET, 19 other neural networks trained on animal or general sound and 16 trained on human speech, each with one classifier. 3. Under session splits, BirdNET re-ID accuracy ranged from 0.16 to 0.97. Within one year, ambient sound alone carried substantial identity information for territorial chiffchaffs (BirdNET balanced accuracy 0.71, against 0.79 for song); background identification exceeded chance for 35 of 38 embeddings and controls. Count-matched random clip splits raised mean accuracy by 0.20 to 0.51 in 18 comparisons across three species. Where several animals shared recordings, only one of three datasets showed clearly that re-ID rested on the animal rather than the recording. With unfamiliar animals present and thresholds calibrated to target 10% stranger acceptance, across 36 neural networks and two scoring methods on wild session-split datasets, the highest median share of known birds' calls accepted and correctly named was 46%, using nearest-clip scoring with four enrolled birds; test-stranger acceptance could exceed the target. No neural network beat BirdNET on every dataset, and network rankings depended on layer choice. 4. The diagnostics need no new models and apply wherever a dataset holds what they require: session labels, background recordings, shared recordings or enough individuals. Re-ID accuracy reported without them can substantially overstate what acoustic re-ID achieves, and should not be used to plan population monitoring until they are applied.

Species distribution models are widely used for the evaluation and analysis of species spatial distributions and responses to environmental changes. Due to this, there's a wide diversity of tools and algorithms to perform these types of analyses. However, despite being a mature field of study, SDM still requires a large amount of research intervention and decision-making to produce reliable models. This is a natural consequence of each species unique response to environmental factors, but in practice it hinders the potential of SDMs to be used effectively and with confidence across large sets of species. Here we present the AutoMaxEnt repository, a collection of functions and tools for the automation of maximum entropy species distribution models. AutoMaxEnt offers a flexible environment to model species distribution models, automating most of the data pre-processing and preparation and giving the user the possibility of exploring multiple model scenarios automatically or with control over how those scenarios are generated. AutoMaxent offers model fit control, multiple background points generation algorithms, automatic model and variable selection, and study area configuration, among other parameters. AutoMaxEnt allows for the automation of model fitting, evaluation and model selection across different species, study areas and time periods.

1. Occurrence records underpin biodiversity analyses, but their coordinates are often erroneous or missing. Misplaced coordinates distort environmental predictors and range estimates used in conservation assessment. Existing tools flag suspect records or georeference locality text, but rarely correct coordinates, provide calibrated uncertainty, or quantify downstream consequences. 2. We present GeoMend, a deterministic, rule-based method that flags and corrects suspect coordinates and georeferences records lacking coordinates from locality text, assigning each a precision tier or uncertainty radius. It uses no machine learning or artificial intelligence. We evaluate it against expert coordinates for 16,913 records and 736 species on Hispaniola, compare georeferencers using 5,000 localities from 72 countries, and measure changes in environmental predictors and the extent of occurrence. 3. On held-out Hispaniola records, GeoMend flags 92% of coordinates more than 5 km from expert truth, versus 27% for CoordinateCleaner. Among the accepted corrections with paired coordinates, the median error falls from 2.19 to 0.82 km, and 68% of the complete held-out pairs improve. On records placed by both GeoMend and GEOLocate that also have original coordinates, the median error is 0.67 km for GeoMend versus 10.69 km for GEOLocate. For records lacking coordinates, evaluated uncertainty radii contain expert truth about 92% of the time in well-supported gazetteer classes. Correction reduces the mean standardized environmental distance from expert truth by 38% and moves the extent of occurrence toward expert truth by a median of 8% (mean of 79%). Corrections and newly georeferenced records give 17 Hispaniola species their first range estimate. 4. On the global benchmark, GeoMend is more accurate than GPT-4o for two-thirds of the records placed by all four comparators, with median errors of 4.4 and 10.8 km, respectively. Applied to Global Biodiversity Information Facility (GBIF) records lacking coordinates, it places records from 82,180 previously unmapped species across 233 countries, ranging from precise locality interpretations to administrative centroids. Automated placement makes the global backlog more approachable, while explicit tiers distinguish uses supported by precise and coarse coordinates. Further georeferencing requires regional expertise to combine automated placement with local knowledge.