Ecological Indicators

Remote sensing mapping of plant functional diversity (PFD) is increasingly used at the science-policy interface. Yet assessments of uncertainty and quality standards for these products are seldom provided, as the impact of measurement uncertainty on PFD estimates, i.e., the variability across pixels, is still poorly understood. We use trait maps and hyperspectral imagery simulations from the Biodiversity Observing System Simulation Experiment (BOSSE) to assess the impact of different types of uncertainty on the estimation PFD. Results show that uncertainties stemming from random errors affect the estimation of PFD (up to three times) more than those arising from systematic errors, with spectral and spatial error correlation reducing their impact. Standardization computing PFD metrics reduces systematic errors, making in-situ-measured plant traits and remote sensing reflectance factors more robust estimators of PFD than other remote sensing proxies derived from reflectance (e.g., spectral indices or optical traits). The impact of uncertainty propagation from reflectance factors to these often-preferred secondary proxies increases with their complexity. Nonetheless, trait uncertainty can spuriously diminish differences between PFD derived from the ground (plant traits) and satellite imagery, as it can reduce uncertainty-induced or inherent biases between the PFD metrics computed from each trait (plant or spectral). This study provides new understanding of how uncertainty propagates into PFD estimates; this knowledge is relevant for optimizing the selection and defining quality requirements for PFD estimation. It also suggests that random and systematic uncertainties should be quantified separately at the reflectance factor level to guide their use and interpretation in PFD analysis.

Forest restoration has become a widespread response to deforestation and biodiversity loss, particularly across the tropics. Monitoring restoration is essential for tracking progress and requires accurate knowledge of baseline communities, with threatened species often used as recovery indicators. Passive Acoustic Monitoring (PAM) combined with AI classifiers is enabling this at scale. However, such tools remain immature and may underperform for rare, threatened species underrepresented in training data. Here, we demonstrate the potential of PAM assisted by the AI classifier BirdNET+ V3.0, a January 2026 beta release, to support monitoring of threatened tropical birds. By validating 995 detections from 8,940 hours of audio across a reforestation landscape in Para in the Brazilian Amazon, we found that BirdNET+ confirmed the presence of ~43% of the IUCN Red List threatened species possible in the region, five of which were the first confirmed records for the study site. We also found that this method outperformed traditional survey methods. However, BirdNET+ identified species with mixed reliability, and we could also not determine whether restoration has had any impact on them. Our results demonstrate that PAM with BirdNET+ is a viable, practical method for detecting threatened birds in tropical forest restoration landscapes, but that conventional validation approaches are not appropriate for such species, for which extensive validation effort is an essential cost. Under circumstances where this is not possible, we demonstrate validating the single highest-confidence detection per occasion as a practical alternative.

The accelerated loss of biodiversity in sub-Saharan Africa threatens the functioning of tropical ecosystems. In Cote dIvoire, the Comoe National Park (PNCOMOE), a Sudano-Guinean savannah, and the Tai National Park (PNTAI), a dense rainforest, both UNESCO World Heritage Sites, are home to fauna assemblages of global importance, whose long-term resilience remains insufficiently quantified. This study assesses and compares, over a decade (2014-2025), the functional stability of vertebrate communities in these two contrasting ecosystems, using nine metrics covering resistance, invariance, persistence, interspecific synchrony, Tilmans stability, Jacobian resilience and a Composite Stability Index (CSI). Abundance data for 107 vertebrate species were collected via foot transects at PNTAI and aerial surveys at PNCOMOE. The stability metrics were calculated using the R package estar, integrated with an alpha diversity analysis (Shannon H', species richness S, Pielous evenness J') and a Jacobian spectral analysis within a multidimensional ecological assessment. PNTAI (0.708) exhibits significantly higher alpha diversity (H' = 2.82; S = 55.7 taxa) and community resilience 4.6 times higher than in the PNCOMOE (0.153). Its interspecific asynchrony index (0.504) reveals a strong portfolio effect, absent in the PNCOMOE (0.232). In contrast, PNCOMOE exhibits higher temporal invariance (0.382 versus 0.116) and Tilman stability (0.276 versus 0.152), reflecting more predictable dynamics. The overall ICS favours the PNTAI (0.484) and (0.370). The Jacobian analysis detects local instability in both parks (Re({lambda}max) = 5.58 at the PNTAI; 3.73 at the PNCOMOE). The two parks exhibit distinct yet complementary stability architectures: PNTAI relies on dynamic stability based on resilience and interspecific compensation, whilst PNCOMOE demonstrates conservative stability through temporal regularity. The absence of calculable resilience at PNCOMOE suggests a potential crossing of a functional degradation threshold, arguing for urgent restoration interventions and differentiated conservation strategies, tailored to the resilience mechanisms specific to each ecosystem.

Deadwood is a fundamental component of forest ecosystem, supporting biodiversity and driving multiple ecological processes. However, structures that may develop on downed coarse woody debris (CWD) create additional microhabitats that are used by numerous organisms and contribute to small-scale habitat heterogeneity. To date, quality of CWD is commonly characterized by its volume, diameter, tree species or stage of decomposition, while fine-scale structures occurring there remain not surveyed and their role in ecosystem is rarely quantified. Here, we introduce the novel concept of microhabitats on CWD. i.e. Deadwood-related Microhabitats (DreMs), defined as distinct features occurring on CWD that may provide shelter, breeding or foraging sites for forest-dwelling organisms. Using an original catalogue comprising 14 groups and 30 types, we inventoried DreMs on 6,003 CWD across 423 study plots located in best-preserved old-growth forests in Poland. We quantified the frequency and richness of DreMs and assessed the link between CWD characteristics and DreM richness in spruce, beech, willow-poplar, fir-beech and oak-lime- hornbeam forests. All inventoried DreMs occurred on both deciduous and coniferous taxa. The most frequent DreM included bryophyte mats, loose bark patches, insect galleries, fungal fruiting bodies and polypores. DreM richness increased with CWD diameter, more complex architecture and the presence of multiple decay classes within single debris. DreM richness peaked at intermediate decay classes and was higher on deciduous than on coniferous taxa. Our study is the first large-scale qualitative and quantitative assessment of DreMs in temperate forests. By focusing on forests characterized by ecological continuity and minimal human-related disturbance, the results provide a reference for downed deadwood-associated structures. By complementing inventory of tree-related microhabitats, DreMs extend potential monitoring schemes of habitat quality and contribute to biodiversity-oriented forest management. Highlights* Downed coarse woody debris (CWD) hosts Deadwood-related microhabitats (DreMs) * Higher DreM richness is associated with CWD diameter * DreM richness peak at intermediate stages of wood decay * CWD of deciduous taxa support more DreMs than coniferous * Diversified CWD and DreMs increase habitat heterogeneity for forest-dwelling taxa

Dead wood has a vital role in maintaining biodiversity and ecosystem processes in boreal forests. A large proportion of deadwood-dependent species are threatened by habitat loss and degradation resulting from intensive forest management practices. In Finland, approximately 25% of all forest-dwelling species, around 5,000 species, are dependent on deadwood, of which 596 are red-listed. Currently, there is no large-scale, long-term monitoring programme for saproxylic species in Finland, and the tools required to implement such monitoring remain underdeveloped. Here, we develop, test and report a DNA-based monitoring method for dead wood associated organisms including fungi, lichens, bryophytes, and insects. Three study sites were selected for the research, one from each boreal vegetation zone. From each study site, 16 study logs were selected and permanently marked for long-term monitoring. Our results show that the method is applicable to all studied species groups. However, especially fungi and lichens show considerable difference between species detected morphologically and those detected using DNA-based methods. We conclude that two partly contrasting strategies emerge for implementing long-term monitoring of deadwood -associated boreal species. The first strategy emphasizes as comprehensive geographical sampling of habitat types and tree species as possible. The second approach focuses on long-term monitoring of individual deadwood units across their full decomposition trajectory. We recommend that solutions for unifying the currently fragmented monitoring research on deadwood-associated organisms would be sought together with potential coordinating institutions.

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.

AimHere we aim to disentangle species-specific bioclimatic drivers of forest site productivity and project their future dynamics, providing a spatially explicit basis for anticipating climate-driven shifts in productivity and their implications for forest carbon sequestration. LocationIberian Peninsula. Time period1985-2014 (calibration); 2071-2100 (projected under CMIP6 scenarios). Major taxa studied21 Iberian tree species. MethodsWe used Site Form (SF) maps derived from the Third Spanish National Forest Inventory, spatially interpolating plot-level SF estimates as a continuous productivity index and relating them to 25 bioclimatic variables. Multiple linear regression models were selected via complementary stepwise and subset regression and validated on independent hold-out data (80%/20% split). ResultsValidated [Formula] ranged from 0.46 (Quercus faginea) to 0.97 (Pinus pinaster); 17 of 21 species reached [Formula]. BI013 precipitation of the wettest month), not BI014, was the most frequently retained predictor (15/17); BI014 was retained in only (11/17 models with a near-even sign split. Combining projected changes in mean productivity and habitat extent under SSP5-8.5, fifteen of sixteen applicable species lose total productivity by 2071-2100, six -- including Fagus sylvatica and Betula alba -- collapsing to below 1% of their reference-period value; only Pinus pinaster gains, and only under the lowest-emission pathway (up to 175%) -- under SSP5-8.5 it too loses productivity, albeit less than any other species (35% of its reference-period value retained). Limiting warming to SSP1-2.6 spares Mediterranean pine and oak species but not Euro-Siberian and montane ones. Main conclusionsThese validated, extrapolation-aware models reveal a near-universal, climate-driven collapse in Iberian forest site productivity, with direct implications for the carbon-sink potential currently attributed to these forest types, and provide a route to dynamic, climate-aware carbon-uptake estimates for the region.

The Community Conservation Index (CCI) developed by Chadd and Extence (2004) has been widely used to assess the conservation value of freshwater macroinvertebrate assemblages by integrating species rarity and community richness into a single metric. However, the original conservation scoring system was based largely on historical rarity designations and regional distribution data that no longer reflect current conservation assessments or recording coverage. This paper presents a comprehensive revision of the Conservation Scores underpinning the CCI. Species scores were updated using the latest IUCN-compliant Red List assessments for British freshwater invertebrates. For taxa classified as Least Concern, Data Deficient, or lacking a recent conservation assessment, scores were derived from national occurrence data across Great Britain for the period 1995-2024 using hectad occupancy. In addition, all non-native species were assigned a conservation score of zero to ensure they do not contribute positively to site conservation value. The revised dataset includes 1,680 taxa, compared with 1,117 in the original index, with 755 taxa receiving Conservation Scores for the first time. Thirty non-native taxa were assigned a score of zero. Among previously scored taxa, 165 received higher scores, 576 received lower scores, and 184 remained unchanged. The predominance of score reductions reflects improved knowledge of species distributions and the replacement of historical rarity classifications with contemporary assessments based on conservation status and national frequency of taxon occupancy. The revised CCI maintains the original strengths of combining rarity and richness while improving transparency, consistency and ecological relevance. By incorporating current Red List assessments, Great Britain-wide distribution data and revised treatment of non-native species, the updated index provides a more robust and nationally representative measure of freshwater macroinvertebrate conservation value. It offers an enhanced tool for biodiversity assessment, conservation prioritisation, site designation and environmental decision-making across freshwater ecosystems in Great Britain.

Global Biodiversity Information Facility (GBIF) occurrence retrievals for an irregularly shaped region are limited by the API spatial query capabilities - rectangular envelopes or size/vertex-limited WKT polygons - neither of which conform to protected areas, sacred groves, wetlands, panchayat or municipal boundaries or any other arbitrary KML polygon of interest queried by users. This paper presents and validates an open, self-contained, adaptive spatial-tiling protocol that (i) ingests any KML polygon of any shape, size and location on earth, breaks it into a set of GBIF API-compatible rectangular tiles, (ii) queries, cleans and clips the individual records to the target polygon, and (iii) summarises the inventory with a generic diversity-completeness-rarefaction module, with minimal manual re-parameterisation between sites. The protocol implements an iterative quadtree refinement algorithm that adapts tile number, size and location to the target polygon geometry, is combined with a fault-tolerant pagination/retry query system, a boundary-exact two-step clipping procedure and a Chao1-based completeness assessment to ensure statistical comparability between sites of different spatial extent and sampling intensity. The algorithm is implemented in open R source (sf, terra, rgbif, tidyverse) with the tiling algorithm controlled by the four parameters only (initial cell size, area floor, tile overlap threshold, recursion limit), with default settings on a new site by simply changing the input file path. This paper describes in detail its five main components - (i) polygon input and validation, (ii) quadtree adaptive tiling, (iii) polygon coverage verification, (iv) tile-wise GBIF query with retry/shrink pagination and partial data retention, (v) boundary-exact deduplication, clipping and diversity estimation. A downstream generic module estimates diversity, Chao1 richness/completeness and Hurlbert rarefaction, for each taxonomic rank and generates rank-ordered diversity tables as output. The generalisability of algorithm to multiple sites has been demonstrated with second polygon (Sonamukhi Sal forest dominated stretch, Bankura district, West Bengal; approx. 610 sq km) that differs from the first (Bishnupur Sal forest dominated stretch; 938 sq km) in both size and complexity (10 vs 34 KML vertices) and report the tiling and diversity metrics comparable results across the two polygons. With no parameter changes, the algorithm generated 135 adaptive query tiles for Sal forest dominated stretch adjoining Bishnupur, and 86 tiles for Sal forest dominated stretch Sonamukhi SDFP, covering completely the area of both polygons. The number of tiles per 100 sq km is comparable between the two runs (14.4 vs 14.1 tiles) despite the 35% difference in polygon size and 3.4x vertex count. The tile-wise querying with retry/shrink pagination retrieved 6,169 GBIF records (excluding errors) with boundary-exact clipping across 404 species for Bishnupur and 1,222 GBIF records (excluding errors) across 271 species for Sonamukhi; the generic diversity module processed the records without further parameter changes and generated comparable metrics for each rank at both sites. The protocol addresses a general bioinformatic challenge in polygon-based GBIF queries, is provided as an open, reusable, documented method which has been validated on two sites. Because the protocol has so far been validated on only two polygons that differ markedly in size, shape and observer regime, it may be regarded as an initial cross-site validation rather than a comprehensive benchmark, and recommend testing on a broader, globally distributed set of polygons before the approach is treated as a general-purpose standard.

Achieving global conservation and restoration targets requires knowing not only how much land to conserve and restore, but also where such actions will have the greatest ecological effect. Conservation and, in particular, restoration priorities are often inferred from habitat condition, opportunity, or broad ecological value, yet these criteria do not necessarily identify where local changes generate the largest landscape-scale consequences. Building on sensitivity-based conservation prioritization, we extend the approach to derive ecological loss and gain potential by differentiating a persistence-relevant connectivity metric with respect to local habitat condition. Sensitivity identifies areas where local degradation or improvement would most strongly affect connected habitat at the landscape scale. Combining sensitivity with current condition distinguishes high loss potential, where degradation would have large ecological consequences, from high gain potential, where ecological improvement could generate large benefits. Applying the framework to national forest-naturalness data in Norway, we show that loss and gain potential are spatially related but far from redundant, and that gain potential is not simply concentrated in the most degraded forests. Both high-loss and high-gain areas were poorly represented within current protected areas, although loss potential was consistently better represented than gain potential. Extending sensitivity-based prioritization in this way provides a general and scalable means of linking local habitat-condition change to persistence-relevant landscape outcomes, while distinguishing the ecological potentials that can inform conservation and restoration decisions.

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.

Soil microbe communities are key indicators of ecosystem recovery, yet their integration into operational restoration monitoring remains limited. We evaluated the potential for benchmarking early Atlantic Forest restoration with reference site-based comparisons using bacteria and fungi. Samples were collected from three geographic clusters and spanned a pasture-restoration-forest land-use gradient. Using metabarcoding, we assessed whether soil microbe community diversity, structure, and functional profiles respond to restoration progress considering influences from spatial arrangement and/or successional stage (determined using NDVI). We found that OTU alpha diversity was not explained by either predictor, whereas the Shannon diversity of bacterial functions declined significantly with increasing NDVI. Bacterial and fungal OTU composition (Bray-Curtis) responded significantly to both NDVI and geographic cluster, as did microbe functional profiles, although only NDVI proved both marginally and conditionally significant. We also found that soil chemical parameters were primarily structured by geographic cluster but not by NDVI, suggesting that many abiotic edaphic conditions reflect soil history rather than current vegetation status. These findings suggest that both microbe taxonomic and functional metrics track revegetation progress and validate eDNA as a scalable, sensitive restoration monitoring framework. Importantly, our results also support the use of reference site-based monitoring for Atlantic Forest restoration.

In northern boreal Europe, the Last Glaciation Maximum resulted in a tabula rasa of earthworm species, post-glacial recolonisation of few peregrine species. Boreal forests represent 11.5% of the Earth's land area, and their cold winters represent the harshest season for earthworms. These biotopes are subject to substantial global changes, notably climate change and land use intensification, which might impact earthworm populations. Yet, no study measuring temporal changes in earthworm population or diversity is known in this biotope. Here, we sampled earthworm communities in two northern and two southern areas in Finland forests. In total, 21 sites were surveyed in 2015 and resurveyed 10 years later in 2025-2026. We aimed at understanding how earthworm diversity and populations changed in the last 10 years, comparing community and species-specific diversity metrics. In ten years, although not significant, earthworm abundance declined (-20%), whereas richness increased (+13%). The cold-tolerant Dendrobaena octaedra decreased, being the only one with a significant change. Temporal community and species dynamics followed a south-north gradient. Southern sites showed a decrease in abundance and D. octaedra populations, with an increase in abundance and occupancy of more heat-tolerant species. Northern sites showed an increase in abundance uniquely due to an increase in D. octaedra populations, but no other species were found there. These changes can be linked to climate changes leading to less rigorous winters in both south and north of Finland, and more rigorous summers in the south, potentially interacting with intense forest management, but could also be attributed to a still ongoing post-glacial recolonisation. These results call for more long-term sampling of earthworm communities in these fragile and rapidly changing environments.

Association network analyses performed on microbial abundance data provides information regarding ecosystems functioning, quality and potential keystone taxa. Samples for network analyses can be collected spatially, temporally or both. Here, we compare the microbiomes and the reconstructed association networks resulting from temporal vs spatial sampling regimes from the Oka estuary in the Spanish Basque Country. The taxonomic composition although similar at Phylum level, diverged more at lower taxonomic levels such as Family. The dissimilarity of spatial replicates was higher than the temporal replicates; however, the dispersion of OTU abundances across samples was similar in the two datasets, indicating that OTU abundances varied similarly across samples. Moreover, the topological properties and keystones differed between networks. The number of edges and the connectance were higher in the spatial network, and keystones were more taxonomically diverse in the temporal network. Among those correlations present in the spatial and temporal networks, we identified potential interactions involved in the sulphur and nitrogen cycles, and in the degradation of organic matter. We conclude that network analyses reveal ecological aspects of the ecosystems functioning, nevertheless, the sampling must be thoroughly planned according to the aim of the study, since different sampling strategies may result in different outcomes.

Plant communities of sub-Arctic and Arctic regions are undergoing changes due to rapid climate change. Permafrost degradation is one of the key drivers of habitat change in these environments and it is considered to impact the biodiversity of northern peatlands. However, the empirical evidence linking permafrost thaw to biodiversity change remains limited. Here, we conducted a vegetation survey of the plant communities (including vascular plants, bryophytes, and lichens) of 14 palsa mires to quantify biodiversity change along different stages of permafrost degradation. We quantified changes in community composition, indicator species, alpha- and beta diversity as well as in two biodiversity indicators. Our results showed that permafrost degradation significantly affected both the plant community composition and the biodiversity of palsa mires. The most pronounced changes in biodiversity were associated with mires from which the permafrost had thawed completely. However, those mires that represented high stage of degradation but still retained remnant permafrost features, were still maintaining plant communities comparable to palsas with low stages of degradation. Thus, our results indicate that collapsing palsas are a clear tipping point for local biodiversity. Almost a complete loss of lichens was observed in completely thawed mires. Furthermore, the shifts in the vascular plant community were recognized to have potential to affect the biodiversity of palsa mires beyond vegetation. This study underscores the role of permafrost sustaining the compositional and taxonomic diversity of plants in palsa mires while also highlighting the varying responses of vascular plants, bryophytes, and lichens to permafrost degradation. Overall, our findings illustrate how the climate-induced changes in habitats and loss of environmental heterogeneity impact the biodiversity and the ecological resilience of the permafrost peatlands.

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.

The tidal wetlands of Louisiana comprise about 25% of those found throughout the conterminous United States yet estimates of wetland loss rates in the region between 1932 and 2016 have exceeded 60 km2 yr-1. To mitigate further degradation and wetland loss in the region, a globally unprecedented $50B, 50-year plan for coastal Louisiana is driving restoration efforts, and demand exists from multiple stakeholders for regularly updated, regional-scale, accurate land cover information. We used machine learning (random forests; RF) and cloud computing to develop a new Landsat-based, marsh vegetation community geospatial dataset. The dataset depicts wetland vegetation community types defined in a previous study at annual (1985-2025) time steps at 30-m resolution. An RF algorithm was used to integrate training samples with feature variables derived from Landsat imagery, and the resulting geospatial data product achieved an overall correct classification rate of 78%. The approach for development of the land cover dataset presented here has potential for application in other coastal wetland habitats throughout the world.

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.

Coastal forests are increasingly threatened by saturated soil and elevated salinity levels resulting from sea level rise, saltwater intrusion, and storm surges. In response to rising salinization and flooding, healthy coastal forests that rely on freshwater (both wetland forests and low-elevation upland forests) are transitioning into landscapes dominated by dead or dying trees, known as ghost forests. Situated among salt-tolerant shrubs and grasses, ghost forests eventually become marshes or open water. Here, our main objective was to quantify the dynamics and pathways of these forest landscape conversions, as well as the factors contributing to the changes, which is vital for understanding the progression of coastal ecosystem degradation and forecasting future changes. We focused first on identifying the best method to track forest landscape change by exploring the role of multiple remote sensing indices (i.e., multispectral, bi-seasonal, topographical, and phenological metrics) in enhancing the performance of deep learning models (convolutional neural networks, CNNs) for land cover classification in the coastal plain of North Carolina using surface reflectance of Landsat 8 and Sentinel-2 images. Then, we used the best available data (Landsat 8) to understand long-term change and identify patterns of land cover change from 1985 to 2021. Our study reveals that incorporating phenology and topographical indices enhances the separability of the ghost forests class from all other vegetation classes. In our assessment, the higher-resolution Sentinel-2 data (F1 Score = 96.3) outperformed Landsat images (F1 score = 93.4) for the 2021 co-available year. However, Landsat remains an important tool used due to its long-term data record. Therefore, we used Landsat to determine that 21% of forests were lost between 1985 and 2021, and that the rate of loss is increasing. Between 2010 and 2021, 23,876 ha of forest were converted to marsh, ghost forest, and shrub, which is 1.5 times higher than the 16,968 ha lost between 1985 and 2010. These conversions from forest to ghost forest and marshes were driven primarily by proximity to the channel, salinity, and the increasing rate of relative sea level rise (RSLR), which are the key environmental drivers of observed changes. By quantifying these changes, we highlight regions most vulnerable to environmental stressors, providing a basis for targeted conservation strategies.