Ecological Indicators

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.

Passive acoustic monitoring (PAM) is increasingly applied in biodiversity research, yet its reliability as a proxy for biodiversity remains insufficiently evaluated. In particular, the spatiotemporal autocorrelation inherent in acoustic indices of PAM is rarely quantified, despite its importance for the standardized application of acoustic monitoring. We conducted an integrated study to investigate these issues using a complete grid-based monitoring system covering the entire region (100 grids of 1 km x 1 km) in southern subtropical climatic zones. Acoustic data from 58 valid sites were combined with camera-trapping and vegetation surveys to evaluate six commonly used acoustic indices in PAM. We found that these indices were more strongly associated with relative abundance and community diversity metrics of bird and mammal than with species richness. Spatially, autocorrelation ranges of some acoustic indices extended to approximately 4 km (i.e., the Bioacoustic Index (BIO) and Normalized difference soundscape index (NDSI)). Temporally, all indices exhibited significant autocorrelation over 2-5 days, exceeding the typical short-term turnover of bird and mammal activity (1-2 days). Our results indicate that acoustic indices are not direct proxies for species richness but provide complementary information on soundscape dynamics. By explicitly quantifying spatiotemporal autocorrelation, this study offers practical guidance for sampling design and statistical analysis in passive acoustic monitoring, supporting more reliable and efficient biodiversity assessment.

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.

Mangroves play a crucial role in supporting global biodiversity and ecosystem functioning, yet how their multidimensional diversity interact and respond under diverse stress conditions remains underexplored. To address this gap, using species, environmental, functional trait and forest structural data collected from the permanent sample plot (PSP) network (110 PSPs) of the worlds largest mangrove ecosystem, the Sundarbans, we answer three key questions: (Q1) How are structural, functional, taxonomic, and phylogenetic diversities interconnected? We hypothesized that these diversity components are positively correlated (H1). (Q2) What are the key environmental stressors and how the diversity components are influenced by multiple stressors? We hypothesized that these stressors negatively affect all diversity components (H2). (Q3) What spatial patterns emerge in the distributions of these diversity components? Here we hypothesized that these diversity components vary across space under changing environmental conditions (H3). Our results show that taxonomic, functional, structural, and phylogenetic diversity have varying degrees of interconnection. While taxonomic and structural diversity are strongly correlated, functional and phylogenetic diversity exhibit more independent patterns, suggesting distinct ecological processes shape each dimension. Salinity, elevation, silt, community structure and downstream-upstream gradient (i.e., upriver position) have strong influences on all the diversity components although the magnitude of the influence varies. GAM results reveal that salinity and siltation act as the primary negative drivers for most dimensions; however, functional richness and divergence show a unique positive response to salinity. Furthermore, we found that community structure and upriver position significantly influence diversity patterns, often in a non-linear fashion. Though taxonomic, structural, and phylogenetic diversity show higher values mainly in the moderate and low saline areas, functional richness shows higher values in high saline areas. Overall, our results provide strong support for all the hypotheses. Our findings highlight the importance of holistic approach integrating taxonomic, structural, functional, and phylogenetic dimensions for maintaining biodiversity and ecosystem functions in dynamic mangrove ecosystems and emphasize the need for conservation efforts that target moderate-stress zones to preserve both ecological and evolutionary diversity. HighlightsO_LIExplored the interconnection between four dimensions of biodiversity (taxonomic, structural, functional, and phylogenetic) and how they respond to multiple stressors in the worlds largest mangrove forest. C_LIO_LIHigh salinity and siltation act as the primary environmental stressors that negatively affect overall biodiversity. C_LIO_LIStructural diversity is strongly related to species richness, serving as a key indicator of ecosystem health. C_LIO_LIFunctional and phylogenetic diversity follow independent spatial patterns, promoting the need for multi-dimensional monitoring. C_LI

Accurate estimation of forest growing stock volume (GSV) at fine spatial scales is essential for sustainable forest management, carbon accounting, and local decision-making. However, traditional forest inventories often lack sufficient sampling density to provide reliable estimates for small areas. This study evaluates the performance of two small area estimation approaches: the Empirical Best Predictor (EBP) based on a nested-error linear regression model, and the Mixed-Effects Random Forest (MERF) for estimating GSV at the forest stand level using multi-source remote sensing data. The analysis was conducted in the Vallombrosa Nature Reserve (Italy), integrating field measurements from 101 plots with auxiliary variables derived from Sentinel-2 imagery and airborne LiDAR. Both methods were applied to estimate the mean and total GSV across 658 forest stands, many of which lacked direct observations. Model performance was assessed using spatial cross-validation, and uncertainty was quantified using root-mean-square error (RMSE). Results show that MERF outperformed EBP in predictive accuracy, achieving higher R2 (0.67 vs. 0.37) and lower RMSE (151 vs. 202 m3 ha{square}1). MERF also produced more stable and precise uncertainty estimates, with improved coverage of observed values. While both methods yielded comparable total GSV estimates, EBP exhibited greater variability and sensitivity to model assumptions. In contrast, MERF effectively captured non-linear relationships and handled multicollinearity among predictors, though at the cost of reduced interpretability and higher computational demand. Overall, findings highlight the advantages of integrating machine learning with mixed-effects modeling for SAE in forestry, particularly under conditions of sparse sampling and complex ecological variability.

Terrestrial Laser Scanning (TLS) captures fine-scaled three-dimensional measurements of ecosystem structure, supporting monitoring of the Essential Biodiversity Variables (EBVs). Yet employing TLS across landscapes remains challenging in remote and topographically complex areas. Remote sensing provides a potential pathway for upscaling TLS-derived structural metrics, but to what extent is unquantified particularly in heterogenous environments, like oceanic islands. Here, we investigated the ability of remote sensing to estimate TLS-derived habitat structure across three contrasting habitats (lowland rainforest, montane cloud forest, and subalpine summit scrub) on La Reunion island. Sentinel-1, Sentinel-2, and Aerial LiDAR (ALS) data were acquired over plots where TLS was completed. We derived defined indices of backscatter coefficients, vegetation indices, and LiDAR metrics and assessed their alignment with TLS measurements using a Procrustes analysis. Subsequently, we used General Additive Models to estimate TLS habitat structure from remote sensing variables. Sentinel-2 exhibited the highest multivariate alignment with TLS (r = 0.51). TLS measurements of horizontal and vertical structure were estimated with the highest cross-validated predictive accuracy (R2 0.39 - 0.73), whilst structural complexity metrics were estimated with greater difficulty (R2 0.02 - 0.20). Multi-sensor models outperformed all single-sensor models in prediction estimates. Model performance also varied across habitats, with the highest agreement between predicted and observed values in the lowland rainforest (r = 0.38), and the lowest agreement (r = 0.35) in the montane cloud forest. Yet the dominant structural feature of each habitat was most accurately captured with remote sensing. Our results demonstrate the potential of integrating multi-sensor remote sensing data to upscale key dimensions of TLS-derived ecosystem structure but remains challenging for fine-scale structural complexity. These findings highlight both the potential and constraints of remote sensing for developing scalable, long-term monitoring frameworks for EBVs, especially in structurally complex and underrepresented island ecosystems.

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.

Field-based monitoring of tree species in forests is often sparse due to logistical constraints. Remote sensing enables repeated, spatially contiguous collection of reflectance data across large areas. Tree species classification accuracy using such data is variable, likely because most studies use observational datasets where species occurrence correlates with environmental variation. We used two sites of a tree biodiversity experiment in Germany (BIOTREE: Kaltenborn and Bechstedt), where different species have been planted with high replication under controlled diversity levels, to assess how well tree species could be classified using reflectance data from airborne imaging spectroscopy and different classification methods (linear discriminant analysis, LDA, and a non-linear support vector machine, SVM). Reflectance data for 589 wavelengths between 400-2400 nm were acquired at 1 m spatial resolution during peak growing season. Reflectance spectra showed large and significant variation between taxonomic classes, orders, and species, and weak, but still significant, interactions between classes or orders and diversity levels. Classification accuracy reached 100% in training datasets, 77%-83% for the four species in Kaltenborn prediction datasets, and 31%-49% for the 16 species in Bechstedt prediction datasets. LDA provided more accurate predictions than SVM; and using similarly-spaced original wavelengths with LDA was as efficient as using principal components derived from the original data. While airborne imaging spectroscopy effectively distinguished up to four tree species in our datasets, classification accuracy was lower in more species-rich plots. In these cases, the methodology may be more useful for functional diversity monitoring than for tree species classification.

Drylands are experiencing increasingly intense and frequent drought events due to climate change. Wetlands in drylands are therefore under increasing pressure as their inundation regimes are altered. In southern African savannas, wetlands are often the only sources of free water for the ecosystem. Changes in the hydroperiod may alter vegetation and water surfaces, which could be early signals of desertification in their immediate vicinity. To investigate trends in surface cover around wetlands, we applied linear multispectral unmixing to Landsat pixels located near wetlands in Hwange National Park. We assessed spatial gradients in vegetation, water, and bare soil dynamics from 1986 to 2022. The studied wetlands were also grouped by hydroperiod to test whether the response of each surface cover differed with the reliability of the water resource. Our results show a significant decrease in the water fraction of wetlands with short hydroperiods, which was significantly negatively correlated with increasing temperature. Furthermore, water fraction was significantly positively correlated with vegetation fraction. This correlation suggests that vegetation could be affected if water surfaces continue to decline. Finally, this study is the first to demonstrate a decline in water surfaces in Hwange National Park, with potential implications for wildlife conservation.

Species have optimal environmental conditions, and ongoing climate warming is reshaping community composition. In particular, many ecosystems exhibit thermophilization, a shift toward species adapted to warmer conditions. However, this process is often slower in forests, leading to a mismatch between community composition and ambient temperature, referred to as climatic debt. Despite increasing attention, its effects on forest productivity remain unclear. Quantifying tree community responses to warming is therefore essential for predicting future forest dynamics and informing biodiversity conservation. In this study, we analyzed natural forests across Japan using data from the 3rd and 4th National Forest Inventory periods (2009-2018). We first assessed compositional consistency between survey periods using the Bray-Curtis index and excluded plots with high dissimilarity ([≥] 0.8). Species-specific thermal optima were estimated using species distribution models and used to calculate the Community Temperature Index (CTI). Thermophilization was quantified as the temporal change in CTI, while climatic debt was defined as the difference between CTI and mean annual temperature. We then examined the relationship between climatic debt and changes in aboveground biomass, used as a proxy for productivity, using linear mixed-effects models. We found a mean thermophilization rate of 0.005 {degrees}C yr-{superscript 1}. Despite this shift, climatic debt increased at an average rate of -0.022 {degrees}C yr-{superscript 1}, indicating a growing mismatch between climate warming and community thermal composition. Although thermophilization showed no statistically significant association with stand structure, it tended to vary with the proportion of small-diameter trees, suggesting the influence of multiple interacting drivers. Importantly, increasing climatic debt was significantly associated with declines in forest primary productivity, even after accounting for stand structure and regional variation. These results demonstrate that delayed thermal adjustment of tree communities can constrain forest productivity under ongoing climate warming, highlighting the importance of evaluating community-level thermal responses for sustaining forest ecosystem functioning.

Metabarcoding is a powerful tool for biodiversity comparisons, where standard-size DNA barcodes (>500 bases) offer better taxonomic resolution than shorter ones. Still, the choice of sequencing platforms and bioinformatics pipelines may strongly affect inferred diversity due to various technical biases. We assessed the relative performance of Illumina MiSeq i100 (2x500 paired-end), PacBio Revio and Oxford Nanopore MinION sequencing and bioinformatics pipelines, using full-length ITS amplicon sequencing datasets from a 103-species mock community and 45 composite soil samples. Despite numerous low-quality reads, PacBio yielded the lowest overall error rate and highest number of taxa. Illumina revealed the highest proportion of chimeric and index-switched reads, along with a strong bias towards shorter amplicons. MinION data analysed using PRONAME and Minovar - a bioinformatics pipeline presented here - had the largest proportion of low-quality data, and rare taxa were lost during data filtering and read polishing steps. Although Minovar enabled amplicon sequence variant (ASV) level precision for common taxa, we recommend clustering ASVs into OTUs. For PacBio, standard filtering approaches outperformed the ASV approach because they retained rare taxa. For Illumina, a stringent ASV approach or removal of rare OTUs would limit artefacts. Across all platforms, excess PCR cycles promoted chimeric and low-quality reads and lost quantitativity in biodiversity assessments. With moderate differences in effect sizes, all analytical approaches supported the conclusion that sampling design determines how we see soil biodiversity responses to land use. For biodiversity surveys based on the full-length ITS metabarcoding, we recommend using PacBio sequencing with standard, non-ASV pipelines.

Coral restoration is recognised as a critical tool to mitigate pantropical degradation of reef ecosystems. Robust monitoring of restoration progress is crucial for projects to evaluate their success, improve practice, and share knowledge. However, traditional visual surveys often fail to capture the full impact of coral restoration on reef function. Therefore, we employed Passive Acoustic Monitoring (PAM) to assess whether the soundscape of a coral restoration site in the Seychelles differs from adjacent healthy and degraded reference reefs. We applied two methods of soundscape analysis: manual detection of unidentified fish sounds; and machine learning-based Uniform Manifold Approximation and Projection analysis. Results were approach-specific: the manual approach highlighted similarities in fish calls between the restoration site and the healthy reference reef, while the machine learning approach extracted broader soundscape patterns, clustering the restoration site alongside the degraded reference reef. Although this is a single-site study, these findings suggest that a) coral restoration alters reef soundscapes, though recovery time may be taxon-specific, and b) multiple metrics are needed to bridge single-taxon and broad soundscape scales. This study contributes to the evolving field of soundscape ecology in coral reef ecosystems, highlighting the utility of PAM in monitoring changes to reef function through coral restoration.

Passive acoustic monitoring (PAM) enables non-invasive sampling of wildlife across broad spatial, temporal and taxonomic scales. Its ongoing and widespread use has generated unprecedented volumes of acoustic data, shifting the primary bottleneck from data collection to the storage, processing, integration, and interpretation of PAM outputs. Although many software tools exist to address these challenges, differences in their design, scope, and usability often create fragmented and complex analytical workflows. To identify the key barriers and opportunities shaping the implementation of PAM surveys, we conducted a structured expert solicitation involving 30 international practitioners working across terrestrial and aquatic ecosystems. Experts identified and ranked their most critical pain points in current PAM workflows, spanning data storage, processing, and interpretation. The top challenge identified related to accurate species identification using deep learning and artificial intelligence (AI) models, especially in noisy soundscapes or for underrepresented taxa. Eight additional priority challenges included workflow fragmentation, limited availability of user-friendly analytical and visualisation tools, uneven access to software, manual validation bottlenecks, computational constraints, and difficulties in data handling, standardisation, and sharing. Participants also proposed practical mitigation strategies for these priority challenges, supported by step-by-step guidance to help overcome key barriers. Together, these insights provide a roadmap toward more scalable, open-access, and collaborative software systems, which are increasingly essential to realise the full potential of PAM in global biodiversity monitoring.

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

Invasive animal species are spreading rapidly across the globe, creating an urgent need for efficient early-detection and monitoring tools. Passive acoustic monitoring has become an established method in biodiversity research, but its application to invasive species monitoring has been less systematically explored. Here, we combine a systematic literature review with a field-based case study to evaluate the potential of passive acoustic monitoring for invasive animal detection. We identified 26 studies on acoustic monitoring of invasive animals, mainly addressing amphibians (11 studies), birds and fish (five each) with most studies from the USA and Australia. The use of acoustic monitoring of invasive species has increased during the past decade, with recent studies applying automated detection, machine learning, and large-scale monitoring frameworks. As a case study, we further tested the feasibility of low-cost acoustic monitoring of the invasive American bullfrog (Lithobates catesbeianus) in southwestern Germany, combined with automated identification using BirdNET. We successfully confirmed bullfrog presence in eight of the eleven monitored lakes, including sites close to a protected nature reserve. Our results highlight the growing potential of passive acoustic monitoring of invasive species under field conditions. In combination with automated species detection, manual validation, and emerging real-time monitoring devices, passive acoustic monitoring becomes an increasingly powerful tool for early intervention and scalable management of biological invasions.

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.

Natural forests are increasingly being replaced by plantation forests, highlighting an urgent need to reconcile forest use and biodiversity conservation. Coastal forests serve as refugia for species of conservation concern, but they have been globally replaced by plantations, and conversion remains ongoing. As natural coastal forests become increasingly scarce, opportunities for direct biodiversity comparisons with coastal plantations are also disappearing. This scarcity leads to underestimation of the conservation value of natural coastal forests, while effective management measures to conserve biodiversity within coastal plantations remain poorly developed. By focusing on the largest natural coastal forests in Japan, we assessed whether plantation coastal forests can substitute for natural coastal habitats across multiple taxonomic groups, functional groups, and threatened species. We found that plantations supported bird species richness comparable to natural forests at the community level and for threatened species. Surveyed plantation forests supported 7% of global breeding populations of the endangered Brown Shrike. However, plantations hosted lower forest plant species and higher introduced plant species richness. Plantations also significantly altered coastal zonation patterns in plant communities: forest species distribution shifted seaward, whereas coastal and introduced species shifted landward. Coastal plantations are unlikely to be complete substitutes for natural coastal forests for plant species, yet still have the potential for restoring historically lost coastal forest ecosystems. Confining further plantation activities to landward areas and plantations with low canopy cover will mitigate the negative impacts of plantations and further conserve unique coastal communities where forest and grassland species can coexist. Highlight{checkmark} Coastal forests have been historically disturbed and converted to plantations {checkmark}We test whether coastal plantations can substitute for natural forests across taxa {checkmark}Bird species richness in plantations was comparable to that in natural forests {checkmark}Plant species richness was lower in plantations and zonation patterns were altered {checkmark}Considering zonation patterns can turn plantations into restoration opportunities

Agriculture has modified the soil structure due to the influence of external factors and processes that affect microbial biodiversity. Metagenomics is a fundamental tool for the study of soil microbial diversity because it provides information about the ecosystem diversity, including both the microorganisms that cannot be isolated in culture media and those that are no longer viable in the analyzed sample. In this work, six soil samples obtained from agroecosystems of central and northern Argentina were subjected to a preliminary 16S metagenomic analysis. Copiotrophic bacteria (Proteobacteria and Actinobacteria) were dominant and one of the samples had a dominance of an oligotrophic Phylum (Acidobacteria). Our findings support previous evidence from traditionally managed agroecosystems and provide new insights into the diversity of soil microbiomes in Argentine regions outside the Pampas. Finally, we analyzed the most common genera with relevant species to agronomy, both beneficial and pathogenic, and their abundance and diversity in the sequenced samples.

Groundwater-dependent ecosystems are biodiversity hotspots that provide habitat for specialised species. The EU Water Framework Directive (WFD) stresses the importance of identifying and protecting these ecosystems. However, they remain poorly mapped in temperate regions, as most studies have focused on (semi-) arid regions, where groundwater use by vegetation is both more prevalent and easier to detect from remote sensing. In this study, we transfer mapping approaches for groundwater-dependent vegetation (GDV) from dry climates into a novel framework for humid climates. To do so, we integrated, ECOSTRESS evapotranspiration data, together with high-resolution remote sensing data, regional geospatial data and field data to identify GDV. To test our framework, we trained and validated Random Forest models with eight predictor variables using 166 ground-truth vegetation plots to map GDV in Saxony-Anhalt (Germany). The final model achieved an overall accuracy of 0.97, identifying 2,067 km2 (41%) of GDV. Currently, only 19% are protected under the EU WFD. The proposed mapping framework offers a new solution for identifying GDV in temperate regions. The new GDV maps can contribute to managing groundwater resources and preserving biodiversity hotspots in regions facing increasing droughts, ultimately supporting implementation of the EU WFD.

Leaf litter ecosystems and their fauna are largely understudied, despite their critical ecological roles. Here, we investigate challenges associated with estimating biodiversity in terrestrial leaf litter. Current methodologies for biodiversity assessment are fraught with limitations, amongst the most significant is a decline in taxonomic expertise, complicating the process of species identification and the significant costs associated with species-level morphological identifications. DNA barcoding employs the mitochondrial gene cytochrome c oxidase I (COI) to identify animal species, and DNA metabarcoding facilitates the identification of multiple species without necessitating taxonomic expertise. Recent studies indicate that environmental DNA (eDNA) may exhibit greater sensitivity compared to traditional methods. To test whether these methods work in a real-world application, we sampled leaf litter across a temperate forest/field ecotone. Leaf litter was dried, ground and processed to extract environmental DNA. We evaluated the DNA extraction protocols to test their relative efficacy. We found that the Qiagen Blood and Tissue Kit was the most effective at recovering invertebrate diversity and that there were notable differences in biodiversity between forest and field habitats. Temperature emerged as a significant factor influencing the composition of the communities observed. Our methodology is applicable across various environments for efficient biodiversity assessment and might be particularly beneficial for monitoring pests and invasive species. Our approach offers a cost-effective and timely alternative to conventional biodiversity assessment methods and underscores the significance of accurate assessment methodologies for leaf litter communities.