Ecological Indicators

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.

Climate change and particularly the frequency and intensity of extreme events is affecting the distribution and abundance of species, with drastic consequences on ecological processes and community structure. Long-term records of environmental parameters are indispensable in climatological studies in order to better understand the processes involved. However, such data is usually unavailable for many geographic areas and certain environments, like Patagonian intertidal shores in the Southwestern Atlantic. The use of reanalysis products can help elucidate the climate of the past when in situ information is missing. In this work, we test the performance of reanalysis datasets in reproducing air temperature patterns and extreme hot events (heatwaves) on rocky intertidal environments of Atlantic Patagonia. Thus, we evaluate the degree of correlation between different reanalysis products and air temperature data from loggers placed on rocky shores. We also test whether those products accurately detect the duration, frequency and number of heatwaves and look for historical trends in their features. Our results showed that reanalysis products perform well for assessing broad-scale changes in air temperature patterns. Products were also capable of detecting heatwaves, with little variation in their features for the period 1960-2024. Additionally, real-time field temperatures to which intertidal organisms are exposed were obtained for the first time in the area; reporting heatwaves events. Thereby, reanalysis products complement local data, providing key information to understand the role that temperature increases and extreme heat can have in events like mussels mass mortalities reported locally. In this sense, our results suggest that heatwaves alone wouldnt be explaining the observed mussel losses. This work provides empiric evidence on the usefulness of reanalysis products of intertidal habitats and encourages similar approaches in order to properly understand climatological patterns that can drive ecological processes on coastal habitats.

Tropical forests, particularly the Amazon, play a critical role in global ecosystems by providing essential services such as climate regulation, carbon sequestration, and biodiversity conservation. However, these ecosystems are increasingly threatened by deforestation and land-use changes driven by agriculture, livestock farming, and other anthropogenic activities. This study investigates habitat composition and temporal changes in Tailandia (Para-Brazil), using high-resolution satellite imagery. Data from 2013 to 2023 were analyzed across 18 research plots and a broader expanded zone to identify patterns of land-use transformation. Results reveal the dominance of Forest Formation habitats, alongside significant increases in Pastures and Oil Palm Crops. Clustering analysis highlighted ecological heterogeneity, with intact forests and heavily altered plots demonstrating varied conservation needs. Results also forecast a 13% decline in forest cover and a 32% rise in pasture areas over the next five years. The findings underscore the urgent need for targeted conservation strategies, robust environmental policies, and sustainable land-use practices. This research demonstrates the utility of remote sensing for large-scale ecological monitoring and its potential to inform effective conservation efforts.

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

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.

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.

Foraminiferal environmental DNA (eDNA) assemblages have recently emerged as a robust and complementary proxy for relative sea level (RSL) reconstruction. However, unlike traditional morphological methods, eDNA assemblages are influenced by diverse DNA sources, including propagules and juveniles, whose effects on RSL reconstruction remain poorly understood. To assess how foraminiferal eDNA from different life stages vary in taxa composition and impact RSL reconstruction, we analyzed foraminiferal eDNA from bulk, 500-63 m and <63 m size fraction sediments from mangrove and mudflat environments in subtropical Hong Kong. The eDNA assemblages in size-fractioned sediments displayed distinct patterns from those in bulk sediment eDNA across different environments. The propagule and juvenile-derived eDNA <63 m fraction exhibited a similar community structure to bulk eDNA in mudflat environments but diverged in mangrove environments, indicating a greater contribution of propagule and juvenile eDNA to the total eDNA pool in the mudflat environment. We applied Bayesian transfer function modeling to estimate the elevation of samples using different size fractions. eDNA assemblages from the <63 m fraction systematically underpredicted elevation in mangrove environments, while elevations inferred from the 500-63 m fraction and bulk sediment eDNA were accurate. Conversely, all eDNA assemblages in the mudflat-mangrove transitional zone led to the overprediction of RSL. These findings confirm the reliability of bulk sediment eDNA for RSL reconstruction in mangrove environments, while highlighting the need for caution when reconstructing RSL in transitional zones.

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.

Accurate mapping of tree species from satellite data remains challenging in heterogeneous mountain forests due to environmental gradients, mixed stands, limited availability of high-purity training labels, and strong illumination-angle effects. Recent geospatial foundation models offer a new approach by learning generic, cloud-agnostic, information-rich representations from large multi-sensor archives suitable for a range of downstream tasks, but their ecological utility for species-level mapping remains incompletely understood. Here, we evaluate two geospatial foundation-model embeddings, AlphaEarth and Tessera, for tree species classification in the Trentino region of northern Italy, using parcel-level forest inventories as reference data (18 species and species groups). We compare their performance against conventional Sentinel-1+2 satellite composites across a series of controlled experiments examining classification accuracy, label efficiency, classifier complexity, robustness to label impurity, and temporal transferability. Foundation-model embeddings consistently outperform composite-based multispectral satellite baselines (weighted F1 = 0.83 vs. 0.80; macro F1 = 0.55 vs. 0.50), reaching near-asymptotic accuracy with as few as 5% of available training parcels and preserving ecologically meaningful structure aligned with functional and taxonomic groupings. However, realising this advantage requires a nonlinear classifier: a compact neural network provides better results than classic machine learning (i.e. Random Forest) and performs as well as deeper neural networks, while a linear classifier on foundation-model embeddings underperforms a neural network on conventional composites. Ancillary environmental covariates offer no additional classification benefit when added to embedding-based models. Classification accuracy remains robust to moderate levels of label impurity, allowing mixed parcels to be retained in the training dataset without substantial penalties, while training with parcel-level species proportions as soft labels achieves higher peak performance (macro F1 = 0.586 for Tessera, 0.589 for AlphaEarth) and lower Proportion L1 error than hard labels without requiring purity filtering, maximising the value of the full range of input data. However, temporal transfer across years reveals performance degradation, with weighted F1 declining by 9% for Tessera and 15% for AlphaEarth, and disproportionate losses for rare species. Overall, our results show that geospatial foundation models shift a primary bottleneck in species mapping from feature engineering toward the availability, quality, and temporal alignment of ecological reference data, while opening new opportunities for scalable biodiversity monitoring and the analysis of ecological change.

ContextAs global urbanization intensifies, Urban Green Spaces (UGS) are pivotal for biodiversity conservation and climate change mitigation. However, comparative assessments of UGS spatial configuration and connectivity across diverse urban landscapes remain limited. ObjectivesThis study aims to assess the spatial arrangement and connectivity of UGS across 28 European capital cities. Additionally, we evaluate how Network Science metrics derived from Graph Theory can complement traditional landscape ecology metrics to provide a more comprehensive understanding of UGS at a large scale. MethodsWe developed a European Urban Vegetation Map using Earth observation data to classify UGS at 10m resolution across the selected capitals. We then analyzed UGS connectivity for each city utilizing 40 traditional landscape metrics and a Graph-Theory-based approach. ResultsWhile traditional landscape metrics effectively quantified fragmentation, they often remain strongly correlated with total vegetation abundance. In contrast, Network Science metrics provided specific insights into UGS functional connectivity, distinguishing the quality of ecological links beyond spatial proximity. This integration allowed us to cluster European capitals into three distinct typologies: unconnected compact cities, large metropolises with complex peri-urban dynamics, and high-connectivity cities with robust networks. These findings demonstrate that graph-based indices effectively complement traditional metrics, highlighting that relying solely on green space percentage is insufficient for assessing the ecological resilience of urban environments. ConclusionsThese results underscore the relevance of Earth observation-based UGS assessment and demonstrate that graph-based landscape connectivity analysis outperforms simple abundance metrics. Therefore, effective assessment requires integrating structural metrics with graph-based connectivity to support resilient urban biodiversity.

Tropical estuaries within the Brazilian Atlantic Forest are biodiversity hotspots facing escalating anthropogenic pressures, yet their ichthyofaunal assemblages remain incompletely documented. We evaluated the combined use of environmental DNA (eDNA) and environmental RNA (eRNA) metabarcoding to characterize fish communities in two estuaries with contrasting levels of urbanization (the Juqueriquere and Escuro rivers) on the northern coast of Sao Paulo, Brazil. Targeting the mitochondrial 12S rRNA (MiFish) fragment, we detected a diverse vertebrate assemblage totaling 93 species. eDNA identified 32 fish species across both systems, while eRNA detected 22 species in the preserved estuary, providing robust signals of metabolically active assemblages. The less impacted estuary exhibited significantly higher diversity indices and a more heterogeneous taxonomic composition. In contrast, the urbanized system displayed clear molecular signatures of anthropogenic influence, including the presence of invasive species (Oreochromis niloticus, O. aureus, and Clarias gariepinus) and domestic animals. This study constitutes the first application of fish eRNA metabarcoding in Brazil and demonstrates that integrating eDNA and eRNA refines ecological interpretation by coupling biodiversity detection with improved inference about contemporary community composition. Our findings highlight the potential of multi-molecule metabarcoding for routine, non-invasive biodiversity assessment in megadiverse and conservation-priority coastal ecosystems.

IntroductionPatterns of soil respiration (Rs) are heterogeneous on temporal and spatial scale. The most important controlling factors of soil respiration are microclimatic conditions such as soil temperature and moisture. However, a strong pronounced seasonality shifts Rs patterns from temperature to moisture-controlled regimes. Rarely investigated patterns are time-lagged effects prior to Rs measurements and influences of trees in mixed forests on large spatio-temporal scales. Material and MethodsWe investigated Rs over two years on a weekly to fortnightly measurement rhythm at an approximately 1 ha area in a mixed forest on 35 predefined locations using the common chamber technique. Analysis was derived using meteorological data and a tree species map. ResultsBy tendency, Rs decreased with increasing distance to the tree and we observed significantly higher Rs in broadleaf patches compared to coniferous and mixed patches during the summer season (+27 %, +18 %, respective). Our data confirmed soil temperature and moisture as important controlling variables. Yet, our results highlight an additional predictor explaining a higher proportion in variability: the vapour pressure in the atmosphere. In contrast to soil temperature and moisture this predictor was able to track a collapse in Rs due to drought and increases following rewetting. ConclusionWe conclude that meteorological conditions might be valuable indicators for CO2 emissions from forest soils. Tree species distribution explained partly the spatial patterns and hot spots of Rs yet additional analysis of local soil properties will enhance our understanding of the soil plant interactions and the resulting Rs.

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.

AimThe Global Biodiversity Information Facility (GBIF) is the most prominent source of species occurrence data for modeling climate niches, but exhibits strong unevenness in its data coverage across different geographic regions. The impact of this spatial bias on the reliability of GBIF-based plant climate niches in Europe remains unexplored. This study aims to address this gap, and to investigate whether the targeted integration of additional atlas data can reduce the potential impact of the spatial bias. LocationEurope. Time period1950s - 2024 Major taxa studiedEuropean grassland plant species. MethodsWe analyzed the climate niches of a large number of grassland species, with diverse distribution patterns across Europe, based on a) GBIF and b) on an enriched version of GBIF with national atlas data from Eastern European countries (GBIF+), where data coverage is currently low in GBIF. We followed best practices in niche characterization, particularly by performing environmental subsampling. The accuracy in climate niche properties was determined by comparing niches based on GBIF and GBIF+ data with niches based on a careful implementation of expert range maps as reference dataset. We focused on niche optimum position and niche similarity. Additionally, we investigated how biogeographical indicators can predict variability in climate niche accuracy. ResultsMost species exhibited reliable climate niche characterization using GBIF data, especially for widely distributed species. Yet, reliability decreased with continentality; that is, when species were primarily distributed in Eastern Europe. Integrating additional data did not significantly reduce this bias in niche characterization. Main conclusionsDespite the spatial bias in its records, GBIF can be used to reliably characterize the climate niches of many species in Europe if uneven sampling effort is accounted for. The laborious integration of additional data to address spatial bias does not yield the desired increase in niche reliability.

Most wildlife species currently inhabit areas transformed by human activity, a hallmark of the Anthropocene. Habitat alterations caused by the creation of roads and other human-made infrastructures shape the spatial distribution of wildlife species and their interaction with the environment. While some sensitive species disappear, more tolerant ones thrive near humans. Therefore, a streamlined tool to quantify the tolerance of different species to human pressures is useful to conservation, in particular to identify more vulnerable species. Here, we present ecoTolerance, an open-source R package that calculates two complementary, continuous metrics: the Road Tolerance Index (RTI), derived from the distance of each occurrence record to the nearest road, and the Human-Footprint Tolerance Index (HFTI), based on the global human-footprint raster. This package is based on a workflow that includes separate functions and arguments to automate data cleaning, spatial thinning, distance extraction, species-level summarization and map generation. As an applied example of its use and application, we processed 3782 records of five species: Copaifera langsdorffii (1407 observations), Bradypus variegatus (724), Sylvilagus brasiliensis (274), Boana faber (1226), and Boana boans (151), revealing RTI values that ranged from 0.183 to 0.654 and HFTI values from 0.111 to 0.392. the values of the two indices varied according to the incidence of road kill, as well as the habitat preference of the particular species. These examples demonstrate that ecoTolerance facilitates a rapid and streamlined assessment of species tolerance and vulnerability, providing valuable insights with potential to inform conservation actions.

O_LITree cores are widely used across a broad range of disciplines in the environmental sciences, most notably as a tool to measure tree growth, estimate tree age, characterise wood anatomy and reconstruct past climate. However, because extracting tree cores is an invasive procedure, concerns about their use are often raised due to perceived risks for tree health. C_LIO_LIHere we comprehensively test the long-term impacts of tree coring on 16 European tree species using a dataset spanning the entire European continent. Over the course of a decade, we tracked the growth and survival of 3334 trees cored in 2012 (including trees cored once and twice) and compared them to that of a cohort of 7413 neighbouring trees that were never cored. C_LIO_LIWe found no evidence that coring had a detrimental impact on either the growth or survival of trees, irrespective of their size, species, climatic environmental or the number of times they were cored. However, we did observe a small positive stem increment response (2.0% for trees cored once and 6.2% for trees cored twice), which we hypothesise is most likely the result of vertical scarring from the coring wound, with potential consequences for the accuracy of repeated diameter measurements collected at the same height. C_LIO_LIOur study supports the use of tree coring as a low-impact method for characterising the growth, age and function of a wide range of tree species. However, to avoid biasing long-term forest census measurements, tree cores should always be collected well above or below the point of measurement of tree stem diameters. C_LI

O_LIRising anthropogenic pressures in the 20th century have caused extensive habitat loss and fragmentation, threatening biodiversity across ecosystems worldwide. In the boreal forest of Fennoscandia, clear-cut forestry has been a major driver of these changes, resulting in a fragmented landscape of even-aged forest stands. Several studies in recent years have investigated the effect of habitat fragmentation and loss on fungal communities associated with deadwood, but these have mainly focused on visible sporocarps and mushrooms. The effects of forestry on the whole fungal community within deadwood have not been explored as extensively, and especially not in the context of long-term effects. C_LIO_LIWe investigated the effects of clear-cutting on deadwood-inhabiting fungi in boreal Picea abies forests in Norway using ITS2 metabarcoding of sawdust samples collected from 459 logs distributed across 24 paired near-natural and previously clear-cut plots harvested 50 to 90 years ago. C_LIO_LIWhile the overall fungal richness associated with deadwood was similar between the two management types, community composition differed markedly. Plot-scale fungal diversity was linked to deadwood heterogeneity, and composition of the common species was additionally affected by the living tree structure. Nearly all red-listed species were exclusively found in the near-natural forest plots. These findings demonstrate that clear-cut forestry shifts fungal community structure rather than reducing the total number of species and that rare and common species are structured by different environmental drivers but respond in similar ways to large scale disturbance. C_LIO_LISynthesis: Our results highlight the importance of maintaining structural complexity of deadwood in boreal forests for fungal conservation. We found a consistent relationship between deadwood volumes and heterogeneity, and community diversity and species richness. This relationship was consistent even for previously clear-cut forests, showing that retaining structural heterogeneity in managed ecosystems has a positive impact on species diversity. C_LI

Passive acoustic monitoring is a valuable tool for studying elusive marine mammals, but analyzing large datasets is typically labor-intensive and costly. In this study, we piloted an automatic approach for sound analysis on extensive datasets of acoustic underwater recordings from freshwater Lake Saimaa over a total of 12 months. Our focus was on "knocking" vocalizations, the most commonly found call type of the endangered Saimaa ringed seal (Pusa saimensis). The annotated datasets of knock sounds (n = 13,179) were used to train and test binary classification systems to detect this sound type. In addition, the fundamental frequencies of the vocalizations were automatically estimated by an ensemble of methods and corroborated by recent literature. The best classifier was a spectrogram-based convolutional neural network that achieved a minimum F1-score of 97.76% on unseen samples from each dataset, demonstrating its ability to detect knockings amongst noise and other events. Moreover, the estimated fundamental frequencies are comparable to the ones manually computed for the same datasets. These automated approaches can significantly reduce labor and costs associated with manual analysis, making long-term species monitoring more feasible and efficient.

Quantitative information on the seasonal dynamics of heterobranch sea slug assemblages remains limited in warm-temperate coastal regions, despite their ecological importance as benthic consumers and indicators of environmental change. Here, we conducted a standardized, multi-seasonal SCUBA-based survey of sea slug assemblages at two rocky reef sites (Tatsunokuchi and Nomozaki-Akase) along the northwestern coast of Kyushu, Japan, from February 2024 to November 2025. Across the study period, a total of 81 species comprising 892 individuals were recorded. Species richness and total abundance exhibited pronounced seasonal variation at both sites, with higher values in winter-spring and marked declines during summer. Assemblage composition shifted seasonally from relatively even communities in winter-spring to dominance by a few taxa in summer, a pattern reflected by concurrent changes in diversity indices. Water temperature displayed clear seasonal cycles and was negatively correlated with both species richness and total abundance, indicating a close association between thermal conditions and seasonal changes in sea slug assemblages. While causal mechanisms were not explicitly tested, these consistent patterns highlight the importance of temporal environmental variability in structuring heterobranch communities in this region. This study provides one of the few quantitative, multi-seasonal baselines of heterobranch sea slug assemblages in warm-temperate coastal Japan, offering a reference framework for future ecological monitoring and assessments of environmental change.