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 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.

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.

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.

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.

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

The use of marine space by human activities is globally increasing, resulting in a competition with spatial management measures for marine conservation. Within the European Union (EU) these measures are currently implemented by the union member states to achieve the UN sustainable development goal (SDG) of protecting at least 10 % of the national marine waters. Further, the EU Marine Strategy Framework Directive (MSFD) and the Nature Restoration Regulation (NRL) are the two main legal means for the implementation of ambitious spatial conservation targets for benthic habitat types, which can range from 10 - 90 %. This study analysis how the targets of the MSFD and NRL are currently met in the German waters of the North Sea and which areas the full implementation of both legislations might require. A spatial optimisation tool ("prioritizr" in R) was used to identify optimised solutions for the conservation of up to 75 % of NRL benthic habitats. The current spatial conservation measures (which ban demersal trawling within certain zones of designated marine protected areas, MPA) are not sufficient to reach the targets of the MSFD and NRL. Extending the exclusion of demersal trawling to the entire area of the MPAs would achieve a sufficient coverage for all habitats except for offshore sand and mud habitats. These could be further protected, when including areas for offshore wind farms, where trawling is also banned. However, to date it is unclear, if and how these (or other human use) areas could be included into spatial conservation regimes, a debate that needs to be resolved to allow for the achievement of the ambitious MSFD and NRL targets.

Large old trees are of eminent importance for organic carbon storage in forest ecosystems and thus play a role in mitigating climate change. Such trees also have an increased risk of internal stem decay and tree cavity formation, which promotes biodiversity, but complicates the prediction of their biomass and carbon stocks, which is usually done from stem diameter and tree height data applying allometric biomass functions. Since the extent of internal stem decay is known to vary widely between different forest ecosystems and data from moist temperate forests exhibited low significance of internal stem decay, we studied dry, frequently fire-exposed Pinus ponderosa forests in central Oregon to capture the other climatic extreme of temperate forests. We hypothesized high significance of internal stem decay for stand aboveground tree biomass, as we assumed widespread stem injury from fire. In addition, we tested the hypothesis that far more than the largest 1% of trees are necessary for 50% stand biomass, as this hypothesis is found in the literature, but has been challenged in other studies. We found low biomass loss due to internal stem decay by only ca. 1% suggesting that also for fire-prone temperate forests of western North America, biomass estimates based on allometric regression are reliable. The 1% largest trees-50% stand aboveground biomass hypothesis has to be rejection for our forests as long as only trees of a size are included that noteworthily contribute to stand biomass. This metrics strongly depends on regeneration density, which is not relevant for stand biomass.

The ongoing decline in biodiversity highlights the need for understanding the causes of population changes. This study uses 25-year, large-scale monitoring dataset to investigate the influence of climate and landscape structure on the annual population growth rates of 84 bird species across Poland. Our methodological framework involves the spatiotemporal decomposition of these environmental drivers to decouple demographic effects of long-term carrying capacities from the short-term effects of environmental perturbations. Using species-specific demographic models followed by a community-wide meta-analysis, we evaluated how individual species responses scale up to shape community-level dynamics. The results reveal significant variation in species-specific responses to individual drivers. At the community level, our findings suggest that bird populations are mainly regulated by the long-term spatial constraints rather than short-term disturbances. Persistent environmental heterogeneity had the strongest positive demographic effect on birds, followed by temperature, forest dominance over croplands, and precipitation. In contrast, rapid temporal shifts in environmental heterogeneity and precipitation anomalies negatively affected population growth, whereas urbanisation consistently exerted a negative effect across both spatiotemporal dimensions. Our results highlight the significance of protecting existing heterogeneous and ecotonal habitats, as well as the need to incorporate features that enhance habitat heterogeneity into urban development. Article impact statementPreserving heterogeneous habitats is essential for the conservation of bird populations.

The automated classification of animals from photos is important in ecology and conservation biology for organizing and understanding the immense diversity of species, as well as facilitating effective conservation and management practices. It is equally important for disease surveillance systems, allowing prompt detection of anomalies in species distributions and boosting citizen-scientist platforms by making user-reported data more accurate and convenient. Image classification uses photos and can also rely on the geographical locations of animals to improve performance. While image classification models have difficulties in classifying low-quality images, unbalanced datasets, and with a small number of images, species distribution models have difficulty in classifying species that coexist in a given region. We propose here strategies for combining image classification models based on deep neural networks with species distribution models using genetic algorithms. The proposal is applied to a real-world dataset comprising fifteen classes of animals from the Brazilian fauna obtained from Fiocruzs citizen-scientist Wildlife Health Information System (SISS-Geo). The SISS-Geo photos portray the reality of animals in their environments, with varying quality, and pose numerous difficulties for classification. Experimental results demonstrate that the proposed integration consistently outperforms standalone models. While individual SDMs achieve Top-1 accuracies of 27.79% (MaxEnt) and 31.76% (Bioclim), and CNN-based classifiers reach 58.17% with ResNet50 and 64.13% with ResNet-152, the hybrid strategies yield substantial improvements. The genetic algorithm-based integration with a single global weight achieves up to 67.96% Top-1 accuracy, whereas the class-specific integration using fifteen parameters attains the best overall performance, reaching 69.03%.

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

O_LICalluna vulgaris is often managed in the UK by rotational burning, but this practice has recently been banned on peat with depth greater than 30-40 cm. It is unclear how then to manage the large areas of Calluna on blanket bogs used for sport shooting because without managed burning, fuel loads and wildfire risk will increase as the Calluna ages within the artificially narrow age distributions created by burn management. C_LIO_LIWe developed a model of Calluna mortality and management to understand duration and persistence of post-management effects. This allows us to assess how long it will take to reach a more natural age structure which would allow increased diversity if management ceases. C_LIO_LIOur results show that management effects persist for around 50 years depending on site-specific mortality rates. Active management may therefore be needed either to mitigate the elevated risk of severe wildfire or to speed up this transition. C_LIO_LISome studies have employed, as unmanaged analogues, Calluna stands that were last managed <50 years ago, but such studies may have unintentionally biased their results by observing Calluna still in post-management recovery leading to an over-estimation of wildfire risk associated with more natural blanket bogs. C_LIO_LISynthesis and applications: with the banning of burning as a management tool for Calluna on deep peat, alternative management is now likely needed as our model shows it could take around 50 years for the Calluna to reach a more natural age distribution. Mowing can replicate some of the effects of managed burning but requires repeated intervention and may compress the peat surface from repeated machine tracking. Rewetting and Sphagnum reintroduction may offer a more sustainable management approach to lowering Calluna fuel loads and reducing severe wildfire risk by creating wetter sub-optimal conditions for Calluna growth and thereby altering the competitive balance between Sphagnum and Calluna. Further work is needed to assess the efficacy of rewetting in controlling fuel loads and how this varies with climate and local pressures. More broadly, this work highlights the need to quantify the persistence of past management regimes to understand ecological trajectories. C_LI

Passive Acoustic Monitoring (PAM) has emerged as a transformative tool for biodiversity assessment in recent years. Despite widespread acceptance and application for conservation-related outcomes, the synergistic effects of hardware limitations, signal propagation, and environmental conditions on how far a signal can be reliably detected remain critically understudied. We quantified changes in signal detectability using Autonomous Recording Units (ARUs) in a tropical agroecosystem using playback experiments of standardised pure-tone (1-8 kHz) in fallow rice paddy fields. We deployed a four-ARU array and broadcast signals over a 50- 300 m distance gradient, and modelled operative detectability of signals using a binomial Generalised Linear Mixed-effects Model (GLMM). Our findings show that the detection space of an ARU is highly frequency-dependent and environmentally modulated. Detection probability for low-frequency signals (1 kHz) decreased rapidly (50% threshold at [~]100 m), whereas mid-range frequencies (4-6 kHz) occupied an acoustic window that remained reliably detectable up to 250 m. Higher relative humidity significantly enhanced overall detection, while increasing temperatures disproportionately reduced low-frequency detectability. The orientation of the ARU to the signal source was important as the detection probability declined from 81% for recorders facing the source (0{degrees}) to 14% for rear-facing units (180{degrees}). Our findings underscore the importance of determining the detection space before undertaking PAM. We propose a Decision Support Framework that provides a pathway for researchers to integrate focal taxa traits with technical constraints to determine detection space and optimise study designs when using PAM for monitoring biodiversity and assessing conservation action.

Statewide tracking of urban tree canopy change is essential for evaluating progress toward policy targets, but detecting real change requires both high-resolution mapping and rigorous uncertainty estimation. We produced a four-year canopy cover time series for all California census-designated places using 60-cm NAIP aerial imagery and a U-Net deep learning model trained with semi-automated LiDAR-derived labels and manually annotated tiles. Canopy cover and change were estimated using stratified, error-adjusted area estimation, enabling comparisons across years. Statewide canopy cover showed a modest negative trend from 2016 to 2022 (Sens slope: -0.60% per year), but confidence intervals included zero across all groups and climate zones, indicating that trends were not statistically distinguishable from no change. Urban canopy cover was consistently lower than non-urban canopy by approximately six percentage points, and canopy cover was highest in the Northern California Coast and lowest in the Southwest Desert. Residential parcels accounted for 55-56% of canopy within incorporated urban areas across all years, indicating that statewide canopy increase goals will require engagement with private landowners. Error adjustment substantially altered canopy estimates relative to raw pixel-count totals, with direct implications for AB 2251 canopy tracking where baselines and targets drawn from unadjusted maps may not reflect true canopy extent. This open-source workflow is transferable to future NAIP acquisition years and other U.S. states, providing a scalable framework for long-term urban forest monitoring.

Plants, as structural elements of habitats, contribute greatly to the maintenance of local biodiversity through their biological interactions. In this study we explore whether their rarity, according to Rabinowitzs (1981) three criteria, is related to the richness and diversity of arthropods and other plants they are associated to, in a gypsum-rich steppe. We first analysed whether the geographic abundance and ecological specialisation of 32 characteristic and dominant plant species are related to the diversity (richness and phylogenetic diversity (MPD)) and degree of local specialisation of arthropods associated with them (1,694 taxa). Then, we focused on a non endemic and non specialized plant in the study area (Krascheninnikovia ceratoides) to explore the effect of population size on two types of interactions: aerial arthropods and plant facilitation. Results indicate that: 1) plant species abundance (geographical range) is not related to the richness or MPD of communities of associated arthropods, 2) plant species ecological specialization (edaphic endemisms or gypsophiles) do not contribute differentially to the maintenance of singular arthropod communities, and 3) the community of aerial arthropods and plants interacting with K. ceratoides in a small population are not necessarily less diverse than those in patches of similar size in a large population. Results also revealed that the two plant species with fewer interactions (one rare, one widespread) do show the highest singularity in their interactions with arthropods. Our study illustrates the important contribution of rare plants to the conservation of local biodiversity.

The release and management of pheasants (Phasianus colchicus) in the UK for recreational shooting exerts a range of effects on the ecosystem into which they are released. We studied possible effect of nutrient deposition on epiphytic tree flora at 20 pheasant release sites distributed through England (18) and Wales (2) during winter and spring 2023/24. Sites were all Ancient Semi-natural Woodlands (ASNWs) and had substantial (600-8000 pheasants) in a single release pen. We measured N-sensitive and N-tolerant indicator bryophyte and lichen species on tree trunks near to the pen and then in plots along a transect 100m, 250m, 500m and 1km+ away from the pen. To achieve a gradient of pheasant use, the transects were located in the opposite direction to the game managed / shooting area. We recorded 1.9 times more coverage of N-tolerant lichens and bryophytes combined on selected tree species at the pen-edge compared to the control plots. The relationship showed a decline from the pen edge to 250m away but then stabilised. We also detected higher levels of coverage of N-sensitive tree flora at 100m and 250 m compared to the penedge plot. These measures were also higher at these mid distances compared to the 500m and 1000m plots. We suggest far plots were nearer wood edges and were affected by ambient inputs of aerial N from farmland and other external sources. The overall interpretation is that concentrations of pheasants in and around release pens for several months from late summer until early winter in ASNWs does affect the balance of N-sensitive and tolerant tree flora up to potentially 250m and this is a consideration when locating release pens in and near to sensitive woods.

Chelating ion exchange resins are widely used to eliminate metal interferences in the analysis of ammonium (NH4+) in soil extraction solutions. However, their potential to co-adsorb NH4+ remains underexplored. Here, synthetic metal ion solutions containing 6-30 mg L-1 NH4+ and the metal cations Ca2+, Mg2+, Cu2+, Mn2+, and Zn2+ were treated with Amberlite IRC-748 resin. The resin efficiently removed Ca2+ (-42.2%), Mg2+ (-21.1%), Cu2+ (-99.9%), Mn2+ (-56.9%), and Zn2+ (-93.6%). However, NH4+ losses of 2.2-5.6% were observed, indicating concentration-dependent co-adsorption. While these losses may be acceptable for concentration measurements via routine assays such as photometric analysis, they may still affect the accuracy of high-precision N analyses that rely on quantitative NH4+ recovery. This highlights a methodological caveat for resin-treated samples, especially in low-NH4+ environments. We therefore recommend including recovery assessments and correction factors when using chelating resins to improve accuracy in NH4+ quantification.

O_LIEnvironmental DNA (eDNA) metabarcoding is an emerging tool for biodiversity assessment in freshwater systems, offering high-resolution insights into community composition. Here, we apply eDNA metabarcoding to evaluate the ecological impacts of Eurasian beaver (Castor fiber) activity within a seminatural enclosure in the Scottish Highlands. C_LIO_LIWe collected seasonal water samples from nine sites, six influenced by beaver dams and three control sites with no evidence of beaver engineering, across a 40-hectare enclosure. Samples were analysed for vertebrate and macroinvertebrate diversity using established 12S and COI markers. C_LIO_LIVertebrate alpha diversity did not differ significantly between beaver and control sites, likely reflecting the small spatial scale and low species richness of upland Scottish streams. However, community composition differed significantly between treatments, especially for fish (PERMANOVA, R2 = 0.55, P < 0.001), with beaver-influenced sites dominated by three-spined stickleback and control sites by brown trout. Macroinvertebrate communities showed a 78% increase in gamma diversity in beaver-modified habitats relative to controls. Species composition varied strongly with beaver presence (PERMANOVA, R2 = 0.29, P < 0.001), likely due to the creation of lentic-lotic mosaics and associated microhabitat diversity. Seasonal variation was significant in both taxonomic groups, with the lowest species richness and highest community dispersion observed in summer, probably reflecting hydrological and temperature-driven dynamics in eDNA production and transport. C_LIO_LIOur findings reinforce previous evidence that beaver dam-building activity enhances beta diversity in headwater systems. Additionally, we demonstrate that eDNA metabarcoding is a sensitive method for detecting spatial patterns in freshwater biodiversity associated with these activities at scales ranging from tens to hundreds of meters. These approaches could inform future monitoring strategies aligned with landscape-scale beaver management and reintroductions. C_LI

QuestionsEcological monitoring, repeated collection of ecological data, is essential to document how ecosystems respond to change. In grasslands, different vegetation monitoring protocols are used across disciplines, making it difficult to address multiple management objectives or research questions. We asked four questions about how three common vegetation monitoring protocols compare. (1) How do the protocols differ in how they collect data? (2) How do the protocols differ in their utility? (3) In what ways do vegetation measurements quantitatively differ across protocols? (4) What are each protocols strengths? LocationThis study was conducted on working ranches in the Southern Great Plains with vegetation consisting mainly of native forbs and grasses. MethodsWe implemented three protocols at each site: (1) the Rangeland Analysis Platform (RAP), (2) the Grassland Effectiveness Monitoring (GEM) protocol, and (3) a typical pollinator ecology survey protocol. We qualitatively compared each protocols utility and quantitatively compared cover measurements that each produced. ResultsAll three protocols displayed positive associations within cover categories, but differed in actual cover measurements. The RAP protocol, which uses remote sensing, measured the highest total vegetation cover. The GEM protocol, a line-point intercept method, had more capability to capture fine-scale cover patterns. The GEM protocol measured the most bare ground while the Pollinator protocol measured more forb coverage. ConclusionFine-scale methods like the GEM protocol are most appropriate to address objectives that require capturing small patterns that would otherwise be overlooked with methods like quadrats or remote sensing. Remote sensing is advantageous when monitoring large areas or inaccessible land, but may over-estimate cover. The Pollinator protocol is best equipped to address questions regarding flower abundance and richness. Similarities among protocols can facilitate synergy across disciplines for more effective monitoring. We emphasize the importance of denoting a clear scale and scope of monitoring objectives before selecting methods.

The small mammals in the tropical savannas of northern Australia, have undergone a degree of change in recent decades, best documented in the Northern Territory. Data is limited from northern Queensland and though the same trends are assumed, the topographic and climatic features differ substantially. In this study we examined data systematically collected from 725 sites between 1998-2012 in three bioregions representing a climatic gradient: from semi-arid to monsoon tropical savannas. We investigated via information-theoretic models and model averaging, the relationship between five mammal groupings and three landscape variables (fractional cover green, elevation and vegetation diversity) to elucidate any consistent or different patterns in the mammal fauna. Key patterns included relationships with increasing elevation (critical weight range species richness positively associated with elevation, rodent species richness negatively associated), increasing rodent and dasyurid species richness with vegetation diversity, and lower macropod and dasyurids abundance with increasing fractional cover green. These relationships underscore a need to consider mammal conservation in Queensland with more nuance than in the more topographically inert Northern Territory. Management strategies need to be more attuned to taxonomic and regional differences, to prevent perverse outcomes.