Ecological Informatics

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%.

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.

Explainable machine learning (ML) methods are gaining increasing attention in environmental and ecological research for their ability to reveal relationships between environmental drivers and population dynamics. However, there remain questions on the reliability of these tools, especially given recent research shows that these explanations can be highly sensitive to model architecture. In ecology, it is typical to use a single ML model, and a comparative evaluation of sensitivity of explainability for different ML approaches is overlooked. In this paper, we develop a novel framework that quantifies explanation consistency between multiple ML model architectures. This framework provides a discrepancy measure for each model prediction, with high discrepancy indicating substantive explanation disagreement across models and low discrepancy indicating strong consensus in explanations across models. We then demonstrate that low explanation discrepancy aligns well with ground truth mechanism. Furthermore, high explanation discrepancy provide a mechanism to identify areas for model refinement and further investigation by domain experts. We do this by using a simulation study based on synthetic coral cover data that incorporate spatio-temporal variability driven by known disturbance effects. Our method provides a quantitative approach to assess the sensitivity of explainable ML in the absence of ground truth. As a result, this enhances the utility of ML approaches in conservation and ecological management. While we focus primarily on ecological modelling for coral reefs, our methods are generally applicable to other ecological and environmental modelling settings.

This paper presents a six-stage methodological framework for Convolutional Neural Net-work (CNN)-based cetacean vocalization detection and classification in Passive Acoustic Monitoring (PAM), implemented as the open-source toolkit ai-pam-pipeline. The frame-work is generalizable across species and fully parameterised through a single configuration file, guaranteeing exact experimental reproducibility. Two experiments are reported. Experiment A examines the effect of FFT window length Nfft [isin] {256, 512, 1024} on binary Bottlenose dolphin (Tursiops truncatus) whistle detection using stratified 10-fold cross-validation on an in-domain dataset (Oltremare, 192 kHz) and a cross-domain benchmark (DCLDE 2022). In-domain performance is uniformly high (macro F1{approx} 0.98; Wilcoxon, all p > 0.05). Cross-domain results diverge substantially: Nfft = 256 is significantly superior (p = 0.006, rank-biserial r = 0.89). The mechanism is an upsampling amplification effect: coarser spectral bins produce wider, higher-contrast FM traces after bilinear resampling to fixed image dimensions. This superiority is threshold-invariant: precision equals 1.000 across all configurations and thresholds{theta} [isin] [0.1, 0.9], confirming that the advantage is not an artifact of threshold choice. These findings demonstrate that preprocessing choices -- often treated as secondary implementation details -- can significantly affect cross-domain generalisation. While Nfft serves here as a controlled case study, the framework is designed to enable systematic, reproducible evaluation of arbitrary preprocessing parameters within a unified experimental protocol. Experiment B demonstrates multiclass capability on five T. truncatus vocalization cate-gories (macro F1 = 0.843); inter-class confusion between click trains and burst-pulse sounds reflects biological signal overlap rather than classifier failure.

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.

Common terns (Sterna hirundo) are regionally threatened migratory seabirds that form large breeding colonies during the North American summer months. They are highly vocal and serve as important bioindicators of aquatic ecosystems. Historically, acoustic studies on colonial seabirds have proven difficult due to the dense aggregations of individuals and high rate of call overlap. However, as passive acoustic monitoring (PAM) becomes increasingly common for studying seabird colonies, quantitative descriptions of species vocalizations are needed to accurately interpret behavioral information from colony soundscapes and support automated analysis of large acoustic datasets. This study aims to quantify the vocal repertoire of adult common terns. We deployed AudioMoths to collect acoustic data at a tern colony on Seavey Island, New Hampshire, USA from across the breeding season. Using RavenPro, unique call types were identified through visual and aural inspection of the acoustic data in the spectrogram. For each call, we then extracted measurements of peak frequency (Hz), bandwidth 90% (Hz), syllable duration 90% (s), and total bout duration (s) to quantify the characteristics of each call type. Statistical analyses for acoustic parameters by call type were performed using Kruskal-Wallis tests, followed by post-hoc Dunn tests. Our results demonstrate that each call type is significantly different from another by at least one parameter, with the exception of the kek and kip/tjuk calls. These findings present the first quantitative analysis of common tern vocalizations for North America. By defining temporal and spectral characteristics for multiple call types, this work helps translate colony soundscape into biologically meaningful information about tern behavior and colony dynamics. These descriptions also provide key parameters for developing automated tools to detect and classify vocalizations in dense, noisy colonies. Integrating quantified vocal characteristics with PAM offers a promising approach for monitoring colony activity and behavior while minimizing disturbance relative to traditional methods.

BioBlitzes are widely used citizen science events that combine biodiversity monitoring, public participation, and environmental awareness through short and intensive observation campaigns. However, applying this model to marine environments presents additional challenges related to safety, access, weather dependency, specialised equipment, species identification, and sustained participation. This paper presents the BioMARathon model as a case study of how BioBlitz-inspired events can be adapted to marine citizen science contexts. The BioMARathon extends the conventional BioBlitz format into a longer, seasonal, and distributed engagement model designed specifically for marine and coastal environments. The paper describes the conceptual foundations of the model in the Janus Engagement Framework, which informed both the design of the BioMARathon and the adaptation of the MINKA citizen science observatory to better support participation, validation, feedback, and continuity over time. BioMARato Catalunya, launched in 2021, is presented as the founding implementation of the model and as the basis for later replication in Portugal. Between 2021 and 2025, BioMARato Catalunya showed continued growth in participation, observations, and taxonomic coverage, while also contributing to the detection of non-indigenous species, first regional records, and climate-related ecological impacts. Beyond biodiversity outcomes, the case suggests that extending participation across a season, distributing activities through local mobilising organisations, and combining expert validation with visible feedback mechanisms can support recurrent participation, retention, and community reactivation in marine citizen science. Rather than offering a formal causal evaluation, this article contributes practical lessons for the design of citizen science initiatives in challenging environments.

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.

Widespread arthropod declines pose risks to ecosystem functioning and agriculture. Assessing this decline or potential remediation implies the need for standardized and scalable population monitoring. Image-based methods, including camera traps and citizen science programs, are increasingly used, but the volume of data collected requires automated analysis. Robust arthropod detection is essential for individual counting or fine-grained classification, yet current datasets and algorithms do not address the vast morphological diversity across arthropod species and often overlook the variety of photographic contexts, such as differences in background, lighting, and image composition, in which arthropods are captured. To address this gap, we developed an arthropod detection dataset, covering all terrestrial families present in France with available validated images on the iNaturalist platform (749 families). To achieve this, we employed an iterative workflow in which a YOLOv11 model pre-annotated images -- using one representative species per family-- followed by manual correction and model retraining. Repeating this process progressively reduced annotation effort and improved model accuracy. The final outcome consists of a publicly available curated detection dataset and a robust arthropod detector for natural background scenes. The detector achieves an F1-score of 0.91, demonstrating strong performance despite substantial interspecific morphological variation and heterogeneity in photographic contexts. We further demonstrated the taxonomical universality of the model showing high F1-score and IoU averaged at the class (0.79, 0.85) and order level (0.82, 0.86) and also a good detection generalizability (F1-score>0.90, IoU>0.83) on species, genera and families never encountered by the model during training. Finally, we show how this model can be improved to generalize to new datasets using data augmentation, complementary training data or fine-tuning and increase detection of small objects. In particular, we report performance of the improved models on three use cases largely used in non lethal insect monitoring: (i) diurnal pollinator monitoring through citizen science or (ii) flower and nocturnal insects monitoring through smartphone time-lapse of a UV-illuminated white panel. These results mark an important step toward automated analysis of arthropod images in natural contexts, from both large-scale automated monitoring approaches or from citizen science monitoring programs.

Intraspecific morphological variability presents a complex challenge for biological systematics and biomonitoring, particularly for organisms with high phenotypic plasticity, such as zooplankton. Morphological differences between individuals of the water flea species Bosmina longirostris (Crustacea: Cladocera) are difficult to distinguish visually, parthenogenetic females look morphologically uniform within the species; nevertheless, they demonstrate differences attributable to their geographic origin and developmental stage. A reference dataset of microscopic images was created for the study, including populations from two geographically separated regions (seven ones from European Russia and seven ones from Sakhalin Island in the Pacific Ocean (Far East of Russia) and two age groups, demonstrating the ability of a neural network classify to successfully the intraspecific morphological variation. This study demonstrates that deep learning methods are prospective for the detection and understanding of fine morphological intraspecific differences in the cladocerans.

Philippine freshwater ecosystems are considered one of the most diverse ecosystems harboring numerous fish species. However, in the Philippines, these ecosystems are threatened by invasive species that potentially disrupt ecological balance. In this study, we focused on the vermiculated sailfin catfish Pterygoplichthys disjunctivus, an invasive aquarium species reported in several Philippine aquatic ecosystems. Despite its documented spread, its potential range under a rapidly changing climate remains poorly understood for the country. Hence, in this study, we utilized the MaxEnt model to predict its near-current and future habitat suitability in the Philippines. Using 11 reported occurrences, our model showed high predictive accuracy (AUC = 0.882{+/-} .034, TSS = 0.7394 {+/-} 0.154, SEDI = 0.971 {+/-} 0.019). Across the current and future scenarios, slope was the primary contributor (78.7% - 81.3%), followed by BIO 10 or the mean temperature of the warmest quarter(18% - 27.8%), and flow accumulation (0% - 5.2%). However, for the SSP126 scenario, BIO10 is projected to triple by 2050 (18 - 48%). Current projections identify high-risk regions, particularly central Luzon (Laguna de Bay and Lake Taal), the Cagayan River Valley, and portions of eastern Mindanao (Agusan Marsh and Lake Mainit). Sankey transition analysis confirms a high habitat stability rate (>73%) for high-suitability pixels in both SSPs, indicating persistent invasion risk. Overall, our study provides a framework for invasive species management and contributes to the conservation of Philippine aquatic ecosystems.

Seafood nutrients from global fisheries are of increasing importance for research and policy in food security and nutrition. As the chemical composition of fish is determined by what they eat, their energetic demands, and the environment in which they live, nutrient content reflects aspects of physiology and life history, ecological and environmental traits, as well as evolutionary history. Here we present data from Bayesian model estimates of 12 key nutrients (calcium, iron, phosphorus, magnesium, selenium, zinc, vitamin A, vitamin B9, vitamin B12, vitamin D, omega-3 fatty acids, and protein) in wild fish, using a database of reported nutrient content for freshwater and marine species. We then predict the nutrient content of 5588 fish species with traits available from FishBase. We compare our previous model using traits alone with a new model of both traits and phylogeny, and present the data, code, and predictions for models coded in PyMC. These models and predictions, made freely available through FishBase, can be used to explore the historical, current, and future nutrient content of fisheries catch.

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.

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 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.

Designing effective spatial management for chondrichthyans (sharks, skates, rays and chimaeras) requires incorporating critical areas, sites essential for population maintenance, such as reproductive and feeding areas. Yet most area-based measures have been developed without consideration of chondrichthyan habitat use. The Important Shark and Ray Area (ISRA) initiative has been pivotal in designating priority areas through a rigorous, consultative process. To complement this, our study offers researchers a testable definition for generating robust evidence to strengthen future critical area delineations and related management decisions. We define critical areas using three criteria: 1) relative frequency of use, (2) extended within-year occupancy and (3) repeated use across years. This framework enables objective comparison among candidate sites and is generalisable across different critical area types. The definition builds upon established early-life-stage habitat concepts and applies these to broader life-history functions. The utility of this framework is then demonstrated through a systematic review of contemporary peer-reviewed literature of critical chondrichthyan areas in the European Atlantic. The review highlighted 62 critical areas with Strong evidence and 41 areas of Moderate strength evidence, which informed the European Atlantic ISRA selection process. Research effort was concentrated in inshore areas, particularly around the British Isles and Portugal, with biases towards large, threatened and commercially valuable species, whilst chimaeras were notably underrepresented. Early-life stage areas were most frequently identified, whereas resting areas were rarely documented. Evidence patterns and biases are examined in the context of evolving critical area concepts to advance their development and improve the quality and breadth of future research. By outlining a testable definition, identifying key knowledge gaps, and proposing research and reporting guidelines, this work enhances the consistency, comparability, and spatial coverage of future chondrichthyan habitat research to support its application to conservation planning.

Reef manta rays (Mobula alfredi) are threatened by fishing and other anthropogenic threats. Which, when coupled with conservative life history traits, have made this species vulnerable to extinction. Spatiotemporal ecological knowledge, such as site fidelity and visitation patterns to key aggregation sites, are imperative for effective conservation management of M. alfredi. A novel method of environmental sensing, remote underwater photo systems (RUPs), was employed to understand drivers of M. alfredi habitat use and resighting patterns. RUPs were deployed at four cleaning sites around Laamu Atoll, Maldives. Between March 2021 and May 2023, 455,458 photos were analysed. Generalised linear models revealed increases in M. alfredi presence in response to high chlorophyll-a concentrations, low illumination moon states, the Southwest Monsoon, and in the morning, while human presence had no effect. Branchial spot patterns allowed for 81 M. alfredi individuals to be identified, from 629 sightings, representing 51.59% of Laamu Atolls previously identified population (n = 157). Cleaning stations are visited more intensively during periods of increased productivity of the Southwest Monsoon, likely in response to greater foraging opportunities near the study areas. Additionally, moon state, used as a proxy for tidal strength, was associated with increased visitation during new moon periods, suggesting that weaker tidal states may facilitate presence. These data support integrating RUPs with observational surveys to improve inferences about habitat use and our understanding of cleaning sites frequented by M. alfredi. This study aims to inform the implementation of Laamu Atolls first marine protected area management plan.

Engagement is widely recognised as central to learning and academic achievement. Electrodermal activity (EDA) has emerged as an objective physiological indicator of engagement, as it measures sympathetic nervous system activation. However, the high cost of wearable EDA sensors has limited its widespread application. This study answers the call for affordable, high-temporal-resolution engagement measures by validating a video-based quantitative assessment method. Researchers collected 75 minutes of synchronised EDA and video data from 12 upper secondary students (aged 17-18) during regular instruction. Novel software was developed to analyse student movement and sound level for academically relevant content. The OpenPose AI model for pose estimation was also applied. This approach produced six distinct movement variables: two AI-based and four non-AI-based. Six linear models using varying movement variables and sound level were tested to predict tonic EDA levels. All models effectively predicted EDA levels, with non-AI-based movement metrics outperforming AI-based alternatives. The four non-AI-based movement models showed similar performance, indicating that compressed versions reduced computational time without sacrificing predictive power. These findings validate a novel, objective method for comparing engagement across learning activities on short timescales. This method is particularly useful for collaborative learning environments and enables controlling for movement and sound in quantitative classroom analyses.

There is an urgent need to gather data on harvest rates of waterbirds in Europe to assess the sustainability of hunting. Estimates of total waterbird harvest in the United Kingdom (UK) and the relative harvest of different huntable species come from two separate surveys, the Value of Shooting (PACEC 2014) and National Gamebag Census (NGC, Aebischer 2019), and these have been recently used to explore the likelihood of unsustainable harvests of wild waterbirds by UK hunters (Ellis and Cameron 2022; Madden et al., 2025). The reliability of these sustainability estimates depends on how representative the original surveys are of hunter behaviour and success. There are also 1-3 million released game-farm mallard (Anas platyrhynchos) that takes up considerable and unquantified proportions of the UK waterbird harvest. Here we explore uncertainties in the UK winter harvest of wild waterfowl by comparing estimates from the NGC dataset with those from the Crown Estate coastal hunting clubs, and a novel approach using analysis of social-media images (2019/20 to 2023/24). We explore the difference in species-specific harvest with and without the uncertainties in the number of released mallard and the total number of duck harvested in the UK. Waterbird harvest estimates differ markedly depending on the input dataset and whether released mallard are included in the analysis. Confidence intervals of each estimate are inflated by uncertainties in the number of released game-farm mallard contributing to, and the size of that national bag. Estimates extrapolated from social media suggest the national harvest of several species may be considerably larger than the corresponding NGC estimates (e.g. Teal *2.07 and gadwall *11.2), while mallard harvests away from formal shoots represented by NGC are significantly lower (*0.71). Excluding released mallard reduces the statistical estimate of total wild duck harvest by 56-63%, which would have biologically significant effects if realised.