Ecological Informatics

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.

Radar aeroecology is dedicated to making ecological inference about aerial wildlife from radar-derived information. While producing unique, large-scale datasets describing biological activity in the sky, radar methodologies are largely incapable of relating these to specific species and are thus taxonomically limited. I describe a computational method to increase taxonomic resolution in vertical looking radar data by dividing detected organisms into morphology and movement-based aerial morphotypes. Using the Birdscan MR1 radar target classifier, wing flapping frequency calculation and target size estimation, I demonstrate a nearly 8 fold increase in classification resolution of bird radar data from the Hula Valley Research station, Israel. Furthermore, by relating each species in the regions species pool to its relevant morphotype, I show that most of these newly separated classes are related to small numbers of species (1-10), providing realistic opurtunities to bridge the taxonomy gap in radar data. By using the morphotype approach, radar aeroecologists can start observing and discussing the concept of "Aerodiversity", analogues to widely used biodiversity, a fundamental measure in ecology and conservation sciences. By analitically adressing taxonomy in radar-aeroecology, practitioners will increase the impact and dissemintation of their work and contribute to a better, more complete understanding of the aerial habitat.

The development of Artificial Intelligence (AI) presents novel opportunities for tackling complex marine resource management challenges. Among AI models, neural networks are a powerful class of tools capable of learning nonlocal and lagged patterns from fisheries data as well as approximating nonlinear relationships among multiple latent variables using estimation methods that automatically implement statistical shrinkage. This gives them potential to effectively handle data obtained from fisheries populations subject to dynamic environments. We highlight two flexible subclasses and one application of neural networks: Long Short-Term Memory (LSTM) and Convolutional Neural networks (CNNs) and policy discovery via Reinforcement Learning. LSTMs are designed to handle sequential data by allowing prediction from past values at both short and long time-lags. CNNs are not explicitly designed to handle temporal information, but can interpolate a spatial latent variable based on its value within a geographic neighborhood, and can be combined with LSTM models for this purpose. This "Food for Thought" paper introduces and applies these neural network approaches, both alone and in combination, to demonstrate their potential application for several foundational topics in fisheries science: 1) the forecasting of population weight-at-age, 2) the standardization of spatio-temporal indices of relative abundance, and 3) the discovery of harvest policies to optimize catches and maintain spawning biomass. Each section provides a simple, simulated example and describes the tradeoffs - particularly the lack of inferential capability - presented by using neural networks over traditional approaches for each topic. We then outline medium-term research questions that may clarify, facilitate or qualify the applicability of these tools to fisheries management science. Finally, we discuss how future combinations of these approaches could result in simplified ways to estimate and forecast stock biomass and provide harvest advice.

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.

Monitoring fish communities is essential for understanding biodiversity dynamics and coral reef ecosystem health. Underwater imaging provides a non-invasive and repeatable approach for such monitoring, yet analysis of large volumes of video data remains extremely time-consuming for experts. Resolving such a bottleneck is today within reach, yet towards automated fish identification, large and high-quality, labelled image datasets are critical for training and testing reliable deep learning models. However, to date, no such dataset exists for the Western Indian Ocean (WIO), a global biodiversity hotspot hosting more than 300 common non-cryptobenthic fish species and facing increasing anthropogenic pressures. This paper presents a novel and publicly available dataset of 114,664 images annotated from 186 videos recorded using fixed underwater cameras on shallow reef habitats from Mayotte archipelago. All images were labelled and validated by trained marine biologists following a standardized protocol. Each image includes detailed metadata describing recording conditions. The dataset comprises 124 reef fish species (including 110 with >200 images) and 8 background classes. This dataset will allow training and testing automated fish classification models.

O_LIBats are a diverse taxonomic group that display a wide range of interesting behaviours. Many bats are keystone species for their ecosystem, are IUCN Red-listed as vulnerable to critically endangered, and subject to human-wildlife conflicts arising from anthropogenic expansion. Yet bats remain understudied both with respect to behaviour, population ecology and conservation status. One of the major challenges when studying bats is obtaining data. Their nocturnal lifestyle and use of ultrasonic echolocation makes them difficult to track and record using traditional methods. Recent advances in passive acoustic monitoring have allowed researchers to record large amounts of data, but the detection and classification of vocalisations remain a challenge. Most available tools are either for profit or are limited to a narrow geographic range, and mostly focus on echolocation search phase calls. C_LIO_LIHere we present BatSpot, a convolutional neural network trained to detect search phase calls, buzzes and social calls. It also offers the option to classify the search phase calls to species(-complex) level. We provide a GUI that allows researchers to retrain or transfer-train the models for their specific needs and validate the performance. C_LIO_LIWe test the performance of all models and show that they perform better than both commercial and open-source solutions (search phase file level F1: 0.97 vs 0.96, buzz detector F1: 0.95 vs 0.11). We furthermore show that retraining the search phase call detector for a new country with examples from just 59 recordings massively improves the performance (F1: 0.48 to 0.79). C_LIO_LIBatSpot will enable bat researchers globally to automate detection and classification with minimal effort and includes novel options for social call and buzz detection, typically not featured in other automated tools for bat monitoring. C_LI

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.

Invasive alien species (IAS) expansions are increasingly impacting the biodiversity and economy of Europe. To more effectively allocate the limited resources available for their management, it is pertinent to accelerate detection of IAS spread and distribution. One largely untapped secondary data source showing much potential lies in the automated tracking of internet activity such as IAS search intensity or mentions across different internet platforms. In this study, we tested if internet activity increases systematically when IAS expand into new EU countries utilizing the combined data of 88 invasive species from various internet platforms. In total, 14 internet platforms were screened and evaluated based on their database accessibility, mined data quality and utility for systematic IAS expansion tracking. We found that the procedure to obtain researcher access to minimal data required for IAS tracking (i.e., information about location, time and place) varies widely across platforms, and is particularly difficult without incurring significant costs for many of the larger ones (X, Google and Tiktok). From the explored species, more charismatic species (i.e., mammals) overall gained more online traction than more cryptic ones (i.e., plants), though online activity of the first proved a worse representation of real-world occurrence patterns. Moreover, while the final five selected internet platforms showed increased activity surrounding the year of invasion in many of the explored invasion scenarios (particularly Wikipedia and Facebook), inconsistencies between species groups, trends per platform and the large variability in data quality currently still hampers systematic integration of such data into existing databases. We conclude that combining IAS activity data from various internet platforms shows potential to accelerate IAS expansion detection across the EU (especially for fish, crustaceans, reptiles, birds and plants). However, incorporation in automated early warning systems is currently hampered by variable data quality, limited researcher access to online data and the few open, accurate and generalizable species classification algorithms with API access.

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.

Minimizing handling of threatened and endangered fish has become increasingly important as populations have dwindled. To minimize handling in morphometric measurements, the HandsFreeFishing program has been developed for juvenile Chinook Salmon (Oncorhynchus tshawytscha). By segmenting a 2D image many morphometric measurements are able to be estimated; from these measurements a weight prediction model is built based on fish whose ground truthed weights were measured using a digital scale. While many segmentation methods may be used, here Metas Segment Anything model (SAM) is employed to produce segmentation masks of raw images. This model is open-source and easily used on any image (of any size) with good performance. In the proposed framework, the user supplies a bounding box around a target fish along with minimal orientation data (left or right facing, upside down or right-side up); the rest of the segmentation, feature extraction, and final weight prediction is completely automated. A main goal of the segmentation is to estimate the surface area of the side profile of the fish. Then, assuming an ellipsoidal shape, this surface area can be related to the volume of the fish, which is directly proportional to the weight. Even on a relatively small dataset of 149 images (fork length 27-90mm) our results confirm the predictive qualities of the morphometric features measured. The model achieved weight prediction with a mean absolute error of 0.16 g with a mean absolute percentage error of 12%, and an r-squared value of 0.99, on fish ranging from 0.31g - 7.74g. The raw images come from a variety of fish viewers, the design of which is relatively inexpensive and reproducible, and, in conjunction with the HandsFreeFishing program, allows for minimal handling compared to traditional length and weight measurement methods.

Accurate monitoring of populations is essential for conservation management, including for vulnerable seabirds. Yet traditional ground-based surveys are logistically challenging and time-consuming, especially in remote environments such as the sub-Antarctic islands. Advances in aerial imagery and artificial intelligence (AI) offer opportunities to improve the efficiency and repeatability of population surveys. In this study, we evaluate an AI-based approach for counting Salvins albatross from high-resolution aerial imagery collected using a piloted fixed-wing aircraft at the Bounty Islands, New Zealand. Imagery acquired during a single-day survey was processed to create orthomosaic images, which were previously analysed using manual counts by an experienced observer. We applied an automated detection and counting model based on a Faster R-CNN architecture with Slicing-Aided Hyper-Inference, and compared AI-derived counts with original human counts in terms of accuracy, consistency, and processing time. The AI achieved an initial F1 score of 92.8% for albatross detection and produced counts within 3% of the manual results, while reducing processing time from approximately 66 hours to just over four minutes. The model was also capable of simultaneously detecting additional species present within the mixed breeding colony, including erect-crested penguins, fulmar prions, and New Zealand fur seals, adding scalable efficiency gains for future surveys. Our results demonstrate that combining piloted aircraft surveys with AI-based image analysis provides a rapid, scalable, and accurate method for monitoring seabird populations, with substantial benefits for conservation management in remote and logistically constrained regions.

Computational bioacoustics has seen significant advances in recent decades. However, the rate of insights from automated analysis of bioacoustic audio lags behind our rate of collecting the data - due to key capacity constraints in data annotation and bioacoustic algorithm development. Gaps in analysis methodology persist: not because they are intractable, but because of resource limitations in the bioacoustics community. To bridge these gaps, we advocate the open science method of data challenges, structured as public contests. We conducted a bioacoustics data challenge named BioDCASE, within the format of an existing event (DCASE). In this work we report on the procedures needed to select and then conduct useful bioacoustics data challenges. We consider aspects of task design such as dataset curation, annotation, and evaluation metrics. We report the three tasks included in BioDCASE 2025 and the resulting progress made. Based on this we make recommendations for open community initiatives in computational bioacoustics.

MotivationThe diet composition of a predator is a direct reflection of its role in a food web, resulting from interactions with prey species. Raptors (including hawks, owls, and falcons) are ubiquitous predators with diverse diets, yet there is no comprehensive database of raptor diet composition. We present a database of over 3500 raw raptor diet records, compiled from more than 1000 studies and representing 173 raptor species from across the world. Our dataset complements existing qualitative summaries of species diets by compiling thousands of quantitative diet "samples" over time and space to present diet data at a uniquely fine resolution. Main types of variable containedThe database comprises published records of raptor diets from pellets, prey remains, direct or photographic observations, prey DNA, and raptor gut or gullet contents. For each diet, we present the taxonomic identity and amounts of consumed prey. We additionally present various metadata for each diet such as location, habitat, and season. Spatial location and grainThe study incorporates diet records collected worldwide, with each record assigned geographic coordinates corresponding to the location where the diet information was obtained. Time period and grainThe database includes diet records from 1893 to 2025. We report a year for each diet record. Major taxa and level of measurementWe recorded raptor diet at the species level, including raptors from three orders: Strigiformes, Falconiformes and Accipitriformes excluding vultures. Most prey are identified to species, but prey taxonomic level varies depending on the extent to which they could be identified. Software formatDiet records and metadata are provided in two files with comma-separated value (.csv) format.

With bird populations across the world being impacted by ever-growing anthropogenic pressures, reliable monitoring is essential to help halt or reverse declines. Existing visual bird monitoring approaches, which employ cameras or radars to deliver automated and large-scale monitoring data, face a variety of issues. Image-based species classification is only possible if the fine-scale features of a bird are clear, which can be difficult to achieve in real monitoring contexts without expensive, high-resolution cameras due to occlusion and lighting. Radar and video-based approaches which analyse longer-term flight behaviour over the course of seconds can achieve more reliable results in real monitoring contexts, particularly from greater distances, but still require expensive equipment and do not account for all the possible types of flight patterns. Here we present a novel approach to track a wide range of bird flight patterns using inexpensive equipment. As a proof-of-concept, we demonstrate how our approach can be used to classify birds between four species, Red Kite, Kestrel, Black-Headed Gull and Sparrowhawk, which represent four different types of flight patterns. The balanced accuracy of the classification is 0.5583, with a recall and precision per species that range from 0.2640-0.7750 and 0.4583-0.5962, respectively. Our proof-of-concept study demonstrates how new and existing visual bird monitoring systems can leverage flight patterns to deliver species-level insights at lower costs and on larger scales than before.

This paper presents a novel workflow leveraging Large Language Models (LLMs) to rapidly extract trait data from fungal species descriptions, addressing a significant bottleneck in ecological research. We developed and evaluated an LLM pipeline to extract morphological trait data from arbuscular mycorrhizal fungi, comparing performance against a manually curated dataset (TraitAM). Results demonstrate the potential of LLMs for automated trait data acquisition, though accuracy varies by trait and model, with systematic biases observed. This framework offers a blueprint for building trait databases across diverse taxa and domains, significantly accelerating ecological research and conservation efforts.

Understanding animal movement behavior is essential for conservation and elucidating various ecological processes. In particular, assessing habitat selection is a central theme in movement ecology, traditionally evaluated by estimating travel distances per unit time across diverse environmental conditions based on tracking data. Integrated step selection analysis (iSSA : Avgar et al., 2016) has been most widely applied in conservation studies and ecosystem service quantifications due to its ease of implementation and interpretability. Despite its popularity, however, iSSA faces a critical issue since it can lead to an underestimation of the travel distance per unit time, potentially biasing estimates of step length. This is primarily due to the assumption of linear interpolation between consecutive observed points, which fails to account for the unobserved locations and the actual, non-linear trajectories taken by the animal. In this paper, we proposed a novel method to improve the estimation of travel distance in iSSA, inspired by multiple imputation, which is a statistical method for missing data. We conducted a simulation study to evaluate the extent to which our proposed method, Multiple Imputation Step Selection Analysis (MiSSA), improves the accuracy of step-length estimation (parameters of gamma distribution) compared to conventional iSSA. In simulation studies across various scenarios, MiSSA estimated the step length more accurately than iSSA. Our study demonstrates that incorporating missing data statistics into the iSSA framework improves the accuracy of travel distance estimations, which serve as the foundation for evaluating habitat selection. MiSSA maintains the core advantages of iSSA while enabling more accurate estimation of travel distances, even with low-resolution data where movement between sampling intervals is non-linear. We anticipate its broad application across various disciplines, with a primary focus on conservation.

Large-scale biodiversity monitoring is often inhibited by taxonomic obstacles. While deep learning has demonstrated efficacy in species identification, the increasing reliance on large Vision Transformers (ViTs) creates computational barriers that restrict usage to cloud-based infrastructure. Recent foundation models, such as BioCLIP and the Insect-1M framework, require parameter counts exceeding 100M, rendering them unsuitable for edge deployment in field operations. This study presents Elytra 1.0, a computer vision model optimized for edge deployment and capable of classifying 3,127 common North American insect species. The dataset, comprising 2.6 million images, includes all insect species in North America with over 1,000 research-grade observations on iNaturalist. An EfficientNet-B0 architecture was trained using transfer learning from ImageNet with adaptive learning rate scheduling. The model achieved 91.27% Top-1 Accuracy and 97.6% Top-5 Accuracy on an internal test set (N=289,151 images). To rigorously evaluate generalization beyond photographer-specific patterns, an independent observer-excluded test set (N=5,780 images, 578 species) was constructed comprising images exclusively from photographers who contributed zero training data. A post-hoc spatiotemporal audit revealed this test set was heavily skewed toward the Neotropics (Mean Lat: 6.05{degrees} N) during the boreal winter (Dec 2025-early Feb 2026). Despite this significant biogeographic and phenological shift from the predominantly temperate training data, the model achieved 86.68% Top-1 Accuracy (95% CI: 85.8-87.5%). This confirms that Elytra 1.0 relies on robust morphological features rather than learning background environmental correlations, maintaining high performance even in novel ecological contexts.The resulting model file size is 30 MB with an inference speed exceeding 700 frames per second (FPS) on mobile hardware. These results indicate that optimized convolutional architectures can achieve competitive accuracy with server-grade transformers while remaining suitable for decentralized, offline monitoring applications.

Applications of machine learning in ecology are rapidly expanding. Symbolic regression is gaining particular attention for its success in reverse-engineering human-readable explanatory population models, including the logistic growth and Lotka-Volterra equations, from simulated and laboratory-based population time series. However, field-based populations often lack the characteristics of the idealized time series used in prior assessments. We evaluated the utility of symbolic regression for such time series by quantifying its success for synthetic data varying in sampling density, population cycle asymmetry, process noise, and the erroneous consideration of spurious variables. We further compared two data preprocessing options for estimating population growth rates, and four evaluation workflows for selecting equations. Results indicate that a trade-off between sampling density and process noise primarily drives equation and variable recovery. Symbolic regression failed to recover the underlying equation at sampling densities below 10 points per cycle; however, at higher sampling densities, process noise did increase equation recovery rates. Importantly, although the true model was frequently recovered at sampling densities of 25 or more points per cycle, it was not consistently selected by the evaluation workflows. This discrepancy highlights a need for more robust post-algorithm selection criteria to identify the focal equation among competing candidates.

Accurately estimating wildlife population sizes, essential for ecological theory and conservation management, yet remains challenging. Although unmanned aerial systems (UASs) combined with machine learning, have revolutionized population estimation, they face limitations in addressing the hierarchical population processes from individual behavior to colony-and population-level dynamics. To overcome this limitation, we developed a data integration framework that jointly analyzes multiple datasets, representing different scales of the same underlying process, were jointly analyzed. Using a seabird colony as a model system, we integrated UAS-based count data in the colony with bio-logging-based tracking data to estimate population size by quantifying both the number of individuals present and the proportion absent from the surveyed area. These complementary datasets were linked using state-space models allowing accurate population estimates with explicit uncertainty quantification. Furthermore, we evaluated the robustness of the estimations with respect to sample size. Sub-sampling simulations revealed that estimation uncertainty was more sensitive to sample size in bio-logging-based tracking data than in UAS-based count data. This finding highlights the importance of understanding dataset-specific properties when designing effective investigations. Overall, our resource-efficient framework is broadly applicable across species and populations and demonstrates how integrating complementary observation methods can improve population estimates and inform conservation practice.