Methods in Ecology and Evolution

1. Monitoring the diel activity patterns of free-living animals is a methodological challenge. Signal strength fluctuations from Very High Frequency (VHF) radio transmitters deployed within the Motus Wildlife Tracking System can be used as a proxy for activity. However, analytical tools to extract behavioral metrics that quantify activity patterns from these data are needed. 2. We developed roostR, an open-source R package that converts Motus detection data into quantitative behavioral metrics, including roost initiation and departure, roost duration, observation time, and restlessness. The package uses a sequential pipeline built around signal volatility and rolling medians to detect transitions between active and inactive states. Default parameter values were tuned using data from 55 dark-eyed juncos (Junco hyemalis) overwintering in southeastern Ohio. 3. We provide an example from a dark-eyed junco over a 58-day period. roostR estimated roost onset on 48 nights and departure on 56 mornings, with higher rolling median signal differences during the day than at night, consistent with a diurnal animal, and variable restlessness periods each night. We also used the package to estimate roost behavior of an American tree sparrow (Spizelloides arborea) over 43-nights. 4. roostR enables researchers to extract individual activity data from Motus datasets. Because all thresholds are user-adjustable, the pipeline is adaptable across species, tag specifications, and ecological contexts, enabling researchers to test hypotheses about how environmental factors influence diel activity patterns.

Risk identification of non-native species is widely used to support prioritization, early warning, horizon scanning and management decisions. Over the past two decades, the Invasiveness Screening Kit (ISK) framework of decision-support tools has developed through aquatic, terrestrial animal and terrestrial plant applications, but until now these toolkits have been distributed and used as separate software environments. ISK v3 provides a single integrated Microsoft Excel/Visual Basic for Applications platform for the Aquatic Species Invasiveness Screening Kit (AS-ISK), Terrestrial Animal Species Invasiveness Screening Kit (TAS-ISK) and Terrestrial Plant Species Invasiveness Screening Kit (TPS-ISK). It preserves the established questionnaires, scoring logic and multilingual implementation of the three toolkits within a single integrated interface, while introducing improved harmonized database workflows, screening record management, taxonomic verification, threshold handling with built-in calibration, risk summaries, reporting, merging and export functions as well as controlled access to separate Excel instances. The platform supports 31 languages, toolkit-specific databases, conversion of compatible v2 databases, import of legacy aquatic first-generation ISK databases into AS-ISK, online verification through major taxonomic and biodiversity data resources, and use of a global a priori categorization dataset for calibration. ISK v3 preserves the reproducible screening structure of the three toolkits while improving consistency, transparency and traceability in non-native species risk identification and reducing fragmentation across toolkits. It provides a common platform for researchers, managers and institutions applying risk identification workflows across aquatic, terrestrial animal and terrestrial plant taxa.

Arthropods drive essential ecosystem processes such as pollination, decomposition, and nutrient cycling and are widely used as indicators of ecosystem condition and function. Among various arthropod-derived metrics, body weight is a key variable in functional ecology and frequently assessed as dry body weight. However, drying arthropod specimens limits the samples future potential for research, as it prevents further processing such as trait measurements or species identification. Here, we present AWET (Arthropod Weight Estimation Tool), an open-source application for automated estimation of individual fresh body weight and extraction of morphometric measurements from standardized images of pre-sorted arthropod samples. AWET combines automated image analysis with taxon-specific allometric regression models to estimate fresh body weight while simultaneously quantifying body length, width, area, and specimen abundance. The software operates with standard imaging equipment, requires no machine-learning-based classification or segmentation, and allows users to define taxonomic groupings according to their objectives. By preserving specimens for downstream analyses while processing large numbers of individuals within milliseconds, AWET provides an efficient, non-destructive, and cost-effective workflow for high-throughput arthropod phenotyping. The software is a practical and expandable tool for biodiversity monitoring projects investigating changes in arthropod biomass, abundance and individual morphometric measures.

1. Understanding how animals acquire and use information from the environment is critical for linking movement to population dynamics, species distributions and conservation. Advances in tracking technologies and growing interest in learning processes have opened opportunities to study behaviours such as habitat exploration in translocated animals or ontogeny of migration and dispersal movements. However, accessible statistical methods for studying these behavioural processes are lacking. 2. We present a learning-explicit step-selection analysis (SSA) that integrates movement, habitat selection, and learning into a single evolving process. At the start of the movement trajectory, the animal is assumed to have no knowledge of the landscape, i.e. its internal habitat quality map is initialized to a constant. As the animal moves, this map is updated dynamically: with each step, only the habitat within its perceptual range becomes known and contributes to future decisions. 3. Through simulations, we show that this method separates true habitat preferences from learning effects and reveals when large-scale behaviours, such as attraction to resources or avoidance of risks, emerge as knowledge accumulates. Critically, we demonstrate that ignoring learning, as in traditional SSA, can lead to biased estimators of habitat selection. 4. Finally, we apply our approach to a real-world GPS dataset of naive individuals, consisting of 10 juvenile red kites dispersing in Switzerland. Using leave-one-individual-out cross-validation, we show that learner SSFs consistently outperform traditional SSFs in predicting movement decisions. 5. We conclude by discussing methodological considerations and future directions for integrating learning into movement ecology. By explicitly modelling the formation of memory that integrates both spatial information and habitat quality, our approach advances process-based movement ecology while remaining compatible with standard SSA workflows, offering practical tools for both theoretical research and applied wildlife management.

Retrospective ecological niche modeling (rENM) combines historical species occurrence records with historical environmental data to reconstruct the spatio-temporal dynamics of species-environment relationships under changing conditions. Despite growing recognition that those relationships can be nonstationary, time-series approaches to ecological niche modeling remain uncommon, and the tools to support them at scale are limited. Here, we describe the rENM Framework, an experimental, open-source suite of R packages that automates a complete rENM workflow spanning data preparation, ensemble time-series construction, trend analysis, AI interpretation, and report generation. The framework integrates eBird occurrence records with environmental variables derived from NASAs MERRA-2 reanalysis across a 45-year study period (1980-2024) and executes a complete analysis for any species with eBird data through a single function call. By treating climatic suitability as a dynamic ecological response surface rather than a static baseline, the framework produces the following analytical products that complement conventional ecological niche modeling approaches: suitability time series, long-term trend and acceleration maps, centroid displacement estimates, bioclimatic velocity metrics, variable contribution trajectories, and hotspot analyses identifying areas of accelerating suitability decline. We illustrate the frameworks outputs with a representative run for Cassins Sparrow (Peucaea cassinii), a grassland species of conservation concern in the arid southwestern United States and the focal species throughout our development work. The frameworks automated, unsupervised pipeline makes systematic application across large numbers of species tractable, with direct implications for conservation assessments, such as State Wildlife Action Plans, where species-specific analytical capacity is often limited by available resources. The rENM Framework is openly available on GitHub and archived on Zenodo.

Automated monitoring camera systems offer an efficient approach for quantifying pollinator biodiversity and plant-pollinator interactions, but financial and logistical constraints limit the number of flowering plant species that can be monitored. Using a large European database of plant-pollinator networks, we evaluated whether plant subsampling can capture key metrics of interest (i.e. pollinator richness and network structure). We compared abundance-based, flower-shape-informed, phylogenetically informed and random plant sampling. Abundance-based sampling consistently outperformed random sampling, while adding flower shape provided little additional benefit and phylogenetic selection performed similarly to random sampling. Overall, monitoring 12-15 flowering species was sufficient to characterize key network properties across varying community sizes. Performance of abundance-based plant sampling declined in species-rich communities and when rare but highly attractive plants were present in the community, while a specific subset of pollinator species was consistently missed even by the best sampling strategy. Based on their relative contribution to interactions within each network, only 12.6% of pollinator species accounted for 95% of interactions, suggesting that AI classifiers could prioritize a relatively small subset of species. Our results provide practical guidance for designing efficient camera-based pollinator monitoring schemes at large spatial scales.

1. For many animals, vision represents the primary sensory modality governing a vast diversity of behaviours. Creating 'virtual twins', or visually realistic digital reconstructions of real individuals interacting within their environments, offers a promising approach for understanding how visual information guides an animal's decisions, behaviours, and interactions. Such systems enable the creation of video scenes from any view, including the viewpoint of the animals themselves. This allows receiver information to then be analysed from the perspective of species-specific visual systems, or for the accurate reconstruction of a real animal's movement to provide social stimuli for behavioural experiments. 2. Despite advances in 3D modelling, photorealistic scene rendering, and animal tracking, efficiently creating realistic digital reconstructions of movement remains challenging for many species. In fishes, a continuously-deforming body due to the absence of discrete joints make it particularly difficult to map tracked body keypoints onto articulated digital models, often resulting in unnatural or erratic animations. 3. Here, we introduce DigiFish, an add-on for the open-source animating software Blender, that generates realistic fish animations from tracking data. By integrating anatomical keypoint tracking with spline-based body curvatures, DigiFish reconstructs biologically-realistic swimming postures and movements of fishes within customisable virtual aquatic environments. The system also supports virtual 'point-of-view' cameras that can be placed arbitrarily within a scene, or dynamically driven by eye-tracking data, enabling unlimited perspectives on how individuals, conspecifics, or landmarks appear against complex backgrounds and how these views change with realistic swimming and eye movements. 4. We provide the Blender add-on and user guide and use DigiFish to reconstruct two different contexts: staged predator-prey interactions in the laboratory and natural schooling of wild fish in the field. Using a further reconstruction of fish swimming in an arena, we also use an Elementary Motion Detector model to show that our virtual reconstructions successfully capture perceptually-realistic movements and postures of individual fish within a digitally reconstructed scene. 5. By integrating photo-realistic scenes with biologically-realistic, simulated motion, DigiFish offers a flexible and accessible platform for investigating visually guided behaviour, perception, and cognition in complex and dynamic environments.

1. Large-scale community-composition datasets, especially from microbiome studies, increasingly provide opportunities to identify major community compositional types (e.g., enterotypes in human microbiomes) and potential transitions depending on environmental factors. Energy landscape analysis based on maximum entropy models has emerged as a promising framework for characterizing such multi-stability in ecological communities. However, its application to diverse, high-dimensional compositional datasets remains limited by computational inefficiency, insufficient evaluation of predictability, and lack of systematic assessment of uncertainty. 2. Here, we present a computationally tractable inference framework for energy landscape analysis of multispecies communities, implemented in the R package ELAplus. We introduce a framework combining cross-validation-based selection of optimization settings, enabling accurate and computationally efficient model fitting across a wide range of simulated community datasets. In addition, we incorporate a bootstrap-based approach to quantify the reliability of inferred stable states, providing a systematic measure of uncertainty in landscape structures. 3. Simulation analyses demonstrate improved predictive performance and robustness compared to existing implementations. Applications to empirical datasets further illustrate how the framework can reveal stable states, basins of attraction, and potential tipping points under varying environmental conditions. The package also provides visualization tools, including disconnectivity graphs and energy surface plots, to facilitate intuitive interpretation of complex ecological landscapes. 4. Our framework enables robust and computationally efficient inference of ecological stability from compositional and environmental data, expanding the applicability of energy landscape approaches in diverse natural communities.

1. Passive acoustic monitoring (PAM) is increasingly used for ecological research, biodiversity monitoring, assessment, and reporting. Automated species classifiers make it feasible to process large audio datasets but generate numerous detections that often need validation before use in downstream analyses or formal outputs. 2. Method development in PAM has focused on classifier building and downstream models that account for imperfect detection, yet the practical step between these - post-classification validation - remains weakly supported and is often implemented through ad hoc workflows. This increases manual handling, creates scope for transcription or consolidation errors, limits transparency and makes it difficult to document what was reviewed. 3. We introduce PAMalytics, an open-source, no-code, local browser-based application to support post-classification validation as a standardised workflow stage. PAMalytics ingests detections from any classifier, allows users to define how detections are sampled for review, and presents selected detections alongside their spectrograms with audio playback in one unified interface. Sampling strategy and review decisions are tracked alongside reviewer identity improving traceability and reproducibility across the validation workflow. 4. Case studies with Conservation International Cambodia and Imperial College London demonstrate PAMalytics in two validation settings. In Cambodia, gibbon predictions from a large, uneven dataset were sampled within sites, with likely classifier errors prioritised for validation. At Imperial, Amazon bird detections were sampled across each species classifier-confidence range before biodiversity metrics were derived. In both cases, PAMalytics reduced manual handling and validation time. By turning an ad hoc step into an accessible, structured workflow for conservation practitioners, PAMalytics fills a practical gap in the PAM bioacoustics pipeline and strengthens the link between automated detections and evidence used in biodiversity monitoring and reporting.

Species distribution models are widely used for the evaluation and analysis of species spatial distributions and responses to environmental changes. Due to this, there's a wide diversity of tools and algorithms to perform these types of analyses. However, despite being a mature field of study, SDM still requires a large amount of research intervention and decision-making to produce reliable models. This is a natural consequence of each species unique response to environmental factors, but in practice it hinders the potential of SDMs to be used effectively and with confidence across large sets of species. Here we present the AutoMaxEnt repository, a collection of functions and tools for the automation of maximum entropy species distribution models. AutoMaxEnt offers a flexible environment to model species distribution models, automating most of the data pre-processing and preparation and giving the user the possibility of exploring multiple model scenarios automatically or with control over how those scenarios are generated. AutoMaxent offers model fit control, multiple background points generation algorithms, automatic model and variable selection, and study area configuration, among other parameters. AutoMaxEnt allows for the automation of model fitting, evaluation and model selection across different species, study areas and time periods.

BackgroundAutomated behavioral tracking is increasingly used in biological and biomedical research; however, robustness across heterogeneous imaging conditions remains a major challenge. Domain shifts caused by changes in illumination, contrast, or acquisition setup can substantially degrade the performance of computer vision and deep learning models, limiting their practical applicability. This issue is particularly relevant for small-scale biological datasets, where extensive annotation and retraining are often impractical. MethodsWe developed a behavioral tracking framework for Galleria mellonella larvae combining a classical computer vision (CV) pipeline, a YOLOv8s-seg + ByteTrack deep learning pipeline, and generative domain adaptation methods. Behavioral recordings were collected in two independent experimental sessions under distinct illumination conditions (top and bottom lighting), creating a natural cross-session domain shift. The classical pipeline was based on contour detection, adaptive preprocessing, temporal smoothing, and trajectory reconstruction. Deep learning models were trained on 320 manually annotated frames and evaluated in both within-session and cross-session settings. To improve generalization, we investigated generative augmentation using StyleGAN2-ADA and unpaired image-to-image translation using CycleGAN. Tracking outputs were further analyzed through behavioral descriptors, including trajectories, traveled distance, velocity, spatial occupancy heatmaps, and directional movement patterns. ResultsThe classical CV pipeline achieved high detection performance in both recording sessions, with mean detection rates of 99.05% and 99.71%, respectively. A YOLOv8s-seg model demonstrated strong within-session performance but exhibited severe degradation under cross-session evaluation, with larval mask segmentation performance dropping to mAP@0.5 = 9.1%, confirming the presence of a substantial domain shift. Despite differences in detection methodology, behavioral metrics derived from YOLO and CV pipelines showed strong agreement at the group level (Pearson correlation r = 0.89; median distance ratio = 0.99). Generative augmentation with StyleGAN2-ADA did not yield meaningful gains in tracking robustness -- likely because baseline performance was already near ceiling -- whereas CycleGAN-based domain adaptation substantially reduced the domain gap and improved cross-session detection performance while preserving biologically relevant trajectory structures and spatial behavioral patterns. ConclusionsCross-session variability represents a critical challenge for automated behavioral tracking in biological experiments. Our results demonstrate that carefully designed classical computer vision approaches can achieve highly reliable tracking in small-data settings, while deep learning models require explicit strategies to address domain shift. Generative domain adaptation, particularly CycleGAN-based image translation, offers an effective solution for improving cross-session generalization without additional manual annotation. The proposed framework provides a robust foundation for scalable behavioral phenotyping of Galleria mellonella and other small biological model organisms.

O_LIQuantifying complex morphology from images remains difficult because predefined descriptors capture only selected traits. Yet, supervised machine learning models for images require labels and often produce task-specific features that are hard to interpret as biological traits. C_LIO_LIWe present MorphQ, a label-free, self-supervised method that learns a quantitative morphospace from standardised specimen images. Its encoder produces feature vectors for statistical analysis, and its decoder converts analysed positions in morphospace into human-interpretable images, including hypothetical forms not represented by sampled specimens or sampled taxa. C_LIO_LIUsing 1,868 Lepidoptera species, we tested whether MorphQs label-free features were more useful for downstream analysis than features from principal component analysis (PCA) or a supervised species-classification machine learning model. As a diagnostic probe of downstream biological utility, MorphQ features supported higher low-label family-classification accuracy than comparator features, and retained stronger family-level similarity for species absent from model training, indicating better generalisation to species not seen during model training. C_LIO_LITwo case studies link MorphQ morphospaces to species-level elevation and assemblage-level functional diversity while keeping statistical patterns visually inspectable. MorphQ provides a reproducible framework for constructing interpretable morphological trait spaces when predefined descriptors are incomplete and labelled data are limited. C_LI Data/code for peer review: An anonymised repository containing the source code, trained model weights, example data, configuration files and scripts required to reproduce the analyses is available at https://anonymous.4open.science/r/MorphQ-ECD4/.

Leaf shape is a fundamental trait of plant ecological strategies, influencing biotic interactions and ecosystem functioning. However, established quantitative metrics fail to capture subtle variations and irregularities, require user-based reference points or are challenging to compare among taxa with broadly different leaf shapes. In addition, established metrics typically conflate (aggregate) leaf edge complexity and macro-shape complexity, despite their independent functional significance and genetic foundations. Here, we introduce an entropy-based framework to quantify two new complexity metrics: edge complexity and macro-shape complexity. Based on three case studies, we show that these metrics outperform aggregate metrics in predicting Quercus robur chemical traits, provide more intuitive interspecific classifications, and strongly align with human perception. In addition, edge and macro-shape complexity show high complementarity, while aggregate metrics are highly redundant and typically strongly related to leaf area. Emerging as the strongest predictor of leaf chemistry and key visual cue for complexity as perceived by humans, the effects of edge complexity highlight the under-appreciated functional significance of leaf margins. Our framework and the proposed entropy-based complexity metrics thus promise to help unlock the potential of growing digital image archives of leaves, including images from herbaria and fossils, and are technically readily applicable to shapes of algae, bacteria, pollen, and beyond. The accompanying package ShapeComplexity enables the broad application of entropy-based metrics, providing a powerful tool to explore how the shape of organisms and biological structures influences ecological strategies, biotic interactions, and ecosystem functioning while tracking spatial and temporal variation.

1. Thermal performance curves (TPCs) are widely used to quantify temperature dependence of biological traits, but estimating them requires experiments across multiple temperatures. Because these experiments can be costly, researchers must balance the number and placement of temperature treatments against replication within treatments. Evenly spaced temperatures are a common default, but may allocate effort inefficiently when thermal responses are nonlinear or strongly asymmetric. 2. We developed TPCdesign, an R package for planning temperature-trait experiments. TPCdesign uses a pseudo-Bayesian (robust) D-optimal experimental design to identify sets of temperatures expected to provide the most information about a TPC, while allowing users to incorporate prior knowledge of thermal limits and curve shape, uncertainty in that knowledge, available numbers of treatments, and practical restrictions on temperature spacing. 3. Using simulation-based worked examples, we show that optimized designs can recover TPCs more accurately than evenly spaced designs, particularly for asymmetric curves and when the number of available temperature treatments is modest. Optimization primarily improves resolution of regions where performance changes rapidly and can substantially improve estimation of upper thermal limits while retaining information across the rest of the curve. 4. TPCdesign provides a practical framework for allocating limited experimental resources according to the biological response researchers seek to estimate. Rather than prescribing a universal set of temperatures, it allows experimental designs to reflect existing biological knowledge, uncertainty, and logistical constraints, improving the information gained from costly thermal performance experiments.

Ecoacoustic monitoring is enabling scientists and land managers to monitor and manage biodiversity more effectively and cost-efficiently in the face of human pressures and rapidly changing climates. Currently, most ecoacoustic surveys use manually deployed static sensors to record data, limiting the scale and reach of surveying efforts. Here we present a proof-of-concept autonomous drone platform that can use intermittent locomotion to conduct ecoacoustic surveys using an onboard sensor. Our custom prototype is able to fly, navigate, and avoid obstacles autonomously, land at a pre-determined location, record audio from an onboard microphone whilst static, before taking off and moving to the next sampling site. Autonomous navigation and operation enable greater sampling flexibility, reach, and scalability. Furthermore, by recording audio only whilst landed, noise from the drone's rotors does not mask signals or disturb animals, simplifying signal processing and downstream ecological analyses. We conducted trials in a scrubland habitat at the Knepp Estate in West Sussex, where our prototype demonstrated successful autonomous navigation and obstacle avoidance. Furthermore, we found that avian biodiversity data collected from the drone platform was comparable to that from traditional static acoustic sensor deployments, and that vocalisation patterns were not significantly impacted by the noise of the drone arriving or leaving a site. While scaled deployments of our technology would require further technical and regulatory challenges to be solved, our first demonstration of autonomous intermittent robotics-assisted ecoacoustic surveys lays the foundations for more cost-effective and far-reaching biodiversity surveys, with transformative potential for conservation, agricultural management, biosecurity, and more.

Advances in camera technology and computer vision techniques have allowed researchers to track animals in 3D in ways which previously were difficult or impossible. Many such 3D tracking tools make use of multiple cameras, but unfamiliarity with the principles and technology involved can make it difficult to employ such techniques. In this protocol, we describe Braid, open-source software for live, multi-camera 3D tracking of insects. Using background-subtraction, Braid performs detection of objects without requiring the use of physical markers affixed to the insect. Braid constructs low-latency 3D position estimates using Kalman filtering and nearest neighbor data association. We document in detail the process of tracking freely flying bees within a flight arena using Braid. This protocol includes instructions on installation, configuration of cameras, setup, calibration, and operation. Within the system described here, we demonstrate that Braid can achieve position estimates accurate to <1 millimeter (within a 0.3 cubic meter volume). These factors make Braid suitable for tracking small, fast-flying animals like insects. Braid's low latency allows live tracking, removing the necessity to collect large video files and making it suitable for integration in closed loop systems such as virtual reality. Code is available at https://github.com/strawlab/strand-braid

Passive acoustic sensors and machine learning classifiers offer a scalable approach to monitoring wildlife populations. These classifiers typically detect whether a species is present in a short time-window of audio; ecologists, however, want ecologically meaningful measures such as indices of site-level abundance. Vocal density, the proportion of time-windows containing a vocalization, is a useful abundance proxy. Recovering vocal density from classifiers requires calibrating their outputs against expert-labeled data. Because classifier performance varies between sites, a single global calibration yields overconfident site-level estimates, yet calibrating every site independently requires prohibitive labeling effort. Estimating site-level vocal density at the scale of modern sensor networks demands a label-efficient alternative. We present a Bayesian hierarchical extension of Platt scaling, a commonly used calibration method, that calibrates classifiers at the site level while borrowing strength across sites. Where site-level covariates are available, a Gaussian-process prior lets the model learn how calibration varies across covariate space. Averaging calibrated per-clip probabilities across a site's recordings yields a posterior distribution over vocal density, and we show how to carry that uncertainty into downstream regressions of vocal density on environmental covariates. Across simulations and two fully annotated field datasets from Hawai'i and the Pacific Northwest, our model attained desired credible-interval coverage of vocal density estimates with narrower intervals than independent site-level calibration at equal labeling effort, and lower mean squared error. Global calibration, by contrast, failed to attain coverage except when simulated sites were genuinely homogeneous. Unlike site-level calibration, our model enables calibration at sites with zero labeled data, and incorporating covariates further improved precision when site heterogeneity was covariate-driven. Applied to a 283-site dataset in Pennsylvania with only two labeled clips per site, we recovered known habitat associations for the declining Wood Thrush (Hylocichla mustelina). Our method provides a principled, label-efficient route from bioacoustic classifier scores to site-level abundance indices with well-quantified uncertainty, making rigorous ecological inference feasible at the scale at which acoustic sensor networks are now deployed.

1. Invasive species pose significant ecological, economic, and public health challenges. Their management requires repeated decisions under uncertainty about when, where and how intensively to intervene. Although structured decision-making and adaptive management provide conceptual frameworks for addressing these challenges, their practical implementation remains limited because optimization methods such as stochastic dynamic programming (SDP) are often perceived as mathematically complex, challenting to apply and difficult to interpret. 2. Here, we provide a practical guide to using SDP and its extensions for invasive species management. Using coypu (*Myocastor coypus*) management as a running case study, we introduce the framework through a minimal working example before progressively extending it to spatial dynamics, imperfect detection using partially observable Markov decision processes (POMDPs), adaptive management under model uncertainty, and value-of-information analyses that quantify the benefits of learning before acting. 3. All examples are accompanied by reproducible implementations in R, together with recommendations on model formulation, parameterization and interpretation. We also discuss the strengths and limitations of SDP. 4. By lowering the technical barrier to SDP, this guide aims to help ecologists and wildlife managers integrate decision theory into invasive species management, enabling transparent, reproducible and objective-driven management strategies under uncertainty. More broadly, the methods presented here are applicable to a wide range of ecological decision problems beyond invasive species, wherever management requires balancing immediate actions against uncertain future outcomes.

O_LIEnvironmental DNA (eDNA) methods are developing rapidly for ecological surveys, and passive eDNA sampling has emerged as a promising approach for integrating DNA signals over deployment time. However, how deployment duration affects the amount of detectable DNA retained by a sampler remains poorly understood. C_LIO_LIHere, an analytical model was developed to examine how DNA input, degradation, finite substrate capacity and residual retention of degraded DNA shape passive eDNA accumulation. The model distinguishes detectable adsorbed DNA from degraded, non-detectable DNA that may remain on the substrate and continue to occupy capacity. The residual-retention parameter,{theta} , represents the fraction of degraded DNA that remains capacity-occupying, with{theta} = 0 corresponding to complete replacement and{theta} = 1 to complete non-replacement. C_LIO_LIThe model predicts three key behaviours. First, when degraded DNA does not occupy substrate capacity ({theta} = 0), detectable eDNA accumulates monotonically towards equilibrium, but equilibrium recovery increases less than proportionally with DNA input. Thus, passive-sampler measurements can compress quantitative differences in environmental DNA supply. Second, when degraded DNA remains capacity-occupying ({theta} > 0), detectable eDNA can reach a finite peak and subsequently decline. Higher DNA input increases peak yield but shifts the peak earlier, whereas greater substrate capacity increases peak yield and delays the peak. Third, under prolonged deployment with{theta} > 0, a higher-input condition can yield less detectable eDNA than a lower-input condition, reversing the expected input-rate ranking. C_LIO_LIThese results show that passive eDNA recovery can follow saturating, unimodal or intermediate dynamics depending on substrate capacity and post-adsorption DNA fate. Thus, retrieval time cannot be optimised by adjusting deployment duration alone. Although investigators can choose deployment duration and sampler design, including substrate capacity, optimisation also requires calibration or explicit assumptions about ambient DNA supply, DNA degradation rate and residual retention of degraded DNA. C_LI

Protecting deep-sea sessile communities as Marine Protected Areas (MPAs) requires accurate estimates of their spatial distribution and abundance. Existing computer-vision detection methods require careful calibration to avoid counting errors; multi-object tracking (MOT) theoretically offers a more robust alternative. In practice, however, poor lighting and turbidity limit the efficacy of purely kinematic MOT algorithms in deep-sea environments. At the same time, the intra-species visual homogeneity typical of deep-sea scenes means traditional per-frame appearance features are often too weak to maintain stable identities in deep-association MOT algorithms. In this paper we introduce JEPA-SORT, an MOT algorithm that injects V-JEPA 2.1 embeddings into detection-to-track association, to show that video world-model embeddings - which capture not only physical appearance but spatiotemporal trajectory across viewpoints - can help maintain stable identities on visually homogeneous specimens. We provide a benchmark of twelve ROV transects surveying deep-sea sessile communities and evaluate JEPA-SORT against standard MOT algorithms across a range of simulated optical environments. We also propose the Biodensity Utility Metric (BDU), which measures tracker performance in the context of downstream ecological modeling. JEPA-SORT outperforms all benchmarked trackers on IDF1, MOTA, and BDU and is competitive on specimen-count accuracy. Ablations indicate its advantage stems primarily from the discriminative power of world-model embeddings on visually homogeneous footage.