Uncertainty Estimation Research Articles

Consensus prediction of cell type labels in single-cell data with popV.

Cell-type classification is a crucial step in single-cell sequencing analysis. Various methods have been proposed for transferring a cell-type label from an annotated reference atlas to unannotated query datasets. Existing methods for transferring cell-type labels lack proper uncertainty estimation for the resulting annotations, limiting interpretability and usefulness. To address this, we propose popular Vote (popV), an ensemble of prediction models with an ontology-based voting scheme. PopV achieves accurate cell-type labeling and provides uncertainty scores. In multiple case studies, popV confidently annotates the majority of cells while highlighting cell populations that are challenging to annotate by label transfer. This additional step helps to reduce the load of manual inspection, which is often a necessary component of the annotation process, and enables one to focus on the most problematic parts of the annotation, streamlining the overall annotation process.

A 'how-to' guide for estimating animal diel activity using hierarchical models.

Animal diel activity patterns can aid understanding of (a) how species behaviourally adapt to anthropogenic and natural disturbances, (b) mechanisms of species co-existence through temporal partitioning, and (c) community or ecosystem effects of diel activity shifts. Activity patterns often vary spatially, a feature ignored by the kernel density estimators (KDEs) currently used for estimating diel activity. Ignoring this source of heterogeneity may lead to biased estimates of uncertainty and misleading conclusions regarding the drivers of diel activity. Thus, there is a need for more flexible statistical approaches for estimating activity patterns and testing hypotheses regarding their biotic and abiotic drivers. We illustrate how trigonometric terms and cyclic cubic splines combined with hierarchical models can provide a valuable alternative to KDEs. Like KDEs, these models accommodate circular data, but they can also account for site-to-site and other sources of variability, correlation amongst repeated measures, and variable sampling effort. They can also more readily quantify and test hypotheses related to the effects of covariates on activity patterns. Through empirical case studies, we illustrate how hierarchical models can quantify changes in activity levels due to seasonality and in response to biotic and abiotic factors (e.g. anthropogenic stressors and co-occurrence). We also describe frequentist and Bayesian approaches for quantifying site-specific (conditional) and population-averaged (marginal) activity patterns. We provide guidelines and tutorials with detailed step-by-step instructions for fitting and interpreting hierarchical models applied to time-stamped data, such as those recorded by camera traps and audio recorders. We conclude that this approach offers a viable, flexible, and effective alternative to KDEs when modelling animal activity patterns.

EBT3 Radiochromic film response in time-dependent thermal environment and water submersion conditions: Its clinical relevance and uncertainty estimation

AimThis study aimed to find the thermal and humidity-dependent behavior of EBT3 film. The changes in the film sensitivity as well as the dosimetric region of interest in the small-size film due to its storage and setup condition were evaluated. Material and methodsThree sets of films were kept at three different thermal storage conditions. 15 films from each set were kept at 2 °C, 20 °C, and 40 °C storage conditions for 24 h and 48 h pre- and post-irradiation. The humidity effect (water submersion) on the film in terms of the film's edge diffusion, and change in the optical density at the central region of the film was estimated by keeping the films underwater. ResultsThe mean relative film sensitivity was found 1.107 ± 0.123 and 0.966 ± 0.028 respectively for the films stored at 40 °C and 2 °C with respect to the sensitivity of the films that were kept at room temperature (20 °C) each for 48 h. The dosimetric result shows that the measured dose in the dose range (2–20 Gy) was overestimated by 7.01 % (percentage mean dose difference (MDD)) in the films stored at 40 °C for 48 h. In contrast, it was underestimated by −4.54 % (MDD) in the films stored at 2 °C for 48 h compared to those kept at an ambient room temperature of 20 °C (MDD = 0.24%). The water-diffusion rate at the border of the EBT3 film used in this study was 0.23 mm/h. An increment of 6.8% in the optical density at the central region of the film was observed for the unexposed control film kept underwater for 24 h. The combined standard uncertainty due to five different sources of uncertainties was 2.91%. ConclusionThe sensitivity of the EBT3 film kept at 40 °C is always higher (overestimate) whereas lower (underestimate) for the films kept at 2 °C as compared to the films at normal room temperature. The correction for the changes in the net optical density due to its storage condition (thermal effect) and the dosimetry condition of water submersion (humidity effect) must be incorporated into the current protocols of film-dosimetry if the facility cannot have temperature-controlled film storage environment and if the film immersion under water is more than 4 h.

Uncertainty-aware genomic deep learning with knowledge distillation.

Deep neural networks (DNNs) have advanced predictive modeling for regulatory genomics, but challenges remain in ensuring the reliability of their predictions and understanding the key factors behind their decision making. Here we introduce DEGU (Distilling Ensembles for Genomic Uncertainty-aware models), a method that integrates ensemble learning and knowledge distillation to improve the robustness and explainability of DNN predictions. DEGU distills the predictions of an ensemble of DNNs into a single model, capturing both the average of the ensemble's predictions and the variability across them, with the latter representing epistemic (or model-based) uncertainty. DEGU also includes an optional auxiliary task to estimate aleatoric, or data-based, uncertainty by modeling variability across experimental replicates. By applying DEGU across various functional genomic prediction tasks, we demonstrate that DEGU-trained models inherit the performance benefits of ensembles in a single model, with improved generalization to out-of-distribution sequences and more consistent explanations of cis-regulatory mechanisms through attribution analysis. Moreover, DEGU-trained models provide calibrated uncertainty estimates, with conformal prediction offering coverage guarantees under minimal assumptions. Overall, DEGU paves the way for robust and trustworthy applications of deep learning in genomics research.

UNIQUE: A Framework for Uncertainty Quantification Benchmarking.

Machine learning (ML) models have become key in decision-making for many disciplines, including drug discovery and medicinal chemistry. ML models are generally evaluated prior to their usage in high-stakes decisions, such as compound synthesis or experimental testing. However, no ML model is robust or predictive in all real-world scenarios. Therefore, uncertainty quantification (UQ) in ML predictions has gained importance in recent years. Many investigations have focused on developing methodologies that provide accurate uncertainty estimates for ML-based predictions. Unfortunately, there is no UQ strategy that consistently provides robust estimates about model's applicability on new samples. Depending on the dataset, prediction task, and algorithm, accurate uncertainty estimations might be unfeasible to obtain. Moreover, the optimum UQ metric also varies across applications, and previous investigations have shown a lack of consistency across benchmarks. Herein, the UNIQUE (UNcertaInty QUantification bEnchmarking) framework is introduced to facilitate a comparison of UQ strategies in ML-based predictions. This Python library unifies the benchmarking of multiple UQ metrics, including the calculation of nonstandard UQ metrics (combining information from the dataset and model), and provides a comprehensive evaluation. In this framework, UQ metrics are evaluated for different application scenarios, e.g., eliminating the predictions with the lowest confidence or obtaining a reliable uncertainty estimate for an acquisition function. Taken together, this library will help to standardize UQ investigations and evaluate new methodologies.

Characterizing the spatial correlation of coseismic slip distributions: A data driven Bayesian approach

Summary The spatial correlation of coseismic slip is a necessary input for generating stochastic seismic rupture models, which are commonly employed in seismic and tsunami hazard assessments. To date, the spatial correlation of individual earthquakes is characterized using finite fault models by finding the combination of parameters of a von Kármán autocorrelation function that best fits the observed autocorrelation function of the finite fault model. However, because a priori spatial correlation conditions (i.e., not in the data) are generally applied in finite fault model generation, the results obtained using this method may be biased. Additionally, robust uncertainty estimates for spatial correlations of coseismic slip are generally not performed. Considering these limitations in the classic method, here, a method is developed based on a Bayesian formulation of Finite Fault Inversion (FFI) with positivity constraints. This method allows for characterizing the spatial correlation of coseismic slip and its uncertainties for an earthquake by using samples of coseismic slip from a posterior probability density function (PDF). Furthermore, a Bayesian model selection criterion called Akaike Bayesian Information Criterion (ABIC) is applied to objectively choose between different prior spatial correlation schemes before computing the posterior, to reduce subjectivity due to this prior condition. The ABIC is calculated using an approximate analytical expression of Bayesian evidence. The method is applied to simulated P-waves, demonstrating that model selection allows for objectively estimating the most suitable prior spatial correlation scheme in FFI. Additionally, the target spatial correlation of coseismic slip is accurately recovered using samples from the posterior PDF, as well as their uncertainties. Moreover, in the simulated experiment, it is shown that a non-robust choice of the prior spatial correlation scheme can significantly bias the estimated spatial correlations of coseismic slip. We apply our method to observed P-waves from the 2015, Illapel earthquake (Mw = 8.3), finding that the spatial correlation of coseismic slip of this earthquake is better described by a von Kármán ACF, with mean correlation lengths of around 47 km and Hurst parameter of 0.58. We conclude that using our method reduces biases associated with prior spatial correlation conditions and allows for robust estimation of spatial correlations of coseismic slip and their uncertainties.

Simulation studies for methodological research in psychology: A standardized template for planning, preregistration, and reporting.

Simulation studies are widely used for evaluating the performance of statistical methods in psychology. However, the quality of simulation studies can vary widely in terms of their design, execution, and reporting. In order to assess the quality of typical simulation studies in psychology, we reviewed 321 articles published in Psychological Methods, Behavior Research Methods, and Multivariate Behavioral Research in 2021 and 2022, among which 100/321 = 31.2% report a simulation study. We find that many articles do not provide complete and transparent information about key aspects of the study, such as justifications for the number of simulation repetitions, Monte Carlo uncertainty estimates, or code and data to reproduce the simulation studies. To address this problem, we provide a summary of the ADEMP (aims, data-generating mechanism, estimands and other targets, methods, performance measures) design and reporting framework from Morris et al. (2019) adapted to simulation studies in psychology. Based on this framework, we provide ADEMP-PreReg, a step-by-step template for researchers to use when designing, potentially preregistering, and reporting their simulation studies. We give formulae for estimating common performance measures, their Monte Carlo standard errors, and for calculating the number of simulation repetitions to achieve a desired Monte Carlo standard error. Finally, we give a detailed tutorial on how to apply the ADEMP framework in practice using an example simulation study on the evaluation of methods for the analysis of pre-post measurement experiments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

New stellar age estimates using SPInS based on Gaia DR3 photometry and LAMOST DR8 abundances

Reliable stellar age estimates are fundamental for testing several problems in modern astrophysics, in particular since they set the timescales of Galactic dynamical and chemical evolution. In this study, we determine ages using only photometry and parallaxes, in combination with interstellar extinction maps, and spectroscopic metallicities and alpha abundances from the latest data release (DR8) of the LAMOST survey. In contrast with previous age estimates, we do not use spectroscopic effective temperatures or surface gravities, and thus we rely on the excellent precision and accuracy of the Gaia photometry. We use a new version of the publicly available SPInS code with improved features, including the on-the-fly computation of the autocorrelation time and the automatic convergence evaluation. We determine reliable age estimates for 35,096 and 243,768 sub-giant and main-sequence turn-off stars in the LAMOST DR8 low- and medium-resolution surveys with typical uncertainties smaller than 10<!PCT!>. In addition, we successfully test our method on more than 4,000 stars of 14 well-studied open and globular star clusters covering a wide range of ages, confirming the reliability of our age and uncertainty estimates.

Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization.

Geometry optimization is an important tool used for computational simulations in the fields of chemistry, physics, and material science. Developing more efficient and reliable algorithms to reduce the number of force evaluations would lead to accelerated computational modeling and materials discovery. Here, we present a delta method-based neural network-density functional theory (DFT) hybrid optimizer to improve the computational efficiency of geometry optimization. Compared to previous active learning approaches, our algorithm adds two key features: a modified delta method incorporating force information to enhance efficiency in uncertainty estimation, and a quasi-Newton approach based upon a Hessian matrix calculated from the neural network; the later improving stability of optimization near critical points. We benchmarked our optimizer against commonly used optimization algorithms using systems including bulk metal, metal surface, metal hydride, and an oxide cluster. The results demonstrate that our optimizer effectively reduces the number of DFT force calls by 2-3 times in all test systems.

TCP-UQ: two-color pyrometry uncertainty quantification using high speed color cameras

Abstract In this study, an uncertainty quantification method is developed for two-color pyrometry (TCP-UQ), using high-speed color cameras. A NIST traceable calibrated light source is used and the relative exposure is determined using spectrometer measurements and color camera response functions. The inherent digitization and noise uncertainty is estimated from a set of calibration images and characterized with probability distribution functions. These uncertainties are then propagated during TCP to two quantities of interest: the temperature and soot volume fraction measurements. TCP-UQ is applied to a polymethyl methacrylate slab burner experiment to determine the temperature and the soot volume fraction fields. Temperature measurements ranged from 2300 K to 2800 K , soot volume fractions are recorded between 0.5 and 10 ppm and are in good agreement with prior measurements reported in the literature. The newly developed TCP-UQ allows for uncertainty estimations in the temperature and the soot volume fraction measurements to be associated with variations in the pixel intensities. For the slab burner experiment, the average uncertainty in the temperature is between 120–140 K and the average uncertainty in the soot volume fraction is around 0.1 ppm. Generally, higher pixel intensities corresponded to lower uncertainty in the temperature measurement.

An Efficient LC–HRMS-Based Approach to Evaluate Pesticide Contamination in Water Bodies with Measurement Uncertainty Considerations

Over recent decades, the global occurrence of pesticide residues in aquatic environments has been a pivotal issue; however, their trace-level concentrations necessitate the establishment of ultra-sensitive and reliable analytical approaches. To this end, the present study describes the optimization and validation of an LC-HRMS-based method for the accurate determination of 18 pesticides in river and sea water, accompanied by a measurement uncertainty estimation. This method was applied to analyze 17 real samples from agriculture and aquaculture-impacted areas in Greece and Albania. Different solid-phase extraction (SPE) protocols were tested. For the analysis, cutting-edge Orbitrap MS technology and MS/MS fragmentation, along with the use of matrix-matched calibration curves, provided unprecedented accuracy (<5 ppm) and sensitivity for the confirmation of positive detections. Regarding method performance, exceptional linearity was obtained; the limits of quantification ranged from 1.7 ng L−1 to 90 ng L−1, recoveries varied from 61% to 96% in river water, while slightly higher recoveries (60–111%) were observed in seawater. In all cases, repeatability and intra-laboratory reproducibility were below 15%. The measurement expanded uncertainty (U′, k = 2) was estimated considering precision and bias. MU% values were lower than 50% in all cases, as recommended in SANTE guidelines and applied to the quantified results. The matrix effect study exhibited negative values (<20%) for all compounds. Application to real samples showed a low pesticide contamination load that should not be underestimated.

A new correlation for estimating solid particle erosion in elbows

Throughout the Literature, solid particle erosion prediction models have been developed based on limited data without considering uncertainties involved in measurements. In this study, a new correlation is designed to estimate the maximum erosion rate caused by solid particles in standard elbow geometries. The novel development process of this correlation includes uncertainty estimation of erosion measurements. To estimate uncertainties in an erosion database, it is imperative to have a model that can be used to calculate accurate erosion values and provide reliable references. For this purpose, a database of repeated erosion measurements for gas-sand, low-pressure conditions collected by previous researchers is used. The database includes standard elbow geometries with 0.0762 m and 0.1016 m internal pipe diameter, and sand particles with mean diameters of 75 and 300 μm. Then, the erosion values in this database are adjusted to agree with the physical behaviors expected in the erosion process. Finally, based on the adjusted erosion values, a new correlation is developed that accounts for important parameters such as pipe and particle diameters, and gas velocity. The results and comparison of this new model with other correlations/models in the literature show that this correlation performs very well in predicting solid-particle erosion values for elbow geometry with an RMSE value of 7.96E-02 mm/kg. The effect of superficial liquid velocity is also investigated and modeled based on a database of gas-liquid-sand erosion, resulting in an updated version of the correlation for predicting erosion in gas-liquid-sand flow conditions. Furthermore, CFD results for higher-pressure fluids and larger pipe diameters of 0.1524 m and 0.2032 m and mean particle sizes of 25, 75, 150, and 300 μm, are used to further validate the correlation for larger pipe sizes and high-pressure fluid flows. In summary, three correlations have been developed in this research; one for low-pressure flows with application in uncertainty estimation, one for predicting erosion in two-phase flow conditions, and one for cases with larger pipe diameters or high-pressure fluids.

Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications

The current study investigates the robustness of deep learning models for accurate medical diagnosis systems with a specific focus on their ability to maintain performance in the presence of adversarial or noisy inputs. We examine factors that may influence model reliability, including model complexity, training data quality, and hyperparameters; we also examine security concerns related to adversarial attacks that aim to deceive models along with privacy attacks that seek to extract sensitive information. Researchers have discussed various defenses to these attacks to enhance model robustness, such as adversarial training and input preprocessing, along with mechanisms like data augmentation and uncertainty estimation. Tools and packages that extend the reliability features of deep learning frameworks such as TensorFlow and PyTorch are also being explored and evaluated. Existing evaluation metrics for robustness are additionally being discussed and evaluated. This paper concludes by discussing limitations in the existing literature and possible future research directions to continue enhancing the status of this research topic, particularly in the medical domain, with the aim of ensuring that AI systems are trustworthy, reliable, and stable.

Robust quantum dots charge autotuning using neural network uncertainty

Abstract This study presents a machine learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural network uncertainty estimations. Tested across three distinct offline experimental datasets representing different single-quantum-dot technologies, this approach achieves a tuning success rate of over 99% in optimal cases, where more than 10% of the success is directly attributable to uncertainty exploitation. The challenging constraints of small training sets containing high diagram-to-diagram variability allowed us to evaluate the capabilities and limits of the proposed procedure.

Inferring stellar parameters and their uncertainties from high-resolution spectroscopy using invertible neural networks

New spectroscopic surveys will increase the number of astronomical objects in need of characterisation by more than an order of magnitude. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine learning algorithms cannot directly estimate error, making them unsuitable for reliable science. We aim to train a supervised deep-learning algorithm tailored for high-resolution observational stellar spectra. This algorithm accurately infers precise estimates while providing coherent estimates of uncertainties by leveraging information from both the neural network and the spectra. We trained a conditional invertible neural network (cINN) on observational spectroscopic data obtained from the GIRAFFE spectrograph (HR10 and HR21 setups) within the Gaia-ESO survey. A key feature of cINN is its ability to produce the Bayesian posterior distribution of parameters for each spectrum. By analysing this distribution, we inferred stellar parameters and their corresponding uncertainties. We carried out several tests to investigate how parameters are inferred and errors are estimated. We achieved an accuracy of 28K in $T_ eff $, 0.06 dex in $ g$, 0.03 dex in Fe/H $, and between 0.05 dex and 0.17 dex for the other abundances for high-quality spectra. Accuracy remains stable with low signal-to-noise ratio (between 5 and 25) spectra, with an accuracy of 39K in $T_ eff $, 0.08 dex in $ g$, and 0.05 dex in Fe/H $. The uncertainties obtained are well within the same order of magnitude. The network accurately reproduces astrophysical relationships both on the scale of the Milky Way and within smaller star clusters. We created a table containing the new parameters generated by our cINN. This neural network represents a compelling proposition for future astronomical surveys. These derived uncertainties are coherent and can therefore be reused in future works as Bayesian priors.

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainty with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of classification of winter precipitation type and regression of surface layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. In order to encourage broader adoption of evidential deep learning, we have developed a new Python package, MILES-GUESS (https://github.com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.

River suspended-sand flux computation with uncertainty estimation using water samples and high-resolution ADCP measurements

Abstract. Measuring suspended-sand fluxes in rivers remains a scientific challenge due to their high spatial and temporal variability. To capture the vertical and lateral gradients of concentration in the cross-section, measurements with point samples are performed. However, the uncertainty related to these measurements is rarely evaluated, as few studies of the major sources of error exist. Therefore, the aim of this study is to develop a method to determine the cross-sectional sand flux and estimate its uncertainty. This SDC (for sand discharge computing) method combines suspended-sand concentrations from point samples with ADCP (acoustic Doppler current profiler) high-resolution depth and velocity measurements. The MAP (for multitransect averaged profile) method allows obtaining an average of several ADCP transects on a regular grid, including the unmeasured areas. The suspended-sand concentrations are integrated vertically by fitting a theoretical exponential suspended-sand profile to the data using Bayesian modeling. The lateral integration is based on the water depth as a proxy for the local bed shear stress to evaluate the bed concentration and sediment diffusion along the river cross-section. The estimation of uncertainty combines ISO standards and semi-empirical methods with a Bayesian approach to estimate the uncertainty due to the vertical integration. The new method is applied to data collected in four rivers under various hydro-sedimentary conditions: the Colorado, Rhône, Isère, and Amazon rivers, with computed flux uncertainties ranging between 18 % and 32 %. The relative difference between the suspended-sand flux in 21 cases calculated with the proposed SDC method compared to the International Organization for Standardization (ISO) 4363 standard method ranges between −40 % and +23 %. This method that comes with a flexible, open-source code is the first to propose an applicable uncertainty estimation that could be adapted to other flux computation methods.

GGR Handbook of Rock and Mineral Analysis Chapter 1 (Part 2) Sampling as Part of the Measurement Process

This chapter (Sampling as Part of the Measurement Process) is a contribution to the Geostandards and Geoanalytical Research Handbook of Rock and Mineral Analysis – an online textbook that is a fully revised and updated edition of A Handbook of Silicate Rock Analysis (P. J. Potts, 1987, Blackie, Glasgow).Chapter 1 (Part 2) forms part of Section 1 of the handbook dealing with fundamentals of measurement and instrument design. The geochemical measurement process is considered to begin when the primary sample is taken from the sampling target, rather than when that sample arrives at the laboratory. This integration of sampling within the measurement procedure enables both sampling and chemical analysis to be optimised in order to achieve a measurement procedure that is fit for its intended geochemical purpose. The key metric in judging this fitness for purpose, and hence validating a measurement procedure, is the uncertainty of each measurement value. This measurement uncertainty is explained, together with methods to estimate and express it in a way that includes the contribution from sampling, with a worked example. The resultant more realistic estimates of measurement uncertainty are shown to improve the reliability of the geochemical interpretation of measurement values.

A Likelihood-Based Triangulation Method for Uncertainties in Through-Water Depth Mapping

Coastal environments, which are crucial for economic and strategic reasons, heavily rely on accurate bathymetry for safe navigation and resource monitoring. Recent advancements in through-water photogrammetry have shown promise in mapping shallow waters efficiently. However, robust uncertainty modeling methods for these techniques, especially in challenging coastal environments, are lacking. This study introduces a novel likelihood-based approach for through-water photogrammetry, focusing on uncertainties associated with camera pose—a key factor affecting depth mapping accuracy. Our methodology incorporates probabilistic modeling and stereo-photogrammetric triangulation to provide realistic estimates of uncertainty in Water Column Depth (WCD) and Water–Air Interface (WAI) height. Using simulated scenarios for both drone and airborne surveys, we demonstrate that viewing geometry and camera pose quality significantly influence resulting uncertainties, often overshadowing the impact of depth itself. Our results reveal the superior performance of the likelihood ratio statistic in scenarios involving high attitude noise, high flight altitude, and complex viewing geometries. Notably, drone-based applications show particular promise, achieving decimeter-level WCD precision and WAI height estimations comparable to high-quality GNSS measurements when using large samples. These findings highlight the potential of drone-based surveys in producing more accurate bathymetric charts for shallow coastal waters. This research contributes to the refinement of uncertainty quantification in bathymetric charting and sets a foundation for future advancements in through-water surveying methodologies.

The Use of Image‐Based Data and Abundance Modelling Approaches for Predicting the Location of Vulnerable Marine Ecosystems in the South Pacific Ocean

ABSTRACTVulnerable marine ecosystems (VMEs) are typically fragile and slow to recover, thereby making them susceptible to disturbance, including fishing. In the high seas, the United Nations General Assembly (UNGA) requested regional fishery management organisations (RFMOs) to implement measures to prevent significant adverse impacts on VMEs. Here, we predict spatial abundances of 15 taxa, 13 VME indicator taxa, in the South Pacific RFMO (SPRFMO) area. Models used seafloor imagery data, an important advance on previously developed presence‐only predictions, to provide information on spatial variation in taxa abundance that is crucial for better inferring likely location of VMEs, rather than just distribution of VME indicator taxa. Abundance models varied in predictive power (mean R2 ranged 0.02–0.40). Uncertainty estimates of model predictions were developed to inform future spatial planning processes for conservation and management of VMEs. Using the VME index concept, abundance model outputs and previously published presence‐only model predictions were weighted using vulnerability scores, to explore how modelled outputs could provide spatial estimates of likely VME distribution. Spatial predictions of abundance improved on previous modelling to provide an almost complete suite of abundance models for VME indicator taxa in the western portion of the SPRFMO Convention area. Nevertheless, to improve utility of modelled outputs, we recommend more high‐quality seafloor imagery data be gathered within the SPRFMO Convention area to (1) validate abundance models developed here with independent data from the model area, (2) update models, if necessary, (3) link abundance information to ecosystem function and (4) explore validity of the adapted VME index approach used here.