Assigning taxonomic identity and thus assessing taxonomic diversity in gelatinous zooplankton is challenging due to their fragility and difficult preservation. Formalin fixation compromises DNA integrity, while flash-freezing, ethanol, and acetone preservation affect morphological features. In this study, we developed a methodological protocol to optimize morphological and molecular identification of pelagic cnidarians and tunicates from the Gulf of Naples (Mediterranean Sea). Specimens were identified while fresh and preserved using four methods: liquid nitrogen (N), ethanol (EtOH), ethanol replaced with acetone (EA), and formalin followed by ethanol and acetone (FEA). Genomic DNA was extracted and PCR-amplified targeting the 18S rRNA gene, being subsequently assessed for concentration, integrity, and sequence quality. FEA specimens retained better morphology compared to N, EtOH, or EA samples, while sequencing performance was comparable between methods. DNA concentrations did not significantly differ among treatments (range: 139-1645 ng ind(-1)) and smaller taxa showed higher values per unit size compared to larger ones. DNA integrity was lower in N and FEA samples, and often undetectable in EA and FEA when assessed by gel electrophoresis. Although 18S : gDNA ratio decreased in some FEA samples, this method still produced high-quality reads (> 500 nucleotides). Our findings show that formalin fixation followed by ethanol and acetone does not compromise molecular analysis, even for species with low DNA content. Unlike other methods, this optimized protocol preserves essential morphological features for accurate taxonomic identification of gelatinous zooplankton while substantially reducing molecular processing time. Overall, the adoption of the proposed protocol could significantly enhance biodiversity assessments of gelatinous taxa.
Concentrations of the photosynthetic pigment chlorophyll a are commonly used as a proxy for phytoplankton biomass in aquatic systems. Traditional methods for extracting chlorophyll a in discrete samples limit measurement frequency, while in situ sensor technology provides high frequency chlorophyll a fluorescence measurements at a more appropriate time scale to study processes related to phytoplankton turnover and a more rapid response to harmful algal blooms. Caveats to in situ measurements include that temperature, fluorescent dissolved organic matter, and turbidity can affect accuracy and that chlorophyll a fluorescence does not always correlate well to extracted chlorophyll a. Therefore, practitioners need guidance to interpret fluorescence data considering potential errors and relationships to extracted chlorophyll a to inform management decisions. Before this project, the nature of those relationships had not been tested across a wide gradient in water properties. To address these gaps, a one-year study was conducted across 12 biogeochemically diverse sites to quantify possible fluorescence interferences and test the predictability of extracted chlorophyll a using uncorrected in situ chlorophyll a (in relative fluorescence units) combined with routinely measured water quality parameters. Overall, sensor fluorescence was positively correlated with extracted chlorophyll a, but the strength and drivers of the relationship varied by site. Temperature, turbidity, and fluorescent dissolved organic matter influenced sensor readings independently of phytoplankton biomass. Hence, considerations for in situ sensor implementation depend on monitoring goals and resources. Where feasible, we recommend the simultaneous use of in situ and extractive approaches to track short-term variability and long-term change in chlorophyll a.
Sediment traps, often used in tandem with preservatives or poisons, are widely used for the collection of particulate organic matter (POM), providing insight into the source to sink mechanisms that shape major biogeochemical cycles and sedimentary carbon sequestration. The effectiveness of these treatments has been studied for marine POM, but is poorly constrained for freshwater POM, whose molecular composition differs due to greater terrigenous input. We tested four treatments commonly used in marine sediment traps, along with untreated controls, over a 1-yr period to assess their ability to retain original elemental and isotopic compositions of organic carbon (%TOC and delta 13Corg) and nitrogen (%TN and delta 15N) in POM collected from a freshwater pond (Willow Pond, Michigan, USA). Consistent with marine studies, 0.005% (w/v) mercuric chloride was the most effective in preserving both C and N geochemical signatures across the 1-yr incubation period. Untreated samples also maintained original POM geochemical signals but only for approximately 6 months, providing an inexpensive, nontoxic alternative that can be used for shorter deployment or storage periods.
While acidification is widely recognized as a critical step influencing the molecular formula distribution of dissolved organic matter (DOM), pH-dependent molecular fractionation remains largely unexplored. Here, we conducted a comprehensive molecular characterization of DOM by direct analysis of original water samples across a pH gradient (non-acidified, 5, 3, 2, and 1) using online ultrahigh-performance liquid chromatography coupled with Orbitrap mass spectrometry. The results demonstrate that acidification-induced molecules experience a compositional shift from saturated compounds to more oxidized polyphenolic and condensed aromatic species as pH decreases from 5 to 2. However, molecules with higher double-bond equivalents and nitrogen-containing compounds are largely detected when the pH is lowered to 1. This is primarily attributed to the enhanced protonation of DOM molecules under strongly acidic conditions, which reduce their ionization, further improves their retention in reversed-phase systems and facilitates their effective separation from inorganic salts, indicating pH-dependent fractionation of ionizable species. Compared with direct analysis of original water under optimized acidification (pH 1), terrestrially derived and microbial-altered components are largely underestimated in DOM from solid-phase extraction. Given that DOM sources and secondary alterations are key processes governing the fate of DOM, the method developed in this study provides new insights into the composition and transformation of DOM in the original water and offers a novel perspective for exploring its biogeochemical implications in aquatic systems. Future studies should systematically analyze samples from diverse environments to constrain the driving factors of DOM compositional heterogeneity and its environmental implications.
The demand for efficient image sorting methods has increased due to technological advancements that enable more intensive phytoplankton monitoring. Both statistical and machine learning algorithms can misidentify algal taxa in taxonomically diverse samples, in which phytoplankton morphology and image traits can vary. We evaluated the statistical filtering performance of the image processing software of an imaging flow cytometer (FlowCam) for two approaches to image library development; these were applied independently to seven commonly occurring algal shapes in mixed natural samples. The "intrinsic method" used a small selection of images (5-15 images of a target taxon) from the same sample being filtered (i.e., intrinsic), whereas the "compiled method" used a larger selection of images (30-80 images of a target taxon) compiled from multiple samples. Filter performance varied with the type of image library, image library size, and target taxon. The largest image libraries offered the highest recall (> 86% for intrinsic, > 94% for compiled) but lower precision (3-85% for intrinsic, < 1-8% for compiled). Precision was highest for the smallest image libraries, and was higher for the intrinsic method (> 75% for most taxa) than the compiled method (< 20% for most taxa). Statistical filtering performance was higher for larger, solitary-celled taxa with relatively uniform features (e.g., Gyrosigma) compared to small-celled colonial species with more complex or variable shapes (e.g., mucilaginous colonial cyanobacteria, and Scenedesmus). Iteratively using the intrinsic statistical filtering method with manual correction between each iteration can be used to augment manual sample classification and reduce processing time.
Recent technological advancements have rapidly expanded our capacity for collecting image data in the marine environment, but processing images into meaningful ecological metrics remains a manual, time-consuming, and biased process. This is particularly challenging with electro-optical cabled imaging systems which generate images at a rate that makes manual identification impractical. To address this challenge, we have developed a machine learning-assisted method for annotating images. Our approach leverages a pre-trained model based on marine-specific imagery from the FathomNet database (499 classes; some classes to species level). We demonstrated the application of this method on a 1-yr time series of images collected at Southern Hydrate Ridge by a digital still camera on the NSF Ocean Observatories Initiative Regional Cabled Array, which resulted in 92,153 benthic megafaunal (organisms > 2 cm) annotations across 10 morphotaxa classes in 50,840 images. This method annotated the full dataset in 6 weeks, compared to an estimated similar to 5.9 yr required for fully manual annotation, representing approximately a 50-fold increase in efficiency. This process also produced a computer vision model with a precision of 0.75, recall of 0.80, mAP50 of 0.84, and mAP50-95 of 0.65. Our method combines machine learning efficiency with human expertise to create high-quality, verified datasets. The output of this methodology is key to achieving the full potential of sustained ecosystem monitoring via cabled observing systems that can capture both short-term and long-term ecological and environmental dynamics.
Monitoring phytoplankton abundance is essential for understanding ecosystem dynamics and detecting harmful algal blooms (HABs). Traditional microscopy-based single-cell counting, while accurate, is time-consuming and poorly suited to high temporal and spatial resolution monitoring. Automated imaging approaches have therefore been developed to assist cell detection and counting, but they face challenges when dealing with colonial forms. In this study, we investigate transfer learning for automated cell counting in digital images of phytoplankton colonies acquired with a FlowCam system. Several Convolutional Neural Network (CNN) architectures pre-trained on the ImageNet database were fine-tuned using annotated datasets of two ecologically relevant colonial taxa commonly observed in the English Channel and the North Sea: Pseudo-nitzschia and Phaeocystis globosa. Model performance and robustness were evaluated using mean absolute error (MAE) and species-specific accuracy metrics. Across both taxa, DenseNet121 architecture achieved the best performance, with a Top 2 accuracy up to 99% for P.-nitzschia and a Top 20% accuracy exceeding 87% for P. globosa. Data augmentation improved model robustness and generalization, particularly for colonies with higher cell numbers. These results demonstrate the ability of deep learning to capture complex spatial patterns and improve counting accuracy across taxa with different morphologies. Beyond methodological performance, this study raises questions about the adequacy of current HAB alert thresholds based on microscopic counts when transitioning to automated imaging systems. Transfer learning provides a robust, fast, and scalable approach that complements traditional monitoring and supports the development of improved environmental assessment strategies.
Mountain lakes are highly sensitive ecosystems and effective sentinels of environmental change, yet the exposure and magnitude of the human footprint remain poorly quantified. In this study, we develop a simple and non-invasive abiotic index to assess cumulative pressures on mountain lakes. The proposed index integrates eight variables grouped into three categories (potential human impacts, accessibility, and recreational exposure) into a single cumulative score, providing a straightforward but informative measure of human pressure on lakes. We employed a dataset comprising 29 lakes from two Iberian protected areas (Sierra de Gredos and Sanabria region) as a case study for assessing the reliability of the index. We observed strong variability among lakes, highlighting the importance of local pressures that are often overlooked by global indicators such as the Human Footprint Index. Our index provides an accessible first-step diagnostic tool that facilitates the integration of human pressure assessment into site-specific monitoring and conservation actions.
Habitat heterogeneity is a key driver of temporal and spatial variability of subtidal marine benthic biodiversity. However, this makes it a challenging environment in which to measure and quantify the factors driving biodiversity in a consistent manner. Current methodologies are either expensive, logistically challenging, require extensive technical knowledge, or are limited to shallow water environments. To overcome some of these limitations, a novel "SeaPen" device was designed as a cost-effective, safe, easily deployable instrument that can provide repeatable and comparable survey results over the small spatial scales required to describe heterogeneous environments. The device, which comprises a tripod frame holding a steel weighted bar in the center, allows gravity to force a graduated metal spindle into the substratum when it is dropped. A video camera fixed to a tripod leg allows the recording of the nature of the seabed and the depth of penetration of the spindle, from which seabed properties can be described. The device does not need to be recovered to the surface between measurements, which allows it to be repeatedly dropped for multiple replicates in a single deployment. The combination of a penetrating spindle and a camera allows for rapid repeat measures and, most importantly, can be used to measure heterogeneity on finer scales (within a site; m) than other more traditional techniques. The SeaPen was used in a shallow water (<50 m) coastal habitat on the West Antarctic Peninsula. The survey area comprised heterogeneous substrata composed of sediments interspersed between boulders, cobbles, and pebbles, which allowed for proof of concept that the SeaPen could discriminate a wide range of seabed habitat types.
Heart rate is a popular proxy of physiological responses, but the highly complex and variable cardiac data obtained from organisms such as marine invertebrates pose a major challenge to efficient and accurate data processing. To address this, we developed a novel, integrative algorithm for rapid and automated cardiac data processing. This algorithm primarily employs autocorrelation for time series analysis to identify recurrent heartbeats and compute heart rates from their periods. A genetic algorithm framework was used to implement such an autocorrelative analysis to filter out noise and extract meaningful signals, maximizing data utilization. A tracking index is also incorporated to reference previous timepoints and reduce errors associated with complex waveforms. To evaluate its performance, we compared the algorithm estimates to manually obtained heart rates of 33 individuals of marine invertebrates (from nine species of gastropods, bivalves, and crustaceans). The results showed that these features collectively improve data utilization (mean percentage count > 90%) and accuracy (mean absolute percentage error = 3%). By avoiding reliance on any predetermined characteristics, this algorithm can not only accommodate case-by-case variability and thus be applicable to diverse taxa, but also potentially extend to analyze periodicity in other biological time series data such as valvometry, acoustics and movement patterns. As an open-source tool, this algorithm encourages collaborative efforts and further developments that refine and expand its applications, thereby enhancing our capabilities in physiological monitoring and analyses.
Greenhouse gas (GHG) emissions from freshwater ecosystems contribute significantly to global carbon budgets, yet they remain poorly constrained due to limited high-frequency measurements. We tested a low-cost, high-frequency GHG measurement system in a long-term mesocosm experiment in Lemming, Denmark, over a 7-month period, focusing on CO2 and CH4 fluxes. We deployed a methodology for calculating CH4 diffusive fluxes using high-frequency sensor data and tested the effects of sampling intervals on emission upscaling. Our findings reveal substantial temporal variability in GHG emissions, particularly for CH4, with ebullitive fluxes dominating and exhibiting large variation. Pronounced diurnal fluctuations were also observed for CO2 and diffusive CH4 fluxes, whereas ebullitive CH4 emissions showed no significant diurnal pattern. Relying solely on daytime measurements led to a significant overestimation of overall CO2 fluxes and CH4 diffusive fluxes. Resampling data at lower frequency showed that reduced sampling frequency leads to an underestimation of total emissions, especially for CH4 ebullitive fluxes. Increasing the sampling interval from daily to monthly markedly increased uncertainty, while weekly sampling better captured overall GHG flux patterns and reduced the uncertainty compared with more infrequent sampling. These results underscore the value of high-frequency GHG measurements in capturing both diurnal and seasonal variations, improving the accuracy of flux estimates, and reducing uncertainties in upscaling emission. We emphasize the need to optimize sampling intervals and incorporate diurnal cycle measurements to enhance the accuracy and reliability of freshwater GHG assessments.
We present a novel motorized platform, a custom-built jet-ski designed for acoustic and radiometric measurements in optically shallow coastal waters. This platform integrates in-water radiometers with single-beam and multi-beam acoustic sensors, along with a suite of active and passive instruments (conductivity-temperature-depth, fluorescence, and backscattering sensors). Such a configuration is particularly well-suited for environments where water depth, bottom type, and optical properties must be jointly characterized. Here, we perform a detailed assessment of the platform's design and its impact on the uncertainty of radiometric measurements and derived apparent optical properties (AOPs). Using a Monte Carlo approach, we propagated uncertainties associated with radiometric measurements, sensor depth, platform tilt, and self-shading corrections. The total uncertainty budget was decomposed to identify the relative contribution of each component. Our results show that over half of the total uncertainty originates from upwelling radiance measurements at two depths near the sea-air interface. The shallower sensor ( 15 cm) contributes 37-48% of the uncertainty, while the deeper sensor ( 30 cm) accounts for 19-27%. Downwelling irradiance above the surface contributes an additional 12-20%. Overall, the uncertainty associated with in-water radiometry from the jet-ski was comparable to that of above-water systems (e.g., HyperSAS) operated simultaneously at two stations. These findings support the use of the jet-ski platform for research applications in optically shallow waters, offering a reliable and mobile solution for spatially extensive sampling and remote sensing validation.
Particles sinking from the surface to the deep ocean play a key role in the biological carbon pump, whose efficiency depends partly on sinking velocities. Over the last decade, in situ imaging has enabled critical advances in our understanding of particle dynamics in the ocean. Yet, in situ velocity measurements are scarce and often inferred only from the bulk population of particles. Here, we introduce the VisuTrap, a new tool to measure in situ velocities of marine particles. It consists of an Underwater Vision Profiler 6 (UVP6) camera inserted into different types of sediment traps, which isolate a volume of water. Continuous image acquisition during short-term or long-term deployments enables reconstruction of particle tracks and estimation of their in situ vertical velocities. We detail the configuration and special UVP6 settings for this application, as well as the image processing and track analysis pipeline. Then, we present results from several experiments in the Mediterranean Sea to illustrate the VisuTrap's use as a new approach to understand the dynamical behavior of marine particles in situ. In light of the broad range of morphological data generated by the UVP6, we discuss technical additions to refine in situ velocity measurements and the possibility of integrating such data into carbon flux assessments.
Understanding a population's distribution depends on observing the presence and movement of individuals throughout their range. For highly mobile marine species, these observations typically rely on high effort monitoring programs. Tracking enough individuals to understand trends in movement behavior is not always logistically feasible, and animals are less likely to be observed in migratory or transitional habitats. To optimize observation data we built a Brownian bridge movement model to generate spatially and temporally explicit space use estimations of individuals along tracks created from intermittent sightings of North Atlantic right whales from 1980 to 2022. Right whales can be identified by unique callosities and markings, providing a noninvasive opportunity to link sightings to individual movement. This model generated location probability estimates of medium- and large-scale movements in biologically plausible habitats. A total of 351,214 d of occurrence distributions were calculated from 67,840 sightings attributed to 806 individuals, representing more than a five-fold increase in individual spatial information. From 1980 to 2022, the model generated space use estimates for at least one whale on 75.7% of days, compared to the underlying visual sightings data, which only provided observations on 38.7% of days. Model outputs compared to tracks of tagged whales demonstrated proficiency estimating space use across regions. The occurrence distributions produced depict known changes in seasonal and decadal space use and estimate transitional space use where observations are sparse. These methods improve spatial distribution predictions in intermittently observed animals and expand the utility of stationary sightings without constraining predictions to historical relationships with the environment.
Accurate direct measurements of coccolithophore particulate inorganic and organic carbon ratios (PIC/POC) are crucial for understanding the physiology and role these microbes play in global ocean carbon flux. Direct measurements of POC are commonly obtained by acid fuming or applying acid directly to filters to dissolve PIC (CaCO3) prior to CN analysis. Review of the literature revealed considerable variation in methods for this decalcification step demonstrating a clear need for a validated standard operating procedure. Importantly, visual verification of complete decalcification after acid treatment was absent. We therefore systematically tested the efficacy of two common acid decalcification methods for several species of coccolithophores. We examined acid fuming wet or dry filters, time spent fuming, and whether there are significant differences in PIC/POC determined using decalcification by acid fuming or direct addition. Scanning electron microscopy was used to visually verify acid fuming completely decalcified filters and there was no statistically significant difference in PIC/POC obtained by fuming for 5 min to 24 h. Direct addition of 0.5 M HCl to wet filters also yielded complete decalcification across all species and cell densities tested with less variation in subsequent CN-analysis. There were no statistically significant differences in PIC/POC obtained from samples treated either by direct acid addition, 30 min acid fuming, or 24 h acid fuming. Moreover, by utilizing the direct acid addition method we were able to reliably resolve PIC/POC for haploid coccolithophores. This study provides important recommendations for obtaining accurate and precise PIC/POC utilizing either acid fuming or direct addition protocols.
Absorption of light by colored dissolved organic matter (CDOM) is often measured for in situ samples using benchtop spectrophotometers, whose valid wavelength ranges are set by the CDOM concentration of the sample and by instrument constraints such as linear response limit and instrument uncertainty. As tools for algorithm development, calibration, and validation, these measurements are most useful when they report CDOM absorption coefficients across the greatest wavelength range, which often means extending further into ultraviolet wavelengths. To maximize the reported spectral range while delivering the highest-quality measurements across ultraviolet-visible wavelengths we developed and evaluated two approaches, the value threshold and percent difference approaches, to combine CDOM absorption spectra of the same sample measured on two different spectrophotometers that have different but overlapping spectral ranges. Colored dissolved organic matter absorption measurements from 1001 samples were examined, which were sourced from nine different field campaigns with broad optical water type properties. The value threshold approach successfully produces a merged product with larger spectral range for coastal ocean and open ocean samples, transitioning between the input measurements over a span of 40-80 nm where both measurements are valid based on the instrument characteristics of linear response limit and instrument uncertainty. The percent difference approach relies on the percent difference between spectra from the two instruments and works for coastal ocean waters but fails for most open ocean samples where there is more uncertainty proportional to CDOM signal. The value threshold approach can be modified to reduce noise propagation in the merged product and can also be adapted for use with other benchtop spectrometer models, making it highly generalizable.
Dissolved black carbon (DBC) is an important component of dissolved organic matter (DOM), and it plays an essential role in global carbon cycling through its ubiquity and roles in mediating biogeochemical and environmental reactions. Currently, DBC in DOM is quantified by oxidation with nitric acid to produce benzene polycarboxylic acids (BPCAs) and subsequently measured by ion-pair reverse-phase liquid (IP-RP) chromatography with photodiode array (PDA) detection. This popular method relies solely on retention times and light absorbance for analyte identification and quantitation, leading to potential errors if co-eluting and light absorbing non-BPCA compounds are present. Moreover, this method is incompatible with mass spectrometry (MS) detection due to its mobile phase composition. To address these limitations, our study evaluated an alternative MS-compatible quantification method that can be used to corroborate DBC values determined by IP-RP. This alternative method separates the target analytes using multi-mode ion exchange-reverse phase columns that elute the necessary BPCAs with MS-friendly mobile phases. We analyzed various DOM samples from different environments. Detection was performed using high-performance liquid chromatograph with both a PDA and a single quadrupole electrospray ionization mass spectrometer detector arranged in series. This MS-friendly method enabled us to identify BPCA with higher confidence relative to methods that rely solely on retention times and PDA light absorbance, while also introducing the potential for superior limits of detection relative to current methods in future studies utilizing higher sensitivity tandem MS instruments.
Lipids are known to affect stable isotope ratio of organisms, especially delta 13C values, and simple arithmetic lipid-correction procedures have been developed based on the carbon to nitrogen ratio (C : N) that is a proxy for lipid content. Equivalent issues will likely arise with the increasing use of hydrogen isotopes in ecology, but as yet no procedure has been available to handle this. We extracted lipids from muscle and from whole fish using a standard chemical extraction procedure. Using isotope ratio mass spectrometry, we then compared the delta 2H values of untreated tissues, lipid-extracted tissues, and the extracted lipids. In the process of performing carbon and nitrogen stable isotope analysis on the same sample, elemental data on carbon and nitrogen content were obtained simultaneously. Using these data, we determined the relationship between lipid concentration and C : N. Extracted lipids were strongly depleted in the heavy isotope of hydrogen, so that tissues from which lipids had been extracted had higher delta 2H values than those of untreated tissues. The lipid content of the samples was directly related to their C : N, so the change in tissue delta 2H values following lipid extraction was also strongly dependent on tissue C : N. This strong dependence allowed the development of a simple arithmetic procedure to correct delta 2H values for variations in tissue lipid content. Arithmetic correction offers a way to normalize delta 2H values from animal tissues with respect to the variations in lipid content and can find wide application in ecological studies using hydrogen isotopes.
Bottom sediments in tidal estuaries influence organic matter and nutrient cycling, habitat suitability, and geomorphological processes. Characterizing the grain size distribution of bottom sediments is essential for predicting sediment transport, channel stability, and ecosystem health. However, this information can be challenging to acquire over large areas as traditional in situ approaches provide only point-based observations that are spatially limited. This study addresses this limitation by applying the sediment balance equation to remotely sensed maps of total suspended solids concentration and numerical model outputs to derive a high-resolution, spatially explicit sediment grain size distribution within a tidal channel of a New England mesotidal estuary. Results reveal a distinct gradient in sediment grain size, with coarse sediments near the inlet transitioning to finer sediments landward. Fine sand covers over 85% of the channel bottom, while medium and coarse sand occupy 14% and 1%, respectively. Peaks in settling velocity identify zones of sediment convergence controlled by tidal forcing and river inflows. The positive correlation between , estimated through the depth integrated suspended sediment continuity equation, and bottom grain size confirms the effectiveness of this approach for sediment classification in estuarine environments.
High-resolution mapping of seafloor habitats has wide applications for fisheries, conservation efforts, offshore infrastructure planning, mineral extraction, and scientific modeling. This study leverages existing widespread single-beam acoustic data and machine learning to create habitat maps for the Gulf of Alaska at a spatial resolution of less than 30 m. A convolutional neural network was trained using concurrent underwater images as the ground truth. To extract habitat type from the images, a semantic segmentation based on a random forest model was used to classify the interspersed substrate types. For the acoustic data, two models were trained: a full model using five transducer frequencies, 18, 38, 70, 120, and 200 kHz, and a reduced two-frequency model using data at 38 and 120 kHz. The acoustic inputs were normalized and scaled to reduce the effects of varying bottom depths and vessel speeds. Model outputs are seabed rugosity and proportions of substrates: sand/mud, gravel, cobble, boulder, and bedrock. Both models have similar performance, with errors of about 12% in compositions. Comparing results across different survey years shows an agreement of 95% at similar locations. The resulting substrate maps appear to correctly identify known geographic features in the Gulf of Alaska. Descriptions of co-occurrence of substrate types, their spatial distribution, and correlations in the study area are also provided.
