Limnology and Oceanography: Methods

The decline in fishery yields in Japan's enclosed coastal seas has been accompanied by a reduction in phytoplankton biomass, suggesting a widespread decrease in ecosystem productivity. Oligotrophication in Mikawa Bay has caused substantial declines in phytoplankton biomass and Manila clam (Ruditapes philippinarum) production. To assess whether external nutrient enhancement could stimulate phytoplankton growth, a controlled demonstration experiment using treated effluent from two sewage treatment plants (WWTPs) was conducted (spanning fiscal years 2022 to 2024) based on analysis using a three-dimensional non-hydrostatic model. Although effluent DIP and nitrogen concentrations were increased, model results showed that WWTP-derived nutrients represent only a minor portion of the total nutrient budget. Field observations revealed limited increases in surface water phosphorus, strong seasonal variability, and only temporary rises in chlorophyll-a and clam harvests. Monthly TP data demonstrated that seasonal DIP pulses, especially spring phosphorus bursts, dominate nutrient dynamics and overshadow external loading effects. Laboratory experiments further showed that phosphate is efficiently fixed by Fe(III) under oxidizing conditions and rapidly released during reductive dissolution of [FeOOH-HPO4]2- complexes, confirming iron as the principal regulator of internal P cycling. Because current numerical models omit iron-driven internal cycling, they cannot accurately predict bioavailable P or ecosystem responses. Overall, the study indicates that localized nutrient enhancement is insufficient to counter long-term oligotrophication, and effective management must integrate nitrogen limitation, internal Fe and P cycling, oceanic exchange, and seasonal variability in Ise and Mikawa Bays.

Understanding the consequences of global change for our ecosystems requires reliable, accurate, and scalable tools for detecting ecological change. To this end, recent efforts have focused on scaling up the quantification of demographic processes from individual organisms (the fundamental ecological unit) to the larger spatial scales they inhabit to yield more precise and informative estimations of ecological change, especially in response to disturbance. Here, we describe a transferable workflow that combines robust underwater photogrammetry techniques with machine-learning-assisted image analysis to quantify demographic changes in coral colonies (growth, stasis, partial mortality, and complete mortality) through time on the reef landscape. We then demonstrate several analytical applications of this workflow that can be broadly applied to sessile and/or clonal organisms in other ecosystems. First, we tracked the fates of 3,162 coral colonies through a marine heatwave that caused substantial mortality. Second, our approach enabled detection of size- and taxon-dependent patterns in demographic changes from before to after the heatwave. Finally, for a subset (425) of these colonies, we further demonstrated how our method can be used to track fates across multiple years, including a year of change before and after the heatwave took place, revealing nuanced patterns of survival and mortality, as well as changes in size structure that occurred before and after the disturbance. Our workflow offers an important advance for increasing the accuracy of change detection on coral reefs and other ecosystems by linking demographic changes in individual organisms with spatially extensive image-based surveys through time.

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

(1) Kelp forests support ecologically, commercially, and culturally important species, yet their benthic communities remain difficult to monitor consistently across space and time. Scientific SCUBA surveys provide valuable observations but can be constrained by safety, bottom time, depth, and the spatial extent that divers can efficiently cover, motivating complementary approaches for collecting standardized benthic imagery. (2) We developed and field-tested a standardized, open-source framework for benthic visual-imaging surveys using a small, modular remotely operated vehicle (ROV). Across four cost-scalable hardware configurations, we integrated navigation sensors, custom BlueOS Extensions, and our surftrak contribution to ArduSub, which enables fixed-altitude flight above the seafloor, and developed two open-source desktop applications that streamline field operations (rov_flight_ops) and imagery processing (rov_imagery_processing). We also evaluated power supplies, benthic lighting, camera settings, field protocols, and telemetry-processing workflows over 303 transects conducted on 64 survey days from 2022 to 2026. (3) The ROV operated across soft-sediment and rocky habitats, currents of 0-3 kt, and underwater visibility of 0-10 m. surftrak held the ROV within 0.2 m of its target altitude for 96% of 21 h of survey transects, producing an approximately 1 m^2 standardized image footprint. Sufficient illumination and optimized camera settings yielded high-resolution imagery in visibility below 2 m, while topside power increased endurance and operational flexibility. Combining Doppler velocity log and underwater acoustic positioning data through the vehicle's extended Kalman filter improved navigation and post hoc transect reconstruction. During 2026 surveys, rov_flight_ops and rov_imagery_processing automated flight logging, log retrieval, fault diagnosis, and the sorting, development, and telemetry annotation of survey imagery. Dense kelp canopies nevertheless constrained vehicle operation, and downward-facing imagery provided limited observations of cryptic or obscured organisms. (4) We provide configuration-specific bills of materials, hardware and wiring documentation, software and firmware inventories, camera settings, open-source field and imagery-processing software, step-by-step field protocols, and pre- and post-dive checklists to support replication and adaptation of the framework. Together, these resources provide a field-tested starting point for expanding standardized benthic imaging while complementing diver surveys across kelp forests and other shallow coastal ecosystems.

Studying algae requires specialized equipment to characterize culture growth and physiological performance and non-invasive monitoring of these cultures typically relies on optical density (OD) measurements. Currently, OD is often measured using standard spectrometers that suffer from detector saturation at high cell densities, which requires time consuming sample dilutions and lacks compatibility with flat-walled cell culture flasks like Roux bottles. Here we describe a 3D-printable device to measure optically dense algal cultures directly within these cultivation vessels. This instrument combines light attenuation and scattering, resulting in a dual-sensor system that utilizes near-infrared light (950 nm) to precisely quantify biomass without interference from changing photosynthetic pigments. Cultures that reach high cell densities can be assumed to be accurately quantified in situ using the scattered signal, preserving the integrity of the culture and eliminating the need for subsampling. This method is adaptable to the most common geometries of Roux bottles (T-25, T-75, and T-175) and could be used to monitor small-scale batch cultures without contamination risks. A cyanobacteria algal species (Limnospira platensis) and three flask sizes were analysed on light attenuation linearity and scattering detection limits at different cell concentration, demonstrating that the light attenuation module can reliably measure biomass up to an OD of 3.1 (equivalent to >1.5 g/L) without requiring dilution.

Environmental nucleic acid (eRNA and eDNA) metabarcoding is a promising tool for biodiversity assessment as it supports low-cost, noninvasive biological surveys. A critical challenge for eRNA/eDNA analyses is accounting for nucleic acid contamination that can compromise the accuracy of biomonitoring results. The primary aim of the current study was to examine carcasses as potential sources of eRNA/eDNA contamination, a contribution that remains poorly understood. Using Japanese medaka (Oryzias latipes), we quantified the detectability and persistence of carcass-derived eRNA/eDNA in water through two complementary experiments: (i) a shedding study comparing eRNA/eDNA release from live and dead individuals and (ii) a decay experiment measuring temperature-dependent degradation from 4 to 34 {degrees}C. Dead fish released detectable amounts of both eDNA and eRNA, indicating that eRNA-based surveys cannot fully eliminate carcass-derived signals. Arrhenius-based analysis showed that eRNA degradation was more temperature-dependent than eDNA degradation. These results suggest that eRNA may improve temporal resolution for recent organismal presence under warm conditions, whereas carcass-derived eRNA may persist under cold conditions and complicate interpretation of positive detections. Overall, eRNA may help reduce carcass-related false positives, particularly in warm conditions, but should be paired with temperature-aware sampling strategies and temporal replication during colder periods.

Remote sensing mapping of plant functional diversity (PFD) is increasingly used at the science-policy interface. Yet assessments of uncertainty and quality standards for these products are seldom provided, as the impact of measurement uncertainty on PFD estimates, i.e., the variability across pixels, is still poorly understood. We use trait maps and hyperspectral imagery simulations from the Biodiversity Observing System Simulation Experiment (BOSSE) to assess the impact of different types of uncertainty on the estimation PFD. Results show that uncertainties stemming from random errors affect the estimation of PFD (up to three times) more than those arising from systematic errors, with spectral and spatial error correlation reducing their impact. Standardization computing PFD metrics reduces systematic errors, making in-situ-measured plant traits and remote sensing reflectance factors more robust estimators of PFD than other remote sensing proxies derived from reflectance (e.g., spectral indices or optical traits). The impact of uncertainty propagation from reflectance factors to these often-preferred secondary proxies increases with their complexity. Nonetheless, trait uncertainty can spuriously diminish differences between PFD derived from the ground (plant traits) and satellite imagery, as it can reduce uncertainty-induced or inherent biases between the PFD metrics computed from each trait (plant or spectral). This study provides new understanding of how uncertainty propagates into PFD estimates; this knowledge is relevant for optimizing the selection and defining quality requirements for PFD estimation. It also suggests that random and systematic uncertainties should be quantified separately at the reflectance factor level to guide their use and interpretation in PFD analysis.

1. Soil invertebrate biomass is closely linked to ecosystem processes, yet its routine quantification remains constrained by the effort required to weigh large numbers of small individuals. Length-mass relationships provide an indirect alternative, but body length can be difficult to standardise and extract automatically from images of curved, coiled or contracted organisms. Projected body area may provide a more tractable image-derived predictor, but its performance across diverse soil-faunal taxa and at the community scale remains insufficiently evaluated. 2. Here, we developed a framework using projected body area and contour length to estimate fresh and dry mass from measurements of 1,792 soil invertebrates representing 15 higher-level taxa. We fitted taxon-specific models for six well-represented groups and general models across taxa, and evaluated predictive performance using leave-one-out cross-validation. We then assessed cross-dataset transferability by comparing our data with those of Lemoine et al. (2026) and pooled the two datasets to derive combined models. Finally, we tested whether individual predictions could be scaled to independently measured community biomass. 3. Taxon-specific models generally reduced prediction error relative to general models, with mean absolute percentage error (MAPE) values of 6.9-37.3% for fresh mass and 7.6-30.2% for dry mass. General models retained MAPEs of 27.2% and 22.4%, respectively. Comparisons of our dataset with that of Lemoine et al. (2026) showed no detectable differences in six of eight taxon-by-mass slopes. Although intercepts frequently differed, no overall dataset effect was detected after accounting for taxa. Pooling both datasets broadened model development, while taxon-specific models generally retained lower error than the combined general model. Finally, community-level predictions tracked measured biomass across all model sets (R2 = 0.87-0.99), but absolute error varied among models and faunal groups (MAPE = 9.6-52.5%). 4. Body area and contour length extracted from images therefore provide a practical basis for scaling soil invertebrate biomass estimation from individuals to communities. Integration with automated imaging and segmentation tools such as flatbug, BIODISCOVER and CollembolAI could extend soil invertebrate monitoring at larger scales beyond abundance and taxonomic composition to biomass, while providing scalable inputs for analyses of food webs and general ecosystem ecology.

Eutrophication-driven hypoxia and harmful algal blooms are an expanding threat to water quality in estuaries. Engineered aeration is a technology used in freshwater and, to a lesser extent, estuarine systems to mitigate both hypoxia and algal blooms. However, the efficacy of engineered aeration has not been well studied within estuarine ecosystems and very little is known about the impact of aeration on microbial communities in either estuarine or freshwater environments. Here we present a time series investigation of microbial community response to aeration and cessation of aeration in a eutrophic estuary. Samples for 16S rRNA gene analysis were collected over about a month-long period during which time the aerators transitioned from being "on" to "off" to "on" again. Cessation of aeration selected for the dominance of a eukaryotic algae similar to Heterosigma akashiwo. After the resumption of aeration, H. akashiwo dominance decreased rapidly. Cessation of aeration also shifted the structure of the entire microbial community, where diversity decreased and the structure of microbial communities at the surface diverged from those from the bottom of the water column. These changes did not occur at non-aerated control sites. Distinct groups of taxa responded to the disruption in a successional pattern through time, with most taxa never returning to their pre-aeration-cessation abundance during the observational period. These findings demonstrate the complex community response to anthropogenic interventions and provide support for bloom suppression through aeration.

Background: Amplicon sequencing of the 16S rRNA gene is widely used to characterise microbial communities, but the performance of commonly used clustering and denoising pipelines has not been systematically evaluated for highly diverse environmental datasets. We therefore benchmarked four established clustering and denoising pipelines, VSEARCH cluster_size, UNOISE as implemented in VSEARCH, Swarm, and DADA2, using simulated microbial communities with known ground-truth compositions and evaluated how performance varied with species richness, abundance unevenness, sequencing depth, and minimum abundance threshold. Results: Differences among pipelines were small at low species richness but became greater as species richness increased. High average cluster purity did not necessarily correspond to accurate species-level recovery, as species could be split across multiple clusters or recovered incompletely. Among the pipelines, UNOISE may be preferable when high species representation and cluster purity are prioritised, while also showing strong reconstruction of relative abundance profiles, albeit with extensive species splitting and many unclustered reads. DADA2 showed similarly strong reconstruction of relative abundance profiles and less species splitting but represented fewer species and had lower cluster purity at higher richness. Swarm provided a balanced compromise between species representation and splitting, whereas cluster_size may be useful when limiting species splitting is a priority, despite weaker reconstruction of relative abundance profiles. Increasing the minimum abundance threshold reduced species splitting but also reduced the number of clusters and perfect clusters and, at higher thresholds, decreased concordance with ground-truth relative abundance profiles. These effects were more pronounced at lower sequencing depths. Analysis of seafloor sediment samples also showed threshold-dependent sequence loss, including the loss of sequences repeatedly detected across replicate samples. Conclusions: Pipeline performance in highly diverse 16S rRNA amplicon datasets varies with dataset characteristics, evaluation criteria, and parameter settings. Pipeline selection should therefore reflect dataset characteristics and the analytical objective rather than rely on a single measure of performance, and minimum abundance thresholds should be evaluated in relation to sequencing depth rather than applied as fixed defaults. These findings emphasise the importance of reconsidering established analytical practices and transparently reporting bioinformatic settings as 16S rRNA amplicon sequencing is increasingly applied to highly diverse environmental microbial communities.

Background Comparisons of single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq) data have been described in some mammalian tissues and, subsequently, in Drosophila, but remain unexplored in most invertebrate lineages. The xenacoelomorphs occupy key phylogenetic positions, yet they differ anatomically from mammals. They have a reduced extracellular matrix, high-salt body fluid, and no circulatory system. Despite these differences, they possess a well-developed nervous system. One of the xenacoelomorphs, the photosymbiotic acoel (Praesagittifera naikaiensis) also harbours symbiotic Tetraselmis algae, whose RNA can be co-captured with host RNA. Results We compared scRNA-seq and snRNA-seq data from whole P. naikaiensis specimens. Both methods yielded high-quality data with comparable gene detection but a larger share of scRNA-seq reads derived from symbiotic algae. Gene-level analyses revealed that neural genes were enriched in snRNA-seq relative to non-neural genes. Cross-method label transfer and integration-based validation identified six snRNA-seq clusters, including some neural populations, that lacked a clear scRNA-seq counterpart. In contrast, three cell populations, including muscle and metabolically active clusters, were reciprocally validated as captured by both methods. Conclusions Our results show that key snRNA-seq advantages, particularly the enhanced recovery of neural transcripts, are recapitulated in our dataset, which is consistent with previous reports in mammals. Furthermore, snRNA-seq reduces symbiont-derived reads and recovers several cell populations underrepresented in scRNA-seq. These findings provide practical guidance for cell atlas construction in non-model, symbiotic invertebrates.

Microplastics are pervasive environmental pollutants that pose a growing concern for human health. Oral ingestion is a common route of human microplastic exposure, yet the proteomic response of the gut epithelium to microplastics remains unclear. This study aimed to investigate the cellular effects of pristine and artificially digested microplastic exposure in primary rat duodenal epithelial cells using untargeted proteomics. Cells were exposed to pristine or digested 0.5 um polystyrene microplastics at 10 or 100 ug/mL for 72 hours and were then analyzed by tandem liquid chromatography and mass spectrometry (LC-MS). Proteins that were both significantly different in intensity compared to controls, with a threshold change of 1.3 or greater, were considered to be differentially expressed. This criterion identified 41 differentially expressed proteins after 100 ug/mL pristine MP exposure, with 19 downregulated and 21 upregulated. Following exposure to 100 ug/mL digested MP, only 3 differentially expressed proteins were upregulated and 7 were down regulated, demonstrating the impact of microplastic physicochemistry. FGSEA pathway analysis revealed that 270 Reactome pathways were significantly altered following microplastic exposure in either condition at both concentrations. These pathways contributed to functional domains including protein synthesis, DNA replication, cell cycle control and aerobic respiration. Overall, microplastic exposure was associated with upregulated mitochondrial respiration, and downregulation of nuclear-related processes including DNA synthesis, transcription and cell proliferation. This study provides targets for future investigation (mitochondria and nucleus) and emphasizes the need to consider biological and environmental conditions for in vitro models of microplastic exposure.

Background: Long non protein coding (lnc)RNAs are ubiquitous transcripts known to be major regulators of cellular processes in eukaryotes. In diatoms, specific lncRNA expression is tightly correlated to nutrient fluctuations, which suggests function, but their biological relevance remains to be established. In order to fill this knowledge gap, we sought to design a protocol to generate diatom lncRNA knockout mutants by CRISPR-Cas9 enabling subsequent functional studies to shed light into their mechanistic roles. Since lncRNAs do not contain open reading frames that can be targeted by point mutations for the generation of functional knockouts, we designed a multiplexed CRISPR-Cas9 plasmid harboring two independent gRNA for the specific ablation of lncRNA loci in P. tricornutum. As proof of concept, we used our plasmid-based system to target three phosphate depletion responsive lncRNAs. Results: Using the golden gate based uLoop system, we assembled a single plasmid containing all the necessary components to generate CRISPR-Cas9 large genomic ablations of lncRNAs in P. tricornutum using bacterial conjugation. Three intergenic lncRNAs (Linc91, Linc77 and Linc24) responsive to phosphate fluctuations were selected and independently targeted. Around 32% of the exconjugants presented a monoallelic ablation of the targeted loci and 11% presented a biallelic ablation on the first PCR-screening round. Mutated lines, harboring biallelic lncRNAs ablations for each lncRNA targeted, were successfully generated for each of the targeted lncRNAs. Additionally, a first line of phenotypic analysis revealed significant growth rate differences for one of the mutants ({Delta}Lnc24) under both full and phosphate depleted conditions, suggesting its involvement in cell division regulation under nutrient fluctuations. Conclusions: This work provides a simple and efficient method for the generation of lncRNA knock-out mutants in P. tricornutum. Furthermore, this approach can be easily adapted to other microalgae amenable to transformation, enabling the enhancement of our understanding of these important cellular regulatory transcripts in the marine environment.

Animal movements can transport materials across ecosystem boundaries, while simultaneously modifying habitats through physical engineering. We integrated GPS telemetry, camera trapping, sediment measurements and physicochemical sampling of ponds on Santa Cruz Island, Galapagos, to evaluate how giant tortoises connect and shape terrestrial and freshwater ecosystems. Telemetry revealed 2,254 visits by 21 tortoises to 12 ponds, with visits lasting up to 21.38 h and showing strong seasonal and diel variation, while camera traps showed peak pond use in January and during early morning and late afternoon. Greater tortoise abundance was associated with higher ammonium, dissolved organic carbon and total phosphorus concentrations, while ponds with persistently greater tortoise abundance had higher total suspended solids. Departing tortoises carried a mean 505.9 g of sediment, yielding a conservative potential export of 8.20 kg per tortoise per year and tens of tonnes annually. These findings identify giant tortoises as mobile zoogeochemical engineers linking terrestrial and freshwater ecosystems through physical and biogeochemical pathways.

Data curation-the process of collecting, cleaning, and joining raw datasets for unified analysis-is a time-consuming yet crucial part of any data science project. With the emergence of powerful agentic coding tools, it is tempting to "vibe curate"-i.e., to prompt AI agents to perform data curation operations and blindly trust their outputs in order to finish work more quickly. However, this can exacerbate two key challenges that already plague manual data curation workflows: reproducibility-the ability to trace the exact set of modifications applied to raw datasets-and verifiability-the ability to audit changes and confirm that data is processed properly. To address these issues, we introduce Scrub Data, a framework that enables the use of AI agents in data curation workflows while transparently maintaining both reproducibility and verifiability. The framework involves an interactive data curation loop between a user and an AI agent, backed by a data provenance graph that tracks and versions all dataset updates. The data graph requires that all updates are formatted as self-contained executable steps, allowing any version of a dataset to be reconstructed from raw data by playing forward the transformations stored in the graph. This workflow takes place within a lightweight web application that is designed to be modified by users and agents in real-time to create custom visualization tools for identifying data curation needs and verifying outcomes. We demonstrate the usefulness of the framework with a real data curation use case from animal movement ecology. Starting with 89M raw GPS coordinates, we use the framework to curate a benchmark dataset of 2.5M coordinates to be used for machine learning or statistical analysis. The framework is available for use and extension at https://github.com/justinkay/scrubdata.

Mesophotic coral ecosystems are commonly interpreted as thin biological veneers over relict shallow-water reef structures, but their capacity to generate late Holocene carbonate deposits remains poorly quantified. Here we combine diver excavations, rubble-bed thickness surveys, radiocarbon age-depth modelling, sediment grain-size analyses, coral-rubble assemblages, trace-element geochemistry, and modern Alveopora allingi growth measurements from two 40-65 m deep coral-rubble fields in the northern Gulf of Eilat/Aqaba, Red Sea. Rubble deposits are at least 1.0-1.5 m thick and accumulated at mean rates of 2.21 {+/-} 0.52 mm yr-1 at the Dekel site and 1.33 {+/-} 0.13 mm yr-1 at the IUI site, implying persistent carbonate accumulation over centuries to >1 kyr. Age-depth models indicate quasi-continuous deposition despite pronounced faunal changes, including a shift from A. allingi-dominated rubble to upper layers enriched in Euphyllia paradivisa at Dekel. The dominant modern rubble-forming coral, A. allingi, extends at 3.04 {+/-} 0.87 mm yr-1 and has skeletal density of 1.17 {+/-} 0.27 g cm-3. First-order cover-based accretion estimates at present coral cover fall well below the radiocarbon-derived rates, indicating that long-term mesophotic rubble accumulation integrates historical cover, secondary calcification and sediment retention rather than present live coral cover alone. These data demonstrate that mesophotic rubble fields can act as active carbonate factories and palaeoecological archives, challenging the assumption that modern mesophotic habitats are geomorphologically passive.

Dissolved organic matter (DOM) is a central component of biogeochemical cycling, but molecular observations generated by ultrahigh-resolution mass spectrometry remain fragmented across studies, limiting quantitative comparisons among ecosystems. Here, we compile and harmonize a global dataset of DOM molecular traits comprising 3,999 samples from 337 studies published between 2005 and 2026. The dataset includes eight intensity-weighted molecular traits: hydrogen-to-carbon (H/C), oxygen-to-carbon (O/C), nitrogen-to-carbon (N/C), and sulfur-to-carbon (S/C) ratios; molecular mass; double-bond equivalents (DBE); modified aromaticity index (AImod); and nominal oxidation state of carbon (NOSC). Samples span aquatic, terrestrial, atmospheric, plant-associated, petroleum-related, and other environments, and are linked, where available, to geographic, physicochemical, nutrient, and climatic metadata. Molecular data were harmonized using common definitions and restricted to negative-mode electrospray-ionization FT-ICR MS to improve cross-study comparability. This resource enables cross-system comparisons of DOM molecular traits and supports analyses of environmental gradients, geographic coverage, and global DOM chemogeography.

Reliable demographic data are critical for seal population assessment and management, yet remain challenging to obtain from drone surveys without automated tools. Using data from harbor seal (Phoca vitulina) haulout surveys conducted in British Columbia, Canada, in 2023-2024, we developed and tested an automated pipeline that integrates detection, morphometric extraction, and size-class classification of seals into a single system. The pipeline combines cascaded DeepLabV3+ segmentation (water removal, colony-level seal detection, and individual body-contour refinement), principal component analysis (PCA)-based extraction of body length and maximum width, and three-class size-class determination based on body length (pups, juveniles, subadults/adults). We evaluated the pipeline on 2,483 manually annotated seals across four drone platforms (DJI Mavic 3 Thermal, Mavic 2 Pro, Mini 3 Pro and Mini 2), five substrate types, and two sensor modalities (RGB, thermal infrared). Aggregate detection F1 was 0.914. Orthomosaic geometric accuracy, quantified as the RMSE of calibration-rod length on the orthomosaic, ranged from 1.0 cm to 18.0 cm depending on the platform-altitude-substrate combination, a spread that aggregate metrics would mask. Relative to manual annotation on the same orthomosaics, automated RGB body lengths showed near-zero bias (-0.6 cm; RMSE = 12.5 cm), whereas automated thermal lengths were systematically shorter (bias -19.9 cm, RMSE = 24.1 cm), establishing the unsuitability of thermal imagery for automated quantitative pinniped morphometry. For RGB imagery, threshold classification by body length reached an overall accuracy (OA) of 78.7%; for individuals outside the uncertainty zones (defined as {+/-}2 x the geometric RMSE on calibration rods around each class boundary), OA reached 90.9%. Within the RGB subset, OA was 78.9% at 20 m altitude, declining to 75.0% at 40 m where geometric error dominated. The pipeline enables automated demographic monitoring of pinniped haulouts with quantitatively characterized uncertainty across survey conditions.

Biological filtration in newly established aquaria depends on the establishment of nitrifying microorganisms that prevent accumulation of toxic ammonia and nitrite. Many aquarists use commercial nitrifying supplements to accelerate biofilter establishment and reduce ammonia and nitrite accumulation. However, the microbial composition and in situ efficacy of these products remain poorly characterized. We evaluated the ability of enrichment cultures containing comammox Nitrospira (CX) and the ammonia-oxidizing archaeon Ca. Nitrosotenuis aquarius (AQ) to serve as aquarium nitrifying supplements, tested alongside a conventional supplement (CS) containing ammonia-oxidizing bacteria. Inoculation of new aquaria containing zebrafish (Danio rerio) with nitrifying supplements resulted in distinct nitrifier profiles within biofilters of each treatment group corresponding with significant reductions in peak ammonia and nitrite concentrations, which were most evident when applied at the maximum tested supplement dose. Treating aquaria with CX, AQ, or CS maintained ammonia and nitrite at low concentrations, and CX effectively prevented ammonia and nitrite accumulation through all stages of aquarium operation and biofilter establishment. After removing all fish from the aquaria, ammonia "spike-in" experiments demonstrated biofilters treated with CX and AQ conferred long-term protection against nitrite accumulation following acute ammonia loading events. Whereas 16S rRNA gene sequences associated with CS were rarely detected within biofilter profiles, those associated with CX and AQ established early and persisted temporally. Overall, results demonstrate how nitrifiers might be differentially adapted to home aquarium conditions and the potential of AOA and comammox Nitrospira to improve biofilter establishment and maintaining safe water quality in newly established freshwater aquaria.

Cell-type-specific molecular responses are obscured in bulk RNA sequencing, whereas generating matched single-cell datasets across environmental conditions remains costly and technically challenging, particularly in non-model organisms. Here, we present a workflow that integrates existing bulk and single-cell RNA-seq resources to prioritize cell-type-associated environmental-response genes. We demonstrate the workflow by examining heat-stress-associated neuronal genes in the reef-building coral Stylophora pistillata. Neuronal genes were first identified based on cell type-specific expression in single-cell transcriptomic data and then intersected with differentially expressed genes from bulk RNA-seq datasets under heat-stress. In parallel, we applied a deconvolution approach to infer cell type-specific expression changes directly from bulk data, independently capturing neuronal transcriptional responses. This integrated pipeline reduced thousands of heat-responsive genes previously identified in bulk RNA-seq studies to nine heat-associated, neuron-enriched candidates. Eight of the nine candidates showed their highest mean expression in the same neuronal subcluster. Targeted sequence-based characterization supported the identification of a TRPA-family ion channel, two calmodulin/centrin-family Ca2+-binding proteins, and a Class A/rhodopsin-like GPCR of unresolved ligand specificity. Two additional candidates were predicted secreted proteins belonging to conserved coral protein families, while the molecular functions of the remaining candidates remained unresolved. Their coordinated regulation suggests heat-associated remodeling of neuronal sensory and intracellular signaling processes. Our workflow provides a scalable framework for extracting cell type-associated molecular responses from existing transcriptomic resources and prioritizing candidates for further investigation. More broadly, it offers a generalizable strategy for investigating cell type-specific gene expression responses across organisms, cell types, and environmental conditions without requiring new single-cell datasets.