Limnology and Oceanography: Methods

The advancement in deep learning techniques has made Convolutional Neural Networks (CNNs) a powerful tool for the fully automated classification of zooplankton images. In this study, we systematically investigate how network selection, colour information and differences in imaging instruments affect the classification of zooplankton images by comparing multiple state-of-the-art CNNs on images of zooplankton and marine snow from the in situ Continuous Particle Imaging and Classification Sensor (CPICS), Video Plankton Recorder (VPR), In Situ Ichtyoplankton Imaging System (ISIIS), and the on-board Plankton Imager (Pi-10). With differences between models of 7.8 to 19% in F1-score, we find that model selection strongly affects the classification performance, with EfficientNetV2S showing the most reliable overall performance. Moreover, differences between model architectures are largest for the least abundant classes (<100 labeled images), which implies that when these are present, careful model selection is most beneficial. The high image quality of the Pi-10 strongly increases the performance for the least abundant classes compared to the other instruments. In addition, we find a significant correlation (r = 0.597) between ImageNet the performance and F1-score on zooplankton images, which implies that more generally, a model that performs well on ImageNet will perform well for zooplankton classification. Colour information increases the F1-score of the best performing classifier with 2.8%, but provides a stronger benefit (25% F1-score) for classes with <100 images. The overall performance increase of colour information is less than expected and questions the advantage of recording colour information for zooplankton.

Freshwater salinization is of increasing concern for integrity and functioning of freshwater habitats worldwide. Experiments so far often have studied drastic salt additions, while gradient designs have been performed less commonly. We tested the effect of freshwater salinization in a mesocosm exposing the plankton community of the oligotrophic Lake Lunz, Austria, to a four-fold salinization gradient (control, 0.2, 1, a 5 ppt salt). Salinity was manipulated in a factorial design with enrichment, with 10 g L-1 and 30 g L-1 phosphorus, resulting in 8 treatments with 3 replicates each. We followed the effects of salinization on diversity, community composition and resource use over 36 days. Community composition was assessed by amplicon sequencing, Diversity loss and community turnover followed upon salt addition. All levels of salinization caused pronounced changes in community composition, with 5 ppt causing the most drastic changes. Salinization caused trophic downgrading by kicking out especially protistan consumers and rotifers, while some green algae and chrysophytes were especially tolerant, resulting in reduced phylogenetic and functional diversity with increasing salinization. In line with reduced top down control, salinization affected temporal variability in chlorophyll-a (chl-a) and resource use (RUE), with higher salinity causing more extreme fluctuations in chl-a and RUE. Enrichment overall aggravated salinization, enhancing temporal turnover and temporal fluctuations in resource use.

Environmental DNA (eDNA) has the potential to be a powerful tool in blue carbon science for characterizing and quantifying the contribution of marine macrophytes; but its complex, dynamic relationship with bulk biomass is poorly understood. Here, we used eDNA to examine the degradation dynamics of sugar kelp (Saccharina latissima) in muddy, anaerobic marine sediment. This involved three 16-week incubations; with additions of lyophilized sugar kelp alone, a mix of lyophilized marine macrophytes including sugar kelp, and sugar kelp holdfasts buried in sediment. We used species-specific digital polymerase chain reaction assays for mitochondrial, chloroplast and nuclear markers, and metabarcoding for the 16S and 18S ribosomal RNA genes. In the former two incubations, all sugar kelp eDNA markers showed rapid log exponential declines (up to 98-99%) to asymptotes greater than the unamended controls, even as part of a more complex mix of macrophytes. In contrast, for the buried kelp holdfasts, sugar kelp eDNA increased to an asymptote (by up to [~]15X), which may be reflective of the different nature of added biomass. Overall, we demonstrate substantial preservation of environmental DNA and total organic carbon under anaerobic conditions, and the potential to use environmental DNA to quantify biomass in a blue carbon context.

Freshwater monitoring programmes like the European Unions Water Framework Directive (WFD) provide a wealth of data on European lake status, including water quality and macrophytes (aquatic plants) as critical habitat features that support health of humans and wildlife. Easier WFD data access can enable external management and research to better safeguard human and natural freshwater use. We demonstrate a replicable workflow to easily download and process multi-year (2007-2024) observations of lake macrophytes (425 sites) and complementary water quality variables (202 sites) from Swedish WFD data. Then, we illustrate the value of improved data access to address ecological questions that drive conservation, investigating how spatial scales influence macrophyte richness and associated water quality relationships using a spatial random intercept model. Decomposing the spatial intercept links small scales (<10 km) to site-level gradients and large scales (>100 km) to biogeographical drivers. Stochastic and environmentally-structured processes coexisted at intermediate scales (10-100 km). Adding water quality rarely improved overall predictive performance of macrophyte diversity models but consistently influences the role of different spatial scales. Water quality variables showed consistent spatially structured variation at intermediate scales and unique spatial patterns in tandem, overlapping with large-scale biogeographical influences. Altogether, we show context-dependencies for spatial model interpretation and provide guidance in accounting for spatial confounding to improve inferential and predictive performance. Our workflow and results show a clear way forward for accessing high-quality macrophyte and water quality data sets and their utility for addressing ecological questions that guide macrophyte protection under the WFD. HighlightsO_LIyears Swedish of macrophyte and water quality monitoring data were extracted. C_LIO_LIrichness showed scale-specific patterns linked to geographic gradients. C_LIO_LIbest predictive models for richness had no water quality at all. C_LIO_LIoverlap in their spatial scales and must be carefully separated. C_LIO_LIpen access data and multiscale analysis can apply to many ecological questions. C_LI

Arctic ecosystems are critically endangered by rising temperatures and changing hydrography, especially the intrusion of increasingly warm water from the Atlantic Ocean known as Atlantification. In addition to housing fragile biodiversity, Arctic copepods and their lipids play a crucial role in cycling carbon by transporting carbon into the deep ocean through their diapausing behaviors. Here, we explored the lipidomes of the Arctic copepod Calanus glacialis, collected from three fjords around Svalbard during November 2022 when C. glacialis are known to be in diapause. These three field sites provide a natural laboratory experiment, as they are influenced by different water masses with varying degrees of Atlantic water, and experience vast differences in sea ice coverage over the year. These environmental differences were clearly reflected in the lipidomic analysis, with stations influenced most by Atlantic Warm Water having the lowest total lipid concentrations and the lowest accumulation of storage lipids necessary for entering diapause. Membrane lipids were a significant proportion of the Svalbard copepod lipidomes, with the highest ratios observed at the Atlantified site. The high membrane lipid and high triacylglycerol concentrations were interpreted as signs of active feeding. This was further corroborated by fatty acid composition analysis, which revealed dietary biomarkers of carnivory at Atlantified sites. The copepods from the site most insulated from Atlantic influence had more than double the amount of storage lipids per individual and fatty acids associated with diatom biomass, indicating assimilation in the spring. Ultimately, the decrease in lipid content observed in association with Atlantification around Svalbard will impact diapause patterns, as Calanus species need 20-30% more WE to successfully complete diapause. In turn, this will impact the magnitude of carbon sequestration through the seasonal lipid pump, not to mention having radiating effects through the Arctic food web where Calanus glacialis plays an important role. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/738257v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@fc9321org.highwire.dtl.DTLVardef@1f6336org.highwire.dtl.DTLVardef@aaa3a1org.highwire.dtl.DTLVardef@dcd2a8_HPS_FORMAT_FIGEXP M_FIG C_FIG

Artificial intelligence (AI)-based image analysis can efficiently estimate fish length, but differences in devices, imaging conditions, operators, and AI models limit comparability among surveys. We propose a standardization framework that estimates a bin-specific error matrix from paired reference measurements and AI-derived lengths and applies it to standardize (correct) AI-derived length-frequency distributions. The Richardson-Lucy expectation-maximization algorithm was used, with the number of iterations selected via cross-validation. Simulations based on empirical length-frequency data from 110 species showed that standardization reduced relative bias and distributional discrepancy; median relative-bias and root mean square error ratios were below 1, and the performance was more affected by the amount of paired data than by the number of cross-validation folds. In real data from 957 Japanese jack mackerel, standardized AI-derived distributions approached human-observer histograms, although discrepancies remained in the range of 160-230 mm. The proposed framework provides a practical approach for improving the comparability of image-derived length-frequency data using paired calibration data, without retraining the underlying AI model.

Quagga mussels (Dreissena rostriformis bugensis) are ecosystem engineers that can alter nutrient cycling, benthic-pelagic coupling, and food-web structure in deep lakes. Although their invasion trajectories are well documented in the Laurentian Great Lakes in North America, depth-specific population dynamics remain poorly resolved in recently invaded European perialpine lakes. We analyzed five annual lake-wide surveys (2021-2025) from 54 stations spanning 2.4-253 m depth in Lake Constance to quantify changes in quagga mussel density, biomass, and shell-length distribution. Contrary to expectations of lake-wide exponential growth, shallow-water populations (< 20 m) showed no significant increase during the study period and appear to have reached carrying capacity before monitoring began. In contrast, densities increased monotonically at intermediate depths (40-125 m), indicating ongoing expansion into deeper strata. Mean shell length declined with depth, and size distributions in shallow waters shifted toward larger individuals, consistent with a transition from active recruitment to somatic growth of established mussels. Compared with the Laurentian Great Lakes, Lake Constance already has substantially higher shallow-water biomass, whereas deeper invasion trajectories are broadly similar. These results show that quagga mussel invasion in deep European lakes can combine rapid littoral saturation with slower profundal expansion, complicating direct transfer of predictions from the Great Lakes. Continued depth-stratified monitoring will be essential for anticipating future ecosystem effects in perialpine lakes.

The global coral reef crisis has prompted restoration initiatives worldwide. Targeting the coral larval stage is among the most scalable approaches as recruitment operates over large spatial scales. It thus represents one of the best levers for coral population recovery. Active coral larval seeding has shown considerable success, and passive substrate engineering has emerged as a promising complementary strategy. Coral settlement modules featuring helix recesses have increased settlement and survival by up to 80-fold on small experimental units, but whether these results translate to tools deployable at the scale of thousands of units, remains yet an open question. Here, we transferred structural features from successful experimental coral settlement designs into production-ready concrete modules to (i) evaluate coral recruitment on five designs at four reef sites differing in flow regime and coral cover over one year; (ii) compare production-scale performance against experimental clay modules and natural reef substrate; and (iii) identify key parameters for large-scale production. The helix recess geometry of coral settlement modules outperformed the featureless control design approximately 20-fold and exceeded natural reef recruitment at least 3- to 32-fold. The helix features were successfully transferred from experimental clay to production-scale concrete modules, yielding comparable settlement densities when standardized to crevice length, which proved to be the biologically relevant unit of available habitat. Production feasibility was demonstrated by producing 690 modules for deployment on a hybrid reef on the west side of Oahu, Hawaii. The passive coral larval recruitment approach presented here could substantially improve the logistical and economic feasibility of large-scale coral reef restoration. This approach requires neither coral larval rearing, handling, nor coral fragmenting, and is compatible with active larval seeding where genetic diversity or larvae supply are limiting factors. The coral settlement modules can be cast in standardized concrete molds at precast facilities. Modules have demonstrated consistent coral recruitment enhancement across reef environments with contrasting flow and coral cover. Deploying mixed arrays of helix-recess structures with designs offering multi-level complexity and three-dimensional rugosity maximizes outcomes for coral, fish, and invertebrate communities simultaneously. Site selection is the most critical deployment decision and should consider larval supply, hydrodynamics, and substrate stability which drive recruitment outcomes more than design choice alone. The modules offer a range of application potential, ranging from integration into existing coastal infrastructure over stand-alone reef restoration approaches, to substrate-consolidating interconnected arrangements.

John Raven pioneered the field of algae ecophysiology, advancing our understanding of cellular resource economics, carbon acquisition, and energy allocation. His work laid the foundation for investigating integrative physiology, linking growth-survival trade-offs across diverse environments. The sea ice habitat provides an excellent framework to continue the research John championed. With steep temperature-salinity gradients, algae survival requires a shift in physiology that we are only beginning to understand. We developed two small scale, reproducible icecosms to investigate physiological changes associated with incorporation into sea ice and survival potential post-melt. Fragilariopsis cylindrus and Nitzschia frigida, known for their association with the ice environment, and Porosira glacialis, known for its association with the ice edge, were used to mechanistically link physical properties with algal physiology and post-melt survival. We observe incorporation into the ice of F. cylindrus and N. frigida alongside vertical photophysiological profiles of F. cylindrus revealing inhospitable conditions in the top compared to the bottom layers of ice. N. frigida and P. glacialis remain viable within the ice and retain the capacity to seed populations following melt. Our results establish icecosms as experimental framework to investigate ecophysiological responses of sea ice algae and provide a foundation toward ecological and evolutionary questions.

DNA metabarcoding of faeces is a powerful, non-invasive method for assessing predator diets. However, when studying the diet of generalist predators, broad PCR primers are used to amplify the wide range of potential prey species and metabarcoding outputs are often dominated by sequences from the predator. While blocking primers can be used to reduce PCR amplification of predator DNA, they frequently cause partial predator suppression and unintended prey blocking. Peptide nucleic acid (PNA) clamps, offer a promising, underutilised alternative by binding strongly and selectively to predator DNA to block its PCR amplification. In this study we designed and validated a novel PNA clamp targeting the 18S rRNA gene to suppress bird and mammal predator DNA in dietary samples. We tested this clamp on tissue mixtures and faecal samples from three seabird and two seal species across temperate, subantarctic, and Antarctic regions. The PNA clamp substantially increased the proportion of prey reads recovered while maintaining consistent prey community composition across all predator species. Our results demonstrate not only the general effectiveness of PNA clamps over standard blocking primers, but also provide a powerful, broadly applicable new tool to improve the accuracy in DNA diet metabarcoding studies.

Scientific bottom-trawl surveys provide essential fisheries-independent data for fisheries and ecosystem research. In the Northeast Atlantic, the ICES Database of Trawl Surveys (DATRAS) compiles haul-level information, species- and length-specific catch data, and individual biological observations across multiple long-term surveys. However, reproducible workflows for processing and integrating these relational datasets remain challenging. We present DATRASextra, an open-source R package that provides modular end-to-end workflows for accessing, cleaning, harmonising, quality-controlling, and analysing DATRAS survey data. The package supports derivation of standardised haul-level survey variables, integration of multiple surveys, and generation of analysis-ready datasets for downstream applications including stock assessment, biodiversity analyses, and large-scale synthesis efforts such as FishGlob.

Flow imaging microscopy (FIM) is an important technology for high-throughput characterization of microscopic particles and microorganisms. However, conventional FIM relies on single-plane imaging (SPI), resulting in out-of-focus particles, reduced measurement precision, and incomplete characterization of irregularly shaped objects extending along the z-axis. To address these limitations, a volumetric flow imaging (VFI) framework was developed and implemented on the portable ARTiMiS platform. This approach captures multiple frames along the z-axis and extracts the highest fidelity image for each particle, which can also be used for single image generation with all particles in focus (i.e., all in focus image) and for three-dimensional reconstruction of irregularly shaped objects. Benchmarking VFI with microspheres, live cells (Chlorella vulgaris), and filamentous cyanobacteria demonstrated increased fraction of particles in focus, reduced variability in particle size measurement, and increased resolvability of elongated particles in comparison to conventional SPI on commercially available FIM technologies. For C. vulgaris, VFI-derived size distributions closely matched curated FlowCam measurements without requiring post-processing to exclude out-of-focus particles. All-in-focus image reconstruction enabled simultaneous visualization of particles distributed across multiple depths and consistently resolved a greater proportion of filamentous structures as compared to SPI. For Aphanizomenon sp., Dolichospermum sp., and Planktothrix agardhii, the SPI approach captured only 84%, 61%, and 58%, respectively, of the total filament length resolved by AIF reconstruction. Beyond image-based characterization, VFI enabled estimation of dynamic particle properties such as sinking velocity and mass density. Application of this framework to C. vulgaris cultures revealed distinct mass-density trajectories under nitrogen-replete and nitrogen-deplete conditions, with cell mass density increasing over time under nitrogen-replete conditions and decreasing under nitrogen deprivation. Collectively, these results establish VFI as a next-generation framework for FIM that expands its analytical capabilities beyond conventional morphometric characterization and provides new opportunities for single-cell-enabled environmental monitoring and biomanufacturing.

Premise: Rapid and accurate plant species identification is a critical challenge exacerbated by the taxonomic impediment. Although portable near-infrared (Micro NIR) spectroscopy represents a promising solution, the current absence of standardized protocols and a fundamental understanding of how critical acquisition and analysis parameters influence accuracy remain significant barriers. This study focused on the systematic optimization and validation of a comprehensive workflow designed to maximize the reliability of plant identification using this technology. To ensure methodological robustness across diverse foliar matrices, four vascular plant species were strategically selected as a representative test set to encompass morphological extremes, including significant variations in leaf thickness, pubescence, and surface texture. Methods: Using a portable spectrometer on herbarium specimens (exsiccate) of four vascular plant species, we systematically tested five spectral backgrounds, seven pre-processing methods, and four classification models. Subsequently, we optimized the number of spectral readings and evaluated the influence of the leaf scanning surface (adaxial vs. abaxial) on model accuracy. Results: The highest-performing combination was a Shiny Aluminum background, Second Derivative pre-processing, and a Random Forest model, which achieved a mean cross-validated accuracy of 99%. An average of just three spectral readings from the adaxial (upper) leaf face was sufficient to saturate model performance, proving statistically superior to other approaches (p < 0.001). Discussion: This study establishes a validated, high-accuracy protocol for plant species identification from herbarium specimens using portable NIR, offering a powerful tool for biodiversity studies. Direct applicability to fresh plants in the field requires future validation to account for the spectral influence of moisture variability.

Paleodietary reconstruction requires isotopic data from both humans and their accessible food resources. However, ideally defined accessible food resources, namely those from the same sites and periods as the target human individuals, are not always available for all ancient individuals. The number of sites with human C/N isotopic data far exceeds that with food-resource isotopic data. Consequently, many individuals cannot be linked to corresponding accessible food resources, a limitation that becomes more pronounced in large-scale quantitative dietary reconstructions incorporating a wide range of food-resource categories. Therefore, this study aims to broaden the definition of accessible food resources. To achieve this aim, we compiled food-resource and soil isotopic data ({delta}13C and {delta}15N) from Britain, including 4,012 ancient faunal and plant remains, 394 modern plant samples, and 260 modern soil samples. Region-period combined groups were established for the five major food-resource categories and served as the basic analytical units for detailed isotopic comparisons. Based on these comparisons, we propose broader criteria for defining accessible food resources. No significant intra-group variation was observed in the isotopic values of terrestrial herbivores and omnivores, suggesting that animals within each region-period combined group can serve as accessible food resources for humans from the same group. C3 plants showed substantial spatial variation but limited temporal variation. Accordingly, accessible food resources for C3 plants should be defined by region, namely England, Wales, and Scotland, regardless of chronological period, with humans from each region assigned plant data from their respective region. Marine and freshwater fish showed no clear temporal or spatial variation, and therefore unified datasets can be applied across all human individuals. Our findings enable each ancient human individual to be assigned appropriate accessible food resources, and therefore appropriate food-resource isotope baselines. We further demonstrate that such assignments can effectively reduce sampling bias arising from the use of traditionally defined accessible food resources, which are often limited by small sample sizes.

Capillary electrophoresis-mass spectrometry (CE-MS) offers unique analytical advantages for polar metabolite profiling but has remained underutilized in metabolomics relative to liquid chromatography-MS (LC-MS), in part due to challenges in managing migration time drift during data analysis. Here we introduce the use of indexed migration time (iMT) for easily managing this aspect of CE-MS data for metabolomics. Migration time indexing using a panel of stable isotope-labeled (SIL) amino acid reference standards, stored as an iRT database in Skyline, outperformed both uncorrected migration time and relative migration time (RMT) correction across three independent analytical batches spanning 90 samples from four biological matrices. The indexed migration time approach achieved sub-1% relative standard deviation (RSD) in migration index across batches, compared to up to [~]15% RSD for uncorrected migration times. Additionally, we evaluate the use of single-point external calibration in Skyline for the purposes of metabolite quantification from complex matrices in order to ease the burden of translational metabolite quantification from metabolomics using high-resolution mass spectrometry (HRMS). Single-point external calibration using a biological matrix-based calibrator was benchmarked against a 13-point linear calibration curve across a panel of amino acids; above 1 M, greater than 95% of back-calculated concentrations fell within {+/-}20% of multi-point calibration. Application of the complete workflow to plasma, serum, urine, and NIST Standard Reference Material (SRM)-1950 demonstrated low inter-batch variability by principal components analysis, broad metabolite coverage across 126 quantifiable analytes, and strong quantitative concordance (Deming slope = 0.862, pseudo-R2 = 0.994, n = 64 analytes) with an independent comprehensive reference dataset for NIST SRM-1950. Together, these results establish a practical mCE-HRMS metabolomics workflow that bridges targeted and discovery metabolomics paradigms and lays the groundwork for single-point external calibration as a powerful tool for translational metabolomics.

The Southern Hemisphere is home to extensive forests of giant kelp (Macrocystis pyrifera), including in Argentina and the southern islands of Tierra del Fuego, which has been proposed as a potential climate refugium. This study presents the first regional time series of M. pyrifera canopy dynamics in Argentina using Landsat satellite imagery from 1985 to 2023. The forests analyzed support 247.61 km{superscript 2} of emergent canopy and are situated in the coastal waters of Argentina and a small portion of Chilean islands, with 4%, 28%, and 68% in the Chubut, Santa Cruz, and Tierra del Fuego A.e.I.A.S, respectively. The small portion of Chilean Islands are included as part of the Tierra del Fuego province analyses. Range limits were scrutinized, in part, using expert knowledge and multisatellite comparisons. Linear regression shows that between 1998 and 2023, 7.4% of kelp sites exhibited a significant trend in annual canopy area, with all observed significant trends in the positive direction. Partitioning by province boundaries, linear regression produces significant positive increases in kelp canopy area across all three provinces, although reassessment when longer temporal continuity is also warranted, where available. Observed seawater nitrate concentrations were high throughout the region (7-23 {micro}mol L-{superscript 1}), suggesting that nitrate availability was not a primary driver of canopy variability. However, positive relationships between kelp canopy and the Antarctic Oscillation suggest that regional climate variability--which alters sea surface temperature and other oceanographic conditions--may be exerting a strong influence on kelp dynamics in this region. These findings document relative stability of kelp forest area in Argentina over the most recent two and a half decades and provide preliminary evidence supporting possible increases in kelp area for the region.

Lake Constance is a pre-Alpine, monomictic, oligotrophic lake situated at the southern end of Germany composed of two main water bodies: deep, oligotrophic Upper Lake Constance (ULC) and the shallow, more mesotrophic Lower Lake Constance (LLC). To date, no sequencing-based study exists of the seasonal succession of the microbial plankton in Lake Constance. Over one-year, microbial plankton communities were sampled biweekly from the top 20 m of the water column in both sites and separated into nanoplankton (NP) and picoplankton (PP). Communities were analysed using rDNA amplicon sequencing: NP samples were analysed by 18S rDNA, and PP samples by 18S and 16S rDNA sequencing. Temporal community diversity was compared between sites and the effect of two major environmental perturbations, winter vertical mixing in ULC and oxygen depletion of the bottom-water layer in LLC, on the community was examined. Despite strong environmental contrasts, microbial plankton communities exhibited conserved seasonal temporal dynamics across basins. In contrast, pronounced compositional shifts occurred during mixing and oxygen depletion events. Approximately 20% of detected taxa were positively associated with these events, with log fold changes reaching 9.82, reflecting rare or undetectable taxa outside these periods. Taxa favoured by these perturbations commonly exhibited high metabolic flexibility, including mixotrophy, fermentation, or anaerobic respiration, or possessed functional traits conferring tolerance to altered redox and mixing regimes. Our results suggest that the temporal dynamics of freshwater microbial plankton communities are driven by deterministic processes and highlight the profound impact of large, and less known, environmental changes on these communities.

The preparation of multi panel figures remains a labor intensive step in scientific publication. Albeit there are specific tools available to solve this problem, they are often highly specialized, difficult to install, or time consuming to learn. Griphus is a standalone graphical application designed for rapid composition and experimentation with multi panel figures, developed by and for zoological taxonomists. Functions specifically designed for multi panel composition include automatic figure numbering and placement, aspect ratio operations, spacers, layout rotation, layout suggestions, and automatic generation of figure legends, including scale bar descriptions. The software can perform both spatial interpretation of images on the canvas and work with a simple, editable layout formula. It also enables instant multi panel composition, with numbered images and automatic contrast selection for the numbers, obtained simply by loading images. User defined parameters such as target printable dimensions, resolution, spacing, and color mode are preserved throughout the work. The program produces coordinated outputs consisting of the final composite figure, a readable file describing the layout structure, and a .gri file storing images, transformations, and parameters for exact regeneration. Griphus is intended as a complementary tool to professional image software, providing a simple and efficient environment for constructing high quality multi panel figures.

Littoral mysids facilitate benthic-pelagic coupling through horizontal migration, yet quantitative monitoring in structurally complex habitats remains methodologically challenged where traditional active gears fail. We evaluated the efficacy of standardized light traps for monitoring littoral mysids (Neomysis integer, Praunus flexuosus) and mesopredatory three-spined sticklebacks (Gasterosteus aculeatus) in the northern Baltic proper, Baltic Sea. Using a paired experimental design with predator-exclusion and unmodified traps, alongside concurrent passive benthic trapping, we assessed abiotic drivers affecting catchability, biotic interactions, and statistical power to monitor changes in population size over time. Results indicated significant biotic interference: unmodified traps attracted high densities of sticklebacks, which reduced mysid catches by approximately 85% through predation or behavioural avoidance. Consequently, physical predator exclusion is mandatory for accurate mysid sampling. Generalized Linear Mixed Models (GLMMs) confirmed that catch rates for all taxa were primarily driven by night duration rather than water temperature. While passive benthic trap catches tracked metabolic activity (peaking in warm summer months), light trap efficiency peaked in spring and collapsed during summer, confirming that sampling efficiency was strictly limited by the short duration of the night. Simulation-based power analysis revealed a stark contrast in monitoring utility based on spatial aggregation. For highly aggregated mysids, the method demonstrated low precision (Power < 0.25 to detect a 50% decline), rendering it suitable primarily for detecting substantial population collapses (>90%). In contrast, for less aggregated sticklebacks, the method achieved a more robust statistical power (>0.80 for a 60% decline), validating light traps as a precise tool for monitoring these abundant mesopredators. We conclude that light traps fill a critical methodological gap for winter and early spring monitoring when traditional passive gears underperform. Appropriate abundance indices should be based on statistical models accounting for night duration and strictly employ physical exclusion barriers when targeting mysids.

Animal COI (mitochondrial cytochrome oxidase I) metabarcoding of environmental DNA (eDNA) is increasingly used to assess biodiversity in complex substrates such as soil. However, due to read-length constraints of second-generation sequencing platforms, mini-barcodes have been used instead of the full barcode region. Long-read sequencing technologies now enable the recovery of full-length barcode sequences, and are more commonly applied for studying microbes, but their use for metabarcoding the full-length standard COI barcoding region in animals remains limited. In this study, we compared three COI amplicon sets -- 313 bp, 660 bp, and 1,256 bp -- amplified from soil eDNA samples and sequenced using Illumina and PacBio platforms to evaluate their overall concurrence, the effectiveness of identifying nuclear mitochondrial DNA segments (NUMTs) and chimeras, as well as their respective taxonomic resolution. The long-read datasets exhibited a higher identification rate of NUMTs and true chimeras, suggesting that longer sequences improve the detection of noise in COI metabarcoding data, thereby reducing the occurrence of spurious taxa. Taxonomy assignment confidence was similar between the 313 bp and 660 bp datasets, whereas extending the amplicon beyond the standard COI barcode region (1,256 bp) reduced confidence, likely because longer reads extend into regions poorly represented in barcode reference databases. Despite substantially lower sequencing depth in the 660 bp dataset, per-sample OTU richness did not differ significantly from that recovered with the Illumina 313 bp amplicon set. Similarly, the relationships between samples were strongly correlated across the detected OTU communities, indicating consistent ecological interpretations between short and long amplicons. We conclude that the standard ~658 bp COI barcode is an optimal marker for soil animal metabarcoding from eDNA, balancing target recovery, artifact detection, taxonomic assignment and ecological interpretability. As COI eDNA metabarcoding becomes increasingly used in biodiversity assessment and is increasingly adopted in large-scale monitoring initiatives, this study provides methodological guidance for improving the robustness of soil animal community biomonitoring.