Canadian Journal of Fisheries and Aquatic Sciences

Accurate estimation of post-release survival is fundamental to fisheries stock assessment and effective management. Conventional tag-return studies and acoustic telemetry are commonly used to estimate this probability, yet each approach has limitations when applied independently. Using gag (Mycteroperca microlepis) as a case study, we integrated data from a large-scale conventional tagging program and an acoustic telemetry experiment within a discrete-time statistical modeling framework that links relative recapture risk with telemetry-derived fate. This approach enabled estimation of post-release survival across a broad gradient of capture depths representative of recreational fishing conditions. Estimated survival was high in shallow waters ({approx}97%) but declined with increasing capture depth, consistent with depth-related barotrauma. Applying model predictions to depth distributions from the recreational fishery yielded annual and monthly post-release survival probabilities. Annual estimates were consistent with values assumed in recent stock assessments, while monthly values highlighted seasonal patterns potentially relevant for management. This integrated framework advances post-release survival estimation by combining the extensive sample sizes and environmental coverage characteristic of conventional tagging data with the direct fate observations provided by acoustic telemetry, and offers a transferable approach for other highly targeted fisheries.

The hockey-stick (HS) stock recruitment relationship (SRR) has been widely used as an empirical alternative to conventional SRRs such as the Beverton-Holt (BH) and Ricker (RI) models. However, the management performance and risks associated with estimating maximum-sustainable-yield (MSY) reference points (RPs) based on HS remain insufficiently understood. This study first defines deterministic and stochastic MSY RPs under the HS model and provides an overview of their properties. We then conduct simulation experiments to investigate the bias and management consequences that arise when MSY RPs are estimated from the HS model (HS-derived MSY RPs) rather than from the true SRR (e.g., BH) across a range of biological and stochastic parameters, with particular focus on scenarios with insufficient data contrast. Our results show that HS-derived MSY RPs tend to exhibit higher bias but lower variance than MSY RPs derived from the true SRR. Management strategy evaluation simulations further reveal that management procedures combining HS-derived MSY RPs with adaptive model learning and some precautionary measures gradually reduce this bias and achieve average spawning biomass and yield that are comparable to those obtained under management based on the true BH SRR. We also show that the management effectiveness of the precautionary measures depends on life-history traits and recruitment variability. These findings indicate that although HS-derived MSY RPs may be biased and require cautious use, combining them with appropriate precautionary measures allows management to remain robust while limiting variability and yield losses. This broadens the range of management options that are available for supporting sustainable fisheries management.

Catch inequality--the disproportionate distribution of catch across anglers-- is a fundamental but overlooked driver of recreational fisheries dynamics. Here, we use 11 years (2012-2022) of compulsory angler report cards to characterize long-term catch dynamics in the specialized recreational steelhead (Oncorhynchus mykiss) fishery in California, U.S.A. Spatialized catch data reveal the fishery is principally supported by wild fish, despite evidence of widespread hatchery straying. California steelhead appear to represent the most catch-unequal recreational fishery studied yet, exhibiting a statewide Gini coefficient of 0.81. Across basins, inequality varies substantially but remains relatively stable over time and flow conditions; high inequality is primarily driven by significant proportions of zero-catch anglers. We find the relationship between sample size and inequality measures is especially influential in fisheries data. Hence, we develop a three-prong approach for identifying minimal sample sizes required for robust Gini estimation. Across basins and years, an average minimum of 77 report cards were required for the present fishery. Collectively, these findings demonstrate the necessity of considering catch inequality in fisheries management, particularly when utilizing angler data. Graphical AbstractN.a.

Red Knots (Calidris canutus rufa) rely on Atlantic horseshoe crab (Limulus polyphemus) eggs in the Delaware Bay to refuel during northward migration. Intensive harvest of horseshoe crabs in the 1990s contributed to declines in Red Knot numbers. In 2013, the Atlantic States Marine Fisheries Commission adopted an Adaptive Resource Management (ARM) framework to balance sustainable horseshoe crab harvest with ecosystem integrity and Red Knot recovery, requiring annual stopover population estimates. We estimated the 2025 passage population of Red Knots at Delaware Bay using a Bayesian analysis of a Jolly-Seber mark-resight model which accounts for population turnover and imperfect detection. We also evaluated change in migration timing between 2011 and 2025 with model-derived estimates of arrival at the Delaware Bay each year. The 2025 passage population was 54,043 individuals (95% credible interval: 47,926-61,928), an increase of approximately 17% over 2024 and only the second year since 2011 to exceed 50,000 individuals. Despite the increase, overlapping credible intervals across years indicate a stable stopover population. Migration timing has remained consistent, with 50% of the population typically arriving by 18 May and no evidence of advancement since 2011. These findings provide meaningful input for the ARM framework, supporting sustainable harvest of horseshoe crabs while maintaining adequate foraging opportunities for Red Knots and other shorebirds. Parts of the Introduction, Methods, and Appendices were originally published in Lyons (2024) and are summarized herein.

Invasive round goby Neogobius melanostomus have advanced eastward through the state of New York and provinces of Ontario and Quebec over the past two decades and are approaching Lake Champlain, one of the largest lakes in North America. This manuscript describes international efforts to monitor round goby populations during 2021-2025 on (a) the southern approach to Lake Champlain via the Hudson River and Champlain Canal, and (b) the northern approach to Lake Champlain via the Saint Lawrence River and Richelieu River. Monitoring utilized environmental DNA (eDNA), backpack electrofishing, beach seining, benthic trawling, and viral hemorrhagic septicemia virus (VHSV) testing. In the Champlain Canal, round goby were captured as far north as the downstream side of the C1 dam (97 kilometers [km] from Lake Champlain) while eDNA detections occurred as far north as the upstream side of the C2 dam (90 km from Lake Champlain). In the Richelieu River, round goby were captured as far south as Saint-Marc-sur-Richelieu (82 km from Lake Champlain) while the southern-most eDNA detections occurred near the Canadian side of the international border (4 km from Lake Champlain). Water temperature influenced habitat usage of round goby in the Champlain Canal, with catch rates in near-shore areas declining at < 10 {degrees}C. All VHSV test results were non-detections at the mouth of the Richelieu River, while one positive and two inconclusive results occurred along the Champlain Canal. Together, these data have informed multiple mitigation measures and have implications for management of aquatic invasive species across North America.

The North American beaver (Castor canadensis) is an ecosystem engineer that strongly influences stream hydrology and ecosystems by constructing dams and canals. Previous research has shown that changes in the extent of beaver ponds and wetlands mapped using aerial photographs can serve as a proxy indicator of shifting regional abundance of beavers. In this study we investigated the use of freely available optical satellite data to measure changes in beaver pond surface water area on the 184 km2 Michipicoten Island in Lake Superior (Ontario, Canada) after a large decline in the beaver population that followed the arrival of grey wolves (Canis lupus). Inter-annual variability in pond extents was measured using sub-pixel mapping methods applied to the 30 m resolution Landsat (1985-2023) and 10 m Sentinel-2 (2016-2023) satellite records. After a > 90% decline in the number of surveyed beaver colonies between 2015-2018, beaver pond surface water area was reduced by 38-42% for ponds < 0.5 ha and by 48% for ponds < 0.1 ha by 2023. While these recent ponding reductions occurred during a period of above average precipitation, two previous smaller reductions were associated with low precipitation, water balance index, and Lake Superior water levels, suggesting that they were caused by drought and not beaver colony declines. While further testing is warranted, our results show that satellite-mapped changes in beaver ponds can provide a cost-effective metric for assessing large-scale population trends in the boreal zone.

There is growing interest in establishing more protective regulations for native fishes historically classified as "rough fish"- a term ascribed to species of low-to-zero perceived commercial value. Yet high-quality population data are lacking for most species and populations, precluding determination of sustainable harvest limits using standard methods. Here, we present an inductive and ecosystem -based approach for comparing and aligning harvest limits of diverse fish species. Our approach centers on the production/biomass (P/B) ratio as the key instrument for gauging sustainable harvest. P/B is the biomass turnover rate in populations and therefore quantifies the return rate of any removed biomass in populations. We extracted and summarized data from existing studies, representing a total of 517 empirical estimates of secondary production, biomass, and P/B ratios. We subsequently developed a highly predictive statistical model (R2 = 0.90), demonstrating P/B is largely a function of maximum age across species. We then developed a separate database on age, growth, and longevity data for most native fishes of interest across the USA. For each species and population, we leveraged the above statistical model to predict and compare mean P/B across species. Results show most native fishes express P/B values similar to, or lower than, traditional game fish species. Accordingly, harvest limits across species groups can be harmonized with those of other managed species. For example, native nongame species like Bigmouth Buffalo Ictiobus cyprinellus and Freshwater Drum Aplodinotus grunniens are long-lived with slow replacement rates that are statistically clustered with those observed in Lake Sturgeon Acipenser fulvescens and trophy Muskellunge Esox masquinongy populations, two popular game fish species. Harvest limits for these nongame species would therefore need to be similarly low for these species to ensure comparable sustainability. To understand broad patterns of harvest limit alignment, we modeled relationships between daily bag limits of managed species and P/B for five test states. Model uniformly showed non-linear trends with high residuals (suggesting excessive bag limits) common for panfish species and low residuals (suggesting overly conservative bag limits) common for trout species. Managers can use the results of this study to estimate harvest limits native fishes.

The development of Artificial Intelligence (AI) presents novel opportunities for tackling complex marine resource management challenges. Among AI models, neural networks are a powerful class of tools capable of learning nonlocal and lagged patterns from fisheries data as well as approximating nonlinear relationships among multiple latent variables using estimation methods that automatically implement statistical shrinkage. This gives them potential to effectively handle data obtained from fisheries populations subject to dynamic environments. We highlight two flexible subclasses and one application of neural networks: Long Short-Term Memory (LSTM) and Convolutional Neural networks (CNNs) and policy discovery via Reinforcement Learning. LSTMs are designed to handle sequential data by allowing prediction from past values at both short and long time-lags. CNNs are not explicitly designed to handle temporal information, but can interpolate a spatial latent variable based on its value within a geographic neighborhood, and can be combined with LSTM models for this purpose. This "Food for Thought" paper introduces and applies these neural network approaches, both alone and in combination, to demonstrate their potential application for several foundational topics in fisheries science: 1) the forecasting of population weight-at-age, 2) the standardization of spatio-temporal indices of relative abundance, and 3) the discovery of harvest policies to optimize catches and maintain spawning biomass. Each section provides a simple, simulated example and describes the tradeoffs - particularly the lack of inferential capability - presented by using neural networks over traditional approaches for each topic. We then outline medium-term research questions that may clarify, facilitate or qualify the applicability of these tools to fisheries management science. Finally, we discuss how future combinations of these approaches could result in simplified ways to estimate and forecast stock biomass and provide harvest advice.

Since 1995, European fisheries of Pecten maximus have faced the presence of Pseudo-nitzschia species, which are able to produce the neurotoxin domoic acid responsible for Amnesic Shellfish Poisoning (ASP). As filter-feeders, scallops can accumulate and retain domoic acid much longer than most other bivalves, from months to years. When concentrations exceed the regulatory threshold, fisheries are closed leading to economic concern. Inter-individual variability increases the difficulty to predict the depuration dynamics. Quantifying the correlations between domoic acid depuration in P. maximus and individual physiological traits, particularly body size, could improve the understanding of contamination and depuration. We analysed toxin dynamics in organs and assessed the effects of body size and growth. This analysis was based on two datasets from an experimental and an in situ depuration monitoring of P. maximus exposed to a natural bloom of toxic P. australis. Results showed that the distribution of domoic acid shifted among organs between contamination and two months of depuration. Toxin concentrations correlated negatively with body size during contamination and after two months of depuration, but shifted to a positive correlation after 7 months of depuration. This suggested that smaller scallops both accumulate more domoic acid and depurate it more rapidly. Dilution by growth appeared to explain the inversion of the correlation between domoic acid and body size throughout depuration. These results yield useful information for modelling these mechanisms, thus providing valuable tools for scallop fishery management facing ASP. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=60 SRC="FIGDIR/small/708139v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1fd317org.highwire.dtl.DTLVardef@15b9032org.highwire.dtl.DTLVardef@57dae8org.highwire.dtl.DTLVardef@1e4c7fc_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIExperimental and in situ datasets allowed to quantify DA proportion dynamics in organs in P. maximus C_LIO_LIDA concentration and body size are negatively correlated during contamination phase, but positively after a 7-month depuration C_LIO_LIConsidering dilution by growth is important for young scallops to assess DA depuration dynamics C_LIO_LIBoth depuration rate and dilution by growth need to be considered to model DA depuration over the whole scallop size range C_LI

Pacific cod is a key species in North Pacific fisheries, and its stock assessment and management units are separated according to biological, geographical, and administrative information. Understanding the fine-scale genetic population structure of this species is crucial for effective management, particularly in regions such as Japan, where complex coastal geography and localised fisheries management prevail. Therefore, in this study, we analysed genome-wide single nucleotide polymorphisms (SNPs; 6,035 loci) in 496 individuals of Pacific cod sampled from 33 sites around the Japanese archipelago via genotyping by random amplicon sequencing-direct (GRAS-Di) analysis. Our analyses revealed three major genetic groups: Japanese Broad Range, Northernmost Honshu-Hokkaido (NHH), and Western Sea of Japan groups. These groups exhibited significant genetic differentiation (global FST = 0.056), distinct levels of nucleotide diversity, and group-specific genome-wide patterns of Tajimas D. Moreover, demographic history reconstruction based on whole-genome sequencing of three representative individuals revealed that each genetic group followed distinct demographic trajectories since the last glacial period. Importantly, the NHH group, related to the Mutsu Bay spawning aggregation and previously shown to exhibit strong natal homing in tagging surveys, was genetically identified for the first time in this study. Isolation-by-distance was observed across Japanese waters and within the Japanese Broad Range group, but not within the NHH group, suggesting that gene flow is generally restricted by geographic distance, except within the NHH group. To evaluate the potential for genetic stock identification, we extended a resampling-based cross-validation framework by incorporating outlier detection to assess marker selection strategies. Over 500 background SNPs were required to achieve >90% assignment accuracy for genetic stock identification, whereas only eight or more outlier SNPs showed comparable performance. These findings suggest that carefully selected SNP panels, particularly those including outlier loci, substantially improve stock discrimination. Overall, our study demonstrates the fine-scale genetic structure and demographic history of Pacific cod in Japanese waters and highlights the utility of practical marker strategies for enhancing the biological realism of fisheries assessment and supporting sustainable fisheries management.

The yellownose skate (Dipturus chilensis) is an endangered skate with a narrow distribution in the southeastern Pacific, facing intense fishing pressure and potential climate threats. Using a species distribution model, we projected the current and future distribution of D. chilensis under contrasting climate change scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for mid-century (2050) and end-of-century (2100). Our models, which demonstrated robust predictive performance significantly better than random expectations, identified maximum temperature and minimum oxygen as the primary environmental drivers of habitat suitability. Projections revealed a consistent poleward range shift towards the Channels and Fjords of Southern Chile ecoregion across all scenarios. While localized habitat loss was projected in Central Chile and Araucanian ecoregions, particularly under high emissions (SSP5-8.5), these losses were outweighed by southern expansions, leading to a net increase in total suitable habitat by 2100. These findings underscore the critical need for climate-adaptive management strategies, including the protection of emerging southern refugia and dynamic fisheries regulations, to ensure the long-term persistence of D. chilensis.

We developed an agent-based model (ABM) to assess how area-based controls on fishing methods can reduce fishing mortality and population declines. The model incorporates the behavior and distributions of dolphins and fishing vessels, and realistic displacement of fishing effort when protection is extended. Our case study is New Zealand dolphin - Hectors and Maui dolphins. The model was designed and calibrated using pattern-oriented modeling. Our results show that mortality due to entanglement in fishing gears has been reduced thanks to a gradual increase in dolphin protection. However, current protection is not as effective as previously thought, and scarce populations are negatively affected by Allee effects. Neither national nor international goals for reducing bycatch are met by current dolphin protection. The IUCN has recommended banning gillnet and trawl fisheries in New Zealand waters < 100m deep. For most New Zealand dolphin populations, this would be effective in achieving national and international goals for reducing bycatch. Only two populations would require additional protection. This modelling approach is also suitable for assessing impacts of bycatch and ship strikes for other marine species, making it suitable for informing management decisions in many regions.

Spatial and spatiotemporal models are increasingly critical for understanding species distributions, tracking population change, and informing conservation decisions. As biological processes are influenced by increasing external pressures, including human disturbance or environmental change, accurate model predictions become essential for adaptive management. However, the reliability of spatial predictions depends on often-overlooked modelling choices, including the spatial resolution used to approximate underlying processes. Using long term monitoring data from a large-scale groundfish survey in the California Current ecosystem, we investigated how spatial model complexity affects the quality of ecological predictions and derived indices used for management. We fit spatial and spatiotemporal models of ocean temperature and fish biomass density for 27 commercially important species using varying levels of spatial resolution. We evaluated both in-sample and out-of-sample prediction, and effects on area-weighted biomass indices. Counter to common assumptions, increasing spatial approximation resolution did not universally improve predictions. Our case studies demonstrate that for many datasets, out-of-sample prediction quality peaked at intermediate spatial resolutions and declined at the finest scales. Through simulation testing, we found this pattern was strongest when spatial patterning had a small range and high spatial variance, and observation error was low. For most species, spatial resolution had a minimal effect on biomass trend estimates used in management, but for several commercially important rockfish species, resolution choices substantially affected both the scale and uncertainty of population indices. Our findings demonstrate that spatial model specification can substantially affect ecological inference, with direct implications for management and conservation planning. We provide practical guidance for ecologists on selecting appropriate spatial complexity through cross-validation. When out-of-sample prediction is a focus, appropriate approximation complexity should improve both parameter estimation accuracy and derived quantities.

BackgroundCryopreservation is a valuable tool in aquaculture and conservation programs, yet it exposes spermatozoa to physiological and molecular stresses that may impair motility, fertilisation capacity, and genomic stability. In fishes, where sperm motility is brief and easily activated, post-thaw separation of high-quality sperm remains technically challenging and poorly understood. This study evaluated whether density-gradient centrifugation can isolate a functionally superior subpopulation of sterlet (Acipenser ruthenus) spermatozoa with enhanced motility, fertilising ability, and reduced cryopreservation-induced epigenetic alterations. We further examined whether the use of this selected fraction influences DNA methylation patterns in resulting embryos. ResultsCryopreservation substantially reduced the proportion of motile spermatozoa, while density-gradient centrifugation consistently enriched motile cells both before and after freezing. Motility enhancement reflected a higher proportion of cells capable of activation rather than changes in kinematic behaviour. Fertilisation trials confirmed that the selected post-thaw fraction produced fewer malformed embryos compared with unselected cryopreserved sperm. Cryopreservation and post-thaw selection induced small but significant methylation changes in sperm, predominantly in intergenic regions and promoter-proximal elements. However, these epigenetic differences were not maintained in embryos. Embryo methylomes showed minimal variation between treatments, no distinct clustering by sperm origin, and negligible numbers of differentially methylated regions. Thus, although cryopreservation and sperm selection influenced sperm DNA methylation, these alterations did not translate into measurable changes in embryo methylation patterns. ConclusionsDensity-gradient centrifugation effectively isolates a motile, functionally improved sterlet sperm fraction after cryopreservation, enhancing fertilisation outcomes and reducing developmental abnormalities. Cryopreservation and sperm selection introduce detectable but limited methylation variation in spermatozoa; however, these changes are not inherited by embryos. The findings highlight the utility of post-thaw sperm selection in aquaculture practice and indicate that cryopreservation-associated epigenetic variation in sterlet sperm does not propagate to early developmental stages.

Estimating the number of spawning events per female is key to understanding individual reproductive output in batch-spawning species, yet direct observation of spawning is often infeasible in the wild. Recent advances in genetic kinship inference enable the identification of maternal half siblings from young-of-the-year genomic samples, while otolith-based age determination provides reconstruction of offspring birth dates. Here we develop an offspring-based framework for estimating the number of clutches produced by individual females by integrating sibling structure inferred from genomic data with otolith-derived birth-date information. By recasting clutch identification as a richness estimation problem, we apply the Chao1 estimator to infer the total number of spawning events from incomplete offspring samples. Using simulation experiments, we evaluate how sampling effort and heterogeneity in clutch size influence clutch detection and estimation. Under uniform clutch-size distributions, modest numbers of offspring sampled per maternal family (10-20 offspring) yield accurate estimates of the total number of clutches, substantially outperforming naive counts of observed birth-date classes by recovering information from rare or unobserved spawning events. In contrast, skewed or multimodal clutch-size distributions lead to underestimation at low sample sizes, indicating that uneven reproductive output increases sampling effort required for reliable inference. Overall, our results demonstrate how offspring genomic data and otolith-derived birth dates can be jointly leveraged to reconstruct individual spawning histories under realistic sampling constraints. This perspective provides a framework for inferring within-season reproductive schedules in batch-spawning species, and highlights opportunities for integrating genomic and life-history data in fisheries monitoring and reproductive ecology.

Climate change can affect salmon and steelhead (Oncorhynchus spp.) throughout their anadromous life cycles, yet there have been no assessments of which Canadian populations face the greatest exposure. We developed a framework to quantify relative climate change exposure of salmon and steelhead populations based on the spatial and temporal distribution of different life stages. Exposure was calculated from climate model projections for freshwater and marine climate variables considering unique impact thresholds for each population and life stage. We applied this framework to 60 Conservation Units of Pacific salmon and steelhead in the Fraser River basin, British Columbia. Lake-type sockeye had the highest exposure, driven by elevated stream temperatures during adult freshwater migration and spawning stages and relatively low thermal tolerance of marine stages. Chinook salmon were the next most exposed, while coho, pink, and chum salmon had relatively low exposure. Uniquely, steelhead exposure was driven by high stream temperatures during incubation. Our framework is broadly applicable, and our findings provide critical input for climate change vulnerability assessments and forward-looking resilience planning for Pacific salmon.

Anthropogenic pressures can have detrimental impacts on fish populations, with their effective management and conservation requiring accurate monitoring tools. Yet, this is not straightforward for closely-related, co-existing species that are difficult to distinguish using simple phenotypic or genetic approaches. Coregonids are of cultural and economic importance across Europe but have faced a multitude of pressures over the last century. Yet genomic management tools are lacking. In Lake Constance, a large pre-alpine lake, stocks have drastically collapsed due to a multitude of pressures, leading to a fishery closure. Here, we adopt a cost-effective, whole genome sequencing approach for lake-wide assessment of stock composition, spatial distribution and genetic diversity of highly admixed Lake Constance whitefish (Coregonus spp.). By sequencing 983 adult and larval genomes, we show that nearly 90% of the stock is made up by one of three species, the Gangfisch (C. macrophthalmus), and define the genetic relationship between Upper and Lower Lake Constance whitefish stocks. We also identified strong mixing between Gangfisch and Blaufelchen (C. wartmanni) on traditionally specific-specific spawning grounds, and detected strong admixture in larvae, with potentially drastic impacts on the effectiveness of hatchery supplementation and stocking. Despite the collapse and admixture, species still exhibit low to moderate levels of genetic diversity, maintain ecologically-relevant genetic differences, and seem to show differences in habitat use. Overall, we present a cost-effective, translatable tool for stock-wide sequencing and genetically-informed fisheries management, with our results calling for the re-evaluation of current management practices to avoid the potential genetic mixing between species.

Using linear inverse ecosystem modeling as a data assimilation tool, we compare spawning grounds of Atlantic and Southern Bluefin Tuna (ABT and SBT, respectively) based on results from field campaigns in the Gulf of Mexico (GoM) and eastern Indian Ocean off northwest Australia (Argo Basin). Both regions are warm, stratified, low-nutrient waters dominated by cyanobacteria (Prochlorococcus). Despite these similarities, the Argo Basin is more productive, with [~]1.5X higher net primary production and nearly 2X higher production of top trophic levels in the model (tuna larvae, planktivorous fish, and predatory gelatinous zooplankton). Higher primary production in the Argo Basin is mainly driven by higher N2 fixation and storm mixing of new nutrients in the upper and lower euphotic zone, respectively. Increased ecosystem efficiency (secondary production of top trophic levels / primary production) results from differences in plankton food web organization. In the GoM, protistan zooplankton are the direct consumers of nearly all phytoplankton production. In contrast, higher rates of herbivory by crustaceans feeding on nanophytoplankton combines with a higher impact of appendicularians on cyanobacteria to convert plankton production into larval tuna prey more efficiently in the Argo Basin. Despite similarities in the proportions of phytoplankton production mediated by cyanobacteria and other picoplankton in both systems, food web pathways to larval tuna and other planktivorous fish are substantially shorter in the Argo Basin. Our results highlight the impact of distinct zooplankton ecological niches on ecosystem efficiency and suggest a need for better inclusion of plankton food-web structure in models simulating climate impacts on fisheries production. HIGHLIGHTSO_LIDeveloped food web models of tuna spawning habitat (Indian Ocean & Gulf of Mexico) C_LIO_LISpawning habitats in the Argo Basin and Gulf of Mexico (GoM) are both oligotrophic C_LIO_LIArgo Basin had higher net primary production in part as a result of nitrogen fixation C_LIO_LIArgo Basin had higher rates of direct herbivory by metazoan zooplankton C_LIO_LIThis resulted in greater ecosystem efficiency in the Argo Basin. C_LI

Transcriptomic analyses are widely used to elucidate the molecular mechanisms driving gametogenesis and reproduction in fish, yet their accuracy depends heavily on appropriate normalization of gene expression data. Conventional approaches that rely on single or multiple reference genes are problematic during teleost oogenesis, as profound structural and physiological remodeling of the ovary challenges the assumption that commonly used reference transcripts remain stable. In this study, we assessed by qPCR the transcriptional variability of four widely used reference genes (actb, ef-1, gapdh, and 18S rRNA) throughout the oogenic cycle of the thicklip grey mullet (Chelon labrosus), using geNorm and NormFinder analyses, and we additionally evaluated total cDNA concentration as an alternative normalization factor. To examine the performance and interpretive consequences of each normalization strategy, we compared expression patterns of key steroidogenic genes (star, cyp19a1a, and cyp11b) normalized by individual reference genes, combinations of reference genes, or total cDNA concentration. All evaluated reference genes displayed notable transcriptional variability across oogenesis, confirming their limited suitability as sole internal controls. In contrast, normalization approaches integrating multiple reference genes and/or total cDNA concentration consistently provided greater stability and more reliable biological interpretation. These results support a refined and more robust normalization framework for transcriptional analyses in fish ovaries, particularly during stages of extensive tissue remodeling. Our findings demonstrate cDNA-based normalization is straightforward, rapid, and easy to implement across laboratories, providing a practical alternative for achieving accurate, reproducible transcript quantification in fish ovary studies.

Effective monitoring of the critically endangered European eel (Anguilla anguilla) is essential for conservation planning and regulatory decision-making, particularly in heavily fragmented rivers. Environmental DNA (eDNA) methods offer sensitive alternatives to traditional surveys, but there is uncertainty around whether targeted assays or community-wide approaches are better suited to achieve monitoring objectives. We compared eDNA metabarcoding and species-specific quantitative PCR (qPCR) for detecting A. anguilla across 145 pumped catchments in the Fens, East Anglia, England. All sites were sampled once initially, and sites negative for A. anguilla were re-sampled based on metabarcoding results. This allowed comparison of detection rates from a single water sample and site-level retrospective identification of sites where qPCR could have identified A. anguilla in earlier samples. The findings were also set in the context of the wider biodiversity information generated by metabarcoding. From the initial (single) water sample, qPCR detected A. anguilla at seven more sites than metabarcoding (17 versus 10). With repeated sampling, metabarcoding detected A. anguilla at 43 sites, including all but one of the sites where qPCR detected A. anguilla, and ten sites where qPCR did not detect A. anguilla within the same number of samples. Indeed, the additional sampling effort required to detect A. anguilla with metabarcoding at sites also positive with qPCR was small relative to the overall sampling effort. Furthermore, metabarcoding additionally detected 28 non-target fish species alongside fish, amphibian and mammal species of conservation concern. Our results highlight trade-offs between target-species sensitivity and the broader ecological information provided by each method, and support metabarcoding as an effective tool for a holistic conservation approach, with the additional community data outweighing the marginally increased sensitivity of qPCR.