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.

Understanding population structure is critical for effective fisheries management in species with complex life histories and variable recruitment. Southern flounder (Paralichthys lethostigma) is a valuable flatfish species with declining populations in the Southeast United States. Improved management may depend on a better understanding of fine-scale and temporal population genetic structure in this region; however, such structure remains poorly characterized. To address our lack of understanding of the spatial and temporal population structure of this important species, we used double digest reduced-representation genome sequencing (ddRADSeq) on juveniles from estuaries in North Carolina and Texas between 2014 and 2023. We found significant genetic differentiation between the Gulf of Mexico and Atlantic populations, supporting the management of these regions as distinct stocks. By contrast, we detected significant variance in genetic structure within Texas and North Carolina populations that was not consistent across sampling years between estuaries in close proximity. The population genetic structure of southern flounder suggests significant, temporally variable genetic differences within estuarine locations that may result from variation in larval dispersal and recruitment patterns. Our findings highlight the value of integrating fine-scale, multi-year genetic data to capture temporal dynamics and avoid misleading conclusions based on single-year or broad-scale sampling.

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 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.

Mid-Trophic Level (MTL) organisms--including krill, forage fish, and mesopelagic fish-- are abundant in the California Current System (CCS) and play an essential role in transferring energy and biomass from primary producers to top predators. However, their spatiotemporal distribution and variability remain poorly understood, particularly with respect to vertical structure across epipelagic and mesopelagic habitats and coastal-offshore gradients. This lack of understanding emerges from both the complexity of MTL interactions with a heterogeneous environment and the challenges associated with sampling these organisms at high spatial and temporal resolution. To address this gap, we analyze 11 years of fisheries acoustic observations in the CCS (2006-2016) to characterize the spatiotemporal dynamics of MTLs as inferred from acoustic backscatter. Acoustic observations at 38 and 120 kHz, collected during day and night across depth strata from 15 to 495 m, reveal consistent cross-shore, seasonal, and latitudinal patterns in the backscatter of acoustically defined zooplankton, epipelagic fish, and mesopelagic fish communities. These patterns include: (1) weaker cross-shore gradients in mesopelagic relative to epipelagic communities; (2) a temporal succession among communities associated with seasonal upwelling; and (3) a multimodal latitudinal distribution with distinct coastal backscatter peaks. We further investigate relationships between acoustic backscatter and co-located environmental variables from in situ, remote sensing, and reanalysis products to elucidate plausible mechanisms underlying MTL dynamics. HighlightsO_LIFisheries acoustics resolve variability in mid-trophic communities C_LIO_LIEleven years of backscatter reveal consistent patterns in the California Current C_LIO_LIEpipelagic backscatter declines faster from the coast to offshore than mesopelagic C_LIO_LISeasonal changes in community composition are linked to upwelling dynamics C_LIO_LIBackscatter exhibits multimodal latitudinal distributions with distinct peaks C_LI

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.

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.

Philippine freshwater ecosystems are considered one of the most diverse ecosystems harboring numerous fish species. However, in the Philippines, these ecosystems are threatened by invasive species that potentially disrupt ecological balance. In this study, we focused on the vermiculated sailfin catfish Pterygoplichthys disjunctivus, an invasive aquarium species reported in several Philippine aquatic ecosystems. Despite its documented spread, its potential range under a rapidly changing climate remains poorly understood for the country. Hence, in this study, we utilized the MaxEnt model to predict its near-current and future habitat suitability in the Philippines. Using 11 reported occurrences, our model showed high predictive accuracy (AUC = 0.882{+/-} .034, TSS = 0.7394 {+/-} 0.154, SEDI = 0.971 {+/-} 0.019). Across the current and future scenarios, slope was the primary contributor (78.7% - 81.3%), followed by BIO 10 or the mean temperature of the warmest quarter(18% - 27.8%), and flow accumulation (0% - 5.2%). However, for the SSP126 scenario, BIO10 is projected to triple by 2050 (18 - 48%). Current projections identify high-risk regions, particularly central Luzon (Laguna de Bay and Lake Taal), the Cagayan River Valley, and portions of eastern Mindanao (Agusan Marsh and Lake Mainit). Sankey transition analysis confirms a high habitat stability rate (>73%) for high-suitability pixels in both SSPs, indicating persistent invasion risk. Overall, our study provides a framework for invasive species management and contributes to the conservation of Philippine aquatic ecosystems.

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.

Guinea worm disease eradication efforts may benefit from environmental surveillance methods capable of detecting infected copepod intermediate hosts in aquatic habitats. We developed a three-dimensional, spatially explicit agent-based model to examine how ecological processes influence detection probability for a hypothetical water sampling method. The results show that surveillance sensitivity is shaped by the combined effects of larval diffusion, copepod density, and pond size, with interactions among these factors producing nonlinear relationships. Detection, in our model, was concentrated within a relatively restricted period after larvae matured to the infective stage and before dispersal and mortality reduced presence, indicating a limited spatiotemporal window for effective sampling. Surveillance performance peaked under intermediate dispersal regimes that generated sufficient spatial overlap between larvae and intermediate hosts, while both limited dispersal and excessive diffusion reduced detection by constraining encounters or diluting larval concentrations. Increasing habitat size reduced detection by diluting larval concentrations, but the magnitude of this effect depended on copepod density and dispersal dynamics, producing nonlinear and threshold responses rather than simple scaling with pond volume. Spatial and temporal patterns of detection shifted as larvae dispersed, with the most favorable detection periods occurring when both larval abundance and intermediate host encounters were elevated. These findings indicate that surveillance can be guided by local ecological conditions. When the timing of larval introduction is uncertain, effective surveillance requires repeated sampling over time to capture transient windows of detectability and the sampling will be less effective in very stagnant and highly mixed waterbodies. Overall, this study demonstrates how mechanistic modeling can support the design and interpretation of environmental surveillance strategies for Guinea worm eradication programs. Author summaryGuinea worm disease is close to eradication but confirming that transmission has fully stopped remains difficult because detecting infectious larvae in water is challenging. Transmission depends on freshwater copepods that become infected after ingesting Guinea worm larvae. These copepods are short-lived and unevenly distributed within ponds, and infected individuals may die before larvae reach the infective stage. As a result, environmental detection is inherently uncertain. We developed a three-dimensional agent-based model to simulate larval dispersal, copepod infection, and water sampling in a pond environment. The model shows that detection is constrained to a brief period when mature larvae and copepods overlap in space and time, and that this window depends strongly on local ecological conditions such as larval dispersal, copepod density, and pond size. Because infected copepods can be present outside these narrow detection windows, negative water samples do not necessarily indicate absence of transmission, highlighting the need for repeated, spatially targeted surveillance during the final stages of eradication.

Global climate change has increased the frequency and severity of stressful temperatures that freshwater fishes experience, necessitating rapid and sensitive methods to monitor wild populations. Tissues used to measure transcriptional responses traditionally involved invasive or lethal sampling, which may be undesirable for imperilled species. Epidermal mucus offers a non-lethal and minimally invasive alternative, but whether thermal thresholds can be detected in mucus to identify fish experiencing thermal stress is unclear. Bull trout (Salvelinus confluentus) are a legally protected salmonid and cold-water specialist, generally occupying waters 12 {degrees}C and below, with higher temperatures resulting in cellular stress. Therefore, we measured a suite of 56 genes using high-throughput qPCR to compare machine learning classifiers developed with transcriptional profiles of epidermal mucus, gill, liver, and muscle to classify laboratory reared juvenile bull trout as below (9 {degrees}C, 12 {degrees}C) or above (15 {degrees}C, 18 {degrees}C) cellular thermal thresholds. Mucus profiles most resembled gills but represented an intermediate transcriptional response to all tissues. A reduced biomarker panel of 10 genes in mucus assigned fish to stress categories with 94.1% (95% CI = 71.3-99.9%) accuracy, which was comparable to gill (100.0%, CI = 82.4- 100%), liver (95.0%, CI = 75.1-99.9%), and muscle (100.0%, CI = 80.5-100.0%). Sex-specific temperature effects were evident in all tissues, but less pronounced in mucus and gill than in liver and muscle. Our findings demonstrate that transcriptional profiling of mucus can reliably identify individuals experiencing thermal stress, highlighting the promise of this non-lethal approach for monitoring at-risk species.

Indo-Pacific humpback dolphin (Sousa chinensis) and finless porpoise (Neophocaena phocaenoides) are increasingly threatened across their native range, yet the relative influence of multiple stressors in shaping their population dynamics remains unclear. Current conservation strategies for both species are limited by incomplete data and limited assessment of affecting factors. Here, we integrated eDNA metabarcoding with Joint Species Distribution Modeling (JSDM) to assess how environmental gradients, pollution, and trophic associations interactively influence cetacean distributions in Hong Kong waters. We show that degraded water quality and intensified human activity negatively associated with cetacean occurrence, with clear species-specific responses to different stressors. S. chinensis covaried most strongly with Secchi disc depth, and presence of vessels, while N. phocaenoides was negatively associated with nitrate nitrogen and microbial pollution (sewage). The JSDM variance partitioning analysis highlighted that the occurrence of S. chinensis was primarily associated with anthropogenic disturbances (30.04%), followed by water physical properties (26.63%), whereas N. phocaenoides was more strongly associated with physical (40.9%) and anthropogenic disturbances (35.2%). By integrating eDNA and JSDM, our approach provides fine-scale diagnostics of species-specific vulnerabilities, supporting adaptive conservation strategies and guiding the realignment of protected areas to mitigate biodiversity loss in urbanized marine ecosystems. Environmental ImplicationOur study demonstrates that hazardous water pollutants, including microbial contamination, nutrient enrichment, and chemical stressors, vessel pressure, show strong, species-specific impacts on resident cetaceans in Hong Kong. By integrating eDNA metabarcoding with joint species distribution models, we provide a diagnostic framework that directly links pollutant profiles to ecological risk. These findings highlight that conventional conservation strategies overlooking pollution drivers are insufficient for marine megafauna persistence. Our approach offers an early-warning system for monitoring hazardous pollutants in coastal ecosystems and supports adaptive management strategies to mitigate biodiversity loss in urbanized seascapes.