ICES Journal of Marine Science

Bottom trawls have been used for centuries, yet studies of their impact on marine ecosystems have largely been restricted to recent decades. Here, we reconstruct fishing effort for the international trawler fleets in the North Sea from the age of sail to early steam trawling, by synthesising historical data describing vessel numbers and specifications, gear dimensions, fishing grounds, and operational characteristics. The trawler fleet increased from ca 800 sailing vessels in the 1820s to ca 3500 at the peak in sail trawling in the 1880s. Subsequently, steam trawling fleets emerged, increasing to almost 2000 vessels in the 1910s, while sailing fleets declined. Trawling grounds, covering ca 7% of the North Sea in 1820s, expanded from coastal to offshore grounds, reaching ca 25% in the 1880s, and 46% in the 1910s after the transition to steam trawling. Using a hydro- and aerodynamic approach to model the wind conditions required for sail trawling, we show that about 55% to 80% of the time at sea was suitable for trawling, providing a new quantitative basis for estimating historical fishing effort. The surface area swept by the trawl per year increased from 35,000 km2 in 1820s to 225,000 km2 in 1880s and 500,000 km2 in 1910s, corresponding to ca 60% of present levels. The trawling intensity (swept area ratio) varied between 0.9-1.7 year-1 in the era of sail, increasing to ca 2.0 in the 1910s. The trawling footprint (unique area trawled) increased to 160,000 km2, about half the present level.

As anthropogenic threats have intensified over the past 500 years, we find ourselves in the midst of a sixth mass extinction, with continued losses of biodiversity threatening ecosystem stability. This biodiversity loss has caused species extinctions across taxa, and placed several others at high risk of functional extinction. These disturbance-driven impacts represent one of the most acute biodiversity crises facing global marine systems. Species exhibiting slow life histories characteristically have low resilience to disturbance. Here, we assess the risk of functional extinction and identify policy pathways for population recovery of the slow-living, Critically Endangered elasmobranch, the spinetail devil ray (Mobula mobular). We develop a stochastic, state-structured Integral Projection Model (IPM) parameterised with demographic data collected from fishery landings data in India, the world's largest mobulid fishery, and supplemented with data on vital rates from published literature. Using the IPM, we estimate that the population is declining at approximately 12% annually, experiencing substantial limiting pressure from fisheries overexploitation and failing to approach its biological maximum growth potential. Our results indicate that populations of M. mobular will be at high risk of functional extinction if 'business as usual' harvest scenario persists for another decade. We further show that long-term population recovery is only possible if survival increases significantly across all size classes, especially among large reproductive females, alongside a concurrent increase in fecundity. We conclude that no single policy measure is sufficient to recover population of M. mobular along the southeastern coast of India. Instead, combined protection through maximum bycatch mitigation and protection of nursery areas in no-take zones will be required for population recovery. This research demonstrates that recovery of overexploited populations often requires integrated resource management across life stages, and that the Critically Endangered M. mobular warrants urgent conservation action to avoid functional extinction.

Offshore wind farms are rapidly expanding to meet growing demands for renewable energy, with development expected to extend further offshore into deeper waters. This expansion requires a robust understanding of the long-term ecological consequences of offshore wind farms (OWFs) and how these may interact with ongoing climate change. We used the coupled hydrodynamic-ecosystem-biogeochemical water-column model (GOTM-ERSEM-BFM) to investigate ecosystem-wide responses to environmental changes associated with OWFs and climate warming. Specifically, we examined OWF-related scenarios of reduced benthic suspension-feeding activity, representing potential effects of contaminant emissions from OWFs, and reduced wind forcing, together with increased sea surface temperature. The scenarios were simulated individually and in combination to explore potential interactive effects. These scenarios were simulated at two contrasting locations in the North Sea, representing a well-mixed coastal site and a seasonally stratified offshore site. The coastal site exhibited comparatively modest ecosystem responses across the scenarios, whereas responses were generally stronger at the deeper offshore site. At the offshore site, changes in stratification altered vertical nutrient dynamics and contributed to pronounced differences in ecosystem responses between the surface and bottom layers. Our results demonstrate that ecosystem responses to OWF-related and climate-driven environmental changes are strongly dependent on local environmental conditions, suggesting that ecological consequences may differ substantially as wind farm development expands into deeper offshore environments.

The management of Atlantic bluefin tuna (Thunnus thynnus) is currently defined by a long-standing life-history paradox: a stark divergence in maturation schedules between the Eastern and Western stocks. While the Mediterranean contingent matures at approximately 104 cm straight fork length (SFL; age 3-5), the western stock has long been characterized as a late-maturing, at [~]190 cm SFL (age 8+), and as late as age 15.8, based on size-at-catch data in the presumed exclusive spawning areas of the Gulf of Mexico. This discrepancy defies maturity states confirmed by endocrine hormones as well as established life-history principles that link metabolic energetics to reproductive biology. To resolve this critical issue in stock assessment, we present a sensitivity analysis based on the first histological evidence of spawning- capable individuals from 42 bluefin females (97-244 cm curved fork length or [~]95- 236 cm SFL) sampled in June 2025 within the Slope Sea, Northwest Atlantic. This historically overlooked, temperate spawning ground must be recognized and past estimates of stock productivity reevaluated. Our data provide a conservative estimate of length-at-50% maturity, L50 for females at 123.3 cm SFL, effectively reconciling the reproductive parameters of the two stocks based on histology. Our findings align established bluefin tuna metabolic and evolutionary symmetries in life history, irrespective of management boundaries. Incorporating this previously unrecognized spawning group (i.e., smaller, younger fish) calls for a substantial upward revision of Western spawning stock biomass (SSB). This biomass represents an intrinsic biological buffer that could contribute to the Atlantic bluefins adaptation and resilience. Appreciating demographic diversity is critical to accurately diagnosing stock vulnerability, ensuring that management frameworks protect long-term population stability in the face of climate-driven oceanographic shifts and ongoing exploitation pressure.

Habitat degradation and biodiversity loss in the Black and Azov Seas necessitate improved tools for spatially explicit conservation planning. We employed stacked species distribution modelling (SSDM) to assess habitat quality for the three resident cetacean species, the common dolphin (Delphinus delphis ponticus), the bottlenose dolphin (Tursiops truncatus ponticus), and the harbour porpoise (Phocoena phocoena relicta), which serve as apex predators and indicators of ecosystem health. Occurrence data were compiled from the Global Biodiversity Information Facility (GBIF), and ensemble species distribution models (ESDMs) were constructed using nine algorithms within the SSDM framework, with eight environmental predictors extracted from Bio-ORACLE v3.0. Individual ESDMs demonstrated excellent predictive performance (AUC: from 0.82 to 0.83; TSS: from 0.65 to 0.67; prop.correct: from 0.82 to 0.83). However, the initial continuous stacking method (pSSDM) yielded low community-level prediction success (0.36), prompting evaluation of three correction approaches. The Probability Ranking Rule (PRR) substantially improved performance (prediction.success = 0.459, sensitivity = 0.704, Jaccard = 0.465), effectively mitigating the overprediction bias inherent in stacked models. Species richness mapping identified multi-species hotspots along the southwestern Black Sea shelf, the Crimean coast, the Kerch Strait, and parts of the eastern coast, while the deep central basin exhibited the lowest richness. Variable importance ranking revealed bathymetry as the primary community-level driver (41.2%), followed by dissolved oxygen (13.8%), sea surface temperature (11.9%), and salinity (10.4%). Species-specific importance patterns confirmed ecological niche segregation, with common dolphins favouring deeper offshore waters and bottlenose dolphins and harbour porpoises associated with shallower shelf environments. The moderate richness observed in the highly productive northwestern shelf, despite high nutrient inputs, may reflect a combination of natural factors (elevated turbidity, reduced salinity) and anthropogenic pressures (fisheries bycatch, shipping, coastal development, and military activity) that limit species co-occurrence. Our findings demonstrate that PRR-corrected SSDM provides a robust framework for mapping cetacean habitat quality and identifying conservation priorities in the Black and Azov Seas, offering an evidence-based tool to inform ecosystem-based management in this ecologically unique and increasingly pressured marine region.

Genomic prediction of complex traits is limited when phenotype records are restricted and when using linear models. Increasing the amount of phenotypic data with high-throughput, image-based phenotyping could result in better genomic prediction and stronger signals in variant detection. Here, we analysed phenotypic and genomic data from a selectively bred cohort of the Australasian snapper (Chrysophrys auratus) to identify genetic variants associated with growth traits. We used a high-throughput phenotyping pipeline to extract 13 measurements of size from images. Phenotypic correlations among image-derived and manually measured traits (weight, fork length), together with heritabilities, were analysed. All measurements were significantly positively correlated with each other, and heritability ranged from 0.20-0.38. Genome-wide association studies (GWAS) identified 28 growth-associated SNPs, while GBLUP was used to predict phenotypes, and XGBoost machine-learning models were used to jointly predict phenotypes and report important variants. Both GBLUP (mean R2 = 0.50) and XGBoost (mean R2 = 0.77) performed well on the training data, but performance dropped on testing sets (both = 0.11), which decreased further when accounting for genetic relatedness (both = 0.06). Despite this, approximately 20% of the genetic variance for growth traits was captured by the models, and feature importance from XGBoost reflected signals seen in GWAS. Our findings highlight the utility of integrating computer vision-based phenotyping with GWAS, GBLUP, and ML for trait prediction. Despite detecting shared biological signals as GWAS, genomic prediction faces challenges with population structure and relatedness that are inherent in breeding programmes of mass spawning species, including many aquatic species. Article summaryIncorporating genetic information into selective breeding programmes can accelerate gains but may also miss gene interactions in complex traits. Machine learning approaches, such as decision trees, can capture those relationships and potentially improve genomic predictions. We used high-throughput computer vision phenotyping to uncover biological signal for genetic growth variants involved in the Australasian snapper. Both types of models captured 18-40% of the genetic variation, but the prediction accuracies were hampered by the population structure in this cohort of mass-spawning fish.

Chinstrap penguins (Pygoscelis antarcticus) in the Antarctic Peninsula and Scotia Arc regions (CCAMLR subareas 48.1 and 48.2) have experienced substantial declines, yet uncertainty remains about regional variation and conservation priorities. We applied a Bayesian hierarchical model to 1,072 nest counts from 194 colonies (1970 to 2024) to quantify population trends, estimate probabilities of exceeding IUCN Red List thresholds, and assess compensation potential across four locations. The regional population declined by 41.9% over three generations, with 91.4% probability of exceeding the 30% Vulnerable threshold but only 9.2% probability of exceeding the 50% Endangered threshold. Declines were spatially heterogeneous: South Shetland Islands (40.2% of regional population) declined by 74.7% with near-certainty of exceeding both thresholds, while South Orkney Islands showed stability with high uncertainty (median -5%, 95% CI: -31.5% to +36.8%). The probability of increasing colonies compensating for regional losses was only 7%. These results support a three-zone conservation strategy for CCAMLR's proposed Marine Protected Area: urgent protection for the South Shetland Islands, enhanced monitoring for South Orkney and Elephant Islands, and adaptive management for the Antarctic Peninsula. Our uncertainty-quantified framework provides evidence-based guidance for balancing krill fishery access with predator protection in a rapidly changing Southern Ocean.

Fledging is a critical period of a seabird life cycle. Using satellite telemetry, we compared movements and survival proxies (transmission duration) of chinstrap penguin fledglings tracked in 2017 (n=8) and 2025 (n=17) relative to krill fishing vessel activity. In 2017, fishing vessels operated intensively near colonies during summer, resulting in early, frequent encounters (median 1.3 days post-fledging) and short transmission durations (median 9.2 days). In 2025, reduced fishing delayed encounters (median 10.0 days) and tripled tracking duration (median 24.0 days). Hidden Markov Models revealed that vessel encounters reduced the probability of transitioning from foraging to transit behavior ({beta} = -0.76), an effect stronger than the productivity ({beta} = -0.11). While 87.5% of 2017 fledglings ceased transmission prematurely within weeks (half of those right after entering areas intensively used by fishing vessels), 65% of 2025 fledglings survived beyond March, with half of those five transmitting until May after dispersing eastward to the South Orkney Islands. These findings suggest that spatiotemporal overlap with krill fisheries during the critical post-fledging window affected foraging behavior and was associated with shorter transmission durations. Our results support further research of post-fledging penguin ecology to better understand the potential impact of fishery, and, following the precautionary principle, support fishing seasonal protection of important areas during critical periods of krill predators life cycle.

Populations are exposed to multiple anthropogenic stressors simultaneously; however, the combined effects are poorly understood. Climate change is starkly impacting marine ecosystems and consequently fishes, with warming temperatures and increases in frequencies and durations of extreme climate events. Concurrently, plastics, such as nylon used in fishing industries, are contaminating marine waters at unprecedented levels, with detrimental effects on fishes. Here we studied the cumulative effects of warming and nylon microplastic fibres on the behavior and physiology of threespine stickleback (Gasterosteus aculeatus) by exposing fish to conditions of 15{degrees}C and 20{degrees}C and nylon concentrations of 0, 1, 10, and 100 mg/g (mg nylon/g food) for 4 weeks. Feeding rates responded complexly to multiple stressors, as increasing concentrations of plastic reduced feeding rates, with warming having an antagonistic effect. Furthermore, we observed "coughing" behaviors in response to ingestion of microfibres and a unique reselection tendency of food items previously selected by conspecifics. Under warming conditions, critical thermal maximum (CTmax) increased; however, exposure to plastics led to reductions in CTmax and thermal safety margins. Given these results, we anticipate reduced acclimation capacities, greater anxiety, and reductions in foraging efficiencies with increasing concentrations of plastic. Cumulatively, these stressors will yield greater energetic trade-offs and decreased accuracy in food selection with stark implications for marine ecosystem dynamics.

Cold stress is a recurring risk in tropical and subtropical aquaculture nursery ponds, yet warning tools remain limited where continuous automated sensors are impractical. This study developed a low-resource cold-stress early warning framework using four years (2022-2025) of 6-hourly manual air and pond-water temperature readings from a Nile tilapia (Oreochromis niloticus) nursery pond in Cumilla, Bangladesh. Models were fitted on 2022-2023, validated on 2024 for threshold selection, and tested on 2025 as an independent year. Cold stress (daily mean water temperature <20{degrees}C) occurred on 133 days; heat stress (>35{degrees}C) on only 4 days. Air-water coupling was strong overall (r = 0.976) but weakened in winter (r = 0.776) and further within the 18-22{degrees}C boundary zone where cold-stress classification is most sensitive. Solar radiation only marginally increased boundary-zone classification AUC from 0.782 to 0.789. In 6-hour regression, the same-hour-yesterday baseline (MAE = 1.117{degrees}C) nearly matched Extreme Gradient Boosting (XGBoost) with MAE of 1.116{degrees}C, cold-zone bias +0.36{degrees}C, and train-test gap 0.02{degrees}C; Random Forest (RF) and Long Short-Term Memory (LSTM) had MAEs of 1.195{degrees}C and 1.244{degrees}C, respectively. For cold-stress classification, Multiple Linear Regression (MLR) gave the highest F1 (0.755), while XGBoost provided the more protective operating point, detecting 95 of 108 cold-stress readings at 6-hour lead time (sensitivity = 0.880, F1 = 0.739). XGBoost warning skill extended to 12, 18, and 24-hour lead times, with F1 scores of 0.722, 0.646, and 0.704, respectively. The framework converts routine manual thermometer readings into short-lead cold-stress alerts for nursery management decisions. Multi-pond validation is needed before deployment.

After introduction, many non-native marine species are dispersed planktonically. Secondary spread within the non-native range has been shown to prevent the establishment of the introduced species if the advection of larvae prevents sufficient return of larvae to maintain the population in the face of competition with native species. However, those studies have largely neglected the effects of spatial variation in alongshore larval transport. We examine the introduction of a novel species with planktonic dispersal into a more realistic coastal environment which includes spatial variation in larval transport estimated from the Mercator Ocean 1/12th degree global circulation model. The introduction may either be from a distant habitat, or through range expansion. We find that there are locations in the global coastal ocean where introduced species are more likely to persist because of spatial variation of coastal currents. These include regions where alongshore larval transport diverges, such as estuaries. The location where a non-native species is introduced may not be where it flourishes - it cannot be assumed that the region where invading species are first noticed to be abundant is the region where it was introduced. We extend closed-population theory to open coastal systems to estimate persistence as a function of local circulation, habitat extent, and the competitive advantage of the introduced species. Software is provided which allows the estimations of regions where introduced species are more likely to persist and flourish as a function of larval depth behavior, planktonic duration and release timing.

Small-scale fisheries provide essential coastal livelihoods in developing regions, yet they often confront severe institutional voids within fragile-state contexts. Adopting a social-ecological systems framework, this study addresses three sequential objectives: characterizing the contemporary institutional and demographic status of fishery cooperatives across the five South-Central Federal Member States in comparison to their late-1980s historical peak; ranking the socio-economic, infrastructural, regulatory, and environmental impediments to cooperative efficacy; and evaluating the contributions of these cooperatives to sustainable fisheries development. This investigation analyzes how fishery cooperatives act as institutional intermediaries to bolster economic resilience within the Somali coastal economy--a sector central to the National Transformation Plan, despite persistent regulatory fragmentation. Through semi-structured qualitative interviews with key informants--analyzed via reflexive thematic analysis and chi-square testing of age-cohort structure--the study finds that illegal, unreported, and unregulated fishing and inadequate post-harvest infrastructure are the principal constraints, whereas training, education, and community collaboration constitute the primary contributions. The results characterize these cooperatives as hybrid institutions that compensate for limited central-state capacity in remote coastal settlements while serving as intermediaries for the Federal Ministry of Fisheries and Blue Economy where co-management mandates are implemented. By extending the actor-community dimension of the SES framework to include age-cohort structure, this research demonstrates that clan-based reciprocal arrangements can serve as functional substitutes for Ostroms first design principle in contexts of persistent fragility. The study recommends multi-stakeholder partnerships to advance targeted capacity-building, financial literacy, and technological integration, and suggests future research into the post-harvest and retail sectors to examine the role of women in the Somali blue economy.

Many Sockeye salmon (Oncorhynchus nerka) populations have declined over recent decades, and climate change is likely to exacerbate these declines through direct and indirect ecological effects. The response to the associated environmental changes is likely to vary among life stages, populations, and regions. Quantitative estimates of climate change driven impacts that account for this variability could fill a critical gap and provide forward-looking insights into how sockeye are expected to respond to future climate-driven change across their lifecycle. To address this need we developed a hierarchical population dynamics model parameterized with juvenile, adult return and spawner abundance data from 13 sockeye salmon populations from Washington State to northern British Columbia. We used a formal causal inference framework that paired salmon abundance data with a suite of environmental covariates hypothesized to represent ecological conditions across the lifecycle. We used the model to estimate population-specific responses to each environmental driver, then combined parameter estimates with projections from down-scaled climate change models to estimate productivity responses to anticipated environmental change. We found that historical sockeye productivity was strongly associated with environmental covariates, which explained more interannual variability in return abundance than spawner abundance in most populations. However, the life stages and specific environmental covariates with the largest impacts differed among populations and regions, often displaying a latitudinal gradient. Increases in coastal ocean temperatures and mixed layer depth generally had negative effects though they varied among regions. Increased freshwater summer rearing and return migration temperatures had weaker but consistently negative effects. Under future climate conditions, projected changes in these environmental covariates are expected to result in substantial declines in productivity across most populations. Sockeye salmon display varying degrees of sensitivity to climate change across life stages, populations, and regions. Effective future management will require explicitly accounting for these life stage and population-specific responses.

Incorporating the complexities of climate change into conservation planning can be challenging. Climate change is projected to change the mean and variability of temperature and precipitation, and change dynamics of extreme events of many climate variables globally. These changes will all have compounding effects on ecosystems worldwide, affecting species distributions, seasonal timings, and population dynamics. However, most recent climate-informed conservation planning frameworks only focus on changes in mean conditions, leaving out ecologically important information about seasonality and extreme events. Using a case study of estuaries in the Pacific Northwest, this research seeks to answer the question "How can the effects of climate change on estuaries best be modelled and described for practical use in ecological management?" To answer this question, we used downscaled climate projection data to estimate changes in the mean and variability of temperature and precipitation. Applying extreme value theory to these projection data, we also projected changes in the magnitude of extremes in temperature and precipitation for estuaries in the region. Using descriptive statistics and open source data, our results present a novel, holistic method of understanding climate risk, including estimating extreme events, highlighting a key research gap in conservation planning. These results also highlight that trends in the mean, variability, and magnitude of climate extremes are not consistent with each other, further underscoring the importance of considering multiple dimensions of climate change together. Despite uncertainty given by climate models, the methods presented here provide a reasonable approach to plan for conservation management in the face of climate uncertainty.

Antarctic benthic organisms are highly diverse and play a critical role in the Southern Ocean ecosystem. Despite decades of sampling, vast areas of the Antarctic continental shelf remain biologically unsurveyed due to logistical and financial constraints, limiting baseline knowledge essential for effective conservation planning. Species distribution models (SDMs) allow biodiversity to be inferred in the absence of biological data by linking benthic community patterns to environmental predictors. However, the resolution of the environmental predictors, particularly bathymetry, varies significantly between regions, casting doubt about how reliably SDMs can be used to predict into regions where only coarse-resolution data are available. Here, we show that SDMs trained on high-resolution data underestimate Antarctic benthic morphospecies richness by up to 18% when applied to aggregated coarse-resolution environmental data (and up to 50% when using satellite-derived ETOPO bathymetry). Using six systematically degraded versions of high-resolution multibeam bathymetry and annotated seafloor imagery across three Antarctic regions, we evaluate SDM performance both with and without additional environmental variables. High-resolution bathymetry captures terrain complexity most effectively, but we find that the spatial distribution of richness hotspots and the median richness per cell remain consistent, provided models are applied at the same resolution at which they were trained. Our results suggest that while high-resolution bathymetry may enhance local predictions, coarse-resolution data may be more robust for regional-scale predictions, such as those used for Antarctic shelf-wide spatial planning.

The age at which Atlantic salmon reaches sexual maturity shows a strong hereditary component associated with the vgll3a locus. The role of Vgll3 in maturation has remained unknown in vertebrates until recently, when it has been linked to pleiotropic roles in killifish, both delaying male maturation and affecting lifespan by protecting against cancer. As Atlantic salmon has two vgll3 paralogs, where only vgll3a has been associated with sexual maturation, it may provide a suitable model for studying the maturation-specific function of vgll3, as the other paralog may buffer for pleiotropic roles of vgll3. To address this, we used CRISPR/Cas9 to generate fish highly mutated in the vgll3a gene. We monitored their maturation and crossed highly mutated crispants to generate two year-classes of complete loss-of-function. All groups were reared under environmental conditions triggering early maturation in one-year-old males. We found a clear difference in the proportion of sexually maturing or mature fish between the different genotypes: in all experiments significantly fewer vgll3a-/- males entered puberty and reached final maturation compared to vgll3a+/- and vgll3a+/+ males. Furthermore, loss of vgll3a resulted in lower frequencies of maturation also in females. We conclude that Vgll3a stimulates maturation and that its complete removal significantly reduced maturation rates in both sexes in Atlantic salmon. Our findings also identify vgll3a as the causative gene in the locus associated with age at sexual maturity. Together, our findings support a new role for Vgll3 in initiating puberty in vertebrates and identifying salmon as a promising model for functional studies regarding the timing of sexual maturation.

The ability to model the complex and uncertain population dynamics of endangered species has improved dramatically in recent decades. However, approaches to identify optimal decisions often require a simplified representation of population dynamics. This leads to a conundrum where managers may be unsure about the output of dynamic decision models because they rely on simplified assumptions of the underlying population dynamics. Here, by pairing integrated population models (IPM) that synthesize diverse ecological data with deep reinforcement learning (DRL) capable of optimizing decisions with high-dimensional uncertainty, we introduce a framework that delivers data-driven and ecologically detailed adaptive management strategies. We demonstrate its utility through application to the supplementation program for the endangered Rio Grande silvery minnow. Using our IPM-DRL framework, we developed an adaptive decision model that selects production and distribution decisions of the supplementation program in response to the observed demographic, hydrological, and genetic environment. The decision model outperformed all heuristic approaches in the simulation across management objectives that weighed persistence and effective population size-related genetic impact differently. For example, the currently deployed supplementation strategy performed 5.3% worse than the decision model under the persistence-focused objective scoring and 185% worse under the genetics-focused one. Analysis of the models decisions in relation to demographic and environmental covariates revealed that minimum sub-population size and total population size were primary drivers of the models decisions. The results demonstrate that the IPM-DRL framework offers a high-performing and interpretable decision-support tool for managing endangered species. SignificanceConservation problems, like imperiled species management, are often challenging because the system dynamics are complex and uncertain. We demonstrate how combining an integrated population model that infers key demographic processes from noisy ecological data with a deep reinforcement learning framework that optimizes management actions addresses these challenges by generating high-performing supplementation strategies for a conservation-dependent species. Our approach embeds two decades of monitoring data within a multi-objective decision-making environment that accounts for ecological uncertainty. The result is a generalizable framework that links ecological inference directly to actionable policy outcomes, enabling scientists and managers to move beyond describing system states and processes toward identifying optimal management actions.

Coral reef monitoring needs scalable, non-invasive tools to complement resource-intensive traditional survey methods. Passive Acoustic Monitoring (PAM) offers a promising supplement, but its effectiveness is limited by the difficulty of attributing recorded sounds to species outside of previously well-characterized taxa. Using Omnidirectional Underwater Passive Acoustic Cameras (UPAC-360), we identified sounds from 31 reef fish species across 14 families on the Kona coast of Hawaii Island, including 13 not previously documented as soniferous. By releasing video and audio specimens, we have created the largest open-access collection of in-situ reef fish sounds to date for the Pacific. A subset of acoustically distinctive taxa--such as Hawaiian Dascyllus (Dascyllus albisella), Lei Triggerfish (Sufflamen bursa), soldierfishes (Myripristis spp.), wrasses, and herbivorous grazers--were identifiable in PAM recordings through manual acoustic and spectrogram review. Through identifying particular sounds linked to species with different ecological roles, these sounds have the potential to serve as indicators of reef function to increase the information and value coming from PAM surveys of Hawaiian and Pacific coral reefs.

Biological invasions are a major driver of global change, reshaping ecosystems and threatening biodiversity worldwide. Anticipating where invaders will establish and where they will exert the strongest ecological impacts are key challenges for early detection and targeted management. Although Species Distribution Models (SDMs) are widely used to forecast biological invasions, they often provide uncertain estimates of establishment ranges and limited insight into invader performance, making it difficult to anticipate ecological impacts. Here, we address these limitations by integrating physiological information on invader performance with SDMs to identify regions of high invasion risk. Using the brown alga Rugulopteryx okamurae, one of the most prominent marine invaders in Europe, we first test alternative hypotheses of northern establishment limits: (i) a cold-survival constraint driven by winter temperatures and (ii) a growth constraint derived from the species' thermal performance. To identify the more likely scenario, we combine cold-tolerance experiments with seasonal growth comparisons between the invader and a native macroalga Dictyota dichotoma, whose established distribution allows physiological performance to be directly related to realised presence. Finally, we project seasonal growth of the invader across the predicted establishment range as a proxy for biomass accumulation and potential ecological impacts. Our results indicate that northern limit in Europe will be more likely constrained by winter survival rather than growth, extending the potential establishment range of Rugulopteryx to mid-Norway. In contrast, the highest impacts are likely to remain concentrated in southern Europe, where thermal conditions sustain high year-round growth. Overall, our approach illustrates how understanding the physiological response of invaders to their environment can improve the interpretation of SDM outputs and help identify areas at greatest risk of impact within their potential establishment range.

AimSeamounts are key components of deep-sea ecosystems. Yet, their biodiversity patterns and conservation needs remain poorly understood, presenting a critical knowledge gap as they face multiple environmental stressors. Here, we aim to characterize benthic community structure across multiple seamounts along the Kyushu-Palau Ridge (KPR) and identify conservation priorities. LocationPhilippine Sea in the Western Pacific. Major taxa studiedDeep-sea benthos. MethodsWe surveyed six transects (13.3{degrees}N-22.9{degrees}N; 614-5,055 m depth; 165 kilometers in total length) using a towed camera system, yielding 31,342 images and 14,218 identified individuals. Community structure was assessed through species depth-range overlap analysis to distinguish nestedness versus turnover patterns. {beta}-diversity was partitioned into local contributions (LCBD) and species contributions (SCBD) to evaluate site-level uniqueness and species-level influence. Generalized linear mixed models were used to relate LCBD and SCBD to depth, slope, aspect, bathymetric position, and functional traits. ResultsCommunity structures varied among slopes and seamounts. Western, steeper slopes were dominated by nestedness, whereas gentler eastern slopes showed mixed patterns of nestedness and gradual turnover. Sessile species largely exhibited nestedness, while mobile taxa showed mixed structures. LCBD declined with depth overall but increased on north-facing slopes and sites with higher broad-scale bathymetric position. Wide-ranging generalist species, particularly among Echinoidea and Holothuroidea, contributed disproportionately to {beta}-diversity. Main conclusionsSeamount benthic communities along the KPR are structured by interactions between depth, geomorphology, and species ecological breadth. The dominance of nestedness on western slopes suggests that protecting shallow, species-rich habitats could capture much of the biodiversity therein. In contrast, the mixed structures on eastern slopes indicate the need for a hybrid conservation approach that includes both shallow summits and deeper, compositionally distinct zones. Our findings highlight the importance of slope-specific, depth-informed strategies to conserve these vulnerable marine ecosystems.