ICES Journal of Marine Science

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.

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.

Larval dispersal models are central to mapping and predicting ichthyoplankton dynamics in the ocean, yet despite decades of refinement they remain fundamentally limited by their ability to represent adaptive behaviors, relying instead on static trait parameterizations. This deficiency constrains our capacity to design effective restoration and mitigation strategies in an increasingly stressed ocean. SWARM (Simulating Waterborne Agent Routes for Marine connectivity) overcomes this barrier by integrating Large Language Model (LLM)-based behavioral agents with a standard biophysical model to simulate active decision-making during the pelagic larval stage. In both idealized and realistic conditions focusing on Red Snapper larvae in the Gulf of Mexico, agents develop adaptive behaviors that improve settlement and generate explainable vertical distribution patterns. SWARM demonstrates that LLMs can overcome long-standing limitations in dispersal modelling by explicitly representing behavioral drivers of movement, opening new pathways for predicting connectivity and designing effective marine-ecosystem restoration.

Non-compliance with regulations threatens the sustainability of fisheries worldwide. Understanding the interconnected feedbacks of this complex social-ecological problem is key for sustainability but rarely integrated into fisheries management. We provide an adaptive stochastic modelling framework that integrates economic, social behavior, and ecological aspects of the Chilean kelp fishery, which plays a critical economic and ecological role in coastal social-ecological ecosystem. High levels of non-compliance is threatening sustainability, fishers well-being, and ecosystem health. Our model considers inherent environmental uncertainties and enables the assessment of different harvesting and compliance scenarios and the role of market-based economic incentives in reducing non-compliance. Results show that, unlike the sustainability obtained under an idealized full-compliance scenario, under dynamic compliance the social, economic, and ecological feedbacks leads to system collapse. Importantly, price premiums can promote compliance and sustainability, but the probability of collapse, albeit small, still exist. Our generalizable stochastic modeling framework evidenced that accounting for inherent uncertainty in natural resource management is key to designing interventions for sustainability.

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

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.

BioBlitzes are widely used citizen science events that combine biodiversity monitoring, public participation, and environmental awareness through short and intensive observation campaigns. However, applying this model to marine environments presents additional challenges related to safety, access, weather dependency, specialised equipment, species identification, and sustained participation. This paper presents the BioMARathon model as a case study of how BioBlitz-inspired events can be adapted to marine citizen science contexts. The BioMARathon extends the conventional BioBlitz format into a longer, seasonal, and distributed engagement model designed specifically for marine and coastal environments. The paper describes the conceptual foundations of the model in the Janus Engagement Framework, which informed both the design of the BioMARathon and the adaptation of the MINKA citizen science observatory to better support participation, validation, feedback, and continuity over time. BioMARato Catalunya, launched in 2021, is presented as the founding implementation of the model and as the basis for later replication in Portugal. Between 2021 and 2025, BioMARato Catalunya showed continued growth in participation, observations, and taxonomic coverage, while also contributing to the detection of non-indigenous species, first regional records, and climate-related ecological impacts. Beyond biodiversity outcomes, the case suggests that extending participation across a season, distributing activities through local mobilising organisations, and combining expert validation with visible feedback mechanisms can support recurrent participation, retention, and community reactivation in marine citizen science. Rather than offering a formal causal evaluation, this article contributes practical lessons for the design of citizen science initiatives in challenging environments.

The queen conch (Aliger gigas) is a key native species of the Caribbean Sea and a primary source of income for thousands of fishers. Historically, it has been a highly valuable resource for the fishing sectors of countries such as the Bahamas, Turks and Caicos, Honduras, and Nicaragua. However, due to its high economic value, the species has been extensively overfished across the region. Overfishing, combined with limited larval dispersal, low recruitment, and poor population connectivity, has led to a drastic decline in population numbers of the species, resulting in its current classification as Threatened. Despite this status, likely impacts of climate change on its populations remain poorly understood, posing significant challenges to conservation efforts. To address this gap, we integrated occurrence records, climate data, and satellite-derived marine habitat data to develop ecological niche models estimating the current and future distribution of the queen conch under different climate change scenarios. We found substantial losses of suitable areas for queen conch along the northern Atlantic coast of South America and Central America, part of the Greater Antilles and the Lesser Antilles. The entire Caribbean region is projected to lose suitability entirely within 20-30 years under the moderate and most extreme climate scenarios. Conversely, our models estimate some suitable areas to persist or expand along the southeastern coast of the United States at least until sometime between 2040 and 2060. Overall, our results suggest a northward shift in the range of this species, with the magnitude of this shift closely tied to the severity of climate change impacts. This work aims to build upon and enhance existing knowledge about survival of queen conch populations in the Caribbean over time. Anticipating future habitat availability will be key to protecting this economically and ecologically important species.

BACKGROUNDOceanic islands and seamounts are recognised as hotspots of abundance and diversity for marine top predators, such as sharks and large teleosts, and are hypothesised to function as migratory stepping stones for mobile, pelagic species. However, while movements of pelagic predators between such features have been documented in some oceans, evidence from the tropical Atlantic remains limited. RESULTSHere, we report on long-distance migrations of three species of pelagic predator between oceanic islands and seamounts in the tropical south Atlantic recorded via independent passive acoustic telemetry arrays. These include the first recorded trans-Atlantic migration of a Galapagos shark (Carcharhinus galapagensis) from Ascension Island (UK) to the Sao Pedro and Sao Paul Archipelago (Brazil) (minimum distance 1,930 km); the longest documented oceanic migration of an Almaco jack (Seriola rivoliana) from St Helena (UK) to Ascension Island (minimum 1,300 km); and multiple movements of Galapagos and silky sharks (C. falciformis) between two Mid-Atlantic Ridge seamounts and Ascension Island (minimum 265-325 km). These detections are notable given the limited duration, sample sizes, and temporal overlap of acoustic tracking studies in the region, suggesting substantial connectivity across large spatial scales. CONCLUSIONSOur findings provide empirical support for the role of oceanic islands and seamounts as connectivity hubs for pelagic predators in the tropical Atlantic, underscoring their importance as key nodes in marine protected area networks. More broadly, this study demonstrates the value of collaborative regional tracking networks in resolving large-scale movement patterns and informing marine management at ecologically relevant scales.

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.

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 concern about how climate change affects marine ecosystems is growing, despite the international commitment to reduce the rate of CO2 emissions. Predicting amphipod species responses to ocean warming is critical due to their high abundance and key ecological role in marine ecosystems. We applied Maximum Entropy (MaxEnt) modelling on 17 selected benthic amphipod species across different ocean depths and feeding groups to evaluate their response to different future climate change scenarios. We used SSP 2.6 (low CO2 emission scenario) and SSP 8.5 scenarios (high CO2 emission scenario) on a global scale projected to the years 2050 and 2100. We further employed linear mixed-effects models (LMMs) to reveal differences in feeding groups responses across different scenarios and time scales. The projected distributions exhibited the reshaping of amphipod species composition areas, including potential local extinctions and the possibility of invasions into new locations. Multiple environmental variables contributed to the model outputs predicting future distributions across different feeding groups. Chlorophyll concentration and turbidity contributed majorly in predicting the future distribution of deposit feeders, while temperature and O2 were more influential for suspension feeders and herbivorous amphipods. Our findings indicated that trophic ecology mediates climate sensitivity, as a significant interaction between feeding types and two scenarios was observed. These findings highlight that climate change may dramatically alter the functional composition of benthic communities and their ecological roles, beyond simple changes in species distributions, emphasizing the need to consider ecological roles and trophic identity when assessing climate impacts on marine ecosystems.

Conservation biology increasingly relies on ecological forecasting, yet the biodiversity components most urgently targeted by conservation, such as rare species, local assemblages, and hotspot-defined communities, are often those whose dynamics are least predictable. Understanding how predictability varies across biodiversity is therefore essential for aligning management tools with their targets. This study tests whether predictability varies along three axes, how diversity is measured, the spatial scale of observation, and the temporal forecast horizon (which together govern the effective signal-to-noise ratio of ecological dynamics), and uses these patterns to inform conservation strategies. Using long-term monitoring data from seven estuaries along the Texas Gulf Coast, forecasting performance was evaluated for Hill diversity (q = 0, 1, 2) and population-level abundance of eight dominant taxa at local (bay) and regional (coastwide) scales across near-term (1-month) and long-term (12-month) horizons. Multiple time-series model classes were assessed within a rolling-origin cross-validation framework, with performance measured as improvement in root mean square error over a seasonal naive baseline. Forecasting performance increased consistently with Hill number order, reflecting reduced stochastic variation as dominant species are emphasized. The effects of spatial aggregation differed between systems. Aggregation generally improved performance for littoral assemblages but provided limited or no benefit for demersal assemblages, consistent with differences in how predictive signals are distributed across space. Forecast skill declined from 1-to 12-month horizons, with slower decay for dominance-weighted diversity and demersal assemblages than for rare-species-weighted richness and littoral assemblages. Environmental covariates provided limited near-term gains but became an increasingly important source of predictive information at longer horizons for a subset of demersal and crustacean targets. These results define a predictability landscape structured by diversity measurement, spatial scale, and forecast horizon. Three conservation domains, stochastic, transitional, and structured, emerge from this framework, each associated with distinct predictability regimes and management strategies. Aligning conservation approaches with the predictability properties of their targets provides a principled basis for determining when forecast-based management is informative and when precautionary approaches are more appropriate.

Understanding how species use habitats across environmental gradients is central to guiding fisheries management and habitat restoration, yet inference is often limited by heterogeneous data and inconsistent observations. In coastal ecosystems, variation in relationships between nekton and seagrass habitats remains unresolved, in part because habitat structure, environmental context, and sampling methods are rarely integrated in predictive models. Here, we combine multi-gear monitoring data from across the Gulf of Mexico with a transfer-learning framework that incorporates trophic priors to quantify how nekton respond to seagrass structure under varying environmental conditions. We show that environmental gradients--including temperature, salinity, and water clarity--define broad-scale distributions, while seagrass structure refines habitat use at local scales. Apparent inconsistencies in seagrass-nekton relationships are largely attributable to environmental context and differences in observational processes. By integrating observations collected using different sampling methods, our approach reveals consistent species-environment relationships across sites and improves predictive performance, particularly for data-limited species, by using trophic priors. We further show that species differ in their responses to environmental gradients, with some exhibiting consistent patterns across sites and others showing strong context dependence. These results demonstrate that combining heterogeneous datasets can strengthen ecological inference and provide a pathway for scalable, data-driven conservation and restoration in rapidly changing coastal systems.

SO_SCPLOWUMMARYC_SCPLOWAge is a fundamental life-history parameter in animal ecology and wildlife management. Age informs key ecological characteristics including population age structure, recruitment strength, extinction risk, reproductive maturity, and mortality rates. This importance has necessitated the development of chronological age estimation methods for wild animals. However, estimating chronological age is challenging for wild species, with noisy and potentially biased measures typically gathered from morphometrics, physical characteristics or, more recently, molecular methods like DNA methylation. These measures of age require at least some initial validation set of known-age individuals, or known time intervals, which is difficult to obtain for many species. Here, we present a solution to inferring the relationship between chronological age and error-prone observed age that does not require known-age individuals. The model couples the formulae for occurrence rates of half-sibling pairs, which decrease as a function of the birth-year gap between two sampled individuals, with time of capture. A pseudo-likelihood framework is developed for parameter estimation that can resolve linear and non-linear relationships and provide variance parameter estimates. We explore the methods efficacy for estimating chronological age using forward-in-time simulation and validate prior estimates of the relationship between vertebral band counts and chronological age for 3,000 school shark (Galeorhinus galeus) from an Australian fishery.

The Salas y Gomez and Nazca Ridges (SGNRs) in the Southeast Pacific, recognized as an Ecologically or Biologically Significant Area (EBSA), host unique marine ecosystems with one of the highest rates of endemism on the planet. This study provides the first comprehensive trait-based assessment of seabird functional diversity in this globally significant region, focusing on their ecological contributions as top predators. Using at-sea abundance data from 11 oceanographic surveys (2014-2017) across 3,500 km of transects, we recorded 36 seabird species (8,179 individuals). We analysed functional diversity through ten foraging-related traits, including diet, foraging strata, and morphology. Multidimensional trait analyses revealed a seabird assemblage characterised by low functional richness (FRic = 0.0587), moderate-to-low evenness (FEve = 0.3649), and high divergence (FDiv = 0.6609), with non-random patterns confirmed by null models. Nesting (17 species) and non-nesting (19 species) groups showed distinct functional structures, with nesting seabirds exhibiting higher functional divergence and non-nesting seabirds greater functional evenness, though with 61% trait-space overlap. Low functional redundancy suggests that the loss of seabird species would likely translate into the loss of unique functional roles, potentially compromising ecosystem processes such as cross-system nutrient subsidies. With 73% of the SGNRs beyond national jurisdiction, seabirds face threats from unregulated fishing, plastic pollution, and seabed mining. These findings underscore the urgent need for conservation strategies under the High Seas Treaty (BBNJ treaty) to protect not only species richness but also functional roles, ensuring ecosystem resilience in this biodiversity hotspot of over 110 seamounts.

The use of marine space by human activities is globally increasing, resulting in a competition with spatial management measures for marine conservation. Within the European Union (EU) these measures are currently implemented by the union member states to achieve the UN sustainable development goal (SDG) of protecting at least 10 % of the national marine waters. Further, the EU Marine Strategy Framework Directive (MSFD) and the Nature Restoration Regulation (NRL) are the two main legal means for the implementation of ambitious spatial conservation targets for benthic habitat types, which can range from 10 - 90 %. This study analysis how the targets of the MSFD and NRL are currently met in the German waters of the North Sea and which areas the full implementation of both legislations might require. A spatial optimisation tool ("prioritizr" in R) was used to identify optimised solutions for the conservation of up to 75 % of NRL benthic habitats. The current spatial conservation measures (which ban demersal trawling within certain zones of designated marine protected areas, MPA) are not sufficient to reach the targets of the MSFD and NRL. Extending the exclusion of demersal trawling to the entire area of the MPAs would achieve a sufficient coverage for all habitats except for offshore sand and mud habitats. These could be further protected, when including areas for offshore wind farms, where trawling is also banned. However, to date it is unclear, if and how these (or other human use) areas could be included into spatial conservation regimes, a debate that needs to be resolved to allow for the achievement of the ambitious MSFD and NRL targets.

Coral reef management under climate change is challenging due to data sparsity and high uncertainty, yet it is essential for informing conservation strategies. We present CoralBlox, a mechanistic discrete time coral ecology model with the explicit aim of supporting rapid scenario exploration and decision making. The model represents discretized distributions of five coral functional groups across configurable spatial scales while incorporating key ecological processes, including coral growth, reproduction, thermal adaptation, and responses to disturbances. Validation against observed data demonstrates that CoralBlox effectively captures major trends in coral cover dynamics across the Great Barrier Reef, particularly for bleaching-driven mortality and recovery patterns. While simplifying ecological complexities, the model maintains sufficient ecological realism to evaluate and compare the result of distinct management strategies. CoralBlox enables comprehensive assessment of potential management interventions with high computational efficiency and interoperability. The models flexible architecture makes it extensible to coral ecosystems worldwide, providing valuable exploratory capability for reef management. TeaserCoralBlox is an efficient coral reef ecology model supporting rapid scenario testing and management decision making under climate change. HighlightsO_LIMarine ecosystems are characterized by high uncertainty and data sparsity. C_LIO_LIManagement decisions still need to be made under these uncertain contexts. C_LIO_LICoralBlox offers a conceptually simple yet credible representation of ecological processes. C_LIO_LIComparatively fast runtimes across different spatial scales enable rapid exploration of plausible future states. C_LI

A comprehensive understanding of life history is vital to successful species conservation and management. When different life history stages are accompanied by considerable morphological or cryptic variation, such as the egg and larval phases exhibited by most fishes, genomic tools are essential for identifying species so that early-life ecology questions can be studied. Genotyping-in-thousands by sequencing (GT-seq) has recently emerged as a targeted and efficient approach for species identification. We leveraged existing genomic and transcriptomic data to develop a GT-seq panel capable of differentiating the members of the Coregonus artedi complex, a radiation of salmonids in the Laurentian Great Lakes whose members are indistinguishable with mitochondrial DNA barcoding loci and are the focus of bi-national conservation initiatives. Our panel of 494 loci was able to assign fishes in the C. artedi complex to species and lake. We examined cross-amplification in other coregonines with overlapping distributions and found that congeneric Lake Whitefish (C. clupeaformis) cross-amplified at 94% of loci and confamilial Round and Pygmy Whitefish (Prosopium spp.) cross-amplified at 42% and 38% of loci, respectively. We adapted bioinformatic probes to account for Prosopium-specific variants including 22 new SNPs and developed a whitelist of 428 SNPs capable of distinguishing these whitefishes. Finally, we demonstrated performance by identifying 3,066 coregonine larvae and juveniles collected in spring 2019-2021 from Lake Superior. These results hold promise for future insights into the species-specific ecology of early life coregonines and demonstrate the flexibility of GT-seq panels, which may cross-amplify hundreds of informative genome-wide loci in related taxa.

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