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Marine heatwaves have been linked to negative ecological effects in recent decades1,2. If marine heatwaves regularly induce community reorganization and biomass collapses in fishes, the consequences could be catastrophic for ecosystems, fisheries and human communities3,4. However, the extent to which marine heatwaves have negative impacts on fish biomass or community composition, or even whether their effects can be distinguished from natural and sampling variability, remains unclear. We investigated the effects of 248 sea-bottom heatwaves from 1993 to 2019 on marine fishes by analysing 82,322 hauls (samples) from long-term scientific surveys of continental shelf ecosystems in North America and Europe spanning the subtropics to the Arctic. Here we show that the effects of marine heatwaves on fish biomass were often minimal and could not be distinguished from natural and sampling variability. Furthermore, marine heatwaves were not consistently associated with tropicalization (gain of warm-affiliated species) or deborealization (loss of cold-affiliated species) in these ecosystems. Although steep declines in biomass occasionally occurred after marine heatwaves, these were the exception, not the rule. Against the highly variable backdrop of ocean ecosystems, marine heatwaves have not driven biomass change or community turnover in fish communities that support many of the world's largest and most productive fisheries.

Spatio-temporal models are widely applied to standardise research survey data and are increasingly used to generate density maps and indices from other data sources. We developed a spatio-temporal modelling framework that integrates research survey data (treated as a 'reference dataset') and other data sources ('non- reference datasets') while estimating spatially varying catchability for the non-reference datasets. We demonstrated it using two case studies. The first involved bottom trawl survey and observer data for spiny dogfish (Squalus acanthias) on the Chatham Rise, New Zealand. The second involved cod predators as samplers of juvenile snow crab (Chionoecetes opilio) abundance, integrated with industry-cooperative surveys and a bottom trawl research survey in the eastern Bering Sea. Our integrated models leveraged the strengths of individual data sources (the quality of the reference dataset and the quantity of non-reference data), while downweighting the influence of the non-reference datasets via the estimated spatially varying catchabilities. They allowed for the generation of annual density maps for a longer time-period and for the provision of one single index rather than multiple indices each covering a shorter time-period. Key words: data integration, spatio-temporal models, density maps, indices of relative abundance, Introduction Resource management requires information about the dynamics of living marine resources (hereinafter simply 'fishes') in space and time. Habitat managers typically need distribution/density maps to designate essential fish habitat [...]

Generating accurate data for stock assessments is resource-demanding, necessitating periodic evaluation of survey sampling designs and potential impacts on stock assessments. We developed a framework for bootstrapped resampling of survey age data and calculation of input sample sizes as a function of among-bootstrap variance in age compositions. Data from this bootstrap estimator were then used to evaluate the influence of alternative sampling rates and methods on uncertainty in estimates of the overfishing limit (OFL) calculated using stock assessment models. For dusky rockfish (Sebastes variabilis) and Pacific ocean perch (Sebastes alutus), a 10% decrease in the number of tows sampled upon led to a predicted 5%-6% increase in the CV of OFL (log-log slope = -0.576 to -0.486), which was greater than the 0%-2% increase from a 10% decrease in otolithsper-tow (log-log slope = -0.238 to -0.029). Application of this approach across all stocks monitored in the survey of interest is required to identify which stocks (i) benefit the most from increased sampling of ageing structures or (ii) cost the least in terms of OFL uncertainty owing to reduced sampling. Key words: age composition, data-weighting, stock assessment, sample size, otoliths, Introduction Sustainable fisheries management is contingent upon sound advice informed by the results of stock assessments, which describe the population dynamics of the resource and provide recommendations for future catch [...]

The spatial distributions of marine fish populations are influenced by environmental conditions, intrinsic properties of the populations, and prior distribution. The influence of these factors may not be consistent across age classes. For this study, age composition estimates for walleye pollock (Gadus chalcogrammus) collected on bottom-trawl surveys in the Bering Sea were used to estimate range correlation indices, population centers of gravity, and effective area occupied. Age- specific density maps suggested a circular ontogenic migration during the summer feeding season, with the youngest and oldest groups most broadly distributed. Range correlation analysis among age groups and year classes provided clear evidence of a population cohort effect in the spatial distribution of the population. Variance decomposition analysis indicated that the variance in the spatial distribution of age groups during summer was influenced by the initial distribution of that cohort as recruits. Model-based analyses showed that extrinsic temperature variables affected the youngest and oldest age classes the most, but provided no indication of age-related effects for intrinsic population factors. This study showed that both cohort and age-specific factors are important drivers of spatial distribution. Key words: demographics, age composition, distribution, walleye pollock, modeling, VAST La repartition spatiale de populations de poissons marins est influencee par les conditions ambiantes, des proprietes intrinseques des populations et la repartition prealable. L'influence de ces facteurs pourrait ne pas etre uniforme d'une classe d'age a l'autre. Nous avons utilise des estimations de la composition par age pour des goberges de l'Alaska (Gadus chalcogrammus) prelevees dans le cadre d'evaluations au chalut de fond dans la mer de Behring pour estimer des indices de correlation des aires de repartition, les centres de gravite de la population et la superficie occupee effective. Des cartes de densite selon l'age indiqueraient une migration ontogenetique circulaire durant la saison d'alimentation estivale, les groupes les plus jeunes et les plus vieux presentant les repartitions les plus vastes. L'analyse de correlation des aires de repartition de differents groupes d'age et classes d'annee fournit des preuves claires d'un effet populationnel de la cohorte dans la repartition spatiale de la population. L'analyse de decomposition de la variance indique que la variance de la repartition spatiale des groupes d'age durant l'ete est influencee par la repartition initiale de la cohorte au moment du recrutement. Des analyses reposant sur des modeles montrent que des variables extrinseques associees a la temperature exercent la plus grande influence sur les classes d'age les plus jeunes et les plus vieilles, mais elles ne revelent aucun effet relie a l'age de facteurs intrinseques a la population. L'etude demontre que des facteurs associes tant a la cohorte qu'a l'age exercent une importante influence sur la repartition spatiale. [Traduit par la Redaction] Mots-cles : demographie, composition par age, repartition, goberge de l'Alaska, modelisation, VAST, Introduction The spatial distributions of fish populations are influenced by many factors. Environmental variables that define habitat suitability are clearly important. For example, physiological tolerances and resource availability directly affect [...]

Szuwalski argues that varying age structure can affect surplus production and that recruitment is a better metric of productivity. We explain how our null model controlled for age structure and other processes as explanations for the temperature-production relationship. Surplus production includes growth, recruitment, and other processes and provides a more complete description of food production impacts than does recruitment alone.

Climate change is altering habitats for marine fishes and invertebrates, but the net effect of these changes on potential food production is unknown. We used temperature-dependent population models to measure the influence of warming on the productivity of 235 populations of 124 species in 38 ecoregions. Some populations responded significantly positively (n = 9 populations) and others responded significantly negatively (n = 19 populations) to warming, with the direction and magnitude of the response explained by ecoregion, taxonomy, life history, and exploitation history. Hindcasts indicate that the maximum sustainable yield of the evaluated populations decreased by 4.1% from 1930 to 2010, with five ecoregions experiencing losses of 15 to 35%. Outcomes of fisheries management-including long-term food provisioning-will be improved by accounting for changing productivity in a warmer ocean.

ABSTRACT Ecosystem models predict changes in productivity and status for multiple species, and are important for incorporating climate‐linked dynamics in ecosystem‐based fisheries management. However, fishery regulations are primarily informed by single‐species stock assessment models, which estimate unexplained variation in dynamics (e.g., recruitment, survival, fishery selectivity, etc) using random effects. We review the general benefits of estimating random effects in ecosystem models: (1) better representing biomass cycles and trends for focal species; (2) conditioning interactions upon observed biomass for predators and prey; (3) easier replication of model results using formal estimation rather than informal model “tuning;” and (4) attributing process errors via comparison amongst different models. We then demonstrate these by introducing a new state‐space model EcoState (and associated R‐package) that extends mass balance dynamics from Ecopath with Ecosim. This model estimates mass balance (Ecopath) and time‐dynamics (Ecosim) parameters directly via their fit to time‐series data (biomass indices and fisheries catches) while also estimating the magnitude of process errors using RTMB. A real‐world application involving Alaska pollock (Gadus chalcogrammus) in the eastern Bering Sea suggests that fluctuations in krill consumption are associated with cycles of increased and decreased pollock production. A self‐test simulation experiment confirms that estimating process errors can improve estimates of productivity (growth and mortality) rates. Overall, we show that state‐space mass‐balance models can be fitted to time‐series data (similar to surplus‐production stock assessment models), and can attribute time‐varying productivity to both bottom‐up and top‐down drivers including the contribution of individual predator and prey interactions. [ABSTRACT FROM AUTHOR]

Understanding how ecosystem change influences fishery resources through trophic pathways is a key tenet of ecosystem‐based fishery management. System models (SM), which use numerical modeling to describe physical and biological processes, can advance inclusion of ecosystem and prey information in fisheries management; however, incorporating SMs in management requires evaluation against empirical data. The Bering Ecosystem Study Nutrient‐Phytoplankton‐Zooplankton (BESTNPZ) model is an SM (originally created by the Bering Ecosystem Study, which initiated in 2006 and was expanded by Kearney et al.) includes zooplankton biomass hindcasts for the Bering Sea. In the Bering Sea, zooplankton are an important prey item for fishery species, yet the zooplankton component of this SM has not been validated against empirical data. We compared empirical zooplankton data to BESTNPZ hindcast estimates for three zooplankton functional groups and found that the two sources of information are on different absolute scales. We found high correlation between relative seasonal biomass trends estimated by BESTNPZ and empirical data for large off‐shelf copepods (Neocalanus spp.) and low correlations for large on‐shelf copepods and small copepods (Calanus spp. and Pseudocalanus spp., respectively). To address these discrepancies, we constructed hybrid species distribution models (H‐SDM), which predict zooplankton biomass using the BESTNPZ hindcast and environmental covariates. We found that H‐SDMs offered marginal improvements over correlative species distribution models (C‐SDMs) relying solely on empirical data for spatial extrapolation and little improvement for most functional groups when forecasting short‐term temporal zooplankton biomass trends. Overall, we suggest that interpretation of current BESTNPZ hindcasts should be tempered by our understanding of key mismatches in absolute scale, seasonality, and annual indices between BESTNPZ and empirical data. [ABSTRACT FROM AUTHOR]

Changing distribution and abundance of small pelagic fishes may drive changes in predator distributions, affecting predator availability to fisheries and surveys. However, small pelagics are difficult to survey directly, so we developed a novel method of assessing the aggregate abundance of 21 small pelagic forage taxa via predator stomach contents. We used stomach contents collected from 22 piscivore species captured by multiple bottom trawl surveys within a vector autoregressive spatio-temporal model to assess trends of small pelagics on the Northeast US shelf. The goal was to develop a spatial "forage index" to inform survey and (or) fishery availability in the western North Atlantic bluefish (Pomatomus saltatrix) stock assessment. This spatially resolved index compared favorably with more traditional design-based survey biomass indices for forage species well sampled by surveys. However, our stomach content-based index better represented smaller unmanaged forage species that surveys are not designed to capture. The stomach-based forage index helped explain bluefish availability to the recreational fishery for stock assessment and provided insight into pelagic forage trends throughout the regional ecosystem. [ABSTRACT FROM AUTHOR]

Spatiotemporal predictions of bycatch (i.e., catch of nontargeted species) have shown promise as dynamic ocean management tools for reducing bycatch. However, which spatiotemporal model framework to use for generating these predictions is unclear. We evaluated a relatively new method, Gaussian Markov random fields (GMRFs), with two other frameworks, generalized additive models (GAMs) and random forests. We fit geostatistical delta-models to fisheries observer bycatch data for six species with a broad range of movement patterns (e.g., highly migratory sea turtles versus sedentary rockfish) and bycatch rates (percentage of observations with nonzero catch, 0.3%-96.2%). Random forests had better interpolation performance than the GMRF and GAM models for all six species, but random forests performance was more sensitive when predicting data at the edge of the fishery (i.e., spatial extrapolation). Using random forests to identify and remove the 5% highest bycatch risk fishing events reduced the bycatch-to-target species catch ratio by 34% on average. All models considerably reduced the bycatch-to-target ratio, demonstrating the clear potential of species distribution models to support spatial fishery management. Les predictions spatiotemporelles de prises accessoires (c.-a-d. prises d'especes non visees) sont prometteuses comme outils de gestion dynamique des oceans pour reduire ces prises. Cependant, la nature du cadre de modelisation spatiotemporelle a utiliser pour produire ces predictions n'est pas bien etablie. Nous avons evalue une methode relativement nouvelle, les champs aleatoires de Markov gaussiens (CAMG), et deux autres cadres, les modeles additifs generalises (MAG) et les forets aleatoires. Nous avons cale des modeles delta geostatistiques a des donnees d'observateurs des peches sur les prises accessoires representatives d'un vaste eventail de motifs de deplacement (p. ex. de tortues de mer tres migratrices a des sebastes sedentaires) et de frequences de prises accessoires (pourcentages d'observations de prise non nulle de 0,3 % a 96,2 %). Les forets aleatoires donnent de meilleures interpolations que les modeles CAMG et MAG pour les six especes, mais la performance des forets aleatoires est plus sensible quand il s'agit de predire des donnees a la limite de la peche (c.-a-d. pour l'extrapolation spatiale). L'utilisation de forets aleatoires pour cerner et retirer les 5 % des evenements de peche qui presentent le risque le plus eleve de prises accessoires reduit le rapport des prises accessoires et des prises de l'espece visee de 34 % en moyenne. Tous les modeles reduisent considerablement ce rapport, ce qui demontre clairement le potentiel des modeles de distribution d'especes comme outil pour appuyer la gestion spatiale des peches. [Traduit par la Redaction], Introduction Bycatch--catch of nontargeted species--occurs in nearly every commercial and recreational fishery and in many cases is a serious environmental and economic problem (Alverson et al. 1994; Davies et al. [...]