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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]

Hierarchical models can express ecological dynamics using a combination of fixed and random effects, and measurement of their complexity (effective degrees of freedom, EDF) requires estimating how much random effects are shrunk toward a shared mean. Estimating EDF is helpful to (1) penalize complexity during model selection and (2) to improve understanding of model behavior. I applied the conditional Akaike Information Criterion (cAIC) to estimate EDF from the finite‐difference approximation to the gradient of model predictions with respect to each datum. I confirmed that this has similar behavior to widely used Bayesian criteria, and I illustrated ecological applications using three case studies. The first compared model parsimony with or without time‐varying parameters when predicting density‐dependent survival, where cAIC favors time‐varying demographic parameters more than conventional Akaike Information Criterion. The second estimates EDF in a phylogenetic structural equation model, and identifies a larger EDF when predicting longevity than mortality rates in fishes. The third compares EDF for a species distribution model fitted for 20 bird species and identifies those species requiring more model complexity. These highlight the ecological and statistical insight from comparing EDF among experimental units, models, and data partitions, using an approach that can be broadly adopted for nonlinear ecological models. [ABSTRACT FROM AUTHOR]

Ecological analyses typically involve many interacting variables. Ecologists often specify lagged interactions in community dynamics (i.e. vector‐autoregressive models) or simultaneous interactions (e.g. structural equation models), but there is less familiarity with dynamic structural equation models (DSEM) that can include any simultaneous or lagged effect in multivariate time‐series analysis.We propose a novel approach to parameter estimation for DSEM, which involves constructing a Gaussian Markov random field (GMRF) representing simultaneous and lagged path coefficients, and then fitting this as a generalized linear mixed model to missing and/or non‐normal data. We provide a new R‐package dsem, which extends the 'arrow interface' from path analysis to represent user‐specified lags when constructing the GMRF. We also outline how the resulting nonseparable precision matrix can generalize existing separable models, for example, for time‐series and species interactions in a vector‐autoregressive model.We first demonstrate dsem by simulating a two‐species vector‐autoregressive model based on wolf–moose interactions on Isle Royale. We show that DSEM has improved precision when data are missing relative to a conventional dynamic linear model. We then demonstrate DSEM via two contrasting case studies. The first identifies a trophic cascade where decreased sunflower starfish has increased urchin and decreased kelp densities, while sea otters have a simultaneous positive effect on kelp in the California Current from 1999 to 2018. The second estimates how declining sea ice has decreased cold‐water habitats, driving a decreased density for fall copepod predation and inhibiting early‐life survival for Alaska pollock from 1963 to 2023.We conclude that DSEM can be fitted efficiently as a GLMM involving missing data, while allowing users to specify both simultaneous and lagged effects in a time‐series structural model. DSEM then allows conceptual models (developed with stakeholder input or from ecological expertise) to be fitted to incomplete time series and provides a simple interface for granular control over the number of estimated time‐series parameters. Finally, computational methods are sufficiently simple that DSEM can be embedded as component within larger (e.g. integrated population) models. We therefore recommend greater exploration and performance testing for DSEM relative to familiar time‐series forecasting methods. [ABSTRACT FROM AUTHOR]

In the face of biodiversity loss worldwide, it is paramount to quantify species' extinction risk to guide conservation efforts. The International Union for the Conservation of Nature (IUCN)'s Red List is considered the global standard for evaluating extinction risks. IUCN criteria also inform national extinction risk assessments. Bayesian models, including the state‐of‐the‐art JARA ('Just Another Red List Assessment') tool, deliver probabilistic statements about species falling into extinction risk categories, thereby enabling characterisation and communication of uncertainty in extinction risk assessments.We coupled the state‐of‐the‐art VAST ('Vector Autoregressive Spatio‐Temporal') modelling tool and JARA, for better informed Red List assessments of marine fishes. In this framework, VAST is fitted to scientific survey catch rate data to provide indices to JARA whose uncertainty is propagated to JARA outcomes suggesting extinction risk categories (under the population reduction criterion). In addition, VAST delivers a valuable habitat assessment to better understand what may be driving extinction risk in the study region. Here, we demonstrate the coupled VAST‐JARA modelling framework by applying it to five contrasting North Sea species, with or without a quantitative stock assessment and with different conservation statuses according to the latest global Red List assessments.The North Sea application coupled with previous assessments and studies suggest that, among the three elasmobranchs, starry ray is in most need of urgent research (and conservation actions where appropriate), followed by spurdog, while lesser‐spotted dogfish is increasing in biomass. Moreover, both the VAST‐JARA modelling framework and previous research indicate that, while European plaice is not of conservation concern, cod has likely met the IUCN criteria for being listed as Endangered recently.Synthesis and applications. The predictions of the VAST‐JARA modelling framework for North Sea species, including JARA output and VAST habitat assessment, constitute valuable supporting information to make interpretations based on Red List guidelines, which will help decision‐makers in their next North Sea Red List assessment. We foresee applications of the modelling framework to assist Red List assessments of numerous marine fishes worldwide. Our modelling framework has many potential advantageous uses, including informing resource management about climate change impacts on species' extinction risks. [ABSTRACT FROM AUTHOR]

Designing effective spatial management strategies is challenging because marine ecosystems are highly dynamic and opaque, and extractive entities such as fishing fleets respond endogenously to ecosystem changes in ways that depend on ecological and policy context. We present a modelling framework, marlin, that can be used to efficiently simulate the bio‐economic dynamics of marine systems in support of both management and research. We demonstrate marlin's capabilities by focusing on two case studies on the conservation and food production impacts of marine protected areas (MPAs): a coastal coral reef and a pelagic tuna fishery. In the coastal coral reef example, we explore how heterogeneity in species distributions and fleet preferences can affect distributional outcomes of MPAs. In the pelagic case study, we show how our model can be used to assess the climate resilience of different MPA design strategies, as well as the climate sensitivity of different fishing fleets. This paper demonstrates how intermediate complexity simulation of coupled bio‐economic dynamics can help communities predict and potentially manage trade‐offs among conservation, fisheries yields and distributional outcomes of management policies affected by spatial bio‐economic dynamics. [ABSTRACT FROM AUTHOR]

Accounting for marine stocks spatiotemporal complexity has become one of the most pressing improvements that should be added to the new generation of stock assessment. Disentangling persistent and dynamic population subcomponents and understanding their main drivers of variation are still stock-specific challenges. Here, we hypothesized that the spatiotemporal variability of density in two adjacent fish stocks is associated with spatially structured environmental processes across multiple spatiotemporal scales. To test this, we applied a generalized empirical orthogonal function and dynamic factor analysis to fishery-independent and -dependent data of red mullet, a highly commercial species, in the Western Mediterranean Sea. Areas with persistent and dynamic high aggregations were detected for both stock units. A large-scale climatic index and local open-ocean convection were associated with both stocks, while other variables exhibited stock-specific effects. We also revealed spatially structured density dynamics within the examined management units. This suggests a metapopulation structure and supports the future implementation of a spatial stock assessment. Considering the common—generally unrealistic—assumptions of panmictic structure and absence of connectivity with neighbouring stock units, our methodology can be applied to other species and systems with putative spatial complexity to inform more accurate population dynamics and structure. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are an important tool for conservation and resource management. However, managers are often interested in derived quantities such as range or area occupied, and how these are calculated can have a large impact.Ecosystem‐based management typically requires spatial information about species distributions, which is increasingly generated from SDMs that are then processed to identify occupied habitat. Many types of SDMs exist, but there is little research regarding how this model‐choice affects outcomes when defining occupied habitat, in part because these models generate different types of output.We fit a suite of five SDMs to data for 208 species/life stage combinations in three marine ecosystems while ensuring that they all estimate a 'common currency' of numerical abundance. We then calculate out‐of‐sample predictive performance to weight these constituents in an ensemble SDM.Results show that this approach can reduce bias arising from a priori specification of individual SDMs resulting in a better fit to survey data (constituent SDMs had a median of 7% higher RMSE). The SDMs had a range of responses relative to the ensemble, with MaxEnt typically predicting a median 1.3% higher area occupied, and negative‐binomial GAMs predicting 21.4% lower area occupied.Two potential methods of identifying the area of occupied habitat from SDM outputs are compared—probability‐based and cumulative density‐based methods. We find that cumulative densities result in smaller estimates of area occupied, and we recommend careful consideration of how model‐choice affects occupied‐habitat estimates in spatial management.Policy implications: Finally, we discuss how the patterns identified during the 5‐year Review of Essential Fish Habitat for Alaska should be carefully considered by managers using SDMs to identify habitat that may be impacted by anthropogenic activities. [ABSTRACT FROM AUTHOR]

Scientific bottom-trawl surveys are ecological observation programs conducted along continental shelves and slopes of seas and oceans that sample marine communities associated with the seafloor. These surveys report taxa occurrence, abundance and/or weight in space and time, and contribute to fisheries management as well as population and biodiversity research. Bottom-trawl surveys are conducted all over the world and represent a unique opportunity to understand ocean biogeography, macroecology, and global change. However, combining these data together for cross-ecosystem analyses remains challenging. Here, we present an integrated dataset of 29 publicly available bottom-trawl surveys conducted in national waters of 18 countries that are standardized and pre-processed, covering a total of 2,170 sampled fish taxa and 216,548 hauls collected from 1963 to 2021. We describe the processing steps to create the dataset, flags, and standardization methods that we developed to assist users in conducting spatio-temporal analyses with stable regional survey footprints. The aim of this dataset is to support research, marine conservation, and management in the context of global change. [ABSTRACT FROM AUTHOR]

Fisheries scientists compare processes among species to estimate species productivity, management reference points, and climate sensitivities. Ecologists have developed "phylogenetic comparative methods" (PCMs) to address these questions, but there is surprisingly little application of PCM within fisheries science. Here, I bridge this gap by introducing PCM (including Brownian motion, Ornstein–Uhlenbeck, and Pagel's kappa and lambda models for species covariance), thereby showing that PCM generalizes the nested taxonomic random effects that are commonly used in fisheries science. I next summarize phylogenetic structural equation models (PSEMs), which extend the linear models that are commonly used in fisheries. Finally, I re‐analyse a high‐quality database used to predict mortality rates from longevity and/or growth parameters. I specifically propose a PSEM that reverts to a longevity‐based prediction when longevity information is available but uses phylogenetic corrected growth parameters otherwise. Using this single PSEM replaces the common practice of fitting and predicting using separate linear models depending upon what data are available for a given species. Cross‐validation suggests that the relationship between log‐mortality rate and longevity does not vary based on phylogeny, and therefore, linear models and PSEM both explain 82% of variance when longevity is available. When longevity is unavailable, by contrast, the linear model explains only 37% of variance while the PSEM explains 52% of variance, where this gain occurs from conditioning predictions on phylogenetic similarities. I therefore conclude that PCM and PSEM provide a general and user‐friendly replacement for linear models and can improve performance for fisheries meta‐analyses that are used for fisheries management applications. [ABSTRACT FROM AUTHOR]

Weighting data appropriately in stock assessment models is necessary to diagnose model mis-specification, estimate uncertainty, and when combining data sets. Age- and length-composition data are often fitted using a multinomial distribution and then reweighted iteratively, and the Dirichlet-multinomial ("DM") likelihood provides a model-based alternative that estimates an additional parameter and thereby "self-weights" data. However, the DM likelihood requires specifying an input sample size (n input), which is often unavailable and results are sensitive to n input. We therefore introduce the multivariate-Tweedie (MVTW) as alternative with three benefits: (1) it can identify both overdispersion (downweighting) or underdispersion (upweighting) relative to the n input; (2) proportional changes in ninput are exactly offset by parameters; and (3) it arises naturally when expanding data arising from a hierarchical sampling design. We use an age-structured simulation to show that the MVTW (1) can be more precise than the DM in estimating data weights, and (2) can appropriately upweight data when needed. We then use a real-world state-space assessment to show that the MVTW can easily be adapted to other software. We recommend that stock assessments explore the sensitivity to specifying DM, MVTW, and logistic-normal likelihoods, particularly when the DM estimates an effective sample size approaching ninput. [ABSTRACT FROM AUTHOR]

Many mobile marine taxa are changing their distributions in response to climate change. Such movements pose a challenge to fisheries monitoring and management, particularly in systems where climate‐adaptive and ecosystem‐based management objectives are emphasized. While shifts in species distributions can be discerned from long‐term fisheries‐independent monitoring data, distilling coherent patterns across space and time from such datasets can be challenging, particularly for transboundary stocks. One approach for identifying dominant patterns of spatiotemporal variation that has been widely used in physical atmospheric and oceanographic studies is empirical orthogonal function (EOF) analysis, wherein spatiotemporal variation is separated into time‐series of annual factor loadings and spatial response maps. Here, we apply an extension of EOF analysis that has been modified for compatibility with biological sampling data to a combined US–Russian fisheries‐independent survey dataset that spans the eastern (United States) and western (Russia) Bering Sea shelf to estimate dominant patterns of spatiotemporal variation for 10 groundfish species at a shelf‐wide scale. EOF identified one axis of variability that was coherent with the extent of cold (≤0°C) near‐bottom waters (the cold pool) previously shown to be a key influence on species distributions and ecosystem structure for the Bering Sea. However, the leading axis of variability identified by our EOF analysis was characterized by low frequency changes in the distributions of several species over longer time scales. Our analysis has important implications for predicting variation in species distributions over time and demonstrates a widely applicable method for leveraging combined fisheries‐independent survey datasets to characterize community‐level responses to ecosystem change at basin‐wide scales. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Phylogenetic comparative methods (PCMs) can be used to study evolutionary relationships and trade‐offs among species traits. Analysts using PCM may want to (1) include latent variables, (2) estimate complex trait interdependencies, (3) predict missing trait values, (4) condition predicted traits upon phylogenetic correlations and (5) estimate relationships as slope parameters that can be compared with alternative regression methods. The Comprehensive R Archive Network (CRAN) includes well‐documented software for phylogenetic linear models (phylolm), phylogenetic path analysis (phylopath), phylogenetic trait imputation (Rphylopars) and structural equation models (sem), but none of these can simultaneously accomplish all five analytical goals. We therefore introduce a new package phylosem for phylogenetic structural equation models (PSEM) and summarize features and interface. We also describe new analytical options, where users can specify any combination of Ornstein‐Uhlenbeck, Pagel's‐δ and Pagel's‐λ transformations for species covariance. For the first time, we show that PSEM exactly reproduces estimates (and standard errors) for simplified cases that are feasible in sem, phylopath, phylolm and Rphylopars and demonstrate the approach by replicating a well‐known case study involving trade‐offs in plant energy budgets. [ABSTRACT FROM AUTHOR]

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.Of 248 marine heatwaves between 1993 and 2019 in North American and European seas, the effects on fish biomass were often minimal, and the heatwaves were not consistently associated with tropicalization or deborealization. [ABSTRACT FROM AUTHOR]

State-space assessment models (SSMs) have garnered attention recently because of their ability to estimate time variation in biological and fisheries processes such as recruitment, natural mortality, catchability, and selectivity. However, current SSMs cannot model time-varying growth internally nor accept length data, limiting their use. Here, we expand the Woods Hole Assessment Model to incorporate new approaches to modelling changes in growth using a combination of parametric and nonparametric approaches while fitting to length and weight data. We present these new features and apply them to data for three important Alaskan stocks with distinct data and model needs. We conduct a "self-test" simulation experiment to ensure the unbiasedness and statistical efficiency of model estimates and predictions. This research presents the first SSM that can be applied when length data are a key source of information, variation in growth is an essential part of the dynamics of the assessed stock, or when linking climate variables to growth in hindcasts or forecasts is relevant. Consequently, the state-space approach and growth estimation can be applied to more fish stocks worldwide, facilitating real-world applications and implementation of simulation experiments for performance evaluation of SSMs for the many stocks whose assessments rely on length data. [ABSTRACT FROM AUTHOR]

Marine population modeling, which underpins the scientific advice to support fisheries interventions, is an active research field with recent advancements to address modern challenges (e.g., climate change) and enduring issues (e.g., data limitations). Based on discussions during the 'Land of Plenty' session at the 2021 World Fisheries Congress, we synthesize current challenges, recent advances, and interdisciplinary developments in biological fisheries models (i.e., data-limited, stock assessment, spatial, ecosystem, and climate), management strategy evaluation, and the scientific advice that bridges the science-policy interface. Our review demonstrates that proliferation of interdisciplinary research teams and enhanced data collection protocols have enabled increased integration of spatiotemporal, ecosystem, and socioeconomic dimensions in many fisheries models. However, not all management systems have the resources to implement model-based advice, while protocols for sharing confidential data are lacking and impeding research advances. We recommend that management and modeling frameworks continue to adopt participatory co-management approaches that emphasize wider inclusion of local knowledge and stakeholder input to fill knowledge gaps and promote information sharing. Moreover, fisheries management, by which we mean the end-to-end process of data collection, scientific analysis, and implementation of evidence-informed management actions, must integrate improved communication, engagement, and capacity building, while incorporating feedback loops at each stage. Increasing application of management strategy evaluation is viewed as a critical unifying component, which will bridge fisheries modeling disciplines, aid management decision-making, and better incorporate the array of stakeholders, thereby leading to a more proactive, pragmatic, transparent, and inclusive management framework–ensuring better informed decisions in an uncertain world. [ABSTRACT FROM AUTHOR]

Traits underlie organismal responses to their environment and are essential to predict community responses to environmental conditions under global change. Species differ in life‐history traits, morphometrics, diet type, reproductive characteristics and habitat utilization.Trait associations are widely analysed using phylogenetic comparative methods (PCM) to account for correlations among related species. Similarly, traits are measured for some but not all species, and missing continuous traits (e.g. growth rate) can be imputed using 'phylogenetic trait imputation' (PTI), based on evolutionary relatedness and trait covariance. However, PTI has not been available for categorical traits, and estimating covariance among traits without ecological constraints risks inferring implausible evolutionary mechanisms.Here, we extend previous PCM and PTI methods by (1) specifying covariance among traits as a structural equation model (SEM), and (2) incorporating associations among both continuous and categorical traits. Fitting a SEM replaces the covariance among traits with a set of linear path coefficients specifying potential evolutionary mechanisms. Estimated parameters then represent regression slopes (i.e. the average change in trait Y given an exogenous change in trait X) that can be used to calculate both direct effects (X impacts Y) and indirect effects (X impacts Z and Z impacts Y).We demonstrate phylogenetic structural‐equation mixed‐trait imputation using 33 variables representing life history, reproductive, morphological, and behavioural traits for all >32,000 described fishes worldwide. SEM coefficients suggest that one degree Celsius increase in habitat is associated with an average 3.5% increase in natural mortality (including a 1.4% indirect impact that acts via temperature effects on the growth coefficient), and an average 3.0% decrease in fecundity (via indirect impacts on maximum age and length). Cross‐validation indicates that the model explains 54%–89% of variance for withheld measurements of continuous traits and has an area under the receiver‐operator‐characteristics curve of 0.86–0.99 for categorical traits.We use imputed traits to classify all fishes into life‐history types, and confirm a phylogenetic signal in three dominant life‐history strategies in fishes. PTI using phylogenetic SEMs ensures that estimated parameters are interpretable as regression slopes, such that the inferred evolutionary relationships can be compared with long‐term evolutionary and rearing experiments. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are widely used to relate species occurrence and density to local environmental conditions, and often include a spatially correlated variable to account for spatial patterns in residuals. Ecologists have extended SDMs to include spatially varying coefficients (SVCs), where the response to a given covariate varies smoothly over space and time. However, SVCs see relatively little use perhaps because they remain less known relative to other SDM techniques. We therefore review ecological contexts where SVCs can improve the interpretability and descriptive power from SDMs, including local responses to regional indices that represent ecological teleconnections; density‐dependent habitat selection; spatially varying detectability; and context‐dependent covariate responses that represent interactions with unmeasured covariates. We then illustrate three additional examples in detail using the vector autoregressive spatio‐temporal (VAST) model. First, a spatially varying decadal trends model identifies decadal trends for arrowtooth flounder Atheresthes stomias density in the Bering Sea from 1982 to 2019. Second, a trait‐based joint SDM highlights the role of body size and temperature in spatial community assembly in the Gulf of Alaska. Third, an age‐structured SDM for walleye pollock Gadus chalcogrammus in the Bering Sea contrasts cohorts with broad spatial distributions (1996 and 2009) and those that are more spatially constrained (2002 and 2015). We conclude that SVCs extend SDMs to address a wide variety of ecological contexts and can be used to better understand a range of ecological processes, e.g. density dependence, community assembly and population dynamics. [ABSTRACT FROM AUTHOR]

Aim: There has been a wide interest in the effect of biotic interactions on species' occurrences and abundances at large spatial scales, coupled with a vast development of the statistical methods to study them. Still, evidence for whether the effects of within‐trophic‐level biotic interactions (e.g. competition and heterospecific attraction) are discernible beyond local scales remains inconsistent. Here, we present a novel hypothesis‐testing framework based on joint dynamic species distribution models and functional trait similarity to dissect between environmental filtering and biotic interactions. Location: France and Finland. Taxon: Birds. Methods: We estimated species‐to‐species associations within a trophic level, independent of the main environmental variables (mean temperature and total precipitation) for common species at large spatial scale with joint dynamic species distribution (a multivariate spatiotemporal delta model) models. We created hypotheses based on species' functionality (morphological and/or diet dissimilarity) and habitat preferences about the sign and strength of the pairwise spatiotemporal associations to estimate the extent to which they result from biotic interactions (competition, heterospecific attraction) and/or environmental filtering. Results: Spatiotemporal associations were mostly positive (80%), followed by random (15%), and only 5% were negative. Where detected, negative spatiotemporal associations in different communities were due to a few species. The relationship between spatiotemporal association and functional dissimilarity among species was negative, which fulfils the predictions of both environmental filtering and heterospecific attraction. Main conclusions We showed that processes leading to species aggregation (mixture between environmental filtering and heterospecific attraction) seem to dominate assembly rules, and we did not find evidence for competition. Altogether, our hypothesis‐testing framework based on joint dynamic species distribution models and functional trait similarity is beneficial in ecological interpretation of species‐to‐species associations from data covering several decades and biogeographical regions. [ABSTRACT FROM AUTHOR]

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 otoliths-per-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. [ABSTRACT FROM AUTHOR]

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Although species distribution models (SDMs) are commonly used to hindcast fine‐scale population metrics, there remains a paucity of information about how well these models predict future responses to climate. Many conventional SDMs rely on spatially‐explicit but time‐invariant conditions to quantify species distributions and densities. We compared these status quo 'static' models with more climate‐informed 'dynamic' SDMs to assess whether the addition of time‐varying processes would improve hindcast performance and/or forecast skill. Here, we present two groundfish case studies from the Bering Sea – a high latitude system that has recently undergone considerable warming. We relied on conventional statistics (R2, % deviance explained, UBRE or GCV) to evaluate hindcast performance for presence–absence, numerical abundance and biomass of arrowtooth flounder Atheresthes stomias and walleye pollock Gadus chalcogrammus. We then used retrospective skill testing to evaluate near‐term forecast skill. Retrospective skill testing enables direct comparisons between forecasts and observations through a process of fitting and forecasting nested submodels within a given time series. We found that the inclusion of time‐varying covariates improved hindcasts. However, dynamic models either did not improve or decreased forecast skill relative to static SDMs. This is likely a result of rapidly changing temperatures within the ecosystem, which required models to predict species responses to environmental conditions that were outside the range of observed values. Until additional model development allows for fully dynamic predictions, static model forecasts (or persistence forecasts from dynamic models) may serve as reliable placeholders, especially when anomalous conditions are anticipated. Nonetheless, our findings demonstrate support for the use of retrospective skill testing rather than selecting forecast models a priori based on their ability to quantify species–habitat associations in the past. [ABSTRACT FROM AUTHOR]

A robust assessment of the American eel (Anguilla rostrata) stock, required to guide conservation efforts, is challenged by the species' vast range, high variability in demographic parameters and data inadequacies. Novel ideas and underutilised resources that may assist both analytic assessments and spatially oriented modelling include (1) species and environmental databases; (2) mining of data from scattered sources; (3) infilling of data gaps by spatial analysis; (4) age estimation from measurements of DNA methylation; evaluation of eel abundance by (5) larval, (6) glass‐bottom boat, (7) net enclosure and (8) eDNA surveys; (9) accounting for dam‐induced habitat increases in eel watercourse modelling; (10) spatially oriented modelling with and without temporal components; (11) geographically nested modelling of glass eel recruitment; (12) spawner per recruit modelling and (13) life cycle modelling to examine larval allocation effects. Eel biologists are too few to gather the required assessment data across all of the species' range. Public posting of electrofishing and eDNA metabarcoding data sets and the use of machine learning techniques to comprehensively inventory small dams will help meet some data needs. These approaches address only a small proportion of the assessment challenges that face American eels. Worldwide collaboration amongst Anguilla scientists is a key enabler of progress towards stock assessment goals. [ABSTRACT FROM AUTHOR]

An abundance of studies in marine systems have documented species range shifts in response to climate change, and many more have used species distribution models to project species ranges under future conditions. However, there is increasing interest in moving beyond a single‐species focus to understand how species redistribution alters ecosystem dynamics via changes in trophic interactions. We employed spatiotemporal models to characterize decadal‐scale changes in spatial overlap between the distribution of juvenile walleye pollock Gadus chalcogrammus and the distributions of four of its groundfish predators: arrowtooth flounder Atheresthes stomias, Pacific cod Gadus macrocephalus, Pacific halibut Hippoglossus stenolepis and adult walleye pollock. These fishes represent ecologically and commercially important species in a rapidly changing sub‐Arctic ecosystem, the eastern Bering Sea, Alaska, USA. We then examined whether changes in spatial overlap corresponded to changes in predation, using spatiotemporal models of predator stomach contents. We found marked shifts in spatial overlap between juvenile pollock and two predators (arrowtooth flounder and Pacific halibut) over 34 years, with changes in overlap corresponding to increases in population‐scale predation pressure. By contrast, we did not find clear relationships between spatial overlap and predation for Pacific cod and adult pollock, the two predators for which juvenile pollock constitute a much smaller diet proportion. Our findings highlight the complexity of predicting predation dynamics for generalist marine species and suggest a need for better process‐based methods for understanding the potential future ecological impacts of coupled species range shifts. However, simple metrics of spatial overlap between relatively specialized predators and their prey offer promise as a means to integrate predictions from species distribution models into ecosystem‐based fisheries management. [ABSTRACT FROM AUTHOR]

Shifts in the distribution of groundfish species as oceans warm can complicate management efforts if species distributions expand beyond the extent of existing scientific surveys, changing the proportion of groundfish available to any one survey each year. We developed the first-ever model-based biomass estimates for three Bering Sea groundfishes (walleye pollock (Gadus chalcogrammus), Pacific cod (Gadus macrocephalus), and Alaska plaice (Pleuronectes quadrituberculatus)) by combining fishery-independent bottom trawl data from the U.S. and Russia in a spatiotemporal framework using Vector Autoregressive Spatio-Temporal (VAST) models. We estimated a fishing-power correction to calibrate disparate data sets and the effect of an annual oceanographic index to explain variation in groundfish spatiotemporal density. Groundfish densities shifted northward relative to historical densities, and high-density areas spanned the international border, particularly in years warmer than the long-term average. In the final year of comprehensive survey data (2017), 49%, 65%, 47% of biomass was in the western and northern Bering Sea for pollock, cod, and plaice, respectively, suggesting that availability of groundfish to the more regular eastern Bering Sea survey is declining. We conclude that international partnerships to combine past data and coordinate future data collection are necessary to track fish as they shift beyond historical survey areas. [ABSTRACT FROM AUTHOR]

Developing Species Distribution Models (SDM) for marine exploited species is a major challenge in fisheries ecology. Classical modelling approaches typically rely on fish research survey data. They benefit from a standardized sampling design and a controlled catchability, but they usually occur once or twice a year and they may sample a relatively small number of spatial locations. Spatial monitoring of commercial data (based on logbooks crossed with Vessel Monitoring Systems) can provide an additional extensive data source to inform fish spatial distribution. We propose a spatial hierarchical framework integrating both data sources while accounting for preferential sampling (PS) of commercial data. From simulations, we demonstrate that PS should be accounted for in estimation when PS is actually strong. When commercial data far exceed scientific data, the later bring little information to spatial predictions in the areas sampled by commercial data, but bring information in areas with low fishing intensity and provide a validation dataset to assess the integrated model consistency. We applied the framework to three demersal species (hake, sole, and squids) in the Bay of Biscay that emphasize contrasted PS intensity and we demonstrate that the framework can account for several fleets with varying catchabilities and PS behaviours. [ABSTRACT FROM AUTHOR]

Diet analysis integrates a wide variety of visual, chemical, and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, for example, highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package mvtweedie that extends capabilities of widely used packages in R (mgcv and ggplot2) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted Puffins (Fratercula cirrhata) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980–2000) and then Pacific herring and capelin (2000–2020), while wolves (Canis lupus ligoni) in southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines. [ABSTRACT FROM AUTHOR]

Data-limited species are often grouped into a species complex to simplify management. Commonalities between species that may indicate if species can be adequately managed as a complex include the following: shared habitat utilization (e.g. overlapping fine-scale spatial distribution), synchrony in abundance trends, consistent fishing pressure or gear susceptibility, or life history parameters resulting in similar productivity. Using non-target rockfish species in the Gulf of Alaska as a case study, we estimate spatial and temporal similarities among species to develop species complexes using the vector autoregressive spatio-temporal (VAST) model, which is a joint dynamic species distribution model. Species groupings are identified using Ward's hierarchical cluster analysis based on spatial and temporal species correlations. We then compare the spatial and temporal groupings with cluster analysis groupings that use exploitation and life history characteristics data. Based on the results, we conclude that there are some rockfish species that consistently group together, but the arrangement and number of clusters differ slightly depending on the data used. Developing species complexes for fisheries management requires a variety of analytical approaches including species distribution models and cluster analyses, and these can be applied across the full extent of available data sources. [ABSTRACT FROM AUTHOR]

Many marine fish species are widely distributed over large areas. Failing to acknowledge that such species may be composed of distinct populations may result in overestimation of the stock's true harvest potential. To avoid overexploitation, ways to identify population structuring are therefore needed. In this study, we developed and applied a statistical approach to identify biologically relevant population boundaries for a widely distributed marine fish species, European sprat (Sprattus sprattus). Specifically, we compiled and standardized multiple trawl-survey data sets and used a range of statistical tools to assess whether the current management boundaries adequately account for potential population structuring. Our results demonstrate regional differences in spatial abundance patterns, temporal dynamics and population demographics. These findings are in line with recent genetic studies of sprat, indicating reproductive isolation between the Baltic Sea/Kattegat and a larger cluster containing the North-, Irish-, Celtic Sea, and Bay of Biscay. Since relying on routinely collected survey data, our statistical approach can be a cost-effective complement to population genetic methods for detecting population structuring. These can be used to guide spatial management efforts and ensure sustainable exploitation, especially under climate change and the expected changes in species distributions across current management borders. [ABSTRACT FROM AUTHOR]

Population sizes of many birds are declining alarmingly and methods for estimating fluctuations in species' abundances at a large spatial scale are needed. The possibility to derive indicators from the tendency of specific species to co‐occur with others has been overlooked. Here, we tested whether the abundance of resident titmice can act as a general ecological indicator of forest bird density in European forests. Titmice species are easily identifiable and have a wide distribution, which makes them potentially useful ecological indicators. Migratory birds often use information on the density of resident birds, such as titmice, as a cue for habitat selection. Thus, the density of residents may potentially affect community dynamics. We examined spatio‐temporal variation in titmouse abundance and total bird abundance, each measured as biomass, by using long‐term citizen science data on breeding forest birds in Finland and France. We analyzed the variation in observed forest bird density (excluding titmice) in relation to titmouse abundance. In Finland, forest bird density linearly increased with titmouse abundance. In France, forest bird density nonlinearly increased with titmouse abundance, the association weakening toward high titmouse abundance. We then analyzed whether the abundance (measured as biomass) of random species sets could predict forest bird density better than titmouse abundance. Random species sets outperformed titmice as an indicator of forest bird density only in 4.4% and 24.2% of the random draws, in Finland and France, respectively. Overall, the results suggest that titmice could act as an indicator of bird density in Northern European forest bird communities, encouraging the use of titmice observations by even less‐experienced observers in citizen science monitoring of general forest bird density. [ABSTRACT FROM AUTHOR]

Density dependence is included in many population–dynamics models, but few options exist within species distribution models (SDMs). One option for density-dependence in SDMs proceeds by including an independent time-series of population abundance as covariate using a spatially varying coefficient (SVC). We extend this via three alternative approaches that replace the independent time-series with information available within the SDM. We recommend the "intermediate complexity" approach that estimates a SVC responding to median abundance in each time; this SVC indicates whether a given location has a smaller- or greater-than-average sensitivity to changes in median abundance. We next develop a reaction–advection–diffusive simulation model, wherein individuals avoid habitats that exceed a threshold in local density. This movement model results in an estimated SVC that is negatively correlated with the average spatial distribution. Finally, we show that a SVC can be identified using bottom trawl data for four species in the eastern Bering Sea from 1982 to 2019. We conclude that the common "basin-model" for animal movement results in an ecological teleconnection, wherein population depletion or recovery at one locations will affect resulting dynamics at geographically distant habitats, and that this form of density dependence can be detected using SDMs. [ABSTRACT FROM AUTHOR]

Fisheries scientists and managers must track rapid shifts in fish spatial distribution to mitigate stakeholder conflict and optimize survey designs, and these spatial shifts result in part from animal movement. Information regarding animal movement can be obtained from selection experiments, tagging studies, flux through movement gates (e.g. acoustic arrays), fishery catch‐per‐unit effort (CPUE), resource surveys and genetic/chemical markers. However, there are few accessible approaches to combine these data types while accounting for spatially correlated residual patterns. We therefore discuss a movement model involving diffusion (random movement), taxis (movement towards preferred habitat) and advection (passive drift following ocean currents). We specifically outline how these movement processes can be fitted to data while discretizing space and time and estimating non‐linear habitat preferences using environmental layers as well as spatial process errors. Finally, we introduce an R package, ATM, by fitting the model to bottom trawl survey, longline fishery and tagging data for Pacific cod (Gadus macrocephalus, Gadidae) in the Bering Sea during winter/summer seasons from 1982 to 2019. Combining data types predicts an increasing proportion of cod residing in the northern Bering Sea from 2013 to 2019, and estimates are informative in a recent stock assessment model. We fit sensitivity analyses by dropping tag, survey or fishery data, and this analysis shows that tagging data are necessary to identify diffusion rates, while survey data are informative about movement among biogeographic strata. This "hybrid" species distribution model can help explain poleward movement, project distributions under future climate conditions and evaluate alternative tag‐deployment scenarios to optimize tagging designs. [ABSTRACT FROM AUTHOR]

Implementation of the United Nations Sustainable Development Goals requires assessments of the global state of fish populations. While we have reliable estimates of stock status for fish populations accounting for approximately half of recent global catch, our knowledge of the state of the majority of the world's "unassessed" fish stocks remains highly uncertain. Numerous publications have produced estimates of the global status of these unassessed fisheries, but limited quantity and quality of data along with methodological differences have produced counterintuitive and conflicting results. Here, we show that despite numerous efforts, our understanding of the status of global fish stocks remains incomplete, even when new sources of broadly available data are added. Estimates of fish populations based primarily on catch histories on average performed 25% better than a random guess. But, on average, these methods assigned fisheries to the wrong FAO status category 57% of the time. Within these broad summaries, the performance of models trained on our tested data sources varied widely across regions. Substantial improvements to estimates of the state of the world's exploited fish populations depend more on expanded collection of new information and efficient use of existing data than development of new modelling methods. [ABSTRACT FROM AUTHOR]

Taxa can expand beyond historical scientific survey footprints and into new areas with different survey protocols as they move to track their preferred climate. In global groundfish fisheries, for example, scientists estimate population dynamics within the spatial extent of a fishery‐independent survey using an index known as a design‐based estimator. Observed changes in species distribution in recent years suggest that some groundfish are moving beyond the spatial extent of single surveys. We must intercalibrate disparate data that cover a larger spatial extent to maintain our ability to accurately index populations as their availability to historical surveys changes.We combine US and Russian data from the northern, eastern and western Bering Sea to understand the proportion of fish biomass within the extent of the eastern survey ('availability'). Surveys are within close proximity to each other, but with different sampling protocols (hence catch a different proportion of local densities, termed 'sampling efficiency ratio'). We use Alaska pollock Gadus chalcogrammus, Pacific cod Gadus macrocephalus and Alaska plaice Pleuronectes quadrituberculatus as case studies to calculate survey efficiency ratios and two area‐swept estimators, termed local and conventional, to summarize groundfish biomass over various spatial scales across the Bering Sea.We estimated variation in spatial availability of transboundary stocks to the eastern Bering Sea (EBS) survey. In 2017, the most recent available year of survey coverage that included all three Bering Sea regions, estimated availability in the EBS of pollock biomass was ~33%, cod biomass was ~27% and plaice biomass was ~26%, down from ~58%, ~71% and ~30%, respectively, in 2010.Synthesis and applications. This is the first study to provide an empirical way to combine Russian and US data in the Bering Sea to assess changes in the availability of groundfish biomass, which, in turn, will alter the interpretations and values of population indices used in regional management. We recommend leveraging this approach using existing global fishery‐independent datasets that span different spatiotemporal footprints to monitor transboundary stocks, and as a template to initiate international cooperation on the assessment of spatial availability of stocks common to multiple countries. [ABSTRACT FROM AUTHOR]

Spatial management has been adopted worldwide to mitigate habitat impacts while achieving fisheries management objectives. However, there is little theory or practice for predicting the impact of spatial regulations on future fishery production; this would provide scientific basis for greater flexibility in fisheries management when balancing fishery and conservation goals. We propose that predicting changes in fishery production resulting from human activities within specific habitats is a "Grand Challenge" for habitat science in the coming decade(s). We then outline three difficulties in resolving this Grand Habitat Challenge, including: (i) stage-structured responses to habitat impacts, (ii) nonlocal responses, and (iii) mechanistic associations among habitat variables. We next discuss analytical approaches to address each difficulty, respectively: (i) ongoing developments for spatial demographic models; (ii) individual movement models and rank-reduction approaches to identify regional variability; (iii) causal analysis involving structural equation models. We demonstrate nonlocal effects in detail using a diffusion-taxis movement model applied to sablefish (Anoplopoma fimbria) in the Gulf of Alaska and discuss all three approaches for deep-sea corals. Despite isolated progress to resolve individual difficulties, we argue that resolving this Grand Habitat Challenge will require a coordinated commitment from science agencies worldwide. [ABSTRACT FROM AUTHOR]

Abundance indices from scientific surveys are key stock assessment inputs, but when the availability of fish varies in space and time, the estimated indices and associated uncertainties do not accurately reflect changes in population abundance. For example, indices for many semi-pelagic species rely on acoustic and bottom trawl gear that differ in water column coverage, and so spatiotemporal trends in fish vertical distribution affect the availability of fish to each gear type. The gears together cover the whole water column, and so in principle allow estimation of more accurate, combined indices of the whole population. Here, we extend previous methods and develop a vertically integrated index, which accounts for spatiotemporal correlation and works with data unbalanced spatially or unpaired from distinct surveys. Using eastern Bering Sea walleye pollock (Gadus chalcogrammus) as an example, we identified clear spatial and temporal patterns in vertical distribution and gear availability from 2007 to 2018. Estimated acoustic annual vertical availability ranged from 0.339 to 0.888 among years, and from 0.588 to 0.911 for the bottom trawl survey. Our results highlight the importance of accounting for the spatiotemporal and vertical distribution of semi-pelagic fish to estimate more accurate indices, and provide important context for gear availability. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of species range edges in the modern era is key to addressing fundamental biogeographic questions about abiotic and biotic drivers of species distributions. Range edges are where colonization and extirpation processes unfold, and so these dynamics are also important to understand for effective natural resource management and conservation. However, few studies to date have analyzed time series of range edge positions in the context of climate change, in part because range edges are difficult to detect. We first quantified positions for 165 range edges of marine fishes and invertebrates from three U.S. continental shelf regions using up to five decades of survey data and a spatiotemporal model to account for sampling and measurement variability. We then analyzed whether those range edges maintained their edge thermal niche—the temperatures found at the range edge position—over time. A large majority of range edges (88%) maintained either summer or winter temperature extremes at the range edge over the study period, and most maintained both (76%), although not all of those range edges shifted in space. However, we also found numerous range edges—particularly poleward edges and edges in the region that experienced the most warming—that did not shift at all, shifted further than predicted by temperature alone, or shifted opposite the direction expected, underscoring the multiplicity of factors that drive changes in range edge positions. This study suggests that range edges of temperate marine species have largely maintained the same edge thermal niche during periods of rapid change and provides a blueprint for testing whether and to what degree species range edges track temperature in general. [ABSTRACT FROM AUTHOR]

Ecosystems are increasingly impacted by human activities, altering linkages among physical and biological components. Spatial community reassembly occurs when these human impacts modify the spatial overlap between system components, and there is need for practical tools to forecast spatial community reassembly at landscape scales using monitoring data. To illustrate a new approach, we extend a generalization of empirical orthogonal function (EOF) analysis, which involves a spatio‐temporal ecosystem model that approximates coupled physical, biological and human dynamics. We then demonstrate its application to five trophic levels for the eastern Bering Sea by fitting to multiple, spatially unbalanced datasets measuring physical characteristics (temperature measurements and climate‐linked forecasts), primary producers (spring and fall size‐fractionated chlorophyll‐a), secondary producers (copepods), juveniles (age‐0 walleye pollock), adult consumers (five commercially important fishes), human activities (seasonal fishing effort) and mobile predators (seabirds). We identify the spatial niche for each ecosystem component, as well as dominant modes of variability that are highly correlated with a known bottom–up driver of dynamics. We then measure spatial overlap between interacting variables (using Schoener's‐D) and identify that age‐0 pollock have decreased spatial overlap with copepods and increased overlap with adult pollock during warm years, and also that adult pollock have increased overlap with arrowtooth flounder and decreased overlap with catcher–processor fishing effort during these warm years. Given the warming conditions that are projected for the coming decade, the model forecasts increased prey and competitor overlap involving adult pollock (between age‐0 pollock, adult pollock and arrowtooth flounder) and decreased overlap with the copepod forage base and with the catcher–processor fishery during future warming. We recommend that joint species distribution models be extended to incorporate 'ecological teleconnections' (correlations between distant locations arising from known mechanisms) arising from behavioral adaptation by mobile animals as well as passive advection of nutrients and planktonic juvenile stages. [ABSTRACT FROM AUTHOR]

The American eel (Anguilla rostrata) occupies a vast range in the West Atlantic Ocean and inflowing waters. Despite its presumed panmictic status, management of this species is geographically fragmented. There have been widespread calls for internationally coordinated efforts towards a range-wide stock assessment, but such an objective faces obstacles of a high degree of heterogeneity in major life history characteristics and the near-absence of such data in the northern, western, and southern parts of the species' range. This paper reviews novel and underutilized methods and data sources that may aid progress to an eventual range-wide assessment. Methods for obtaining information on distribution and abundance include mining of extant data and field surveys by glass bottom boat, electrofishing boat, net enclosures, environmental DNA (eDNA) and ocean larval tows. Analytic resources and tools include environmental databases, fetch as a covariate of abundance, accounting for the net effects of small ponds, GIS-oriented habitat modeling, glass eel-oriented population modeling, estimation of age structure from length structure, use of life history parameter clines to fill gaps in assessment input values, and life cycle modeling. eDNA is a cost-effective technique that has the potential to clarify American eel distribution, and possibly relative abundance, over the very large areas where data for this species are sparse. Most other techniques reviewed in this paper have substantial costs, which may constrain their use in the southern part of the American eel range where resources of aquatic science are often limited. [ABSTRACT FROM AUTHOR]

Detecting and analyzing patterns of distribution shifts and range expansion/contraction is important to understand population dynamics and changes in stock status. Here, we develop a spatio‐temporal model for yellow croaker (Larimichthys polyactis), which was fitted to bottom trawl survey biomass data collected in the Yellow Sea in the winter of 2001–2017. The model accounts for both spatial and spatio‐temporal structure and can potentially include the effects of surface temperature and of an annual index, the Pacific Decadal Oscillation, represented using a recently developed spatially varying coefficient model. We selected a spatio‐temporal model with no covariates based on Akaike's information criterion. The center of gravity for yellow croaker was found to move northwest between 2001 and 2010, and then southwest over the period 2010–2017. These results reflected the predicted progressive disappearance of yellow croaker density hotspots (i.e., highest density areas) in the north and southeast areas of the Yellow Sea, which resulted in the central area of the Yellow Sea becoming the only yellow croaker density hotspot in 2017. This finding has important implications for fisheries management in the context of the China–South Korea fisheries agreement, as it indicates a measurable displacement of yellow croaker biomass toward China. The exclusion of covariates from the spatio‐temporal model was not expected a priori and may be due to the facts that environmental variations are not pronounced in winter in the Yellow Sea and that the representation of spatial and spatio‐temporal structure in spatio‐temporal models accounts for a large proportion of the variability in the data. [ABSTRACT FROM AUTHOR]

The northern Bering Sea is transitioning from an Arctic to subarctic fish community as climate warms. Scientists and managers aim to understand how these changing conditions are influencing fish biomass and spatial distribution in this region, as both are used to inform stock assessments and fisheries management advice. Here, we use a spatio‐temporal model for walleye pollock (Gadus chalcogrammus) to provide two inputs to its stock assessment model: (a) an alternative model‐based biomass index and (b) alternative model‐based age compositions. Both inputs were derived from multiple fishery‐independent data that span different regions of space and time. We developed an assessment model that utilizes both the standard and model‐based inputs from multiple surveys despite inconsistencies in spatial and temporal coverage, and we found that using these data provide an improved spatial and temporal scope of total pollock biomass. Age composition information indicated that pollock density is increasing and moving farther north, particularly for older pollock. We found that including an index of cold pool extent could be used to extrapolate pollock densities in the northern Bering Sea in unsampled years. Stock assessment parameter estimates were similar for standard and model‐based input. This study demonstrates that spatio‐temporal model‐based estimates of a biomass index and age composition can facilitate rapid changes in stock assessment structure in response to climate‐driven shifts in spatial distribution. We conclude that assimilating data from regions neighboring standard survey areas, such as the Chukchi Sea and western Bering Sea, would improve understanding and management efforts as fish distributions change under a warming climate. [ABSTRACT FROM AUTHOR]

Taiwan has a long history of fishery operations and contributes significantly to the global fishery harvest. The East and South China seas are important fishing grounds for which publicly available data are very limited. More efforts are needed to digitise and analyse historical catch rate data to illuminate species and community changes in this region. In this study we digitised historical records of catch and effort from government fishery reports for nine commercial species caught by otter trawl, and reported quarterly from 1970 to 2001, from the East and South China seas. We analysed the four seasons and present abundance indices, distributions and among-species correlations for nine commercially important species from 1970 to 1988 (a period with high fishing effort) using a multispecies spatiotemporal model that estimates both covariation in multispecies catch rates, attributed to spatial habitat preferences and environmental responses, and indices representing trends in abundance and distribution. We found substantial spatial, temporal and spatiotemporal variation in the distribution of fishes and season-specific patterns. We recommend collaborative work from various adjacent countries to digitise historical records of fishing catch rates, because more records would potentially address scientific disagreements regarding trends in the abundance and distribution of commercial fishes in this region through comparative studies. The East and South China seas are important fishing grounds with very limited publicly available data. We digitised historical records of catch and effort from government fishery reports for nine commercial species caught by otter trawl, and reported quarterly from 1970 to 2001, from Taiwan to illuminate species and community changes in this region. We found substantial spatial, temporal and spatiotemporal variation in the distribution of fishes and season-specific patterns. [ABSTRACT FROM AUTHOR]

Climate change is rapidly affecting the seasonal timing of spatial demographic processes. Consequently, resource managers require information from models that simultaneously measure seasonal, interannual, and spatial variation. We present a spatio-temporal model that includes annual, seasonal, and spatial variation in density and then highlight two important uses: (i) standardizing data that are spatially unbalanced within multiple seasons and (ii) identifying interannual changes in seasonal timing (phenology) of population processes. We demonstrate these uses with two contrasting case studies: three bottom trawl surveys for yellowtail flounder (Limanda ferruginea) in the Northwest Atlantic Ocean from 1985 to 2017 and pelagic tows for copepodite stage 3+ copepod (Calanus glacialis/marshallae) densities in the eastern Bering Sea from 1993 to 2016. The yellowtail analysis illustrates how data from multiple surveys can be used to infer density hot spots in an area that is not sampled one or more surveys. The copepod analysis assimilates seasonally unbalanced samples to estimate an annual index of the seasonal timing of copepod abundance and identifies a positive correlation between this index and cold-pool extent. We conclude by discussing additional potential uses of seasonal spatio-temporal models and emphasize their ability to identify climate-driven shifts in the seasonal timing of fish movement and ecosystem productivity. [ABSTRACT FROM AUTHOR]

Ocean management involves monitoring data that are used in biological models, where estimates inform policy choices. However, few science organizations publish results from a recurring, quantitative process to optimize effort spent measuring fish age. We propose that science organizations could predict the likely consequences of changing age-reading effort using four independent and species-specific analyses. Specifically we predict the impact of changing age collections on the variance of expanded age-composition data ("input sample size", Analysis 1), likely changes in the variance of residuals relative to stock-assessment age-composition estimates ("effective sample size", Analysis 2), subsequent changes in the variance of stock status estimates (Analysis 3), and likely impacts on management performance (Analysis 4). We propose a bootstrap estimator to conduct Analysis 1 and derive a novel analytic estimator for Analysis 2 when age-composition data are weighted using a Dirichlet-multinomial likelihood. We then provide two simulation studies to evaluate these proposed estimators and show that the bootstrap estimator for Analysis 1 underestimates the likely benefit of increased age reads while the analytic estimator for Analysis 2 is unbiased given a plausible mechanism for model misspecification. We conclude by proposing a formal process to evaluate changes in survey efforts for stock assessment. [ABSTRACT FROM AUTHOR]

Ecologists and oceanographers inform population and ecosystem management by identifying the physical drivers of ecological dynamics. However, different research communities use different analytical tools where, for example, physical oceanographers often apply rank‐reduction techniques (a.k.a. empirical orthogonal functions [EOF]) to identify indicators that represent dominant modes of physical variability, whereas population ecologists use dynamical models that incorporate physical indicators as covariates. Simultaneously modeling physical and biological processes would have several benefits, including improved communication across sub‐fields; more efficient use of limited data; and the ability to compare importance of physical and biological drivers for population dynamics. Here, we develop a new statistical technique, EOF regression, which jointly models population‐scale dynamics and spatially distributed physical dynamics. EOF regression is fitted using maximum‐likelihood techniques and applies a generalized EOF analysis to environmental measurements, estimates one or more time series representing modes of environmental variability, and simultaneously estimates the association of this time series with biological measurements. By doing so, it identifies a spatial map of environmental conditions that are best correlated with annual variability in the biological process. We demonstrate this method using a linear (Ricker) model for early‐life survival ("recruitment") of three groundfish species in the eastern Bering Sea from 1982 to 2016, combined with measurements and end‐of‐century projections for bottom and sea surface temperature. Results suggest that (a) we can forecast biological dynamics while applying delta‐correction and statistical downscaling to calibrate measurements and projected physical variables, (b) physical drivers are statistically significant for Pacific cod and walleye pollock recruitment, (c) separately analyzing physical and biological variables fails to identify the significant association for walleye pollock, and (d) cod and pollock will likely have reduced recruitment given forecasted temperatures over future decades. [ABSTRACT FROM AUTHOR]

Accounting for variation in prey mortality and predator metabolic potential arising from spatial variation in consumption is an important task in ecology and resource management. However, there is no statistical method for processing stomach content data that accounts for fine‐scale spatio‐temporal structure while expanding individual stomach samples to population‐level estimates of predation. Therefore, we developed an approach that fits a spatio‐temporal model to both prey‐biomass‐per‐predator‐biomass data (i.e. the ratio of prey biomass in stomachs to predator weight) and predator biomass survey data, to predict "predator‐expanded‐stomach‐contents" (PESCs). PESC estimates can be used to visualize either the annual landscape of PESCs (spatio‐temporal variation), or can be aggregated across space to calculate annual variation in diet proportions (variation among prey items and among years). We demonstrated our approach in two contrasting scenarios: a data‐rich situation involving eastern Bering Sea (EBS) large‐size walleye pollock (Gadus chalcogrammus, Gadidae) for 1992–2015; and a data‐limited situation involving West Florida Shelf red grouper (Epinephelus morio, Epinephelidae) for 2011–2015. Large walleye pollock PESC was predicted to be higher in very warm years on the Middle Shelf of the EBS, where food is abundant. Red grouper PESC was variable in north‐western Florida waters, presumably due to spatio‐temporal variation in harmful algal bloom severity. Our approach can be employed to parameterize or validate diverse ecosystem models, and can serve to address many fundamental ecological questions, such as providing an improved understanding of how climate‐driven changes in spatial overlap between predator and prey distributions might influence predation pressure. [ABSTRACT FROM AUTHOR]

Fisheries managers use biological reference points (BRPs) as targets or limits on fishing and biomass to maintain productive levels of fish stock biomass. There are multiple ways to calculate BRPs when biological parameters are time varying. Using summer flounder (Paralichthys dentatus) as a case study, we investigated time-varying approaches in concert with climate-linked population models to understand the impact of environmentally driven variability in natural mortality, recruitment, and size-at-age on two commonly used BRPs [ B 0 (t) and F 35%(⁠ t ⁠)]. We used the following two approaches to calculate time-varying BRPs: dynamic-BRP and moving-average-BRP. We quantified the variability and uncertainty of different climate dependencies and estimation approaches, attributed BRP variation to variation in life-history processes, and evaluated how using different approaches impacts estimates of stock status. Results indicate that the dynamic-BRP approach using the climate-linked natural mortality model produced the least variable reference points compared to others calculated. Summer flounder stock status depended on the estimation approach and climate model used. These results emphasize that understanding climate dependencies is important for summer flounder reference points and perhaps other species, and careful consideration is warranted when considering what time-varying approach to use, ideally based upon simulation studies within a proposed set of management procedures. [ABSTRACT FROM AUTHOR]

Characterizing population distribution and abundance over space and time is central to population ecology and conservation of natural populations. However, species distribution models and population dynamic models have rarely been integrated into a single modelling framework. Consequently, fine‐scale spatial heterogeneity is often ignored in resource assessments. We develop and test a novel spatiotemporal assessment framework to better address fine‐scale spatial heterogeneities based on theories of fish population dynamic and spatiotemporal statistics. The spatiotemporal model links species distribution and population dynamic models within a single statistical framework that is flexible enough to permit inference for each state variable through space and time. We illustrate the model with a simulation–estimation experiment tailored to two exploited marine species: snow crab (Chionoecetes opilio, Oregoniidae) in the Eastern Bering Sea and northern shrimp (Pandalus borealis, Pandalidae) in the Gulf of Maine. These two species have different types of life history. We compare the spatiotemporal model with a spatially aggregated model and systematically evaluate the spatiotemporal model based on simulation experiments. We show that the spatiotemporal model can recover spatial patterns in population and exploitation pressure as well as provide unbiased estimates of spatially aggregated population quantities. The spatiotemporal model also implicitly accounts for individual movement rates and can outperform spatially aggregated models by accounting for time‐and‐size varying selectivity caused by spatial heterogeneity. We conclude that spatiotemporal modelling framework is a feasible and promising approach to address the spatial structure of natural resource populations, which is a major challenge in understanding population dynamics and conducting resource assessments and management. [ABSTRACT FROM AUTHOR]

Fisheries scientists use biological models to determine sustainable fishing rates and forecast future dynamics. These models require both life‐history parameters (mortality, maturity, growth) and stock‐recruit parameters (juvenile production). However, there has been little research to simultaneously predict life‐history and stock‐recruit parameters. I develop the first data‐integrated life‐history model, which extends a simple model of evolutionary dynamics to field measurements of life‐history parameters as well as historical records of spawning output and subsequent recruitment. This evolutionary model predicts recruitment productivity (steepness) and variability (variance and autocorrelation in recruitment deviations) as well as mortality, maturity, growth, and size, and uses these to predict intrinsic growth rate (r) for all described fishes. The model confirms previous analysis showing little correlation between steepness and either natural mortality or asymptotic maximum size (W∞). However, it does reveal taxonomic patterns, where family Sebastidae has lower steepness (mean=0.72) and Salmonidae has elevated steepness (mean=0.79) relative to the prediction for bony fishes (class Actinopterygii, mean=0.74). Similarly, genus Sebastes has growth rate r (0.09) approaching that of several shark families (Lamniformes: 0.02; Carcharhiniformes: 0.02). A cross‐validation experiment confirms that the model is accurate, explains a substantial portion of variance (32%–67%), but generates standard errors that are somewhat too small. Predictive intervals are tighter for species than for higher‐level organizations (e.g. families), and predictions (including intervals) are available for all fishes worldwide in R package FishLife. I conclude by outlining how multivariate predictions of life‐history and stock‐recruit parameters could be useful for stock assessment, decision theory, ensemble modelling and strategic management. [ABSTRACT FROM AUTHOR]

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