Quantifying broad-scale population trends and distribution change is critical for effective management and conservation of marine species, particularly under climate change. However, fragmented regional survey data often hinder such efforts for transboundary populations. A prime example is Pacific Spiny Dogfish (Squalus suckleyi, Squalidae), a small shark with a remarkably slow life history and wide-ranging distribution. Dogfish are now caught incidentally but were heavily fished along the Pacific US-Canada coast approximate to 80 years ago. Reports on local population trends have conflicted along the coast, suggesting that movement between regions may be responsible. We fit spatiotemporal models integrating data from 10 surveys to synthesise trends in biomass, abundance, distribution and thermal niche for dogfish across their entire eastern Pacific Ocean range. Prior to 2003, Alaskan biomass increased through the 1990s whereas California to British Columbia indices were variable and imprecise. However, during 2003-2023, we found a coastwide 51% (95% CI: 38%-61%) decline in dogfish biomass with mature females and immature dogfish showing the largest proportional declines. Regionally, declines were steepest for the US West Coast (71%-85%) and Canada (58%-82%), while Alaska showed less severe declines (13%-54%). Off the US West Coast, dogfish shifted into deeper waters as temperatures in their habitat increased, but these patterns do not explain the coastwide declines. Our results suggest population declines are primarily driven by reduced abundance rather than between-region movement, indicating elevated coastwide conservation concern and helping focus investigations of causal mechanisms.

Aim The temperature-size rule is often described as a reduction in ectothermic asymptotic or maximum body size with warming. Although this coincides with the expectation that faster growth under warming would lead to larger sizes early in life but smaller sizes later in life, there remain few tests of changes in size across ontogeny. Here, we use > 99,000 observations of weight-at-age of commercially important fishes over 25 years to ask whether changes in size across ontogeny can be explained by temperature. We also examine whether oxygen, a key part of a proposed mechanism behind the temperature-size rule, explains patterns of weight-at-age better than temperature. Understanding how temperature and oxygen affect size across age offers a test of macroecological theory and can inform the future productivity of fisheries. Location Bering Sea, largest subarctic system (Latitude: 52 degrees N-66 degrees N, Longitude: 160 degrees E-170 degrees W). Time Period 1987-2023. Major Taxa Studied Commercially important fishes. Methods We coupled fisheries-independent survey data and climate model output with spatiotemporal generalised linear mixed-effects models, a novel framework for testing the temperature-size rule. These models account for uneven sampling across space and time to assess long-term trends, as well as the effects of temperature and oxygen on size across age. We additionally explore the effect of model structure on our results by comparing functional forms and how spatial and/or spatiotemporal random effects are included. Results Weight-at-age was variable over time for all species with no long-term trend. Temperature better explained this variability than oxygen, but effects were small and not age-specific, counter to the temperature-size rule. Our results were sensitive to model structure as models with shared spatial effects across ages appeared to support temperature-size rule predictions, but this support reflected spatial autocorrelation in size rather than age-specific temperature responses. Collectively, this work shows that support for the temperature-size rule in these fishes was an artifact of spatial patterns in size and growth. Main Conclusions Our work tests macroecological theory using nearly complete body size trajectories to understand not only changes in adult stages but how size changes across age. We highlight that relationships among size, growth, temperature, and oxygen are not as straightforward as theory suggests and illustrate that modelling decisions can have a large effect on tests of ecological theory, and more broadly, our ability to understand biological responses to climate change.

Abstract Fisheries-independent survey(s) (FIS(s)) are conducted worldwide to study the status of marine populations and ecosystems and provide consistent time-series data for use in stock assessments, ecosystem assessments, ecological studies, and forecasting. These time series allow researchers to track changes in populations and ecosystem responses to environmental variation over time and can be used to reveal trends and patterns that single observations cannot capture. However, the consistency and continuity of a FIS time series may be impacted by natural forces (e.g. changes in environment, stock distribution, and weather) or anthropogenic actions (e.g. funding, vessel availability, new sampling objectives, new technology, and reduction of sampling area due to spatial closures or restrictions). These issues, and others, are increasingly common due to changes in marine ecosystems, increased human activities in and around FIS areas, and the evolution of FIS technologies and statistical methods. We review drivers that are triggering changes in FISs and propose pathways for adapting existing FISs to these changes while ensuring consistency in the information provided by FIS time series. We show that there is potential for advanced technologies (e.g. autonomous vehicles, remotely operated vehicles, and eDNA) to improve information collected from traditional FIS methods (e.g. trawls, longlines, acoustics, and pots). The modernization of FISs is best achieved through incremental incorporation and calibration of new technologies to existing FIS designs and operations, rather than a wholesale replacement of existing methods that would disrupt the historical baseline of a time series. These transitions of FISs in a changing world can be aided by developing methods to collect FIS data using multiple types of equipment and/or from multiple FIS platforms.

Research trawl surveys in Alaska waters demonstrate that rockfishes are often patchily distributed, increasing the uncertainty of trawl survey biomass estimates. The availability of echosounders on bottom trawl survey vessels can provide additional acoustic information on spatial aggregations, which may be useful for trawl survey designs. In this study, we examined acoustic backscatter at demersal trawl locations in the Gulf of Alaska. We describe the aggregation patterns at tow locations with either >= 80% rockfish (Sebastes spp., mostly Pacific ocean perch, Sebastes alutus) or >= 80% walleye pollock (Gadus chalcogrammus, hereafter "pollock"), a commonly co-occurring species in Alaska waters; we then classified species aggregations using random forest classification techniques. We examined the relationship between near-bottom acoustic backscatter S-v (dB re 1 m(-1)) along the tow path and demersal catches from mixed species where bottom depths were 100-150 m, 150-200 m, 200-250 m, and > 250 m. Data were collected during groundfish bottom trawl surveys in 2007 and 2009, and during a rockfish bottom trawl survey in 2009. Aggregation patterns from monospecific tow locations varied in shape, depth, and acoustic intensity. Two aggregation types were identified based on length as being either greater than or less than 800 m, and approximately 90% of the rockfish and pollock aggregations were < 800 m. A patch index was developed using the aggregation length metric, and indices < 0.3 were correlated with ln(CPUE). Based on the acoustically-derived aggregation metrics, a random forest model predicted rockfish and pollock aggregations 85% of the time (F1 score = 80%), and when bottom depth and bottom temperature metrics were included, the model predicted species correctly 89% of the time (F1 score = 85%). These results indicate potential for using acoustic aggregation patterns to identify species within bottom trawl survey designs, but more research is needed on mixed-species aggregations (e.g., when the swimbladdered species is not dominated by rockfish or pollock). The correlations between acoustic backscatter and demersal catches from mixed species during the groundfish bottom trawl surveys were consistently strong where bottom depths were 150-200 m (r >= 0.76, p < 0.001), suggesting that in this depth stratum, a two-phase sampling design that uses acoustic backscatter as an auxiliary variable to infer information on catch could be a useful strategy to increase precision for pollock and rockfish abundance estimates.

Fisheries management faces challenges due to political, spatial, and ecological complexities, which are further exacerbated by variation or shifts in species distributions. Effective management depends on the ability to integrate fisheries data across political and geographic boundaries. However, such efforts may be hindered by inconsistent data formats, limited data sharing, methodological differences in sampling, and regional governance differences. To address these issues, we introduce the surveyjoin R package, which combines and provides public access to bottom trawl survey data collected in the Northeast Pacific Ocean by NOAA Fisheries and Fisheries and Oceans Canada. This initial database integrates over 3.3 million observations from 14 bottom trawl surveys spanning Alaska, British Columbia, Washington, Oregon, and California from the 1980s to present. This database standardizes variables such as catch-per-unit-effort (CPUE), haul data, and in-situ measurements of bottom temperature. We demonstrate the utility of this database through three case studies. Our first case study develops a coastwide biomass index for Pacific hake (Merluccius productus) using geostatistical index standardization, comparing results to independent acoustic survey estimates. The second case study examines spatial patterns in groundfish community structure, highlighting breakpoints between assemblages in their mixture of life histories and trophic compositions. Our third example applies spatially varying coefficient models to assess sablefish (Anoplopoma fimbria) biomass trends, identifying regional variability in increases in occurrence and biomass. Together, these case studies demonstrate how the surveyjoin R package and database may improve species and ecosystem assessments by providing insights into population trends across geopolitical boundaries. This database and package represent an important step toward offering a scalable framework that can be extended to include additional data types, surveys, and species. By fostering collaboration, transparency, and data-driven decision making, surveyjoin supports international efforts to sustainably manage shared marine resources under dynamic environmental conditions.

Fisheries-independent surveys are used to track trends in fish stocks globally. Survey alterations occur for a myriad of reasons (e.g., vessel availability, changes in fish distribution, funding shortfalls). Understanding the sensitivity of survey estimates to changes in sampling is pivotal to sustainable management. We present a case-study of an annual multispecies survey. Simulating distributions for four species with a spatiotemporal model, we evaluated simple random, stratified random, and systematic grid sampling designs across four sampling intensities. The systematic design yielded higher-precision estimates at all sampling intensities. However, the commonly used standard error estimator thereof resulted in mean biases from 24% to 63%. We evaluated two alternative standard error estimators that successfully mitigated these biases. Our results indicate that estimates from systematic survey designs may be robust to decreases in sampling intensity, and that analyses such as integrated stock assessments which use these estimates may mitigate model misspecification by applying alternative variance estimators.

Spatial models that identify statistical relationships between environmental conditions and species distributional data are commonly used in fisheries research to evaluate habitat suitability and predict distributional shifts, such as those driven by changing ocean temperature and oxygen levels. However, a lack of environmental data-particularly dissolved oxygen-at the same temporal and spatial resolution as biological data can limit these analyses. We evaluate the ability to predict bottom dissolved oxygen via imputation and extrapolation and with biophysical oceanographic models in the northeastern Pacific Ocean (Aleutian Islands, Eastern Bering Sea, Gulf of Alaska, British Columbia, and California Current). Specifically, we measure predictive skill compared to in situ observations (measured concurrently with bottom trawl data) for (1) predictions from an empirical statistical model fit to integrated dissolved oxygen observations and (2) a commonly used dynamical oceanographic model estimate of oxygen, the Global Oceanographic Biogeochemistry Hindcast (GOBH). Lastly, we evaluate how estimation and interpretation of a species distribution model are impacted by the use of different oxygen data sources. For year-out cross-validation, we find that the empirical statistical model predicts bottom dissolved oxygen for fish catch sampling events with relatively high accuracy in only certain regions (California Current and British Columbia) (root mean squared error [RMSE] similar to 16-30 mu mol kg(-1)). Prediction skill was more than two times lower in Alaska regions that did not have extensive data (around < 0.075 observations per square kilometer), and this approach would likely not provide sufficiently accurate oxygen values for SDMs in these regions. The Copernicus Global Oceanographic Biogeochemistry Hindcast had a substantially lower prediction skill than the integrated statistical predictions (RMSE similar to 30-90 mu mol kg(-1)). When applied to species distribution models, the estimated dissolved oxygen thresholds differed by 20-50 mu mol kg(-1) when fit to different dissolved oxygen data sources. We focus on oxygen in the northeastern Pacific, yet our approach is generalizable to other variables and systems. We recommend increased attention to validating oceanographic models when operationalized to fisheries applications and evaluating the robustness of conclusions to environmental covariate data sources.

Species distribution modeling is increasingly used to describe and anticipate consequences of a warming ocean. These models often identify statistical associations between distribution and environmental conditions such as temperature and oxygen, but rarely consider the mechanisms by which these environmental variables affect metabolism. Oxygen and temperature jointly govern the balance of oxygen supply to oxygen demand, and theory predicts thresholds below which population densities are diminished. However, parameterizing models with this joint dependence is challenging because of the paucity of experimental work for most species, and the limited applicability of experimental findings in situ. Here we ask whether the temperature-sensitivity of oxygen can be reliably inferred from species distribution observations in the field, using the U.S. Pacific Coast as a model system. We developed a statistical model that adapted the metabolic index - a compound metric that incorporates these joint effects on the ratio of oxygen supply and oxygen demand by applying an Arrhenius equation - and used a non-linear threshold function to link the index to fish distribution. Through simulation testing, we found that our statistical model could not precisely estimate the parameters due to inherent features of the distribution data. However, the model reliably estimated an overall metabolic index threshold effect. When applied to case studies of real data for two groundfish species, this new model provided a better fit to spatial distribution of one species, sablefish Anoplopoma fimbria, than previously used models, but did not for the other, longspine thornyhead Sebastolobus altivelis. This physiological framework may improve predictions of species distribution, even in novel environmental conditions. Further efforts to combine insights from physiology and realized species distributions will improve forecasts of species' responses to future environmental changes.

Geostatistical spatial or spatiotemp oral data are common across scientific fields. However, appropriate models to analyze these data, such as generalized linear mixed effects models (GLMMs) with Gaussian Markov random fields (GMRFs), are computationally intensive and challenging for many users to implement. Here, we introduce the R package sdmTMB, which extends the flexible interface familiar to users of lme4, glmmTMB, and mgcv to include spatial and spatiotemp oral latent GMRFs using the stochastic partial differential equation (SPDE) approach. SPDE matrices are constructed with fmesher, and estimation is conducted via maximum marginal likelihood with TMB or via Bayesian inference with tmbstan and rstan. We describe the model and explore case studies that illustrate sdmTMB's flexibility in implementing penalized smoothers, non-stationary processes (time-varying and spatially varying coefficients), hurdle models, cross-validation, and anisotropy (directionally dependent spatial correlation). Finally, we compare the functionality, speed, and interfaces of related software, demonstrating that sdmTMB can be an order of magnitude faster than R-INLA. We hope sdmTMB will help open this useful class of models to more geostatistical analysts.

The temperature size-rule is often described as a reduction in ectothermic body size with warming. This coincides with the expectation that faster growth under warming would lead to larger sizes early in life but smaller sizes later in life. Here, we use > 99,000 observations of weight-at-age of four commercially-important fishes over 25 years to ask whether changes in size and growth can be explained by temperature. We also examine whether oxygen, a key part of a proposed mechanism behind the temperature size-rule, explains patterns of weight-at-age better than temperature. Changes in weight-at-age over time were more related to temperature than oxygen but the effect of temperature on weight was small and did not vary by age, counter to the temperature size-rule. Importantly, these results were sensitive to how the relationship between weight-at-age and temperature was modeled. While the functional form (linear or polynomial) mattered little, how space was included in the model led to different conclusions regarding how temperature affects size and growth. Assuming that spatial and spatiotemporal random effects - those that account for the higher degree of similarity between observations collected closer in space and time - are shared across ages led to different results compared to allowing these effects to vary by age. Models with shared effects suggested weight for younger ages had positive relationships with temperature and negative relationships for older ages. However, these models provided spurious support for the temperature size-rule as they had less statistical support than models that allowed spatial effects to vary by age. Overall, our work highlights that relationships among size, growth, temperature, and oxygen may not be as straightforward as theory suggests and illustrates that modeling decisions can have a large effect on tests of ecological theory, and more broadly, our ability to understand biological responses to climate change. ### Competing Interest Statement The authors have declared no competing interest. National Science Foundation, 2109411

Fisheries management faces challenges due to political, spatial, and ecological complexities, which are further exacerbated by variation or shifts in species distributions. Effective management depends on the ability to integrate fisheries data across political and geographic boundaries. However, such efforts may be hindered by inconsistent data formats, limited data sharing, methodological differences in sampling, and regional governance differences. To address these issues, we introduce the surveyjoin R package, which combines and provides public access to bottom trawl survey data collected by NOAA Fisheries and Fisheries and Oceans Canada in the Northeast Pacific Ocean. This initial database integrates over 3.3 million observations from 14 bottom trawl surveys spanning Alaska, British Columbia, Washington, Oregon, and California from the 1980s to present. This effort standardizes variables such as catch-per-unit-effort (CPUE), haul data, and in-situ measurements of bottom temperature. We demonstrate the utility of this database through three case studies. The first develops a coastwide biomass index for Pacific hake (Merluccius productus) using geostatistical index standardization, comparing results to independent acoustic survey estimates. The second examines changes in the spatial distribution of groundfish species across marine heatwave and non-heatwave years, highlighting species-specific and community-level responses to warming events. The third applies spatially varying coefficient models to assess sablefish (Anoplopoma fimbria) biomass trends, identifying regional variability in increases in occurrence and biomass. Together, these case studies demonstrate how the surveyjoin R package and database may improve species and ecosystem assessments by providing insights into population trends across geopolitical boundaries. This database and package represent an important step toward offering a scalable framework that can be extended to include additional data types, surveys, and species. By fostering collaboration, transparency, and data-driven decision-making, surveyjoin supports international efforts to sustainably manage shared marine resources under dynamic environmental conditions. ### Competing Interest Statement The authors have declared no competing interest.

From fishers to farmers, people across the planet who rely directly upon natural resources for their livelihoods and well-being face extensive impacts from climate change. However, local- and regional-scale impacts and associated risks can vary geographically, and the implications for development of adaptation pathways that will be most effective for specific communities are underexplored. To improve this understanding at relevant local scales, we developed a coupled social-ecological approach to assess the risk posed to fishing fleets by climate change, applying it to a case study of bottom trawl groundfish fleets that are a cornerstone of fisheries along the U.S. West Coast. Based on the mean of three high-resolution climate projections, we found that more poleward fleets may experience twice as much local temperature change as equatorward fleets, and 3-4 times as much depth displacement of historical environmental conditions in their fishing grounds. Not only are they more highly exposed to climate change, but more poleward fleets can be >10x more economically-dependent on groundfish. While we show clear regional differences in fleets’ flexibility to shift to new fisheries (‘adapt in-place’) or shift their fishing grounds in response to future change (‘adapt on-the-move’), these differences do not completely mitigate the greater exposure and economic dependence of poleward fleets. Therefore, on the U.S. West Coast more poleward fishing fleets may be at greater overall risk due to climate change, in contrast to expectations for greater equatorward risk in other parts of the world. Through integration of climatic, ecological, and socio-economic data, this case study illustrates the potential for widespread implementation of risk assessment at scales relevant to fishers, communities, and decision makers. Such applications will help identify the greatest opportunities to mitigate climate risks through adaptation that enhances mobility and diversification in fisheries.

Data from fishery-independent surveys are critical inputs to stock assessments, ecosystem-based fishery management, and applied ecological research. However, environmental change may affect species distributions and their availability to surveys, with consequences for the consistency and precision of abundance estimates over time. We investigated whether defining survey stratum boundaries by environmental conditions improves the precision and accuracy of abundance estimates in a multispecies survey. We fitted univariate spatiotemporal species distribution models to 16 stocks (14 species) using historical observations of fishery-independent bottom trawl survey catch-per-unit-effort and sea bottom temperature in the eastern and northern Bering Sea from 1982 to 2022. These spatiotemporal models were used to simulate species distributions and survey observations under a variety of environmental conditions and survey designs. The predicted density of each species at each location and time was passed to a multivariate optimization routine to determine whether this could increase the accuracy of estimates of abundance per unit of survey effort across species relative to traditional survey designs. Historical and projected future abundances for 10 of the 16 stocks were estimated more precisely under optimized designs-up to 4x as precise as the existing design. The accuracy of the estimate of abundance precision was always lowest for systematic sample allocation and highest for random or balanced random sampling within strata, suggesting that designs optimized with historical biological and environmental data lead to a better ability to quantify survey precision. The approach developed here can be applied in other ecosystems experiencing change to support the design of flexible survey designs that could increase the efficiency of sampling marine resources under current and future climates.

Understanding the dynamic relationship between marine species and their changing environments is critical for ecosystem based management, particularly as coastal ecosystems experience rapid change (e.g., general warming, marine heat waves). In this paper, we present a novel statistical approach to robustly estimate and track the thermal niches of 30 marine fishes along the west coast of North America. Leveraging three long-term fisheries-independent datasets, we use spatiotemporal modeling tools to capture spatiotemporal variation in species densities. Estimates from our models are then used to generate species-specific estimates of thermal niches through time at several scales: coastwide and for each of the three regions. By synthesizing data across regions and time scales, our modeling approach provides insights into how these marine species may be tracking or responding to changes in temperature. While we did not find evidence of consistent temperature-density relationships among regions, we are able to contrast differences across species: Dover sole and shortspine thornyhead have relatively broad thermal niche estimates that are static over time, whereas several semi-pelagic species (e.g., Pacific hake, walleye pollock) have niches that are both becoming warmer over time and simultaneously narrowing. This illustrates how several economically and ecologically valuable species are facing contrasting fates in a changing environment, with potential consequences for fisheries and ecosystems. Our modeling approach is flexible and can be easily extended to other species or ecosystems, as well as other environmental variables. Results from these models may be broadly useful to scientists, managers, and stakeholders — monitoring trends in the direction and variability of thermal niches may be useful in identifying species that are more susceptible to environmental change, and results of this work can form quantitative metrics that may be included in climate vulnerability assessments, estimation of dynamic essential fish habitat, and assessments of climate risk posed to fishing communities.

We assessed the effect of survey effort reduction on the accuracy and precision of estimates of abundance for 4 commercially or ecologically important species with differing distributions observed in a bottom-trawl survey conducted in the Gulf of Alaska. Simulations from a spatiotemporal generalized linear mixed model based on historical observations of catch densities were used to evaluate the statistical robustness, measured in terms of coefficient of variation, relative bias, and relative root mean square error, of the abundance estimates and their variances. These metrics were used to compare estimates between the traditional design-based estimator and the alternative estimator, based on a vector autoregressive spatiotemporal model, at 4 different sampling densities, representing 2 historical and 2 theoretical sampling effort levels on either side of the historical range. The recent reduction in the density of survey sampling from 820 to 550 stations had only a modest effect on the performance metrics for both estimators for arrowtooth flounder (Atheresthes stomias), Pacific cod (Gadus macrocephalus), and Pacific ocean perch (Sebastes alutus). However, the effect on the abundance estimates for sablefish (Anoplopoma fimbria) was substantial. We attribute this difference in results to the wider depth range utilized by sablefish, which preferentially occupy the relatively under-sampled deep strata (>500 m), and to the truncated survey area at the reduced sampling levels where the deepest strata (>700 m) have been eliminated.

The US Chukchi Sea consists of the waters off the northwest of Alaska and is a naturally dynamic ice-driven ecosystem. The impacts from climate change are affecting the Arctic marine ecosystem as well as the coastal communities that rely on healthy marine ecosystems. In anticipation of increased ecosystem monitoring in the area, there is an opportunity to evaluate improved sampling designs for future ecological monitoring of the Chukchi Sea, an area that is sampled less comprehensively compared to other regions in Alaska. This analysis focused on standardized NOAA-NMFS-AFSC bottom trawl surveys (otter and beam trawls) and three types of survey designs: simple random, stratified random, and systematic. First, spatiotemporal distributions for 18 representative demersal fish and invertebrate taxa were fitted using standardized catch and effort data. We then simulated spatiotemporal taxon densities to replicate the three survey design types to evaluate design-based estimates of abundance and precision across a range of sampling effort. Modest increases in precision were gained from stratifying the design when compared to a simple random design with either similar or lower uncertainty and bias of the precision estimates. There were often strong tradeoffs between the precision and bias of the systematic estimates of abundance (and associated variance) across species and gear type. The stratified random design provided the most consistent, reliable, and precise estimates of abundance indices and is likely to be the most robust to changes in the survey design. This analysis provides a template for changing bottom trawl survey designs in the Chukchi Sea and potentially other survey regions in Alaska going forward and will be important when integrating new survey objectives that are more ecosystem-focused.

Climate change drives species distribution shifts, affecting the availability of resources people rely upon for food and livelihoods. These impacts are complex, manifest at local scales, and have diverse effects across multiple species. However, for wild capture fisheries, current understanding is dominated by predictions for individual species at coarse spatial scales. We show that species-specific responses to localized environmental changes will alter the collection of co-occurring species within established fishing footprints along the U.S. West Coast. We demonstrate that availability of the most economically valuable, primary target species is highly likely to decline coastwide in response to warming and reduced oxygen concentrations, while availability of the most abundant, secondary target species will potentially increase. A spatial reshuffling of primary and secondary target species suggests regionally heterogeneous opportunities for fishers to adapt by changing where or what they fish. Developing foresight into the collective responses of species at local scales will enable more effective and tangible adaptation pathways for fishing communities.

A bottom -trawl survey with a stratified-random sampling design has been used to inform stock assess-ments for commercially important spe-cies in the Gulf of Alaska since 1984. A new stratified sampling design was evaluated to determine whether its use could improve the precision and accuracy of abundance estimates. In this proposed approach to defining strata, historical survey data are used to generate what we refer to as infor-mation scores (ISes). We compared the traditional stratification scheme with the new method and 2 other sampling designs, using both a design -based esti-mator and a model -based estimator with each design, to determine if the existing approach is optimal. Statistical robustness, measured in terms of coef-ficient of variation, bias, and root mean square error, was compared among 7 scenarios with different combinations of estimators and sampling designs by using simulation with a spatiotem-poral generalized linear mixed model conditioned on historical observations of catch per unit of effort of 3 species. The combination of the design -based estimator with the IS -based stratifi-cation scheme was the best scenario across all performance metrics for all species. This scenario consistently had the lowest variance and smallest total error, and it was generally unbiased. In contrast, the pairing of the model -based estimator with this sampling design was by far the worst-performing scenario. The performance of the exist-ing approach was average.

Many marine species are shifting their distributions in response to changing ocean conditions, posing significant challenges and risks for fisheries management. Species distribution models (SDMs) are used to project future species distributions in the face of a changing climate. Information to fit SDMs generally comes from two main sources: fishery-independent (scientific surveys) and fishery-dependent (commercial catch) data. A concern with fishery-dependent data is that fishing locations are not independent of the underlying species abundance, potentially biasing predictions of species distributions. However, resources for fishery-independent surveys are increasingly limited; therefore, it is critical we understand the strengths and limitations of SDMs developed from fishery-dependent data. We used a simulation approach to evaluate the potential for fishery-dependent data to inform SDMs and abundance estimates and quantify the bias resulting from different fishery-dependent sampling scenarios in the California Current System (CCS). We then evaluated the ability of the SDMs to project changes in the spatial distribution of species over time and compare the time scale over which model performance degrades between the different sampling scenarios and as a function of climate bias and novelty. Our results show that data generated from fishery-dependent sampling can still result in SDMs with high predictive skill several decades into the future, given specific forms of preferential sampling which result in low climate bias and novelty. Therefore, fishery-dependent data may be able to supplement information from surveys that are reduced or eliminated for budgetary reasons to project species distributions into the future.

Fisheries-independent surveys provide critical data products used to estimate stock status and inform management decisions. While it can be possible to redistribute sampling effort to improve survey efficiency and address changing monitoring needs in the face of unforeseen challenges, it is important to assess the consequences of such changes. Here, we present an approach that relies on existing survey data and simulations to evaluate the impacts of strategic reductions in survey sampling effort. We apply this approach to assess the potential effects of reducing high density sampling near St. Matthew Island and the Pribilof Islands in the NOAA eastern Bering Sea (EBS) bottom trawl survey. These areas contain high density “corner stations” that were implemented for finer-scale monitoring of associated blue king crab stocks (Paralithodes platypus) which historically supported commercial fisheries but have since declined and are seldom eligible for harvest. We investigate the effects of removing these corner stations on survey data quality for focal P. platypus stocks and other crab and groundfish species monitored by the EBS survey. We find that removing the St. Matthew and Pribilof Islands corner stations has negligible effects on data quality for most stocks, except for those whose distributions are concentrated in these areas. However, the data quality for such stocks was relatively low even with higher density sampling, and corner station removal had only minor effects on stock assessment outcomes. The analysis we present here provides a generic approach for evaluating strategic reductions in sampling effort for systematic survey designs and can be applied by scientists and managers facing similar decisions elsewhere.