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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]

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]

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]

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]

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]

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]

Multispecies models are widely used to evaluate management trade‐offs arising from species interactions. However, identifying climate impacts and sensitive habitats requires integrating spatial heterogeneity and environmental impacts into multispecies models at fine spatial scales. We therefore develop a spatio‐temporal model of intermediate complexity for ecosystem assessments (a "MICE‐in‐space"), which is fitted to survey sampling data and time series of fishing mortality using maximum‐likelihood techniques. The model is implemented in the VAST R package, and it can be configured to range from purely descriptive to including ratio‐dependent interactions among species. We demonstrate this model using data for four groundfishes in the Gulf of Alaska using data from 1982 to 2015. Model selection for this case‐study shows that models with species interactions are parsimonious, although a model specifying separate density dependence without interactions also has substantial support. The AIC‐selected model estimates a significant, negative impact of Alaska pollock (Gadus chalcogrammus, Gadidae) on productivity of other species and suggests that recent fishing mortality for Pacific cod (G. microcephalus, Gadidae) is above the biological reference point (BRP) resulting in 40% of unfished biomass; other models show similar trends but different scales due to different BRP estimates. A simulation experiment shows that fitting a model with fewer species at a coarse spatial resolution degrades estimation performance, but that interactions and biological reference points can still be estimated accurately. We conclude that MICE‐in‐space models can simultaneously estimate fishing impacts, species trade‐offs, biological reference points and habitat quality. They are therefore suitable to forecast short‐term climate impacts, optimize survey designs and designate protected habitats. [ABSTRACT FROM AUTHOR]

• Empirical Orthogonal Function analysis (EOF) is widely used in oceanography. • We generalize EOF to fit noisy, multivariate, zero-inflated samples of fish density. • We demonstrate this using 14 fish and crab species in the eastern Bering Sea, 1982–2017. • EOF shows an index that is highly correlated with the spatial extent of cold water. • Our model is publicly available, and is a new way to connect physics and biology. Multivariate data reduction techniques are widely used to describe modes of variability in atmospheric and oceanographic conditions for the world's oceans. Dominant modes of variability such as the Pacific Decadal Oscillation (PDO) are typically defined as a statistical summary of physical measurements, and include both principle components representing modes of variability over time, and an empirical orthogonal function (EOF) giving the spatial pattern associated with a positive or negative phase for each mode. Typically, these indices are compared with biological conditions to describe or predict physical drivers of ecological dynamics. In some circumstances, however, it may instead be useful to apply EOF analysis directly to biological measurements, estimating indices of biological variability as well as maps of biological response associated with each index. We therefore develop a generalization of EOF analysis that can be applied directly to multispecies biological samples using a multivariate spatio-temporal model. These biologically derived indices can then be compared with relevant indices derived from physical data, or used as covariates in spatially-varying coefficient models. We first show that a spatio-temporal model can replicate previous EOF estimates of the PDO and North Pacific Gyre Oscillation. We then identify three axes of variability in the eastern Bering Sea using biomass-sampling data for fourteen bottom-associated fishes and decapod crustaceans from 1982 to 2017. The first axis represents habitat preferences that are stable over time, and the second represents a multi-decadal trend in distribution for most species; for example, showing an increasing density for Alaska skate and arrowtooth flounder in the middle and inner domain. Finally, the third axis shows high interannual variability from 1982 to 1998 switching to multiyear stanzas from 1999 to 2017 and is highly correlated (0.87) with the extent of the cold bottom temperatures in this region and associated impacts on Alaska pollock and Pacific cod. These axes represent ecological dynamics for adult fishes and therefore integrate the impact of bottom-up and top-down processes, and they also confirm the importance of cold-pool extent for fish distribution in the Bering Sea while visualizing its varied impact on individual species. Moreover, this spatio-temporal approach allows oceanographers to define annual indices representing modes of variability in diverse biological communities from widely available field-sampling data. [ABSTRACT FROM AUTHOR]

• Modeling nearshore habitats is essential for resource management and conservation. • The Nearshore Fish Atlas of Alaska provides a large amount of data for this task. • Habitat information is available for the entire Alaska coastline from ShoreZone. • We modeled nearshore habitats using both databases and generalized additive models. • Our work will establish a reference for similar nearshore habitat modeling efforts. Nearshore areas represent important habitats for many species, at least for part of their life cycle. Therefore, modeling and mapping nearshore habitats is essential for natural resource management and conservation, such as determining potential impacts to marine populations and their habitats from human activities and identifying conservation measures. Although fish survey and habitat data are uncommon for nearshore areas, two regional databases, the Nearshore Fish Atlas of Alaska (NFA) and ShoreZone , provide a rare opportunity to evaluate nearshore habitats for Alaska's shallow, nearshore fish assemblages. In the present study, we used the NFA and ShoreZone databases in a practical approach to model and map Alaska nearshore fish habitats. Specifically, we fitted generalized additive models (GAMs) to NFA and ShoreZone data to map the spatial patterns of probability of encounter and density of Pacific cod (Gadus macrocephalus) early juveniles in the northern southeastern Alaska (NSEA) area and walleye pollock (Gadus chalcogrammus) early juveniles in Prince William Sound (PWS). The density of Pacific cod early juveniles was found to be high in all of the western part of the NSEA area, particularly around Port Alexander. The density hotspots of walleye pollock early juveniles were found to be located in the northern and southernmost parts of PWS. Data inventories and modeling and mapping Alaska nearshore fish habitats provide valuable information to manage marine resources and human activities (e.g., to identify the main nursery areas of commercially important species along the Alaska coastline), and allow for other important ecological and ecosystem issues to be addressed (e.g., producing marine protected area planning scenarios to protect forage fishes used by large marine predators). The NFA and ShoreZone are valuable resources, and our efforts to leverage them to model and map nearshore fish habitats establishes a reference for similar efforts throughout Alaska's regions and beyond. [ABSTRACT FROM AUTHOR]