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Predictions of the distribution of groundfish species are needed to support ongoing marine spatial planning initiatives in Canadian Pacific waters. Data to inform species distribution models are available from several fishery-independent surveys. However, no single survey covers the entire region and different gear types are required to survey the range of relevant habitat. Here, we demonstrated a method for integrating presence–absence data across surveys and gear types that allows us to predict the coastwide distributions of 65 groundfish species in British Columbia. Our model leverages data from multiple surveys to estimate how species respond to environmental gradients while accounting for differences in survey catchability. We find that this method has two main benefits: (1) it increases the accuracy of predictions in data-limited surveys and regions while having negligible impacts on accuracy when data are already sufficient, and (2) it reduces uncertainty, resulting in tighter confidence intervals on predicted occurrences. These benefits are particularly relevant in areas of our coast where our understanding of habitat suitability is limited due to a lack of spatially comprehensive long-term groundfish surveys.

Ecologists and fisheries scientists are faced with forecasting the ecological responses of non-stationary processes resulting from climate change and other drivers. While much is known about temporal change, and resulting responses vis-à-vis species distributional shifts, less is known about how spatial variability in population structure changes through time in response to temporal trends in drivers. A population experiencing decreasing spatial variability would be expected to be more evenly spatially distributed over time, and an increasing trend would correspond to greater extremes or patchiness. We implement a new approach for modelling this spatiotemporal variability in the R package sdmTMB. As a real-world application, we focus on a long-term groundfish monitoring dataset, from the west coast of the USA. Focusing on the 36 species with the highest population densities, we compare our model with dynamic spatiotemporal variance to a model with constant spatiotemporal variance. Of the 36 species examined, 13 had evidence to support increasing patchiness, including darkblotched rockfish, lingcod, and petrale sole. Species appearing to be more uniformly spatially distributed over time included: Dover sole, Pacific ocean perch, and Dungeness crab. Letting spatiotemporal variation change through time generally results in small differences in population trend estimates, but larger estimated differences in precision.

The fields of ecology and conservation have evolved rapidly over the past century. Synthesizing larger trends in these disciplines remains a challenge yet is critical to bridging subdisciplines, guiding research, and informing educational frameworks. Here, we provide what we believe is the largest full‐text culturomic analysis of ecology and conservation journals, covering 80 years, 52 journals, and half a billion words. Our analysis illuminates the boom‐and‐bust of ecological hypotheses and theories; the adoption of statistical, genetic, and social‐science approaches; and the domination of terms that have emerged in recent decades (eg climate change, invasive species, ecosystem services, meta‐analysis, and supplementary material, which largely replaced unpublished data). We track the evolution of ecology from a largely descriptive field focused on natural history and observational studies to a more data‐driven, multidisciplinary field focused on applied environmental issues. Overall, our analysis highlights the increasing breadth of the field, illustrating that there is room for more diversity of ecologists and conservationists today than ever before.

An organism’s body condition describes its mass given its length and is often positively associated with fitness. The condition of Atlantic cod (Gadus morhua) in the Baltic Sea has declined dramatically since the early 1990s, possibly due to increased competition, food limitation, and hypoxia. However, the effect of biotic and abiotic variables on body condition have not been evaluated at local scales, which is important given spatial heterogeneity. We evaluate changes in distribution, experienced environmental conditions, and individual-level condition of cod in relation to covariates at different spatial scales using geostatistical models with spatial and spatiotemporal random effects. Oxygen, sprat biomass, and temperature were positively related to condition, and depth negatively associated, but the effect sizes of these variables were small—spatial and spatiotemporal latent variables explained almost five times more variation than fixed effects. We also show that accounting for the heterogenous distribution of cod leads to both lover levels and steeper trends over time in experienced oxygen compared to those in the environment. Understanding the drivers of spatiotemporal variation in body condition is critical for predicting responses to environmental change and to effective fishery management; yet low explanatory power of covariates on individual condition constitutes a major challenge.

We develop a mechanistically motivated von Bertalanffy growth model to estimate growth rate and its predictors from spatial–temporal data and compare this model’s performance with a suite of commonly used mixed-effects growth models. We test these models with simulated data and then apply them to test whether concerns that high density is causing growth suppression of walleye (Sander vitreus) in Alberta, Canada, are supported using data collected during 2000–2017. Simulation experiments demonstrated that models that failed to account for complex dependency structures often resulted in growth rate estimates that were less accurate and biased low as judged by median absolute relative error and median relative error, respectively. The magnitude of this bias depended on the parameter values used for simulation. For the case study, a spatial–temporal model was more parsimonious and had higher predictive performance relative to simpler models and did not support the slow-growing walleye hypothesis in Alberta. These findings demonstrate the importance of considering spatial–temporal correlation in analyses that rely on surveillance-style monitoring datasets, particularly when examining relationships between life-history traits and environmental characteristics.

Geostatistical data—spatially referenced observations related to some continuous spatial phenomenon— are ubiquitous in ecology and can reveal ecological processes and inform management decisions. However, appropriate models to analyze these data, such as generalized linear mixed effects models (GLMMs) with Gaussian random fields, are often computationally intensive and challenging to implement, interpret, and evaluate.Here, we introduce the R package sdmTMB, which implements predictive-process SPDE- (stochastic partial differential equation) based spatial and spatiotemporal models. Estimation is conducted via maximum marginal likelihood with Template Model Builder (TMB) but can be extended to penalized likelihood or Bayesian inference. We describe the statistical model, illustrate the package’s use through two case studies, and compare the functionality, speed, and interface to related software.We highlight advantages of using sdmTMB for this class of models: (1) sdmTMB provides a flexible interface familiar to users of glm(), lme4, glmmTMB, or mgcv; (2) estimation is often faster than alternatives; (3) sdmTMB provides simple out-of-sample cross validation; (4) non-stationary processes (time-varying and spatially varying coefficients) are easily constructed with a formula interface; and (5) sdmTMB includes features not available as a combination in related packages (e.g., delta/hurdle models, penalized smoothers and break-point effects, anisotropy, abundance index standardization).We hope that sdmTMB’s user-friendly interface will open this useful class of models to a wider audience within species distribution modelling and beyond.

We assembled estimated biomass (B) time series from stock assessments for 24 Pacific Canadian groundfish stocks and modelled average and stock status through 2020 based on biomass relative to each stock’s (1) Limit Reference Point (B/LRP), (2) Upper Stock Reference (B/USR), and (3) biomass at maximum sustainable yield (B/BMSY). The overall mean B/LRP in 2020 was 3.2 (95% credible interval [CI]: 2.6–3.9). The overall mean B/USR and B/BMSY in 2020 was 1.5 (95% CI: 1.3–1.9) and 1.4 (95% CI: 1.1–1.7), respectively. Average stock status declined from 1950 to around 2000 and has remained relatively stable since then. The change around 2000 followed the implementation of ITQs (individual transferable quotas) for the trawl fleet and the commencement of the synoptic trawl surveys. As of their last assessment, four stocks (Strait of Georgia Lingcod [Area 4B], coastwide Bocaccio, and inside and outside Quillback Rockfish) had a greater than 5% probability of being below their LRP (i.e., in the “ critical zone”); Pacific Cod in Area 3CD had a 4.6% probability. Roughly one-third of stocks had a greater than 1 in 4 chance of being below their USR (i.e., in the “ cautious zone”). Conversely, two-thirds of assessed groundfish stocks had a high (>75%) probability of being above the USR (i.e., in the “ healthy zone”).

Sustainability indices are proliferating, both to help synthesize scientific understanding and inform policy. However, it remains poorly understood how such indices are affected by underlying assumptions of the data and modelling approaches used to compute indicator values. Here, we focus on one such indicator, the fisheries goal within the Ocean Health Index (OHI), which evaluates the sustainable provision of food from wild fisheries. We quantify uncertainty in the fisheries goal status arising from the (a) approach for estimating missing data (i.e., fish stocks with no status) and (b) reliance on a data‐limited method (catch‐MSY) to estimate stock status (i.e., B/BMSY). We also compare several other models to estimate B/BMSY, including an ensemble approach, to determine whether alternative models might reduce uncertainty and bias. We find that the current OHI fisheries goal model results in overly optimistic fisheries goal statuses. Uncertainty and bias can be reduced by (a) using a mean (vs. median) gap‐filling approach to estimate missing stock scores and (b) estimating fisheries status using the central tendency from a simulated distribution of status scores generated by a bootstrap approach that incorporates error in B/BMSY. This multitiered approach to measure and describe uncertainty improves the transparency and interpretation of the indicator and allows us to better understand uncertainty around our OHI fisheries model and outputs for country‐level interpretation and use.

Understanding and tracking how ecosystems respond to changing environments is an ongoing challenge. Marine ecosystems in the North Pacific support productive fisheries and diverse ecosystem services, and they are subject to large-scale environmental, human, and ecological perturbations. Ichthyoplankton time-series from these ecosystems may provide an important indicator of lower trophic level dynamics and ecosystem functioning. Here we present a spatiotemporal analysis using data from three decades of ichthyoplankton surveys in the Gulf of Alaska to investigate temporal patterns in indicators of species richness, Shannon diversity, and synchrony. Then we use Dynamic Factor Analysis (DFA) to synthesize the ichthyoplankton assemblage with two dominant trends. We relate the biodiversity indices and DFA trends to local and regional climate indices in the North Pacific. We find evidence for increased temperatures driving increased species richness, and changes in synchrony coincident with shifting assemblage composition and the 1988/1989 regime shift. Shannon diversity was largely driven by the dominance of larval walleye pollock (Gadus chalcogrammus). Correlations between climate drivers and DFA trends suggest that the influence of basin scale drivers (North Pacific Gyre Oscillation and the Pacific Decadal Oscillation) were stronger than the influence of local-scale drivers like regional sea surface temperature. Our work demonstrates the potential value of ichthyoplankton surveys to provide indicators of climate-driven ecosystem variability and long-term ecological change.

Estimates of the basic reproduction number (R0) for Coronavirus disease 2019 (COVID-19) are particularly variable in the context of transmission within locations such as long-term health care (LTHC) facilities. We sought to characterise the heterogeneity ofR0across known outbreaks within these facilities. We used a unique comprehensive dataset of all outbreaks that have occurred within LTHC facilities in British Columbia, Canada. We estimatedR0with a Bayesian hierarchical dynamic model of susceptible, exposed, infected, and recovered individuals, that incorporates heterogeneity ofR0between facilities. We further compared these estimates to those obtained with standard methods that utilize the exponential growth rate and maximum likelihood. The total size of an outbreak varied dramatically, with a range of attack rates of 2%–86%. The Bayesian analysis provides more constrained overall estimates ofR0= 2.19 (90% CrI [credible interval] 0.19–6.69) than standard methods, with a range within facilities of 0.48–10.08. We further estimated that intervention led to 57% (47%–66%) of all cases being averted within the LTHC facilities, or 73% (63%–78%) when using a model with multi-level intervention effect. Understanding the risks and impact of intervention are essential in planning during the ongoing global pandemic, particularly in high-risk environments such as LTHC facilities.

Euphausiids are a keystone species in coastal food webs due to their high lipid content and seasonally high biomass. Understanding the habitat and environmental drivers that lead to areas of high biomass, or ‘hotspots’, and their seasonal persistence, will support the identification of important foraging regions for mid- and upper- trophic level predators. We quantify the distribution of hotspots of the two dominant species of euphausiid in the north-east Pacific Ocean: Euphausia pacifica and Thysanoessa spinifera, as well as euphausiid larvae (mixed species). The Canadian coast encompasses the northern California Current Ecosystem and the transition zone to the Alaska current, and is a highly productive region for fisheries, marine mammals, and seabirds. We used spatiotemporal modelling to predict the distribution of these three euphausiid groups in relation to geomorphic and environmental variables during the important spring-summer months (April through September) when euphausiid biomass is highest. We quantified the area, intensity, and persistence of biomass hotspots across months according to specific oceanographic ecosections developed for marine spatial planning purposes. Persistent hotspots of both adult species were predicted to occur along the 200 m depth contour of the continental slope; however, differences were predicted on the shallower Dixon shelf, which was a key area for T. spinifera, and within the Juan de Fuca Eddy system where E. pacifica hotspots occurred. The continental slope along the west coast of Vancouver Island was the only persistent hotspot region common between both adult species and euphausiid larvae. Larval distribution was more correlated with T. spinifera than E. pacifica biomass. Hotspots of adults were more persistent across months than hotspots of euphausiid larvae, which were seasonally patchy. The persistence of biomass hotspots of forage species through periods of low overall biomass could maintain trophic connectivity through perturbation events and increase ecosystem resilience to climate change.

Population-specific spatial and temporal distribution data are necessary to identify mechanisms regulating abundance and to manage anthropogenic impacts. However the distributions of highly migratory species are often difficult to resolve, particularly when multiple populations’ movements overlap. Here we present an integrated model to estimate spatially-stratified, seasonal trends in abundance and population composition, using data from extensive genetic sampling of commercial and recreational Chinook salmon (Oncorhynchus tshawytscha) fisheries in southern British Columbia. We use the model to estimate seasonal changes in population-specific standardized catch per unit effort (a proxy for abundance) across six marine regions, while accounting for annual variability in sampling effort and uncertain genetic stock assignment. We also share this model as an R package stockseasonr for application to other regions and species. Even at the relatively small spatial scales considered here, we found that patterns in seasonal abundance differed among regions and stocks. While certain locations were clearly migratory corridors, regions within the Salish Sea exhibited diverse, and often weak, seasonal patterns in abundance, emphasizing that they are important, year-round foraging habitats. Furthermore, we found evidence that stocks with similar freshwater life histories and adult run timing, as well as relatively proximate spawning locations, exhibited divergent distributions. Our findings highlight subtle, but important differences in how adult Chinook salmon use marine habitats. Down-scaled model outputs could be used to inform ecosystem-based management efforts by resolving the degree to which salmon overlap with other species of concern, as well as specific fisheries. More broadly, variation in stock-specific abundance among regions indicates efforts to identify mechanisms driving changes in size-at-maturity and natural mortality should account for distinct marine distributions.

Extensive non-pharmaceutical and physical distancing measures are currently the primary interventions against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing, with the timing of distancing measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to reported COVID-19 cases in British Columbia (BC), Canada, and five other jurisdictions, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimated the impact that physical distancing (social distancing) has had on the contact rate and examined the projected impact of relaxing distancing measures. We found that, as of April 11 2020, distancing had a strong impact in BC, consistent with declines in reported cases and in hospitalization and intensive care unit numbers; individuals practising physical distancing experienced approximately 0.22 (0.11–0.34 90% CI [credible interval]) of their normal contact rate. The threshold above which prevalence was expected to grow was 0.55. We define the “contact ratio” to be the ratio of the estimated contact rate to the threshold rate at which cases are expected to grow; we estimated this contact ratio to be 0.40 (0.19–0.60) in BC. We developed an R package ‘covidseir’ to make our model available, and used it to quantify the impact of distancing in five additional jurisdictions. As of May 7, 2020, we estimated that New Zealand was well below its threshold value (contact ratio of 0.22 [0.11–0.34]), New York (0.60 [0.43–0.74]), Washington (0.84 [0.79–0.90]) and Florida (0.86 [0.76–0.96]) were progressively closer to theirs yet still below, but California (1.15 [1.07–1.23]) was above its threshold overall, with cases still rising. Accordingly, we found that BC, New Zealand, and New York may have had more room to relax distancing measures than the other jurisdictions, though this would need to be done cautiously and with total case volumes in mind. Our projections indicate that intermittent distancing measures—if sufficiently strong and robustly followed—could control COVID-19 transmission. This approach provides a useful tool for jurisdictions to monitor and assess current levels of distancing relative to their threshold, which will continue to be essential through subsequent waves of this pandemic.

Marine biota are redistributing at a rapid pace in response to climate change and shifting seascapes. While changes in fish populations and community structure threaten the sustainability of fisheries, our capacity to adapt by tracking and projecting marine species remains a challenge due to data discontinuities in biological observations, lack of data availability, and mismatch between data and real species distributions. To assess the extent of this challenge, we review the global status and accessibility of ongoing scientific bottom trawl surveys. In total, we gathered metadata for 283,925 samples from 95 surveys conducted regularly from 2001 to 2019. We identified that 59% of the metadata collected are not publicly available, highlighting that the availability of data is the most important challenge to assess species redistributions under global climate change. Given that the primary purpose of surveys is to provide independent data to inform stock assessment of commercially important populations, we further highlight that single surveys do not cover the full range of the main commercial demersal fish species. An average of 18 surveys is needed to cover at least 50% of species ranges, demonstrating the importance of combining multiple surveys to evaluate species range shifts. We assess the potential for combining surveys to track transboundary species redistributions and show that differences in sampling schemes and inconsistency in sampling can be overcome with spatio‐temporal modeling to follow species density redistributions. In light of our global assessment, we establish a framework for improving the management and conservation of transboundary and migrating marine demersal species. We provide directions to improve data availability and encourage countries to share survey data, to assess species vulnerabilities, and to support management adaptation in a time of climate‐driven ocean changes., While many species are changing their spatial distributions rapidly with climate change, the global capacity to track these changes and then adapt management and policy has been limited. To build this capacity for demersal fishes, we compiled the largest worldwide collection of bottom trawl survey metadata and demonstrate how surveys can be combined to follow species across national and survey boundaries. Open data and transparent scientific assessment can be a key step towards improved management of redistributing transboundary species and stocks.

Ocean ecosystems are vulnerable to climate-driven perturbations, which are increasing in frequency and can have profound effects on marine social-ecological systems. Thus, there is an urgency to develop tools that can detect the response of ecosystem components to these perturbations as early as possible. We used Bayesian Dynamic Factor Analysis (DFA) to develop a community state indicator for the California Current Ecosystem (CCE) to track the system’s response to climate perturbations, and to forecast future changes in community state. Our key objectives were to (1) summarize environmental and biological variability in the southern and central regions of the CCE during a recent and unprecedented marine heatwave in the northeast Pacific Ocean (2014–2016) and compare these patterns to past variability, (2) examine whether there is evidence of a shift in the community to a new state in response to the heatwave, (3) identify relationships between community variability and climate variables; and (4) test our ability to create one-year ahead forecasts of individual species responses and the broader community response based on ocean conditions. Our analysis detected a clear community response to the marine heatwave, although it did not exceed normal variability over the past six decades (1951–2017), and we did not find evidence of a shift to a new community state. We found that nitrate flux through the base of the mixed layer exhibited the strongest relationship with species and community-level responses. Furthermore, we demonstrated skill in creating forecasts of species responses and community state based on estimates of nitrate flux. Our indicator and forecasts of community state show promise as tools for informing ecosystem-based and climate-ready fisheries management in the CCE. Our modeling framework is also widely applicable to other ecosystems where scientists and managers are faced with the challenge of managing and protecting living marine resources in a rapidly changing climate.

Following successful non-pharmaceutical interventions (NPI) aiming to control COVID-19, many jurisdictions reopened their economies and borders. As little immunity had developed in most populations, re-establishing higher contact carried substantial risks, and therefore many locations began to see resurgence in COVID-19 cases. We present a Bayesian method to estimate the leeway to reopen, or alternatively the strength of change required to re-establish COVID-19 control, in a range of jurisdictions experiencing different COVID-19 epidemics. We estimated the timing and strength of initial control measures such as widespread distancing and compared the leeway jurisdictions had to reopen immediately after NPI measures to later estimates of leeway. Finally, we quantified risks associated with reopening and the likely burden of new cases due to introductions from other jurisdictions. We found widely varying leeway to reopen. After initial NPI measures took effect, some jurisdictions had substantial leeway (e.g., Japan, New Zealand, Germany) with > 0.99 probability that contact rates were below 80% of the threshold for epidemic growth. Others had little leeway (e.g., the United Kingdom, Washington State) and some had none (e.g., Sweden, California). For most such regions, increases in contact rate of 1.5–2 fold would have had high (> 0.7) probability of exceeding past peak sizes. Most jurisdictions experienced June–August trajectories consistent with our projections of contact rate increases of 1–2-fold. Under such relaxation scenarios for some regions, we projected up to ∼ 100 additional cases if just one case were imported per week over six weeks, even between jurisdictions with comparable COVID-19 risk. We provide an R package covidseir to enable jurisdictions to estimate leeway and forecast cases under different future contact patterns. Estimates of leeway can establish a quantitative basis for decisions about reopening. We recommend a cautious approach to reopening economies and borders, coupled with strong monitoring for changes in transmission.

Extensive physical distancing measures are currently the primary intervention against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing measures, with the timing of these measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to reported COVID-19 cases in British Columbia, Canada, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimate the impact that physical distancing (also known as social distancing) has had on the contact rate and examine the projected impact of relaxing distancing measures. We find that distancing has had a strong impact, consistent with declines in reported cases and in hospitalization and intensive care unit numbers. We estimate that approximately 0.78 (0.66–0.89 90% CI) of contacts have been removed for individuals in British Columbia practising physical distancing and that this fraction is above the threshold of 0.45 at which prevalence is expected to grow. However, relaxing distancing measures beyond this threshold re-starts rapid exponential growth. Because the extent of underestimation is unknown, the data are consistent with a wide range in the prevalence of COVID-19 in the population; changes to testing criteria over time introduce additional uncertainty. Our projections indicate that intermittent distancing measures—if sufficiently strong and robustly followed— could control COVID-19 transmission, but that if distancing measures are relaxed too much, the epidemic curve would grow to high prevalence.

Many of the world's fisheries are unassessed, with little information about population status or risk of overfishing. Unassessed fisheries are particularly predominant in developing countries and in small-scale fisheries, where they are important for food security. Several catch-only methods based on time series of fishery catch and commonly available life-history traits have been developed to estimate stock status (defined as biomass relative to biomass at maximum sustainable yield: B/B-MSY). While their stock status performance has been extensively studied, performance of catch-only models as a management tool is unknown. We evaluated the extent to which a superensemble of three prominent catch-only models can provide a reliable basis for fisheries management and how performance compares across management strategies that control catch or fishing effort. We used a management strategy evaluation framework to determine whether a superensemble of catch-only models can reliably inform harvest control rules (HCRs). Across five simulated fish life histories and two harvest-dynamic types, catch-only models and HCR combinations reduced the risk of overfishing and increased the proportion of stocks above B-MSY compared to business as usual, though often resulted in poor yields. Precautionary HCRs based on fishing effort were robust and insensitive to error in catch-only models, while catch-based HCRs caused high probabilities of overfishing and more overfished populations. Catch-only methods tended to overestimate B/B-MSY for our simulated data sets. The catch-only superensemble combined with precautionary effort-based HCRs could be part of a stepping stone approach for managing some data-limited stocks while working towards more data-moderate assessment methods.

Individuals relying on natural resource extraction for their livelihood face high income variability driven by a mix of environmental, biological, management, and economic factors. Key to managing these industries is identifying how regulatory actions and individual behavior affect income variability, financial risk, and, by extension, the economic stability and the sustainable use of natural resources. In commercial fisheries, communities and vessels fishing a greater diversity of species have less revenue variability than those fishing fewer species. However, it is unclear whether these benefits extend to the actions of individual fishers and how year-to-year changes in diversification affect revenue and revenue variability. Here, we evaluate two axes by which fishers in Alaska can diversify fishing activities. We show that, despite increasing specialization over the last 30 years, fishing a set of permits with higher species diversity reduces individual revenue variability, and fishing an additional permit is associated with higher revenue and lower variability. However, increasing species diversity within the constraints of existing permits has a fishery-dependent effect on revenue and is usually (87% probability) associated with increased revenue uncertainty the following year. Our results demonstrate that the most effective option for individuals to decrease revenue variability is to participate in additional or more diverse fisheries. However, this option is expensive, often limited by regulations such as catch share programs, and consequently unavailable to many individuals. With increasing climatic variability, it will be particularly important that individuals relying on natural resources for their livelihood have effective strategies to reduce financial risk.

River networks may dampen local hydrologic signals of climate change through the aggregation of upstream climate portfolio assets. Here we examine this hypothesis using flow and climate trend estimates (1970–2007) at 55 hydrometric gauge stations and across their contributing watersheds' within the Fraser River basin in British Columbia, Canada. Using a null hypothesis framework, we compared our observed attenuation of river flow trends as a function of increasing area and climate trend diversity, with null-simulated estimates to gauge the likelihood and strength of our observations. We found the Fraser River reduced variability in downstream long-term discharge by >91%, with >3.1 times the attenuation than would be expected under null simulation. Although the strength of dampening varied seasonally, our findings indicate that large free-flowing rivers offer a powerful and largely unappreciated process of climate change mitigation. River networks that integrate a diverse climate portfolio can dampen local extremes and offer climate change relief to riverine biota.

Black swans are improbable events that nonetheless occur-often with profound consequences. Such events drive important transitions in social systems (e.g., banking collapses) and physical systems (e.g., earthquakes), and yet it remains unclear the extent to which ecological population numbers buffer or suffer from such extremes. Here, we estimate the prevalence and direction of black-swan events (heavy-tailed process noise) in 609 animal populations after accounting for population dynamics (productivity, density dependence, and typical stochasticity). We find strong evidence for black-swan events in [Formula: see text]4% of populations. These events occur most frequently for birds (7%), mammals (5%), and insects (3%) and are not explained by any life-history covariates but tend to be driven by external perturbations such as climate, severe winters, predators, parasites, or the combined effect of multiple factors. Black-swan events manifest primarily as population die-offs and crashes (86%) rather than unexpected increases, and ignoring heavy-tailed process noise leads to an underestimate in the magnitude of population crashes. We suggest modelers consider heavy-tailed, downward-skewed probability distributions, such as the skewed Student [Formula: see text] used here, when making forecasts of population abundance. Our results demonstrate the importance of both modeling heavy-tailed downward events in populations, and developing conservation strategies that are robust to ecological surprises.

Fishery managers must often reconcile conflicting estimates of population status and trend. Superensemble models, commonly used in climate and weather forecasting, may provide an effective solution. This approach uses predictions from multiple models as covariates in an additional "superensemble" model fitted to known data. We evaluated the potential for ensemble averages and superensemble models (ensemble methods) to improve estimates of population status and trend for fisheries. We fit four widely applicable data-limited models that estimate stock biomass relative to equilibrium biomass at maximum sustainable yield (B/BMSY). We combined these estimates of recent fishery status and trends in B/BMSY with four ensemble methods: an ensemble average and three superensembles (a linear model, a random forest and a boosted regression tree). We trained our superensembles on 5,760 simulated stocks and tested them with cross-validation and against a global database of 249 stock assessments. Ensemble methods substantially improved estimates of population status and trend. Random forest and boosted regression trees performed the best at estimating population status: inaccuracy (median absolute proportional error) decreased from 0.42 -0.56 to 0.32 -0.33, rank-order correlation between predicted and true status improved from 0.02 - 0.32 to 0.44 - 0.48 and bias (median proportional error) declined from - 0.22 - 0.31 to - 0.12 - 0.03. We found similar improvements when predicting trend and when applying the simulation-trained superensembles to catch data for global fish stocks. Superensembles can optimally leverage multiple model predictions; however, they must be tested, formed from a diverse set of accurate models and built on a data set representative of the populations to which they are applied.

Heterogeneity in human responses and decision‐making can contribute to the resilience of social–ecological systems in the face of environmental, political and economic pressures. In fishery systems worldwide, the ability of harvesters to maintain a diverse portfolio of fishing strategies is important for building adaptive capacity. We used a case‐study approach to examine the complexity of factors that inhibit or promote diversification in fisheries of Alaska, one of the major fishing regions of the world. Through a combination of harvest records and literature review, we explored shifts in participation and portfolio diversity in Alaskan fisheries over three decades. The four case‐studies examined the responses of fishers, fleets and communities to multiple, intersecting pressures, including biological declines, market and price dynamics, fishery privatization and the 1989 Exxon Valdez oil spill. These cases illustrate how stressors acting at multiple scales can encourage or constrain opportunities for diversification, and that these opportunities may be spread inequitably across participants. Overall, we found evidence for reduced participation and increasing specialization in Alaskan commercial fisheries. While numerous factors explain these trends, policies like individual quota systems and the increasing cost of entry into fisheries are forcing consolidation at local to regional scales. A portfolio approach to managing fisheries that reduces barriers to diversification and includes broad representation of resource users and communities in management may help to maintain opportunity and choice for fishers.

Somatic growth in fishes often varies through time. Despite this, most stock assessments either fix or estimate a time-invariant growth relationship because estimating time-varying growth parameters can be data intensive and subject to multiple sources of bias. Additionally, estimates of growth are often confounded with estimates of selectivity, particularly if selectivity is also time varying. Incorporating empirical weight-at-age (EWAA) information into assessments is a little-studied alternative that accounts for time-varying growth, but foregoes fixing or estimating growth and length–weight relationships. However, this method requires annual measures of fish weights at each age, and missing values must therefore be interpolated. We used Stock Synthesis in a simulation-testing framework to compare the effect of estimating a single time-invariant growth curve, time-varying growth curves, and incorporating EWAA information on management quantities and parameter estimates. We ran simulations across two fish life histories (hake-like and rockfish-like) and three data cases (data-rich, data-rich with a late-starting survey, and data-moderate). We found that when growth was time invariant, the EWAA approach was unbiased but had twice the median average relative error compared to a model that estimated growth from age and length data. However, for data-rich cases with time-varying growth, the EWAA method resulted in more accurate estimates of spawning stock biomass compared to the approach that estimated time-invariant and time-varying growth parameters, as evidenced by at least a five-fold reduction in range of median relative errors. The magnitude of this effect was greatest for the long-lived, slow-growing life history. For the relatively fast-growing species, estimates from the EWAA method were particularly sensitive to interpolating missing values.

Analysts conducting stock assessments using integrated, age-structured models must discretize length data into a limited number of bins (data bins). Furthermore, some modeling frameworks also allow users to specify a distinct structure for how lengths of fish are represented in the model (model bins). The effect of choices regarding the number and width of these bins on model output is unclear, and these choices are made inconsistently in assessments across regions and species. Here, we used the stock synthesis modeling framework, and the ss3sim stock assessment simulation package, to explore the effects of choices about length discretization on stock assessment performance for three fish life-history types and four data cases. We found that, with all other aspects of a model fixed, increasing the model bin width tended to increase estimates of spawning biomass, but this effect depended on the shape of length-based processes (e.g., growth, maturity, and selectivity). Thus, we suggest analysts using model bins wider than 1 cm explore the effect of this decision on derived management quantities. In the context of estimation, there generally was a predictable tradeoff between estimation accuracy and model run time, with finer model and data bins always improving estimation accuracy and model convergence, but increasing run time. In some cases, wider data bins reduced run time (by up to 50%) with little sacrifice in model estimation performance, particularly those using conditional age-at-length data. This study identifies key aspects to consider when binning length, and provides pertinent information for stock assessment best practice guidelines.

The Gulf of Alaska experienced extreme temperatures during 2014–2019, including the four warmest years ever observed. The goal of this study is to evaluate the ecological consequences of that warming event, across multiple trophic levels and taxa. We tested for evidence that observed sea surface temperature (SST) anomalies were outside the envelope of natural climate variability in order to evaluate the risk of novel ecosystem configurations. We also tested for state changes in shared trends of climate (n = 11) and biology (n = 48) time series, using a Bayesian implementation of Dynamic Factor Analysis (DFA). And we tested for evidence of novel ecological relationships during 2014–2019. We found that 3-year running mean SST anomalies during 2014–2019 were outside the range of anomalies from preindustrial simulations in CMIP5 models, indicating that the combined magnitude and duration of the warming event was outside the range of natural variability. A DFA model of climate variability also returned a shared trend in climate time series that was at unprecedented levels during 2014–2019. However, DFA models fit to biology data did not show shared trends of variability at unprecedented levels, and Hidden Markov Models fit to shared trends from the climate and biology models failed to find evidence of shifts to a new ecosystem state during 2014–2019. Conversely, we did find preliminary indications that community responses to SST variability strengthened during 2014–2019 after decades of a mostly neutral relationship. Tests for nonstationary patterns of shared variability suggest that covariance between SST and other ecologically-important climate variables remained weaker than during the 1970s Pacific Decadal Oscillation shift, suggesting the potential for muted ecological responses to the 2014–2019 event. Finally, we found that recent patterns of community variability appear to be highly dissimilar to those associated with the 1970s event, suggesting the potential for novel community states with continued warming. In summary, we find no evidence for wholesale ecosystem reorganization during 2014–2019, though nonstationary relationships among climate and community variables suggest the ongoing possibility of novel patterns of ecosystem functioning with continued warming.

Demand for data-limited stock assessment methods is increasing, and new methods are being developed rapidly. One class of these methods requires only catch time series and, in some cases, information about life history or fishery characteristics, to estimate stock status. These catch-only methods (COMs) range from statistical models trained on data-rich stocks to mechanistic population models that make assumptions about changes in fishing effort. We review 11 COMs, comparing performance through application to data-rich stocks and simulated fisheries. The catch-only methods evaluated here produce imprecise and biased estimates of B/BMSY, especially for stocks that are lightly exploited. They were also generally poor classifiers of stock status. While no method performed best across all stocks, ensembles of multiple COMs generally performed better than individual COMs. We advocate for testing new COMs using this common platform. We also caution that performance in estimating stock status is not sufficient for gauging the usefulness of COMs in managing fisheries. Greater use of management strategy evaluation is needed before COMs can be considered a reliable tool for management.

The exploitation status of marine fisheries stocks worldwide is of critical importance for food security, ecosystem conservation, and fishery sustainability. Applying a suite of data-limited methods to global catch data, combined through an ensemble modeling approach, we provide quantitative estimates of exploitation status for 785 fish stocks. Fifty six percent (439 stocks) are below BMSY and of these, 261 are estimated to be below 80% of the BMSY level. While the 178 stocks above 80% of BMSY are conventionally considered “fully exploited”, stocks staying at this level for many years, forego substantial yield. Our results enable managers to consider more detailed information than simply a categorization of stocks as “fully” or “over” exploited. Our approach is reproducible, allows consistent application to a broad range of stocks, and can be easily updated as new data become available. Applied on an ongoing basis, this approach can provide critical, more detailed information for resource management for more exploited fish stocks than currently available. This article is protected by copyright. All rights reserved

We thank Youngflesh and Lynch (1) for their thoughtful comments on our paper (2). As they note, we should have mentioned immigration and emigration alongside the intrinsic population properties (e.g., population birth rate, mortality, and age at maturity) and extrinsic causes of black-swan events (e.g., extreme climate, disease, predation, competition, exploitation, and habitat destruction). After all, immigration and observation error are the only possible explanations for sudden abundance increases above those possible from the maximum biological rate of increase. Youngflesh and Lynch (1) use a simple approach to flag which time series have population increases ( r ) that are greater than the demographic maximum [Cole’s ρ (3), r > ρ ], and hence may be driven by migration. However, they estimate the greatest realized r without accounting for observation error (uncertainty in measuring population abundance); consequently, there will be false-positive cases of apparent high r (Fig. 1 A – C ). If we calculate r for the populations referenced by Youngflesh and Lynch (1) while accounting for moderate observation error [coefficient of variation (CV) = 0.2], only six populations remain with probability Pr( r > ρ ) > 0.5 and the 95% credible interval excludes ρ in only … [↵][1]2To whom correspondence should be addressed. Email: sean.anderson{at}dfo-mpo.gc.ca. [1]: #xref-corresp-1-1

In ecological systems, extremes can happen in time, such as population crashes, or in space, such as rapid range contractions. However, current methods for joint inference about temporal and spatial dynamics (e.g., spatiotemporal modeling with Gaussian random fields) may perform poorly when underlying processes include extreme events. Here we introduce a model that allows for extremes to occur simultaneously in time and space. Our model is a Bayesian predictive-process GLMM (generalized linear mixed-effects model) that uses a multivariate-t distribution to describe spatial random effects. The approach is easily implemented with our flexible R package glmmfields. First, using simulated data, we demonstrate the ability to recapture spatiotemporal extremes, and explore the consequences of fitting models that ignore such extremes. Second, we predict tree mortality from mountain pine beetle (Dendroctonus ponderosae) outbreaks in the U.S. Pacific Northwest over the last 16 yr. We show that our approach provides more accurate and precise predictions compared to traditional spatiotemporal models when extremes are present. Our R package makes these models accessible to a wide range of ecologists and scientists in other disciplines interested in fitting spatiotemporal GLMMs, with and without extremes.