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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.

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.

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.

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.

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.

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.

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.

Recognizing the threat of additive risk Humans are accelerating the extinction rates of species in both terrestrial and marine environments. However, species extinctions have occurred across time for a variety of other reasons. Finnegan et al. looked at the extinction rates across marine genera (groups of species) over the past 23 million years to determine intrinsic extinction rates and what traits or regions correspond to the highest rates. Combining patterns of intrinsic extinction with regions of high anthropogenic threat revealed taxa and areas, particularly in the tropics, where the risk of extinction will be especially high. Science , this issue p. 567

Despite a growing interest in identifying tipping points in response to environmental change, our understanding of the ecological mechanisms underlying non-linear ecosystem dynamics is limited. Ecosystems governed by strong species interactions can provide important insight into how non-linear relationships between organisms and their environment propagate through ecosystems, and the potential for environmentally mediated species interactions to drive or protect against sudden ecosystem shifts. Here, we experimentally determine the functional relationships (i.e., the shapes of the relationships between predictor and response variables) of a seagrass assemblage with well-defined species interactions to ocean acidification (enrichment of CO2) in isolation and in combination with nutrient loading. We demonstrate that the effect of ocean acidification on grazer biomass (Phyllaplysia taylori and Idotea resecata) was quadratic, with the peak of grazer biomass at mid-pH levels. Algal grazing was negatively affected by nutrients, potentially due to low grazer affinity for macroalgae (Ulva intestinalis), as recruitment of both macroalgae and diatoms were favored in elevated nutrient conditions. This led to an exponential increase in macroalgal and epiphyte biomass with ocean acidification, regardless of nutrient concentration. When left unchecked algae can cause declines in seagrass productivity and persistence through shading and competition. Despite quadratic and exponential functional relationships to stressors that could cause a non-linear decrease in seagrass biomass, productivity of our model seagrass – the eelgrass (Zostera marina)- remained highly resilient to increasing acidification. These results suggest that important species interactions governing ecosystem dynamics may shift with environmental change, and ecosystem state may be decoupled from ecological responses at lower levels of organization. This article is protected by copyright. All rights reserved.

In recent decades, a substantial number of popular press articles have described an increase in demand for certain species in the pet trade due to films such as “Finding Nemo”, “Ninja turtles”, and “Harry Potter”. Nevertheless, such assertions are largely supported only by anecdotal evidence. Given the role of the wildlife trade in the spread of pathogens and zoonosis, the introduction of invasive species, the overexploitation of biodiversity, and the neglect of animal welfare, it is crucial to understand what factors drive demand for a species. Here, we investigate the effect the movie industry may have on wildlife trade by examining the relationship between the “Harry Potter” cultural phenomenon and the trade in owls within the United Kingdom (UK). We gathered data from the UK box office, book sales, and newspaper mentions, and examined their relationship with data from three independent sources reflecting the legal ownership of owls in the UK, which is likely to involve several thousands of animals. Additionally, we conducted a questionnaire survey with UK animal sanctuaries to study the presumed mass abandonment of pet owls when the film series ended. Counter to common assertions, we find no evidence that the “Harry Potter” phenomenon increased the legal trade in owls within the UK, even when possible time-lag effects were taken into account. Only one indicator, the number of movie tickets sold, showed a weak but contradictory relationship with demand for owls, with a recorded drop of 13% (95% CI: 3–27%) per 1 SD in tickets sold in the original analysis but an increase of 4% (95% CI: 0–8%) with a one-year lag. In addition, our results suggest that the end of the Harry Potter series did not have a noticeable impact on the number of owls abandoned in UK wildlife sanctuaries, as only two of the 46 animal sanctuaries we contacted independently stated they had seen an increase in owls received and believed this was due to the Harry Potter series. We highlight the importance of further research on the drivers of demand for wildlife to better manage this global trade, and discuss the potential to use films to positively influence behaviour.

Human-wildlife conflicts impose considerable costs to people and wildlife worldwide. Most research focuses on proximate causes, offering limited generalizable understanding of ultimate drivers. We tested three competing hypotheses (problem individuals, regional population saturation, limited food supply) that relate to underlying processes of human-grizzly bear (Ursus arctos horribilis) conflict, using data from British Columbia, Canada, between 1960–2014. We found most support for the limited food supply hypothesis: in bear populations that feed on spawning salmon (Oncorhynchus spp.), the annual number of bears/km2 killed due to conflicts with humans increased by an average of 20% (6–32% [95% CI]) for each 50% decrease in annual salmon biomass. Furthermore, we found that across all bear populations (with or without access to salmon), 81% of attacks on humans and 82% of conflict kills occurred after the approximate onset of hyperphagia (July 1st), a period of intense caloric demand. Contrary to practices by many management agencies, conflict frequency was not reduced by hunting or removal of problem individuals. Our finding that a marine resource affects terrestrial conflict suggests that evidence-based policy for reducing harm to wildlife and humans requires not only insight into ultimate drivers of conflict, but also management that spans ecosystem and jurisdictional boundaries.

Theory predicts that bottom-heavy biomass pyramids or ‘stacks’ should predominate in real-world communities if trophic-level increases with body size (mean predator-to-prey mass ratio (PPMR) more than 1). However, recent research suggests that inverted biomass pyramids (IBPs) characterize relatively pristine reef fish communities. Here, we estimated the slope of a kelp forest fish community biomass spectrum from underwater visual surveys. The observed biomass spectrum slope is strongly positive, reflecting an IBP. This is incongruous with theory because this steep positive slope would only be expected if trophic position decreased with increasing body size (consumer-to-resource mass ratio, less than 1). We then used δ 15 N signatures of fish muscle tissue to quantify the relationship between trophic position and body size and instead detected strong evidence for the opposite, with PPMR ≈ 1650 (50% credible interval 280–12 000). The natural history of kelp forest reef fishes suggests that this paradox could arise from energetic subsidies in the form of movement of mobile consumers across habitats, and from seasonally pulsed production inputs at small body sizes. There were four to five times more biomass at large body sizes (1–2 kg) than would be expected in a closed steady-state community providing a measure of the magnitude of subsidies.

Scientific management of wildlife requires confronting the complexities of natural and social systems. Uncertainty poses a central problem. Whereas the importance of considering uncertainty has been widely discussed, studies of the effects of unaddressed uncertainty on real management systems have been rare. We examined the effects of outcome uncertainty and components of biological uncertainty on hunt management performance, illustrated with grizzly bears (Ursus arctos horribilis) in British Columbia, Canada. We found that both forms of uncertainty can have serious impacts on management performance. Outcome uncertainty alone – discrepancy between expected and realized mortality levels – led to excess mortality in 19% of cases (population-years) examined. Accounting for uncertainty around estimated biological parameters (i.e., biological uncertainty) revealed that excess mortality might have occurred in up to 70% of cases. We offer a general method for identifying targets for exploited species that incorporates uncertainty and maintains the probability of exceeding mortality limits below specified thresholds. Setting targets in our focal system using this method at thresholds of 25% and 5% probability of overmortality would require average target mortality reductions of 47% and 81%, respectively. Application of our transparent and generalizable framework to this or other systems could improve management performance in the presence of uncertainty. &nbsp

BackgroundWorldwide, finfish fisheries are receiving increasing assessment and regulation, slowly leading to more sustainable exploitation and rebuilding. In their wake, invertebrate fisheries are rapidly expanding with little scientific scrutiny despite increasing socio-economic importance.Methods and findingsWe provide the first global evaluation of the trends, drivers, and population and ecosystem consequences of invertebrate fisheries based on a global catch database in combination with taxa-specific reviews. We also develop new methodologies to quantify temporal and spatial trends in resource status and fishery development. Since 1950, global invertebrate catches have increased 6-fold with 1.5 times more countries fishing and double the taxa reported. By 2004, 34% of invertebrate fisheries were over-exploited, collapsed, or closed. New fisheries have developed increasingly rapidly, with a decrease of 6 years (3 years) in time to peak from the 1950s to 1990s. Moreover, some fisheries have expanded further and further away from their driving market, encompassing a global fishery by the 1990s. 71% of taxa (53% of catches) are harvested with habitat-destructive gear, and many provide important ecosystem functions including habitat, filtration, and grazing.ConclusionsOur findings suggest that invertebrate species, which form an important component of the basis of marine food webs, are increasingly exploited with limited stock and ecosystem-impact assessments, and enhanced management attention is needed to avoid negative consequences for ocean ecosystems and human well-being.