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Abstract Background Ecological and physical conditions vary with depth in aquatic ecosystems, resulting in gradients of habitat suitability. Although variation in vertical distributions among individuals provides evidence of habitat selection, it has been challenging to disentangle how processes at multiple spatio-temporal scales shape behaviour. Methods We collected thousands of observations of depth from $$>~300$$ > 300 acoustically tagged adult Chinook salmon Oncorhynchus tshawytscha, spanning multiple seasons and years. We used these data to parameterize a machine-learning model to disentangle the influence of spatial, temporal, and dynamic oceanographic variables while accounting for differences in individual condition and maturation stage. Results The top performing machine learning model used bathymetric depth ratio (i.e., individual depth relative to seafloor depth) as a response. We found that bathymetry, season, maturation stage, and spatial location most strongly influenced Chinook salmon depth. Chinook salmon bathymetric depth ratios were deepest in shallow water, during winter, and for immature individuals. We also identified non-linear interactions among covariates, resulting in spatially-varying effects of zooplankton concentration, lunar cycle, temperature and oxygen concentration. Conclusions Our results suggest Chinook salmon vertical habitat use is a function of ecological interactions, not physiological constraints. Temporal and spatial variation in depth distributions could be used to guide management decisions intended to reduce fishery impacts on Chinook salmon. More generally, our findings demonstrate how complex interactions among bathymetry, seasonality, location, and life history stage regulate vertical habitat selection.

Abstract Nutrient‐rich waters along the Pacific coast of North America support diverse fish communities that have helped sustain coastal peoples for millennia. Five Nuu‐chah‐nulth First Nations on the west coast of what is now known as Vancouver Island, Canada, hold constitutional Indigenous rights to conduct a multispecies community fishery, which includes Pacific Halibut (Hippoglossus stenolepis). A 2009 court decision defined the extent of these Indigenous rights to be within 9 nm of the coast, thereby not fully recognizing the knowledge and authority of the traditional leadership and raising concerns about the potential for an increase in rockfish bycatch. Yelloweye Rockfish (Sebastes ruberrimus) are a potential ‘choke’ species for this fishery because the two species occupy similar depth ranges. A choke species is one that is caught incidentally while targeting other species and, if caught in excess of its quota limits, can trigger a halt to fishing on the target species. Guided by the insights of local Indigenous peoples and using both fishery‐independent survey and commercial longline catch data, we investigated the effects of fishing depth and spatial restriction on the relative catch weights of these two species using spatiotemporal models. We find evidence that a confined fishing area can limit opportunities for avoiding choke species. Specifically, fishing at depths deeper than 175 m, which occur outside the court defined area (CDA), would provide more opportunities for catching halibut while avoiding Yelloweye Rockfish than are currently available within the CDA. This Indigenous‐informed, analytical approach to a management problem is just one example of how Western scientists can engage in coproduction of knowledge with Indigenous peoples to transition from the ‘status quo’ towards a practice of ‘Two‐Eyed Seeing’ that more effectively balances Indigenous rights and species conservation. Policy implications: Our study highlights (1) the importance of considering choke species distributions and opportunities for their avoidance when implementing spatial harvest restrictions and (2) how related analytical and management decisions can benefit from being guided by the advice of Indigenous knowledge holders. Read the free Plain Language Summary for this article on the Journal blog.

The use of species distribution models (SDMs) has rapidly increased over the last decade, driven largely by increasing observational evidence of distributional shifts of terrestrial and aquatic populations. These models permit, for example, the quantification of range shifts, the estimation of species co-occurrence, and the association of habitat to species distribution and abundance. The increasing complexity of contemporary SDMs presents new challenges—as the choices among modeling options increase, it is essential to understand how these choices affect model outcomes. Using a combination of original analysis and literature review, we synthesize the effects of three common model choices in semi-parametric predictive process species distribution modeling: model structure, spatial extent of the data, and spatial scale of predictions. To illustrate the effects of these choices, we develop a case study centered around sablefish (Anoplopoma fimbria) distribution on the west coast of the USA. The three modeling choices represent decisions necessary in virtually all ecological applications of these methods, and are important because the consequences of these choices impact derived quantities of interest (e.g., estimates of population size and their management implications). Truncating the spatial extent of data near the observed range edge, or using a model that is misspecified in terms of covariates and spatial and spatiotemporal fields, led to bias in population biomass trends and mean distribution compared to estimates from models using the full dataset and appropriate model structure. In some cases, these suboptimal modeling decisions may be unavoidable, but understanding the tradeoffs of these choices and impacts on predictions is critical. We illustrate how seemingly small model choices, often made out of necessity or simplicity, can affect scientific advice informing management decisions—potentially leading to erroneous conclusions about changes in abundance or distribution and the precision of such estimates. For example, we show how incorrect decisions could cause overestimation of abundance, which could result in management advice resulting in overfishing. Based on these findings and literature gaps, we outline important frontiers in SDM development.

Estimates of the basic reproduction number (R0) for COVID-19 are particularly variable in the context of transmission within locations such as long-term healthcare (LTHC) facilities. We sought to characterize the heterogeneity of R0 across known outbreaks within these facilities. We used a unique comprehensive dataset of all outbreaks that occurred within LTHC facilities in British Columbia, Canada as of 21 September 2020. We estimated R0 in 18 LTHC outbreaks with a novel Bayesian hierarchical dynamic model of susceptible, exposed, infected and recovered individuals, incorporating heterogeneity of R0 between facilities. We further compared these estimates to those obtained with standard methods that use the exponential growth rate and maximum likelihood. The total size of outbreaks varied dramatically, with range of attack rates 2%–86%. The Bayesian analysis provided an overall estimate of R0 = 2.51 (90% credible interval 0.47–9.0), with individual facility estimates ranging between 0.56 and 9.17. Uncertainty in these estimates was more constrained than standard methods, particularly for smaller outbreaks informed by the population-level model. We further estimated that intervention led to 61% (52%–69%) of all potential cases being averted within the LTHC facilities, or 75% (68%–79%) when using a model with multi-level intervention effect. Understanding of transmission risks and impact of intervention are essential in planning during the ongoing global pandemic, particularly in high-risk environments such as LTHC facilities.

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.

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.

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.

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.

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.

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.

Simulation testing is an important approach to evaluating fishery stock assessment methods. In the last decade, the fisheries stock assessment modeling framework Stock Synthesis (SS3) has become widely used around the world. However, there lacks a generalized and scriptable framework for SS3 simulation testing. Here, we introduce ss3sim, an R package that facilitates reproducible, flexible, and rapid end-to-end simulation testing with SS3. ss3sim requires an existing SS3 model configuration along with plain-text control files describing alternative population dynamics, fishery properties, sampling scenarios, and assessment approaches. ss3sim then generates an underlying 'truth' from a specified operating model, samples from that truth, modifies and runs an estimation model, and synthesizes the results. The simulations can be run in parallel, reducing runtime, and the source code is free to be modified under an open-source MIT license. ss3sim is designed to explore structural differences between the underlying truth and assumptions of an estimation model, or between multiple estimation model configurations. For example, ss3sim can be used to answer questions about model misspecification, retrospective patterns, and the relative importance of different types of fisheries data. We demonstrate the software with an example, discuss how ss3sim complements other simulation software, and outline specific research questions that ss3sim could address.

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.