Search

In the face of biodiversity loss worldwide, it is paramount to quantify species' extinction risk to guide conservation efforts. The International Union for the Conservation of Nature (IUCN)'s Red List is considered the global standard for evaluating extinction risks. IUCN criteria also inform national extinction risk assessments. Bayesian models, including the state‐of‐the‐art JARA ('Just Another Red List Assessment') tool, deliver probabilistic statements about species falling into extinction risk categories, thereby enabling characterisation and communication of uncertainty in extinction risk assessments.We coupled the state‐of‐the‐art VAST ('Vector Autoregressive Spatio‐Temporal') modelling tool and JARA, for better informed Red List assessments of marine fishes. In this framework, VAST is fitted to scientific survey catch rate data to provide indices to JARA whose uncertainty is propagated to JARA outcomes suggesting extinction risk categories (under the population reduction criterion). In addition, VAST delivers a valuable habitat assessment to better understand what may be driving extinction risk in the study region. Here, we demonstrate the coupled VAST‐JARA modelling framework by applying it to five contrasting North Sea species, with or without a quantitative stock assessment and with different conservation statuses according to the latest global Red List assessments.The North Sea application coupled with previous assessments and studies suggest that, among the three elasmobranchs, starry ray is in most need of urgent research (and conservation actions where appropriate), followed by spurdog, while lesser‐spotted dogfish is increasing in biomass. Moreover, both the VAST‐JARA modelling framework and previous research indicate that, while European plaice is not of conservation concern, cod has likely met the IUCN criteria for being listed as Endangered recently.Synthesis and applications. The predictions of the VAST‐JARA modelling framework for North Sea species, including JARA output and VAST habitat assessment, constitute valuable supporting information to make interpretations based on Red List guidelines, which will help decision‐makers in their next North Sea Red List assessment. We foresee applications of the modelling framework to assist Red List assessments of numerous marine fishes worldwide. Our modelling framework has many potential advantageous uses, including informing resource management about climate change impacts on species' extinction risks. [ABSTRACT FROM AUTHOR]

As ecosystem-based fisheries management becomes more ingrained into the way fisheries agencies do business, a need for ecosystem and multispecies models arises. Yet ecosystems are complex, and model uncertainty can be large. Model ensembles have historically been used in other disciplines to address model uncertainty. To understand the benefits and limitations of multispecies model ensembles (MMEs), cases where they have been used in the United States to address fisheries management issues are reviewed. The cases include: (1) development of ecological reference points for Atlantic Menhaden, (2) the creation of time series to relate harmful algal blooms to grouper mortality in the Gulf of Mexico, and (3) fostering understanding of the role of forage fish in the California Current. Each case study briefly reviews the management issue, the models used and model synthesis approach taken, and the outcomes and lessons learned from the application of MMEs. Major conclusions drawn from these studies highlight how the act of developing an ensemble model suite can improve the credibility of multispecies models, how qualitative synthesis of projections can advance system understanding and build confidence in the absence of quantitative treatments, and how involving a diverse set of stakeholders early is useful for ensuring the utility of the models and ensemble. Procedures for review and uptake of information from single-species stock assessment models are well established, but the absence of well-defined procedures for MMEs in many fishery management decision-making bodies poses a major obstacle. The benefits and issues identified here should help accelerate the design, implementation, and utility of MMEs in applied fisheries contexts.

Both trophic structure and biomass flow within marine food webs are influenced by the abiotic environment and anthropogenic stressors such as fishing. The abiotic environment has a large effect on species spatial distribution patterns and productivity and, consequently, spatial co-occurrence between predators and prey, while fishing alters species abundances and food-web structure. In order to disentangle the impacts of the abiotic environment and fishing in the Celtic Sea ecosystem, we developed a spatio-temporal trophic model, specifically an Ecopath with Ecosim with Ecospace model, for the period 1985–2016. In this model, particular attention was paid to the parameterization of the responses of all trophic levels to abiotic environmental changes. Satellite remote sensing data were employed to determine the spatial distribution and annual fluctuations of primary production (PP). Spatial and temporal changes in the habitat favorable for zooplankton were predicted with a novel ecological-niche approach using daily detection of productivity fronts from satellite ocean color. Finally, functional responses characterizing the effect of several abiotic environmental variables (including, among others, temperature, salinity and dissolved oxygen concentration, both at the surface and at the bottom) on fish species groups’ habitat suitability were produced from the predictions of statistical habitat models fitted to presence-absence data collected by multiple fisheries-independent surveys. The dynamic component of our model (Ecosim) was driven by time-series of fishing effort, PP, zooplankton habitat suitability and abiotic environmental variables, and was fitted to abundance and fisheries catch data. The spatial component of our model (Ecospace) was constructed, for specific years of the period 1985–2016 with contrasted abiotic environmental conditions, to predict the variable distribution of the biomass of all functional groups. We found that fishing was the main driver of observed ecosystem changes in the Celtic Sea over the period 1985–2016. However, the integration of the environmental variability into the model and the subsequent improvement of the fit of the dynamic Ecosim component highlighted (i) the control of the overall pelagic production by PP and (ii) the influence of temperature on the productivity of several trophic levels in the Celtic Sea, especially on trophic groups with warm and cold water affinities. In addition, Ecospace predictions indicated that the spatial distributions of commercial fish species may have substantially changed over the studied period. These spatial changes mainly appeared to be driven by temperature and may, therefore, largely impact future fisheries given the continuity of climatic changes.

The marine ecosystem off British Columbia (BC), Canada, has experienced various changes in the last two decades, including reduced lipid-rich zooplankton biomass, increased marine mammals, and deteriorated commercial fisheries, particularly those targeting pelagic species such as Pacific Herring (Clupea pallasii). Understanding how stressors interactively and cumulatively affect commercially important fish species is key to moving toward ecosystem-based fisheries management. Because it is challenging to assess the cumulative effects of multiple stressors by using empirical data alone, a dynamic, individual-based spatially explicit ecosystem modeling platform such as Object-oriented Simulator of Marine Ecosystems (OSMOSE) represents a valuable tool to simulate ecological processes and comprehensively evaluate how stressors cumulatively impact modeled species. In this study, we employed OSMOSE to investigate the cumulative effects of fishing, plankton biomass change, and marine mammal consumption on the dynamics of some fish species and the BC marine ecosystem as a whole. We specifically simulated ecosystem dynamics during the last 20 years under two sets of scenarios: (1) unfavorable conditions from the perspective of commercial fish species (i.e., doubling fishing mortality rates, halving plankton biomass, and doubling marine mammal biomass, acting individually or collectively); and (2) favorable conditions with the three factors having opposite changes (i.e., halving fishing mortality rates, doubling plankton biomass, and halving marine mammal biomass, acting individually or collectively). Our results indicate that, under unfavorable conditions, the degree to which species biomass was reduced varied among species, and that negative synergistic and negative dampened effects were dominant under historical and doubled fishing mortality rates, respectively. Under favorable conditions, species biomasses did not increase as much as expected due to the existence of complex predator-prey interactions among fish species, and positive synergistic and positive dampened effects were prevailing under historical and halved fishing mortality rates, respectively. The ecosystem total biomass and the biomass to fisheries yield ratio were found to be good ecological indicators to represent ecosystem changes and track the impacts from the multiple drivers of change. Our research provides insights on how fisheries management should adapt to prepare for potential future impacts of climate change.

Resource managers in the United States and worldwide are tasked with identifying and mitigating trade-offs between human activities in the deep sea (e.g., fishing, energy development, and mining) and their impacts on habitat-forming invertebrates, including deep-sea corals, and sponges (DSCS). Related management decisions require information about where DSCS occur and in what densities. Species distribution modeling (SDM) provides a cost-effective means of identifying potential DSCS habitat over large areas to inform these management decisions and data collection. Here we describe good practices for DSCS SDM, especially in the context of data collection and management applications. Managers typically need information regarding DSCS encounter probabilities, densities, and sizes, defined at sub-regional to basin-wide scales and validated using subsequent, targeted data collections. To realistically achieve these goals, analysts should integrate available data sources in SDMs including fine-scale visual sampling and broad-scale resource surveys (e.g., fisheries trawl surveys), include environmental predictor variables representing multiple spatial scales, model residual spatial autocorrelation, and quantify prediction uncertainty. When possible, models fitted to presence-absence and density data are preferred over models fitted only to presence data, which are difficult to validate and can confound estimated probability of occurrence or density with sampling effort. Ensembles of models can provide robust predictions, while multi-species models leverage information across taxa, and facilitate community inference. To facilitate the use of models by managers, predictions should be expressed in units that are widely understood and validated at an appropriate spatial scale using a sampling design that provides strong statistical inference. We present three case studies for the Pacific Ocean that illustrate good practices with respect to data collection, modeling, and validation; these case studies demonstrate it is possible to implement our good practices in real-world settings.

Spatio-temporal models are widely applied to standardise research survey data and are increasingly used to generate density maps and indices from other data sources. We developed a spatio-temporal modelling framework that integrates research survey data (treated as a "reference dataset") and other data sources ("non-reference datasets") while estimating spatially varying catchability for the non-reference datasets. We demonstrated it using two case studies. The first involved bottom trawl survey and observer data for spiny dogfish (Squalus acanthias) on the Chatham Rise, New Zealand. The second involved cod predators as samplers of juvenile snow crab (Chionoecetes opilio) abundance, integrated with industry-cooperative surveys and a bottom trawl research survey in the eastern Bering Sea. Our integrated models leveraged the strengths of individual data sources (the quality of the reference dataset and the quantity of non-reference data), while downweighting the influence of the non-reference datasets via the estimated spatially varying catchabilities. They allowed for the generation of annual density maps for a longer time-period and for the provision of one single index rather than multiple indices each covering a shorter time-period. [ABSTRACT FROM AUTHOR]

Estimating the growth of fishes is critical to understanding their life history and conducting fisheries assessments. It is imperative to sufficiently sample each size and age class of fishes to construct models that accurately reflect biological growth patterns, but this may be a challenging endeavor for highly-exploited species in which older fish are rare. Here, we use the Gulf Corvina (Cynoscion othonopterus), a vulnerable marine fish that has been persistently overfished for two decades, as a model species to compare the performance of several growth models. We fit the von Bertalanffy, Gompertz, logistic, Schnute, and Schnute–Richards growth models to length-at-age data by nonlinear least squares regression and used simple indicators to reveal biased data and ensure our results were biologically feasible. We then explored the consequences of selecting a biased growth model with a per-recruit model that estimated female spawning-stock-biomass-per-recruit and yield-per-recruit. Based on statistics alone, we found that the Schnute–Richards model described our data best. However, it was evident that our data were biased by a bimodal distribution of samples and underrepresentation of large, old individuals, and we found the Schnute–Richards model output to be biologically implausible. By simulating an equal distribution of samples across all age classes, we found that sample distribution distinctly influenced model output for all growth models tested. Consequently, we determined that the growth pattern of the Gulf Corvina was best described by the von Bertalanffy growth model, which was the most robust to biased data, comparable across studies, and statistically comparable to the Schnute–Richards model. Growth model selection had important consequences for assessment, as the per-recruit model employing the Schnute–Richards model fit to raw data predicted the stock to be in a much healthier state than per-recruit models employing other growth models. Our results serve as a reminder of the importance of complete sampling of all size and age classes when possible and transparent identification of biased data when complete sampling is not possible.

To be able to simulate spatial patterns of predator-prey interactions, many spatially-explicit ecosystem modeling platforms, including Atlantis, need to be provided with distribution maps defining the annual or seasonal spatial distributions of functional groups and life stages. We developed a methodology combining extrapolation and interpolation of the predictions made by statistical habitat models to produce distribution maps for the fish and invertebrates represented in the Atlantis model of the Gulf of Mexico (GOM) Large Marine Ecosystem (LME) (“Atlantis-GOM”). This methodology consists of: (1) compiling a large monitoring database, gathering all the fisheries-independent and fisheries-dependent data collected in the northern (U.S.) GOM since 2000; (2) compiling a large environmental database, storing all the environmental parameters known to influence the spatial distribution patterns of fish and invertebrates of the GOM; (3) fitting binomial generalized additive models (GAMs) to the large monitoring and environmental databases, and geostatistical binomial generalized linear mixed models (GLMMs) to the large monitoring database; and (4) employing GAM predictions to infer spatial distributions in the southern GOM, and GLMM predictions to infer spatial distributions in the U.S. GOM. Thus, our methodology allows for reasonable extrapolation in the southern GOM based on a large amount of monitoring and environmental data, and for interpolation in the U.S. GOM accurately reflecting the probability of encountering fish and invertebrates in that region. We used an iterative cross-validation procedure to validate GAMs. When a GAM did not pass the validation test, we employed a GAM for a related functional group/life stage to generate distribution maps for the southern GOM. In addition, no geostatistical GLMMs were fit for the functional groups and life stages whose depth, longitudinal and latitudinal ranges within the U.S. GOM are not entirely covered by the data from the large monitoring database; for those, only GAM predictions were employed to obtain distribution maps for Atlantis-GOM. Pearson residuals were computed to validate geostatistical binomial GLMMs. Ultimately, 53 annual maps and 64 seasonal maps (for 32 different functional groups/life stages) were produced for Atlantis-GOM. Our methodology could serve other world's regions characterized by a large surface area, particularly LMEs bordered by several countries.