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This paper aims to highlight the risk of climate change on coupled marine human and natural systems and explore possible solutions to reduce such risk. Specifically, it explores some of the key responses of marine fish stocks and fisheries to climate change and their implications for human society. It highlights the importance of mitigating carbon emission and achieving the Paris Agreement in reducing climate risk on marine fish stocks and fisheries. Finally, it discusses potential opportunities for helping fisheries to reduce climate threats, through local adaptation. A research direction in fish biology and ecology is proposed that would help support the development of these potential solutions.

A first mass balance food web model was developed to parameterize and characterize the structure and functioning of the upwelling ecosystem in the Southern Taiwan Strait (STWS). The model representing the ecosystem state and trophic flows in 2001 includes 50 functional groups from primary producers to marine mammal and 6 fishing fleets. The basic outputs and trophic flows in the ecosystem are analyzed using ecological indicators. Results indicate that phytoplankton, zooplankton, benthos and shrimps are structuring groups while dolphins, top predatory fishes, seabirds, lizard fish, hairtail and mantis shrimps are the keystone groups within the ecosystem. Based on the maturity indicators, STWS is at a mature state. An analysis of control mechanisms indicates that the top-down effect was the primary ecosystem control mechanism, although bottom-up effects were also in play. In addition, fishing impacts were analyzed throughout the food web. Such modelling of the structure and functioning of ecosystem provides basic information for stakeholders and policy-makers to implement ecosystem-based management.

The increase in anthropogenic CO 2 emissions over the last century has modified oceanic conditions, affecting marine ecosystems and the goods and services that they provide to society. Pacific Island countries and territories are highly vulnerable to these changes because of their strong dependence on ocean resources, high level of exposure to climate effects, and low adaptive capacity. Projections of mid-to-late 21st century changes in sea surface temperature (SST), dissolved oxygen, pH, and net primary productivity (NPP) were synthesized across the tropical Western Pacific under strong climate mitigation and business-as-usual scenarios. These projections were used to model impacts on marine biodiversity and potential fisheries catches. Results were consistent across three climate models, indicating that SST will rise by ≥ 3 °C, surface dissolved oxygen will decline by ≥ 0.01 ml L −1 , pH will drop by ≥ 0.3, and NPP will decrease by 0.5 g m −2 d −1 across much of the region by 2100 under the business-as-usual scenario. These changes were associated with rates of local species extinction of > 50% in many regions as fishes and invertebrates decreased in abundance or migrated to regions with conditions more suitable to their bio-climate envelope. Maximum potential catch (MCP) was projected to decrease by > 50% across many areas, with the largest impacts in the western Pacific warm pool. Climate change scenarios that included strong mitigation resulted in substantial reductions of MCP losses, with the area where MCP losses exceeded 50% reduced from 74.4% of the region under business-as-usual to 36.0% of the region under the strong mitigation scenario.

Fish are rich in bioavailable micronutrients, such as zinc and iron, deficiencies of which are a global food security concern.1,2 Global marine fisheries yields are threatened by climate change and overfishing,3,4 yet understanding of how these stressors affect the nutrients available from fisheries is lacking.5,6 Here, using global assessments of micronutrient content2 and fisheries catch data,7 we investigate how the vulnerability status of marine fish species8,9 may translate into vulnerability of micronutrient availability at scales of both individual species and entire fishery assemblages for 157 countries. We further quantify the micronutrient evenness of catches to identify countries where interventions can optimize micronutrient supply. Our global analysis, including >800 marine fish species, reveals that, at a species level, micronutrient availability and vulnerability to both climate change and overfishing varies greatly, with tropical species displaying a positive co-tolerance, indicating greater persistence to both stressors at a community level.10 Global fisheries catches had relatively low nutritional vulnerability to fishing. Catches with higher species richness tend to be nutrient dense and evenly distributed but are more vulnerable to climate change, with 40% of countries displaying high vulnerability. Countries with high prevalence of inadequate micronutrient intake tend to have the most nutrient-dense catches, but these same fisheries are highly vulnerable to climate change, with relatively lower capacity to adapt.11 Our analysis highlights the need to consolidate fisheries, climate, and food policies to secure the sustainable contribution of fish-derived micronutrients to food and nutrition security.

We assess progress towards Aichi Biodiversity Target 6, which aims to achieve global fisheries sustainability by 2020. Current trends suggest that the proportion of fish stocks within safe ecological limits is likely to decline until 2020. While model projections show a considerable reduction in overexploited stocks by 2050 if climate change is not considered, there will be a substantial increase in the risk of overexploited fish stocks if climate change is taken into account. Overall, although there is progress toward rebuilding fisheries in some developed nations, this improvement is insufficient to meet the Aichi Target by 2020; there is a need for substantial changes to current fisheries policy and management if Target 6 is to be met.

Recent proliferation of ubiquitous smart phones has led to the emergence of a wide variety of apps. Selecting apps through keyword search or recommendations from friends or social networks (e.g., Facebook) may not match the real preferences of users, especially when the need is just-in-time and context specific. Although there are many collaborative filtering approaches that are capable of generating time-aware recommendations, most of them work on modeling of the time stamps (the time that events happen) rather than modeling of the sequential patterns (in cases that time stamps are not available) as well as investigating the factors behind those patterns. In this paper, we propose a mechanism for modeling three important factors governing the app installation of smart phone users: (1) short-term context, (2) co-installation pattern, and (3) random choice. Specifically, we use a hidden Markov model equipped with heterogeneous emission distributions to incorporate these factors. Apps being installed are probabilistically categorized into one of these factors, and app recommendations for users are carried out accordingly. This coherent model can be inferred effectively by using Gibbs sampling. The formulation has a significant advantage that the performance is less sensitive to data sparsity and incompleteness. Empirical results show that it has higher performance in recommending mobile apps to smart phone users, measured in terms of precision and area under the ROC curve (AUC). Besides, the proposed model allows the nature of the apps, with respect to the three factors, to be revealed as well as the extent to which each user is affected by the three factors to be inferred, providing additional insights on the users’ behavior.

Marine species are being impacted by climate change and ocean acidification, although their level of vulnerability varies due to differences in species' sensitivity, adaptive capacity and exposure to climate hazards. Due to limited data on the biological and ecological attributes of many marine species, as well as inherent uncertainties in the assessment process, climate change vulnerability assessments in the marine environment frequently focus on a limited number of taxa or geographic ranges. As climate change is already impacting marine biodiversity and fisheries, there is an urgent need to expand vulnerability assessment to cover a large number of species and areas. Here, we develop a modelling approach to synthesize data on species-specific estimates of exposure, and ecological and biological traits to undertake an assessment of vulnerability (sensitivity and adaptive capacity) and risk of impacts (combining exposure to hazards and vulnerability) of climate change (including ocean acidification) for global marine fishes and invertebrates. We use a fuzzy logic approach to accommodate the variability in data availability and uncertainties associated with inferring vulnerability levels from climate projections and species' traits. Applying the approach to estimate the relative vulnerability and risk of impacts of climate change in 1074 exploited marine species globally, we estimated their index of vulnerability and risk of impacts to be on average 52 ± 19 SD and 66 ± 11 SD, scaling from 1 to 100, with 100 being the most vulnerable and highest risk, respectively, under the 'business-as-usual' greenhouse gas emission scenario (Representative Concentration Pathway 8.5). We identified 157 species to be highly vulnerable while 294 species are identified as being at high risk of impacts. Species that are most vulnerable tend to be large-bodied endemic species. This study suggests that the fuzzy logic framework can help estimate climate vulnerabilities and risks of exploited marine species using publicly and readily available information.

International audience; Fisheries and aquaculture make a crucial contribution to global food security, nutrition and livelihoods. However, the UN Sustainable Development Goals separate marine and terrestrial food production sectors and ecosystems. To sustainably meet increasing global demands for fish, the interlinkages among goals within and across fisheries, aquaculture and agriculture sectors must be recognized and addressed along with their changing nature. Here, we assess and highlight development challenges for fisheries-dependent countries based on analyses of interactions and trade-offs between goals focusing on food, biodiversity and climate change. We demonstrate that some countries are likely to face double jeopardies in both fisheries and agriculture sectors under climate change. The strategies to mitigate these risks will be context-dependent, and will need to directly address the trade-offs among Sustainable Development Goals, such as halting biodiversity loss and reducing poverty. Countries with low adaptive capacity but increasing demand for food require greater support and capacity building to transition towards reconciling trade-offs. Necessary actions are context-dependent and include effective governance, improved management and conservation, maximizing societal and environmental benefits from trade, increased equitability of distribution and innovation in food production, including continued development of low input and low impact aquaculture.

One of the main expected responses of marine fishes to ocean warming is decrease in body size, as supported by evidence from empirical data and theoretical modeling. The theoretical underpinning for fish shrinking is that the oxygen supply to large fish size cannot be met by their gills, whose surface area cannot keep up with the oxygen demand by their three-dimensional bodies. However, Lefevre et al. (Global Change Biology, 2017, 23, 3449–3459) argue against such theory. Here, we re-assert, with the Gill-Oxygen Limitation Theory (GOLT), that gills, which must retain the properties of open surfaces because their growth, even while hyperallometric, cannot keep up with the demand of growing three-dimensional bodies. Also, we show that a wide range of biological features of fish and other water-breathing organisms can be understood when gill area limitation is used as an explanation. We also note that an alternative to GOLT, offering a more parsimonious explanation for these features of water-breathers has not been proposed. Available empirical evidence corroborates predictions of decrease in body sizes under ocean warming based on GOLT, with the magnitude of the predicted change increases when using more species-specific parameter values of metabolic scaling.

The world's oceans are highly impacted by climate change and other human pressures, with significant implications for marine ecosystems and the livelihoods that they support. Adaptation for both natural and human systems is increasingly important as a coping strategy due to the rate and scale of ongoing and potential future change. Here, we conduct a review of literature concerning specific case studies of adaptation in marine systems, and discuss associated characteristics and influencing factors, including drivers, strategy, timeline, costs, and limitations. We found ample evidence in the literature that shows that marine species are adapting to climate change through shifting distributions and timing of biological events, while evidence for adaptation through evolutionary processes is limited. For human systems, existing studies focus on frameworks and principles of adaptation planning, but examples of implemented adaptation actions and evaluation of outcomes are scarce. These findings highlight potentially useful strategies given specific social-ecological contexts, as well as key barriers and specific information gaps requiring further research and actions. This article is protected by copyright. All rights reserved.

This article presents the first bottom-up analysis of the proportion of global marine fisheries subsidies to small-scale fisheries (SSF). Using existing data, the reported national subsidy amounts are split into the fraction that goes to small- and large-scale fishing sectors. Results reveal a major imbalance in subsidy distribution, with SSF receiving only about 16% of the total global fisheries subsidy amount of $35 billion in 2009. To bring this into perspective, a person engaged in large-scale fishing received around 4 times the amount of subsidies received by their SSF counterparts. Furthermore, almost 90% of capacity-enhancing subsidies, which are known to exacerbate overfishing go to large-scale fisheries, thus increasing the unfair competitive advantage that large-scale fisheries already have. The developmental, economic and social consequences of this inequity are huge and impair the economic viability of the already vulnerable small-scale fishing sector. Conclusions indicate that taxpayers' money should be used to support sustainable fishing practices and in turn ocean conservation, and not to foster the degradation of marine ecosystems, often a result of capacity-enhancing subsidies. Reducing capacity-enhancing subsidies will have minimal negative effects on SSF communities since they receive very little of these subsidies to begin with. Instead, it will help correct the existing inequality, enhance SSF economic viability, and promote global fisheries sustainability.

In Bangladesh, export-oriented shrimp farming is one of the most important sectors of the national economy. However, shrimp farming in coastal Bangladesh has devastating effects on mangrove forests. Mangroves are the most carbon-rich forests in the tropics, and blue carbon (i.e., carbon in coastal and marine ecosystems) emissions from mangrove deforestation due to shrimp cultivation are accumulating. These anthropogenic carbon emissions are the dominant cause of climate change, which in turn affect shrimp cultivation. Some adaptation strategies including Integrated Multi-Trophic Aquaculture (IMTA), mangrove restoration, and Reducing Emissions from Deforestation and forest Degradation (REDD+) could help to reduce blue carbon emissions. Translocation of shrimp culture from mangroves to open-water IMTA and restoration of habitats could reduce blue carbon emissions, which in turn would increase blue carbon sequestration. Mangrove restoration by the REDD+ program also has the potential to conserve mangroves for resilience to climate change. However, institutional support is needed to implement the proposed adaptation strategies.

Diffusion on social networks refers to the process where opinions are spread via the connected nodes. Given a set of observed information cascades, one can infer the underlying diffusion process for social network analysis. The independent cascade model (IC model) is a widely adopted diffusion model where a node is assumed to be activated independently by any one of its neighbors. In reality, how a node will be activated also depends on how its neighbors are connected and activated. For instance, the opinions from the neighbors of the same social group are often similar and thus redundant. In this paper, we extend the IC model by considering that: 1) the information coming from the connected neighbors are similar and 2) the underlying redundancy can be modeled using a dynamic structural diversity measure of the neighbors. Our proposed model assumes each node to be activated independently by different communities (or components) of its parent nodes, each weighted by its effective size. An expectation maximization algorithm is derived to infer the model parameters. We compare the performance of the proposed model with the basic IC model and its variants using both synthetic data sets and a real-world data set containing news stories and Web blogs. Our empirical results show that incorporating the community structure of neighbors and the structural diversity measure into the diffusion model significantly improves the accuracy of the model, at the expense of only a reasonable increase in run-time.

Marine heatwaves (MHWs) have occurred in all ocean basins with severe negative impacts on coastal and ocean ecosystems. The northeast Pacific 2013–2015 MHW in particular received major societal concerns. Yet, our knowledge about how MHWs impact fish stocks is limited. Here, we combine outputs from a large ensemble simulation of an Earth system model with a fish impact model to simulate responses of major northeast Pacific fish stocks to MHWs. We show that MHWs cause biomass decrease and shifts in biogeography of fish stocks that are at least four times faster and bigger in magnitude than the effects of decadal-scale mean changes throughout the 21st century. With MHWs, we project a doubling of impact levels by 2050 amongst the most important fisheries species over previous assessments that focus only on long-term climate change. Our results underscore the additional challenges from MHWs for fisheries and their management under climate change.

WOS:000579835900001; International audience; The global ocean is commonly partitioned into 4 biomes subdivided into 56 biogeochemical provinces (BGCPs) following the accepted partition proposed by Longhurst in 1998. Each province corresponds to a unique regional environment that shapes biodiversity and constrains ecosystem structure and functions. BGCPs are dynamic entities that change their spatial extent and position with climate and are expected to be pertubated in the near future by global climate change. Here, we characterize the changes in spatial distribution of BGCPs from 1950 to 2100 using three earth system models under two representative concentration pathways (RCP 2.6 and 8.5). We project a reorganisation of current distribution of BGCPs driven mostly by a poleward shift of their distributions (18.4 km in average per decade). Projection of the future distribution of BGCPs also revealed the emergence of new climate that has no analogue with past and current environmental conditions experienced by BGCPs . This novel environmental conditions, here named No-Analogue BGCPs State (NABS), will expand from 2040 to 2100 at a rate of 4.3 Mkm2 per decade (1.2% of the global ocean). We subsequently quantified the potential amount of marine species and fisheries catch that would experience such novel environmental conditions to roughly evaluate NABS impact on ecosystem services.

Unidad de excelencia María de Maeztu CEX2019-000940-M Identificadors digitals: Digital object identifier for the 'European Research Council' (http://dx.doi.org/10.13039/501100000781) Digital object identifier for 'Horizon 2020' (http://dx.doi.org/10.13039/501100007601) - BIGSEA project The global wild capture fishery expanded rapidly over the 20th century as fishing technology improved, peaking in the 1990s as most fisheries transitioned to fully- or over-exploited status. Historical records for individual large marine ecosystems (LMEs) tend to echo this same progression, but with local variations in the timing and abruptness of catch peaks. Here, we provide objective descriptions of these catch peaks, which generally progressed from high- to low-latitude LMEs, and attribute the temporal progression to a combination of economic and ecological factors. We show that the ecological factors can be remarkably strong by using a spatially resolved, observationally-constrained, coupled macroecological-economic model to which we impose an idealized, globally homogeneous increase in catchability. The globally-uniform technology creep produces a spatial progression of fishing from high-to-low latitudes that is similar to observations, primarily due to the impact of temperature on ecosystem metabolism. In colder LMEs, low respiration rates allow the build-up of larger pristine standing stocks, so that high-latitude fisheries are profitable earlier, at lower levels of fishing technology. We suggest that these bioenergetic characteristics contributed significantly to the historical progression of this human-ecological system.

Ocean acidification is an emerging consequence of anthropogenic carbon dioxide emissions. The full extent of the biological impacts are currently not entirely defined. However, it is expected that invertebrate species that rely on the mineral calcium carbonate will be directly affected. Despite the limited understanding of the full extent of potential impacts and responses there is a need to identify potential pathways for human societies to be affected by ocean acidification. Research on these social implications is a small but developing field. This research contributes to this field by using an impact assessment framework, informed by a biophysical model of future species distributions, to investigate potential impacts facing Atlantic Canadian society from potential changes in shellfish fisheries driven by ocean acidification and climate change. New Brunswick and Nova Scotia are expected to see declines in resource accessibility but are relatively socially insulated from these changes. Conversely, Prince Edward Island, along with Newfoundland and Labrador are more socially vulnerable to potential losses in fisheries, but are expected to experience relatively minor net changes in access.

The health of the ocean, central to human well-being, has now reached a critical point. Most fish stocks are overexploited, climate change and increased dissolved carbon dioxide are changing ocean chemistry and disrupting species throughout food webs, and the fundamental capacity of the ocean to regulate the climate has been altered. However, key technical, organizational, and conceptual scientific barriers have prevented the identification of policy levers for sustainability and transformative action. Here, we recommend key strategies to address these challenges, including (1) stronger integration of sciences and (2) ocean-observing systems, (3) improved science-policy interfaces, (4) new partnerships supported by (5) a new ocean-climate finance system, and (6) improved ocean literacy and education to modify social norms and behaviors. Adopting these strategies could help establish ocean science as a key foundation of broader sustainability transformations.

Climate change is shifting the distribution of fish stocks that straddle between exclusive economic zones (EEZ), challenging transboundary fisheries management. Here, we examine the projected sharing of jointly managed transboundary fish stocks between Canada and the United States. We hypothesize that ocean warming will alter the sharing of fish stocks between the two countries, and that such changes will intensify under a high carbon emission scenario. We look at the specific cases of the International Pacific Halibut Commission that manages Pacific halibut (Hippoglossus stenolepis) and a resource sharing arrangement in the Gulf of Maine for cod (Gadus morhua), haddock (Melanogrammus aeglefinus), and yellowtail flounder (Limanda ferruginea) to discuss the management consequences of shifts in transboundary stocks. We rely on multiple Earth system models' simulations and species distribution models to estimate the change in catch potential and stock share ratio of each transboundary stock in the 21st century under two climate change scenarios. Results show that, even under a low emission scenario, most transboundary fish stocks sharing ratios, i.e., the proportion of the total catch of a fish stock taken by a given country, will change by 2050 relative to present. The overall reduction in catch potential, in addition to the changes in stock-share will further exacerbate trade-offs between changes in species catch potential. Such trade-offs in the Atlantic and Pacific regions will be amplified if a high emission scenario is followed, relative to a low carbon emission scenario. Based on the simulation results, we examine possible solution options to reduce climate risks on transboundary fish stocks and fisheries.

Learning representations of medical concepts from the Electronic Health Records (EHR) has been shown effective for predictive analytics in healthcare. Incorporation of medical ontologies has also been explored to further enhance the accuracy and to ensure better alignment with the known medical knowledge. Most of the existing work assumes that medical concepts under the same ontological category should share similar representations, which however does not always hold. In particular, the categorizations in medical ontologies were established with various factors being considered. Medical concepts even under the same ontological category may not follow similar occurrence patterns in the EHR data, leading to contradicting objectives for the representation learning. In this paper, we propose a deep learning model called MMORE which alleviates this conflicting objective issue by allowing multiple representations to be inferred for each ontological category via an attention mechanism. We apply MMORE to diagnosis prediction and our experimental results show that the representations obtained by MMORE can achieve better predictive accuracy and result in clinically meaningful sub-categorization of the existing ontological categories.

With the prevalence of smartphones, many new business opportunities are being spawned by exploiting the locations or routes of the users. We propose a probabilistic Wi-Fi localization approach to meet the special needs of the exhibition industry: tight schedules, large venues and dynamic environment. Unlike the popular fingerprinting approach which requires extensive training time and the need for retraining, our approach has a much lower start-up cost which means that it doesn't need the labor-intensive training phase while having comparable accuracy for localization. This is achieved by employing a real-time calibration of signal degradation model, which also makes our localization model be adaptive to the dynamic environment. We also propose practical optimization techniques which can dynamically control the computational cost during device localization and hence more devices can be tracked. Experiments with our testbed show that our proposed approach can achieve acceptable and stable localization results in different environment based on real-time calibration. From other tests of our system in real-world exhibition event held in a venue having of 4,000 square meters, over half of the localization errors are within 4.5 meters.

6 pages, 5 figures, supporting information https://doi.org/10.1073/pnas.1900194116.-- All data reported in this paper are archived and publicly available at http://dataservices.gfz-potsdam.de/pik/showshort.php?id=escidoc:2956913., While the physical dimensions of climate change are now routinely assessed through multimodel intercomparisons, projected impacts on the global ocean ecosystem generally rely on individual models with a specific set of assumptions. To address these single-model limitations, we present standardized ensemble projections from six global marine ecosystem models forced with two Earth system models and four emission scenarios with and without fishing. We derive average biomass trends and associated uncertainties across the marine food web. Without fishing, mean global animal biomass decreased by 5% (±4% SD) under low emissions and 17% (±11% SD) under high emissions by 2100, with an average 5% decline for every 1 °C of warming. Projected biomass declines were primarily driven by increasing temperature and decreasing primary production, and were more pronounced at higher trophic levels, a process known as trophic amplification. Fishing did not substantially alter the effects of climate change. Considerable regional variation featured strong biomass increases at high latitudes and decreases at middle to low latitudes, with good model agreement on the direction of change but variable magnitude. Uncertainties due to variations in marine ecosystem and Earth system models were similar. Ensemble projections performed well compared with empirical data, emphasizing the benefits of multimodel inference to project future outcomes. Our results indicate that global ocean animal biomass consistently declines with climate change, and that these impacts are amplified at higher trophic levels. Next steps for model development include dynamic scenarios of fishing, cumulative human impacts, and the effects of management measures on future ocean biomass trends, Financial support was provided by the German Federal Ministry of Education and Research through ISI-MIP (Grant01LS1201A1), the European Union’s Horizon 2020 Research and Innovation Program (Grant 678193), and the Ocean Frontier Institute (Module G). We acknowledge additional financial support as follows: to H.K.L., W.W.L.C., and B.W. from the Natural Sciences and Engineering Research Council (NSERC) of Canada; to D.P.T. from the Kanne Rasmussen Foundation Denmark; to A.B.-B. from the NSERC Transatlantic Ocean Science and Technology Program; to W.W.L.C. and T.D.E. from the Nippon Foundation-Nereus Program; to E.D.G., M.C. and J. Steenbeek from the European Union’s Horizon 2020 Re-search and Innovation Program (Grants 682602 and 689518); to E.A.F., J.L.B., andT.R. from Commonwealth Scientific and Industrial Research Organization and the Australian Research Council; to N.B., L.B., and O.M. from the French Agence Nationale de la Recherche and Pôle de Calcul et de Données pour la Mer; and to S.J. from the UK Department of Environment, Food and Rural Affairs

Missing data has been widely recognized as a key challenge of clinical time series analysis, which hinders the practical application of data-driven approaches to clinical data analytics [1], [2]. Various methods have been proposed to perform the time series imputation to alleviate this issue, yet most of them impose strong assumptions on the missing data, for instance, locality in Gaussian Process based models [3], lowrankness and temporal regularity in matrix/tensor factorization models [4], etc. More recently, researchers proposed to apply the Recurrent Neural Networks (RNNs) to tackle the missing data imputation problem for time series, where the RNNs try to capture and summarize the temporal dynamics using hidden state vectors [5]–[7].

This study synthesizes results from observations, laboratory experiments and models to showcase how the integration of scientific methods and indigenous knowledge can improve our understanding of (a) past and projected changes in environmental conditions and marine species; (b) their effects on social and ecological systems in the respective communities; and (c) support management and planning tools for climate change adaptation and mitigation. The study links climate-ecosystem-economic (CEE) models and discusses uncertainties within those tools. The example focuses on the key forage species in the Inuvialuit Settlement Region (Western Canadian Arctic), i.e., Arctic cod (Boreogadus saida). Arctic cod can be trophically linked to sea-ice algae and pelagic primary producers and are key vectors for energy transfers from plankton to higher trophic levels (e.g., ringed seals, beluga), which are harvested by Inuit peoples. Fundamental changes in ice and ocean conditions in the region affect the marine ecosystem and fish habitat. Model simulations suggest increasing trends in oceanic phytoplankton and sea-ice algae with high interannual variability. The latter might be linked to interannual variations in Arctic cod abundance and mask trends in observations. CEE simulations incorporating physiological temperature limits data for the distribution of Arctic cod, result in an estimated 17% decrease in Arctic cod populations by the end of the century (high emission scenario), but suggest increases in abundance for other Arctic and sub-Arctic species. The Arctic cod decrease is largely caused by increased temperatures and constraints in northward migration, and could directly impact key subsistence species. Responses to acidification are still highly uncertain, but sensitivity simulations suggests an additional 1% decrease in Arctic cod populations due to pH impacts on growth and survival. Uncertainties remain with respect to detailed future changes, but general results are likely correct and in line with results from other approaches. To reduce uncertainties, higher resolution models with improved parameterizations and better understanding of the species' physiological limits are required. Arctic communities should be directly involved, receive tools and training to conduct local, unified research and food chain monitoring while decisions regarding commercial fisheries will need to be precautionary and adaptive in light of the existing uncertainties.

With the fast development of social networks, there exists a huge amount of users information as well as their social ties. Such information generally contains sensitive and correlated users' personal data. How to accurately analyze this large and correlated social graph data while protecting users' privacy has become a challenging research issue. In the literature, various research efforts have been made on this topic. Unfortunately, correlation based privacy protection techniques for social graph data have seldom been investigated. To the best of our knowledge, this paper is the first attempt to resolve this research issue. Particularly, this paper first protects users' raw data via local differential privacy technique and then a correlation based privacy protection approach is designed. Last, a K-means algorithm is applied on the perturbed local data and the clustering results are used to generate the synthetic graphs for further data analytics. Experiments are evaluated on two real-world data sets, i.e. Facebook dataset and Enron dataset, and the promising experimental results demonstrate that the proposed approach is superior to the state-of-the-art LDPGen and the baseline method, e.g. the DGG, with respect to the accuracy and utility evaluation criteria.

Ocean warming and deoxygenation are affecting the physiological performance of marine species by increasing their oxygen demand while reducing oxygen supply. Impacts on organisms (e.g., growth and reproduction) can eventually affect entire populations, altering macroecological dynamics and shifting species’ distribution ranges. To quantify the effect of warming and deoxygenation on marine organisms, Penn et al. (2018) and Deutsch et al. (2020) developed two metabolic indices that integrate physiological, biogeographic and climatic data. Here, we develop an alternative index, referred to as Aerobic Growth Index (AGI) based on an approach that integrates the von Bertalanffy growth and metabolic theory. We compare the results derived from the application of AGI with those of the two previously published metabolic indices for six species: Atlantic blue crab (Callinectes sapidus), sharpsnout seabream (Diplodus puntazzo), Atlantic cod (Gadus morhua), Australian spiny lobster (Panulirus cygnus), red drum (Sciaenops ocellatus) and common cuttlefish (Sepia officinalis). The baseline (1971–2000) habitat suitability values of AGI are significantly and positively correlated with both metabolic indices (R2 ≥ 0.92). All three indices also show similar spatial patterns and magnitudes of viable habitat loss by the end of the 21st century (2071–2100) relative to baseline conditions under a high greenhouse gas trajectory (Representative Concentration Pathway 8.5). Our results support the applicability and use of AGI to better understand the impacts of warming and deoxygenation on global marine fishery resources. Given the uncertainties surrounding mechanisms linking temperature, oxygen and biogeography, there is a need for different indicators to account for these uncertainties in climate change projections.

In this Article, the affiliation of author Cristina Garilao was incorrectly given as ‘Evolutionary Biology and Ecology Laboratory, Albert Ludwigs University, Freiburg, Germany.’ (affiliation 8). It should be ‘GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany’ (affiliation 18). The original Article has been corrected online.

Oceanic ecosystem services support a range of human benefits, and Canada has extensive research networks producing growing data sets. We present a first effort to compile, link, and harmonize available information to provide new perspectives on the status of Canadian ocean ecosystems and corresponding research. The metadata database currently includes 1094 individual assessments and data sets from government (n = 716), nongovernment (n = 320), and academic sources (n = 58), comprising research on marine species, natural drivers and resources, human activities, ecosystem services, and governance, with data sets spanning 1979–2012 on average. Overall, research shows a strong prevalence towards single-species fishery studies, with an underrepresentation of economic and social aspects, and of the Arctic region in general. Nevertheless, the number of studies that are multispecies or ecosystem-based have increased since the 1960s. We present and discuss two illustrative case studies — marine protected area establishment in Canada and herring resource use by the Heiltsuk First Nation — highlighting the potential use of multidisciplinary data sets drawn from metadata records. Identifying knowledge gaps is key to achieving the comprehensive, accessible and interdisciplinary data sets and subsequent analyses necessary for new sustainability policies that meet both ecological and socioeconomic needs.

There is a critical need to develop effective strategies for the long-term sustainability of Canada’s oceans. However, this is challenged by uncertainty over future impacts of global environmental and socioeconomic change on marine ecosystems, and how coastal communities will respond to these changes. Scenario analysis can address this uncertainty by exploring alternative futures for Canadian oceans under different pathways of climate change, economic development, social and policy changes. However, there has, to date, been no scenario analysis of Canada’s future ocean sustainability at a national scale. To facilitate this process, we review whether the literature on existing scenarios of Canada’s fisheries and marine ecosystems provides an integrative, social-ecological perspective about potential future conditions. Overall, there is sufficient national-level oceanographic data and application of ecosystem, biophysical, and socioeconomic models to generate projections of future ocean and socioeconomic trends in Canada. However, we find that the majority of marine-related scenario analyses in Canada focus on climate scenarios and the associated oceanographic and ecological changes. There is a gap in the incorporation of social, economic, and governance drivers in scenarios, as well as a lack of scenarios which consider the economic and social impact of future change. Moreover, available marine scenario studies mostly do not cover all three Canadian oceans simultaneously. To address these gaps, we propose to develop national-level scenarios using a matrix framework following the concept of Shared Socioeconomic Pathways, which would allow a social-ecological examination of Canada’s oceans in terms of the state of future uncertainties.

The prevalence of smartphones and mobile social networks allow the users to share their location-based life experience much easier. The large amount of data generated in related location-based social networks provides informative cues on user's behaviors and preferences to support personalized location-based services, like point-of-interest (POI) recommendation. Yet achieving accurate personalized POI recommendation is challenging as the data available for each user is highly sparse. In addition, the computational complexity is high due to the large number of users. In this paper, a novel methodology for personalized successive POI recommendation is proposed. First, the preferred successive category of location is predicted using a third-rank tensor computed based on the partially observed transitions between the categories of user's successive locations where the missing transitions are uncovered by inferring the group preference. The group is achieved according to users' demographics and frequently visited locations. Then, a bipartite graph is constructed based on the recommended categories for each user. To obtain the personalized ranking of locations, a distance weighted HITS algorithm is proposed so that the location authority score is updated iteratively according to the visiting frequency of the group and some distance constraints. The proposed two-step approach with the category prediction incorporated aims to boost the location prediction performance via the smoothing and at the same time reduce the complexity. Experimental results obtained based on the real-world location-based social network data show that the proposed approach outperforms the existing state-of-the-art methods by a large margin.

Climate change affects ocean conditions, fish stocks and hence fisheries. In West Africa, climate change impacts on fisheries were projected to be mainly negative through multi-facet ways. However, analysis of adaptation responses of fishers to climate change impacts is scarce. This paper reviews the impacts on climate change on fisheries in West Africa and discusses the potential adaptation strategies adopted by both the artisanal and industrial fishing sectors. Overall, climate change and over-exploitation have altered species composition of fisheries catches in West Africa. The effect of ocean warming on fisheries is indicated by the increase in dominance of warmer water species in the landings, shown from an increase in Mean Temperature of Catch, in the region. Climate change induced changes in potential catch and species composition, which inherently have similar symptoms as over-exploitation, are expected to have repercussions on the economic and social performance of fisheries. Both artisanal and industrial sectors may adapt to these changes mainly through expansion of fishing ground that increases operation costs. Our results highlight that historical changes in target species are more common in industrial than artisanal fisheries. This result challenges the prevailing assumption that artisanal fisheries, given their limited movement capacity, would adapt to climate change by shifting target species and/or gear type.

Climate change is projected to redistribute fisheries resources, resulting in tropical regions suffering decreases in seafood production. While sustainably managing marine ecosystems contributes to building climate resilience, these solutions require transformation of ocean governance. Recent studies and international initiatives suggest that conserving high seas biodiversity and fish stocks will have ecological and economic benefits; however, implications for seafood security under climate change have not been examined. Here, we apply global-scale mechanistic species distribution models to 30 major straddling fish stocks to show that transforming high seas fisheries governance could increase resilience to climate change impacts. By closing the high seas to fishing or cooperatively managing its fisheries, we project that catches in exclusive economic zones (EEZs) would likely increase by around 10% by 2050 relative to 2000 under climate change (representative concentration pathway 4.5 and 8.5), compensating for the expected losses (around −6%) from ‘business-as-usual’. Specifically, high seas closure increases the resilience of fish stocks, as indicated by a mean species abundance index, by 30% in EEZs. We suggest that improving high seas fisheries governance would increase the resilience of coastal countries to climate change.

In 2005, the Sea Around Us described a website (www.seaaroundus.org) which presented, for all maritime countries and large marine ecosystems in the world, one of the most basic information items required by policy makers and fisheries managers: what catch was taken within their jurisdictional boundaries, and which countries took it. Surprisingly, for many countries this kind of jurisdictionally and/or ecologically assigned data had not been readily available before then. Since the release of these spatialized data, this material has had major influence on how fisheries are perceived by policy makers in various countries and by the global scientific community, as well as by a growing list of other stakeholders such as non-governmental environmental organizations and the general public. Here, the Sea Around Us updates the fisheries science, policy, conservation and management audience on the extensively modified spatial allocation method and a substantially improved new website. Also, this contribution points to and describes the much improved catch data underlying this website. These data now account for catches for all countries in the world by fisheries sectors (industrial, artisanal, subsistence, recreational), after augmenting the officially reported landings data through the inclusion of comprehensively reconstructed data of previously unreported catches and major discards, for every maritime country or territory in the world, and their Exclusive Economic Zone (EEZ). Also presented are the extensively improved spatial allocation procedures which assign global catch data to the 180,000 half degree spatial cells used by the Sea Around Us to subdivide the global ocean. The reconstructed data for 1950–2010 for all countries in the world and the High Seas, freely accessible and downloadable through the Sea Around Us web portal, will be updated regularly. It is hoped that these revised data and the substantially improved web utility will invigorate and assist the debate about the role of fisheries in a global framework as well as in national food security settings.

Discovering global knowledge from distributed data sources is challenging as there exist several practical concerns such as bandwidth limitation and data privacy. By appropriately abstracting distributed data, various global data mining tasks could still be implemented on the basis of local data abstractions. This article reviews existing techniques related to distributed data mining in abstraction-based data mining. It then discusses open research challenges on mining tasks performed on distributed and abstracted data, describes how global data models clustering and manifold discovery could be learnt based on local data models, and points out future research directions. WIREs Data Mining Knowl Discov 2016, 6:167-176. doi: 10.1002/widm.1182

Christopher Golden and colleagues calculate that declining numbers of marine fish will spell more malnutrition in many developing nations.

The global ocean is projected to be warmer, less oxygenated and more acidic in the 21st century relative to the present day, resulting in changes in the biogeography and productivity of marine organisms and ecosystems. Previous studies using a Dynamic Bioclimate Envelope Model (DBEM) projected increases in potential catch in high latitude regions and decreases in tropical regions over the next few decades. A major structural uncertainty of the projected redistribution of species and fisheries catches can be attributed to the habitat suitability algorithms used. Here, we compare the DBEM projections of potential catches of 500 species of exploited marine fishes and invertebrates from 1971 to 2060 using three versions of DBEM that differ by the algorithm used to predict relative habitat suitability: DBEM-Basic, DBEM-Maxent and DBEM-Aquamaps. All the DBEM models have similar skill in predicting the occurrence of exploited species and distribution of observed fisheries production. Globally, the models project a decrease in catch potential of 3% to 13% by 2050 under a high emissions scenario (Representative Concentration Pathway 8.5). For the majority of the modelled species, projections by DBEM-Maxent are less sensitive to changes in ocean properties than those by DBEM-Aquamaps. The mean magnitude of projected changes relative to differences between projections differ between regions, being highest (>1 times the standard deviation) in the tropical regions and Arctic Ocean and lowest in three of the main Eastern Boundary Upwelling regions, the eastern Indian Ocean and the Southern Ocean. These results suggest that the qualitative patterns of changes in catch potential reported in previous studies are not affected by the structural uncertainty of DBEM, particularly in areas where catch potential was projected to be most sensitive to climate change. However, when making projections of fish stocks and their potential catches using DBEM in the future, multiple versions of DBEM should be used to quantify the uncertainty associated with structural uncertainty of the models. Overall, this study contributes to improving projection of future changes in living marine resources by exploring one aspect of the cascade of uncertainty associated with such projections.

In recent decades, the relationships between species distributional shifts and climate change have been investigated at various geographic scales, yet there is still a gap in understanding the impacts of climate change on marine commercial fish species surrounding the Antarctic Peninsula. The dynamic bioclimate envelope model (DBEM) is a mechanistic model that encompass species distribution model and population dynamic model approaches to project the spatiotemporal change of marine commercial fish species driven by various climate change scenarios in the Southern Ocean. This paper focuses on the spatiotemporal changes of marine commercial fish species surrounding the Antarctic Peninsula under a high emissions scenario (RCP8.5) and a low emissions scenario (RCP2.6) from 1970 to 2060 following three different Earth System Models (ESMs), namely, the GFDL-ESM 2G, IPSL-CM5A-MR and MPI-ESM-MR. Results reveal that: i) The general latitudinal gradient patterns in species richness shifts poleward associated with a global abundance decrease ii) The Spp. richness in Eastern Antarctic Peninsula (EAP) is higher than in the Western Antarctic Peninsula (WAP) at the same latitude (>65°S latitude). iii) The reasons are that the krill-dependent predators in WAP could face a higher risk of depletion than that in EAP due to ocean warming and anthropogenic activities.

The Paris Agreement is good for fish, fishers, seafood workers, and consumers., The Paris Agreement aims to mitigate the potential impacts of climate change on ecological and social systems. Using an ensemble of climate-marine ecosystem and economic models, we explore the effects of implementing the Agreement on fish, fishers, and seafood consumers worldwide. We find that implementing the Agreement could protect millions of metric tons in annual worldwide catch of top revenue-generating fish species, as well as billions of dollars annually of fishers’ revenues, seafood workers’ income, and household seafood expenditure. Further, our analysis predicts that 75% of maritime countries would benefit from this protection, and that ~90% of this protected catch would occur within the territorial waters of developing countries. Thus, implementing the Paris Agreement could prove to be crucial for the future of the world’s ocean ecosystems and economies.

Variations in weather and climate create and interact with ocean fluctuations occurring over days to decades. In some cases these fluctuations are local. In others they stretch across ocean basins. Marine organisms respond to environmental changes in diverse and sometimes dramatic ways. Over the past century natural ocean fluctuations have been augmented by a variety of anthropogenic drivers. The ocean has absorbed vast amounts of carbon dioxide, excess heat arising from the accumulation of greenhouse gases, nutrients from fertilizers, and other pollutants. While this has moderated climate change and pollution impacts on terrestrial systems, it has had diverse consequences for the ocean. This chapter provides a brief overview of ocean changes of particular relevance for marine life, including ocean acidification, warming, melting ice, shifting ocean productivity baselines, deoxygenation, coastal development, and pollution. We highlight contributions from the Nereus Program, and attempt to provide a broad context for the more detailed discussion of select topics in other chapters in this section. Anthropogenic ocean changes pose a considerable challenge to sustaining marine resources. Continued advances in understanding and predicting ocean changes, such as those described herein, are essential for meeting this challenge.

Background Traditional food systems are under pressure from various stressors, including climate change which is projected to negatively alter the abundance of marine species harvested by coastal First Nations (FNs) in British Columbia (BC). Objective To model the potential impacts of the climate-related declines in seafood production on the nutritional status of coastal BC FNs. In addition, we projected potential changes in nutrient intakes, under different scenarios of substitution where traditional seafood is replaced with alternative non-traditional foods. Methods The study design is a mixed-method approach that combines two datasets: projected scenarios of climate-related change on seafood catch potential for coastal BC FNs and data derived from the cross-sectional First Nations Food, Nutrition, and Environment Study. The consumption of seafood was estimated using a food frequency questionnaire among 356 FNs. The contribution of seafood consumption to protein, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), vitamins (A, B12, D, niacin), and minerals (zinc, selenium and iron) requirements was assessed using Dietary Reference Intakes (DRIs). Results Traditional seafood consumption provided daily recommendations of EPA+DHA (74-184%) and vitamin B12 (84-152%) and substantial levels of niacin (28-55%), selenium (29-55%), vitamin D (15-30%) and protein (14-30%). Projected climate change was estimated to reduce the intakes of essential nutrients by 21% and 31% under 'strong mitigation' (Representative Concentration Pathway, RCP2.6) and 'business-as-usual' (RCP8.5) climate change scenarios, respectively, by the year 2050 relative to 2000. The hypothetical substitution of seafood with selected alternative non-traditional foods does not provide adequate amounts of nutrients. Conclusion Traditionally-harvested seafood remains fundamental to the contemporary diet and health of coastal BC FNs. Potential dietary shifts aggravated by climate-related declines in seafood consumption may have significant nutritional and health implications for BC FN. Strategies to improve access to seafood harvest potential in coastal communities are needed to ensure nutritional health and overall well-being and to promote food security and food sovereignty in coastal FNs.

While broadly applicable to many natural language processing (NLP) tasks, variational autoencoders (VAEs) are hard to train due to the posterior collapse issue where the latent variable fails to encode the input data effectively. Various approaches have been proposed to alleviate this problem to improve the capability of the VAE. In this paper, we propose to introduce a mutual information (MI) term between the input and its latent variable to regularize the objective of the VAE. Since estimating the MI in the high-dimensional space is intractable, we employ neural networks for the estimation of the MI and provide a training algorithm based on the convex duality approach. Our experimental results on three benchmark datasets demonstrate that the proposed model, compared to the state-of-the-art baselines, exhibits less posterior collapse and has comparable or better performance in language modeling and text generation. We also qualitatively evaluate the inferred latent space and show that the proposed model can generate more reasonable and diverse sentences via linear interpolation in the latent space.

Global changes to marine systems will largely be experienced by humans through changing fisheries and seafood supply. The chapters in this section of the book focus on four components that are important for projecting the future contributions and sustainability of fish stocks to food security, safety, and human health. These components are (1) fisheries management, (2) fisheries and food supply, (3) security and safety in seafood production, and (4) seafood and human well-being. We summarize the key messages of the chapters in this section and offer a synthesis of how these chapters contribute to projecting future scenarios for fisheries, seafood supply, and security under changing oceans. Finally, we identify key areas that will require additional information to inform the development of pathways for sustainable future oceans.

14 pages, 5 figures, 1 table, supporting information https://doi.org/10.1111/gcb.14512, Climate change effects on marine ecosystems include impacts on primary production, ocean temperature, species distributions, and abundance at local to global scales. These changes will significantly alter marine ecosystem structure and function with associated socio‐economic impacts on ecosystem services, marine fisheries, and fishery‐dependent societies. Yet how these changes may play out among ocean basins over the 21st century remains unclear, with most projections coming from single ecosystem models that do not adequately capture the range of model uncertainty. We address this by using six marine ecosystem models within the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish‐MIP) to analyze responses of marine animal biomass in all major ocean basins to contrasting climate change scenarios. Under a high emissions scenario (RCP8.5), total marine animal biomass declined by an ensemble mean of 15%–30% (±12%–17%) in the North and South Atlantic and Pacific, and the Indian Ocean by 2100, whereas polar ocean basins experienced a 20%–80% (±35%–200%) increase. Uncertainty and model disagreement were greatest in the Arctic and smallest in the South Pacific Ocean. Projected changes were reduced under a low (RCP2.6) emissions scenario. Under RCP2.6 and RCP8.5, biomass projections were highly correlated with changes in net primary production and negatively correlated with projected sea surface temperature increases across all ocean basins except the polar oceans. Ecosystem structure was projected to shift as animal biomass concentrated in different size‐classes across ocean basins and emissions scenarios. We highlight that climate change mitigation measures could moderate the impacts on marine animal biomass by reducing biomass declines in the Pacific, Atlantic, and Indian Ocean basins. The range of individual model projections emphasizes the importance of using an ensemble approach in assessing uncertainty of future change, Financial support was provided by the German Federal Ministry of Education and Research (BMBF, grant no. 01LS1201A1) and through the Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP). ABB acknowledges financial support from the NSERC CREATE Transatlantic Ocean Science and Technology Program (TOSST), HKL from the Natural Sciences and Engineering Research Council (NSERC) of Canada, and DPT from the Kann Rasmussen Foundation, Denmark

The ocean and its biodiversity provide ecosystem services that are important for the well-being of human societies. However, environmental impacts from human activities such as overfishing, climate change, and pollution are reducing the capacity of the ocean to support these services. We are now at the crossroads of deciding the relationship between the ocean and people. Understanding such relationships and “predicting” (or projecting) its future provide important knowledge to inform decisions and actions that shape ocean sustainability. This chapter explains the key theoretical and analytical frameworks developed by the Nippon Foundation Nereus Program that contributes to the goal of “Predicting the future ocean.” Particular focuses are put on four components: (1) characterizing the coupled human–natural marine system, (2) exploring the confidence and uncertainty in future ocean projections, (3) examining adaptation to the changing ocean, and (4) elucidating the linkages between the ocean and sustainable development. These frameworks synthesize the understanding of the functioning of marine systems and use models and scenarios to generate projections of the future ocean to inform policies and decision-making. It also helps understand the responses of human communities to the changing ocean and develop new perspectives of viewing the ocean in the context of human society.

Climate change is impacting marine ecosystems and their goods and services in diverse ways, which can directly hinder our ability to achieve the Sustainable Development Goals (SDGs), set out under the 2030 Agenda for Sustainable Development. Through expert elicitation and a literature review, we find that most climate change effects have a wide variety of negative consequences across marine ecosystem services, though most studies have highlighted impacts from warming and consequences of marine species. Climate change is expected to negatively influence marine ecosystem services through global stressors—such as ocean warming and acidification—but also by amplifying local and regional stressors such as freshwater runoff and pollution load. Experts indicated that all SDGs would be overwhelmingly negatively affected by these climate impacts on marine ecosystem services, with eliminating hunger being among the most directly negatively affected SDG. Despite these challenges, the SDGs aiming to transform our consumption and production practices and develop clean energy systems are found to be least affected by marine climate impacts. These findings represent a strategic point of entry for countries to achieve sustainable development, given that these two goals are relatively robust to climate impacts and that they are important pre‐requisite for other SDGs. Our results suggest that climate change impacts on marine ecosystems are set to make the SDGs a moving target travelling away from us. Effective and urgent action towards sustainable development, including mitigating and adapting to climate impacts on marine systems are important to achieve the SDGs, but the longer this action stalls the more distant these goals will become.