Search

The East China Sea (ECS) Large Marine Ecosystem (LME) is a globally significant fishing ground, but the absence of cooperative regional management impedes the future sustainability of ECS fisheries. To navigate around nationally focused perspectives, we provide an up to date synthesis about the socio-economic importance and status of ECS fisheries at the ecosystem level, which is currently lacking in the literature. Our review indicates that ECS LME fisheries contribute around 6 million t in catch and USD 13 billion in landed value annually, and employ up to an estimated 1.4 million, the majority of who are small-scale fishers. However, the fisheries benefits are threatened by intense fishing pressure and rapid economic development which exacerbates the effects of overfishing. The future of ECS fisheries also faces climate uncertainties, which has already been associated with shifts in species distribution and spatial distribution of fishing effort. At the LME level, political disputes that inhibit crucial multilateral fisheries management threaten the future sustainability of ECS fish stocks, and also weaken the effects of national management measures which have largely failed to address fisheries overcapacity and coastal marine degradation. Continuing on a path focussed on national interests without considering LME wide dynamics risks jeopardising the significant fisheries socio-economic and ecological benefits that accrue to all LME countries. Thus, our review emphasises the urgency for multilateral ECS fisheries management to enhance ecosystem resilience so that fisheries resources can continue to support the region’s human, social, and economic well-being into the future.

Climate change is shifting the distribution of shared fish stocks between neighboring countries' Exclusive Economic Zones (EEZs) and the high seas. The timescale of these transboundary shifts determines how climate change will affect international fisheries governance. Here, we explore this timescale by coupling a large ensemble simulation of an Earth system model under a high emission climate change scenario to a dynamic population model. We show that by 2030, 23% of transboundary stocks will have shifted and 78% of the world's EEZs will have experienced at least one shifting stock. By the end of this century, projections show a total of 45% of stocks shifting globally and 81% of EEZs waters with at least one shifting stock. The magnitude of such shifts is reflected in changes in catch proportion between EEZs sharing a transboundary stock. By 2030, global EEZs are projected to experience an average change of 59% in catch proportion of transboundary stocks. Many countries that are highly dependent on fisheries for livelihood and food security emerge as hotspots for transboundary shifts. These hotspots are characterized by early shifts in the distribution of an important number of transboundary stocks. Existing international fisheries agreements need to be assessed for their capacity to address the social-ecological implications of climate-change-driven transboundary shifts. Some of these agreements will need to be adjusted to limit potential conflict between the parties of interest. Meanwhile, new agreements will need to be anticipatory and consider these concerns and their associated uncertainties to be resilient to global change.El cambio climático está afectando la distribución de las poblaciones de fauna marina compartidas por Zonas Económicas Exclusivas (ZEEs) de países vecinos y en el alta mar. Los efectos del cambio climático en el manejo pesquero internacional estarán determinados por la escala temporal de dichos desplazamientos transfronterizos. Para determinar esa escala temporal, el presente estudio combinó un modelo dinámico poblacional, con una serie de simulaciones de un modelo del sistema terrestre, bajo un escenario de cambio climático de altas emisiones. Los resultados siguieren que para 2030, el 23% de las poblaciones transfronterizas se habrán desplazado y en el 78% de las ZEEs del mundo habrán experimentado cambios en la distribución de al menos una población transfronteriza. Para fines de este siglo, las proyecciones muestran que el 81% de las ZEEs tendrán al menos una población en movimiento y 45% de las poblaciones transfronterizas globales habrán cambiado su distribución. La magnitud de tal desplazamiento se reflejará en un cambio promedio del 59% de la proporción de captura de poblaciones transfronterizas entre ZEEs vecinas para el 2030. Muchos países que dependen de la pesca para sustento económico y seguridad alimentaria emergen como zonas críticas de cambios transfronterizos. Estas zonas se caracterizan por cambios tempranos en la distribución de un número importante de poblaciones transfronterizas. Por lo tanto, los acuerdos pesqueros internacionales deben evaluarse por su capacidad para responder a los impactos socio-ecológicos del desplazamiento de poblaciones transfronterizas debido al cambio climático. Dichos acuerdos deberán de ser ajustados para limitar los posibles conflictos entre las partes de interés y evitar amenazar la sustentabilidad del recurso. Así mismo, los nuevos acuerdos que vayan a establecerse deberán considerar los posibles cambios en la distribución de poblaciones compartidas (y la incertidumbre asociada) para anticiparse a dichos conflictos y aumentar la resiliencia frente al cambio climático.Le changement climatique altère la distribution des stocks de poissons exploités posant de sérieux problèmes de juridiction et gestion des espèces partagées entre pays voisins, et/ou avec la haute mer. C’est en analysant l’échelle de temps de ces migrations transfrontalières que l’impact du changement climatique sur la gouvernance mondiale des pêches peut être évalué. Dans cette étude, nous explorons cette échelle de temps à l'aide d’un modèle de dynamique des populations marines exploitées couplé à des simulations dérivées d’un ensemble de modèles globaux océan-atmosphère. Les résultats montrent que d’ici 2030, pour le scénario à hautes émissions, 23% des stocks transfrontaliers auront changé de distribution et que 78% des zones économiques exclusives (ZEE) expérimenteront au moins une nouvelle espèce transfrontalière. A la fin du siècle, et pour ce même scénario, 81% des ZEE auront au moins une espèce transfrontalière et 45% des stocks transfrontaliers auront changé de distribution. La magnitude de tels changements de distribution est ici quantifiée par la variation dans la proportion de capture entre ZEE partageant ce stock transfrontalier. D’ici 2030, de tels changements entre ZEE seront de l’ordre de 59% à l'échelle globale, avec de nombreux pays dont la qualité de vie et la sécurité alimentaire dépendent de la pêche émergeant comme zones à haut risque. Ces zones se caractérisent par le déplacement précoce d’un grand nombre de stocks transfrontaliers. A la lumière de ces résultats, les traités et accords de pêche internationaux doivent être évalués pour leur capacité à répondre aux implications socio-écologiques du changement climatique et renégocier afin d’éviter tout conflit entre pays voisins. En anticipant des changements potentiels de distribution entre stocks transfrontaliers, tout nouvel accord de pêche se voudra plus résilient aux effets du changement climatique.As mudanças climáticas vêm promovendo alterações na distribuição dos estoques de peixes compartilhados por países vizinhos, tanto nas suas Zonas Econômicas Exclusivas (ZEE) como em águas oceânicas internacionais. A escala de tempo desse deslocamento transfronteiriço vai determinar como as mudanças climáticas afetarão o manejo pesqueiro internacional. Diante disso, o presente trabalho teve por objetivo analisar essa escala de tempo, combinando um amplo conjunto de simulações de um modelo do sistema terrestre sob um cenário de mudanças climáticas de altas emissões a um modelo de dinâmica populacional. Foi observado que, para 2030, 23% dos estoques transfronteiriços terão suas distribuições alteradas e 78% das ZEEs do mundo terão experimentado deslocamentos em pelo menos um estoque transfronteiriço. No final deste século, as projeções mostram que 45% dos estoques transfronteiriços do mundo sofrerão alterações e que 81% das ZEEs apresentarão alterações em pelo menos um estoque. A magnitude de tal deslocamento será refletida por uma mudança média de 59% na proporção de capturas de estoques transfronteiriços entre ZEEs vizinhas no ano de 2030. Muitos países que são altamente dependentes da pesca para subsistência e segurança alimentar surgem como pontos críticos para mudanças transfronteiriças. Estes são caracterizados por mudanças iniciais na distribuição de um número importante de estoques transfronteiriços. Os acordos internacionais de pesca precisam ser avaliados quanto à sua capacidade de abordar as implicações sócio-ecológicas de deslocamentos transfronteiriços impulsionados pelas mudanças climáticas e ajustados para limitar um possível conflito entre as partes de interesse. Da mesma forma, novos acordos devem considerar possíveis mudanças na distribuição de populações transfronteiriças a fim de antecipar tais conflitos e construir resiliência em face das mudanças climáticas e das incertezas que as acompanha.

Altres ajuts: Unidad de excelencia María de Maeztu CEX2019-000940-M Transformative governance is key to addressing the global environmental crisis. We explore how transformative governance of complex biodiversity-climate-society interactions can be achieved, drawing on the first joint report between the Intergovernmental Panel on Climate Change and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services to reflect on the current opportunities, barriers, and challenges for transformative governance. We identify principles for transformative governance under a biodiversity-climate-society nexus frame using four case studies: forest ecosystems, marine ecosystems, urban environments, and the Arctic. The principles are focused on creating conditions to build multifunctional interventions, integration, and innovation across scales; coalitions of support; equitable approaches; and positive social tipping dynamics. We posit that building on such transformative governance principles is not only possible but essential to effectively keep climate change within the desired 1.5 degrees Celsius global mean temperature increase, halt the ongoing accelerated decline of global biodiversity, and promote human well-being.

Ocean acidification (OA) affects marine organisms through various physiological and biological processes, yet our understanding of how these translate to large-scale population effects remains limited. Here, we integrated laboratory-based experimental results on the life history and physiological responses to OA of the American lobster, Homarus americanus, into a dynamic bioclimatic envelope model to project future climate change effects on species distribution, abundance, and fisheries catch potential. Ocean acidification effects on juvenile stages had the largest stage-specific impacts on the population, while cumulative effects across life stages significantly exerted the greatest impacts, albeit quite minimal. Reducing fishing pressure leads to overall increases in population abundance while setting minimum size limits also results in more higher-priced market-sized lobsters (> 1 lb), and could help mitigate the negative impacts of OA and concurrent stressors (warming, deoxygenation). However, the magnitude of increased effects of climate change overweighs any moderate population gains made by changes in fishing pressure and size limits, reinforcing that reducing greenhouse gas emissions is most pressing and that climate-adaptive fisheries management is necessary as a secondary role to ensure population resiliency. We suggest possible strategies to mitigate impacts by preserving important population demographics.

Ocean net primary production (NPP) results from CO2 fixation by marine phytoplankton, catalysing the transfer of organic matter and energy to marine ecosystems, supporting most marine food webs, and fisheries production as well as stimulating ocean carbon sequestration. Thus, alterations to ocean NPP in response to climate change, as quantified by Earth system model experiments conducted as part of the 5th and 6th Coupled Model Intercomparison Project (CMIP5 and CMIP6) efforts, are expected to alter key ecosystem services. Despite reductions in inter-model variability since CMIP5, the ocean components of CMIP6 models disagree roughly 2-fold in the magnitude and spatial distribution of NPP in the contemporary era, due to incomplete understanding and insufficient observational constraints. Projections of NPP change in absolute terms show large uncertainty in CMIP6, most notably in the North Atlantic and the Indo-Pacific regions, with the latter explaining over two-thirds of the total inter-model uncertainty. While the Indo-Pacific has previously been identified as a hotspot for climate impacts on biodiversity and fisheries, the increased inter-model variability of NPP projections further exacerbates the uncertainties of climate risks on ocean-dependent human communities. Drivers of uncertainty in NPP changes at regional scales integrate different physical and biogeochemical factors that require more targeted mechanistic assessment in future studies. Globally, inter-model uncertainty in the projected changes in NPP has increased since CMIP5, which amplifies the challenges associated with the management of associated ecosystem services. Notably, this increased regional uncertainty in the projected NPP change in CMIP6 has occurred despite reduced uncertainty in the regional rates of NPP for historical period. Improved constraints on the magnitude of ocean NPP and the mechanistic drivers of its spatial variability would improve confidence in future changes. It is unlikely that the CMIP6 model ensemble samples the complete uncertainty in NPP, with the inclusion of additional mechanistic realism likely to widen projections further in the future, especially at regional scales. This has important consequences for assessing ecosystem impacts. Ultimately, we need an integrated mechanistic framework that considers how NPP and marine ecosystems respond to impacts of not only climate change, but also the additional non-climate drivers.

16 pages, 6 figures, 3 tables, supplementary data https://doi.org/10.1016/j.pocean.2021.102659.-- Code and data availability: The experimental protocol in this paper has no code associated with it. Forcing data from CMIP5 used for the protocol, and the FishMIP model outputs presented in this paper are available on the ISIMIP servers (https://www.isimip.org/), Climate change is warming the ocean and impacting lower trophic level (LTL) organisms. Marine ecosystem models can provide estimates of how these changes will propagate to larger animals and impact societal services such as fisheries, but at present these estimates vary widely. A better understanding of what drives this inter-model variation will improve our ability to project fisheries and other ecosystem services into the future, while also helping to identify uncertainties in process understanding. Here, we explore the mechanisms that underlie the diversity of responses to changes in temperature and LTLs in eight global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP). Temperature and LTL impacts on total consumer biomass and ecosystem structure (defined as the relative change of small and large organism biomass) were isolated using a comparative experimental protocol. Total model biomass varied between −35% to +3% in response to warming, and -17% to +15% in response to LTL changes. There was little consensus about the spatial redistribution of biomass or changes in the balance between small and large organisms (ecosystem structure) in response to warming, an LTL impacts on total consumer biomass varied depending on the choice of LTL forcing terms. Overall, climate change impacts on consumer biomass and ecosystem structure are well approximated by the sum of temperature and LTL impacts, indicating an absence of nonlinear interaction between the models’ drivers. Our results highlight a lack of theoretical clarity about how to represent fundamental ecological mechanisms, most importantly how temperature impacts scale from individual to ecosystem level, and the need to better understand the two-way coupling between LTL organisms and consumers. We finish by identifying future research needs to strengthen global marine ecosystem modelling and improve projections of climate change impacts, JDE was funded by Australian Research Council Discovery Projects DP150102656 and DP190102293. MC, JS, NB and OM received financial support by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 817578 (Triatlas project). CH received funding from the Open Philanthropy Project. NB and OM also acknowledge the support of the French ANR project CIGOEF (grant ANR-17-CE32-0008-01). DPT acknowledges funding from the ISI-MIP project to support a workshop on this topic, and the Jarislowsky Foundation. EDG received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 682602, BIGSEA). RFH was funded by the Spanish Ministry of Science, Innovation and Universities through the Acciones de Programación Conjunta Internacional (PCIN-2017-115). MC acknowledges the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S) to the Institute of Marine Science (ICM-CSIC). TDE acknowledges funding from the ISIMIP project to support a workshop on this topic and the Fisheries and Oceans Canada Atlantic Fisheries Fund. All authors declare no conflict of interest with respect to this study. JAFS received funding from the European Union’s Horizon 2020 FutureMARES project (#869300).

Extreme temperature events have occurred in all ocean basins in the past two decades with detrimental impacts on marine biodiversity, ecosystem functions, and services. However, global impacts of temperature extremes on fish stocks, fisheries, and dependent people have not been quantified. Using an integrated climate-biodiversity-fisheries-economic impact model, we project that, on average, when an annual high temperature extreme occurs in an exclusive economic zone, 77% of exploited fishes and invertebrates therein will decrease in biomass while maximum catch potential will drop by 6%, adding to the decadal-scale mean impacts under climate change. The net negative impacts of high temperature extremes on fish stocks are projected to cause losses in fisheries revenues and livelihoods in most maritime countries, creating shocks to fisheries social-ecological systems particularly in climate-vulnerable areas. Our study highlights the need for rapid adaptation responses to extreme temperatures in addition to carbon mitigation to support sustainable ocean development.

Harmful fisheries subsidies contribute to overfishing leading to environmental and societal impacts1. If only fisheries within the subsidising nations’ jurisdiction were affected, then unilateral actions might be sufficient to help safeguard our ocean and the people reliant upon it. However, just as fish move between jurisdictions2, so too do the subsidised fishing fleets targeting them3. As such, the impacts and solutions to subsidies-induced overfishing are matters of international concern. Mapping that impact is therefore key to understanding these concerns and informing multilateral reform. Here we combine existing datasets4–6 to quantify the amount of harmful fisheries subsidies impacting the high seas, domestic and foreign waters, respectively. We estimate that between 24% and 43% of all harmful fisheries subsidies impact foreign waters or the high seas. We show that harmful subsidies primarily originate from countries with high-Human Development Index (HDI), strong fisheries management capacity and relatively sustainable fish stocks, yet disproportionately impact countries with low-HDI, lower management capacity and more vulnerable stocks. Indeed, over 40% of the harmful subsidies impacting low-HDI countries originate from high-HDI countries. This discrepancy between the source of harmful subsidies and the nations that are ultimately impacted is unsustainable and unjust. Policy-makers from all nations must push for effective multilateral subsidies reform. Prohibiting subsidies to distant-water fishing should be prioritised to support equitable and sustainable fisheries worldwide.

Previous studies have focused on changes in the geographical distribution of terrestrial biomes and species targeted by marine capture fisheries due to climate change impacts. Given mariculture's substantial contribution to global seafood production and its growing significance in recent decades, it is essential to evaluate the effects of climate change on mariculture and their socio‐economic consequences. Here, we projected climate change impacts on the marine aquaculture diversity for 85 of the currently most commonly farmed fish and invertebrate species in the world's coastal and/or open ocean areas. Results of ensemble projections from three Earth system models and three species distribution models show that climate change may lead to a substantial redistribution of mariculture species richness potential, with an average of 10%–40% decline in the number of species being potentially suitable to be farmed in tropical to subtropical regions. In contrast, mariculture species richness potential is projected to increase by about 40% at higher latitudes under the ‘no mitigation policy’ scenario (RCP 8.5) by the mid‐21st century. In Exclusive Economic Zones where mariculture is currently undertaken, we projected an average future decline of 1.3% and 5% in mariculture species richness potential under RCP 2.6 (‘strong mitigation’) and RCP 8.5 scenarios, respectively, by the 2050s relative to the 2000s. Our findings highlight the opportunities and challenges for climate adaptation in the mariculture sector through the redistribution of farmed species and expansion of mariculture locations. Our results can help inform adaptation planning and governance mechanisms to minimize local environmental impacts and potential conflicts with other marine and coastal sectors in the future., We analysed the impacts of climate change on global mariculture species richness for 85 of the currently most commonly farmed species by the 2050s relative to the 2005s. Results of ensemble projections show that climate change may lead to a substantial redistribution of mariculture species richness potential, with an average of 10%–40% decline in the number of species being potentially suitable to be farmed in tropical to subtropical regions. In contrast, potential mariculture species diversity is projected to increase by about 40% at higher latitudes under the ‘no mitigation policy’ scenario (RCP 8.5) scenario by the mid‐21st century.

International audience; Sea water temperature affects all biological and ecological processes that ultimately impact ecosystem functioning. In this study, we examine the influence of temperature on global biomass transfers from marine secondary production to fish stocks. By combining fisheries catches in all coastal ocean areas and life history traits of exploited marine species, we provide global estimates of two trophic transfer parameters which determine biomass flows in coastal marine food web: the trophic transfer efficiency and the biomass residence time in the food web. We find that biomass transfers in tropical ecosystems are less efficient and faster than in areas with cooler waters. In contrast, biomass transfers through the food web became faster and more efficient between 1950 and 2010. Using simulated changes in sea water temperature from three Earth system models, we project that the mean trophic transfer efficiency in coastal waters would decrease from 7.7% to 7.2% between 2010 and 2,100 under the ‘no effective mitigation’ Representative Concentration Pathway (RCP 8.5), while biomass residence time between trophic level 2 and 4 is projected to decrease from 2.7 to 2.3 year on average. Beyond the global trends, we show that the trophic transfer efficiencies and biomass residence times may vary substantially among ecosystem types and that the polar ecosystems may be the most impacted ecosystems. The detected and projected changes in mean trophic transfer efficiency and biomass residence time will undermine food web functioning. Our study provides quantitative understanding of temperature effects on trophodynamic of marine ecosystems under climate change.

Diffusion on social networks refers to the phenomena that opinions are spread via connected nodes. Given a set of observed cascades, the underlying diffusion process can be inferred for social network analysis. Earlier studies for modeling the diffusion process often assume that the activation of a node depends independently on the activations of its neighbors (or called parent nodes). Nevertheless, the activation of a node also depends on the connectivity of its neighbors. For instance, the opinions from the neighbors of the same closely connected social group are often similar, and thus those neighbors exhibit similar influence. Some recent studies incorporate the structural dependency of neighbors as connected components, which allow more accurate diffusion models to be inferred. However, the effectiveness of such component-based models often depends on how the components are identified. Existing methods are not designed to directly preserve the local connectivity of neighbors. In this paper, we propose to incorporate network embedding to enhance the performance of component-based diffusion models in social networks. In particular, we embed nodes in a social network in a latent vector space with local connectivity of the nodes preserved. Parent component identification then becomes a clustering task in the embedding space. A united probabilistic framework is proposed so that the parent components and the component-based diffusion models can be inferred simultaneously using a two-level EM algorithm based on observed information cascades. For performance evaluation, we apply the proposed model to both synthetic and real world data sets with promising results obtained. The empirical results also show how the use of the embedding-based framework can enhance both the component identification and the diffusion model.

Ocean conditions can affect human health in a variety of ways that are often overlooked and unappreciated. Oceans adjacent to Canada are affected by many anthropogenic stressors, with implications for human health and well-being. Climate change further escalates these pressures and can expose coastal populations to unique health hazards and distressing conditions. However, current research efforts, education or training curriculums, and policies in Canada critically lack explicit consideration of these ocean–public health linkages. The objective of this paper is to present multiple disciplinary perspectives from academics and health practitioners to inform the development of future directions for research, capacity development, and policy and practice at the interface of oceans and human health in Canada. We synthesize major ocean and human health linkages in Canada, and identify climate-sensitive drivers of change, drawing attention to unique considerations in Canada. To support effective, sustained, and equitable collaborations at the nexus of oceans and human health, we recommend the need for progress in three critical areas: ( i) holistic worldviews and perspectives, ( ii) capacity development, and ( iii) structural supports. Canada can play a key role in supporting the global community in addressing the health challenges of climate and ocean changes.

Climate change poses a challenge to the management of marine ecosystems and fisheries. Estuarine ecosystems in particular are exposed to a broad range of environmental changes caused by the effects of climate change both on land and in the ocean, and such ecosystems have also had a long history of human disturbance from over-exploitation and habitat changes. In this study, we examine the effects of climate change and fishing on the Pearl River Estuary (PRE) ecosystem using Ecopath with Ecosim. Our results show that changes in net primary production and ocean warming are the dominant climatic factors impacting biomass and fisheries productivity in the PRE. Additionally, physiological changes of fishes and invertebrates that are induced by climate change were projected to be modified by trophic interactions. Overall, our study suggests that the combined effects of climate change and fishing will reduce the potential fisheries catches in the PRE. Reducing fishing efforts can reduce the impacts of climate change on selected functional groups; however, some prey fishes are expected to experience higher predation mortality and consequently decreases in biomass under low fishing intensity scenarios. Thus, our study highlights the non-linearity of the responses of estuarine ecosystems when climate change interacts with other human stressors. In this study, the whole-ecosystem model (Ecopath with Ecosim) is used to examine the effects of climate change and fishing on a highly developed estuarine ecosystem (Pearl River Estuary, PRE) in the subtropical western Pacific. The oceans variables are extracted from the global earth system model (GFDL ESM2M), including changes in sea surface temperature (SST), hydrogen ion concentration (pH), dissolved oxygen (DO) concentration and net primary production (NPP) under the two scenarios RCP2.6 and RCP8.5. We developed a EwE model of the PRE ecosystem and simulated the effects of changing ocean conditions under alternative climate change scenarios as well as three fishing scenarios on the biodiversity and fisheries productivity of the PRE.

Non-negative Tensor Factorization (NTF) has been shown effective to discover clinically relevant and interpretable phenotypes from Electronic Health Records (EHR). Existing NTF based computational phenotyping models aggregate data over the observation window, resulting in the learned phenotypes being mixtures of disease states appearing at different times. We argue that by separating the clinical events happening at different times in the input tensor, the temporal dynamics and the disease progression within the observation window could be modeled and the learned phenotypes will correspond to more specific disease states. Yet how to construct the tensor for data samples with different temporal lengths and properly capture the temporal relationship specific to each individual data sample remains an open challenge. In this paper, we propose a novel Collective Non-negative Tensor Factorization (CNTF) model where each patient is represented by a temporal tensor, and all of the temporal tensors are factorized collectively with the phenotype definitions being shared across all patients. The proposed CNTF model is also flexible to incorporate non-temporal data modality and RNN-based temporal regularization. We validate the proposed model using MIMIC-III dataset, and the empirical results show that the learned phenotypes are clinically interpretable. Moreover, the proposed CNTF model outperforms the state-of-the-art computational phenotyping models for the mortality prediction task.

Quantization has been widely adopted for large-scale multimedia retrieval due to its effectiveness of coding highdimensional data. Deep quantization models have been demonstrated to achieve the state-of-the-art retrieval accuracy. However, training the deep models given a large-scale database is highly time-consuming as a large amount of parameters are involved. Existing deep quantization methods often sample only a subset from the database for training, which may end up with unsatisfactory retrieval performance as a large portion of label information is discarded. To alleviate this problem, we propose a novel model called Similarity Preserving Deep Asymmetric Quantization (SPDAQ) which can directly learn the compact binary codes and quantization codebooks for all the items in the database efficiently. To do that, SPDAQ makes use of an image subset as well as the label information of all the database items so the image subset items and the database items are mapped to two different but correlated distributions, where the label similarity can be well preserved. An efficient optimization algorithm is proposed for the learning. Extensive experiments conducted on four widely-used benchmark datasets demonstrate the superiority of our proposed SPDAQ model.

Global warming is already affecting the oceans through changes in water temperature, acidification, oxygen content and sea level rise, amongst many others. These changes are having multiple effects on marine species worldwide, with subsequent impacts on marine fisheries, peoples' livelihoods and food security. This work presents a review of the recent literature on the current and projected impacts of climate change on Canada's Pacific marine ecosystem. We find that there is an increasing number of studies in British Columbia focusing on changes in ocean conditions and marine species responses under climate change, including an emerging literature on the socio-economic impacts of these changes considered to be a knowledge gap. According to the literature, it is well established that ocean temperatures are increasing over the long-term, especially, in southern areas of British Columbia. Warming trends are increasing in the spring and are strongest in summer. However, there are important uncertainties regarding other climate drivers, such as oxygen concentration and acidification, stemming mainly from the insufficiency of data. Pacific salmon, elasmobranchs, invertebrates and rockfishes are amongst the most vulnerable species groups to climate change in British Columbia. Also, shifts in stock distribution and fish abundance under climate change may have a significant impact on fish supply affecting the livelihoods and food security of some British Columbians. The magnitude of these impacts is likely to vary according to a latitudinal gradient, with southern coastal areas being more affected than northern and central areas; challenging multiple areas of governance, such as equity and fishing access amongst First Nations; and institutional arrangements for transboundary stocks between the U.S. and Canada.

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely applied to reconstruct the cell-type-specific gene regulatory networks by exploring the dynamic changes of gene expression between transcription factors (TFs) and their target genes. The existing algorithms were commonly designed to analyze bulk gene expression data and could not deal with the dropouts and cell heterogeneity in scRNA-seq data. In this paper, we developed dynDeepDRIM that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. dynDeepDRIM integrated the primary image, neighbor images with time-course into a four-dimensional tensor and trained a convolutional neural network to predict the direct regulatory interactions between TFs and genes. We evaluated the performance of dynDeepDRIM on five time-course gene expression datasets. dynDeepDRIM outperformed the state-of-the-art methods for predicting TF-gene direct interactions and gene functions. We also observed gene functions could be better performed if more neighbor images were involved.

Climate change is shifting the distribution of shared fish stocks between neighboring countries’ Exclusive Economic Zones (EEZ) and the high seas. The timescale of these transboundary shifts determines how climate change will affect international fisheries governance. Coupling a large ensemble simulation of an Earth system model to a species distribution model, we show that by 2030, 23% of transboundary stocks would have shifted and 78% of the world EEZs will experience at least one shifting stock under a high emission climate change scenario. By the end of this century, 81% of EEZ waters will see at least one shifting stock with a total of 45% of stocks shifting globally, under a high emissions scenario. Importantly, many countries that are highly dependent on fisheries for income, food and nutrition security, as well as livelihoods emerge as hotspots for transboundary shifts showing early, and sometimes past shifts. Existing fisheries agreements need to be assessed for their capacity in addressing transboundary shifts, and strengthened where necessary to limit conflict over these fish stocks while new agreements are urged to considere this problematic in order to be resilient to global change.

Single-cell RNA sequencing has enabled to capture the gene activities at single-cell resolution, thus allowing reconstruction of cell-type-specific gene regulatory networks (GRNs). The available algorithms for reconstructing GRNs are commonly designed for bulk RNA-seq data, and few of them are applicable to analyze scRNA-seq data by dealing with the dropout events and cellular heterogeneity. In this paper, we represent the joint gene expression distribution of a gene pair as an image and propose a novel supervised deep neural network called DeepDRIM which utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRN from scRNA-seq data. Due to the consideration of TF-gene pair’s neighborhood context, DeepDRIM can effectively eliminate the false positives caused by transitive gene–gene interactions. We compared DeepDRIM with nine GRN reconstruction algorithms designed for either bulk or single-cell RNA-seq data. It achieves evidently better performance for the scRNA-seq data collected from eight cell lines. The simulated data show that DeepDRIM is robust to the dropout rate, the cell number and the size of the training data. We further applied DeepDRIM to the scRNA-seq gene expression of B cells from the bronchoalveolar lavage fluid of the patients with mild and severe coronavirus disease 2019. We focused on the cell-type-specific GRN alteration and observed targets of TFs that were differentially expressed between the two statuses to be enriched in lysosome, apoptosis, response to decreased oxygen level and microtubule, which had been proved to be associated with coronavirus infection.

Elevated atmospheric carbon dioxide (CO2) is causing global ocean changes and drives changes in organism physiology, life-history traits, and population dynamics of natural marine resources. However, our knowledge of the mechanisms and consequences of ocean acidification (OA) – in combination with other climatic drivers (i.e., warming, deoxygenation) – on organisms and downstream effects on marine fisheries is limited. Here, we explored how the direct effects of multiple changes in ocean conditions on organism aerobic performance scales up to spatial impacts on fisheries catch of 210 commercially exploited marine invertebrates, known to be susceptible to OA. Under the highest CO2 trajectory, we show that global fisheries catch potential declines by as much as 12% by the year 2100 relative to present, of which 3.4% was attributed to OA. Moreover, OA effects are exacerbated in regions with greater changes in pH (e.g., West Arctic basin), but are reduced in tropical areas where the effects of ocean warming and deoxygenation are more pronounced (e.g., Indo-Pacific). Our results enhance our knowledge on multi-stressor effects on marine resources and how they can be scaled from physiology to population dynamics. Furthermore, it underscores variability of responses to OA and identifies vulnerable regions and species.

International audience; Climate change impacts on marine life in the world ocean are expected to accelerate over the 21st century, affecting the structure and functioning of food webs. We analyzed a key aspect of this issue, focusing on the impact of changes in biomass flow within marine food webs and the resulting effects on ecosystem biomass and production. We used a modeling framework based on a parsimonious quasi-physical representation of biomass flow through the food web, to explore the future of marine consumer biomass and production at the global scale over the 21st century. Biomass flow is determined by three climate-related factors: primary production entering the food web, trophic transfer efficiency describing losses in biomass transfers from one trophic level (TL) to the next, and flow kinetic measuring the speed of biomass transfers within the food web. Using climate projections of three earth system models, we calculated biomass and production at each TL on a 1 degrees latitude x1 degrees longitude grid of the global ocean under two greenhouse gas emission scenarios. We show that the alterations of the trophic functioning of marine ecosystems, mainly driven by faster and less efficient biomass transfers and decreasing primary production, would lead to a projected decline in total consumer biomass by 18.5% by 2090-2099 relative to 1986-2005 under the "no mitigation policy" scenario. The projected decrease in transfer efficiency is expected to amplify impacts at higher TLs, leading to a 21.3% decrease in abundance of predators and thus to a change in the overall trophic structure of marine ecosystems. Marine animal production is also projected to decline but to a lesser extent than biomass. Our study highlights that the temporal and spatial projected changes in biomass and production would imply direct repercussions on the future of world fisheries and beyond all services provided by Ocean.

Marine protected areas (MPAs) are key to averting continued loss of species and ecosystem services in our oceans, but concerns around economic trade-offs hamper progress. Here we provide optimized planning scenarios for global MPA networks that secure species habitat while minimizing impacts on fisheries revenues. We found that MPA coverage requirements differ vastly among nations, and that two-thirds of nations benefit economically from a collaborative approach. Immediate global protection of marine biodiversity habitat comes with losses of ~19% of total fisheries revenues, but international cooperation in concert with high seas protection improves economic losses for most countries, safeguards all species, and could save ~5B USD annually worldwide. Nations and fishery economies both share benefits from a coordinated approach to conserving marine biodiversity, with direct relevance to current international policies.

Arctic sea ice loss has direct consequences for predators. Climate-driven distribution shifts of native and invasive prey species may exacerbate these consequences. We assessed potential changes by modelling the prey base of a widely distributed Arctic predator (ringed seal; Pusa hispida) in a sentinel area for change (Hudson Bay) under high- and low-greenhouse gas emission scenarios from 1950 to 2100. All changes were relatively negligible under the low-emission scenario, but under the high-emission scenario, we projected a 50% decline in the abundance of the well-distributed, ice-adapted and energy-rich Arctic cod (Boreogadus saida) and an increase in the abundance of smaller temperate-associated fish in southern and coastal areas. Furthermore, our model predicted that all fish species declined in mean body size, but a 29% increase in total prey biomass. Declines in energy-rich prey and restrictions in their spatial range are likely to have cascading effects on Arctic predators.

Cancer is common among older Chinese American immigrants. Psychological distress may be associated with cancer pain, yet prior studies have not examined this relationship. We conducted a secondary analysis of 514 Chinese Americans with cancer-related pain. Patients completed validated questionnaires, including the Chinese Health Questionnaire-12 (CHQ-12). Analyses evaluated associations among sociodemographics, acculturation, psychological distress, and pain variables. Most patients had low acculturation and socioeconomic levels. Overall, 51.9% of patients reported moderate-severe psychological distress, 35.8% reported worst pain intensity ≥7/10 over the previous week and 41.2% had high pain-related distress. Higher CHQ-12 scores were associated with younger age (β = −0.13); lower educational level (β = −0.12); birthplace in China (β = −0.18); lack of a caregiver (β = −0.10); higher worst pain intensity (β = 0.15); and higher pain-related distress (β = 0.28; all p

There are many sources of uncertainty in scenarios and models of socio-ecological systems, and understanding these uncertainties is critical in supporting informed decision-making about the management of natural resources. Here, we review uncertainty across the steps needed to create socio-ecological scenarios, from narrative storylines to the representation of human and biological processes in models and the estimation of scenario and model parameters. We find that socio-ecological scenarios and models would benefit from moving away from “stylized” approaches that do not consider a wide range of direct drivers and their dependency on indirect drivers. Indeed, a greater focus on the social phenomena is fundamental in understanding the functioning of nature on a human-dominated planet. There is no panacea for dealing with uncertainty, but several approaches to evaluating uncertainty are still not routinely applied in scenario modeling, and this is becoming increasingly unacceptable. However, it is important to avoid uncertainties becoming an excuse for inaction in decision-making when facing environmental challenges.

Sustainably managed wild fisheries support food and nutritional security, livelihoods, and cultures (1). Harmful fisheries subsidies—government payments that incentivize overcapacity and lead to overfishing—undermine these benefits yet are increasing globally (2). World Trade Organization (WTO) members have a unique opportunity at their ministerial meeting in November to reach an agreement that eliminates harmful subsidies (3). We—a group of scientists spanning 46 countries and 6 continents—urge the WTO to make this commitment...

Social network alignment aims at aligning person identities across social networks. Embedding based models have been shown effective for the alignment where the structural proximity preserving objective is typically adopted for the model training. With the observation that ``overly-close'' user embeddings are unavoidable for such models causing alignment inaccuracy, we propose a novel learning framework which tries to enforce the resulting embeddings to be more widely apart among the users via the introduction of carefully implanted pseudo anchors. We further proposed a meta-learning algorithm to guide the updating of the pseudo anchor embeddings during the learning process. The proposed intervention via the use of pseudo anchors and meta-learning allows the learning framework to be applicable to a wide spectrum of network alignment methods. We have incorporated the proposed learning framework into several state-of-the-art models. Our experimental results demonstrate its efficacy where the methods with the pseudo anchors implanted can outperform their counterparts without pseudo anchors by a fairly large margin, especially when there only exist very few labeled anchors.

22 pages, 5 figures, 1 table, supplementary information https://doi.org/10.1038/s41558-021-01173-9.-- Data availabilityAll standardized forcing variables from the ESMs are available at https://doi.org/10.48364/ISIMIP.575744.1; all outputs from the MEMs are available via ISIMIP (https://www.isimip.org/gettingstarted/data-access/).-- Code availabilityAll code used to analyse simulations is available at https://github.com/Fish-MIP/CMIP5vsCMIP6, Projections of climate change impacts on marine ecosystems have revealed long-term declines in global marine animal biomass and unevenly distributed impacts on fisheries. Here we apply an enhanced suite of global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP), forced by new-generation Earth system model outputs from Phase 6 of the Coupled Model Intercomparison Project (CMIP6), to provide insights into how projected climate change will affect future ocean ecosystems. Compared with the previous generation CMIP5-forced Fish-MIP ensemble, the new ensemble ecosystem simulations show a greater decline in mean global ocean animal biomass under both strong-mitigation and high-emissions scenarios due to elevated warming, despite greater uncertainty in net primary production in the high-emissions scenario. Regional shifts in the direction of biomass changes highlight the continued and urgent need to reduce uncertainty in the projected responses of marine ecosystems to climate change to help support adaptation planning, This work was supported by the Jarislowsky Foundation (D.P.T.), the Natural Sciences and Engineering Research Council of Canada Discovery Grant programme (D.P.T., H.K.L., T.D.E., W.W.L.C., J.P.-A. and V.C.); Australian Research Council (ARC) Discovery Projects DP170104240 (J.L.B. and C.N.), DP190102293 (J.L.B., C.N., A.J.R., J.D.E. and D.P.T.) and DP150102656 (J.D.E.); the European Union’s Horizon 2020 research and innovation programme under grant agreements 817578 (TRIATLAS) (M.C., J.S., L.S., O.M., L.B., Y.-J.S., N.B. and J.R.), 869300 (FutureMARES) (J.A.F.-S.,Y.-J.S. and M.C.) and 862428 (MISSION ATLANTIC (J.A.F.-S, Y.-J.S. and M.C.); the Spanish National Project ProOceans (PID2020-118097RB-I00) (M.C. and J.S.); the Open Philanthropy Project (C.S.H.); the United Kingdom Research and Innovation (UKRI) Global Challenges Research Fund (GCRF) One Ocean Hub (NE/S008950/1) (K.O.-C. and L.S.); the Simons Foundation (nos. 54993, 645921) (G.L.B.); the Belmont Forum and BiodivERsA under the BiodivScen ERA-Net COFUND programme (SOMBEE project, ANR-18-EBI4-0003-01) (Y.-J.S. and N.B.); the MEOPAR Postdoctoral Fellowship Award 2020–2021 and the Ocean Frontier Institute (Module G) (A.B.-B.); the French ANR project CIGOEF (grant ANR-17-CE32-0008-01) (O.M., L.B. and J.R.); the California Ocean Protection Council Grant C0100400, the Alfred P. Sloan Foundation and the Extreme Science and Engineering Discovery Environment (XSEDE) allocation TG-OCE170017 (D.B. and J.G.); the National Oceanographic and Atmospheric Association (NA20OAR4310441, NA20OAR4310442) (C.M.P.). M.C. acknowledges the Severo Ochoa Centre of Excellence accreditation (CEX2019-000928-S) to the Institute of Marine Science (ICM-CSIC)

One of the aims of the United Nations (UN) negotiations on the conservation and sustainable use of marine biodiversity in areas beyond national jurisdiction (ABNJ) is to develop a legal process for the establishment of area-based management tools, including marine protected areas, in ABNJ. Here we use a conservation planning algorithm to integrate 55 global data layers on ABNJ species diversity, habitat heterogeneity, benthic features, productivity, and fishing as a means for highlighting priority regions in ABNJ to be considered for spatial protection. We also include information on forecasted species distributions under climate change. We found that parameterizing the planning algorithm to protect at least 30% of these key ABNJ conservation features, while avoiding areas of high fishing effort, yielded a solution that highlights 52,545,634 km2 (23.7%) of ABNJ as high priority regions for protection. Instructing the planning model to avoid ABNJ areas with high fishing effort resulted in relatively minor shifts in the planning solution, when compared to a separate model that did not consider fishing effort. Integrating information on climate change had a similarly minor influence on the planning solution, suggesting that climate-informed ABNJ protected areas may be able to protect biodiversity now and in the future. This globally standardized, data-driven process for identifying priority ABNJ regions for protection serves as a valuable complement to other expert-driven processes underway to highlight ecologically or biologically significant ABNJ regions. Both the outputs and methods exhibited in this analysis can additively inform UN decision-making concerning establishment of ABNJ protected areas.

We apply the concept of threat points in game theory to explore the stability of current joint management arrangements for shared transboundary fish stocks between Canada and the United States. We use three examples to explore the effects of projected impacts of climate change on the productivity and distribution of these stocks between the exclusive economic zones of the two countries. The three stocks that we study are: Atlantic cod (Gadus morhua) and yellowtail flounder (Limanda ferruginea) within the Gulf of Maine, and Pacific halibut (Hippoglossus stenolepis) in the Pacific Coast. We define a threat point as the payoffs that the fisheries in Canada and the United States take home given the current management agreement between the two countries. This is an application of John Nash's threat point, defined as the minimum payoffs that each player in a game theoretic model must receive for the solution to a cooperative game to be stable, which is usually the outcome of a noncooperative game. First, we compute the threat points, that is, the current profits that Canada and the United States derive from the three shared stocks, respectively. Next, we build an ensemble of climate-marine ecosystem and economic models and use them to determine how climate change is likely to change current profits received by each country relative to the shifts in their threat points. We find that in some cases the profits obtained by fisheries in Canada and the United States would change under climate change both in absolute and relative terms resulting in relative changes in threat points. These relative changes in threat points serve as the basis for our discussion of the stability of current transboundary management agreements between Canada and the United States for these important shared stocks in the face of climate change.

Removing large fish from the ocean limits blue carbon sequestration through the sinking of their carcasses., Contrary to most terrestrial organisms, which release their carbon into the atmosphere after death, carcasses of large marine fish sink and sequester carbon in the deep ocean. Yet, fisheries have extracted a massive amount of this “blue carbon,” contributing to additional atmospheric CO2 emissions. Here, we used historical catches and fuel consumption to show that ocean fisheries have released a minimum of 0.73 billion metric tons of CO2 (GtCO2) in the atmosphere since 1950. Globally, 43.5% of the blue carbon extracted by fisheries in the high seas comes from areas that would be economically unprofitable without subsidies. Limiting blue carbon extraction by fisheries, particularly on unprofitable areas, would reduce CO2 emissions by burning less fuel and reactivating a natural carbon pump through the rebuilding of fish stocks and the increase of carcasses deadfall.

Climate-driven trends in ocean temperature and primary productivity are projected to differ greatly across the globe, triggering variable levels of concern for marine biota and ecosystems. Quantifying these changes, and the complex ways in which resource-dependent communities will need to respond, is inherently difficult. Existing uncertainty about the structure, function and responses of marine ecosystems, means that a multi-model or ensemble model approach is the most prudent means of assessing the potential ecosystem responses to climate change. In this study, climate-ecological projections of 13 marine ecosystem models for regions around Australia were evaluated. Model types included dynamic food web, spatial whole of ecosystem, intermediate complexity, species distribution, and size spectrum models and were all forced by high-resolution ocean model data. Each Australian region and fishery will face its own challenges in terms of ecosystem shifts and fisheries management responses over the next 30 years. Across regions, demersal systems appear to be more strongly affected by climate change than pelagic systems, with invertebrate species in shallow waters likely to respond first and to a larger degree. With the assistance of qualitative confidence evaluations, the multi-model approach was useful for identifying the likely state of concern for each functional group and thus adaptive management and research priorities. Largest model discrepancies were found between the regional ecosystem models that represent trophic interactions and the species distribution models, with implications for future assessments and adaption planning. Study results highlight that fisheries and their management will need to foster pro-active and flexible adaptation options to make the most of coming opportunities and to minimize risks or negative outcomes.

Properly analyzing health insurance claims data could lead to significant business insights and benefits for health service providers and insurance companies. Yet, health insurance data is often high dimensional and contains complex interleave sequences of claims. Instead of conducting machine learning tasks directly on the raw data, a better approach is performing the tasks on high-quality embeddings of the raw data. Driven by the real business need of Solution Segic Inc., a Canadian technology company in the group insurance industry, we extract health insurance claims embeddings with neural networks in the context of anomaly detection. We propose and thoroughly examine six embedding components that are customized based on different possible assumptions made on the data. One of our proposed embedding components, EC-ReStepRec, significantly outperforms other candidates on two anomaly detection tasks. This is the first embedding study done on health insurance claims for anomaly detection.

Small- and intermediate-size pelagic fisheries are highly impacted by environmental variability and climate change. Their wide geographical distribution and high mobility makes them more likely to shift their distribution under climate change. Here, we explore the potential impact of different climate change scenarios on the four main commercial pelagic species in the North-East Atlantic (NEA): Atlantic mackerel (Scomber scombrus), European sprat (Sprattus sprattus), Atlantic herring (Clupea harengus) and blue whiting (Micromesistius poutassou). We used a process-based fisheries model (SS-DBEM), where all the target species were exploited at their maximum sustainable yield (MSY), to project future potential catches under a high- and low-future-greenhouse-gas scenario (RCP 2.6 and 8.5, respectively). Two ocean biogeochemical models (GDFL and MEDUSA) were used to force the environmental conditions. Mackerel and sprat are projected to have increases in a potential catch under both scenarios. Herring and blue whiting are projected to increase under the RCP2.6, but future projections under RCP8.5 show mixed responses with decreases or no changes forecasted. Overall, the potential catch is projected to increase in the northern area of the NEA but is projected to decrease in the southern area. These projected changes are mainly driven by changes in temperature and primary production. Shifts in the distribution of pelagic resources may destabilize existing international agreements on sharing of straddling resources as exemplified by the dispute in sharing of quota for Atlantic mackerel. Novel climate-ready policy approaches considering full species distribution are needed to complement current stock-based approaches.

Binary data with one-class missing values are ubiquitous in real-world applications. They can be represented by irregular tensors with varying sizes in one dimension, where value one means presence of a feature while zero means unknown (i.e., either presence or absence of a feature). Learning accurate low-rank approximations from such binary irregular tensors is a challenging task. However, none of the existing models developed for factorizing irregular tensors take the missing values into account, and they assume Gaussian distributions, resulting in a distribution mismatch when applied to binary data. In this paper, we propose Logistic PARAFAC2 (LogPar) by modeling the binary irregular tensor with Bernoulli distribution parameterized by an underlying real-valued tensor. Then we approximate the underlying tensor with a positive-unlabeled learning loss function to account for the missing values. We also incorporate uniqueness and temporal smoothness regularization to enhance the interpretability. Extensive experiments using large-scale real-world datasets show that LogPar outperforms all baselines in both irregular tensor completion and downstream predictive tasks. For the irregular tensor completion, LogPar achieves up to 26% relative improvement compared to the best baseline. Besides, LogPar obtains relative improvement of 13.2% for heart failure prediction and 14% for mortality prediction on average compared to the state-of-the-art PARAFAC2 models.

Humanity is on a deeply unsustainable trajectory. We are exceeding planetary boundaries and unlikely to meet many international sustainable development goals and global environmental targets. Until recently, there was no broadly accepted framework of interventions that could ignite the transformations needed to achieve these desired targets and goals. As a component of the IPBES Global Assessment, we conducted an iterative expert deliberation process with an extensive review of scenarios and pathways to sustainability, including the broader literature on indirect drivers, social change and sustainability transformation. We asked, what are the most important elements of pathways to sustainability? Applying a social–ecological systems lens, we identified eight priority points for intervention (leverage points) and five overarching strategic actions and priority interventions (levers), which appear to be key to societal transformation. The eight leverage points are: (1) Visions of a good life, (2) Total consumption and waste, (3) Latent values of responsibility, (4) Inequalities, (5) Justice and inclusion in conservation, (6) Externalities from trade and other telecouplings, (7) Responsible technology, innovation and investment, and (8) Education and knowledge generation and sharing. The five intertwined levers can be applied across the eight leverage points and more broadly. These include: (A) Incentives and capacity building, (B) Coordination across sectors and jurisdictions, (C) Pre-emptive action, (D) Adaptive decision-making and (E) Environmental law and implementation. The levers and leverage points are all non-substitutable, and each enables others, likely leading to synergistic benefits. Transformative change towards sustainable pathways requires more than a simple scaling-up of sustainability initiatives—it entails addressing these levers and leverage points to change the fabric of legal, political, economic and other social systems. These levers and leverage points build upon those approved within the Global Assessment's Summary for Policymakers, with the aim of enabling leaders in government, business, civil society and academia to spark transformative changes towards a more just and sustainable world. A free Plain Language Summary can be found within the Supporting Information of this article. Fil: Chan, Kai M. A.. University of British Columbia; Canadá Fil: Boyd, David R.. University of British Columbia; Canadá Fil: Gould, Rachelle. University of Vermont; Estados Unidos Fil: Jetzkowitz, Jens. Staatliches Museum fur Naturkunde Stuttgart; Alemania Fil: Liu, Jianguo. Michigan State University; Estados Unidos Fil: Muraca, Bárbara. University of Oregon; Estados Unidos Fil: Naidoo, Robin. University of British Columbia; Canadá Fil: Beck, Paige. University of British Columbia; Canadá Fil: Satterfield, Terre. University of British Columbia; Canadá Fil: Selomane, Odirilwe. Stellenbosch University; Sudáfrica Fil: Singh, Gerald G.. University of British Columbia; Canadá Fil: Sumaila, Rashid. University of British Columbia; Canadá Fil: Ngo, Hien T.. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; Alemania Fil: Boedhihartono, Agni Klintuni. University of British Columbia; Canadá Fil: Agard, John. The University Of The West Indies; Trinidad y Tobago Fil: de Aguiar, Ana Paula D.. Stockholms Universitet; Suecia Fil: Armenteras, Dolors. Universidad Nacional de Colombia; Colombia Fil: Balint, Lenke. BirdLife International; Reino Unido Fil: Barrington-Leigh, Christopher. Mcgill University; Canadá Fil: Cheung, William W. L.. University of British Columbia; Canadá Fil: Díaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina Fil: Driscoll, John. University of British Columbia; Canadá Fil: Esler, Karen. Stellenbosch University; Sudáfrica Fil: Eyster, Harold. University of British Columbia; Canadá Fil: Gregr, Edward J.. University of British Columbia; Canadá Fil: Hashimoto, Shizuka. The University Of Tokyo; Japón Fil: Hernández Pedraza, Gladys Cecilia. The World Economy Research Center; Cuba Fil: Hickler, Thomas. Goethe Universitat Frankfurt; Alemania Fil: Kok, Marcel. PBL Netherlands Environmental Assessment Agency; Países Bajos Fil: Lazarova, Tanya. PBL Netherlands Environmental Assessment Agency; Países Bajos Fil: Mohamed, Assem A. A.. Central Laboratory for Agricultural Climate; Egipto Fil: Murray-Hudson, Mike. University Of Botswana; Botsuana Fil: O'Farrell, Patrick. University of Cape Town; Sudáfrica Fil: Palomo, Ignacio. Basque Centre for Climate Change; España Fil: Saysel, Ali Kerem. Boğaziçi University; Turquía Fil: Seppelt, Ralf. Martin-universität Halle-wittenberg; Alemania Fil: Settele, Josef. German Centre for Integrative Biodiversity Research-iDiv; Alemania Fil: Strassburg, Bernardo. International Institute for Sustainability, Estrada Dona Castorina; Brasil Fil: Xue, Dayuan. Minzu University Of China; China Fil: Brondízio, Eduardo S.. Indiana University; Estados Unidos

arge‐scale and long‐term changes in fish abundance and distribution in response to climate change have been simulated using both statistical and process‐based models. However, national and regional fisheries management requires also shorter term projections on smaller spatial scales, and these need to be validated against fisheries data. A 26‐year time series of fish surveys with high spatial resolution in the North‐East Atlantic provides a unique opportunity to assess the ability of models to correctly simulate the changes in fish distribution and abundance that occurred in response to climate variability and change. We use a dynamic bioclimate envelope model forced by physical–biogeochemical output from eight ocean models to simulate changes in fish abundance and distribution at scales down to a spatial resolution of 0.5°. When comparing with these simulations with annual fish survey data, we found the largest differences at the 0.5° scale. Differences between fishery model runs driven by different biogeochemical models decrease dramatically when results are aggregated to larger scales (e.g. the whole North Sea), to total catches rather than individual species or when the ensemble mean instead of individual simulations are used. Recent improvements in the fidelity of biogeochemical models translate into lower error rates in the fisheries simulations. However, predictions based on different biogeochemical models are often more similar to each other than they are to the survey data, except for some pelagic species. We conclude that model results can be used to guide fisheries management at larger spatial scales, but more caution is needed at smaller scales.

The future of the global ocean economy is currently envisioned as advancing towards a 'blue economy'-socially equitable, environmentally sustainable and economically viable ocean industries

Earth System Models project that global climate change will reduce ocean net primary production (NPP), upper trophic level biota biomass and potential fisheries catches in the future, especially in the eastern equatorial Pacific. However, projections from Earth System Models are undermined by poorly constrained assumptions regarding the biological cycling of iron, which is the main limiting resource for NPP over large parts of the ocean. In this study, we show that the climate change trends in NPP and the biomass of upper trophic levels are strongly affected by modifying assumptions associated with phytoplankton iron uptake. Using a suite of model experiments, we find 21st century climate change impacts on regional NPP range from -12.3% to +2.4% under a high emissions climate change scenario. This wide range arises from variations in the efficiency of iron retention in the upper ocean in the eastern equatorial Pacific across different scenarios of biological iron uptake, which affect the strength of regional iron limitation. Those scenarios where nitrogen limitation replaced iron limitation showed the largest projected NPP declines, while those where iron limitation was more resilient displayed little future change. All model scenarios have similar skill in reproducing past inter-annual variations in regional ocean NPP, largely due to limited change in the historical period. Ultimately, projections of end of century upper trophic level biomass change are altered by 50%-80% across all plausible scenarios. Overall, we find that uncertainties in the biological iron cycle cascade through open ocean pelagic ecosystems, from plankton to fish, affecting their evolution under climate change. This highlights additional challenges to developing effective conservation and fisheries management policies under climate change.

Flatfishes (order Pleuonectiformes) are important to fisheries and contribute substantially to seafood production and people's livelihood. However, the sustainability of flatfish fisheries is being challenged by climate change, in addition to other non-climatic human stressors. There is an urgent need to expand our understanding of the vulnerability and risk of impacts of flatfishes and their fisheries to climate change, and identify possible options to moderate such impacts. In this paper, firstly, we explain the importance of ocean temperature and thermal characteristics of flatfishes in determining their biogeography. Secondly, we discuss the biological vulnerability of flatfishes in the world to climate change as indicated by quantitative indices estimated from a fuzzy logic algorithm. Thirdly, by presenting projections of future distribution and potential catches of exploited flatfishes from computer simulation models, we highlight specific regions and species that are expected to be most impacted by climate change. Finally, we discuss potential human interventions that could help reduce such impacts, including the potential for mariculture. This paper underscores the need for immediate actions to integrate climate change into flatfish conservation and fisheries management measures.

Indoor localization has attracted much attention these years and been applied into different fields and sites. This paper focuses on Wi-Fi based localization system in exhibition venues which have some unique features, for example, many heterogeneous mobile devices with different transmission characteristics. We propose a customized indoor localization system and an efficient positioning approach which can give fast and accurate location estimations. For tackling heterogeneous mobile device problem, we use Expectation-maximization (EM) algorithm to infer the parameters of the signal-strength-to-distance model so that each mobile device has its own signal strength degradation model . This is suitable for the exhibition scenario since it not only improves the localization accuracy for diverse mobile devices, but also reduces the offline data acquisition time as in the case of employing the fingerprinting approach. We have conducted experiments in our testbed as well as pilot tests at the Hong Kong Convention and Exhibition Centre (HKCEC). The experimental results show that our system has good localization performance and is robust to heterogeneous mobile devices.

Ocean acidification (OA) driven by anthropogenic CO 2 emissions affects marine ecosystems, fisheries and aquaculture. Assessing the impacts of OA using projection models facilitates the development of future scenarios and potential solutions. Here, we explored various ways to incorporate OA impacts into a multi-stressor dynamic bioclimatic envelope model to project biogeographic changes of ten commercially exploited invertebrate species. We examine three dimensions of uncertainties in modelling biophysical OA effects: model structure, parameterization, and scenario uncertainty. Our results show that projected OA impacts are most sensitive to the choice of structural relationship between OA and biological responses, followed by the choice of climate change emission scenarios and parameterizations of the size of OA effects. Species generally showed negative effects to OA but sensitivity to the various sources of uncertainty were not consistent across or within species. For example, some species showed higher sensitivity to structural uncertainty and very low sensitivity to parameter uncertainty, while others showed greatest sensitivity to parameter uncertainty. This variability is largely due to geographic variability and difference in life history traits used to parameterize model simulations. Our model highlights the variability across the sources of uncertainty and contributes to the development of integrating OA impacts in species distribution models. We further stress the importance of defining the limitations and assumptions, as well as exploring the range of uncertainties associated with modelling OA impacts.

Risk of impact of marine fishes to fishing and climate change (including ocean acidi- fication) depend on the species’ ecological and biological characteristics, as well as their exposure to over‐exploitation and climate hazards. These human‐induced haz- ards should be considered concurrently in conservation risk assessment. In this study, we aim to examine the combined contributions of climate change and fishing to the risk of impacts of exploited fishes, and the scope for climate‐risk reduction from fisheries management. We combine fuzzy logic expert system with species dis- tribution modeling to assess the extinction risks of climate and fishing impacts of 825 exploited marine fish species across the global ocean. We compare our calcu- lated risk index with extinction risk of marine species assessed by the International Union for Conservation of Nature (IUCN). Our results show that 60% (499 species) of the assessed species are projected to experience very high risk from both over- fishing and climate change under a “business‐as‐usual” scenario (RCP 8.5 with cur- rent status of fisheries) by 2050. The risk index is significantly and positively related to level of IUCN extinction risk (ordinal logistic regression, p20%) chance of having high extinction risk in the next few dec- ades (equivalent to the IUCN categories of vulnerable, endangered or critically endangered). Areas with more at‐risk species to climate change are in tropical and subtropical oceans, while those that are at risk to fishing are distributed more broadly, with higher concentration of at‐risk species in North Atlantic and South Pacific Ocean. The number of species with high extinction risk would decrease by 63% under the sustainable fisheries‐low emission scenario relative to the “business ‐as‐usual” scenario. This study highlights the substantial opportunities for climate ‐risk reduction through effective fisheries management.

Achieving the United Nations’ 17 Sustainable Development Goals (SDGs) results in many ecological, social, and economic consequences that are inter-related. Understanding relationships between sustainability goals and determining their interactions can help prioritize effective and efficient policy options. This paper presents a framework that integrates existing knowledge from literature and expert opinions to rapidly assess the relationships between one SDG goal and another. Specifically, given the important role of the oceans in the world's social-ecological systems, this study focuses on how SDG 14 (Life Below Water), and the targets within that goal, contributes to other SDG goals. This framework differentiates relationships based on compatibility (co-benefit, trade-off, neutral), the optional nature of achieving one goal in attaining another, and whether these relationships are context dependent. The results from applying this framework indicate that oceans SDG targets are related to all other SDG goals, with two ocean targets (of seven in total) most related across all other SDG goals. Firstly, the ocean SDG target to increase economic benefits to Small Island Developing States (SIDS) and least developed countries for sustainable marine uses has positive relationships across all SDGs. Secondly, the ocean SDG target to eliminate overfishing, illegal and destructive fishing practices is a necessary pre-condition for achieving the largest number of other SDG targets. This study highlights the importance of the oceans in achieving sustainable development. The rapid assessment framework can be applied to other SDGs to comprehensively map out the subset of targets that are also pivotal in achieving sustainable development.

The elderly living in smart homes can have their daily movement recorded and analyzed. As different elders can have their own living habits, a methodology that can automatically identify their daily activities and discover their daily routines will be useful for better elderly care and support. In this article, we focus on automatic detection of behavioral patterns from the trajectory data of an individual for activity identification as well as daily routine discovery. The underlying challenges lie in the need to consider longer-range dependency of the sensor triggering events and spatiotemporal variations of the behavioral patterns exhibited by humans. We propose to represent the trajectory data using a behavior-aware flow graph that is a probabilistic finite state automaton with its nodes and edges attributed with some local behavior-aware features. We identify the underlying subflows as the behavioral patterns using the kernel k -means algorithm. Given the identified activities, we propose a novel nominal matrix factorization method under a Bayesian framework with Lasso to extract highly interpretable daily routines. For empirical evaluation, the proposed methodology has been compared with a number of existing methods based on both synthetic and publicly available real smart home datasets with promising results obtained. We also discuss how the proposed unsupervised methodology can be used to support exploratory behavior analysis for elderly care.