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ERA-Interim data were obtained from the ECMWF data server (http://data.ecmwf.int/data). Pathfinder v5.2 data were provided by GHRSST and the US National Oceanographic Data Center. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com. The altimeter products were produced by Ssalto/Duacs and distributed by Aviso, with support from Cnes (http://www.aviso.oceanobs.com/duacs/). ICOADS data for this study have been retrieved from the Research Data Archive (RDA) which is maintained by the Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research (NCAR). Word Ocean Database data were obtained from http://www.nodc.noaa.gov/OC5/WOD/pr_wod.html. DINEOF source code is freely available at http://modb.oce.ulg.ac.be/mediawiki/index.php/DINEOF. G. Esnaola is supported by a research grant (Interaccion Atmosfera-Oceano en el Golfo de Bizkaia) from Fundacion Centros Tecnologicos Inaki Goenaga. Jon Saenz would like to thank the financial support from project CGL2008-03321 (Spanish National R+D+I Programme). He also thanks funding provided by the University of the Basque Country (UFI 11/55, PPH12/01 and GIU 11/01). The Basque Government (Department of Agriculture, Fisheries and Food) has funded the project VARIACIONES. The authors want to express their gratitude to A. Caballero from AZTI-Tecnalia for her help with the Altimetry data and to B. Le Cann from UBO-CNRS-IRD-IFREMER for his useful comments on an early draft of this document. This is contribution number 649 of the Marine Research Division of AZTI-Tecnalia. The combination of remotely sensed gappy Sea surface temperature (SST) images with the missing data filling DINEOF (data interpolating empirical orthogonal functions) technique, followed by a principal component analysis of the reconstructed data, has been used to identify the time evolution and the daily scale variability of the wintertime surface signal of the Iberian Poleward Current (IPC), or Navidad, during the 1981-2010 period. An exhaustive comparison with the existing bibliography, and the vertical temperature and salinity profiles related to its extremes over the Bay of Biscay area, show that the obtained time series accurately reflect the IPC-Navidad variability. Once a time series for the evolution of the SST signal of the current over the last decades is well established, this time series is used to propose a physical mechanism in relation to the variability of the IPC-Navidad, involving both atmospheric and oceanic variables. According to the proposed mechanism, an atmospheric circulation anomaly observed in both the 500 hPa and the surface levels generates atmospheric surface level pressure, wind-stress and heat-flux anomalies. In turn, those surface level atmospheric anomalies induce mutually coherent SST and sea level anomalies over the North Atlantic area, and locally, in the Bay of Biscay area. These anomalies, both locally over the Bay of Biscay area and over the North Atlantic, are in agreement with several mechanisms that have separately been related to the variability of the IPC-Navidad, i.e. the south-westerly winds, the joint effect of baroclinicity and relief (JEBAR) effect, the topographic beta effect and a weakened North Atlantic gyre.

Funding for this research was provided by the European Research Council under the European Union’s Horizon 2020 research and innovation program [ERC Starting Grant for project CLOCK, grant no. 679812 ]. Diego Salgueiro Otero was supported by Xunta de Galicia , Consellería de Educación e Ordenación Universitaria ; Xochitl Elías Ilosvay has been supported by the University of Vigo predoc Fellowship ; Juan Bueno-Pardo has been funded by European Union’s Horizon 2020 project FutureMARES (grant no. 869300); and Elena Ojea thanks the Axencia Galega de INnovacin (GAIN) from Xunta de Galicia for the Oportunius fellowship and Consellería de Educación for financial support. Funding for open access has been provided by Universidade de Vigo/CISUG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the paper. Funding for this research was provided by the European Research Council under the European Union's Horizon 2020 research and innovation program [ERC Starting Grant for project CLOCK, grant no. 679812]. Diego Salgueiro Otero was supported by Xunta de Galicia, Consellería de Educación e Ordenación Universitaria; Xochitl Elías Ilosvay has been supported by the University of Vigo predoc Fellowship; Juan Bueno-Pardo has been funded by European Union's Horizon 2020 project FutureMARES (grant no. 869300); and Elena Ojea thanks the Axencia Galega de INnovacin (GAIN) from Xunta de Galicia for the Oportunius fellowship and Consellería de Educación for financial support. Funding for open access has been provided by Universidade de Vigo/CISUG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the paper. The current magnitude of ocean extreme events already exceeds the end-of-the-decade scenario estimates, and therefore incremental adaptation measures will render insufficient for seafood-dependent livelihoods. Nevertheless, transformational change is deemed promising, but uncertainties remain as to what activates such processes and how maladaptation outcomes can be avoided. While the science on extreme events is advancing fast, little is known about livelihood adaptation and transformation processes in the context of single or compound ocean extreme events. We identify a set of research priorities: 1) the identification of hotspot areas for coastal compound extreme events, 2) the development of bottom-up case study analysis of adaptation to extreme events, 3) the identification of constrainers and enablers to livelihood adaptation and transformation under abrupt change, and 4) directing research to contribute to climate-change policy. An effort addressing these key gaps will inform seafood-dependent livelihood adaptation policies for the 2030 Agenda and beyond. © 2023 The Authors

We assessed the effects of nutrient enrichment on three stream ecosystems running through distinct biomes (Mediterranean, Pampean and Andean). We increased the concentrations of N and P in the stream water 1.6–4-fold following a before–after control–impact paired series (BACIPS) design in each stream, and evaluated changes in the biomass of bacteria, primary producers, invertebrates and fish in the enriched (E) versus control (C) reaches after nutrient addition through a predictive-BACIPS approach. The treatment produced variable biomass responses (2–77% of explained variance) among biological communities and streams. The greatest biomass response was observed for algae in the Andean stream (77% of the variance), although fish also showed important biomass responses (about 9–48%). The strongest biomass response to enrichment (77% in all biological compartments) was found in the Andean stream. The magnitude and seasonality of biomass responses to enrichment were highly site specific, often depending on the basal nutrient concentration and on windows of ecological opportunity (periods when environmental constraints other than nutrients do not limit biomass growth). The Pampean stream, with high basal nutrient concentrations, showed a weak response to enrichment (except for invertebrates), whereas the greater responses of Andean stream communities were presumably favored by wider windows of ecological opportunity in comparison to those from the Mediterranean stream. Despite variation among sites, enrichment globally stimulated the algal-based food webs (algae and invertebrate grazers) but not the detritus-based food webs (bacteria and invertebrate shredders). This study shows that nutrient enrichment tends to globally enhance the biomass of stream biological assemblages, but that its magnitude and extent within the food web are complex and are strongly determined by environmental factors and ecosystem structure. This study was funded by the project GLOBRIO of the Banco Bilbao Vizcaya Argentaria (BBVA) Foundation. Additional funding was obtained from the projects SCARCE (Consolider Ingenio 2010, CSD2009-00065) and CARBONET (CGL2011-30474-C02-01) of the Spanish Ministry of Science and Innovation. 13 p. + 2 p. (Erratum)

Aim: Understanding the variation in community composition and species abundances (i.e., β-diversity) is at the heart of community ecology. A common approach to examine β-diversity is to evaluate directional variation in community composition by measuring the decay in the similarity among pairs of communities along spatial or environmental distance. We provide the first global synthesis of taxonomic and functional distance decay along spatial and environmental distance by analysing 148 datasets comprising different types of organisms and environments. Location: Global. Time period: 1990 to present. Major taxa studied: From diatoms to mammals. Method: We measured the strength of the decay using ranked Mantel tests (Mantel r) and the rate of distance decay as the slope of an exponential fit using generalized linear models. We used null models to test whether functional similarity decays faster or slower than expected given the taxonomic decay along the spatial and environmental distance. We also unveiled the factors driving the rate of decay across the datasets, including latitude, spatial extent, realm and organismal features. Results: Taxonomic distance decay was stronger than functional distance decay along both spatial and environmental distance. Functional distance decay was random given the taxonomic distance decay. The rate of taxonomic and functional spatial distance decay was fastest in the datasets from mid-latitudes. Overall, datasets covering larger spatial extents showed a lower rate of decay along spatial distance but a higher rate of decay along environmental distance. Marine ecosystems had the slowest rate of decay along environmental distances. Main conclusions: In general, taxonomic distance decay is a useful tool for biogeographical research because it reflects dispersal-related factors in addition to species responses to climatic and environmental variables. Moreover, functional distance decay might be a cost-effective option for investigating community changes in heterogeneous environments. Caio Graco-Roza was funded by the Coordination for the Improvement of Higher Education Personnel (CAPES), the Carlos Chagas Filho Research Support Foundation (FAPERJ) and the Ella and Georg Erhnrooth Foundation; Jan Altman by research grants INTER-EXCELLENCE LTAUSA19137 provided by Czech Ministry of Education, Youth and Sports, 20-05840Y of the Czech Science Foundation, and long-term research development project no. RVO 67985939 of the Czech Academy of Sciences; Otso Ovaskainen was funded by Academy of Finland (grant no. 309581), Jane and Aatos Erkko Foundation, Research Council of Norway through its Centres of Excellence Funding Scheme (223257), and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 856506; ERC-synergy project LIFEPLAN); and Jianjun Wang was funded by CAS Key Research Program of Frontier Sciences (QYZDB-SSW-DQC043) and National Natural Science Foundation of China (91851117). The "sPlot" project was initiated by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (DFG FZT 118), and is now a platform of iDiv. The study was supported by the TRY initiative on plant traits (). We are also grateful to Jens Kattge and TRY database. TRY is hosted, developed and maintained at the Max Planck Institute for Biogeochemistry (MPI-BGC) in Jena, Germany, in collaboration with the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. The CESTES database of metacommunities is also an initiative of iDiv led by Alienor Jeliazkov. We thank sDiv for supporting the open science initiative. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ) Ministry of Education, Youth & Sports - Czech Republic LTAUSA19137 Grant Agency of the Czech Republic 20-05840Y Czech Academy of Sciences RVO 67985939 Research Council of Norway through its Centres of Excellence Funding Scheme 223257 CAS Key Research Program of Frontier Sciences QYZDB-SSW-DQC043 National Natural Science Foundation of China (NSFC) 91851117 German Research Foundation (DFG) DFG FZT 118 European Research Council (ERC) 856506 Ella and Georg Erhnrooth Foundation Jane and Aatos Erkko Foundation TRY initiative on plant traits Academy of Finland 309581

An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep¿sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59% This work was developed within the framework of the Tecnoterra (ICM-CSIC/UPC) and the following project activities: ARIM (Autonomous Robotic Sea-Floor Infrastructure for Benthopelagic Monitoring; MarTERA ERA-Net Cofound) and RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades) With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI) Special issue Imaging Sensor Systems for Analyzing Subsea Environment and Life).-- 25 pages, 8 figures, 4 tables, 1 appendix