Innovative and practical tools for monitoring and assessing biodiversity status and impacts of multiple human pressures in marine systems

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Five Horizon European projects have joined forces to collaboratively increase our knowledge and skills to monitor and assess the ocean in an innovative way, assisting managers and policy-makers in taking decisions to maintain sustainable activities at sea. Here we present and discuss the status of some methods revised during a summer school, aiming at a better management of coasts and seas. We include novel methods to monitor the coastal and ocean waters (e.g. environmental DNA, drones, imaging and artificial intelligence, climate modelling and spatial planning) and innovative tools to assess the status (e.g. cumulative impacts assessment, multiple pressures, Nested Environmental status Assessment Tool (NEAT), ecosystem services assessment, or a new unifying approach). As a concluding remark, some of the most important challenges ahead is assessing pros and cons of novel methods, comparing them with benchmark technologies, and integrating these into long standing time series for data continuity. This requires transition periods and careful planning, which can be covered through an intense collaboration of current and future European projects on marine biodiversity and ecosystem health. cumulative effects environmental status eDNA drones artificial intelligence imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Human needs (e.g., food, energy, recreation) result in an increasing number of activities at sea, including fishing, aquaculture, shipping, infrastructure construction (e.g., coastal defence, renewable energies, oil and gas platforms, etc.), as well as on land-based activities (Dailianis et al., 2018). These human activities lead to pressures affecting various ecosystem components; this may result in the degradation of ocean health (Halpern et al., 2008a, 2008b; Reker et al., 2019; Korpinen et al., 2021) which in turn can result in a diminution in ecosystem services supply and, ultimately, affect human well-being and health (Borja et al., 2020). Despite efforts to minimize human pressure effects in marine ecosystems and their services (e.g. European Commission et al., 2022), these effects will most likely increase with increasing use of the ocean (Nash et al., 2020). Among the main legal instruments available to prevent and mitigate human pressures and impacts, and promote the sustainable use of European seas, are the Maritime Spatial Planning Directive (MSPD; European Union, 2014) and the Marine Strategy Framework Directive (MSFD; European Commission, 2008). Their implementation should support a sustainable blue economy, which is crucial to achieving the Biodiversity Strategy targets for 2030 (European Commission, 2020) and the Good Environmental Status (GES) under the MSFD, by 2026. Also, when those objectives are not achieved, the Nature Restoration Law defines targets to restore degraded habitats in terrestrial, aquatic and marine systems, by 2030 and 2050 (Hering et al., 2023). To make development and conservation compatible, instruments are needed (European Commission, 2021), including innovative and practical monitoring and assessment methods that can assist managers in decisions making. Several examples of reviews of methods for monitoring (Danovaro et al., 2016; Mack et al., 2020), modelling (Lynam et al., 2016; European Commission, 2022) or status assessment already exist (Borja et al., 2016). Materializing some of the developed ideas, in the last 2-3 years, some Horizon Europe projects have been initiated, providing the opportunity to dramatically increase our knowledge and skills to monitor and assess the ocean with new tools (Borja et al., 2024). Five of such projects (GES4SEAS [1] , OBAMA-NEXT [2] , BIOcean5D [3] , ACTNOW [4] , MARBEFES [5] ) joined forces in a summer school held in San Sebastián (Spain), from 5 th to 7 th June 2023, to revise and discuss with more than 50 attendees, the innovative methods that are being developed and the ways in which these can assist managers and policy-makers. This manuscript presents the status of the most important methods reviewed during the school and discusses the progress they represent towards a better management of the seas, in Europe and beyond. The manuscript is divided in three sections: (i) novel methods to monitor coastal and ocean waters (e.g. environmental DNA (eDNA), drones, imaging and artificial intelligence, climate modelling and spatial planning); (ii) innovative tools to assess the status (e.g. cumulative impacts assessment, multiple pressures, Nested Environmental status Assessment Tool (NEAT), ecosystem services assessment, or a new unifying approach); and (iii) discussion on the next steps in monitoring and assessing marine biodiversity and ecosystem health. Some novel methods to monitor the ocean Analyzing eDNA to monitor different ecosystem components at sea eDNA refers to DNA collected from environmental samples, such as for example from marine water (Pawlowski et al., 2020). A few liters of marine water contain organisms such as bacteria, microbial eukaryotes or small planktonic animals, but also traces of larger organisms such as fish or marine mammals in form or scales, tissues, cells or gametes (Rodriguez-Ezpeleta et al., 2021a). Thus, sampling and then analyzing eDNA from marine water allows studying the diversity and abundance of several ecosystem components. This approach has the potential to revolutionize marine monitoring, which often requires time consuming surveys involving expensive sampling gear not suitable for all locations and depths. Using eDNA for biomonitoring involves several steps upon which the accuracy of the obtained results largely relies (Rodriguez-Ezpeleta et al., 2021b). Sampling can involve collecting from a few to hundreds of liters of water from the desired location and depth and then passing it through a filter of a given pore size (often 0.22 mm for bacteria and up to 3 mm for eukaryotic material). eDNA is then extracted from the filtrate and used for species specific assays or for metabarcoding (Figure 1). Species specific assays consist of applying quantitative or digital PCR (qPCR/dPCR) to detect and quantify the DNA from a species of interest for which the assay is specifically designed (Salter et al., 2019). This approach is particularly interesting for early detection of non-indigenous species (Nathan et al., 2014) or for monitoring movements and spawning activity (Erikson et al., 2016), and it has recently been proven as a promising approach for fisheries surveys, providing similar outcomes to those obtained through active acoustics (Shelton et al., 2022). Alternatively, DNA metabarcoding allows to study the whole community of a given taxonomic group. Using this approach, the DNA from a given ecosystem component (e.g. cephalopods, fish, …) is amplified and sequenced; the resulting DNA reads are then compared against a reference database that contains the correspondence between DNA sequences (barcodes) and species names. This approach is particularly interesting for monitoring diversity and relative abundance patterns, including changes across time and space. This method has proven to detect systematically more species than traditional approaches, such as plankton nets, trawling, video, etc. (Fraija-Fernández et al., 2020). Despite the demonstrated power of eDNA based analyses for biodiversity monitoring, there are several inherent challenges associated to the use of eDNA as a proxy of species presence. Several researchers have established the term “ecology of eDNA” (Barnes and Turner, 2016) to refer to the origin, state, transport and fate of eDNA within the environment, factors that should be account when interpreting eDNA derived biodiversity assessments. Indeed, finding eDNA from a species at a given location is not always indicative that the species was there, alive at that moment (Burian et al., 2021). Yet, despite recognized uncertainties, experiments in controlled environments and open ocean suggest that eDNA detection is a good proxy of species presence (Holman et al., 2021), which is also supported by studies revealing the power of eDNA to detect species vertical structuring (Canals et al., 2021). In order to go beyond species lists with uncertain interpretation, it is now time that the knowledge accumulated over 15 years of using eDNA for studies of macroorganismal biodiversity is coupled with models that account for the factors that shape the “ecology of eDNA”. eDNA studies are also affected by the inherent biases of the species-specific and metabarcoding assays. Some of them, such as contaminations, can be controlled with appropriate laboratory practices (Burian et al., 2021), while others such as variable DNA extraction efficiency, inhibition in DNA amplification, low specificity of the developed assay (non-target species are also detected) or primer bias (under and over amplification of DNA of some species) are difficult to detect. An important aspect that mostly affect metabarcoding based studies is the lack of completeness and accuracy of reference databases, which can severely bias obtained results (Claver et al., 2023); thus, it is advised that a custom-curated database is used as reference and that, when not complete, an assessment of its completeness is performed. Besides advances in improving laboratory and data analyses protocols as well as models for data interpretation, advances in eDNA research include the use of autonomous sampling (Preston et al., 2023) and in situ analysis (Hansen et al., 2020) devices, the use of eDNA for population genomics (Sigsgaard et al., 2019), improvements in the use of eDNA approaches for biomass estimation (Shelton et al., 2022) and other advances such as estimating the developmental status of the targeted species (Parsley and Goldberg, 2023). Thus, considering also advances in overcoming the challenges associated with standardization and reproducibility, it is anticipated that the position of eDNA as a game-changer in environmental monitoring will be further consolidated in the future. Drones for mapping and monitoring shallow water benthic communities In shallow water benthic habitats, ecosystem services are largely linked to the presence and livelihood of marine vegetation such as seagrass, rockweeds, kelp forests and tidal marshes (Krause-Jensen et al., 2022). Standard techniques to monitor and map the coastal zones include satellite remote sensing, research vessels, small boats, and diving operations. However, these methods often come short for shallow water coastal zones. Satellite remote sensing offers large spatial coverage but low spatial resolution, with RGB and spectral data acquisition at resolution of tens to hundreds of meter scales (Lønborg et al., 2022). Larger research vessels are limited to water depths deeper than ~10 meters and have high running costs. Smaller boats can access shallow waters at lower costs but with limited spatial coverage and a significant time investment. Similarly, SCUBA diving for underwater mapping and monitoring is also expensive and time-intensive (Féral and Norro, 2023). The latter two, in addition, often suffer from not being spatially explicit (Murphy and Jenkins, 2010). Flying drones offer a novel technology and a strong complement to these existing methodologies and is filling a gap by providing cost-efficient data collection with a high spatial and temporal resolution (centimeter-scale and on-demand data collection), capabilities to operate below cloud cover, and facilitating systematic and repetitive data acquisition for shallow water research and monitoring purposes (Joyce et al., 2023). Over the past few years, there has been an explosion in the technical development of flying drones and a following steep development of applications in research, mapping and monitoring of aquatic habitats (Joyce et al., 2023). Flying drones are scientifically often referred to as Unoccupied, Uncrewed, or Unmanned Aerial Vehicles (UAV). Or even Unmanned Aerial Systems (UAS) or Remotely Piloted Aircraft Systems (RPAS), as frequently used in the aviation industry. We suggest using the gender-neutral Unoccupied or Uncrewed Aerial Vehicles (UAV) in science and management context, as advocated for by Joyce et al. (2021). UAVs are well underway changing the game of shallow water benthic habitat mapping, by facilitating efficient, precise, and reproducible data collection (Joyce et al., 2023). Using sophisticated light-weight sensors and high-resolution cameras, combined with artificial intelligence (AI) for data analysis and high-performance computers, research communities as well as management and mapping authorities now have tools at hand to capture detailed geospatial data of coastal and aquatic communities with ease. By providing a birds-eye perspective on coastal ecosystems from a much lower altitude than satellites, drones can capture data at ~100 times better resolution than satellites, with a flexibility in timing and frequency allowing studies at certain tide stages, or before and after events (Casella et al., 2017), even in hazardous environments. Drones have already shown its efficiency and provide unique data and novel insight to a range of coastal subjects, such as mapping of seagrass (Duffy et al., 2019), kelp forests (Cavanaugh et al., 2021), coastal wetlands (Doughty and Cavanaugh, 2019), abundance and community dynamics of seabirds (Edney et al., 2023) and marine mammals (Álvarez-González et al., 2023), as well as anthropogenic substances such as plastic pollution (Torsvik et al., 2019) and oil spills (Adade et al., 2021). Coastal habitat mapping typically employs three main categories of UAVs: rotor drones, fixed-wing drones, and vertical take-off and landing (VTOL) drones (Figure 2, Kvile et al., submitted). The VTOL drones integrate features from both fixed-wing and rotor systems, utilizing rotors for vertical take-off and landing of a fixed-wing drone. The type of drone to deploy depends on the objectives of the survey. Rotor drones excel in maneuverability, low-speed flight, and hovering capabilities. In contrast, fixed-wing drones are more energy efficient and thus facilitate extended flight durations, making them suitable for long-distance missions. However, their reliance on specified take-off and landing zones often poses challenges in coastal environments, a challenge subdued with VTOL drones for easier landings. See Kvile et al. (submitted) for an evaluation of these in coastal mapping applications. Drone-based recognition of coastal habitats builds on optical identification of species and habitats, also referred to as an optical fingerprint. It describes a distinctive spectral signature of the target object (e.g. seagrass, macroalgae, etc.) and quantifies how the object interacts with light across different wavelengths of the electromagnetic spectrum, typically within the band of visual light and at slightly longer wavelengths (400 to 1000 nm). In habitat mapping, optical sensors like RGB, multispectral (MSI) and hyperspectral (HSI) imagery are applied enabling precise identification and differentiation of objects or elements (Figure 2), such as vegetation or animals by comparing the collected spectral data to established databases or by training of AI algorithms and recognition networks. This approach facilitates accurate classification and mapping in addition to uncertainty estimation of both quantitative (uncertainty in coverage and distribution) and qualitative (probability of prediction) parameters. Seagrass and kelp forests have been difficult to map with high levels of accuracy and precision with satellite imagery due to complex and mixed optical signatures from vegetative and abiotic spectra and near-coast optical distortion (e.g. Cavanaugh et al., 2021). The use of drones, however, enabling mapping of such ecosystems with higher spatial resolution and more precise georeferencing, differentiating between biotic and abiotic matter using higher spectral resolution, and with minimal optical distortion. Figure 3 illustrates an example of seagrass mapping in a shallow bay area in the Oslofjord, Norway. Using a fixed wing drone and combined data from RGB and MSI cameras (as A, D, and E in Figure 2, respectively), around 500 single images were collected and subsequently stitched together to form a single orthomosaic which was georeferenced to a pression of +/- 3 cm (according to the drone data collection protocol by Kvile et al., submitted). The orthomosaic holds a ground sampling distance (image resolution) of 3 cm/pixel. Major coastal components such as seagrass, various macroalgae species, sandy sediments, and rocks were annotated from in situ (ground truth) observations, and subsequently used to train a machine learning (ML) algorithm (in this case random forest) for separating and predicting the habitat classes in the orthomosaic (see legend in Figure 3). The predictive accuracy of the classification (a so-called confusion matrix) showed that Eelgrass ( Zostera marina ) was predicted with 86%, brown seaweed with 87%, sandy sediment with 79% accuracy, and so forth (Figure 3). More recently, convolutional Neural Network (CNN) analysis has improved model performance (Liu et al., 2022) and has enabled habitats predictions at species level (Kvile et al., submitted). Ultimately, ML models may allow mapping and classification of coastal habitats without the need for additional collection of ground truth data, building on existing networks. However, ground-truthing will still be required when training new algorithms. Further advancement within drone technology for shallow water benthic habitat mapping includes higher resolution RGB sensors (a fast-developing industry), the full utilization of spectrally resolved optical fingerprinting data such as that available from MSI and HSI data. It also involves the development of neural networks from foundation models and faster and larger drones with increased payload capabilities. Also, infrared (thermal) sensors will improve the detection and recognition of animals and facilitate for detailed studies of surface temperature variabilities of water and vegetation. This could, for instance, improve our understanding of ocean warming and its effects, and aid in making more accurate predictions. We anticipate that LIDAR (Light Detection And Ranging) systems, now available for medium sized (<25 kg) research drones, will come to add a new range of applications for high resolution UAV-based assessments of shallow water bathymetry, sedimentation and erosion studies, detailed 3D seafloor mapping (Chust et al., 2010), and likely for coastal health and ecosystem services assessments too. Thus, while drone imagery cannot replace traditional methods for coastal mapping completely, it offers a cost-efficient supplement, and, in concert with other new technologies like unoccupied surface and underwater vehicles, much needed opportunities for digitalized and reproducible ecosystem monitoring and research. In light of these advancements, drone-based methodologies may be recognized as recommended and protocol procedures for ecosystem mapping and monitoring under directives like the Maritime Spatial Planning Directive, the MSFD, and for biodiversity assessments in shallow-water coastal regions. This recognition could contribute to enhancing future coastal management and regulations by providing accurate, comprehensive and up-to-date information for decision-makers. Use of imaging and artificial intelligence (AI) for monitoring pelagic communities Within pelagic communities, plankton are plants and animals which are critical to all life on earth. Whilst phytoplankton play a key role in carbon flux as primary producers, absorbing more carbon dioxide than all trees on land, zooplankton occupy a central position in the food web, often controlling phytoplankton by grazing and providing food for many important larval and adult fish and seabirds (Pitois et al., 2012; Lauria et al., 2013). Decades of laboratory and field investigations have shown major impacts of changing oceans on zooplankton physiology, community composition, and distribution, and their resulting influence on both biogeochemistry and productivity of the oceans (Ratnarajah et al., 2023). It is therefore critical to further our understanding of how plankton community structures and abundances are likely to respond to climate change in the future, and their effects on ecosystem dynamics. Plankton are an extremely diverse group, covering a wide range of sizes (from sub-microns to several meters) and behaviours. As a result, no single sampling device can sample all the plankton components at any one-time, leading researchers to select the system they think best fulfills the aims of the particular studies (Owens et al., 2013; Skjoldal et al., 2013). As there are few dedicated plankton monitoring sites and surveys, our knowledge of their biomass, size composition and rates of production, in many continental shelf seas, remains fragmented. The implementation of the MSFD (European Commission, 2008) has resulted in increased volumes of plankton data needed to meet its requirements. However, the traditional collection of plankton samples, using bottles and nets followed by taxonomic analysis of the preserved samples using microscopes by a trained specialist, is a labor intensive and time-consuming process (Wiebe and Benfield, 2003; Benfield et al., 2007). Limited resources and budgets for monitoring have been a major driver for the development of cost-effective methods to gather plankton information (Danovaro et al., 2016). Nowadays there is a wide range of instruments including for example molecular, optical, remote sensing, and automated techniques (Pitois et al., 2023), most have been reviewed by Lombard et al. (2019). In particular, imaging instruments combined with Machine Learning tools to automatically classify the collected images (e.g., Weldrick, 2022), have received a high level of interest, due to their ability to provide rapid and unbiased data that can be stored digitally. Thus, they offer the opportunity to help overcome many of the limitations that characterize traditional methods of collecting and analyzing zooplankton samples. These can be used on collected samples (e.g. PlanktoScope (Mériguet et al., 2022; Pollina et al., 2022), FlowCam (Mériguet et al., 2022)), or in situ (e.g., Underwater Vision Profiler (Drago et al., 2022), Plankton Imager (PI) (Scott et al., 2023), In Situ Ichthyoplankton Imaging System (ISIIS) (Panaïotis et al., 2022), Scripps Plankton Camera (SPC) (Le et al., 2022), Imaging FlowCytobot (Kraft et al., 2022); Video Plankton Recorder (VPR) (Plonus et al., 2021). A specific example of an all-in-one tool that combines zooplankton sampling and image analysis is the Plankton Imager (PI) (Figure 4). The PI was shown to provide a robust description of zooplankton abundance, distributions and community structures (Pitois et al., 2018; Scott et al., 2021). Its ability to capture in situ size information was applied to the development of ecological indicators (Pitois et al., 2021). Finally, the PI collects over 150 images per second, thus allowing for fine spatial resolution of the collected images (Scott et al., 2023). The ability of the PI to continuously collect fine resolution data opens the door to data intensive plankton studies. But the pinnacle of pelagic studies will be the monitoring of plankton (phytoplankton and zooplankton) along environmental (e.g., temperature, salinity) and other biological (e.g., fish) variables, at high-frequency and in real-time. This offers the opportunity to quickly respond according to observed changes and adjust data collection strategy for example (during a survey for example), but also increases capabilities and potential towards building a digital twin of the oceans (Chen et al., 2023). However, the concept of digital twin of the ocean, which seeks to integrate marine big data and AI, is in the initial stage of development and major data challenges need addressing first: An ever-increasing number of images is being produced. These need to be stored and made accessible. But the biggest bottleneck is in the processing and interpretation step. Machine learning tools are key to automate this process and are reliant on comprehensive libraries of pre-classified images (training set). Whilst building training sets is a time-consuming exercise, these are key to the development of high performing algorithms trusted by taxonomists in particular (Giering et al., 2022). Another major bottleneck resides in the transfer of high numbers of relatively large data files (images) from offshore to land locations as soon as images are collected faster than they can be transferred. Data are being collected under different formats and at varying scales: for example, continuous temperature values, plankton images, acoustic signals, satellite imagery, etc. Integrating multimodal data is a challenge in itself (Grossmann et al., 2022). Addressing these challenges will require (i) substantial investments to build high quality and comprehensive training libraries, and (ii) a data driven approach to automate end-to-end data flows. The latter, which has only recently become possible thanks to the latest advances in machine learning tools, in par and high-performance computing (e.g. Kraft et al., 2022), whist the use of Edge-AI allows for image processing to be done closed to the point of collection so that only the data output from the images is transferred to the cloud rather than the image itself (Schmid et al., 2023). The use of imaging and AI tools is still in its infancy, but early results suggest that technological advances in this field have the potential to revolutionize how we monitor our seas. Climate modelling as a decision support tool for marine spatial planning and biodiversity conservation The pressures on marine resources are increasing to the extent that societal demand for goods and services often exceeds the capacity of the marine environment to meet them all simultaneously (Ehler, 2021). Without regulation, these demands can lead to over-use, conflicts and eventual degradation of marine ecosystems. In order to ensure the sustainable use and conservation of the world ocean, a public process must be undertaken to decide what, where and when human activities should take place in marine areas – e.g. marine spatial planning (MSP) (Frazão Santos et al., 2020). MSP has the potential to balance multiple, often conflicting, human demands and to protect marine ecosystems in a spatially explicit manner (Frazão Santos et al., 2020). As a management process, MSP has spread widely over the last 15 years and marine spatial plans are in development in around 70 countries, encompassing six continents and four ocean basins (Frazão Santos et al., 2021). However, despite this widespread acceptance, development and implementation of MSP still faces some challenges, not least of all global climate change. Anthropogenic climate change is altering marine ecosystems at unprecedented rates. Altered environmental conditions (e.g. warming, acidification, deoxygenation, changes in salinity and hydrodynamics, among others; Pörtner et al., 2021, 2022) and habitat suitability cause marine species to respond by tracking their optimum habitat (when possible), resulting in changing distributions (Dulvy et al., 2008). This is expressed locally as changes (and often loss) of species biodiversity (Pecl et al., 2017), impacting on conservation efforts as well as on sectors such as capture fisheries and aquaculture. Such changes may increase the potential for conflicts between users of marine space, and increase cumulative impacts on the marine environment, all of which will have implications for human well-being and prosperity (Frazão Santos et al., 2020). Designing MSP that addresses the effects of climate change (climate-smart MSP) is therefore a pressing ambition for ocean managers and marine planners. However, it remains - with very few exceptions – an ambition without practical implementation (Queirós et al., 2021). Designing effective climate-smart MSP requires an objective means to assess how ecosystem components may respond to climate change. Given the highly dynamic nature of the marine environment, determining whether climate change is a driver of change in habitat conditions or species distributions necessitates a careful examination of environmental and species means over time, as well as their variability (Hawkins and Sutton, 2012). Ocean climate modelling (e.g. physical-biogeochemical modelling and species distribution modelling) has therefore been identified as an essential decision support tool for planners seeking climate-smart design (Queirós et al., MSPACE report, 2023). The benefits of such models are clear – plans can be tested under different climate change scenarios, and assessments of possible trade-offs can be made; these in turn can inform those stakeholder consultations, which are so critical to planning (Pınarbaşı et al., 2017; Frazão-Santos et al., 2020). To date, however, the uptake of climate modelling projections has been slow. This is largely due to the mismatch between traditional climate modelling analyses (which tend to focus on long-term climate impacts and individual ecosystem components separately) and the time-scales and interests of planners, who see climate change as a holistic process that may affect the activity of sectors reliant on those species and services impacted by changing ocean conditions (Queirós et al., 2021). To address this mismatch, the “bright-spots framework” was developed. Climate change drives concurrent changes in multiple ecosystem attributes, which change spatially and temporally at different speeds and with different magnitudes (Pörtner et al., 2022). The bright-spots framework uses techniques established in the meta-analysis statistics field to identify the emergence of ecosystem-level climate change signals (cf. pressure by pressure as in traditional analyses), which allows for a mapping of the emergence of: (i) climate change refugia - i.e. where environmental conditions or species abundances may be resilient to climate change, and where current uses may be sustainable; (ii) climate change hotspots – i.e. areas where climate driven trends force ecosystem components or species abundances into a new state beyond their natural variability; or (iii) climate change bright-spots, indicating a change in environmental conditions or species abundances (outside of present day variability) in opposition to predicted climate change trends, generally as a result of climate cycles (Queirós et al., 2021). Capitalising on refugia and bright spots in climate-smart planning design may be beneficial in the mid-term delivery of sustainability goals, and for increasing the climate resilience of important conservation sites and maritime sectors. It may also function as a time buying strategy while we reduce global emissions and slow the pace of climate change. This method has already been used in several globally distributed research programmes including those informing marine management in Ireland (Queirós et al., 2021), The UK (Queiros et al., 2023) Philippines (Talbot et al., in review) and Vietnam (Queirós et al., 2022). It is also in use in a number of case studies across the European Union (www.futuremares.eu). To further illustrate, a case study using the bright spots framework was recently conducted to inform the development of proposed new marine protected areas in Irish waters (Queiros et al., FairSeas report, in prep), in support of the move towards the Global Biodiversity Frameworks 30x30 target. That study identified areas in the Irish marine planning area which consistently (>80% of the time) appeared as climate refugia between 2026 and 2069 across two emissions scenarios (RCP4.5 and RCP8.5), for habitats and species of conservation interest. Specifically, four separate analyses were conducted: two used modelling datasets that described the spatial distribution of climate resilience and vulnerability in the environmental conditions and prey species required by benthic and pelagic megafauna. A further two analyses used datasets that described benthic and pelagic habitats directly (Queiros et al., FairSeas report, in prep). Figure 5 shows the locations of those refugia, along with the current Natura 2000 network of marine protected areas (MPAs) in Irish waters, and the proposed locations of new ones. While identified long-term refugia for pelagic habitats and megafauna were rather sparse, a substantial proportion of the proposed new MPA sites fell in refugia for benthic habitats, particularly those in deeper offshore waters (Queiros et al., FairSeas report, in prep). The identification of such consistent, long-term refugia can provide marine planners and managers with a better idea of those areas which may support effective conservation into the future, despite climate change pressures in the broader region. Some novel methods to assess the status of the ocean Cumulative Impacts Assessment to guide Ecosystem-based Management The Ecosystem Approach (Kirkfeldt, 2019) offers a powerful strategy for the integrated management of living resources and was adopted as the primary framework for the conservation and sustainable use of biodiversity under several EU policy frameworks (e.g. the MSFD). The many definitions, though broadly similar, vary in their emphasis but could be structured into three broad themes representing the main aspects of Ecosystem-Based Management (EBM) using a review of Long et al. (2015), providing a set of key principles: (i) capturing the integrity, functioning and dynamics of the marine ecosystem in its environmental context; (ii) accounting for all relevant human activities and their interconnections with the marine ecosystem as part of the wider socio-economic context; and (iii) organising the process regarding governance and management, taking account of the institutional context. The first requires the monitoring and assessment of the multitude of indicators put forward in the MSFD and other frameworks intended to capture the many different aspects and interactions encapsulated in the ecosystem integrity, functioning and dynamics biodiversity concepts (Teixeira et al., 2016). The second follows the rationale that EBM is about managing human activities, not the ecosystem itself (Long, 2012; O’Hagan, 2020). An assessment of the potential impacts of all relevant human activities and their pressures is therefore key to guide management towards the achievement of the policy objectives for the marine ecosystem (Borja et al., 2024). A recent study by Piet et al. (2023) introduced SCAIRM (Spatial Cumulative Assessment of Impact Risk for Management), a spatially explicit cumulative impact assessment (CIA), a method that has been specifically developed to guide an ecosystem approach. CIAs are considered key to policymaking and planning in governance and management but their actual implementation in MSP and management processes is often limited (Stelzenmüller et al., 2018; EC-CINEA, 2021). Management is required to reduce the effects of human activities on ecosystem components and their functioning in order to achieve policy objectives in support of societal and environmental goals (Cormier et al., 2017). However, it is the implementation of management measures developed by authorities that should ‘carry into effect’ the objectives identified at the onset of the process required as part of an ecosystem approach (Elliott et al., 2020). Risk-based approaches have often been at the basis of CIAs for marine management (Stelzenmüller et al., 2018). SCAIRM combines elements from both a likelihood-consequence approach (i.e., calamities; Williams et al., 2011), and an exposure-effect approach, which is considered more suitable when assessing existing and (more or less) continuous or frequently occurring pressures (Smith et al., 2007; Knights et al., 2015) into a method able to handle pressure events as well as continuous pressures. Exposure and Effect are estimated separately using risk-based approaches based on (i) qualitative categories and expert-judgement scores, or (ii) quantitative information applying actual data. The former has the advantage that it allows comprehensive assessments that covers all potential impact chains (i.e. a causal chain connecting every single human activity-pressure-ecosystem component) and can (and needs to) be applied in data-poor areas (Knights et al., 2015; Borgwardt et al., 2019). The latter has the advantage of higher accuracy and more confidence in the outcome; however, is limited to only those impact chains for which adequate information is available and is therefore more appropriate in data-rich areas, such as the North Sea (Piet et al., 2021a). The SCAIRM method is essentially a harmonization of this work, allowing to mix qualitative and quantitative information. An ecosystem approach requires policy objectives to be achieved through what are essentially sectoral operational controls, which are not designed to achieve ecosystem scale outcomes (Cormier et al., 2019). While sectors may individually fully conform to sector-specific operational standards and regulatory requirements, integration across sectors is lacking (Garcia and Charles, 2014). Thus, instead of a lack of legislation, regulations or standards, the problem may be caused by a lack of coherence and alignment between sector-specific operational practices and environmental legislation and policy objectives (Cavallo, 2018). Some studies show how an advanced CIA method like SCAIRM can be used to provide the type of operational-centric approach (Murawski, 2007; Cormier et al., 2017, 2019) required to achieve environmental policy objectives. Impact Risk is the key output of SCAIRM and can be estimated per impact chain as Exposure*Effect Potential. Impact Risk reflects the expected pressure-induced loss of a specific ecosystem component relative to an undisturbed situation based on state-of-the-art (qualitative or quantitative) information. To guide (an ecosystem approach to) MSP or management first a recent baseline situation needs to be established which can be evaluated against any future scenarios. Aggregation of the impact risk from cumulative pressures across ecosystem components (but now focusing only on a suite of bird and mammal species) then reflects the degree to which biodiversity or ecosystem integrity is at risk from anthropogenic activities. Here, we provide a North Sea example of such scenarios for 2030 and 2040 (Table 1). The main driver for MSP in the North Sea is the transition towards renewable energy, notably offshore wind (van de Pol et al., 2023). For an ecosystem approach to MSP, the implementation of offshore windfarms needs to be considered as part of scenarios involving all the main human activities. Table 1. Scenario values (% of study area) for the main human activities in the North Sea study area (Piet et al., 2021a). Activity Phase Baseline (2022) Scenario for 2030 Scenario for 2040 Aquaculture: fish Operation 0.00356 0.00625 0.10861 Aquaculture: fish Set-up 0.00036 0.00027 0.00512 Aquaculture: macroalgae Operation 0.00105 0.00184 0.03194 Aquaculture: macroalgae Set-up 0.00105 0.00184 0.03194 Aquaculture: shellfish Operation 0.00545 0.00956 0.16611 Aquaculture: shellfish Set-up 0.00054 0.00041 0.00783 Fishing: benthic trawling Mooring/anchoring 0.89099 0.83975 0.78081 Fishing: benthic trawling Operation 89.09860 83.97510 78.08124 Fishing: Nets Operation 6.86605 6.47122 6.01704 Fishing: Nets Set-up 6.86605 6.47122 6.01704 Fishing: Pelagic trawls Mooring/anchoring 0.17532 0.16524 0.15364 Fishing: Pelagic trawls Operation 17.53183 16.52368 15.36396 Oil and Gas Construction 0.00538 0.00538 0.00000 Oil and Gas Operation 0.10760 0.10760 0.00230 Sand/gravel mining Operation 2.55918 3.37578 4.09468 Sand/gravel mining Disposal 2.55918 3.37578 4.09468 Shipping Mooring/anchoring 0.07972 0.07972 0.11081 Shipping Operation 20.86627 20.86627 29.00411 Telecoms and Electricity Operation 0.04168 0.05602 0.07762 Telecoms and Electricity Laying cables 0.00104 0.00143 0.00108 Wind farms Construction 0.06144 0.17251 0.27723 Wind farms Operation 0.73154 2.11163 4.88391 SCAIRM was then applied to assess the consequences of these scenarios in terms of (changes in) Impact Risk (Figure 6). Table 2 shows which ecosystem components are likely to suffer from an increased Impact Risk under these scenarios. Table 2. Change in cumulative impact risk (%) of human activities on ecological components in two future scenarios (2030, 2040) relative to the baseline (2022). An increase is shown in bold. Ecological component Change in impact risk (%) 2030 vs. 2022 Change in impact risk (%) 2040 vs. 2022 Birds 0.4% 2.9% Mammals -0.3% -0.4% Fish & Cephalopods -1.7% -3.7% Pelagic water column -0.8% -1.7% Littoral sediment 0.0% 0.2% Littoral rock and other hard substrata 0.0% 0.2% Sublittoral sediment -0.6% -1.6% Circalittoral rock and other hard substrata -0.2% 0.0% Infralittoral rock and other hard substrata -0.2% 0.0% Deep-sea bed -1.6% -3.6% This assessment thus shows that notably for birds and to a lesser extent for seabed habitats in the littoral zone, require additional management measures in order not to (further) compromise the achievement of the MSFD objectives. Assessing multiple pressures and cumulative impacts for Europe’s seas The European Union’s policy objective to achieve GES of marine environment under the MSFD, cannot be successful without understanding which anthropogenic pressures are preventing GES and where that takes place. This becomes more complex when multiple simultaneous pressures act together exerting cumulative impacts. Tools to identify, map and assess the cumulative impacts of multiple pressures have been developed for several Europe’s marine areas (Korpinen and Andersen, 2016) and CIAs have been published since 2010s (Korpinen et al., 2012, 2021; Coll et al., 2012; Andersen et al., 2013; Micheli et al., 2013). A key challenge in CIAs is the integration of tens or hundreds of ecosystem impacts into a common scale (Halpern and Fujita, 2013). In simpler models, such as fishery impacts on marine populations (Coll et al., 2016) or impacts of multiple pressures on a single population (Marcotte et al., 2015), the integration can be made with data-based pressure-population response functions, but the integration of tens of different pressures on tens of very different ecosystem features is beyond the possibilities of ecosystem models. The framework to address the complex CIAs was introduced by Halpern et al. (2008b). It represents the pressure-ecosystem response functions as simple sensitivity estimates. These are scores representing the sensitivity of an ecosystem feature to a single pressure and they are surveyed from a wide pool of experts (Halpern et al., 2007). While the use of expert judgement is not ideal, the approach has served tens of peer-reviewed CIAs worldwide (Korpinen and Andersen, 2016) and many other ecosystem studies (e.g. Isbell et al. 2022). So far, only a few studies have been made to assess how well the experts agree with each other in environmental studies. It is also unclear if such sensitivity estimates should be local or regional or whether they can be global (Halpern et al., 2007). Korpinen et al. (2021) carried out a pan-European survey of marine sensitivity estimates and concluded that the expert elicitation was relatively reliable: 394 out of 450 combinations of pressure-ecosystem estimates did not differ from each other by the experts. The remaining sensitivity estimates represented cases where there were too few responses, the knowledge of the sensitivity was arguably weak, or for reasons not known for the authors. In that study, the CIA was made for the whole European Sea area using the same sensitivity estimates. To our knowledge, more comprehensive analyses of the coherence of the sensitivity estimates have not been made. The pan-European CIA by Korpinen et al. (2021) was carried out to support the European Environment Agency (EEA) in locating the areas of too high cumulative impacts (which can potentially prevent achieving GES) and identifying the human activities and pressures causing them (hereafter the ‘EEA study’). The objective of this study was to map, on a European scale, the main anthropogenic pressures and estimate their level of intensity in each assessment grid cell, which were 10 km x 10 km in size. From the management point of view, it is important to use standardized definitions of ‘pressures’ and ‘human activities’ as given by the respective policy, in the case of the EEA study the MSFD definitions were used (European Commission, 2017). Therefore, the EEA study categorized the pressures into 14 classes, following the MSFD Annex III (European Commission, 2017). The CIAs certainly have different objectives which determine how detailed data – human activities, pressures or ecosystem features – are necessary to use. In the EEA’s CIA, the objective was not to enable managing detailed activities but to get an overall picture of the ‘problem areas’ and identify the problems. For that objective the 14 pressures were a sufficient level of detail. Visual and analytical assessments of integrated pressures is sometimes enough for management, but more accurate results may be produced by estimating their cumulative impacts, i.e., producing a full CIA. The EEA study estimated the cumulative impacts of 14 pressures on 31 ecosystem features, which were both habitats and species groups. These needed 14 x 31 estimates for ecosystem sensitivity. Thus, a practical limiting factor for a large-scale CIA is also the determination of the sensitivity estimates. There is no guideline on how widely and deeply marine biodiversity should be included in CIAs. In the EEA study it was, however, ensured that the ecosystem features should cover benthic and pelagic habitats and the main mobile species groups (mammals, birds and fish). A CIA in the Baltic Sea compared the influence of increasing or decreasing the number of ecosystem features and concluded that the higher number of features adds fine-scale details in the spatial presentation of the results but the main conclusions of the CIA – on the marine region scale – were not jeopardized (Korpinen et al., 2012). The EEA study was a necessary landmark in the EU ambition to map and identify the preventing factors to achieve GES in Europe. It showed that there are multiple pressures and cumulative impacts in Europe’s seas that probably prevent GES and that the main pressures in the shelf areas are physical disturbance by bottom-contacting fishing gears, invasive alien species and species disturbance by human presence, and in the whole sea area, increased surface water temperature and underwater noise. The coastal and shelf areas had significantly higher cumulative impacts than the offshore areas. Do these pressures cause such high impact that GES is not achieved? There are a few studies which have compared the CIA results with environmental monitoring data. Bevilacqua et al. (2018) showed how the condition of coralligenous algae assemblages depended on the level of a CIA score, and Clark et al. (2016) showed correlation with benthic communities in an estuary. The EEA’s Marine Messages II report successfully correlated the CIA with an integrated assessment of marine ecosystem status on the pan-European scale (Reker et al., 2019), and Korpinen et al. (2021) showed how the CIA explained the ecological status of coastal waters. Even if the large-scale CIAs such as the EEA study are only rough estimations of the cumulative impacts, they are useful tools to guide marine management into the right direction. The work is far from building a model to predict good or not good status of marine ecosystem, which is still best to assess with biological monitoring, but it is unarguable that the spatial and temporal resolution of CIAs is much sharper than in biological monitoring. The CIAs can indicate areas of high risks of failing policy objectives such as GES. Such an approach is already adopted with the MSFD assessment of the benthic habitats where pressure impacts on seabed habitats are seen as the major data source informing of the need for management measures (European Commission, 2022b). Assessing the status of marine systems using NEAT Among the tools and methods for assessing the status of the sea, in an integrated way, one is NEAT (Borja et al., 2016). This method was developed for assessing the marine environmental status under the MSFD and was first validated in 10 locations in the four European regional seas (in the Black Sea, Mediterranean Sea, Atlantic Ocean, Baltic Sea) and the Arctic (Uusitalo et al., 2016). Later, it has been applied in different locations and considering different pressures, e.g. (i) in the Iranian Caspian Sea, to assess the status of bathing waters (Nemati et al., 2017); (ii) in the Mediterranean, for assessing the spatio-temporal recovery of a Greek bay (Pavlidou et al., 2019); to compare NEAT and Maltese official MSFD assessments (Borja et al., 2021); or to identify the contribution of MPAs to the status of this sea (Fraschetti et al., 2022); (iii) in the Atlantic, to assess the status of MPAs in deep-sea (Kazanidis et al., 2020); and testing the use of multi-source and multi-scale data (Menchaca et al., 2022); (iv) in the Black Sea, to assess the status of Romanian (Marin et al., 2020) and Turkish waters (Tan et al., 2023); and (v) in the whole Europe, to test some MSFD descriptors (Borja et al., 2019). As shown in all the above-mentioned studies, NEAT (version 1.4) is a software designed to assess the environmental status of marine waters, which can be freely downloaded from https://www.azti.es/en/productos/neat-software-nested-environmental-status-assessment-tool/. The central principle of NEAT is its hierarchical and nested structure of the environmental status of a given area, or Spatial Assessment Unit (SAU), and the associated habitats. This means that information belonging to a SAU contributes to the specific assessment of this SAU, as well as to the assessment of any other larger SAU that encompasses it, and therefore, to the overall assessment. The order of these hierarchies is such that the assessment begins with the hierarchically nested SAUs. In the example below (Figure 7), information belonging to the “Sardinia” will contribute to the specific assessment of the “Sardinia”, as well as that of the “Western Mediterranean” and the “Mediterranean Sea”. This tool aggregates the values of a set of indicators that are specific to a SAU, habitat, ecosystem component and MSFD descriptors in a comparable and systematic way (Uusitalo et al., 2016; Borja et al., 2019, 2021). Each indicator must have at least: (i) a range (i.e., a minimum and a maximum value), in which the values can be found, (ii) the class boundary values between the different quality classes (e.g. the boundary between good and non-good status), (iii) the actual measured indicator value, and (iv) its standard error value (Borja et al., 2016, 2021). After selecting the indicators to be used, the software normalizes them to a uniform scale ranging from 0 (worst environmental status) to 1 (best environmental status) with equidistant classes, allowing for integration across different ecosystem components and indicator types. (Borja et al., 2016, 2019, 2021). The resulting NEAT value can be calculated at different levels, e.g. it can contain all indicators belonging to a certain set of descriptors. The NEAT value is interpreted as the environmental status for this specific descriptor(s). Another result can be the NEAT value of a specific ecosystem component, habitat or SAU (Figure 8). The final NEAT values are calculated as a weighted mean of all indicator values assigned to a certain defined SAU, habitat or combination of both, and the value can be visualized as SAU + habitat (+descriptor) or SAU + ecosystem component (+descriptor) (Figure 8). The initial version of the software only matched the original MSFD criteria within the 11 descriptors, and Borja et al. (2021) implemented a version adapted to the ecosystem components of the Directive (e.g., pelagic and demersal commercial fish species, type of nutrients or contaminants). These authors redefined the hierarchies following the hierarchical structure for habitats and considering relevant species groups, habitat types, ecosystems and pressures, as well as criteria, according to the updated European Commission (2017) Decision. In this sense, it can be confirmed that NEAT is an adequate and efficient tool to aggregate multiple indicators from different ecosystem components (and multiple sources) to assess (spatially and temporally) the health status of marine waters. NEAT was found to be useful for managers, policy-makers and scientists in taking decisions for future management measures and assessment approaches. Ecosystem services mapping and assessment In everyday life, individuals constantly make choices depending on their needs. Individuals’ choices depend on the limited, scarce resources the individual possesses, and the priority each option assumes for that individual (Daly and Farley, 2004). Scarce resources to consider include, for example, time and money. Policy and decision makers will also have to make choices for the benefit of society, e.g., decisions on how to invest the available budget within a country will include considering trade-offs between building a new road to connect villages or conserving a forest for recreational purposes. These kinds of trade-offs, where natural resources are involved, consider potential alternative uses of the natural capital and the ecosystem services that it provides. Environmental economics methods and techniques, including natural capital accounting and environmental valuation, are particularly useful to support decision making on policy and management of natural resources (Bagstad et al., 2021). Natural capital is defined as “the elements of nature that produce value or benefits to people either directly or indirectly, for example, the stock of forests, rivers, land, minerals and oceans [these are the natural assets] as well as the natural processes and functions that underpin their operation [these are the ecosystem services]” (Natural Capital Committee, 2014). When considering the economic valuation of natural capital, it becomes important to subdivide ecosystem services in two categories: intermediate and final ecosystem services (Fisher et al., 2009). This distinction is important to avoid double counting the same ecosystem service, considering only those final ecosystem services which provide a benefit to society. Some final ecosystem services can coincide with the benefits, although usually benefits are provided with the support of other capital inputs such as human capital, infrastructure, etc. For example, all fish in the sea is a final ecosystem service , whereas the commercial fish to be sold in the market, that is caught by fishers using vessels, is a benefit . The economic valuation is in fact performed only on the benefits, e.g., valuing nutrient cycling on its own and also valuing water quality could be problematic as it may double count the nutrient cycling service as this is already part of the water quality assessment. Similarly, valuing places and seascapes separately from recreation may also double count the value of the beauty of the environment (Figure 9). Ultimately, we want to be sure that the natural capital assets are managed in a way that they will continue to be productive and provide current and potential benefits to society. There are well-established steps that need to be followed to perform an appropriate environmental economic assessment. Since ecosystem services are context dependent (Morse-Jones et al., 2011), their valuation must be spatially explicit; this means that the area where the ecosystem service arises must be known. The extent of the spatial area of the natural capital asset (i.e. the habitat) may be calculated using geographic information system (GIS) mapping. The process and function of the ecosystem service to be valued has also to be known as well as their economic value (€) per unit of measure (e.g. tCO 2eq km -1 ). These data will allow initial calculations of the economic value of the ecosystem service. Natural capital accounting, which follows a more restrictive set of rules to value the natural capital and its ecosystem services, requires further information on the condition of the natural capital (United Nations, 2021). Condition refers to the health of the natural capital assets and if they are still providing relevant ecosystem services and they are in the condition to continue to do so in the future. However, data collection and understanding on condition are demanding and therefore this information is currently often missing from natural capital accounts. As an example, Luisetti et al. (2011) illustrate a step-by-step approach to support decision making when dealing with coastal management assessment and evaluation. Steps are sequential, although at times overlapping, and deal with well-known issues such as the need of spatial explicitness when dealing with ecosystem services valuation, the need to avoid double counting of ecosystem services and non-linearities in services provision (Morse-Jones et al., 2011). The Luisetti et al. (2011) paper is concerned with managed realignment, an ecological engineering technique that provides a natural sea defense through saltmarshes. The project is assumed to be developed in the Blackwater estuary (UK) and for which an estimate of the economic value of the benefits provided by newly re-created saltmarshes in the area was investigated. The benefits estimated were the provision of a healthy climate through carbon sequestration and storage, recreation in saltmarsh areas including nature watching and spiritual wellbeing, and fish nursery benefits for commercially valued wild fish (seabass). For the healthy climate in blue carbon ecosystems (i.e. the saltmarshes), following best practice, GIS maps estimating the areal extent of the new intertidal habitats created with managed realignment were produced and an estimate of the tons of carbon stored each year per hectare was provided. These data are used to know the amount of carbon stored within the area of interest. The economic values of carbon used considered a sensitivity analysis of several values using the social cost of carbon method. More recently, values estimated using other methods are usually included as well, such as the abatement cost method (Luisetti et al., 2019). The carbon price from regulated carbon markets is still considered too volatile to be included in this kind of assessment. For the benefit of recreation in saltmarsh areas , in the Blackwater project a specific primary data collection exercise to obtain the willingness to pay to estimate the value of that benefit was undertaken. The willingness to pay data was elicited with a choice experiment method and provided an estimated economic value for the saltmarshes around the Blackwater estuary in the case of potential saltmarsh re-creation. When primary data collection is too onerous, it is possible to use a benefit transfer technique, although that technique has many challenges and limitations (Luisetti et al., 2014). Finally, the fish nursery benefits were estimated looking at the potential increased production in the Blackwater newly re-created saltmarshes of seabass. For this valuation, it was important to take account only of the fish that were not going to leave the estuary so to estimate only the contribution of the saltmarshes and not confound the data with other, bigger, seabass entering the estuary. All these benefit values were then used in a cost-benefit analysis (CBA) to assess the viability of undertaking such a policy through this project. For this reason, assumptions under different scenarios were developed; from a hold the line and a business-as-usual scenario to greener scenarios deeply rooted in ecological restoration. This example is interesting as it shows a trade-off analysis and calculations of opportunity costs between the use of the land for agriculture or for coastal protection, flooding agricultural land and re-creating saltmarshes. Environmental economics methods and techniques provide decision makers, of either policy design or project development, with tools to evaluate their decision from an economics perspective. The interdisciplinary nature of environmental economics and of natural capital approaches enable the decision maker to gather both ecological and economic information (Bagstad et al., 2021). Natural capital accounting, although requiring a more restricted set of economic rules, can provide comprehensive time-series monitoring of marine biodiversity and natural resources within a country’s national waters while scrutinizing any increment or decrement in their economic value and contribution to economic activities (Luisetti and Schratzberger, 2023). Environmental valuation techniques encompass a wider set of options to value the marine natural resources compared to natural capital accounting (Bartkowski et al., 2015). These techniques can also support the decision maker in evaluating a post-implementation policy or project. Both sets of methods and techniques support the decision maker with the level of information needed depending on the economic analysis needed, including trade-offs analysis such as those for marine spatial planning, and are therefore encouraged. Towards a unifying framework for assessing ’cumulative effects’ and ’environmental status’ The basic method of doing CIA (see Section 3.1) or cumulative effects assessment (CEA) (Halpern et al., 2008a; Korpinen et al., 2013, 2021; Piet et al., 2021a) is to develop a so-called linkage framework or mental model of all the relevant links (or impact chains) between Activities, Pressures and State (or ecosystem) components, i.e. A-P-S from DAPSI(W)R(M) (Elliott et al., 2017a) that may potentially be impacted and apply a risk-based approach to assess the cumulative pressures on the ecosystem and estimate the relative contributions of each of those stressors. A typical risk assessment consists of two aspects of risk: exposure and effect. The estimation of exposure requires the collection of spatial data from as many as possible of the pressures (e.g. nutrients, pollution, trawling, shipping, acidification) and ecosystem components (e.g. mangroves, reefs, seagrass, open waters) to estimate the spatial overlap. Estimation of effect requires a numerical sensitivity score expressing the severity of the effect the pressure can have on the component. Additionally, the effect can be translated into a potential impact using the population dynamics of the ecosystem component. Finally, all individual results are added up (cumulation by addition) where the difference between CEA and CIA lies in whether or not the final step is included (Piet et al., 2021a). This method is simple to implement. While the results may give a good estimate of the overall additive effects of human pressures, it still has a number of drawbacks. Some of them were recognized early on by Halpern et al. (2008b) but have not yet been implemented in typical applications of CIA/CEA. We have identified these six most important issues: Flat structure: Every ecosystem component makes the same (numerical) contribution to the final result. As an example, using one species of five different phyla, plus 10 bird species, will mean that the assessment result will be largely determined by the birds and not by any one of the species of some other phylum, regardless of their functional role or importance in the ecosystem. No uniform scaling: Typically, the minimum and maximum observed pressure intensities are rescaled to a common scale of 0–1, possibly doing some logarithmic transformation to keep numbers within a few magnitudes (e.g. Halpern et al., 2008a, 2008b). This, however, does not consider that the minimum/maximum pressure intensities of any two pressures from the dataset do not necessarily result in the same biological effect on the ecosystem component. The sensitivity score alone is not able to compensate for this. Further, ecosystem components are not on the same scale just because the same numeric value was measured for whatever parameter was used to quantify them. As an example, a species richness of five benthic fauna species in a given area is not the same as 5 g m-² biomass of the same or another species or the whole community in that same area. Weighting factors: Adding weighting factors to certain ecosystem components as a proxy for their importance will likely just increase the bias introduced by the flat structure (see #1) as some species gain even more influence on the final result of an assessment. Static sensitivity scores: Typically, sensitivity scores are derived from expert surveys and are static, i.e. represented by one single value. This leads to a bias. With low levels of a pressure, the reaction of an ecosystem component is often less pronounced than in situations where the pressure level is higher. The reaction does not follow a linear relationship with respect to the pressure level. Situations where pressure levels exceed a specific threshold value, leading to the death of a species, are not considered either. No uncertainty information: The amount of uncertainty in the final result is often unknown and typically underestimated. This can easily lead to misinterpretations, e.g. when using the data for management decisions where one area must be prioritized over another for conservation. An area might be chosen that would not be considered when the potentially high amount of uncertainty was known. No interaction: In the sectoral approach all individual effects are treated independently from each other and cumulation occurs by addition. In reality, however, an effect from one pressure on a specific species may be altered by that of another pressure e.g. when disturbed by noise it is more susceptible to collision or when mortality increases in a species suffering from contaminants and it is additionally disturbed from its preferred feeding grounds. Another type of interaction is a pressure on one species influencing the effect of that same pressure on another species (often called indirect or knock-on effects). For example, when a mammal species gets scared away by underwater noise and leaves a specific area, this will mean it will not feed on e.g. the fish species present in that area anymore. These fish might not be so sensitive to the underwater noise and the larger number of mammals in the other area may result in more fish entering the noisy region, thus increasing the number of fish affected by the noise. Thus, the sole usage of adding effects per component over all pressures may lead to misinterpretations. Lack of information on these interactions and the conceptual simplicity of many CIA/CEAs then lead to ignoring synergistic and antagonistic interactions. For a reliable and transparent CIA/CEA, these six issues should be considered in any method aiming to assess the cumulative effects of pressures. Here, we outline solutions to every one of these, which we are developing in a new framework to assess cumulative pressures, environmental status and supply of ecosystem services (Borja et al., 2024): i. Hierarchical structure: Instead of using an approach where each ecosystem component is treated identically, the structure of the assessment can be made hierarchical, e.g. reflecting the taxonomic relationships of the ecosystem components or using biological traits or other structural information important to the ecosystem. The structuring by taxonomy was already developed in NEAT (see Section 3.3). In this approach, the weight of every ecosystem component (e.g. a species) in the assessment is determined by its position within the taxonomic tree. The specific distribution of weights ensures that every taxonomic level of the taxonomic tree get the same share of the total weight. In practice, taking the example given above, all phyla in the assessment share the weight equally and when there is more than one species in a specific phylum, the weight of that phylum is again distributed evenly among itself and its lower taxonomic groups. Thus, a set of 10 bird species are not able to out-rule the other ecosystem components. This hierarchical structure can also be applied to a hierarchy of habitats or biotopes (e.g. using the European Union Nature Information System (EUNIS; Davies and Moss, 2002). ii. Uniform scaling: Instead of a pure mathematical scaling, a benchmark scale can be used. Such a scale means that the same value of the overall magnitude of a given pressure will result in a similar (biological) effect on an ecosystem component. For this, the scale needs to use common anchor or reference points that define specific reactions of ecosystem components that can be compared, e.g. 0 = no pressure; 0.4 = pressure is starting to have reproductive consequences on the ecosystem component; 0.6 = pressure is starting to have increased mortality; and 1 = the ecosystem component is lost. In order to determine these points, the overall pressure magnitude can be deconstructed into its constituents: intensity, frequency, range, type (direct or indirect), and recovery time after loss. Regarding the ecosystem components, it must be ensured that the same parameter is used for representing the ecosystem component, for example abundance or biomass only. When only mixed data are present, the data can either be reduced to only presence/absence (reducing the level of information by a large margin) or one parameter can be translated into the other by estimation or using a (numerical) translation function. iii. Weighting: A natural weighting can be achieved by utilizing the hierarchical structure from #1. The importance of an ecosystem component can thus be determined by its position within the taxonomical tree. A manual weighting factor can then be applied on top of the natural weighting. This should, however, only be done when a special kind of importance needs to be applied. This may be the case when legal settings require to put more weight on e.g. protected or rare species. iv. Dynamic sensitivities: Non-linear dose-response relationships can be used instead of a single sensitivity score. A single sensitivity value is equivalent of having a direct linear relationship between the magnitude of the pressure and the resulting (biological) effect on the ecosystem component. This can be modified in various ways, to reflect the typical reactions observed in nature: low levels of pressure may not increase the reaction until a threshold point from which the linear response starts; the response may be linear but with different values in specific ranges of the pressure magnitude, leading to a piece-wise linear relationship with different slopes; the response can be logarithmic, quickly increasing the response in the first place but then levelling off at higher pressure levels; the response may show a sigmoid behaviour with a slow increase of the reaction followed by a (tipping-point like) sharp increase and then slowly levelling off again. Instead of using a single value for the sensitivity, the procedure will thus use a sensitivity function that describes the dose-response relationship depending on the magnitude of the pressure on the uniform scale from issue ii. v. Uncertainty framework: A dedicated uncertainty framework can be applied to the data. Each pressure value and each value representing the presence of an ecosystem component should have an accompanying uncertainty value capturing the range of uncertainty about the measurement. As an example, this can be a standard error. The same can be applied to sensitivity scores or other measured and estimated data used in the assessment. This enables the application to use e.g. a bootstrapping procedure that will propagate the uncertainty through the whole assessment procedure, resulting in an overall uncertainty for the assessment result. With this, it can be quantified how large the range of uncertainty around the final assessment value is (Carstensen and Lindegarth, 2016). vi. Interaction: Known important interactions can be parameterized to support synergistic and antagonistic interactions between ecosystem components, in addition to the additive cumulation being used in the assessment procedure. In its easiest form this can be done by applying a matrix which gives a factor of 1 for all ecosystem component pairs that have no known interactions. Numbers above 1 represent synergistic interactions, increasing the total value of the effect for the paired components. Numbers below 1 represent antagonistic interactions, decreasing the total value. Ideally, these factors would only be applied in one direction, identifying a source and a recipient ecosystem component. Then, the factors can have two different values for each pair of ecosystem components, depending on for which components the cumulative effect is calculated (which component is the recipient component). In a more detailed assessment, the interaction values could be pressure-specific. To derive such detailed information about the interactions between components, it can be helpful to build a conceptual ecosystem model of the ecosystem at hand. If more detailed models are needed, a specific food web model may be used to quantify interactions. This will allow to analyze the relationships of the constituent ecosystem components in a systematic and transparent way. Given this framework of parameters of a CIA/CEA, other types of assessments can be applied. Instead of using ecosystem components the procedure can be applied to ecosystem functions instead. The estimated sensitivities/vulnerabilities of the various ecosystem components can be translated to the effect on the ecosystem functions which the components have. Ultimately, this is a proxy for their capacity to supply ecosystem services resulting from those functions. To make such assessments, a translation needs to be done, transferring the spatial and quantitative distribution of the ecosystem components into their ecosystem functions as an indication of their capacity to supply ecosystem services. Often, a CIA/CEA is one of the indicators as part of a status assessment. E.g., CIA/CEA is one of the pillars for achieving good environmental status, sensu MSFD (Borja et al., 2024). It is important to note the semantic difference between an effect and a potential impact (Piet et al., 2021a) as estimated in respectively a CEA and CIA as presented here. These potential impacts are different from the concrete impacts as they are measured with monitoring programs that are the basis of environmental status assessments. CIA rather is intended to guide management towards minimizing and mitigating pressures in order to improve environmental status and/or predicting the risk of further deterioration given the current monitored situation. Thus, not every effect is automatically an impact. Further, a potential impact as estimated by CIA does not directly translate into a "status" in the sense of a status assessment (Borja et al., 2013). These considerations show the necessity of a unifying framework for assessing cumulative effects and applying these together with environmental status assessments as part of EBM. We here propose a systematic approach to CIA/CEA to form such a unified framework. Not every aspect of the framework must be employed every time an assessment is made, but the users should be aware of them, assessing their influence on the final result, transparently documenting what is left out from the analysis and to which degree this increases the uncertainty of the result on top of the pure uncertainty imposed by the data. As a common foundation of the framework, we propose to use DAPSI(W)R(M) (Elliott et al., 2017a), for structuring the problem and determining the implementation of the management cycle. DAPSI(W)R(M) links the human system of Drivers, Activities, Pressures, Status, Impacts, Welfare, Responses and Measures to the natural system of environmental status. The proposed framework follows a typical EBM cycle and starts with the environmental status of the natural system: (1) Assess the environmental status, using state indicators alone; (2) identify the problematic ecosystem components (the ones not allowing to achieve good status); (3) do cumulative effects assessment; (4) identify the pressures that likely prevent achieving good status for the identified ecosystem components (to inform management); (5) take measures, set or strengthen the thresholds for the respective pressure and wait for the environmental response to happen; and (6) repeat. This cycle of actions and assessments will allow management to iteratively advance closer and closer to a good environmental status and thus to good ocean health. It uses distinct management phases and separates status assessment from CIA/CEA by putting them into different stages of the cycle. Nonetheless, both status and impact analysis are still integral parts of the cycle and need to be used to complement each other. Discussion: looking forward in monitoring and assessing the ocean Long-term marine monitoring has demonstrated high value for informed management decisions addressed to reduce the impacts of human activities and pressures (Borja and Elliott, 2019). Therefore, maintenance of these long-term monitoring networks is critical, and relies on the development of cost-effective implementation approaches (Borja and Elliott, 2013) and methods (Hyvärinen et al., 2021). These methods can be either traditional or rely on novel technologies, with a broad range of sampling, observation and analytical techniques (Danovaro et al., 2016), such as: instruments (e.g. buoys to deploy sensors, physico-chemical sensors, remotely operated vehicles, autonomous vehicles, biosensors, biologgers, passive sampling), molecular approaches (e.g. metabarcoding of eDNA), acoustic devices (e.g. multibeam), flow cytometry, imaging, remote sensing (optical, thermal and radar images from airborne, including drones, and satellite sensors), artificial substrata, etc. Traditional methods are often time consuming, and costly, which typically translates into pronounced limitations of the spatio-temporal resolution and coverage that is required by policy-makers, stakeholders and decision-makers (Borja and Elliott, 2013; Mack et al., 2020). To improve cost-efficiency, reproducibility and spatio-temporal resolution, novel methods are being proposed, i.e. as proof-of-concept, or already operational but with little application in the marine realm, using five basic criteria (Mack et al., 2020): (i) technology readiness level of seven or higher; (ii) comprehensive and standalone techniques; (iii) filling a gap in the current monitoring frameworks; (iv) novel and not in general use; and (v) evaluated as cost-efficient in terms of their cost-benefit ratio (including investment costs, monitoring costs, reliability, environmental impact, added value, limitations, required expertise). These criteria were all identified and rated for 22 novel methods relevant to marine monitoring, with some of them being included in the presented review (e.g. molecular methods, drones, etc.). For novel methodologies to prove their usefulness and value to monitor and assess marine ecosystem status and health of coasts and seas, they need to score well on relevant criteria, as those proposed by Mack et al., (2020) and in addition be properly communicated to end-users, especially to stakeholders as management agencies who will be using data generated by such innovative tools for improve management actions (Seys et al., 2022). Some marine organizations have proposed step-by-step tailored guides for an accurate and efficient communication (Seeyave et al., 2017). Usually, this communication requires (i) identifying the target audience (e.g. end-users of monitoring and assessment methods, policy-makers, decision-makers, managers); (ii) break down the communication objectives into relevant messages for each target audience, starting with those with highest priority (e.g. those deciding on monitoring and assessment infrastructures); (iii) for each target, decide the most appropriate channel to communicate (e.g., hard copy, digital, in-person, etc.); and (iv) build the communication implementation plan, including the objectives, target audience, tasks and methods, timing and key performance indicators (Seeyave et al., 2017). An example of a communication plan, from one of the projects participating in the summer school (GES4SEAS), can be seen in Figure 10 (Borja et al., 2024). It includes the outcomes and key messages to be transmitted (“What?”), the target audience (“To whom?”), the multiple channels used, which can include social media, videos, infographics, summer schools, specialized workshops, etc. (“Which channel?”), and also the expected effects on the audience (“What effect?”). Research programmes on monitoring and assessment, based on individual and unconnected projects, can lead to research fragmentation, wasting resources through duplication, dispersion and overlap (Elliott et al., 2017b). To minimize these deficits, the Horizon Europe programme offers an instrument for collaboration among “sister” projects (i.e. those under the same call or addressing close topics), facilitating synergies in their outcomes, communication, stakeholders' participation and increase of knowledge. This has been the case of the summer school which has resulted in this contribution, in which five projects (i.e., GES4SEAS, OBAMA-NEXT, BIOcean5D, ACTNOW and MARBEFES) have collaborated to bring to the scientific and stakeholders’ community the most recent advances in marine monitoring and assessment and the way forward, to better manage the ocean and reduce the effects of multiple human pressures on ecosystem components (Borja et al., 2024). Regarding those advances, in the summer school discussions the following tools emerged and were evaluated to be important and useful for future monitoring, including remote sensing, drones, eDNA metabarcoding, video, biological sensors, imaging and machine learning. The applicability and their and pros and cons for marine coastal and marine monitoring have been discussed in the sections above. In the case of assessment methods, the availability of integrative and flexible tools was discussed, highlighting the importance of having capacity to be feed by large open access datasets (e.g. Lowndes et al., 2017; Borja et al., 2019) and having an associated confidence of status classification system. Regarding the attendees comments about the expectations from a marine monitoring tool, they included minimizing the environmental impact (i.e., non-destructive methods, or related to the animal ethics, as in the case of biologging), maximizing repeatability throughout time and space, having reasonable affordability for operation and maintenance, demonstrating the ability to monitor spatially and work autonomously for a long time-period, standardizing measurements, and being cheap, easy to use, precise and efficient. As a concluding remark, the attendees agreed that one of the most important challenges ahead is associated with integrating the novel methods presented here (or others) into long standing time series for data continuity, alongside current benchmark methodologies. This requires dedicated and well-planned transition periods allowing for quantitative and qualitative comparison of methods to secure consistent and data-driven ecosystem assessments. Such actions can be cover through intense collaborations between current and future European projects on marine biodiversity and ecosystem health. Declarations Funding section This manuscript is a result of GES4SEAS (Achieving Good Environmental Status for maintaining ecosystem services, by assessing integrated impacts of cumulative pressures) project, funded by the European Union under the Horizon Europe program (grant agreement no. 101059877), www.ges4seas.eu; OBAMA-NEXT (Observing and Mapping Marine Ecosystems – Next Generation Tools) project, funded by the European Union under the Horizon Europe program (grant agreement no. 101081642), www.obama-next.eu; BIOcean5D (Marine Biodiversity Assessment and Prediction Across Spatial, Temporal and Human Scales) project, co-funded by the European Union under the Horizon Europe program (grant agreement no. 101059915), the UK government Horizon Europe Guarantee, UKRI (grant reference no. 10039266) and the Swiss State Secretariat for Education, Research and Innovation, SERI (contract no. 22.00255), www.biocean5d.org; FutureMARES, funded by the European Union under the Horizon Europe program (grant agreement no. 869300), http://www.futuremares.eu"www.futuremares.eu; ACTNOW, funded by the European Union under the Horizon Europe program (grant agreement no. 101060072), www.actnow-project.eu; MARBEFES (MARine Biodiversity and Ecosystem Functioning leading to Ecosystem Services) project, funded by the European Union under the Horizon Europe program (grant agreement no. 101060937) and supported by Innovate UK through the Horizon Europe Guarantee scheme (grant number 10040216), https://marbefes.eu/http://www.actnow-project.eu/; and the Norwegian Infrastructure for drone-based research, mapping, and monitoring in the coastal zone - SeaBee, funded by the Research Council of Norway (grant agreement 296478), www.seabee.no. Acknowledgements This manuscript is based on the discussions taken during the AZTI’s summer school (2 nd GES4SEAS), held in the Aquarium of San Sebastián (Spain), on 5 th to 7 th June 2023. Science Crunchers have designed Figure 10. This is contribution nr XXX from AZTI’s Marine Research, Basque Research and Technology Alliance (BRTA). Data Availability declaration Not applicable, since no data are associated to this manuscript. Statements and declarations The authors declare no competing interests, financial or non-financial. Ethics approval and consent to participate All authors have read the last version of the manuscript prepared and approved it before submission. Author Contributions AB coordinated the group and wrote the first draft. NRE wrote section on eDNA, KH, AGH, HG, and JES wrote section on drones, SP wrote section on AI and pelagic environments, ET wrote section on climate modelling, GJP wrote section on CIA, SK wrote section on assessing multiple pressures in Europe, IM wrote section on NEAT, TL wrote section on ecosystem services mapping, TB and CM wrote section on a unifying framework, MCU and MCL contributed to communication parts. All authors collaboratively wrote the introduction, discussion and conclusions, contributed to the revision of the article, and approved the version submitted for publication. References Adade, R., Aibinu, A. M., Ekumah, B., Asaana, J, 2021. Unmanned Aerial Vehicle (UAV) applications in coastal zone management – a review. Environmental Monitoring and Assessment, 193: 154. Álvarez-González, M., Suarez-Bregua, P., Pierce, G. J., Saavedra, C, 2023. Unmanned Aerial Vehicles (UAVs) in Marine Mammal Research: A Review of Current Applications and Challenges. Drones 7: 667, https://doi.org/10.3390/drones7110667 Andersen, J.H., A. Stock, S. Heinänen, M. Mannerla, M. Vinther, 2013. Human Uses, Pressures and Impacts in the Eastern North Sea. Technical Report from DCE – Danish Centre for Environment and Energy No. 18. Aarhus University, Roskilde. Aniceto, A. S., Biuw, M., Lindstrøm, U., Solbø, S. A., Broms, F., Carroll, J, 2018. Monitoring marine mammals using unmanned aerial vehicles: quantifying detection certainty. Ecosphere 9(3):e02122. 10.1002/ecs2.2122 Bagstad, K.J., J.C. Ingram, C.D. Shapiro, A. La Notte, J. Maes, S. Vallecillo, C.F. Casey, P.D. Glynn, M.P. Heris, J.A. Johnson, C. Lauer, J. Matuszak, K.L.L. Oleson, S.M. Posner, C. Rhodes, B. Voigt, 2021. Lessons learned from development of natural capital accounts in the United States and European Union. Ecosystem Services, 52: 101359. Barnes, M.A., C.R. Turner, 2016. The ecology of environmental DNA and implications for conservation genetics. Conservation Genetics, 17: 1-17. Bartkowski, B., N. Lienhoop, B. Hansjürgens, 2015. Capturing the complexity of biodiversity: A critical review of economic valuation studies of biological diversity. Ecological Economics, 113: 1-14. Benfield, M.C., P. Grosjean, P.F. Culverhouse, X. Irigoien, M.E. Sieracki, A. Lopez-Urrutia, H.G. Dam, Q. Hu, C.S. Davis, A. Hansen, C.H. Pilskaln, E.M. Riseman, H. Schultz, P.E. Utgoff, G. Gorsky, 2007. RAPID: research on automated plankton identification. Oceanography, 20(2), 172-187. Bevilacqua, S., G. Guarnieri, G. Farella, A. Terlizzi, S. Fraschetti, 2018. A regional assessment of cumulative impact mapping on Mediterranean coralligenous outcrops. Scientific Reports, 8:1757, DOI:10.1038/s41598-018-20297-1 Borgwardt, F., L. Robinson, D. Trauner, H. Teixeira, A.J.A. Nogueira, A.I. Lillebø, G. Piet, M. Kuemmerlen, T. O'Higgins, H. McDonald, J. Arevalo-Torres, A.L. Barbosa, A. Iglesias-Campos, T. Hein, F. Culhane, 2019. Exploring variability in environmental impact risk from human activities across aquatic ecosystems. Science of The Total Environment, 652: 1396-1408. Borja, A., M. Elliott, 2013. Marine monitoring during an economic crisis: The cure is worse than the disease. Marine Pollution Bulletin, 68: 1-3. Borja, A., M. Elliott, 2021. From an economic crisis to a pandemic crisis: The need for accurate marine monitoring data to take informed management decisions. Advances in Marine Biology, 89: 79-114. Borja, A., M. Elliott, J.H. Andersen, A.C. Cardoso, J. Carstensen, J.G. Ferreira, A.-S. Heiskanen, J.C. Marques, J.M. Neto, H. Teixeira, L. Uusitalo, M.C. Uyarra, N. Zampoukas, 2013. Good Environmental Status of marine ecosystems: What is it and how do we know when we have attained it? Marine Pollution Bulletin, 76: 16-27. Borja, A., M. Elliott, J.H. Andersen, T. Berg, J. Carstensen, B.S. Halpern, A.-S. Heiskanen, S. Korpinen, J.S.S. Lowndes, G. Martin, N. Rodriguez-Ezpeleta, 2016. Overview of integrative assessment of marine systems: the Ecosystem Approach in practice. Frontiers in Marine Science, 3: doi: 10.3389/fmars.2016.00020. Borja, A., J.M. Garmendia, I. Menchaca, A. Uriarte, Y. Sagarmínaga, 2019. Yes, We Can! Large-Scale Integrative Assessment of European Regional Seas, Using Open Access Databases. Frontiers in Marine Science, 6: 10.3389/fmars.2019.00019. Borja, A., M.P. White, E. Berdalet, N. Bock, C. Eatock, P. Kristensen, A. Leonard, J. Lloret, S. Pahl, M. Parga, J.V. Prieto, S. Wuijts, L.E. Fleming, 2020. Moving Toward an Agenda on Ocean Health and Human Health in Europe. Frontiers in Marine Science, 7: 10.3389/fmars.2020.00037. Borja, A., I. Menchaca, J.M. Garmendia, J. Franco, J. Larreta, Y. Sagarminaga, Y. Schembri, R. González, R. Antón, T. Micallef, S. Camilleri, O. Solaun, A. Uriarte, M.C. Uyarra, 2021. Big Insights From a Small Country: The Added Value of Integrated Assessment in the Marine Environmental Status Evaluation of Malta. Frontiers in Marine Science, 8: 10.3389/fmars.2021.638232. Borja, A., Elliott, M., Teixeira, H., Stelzenmuller, V., Katsanevakis, S., Coll, M., Galparsoro, I., Fraschetti, S., Papadopoulou, N., Lynam, C., Berg, T., Andersen, J.H., Carstensen, J., Leal, M.C. and Uyarra, M.C., 2024. Addressing the cumulative impacts of multiple human pressures in marine systems, for a sustainable use of the seas. Frontiers in Ocean Sustainability, 1:1308125. doi: 10.3389/focsu.2023.1308125 Burian, A., Q. Mauvisseau, M. Bulling, S. Domisch, S. Qian, M. Sweet, 2021. Improving the reliability of eDNA data interpretation. Molecular Ecology Resources, 21: 1422-1433. Canals, O., I. Mendibil, M. Santos, X. Irigoien, N. Rodríguez-Ezpeleta, 2021. Vertical stratification of environmental DNA in the open ocean captures ecological patterns and behavior of deep-sea fishes. Limnology and Oceanography Letters, 6: 339-347. Carstensen, J., M. Lindegarth, 2016. Confidence in ecological indicators: A framework for quantifying uncertainty components from monitoring data. Ecological Indicators, 67: 306-317. Casella, E., Collin, A., Harris, D., Ferse, S., Bejarano, S., Parravicini, V., Hench, J. L. Rovere, A, 2017. Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques. Coral Reefs (2017) 36:269–275. doi:10.1007/s00338-016-1522-0 Cavallo, M., Elliott, M., Quintino, V., Touza, J. 2018. Can national management measures achieve good status across international boundaries? - a case study of the Bay of Biscay and Iberian coast sub-region. Ocean Coast. Manag. 160, 93–102. Cavanaugh, K. C., Cavanaugh, K. C., Bell, T. W., Hockridge, E. G. 2021. Front. Environ. Sci. 17, https://doi.org/10.3389/fenvs.2020.587354 Chen, G., J. Yang, B. Huang, C. Ma, F. Tian, L. Ge, L. Xia, J. Li, 2023. Toward digital twin of the ocean: from digitalization to cloning. Intelligent Marine Technology and Systems, 1(1): 3. Chust, G., M. Grande, I. Galparsoro, A. Uriarte, Á. Borja, 2010. Capabilities of the bathymetric Hawk Eye LiDAR for coastal habitat mapping: A case study within a Basque estuary. Estuarine, Coastal and Shelf Science, 89: 200-213. Clark, D., E. Goodwin, J. Sinner, J. Ellis, G. Singh, 2016. Validation and limitations of a cumulative impact model for an estuary. Ocean and Coastal Management, 120: 88-98. Claver, C., O. Canals, L. G. de Amézaga, I. Mendibil, N. Rodriguez-Ezpeleta, 2023. An automated workflow to assess completeness and curate GenBank for environmental DNA metabarcoding: The marine fish assemblage as case study. Environmental DNA, 5: 634-647. Coll, M., C. Piroddi, C. Albouy, F. Ben Rais Lasram, W. W. L. Cheung, V. Christensen, V. S. Karpouzi, F. Guilhaumon, D. Mouillot, M. Paleczny, M. L. Palomares, J. Steenbeek, P. Trujillo, R. Watson, D. Pauly, 2012. The Mediterranean Sea under siege: spatial overlap between marine biodiversity, cumulative threats and marine reserves. Global Ecology and Biogeography, 21: 465-480. Coll, M., J. Steenbeek, J. Sole, I. Palomera, V. Christensen, 2016. Modelling the cumulative spatial–temporal effects of environmental drivers and fishing in a NW Mediterranean marine ecosystem. Ecological Modelling, 331: 100-114. Cormier, R., C.R. Kelble, M.R. Anderson, J.I. Allen, A. Grehan, Ó. Gregersen, 2017. Moving from ecosystem-based policy objectives to operational implementation of ecosystem-based management measures. ICES Journal of Marine Science, 74: 406-413. Cormier, R., M. Elliott, J. Rice, 2019. Putting on a bow-tie to sort out who does what and why in the complex arena of marine policy and management. Science of The Total Environment, 648: 293-305. Dailianis, T., C.J. Smith, N. Papadopoulou, V. Gerovasileiou, K. Sevastou, T. Bekkby, M. Bilan, D. Billett, C. Boström, M. Carreiro-Silva, R. Danovaro, S. Fraschetti, K. Gagnon, C. Gambi, A. Grehan, S. Kipson, J. Kotta, C.J. McOwen, T. Morato, H. Ojaveer, C.K. Pham, R. Scrimgeour, 2018. Human activities and resultant pressures on key European marine habitats: An analysis of mapped resources. Marine Policy, 98: 1-10. Daly, H.E., J. Farley, 2004. Ecological Economics – principles and application. Island Press. Washington, DC (USA). Danovaro, R., L. Carugati, M. Berzano, A.E. Cahill, S. Carvalho, A. Chenuil, C. Corinaldesi, S. Cristina, R. David, A. Dell'Anno, N. Dzhembekova, E. Garcés, J.M. Gasol, P. Goela, J.-P. Féral, I. Ferrera, R.M. Forster, A.A. Kurekin, E. Rastelli, V. Marinova, P.I. Miller, S. Moncheva, A. Newton, J.K. Pearman, S.G. Pitois, A. Reñé, N. Rodríguez-Ezpeleta, V. Saggiomo, S.G.H. Simis, K. Stefanova, C. Wilson, M. Lo Martire, S. Greco, S.K.J. Cochrane, O. Mangoni, A. Borja, 2016. Implementing and Innovating Marine Monitoring Approaches for Assessing Marine Environmental Status. Frontiers in Marine Science, 3: 10.3389/fmars.2016.00213. Data Study Group team, 2022. Data Study Group Final Report: Centre for Environment, Fisheries and Aquaculture Science, Plankton image classification. Alan Turing Institute. Zenodo. https://doi.org/10.5281/zenodo.6799166. Davies, C.E., Moss, D., 2002. EUNIS Habitat Classification, February 2002. P. European Topic Centre on Nature Protection and Biodiversity. Doughty, C. L., Cavanaugh, K. C, 2019. Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery. Remote Sens. 11: 540, https://doi.org/10.3390/rs11050540 Drago, L., T. Panaïotis, J.-O. Irisson, M. Babin, T. Biard, F. Carlotti, L. Coppola, L. Guidi, H. Hauss, L. Karp-Boss, F. Lombard, A.M.P. McDonnell, M. Picheral, A. Rogge, A.M. Waite, L. Stemmann, R. Kiko, 2022. Global distribution of zooplankton biomass estimated by in situ imaging and machine learning. Frontiers in Marine Science, 9: 10.3389/fmars.2022.894372 Duffy, J. P., Pratt, L., Anderson, K., Land, P. E., Shutler, J. D., 2018. Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone. Estuarine, Coastal and Shelf Science, 200: 169-180 Dulvy N.K., S.I. Rogers, S. Jennings, V. Stelzenmüller, S.R. Dye, H.R. Skjoldal, 2008. "limate change and deepening of the North Sea fish assemblage: a biotic indicator of warming seas. Journal of Applied Ecology 45(4): 1029-1039 EC-CINEA. 2021. Guidelines for implementing an ecosystem-based approach in maritime spatial planning : including a method for the evaluation, monitoring and review of EBA in MSP. ISBN 978-92-95225-17-6, DOI 10.2926/84261, Catalogue number HZ-01-21-323-EN-N. Edney, A. J., Hart, T., Jessopp, M. J., Banks, A., Clarke, L. E., Cugnière, L., Elliot, K. H., Juarez Martinez, I., Kilcoyne, A., Murphy, M., Nager, R. G., Ratcliffe, N., Thompson, D. L., Ward, R. M., Wood, M. J, 2023. Best practices for using drones in seabird monitoring and research. Marine Ornithology 51: 265-280 Ehler, C.N., 2021. Two decades of progress in Marine Spatial Planning. Marine Policy, 132: 10.1016/j.marpol.2020.104134 Elliott, M., D. Burdon, J.P. Atkins, A. Borja, R. Cormier, V.N. de Jonge, R.K. Turner, 2017a. “And DPSIR begat DAPSI(W)R(M)!” - A unifying framework for marine environmental management. Marine Pollution Bulletin, 118: 27-40. Elliott, M., P. Snoeijs-Leijonmalm, S. Barnard, 2017b. ‘The dissemination diamond’ and paradoxes of science-to-science and science-to-policy communication: Lessons from large marine research programmes. Marine Pollution Bulletin, 125: 1-3. Elliott, M., Á. Borja, R. Cormier, 2020. Managing marine resources sustainably: A proposed integrated systems analysis approach. Ocean & Coastal Management, 197: 105315. Erickson, R.A., C.B. Rees, A.A. Coulter, C.M. Merkes, S.G. McCalla, K.F. Touzinsky, L. Walleser, R.R. Goforth, J.J. Amberg, 2016. Detecting the movement and spawning activity of bigheaded carps with environmental DNA. Molecular Ecology Resources, 16: 957-965. European Commission, 2008. Directive 2008/56/EC of the European Parliament and of the Council establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework Directive). Official Journal of the European Union, L164: 19-40. European Commission, 2017. Commission Directive (EU) 2017/845 of 17 May 2017 amending Directive 2008/56/EC of the European Parliament and of the Council as regards the indicative lists of elements to be taken into account for the preparation of marine strategies Official Journal of the European Communities, L125: 27-33. European Commission, 2020. Communication from the Commission of the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, EU Biodiversity Strategy for 2030, Bringing nature back into our lives. Brussels, 20.5.2020; COM(2020) 380 final 27 pp. European Commission, 2021. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions on a new approach for a sustainable blue economy in the EU. Transforming the EU's Blue Economy for a Sustainable Future. Brussels, 17.5.2021 COM(2021) 240 final 21 pp. European Commission. 2022a. Marine biodiversity modelling study. Directorate General for Research and Innovation; Publications Office of the European Union. European Commission, 2022b. MSFD CIS Guidance Document No. 19, Article 8 MSFD, May 2022. European Commission, Directorate General for Maritime Affairs & Fisheries, A. Addamo, A. Calvo Santos, J. Guillén, S. Neehus, A. Peralta Baptista, S. Quatrini, T. Telsnig, G. Petrucco. 2022. The EU blue economy report 2022. Publications Office of the European Union. European Union, 2014. Directive 2014/89/EU of the European Parliament and of the Council of 23 July 2014 establishing a framework for maritime spatial planning. Official Journal of the European Union, L257: 135-145. Féral, J.-P., A. Norro, 2023. Specific initial training standards are needed to dive for science in Europe, Occupational vs. Citizen Science Diving. Frontiers in Marine Science, 10: 10.3389/fmars.2023.1134494 Fisher, B., Turner R.K., Morling P., 2009. Defining and classifying ecosystem services for decision making, Ecological Economics, 68(3): 643-653. https://doi.org/10.1016/j.ecolecon.2008.09.014. Fraija-Fernández, N., M.-C. Bouquieaux, A. Rey, I. Mendibil, U. Cotano, X. Irigoien, M. Santos, N. Rodríguez-Ezpeleta, 2020. Marine water environmental DNA metabarcoding provides a comprehensive fish diversity assessment and reveals spatial patterns in a large oceanic area. Ecology and Evolution, 10: 7560-7584. Fraschetti, S., E. Fabbrizzi, L. Tamburello, M.C. Uyarra, F. Micheli, E. Sala, C. Pipitone, F. Badalamenti, S. Bevilacqua, J. Boada, E. Cebrian, G. Ceccherelli, M. Chiantore, G. D'Anna, A. Di Franco, S. Farina, S. Giakoumi, E. Gissi, I. Guala, P. Guidetti, S. Katsanevakis, E. Manea, M. Montefalcone, M. Sini, V. Asnaghi, A. Calò, M. Di Lorenzo, J. Garrabou, L. Musco, A. Oprandi, G. Rilov, A. Borja, 2022. An integrated assessment of the Good Environmental Status of Mediterranean Marine Protected Areas. Journal of Environmental Management, 305: 114370. Frazão-Santos, C., T. Agardy, F. Andrade, H. Calado, L.B. Crowder, C.N. Ehler, S. García-Morales, E. Gissi, B.S. Halpern, M.K. Orbach, H.-O. Pörtner, R. Rosa, 2020. Integrating climate change in ocean planning. Nature Sustainability: 1-12. Frazão-Santos, C., T. Agardy, F. Andrade, H., L.B. Crowder, C.N. Ehler, M.K. Orbach, 2021. Major challenges in developing marine spatial planning. Marine Policy, 132: 10.1016/j.marpol.2018.08.032 Garcia, S.M. R.,J.; Charles, A. 2014. Governance of Marine Fisheries and Biodiversity Conservation: Interaction and Coevolution. 511 pp. Giering, S.L., Culverhouse, P.F., Johns, D.G., McQuatters-Gollop, A., Pitois, S.G. 2022. Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making. Frontiers in Marine Science, 9: 986206. Grossmann, V., Nakath, D., Urlaub, M., Oppelt, N., Koch, R., Köser, K. 2022. Digital twinning in the ocean-chanllenges in multimodal sensing and multiscale fusion based on faithful visual models. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4: 345-352. Haines-Young, R., M.B. Potschin, 2018. Common International Classification of Ecosystem Services (CICES) V5.1 and Guidance on the Application of the Revised Structure. Available from www.cices.eu 53 pp. Halpern, B.S., R. Fujita, 2013. Assumptions, challenges, and future directions in cumulative impact analysis. Ecosphere. https://doi.org/10.1890/ES13-00181. Halpern, B.S., K.A. Selkoe, F. Micheli, C.V. Kappel, 2007. Evaluating and Ranking the Vulnerability of Global Marine Ecosystems to Anthropogenic Threats. - Conservation Biology, 21(5): 1301-1315 Halpern, B.S., K.L. McLeod, A.A. Rosenberg, L.B. Crowder, 2008a. Managing for cumulative impacts in ecosystem-based management through ocean zoning. Ocean & Coastal Management, 51: 203-211. Halpern, B.S., S. Walbridge, K.A. Selkoe, C.V. Kappel, F. Micheli, C. D'Agrosa, J.F. Bruno, K.S. Casey, C. Ebert, H.E. Fox, R. Fujita, D. Heinemann, H.S. Lenihan, E.M.P. Madin, M.T. Perry, E.R. Selig, M. Spalding, R. Steneck, R. Watson, 2008b. A Global Map of Human Impact on Marine Ecosystems. Science, 319: 948-952. Hansen, B.K., M.W. Jacobsen, A.L. Middelboe, C.M. Preston, R. Marin, D. Bekkevold, S.W. Knudsen, P.R. Møller, E.E. Nielsen, 2020. Remote, autonomous real-time monitoring of environmental DNA from commercial fish. Scientific Reports, 10: 13272. Hawkins, E., R. Sutton, 2012. Time of emergence of climate signals. Geophysical Research Letters 39(1). Hering, D., C. Schürings, F. Wenskus, K. Blackstock, A. Borja, S. Birk, C. Bullock, L. Carvalho, M.B. Dagher-Kharrat, S. Lakner, N. Lovrić, S. McGuinness, G.-J. Nabuurs, A. Sánchez-Arcilla, J. Settele, G. Pe’er, 2023. Securing success for the Nature Restoration Laws. Science, 382: 1248-1250. doi:10.1126/science.adk1658 Holman, L.E., Y. Chng, M. Rius, 2022. How does eDNA decay affect metabarcoding experiments? Environmental DNA, 4: 108-116. Hyvärinen, H., A. Skyttä, S. Jernberg, K. Meissner, H. Kuosa, L. Uusitalo, 2021. Cost-efficiency assessments of marine monitoring methods lack rigor—a systematic mapping of literature and an end-user view on optimal cost-efficiency analysis. Environmental Monitoring and Assessment, 193: 400. Isbell, F., Balvanera, P., Mori, A.S., He, J.S., Bullock, J.M., Regmi, G.M., et al. 2022. Expert perspectives on global biodiversity loss and its drivers and impacts on people. Front Ecol Environ 2023; 21(2): 94–103, doi:10.1002/fee.2536. Joyce, K. E., Andersen, K., Bartolo, R. E. 2021. Of course we fly unmanned – we're women! Drones 5, 21. https://doi.org/10.3390/drones5010021 Joyce, Fickas, K. C., Kalamandeen, M, 2023. The unique value proposition for using drones to map coastal ecosystems. Cambridge Prisms: Coastal Futures, 1, e6. https://doi.org/10.1017/cft.2022.7 Kazanidis, G., C. Orejas, A. Borja, E. Kenchington, L.-A. Henry, O. Callery, M. Carreiro-Silva, H. Egilsdottir, E. Giacomello, A. Grehan, L. Menot, T. Morato, S.Á. Ragnarsson, J.L. Rueda, D. Stirling, T. Stratmann, D. van Oevelen, A. Palialexis, D. Johnson, J.M. Roberts, 2020. Assessing the environmental status of selected North Atlantic deep-sea ecosystems. Ecological Indicators, 119: 106624. Kirkfeldt, T.S., 2019. An ocean of concepts: Why choosing between ecosystem-based management, ecosystem-based approach and ecosystem approach makes a difference. Marine Policy, 106: 103541. Knights, A.M., G.J. Piet, R.H. Jongbloed, J.E. Tamis, L. White, E. Akoglu, L. Boicenco, T. Churilova, O. Kryvenko, V. Fleming-Lehtinen, J.-M. Leppanen, B.S. Galil, F. Goodsir, M. Goren, P. Margonski, S. Moncheva, T. Oguz, K.N. Papadopoulou, O. Setälä, C.J. Smith, K. Stefanova, F. Timofte, L.A. Robinson, 2015. An exposure-effect approach for evaluating ecosystem-wide risks from human activities. ICES Journal of Marine Science, 72: 1105-1115. Korpinen, S., J.H. Andersen, 2016. A global review of cumulative pressure and impact assessments in marine environments. Frontiers in Marine Science, 3. https://doi.org/10.3389/fmars.2016.00153. Korpinen, S., L. Meski, J.H. Andersen, M. Laamanen. 2012. Human pressures and their potential impact on the Baltic Sea ecosystem. Ecological Indicators, 15: 105–114. Korpinen, S., M. Meidinger, M. Laamanen, 2013. Cumulative impacts on seabed habitats: An indicator for assessments of good environmental status. Marine Pollution Bulletin, 74: 311-319. Korpinen, S., L. Laamanen, L. Bergström, M. Nurmi, J.H. Andersen, J. Haapaniemi, E.T. Harvey, C.J. Murray, M. Peterlin, E. Kallenbach, K. Klančnik, U. Stein, L. Tunesi, D. Vaughan, J. Reker, 2021. Combined effects of human pressures on Europe’s marine ecosystems. AMBIO, 50: 1325-1336. Kraft, K., O. Velhonoja, T. Eerola, S. Suikkanen, T. Tamminen, L. Haraguchi, P. Ylöstalo, S. Kielosto, M. Johansson, L. Lensu, H. Kälviäinen, H. Haario, J. Seppälä, 2022. Towards operational phytoplankton recognition with automated high-throughput imaging, near-real-time data processing, and convolutional neural networks. Frontiers in Marine Science, 9: 867695. Krause-Jensen D, Gundersen H, Björk M, Gullström M, Dahl M, Asplund ME, Boström C, Holmer M, Banta G, Graversen AEL, Pedersen MF, Bekkby T, Frigstad H, Skjellum SF, Thormar J, Gyldenkærne S, Howard J, Pidgeon E, Ragnarsdóttir SB, Mols-Mortensen A, Hancke K. 2022. Nordic Blue Carbon ecosystems: Status and outlook. Frontiers in Marine Science, Review article. doi: 10.3389/fmars.2022.847544 Kvile, K. Ø., Gundersen, H., Sample, J. E., Buls, T., Poulsen, R. N., Bekkby, T., Ghareeb, M. E., Salberg, A.-B., Hancke, K. (Submitted). Drone and ground-truth data collection and image annotation: A protocol for coastal habitat mapping and classification. MethodsX Journal Lauria, V., Attrill, M.J., Brown, A., Edwards, M., Votier, S.C., 2013. Regional variation in the impact of climate change: evidence that bottom-up regulation from plankton to seabirds is weak in parts of the Northeast Atlantic. Marine Ecology Progress Series, 488: 11-22. Le, K.T., Z. Yuan, A. Syed, D. Ratelle, E.C. Orenstein, M.L. Carter, S. Strang, K.M. Kenitz, P. Morgado, P.J.S. Franks, N. Vasconcelos, J.S. Jaffe, 2022. Benchmarking and automating the image recognition capability of an in situ plankton imaging system. Frontiers in Marine Science, 9: 869088. Liu, Y., Liu, Q., Sample, J. E., Hancke, K., Salberg, A.-B, 2022. Coastal habitat mapping with UAV multi-sensor data: an experiment among DCNN-based approaches. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 439-445 Lombard, F., E. Boss, A.M. Waite, M. Vogt, J. Uitz, L. Stemmann, H.M. Sosik, J. Schulz, J.-B. Romagnan, M. Picheral, J. Pearlman, M.D. Ohman, B. Niehoff, K.O. Möller, P. Miloslavich, A. Lara-Lpez, R. Kudela, R.M. Lopes, R. Kiko, L. Karp-Boss, J.S. Jaffe, M.H. Iversen, J.-O. Irisson, K. Fennel, H. Hauss, L. Guidi, G. Gorsky, S.L.C. Giering, P. Gaube, S. Gallager, G. Dubelaar, R.K. Cowen, F. Carlotti, C. Briseño-Avena, L. Berline, K. Benoit-Bird, N. Bax, S. Batten, S.D. Ayata, L.F. Artigas, W. Appeltans, 2019. Globally consistent quantitative observations of planktonic ecosystems. Frontiers in Marine Science, 6: 10.3389/fmars.2019.00196 Long, R. 2012. Legal aspects of ecosystem-based marine management in Europe. In A. Chircop, M. L. McConnell, & S. Coffen-Smout (Eds.), Ocean yearbook (Vol. 26, pp. 417–484). Boston: Brill Academic Publishers. Long, R.D., A. Charles, R.L. Stephenson, 2015. Key principles of marine ecosystem-based management. Marine Policy, 57: 53-60. Lowndes, J.S.S., B.D. Best, C. Scarborough, J.C. Afflerbach, M.R. Frazier, C.C. O’Hara, N. Jiang, B.S. Halpern, 2017. Our path to better science in less time using open data science tools. Nature Ecology & Evolution, 1: 0160. Luisetti, T., M. Schratzberger, 2023. Including biological diversity in natural capital accounts for marine biodiversity conservation and human well-being. Biodiversity Conservation, 32: 405–413 (2023). https://doi.org/10.1007/s10531-021-02342-0 Luisetti, T., R.K. Turner, I. Bateman, S. Morse-Jones, C. Adams, L. Fonseca, 2011. Coastal and Marine Ecosystem Services Valuation for Policy and Management: Managed Realignment Case Studies in England. Ocean & Coastal Management, 54: 212-224. Luisetti, T., R.K. Turner, T. Jickells, J. Andrews, M. Elliott, M. Schaafsma, N. Beaumont, S. Malcolm, D. Burdon, C. Adams, W. Watts, 2014. Coastal Zone Ecosystem Services: From science to values and decision making; a case study. Science of the Total Environment, 493: 682–693. Luisetti T., Turner R.K., Andrews J., Jickells T., Kroeger S., Diesing M., Paltriguera L., Johnson M., Parker R., Bakker D.C.E., Weston K., 2019. Quantifying and valuing carbon flows and stores in coastal and shelf ecosystems in the UK. Ecosystem Services, 35: 67–76 Lynam, C., L. Uusitalo, J. Patrício, C. Piroddi, A. Queiros, H. Teixeira, A. Rossberg, Y. Sagarminaga, K. Hyder, N. Niquil, C. Möllmann, C. Wilson, G. Chust, I. Galparsoro, R. Forster, H. Verissimo, L. Tedesco, M. Revilla, S. Neville, 2016. Uses of innovative modelling tools within the implementation of the marine strategy framework directive. Frontiers in Marine Science, 3: 10.3389/fmars.2016.00182. Lønborg, C., Thomasberger, A., Stæhr, P. A. U., Stockmarr, A., Sengupta, S., Rasmussen, M. L., Nielsen, L. T., Hansen, L. B., Timmermann, K, 2022. Submerged aquatic vegetation - overview of monitoring techniques used for the identification and determination of spatial distribution in European coastal waters. Integrated Environmental Assessment and Management, 18: 892-908 Mack, L., J. Attila, E. Aylagas, A. Beermann, A. Borja, D. Hering, M. Kahlert, F. Leese, R. Lenz, M. Lehtiniemi, A. Liess, U. Lips, O.-P. Mattila, K. Meissner, T. Pyhälahti, O. Setälä, J. S. Strehse, L. Uusitalo, A. Willstrand Wranne, S. Birk, 2020. A Synthesis of Marine Monitoring Methods with the Potential to Enhance the Status Assessment of the Baltic Sea. Frontiers in Marine Science, 7: 10.3389/fmars.2020.552047. Marcotte, D., S.K. Hung, S. Caquard, 2015. Mapping cumulative impacts on Hong Kong's pink dolphin population. Ocean and Coastal Management, 109: 51-63 Marin, O., L. Boicenco, V. Abaza, E. Bișinicu, V. Coatu, M. Galațchi, L. Lazăr, A. Oros, E. Pantea, C. Tabarcea, G. Țiganov, O. Vlas, 2020. Performing First Integrative Evaluation of the Ecological Status of Romanian Black Sea Waters Using Nested Environmental Status Assessment Tool (NEAT). Cercetări Marine, 50: 108-125. Menchaca, I., Á. Borja, I. Galparsoro, J. Franco, M.C. Uyarra, A. Uriarte, G. Chust, L. Ibaibarriaga, J. Bald, 2022. Multi-source and multi-scale data integration for the assessment of the marine environmental status of the Basque Coast (SE Bay of Biscay). Estuarine, Coastal and Shelf Science, 277: 108055. Mériguet, Z., A. Oddone, D. Le Guen, T. Pollina, R. Bazile, C. Moulin, R. Troublé, M. Prakash, C. de Vargas, F. Lombard, 2022. Basin-scale underway quantitative survey of surface microplankton using affordable collection and imaging tools deployed from Tara. Frontiers in Marine Science, 9: 916025. Micheli, F., B. S. Halpern, S. Walbridge, S. Ciriaco, F. Ferretti, S. Fraschetti, R. Lewison, L. Nykjaer, A. A. Rosenberg, 2013. Cumulative Human Impacts on Mediterranean and Black Sea Marine Ecosystems: Assessing Current Pressures and Opportunities. Plos ONE, 8: e79889. Morse-Jones, S., T. Luisetti, R.K. Turner, B. Fisher, 2011. Ecosystem valuation: some principles and a partial application. Environmetrics, 22: 675-685. Murawski, S.A. 2007. Ten myths concerning ecosystem approaches to marine resource management. Mar. Policy 31, 681–690. Murphy, H., Jenkins, G, 2010. Observational methods used in marine spatial monitoring of fishes and associated habitats: a review. Marine and Freshwater Research 61: 236-252. doi:10.1071/MF09068 Nash, K.L., J.L. Blythe, C. Cvitanovic, E.A. Fulton, B.S. Halpern, E.J. Milner-Gulland, P.F.E. Addison, G.T. Pecl, R.A. Watson, J.L. Blanchard, 2020. To Achieve a Sustainable Blue Future, Progress Assessments Must Include Interdependencies between the Sustainable Development Goals. One Earth. Nathan, L.M., M. Simmons, B.J. Wegleitner, C.L. Jerde, A.R. Mahon, 2014. Quantifying Environmental DNA Signals for Aquatic Invasive Species Across Multiple Detection Platforms. Environmental Science & Technology, 48: 12800-12806. Natural Capital Committee, 2014. The State of Natural Capital: Restoring our Natural Assets. Second report to the Economic Affairs Committee - March 2014, UK Government. Nemati, H., M.R. Shokri, Z. Ramezanpour, G.H. Ebrahimi Pour, I. Muxika, Á. Borja, 2017. Using multiple indicators to assess the environmental status in impacted and non-impacted bathing waters in the Iranian Caspian Sea. Ecological Indicators, 82: 175-182. O’Hagan, A.M. 2020. Ecosystem-Based Management (EBM) and Ecosystem Services in EU Law, Policy and Governance. In Ecosystem-Based Management, Ecosystem Services and Aquatic Biodiversity : Theory, Tools and Applications, pp. 353-372. Ed. by T.G. O’Higgins, M. Lago, and T.H. DeWitt. Springer International Publishing, Cham. Owens, N.J.P., Hosie, G.W., Batten, S.D., Edwards, M., Johns, D.G., Beaugrand, G., 2013. All plankton sampling systems underestimate abundance: response to “Continuous plankton recorder underestimates zooplankton abundance” by JW Dippner and M. Krause. Journal of Marine Systems, 128: 240-242. Panaïotis, T., L. Caray–Counil, B. Woodward, M.S. Schmid, D. Daprano, S.T. Tsai, C. M. Sullivan, R.K. Cowen, J.-O. Irisson, 2022. Content-aware segmentation of objects spanning a large size range: Application to plankton images. Frontiers in Marine Science, 9: 870005. Parsley, M.B., C.S. Goldberg, 2023. Environmental RNA can distinguish life stages in amphibian populations. Molecular Ecology Resources, n/a. Pavlidou, A., N. Simboura, Κ. Pagou, G. Assimakopoulou, V. Gerakaris, I. Hatzianestis, P. Panayotidis, M. Pantazi, N. Papadopoulou, S. Reizopoulou, C. Smith, M. Triantaphyllou, M.C. Uyarra, I. Varkitzi, V. Vassilopoulou, C. Zeri, A. Borja, 2019. Using a holistic ecosystem-integrated approach to assess the environmental status of Saronikos Gulf, Eastern Mediterranean. Ecological Indicators, 96: 336-350. Pawlowski, J., L. Apothéloz-Perret-Gentil, F. Altermatt, 2020. Environmental DNA: What's behind the term? Clarifying the terminology and recommendations for its future use in biomonitoring. Molecular Ecology, 29: 4258-4264. Pecl, G.T., M.B. Araújo, J.D. Bell, J. Blanchard, T.C. Bonebrake, I.-C. Chen, T.D. Clark, R.K. Colwell, F. Danielsen, B. Evengård, L. Falconi, S. Ferrier, S. Frusher, R.A. Garcia, R.B. Griffis, A.J. Hobday, C. Janion-Scheepers, M.A. Jarzyna, S. Jennings, J. Lenoir, H.I. Linnetved, V.Y. Martin, P.C. McCormack, J. McDonald, N.J. Mitchell, T. Mustonen, J.M. Pandolfi, N. Pettorelli, E. Popova, S.A. Robinson, B.R. Scheffers, J.D. Shaw, C.J.B. Sorte, J.M. Strugnell, J.M. Sunday, M.-N. Tuanmu, A. Vergés, C. Villanueva, T. Wernberg, E. Wapstra, S.E. Williams, 2017. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science, 355. Piet, G.J., J.E. Tamis, J. Volwater, P. de Vries, J.T. van der Wal, R.H. Jongbloed, 2021a. A roadmap towards quantitative cumulative impact assessments: Every step of the way. Science of The Total Environment, 784: 146847. Piet G.J., Tamis J.E., van der Wal J.T., Jongbloed R.H., 2021b. Cumulative impacts of wind farms on the North Sea ecosystem. Wageningen Marine Research report C081/21. https://doi.org/10.18174/556024 Piet, G.J., Grundlehner, A., Jongbloed, R., Tamis, J. de Vries, P., 2023. SCAIRM: A spatial cumulative assessment of impact risk for management. Ecological Indicators 157. https://doi.org/10.1016/j.ecolind.2023.111157 Pınarbaşı, K., I. Galparsoro, Á. Borja, V. Stelzenmüller, C.N. Ehler, A. Gimpel, 2017. Decision support tools in marine spatial planning: Present applications, gaps and future perspectives. Marine Policy, 83: 83-91. Pitois, S.G., C.A. Graves, H. Close, C. Lynam, J. Scott, J. Tilbury, J. van der Kooij, P. Culverhouse, 2021. A first approach to build and test the Copepod Mean Size and Total Abundance (CMSTA) ecological indicator using in-situ size measurements from the Plankton Imager (PI). Ecological Indicators, 123: 107307. Pitois, S.G., Lynam, C.P., Jansen, T., Halliday, N., Edwards, M., 2012. Bottom-up effects of climate on fish populations: data from the Continuous Plankton Recorder. Marine Ecology Progress Series, 456: 169-186. Pitois, S.G., Tilbury, J., Bouch, P., Close, H., Barnett, S., Culverhouse, P.F., 2018. Comparison of a cost-effective integrated plankton sampling and imaging instrument with traditional systems for mesozooplankton sampling in the Celtic Sea. Frontiers in Marine Science, 5: 5 Pitois, S.G., Fileman, E.S., Benfield, M.C., Wiebe, P.H., Lombard, F., 2023. Technological advances for measuring planktonic components of the pelagic ecosystem: An integrated approach to data collection and analysis. Frontiers in Marine Science, 10: 1100998. Plonus, R.M., Vogl, S., Floeter, J., 2021. Automatic Segregation of Pelagic Habitats. Frontiers in Marine Science, 8, 754375. Pollina, T., A.G. Larson, F. Lombard, H. Li, D. Le Guen, S. Colin, C. de Vargas, M. Prakash, 2022. PlanktoScope: affordable modular quantitative imaging platform for citizen oceanography. Frontiers in Marine Science, 9: 949428. Pörtner H.-O., R.J. Scholes, J. Agard, E. Archer, A. Arneth, X. Bai, D. Barnes, M. Burrows, L. Chan, W. Cheung, 2021. IPBES-IPCC co-sponsored workshop report on biodiversity and climate change. IPBES and IPCC 28 Pörtner H.-O., D.C. Roberts, H. Adams, C. Adler, P. Aldunce, E. Ali, R.A. Begum, R. Betts, R.B. Kerr, R. Biesbroek, 2022. Climate Change 2022: Impacts, adaptation and vulnerability, IPCC Geneva, Switzerland. Preston, C., K. Yamahara, D. Pargett, C. Weinstock, J. Birch, B. Roman, S. Jensen, B. Connon, R. Jenkins, J. Ryan, C. Scholin, 2024. Autonomous eDNA collection using an uncrewed surface vessel over a 4200-km transect of the eastern Pacific Ocean. Environmental DNA, 6: e468. Queirós, A.M., E. Talbot, N.J. Beaumont, P.J. Somerfield, S. Kay, C. Pascoe, S. Dedman, J.A. Fernandes, A. Jueterbock, P.I. Miller, S.F. Sailley, G. Sará, L.M. Carr, M.C. Austen, S. Widdicombe, G. Rilov, L.A. Levin, S.C. Hull, S.F. Walmsley, C. Nic Aonghusa, 2021. Bright spots as climate-smart marine spatial planning tools for conservation and blue growth. Global Change Biology, 27: 5514-5531. Queirós A.M., E. Talbot, S. Kay, S. Sailley, T. Vu Hoang Le, C. Thi Pham, S. Widdicombe, 2022. Climate-smart spatial planning assessment in support of conservation and blue growth in Da Nang city's marine environment. 10.17031/dxfj-a468 Queirós A.M., S. Kay, M. Sciberras, E. Talbot, M. Kaiser, R.J. Wilson, S. Sailley, S. Marra, L. Matear, J. Fernandes, A. Aldridge, R. McEwan, K. Morris, C.L. McNeill, J. Nunes, K. Woodcock, S. Duncombe-Smith, A. Smith, C. Lynam, C. Vina-Herbon, P. Boulcott, B. Hunter, R. Parker, K. Robinson, C. Sweeting, M. Frost, P.J. Somerfield, 2023. Early-warning system: Climate-smart spatial management of UK fisheries, aquaculture and conservation. A report of the NERC/ESRC Marine Spatial Planning Addressing Climate Effects project. 58 pp. 10.14465/2023.msp02.tec Queirós A.M, E. Talbot, S. Kay, S.Sailley, J.A. Fernandes. in prep. A climate-resilient path for Ireland’s Marine Protected Areas network. Addendum chapter to Revitalising Our Seas report: Identifying Areas of Interest for Marine Protected Area Designation in Irish Waters Ratnarajah, L., R. Abu-Alhaija, A. Atkinson, S. Batten, N.J. Bax, K.S. Bernard, G. Canonico, A. Cornils, J.D. Everett, M. Grigoratou, N.H.A. Ishak, D. Johns, F. Lombard, E. Muxagata, C. Ostle, S. Pitois, A.J. Richardson, K. Schmidt, L. Stemmann, K.M. Swadling, G. Yang, L. Yebra, 2023. Monitoring and modelling marine zooplankton in a changing climate. Nature Communications, 14(1): 564. Reker, J., C. Murray, E. Royo Gelabert, K. Abhold, S. Korpinen, M. Peterlin, D. Vaughan, J.H. Andersen, 2019. Marine Messages II. Navigating the course towards clean, healthy and productive seas through implementation of an ecosystem-based approach. European Environment Agency, EEA Report, 17/2019: 82 pp. Rodriguez-Ezpeleta, N., O. Morissette, C.W. Bean, S. Manu, P. Banerjee, A. Lacoursière-Roussel, K.C. Beng, S.E. Alter, F. Roger, L.E. Holman, K.A. Stewart, M.T. Monaghan, Q. Mauvisseau, L. Mirimin, O.S. Wangensteen, C.M. Antognazza, S.J. Helyar, H. de Boer, M.-E. Monchamp, R. Nijland, C.L. Abbott, H. Doi, M.A. Barnes, M. Leray, P.I. Hablützel, K. Deiner, 2021a. Trade-offs between reducing complex terminology and producing accurate interpretations from environmental DNA: Comment on “Environmental DNA: What's behind the term?” by Pawlowski et al., (2020). Molecular Ecology, 30: 4601-4605. Rodríguez-Ezpeleta, N., L. Zinger, A. Kinziger, H.M. Bik, A. Bonin, E. Coissac, B.C. Emerson, C.M. Lopes, T.A. Pelletier, P. Taberlet, S. Narum, 2021b. Biodiversity monitoring using environmental DNA. Molecular Ecology Resources, 21: 1405-1409. Salter, I., M. Joensen, R. Kristiansen, P. Steingrund, P. Vestergaard, 2019. Environmental DNA concentrations are correlated with regional biomass of Atlantic cod in oceanic waters. Communications Biology, 2: 461. Schmid, M.S., D. Daprano, M.M. Damle, C.M. Sullivan, S. Sponaugle, C. Cousin, C. Guigand, R.K. Cowen, 2023. Edge computing at sea: high-throughput classification of in-situ plankton imagery for adaptive sampling. Frontiers in Marine Science, 10: 1187771. Scott, J., Pitois, S., Close, H., Almeida, N., Culverhouse, P., Tilbury, J., Malin, G., 2021. In situ automated imaging, using the Plankton Imager, captures temporal variations in mesozooplankton using the Celtic Sea as a case study. Journal of Plankton Research, 43(2): 300-313. Scott, J., Pitois, S., Creach, V., Malin, G., Culverhouse, P., Tilbury, J., 2023. Resolution changes relationships: Optimizing sampling design using small scale zooplankton data. Progress in Oceanography, 210: 102946. Seeyave, S., P. Simpson, S. Burg, K.M. Davidson, F. Beckman, V. Cheung, A. Miller, A. Ribeiro, T. Keizer, E. Smail, A. Villwock, 2017. Writing a Communication Strategy: A Step-by-Step Guide and Template Tailored for International (Marine) Science Organisations Ocean Communicators United: 36 pp. Seys, J., L. Cox, E. Şahin Yücel, D. Ezgeta-Balić, M. Faimali, F. Garaventa, M.-C. García-Martínez, C. Gili, K. Kopke, K. Moreau, F. Petrera, D. Simon, A. Villwock, T. Zielinski, A. Muñiz Piniella, 2022. Marine Science Communication in Europe – A way forward. Muñiz Piniella, A., Kellett, P., van den Brand, R., Alexander, B., Rodriguez Perez, A., Van Elslander, J., Heymans, J. J. [Eds.] Future Science Brief 8 of the European Marine Board, Ostend, Belgium. 48pp. ISBN: 9789464206166 ISSN: 2593-5232 DOI: 10.5281/zenodo.6444143 Shelton, A.O., A. Ramón-Laca, A. Wells, J. Clemons, D. Chu, B.E. Feist, R.P. Kelly, S.L. Parker-Stetter, R. Thomas, K.M. Nichols, L. Park, 2022. Environmental DNA provides quantitative estimates of Pacific hake abundance and distribution in the open ocean. Proceedings of the Royal Society B: Biological Sciences, 289: 20212613. Sigsgaard, E.E., M.R. Jensen, I.E. Winkelmann, P.R. Møller, M.M. Hansen, P.F. Thomsen, 2020. Population-level inferences from environmental DNA—Current status and future perspectives. Evolutionary Applications, 13: 245-262. Skjoldal, H.R., Wiebe, P.H., Postel, L., Knutsen, T., Kaartvedt, S., Sameoto, D.D., 2013. Intercomparison of zooplankton (net) sampling systems: Results from the ICES/GLOBEC sea-going workshop. Progress in oceanography, 108: 1-42. Smith, A.D.M., Fulton, E.J., Hobday, A.J., Smith, D.C., Shoulder, P. 2007. Scientific tools to support the practical implementation of ecosystem-based fisheries management. ICES Journal of Marine Science, 64: 633-639. Stelzenmüller, V., M. Coll, A.D. Mazaris, S. Giakoumi, S. Katsanevakis, M.E. Portman, R. Degen, P. Mackelworth, A. Gimpel, P.G. Albano, V. Almpanidou, J. Claudet, F. Essl, T. Evagelopoulos, J.J. Heymans, T. Genov, S. Kark, F. Micheli, M.G. Pennino, G. Rilov, B. Rumes, J. Steenbeek, H. Ojaveer, 2018. A risk-based approach to cumulative effect assessments for marine management. Science of The Total Environment, 612: 1132-1140. Talbot, E., J-B S. Jontila, B.J. Gonzales, R.G. Dolorosa, E.D. Jose, R. Sajorne, S. Sailley, S. Kay, A.M. Queirós, In review. Incorporating climate-readiness into fisheries management strategies Tan, I., H. Atabay, A. Evcen, G. Kurt, E. Taşkın, Ç. Polat Beken, 2023. Integrated assessment of eutrophication in the southern Black Sea waters, using the Nested Environmental Status Assessment Tool. Marine Pollution Bulletin, 195: 115424. Teixeira, H., T. Berg, L. Uusitalo, K. Fürhaupter, A.-S. Heiskanen, K. Mazik, C. Lynam, S. Neville, J.G. Rodriguez, N. Papadopoulou, S. Moncheva, T. Churilova, O. Krivenko, D. Krause-Jensen, A. Zaiko, H. Verissimo, M. Pantazi, S. Carvalho, J. Patrício, M.C. Uyarra, A. Borja, 2016. A Catalogue of marine biodiversity indicators. Frontiers in Marine Science, 3: 10.3389/fmars.2016.00207. Torsvik, B. M., Poulsen, R. N., van Bavel, B., Gundersen, H., Hancke, K, 2020. Detection of macroplastic on beaches using drones and object-based image analysis. NIVA report 7553-2020, 47 pp. Turner, R., M. Schaafsma, M. Elliott, D. Burdon, J. Atkins, T. Jickells, P. Tett, L. Mee, S. van Leeuwen, S. Barnard, T. Luisetti, L. Paltriguera, G. Palmieri, J. Andrews, 2014. UK National Ecosystem Assessment Follow-on. Work Package Report 4: Coastal and Marine Ecosystem Services: Principles and Practice. UNEP-WCMC, LWEC, UK. http://uknea.unep-wcmc.org/LinkClick.aspx?fileticket=KLy76Rak0WQ%3D&tabid=82 United Nations, 2021. System of Environmental-Economic Accounting— Ecosystem Accounting (SEEA EA). White cover publication, pre-edited text subject to official editing. Available at: https://seea.un.org/ecosystem-accounting. Uusitalo, L., H. Blanchet, J. Andersen, O. Beauchard, T. Berg, S. Bianchelli, A. Cantafaro, J. Carstensen, L. Carugati, S. Cochrane, R. Danovaro, A.-S. Heiskanen, V. Karvinen, S. Moncheva, C. Murray, J. Neto, H. Nygård, M. Pantazi, N. Papadopoulou, N. Simboura, G. Srėbalienė, M.C. Uyarra, A. Borja, 2016. Indicator-based assessment of marine biological diversity – lessons from 10 case studies across the European Seas. Frontiers in Marine Science, 3: 10.3389/fmars.2016.00159. Van de Pol, L., K. Van der Biest, S.E. Taelman, L. De Luca Peña, G. Everaert, S. Hernandez, F. Culhane, A. Borja, J.J. Heymans, G. Van Hoey, J. Vanaverbeke, P. Meire, 2023. Impacts of human activities on the supply of marine ecosystem services: A conceptual model for offshore wind farms to aid quantitative assessments. Heliyon, 9: e13589. Weldrick, C.K., 2022. A promising approach to quantifying pteropod eggs using image analysis and machine learning. Frontiers in Marine Science, 9: 869252. Wiebe, P.H., Benfield, M.C., 2003. From the Hensen net toward four-dimensional biological oceanography. Progress in Oceanography, 56(1): 7-136. Williams, A., Dowdney, J., Smith, A.D.M., Hobday, A.J., Fuller, M. 2011. Evaluating impacts of fishing on benthic habitats: A risk assessment framework applied to Australian fisheries. Fisheries Research, 112: 154-167. Footnotes [1] www.ges4seas.eu [2] www.obama-next.eu [3] www.biocean5d.org [4] www.actnow-project.eu [5] www.marbefes.eu Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Mar, 2024 Submission checks completed at journal 08 Mar, 2024 Editor assigned by journal 08 Mar, 2024 First submitted to journal 26 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4047391","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277793666,"identity":"85cdd9ed-2096-4324-bec6-a25677fcafe8","order_by":0,"name":"Angel Borja","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYDACCTC2AZKMDSRpSWNgYCNJCwPDYaAWYt3FP7v52AeLmvPy8vObGxg+/CHGkjvHkmdIHLtt2NjG2MA4s40Ya27kGDNIsN1mbAb6hZm3gQgd8mAt/87Zt4G0/CHGYQYgLZJtBxJ7QFqICgNDoF8YJPuSk2ewJTYc7CXGL3K3mw8zS3yzs53ffPzhgx/EOAwEmCWgjANEagCmlA9EKx0Fo2AUjIIRCQAPITPf89fovAAAAABJRU5ErkJggg==","orcid":"","institution":"AZTI","correspondingAuthor":true,"prefix":"","firstName":"Angel","middleName":"","lastName":"Borja","suffix":""},{"id":277793667,"identity":"eb33a9c3-2a66-4d2c-86cb-db86e9106592","order_by":1,"name":"Torsten Berg","email":"","orcid":"","institution":"MariLim aquatic research GmbH","correspondingAuthor":false,"prefix":"","firstName":"Torsten","middleName":"","lastName":"Berg","suffix":""},{"id":277793668,"identity":"abec9588-5176-4b38-a615-838deab398d2","order_by":2,"name":"Hege Gundersen","email":"","orcid":"","institution":"Norwegian Institute for Water Research (NIVA)","correspondingAuthor":false,"prefix":"","firstName":"Hege","middleName":"","lastName":"Gundersen","suffix":""},{"id":277793669,"identity":"ead792ee-f61f-42fd-912a-421d19a0b079","order_by":3,"name":"Anders Gjørwad Hagen","email":"","orcid":"","institution":"Norwegian Institute for Water Research (NIVA)","correspondingAuthor":false,"prefix":"","firstName":"Anders","middleName":"Gjørwad","lastName":"Hagen","suffix":""},{"id":277793670,"identity":"142ee268-e40f-4884-b15d-ebaea8659276","order_by":4,"name":"Kasper Hancke","email":"","orcid":"","institution":"Norwegian Institute for Water Research (NIVA)","correspondingAuthor":false,"prefix":"","firstName":"Kasper","middleName":"","lastName":"Hancke","suffix":""},{"id":277793671,"identity":"1b692a07-8e43-4be1-8026-1c2f5feeab76","order_by":5,"name":"Samuli Korpinen","email":"","orcid":"","institution":"Finnish Environment Institute","correspondingAuthor":false,"prefix":"","firstName":"Samuli","middleName":"","lastName":"Korpinen","suffix":""},{"id":277793672,"identity":"8931f3b6-bc6f-4703-bcf4-12ffc3055f30","order_by":6,"name":"Miguel C. Leal","email":"","orcid":"","institution":"Science Crunchers","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"C.","lastName":"Leal","suffix":""},{"id":277793673,"identity":"b77dfc05-5d72-4e6c-8c1d-4aac4a9d166d","order_by":7,"name":"Tiziana Luisetti","email":"","orcid":"","institution":"CEFAS","correspondingAuthor":false,"prefix":"","firstName":"Tiziana","middleName":"","lastName":"Luisetti","suffix":""},{"id":277793674,"identity":"7664c644-43f0-47e3-9482-c7e53317ad19","order_by":8,"name":"Iratxe Menchaca","email":"","orcid":"","institution":"AZTI","correspondingAuthor":false,"prefix":"","firstName":"Iratxe","middleName":"","lastName":"Menchaca","suffix":""},{"id":277793675,"identity":"c966a47c-0e30-4902-b926-04232482d6a0","order_by":9,"name":"Ciaran Murray","email":"","orcid":"","institution":"NIVA Denmark Water Research","correspondingAuthor":false,"prefix":"","firstName":"Ciaran","middleName":"","lastName":"Murray","suffix":""},{"id":277793676,"identity":"39541d14-4442-4f2c-bdea-178eb085167d","order_by":10,"name":"GerJan Piet","email":"","orcid":"","institution":"Wageningen University and Research, Wageningen Marine Research","correspondingAuthor":false,"prefix":"","firstName":"GerJan","middleName":"","lastName":"Piet","suffix":""},{"id":277793677,"identity":"1369ec8e-4958-48a6-a6f3-ca27b04377c3","order_by":11,"name":"Sophie Pitois","email":"","orcid":"","institution":"CEFAS","correspondingAuthor":false,"prefix":"","firstName":"Sophie","middleName":"","lastName":"Pitois","suffix":""},{"id":277793678,"identity":"d009a7d3-6d50-4fcc-978d-fb8c0eebffe4","order_by":12,"name":"Naiara Rodríguez-Ezpeleta","email":"","orcid":"","institution":"AZTI","correspondingAuthor":false,"prefix":"","firstName":"Naiara","middleName":"","lastName":"Rodríguez-Ezpeleta","suffix":""},{"id":277793679,"identity":"890f3749-4c45-43d6-8543-782b027f33d0","order_by":13,"name":"James E. Sample","email":"","orcid":"","institution":"Norwegian Institute for Water Research (NIVA)","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"E.","lastName":"Sample","suffix":""},{"id":277793680,"identity":"bae15690-b78b-463a-a62e-597ffd73e850","order_by":14,"name":"Elizabeth Talbot","email":"","orcid":"","institution":"Plymouth Marine Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Talbot","suffix":""},{"id":277793681,"identity":"fccbd6c6-7ac6-49d2-9c21-02ecdef8d91e","order_by":15,"name":"María C. Uyarra","email":"","orcid":"","institution":"AZTI","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"C.","lastName":"Uyarra","suffix":""}],"badges":[],"createdAt":"2024-03-08 17:00:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4047391/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4047391/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52447909,"identity":"dcee588d-8cac-46f4-a32c-b38fadcf43e7","added_by":"auto","created_at":"2024-03-11 18:32:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":295222,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the steps involved in the process of using environmental DNA for biodiversity assessment. Water samples are collected using Niskin bottles or less sophisticated gear and then filtered before environmental DNA extraction. For the metabarcoding process, environmental DNA is amplified using group specific primers and then sequenced; resulting sequences are compared against a reference database to obtain the relative DNA abundance (percentage of sequences) of each species. In the species-specific assay process, environmental DNA is amplified using a species-specific quantitative or digital PCR assay to obtain the absolute abundance (number of DNA copies per liter) of the target species.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4047391/v1/a9fb8f69915798e10ab58710.png"},{"id":52447908,"identity":"eb2080f9-4087-43cb-9fa5-bd773ee1804b","added_by":"auto","created_at":"2024-03-11 18:32:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":449056,"visible":true,"origin":"","legend":"\u003cp\u003eNovel drone and sensor types used for coastal habitat mapping: A) fixed-wing drone (eBee X mapping drone, AgEagle Aerial Systems); B) rotor drone (DJI Matrice 300, photo by SeaBee); C) vertical take-off and landing (VTOL) drone (DeltaQuad Pro, DeltaQuad, photo by SeaBee); D) Red, green and blue (RGB) sensor (Sony RXR II, Sony Group Corporation); E) multispectral imagery (MSI) sensor (MicaSense Altum, Ag Eagle Inc.); F) hyperspectral imagery (HSI) sensor (Specim AFX10, Specim, Spectral Imaging Ltd.). Adapted from Kvile et al. submitted.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4047391/v1/eafcbda041557e8c7d3ecd1e.png"},{"id":52447904,"identity":"385ed74c-0fa2-47b8-aafc-92dcd7571344","added_by":"auto","created_at":"2024-03-11 18:32:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1560863,"visible":true,"origin":"","legend":"\u003cp\u003eA drone-based map (orthomosaic) and the habitat classes it hosts in a typical coastal bay of the Oslofjord, Norway. The orthomosaic is stitched from ~500 single drone collected RGB images, and the corresponding habitat map is a result of an AI-based prediction algorithm. The predictive success is shown as the inserted confusion matrix, giving 86% prediction success for seagrass. Data by Hancke et al., unpubl., collected as described by Kvile et al., submitted.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4047391/v1/9b131b7be1b6a2953a2d21d1.png"},{"id":52447901,"identity":"eddfc5be-5db6-429f-a5d7-1b5cf6c89ae2","added_by":"auto","created_at":"2024-03-11 18:32:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":374964,"visible":true,"origin":"","legend":"\u003cp\u003eThe Plankton Imager (PI) set-up onboard RV Cefas Endeavour. (A) The high-speed line scan camera images all particles (0.18-25 mm) continuously in a through-flow system connected to the ship water supply and can work in all weathers; (B) A machine algorithm (Data Study Group team, 2022) runs to automatically classify the (C) Images are saved along with GPS, time and size information.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4047391/v1/a16224b1f8d7307baf4f0788.png"},{"id":52447899,"identity":"a61b5b30-29e6-428c-b791-676ada116a34","added_by":"auto","created_at":"2024-03-11 18:32:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":628805,"visible":true,"origin":"","legend":"\u003cp\u003eAreas in which long-term climate change refugia were identified for benthic habitats, pelagic habitats, and megafauna reliant on either benthic or pelagic species and habitats. Refugia appeared consistently across both emissions scenarios (RCP4.5 and RCP8.5), between 2026 and 2069. Proposed Marine Protected Areas locations (Areas of Interest) were identified as being important conservation sites by the FairSeas coalition of Irelands leading environmental NGOs and environmental networks. Figure reworked with permission.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4047391/v1/ca0ca75cee3613fb687647da.png"},{"id":52447911,"identity":"308bb785-3020-4dac-91f9-6f01f12ec7ec","added_by":"auto","created_at":"2024-03-11 18:32:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":514733,"visible":true,"origin":"","legend":"\u003cp\u003eBaseline situation and two future scenarios (2030 and 2040) from a North Sea application of SCAIRM indicating the main ecosystem components at risk with the 16 main stressors causing this risk. The remainder are grouped under “other” for presentation reasons but can be specified if needed.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4047391/v1/7ac78c3dea47e4da6b1dc32c.png"},{"id":52447900,"identity":"beb4eef0-9148-405e-80c9-701bf6246e4b","added_by":"auto","created_at":"2024-03-11 18:32:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":124059,"visible":true,"origin":"","legend":"\u003cp\u003eHypothetical example of Spatial Assessment Units (SAUs) for the Mediterranean Sea, that are nested and hierarchically structured. The SAUs on the higher level always comprise all the units below in the hierarchy.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4047391/v1/7bbd650f141cea7bb6176c92.png"},{"id":52447913,"identity":"9e8c9ca0-6d63-4bef-944e-d938b4efdfed","added_by":"auto","created_at":"2024-03-11 18:32:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":305105,"visible":true,"origin":"","legend":"\u003cp\u003eExample of NEAT results from different Spatial Assessment Units (SAUs), corresponding to Mediterranean Sea and aggregation at different levels by ecosystem component, habitat or descriptor of the Marine Strategy Framework Directive (modified from Borja et al., 2019). [D3: Commercial (Com) fish; D5: eutrophication; D8: contaminants], and habitat [Pelagic; Demersal/Benthic: Dem./Ben.]. BoB, Bay of Biscay. The colors indicate the status: High: blue; Good: green; Moderate: yellow; Poor: orange. Confidence (Confid.) refers to the certainty (in %) that the status class corresponds to that calculated.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4047391/v1/cfe68c51f8c8191cb67ef7a2.png"},{"id":52447912,"identity":"698c8a16-0233-4ad1-8cd0-d32cd41a75f3","added_by":"auto","created_at":"2024-03-11 18:32:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":210221,"visible":true,"origin":"","legend":"\u003cp\u003eEcosystem services classification (Source: Turner et al., 2014).\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4047391/v1/e27672e585b25cd766e02f0c.png"},{"id":52447907,"identity":"b9d7fe1c-432b-4936-a675-49cfba1285d0","added_by":"auto","created_at":"2024-03-11 18:32:15","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":311666,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical example of the communication plan from Horizon Europe project GES4SEAS. EBM: Ecosystem-Based Management; PAB: Practitioners Advisory Board; HAB: Harmful Algal Blooms; MOOC: Massive Open Online Courses. The colours associated to each communication channel refer to the target audiences that each channel can reach. Modified from Borja et al. (2024).\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4047391/v1/b76ed69e54fba1865ebfda59.png"},{"id":52448223,"identity":"f8f09440-8dde-4433-b311-1978604248b3","added_by":"auto","created_at":"2024-03-11 18:40:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4526475,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4047391/v1/3a2f7282-07d4-4081-8587-b54f09393214.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Innovative and practical tools for monitoring and assessing biodiversity status and impacts of multiple human pressures in marine systems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman needs (e.g., food, energy, recreation) result in an increasing number of activities at sea, including fishing, aquaculture, shipping, infrastructure construction (e.g., coastal defence, renewable energies, oil and gas platforms, etc.), as well as on land-based activities (Dailianis et al., 2018). These human activities lead to pressures affecting various ecosystem components; this may result in the degradation of ocean health (Halpern et al., 2008a, 2008b; Reker et al., 2019; Korpinen et al., 2021) which in turn can result in a diminution in ecosystem services supply and, ultimately, affect human well-being and health (Borja et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite efforts to minimize human pressure effects in marine ecosystems and their services (e.g. European Commission et al., 2022), these effects will most likely increase with increasing use of the ocean (Nash et al., 2020). Among the main legal instruments available to prevent and mitigate human pressures and impacts, and promote the sustainable use of European seas, are the Maritime Spatial Planning Directive (MSPD; European Union, 2014) and the Marine Strategy Framework Directive (MSFD; European Commission, 2008). Their implementation should support a sustainable blue economy, which is crucial to achieving the Biodiversity Strategy targets for 2030 (European Commission, 2020) and the Good Environmental Status (GES) under the MSFD, by 2026. Also, when those objectives are not achieved, the Nature Restoration Law defines targets to restore degraded habitats in terrestrial, aquatic and marine systems, by 2030 and 2050 (Hering et al., 2023). To make development and conservation compatible, instruments are needed (European Commission, 2021), including innovative and practical monitoring and assessment methods that can assist managers in decisions making.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral examples of reviews of methods for monitoring (Danovaro et al., 2016; Mack et al., 2020), modelling (Lynam et al., 2016; European Commission, 2022) or status assessment already exist (Borja et al., 2016). Materializing some of the developed ideas, in the last 2-3 years, some Horizon Europe projects have been initiated, providing the opportunity to dramatically increase our knowledge and skills to monitor and assess the ocean with new tools (Borja et al., 2024). Five of such projects (GES4SEAS\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e[1]\u003c/a\u003e, OBAMA-NEXT\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e[2]\u003c/a\u003e, BIOcean5D\u003ca href=\"#_ftn3\" name=\"_ftnref3\" title=\"\"\u003e[3]\u003c/a\u003e, ACTNOW\u003ca href=\"#_ftn4\" name=\"_ftnref4\" title=\"\"\u003e[4]\u003c/a\u003e, MARBEFES\u003ca href=\"#_ftn5\" name=\"_ftnref5\" title=\"\"\u003e[5]\u003c/a\u003e) joined forces in a summer school held in San Sebasti\u0026aacute;n (Spain), from 5\u003csup\u003eth\u003c/sup\u003e to 7\u003csup\u003eth\u003c/sup\u003e June 2023, to revise and discuss with more than 50 attendees, the innovative methods that are being developed and the ways in which these can assist managers and policy-makers. This manuscript presents the status of the most important methods reviewed during the school and discusses the progress they represent towards a better management of the seas, in Europe and beyond. The manuscript is divided in three sections: (i) novel methods to monitor coastal and ocean waters (e.g. environmental DNA (eDNA), drones, imaging and artificial intelligence, climate modelling and spatial planning); (ii) innovative tools to assess the status (e.g. cumulative impacts assessment, multiple pressures, Nested Environmental status Assessment Tool (NEAT), ecosystem services assessment, or a new unifying approach); and (iii) discussion on the next steps in monitoring and assessing marine biodiversity and ecosystem health.\u003c/p\u003e"},{"header":"Some novel methods to monitor the ocean","content":"\u003ch2\u003e\u003cem\u003eAnalyzing eDNA to monitor different ecosystem components at sea\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eeDNA refers to DNA collected from environmental samples, such as for example from marine water (Pawlowski et al., 2020). A few liters of marine water contain organisms such as bacteria, microbial eukaryotes or small planktonic animals, but also traces of larger organisms such as fish or marine mammals in form or scales, tissues, cells or gametes (Rodriguez-Ezpeleta et al., 2021a). Thus, sampling and then analyzing eDNA from marine water allows studying the diversity and abundance of several ecosystem components. This approach has the potential to revolutionize marine monitoring, which often requires time consuming surveys involving expensive sampling gear not suitable for all locations and depths.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing eDNA for biomonitoring involves several steps upon which the accuracy of the obtained results largely relies (Rodriguez-Ezpeleta et al., 2021b). Sampling can involve collecting from a few to hundreds of liters of water from the desired location and depth and then passing it through a filter of a given pore size (often 0.22 mm for bacteria and up to 3 mm for eukaryotic material). eDNA is then extracted from the filtrate and used for species specific assays or for metabarcoding (Figure 1). Species specific assays consist of applying quantitative or digital PCR (qPCR/dPCR) to detect and quantify the DNA from a species of interest for which the assay is specifically designed (Salter et al., 2019). This approach is particularly interesting for early detection of non-indigenous species (Nathan et al., 2014) or for monitoring movements and spawning activity (Erikson et al., 2016), and it has recently been proven as a promising approach for fisheries surveys, providing similar outcomes to those obtained through active acoustics (Shelton et al., 2022). Alternatively, DNA metabarcoding allows to study the whole community of a given taxonomic group. Using this approach, the DNA from a given ecosystem component (e.g. cephalopods, fish, \u0026hellip;) is amplified and sequenced; the resulting DNA reads are then compared against a reference database that contains the correspondence between DNA sequences (barcodes) and species names. This approach is particularly interesting for monitoring diversity and relative abundance patterns, including changes across time and space. This method has proven to detect systematically more species than traditional approaches, such as plankton nets, trawling, video, etc. (Fraija-Fern\u0026aacute;ndez et al., 2020).\u003c/p\u003e\n\u003cp\u003eDespite the demonstrated power of eDNA based analyses for biodiversity monitoring, there are several inherent challenges associated to the use of eDNA as a proxy of species presence. Several researchers have established the term \u0026ldquo;ecology of eDNA\u0026rdquo; (Barnes and Turner, 2016) to refer to the origin, state, transport and fate of eDNA within the environment, factors that should be account when interpreting eDNA derived biodiversity assessments. Indeed, finding eDNA from a species at a given location is not always indicative that the species was there, alive at that moment (Burian et al., 2021). Yet, despite recognized uncertainties, experiments in controlled environments and open ocean suggest that eDNA detection is a good proxy of species presence (Holman et al., 2021), which is also supported by studies revealing the power of eDNA to detect species vertical structuring (Canals et al., 2021). In order to go beyond species lists with uncertain interpretation, it is now time that the knowledge accumulated over 15 years of using eDNA for studies of macroorganismal biodiversity is coupled with models that account for the factors that shape the \u0026ldquo;ecology of eDNA\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eeDNA studies are also affected by the inherent biases of the species-specific and metabarcoding assays. Some of them, such as contaminations, can be controlled with appropriate laboratory practices (Burian et al., 2021), while others such as variable DNA extraction efficiency, inhibition in DNA amplification, low specificity of the developed assay (non-target species are also detected) or primer bias (under and over amplification of DNA of some species) are difficult to detect. An important aspect that mostly affect metabarcoding based studies is the lack of completeness and accuracy of reference databases, which can severely bias obtained results (Claver et al., 2023); thus, it is advised that a custom-curated database is used as reference and that, when not complete, an assessment of its completeness is performed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBesides advances in improving laboratory and data analyses protocols as well as models for data interpretation, advances in eDNA research include the use of autonomous sampling (Preston et al., 2023) and \u003cem\u003ein situ\u003c/em\u003e analysis (Hansen et al., 2020) devices, the use of eDNA for population genomics (Sigsgaard et al., 2019), improvements in the use of eDNA approaches for biomass estimation (Shelton et al., 2022) and other advances such as estimating the developmental status of the targeted species (Parsley and Goldberg, 2023). Thus, considering also advances in overcoming the challenges associated with standardization and reproducibility, it is anticipated that the position of eDNA as a game-changer in environmental monitoring will be further consolidated in the future.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eDrones for mapping and monitoring shallow water benthic communities\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eIn shallow water benthic habitats, ecosystem services are largely linked to the presence and livelihood of marine vegetation such as seagrass, rockweeds, kelp forests and tidal marshes (Krause-Jensen et al., 2022). Standard techniques to monitor and map the coastal zones include satellite remote sensing, research vessels, small boats, and diving operations. However, these methods often come short for shallow water coastal zones. Satellite remote sensing offers large spatial coverage but low spatial resolution, with RGB and spectral data acquisition at resolution of tens to hundreds of meter scales (L\u0026oslash;nborg et al., 2022). Larger research vessels are limited to water depths deeper than ~10 meters and have high running costs. Smaller boats can access shallow waters at lower costs but with limited spatial coverage and a significant time investment. Similarly, SCUBA diving for underwater mapping and monitoring is also expensive and time-intensive (F\u0026eacute;ral and Norro, 2023). The latter two, in addition, often suffer from not being spatially explicit (Murphy and Jenkins, 2010).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFlying drones offer a novel technology and a strong complement to these existing methodologies and is filling a gap by providing cost-efficient data collection with a high spatial and temporal resolution (centimeter-scale and on-demand data collection), capabilities to operate below cloud cover, and facilitating systematic and repetitive data acquisition for shallow water research and monitoring purposes (Joyce et al., 2023).\u003c/p\u003e\n\u003cp\u003eOver the past few years, there has been an explosion in the technical development of flying drones and a following steep development of applications in research, mapping and monitoring of aquatic habitats (Joyce et al., 2023). Flying drones are scientifically often referred to as Unoccupied, Uncrewed, or Unmanned Aerial Vehicles (UAV). Or even Unmanned Aerial Systems (UAS) or Remotely Piloted Aircraft Systems (RPAS), as frequently used in the aviation industry. We suggest using the gender-neutral Unoccupied or Uncrewed Aerial Vehicles (UAV) in science and management context, as advocated for by Joyce et al. (2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUAVs are well underway changing the game of shallow water benthic habitat mapping, by facilitating efficient, precise, and reproducible data collection (Joyce et al., 2023). Using sophisticated light-weight sensors and high-resolution cameras, combined with artificial intelligence (AI) for data analysis and high-performance computers, research communities as well as management and mapping authorities now have tools at hand to capture detailed geospatial data of coastal and aquatic communities with ease. By providing a birds-eye perspective on coastal ecosystems from a much lower altitude than satellites, drones can capture data at ~100 times better resolution than satellites, with a flexibility in timing and frequency allowing studies at certain tide stages, or before and after events (Casella et al., 2017), even in hazardous environments. Drones have already shown its efficiency and provide unique data and novel insight to a range of coastal subjects, such as mapping of seagrass (Duffy et al., 2019), kelp forests (Cavanaugh et al., 2021), coastal wetlands (Doughty and Cavanaugh, 2019), abundance and community dynamics of seabirds (Edney et al., 2023) and marine mammals (\u0026Aacute;lvarez-Gonz\u0026aacute;lez et al., 2023), as well as anthropogenic substances such as plastic pollution (Torsvik et al., 2019) and oil spills (Adade et al., 2021).\u003c/p\u003e\n\u003cp\u003eCoastal habitat mapping typically employs three main categories of UAVs: rotor drones, fixed-wing drones, and vertical take-off and landing (VTOL) drones (Figure 2, Kvile et al., submitted). The VTOL drones integrate features from both fixed-wing and rotor systems, utilizing rotors for vertical take-off and landing of a fixed-wing drone. The type of drone to deploy depends on the objectives of the survey. Rotor drones excel in maneuverability, low-speed flight, and hovering capabilities. In contrast, fixed-wing drones are more energy efficient and thus facilitate extended flight durations, making them suitable for long-distance missions. However, their reliance on specified take-off and landing zones often poses challenges in coastal environments, a challenge subdued with VTOL drones for easier landings. See Kvile et al. (submitted) for an evaluation of these in coastal mapping applications.\u003c/p\u003e\n\u003cp\u003eDrone-based recognition of coastal habitats builds on optical identification of species and habitats, also referred to as an optical fingerprint. It describes a distinctive spectral signature of the target object (e.g. seagrass, macroalgae, etc.) and quantifies how the object interacts with light across different wavelengths of the electromagnetic spectrum, typically within the band of visual light and at slightly longer wavelengths (400 to 1000 nm). In habitat mapping, optical sensors like RGB, multispectral (MSI) and hyperspectral (HSI) imagery are applied enabling precise identification and differentiation of objects or elements (Figure 2), such as vegetation or animals by comparing the collected spectral data to established databases or by training of AI algorithms and recognition networks. This approach facilitates accurate classification and mapping in addition to uncertainty estimation of both quantitative (uncertainty in coverage and distribution) and qualitative (probability of prediction) parameters.\u003c/p\u003e\n\u003cp\u003eSeagrass and kelp forests have been difficult to map with high levels of accuracy and precision with satellite imagery due to complex and mixed optical signatures from vegetative and abiotic spectra and near-coast optical distortion (e.g. Cavanaugh et al., 2021). The use of drones, however, enabling mapping of such ecosystems with higher spatial resolution and more precise georeferencing, differentiating between biotic and abiotic matter using higher spectral resolution, and with minimal optical distortion. Figure 3 illustrates an example of seagrass mapping in a shallow bay area in the Oslofjord, Norway. Using a fixed wing drone and combined data from RGB and MSI cameras (as A, D, and E in Figure 2, respectively), around 500 single images were collected and subsequently stitched together to form a single orthomosaic which was georeferenced to a pression of +/- 3 cm (according to the drone data collection protocol by Kvile et al., submitted). The orthomosaic holds a ground sampling distance (image resolution) of 3 cm/pixel. Major coastal components such as seagrass, various macroalgae species, sandy sediments, and rocks were annotated from \u003cem\u003ein situ\u003c/em\u003e (ground truth) observations, and subsequently used to train a machine learning (ML) algorithm (in this case random forest) for separating and predicting the habitat classes in the orthomosaic (see legend in Figure 3). The predictive accuracy of the classification (a so-called confusion matrix) showed that Eelgrass (\u003cem\u003eZostera marina\u003c/em\u003e) was predicted with 86%, brown seaweed with 87%, sandy sediment with 79% accuracy, and so forth (Figure 3). More recently, convolutional Neural Network (CNN) analysis has improved model performance (Liu et al., 2022) and has enabled habitats predictions at species level (Kvile et al., submitted). Ultimately, ML models may allow mapping and classification of coastal habitats without the need for additional collection of ground truth data, building on existing networks. However, ground-truthing will still be required when training new algorithms.\u003c/p\u003e\n\u003cp\u003eFurther advancement within drone technology for shallow water benthic habitat mapping includes higher resolution RGB sensors (a fast-developing industry), the full utilization of spectrally resolved optical fingerprinting data such as that available from MSI and HSI data. It also involves the development of neural networks from foundation models and faster and larger drones with increased payload capabilities. Also, infrared (thermal) sensors will improve the detection and recognition of animals and facilitate for detailed studies of surface temperature variabilities of water and vegetation. This could, for instance, improve our understanding of ocean warming and its effects, and aid in making more accurate predictions. We anticipate that LIDAR (Light Detection And Ranging) systems, now available for medium sized (\u0026lt;25 kg) research drones, will come to add a new range of applications for high resolution UAV-based assessments of shallow water bathymetry, sedimentation and erosion studies, detailed 3D seafloor mapping (Chust et al., 2010), and likely for coastal health and ecosystem services assessments too.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThus, while drone imagery cannot replace traditional methods for coastal mapping completely, it offers a cost-efficient supplement, and, in concert with other new technologies like unoccupied surface and underwater vehicles, much needed opportunities for digitalized and reproducible ecosystem monitoring and research. In light of these advancements, drone-based methodologies may be recognized as recommended and protocol procedures for ecosystem mapping and monitoring under directives like the Maritime Spatial Planning Directive, the MSFD, and for biodiversity assessments in shallow-water coastal regions. This recognition could contribute to enhancing future coastal management and regulations by providing accurate, comprehensive and up-to-date information for decision-makers.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eUse of imaging and artificial intelligence (AI) for monitoring pelagic communities\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eWithin pelagic communities, plankton are plants and animals which are critical to all life on earth. Whilst phytoplankton play a key role in carbon flux as primary producers, absorbing more carbon dioxide than all trees on land, zooplankton occupy a central position in the food web, often controlling phytoplankton by grazing and providing food for many important larval and adult fish and seabirds (Pitois et al., 2012; Lauria et al., 2013). Decades of laboratory and field investigations have shown major impacts of changing oceans on zooplankton physiology, community composition, and distribution, and their resulting influence on both biogeochemistry and productivity of the oceans (Ratnarajah et al., 2023). It is therefore critical to further our understanding of how plankton community structures and abundances are likely to respond to climate change in the future, and their effects on ecosystem dynamics.\u003c/p\u003e\n\u003cp\u003ePlankton are an extremely diverse group, covering a wide range of sizes (from sub-microns to several meters) and behaviours. As a result, no single sampling device can sample all the plankton components at any one-time, leading researchers to select the system they think best fulfills the aims of the particular studies (Owens et al., 2013; Skjoldal et al., 2013). As there are few dedicated plankton monitoring sites and surveys, our knowledge of their biomass, size composition and rates of production, in many continental shelf seas, remains fragmented.\u003c/p\u003e\n\u003cp\u003eThe implementation of the MSFD (European Commission, 2008) has resulted in increased volumes of plankton data needed to meet its requirements. However, the traditional collection of plankton samples, using bottles and nets followed by taxonomic analysis of the preserved samples using microscopes by a trained specialist, is a labor intensive and time-consuming process (Wiebe and Benfield, 2003; Benfield et al., 2007). Limited resources and budgets for monitoring have been a major driver for the development of cost-effective methods to gather plankton information (Danovaro et al., 2016). Nowadays there is a wide range of instruments including for example molecular, optical, remote sensing, and automated techniques (Pitois et al., 2023), most have been reviewed by Lombard et al. (2019).\u003c/p\u003e\n\u003cp\u003eIn particular, imaging instruments combined with Machine Learning tools to automatically classify the collected images (e.g., Weldrick, 2022), have received a high level of interest, due to their ability to provide rapid and unbiased data that can be stored digitally. Thus, they offer the opportunity to help overcome many of the limitations that characterize traditional methods of collecting and analyzing zooplankton samples. These can be used on collected samples (e.g. PlanktoScope (M\u0026eacute;riguet et al., 2022; Pollina et al., 2022), FlowCam (M\u0026eacute;riguet et al., 2022)), or \u003cem\u003ein situ\u003c/em\u003e (e.g., Underwater Vision Profiler (Drago et al., 2022), Plankton Imager (PI) (Scott et al., 2023), In Situ Ichthyoplankton Imaging System (ISIIS) (Pana\u0026iuml;otis et al., 2022), Scripps Plankton Camera (SPC) (Le et al., 2022), Imaging FlowCytobot (Kraft et al., 2022); Video Plankton Recorder (VPR) (Plonus et al., 2021).\u003c/p\u003e\n\u003cp\u003eA specific example of an all-in-one tool that combines zooplankton sampling and image analysis is the Plankton Imager (PI) (Figure 4).\u003c/p\u003e\n\u003cp\u003eThe PI was shown to provide a robust description of zooplankton abundance, distributions and community structures (Pitois et al., 2018; Scott et al., 2021). Its ability to capture \u003cem\u003ein situ\u003c/em\u003e size information was applied to the development of ecological indicators (Pitois et al., 2021). Finally, the PI collects over 150 images per second, thus allowing for fine spatial resolution of the collected images (Scott et al., 2023).\u003c/p\u003e\n\u003cp\u003eThe ability of the PI to continuously collect fine resolution data opens the door to data intensive plankton studies. But the pinnacle of pelagic studies will be the monitoring of plankton (phytoplankton and zooplankton) along environmental (e.g., temperature, salinity) and other biological (e.g., fish) variables, at high-frequency and in real-time. This offers the opportunity to quickly respond according to observed changes and adjust data collection strategy for example (during a survey for example), but also increases capabilities and potential towards building a digital twin of the oceans (Chen et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, the concept of digital twin of the ocean, which seeks to integrate marine big data and AI, is in the initial stage of development and major data challenges need addressing first:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eAn ever-increasing number of images is being produced. These need to be stored and made accessible. But the biggest bottleneck is in the processing and interpretation step. Machine learning tools are key to automate this process and are reliant on comprehensive libraries of pre-classified images (training set). Whilst building training sets is a time-consuming exercise, these are key to the development of high performing algorithms trusted by taxonomists in particular (Giering et al., 2022).\u003c/li\u003e\n \u003cli\u003eAnother major bottleneck resides in the transfer of high numbers of relatively large data files (images) from offshore to land locations as soon as images are collected faster than they can be transferred.\u003c/li\u003e\n \u003cli\u003eData are being collected under different formats and at varying scales: for example, continuous temperature values, plankton images, acoustic signals, satellite imagery, etc. Integrating multimodal data is a challenge in itself (Grossmann et al., 2022).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAddressing these challenges will require (i) substantial investments to build high quality and comprehensive training libraries, and (ii) a data driven approach to automate end-to-end data flows. The latter, which has only recently become possible thanks to the latest advances in machine learning tools, in par and high-performance computing (e.g. Kraft et al., 2022), whist the use of Edge-AI allows for image processing to be done closed to the point of collection so that only the data output from the images is transferred to the cloud rather than the image itself (Schmid et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe use of imaging and AI tools is still in its infancy, but early results suggest that technological advances in this field have the potential to revolutionize how we monitor our seas.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eClimate modelling as a decision support tool for marine spatial planning and biodiversity conservation\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe pressures on marine resources are increasing to the extent that societal demand for goods and services often exceeds the capacity of the marine environment to meet them all simultaneously (Ehler, 2021). Without regulation, these demands can lead to over-use, conflicts and eventual degradation of marine ecosystems. In order to ensure the sustainable use and conservation of the world ocean, a public process must be undertaken to decide what, where and when human activities should take place in marine areas \u0026ndash; e.g. marine spatial planning (MSP) (Fraz\u0026atilde;o Santos et al., 2020). MSP has the potential to balance multiple, often conflicting, human demands and to protect marine ecosystems in a spatially explicit manner (Fraz\u0026atilde;o Santos et al., 2020). As a management process, MSP has spread widely over the last 15 years and marine spatial plans are in development in around 70 countries, encompassing six continents and four ocean basins (Fraz\u0026atilde;o Santos et al., 2021). However, despite this widespread acceptance, \u0026nbsp; development and implementation of MSP still faces some challenges, not least of all global climate change. Anthropogenic climate change is altering marine ecosystems at unprecedented rates. Altered environmental conditions (e.g. warming, acidification, deoxygenation, changes in salinity and hydrodynamics, among others; P\u0026ouml;rtner et al., 2021, 2022) and habitat suitability cause marine species to respond by tracking their optimum habitat (when possible), resulting in changing distributions (Dulvy et al., 2008). This is expressed locally as changes (and often loss) of species biodiversity (Pecl et al., 2017), impacting on conservation efforts as well as on sectors such as capture fisheries and aquaculture. Such changes may increase the potential for conflicts between users of marine space, and increase cumulative impacts on the marine environment, all of which will have implications for human well-being and prosperity (Fraz\u0026atilde;o Santos et al., 2020). Designing MSP that addresses the effects of climate change (climate-smart MSP) is therefore a pressing ambition for ocean managers and marine planners. However, it remains - with very few exceptions \u0026ndash; an ambition without practical implementation (Queir\u0026oacute;s et al., 2021).\u003c/p\u003e\n\u003cp\u003eDesigning effective climate-smart MSP requires an objective means to assess how ecosystem components may respond to climate change. Given the highly dynamic nature of the marine environment, determining whether climate change is a driver of change in habitat conditions or species distributions necessitates a careful examination of environmental and species means over time, as well as their variability (Hawkins and Sutton, 2012). Ocean climate modelling (e.g. physical-biogeochemical modelling and species distribution modelling) has therefore been identified as an essential decision support tool for planners seeking climate-smart design (Queir\u0026oacute;s et al., MSPACE report, 2023). The benefits of such models are clear \u0026ndash; plans can be tested under different climate change scenarios, and assessments of possible trade-offs can be made; these in turn can inform those stakeholder consultations, which are so critical to planning (Pınarbaşı et al., 2017; Fraz\u0026atilde;o-Santos et al., 2020). To date, however, the uptake of climate modelling projections has been slow. This is largely due to the mismatch between traditional climate modelling analyses (which tend to focus on long-term climate impacts and individual ecosystem components separately) and the time-scales and interests of planners, who see climate change as a holistic process that may affect the activity of sectors reliant on those species and services impacted by changing ocean conditions (Queir\u0026oacute;s et al., 2021). To address this mismatch, the \u0026ldquo;bright-spots framework\u0026rdquo; was developed.\u003c/p\u003e\n\u003cp\u003eClimate change drives concurrent changes in multiple ecosystem attributes, which change spatially and temporally at different speeds and with different magnitudes (P\u0026ouml;rtner et al., 2022). The bright-spots framework uses techniques established in the meta-analysis statistics field to identify the emergence of ecosystem-level climate change signals (cf. pressure by pressure as in traditional analyses), which allows for a mapping of the emergence of: (i) climate change refugia - i.e. where environmental conditions or species abundances may be resilient to climate change, and where current uses may be sustainable; (ii) climate change hotspots \u0026ndash; i.e. areas where climate driven trends force ecosystem components or species abundances into a new state beyond their natural variability; or (iii) climate change bright-spots, indicating a change in environmental conditions or species abundances (outside of present day variability) in opposition to predicted climate change trends, generally as a result of climate cycles (Queir\u0026oacute;s et al., 2021). Capitalising on refugia and bright spots in climate-smart planning design may be beneficial in the mid-term delivery of sustainability goals, and for increasing the climate resilience of important conservation sites and maritime sectors. It may also function as a time buying strategy while we reduce global emissions and slow the pace of climate change. This method has already been used in several globally distributed research programmes including those informing marine management in Ireland (Queir\u0026oacute;s et al., 2021), The UK (Queiros et al., 2023) Philippines (Talbot et al., in review) and Vietnam (Queir\u0026oacute;s et al., 2022). It is also in use in a number of case studies across the European Union (www.futuremares.eu).\u003c/p\u003e\n\u003cp\u003eTo further illustrate, a case study using the bright spots framework was recently conducted to inform the development of proposed new marine protected areas in Irish waters (Queiros et al., FairSeas report, in prep), in support of the move towards the Global Biodiversity Frameworks 30x30 target. That study identified areas in the Irish marine planning area which consistently (\u0026gt;80% of the time) appeared as climate refugia between 2026 and 2069 across two emissions scenarios (RCP4.5 and RCP8.5), for habitats and species of conservation interest. Specifically, four separate analyses were conducted: two used modelling datasets that described the spatial distribution of climate resilience and vulnerability in the environmental conditions and prey species required by benthic and pelagic megafauna. A further two analyses used datasets that described benthic and pelagic habitats directly (Queiros et al., FairSeas report, in prep). Figure 5 shows the locations of those refugia, along with the current Natura 2000 network of marine protected areas (MPAs) in Irish waters, and the proposed locations of new ones. While identified long-term refugia for pelagic habitats and megafauna were rather sparse, a substantial proportion of the proposed new MPA sites fell in refugia for benthic habitats, particularly those in deeper offshore waters (Queiros et al., FairSeas report, in prep). The identification of such consistent, long-term refugia can provide marine planners and managers with a better idea of those areas which may support effective conservation into the future, despite climate change pressures in the broader region.\u003c/p\u003e"},{"header":"Some novel methods to assess the status of the ocean","content":"\u003ch2\u003e\u003cem\u003eCumulative Impacts Assessment to guide Ecosystem-based Management\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe Ecosystem Approach (Kirkfeldt, 2019) offers a powerful strategy for the integrated management of living resources and was adopted as the primary framework for the conservation and sustainable use of biodiversity under several EU policy frameworks (e.g. the MSFD). The many definitions, though broadly similar, vary in their emphasis but could be structured into three broad themes representing the main aspects of Ecosystem-Based Management (EBM) using a review of Long et al. (2015), providing a set of key principles: (i) capturing the integrity, functioning and dynamics of the marine ecosystem in its environmental context; (ii) accounting for all relevant human activities and their interconnections with the marine ecosystem as part of the wider socio-economic context; and (iii) organising the process regarding governance and management, taking account of the institutional context.\u003c/p\u003e\n\u003cp\u003eThe first requires the monitoring and assessment of the multitude of indicators put forward in the MSFD and other frameworks intended to capture the many different aspects and interactions encapsulated in the ecosystem integrity, functioning and dynamics biodiversity concepts (Teixeira et al., 2016). The second follows the rationale that EBM is about managing human activities, not the ecosystem itself (Long, 2012; O\u0026rsquo;Hagan, 2020). An assessment of the potential impacts of all relevant human activities and their pressures is therefore key to guide management towards the achievement of the policy objectives for the marine ecosystem (Borja et al., 2024).\u003c/p\u003e\n\u003cp\u003eA recent study by Piet et al. (2023) introduced SCAIRM (Spatial Cumulative Assessment of Impact Risk for Management), a spatially explicit cumulative impact assessment (CIA), a method that has been specifically developed to guide an ecosystem approach. CIAs are considered key to policymaking and planning in governance and management but their actual implementation in MSP and management processes is often limited (Stelzenm\u0026uuml;ller et al., 2018; EC-CINEA, 2021). Management is required to reduce the effects of human activities on ecosystem components and their functioning in order to achieve policy objectives in support of societal and environmental goals (Cormier et al., 2017). However, it is the implementation of management measures developed by authorities that should \u0026lsquo;carry into effect\u0026rsquo; the objectives identified at the onset of the process required as part of an ecosystem approach (Elliott et al., 2020).\u003c/p\u003e\n\u003cp\u003eRisk-based approaches have often been at the basis of CIAs for marine management (Stelzenm\u0026uuml;ller et al., 2018). SCAIRM combines elements from both a likelihood-consequence approach (i.e., calamities; Williams et al., 2011), and an exposure-effect approach, which is considered more suitable when assessing existing and (more or less) continuous or frequently occurring pressures (Smith et al., 2007; Knights et al., 2015) into a method able to handle pressure events as well as continuous pressures. Exposure and Effect are estimated separately using risk-based approaches based on (i) qualitative categories and expert-judgement scores, or (ii) quantitative information applying actual data. The former has the advantage that it allows comprehensive assessments that covers all potential impact chains (i.e. a causal chain connecting every single human activity-pressure-ecosystem component) and can (and needs to) be applied in data-poor areas (Knights et al., 2015; Borgwardt et al., 2019). The latter has the advantage of higher accuracy and more confidence in the outcome; however, is limited to only those impact chains for which adequate information is available and is therefore more appropriate in data-rich areas, such as the North Sea (Piet et al., 2021a). The SCAIRM method is essentially a harmonization of this work, allowing to mix qualitative and quantitative information.\u003c/p\u003e\n\u003cp\u003eAn ecosystem approach requires policy objectives to be achieved through what are essentially sectoral operational controls, which are not designed to achieve ecosystem scale outcomes (Cormier et al., 2019). While sectors may individually fully conform to sector-specific operational standards and regulatory requirements, integration across sectors is lacking (Garcia and Charles, 2014). Thus, instead of a lack of legislation, regulations or standards, the problem may be caused by a lack of coherence and alignment between sector-specific operational practices and environmental legislation and policy objectives (Cavallo, 2018). Some studies show how an advanced CIA method like SCAIRM can be used to provide the type of operational-centric approach (Murawski, 2007; Cormier et al., 2017, 2019) required to achieve environmental policy objectives.\u003c/p\u003e\n\u003cp\u003eImpact Risk is the key output of SCAIRM and can be estimated per impact chain as Exposure*Effect Potential. Impact Risk reflects the expected pressure-induced loss of a specific ecosystem component relative to an undisturbed situation based on state-of-the-art (qualitative or quantitative) information. To guide (an ecosystem approach to) MSP or management first a recent baseline situation needs to be established which can be evaluated against any future scenarios. Aggregation of the impact risk from cumulative pressures across ecosystem components (but now focusing only on a suite of bird and mammal species) then reflects the degree to which biodiversity or ecosystem integrity is at risk from anthropogenic activities.\u003c/p\u003e\n\u003cp\u003eHere, we provide a North Sea example of such scenarios for 2030 and 2040 (Table 1). The main driver for MSP in the North Sea is the transition towards renewable energy, notably offshore wind (van de Pol et al., 2023). For an ecosystem approach to MSP, the implementation of offshore windfarms needs to be considered as part of scenarios involving all the main human activities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Scenario values (% of study area) for the main human activities in the North Sea study area (Piet et al., 2021a).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"551\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eActivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline (2022)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScenario for 2030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScenario for 2040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eAquaculture: fish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.00356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.10861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eAquaculture: fish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eSet-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.00036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eAquaculture: macroalgae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.00105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.03194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eAquaculture: macroalgae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eSet-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.00105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.03194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eAquaculture: shellfish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.00545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.16611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eAquaculture: shellfish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eSet-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.00054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eFishing: benthic trawling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eMooring/anchoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.89099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.83975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.78081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eFishing: benthic trawling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e89.09860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e83.97510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e78.08124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eFishing: Nets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e6.86605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e6.47122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e6.01704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eFishing: Nets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eSet-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e6.86605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e6.47122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e6.01704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eFishing: Pelagic trawls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eMooring/anchoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.17532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.16524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.15364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eFishing: Pelagic trawls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e17.53183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e16.52368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e15.36396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eOil and Gas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eConstruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.00538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eOil and Gas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.10760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.10760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eSand/gravel mining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e2.55918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e3.37578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e4.09468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eSand/gravel mining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eDisposal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e2.55918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e3.37578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e4.09468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eShipping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eMooring/anchoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.07972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.07972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.11081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eShipping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e20.86627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e20.86627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e29.00411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eTelecoms and Electricity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.04168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.05602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.07762\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eTelecoms and Electricity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eLaying cables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.00104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.00108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eWind farms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eConstruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.06144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.17251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e0.27723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.797101449275363%\" valign=\"top\"\u003e\n \u003cp\u003eWind farms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.63768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.768115942028986%\" valign=\"top\"\u003e\n \u003cp\u003e0.73154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e2.11163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.398550724637682%\" valign=\"top\"\u003e\n \u003cp\u003e4.88391\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSCAIRM was then applied to assess the consequences of these scenarios in terms of (changes in) Impact Risk (Figure 6).\u003c/p\u003e\n\u003cp\u003eTable 2 shows which ecosystem components are likely to suffer from an increased Impact Risk under these scenarios.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Change in cumulative impact risk (%) of human activities on ecological components in two future scenarios (2030, 2040) relative to the baseline (2022). An increase is shown in bold.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"512\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.046783625730995%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcological component\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange in impact risk (%)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2030 vs. 2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange in impact risk (%)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2040 vs. 2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.046783625730995%\" valign=\"top\"\u003e\n \u003cp\u003eBirds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.046783625730995%\" valign=\"top\"\u003e\n \u003cp\u003eMammals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e-0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e-0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.046783625730995%\" valign=\"top\"\u003e\n \u003cp\u003eFish \u0026amp; Cephalopods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e-1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e-3.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.046783625730995%\" valign=\"top\"\u003e\n \u003cp\u003ePelagic water column\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e-0.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e-1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.046783625730995%\" valign=\"top\"\u003e\n \u003cp\u003eLittoral sediment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.046783625730995%\" valign=\"top\"\u003e\n \u003cp\u003eLittoral rock and other hard substrata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.046783625730995%\" valign=\"top\"\u003e\n \u003cp\u003eSublittoral sediment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e-0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e-1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.046783625730995%\" valign=\"top\"\u003e\n \u003cp\u003eCircalittoral rock and other hard substrata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e-0.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.046783625730995%\" valign=\"top\"\u003e\n \u003cp\u003eInfralittoral rock and other hard substrata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e-0.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.046783625730995%\" valign=\"top\"\u003e\n \u003cp\u003eDeep-sea bed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e-1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.976608187134502%\" valign=\"top\"\u003e\n \u003cp\u003e-3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThis assessment thus shows that notably for birds and to a lesser extent for seabed habitats in the littoral zone, require additional management measures in order not to (further) compromise the achievement of the MSFD objectives.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eAssessing multiple pressures and cumulative impacts for Europe\u0026rsquo;s seas\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe European Union\u0026rsquo;s policy objective to achieve GES of marine environment under the MSFD, cannot be successful without understanding which anthropogenic pressures are preventing GES and where that takes place. This becomes more complex when multiple simultaneous pressures act together exerting cumulative impacts. Tools to identify, map and assess the cumulative impacts of multiple pressures have been developed for several Europe\u0026rsquo;s marine areas (Korpinen and Andersen, 2016) and CIAs have been published since 2010s (Korpinen et al., 2012, 2021; Coll et al., 2012; Andersen et al., 2013; Micheli et al., 2013).\u003c/p\u003e\n\u003cp\u003eA key challenge in CIAs is the integration of tens or hundreds of ecosystem impacts into a common scale (Halpern and Fujita, 2013). In simpler models, such as fishery impacts on marine populations (Coll et al., 2016) or impacts of multiple pressures on a single population (Marcotte et al., 2015), the integration can be made with data-based pressure-population response functions, but the integration of tens of different pressures on tens of very different ecosystem features is beyond the possibilities of ecosystem models. The framework to address the complex CIAs was introduced by Halpern et al. (2008b). It represents the pressure-ecosystem response functions as simple sensitivity estimates. These are scores representing the sensitivity of an ecosystem feature to a single pressure and they are surveyed from a wide pool of experts (Halpern et al., 2007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile the use of expert judgement is not ideal, the approach has served tens of peer-reviewed CIAs worldwide (Korpinen and Andersen, 2016) and many other ecosystem studies (e.g. Isbell et al. 2022). So far, only a few studies have been made to assess how well the experts agree with each other in environmental studies. It is also unclear if such sensitivity estimates should be local or regional or whether they can be global (Halpern et al., 2007). Korpinen et al. (2021) carried out a pan-European survey of marine sensitivity estimates and concluded that the expert elicitation was relatively reliable: 394 out of 450 combinations of pressure-ecosystem estimates did not differ from each other by the experts. The remaining sensitivity estimates represented cases where there were too few responses, the knowledge of the sensitivity was arguably weak, or for reasons not known for the authors. In that study, the CIA was made for the whole European Sea area using the same sensitivity estimates. To our knowledge, more comprehensive analyses of the coherence of the sensitivity estimates have not been made.\u003c/p\u003e\n\u003cp\u003eThe pan-European CIA by Korpinen et al. (2021) was carried out to support the European Environment Agency (EEA) in locating the areas of too high cumulative impacts (which can potentially prevent achieving GES) and identifying the human activities and pressures causing them (hereafter the \u0026lsquo;EEA study\u0026rsquo;). The objective of this study was to map, on a European scale, the main anthropogenic pressures and estimate their level of intensity in each assessment grid cell, which were 10 km x 10 km in size. From the management point of view, it is important to use standardized definitions of \u0026lsquo;pressures\u0026rsquo; and \u0026lsquo;human activities\u0026rsquo; as given by the respective policy, in the case of the EEA study the MSFD definitions were used (European Commission, 2017). Therefore, the EEA study categorized the pressures into 14 classes, following the MSFD Annex III (European Commission, 2017). The CIAs certainly have different objectives which determine how detailed data \u0026ndash; human activities, pressures or ecosystem features \u0026ndash; are necessary to use. In the EEA\u0026rsquo;s CIA, the objective was not to enable managing detailed activities but to get an overall picture of the \u0026lsquo;problem areas\u0026rsquo; and identify the problems. For that objective the 14 pressures were a sufficient level of detail.\u003c/p\u003e\n\u003cp\u003eVisual and analytical assessments of integrated pressures is sometimes enough for management, but more accurate results may be produced by estimating their cumulative impacts, i.e., producing a full CIA. The EEA study estimated the cumulative impacts of 14 pressures on 31 ecosystem features, which were both habitats and species groups. These needed 14 x 31 estimates for ecosystem sensitivity. Thus, a practical limiting factor for a large-scale CIA is also the determination of the sensitivity estimates. There is no guideline on how widely and deeply marine biodiversity should be included in CIAs. In the EEA study it was, however, ensured that the ecosystem features should cover benthic and pelagic habitats and the main mobile species groups (mammals, birds and fish). A CIA in the Baltic Sea compared the influence of increasing or decreasing the number of ecosystem features and concluded that the higher number of features adds fine-scale details in the spatial presentation of the results but the main conclusions of the CIA \u0026ndash; on the marine region scale \u0026ndash; were not jeopardized (Korpinen et al., 2012).\u003c/p\u003e\n\u003cp\u003eThe EEA study was a necessary landmark in the EU ambition to map and identify the preventing factors to achieve GES in Europe. It showed that there are multiple pressures and cumulative impacts in Europe\u0026rsquo;s seas that probably prevent GES and that the main pressures in the shelf areas are physical disturbance by bottom-contacting fishing gears, invasive alien species and species disturbance by human presence, and in the whole sea area, increased surface water temperature and underwater noise. The coastal and shelf areas had significantly higher cumulative impacts than the offshore areas. Do these pressures cause such high impact that GES is not achieved? There are a few studies which have compared the CIA results with environmental monitoring data. Bevilacqua et al. (2018) showed how the condition of coralligenous algae assemblages depended on the level of a CIA score, and Clark et al. (2016) showed correlation with benthic communities in an estuary. The EEA\u0026rsquo;s Marine Messages II report successfully correlated the CIA with an integrated assessment of marine ecosystem status on the pan-European scale (Reker et al., 2019), and Korpinen et al. (2021) showed how the CIA explained the ecological status of coastal waters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEven if the large-scale CIAs such as the EEA study are only rough estimations of the cumulative impacts, they are useful tools to guide marine management into the right direction. The work is far from building a model to predict good or not good status of marine ecosystem, which is still best to assess with biological monitoring, but it is unarguable that the spatial and temporal resolution of CIAs is much sharper than in biological monitoring. The CIAs can indicate areas of high risks of failing policy objectives such as GES. Such an approach is already adopted with the MSFD assessment of the benthic habitats where pressure impacts on seabed habitats are seen as the major data source informing of the need for management measures (European Commission, 2022b).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eAssessing the status of marine systems using NEAT\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eAmong the tools and methods for assessing the status of the sea, in an integrated way, one is NEAT (Borja et al., 2016). This method was developed for assessing the marine environmental status under the MSFD and was first validated in 10 locations in the four European regional seas (in the Black Sea, Mediterranean Sea, Atlantic Ocean, Baltic Sea) and the Arctic (Uusitalo et al., 2016). Later, it has been applied in different locations and considering different pressures, e.g. (i) in the Iranian Caspian Sea, to assess the status of bathing waters (Nemati et al., 2017); (ii) in the Mediterranean, for assessing the spatio-temporal recovery of a Greek bay (Pavlidou et al., 2019); to compare NEAT and Maltese official MSFD assessments (Borja et al., 2021); or to identify the contribution of MPAs to the status of this sea (Fraschetti et al., 2022); (iii) in the Atlantic, to assess the status of MPAs in deep-sea (Kazanidis et al., 2020); and testing the use of multi-source and multi-scale data (Menchaca et al., 2022); (iv) in the Black Sea, to assess the status of Romanian (Marin et al., 2020) and Turkish waters (Tan et al., 2023); and (v) in the whole Europe, to test some MSFD descriptors (Borja et al., 2019).\u003c/p\u003e\n\u003cp\u003eAs shown in all the above-mentioned studies, NEAT (version 1.4) is a software designed to assess the environmental status of marine waters, which can be freely downloaded from https://www.azti.es/en/productos/neat-software-nested-environmental-status-assessment-tool/. The central principle of NEAT is its hierarchical and nested structure of the environmental status of a given area, or Spatial Assessment Unit (SAU), and the associated habitats. This means that information belonging to a SAU contributes to the specific assessment of this SAU, as well as to the assessment of any other larger SAU that encompasses it, and therefore, to the overall assessment. The order of these hierarchies is such that the assessment begins with the hierarchically nested SAUs. In the example below (Figure 7), information belonging to the \u0026ldquo;Sardinia\u0026rdquo; will contribute to the specific assessment of the \u0026ldquo;Sardinia\u0026rdquo;, as well as that of the \u0026ldquo;Western Mediterranean\u0026rdquo; and the \u0026ldquo;Mediterranean Sea\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis tool aggregates the values of a set of indicators that are specific to a SAU, habitat, ecosystem component and MSFD descriptors in a comparable and systematic way (Uusitalo et al., 2016; Borja et al., 2019, 2021). Each indicator must have at least: (i) a range (i.e., a minimum and a maximum value), in which the values can be found, (ii) the class boundary values between the different quality classes (e.g. the boundary between good and non-good status), (iii) the actual measured indicator value, and (iv) its standard error value (Borja et al., 2016, 2021). After selecting the indicators to be used, the software normalizes them to a uniform scale ranging from 0 (worst environmental status) to 1 (best environmental status) with equidistant classes, allowing for integration across different ecosystem components and indicator types. (Borja et al., 2016, 2019, 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe resulting NEAT value can be calculated at different levels, e.g. it can contain all indicators belonging to a certain set of descriptors. The NEAT value is interpreted as the environmental status for this specific descriptor(s). Another result can be the NEAT value of a specific ecosystem component, habitat or SAU (Figure 8). The final NEAT values are calculated as a weighted mean of all indicator values assigned to a certain defined SAU, habitat or combination of both, and the value can be visualized as SAU + habitat (+descriptor) or SAU + ecosystem component (+descriptor) (Figure 8).\u003c/p\u003e\n\u003cp\u003eThe initial version of the software only matched the original MSFD criteria within the 11 descriptors, and Borja et al. (2021) implemented a version adapted to the ecosystem components of the Directive (e.g., pelagic and demersal commercial fish species, type of nutrients or contaminants). These authors redefined the hierarchies following the hierarchical structure for habitats and considering relevant species groups, habitat types, ecosystems and pressures, as well as criteria, according to the updated European Commission (2017) Decision. In this sense, it can be confirmed that NEAT is an adequate and efficient tool to aggregate multiple indicators from different ecosystem components (and multiple sources) to assess (spatially and temporally) the health status of marine waters. NEAT was found to be useful for managers, policy-makers and scientists in taking decisions for future management measures and assessment approaches.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eEcosystem services mapping and assessment\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eIn everyday life, individuals constantly make choices depending on their needs. Individuals\u0026rsquo; choices depend on the limited, scarce resources the individual possesses, and the priority each option assumes for that individual (Daly and Farley, 2004). Scarce resources to consider include, for example, time and money. Policy and decision makers will also have to make choices for the benefit of society, e.g., decisions on how to invest the available budget within a country will include considering trade-offs between building a new road to connect villages or conserving a forest for recreational purposes. These kinds of trade-offs, where natural resources are involved, consider potential alternative uses of the natural capital and the ecosystem services that it provides. Environmental economics methods and techniques, including natural capital accounting and environmental valuation, are particularly useful to support decision making on policy and management of natural resources (Bagstad et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNatural capital is defined as \u003cem\u003e\u0026ldquo;the elements of nature that produce value or benefits to people either directly or indirectly, for example, the stock of forests, rivers, land, minerals and oceans [these are the natural assets] as well as the natural processes and functions that underpin their operation [these are the ecosystem services]\u0026rdquo;\u003c/em\u003e (Natural Capital Committee, 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen considering the economic valuation of natural capital, it becomes important to subdivide ecosystem services in two categories: intermediate and final ecosystem services (Fisher et al., 2009). This distinction is important to avoid double counting the same ecosystem service, considering only those final ecosystem services which provide a benefit to society. Some final ecosystem services can coincide with the benefits, although usually benefits are provided with the support of other capital inputs such as human capital, infrastructure, etc. For example, all fish in the sea is a \u003cem\u003efinal ecosystem service\u003c/em\u003e, whereas the commercial fish to be sold in the market, that is caught by fishers using vessels, is a \u003cem\u003ebenefit\u003c/em\u003e. The economic valuation is in fact performed only on the benefits, e.g., valuing nutrient cycling on its own and also valuing water quality could be problematic as it may double count the nutrient cycling service as this is already part of the water quality assessment. Similarly, valuing places and seascapes separately from recreation may also double count the value of the beauty of the environment (Figure 9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUltimately, we want to be sure that the natural capital assets are managed in a way that they will continue to be productive and provide current and potential benefits to society. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere are well-established steps that need to be followed to perform an appropriate environmental economic assessment. Since ecosystem services are context dependent (Morse-Jones et al., 2011), their valuation must be spatially explicit; this means that the area where the ecosystem service arises must be known. The extent of the spatial area of the natural capital asset (i.e. the habitat) may be calculated using geographic information system (GIS) mapping. The process and function of the ecosystem service to be valued has also to be known as well as their economic value (\u0026euro;) per unit of measure (e.g. tCO\u003csub\u003e2eq\u003c/sub\u003e km\u003csup\u003e-1\u003c/sup\u003e). These data will allow initial calculations of the economic value of the ecosystem service. Natural capital accounting, which follows a more restrictive set of rules to value the natural capital and its ecosystem services, requires further information on the condition of the natural capital (United Nations, 2021). Condition refers to the health of the natural capital assets and if they are still providing relevant ecosystem services and they are in the condition to continue to do so in the future. However, data collection and understanding on condition are demanding and therefore this information is currently often missing from natural capital accounts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs an example, Luisetti et al. (2011) illustrate a step-by-step approach to support decision making when dealing with coastal management assessment and evaluation. Steps are sequential, although at times overlapping, and deal with well-known issues such as the need of spatial explicitness when dealing with ecosystem services valuation, the need to avoid double counting of ecosystem services and non-linearities in services provision (Morse-Jones et al., 2011). The Luisetti et al. (2011) paper is concerned with managed realignment, an ecological engineering technique that provides a natural sea defense through saltmarshes. The project is assumed to be developed in the Blackwater estuary (UK) and for which an estimate of the economic value of the benefits provided by newly re-created saltmarshes in the area was investigated. The benefits estimated were the provision of a \u003cem\u003ehealthy climate\u003c/em\u003e through carbon sequestration and storage, \u003cem\u003erecreation in saltmarsh areas\u003c/em\u003e including nature watching and spiritual wellbeing, and \u003cem\u003efish nursery benefits\u003c/em\u003e for commercially valued wild fish (seabass). For the \u003cem\u003ehealthy climate\u003c/em\u003e in blue carbon ecosystems (i.e. the saltmarshes), following best practice, GIS maps estimating the areal extent of the new intertidal habitats created with managed realignment were produced and an estimate of the tons of carbon stored each year per hectare was provided. These data are used to know the amount of carbon stored within the area of interest. The economic values of carbon used considered a sensitivity analysis of several values using the social cost of carbon method. More recently, values estimated using other methods are usually included as well, such as the abatement cost method (Luisetti et al., 2019). The carbon price from regulated carbon markets is still considered too volatile to be included in this kind of assessment. For the benefit of \u003cem\u003erecreation in saltmarsh areas\u003c/em\u003e, in the Blackwater project a specific primary data collection exercise to obtain the willingness to pay to estimate the value of that benefit was undertaken. The willingness to pay data was elicited with a choice experiment method and provided an estimated economic value for the saltmarshes around the Blackwater estuary in the case of potential saltmarsh re-creation. When primary data collection is too onerous, it is possible to use a benefit transfer technique, although that technique has many challenges and limitations (Luisetti et al., 2014). Finally, the \u003cem\u003efish nursery benefits\u0026nbsp;\u003c/em\u003ewere estimated looking at the potential increased production in the Blackwater newly re-created saltmarshes of seabass. For this valuation, it was important to take account only of the fish that were not going to leave the estuary so to estimate only the contribution of the saltmarshes and not confound the data with other, bigger, seabass entering the estuary. All these benefit values were then used in a cost-benefit analysis (CBA) to assess the viability of undertaking such a policy through this project. For this reason, assumptions under different scenarios were developed; from a hold the line and a business-as-usual scenario to greener scenarios deeply rooted in ecological restoration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis example is interesting as it shows a trade-off analysis and calculations of opportunity costs between the use of the land for agriculture or for coastal protection, flooding agricultural land and re-creating saltmarshes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEnvironmental economics methods and techniques provide decision makers, of either policy design or project development, with tools to evaluate their decision from an economics perspective. The interdisciplinary nature of environmental economics and of natural capital approaches enable the decision maker to gather both ecological and economic information (Bagstad et al., 2021). Natural capital accounting, although requiring a more restricted set of economic rules, can provide comprehensive time-series monitoring of marine biodiversity and natural resources within a country\u0026rsquo;s national waters while scrutinizing any increment or decrement in their economic value and contribution to economic activities (Luisetti and Schratzberger, 2023). Environmental valuation techniques encompass a wider set of options to value the marine natural resources compared to natural capital accounting (Bartkowski et al., 2015). These techniques can also support the decision maker in evaluating a post-implementation policy or project. Both sets of methods and techniques support the decision maker with the level of information needed depending on the economic analysis needed, including trade-offs analysis such as those for marine spatial planning, and are therefore encouraged.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eTowards a unifying framework for assessing \u0026rsquo;cumulative effects\u0026rsquo; and \u0026rsquo;environmental status\u0026rsquo;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe basic method of doing CIA (see Section 3.1) or cumulative effects assessment (CEA) (Halpern et al., 2008a; Korpinen et al., 2013, 2021; Piet et al., 2021a) is to develop a so-called linkage framework or mental model of all the relevant links (or impact chains) between Activities, Pressures and State (or ecosystem) components, i.e. A-P-S from DAPSI(W)R(M) (Elliott et al., 2017a) that may potentially be impacted and apply a risk-based approach to assess the cumulative pressures on the ecosystem and estimate the relative contributions of each of those stressors. A typical risk assessment consists of two aspects of risk: exposure and effect. The estimation of exposure requires the collection of spatial data from as many as possible of the pressures (e.g. nutrients, pollution, trawling, shipping, acidification) and ecosystem components (e.g. mangroves, reefs, seagrass, open waters) to estimate the spatial overlap. Estimation of effect requires a numerical sensitivity score expressing the severity of the effect the pressure can have on the component. Additionally, the effect can be translated into a potential impact using the population dynamics of the ecosystem component. Finally, all individual results are added up (cumulation by addition) where the difference between CEA and CIA lies in whether or not the final step is included (Piet et al., 2021a).\u003c/p\u003e\n\u003cp\u003eThis method is simple to implement. While the results may give a good estimate of the overall additive effects of human pressures, it still has a number of drawbacks. Some of them were recognized early on by Halpern et al. (2008b) but have not yet been implemented in typical applications of CIA/CEA. We have identified these six most important issues:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eFlat structure: Every ecosystem component makes the same (numerical) contribution to the final result. As an example, using one species of five different phyla, plus 10 bird species, will mean that the assessment result will be largely determined by the birds and not by any one of the species of some other phylum, regardless of their functional role or importance in the ecosystem.\u003c/li\u003e\n \u003cli\u003eNo uniform scaling: Typically, the minimum and maximum observed pressure intensities are rescaled to a common scale of 0\u0026ndash;1, possibly doing some logarithmic transformation to keep numbers within a few magnitudes (e.g. Halpern et al., 2008a, 2008b). This, however, does not consider that the minimum/maximum pressure intensities of any two pressures from the dataset do not necessarily result in the same biological effect on the ecosystem component. The sensitivity score alone is not able to compensate for this. Further, ecosystem components are not on the same scale just because the same numeric value was measured for whatever parameter was used to quantify them. As an example, a species richness of five benthic fauna species in a given area is not the same as 5 g m-\u0026sup2; biomass of the same or another species or the whole community in that same area.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWeighting factors: Adding weighting factors to certain ecosystem components as a proxy for their importance will likely just increase the bias introduced by the flat structure (see #1) as some species gain even more influence on the final result of an assessment.\u003c/li\u003e\n \u003cli\u003eStatic sensitivity scores: Typically, sensitivity scores are derived from expert surveys and are static, i.e. represented by one single value. This leads to a bias. With low levels of a pressure, the reaction of an ecosystem component is often less pronounced than in situations where the pressure level is higher. The reaction does not follow a linear relationship with respect to the pressure level. Situations where pressure levels exceed a specific threshold value, leading to the death of a species, are not considered either.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNo uncertainty information: The amount of uncertainty in the final result is often unknown and typically underestimated. This can easily lead to misinterpretations, e.g. when using the data for management decisions where one area must be prioritized over another for conservation. An area might be chosen that would not be considered when the potentially high amount of uncertainty was known.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNo interaction: In the sectoral approach all individual effects are treated independently from each other and cumulation occurs by addition. In reality, however, an effect from one pressure on a specific species may be altered by that of another pressure e.g. when disturbed by noise it is more susceptible to collision or when mortality increases in a species suffering from contaminants and it is additionally disturbed from its preferred feeding grounds. Another type of interaction is a pressure on one species influencing the effect of that same pressure on another species (often called indirect or knock-on effects). For example, when a mammal species gets scared away by underwater noise and leaves a specific area, this will mean it will not feed on e.g. the fish species present in that area anymore. These fish might not be so sensitive to the underwater noise and the larger number of mammals in the other area may result in more fish entering the noisy region, thus increasing the number of fish affected by the noise. Thus, the sole usage of adding effects per component over all pressures may lead to misinterpretations. Lack of information on these interactions and the conceptual simplicity of many CIA/CEAs then lead to ignoring synergistic and antagonistic interactions.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor a reliable and transparent CIA/CEA, these six issues should be considered in any method aiming to assess the cumulative effects of pressures. Here, we outline solutions to every one of these, which we are developing in a new framework to assess cumulative pressures, environmental status and supply of ecosystem services (Borja et al., 2024):\u003c/p\u003e\n\u003cp\u003ei. Hierarchical structure: Instead of using an approach where each ecosystem component is treated identically, the structure of the assessment can be made hierarchical, e.g. reflecting the taxonomic relationships of the ecosystem components or using biological traits or other structural information important to the ecosystem. The structuring by taxonomy was already developed in NEAT (see Section 3.3). In this approach, the weight of every ecosystem component (e.g. a species) in the assessment is determined by its position within the taxonomic tree. The specific distribution of weights ensures that every taxonomic level of the taxonomic tree get the same share of the total weight. In practice, taking the example given above, all phyla in the assessment share the weight equally and when there is more than one species in a specific phylum, the weight of that phylum is again distributed evenly among itself and its lower taxonomic groups. Thus, a set of 10 bird species are not able to out-rule the other ecosystem components. This hierarchical structure can also be applied to a hierarchy of habitats or biotopes (e.g. using the European Union Nature Information System (EUNIS; Davies and Moss, 2002).\u003c/p\u003e\n\u003cp\u003eii. Uniform scaling: Instead of a pure mathematical scaling, a benchmark scale can be used. Such a scale means that the same value of the overall magnitude of a given pressure will result in a similar (biological) effect on an ecosystem component. For this, the scale needs to use common anchor or reference points that define specific reactions of ecosystem components that can be compared, e.g. 0 = no pressure; 0.4 = pressure is starting to have reproductive consequences on the ecosystem component; 0.6 = pressure is starting to have increased mortality; and 1 = the ecosystem component is lost. In order to determine these points, the overall pressure magnitude can be deconstructed into its constituents: intensity, frequency, range, type (direct or indirect), and recovery time after loss. Regarding the ecosystem components, it must be ensured that the same parameter is used for representing the ecosystem component, for example abundance or biomass only. When only mixed data are present, the data can either be reduced to only presence/absence (reducing the level of information by a large margin) or one parameter can be translated into the other by estimation or using a (numerical) translation function.\u003c/p\u003e\n\u003cp\u003eiii. Weighting: A natural weighting can be achieved by utilizing the hierarchical structure from #1. The importance of an ecosystem component can thus be determined by its position within the taxonomical tree. A manual weighting factor can then be applied on top of the natural weighting. This should, however, only be done when a special kind of importance needs to be applied. This may be the case when legal settings require to put more weight on e.g. protected or rare species.\u003c/p\u003e\n\u003cp\u003eiv. Dynamic sensitivities: Non-linear dose-response relationships can be used instead of a single sensitivity score. A single sensitivity value is equivalent of having a direct linear relationship between the magnitude of the pressure and the resulting (biological) effect on the ecosystem component. This can be modified in various ways, to reflect the typical reactions observed in nature:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003elow levels of pressure may not increase the reaction until a threshold point from which the linear response starts;\u003c/li\u003e\n \u003cli\u003ethe response may be linear but with different values in specific ranges of the pressure magnitude, leading to a piece-wise linear relationship with different slopes;\u003c/li\u003e\n \u003cli\u003ethe response can be logarithmic, quickly increasing the response in the first place but then levelling off at higher pressure levels;\u003c/li\u003e\n \u003cli\u003ethe response may show a sigmoid behaviour with a slow increase of the reaction followed by a (tipping-point like) sharp increase and then slowly levelling off again.\u003c/li\u003e\n \u003cli\u003eInstead of using a single value for the sensitivity, the procedure will thus use a sensitivity function that describes the dose-response relationship depending on the magnitude of the pressure on the uniform scale from issue ii.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ev. Uncertainty framework: A dedicated uncertainty framework can be applied to the data. Each pressure value and each value representing the presence of an ecosystem component should have an accompanying uncertainty value capturing the range of uncertainty about the measurement. As an example, this can be a standard error. The same can be applied to sensitivity scores or other measured and estimated data used in the assessment. This enables the application to use e.g. a bootstrapping procedure that will propagate the uncertainty through the whole assessment procedure, resulting in an overall uncertainty for the assessment result. With this, it can be quantified how large the range of uncertainty around the final assessment value is (Carstensen and Lindegarth, 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003evi. Interaction: Known important interactions can be parameterized to support synergistic and antagonistic interactions between ecosystem components, in addition to the additive cumulation being used in the assessment procedure. In its easiest form this can be done by applying a matrix which gives a factor of 1 for all ecosystem component pairs that have no known interactions. Numbers above 1 represent synergistic interactions, increasing the total value of the effect for the paired components. Numbers below 1 represent antagonistic interactions, decreasing the total value. Ideally, these factors would only be applied in one direction, identifying a source and a recipient ecosystem component. Then, the factors can have two different values for each pair of ecosystem components, depending on for which components the cumulative effect is calculated (which component is the recipient component). In a more detailed assessment, the interaction values could be pressure-specific. To derive such detailed information about the interactions between components, it can be helpful to build a conceptual ecosystem model of the ecosystem at hand. If more detailed models are needed, a specific food web model may be used to quantify interactions. This will allow to analyze the relationships of the constituent ecosystem components in a systematic and transparent way.\u003c/p\u003e\n\u003cp\u003eGiven this framework of parameters of a CIA/CEA, other types of assessments can be applied. Instead of using ecosystem components the procedure can be applied to ecosystem functions instead. The estimated sensitivities/vulnerabilities of the various ecosystem components can be translated to the effect on the ecosystem functions which the components have. Ultimately, this is a proxy for their capacity to supply ecosystem services resulting from those functions. To make such assessments, a translation needs to be done, transferring the spatial and quantitative distribution of the ecosystem components into their ecosystem functions as an indication of their capacity to supply ecosystem services.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOften, a CIA/CEA is one of the indicators as part of a status assessment. E.g., CIA/CEA is one of the pillars for achieving good environmental status, \u003cem\u003esensu\u003c/em\u003e MSFD (Borja et al., 2024). It is important to note the semantic difference between an effect and a potential impact (Piet et al., 2021a) as estimated in respectively a CEA and CIA as presented here. These potential impacts are different from the concrete impacts as they are measured with monitoring programs that are the basis of environmental status assessments. CIA rather is intended to guide management towards minimizing and mitigating pressures in order to improve environmental status and/or predicting the risk of further deterioration given the current monitored situation. Thus, not every effect is automatically an impact. Further, a potential impact as estimated by CIA does not directly translate into a \u0026quot;status\u0026quot; in the sense of a status assessment (Borja et al., 2013). These considerations show the necessity of a unifying framework for assessing cumulative effects and applying these together with environmental status assessments as part of EBM.\u003c/p\u003e\n\u003cp\u003eWe here propose a systematic approach to CIA/CEA to form such a unified framework. Not every aspect of the framework must be employed every time an assessment is made, but the users should be aware of them, assessing their influence on the final result, transparently documenting what is left out from the analysis and to which degree this increases the uncertainty of the result on top of the pure uncertainty imposed by the data. As a common foundation of the framework, we propose to use DAPSI(W)R(M) (Elliott et al., 2017a), for structuring the problem and determining the implementation of the management cycle. DAPSI(W)R(M) links the human system of Drivers, Activities, Pressures, Status, Impacts, Welfare, Responses and Measures to the natural system of environmental status. The proposed framework follows a typical EBM cycle and starts with the environmental status of the natural system: (1) Assess the environmental status, using state indicators alone; (2) identify the problematic ecosystem components (the ones not allowing to achieve good status); (3) do cumulative effects assessment; (4) identify the pressures that likely prevent achieving good status for the identified ecosystem components (to inform management); (5) take measures, set or strengthen the thresholds for the respective pressure and wait for the environmental response to happen; and (6) repeat.\u003c/p\u003e\n\u003cp\u003eThis cycle of actions and assessments will allow management to iteratively advance closer and closer to a good environmental status and thus to good ocean health. It uses distinct management phases and separates status assessment from CIA/CEA by putting them into different stages of the cycle. Nonetheless, both status and impact analysis are still integral parts of the cycle and need to be used to complement each other.\u003c/p\u003e"},{"header":"Discussion: looking forward in monitoring and assessing the ocean","content":"\u003cp\u003eLong-term marine monitoring has demonstrated high value for informed management decisions addressed to reduce the impacts of human activities and pressures (Borja and Elliott, 2019). Therefore, maintenance of these long-term monitoring networks is critical, and relies on the development of cost-effective implementation approaches (Borja and Elliott, 2013) and methods (Hyv\u0026auml;rinen et al., 2021). These methods can be either traditional or rely on novel technologies, with a broad range of sampling, observation and analytical techniques (Danovaro et al., 2016), such as: instruments (e.g. buoys to deploy sensors, physico-chemical sensors, remotely operated vehicles, autonomous vehicles, biosensors, biologgers, passive sampling), molecular approaches (e.g. metabarcoding of eDNA), acoustic devices (e.g. multibeam), flow cytometry, imaging, remote sensing (optical, thermal and radar images from airborne, including drones, and satellite sensors), artificial substrata, etc.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTraditional methods are often time consuming, and costly, which typically translates into pronounced limitations of the spatio-temporal resolution and coverage that is required by policy-makers, stakeholders and decision-makers (Borja and Elliott, 2013; Mack et al., 2020). To improve cost-efficiency, reproducibility and spatio-temporal resolution, novel methods are being proposed, i.e. as proof-of-concept, or already operational but with little application in the marine realm, using five basic criteria (Mack et al., 2020): (i) technology readiness level of seven or higher; (ii) comprehensive and standalone techniques; (iii) filling a gap in the current monitoring frameworks; (iv) novel and not in general use; and (v) evaluated as cost-efficient in terms of their cost-benefit ratio (including investment costs, monitoring costs, reliability, environmental impact, added value, limitations, required expertise). These criteria were all identified and rated for 22 novel methods relevant to marine monitoring, with some of them being included in the presented review (e.g. molecular methods, drones, etc.).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor novel methodologies to prove their usefulness and value to monitor and assess marine ecosystem status and health of coasts and seas, they need to score well on relevant criteria, as those proposed by Mack et al., (2020) and in addition be properly communicated to end-users, especially to stakeholders as management agencies who will be using data generated by such innovative tools for improve management actions (Seys et al., 2022). Some marine organizations have proposed step-by-step tailored guides for an accurate and efficient communication (Seeyave et al., 2017). Usually, this communication requires (i) identifying the target audience (e.g. end-users of monitoring and assessment methods, policy-makers, decision-makers, managers); (ii) break down the communication objectives into relevant messages for each target audience, starting with those with highest priority (e.g. those deciding on monitoring and assessment infrastructures); (iii) for each target, decide the most appropriate channel to communicate (e.g., hard copy, digital, in-person, etc.); and (iv) build the communication implementation plan, including the objectives, target audience, tasks and methods, timing and key performance indicators (Seeyave et al., 2017). An example of a communication plan, from one of the projects participating in the summer school (GES4SEAS), can be seen in Figure 10 (Borja et al., 2024). It includes the outcomes and key messages to be transmitted (\u0026ldquo;What?\u0026rdquo;), the target audience (\u0026ldquo;To whom?\u0026rdquo;), the multiple channels used, which can include social media, videos, infographics, summer schools, specialized workshops, etc. (\u0026ldquo;Which channel?\u0026rdquo;), and also the expected effects on the audience (\u0026ldquo;What effect?\u0026rdquo;).\u003c/p\u003e\n\u003cp\u003eResearch programmes on monitoring and assessment, based on individual and unconnected projects, can lead to research fragmentation, wasting resources through duplication, dispersion and overlap (Elliott et al., 2017b). To minimize these deficits, the Horizon Europe programme offers an instrument for collaboration among \u0026ldquo;sister\u0026rdquo; projects (i.e. those under the same call or addressing close topics), facilitating synergies in their outcomes, communication, stakeholders\u0026apos; participation and increase of knowledge. This has been the case of the summer school which has resulted in this contribution, in which five projects (i.e., GES4SEAS, OBAMA-NEXT, BIOcean5D, ACTNOW and MARBEFES) have collaborated to bring to the scientific and stakeholders\u0026rsquo; community the most recent advances in marine monitoring and assessment and the way forward, to better manage the ocean and reduce the effects of multiple human pressures on ecosystem components (Borja et al., 2024).\u003c/p\u003e\n\u003cp\u003eRegarding those advances, in the summer school discussions the following tools emerged and were evaluated to be important and useful for future monitoring, including remote sensing, drones, eDNA metabarcoding, video, biological sensors, imaging and machine learning. The applicability and their and pros and cons for marine coastal and marine monitoring have been discussed in the sections above. In the case of assessment methods, the availability of integrative and flexible tools was discussed, highlighting the importance of having capacity to be feed by large open access datasets (e.g. Lowndes et al., 2017; Borja et al., 2019) and having an associated confidence of status classification system. Regarding the attendees comments about the expectations from a marine monitoring tool, they included minimizing the environmental impact (i.e., non-destructive methods, or related to the animal ethics, as in the case of biologging), maximizing repeatability throughout time and space, having reasonable affordability for operation and maintenance, demonstrating the ability to monitor spatially and work autonomously for a long time-period, standardizing measurements, and being cheap, easy to use, precise and efficient.\u003c/p\u003e\n\u003cp\u003eAs a concluding remark, the attendees agreed that one of the most important challenges ahead is associated with integrating the novel methods presented here (or others) into long standing time series for data continuity, alongside current benchmark methodologies. This requires dedicated and well-planned transition periods allowing for quantitative and qualitative comparison of methods to secure consistent and data-driven ecosystem assessments. Such actions can be cover through intense collaborations between current and future European projects on marine biodiversity and ecosystem health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding section\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript is a result of GES4SEAS (Achieving Good Environmental Status for maintaining ecosystem services, by assessing integrated impacts of cumulative pressures) project, funded by the European Union under the Horizon Europe program (grant agreement no. 101059877), www.ges4seas.eu; OBAMA-NEXT (Observing and Mapping Marine Ecosystems \u0026ndash; Next Generation Tools) project, funded by the European Union under the Horizon Europe program (grant agreement no. 101081642), www.obama-next.eu; BIOcean5D (Marine Biodiversity Assessment and Prediction Across Spatial, Temporal and Human Scales) project, co-funded by the European Union under the Horizon Europe program (grant agreement no. 101059915), the UK government Horizon Europe Guarantee, UKRI (grant reference no. 10039266) and the Swiss State Secretariat for Education, Research and Innovation, SERI (contract no. 22.00255), www.biocean5d.org; FutureMARES, funded by the European Union under the Horizon Europe program (grant agreement no. 869300), http://www.futuremares.eu\u0026quot;www.futuremares.eu; ACTNOW, funded by the European Union under the Horizon Europe program (grant agreement no. 101060072), www.actnow-project.eu; MARBEFES (MARine Biodiversity and Ecosystem Functioning leading to Ecosystem Services) project, funded by the European Union under the Horizon Europe program (grant agreement no. 101060937) and supported by Innovate UK through the Horizon Europe Guarantee scheme (grant number 10040216), https://marbefes.eu/http://www.actnow-project.eu/; and the Norwegian Infrastructure for drone-based research, mapping, and monitoring in the coastal zone - SeaBee, funded by the Research Council of Norway (grant agreement 296478), www.seabee.no.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript is based on the discussions taken during the AZTI\u0026rsquo;s summer school (2\u003csup\u003end\u003c/sup\u003e GES4SEAS), held in the Aquarium of San Sebasti\u0026aacute;n (Spain), on 5\u003csup\u003eth\u003c/sup\u003e to 7\u003csup\u003eth\u003c/sup\u003e June 2023. Science Crunchers have designed Figure 10. This is contribution nr XXX from AZTI\u0026rsquo;s Marine Research, Basque Research and Technology Alliance (BRTA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, since no data are associated to this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatements and declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests, financial or non-financial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read the last version of the manuscript prepared and approved it before submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAB coordinated the group and wrote the first draft. NRE wrote section on eDNA, KH, AGH, HG, and JES wrote section on drones, SP wrote section on AI and pelagic environments, ET wrote section on climate modelling, GJP wrote section on CIA, SK wrote section on assessing multiple pressures in Europe, IM wrote section on NEAT, TL wrote section on ecosystem services mapping, TB and CM wrote section on a unifying framework, MCU and MCL contributed to communication parts. All authors collaboratively wrote the introduction, discussion and conclusions, contributed to the revision of the article, and approved the version submitted for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdade, R., Aibinu, A. M., Ekumah, B., Asaana, J, 2021. Unmanned Aerial Vehicle (UAV) applications in coastal zone management \u0026ndash; a review. Environmental Monitoring and Assessment, 193: 154. \u003c/li\u003e\n\u003cli\u003e\u0026Aacute;lvarez-Gonz\u0026aacute;lez, M., Suarez-Bregua, P., Pierce, G. J., Saavedra, C, 2023. Unmanned Aerial Vehicles (UAVs) in Marine Mammal Research: A Review of Current Applications and Challenges. Drones 7: 667, https://doi.org/10.3390/drones7110667 \u003c/li\u003e\n\u003cli\u003eAndersen, J.H., A. Stock, S. Hein\u0026auml;nen, M. Mannerla, M. Vinther, 2013. Human Uses, Pressures and Impacts in the Eastern North Sea. Technical Report from DCE \u0026ndash; Danish Centre for Environment and Energy No. 18. Aarhus University, Roskilde.\u003c/li\u003e\n\u003cli\u003eAniceto, A. S., Biuw, M., Lindstr\u0026oslash;m, U., Solb\u0026oslash;, S. A., Broms, F., Carroll, J, 2018. Monitoring marine mammals using unmanned aerial vehicles: quantifying detection certainty. Ecosphere 9(3):e02122. 10.1002/ecs2.2122 \u003c/li\u003e\n\u003cli\u003eBagstad, K.J., J.C. Ingram, C.D. Shapiro, A. La Notte, J. Maes, S. Vallecillo, C.F. Casey, P.D. Glynn, M.P. Heris, J.A. Johnson, C. Lauer, J. Matuszak, K.L.L. Oleson, S.M. Posner, C. Rhodes, B. Voigt, 2021. Lessons learned from development of natural capital accounts in the United States and European Union. Ecosystem Services, 52: 101359.\u003c/li\u003e\n\u003cli\u003eBarnes, M.A., C.R. Turner, 2016. The ecology of environmental DNA and implications for conservation genetics. Conservation Genetics, 17: 1-17.\u003c/li\u003e\n\u003cli\u003eBartkowski, B., N. Lienhoop, B. Hansj\u0026uuml;rgens, 2015. Capturing the complexity of biodiversity: A critical review of economic valuation studies of biological diversity. Ecological Economics, 113: 1-14.\u003c/li\u003e\n\u003cli\u003eBenfield, M.C., P. Grosjean, P.F. Culverhouse, X. Irigoien, M.E. Sieracki, A. Lopez-Urrutia, H.G. Dam, Q. Hu, C.S. Davis, A. Hansen, C.H. Pilskaln, E.M. Riseman, H. Schultz, P.E. Utgoff, G. Gorsky, 2007. RAPID: research on automated plankton identification. Oceanography, 20(2), 172-187.\u003c/li\u003e\n\u003cli\u003eBevilacqua, S., G. Guarnieri, G. Farella, A. Terlizzi, S. Fraschetti, 2018. A regional assessment of cumulative impact mapping on Mediterranean coralligenous outcrops. Scientific Reports, 8:1757, DOI:10.1038/s41598-018-20297-1\u003c/li\u003e\n\u003cli\u003eBorgwardt, F., L. Robinson, D. Trauner, H. Teixeira, A.J.A. Nogueira, A.I. Lilleb\u0026oslash;, G. Piet, M. Kuemmerlen, T. O\u0026apos;Higgins, H. McDonald, J. Arevalo-Torres, A.L. Barbosa, A. Iglesias-Campos, T. Hein, F. Culhane, 2019. Exploring variability in environmental impact risk from human activities across aquatic ecosystems. Science of The Total Environment, 652: 1396-1408.\u003c/li\u003e\n\u003cli\u003eBorja, A., M. Elliott, 2013. Marine monitoring during an economic crisis: The cure is worse than the disease. Marine Pollution Bulletin, 68: 1-3.\u003c/li\u003e\n\u003cli\u003eBorja, A., M. Elliott, 2021. From an economic crisis to a pandemic crisis: The need for accurate marine monitoring data to take informed management decisions. Advances in Marine Biology, 89: 79-114.\u003c/li\u003e\n\u003cli\u003eBorja, A., M. Elliott, J.H. Andersen, A.C. Cardoso, J. Carstensen, J.G. Ferreira, A.-S. Heiskanen, J.C. Marques, J.M. Neto, H. Teixeira, L. Uusitalo, M.C. Uyarra, N. Zampoukas, 2013. Good Environmental Status of marine ecosystems: What is it and how do we know when we have attained it? Marine Pollution Bulletin, 76: 16-27.\u003c/li\u003e\n\u003cli\u003eBorja, A., M. Elliott, J.H. Andersen, T. Berg, J. Carstensen, B.S. Halpern, A.-S. Heiskanen, S. Korpinen, J.S.S. Lowndes, G. Martin, N. Rodriguez-Ezpeleta, 2016. Overview of integrative assessment of marine systems: the Ecosystem Approach in practice. Frontiers in Marine Science, 3: doi: 10.3389/fmars.2016.00020.\u003c/li\u003e\n\u003cli\u003eBorja, A., J.M. Garmendia, I. Menchaca, A. Uriarte, Y. Sagarm\u0026iacute;naga, 2019. Yes, We Can! Large-Scale Integrative Assessment of European Regional Seas, Using Open Access Databases. Frontiers in Marine Science, 6: 10.3389/fmars.2019.00019.\u003c/li\u003e\n\u003cli\u003eBorja, A., M.P. White, E. Berdalet, N. Bock, C. Eatock, P. Kristensen, A. Leonard, J. Lloret, S. Pahl, M. Parga, J.V. Prieto, S. Wuijts, L.E. Fleming, 2020. Moving Toward an Agenda on Ocean Health and Human Health in Europe. Frontiers in Marine Science, 7: 10.3389/fmars.2020.00037.\u003c/li\u003e\n\u003cli\u003eBorja, A., I. Menchaca, J.M. Garmendia, J. Franco, J. Larreta, Y. Sagarminaga, Y. Schembri, R. Gonz\u0026aacute;lez, R. Ant\u0026oacute;n, T. Micallef, S. Camilleri, O. Solaun, A. Uriarte, M.C. Uyarra, 2021. Big Insights From a Small Country: The Added Value of Integrated Assessment in the Marine Environmental Status Evaluation of Malta. Frontiers in Marine Science, 8: 10.3389/fmars.2021.638232.\u003c/li\u003e\n\u003cli\u003eBorja, A., Elliott, M., Teixeira, H., Stelzenmuller, V., Katsanevakis, S., Coll, M., Galparsoro, I., Fraschetti, S., Papadopoulou, N., Lynam, C., Berg, T., Andersen, J.H., Carstensen, J., Leal, M.C. and Uyarra, M.C., 2024. Addressing the cumulative impacts of multiple human pressures in marine systems, for a sustainable use of the seas. Frontiers in Ocean Sustainability, 1:1308125. doi: 10.3389/focsu.2023.1308125\u003c/li\u003e\n\u003cli\u003eBurian, A., Q. Mauvisseau, M. Bulling, S. Domisch, S. Qian, M. Sweet, 2021. Improving the reliability of eDNA data interpretation. Molecular Ecology Resources, 21: 1422-1433.\u003c/li\u003e\n\u003cli\u003eCanals, O., I. Mendibil, M. Santos, X. Irigoien, N. Rodr\u0026iacute;guez-Ezpeleta, 2021. Vertical stratification of environmental DNA in the open ocean captures ecological patterns and behavior of deep-sea fishes. Limnology and Oceanography Letters, 6: 339-347.\u003c/li\u003e\n\u003cli\u003eCarstensen, J., M. Lindegarth, 2016. Confidence in ecological indicators: A framework for quantifying uncertainty components from monitoring data. Ecological Indicators, 67: 306-317.\u003c/li\u003e\n\u003cli\u003eCasella, E., Collin, A., Harris, D., Ferse, S., Bejarano, S., Parravicini, V., Hench, J. L. Rovere, A, 2017. Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques. Coral Reefs (2017) 36:269\u0026ndash;275. doi:10.1007/s00338-016-1522-0\u003c/li\u003e\n\u003cli\u003eCavallo, M., Elliott, M., Quintino, V., Touza, J. 2018. Can national management measures achieve good status across international boundaries? - a case study of the Bay of Biscay and Iberian coast sub-region. Ocean Coast. Manag. 160, 93\u0026ndash;102.\u003c/li\u003e\n\u003cli\u003eCavanaugh, K. C., Cavanaugh, K. C., Bell, T. W., Hockridge, E. G. 2021. Front. Environ. Sci. 17, https://doi.org/10.3389/fenvs.2020.587354 \u003c/li\u003e\n\u003cli\u003eChen, G., J. Yang, B. Huang, C. Ma, F. Tian, L. Ge, L. Xia, J. Li, 2023. Toward digital twin of the ocean: from digitalization to cloning. Intelligent Marine Technology and Systems, 1(1): 3.\u003c/li\u003e\n\u003cli\u003eChust, G., M. Grande, I. Galparsoro, A. Uriarte, \u0026Aacute;. Borja, 2010. Capabilities of the bathymetric Hawk Eye LiDAR for coastal habitat mapping: A case study within a Basque estuary. Estuarine, Coastal and Shelf Science, 89: 200-213.\u003c/li\u003e\n\u003cli\u003eClark, D., E. Goodwin, J. Sinner, J. Ellis, G. Singh, 2016. Validation and limitations of a cumulative impact model for an estuary. Ocean and Coastal Management, 120: 88-98.\u003c/li\u003e\n\u003cli\u003eClaver, C., O. Canals, L. G. de Am\u0026eacute;zaga, I. Mendibil, N. Rodriguez-Ezpeleta, 2023. An automated workflow to assess completeness and curate GenBank for environmental DNA metabarcoding: The marine fish assemblage as case study. Environmental DNA, 5: 634-647.\u003c/li\u003e\n\u003cli\u003eColl, M., C. Piroddi, C. Albouy, F. Ben Rais Lasram, W. W. L. Cheung, V. Christensen, V. S. Karpouzi, F. Guilhaumon, D. Mouillot, M. Paleczny, M. L. Palomares, J. Steenbeek, P. Trujillo, R. Watson, D. Pauly, 2012. The Mediterranean Sea under siege: spatial overlap between marine biodiversity, cumulative threats and marine reserves. Global Ecology and Biogeography, 21: 465-480.\u003c/li\u003e\n\u003cli\u003eColl, M., J. Steenbeek, J. Sole, I. Palomera, V. Christensen, 2016. Modelling the cumulative spatial\u0026ndash;temporal effects of environmental drivers and fishing in a NW Mediterranean marine ecosystem. Ecological Modelling, 331: 100-114.\u003c/li\u003e\n\u003cli\u003eCormier, R., C.R. Kelble, M.R. Anderson, J.I. Allen, A. Grehan, \u0026Oacute;. Gregersen, 2017. Moving from ecosystem-based policy objectives to operational implementation of ecosystem-based management measures. ICES Journal of Marine Science, 74: 406-413.\u003c/li\u003e\n\u003cli\u003eCormier, R., M. Elliott, J. Rice, 2019. Putting on a bow-tie to sort out who does what and why in the complex arena of marine policy and management. Science of The Total Environment, 648: 293-305.\u003c/li\u003e\n\u003cli\u003eDailianis, T., C.J. Smith, N. Papadopoulou, V. Gerovasileiou, K. Sevastou, T. Bekkby, M. Bilan, D. Billett, C. Bostr\u0026ouml;m, M. Carreiro-Silva, R. Danovaro, S. Fraschetti, K. Gagnon, C. Gambi, A. Grehan, S. Kipson, J. Kotta, C.J. McOwen, T. Morato, H. Ojaveer, C.K. Pham, R. Scrimgeour, 2018. Human activities and resultant pressures on key European marine habitats: An analysis of mapped resources. Marine Policy, 98: 1-10.\u003c/li\u003e\n\u003cli\u003eDaly, H.E., J. Farley, 2004. Ecological Economics \u0026ndash; principles and application. Island Press. Washington, DC (USA).\u003c/li\u003e\n\u003cli\u003eDanovaro, R., L. Carugati, M. Berzano, A.E. Cahill, S. Carvalho, A. Chenuil, C. Corinaldesi, S. Cristina, R. David, A. Dell\u0026apos;Anno, N. Dzhembekova, E. Garc\u0026eacute;s, J.M. Gasol, P. Goela, J.-P. F\u0026eacute;ral, I. Ferrera, R.M. Forster, A.A. Kurekin, E. Rastelli, V. Marinova, P.I. Miller, S. Moncheva, A. Newton, J.K. Pearman, S.G. Pitois, A. Re\u0026ntilde;\u0026eacute;, N. Rodr\u0026iacute;guez-Ezpeleta, V. Saggiomo, S.G.H. Simis, K. Stefanova, C. Wilson, M. Lo Martire, S. Greco, S.K.J. Cochrane, O. Mangoni, A. Borja, 2016. Implementing and Innovating Marine Monitoring Approaches for Assessing Marine Environmental Status. Frontiers in Marine Science, 3: 10.3389/fmars.2016.00213.\u003c/li\u003e\n\u003cli\u003eData Study Group team, 2022. Data Study Group Final Report: Centre for Environment, Fisheries and Aquaculture Science, Plankton image classification. Alan Turing Institute. Zenodo. https://doi.org/10.5281/zenodo.6799166. \u003c/li\u003e\n\u003cli\u003eDavies, C.E., Moss, D., 2002. EUNIS Habitat Classification, February 2002. P. European Topic Centre on Nature Protection and Biodiversity.\u003c/li\u003e\n\u003cli\u003eDoughty, C. L., Cavanaugh, K. C, 2019. Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery. Remote Sens. 11: 540, https://doi.org/10.3390/rs11050540\u003c/li\u003e\n\u003cli\u003eDrago, L., T. Pana\u0026iuml;otis, J.-O. Irisson, M. Babin, T. Biard, F. Carlotti, L. Coppola, L. Guidi, H. Hauss, L. Karp-Boss, F. Lombard, A.M.P. McDonnell, M. Picheral, A. Rogge, A.M. Waite, L. Stemmann, R. Kiko, 2022. Global distribution of zooplankton biomass estimated by in situ imaging and machine learning. Frontiers in Marine Science, 9: 10.3389/fmars.2022.894372\u003c/li\u003e\n\u003cli\u003eDuffy, J. P., Pratt, L., Anderson, K., Land, P. E., Shutler, J. D., 2018. Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone. Estuarine, Coastal and Shelf Science, 200: 169-180\u003c/li\u003e\n\u003cli\u003eDulvy N.K., S.I. Rogers, S. Jennings, V. Stelzenm\u0026uuml;ller, S.R. Dye, H.R. Skjoldal, 2008. \u0026quot;limate change and deepening of the North Sea fish assemblage: a biotic indicator of warming seas. Journal of Applied Ecology 45(4): 1029-1039\u003c/li\u003e\n\u003cli\u003eEC-CINEA. 2021. Guidelines for implementing an ecosystem-based approach in maritime spatial planning : including a method for the evaluation, monitoring and review of EBA in MSP. ISBN 978-92-95225-17-6, DOI 10.2926/84261, Catalogue number HZ-01-21-323-EN-N.\u003c/li\u003e\n\u003cli\u003eEdney, A. J., Hart, T., Jessopp, M. J., Banks, A., Clarke, L. E., Cugni\u0026egrave;re, L., Elliot, K. H., Juarez Martinez, I., Kilcoyne, A., Murphy, M., Nager, R. G., Ratcliffe, N., Thompson, D. L., Ward, R. M., Wood, M. J, 2023. Best practices for using drones in seabird monitoring and research. Marine Ornithology 51: 265-280\u003c/li\u003e\n\u003cli\u003eEhler, C.N., 2021. Two decades of progress in Marine Spatial Planning. Marine Policy, 132: 10.1016/j.marpol.2020.104134\u003c/li\u003e\n\u003cli\u003eElliott, M., D. Burdon, J.P. Atkins, A. Borja, R. Cormier, V.N. de Jonge, R.K. Turner, 2017a. \u0026ldquo;And DPSIR begat DAPSI(W)R(M)!\u0026rdquo; - A unifying framework for marine environmental management. Marine Pollution Bulletin, 118: 27-40.\u003c/li\u003e\n\u003cli\u003eElliott, M., P. Snoeijs-Leijonmalm, S. Barnard, 2017b. \u0026lsquo;The dissemination diamond\u0026rsquo; and paradoxes of science-to-science and science-to-policy communication: Lessons from large marine research programmes. Marine Pollution Bulletin, 125: 1-3.\u003c/li\u003e\n\u003cli\u003eElliott, M., \u0026Aacute;. Borja, R. Cormier, 2020. Managing marine resources sustainably: A proposed integrated systems analysis approach. Ocean \u0026amp; Coastal Management, 197: 105315.\u003c/li\u003e\n\u003cli\u003eErickson, R.A., C.B. Rees, A.A. Coulter, C.M. Merkes, S.G. McCalla, K.F. Touzinsky, L. Walleser, R.R. Goforth, J.J. Amberg, 2016. Detecting the movement and spawning activity of bigheaded carps with environmental DNA. Molecular Ecology Resources, 16: 957-965.\u003c/li\u003e\n\u003cli\u003eEuropean Commission, 2008. Directive 2008/56/EC of the European Parliament and of the Council establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework Directive). Official Journal of the European Union, L164: 19-40.\u003c/li\u003e\n\u003cli\u003eEuropean Commission, 2017. Commission Directive (EU) 2017/845 of 17 May 2017 amending Directive 2008/56/EC of the European Parliament and of the Council as regards the indicative lists of elements to be taken into account for the preparation of marine strategies Official Journal of the European Communities, L125: 27-33.\u003c/li\u003e\n\u003cli\u003eEuropean Commission, 2020. Communication from the Commission of the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, EU Biodiversity Strategy for 2030, Bringing nature back into our lives. Brussels, 20.5.2020; COM(2020) 380 final 27 pp.\u003c/li\u003e\n\u003cli\u003eEuropean Commission, 2021. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions on a new approach for a sustainable blue economy in the EU. Transforming the EU\u0026apos;s Blue Economy for a Sustainable Future. Brussels, 17.5.2021 COM(2021) 240 final 21 pp.\u003c/li\u003e\n\u003cli\u003eEuropean Commission. 2022a. Marine biodiversity modelling study. Directorate General for Research and Innovation; Publications Office of the European Union.\u003c/li\u003e\n\u003cli\u003eEuropean Commission, 2022b. MSFD CIS Guidance Document No. 19, Article 8 MSFD, May 2022.\u003c/li\u003e\n\u003cli\u003eEuropean Commission, Directorate General for Maritime Affairs \u0026amp; Fisheries, A. Addamo, A. Calvo Santos, J. Guill\u0026eacute;n, S. Neehus, A. Peralta Baptista, S. Quatrini, T. Telsnig, G. Petrucco. 2022. The EU blue economy report 2022. Publications Office of the European Union.\u003c/li\u003e\n\u003cli\u003eEuropean Union, 2014. Directive 2014/89/EU of the European Parliament and of the Council of 23 July 2014 establishing a framework for maritime spatial planning. Official Journal of the European Union, L257: 135-145.\u003c/li\u003e\n\u003cli\u003eF\u0026eacute;ral, J.-P., A. Norro, 2023. Specific initial training standards are needed to dive for science in Europe, Occupational vs. Citizen Science Diving. Frontiers in Marine Science, 10: 10.3389/fmars.2023.1134494\u003c/li\u003e\n\u003cli\u003eFisher, B., Turner R.K., Morling P., 2009. Defining and classifying ecosystem services for decision making, Ecological Economics, 68(3): 643-653. https://doi.org/10.1016/j.ecolecon.2008.09.014.\u003c/li\u003e\n\u003cli\u003eFraija-Fern\u0026aacute;ndez, N., M.-C. Bouquieaux, A. Rey, I. Mendibil, U. Cotano, X. Irigoien, M. Santos, N. Rodr\u0026iacute;guez-Ezpeleta, 2020. Marine water environmental DNA metabarcoding provides a comprehensive fish diversity assessment and reveals spatial patterns in a large oceanic area. Ecology and Evolution, 10: 7560-7584.\u003c/li\u003e\n\u003cli\u003eFraschetti, S., E. Fabbrizzi, L. Tamburello, M.C. Uyarra, F. Micheli, E. Sala, C. Pipitone, F. Badalamenti, S. Bevilacqua, J. Boada, E. Cebrian, G. Ceccherelli, M. Chiantore, G. D\u0026apos;Anna, A. Di Franco, S. Farina, S. Giakoumi, E. Gissi, I. Guala, P. Guidetti, S. Katsanevakis, E. Manea, M. Montefalcone, M. Sini, V. Asnaghi, A. Cal\u0026ograve;, M. Di Lorenzo, J. Garrabou, L. Musco, A. Oprandi, G. Rilov, A. Borja, 2022. An integrated assessment of the Good Environmental Status of Mediterranean Marine Protected Areas. Journal of Environmental Management, 305: 114370.\u003c/li\u003e\n\u003cli\u003eFraz\u0026atilde;o-Santos, C., T. Agardy, F. Andrade, H. Calado, L.B. Crowder, C.N. Ehler, S. Garc\u0026iacute;a-Morales, E. Gissi, B.S. Halpern, M.K. Orbach, H.-O. P\u0026ouml;rtner, R. Rosa, 2020. Integrating climate change in ocean planning. Nature Sustainability: 1-12.\u003c/li\u003e\n\u003cli\u003eFraz\u0026atilde;o-Santos, C., T. Agardy, F. Andrade, H., L.B. Crowder, C.N. Ehler, M.K. Orbach, 2021. Major challenges in developing marine spatial planning. Marine Policy, 132: 10.1016/j.marpol.2018.08.032\u003c/li\u003e\n\u003cli\u003eGarcia, S.M. R.,J.; Charles, A. 2014. Governance of Marine Fisheries and Biodiversity Conservation: Interaction and Coevolution. 511 pp.\u003c/li\u003e\n\u003cli\u003eGiering, S.L., Culverhouse, P.F., Johns, D.G., McQuatters-Gollop, A., Pitois, S.G. 2022. Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making. Frontiers in Marine Science, 9: 986206. \u003c/li\u003e\n\u003cli\u003eGrossmann, V., Nakath, D., Urlaub, M., Oppelt, N., Koch, R., K\u0026ouml;ser, K. 2022. Digital twinning in the ocean-chanllenges in multimodal sensing and multiscale fusion based on faithful visual models. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4: 345-352. \u003c/li\u003e\n\u003cli\u003eHaines-Young, R., M.B. Potschin, 2018. Common International Classification of Ecosystem Services (CICES) V5.1 and Guidance on the Application of the Revised Structure. Available from www.cices.eu 53 pp.\u003c/li\u003e\n\u003cli\u003eHalpern, B.S., R. Fujita, 2013. Assumptions, challenges, and future directions in cumulative impact analysis. Ecosphere. https://doi.org/10.1890/ES13-00181.\u003c/li\u003e\n\u003cli\u003eHalpern, B.S., K.A. Selkoe, F. Micheli, C.V. Kappel, 2007. Evaluating and Ranking the Vulnerability of Global Marine Ecosystems to Anthropogenic Threats. - Conservation Biology, 21(5): 1301-1315 \u003c/li\u003e\n\u003cli\u003eHalpern, B.S., K.L. McLeod, A.A. Rosenberg, L.B. Crowder, 2008a. Managing for cumulative impacts in ecosystem-based management through ocean zoning. Ocean \u0026amp; Coastal Management, 51: 203-211.\u003c/li\u003e\n\u003cli\u003eHalpern, B.S., S. Walbridge, K.A. Selkoe, C.V. Kappel, F. Micheli, C. D\u0026apos;Agrosa, J.F. Bruno, K.S. Casey, C. Ebert, H.E. Fox, R. Fujita, D. Heinemann, H.S. Lenihan, E.M.P. Madin, M.T. Perry, E.R. Selig, M. Spalding, R. Steneck, R. Watson, 2008b. A Global Map of Human Impact on Marine Ecosystems. Science, 319: 948-952.\u003c/li\u003e\n\u003cli\u003eHansen, B.K., M.W. Jacobsen, A.L. Middelboe, C.M. Preston, R. Marin, D. Bekkevold, S.W. Knudsen, P.R. M\u0026oslash;ller, E.E. Nielsen, 2020. Remote, autonomous real-time monitoring of environmental DNA from commercial fish. Scientific Reports, 10: 13272.\u003c/li\u003e\n\u003cli\u003eHawkins, E., R. Sutton, 2012. Time of emergence of climate signals. Geophysical Research Letters 39(1).\u003c/li\u003e\n\u003cli\u003eHering, D., C. Sch\u0026uuml;rings, F. Wenskus, K. Blackstock, A. Borja, S. Birk, C. Bullock, L. Carvalho, M.B. Dagher-Kharrat, S. Lakner, N. Lovrić, S. McGuinness, G.-J. Nabuurs, A. S\u0026aacute;nchez-Arcilla, J. Settele, G. Pe\u0026rsquo;er, 2023. Securing success for the Nature Restoration Laws. Science, 382: 1248-1250. doi:10.1126/science.adk1658\u003c/li\u003e\n\u003cli\u003eHolman, L.E., Y. Chng, M. Rius, 2022. How does eDNA decay affect metabarcoding experiments? Environmental DNA, 4: 108-116.\u003c/li\u003e\n\u003cli\u003eHyv\u0026auml;rinen, H., A. Skytt\u0026auml;, S. Jernberg, K. Meissner, H. Kuosa, L. Uusitalo, 2021. Cost-efficiency assessments of marine monitoring methods lack rigor\u0026mdash;a systematic mapping of literature and an end-user view on optimal cost-efficiency analysis. Environmental Monitoring and Assessment, 193: 400.\u003c/li\u003e\n\u003cli\u003eIsbell, F., Balvanera, P., Mori, A.S., He, J.S., Bullock, J.M., Regmi, G.M., et al. 2022. Expert perspectives on global biodiversity loss and its drivers and impacts on people. Front Ecol Environ 2023; 21(2): 94\u0026ndash;103, doi:10.1002/fee.2536.\u003c/li\u003e\n\u003cli\u003eJoyce, K. E., Andersen, K., Bartolo, R. E. 2021. Of course we fly unmanned \u0026ndash; we\u0026apos;re women! Drones 5, 21. https://doi.org/10.3390/drones5010021 \u003c/li\u003e\n\u003cli\u003eJoyce, Fickas, K. C., Kalamandeen, M, 2023. The unique value proposition for using drones to map coastal ecosystems. Cambridge Prisms: Coastal Futures, 1, e6. https://doi.org/10.1017/cft.2022.7 \u003c/li\u003e\n\u003cli\u003eKazanidis, G., C. Orejas, A. Borja, E. Kenchington, L.-A. Henry, O. Callery, M. Carreiro-Silva, H. Egilsdottir, E. Giacomello, A. Grehan, L. Menot, T. Morato, S.\u0026Aacute;. Ragnarsson, J.L. Rueda, D. Stirling, T. Stratmann, D. van Oevelen, A. Palialexis, D. Johnson, J.M. Roberts, 2020. Assessing the environmental status of selected North Atlantic deep-sea ecosystems. Ecological Indicators, 119: 106624.\u003c/li\u003e\n\u003cli\u003eKirkfeldt, T.S., 2019. An ocean of concepts: Why choosing between ecosystem-based management, ecosystem-based approach and ecosystem approach makes a difference. Marine Policy, 106: 103541.\u003c/li\u003e\n\u003cli\u003eKnights, A.M., G.J. Piet, R.H. Jongbloed, J.E. Tamis, L. White, E. Akoglu, L. Boicenco, T. Churilova, O. Kryvenko, V. Fleming-Lehtinen, J.-M. Leppanen, B.S. Galil, F. Goodsir, M. Goren, P. Margonski, S. Moncheva, T. Oguz, K.N. Papadopoulou, O. Set\u0026auml;l\u0026auml;, C.J. Smith, K. Stefanova, F. Timofte, L.A. Robinson, 2015. An exposure-effect approach for evaluating ecosystem-wide risks from human activities. ICES Journal of Marine Science, 72: 1105-1115.\u003c/li\u003e\n\u003cli\u003eKorpinen, S., J.H. Andersen, 2016. A global review of cumulative pressure and impact assessments in marine environments. Frontiers in Marine Science, 3. https://doi.org/10.3389/fmars.2016.00153. \u003c/li\u003e\n\u003cli\u003eKorpinen, S., L. Meski, J.H. Andersen, M. Laamanen. 2012. Human pressures and their potential impact on the Baltic Sea ecosystem. Ecological Indicators, 15: 105\u0026ndash;114.\u003c/li\u003e\n\u003cli\u003eKorpinen, S., M. Meidinger, M. Laamanen, 2013. Cumulative impacts on seabed habitats: An indicator for assessments of good environmental status. Marine Pollution Bulletin, 74: 311-319.\u003c/li\u003e\n\u003cli\u003eKorpinen, S., L. Laamanen, L. Bergstr\u0026ouml;m, M. Nurmi, J.H. Andersen, J. Haapaniemi, E.T. Harvey, C.J. Murray, M. Peterlin, E. Kallenbach, K. Klančnik, U. Stein, L. Tunesi, D. Vaughan, J. Reker, 2021. Combined effects of human pressures on Europe\u0026rsquo;s marine ecosystems. AMBIO, 50: 1325-1336.\u003c/li\u003e\n\u003cli\u003eKraft, K., O. Velhonoja, T. Eerola, S. Suikkanen, T. Tamminen, L. Haraguchi, P. Yl\u0026ouml;stalo, S. Kielosto, M. Johansson, L. Lensu, H. K\u0026auml;lvi\u0026auml;inen, H. Haario, J. Sepp\u0026auml;l\u0026auml;, 2022. Towards operational phytoplankton recognition with automated high-throughput imaging, near-real-time data processing, and convolutional neural networks. Frontiers in Marine Science, 9: 867695. \u003c/li\u003e\n\u003cli\u003eKrause-Jensen D, Gundersen H, Bj\u0026ouml;rk M, Gullstr\u0026ouml;m M, Dahl M, Asplund ME, Bostr\u0026ouml;m C, Holmer M, Banta G, Graversen AEL, Pedersen MF, Bekkby T, Frigstad H, Skjellum SF, Thormar J, Gyldenk\u0026aelig;rne S, Howard J, Pidgeon E, Ragnarsd\u0026oacute;ttir SB, Mols-Mortensen A, Hancke K. 2022. Nordic Blue Carbon ecosystems: Status and outlook. Frontiers in Marine Science, Review article. doi: 10.3389/fmars.2022.847544 \u003c/li\u003e\n\u003cli\u003eKvile, K. \u0026Oslash;., Gundersen, H., Sample, J. E., Buls, T., Poulsen, R. N., Bekkby, T., Ghareeb, M. E., Salberg, A.-B., Hancke, K. (Submitted). Drone and ground-truth data collection and image annotation: A protocol for coastal habitat mapping and classification. MethodsX Journal\u003c/li\u003e\n\u003cli\u003eLauria, V., Attrill, M.J., Brown, A., Edwards, M., Votier, S.C., 2013. Regional variation in the impact of climate change: evidence that bottom-up regulation from plankton to seabirds is weak in parts of the Northeast Atlantic. Marine Ecology Progress Series, 488: 11-22. \u003c/li\u003e\n\u003cli\u003eLe, K.T., Z. Yuan, A. Syed, D. Ratelle, E.C. Orenstein, M.L. Carter, S. Strang, K.M. Kenitz, P. Morgado, P.J.S. Franks, N. Vasconcelos, J.S. Jaffe, 2022. Benchmarking and automating the image recognition capability of an in situ plankton imaging system. Frontiers in Marine Science, 9: 869088. \u003c/li\u003e\n\u003cli\u003eLiu, Y., Liu, Q., Sample, J. E., Hancke, K., Salberg, A.-B, 2022. Coastal habitat mapping with UAV multi-sensor data: an experiment among DCNN-based approaches. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 439-445\u003c/li\u003e\n\u003cli\u003eLombard, F., E. Boss, A.M. Waite, M. Vogt, J. Uitz, L. Stemmann, H.M. Sosik, J. Schulz, J.-B. Romagnan, M. Picheral, J. Pearlman, M.D. Ohman, B. Niehoff, K.O. M\u0026ouml;ller, P. Miloslavich, A. Lara-Lpez, R. Kudela, R.M. Lopes, R. Kiko, L. Karp-Boss, J.S. Jaffe, M.H. Iversen, J.-O. Irisson, K. Fennel, H. Hauss, L. Guidi, G. Gorsky, S.L.C. Giering, P. Gaube, S. Gallager, G. Dubelaar, R.K. Cowen, F. Carlotti, C. Brise\u0026ntilde;o-Avena, L. Berline, K. Benoit-Bird, N. Bax, S. Batten, S.D. Ayata, L.F. Artigas, W. Appeltans, 2019. Globally consistent quantitative observations of planktonic ecosystems. Frontiers in Marine Science, 6: 10.3389/fmars.2019.00196\u003c/li\u003e\n\u003cli\u003eLong, R. 2012. Legal aspects of ecosystem-based marine management in Europe. In A. Chircop, M. L. McConnell, \u0026amp; S. Coffen-Smout (Eds.), Ocean yearbook (Vol. 26, pp. 417\u0026ndash;484). Boston: Brill Academic Publishers.\u003c/li\u003e\n\u003cli\u003eLong, R.D., A. Charles, R.L. Stephenson, 2015. Key principles of marine ecosystem-based management. Marine Policy, 57: 53-60.\u003c/li\u003e\n\u003cli\u003eLowndes, J.S.S., B.D. Best, C. Scarborough, J.C. Afflerbach, M.R. Frazier, C.C. O\u0026rsquo;Hara, N. Jiang, B.S. Halpern, 2017. Our path to better science in less time using open data science tools. Nature Ecology \u0026amp; Evolution, 1: 0160.\u003c/li\u003e\n\u003cli\u003eLuisetti, T., M. Schratzberger, 2023. Including biological diversity in natural capital accounts for marine biodiversity conservation and human well-being. Biodiversity Conservation, 32: 405\u0026ndash;413 (2023). https://doi.org/10.1007/s10531-021-02342-0\u003c/li\u003e\n\u003cli\u003eLuisetti, T., R.K. Turner, I. Bateman, S. Morse-Jones, C. Adams, L. Fonseca, 2011. Coastal and Marine Ecosystem Services Valuation for Policy and Management: Managed Realignment Case Studies in England. Ocean \u0026amp; Coastal Management, 54: 212-224.\u003c/li\u003e\n\u003cli\u003eLuisetti, T., R.K. Turner, T. Jickells, J. Andrews, M. Elliott, M. Schaafsma, N. Beaumont, S. Malcolm, D. Burdon, C. Adams, W. Watts, 2014. Coastal Zone Ecosystem Services: From science to values and decision making; a case study. Science of the Total Environment, 493: 682\u0026ndash;693.\u003c/li\u003e\n\u003cli\u003eLuisetti T., Turner R.K., Andrews J., Jickells T., Kroeger S., Diesing M., Paltriguera L., Johnson M., Parker R., Bakker D.C.E., Weston K., 2019. Quantifying and valuing carbon flows and stores in coastal and shelf ecosystems in the UK. Ecosystem Services, 35: 67\u0026ndash;76\u003c/li\u003e\n\u003cli\u003eLynam, C., L. Uusitalo, J. Patr\u0026iacute;cio, C. Piroddi, A. Queiros, H. Teixeira, A. Rossberg, Y. Sagarminaga, K. Hyder, N. Niquil, C. M\u0026ouml;llmann, C. Wilson, G. Chust, I. Galparsoro, R. Forster, H. Verissimo, L. Tedesco, M. Revilla, S. Neville, 2016. Uses of innovative modelling tools within the implementation of the marine strategy framework directive. Frontiers in Marine Science, 3: 10.3389/fmars.2016.00182.\u003c/li\u003e\n\u003cli\u003eL\u0026oslash;nborg, C., Thomasberger, A., St\u0026aelig;hr, P. A. U., Stockmarr, A., Sengupta, S., Rasmussen, M. L., Nielsen, L. T., Hansen, L. B., Timmermann, K, 2022. Submerged aquatic vegetation - overview of monitoring techniques used for the identification and determination of spatial distribution in European coastal waters. Integrated Environmental Assessment and Management, 18: 892-908\u003c/li\u003e\n\u003cli\u003eMack, L., J. Attila, E. Aylagas, A. Beermann, A. Borja, D. Hering, M. Kahlert, F. Leese, R. Lenz, M. Lehtiniemi, A. Liess, U. Lips, O.-P. Mattila, K. Meissner, T. Pyh\u0026auml;lahti, O. Set\u0026auml;l\u0026auml;, J. S. Strehse, L. Uusitalo, A. Willstrand Wranne, S. Birk, 2020. A Synthesis of Marine Monitoring Methods with the Potential to Enhance the Status Assessment of the Baltic Sea. Frontiers in Marine Science, 7: 10.3389/fmars.2020.552047.\u003c/li\u003e\n\u003cli\u003eMarcotte, D., S.K. Hung, S. Caquard, 2015. Mapping cumulative impacts on Hong Kong\u0026apos;s pink dolphin population. Ocean and Coastal Management, 109: 51-63\u003c/li\u003e\n\u003cli\u003eMarin, O., L. Boicenco, V. Abaza, E. Bișinicu, V. Coatu, M. Galațchi, L. Lazăr, A. Oros, E. Pantea, C. Tabarcea, G. Țiganov, O. Vlas, 2020. Performing First Integrative Evaluation of the Ecological Status of Romanian Black Sea Waters Using Nested Environmental Status Assessment Tool (NEAT). Cercetări Marine, 50: 108-125.\u003c/li\u003e\n\u003cli\u003eMenchaca, I., \u0026Aacute;. Borja, I. Galparsoro, J. Franco, M.C. Uyarra, A. Uriarte, G. Chust, L. Ibaibarriaga, J. Bald, 2022. Multi-source and multi-scale data integration for the assessment of the marine environmental status of the Basque Coast (SE Bay of Biscay). Estuarine, Coastal and Shelf Science, 277: 108055.\u003c/li\u003e\n\u003cli\u003eM\u0026eacute;riguet, Z., A. Oddone, D. Le Guen, T. Pollina, R. Bazile, C. Moulin, R. Troubl\u0026eacute;, M. Prakash, C. de Vargas, F. Lombard, 2022. Basin-scale underway quantitative survey of surface microplankton using affordable collection and imaging tools deployed from Tara. Frontiers in Marine Science, 9: 916025. \u003c/li\u003e\n\u003cli\u003eMicheli, F., B. S. Halpern, S. Walbridge, S. Ciriaco, F. Ferretti, S. Fraschetti, R. Lewison, L. Nykjaer, A. A. Rosenberg, 2013. Cumulative Human Impacts on Mediterranean and Black Sea Marine Ecosystems: Assessing Current Pressures and Opportunities. Plos ONE, 8: e79889.\u003c/li\u003e\n\u003cli\u003eMorse-Jones, S., T. Luisetti, R.K. Turner, B. Fisher, 2011. Ecosystem valuation: some principles and a partial application. Environmetrics, 22: 675-685.\u003c/li\u003e\n\u003cli\u003eMurawski, S.A. 2007. Ten myths concerning ecosystem approaches to marine resource management. Mar. Policy 31, 681\u0026ndash;690.\u003c/li\u003e\n\u003cli\u003eMurphy, H., Jenkins, G, 2010. Observational methods used in marine spatial monitoring of fishes and associated habitats: a review. Marine and Freshwater Research 61: 236-252. doi:10.1071/MF09068 \u003c/li\u003e\n\u003cli\u003eNash, K.L., J.L. Blythe, C. Cvitanovic, E.A. Fulton, B.S. Halpern, E.J. Milner-Gulland, P.F.E. Addison, G.T. Pecl, R.A. Watson, J.L. Blanchard, 2020. To Achieve a Sustainable Blue Future, Progress Assessments Must Include Interdependencies between the Sustainable Development Goals. One Earth.\u003c/li\u003e\n\u003cli\u003eNathan, L.M., M. Simmons, B.J. Wegleitner, C.L. Jerde, A.R. Mahon, 2014. Quantifying Environmental DNA Signals for Aquatic Invasive Species Across Multiple Detection Platforms. Environmental Science \u0026amp; Technology, 48: 12800-12806.\u003c/li\u003e\n\u003cli\u003eNatural Capital Committee, 2014. The State of Natural Capital: Restoring our Natural Assets. Second report to the Economic Affairs Committee - March 2014, UK Government.\u003c/li\u003e\n\u003cli\u003eNemati, H., M.R. Shokri, Z. Ramezanpour, G.H. Ebrahimi Pour, I. Muxika, \u0026Aacute;. Borja, 2017. Using multiple indicators to assess the environmental status in impacted and non-impacted bathing waters in the Iranian Caspian Sea. Ecological Indicators, 82: 175-182.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Hagan, A.M. 2020. Ecosystem-Based Management (EBM) and Ecosystem Services in EU Law, Policy and Governance. In Ecosystem-Based Management, Ecosystem Services and Aquatic Biodiversity : Theory, Tools and Applications, pp. 353-372. Ed. by T.G. O\u0026rsquo;Higgins, M. Lago, and T.H. DeWitt. Springer International Publishing, Cham.\u003c/li\u003e\n\u003cli\u003eOwens, N.J.P., Hosie, G.W., Batten, S.D., Edwards, M., Johns, D.G., Beaugrand, G., 2013. All plankton sampling systems underestimate abundance: response to \u0026ldquo;Continuous plankton recorder underestimates zooplankton abundance\u0026rdquo; by JW Dippner and M. Krause. Journal of Marine Systems, 128: 240-242. \u003c/li\u003e\n\u003cli\u003ePana\u0026iuml;otis, T., L. Caray\u0026ndash;Counil, B. Woodward, M.S. Schmid, D. Daprano, S.T. Tsai, C. M. Sullivan, R.K. Cowen, J.-O. Irisson, 2022. Content-aware segmentation of objects spanning a large size range: Application to plankton images. Frontiers in Marine Science, 9: 870005. \u003c/li\u003e\n\u003cli\u003eParsley, M.B., C.S. Goldberg, 2023. Environmental RNA can distinguish life stages in amphibian populations. Molecular Ecology Resources, n/a.\u003c/li\u003e\n\u003cli\u003ePavlidou, A., N. Simboura, \u0026Kappa;. Pagou, G. Assimakopoulou, V. Gerakaris, I. Hatzianestis, P. Panayotidis, M. Pantazi, N. Papadopoulou, S. Reizopoulou, C. Smith, M. Triantaphyllou, M.C. Uyarra, I. Varkitzi, V. Vassilopoulou, C. Zeri, A. Borja, 2019. Using a holistic ecosystem-integrated approach to assess the environmental status of Saronikos Gulf, Eastern Mediterranean. Ecological Indicators, 96: 336-350.\u003c/li\u003e\n\u003cli\u003ePawlowski, J., L. Apoth\u0026eacute;loz-Perret-Gentil, F. Altermatt, 2020. Environmental DNA: What\u0026apos;s behind the term? Clarifying the terminology and recommendations for its future use in biomonitoring. Molecular Ecology, 29: 4258-4264.\u003c/li\u003e\n\u003cli\u003ePecl, G.T., M.B. Ara\u0026uacute;jo, J.D. Bell, J. Blanchard, T.C. Bonebrake, I.-C. Chen, T.D. Clark, R.K. Colwell, F. Danielsen, B. Eveng\u0026aring;rd, L. Falconi, S. Ferrier, S. Frusher, R.A. Garcia, R.B. Griffis, A.J. Hobday, C. Janion-Scheepers, M.A. Jarzyna, S. Jennings, J. Lenoir, H.I. Linnetved, V.Y. Martin, P.C. McCormack, J. McDonald, N.J. Mitchell, T. Mustonen, J.M. Pandolfi, N. Pettorelli, E. Popova, S.A. Robinson, B.R. Scheffers, J.D. Shaw, C.J.B. Sorte, J.M. Strugnell, J.M. Sunday, M.-N. Tuanmu, A. Verg\u0026eacute;s, C. Villanueva, T. Wernberg, E. Wapstra, S.E. Williams, 2017. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science, 355.\u003c/li\u003e\n\u003cli\u003ePiet, G.J., J.E. Tamis, J. Volwater, P. de Vries, J.T. van der Wal, R.H. Jongbloed, 2021a. A roadmap towards quantitative cumulative impact assessments: Every step of the way. Science of The Total Environment, 784: 146847.\u003c/li\u003e\n\u003cli\u003ePiet G.J., Tamis J.E., van der Wal J.T., Jongbloed R.H., 2021b. Cumulative impacts of wind farms on the North Sea ecosystem. Wageningen Marine Research report C081/21. https://doi.org/10.18174/556024\u003c/li\u003e\n\u003cli\u003ePiet, G.J., Grundlehner, A., Jongbloed, R., Tamis, J. de Vries, P., 2023. SCAIRM: A spatial cumulative assessment of impact risk for management. Ecological Indicators 157. https://doi.org/10.1016/j.ecolind.2023.111157\u003c/li\u003e\n\u003cli\u003ePınarbaşı, K., I. Galparsoro, \u0026Aacute;. Borja, V. Stelzenm\u0026uuml;ller, C.N. Ehler, A. Gimpel, 2017. Decision support tools in marine spatial planning: Present applications, gaps and future perspectives. Marine Policy, 83: 83-91.\u003c/li\u003e\n\u003cli\u003ePitois, S.G., C.A. Graves, H. Close, C. Lynam, J. Scott, J. Tilbury, J. van der Kooij, P. Culverhouse, 2021. A first approach to build and test the Copepod Mean Size and Total Abundance (CMSTA) ecological indicator using in-situ size measurements from the Plankton Imager (PI). Ecological Indicators, 123: 107307. \u003c/li\u003e\n\u003cli\u003ePitois, S.G., Lynam, C.P., Jansen, T., Halliday, N., Edwards, M., 2012. Bottom-up effects of climate on fish populations: data from the Continuous Plankton Recorder. Marine Ecology Progress Series, 456: 169-186. \u003c/li\u003e\n\u003cli\u003ePitois, S.G., Tilbury, J., Bouch, P., Close, H., Barnett, S., Culverhouse, P.F., 2018. Comparison of a cost-effective integrated plankton sampling and imaging instrument with traditional systems for mesozooplankton sampling in the Celtic Sea. Frontiers in Marine Science, 5: 5\u003c/li\u003e\n\u003cli\u003ePitois, S.G., Fileman, E.S., Benfield, M.C., Wiebe, P.H., Lombard, F., 2023. Technological advances for measuring planktonic components of the pelagic ecosystem: An integrated approach to data collection and analysis. Frontiers in Marine Science, 10: 1100998. \u003c/li\u003e\n\u003cli\u003ePlonus, R.M., Vogl, S., Floeter, J., 2021. Automatic Segregation of Pelagic Habitats. Frontiers in Marine Science, 8, 754375. \u003c/li\u003e\n\u003cli\u003ePollina, T., A.G. Larson, F. Lombard, H. Li, D. Le Guen, S. Colin, C. de Vargas, M. Prakash, 2022. PlanktoScope: affordable modular quantitative imaging platform for citizen oceanography. Frontiers in Marine Science, 9: 949428. \u003c/li\u003e\n\u003cli\u003eP\u0026ouml;rtner H.-O., R.J. Scholes, J. Agard, E. Archer, A. Arneth, X. Bai, D. Barnes, M. Burrows, L. Chan, W. Cheung, 2021. IPBES-IPCC co-sponsored workshop report on biodiversity and climate change. IPBES and IPCC 28\u003c/li\u003e\n\u003cli\u003eP\u0026ouml;rtner H.-O., D.C. Roberts, H. Adams, C. Adler, P. Aldunce, E. Ali, R.A. Begum, R. Betts, R.B. Kerr, R. Biesbroek, 2022. Climate Change 2022: Impacts, adaptation and vulnerability, IPCC Geneva, Switzerland.\u003c/li\u003e\n\u003cli\u003ePreston, C., K. Yamahara, D. Pargett, C. Weinstock, J. Birch, B. Roman, S. Jensen, B. Connon, R. Jenkins, J. Ryan, C. Scholin, 2024. Autonomous eDNA collection using an uncrewed surface vessel over a 4200-km transect of the eastern Pacific Ocean. Environmental DNA, 6: e468.\u003c/li\u003e\n\u003cli\u003eQueir\u0026oacute;s, A.M., E. Talbot, N.J. Beaumont, P.J. Somerfield, S. Kay, C. Pascoe, S. Dedman, J.A. Fernandes, A. Jueterbock, P.I. Miller, S.F. Sailley, G. Sar\u0026aacute;, L.M. Carr, M.C. Austen, S. Widdicombe, G. Rilov, L.A. Levin, S.C. Hull, S.F. Walmsley, C. Nic Aonghusa, 2021. Bright spots as climate-smart marine spatial planning tools for conservation and blue growth. Global Change Biology, 27: 5514-5531.\u003c/li\u003e\n\u003cli\u003eQueir\u0026oacute;s A.M., E. Talbot, S. Kay, S. Sailley, T. Vu Hoang Le, C. Thi Pham, S. Widdicombe, 2022. Climate-smart spatial planning assessment in support of conservation and blue growth in Da Nang city\u0026apos;s marine environment. 10.17031/dxfj-a468\u003c/li\u003e\n\u003cli\u003eQueir\u0026oacute;s A.M., S. Kay, M. Sciberras, E. Talbot, M. Kaiser, R.J. Wilson, S. Sailley, S. Marra, L. Matear, J. Fernandes, A. Aldridge, R. McEwan, K. Morris, C.L. McNeill, J. Nunes, K. Woodcock, S. Duncombe-Smith, A. Smith, C. Lynam, C. Vina-Herbon, P. Boulcott, B. Hunter, R. Parker, K. Robinson, C. Sweeting, M. Frost, P.J. Somerfield, 2023. Early-warning system: Climate-smart spatial management of UK fisheries, aquaculture and conservation. A report of the NERC/ESRC Marine Spatial Planning Addressing Climate Effects project. 58 pp. 10.14465/2023.msp02.tec\u003c/li\u003e\n\u003cli\u003eQueir\u0026oacute;s A.M, E. Talbot, S. Kay, S.Sailley, J.A. Fernandes. in prep. A climate-resilient path for Ireland\u0026rsquo;s Marine Protected Areas network. Addendum chapter to Revitalising Our Seas report: Identifying Areas of Interest for Marine Protected Area Designation in Irish Waters\u003c/li\u003e\n\u003cli\u003eRatnarajah, L., R. Abu-Alhaija, A. Atkinson, S. Batten, N.J. Bax, K.S. Bernard, G. Canonico, A. Cornils, J.D. Everett, M. Grigoratou, N.H.A. Ishak, D. Johns, F. Lombard, E. Muxagata, C. Ostle, S. Pitois, A.J. Richardson, K. Schmidt, L. Stemmann, K.M. Swadling, G. Yang, L. Yebra, 2023. Monitoring and modelling marine zooplankton in a changing climate. Nature Communications, 14(1): 564. \u003c/li\u003e\n\u003cli\u003eReker, J., C. Murray, E. Royo Gelabert, K. Abhold, S. Korpinen, M. Peterlin, D. Vaughan, J.H. Andersen, 2019. Marine Messages II. Navigating the course towards clean, healthy and productive seas through implementation of an ecosystem-based approach. European Environment Agency, EEA Report, 17/2019: 82 pp.\u003c/li\u003e\n\u003cli\u003eRodriguez-Ezpeleta, N., O. Morissette, C.W. Bean, S. Manu, P. Banerjee, A. Lacoursi\u0026egrave;re-Roussel, K.C. Beng, S.E. Alter, F. Roger, L.E. Holman, K.A. Stewart, M.T. Monaghan, Q. Mauvisseau, L. Mirimin, O.S. Wangensteen, C.M. Antognazza, S.J. Helyar, H. de Boer, M.-E. Monchamp, R. Nijland, C.L. Abbott, H. Doi, M.A. Barnes, M. Leray, P.I. Habl\u0026uuml;tzel, K. Deiner, 2021a. Trade-offs between reducing complex terminology and producing accurate interpretations from environmental DNA: Comment on \u0026ldquo;Environmental DNA: What\u0026apos;s behind the term?\u0026rdquo; by Pawlowski et al., (2020). Molecular Ecology, 30: 4601-4605.\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Ezpeleta, N., L. Zinger, A. Kinziger, H.M. Bik, A. Bonin, E. Coissac, B.C. Emerson, C.M. Lopes, T.A. Pelletier, P. Taberlet, S. Narum, 2021b. Biodiversity monitoring using environmental DNA. Molecular Ecology Resources, 21: 1405-1409.\u003c/li\u003e\n\u003cli\u003eSalter, I., M. Joensen, R. Kristiansen, P. Steingrund, P. Vestergaard, 2019. Environmental DNA concentrations are correlated with regional biomass of Atlantic cod in oceanic waters. Communications Biology, 2: 461.\u003c/li\u003e\n\u003cli\u003eSchmid, M.S., D. Daprano, M.M. Damle, C.M. Sullivan, S. Sponaugle, C. Cousin, C. Guigand, R.K. Cowen, 2023. Edge computing at sea: high-throughput classification of in-situ plankton imagery for adaptive sampling. Frontiers in Marine Science, 10: 1187771. \u003c/li\u003e\n\u003cli\u003eScott, J., Pitois, S., Close, H., Almeida, N., Culverhouse, P., Tilbury, J., Malin, G., 2021. In situ automated imaging, using the Plankton Imager, captures temporal variations in mesozooplankton using the Celtic Sea as a case study. Journal of Plankton Research, 43(2): 300-313. \u003c/li\u003e\n\u003cli\u003eScott, J., Pitois, S., Creach, V., Malin, G., Culverhouse, P., Tilbury, J., 2023. Resolution changes relationships: Optimizing sampling design using small scale zooplankton data. Progress in Oceanography, 210: 102946. \u003c/li\u003e\n\u003cli\u003eSeeyave, S., P. Simpson, S. Burg, K.M. Davidson, F. Beckman, V. Cheung, A. Miller, A. Ribeiro, T. Keizer, E. Smail, A. Villwock, 2017. Writing a Communication Strategy: A Step-by-Step Guide and Template Tailored for International (Marine) Science Organisations Ocean Communicators United: 36 pp.\u003c/li\u003e\n\u003cli\u003eSeys, J., L. Cox, E. Şahin Y\u0026uuml;cel, D. Ezgeta-Balić, M. Faimali, F. Garaventa, M.-C. Garc\u0026iacute;a-Mart\u0026iacute;nez, C. Gili, K. Kopke, K. Moreau, F. Petrera, D. Simon, A. Villwock, T. Zielinski, A. Mu\u0026ntilde;iz Piniella, 2022. Marine Science Communication in Europe \u0026ndash; A way forward. Mu\u0026ntilde;iz Piniella, A., Kellett, P., van den Brand, R., Alexander, B., Rodriguez Perez, A., Van Elslander, J., Heymans, J. J. [Eds.] Future Science Brief 8 of the European Marine Board, Ostend, Belgium. 48pp. ISBN: 9789464206166 ISSN: 2593-5232 DOI: 10.5281/zenodo.6444143\u003c/li\u003e\n\u003cli\u003eShelton, A.O., A. Ram\u0026oacute;n-Laca, A. Wells, J. Clemons, D. Chu, B.E. Feist, R.P. Kelly, S.L. Parker-Stetter, R. Thomas, K.M. Nichols, L. Park, 2022. Environmental DNA provides quantitative estimates of Pacific hake abundance and distribution in the open ocean. Proceedings of the Royal Society B: Biological Sciences, 289: 20212613.\u003c/li\u003e\n\u003cli\u003eSigsgaard, E.E., M.R. Jensen, I.E. Winkelmann, P.R. M\u0026oslash;ller, M.M. Hansen, P.F. Thomsen, 2020. Population-level inferences from environmental DNA\u0026mdash;Current status and future perspectives. Evolutionary Applications, 13: 245-262.\u003c/li\u003e\n\u003cli\u003eSkjoldal, H.R., Wiebe, P.H., Postel, L., Knutsen, T., Kaartvedt, S., Sameoto, D.D., 2013. Intercomparison of zooplankton (net) sampling systems: Results from the ICES/GLOBEC sea-going workshop. Progress in oceanography, 108: 1-42. \u003c/li\u003e\n\u003cli\u003eSmith, A.D.M., Fulton, E.J., Hobday, A.J., Smith, D.C., Shoulder, P. 2007. Scientific tools to support the practical implementation of ecosystem-based fisheries management. ICES Journal of Marine Science, 64: 633-639.\u003c/li\u003e\n\u003cli\u003eStelzenm\u0026uuml;ller, V., M. Coll, A.D. Mazaris, S. Giakoumi, S. Katsanevakis, M.E. Portman, R. Degen, P. Mackelworth, A. Gimpel, P.G. Albano, V. Almpanidou, J. Claudet, F. Essl, T. Evagelopoulos, J.J. Heymans, T. Genov, S. Kark, F. Micheli, M.G. Pennino, G. Rilov, B. Rumes, J. Steenbeek, H. Ojaveer, 2018. A risk-based approach to cumulative effect assessments for marine management. Science of The Total Environment, 612: 1132-1140.\u003c/li\u003e\n\u003cli\u003eTalbot, E., J-B S. Jontila, B.J. Gonzales, R.G. Dolorosa, E.D. Jose, R. Sajorne, S. Sailley, S. Kay, A.M. Queir\u0026oacute;s, In review. Incorporating climate-readiness into fisheries management strategies\u003c/li\u003e\n\u003cli\u003eTan, I., H. Atabay, A. Evcen, G. Kurt, E. Taşkın, \u0026Ccedil;. Polat Beken, 2023. Integrated assessment of eutrophication in the southern Black Sea waters, using the Nested Environmental Status Assessment Tool. Marine Pollution Bulletin, 195: 115424.\u003c/li\u003e\n\u003cli\u003eTeixeira, H., T. Berg, L. Uusitalo, K. F\u0026uuml;rhaupter, A.-S. Heiskanen, K. Mazik, C. Lynam, S. Neville, J.G. Rodriguez, N. Papadopoulou, S. Moncheva, T. Churilova, O. Krivenko, D. Krause-Jensen, A. Zaiko, H. Verissimo, M. Pantazi, S. Carvalho, J. Patr\u0026iacute;cio, M.C. Uyarra, A. Borja, 2016. A Catalogue of marine biodiversity indicators. Frontiers in Marine Science, 3: 10.3389/fmars.2016.00207.\u003c/li\u003e\n\u003cli\u003eTorsvik, B. M., Poulsen, R. N., van Bavel, B., Gundersen, H., Hancke, K, 2020. Detection of macroplastic on beaches using drones and object-based image analysis. NIVA report 7553-2020, 47 pp.\u003c/li\u003e\n\u003cli\u003eTurner, R., M. Schaafsma, M. Elliott, D. Burdon, J. Atkins, T. Jickells, P. Tett, L. Mee, S. van Leeuwen, S. Barnard, T. Luisetti, L. Paltriguera, G. Palmieri, J. Andrews, 2014. UK National Ecosystem Assessment Follow-on. Work Package Report 4: Coastal and Marine Ecosystem Services: Principles and Practice. UNEP-WCMC, LWEC, UK. http://uknea.unep-wcmc.org/LinkClick.aspx?fileticket=KLy76Rak0WQ%3D\u0026amp;tabid=82 \u003c/li\u003e\n\u003cli\u003eUnited Nations, 2021. System of Environmental-Economic Accounting\u0026mdash; Ecosystem Accounting (SEEA EA). White cover publication, pre-edited text subject to official editing. Available at: https://seea.un.org/ecosystem-accounting.\u003c/li\u003e\n\u003cli\u003eUusitalo, L., H. Blanchet, J. Andersen, O. Beauchard, T. Berg, S. Bianchelli, A. Cantafaro, J. Carstensen, L. Carugati, S. Cochrane, R. Danovaro, A.-S. Heiskanen, V. Karvinen, S. Moncheva, C. Murray, J. Neto, H. Nyg\u0026aring;rd, M. Pantazi, N. Papadopoulou, N. Simboura, G. Srėbalienė, M.C. Uyarra, A. Borja, 2016. Indicator-based assessment of marine biological diversity \u0026ndash; lessons from 10 case studies across the European Seas. Frontiers in Marine Science, 3: 10.3389/fmars.2016.00159.\u003c/li\u003e\n\u003cli\u003eVan de Pol, L., K. Van der Biest, S.E. Taelman, L. De Luca Pe\u0026ntilde;a, G. Everaert, S. Hernandez, F. Culhane, A. Borja, J.J. Heymans, G. Van Hoey, J. Vanaverbeke, P. Meire, 2023. Impacts of human activities on the supply of marine ecosystem services: A conceptual model for offshore wind farms to aid quantitative assessments. Heliyon, 9: e13589.\u003c/li\u003e\n\u003cli\u003eWeldrick, C.K., 2022. A promising approach to quantifying pteropod eggs using image analysis and machine learning. Frontiers in Marine Science, 9: 869252. \u003c/li\u003e\n\u003cli\u003eWiebe, P.H., Benfield, M.C., 2003. From the Hensen net toward four-dimensional biological oceanography. Progress in Oceanography, 56(1): 7-136.\u003c/li\u003e\n\u003cli\u003eWilliams, A., Dowdney, J., Smith, A.D.M., Hobday, A.J., Fuller, M. 2011. Evaluating impacts of fishing on benthic habitats: A risk assessment framework applied to Australian fisheries. Fisheries Research, 112: 154-167.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003cdiv id=\"ftn1\"\u003e\n \u003cp\u003e[1] www.ges4seas.eu\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"ftn2\"\u003e\n \u003cp\u003e[2] www.obama-next.eu\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"ftn3\"\u003e\n \u003cp\u003e[3] www.biocean5d.org\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"ftn4\"\u003e\n \u003cp\u003e[4] www.actnow-project.eu\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"ftn5\"\u003e\n \u003cp\u003e[5] www.marbefes.eu\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"cumulative effects, environmental status, eDNA, drones, artificial intelligence, imaging ","lastPublishedDoi":"10.21203/rs.3.rs-4047391/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4047391/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Human activities at sea can produce pressures and cumulative effects in ecosystem components, that need to be monitored and assessed, in a cost-effective manner. Five Horizon European projects have joined forces to collaboratively increase our knowledge and skills to monitor and assess the ocean in an innovative way, assisting managers and policy-makers in taking decisions to maintain sustainable activities at sea. Here we present and discuss the status of some methods revised during a summer school, aiming at a better management of coasts and seas. We include novel methods to monitor the coastal and ocean waters (e.g. environmental DNA, drones, imaging and artificial intelligence, climate modelling and spatial planning) and innovative tools to assess the status (e.g. cumulative impacts assessment, multiple pressures, Nested Environmental status Assessment Tool (NEAT), ecosystem services assessment, or a new unifying approach). As a concluding remark, some of the most important challenges ahead is assessing pros and cons of novel methods, comparing them with benchmark technologies, and integrating these into long standing time series for data continuity. This requires transition periods and careful planning, which can be covered through an intense collaboration of current and future European projects on marine biodiversity and ecosystem health.","manuscriptTitle":"Innovative and practical tools for monitoring and assessing biodiversity status and impacts of multiple human pressures in marine systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 18:32:01","doi":"10.21203/rs.3.rs-4047391/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-10T01:12:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-08T16:53:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-08T16:53:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-02-26T16:43:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"588aab98-7473-4254-b6df-19f1a1161989","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-22T13:53:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 18:32:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4047391","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4047391","identity":"rs-4047391","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}