Fishes are the dominant vertebrates in the ocean, yet we know little of their contribution to carbon export flux at regional to global scales. We synthesize the existing information on fish-based carbon flux in coastal and pelagic waters, identify gaps and challenges in measuring this flux and approaches to address them, and recommend research priorities. Based on our synthesis of passive (fecal pellet sinking) and active (migratory) flux of fishes, we estimated that fishes contribute an average (AE standard deviation) of about 16.1% (AE 13%) to total carbon flux out of the euphotic zone. Using the mean value of model-generated global carbon flux estimates, this equates to an annual flux of 1.5 AE 1.2 Pg C yr −1 . High variability in estimations of the fish-based contribution to total carbon flux among previous field studies and reported here highlight significant methodological variations and observational gaps in our present knowledge. Community-adopted methodological standards, improved and more frequent measurements of biomass and passive and active fluxes of fishes, and stronger linkages between observations and models will decrease uncertainty, increase our confidence in the estimation of fish-based carbon flux, and enable identification of controlling factors to account for spatial and temporal variability. Better constraints on this key component of the biological pump will provide a baseline for understanding how ongoing climate change and harvest will affect the role fishes play in carbon flux. * Estimated from DVM fish standing stock in and out of 500 m, an assumed daily ration of 10% body weight, 50% assimilation efficiency, and 5% fecal carbon content. † Uncorrected for capture efficiency. ‡ Estimated from midwater fish standing stock and daily consumption rate assuming an egestion rate of 20% food intake. §Values reported here include only data where anchovy fecal pellets were present in sediment traps (in 12 out of 20 free-drifting sediment trap sampling deployments in fall of 1977 and fall of 1978). ¶ Assumes 14% capture efficiency. ** Maximum respiratory carbon flux as day-time net catches have not been corrected for capture efficiency. † † Value estimated as the geometric mean from the ranges reported in the study. ‡ ‡ Assumes 50% capture efficiency. § § Assumes 14% capture efficiency for Matsuda-Oozeki-Hu trawls and an additional 6% capture efficiency for Isaacs-Kidd midwater trawls. ¶ ¶ Calculated from Davison et al. 2013 (table 9), comparing Vertical Migrant (VM) export and Fish-Mediated Export (FME; vertical migrant + nonmigrant export) to passive POC flux measured from sediment traps at 150 m). ***Estimated from assuming total fish export flux is equivalent to 100% (fecal) plus 180% (excretory) plus 50% (mortality) of the fish standing stock (11.9-66 mg C m −2).**Atmospheric Arpege weather forecast model coupled with geochemical Hamburg ocean carbon cycle model. † † Ecosystem model that consists of several compartments and tracks elements, including carbon, for the biota and detrital pools....

The deep learning (DL) revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. New methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid. Off-the-shelf algorithms find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. These endeavours require collaboration across ecological and data science disciplines, which can be challenging to initiate. To promote the use of DL towards ecosystem-based management of the sea, this paper aims to bridge the gap between marine ecologists and computer scientists. We provide insight into popular DL approaches for ecological data analysis, focusing on supervised learning techniques with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of DL to marine ecology. We present case studies on plankton, fish, marine mammals, pollution, and nutrient cycling that involve object detection, classification, tracking, and segmentation of visualized data. We conclude with a broad outlook of the field’s opportunities and challenges, including potential technological advances and issues with managing complex data sets.

In the last decade, the ocean has absorbed a quarter of the Earth's greenhouse gas emissions through the carbon (C) cycle, a naturally occurring process. Aspects of the ocean C cycle are now being incorporated into climate change mitigation and adaptation plans. Currently, too little is known about marine vertebrate C functions for their inclusion in policies. Fortunately, marine vertebrate biology, behavior, and ecology through the lens of C and nutrient cycling and flux is an emerging area of research that is rich in existing data. This review uses literature and trusted data sources to describe marine vertebrate C interactions, provides quantification where possible, and highlights knowledge gaps. Implications of better understanding the integral functions of marine vertebrates in the ocean C cycle include the need for consideration of these functions both in policies on nature-based climate change mitigation and adaptation, and in management of marine vertebrate populations. ll

The value of interdisciplinarity for solving complex coastal problems is widely recognized. Many early career researchers (ECRs) therefore actively seek this type of collaboration through choice or necessity, for professional development or project funding. However, establishing and conducting interdisciplinary research collaborations as an ECR has many challenges. Here, we identify these challenges through the lens of ECRs working in different disciplines on a common ecosystem, the Norwegian Skagerrak coast. The most densely populated coastline in Norway, the Skagerrak coast, is experiencing a multitude of anthropogenic stressors including fishing, aquaculture, eutrophication, climate change, land runoff, development, and invasive species. The Skagerrak coastline has also been the focus of environmental science research for decades, much of which aims to inform management of these stressors. The region provides a fantastic opportunity for interdisciplinary collaboration, both within and beyond the environmental sciences. This perspective article identifies the barriers ECRs in Norway face in establishing interdisciplinary and collaborative research to inform management of coastal ecosystems, along with their root causes. We believe our discussion will be of broad interest to all research institutions who employ or educate ECRs (in Norway and worldwide), and to those who develop funding mechanisms for ECRs and interdisciplinary research.

The deep learning revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. These new methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid.Off-the-shelf algorithms can find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. Using these opportunities requires collaboration across ecological and data science disciplines, which can be challenging to initiate. To facilitate these collaborations and promote the use of deep learning towards ecosystem-based management of the sea, this paper aims to bridge the gap between marine ecologists and computer scientists. We provide insight into popular deep learning approaches for ecological data analysis in plain language, focusing on the techniques of supervised learning with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of deep learning to marine ecology. We use established and future-looking case studies on plankton, fishes, marine mammals, pollution, and nutrient cycling that involve object detection, classification, tracking, and segmentation of visualized data. We conclude with a broad outlook of the field's opportunities and challenges, including potential technological advances and issues with managing complex data sets.