Temporal and spatial variation in temperature experienced by macrofauna at main endeavour Hydrothermal vent field

A significant focus of hydrothermal vent ecological studies has been to understand how species cope with various stressors through physiological tolerance and biochemical resistance. Yet, the environmental conditions experienced by vent species have not been well characterized. This objective requires continuous observations over time intervals that can capture environmental variability at scales that are relevant to animals. We used autonomous temperature logger arrays (four roughly parallel linear arrays of 12 loggers spaced every 10–12 cm) to study spatial and temporal variations in the thermal regime experienced by hydrothermal vent macrofauna at a diffuse flow vent. Hourly temperatures were recorded over eight months from 2010 to 2011 at Grotto vent in the Main Endeavour vent field on the Juan de Fuca Ridge, a focus area of the Ocean Networks Canada cabled observatory. The conspicuous animal assemblages in video footage contained Ridgeia piscesae tubeworms, gastropods (primarily Lepetodrilus fucensis), and polychaetes (polynoid scaleworms and the palm worm Paralvinella palmiformis). Two dimensional spatial gradients in temperature were generally stable over the deployment period. The average temperature recorded by all arrays, and in some individual loggers, revealed distinctive fluctuations in temperature that often corresponded with the tidal cycle. We postulate that this may be related to changes in bottom currents or fluctuations in vent discharge. A marked transient temperature increase lasting over a period of days was observed in April 2011. While the distributions and behavior of Juan de Fuca Ridge vent invertebrates may be partially constrained by environmental temperature and temperature tolerance, except for the one transient high-temperature event, observed fluid temperatures were generally similar to the thermal preferences for some species, and typically well below lethal temperatures for all species. Average temperatures of the four arrays ranged from 4.1 to 11.0 °C during the deployment, indicating that on an hourly timescale the temperature conditions in this tubeworm community were fairly moderate and stable. The generality of these findings and behavioural responses of vent organisms to predictable rhythmicity and non-periodic temperature shifts are areas for further investigation

Monitoring ecological dynamics on complex hydrothermal structures: A novel photogrammetry approach reveals fine‐scale variability of vent assemblages

We set out to characterize the fine-scale processes acting on interannual dynamics of deep-sea vent fauna by using a novel approach involving a 5-yr time series of 3D photogrammetry models acquired at the Eiffel Tower sulfide edifice (Lucky Strike vent field, Mid-Atlantic Ridge). Consistently, with the overall stability of the vent edifice, total mussel cover did not undergo drastic changes, suggesting that they have been at a climax stage for at least 25 yr based on previous data. Successional patterns showed consistency over time, illustrating the dynamic equilibrium of the ecological system. In contrast, microbial mats significantly declined, possibly due to magmatic events. The remaining environmental variability consisted of decimeter-scale displacement of vent outflows, resulting from their opening or closure or from the progressive accretion of sulfide material. As a result, vent mussels showed submeter variability in the immediate vicinity of vent exits, possibly by repositioning in response to that fine-scale regime of change. As former studies were not able to quantify processes at submeter scales in complex settings, this pioneering work demonstrates the potential of 3D photogrammetry models for conducting long-term monitoring in the deep sea. We observed that the ability of mussels to displace may enable them to cope with changing local conditions in a stable system. However, the long-term stability of mussel assemblages questions their capacity to withstand large-scale disturbances and may imply a low resilience of these “climax” communities. This suggests that they may be particularly vulnerable to the negative effects of mining activities in hydrothermal ecosystems

Convolutional neural networks for hydrothermal vents substratum classification: An introspective study

The increasing availability of seabed images has created new opportunities and challenges for monitoring and better understanding the spatial distribution of fauna and substrata. To date, however, deep-sea substratum classification relies mostly on visual interpretation, which is costly, time-consuming, and prone to human bias or error. Motivated by the success of convolutional neural networks in learning semantically rich representations directly from images, this work investigates the application of state-of-the-art network architectures, originally employed in the classification of non-seabed images, for the task of hydrothermal vent substrata image classification. In assessing their potential, we conduct a study on the generalization, complementarity and human interpretability aspects of those architectures. Specifically, we independently trained deep learning models with the selected architectures using images obtained from three distinct sites within the Lucky-Strike vent field and assessed the models' performances on-site as well as off-site. To investigate complementarity, we evaluated a classification decision committee (CDC) built as an ensemble of networks in which individual predictions were fused through a majority voting scheme. The experimental results demonstrated the suitability of the deep learning models for deep-sea substratum classification, attaining accuracies reaching up to 80% in terms of F1-score. Finally, by further investigating the classification uncertainty computed from the set of individual predictions of the CDC, we describe a semiautomatic framework for human annotation, which prescribes visual inspection of only the images with high uncertainty. Overall, the results demonstrated that high accuracy values of over 90% F1-score can be obtained with the framework, with a small amount of human intervention

Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network

The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada) deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina), as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp.), using a camera deployed in Saanich Inlet (103 m depth). For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters) with Euclidean Distances (ED) on Red-Green-Blue (RGB) channels. The Scale-Invariant Feature Transform (SIFT) features and Fourier Descriptors (FD) of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA) on Mean RGB (RGBv) value for each object and Fourier Descriptors (RGBv+FD) matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected) occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent Coverage and Fractal Dimension. A constant Region of Interest (ROI) was defined and background extraction by a Gaussian Blurring Filter was performed. Image subtraction within ROI was followed by the sum of the RGB channels matrices. Percent Coverage was calculated on the resulting image. Fractal Dimension was estimated using the box-counting method. The images were then resized to a dimension in pixels equal to a power of 2, allowing subdivision into sub-multiple quadrants. In comparisons of manual and automated Percent Coverage and Fractal Dimension estimates, the former showed an overestimation tendency for both parameters. The primary limitations on the automatic analysis of benthic images were habitat variations in sediment texture and water column turbidity. The application of filters for background corrections is a required preliminary step for the efficient recognition of animals and bacterial mat patches

The EMSO Generic Instrument Module (EGIM): standardized and interoperable instrumentation for ocean observation

The oceans are a fundamental source for climate balance, sustainability of resources and life on Earth, therefore society has a strong and pressing interest in maintaining and, where possible, restoring the health of the marine ecosystems. Effective, integrated ocean observation is key to suggesting actions to reduce anthropogenic impact from coastal to deep-sea environments and address the main challenges of the 21st century, which are summarized in the UN Sustainable Development Goals and Blue Growth strategies. The European Multidisciplinary Seafloor and water column Observatory (EMSO), is a European Research Infrastructure Consortium (ERIC), with the aim of providing long-term observations via fixed-point ocean observatories in key environmental locations across European seas from the Arctic to the Black Sea. These may be supported by ship-based observations and autonomous systems such as gliders. In this paper, we present the EMSO Generic Instrument Module (EGIM), a deployment ready multi-sensor instrumentation module, designed to measure physical, biogeochemical, biological and ecosystem variables consistently, in a range of marine environments, over long periods of time. Here, we describe the system, features, configuration, operation and data management. We demonstrate, through a series of coastal and oceanic pilot experiments that the EGIM is a valuable standard ocean observation module, which can significantly improve the capacity of existing ocean observatories and provides the basis for new observatories. The diverse examples of use included the monitoring of fish activity response upon oceanographic variability, hydrothermal vent fluids and particle dispersion, passive acoustic monitoring of marine mammals and time series of environmental variation in the water column. With the EGIM available to all the EMSO Regional Facilities, EMSO will be reaching a milestone in standardization and interoperability, marking a key capability advancement in addressing issues of sustainability in resource and habitat management of the oceans.This work was funded by the project EMSODEV (Grant agreement No 676555) supported by DG Research and Innovation of the European Commission under the Research Infrastructures Programme of the H2020. EMSO-link EC project (Grant agreement No 731036) provided additional funding. Other projects which supported the work include Plan Estatal de Investigación Científica y Técnica y de Innovación 2017–2020, project BITER-LANDER PID2020- 114732RB-C32, iFADO (Innovation in the Framework of the Atlantic Deep Ocean, 2017–2021) EAPA_165/2016. The Spanish Government contributed through the “Severo Ochoa Centre Excellence” accreditation to ICM-CSIC (CEX2019-000928-S) and the Research Unit Tecnoterra (ICM-CSIC/UPC). UK colleagues were supported by Climate Linked Atlantic Sector Science (CLASS) project supported by NERC National Capability funding (NE/R015953/1).Peer ReviewedArticle signat per 33 autors/es: Nadine Lantéri; Henry A. Ruh; Andrew Gates; Enoc Martínez; Joaquin del Rio Fernandez; Jacopo Aguzzi; Mathilde Cannat; Eric Delory; Davide Embriaco; Robert Huber; Marjolaine Matabos;George Petihakis; Kieran Reilly; Jean-François Rolin; Mike van der Schaar; Michel André; Jérôme Blandin; Andrés Cianca; Marco Francescangeli; Oscar Garcia; Susan Hartman; Jean-Romain Lagadec; Julien Legrand; Paris Pagonis; Jaume Piera; Xabier Remirez; Daniel M. Toma; Giuditta Marinaro; Bertrand Moreau; Raul Santana; Hannah Wright; Juan José Dañobeitia; Paolo FavaliPostprint (published version

A blueprint for integrating scientific approaches and international communities to assess basin-wide ocean ecosystem status

Ocean ecosystems are at the forefront of the climate and biodiversity crises, yet we lack a unified approach to assess their state and inform sustainable policies. This blueprint is designed around research capabilities and cross-sectoral partnerships. We highlight priorities including integrating basin-scale observation, modelling and genomic approaches to understand Atlantic oceanography and ecosystem connectivity; improving ecosystem mapping; identifying potential tipping points in deep and open ocean ecosystems; understanding compound impacts of multiple stressors including warming, acidification and deoxygenation; enhancing spatial and temporal management and protection. We argue that these goals are best achieved through partnerships with policy-makers and community stakeholders, and promoting research groups from the South Atlantic through investment and engagement. Given the high costs of such research (€800k to €1.7M per expedition and €30–40M for a basin-scale programme), international cooperation and funding are integral to supporting science-led policies to conserve ocean ecosystems that transcend jurisdictional borders

sFDvent: A global trait database for deep‐sea hydrothermal‐vent fauna

Motivation: Traits are increasingly being used to quantify global biodiversity patterns, with trait databases growing in size and number, across diverse taxa. Despite grow‐ ing interest in a trait‐based approach to the biodiversity of the deep sea, where the impacts of human activities (including seabed mining) accelerate, there is no single re‐ pository for species traits for deep‐sea chemosynthesis‐based ecosystems, including hydrothermal vents. Using an international, collaborative approach, we have compiled the first global‐scale trait database for deep‐sea hydrothermal‐vent fauna – sFD‐ vent (sDiv‐funded trait database for the Functional Diversity of vents). We formed a funded working group to select traits appropriate to: (a) capture the performance of vent species and their influence on ecosystem processes, and (b) compare trait‐based diversity in different ecosystems. Forty contributors, representing expertise across most known hydrothermal‐vent systems and taxa, scored species traits using online collaborative tools and shared workspaces. Here, we characterise the sFDvent da‐ tabase, describe our approach, and evaluate its scope. Finally, we compare the sFD‐ vent database to similar databases from shallow‐marine and terrestrial ecosystems to highlight how the sFDvent database can inform cross‐ecosystem comparisons. We also make the sFDvent database publicly available online by assigning a persistent, unique DOI. Main types of variable contained: Six hundred and forty‐six vent species names, associated location information (33 regions), and scores for 13 traits (in categories: community structure, generalist/specialist, geographic distribution, habitat use, life history, mobility, species associations, symbiont, and trophic structure). Contributor IDs, certainty scores, and references are also provided. Spatial location and grain: Global coverage (grain size: ocean basin), spanning eight ocean basins, including vents on 12 mid‐ocean ridges and 6 back‐arc spreading centres. Time period and grain: sFDvent includes information on deep‐sea vent species, and associated taxonomic updates, since they were first discovered in 1977. Time is not recorded. The database will be updated every 5 years. Major taxa and level of measurement: Deep‐sea hydrothermal‐vent fauna with spe‐ cies‐level identification present or in progress. Software format: .csv and MS Excel (.xlsx).This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited

The EMSO Generic Instrument Module (EGIM): Standardized and interoperable instrumentation for ocean observation

The oceans are a fundamental source for climate balance, sustainability of resources and life on Earth, therefore society has a strong and pressing interest in maintaining and, where possible, restoring the health of the marine ecosystems. Effective, integrated ocean observation is key to suggesting actions to reduce anthropogenic impact from coastal to deep-sea environments and address the main challenges of the 21st century, which are summarized in the UN Sustainable Development Goals and Blue Growth strategies. The European Multidisciplinary Seafloor and water column Observatory (EMSO), is a European Research Infrastructure Consortium (ERIC), with the aim of providing long-term observations via fixed-point ocean observatories in key environmental locations across European seas from the Arctic to the Black Sea. These may be supported by ship-based observations and autonomous systems such as gliders. In this paper, we present the EMSO Generic Instrument Module (EGIM), a deployment ready multi-sensor instrumentation module, designed to measure physical, biogeochemical, biological and ecosystem variables consistently, in a range of marine environments, over long periods of time. Here, we describe the system, features, configuration, operation and data management. We demonstrate, through a series of coastal and oceanic pilot experiments that the EGIM is a valuable standard ocean observation module, which can significantly improve the capacity of existing ocean observatories and provides the basis for new observatories. The diverse examples of use included the monitoring of fish activity response upon oceanographic variability, hydrothermal vent fluids and particle dispersion, passive acoustic monitoring of marine mammals and time series of environmental variation in the water column. With the EGIM available to all the EMSO Regional Facilities, EMSO will be reaching a milestone in standardization and interoperability, marking a key capability advancement in addressing issues of sustainability in resource and habitat management of the oceans