Impact of returning scientific cruises and prolonged on-site presence on litter abundance at the deep-sea nodule fields in the Peru Basin

Marine litter can be found along coasts, continental shelves and slopes, down into the abyss. The absence of light, low temperatures and low energy regimes characterising the deeper habitats ensure the persistence of litter over time. Therefore, manmade items within the deep sea will likely accumulate to increasing quantities. Here we report the litter abundance encountered at the Pacific abyssal nodule fields from the Peru Basin at 4150 m depth. An average density of 2.67 litter items/ha was observed. Litter composed of plastic was the most abundant followed by metal and glass. At least 58 % of the items observed could be linked to the research expeditions conducted in the area and appeared to be mostly accidental disposals from ships. The data gathered was used to address temporal trends in litter abundance as well as the impact of human on-site presence and return cruises in the context of future deep-sea mining efforts

Long-term trends in functional diversity of exploited marine fish in the Azores’ archipelago: past and present

IntroductionEffective fisheries management requires monitoring and quantifying changes in exploited fish communities. Concerns about global fisheries sustainability have led to innovative approaches. Functional diversity, rooted in ecological theory, offers valuable insights into fishery activities and ecosystem processes. A trait-based approach was used to investigate the functional diversity of landed fish species in the Azores archipelago from 1980 to 2021.MethodsLandings data of exploited Actinopterygii and Elasmobranchii were provided by the Azores Fisheries Auction Services (LOTAÇOR/OKEANOS-UAc Fisheries Database). A trait matrix was built, incorporating 12 functional traits assigned to each species, capturing their importance in marine ecological processes. The Quickhull algorithm for convex hull was employed to calculate the volume occupied by the species in the four-dimensional functional space. Functional diversity (FD) was measured using three indices: functional richness (FRic), functional evenness (FEve), and functional divergence (FDiv). Trends in FD indices over the past 42 years were visualized using Generalized Additive Models (GAM) with interaction terms.Results and discussionGAM analysis revealed significant variations in the functional space and FD metrics over time. FRic exhibited peaks in the 1980s and 2010s, declining in the 1990s and from the 2010s onwards, indicating diversification in target species. The recent decrease in FRic can be attributed to the absence of catches of species with unique traits. The distribution of landings and trait combinations showed higher regularity in the functional space during the 1980s and 1990s (high FEve). Actinopterygii species targeted in the 1980s and 1990s had lower trait divergence (low FDiv) compared to those targeted from the 2000s onwards (high FDiv). Variability in FD can be linked to changes in fishing practices, species availability, market demand, environmental factors, and local regulations. This study underscores the importance of considering FD metrics alongside species richness and abundance when assessing the potential effects of fisheries on marine ecosystems and sustainable use of fishery resources

Existing environmental management approaches relevant to deep-sea mining

Deep-sea mining (DSM) may become a significant stressor on the marine environment. The DSM industry should demonstrate transparently its commitment to preventing serious harm to the environment by complying with legal requirements, using environmental good practice, and minimizing environmental impacts. Here existing environmental management approaches relevant to DSM that can be used to improve performance are identified and detailed. DSM is still predominantly in the planning stage and will face some unique challenges but there is considerable environmental management experience in existing related industries. International good practice has been suggested for DSM by bodies such as the Pacific Community and the International Marine Minerals Society. The inherent uncertainty in DSM presents challenges, but it can be addressed by collection of environmental information, area-based/spatial management, the precautionary approach and adaptive management. Tools exist for regional and strategic management, which have already begun to be introduced by the International Seabed Authority, for example in the Clarion-Clipperton Zone. Project specific environmental management, through environmental impact assessment, baseline assessment, monitoring, mitigation and environmental management planning, will be critical to identify and reduce potential impacts. In addition, extractive companies’ internal management may be optimised to improve performance by emphasising sustainability at a high level in the company, improving transparency and reporting and introducing environmental management systems. The DSM industry and its regulators have the potential to select and optimize recognised and documented effective practices and adapt them, greatly improving the environmental performance of this new industry

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

Resilience of benthic deep-sea fauna to mining activities

With increasing demand for mineral resources, extraction of polymetallic sulphides at hydrothermal vents, cobalt-rich ferromanganese crusts at seamounts, and polymetallic nodules on abyssal plains may be imminent. Here, we shortly introduce ecosystem characteristics of mining areas, report on recent mining developments, and identify potential stress and disturbances created by mining. We analyze species' potential resistance to future mining and perform meta-analyses on population density and diversity recovery after disturbances most similar to mining: volcanic eruptions at vents, fisheries on seamounts, and experiments that mimic nodule mining on abyssal plains. We report wide variation in recovery rates among taxa, size, and mobility of fauna. While densities and diversities of some taxa can recover to or even exceed pre-disturbance levels, community composition remains affected after decades. The loss of hard substrata or alteration of substrata composition may cause substantial community shifts that persist over geological timescales at mined sites. (C) 2017 Elsevier Ltd. All rights reserved.European Union Seventh Framework Programme (FP7) under the MIDAS project; FCT [IF/00029/2014/CP1230/CT0002, SFRH/ BPD/110278/2015]; Spanish RTD project NUREIEV [CTM2013-44598-R]; Ministry of Economy and Competitiveness [SGR 1068]; Generalitat de Catalunya autonomous government; European Union Horizon research and innovation programme [689518]; Fundacao para a Ciencia e a Tecnologia [UID/MAR/04292/2013]; German Ministry of Research (BMBF) [03F0707A-G]; Program Investigador FCT [IF/01194/2013/CP1199/CT0002]info:eu-repo/semantics/publishedVersio

Deep learning–assisted biodiversity assessment in deep-sea benthic megafauna communities: a case study in the context of polymetallic nodule mining

Technological developments have facilitated the collection of large amounts of imagery from isolated deep-sea ecosystems such as abyssal nodule fields. Application of imagery as a monitoring tool in these areas of interest for deep-sea exploitation is extremely valuable. However, in order to collect a comprehensive number of species observations, thousands of images need to be analysed, especially if a high diversity is combined with low abundances such is the case in the abyssal nodule fields. As the visual interpretation of large volumes of imagery and the manual extraction of quantitative information is time-consuming and error-prone, computational detection tools may play a key role to lessen this burden. Yet, there is still no established workflow for efficient marine image analysis using deep learning–based computer vision systems for the task of fauna detection and classification. Methods In this case study, a dataset of 2100 images from the deep-sea polymetallic nodule fields of the eastern Clarion-Clipperton Fracture zone from the SO268 expedition (2019) was selected to investigate the potential of machine learning–assisted marine image annotation workflows. The Machine Learning Assisted Image Annotation method (MAIA), provided by the BIIGLE system, was applied to different set-ups trained with manually annotated fauna data. The results computed with the different set-ups were compared to those obtained by trained marine biologists regarding accuracy (i.e. recall and precision) and time. Results Our results show that MAIA can be applied for a general object (i.e. species) detection with satisfactory accuracy (90.1% recall and 13.4% precision), when considered as one intermediate step in a comprehensive annotation workflow. We also investigated the performance for different volumes of training data, MAIA performance tuned for individual morphological groups and the impact of sediment coverage in the training data. Discussion We conclude that: a) steps must be taken to enable computer vision scientists to access more image data from the CCZ to improve the system’s performance and b) computational species detection in combination with a posteriori filtering by marine biologists has a higher efficiency than fully manual analyses

Deep learning–assisted biodiversity assessment in deep-sea benthic megafauna communities: a case study in the context of polymetallic nodule mining

Cuvelier D, Zurowietz M, Nattkemper TW. Deep learning–assisted biodiversity assessment in deep-sea benthic megafauna communities: a case study in the context of polymetallic nodule mining. Frontiers in Marine Science. 2024;11. **Introduction** Technological developments have facilitated the collection of large amounts of imagery from isolated deep-sea ecosystems such as abyssal nodule fields. Application of imagery as a monitoring tool in these areas of interest for deep-sea exploitation is extremely valuable. However, in order to collect a comprehensive number of species observations, thousands of images need to be analysed, especially if a high diversity is combined with low abundances such is the case in the abyssal nodule fields. As the visual interpretation of large volumes of imagery and the manual extraction of quantitative information is time-consuming and error-prone, computational detection tools may play a key role to lessen this burden. Yet, there is still no established workflow for efficient marine image analysis using deep learning–based computer vision systems for the task of fauna detection and classification. **Methods** In this case study, a dataset of 2100 images from the deep-sea polymetallic nodule fields of the eastern Clarion-Clipperton Fracture zone from the SO268 expedition (2019) was selected to investigate the potential of machine learning–assisted marine image annotation workflows. The Machine Learning Assisted Image Annotation method (MAIA), provided by the BIIGLE system, was applied to different set-ups trained with manually annotated fauna data. The results computed with the different set-ups were compared to those obtained by trained marine biologists regarding accuracy (i.e. recall and precision) and time. **Results** Our results show that MAIA can be applied for a general object (i.e. species) detection with satisfactory accuracy (90.1% recall and 13.4% precision), when considered as one intermediate step in a comprehensive annotation workflow. We also investigated the performance for different volumes of training data, MAIA performance tuned for individual morphological groups and the impact of sediment coverage in the training data. **Discussion** We conclude that: a) steps must be taken to enable computer vision scientists to access more image data from the CCZ to improve the system’s performance and b) computational species detection in combination with a posteriori filtering by marine biologists has a higher efficiency than fully manual analyses. </p

Delayed response of hermit crabs carrying anemones to a benthic impact experiment at the deep-sea nodule fields of the Peru Basin?

Highlights • Probeebei mirabilis with or without anemone as indicator of changed environment. • 26 years after disturbance population densities changed significantly. • Possible delayed response to anthropogenic disturbance experiment. • Need for long-term (>30 yrs) monitoring surveys post-disturbance in the abyss. The deep Peru Basin is characterised by a unique abyssal scavenging community featuring large numbers of hermit crabs (Probeebei mirabilis, Decapoda, Crustacea). These are atypical hermit crabs, not carrying a shell, but on some occasions carrying an anemone (Actiniaria). The reason why some hermit crabs carry or not carry anemones is thought to be indicative of a changed environment, outweighing the cost/benefit of their relationship. Here we present the temporal variation of abundances of P. mirabilis with and without anemones, spanning more than two decades, following a benthic impact experiment. An overall decrease in hermit crab densities was observed, most noticeable and significant after 26 years and characterised by a loss of Actiniaria on the Probeebei mirabilis' pleon. Whether this is a delayed response to the benthic impact experiment carried out 26 years’ prior or a natural variation in the population remains to be corroborated by an extension of the time-series. Attention is drawn to the limitations of our knowledge over time and space of the abyssal community dynamics and the urgent necessity to fill in these gaps prior to any type of deep-sea exploitation