Description: 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.

Description: Mining polymetallic nodules on abyssal plains will have adverse impacts on deep‐sea ecosystems, but it is largely unknown whether the impacted ecosystem will recover, and if so at what rate. In 1989 the “DISturbance and reCOLonization” (DISCOL) experiment was conducted in the Peru Basin where the seafloor was disturbed with a plough harrow construction to explore the effect of small‐scale sediment disturbance from deep‐sea mining. Densities of Holothuroidea in the region were last investigated 7 yr post‐disturbance, before 19 yr later, the DISCOL site was re‐visited in 2015. An “ocean floor observatory system” was used to photograph the seabed across ploughed and unploughed seafloor and at reference sites. The images were analyzed to determine the Holothuroidea population density and community composition, which were combined with in situ respiration measurements of individual Holothuroidea to generate a respiration budget of the study area. For the first time since the experimental disturbance, similar Holothuroidea densities were observed at the DISCOL site and at reference sites. The Holothuroidea assemblage was dominated by Amperima sp., Mesothuria sp., and Benthodytes typica, together contributing 46% to the Holothuroidea population density. Biomass and respiration were similar among sites, with a Holothuroidea community respiration of 5.84 x-10-4 +/- 8.74 x 10-5 10−5 mmol C m−2 d−1 at reference sites. Although these results indicate recovery of Holothuroidea, extrapolations regarding recovery from deep‐sea mining activities must be made with caution: results presented here are based on a relatively small‐scale disturbance experiment as compared to industrial‐scale nodule mining, and also only represent one taxonomic class of the megafauna.

Description: Permanent sea-ice cover and low primary productivity in the mostly ice-covered Central Arctic ocean basins result in significantly lower biomass and density of macrobenthos in the abyssal plains compared to the continental slopes. However, little is known on bathymetric and regional effects on the macrobenthos diversity. This study synthesizes new and available macrobenthos data to provide a baseline for future studies of the effects of Arctic change on macrofauna community composition in the Arctic basins. Samples collected during three expeditions (in 1993, 2012 and 2015) at 37 stations on the slope of the Barents and Laptev Seas and in the abyssal of the Nansen and Amundsen Basins in the depth range from 38 m to 4381 m were used for a quantitative analysis of species composition, abundance and biomass. Benthic communities clustered in five depth ranges across the slope and basin. A parabolic pattern of species diversity change with depth was found, with the diversity maximum for macrofauna at the shelf edge at depths of 100–300 m. This deviates from the typical species richness peak at mid-slope depths of 1500–3000 m in temperate oceans. Due to the limited availability of standardized benthos data, it remains difficult to assess if and how the significant sea-ice loss observed in the past decade has affected benthic community composition. The polychaete Ymerana pteropoda and the bryozoan Nolella sp. were found for the first time in the deep Nansen and Amundsen Basins.

Description: Spatial distribution patterns in macrobenthos were studied based on the material collected during the R.V. Polarstern expedition ARK-XXVII/2 in July 2012. Eleven stations along the latitudinal transect at the deep-sea observatory HAUSGARTEN in the Fram Strait were taken at depths of about 2500 m. Macrofauna was obtained using the USNEL box corer. A single core (0.25 m2) was taken at each station and four subcores (0.03 m2) were taken from each core and used for the quantitative analysis. The results suggest that the single highly variable macrobenthic community with the dominance of polychaetes Galathowenia fragilis and Myriochele heeri inhabits the studied area. The prevalence of a mosaic in the community structure with the grain size at least more than the size of a core was detected. However, several abundant species (e.g. the polychaetes Prionospio sp. and Galathowenia fragilis) tend to form patches at the scale less than a core (0.25 m2). Despite the lack of significant differences in species distribution patterns along the latitudinal transect, there is a slight difference in community structure between the northernmost and southernmost stations (~170 km apart), which can be explained by variations in environmental factors (e.g. higher food availability in the northern part of the transect).

Description: During a survey of the H°akon Mosby mud volcano (HMMV), located on the Bear Island fan in the southwest Barents Sea at �1250m water depth, different habitats inside the volcano caldera and outside it were hotographed using a towed camera platform, an Ocean Floor Observation System (OFOS). Three transects were performed across the caldera and one outside, in the background area, each transect was �2 km in length. We compared the density, taxa richness and diversity of nonsymbiotrophic megafauna in areas inside the volcano caldera with different bacterial mat and pogonophoran tubeworm cover. Significant variations in megafaunal composition, density and distribution were found between considered areas. Total megafaunal density was highest in areas of dense pogonophoran populations (mean 52.9 ind.m−2) followed by areas of plain light-coloured sediment that were devoid of bacterial mats and tube worms (mean 37.7 ind.m−2). The lowest densities were recorded in areas of dense bacterial mats (mean �1.4 ind.m−2). Five taxa contributed to most of the observed variation: the ophiuroid Ophiocten gracilis, lysianassid amphipods, the pycnogonid Nymphon macronix, the caprellid Metacaprella horrida and the fish Lycodes squamiventer. In agreement with previous studies, three zones within the HMMV caldera were distinguished, based on different habitats and megafaunal composition: “bacterial mats”, “pogonophoran fields” and “plain light-coloured sediments”. The zones were arranged almost concentrically around the central part of the caldera that was devoid of visible megafauna. The total number of taxa showed little variation inside (24 spp.) and outside the caldera (26 spp.). The density, diversity and composition of megafauna varied substantially between plain lightcoloured sediment areas inside the caldera and the HMMV background. Megafaunal density was lower in the background (mean 25.3 ind.m−2) compared to areas of plain light-coloured sediments inside the caldera. So the effect of the mud-volcano environment on benthic communities is expressed in increasing of biomass, changing of taxa composition and proportions of most taxonomic groups.

Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chapman, A. S. A., Beaulieu, S. E., Colaco, A., Gebruk, A. V., Hilario, A., Kihara, T. C., Ramirez-Llodra, E., Sarrazin, J., Tunnicliffe, V., Amon, D. J., Baker, M. C., Boschen-Rose, R. E., Chen, C., Cooper, I. J., Copley, J. T., Corbari, L., Cordes, E. E., Cuvelier, D., Duperron, S., Du Preez, C., Gollner, S., Horton, T., Hourdez, S., Krylova, E. M., Linse, K., LokaBharathi, P. A., Marsh, L., Matabos, M., Mills, S. W., Mullineaux, L. S., Rapp, H. T., Reid, W. D. K., Rybakova (Goroslavskaya), E., Thomas, T. R. A., Southgate, S. J., Stohr, S., Turner, P. J., Watanabe, H. K., Yasuhara, M., & Bates, A. E. sFDvent: a global trait database for deep-sea hydrothermal-vent fauna. Global Ecology and Biogeography, 28(11), (2019): 1538-1551, doi: 10.1111/geb.12975.

Description: Motivation Traits are increasingly being used to quantify global biodiversity patterns, with trait databases growing in size and number, across diverse taxa. Despite growing 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 repository 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 – sFDvent (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 database, describe our approach, and evaluate its scope. Finally, we compare the sFDvent 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 species‐level identification present or in progress. Software format .csv and MS Excel (.xlsx).

Description: We would like to thank the following experts, who are not authors on this publication but made contributions to the sFDvent database: Anna Metaxas, Alexander Mironov, Jianwen Qiu (seep species contributions, to be added to a future version of the database) and Anders Warén. We would also like to thank Robert Cooke for his advice, time, and assistance in processing the raw data contributions to the sFDvent database using R. Thanks also to members of iDiv and its synthesis centre – sDiv – for much‐valued advice, support, and assistance during working‐group meetings: Doreen Brückner, Jes Hines, Borja Jiménez‐Alfaro, Ingolf Kühn and Marten Winter. We would also like to thank the following supporters of the database who contributed indirectly via early design meetings or members of their research groups: Malcolm Clark, Charles Fisher, Adrian Glover, Ashley Rowden and Cindy Lee Van Dover. Finally, thanks to the families of sFDvent working group members for their support while they were participating in meetings at iDiv in Germany. Financial support for sFDvent working group meetings was gratefully received from sDiv, the Synthesis Centre of iDiv (DFG FZT 118). ASAC was a PhD candidate funded by the SPITFIRE Doctoral Training Partnership (supported by the Natural Environmental Research Council, grant number: NE/L002531/1) and the University of Southampton at the time of submission. ASAC also thanks Dominic, Lesley, Lettice and Simon Chapman for their support throughout this project. AEB and VT are sponsored through the Canada Research Chair Programme. SEB received support from National Science Foundation Division of Environmental Biology Award #1558904 and The Joint Initiative Awards Fund from the Andrew W. Mellon Foundation. AC is supported by Program Investigador (IF/00029/2014/CP1230/CT0002) from Fundação para a Ciência e a Tecnologia (FCT). This study also had the support of Fundação para a Ciência e a Tecnologia, through the strategic project UID/MAR/04292/2013 granted to marine environmental sciences centre. Data compiled by AVG and EG were supported by Russian science foundation Grant 14‐50‐00095. AH was supported by the grant BPD/UI88/5805/2017 awarded by CESAM (UID/AMB/50017), which is financed by FCT/Ministério da Educação through national funds and co‐funded by fundo Europeu de desenvolvimento regional, within the PT2020 Partnership Agreement and Compete 2020. ERLL was partially supported by the MarMine project (247626/O30). JS was supported by Ifremer. Data on vent fauna from the East Scotia Ridge, Mid‐Cayman Spreading Centre, and Southwest Indian Ridge were obtained by UK natural environment research council Grants NE/D01249X/1, NE/F017774/1 and NE/H012087/1, respectively. REBR's contribution was supported by a Postdoctoral Fellowship at the University of Victoria, funded by the Canadian Healthy Oceans Network II Strategic Research Program (CHONe II). DC is supported by a post‐doctoral scholarship (SFRH/BPD/110278/2015) from FCT. HTR was supported by the Research Council of Norway through project number 70184227 and the KG Jebsen Centre for Deep Sea Research (University of Bergen). MY was partially supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (project codes: HKU 17306014, HKU 17311316).