Inter-laboratory comparison of 14 gaseous components for fuel quality for hydrogen fuel cell vehicles - Abigail Siân Olivia Morris; Pierpaolo Mudugno; Samuel Bates; Shirin Khaki; Thor Anders Aarhaug; Thomas Bacquart (Fuel, 2027)

BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series - Maria Dornelas; Laura H. Antão; Amanda E. Bates; Viviana Brambilla; Jonathan M. Chase; Cher F. Y. Chow; Ada Fontrodona‐Eslava; Anne E. Magurran; Inês S. Martins; Faye Moyes; Alban Sagouis; Samuel Adu‐Acheampong; et al. (Global Ecology and Biogeography, 2025)
Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL.

Correction to: Soil Pollution, Animal Contamination and Safe Food Production: The Case of the French West Indies - Pablo Andrés-Domenech; Valérie Angeon; Samuel Bates; Colombine Lesage (Environmental Modeling & Assessment, 2024)

Soil Pollution, Animal Contamination and Safe Food Production: The Case of the French West Indies - Pablo Andrés-Domenech; Valérie Angeon; Samuel Bates; Colombine Lesage (Environmental Modeling & Assessment, 2023)

Laboratory Evaluation of the Thermal Breakout Method for Maximum Horizontal Stress Measurement - Maciej Trzeciak; Hiroki Sone; Samuel Voegeli; Charlotte E. Bate; Herbert Wang (Rock Mechanics and Rock Engineering, 2022)

sFDvent: A global trait database for deep‐sea hydrothermal‐vent fauna - Abbie S. A. Chapman; Stace E. Beaulieu; Ana Colaço; Andrey V. Gebruk; Ana Hilario; Terue C. Kihara; Eva Ramirez‐Llodra; Jozée Sarrazin; Verena Tunnicliffe; Diva J. Amon; Maria C. Baker; Rachel E. Boschen‐Rose; et al. (Global Ecology and Biogeography, 2019)
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 ( s Div‐funded trait database for the F unctional D iversity of vent s). 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).

Biogeochemical and physical factors influencing seawater ƒCO 2 and air‐sea CO 2 exchange on the Bermuda coral reef - Nicholas R. Bates; Leone Samuels; Liliane Merlivat (Limnology and Oceanography, 2001)
It is uncertain whether coral reef ecosystems are oceanic sources or sinks of carbon dioxide (CO 2 ). Understanding the complex interactions between biogeochemical and physical processes within reef ecosystems is important for determining the contribution of coral reefs to the global carbon cycle and the air‐sea flux of CO2 . The influence of biogeochemical and physical processes on CO 2 cycling was examined for 1 month at Hog Reef Flat, part of the rim reef of Bermuda. The fugacity of seawater CO 2 (CO 2 ) was measured hourly by use of a Carbon Interface Ocean Atmosphere buoy, providing the longest time series of seawater CO 2 data on a coral reef ecosystem. Seawater CO 2 ranged from ~340 to 470 µatm, with a diurnal variability ranging from ~20–60 µatm. Air‐sea CO 2 was directed from ocean to atmosphere with a mean flux of 3.3 ± 4.6 mmoles CO 2 m −2 d −1 . The reef data are compared with a seawater CO 2 time series collected at the U.S. Joint Global Ocean Flux Study Bermuda Atlantic Time‐series Study (BATS) site (31°50'N, 64°10'W) in the Sargasso Sea surrounding Bermuda. Sargasso Sea waters are the original source for Bermuda platform water, providing a context for understanding the biogeochemical modification of reef water at Hog Reef Flat. Seawater fCO 2 at Hog Reef Flat was elevated relative to the Sargasso Sea by ~0 to ~120 µatm, primarily as a result of calcium carbonate production. However, the ability of the reef to act either as a source or sink of CO 2 to/from the atmosphere largely depended on the air‐sea CO 2 disequilibrium of offshore Sargasso Sea waters impinging on the reef site. This study also revealed that an assessment of the fate of CO 2 on coral reefs is dependent on understanding the reef's physical regime and forcing. The dynamics of wind, tide, platform circulation, and fluxes of offshore or onshore waters are necessary context for all coral reef sites.