ALBERT

Description: Trait-based approaches that complement taxonomic-based studies have increased in popularity among the scientific community over the last decades. The collection of biological and ecological characteristics of species (i.e., traits) provides insight into species and ecosystem vulnerability to environmental and anthropogenic changes, as well as ecosystem functioning. While most of the available trait databases to date contain essential information to understand the functional diversity of a taxonomic group or functional group based on size, the FUN Azores trait database has an ecosystem-based approach that provides a comprehensive assessment of diverse fauna (meio-, macro-, and megafauna) from benthic and pelagic environments in the Azores Marine Park; including ridges, seamounts, and hydrothermal vents. We used a collaborative approach involving 30 researchers with different expertise to develop the trait database; which contains compiled data on 14 traits representing morphological, behavioral, and life history characteristics for 1210 species, across 10 phyla.

Description: As ocean temperatures rise, species distributions are tracking towards historically cooler regions in line with their thermal affinity(1,2). However, different responses of species to warming and changed species interactions make predicting biodiversity redistribution and relative abundance a challenge(3,4). Here, we use three decades of fish and plankton survey data to assess how warming changes the relative dominance of warm-affinity and cold-affinity species(5,6). Regions with stable temperatures (for example, the Northeast Pacific and Gulf of Mexico) show little change in dominance structure, while areas with warming (for example, the North Atlantic) see strong shifts towards warm-water species dominance. Importantly, communities whose species pools had diverse thermal affinities and a narrower range of thermal tolerance showed greater sensitivity, as anticipated from simulations. The composition of fish communities changed less than expected in regions with strong temperature depth gradients. There, species track temperatures by moving deeper(2,7), rather than horizontally, analogous to elevation shifts in land plants(8). Temperature thus emerges as a fundamental driver for change in marine systems, with predictable restructuring of communities in the most rapidly warming areas using metrics based on species thermal affinities. The ready and predictable dominance shifts suggest a strong prognosis of resilience to climate change for these communities.

Description: © The Author(s), 2018]. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Global Ecology and Biogeography 27 (2018): 760-786, doi:10.1111/geb.12729.

Description: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2). BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.

Description: European Research Council and EU, Grant/Award Number: AdG‐250189, PoC‐727440 and ERC‐SyG‐2013‐610028; Natural Environmental Research Council, Grant/Award Number: NE/L002531/1; National Science Foundation, Grant/Award Number: DEB‐1237733, DEB‐1456729, 9714103, 0632263, 0856516, 1432277, DEB‐9705814, BSR‐8811902, DEB 9411973, DEB 0080538, DEB 0218039, DEB 0620910, DEB 0963447, DEB‐1546686, DEB‐129764, OCE 95‐21184, OCE‐ 0099226, OCE 03‐52343, OCE‐0623874, OCE‐1031061, OCE‐1336206 and DEB‐1354563; National Science Foundation (LTER) , Grant/Award Number: DEB‐1235828, DEB‐1440297, DBI‐0620409, DEB‐9910514, DEB‐1237517, OCE‐0417412, OCE‐1026851, OCE‐1236905, OCE‐1637396, DEB 1440409, DEB‐0832652, DEB‐0936498, DEB‐0620652, DEB‐1234162 and DEB‐0823293; Fundação para a Ciência e Tecnologia, Grant/Award Number: POPH/FSE SFRH/BD/90469/2012, SFRH/BD/84030/2012, PTDC/BIA‐BIC/111184/2009; SFRH/BD/80488/2011 and PD/BD/52597/2014; Ciência sem Fronteiras/CAPES, Grant/Award Number: 1091/13‐1; Instituto Milenio de Oceanografía, Grant/Award Number: IC120019; ARC Centre of Excellence, Grant/Award Number: CE0561432; NSERC Canada; CONICYT/FONDECYT, Grant/Award Number: 1160026, ICM PO5‐002, CONICYT/FONDECYT, 11110351, 1151094, 1070808 and 1130511; RSF, Grant/Award Number: 14‐50‐00029; Gordon and Betty Moore Foundation, Grant/Award Number: GBMF4563; Catalan Government; Marie Curie Individual Fellowship, Grant/Award Number: QLK5‐CT2002‐51518 and MERG‐CT‐2004‐022065; CNPq, Grant/Award Number: 306170/2015‐9, 475434/2010‐2, 403809/2012‐6 and 561897/2010; FAPESP (São Paulo Research Foundation), Grant/Award Number: 2015/10714‐6, 2015/06743‐0, 2008/10049‐9, 2013/50714‐0 and 1999/09635‐0 e 2013/50718‐5; EU CLIMOOR, Grant/Award Number: ENV4‐CT97‐0694; VULCAN, Grant/Award Number: EVK2‐CT‐2000‐00094; Spanish, Grant/Award Number: REN2000‐0278/CCI, REN2001‐003/GLO and CGL2016‐79835‐P; Catalan, Grant/Award Number: AGAUR SGR‐2014‐453 and SGR‐2017‐1005; DFG, Grant/Award Number: 120/10‐2; Polar Continental Shelf Program; CENPES – PETROBRAS; FAPERJ, Grant/Award Number: E‐26/110.114/2013; German Academic Exchange Service; sDiv; iDiv; New Zealand Department of Conservation; Wellcome Trust, Grant/Award Number: 105621/Z/14/Z; Smithsonian Atherton Seidell Fund; Botanic Gardens and Parks Authority; Research Council of Norway; Conselleria de Innovació, Hisenda i Economia; Yukon Government Herschel Island‐Qikiqtaruk Territorial Park; UK Natural Environment Research Council ShrubTundra Grant, Grant/Award Number: NE/M016323/1; IPY; Memorial University; ArcticNet. DOI: 10.13039/50110000027. Netherlands Organization for Scientific Research in the Tropics NWO, grant W84‐194. Ciências sem Fronteiras and Coordenação de Pessoal de Nível Superior (CAPES, Brazil), Grant/Award Number: 1091/13‐1. National Science foundation (LTER), Award Number: OCE‐9982105, OCE‐0620276, OCE‐1232779. FCT ‐ SFRH / BPD / 82259 / 2011. U.S. Fish and Wildlife Service/State Wildlife federal grant number T‐15. Australian Research Council Centre of Excellence for Coral Reef Studies (CE140100020). Australian Research Council Future Fellowship FT110100609. M.B., A.J., K.P., J.S. received financial support from internal funds of University of Lódź. NSF DEB 1353139. Catalan Government fellowships (DURSI): 1998FI‐00596, 2001BEAI200208, MECD Post‐doctoral fellowship EX2002‐0022. National Science Foundation Award OPP‐1440435. FONDECYT 1141037 and FONDAP 15150003 (IDEAL). CNPq Grant 306595‐2014‐1

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).

Description: To predict the threat of biological invasions to native species, it is critical that we understand how increasing abundance of invasive alien species (IAS) affects native populations and communities. The form of this relationship across taxa and ecosystems is unknown, but is expected to depend strongly on the trophic position of the IAS relative to the native species. Using a global metaanalysis based on 1,258 empirical studies presented in 201 scientific publications, we assessed the shape, direction, and strength of native responses to increasing invader abundance. We also tested how native responses varied with relative trophic position and for responses at the population vs. community levels. As IAS abundance increased, native populations declined nonlinearly by 20%, on average, and community metrics declined linearly by 25%. When at higher trophic levels, invaders tended to cause a strong, nonlinear decline in native populations and communities, with the greatest impacts occurring at low invader abundance. In contrast, invaders at the same trophic level tended to cause a linear decline in native populations and communities, while invaders at lower trophic levels had no consistent impacts. At the community level, increasing invader abundance had significantly larger effects on species evenness and diversity than on species richness. Our results show that native responses to invasion depend critically on invasive species’ abundance and trophic position. Further, these general abundance–impact relationships reveal how IAS impacts are likely to develop during the invasion process and when to best manage them.