BioTIME: A database of biodiversity time series for the Anthropocene.

MotivationThe 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.Main types of variables includedThe 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.Spatial location and grainBioTIME 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).Time period and grainBioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurementBioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.Software format.csv and .SQL

Trait structure reveals the processesunderlying fish establishment in theMediterranean

International audienceAim Typically, non-indigenous species have been studied in relation to eitherthe invaded (recipient) community or the donor community. However, we stilllack a broad understanding of the mechanisms underlying the establishment ofnon-indigenous species that combines both perspectives. Since the opening ofthe Suez Canal, hundreds of species have invaded the Mediterranean from theRed Sea, forming a unique system in which the entire species pool (donor,non-indigenous and recipient) is known. Focusing on species ecological traits,we assess the drivers underlying the establishment of non-indigenous speciesfrom the onset of the invasion to the present.Location The Mediterranean and Red Seas.Methods We compiled traits for shallow-water hard-bottom-associated fishesin the Mediterranean (recipient community) and the Red Sea (donorcommunity). We compared the multivariate community trait structure of nonindigenousspecies with the donor and recipient assemblages, using a novelmethod to objectively assign trait-weights, thereby increasing the robustness ofthe results. Patterns were contrasted with two types of null models whichprovide distinct insights into the ecological processes.Results Non-indigenous fishes are very diverse ecologically, substantiallyincreasing the total community trait diversity of the Mediterranean. Traitsimilarity between non-indigenous and indigenous Mediterranean species waslower than expected, indicating that non-indigenous fishes tend to occupyrelatively vacant niches within the Mediterranean. However, we further foundthat over time non-indigenous species display increased trait similarity toindigenous Mediterranean species. This suggests that trait constraintsassociated with successful establishment are weakening.Main conclusions Non-indigenous species establish in relatively vacantecological niches, opening the possibility for predicting establishment usingecological traits. However, the weakening of trait constraints through time suggeststhat forecasting future establishment may be difficult. Faced with an acceleratingpace of invasion, it appears that the Mediterranean is going to be transformed intoan extension of the Red Sea in terms of trait and species composition

Shifts in Eastern Mediterranean Fish Communities: Abundance Changes, Trait Overlap, and Possible Competition between Native and Non-Native Species

During the last few decades the fish community has changed substantially along the Eastern Mediterranean continental shelf, which is a hotspot of invasion by species that had migrated via the Suez Canal. Trawl data from the Israeli coast spanning two periods (1990–1994 and 2008–2011) were compared to identify species with substantial variation in their relative abundance between the two periods. The aim of this study was to examine if certain ecological traits characterize fish species showing an increase or decrease in relative abundance, and if non-indigenous fishes with strongly increasing populations may have caused the decline of native species with similar habitats and diets. We found that the main predictors of population trends were species length, habitat affinity, and maximum depth, with larger and soft bottom species displaying decreasing abundances. Comparing native and non-indigenous fishes with similar habitat and diet, we found a potential for competitive impact of the Indo-Pacific Plotosus lineatus and two Upeneus spp. on the native Mullus species. However, competition with non-indigenous fishes could not generally explain the dramatic decline of many other native species between the two study periods. Alternative causes, such as fishery pressure and increasing water temperature, are discussed

BioTIME: A database of biodiversity time series for the Anthropocene

Motivation: 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. Main types of variables included: 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. Spatial location and grain: 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 km(2) (158 cm(2)) to 100 km(2) (1,000,000,000,000 cm(2)). Time period and grainBio: TIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement: 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.European Research Council; EU [AdG-250189, PoC-727440, ERC-SyG-2013-610028]; Natural Environmental Research Council [NE/L002531/1]; National Science Foundation [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]; National Science Foundation (LTER) [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, DEB-0823293, OCE-9982105, OCE-0620276, OCE-1232779]; Fundacao para a Ciencia e Tecnologia [POPH/FSE SFRH/BD/90469/2012, SFRH/BD/84030/2012, PTDC/BIA-BIC/111184/2009]; Ciencia sem Fronteiras/CAPES [1091/13-1]; Instituto Milenio de Oceanografia [IC120019]; ARC Centre of Excellence [CE0561432]; NSERC Canada; CONICYT/FONDECYT [1160026, ICM PO5-002, 11110351, 1151094, 1070808, 1130511]; RSF [14-50-00029]; Gordon and Betty Moore Foundation [GBMF4563]; Catalan Government; Marie Curie Individual Fellowship [QLK5-CT2002-51518, MERG-CT-2004-022065]; CNPq [306170/2015-9, 475434/2010-2, 403809/2012-6, 561897/2010, 306595-2014-1]; FAPESP (Sao Paulo Research Foundation) [2015/10714-6, 2015/06743-0, 2008/10049-9, 2013/50714-0, 1999/09635-0 e 2013/50718-5]; EU CLIMOOR [ENV4-CT97-0694]; VULCAN [EVK2-CT2000-00094]; DFG [120/10-2]; Polar Continental Shelf Program; CENPES - PETROBRAS; FAPERJ [E-26/110.114/ 2013]; German Academic Exchange Service; New Zealand Department of Conservation; Wellcome Trust [105621/Z/14/Z]; Smithsonian Atherton Seidell Fund; Botanic Gardens and Parks Authority; Research Council of Norway; Conselleria de Innovacio, Hisenda i Economia; Yukon Government Herschel Island-Qikiqtaruk Territorial Park; UK Natural Environment Research Council ShrubTundra Grant [NE/M016323/1]; IPY; Memorial University; ArcticNet; Netherlands Organization for Scientific Research in the Tropics NWO [W84-194]; Ciencias sem Fronteiras and Coordenacao de Pessoal de Nivel Superior (CAPES, Brazil) [1091/13-1]; U.S. Fish and Wildlife Service/State Wildlife federal grant [T-15]; Australian Research Council Centre of Excellence for Coral Reef Studies [CE140100020]; Australian Research Council Future Fellowship [FT110100609]; University of Lodz; NSF DEB [1353139]; Catalan Government fellowships (DURSI) [1998FI-00596, 2001BEAI200208]; MECD Post-doctoral fellowship [EX2002-0022]; FONDECYT [1141037]; FONDAP [15150003]; [SFRH/BD/80488/2011]; [PD/BD/52597/2014]; [REN2000-0278/CCI]; [REN2001-003/GLO]; [CGL2016-79835-P]; [AGAUR SGR-2014453]; [SGR-2017-1005]; [FCT - SFRH / BPD / 82259 / 2011]; [OCE 95-21184]; [OCE-0099226]; [OCE 03-5234]; [OCE-0623874]; [OCE-1031061]; [OCE-1336206]; [DEB-1354563]; [OPP-1440435]12 month embargo; published online: 24 July 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Are rare species useful species? Obstacles to the conservation of tree diversity in the dry forest zone agro-ecosystems of Mesoamerica

Aim To test the potential to conserve rare dry forest tree and shrub species circa situm.Location Oaxaca, Mexico and Southern Honduras.Methods Local uses (timber, posts and firewood) of species were determined principally through semistructured interviews with 20 rural householders in each of four communities in Honduras and four in Oaxaca. Tree and shrub diversity inventories were carried out in a total of 227 forest patches and parcels of farmland in those eight communities. Species’ conservation priorities were determined using the star system of Hawthorne (1996) and IUCN listings.Results Despite a large number of useful species, remarkably few were also conservation priorities. Useful species were found to be substitutable as is illustrated by Bombacopsis quinata, Cordia alliodora, Guaiacum sanctum and G. coulteri.Conclusions In these areas, circa situm conservation is inhibited by the lack of species that are both rare and useful. Usefulness must be interpreted as a function of substitutability. Natural regeneration provides an abundance of diversity, farmers are unlikely to invest in the management of a species when suitable substitutes are freely available. The key to conserving rare species may be in maintaining or enhancing the value of the landscape elements in which they are found

BioTIME:a database of biodiversity time series for the Anthropocene

Abstract Motivation: 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. Main types of variables included: 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. Spatial location and grain: 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). Time period and grain: BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement: 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. Software format: .csv and .SQL