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  • Energy Research
  • 6. Clean water
  • 14. Life underwater
  • Netherlands Research Portal

  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Opreanu, Priscila-Ana;

    Dataset containing meiobenthos data for samples collected during the September 2008 Sesame Cruise in the North-West Black Sea on board of the Romanian R/V Mare Nigrum. Meiobenthos samples were collected in 5 stations, using a multicorer MARK II-400. The dataset includes 5 samples analysed for meiobenthos species composition, abundance and biomass.The entire washed sample was analyzed under the binocular stereomicroscope. Meiobenthic species were identified and enumerated; some meiobenthic species were identified and enumerated only at higher taxonomic level. Taxonomic identification was done at GEOECOMAR.

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    B2FIND
    Dataset . 2015
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 2015
    Data sources: PANGAEA
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 2015
    License: CC BY
    Data sources: PANGAEA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ B2FINDarrow_drop_down
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      B2FIND
      Dataset . 2015
      Data sources: B2FIND
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 2015
      Data sources: PANGAEA
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 2015
      License: CC BY
      Data sources: PANGAEA
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ana Paula Staben Pruchniak (10423459); Graziella dos Santos Portes Silva (10423462); Liliane Schier de Lima (10423465); Sueli Pércio Quináia (4986440);

    Abstract Activated carbon is commonly used as a material for contaminant-adsorption processes in aqueous systems. However, its use is more restricted to charcoal than to coal, for the most part, in view of the fact of the higher cost (~ 40%) if the mineral is a fossil fuel which needs to be extracted from the earth by mining. For this reason, the peach stone that comes from alimentary industrial tailings can be a good choice for the separation of pollutants from aqueous suspensions and other soluble substances. The purpose of this research was the development of a low-cost filter, using stones to remove atrazine from water. Appraisal and characterization studies were performed along with batch experiments to investigate dosing effects of the activated carbon, atrazine concentration, contact time, and adsorption pH on removal procedures. From the results of the experiment, an excellent removal of the analyte in question was observed under conditions that can be considered as close as possible to the environment, such as pH = 6.5, room temperature and 10 minutes of agitation time, always choosing the best alternative with the lowest cost of energy and time. Batch system application has been recommended as versatile for utilization in seasonal problems such as pesticide contamination.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
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    figshare
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    figshare
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Smithsonian figshare
    Dataset . 2020
    License: CC BY
    0
    citations0
    popularityAverage
    influenceAverage
    impulseAverage
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      figshare
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      figshare
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Smithsonian figshare
      Dataset . 2020
      License: CC BY
  • Authors: T.Ya. Ashikhmina; E. V. Tovstik; S. Yu. Ogorodnikova; I. G. Shirokikh;

    In the course of monitoring near the chemical weapons storage and destruction plant "Maradykovsky", more than 100 strains of Streptomyces were stated from the soil surfaces into the pure culture. In order to find the strains which are tolerant to chemical weapons degradation products, the response of natural isolates of actinomycetes to sodium pyrophosphate, methylphosphonic acid, and arsenic concentration gradient was studied. For each pollutant a special dose was set, which could stimulate and inhibit either the growth of actinomycetes at the stage of spores and at the stage of vegetative growth, or their metabolic activity consisting in production of antibiotics, or cellulose decomposition. Actinomycetes cultures were found, which are promising for creating microbial inoculants for remediation of soils impacted with arsenic and phosphorus xenobiotics.

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    Authors: Snejana Moncheva; Ludmila G Senichkina; Dennis Altukhov;

    The samples were concentrated down to 50 cm**3 by slow decantation after storage for 20 days in a cool and dark place. The species identification was done under light microscope OLIMPUS–BS41 connected to a video-interactive image analysis system at magnification of the ocular 10X and objective – 40X. A Sedgwick-Rafter camera (1ml) was used for counting. 400 specimen were counted for each sample, while rare and large species were checked in the whole sample (Manual of phytoplankton, 2005). Species identification was mainly after Carmelo T. (1997) and Fukuyo, Y. (2000).Taxon-specific phytoplankton abundance and biomass were analysed by Moncheva S., B. Parr, 2005. Manual for Phytoplankton Sampling and Analysis in the Black Sea.The cell biovolume was determined based on morpho-metric measurement of phytoplankton units and the corresponding geometric shapes as described in detail in (Edier, 1979).

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ B2FINDarrow_drop_down
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    B2FIND
    Dataset . 2015
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 2015
    License: CC BY
    Data sources: PANGAEA
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 2015
    Data sources: PANGAEA
    0
    citations0
    popularityAverage
    influenceAverage
    impulseAverage
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ B2FINDarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      B2FIND
      Dataset . 2015
      Data sources: B2FIND
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 2015
      License: CC BY
      Data sources: PANGAEA
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 2015
      Data sources: PANGAEA
  • An extensive compilation of field data on net primary productivity (NPP) of natural and agricultural ecosystems worldwide was synthesized in the 1970s and early 1980s by Prof. H. Lieth, Dr. G. Esser and others. Much of this work was carried out at the University of Osnabrueck, Germany. More than 700 single point estimates of NPP or biomass were extracted from the scientific literature, each with a geographical reference (latitude/longitude). The literature cited dates from 1869 to 1982, with the majority of references from the 1960s and 1970s. Although this data set has not been updated since the 1980s, it represents a wealth of information for use in model development and validation. In the early 1970s, a subset of these NPP data was used by Lieth, Esser and co-workers to develop and test a series of statistical-correlative models of NPP as a function of mean annual temperature and precipitation. The later versions of these models included modifications for soil, seasonality, agriculture, and other human influences ("Osnabrück Biosphere Mode,""High Resolution Biosphere Model," etc.). Most of the 720 unique NPP records (632, or 88 percent) have been matched to a bibliography of 356 references from the primary literature. The original form of this bibliography contained many more references than records, including multiple sources for the same author and study, as well as additional references to data on standing biomass, soils, and so forth. Since this is a useful resource in its own right, an edited and corrected compilation of these 858 references is available here with the cross-references to the NPP records highlighted. Of the 720 unique NPP records, about two-thirds have above-ground NPP estimates that range between 1 and 8530 g/m2/year (dry matter) -- or 2923 g/m2/year, excluding doubtful values, wetlands, and crops/pastures and other likely managed systems. Total NPP, for which more than half of the sites have estimates, ranges from 3 to 9320 g/m2/year (dry matter) -- or 3580 g/m2/year, excluding doubtful values, wetlands, and crops/pastures and other likely managed systems. Each record includes a site identifier, latitude, longitude, author, country, NPP estimates, vegetation type, and other variables. The vegetation-type field begins with a generalized biome type (including tundra, forest, Mediterranean, savanna, grassland, desert, wetland, and a number of managed vegetation types) and is followed by more specific vegetation terminology derived from the original data. Caution is advised in using these biome/vegetation types because they were not defined consistently within the original data set and nearly 200 sites lack any vegetation designation. To achieve completeness in a single synthesis file, a single NPP value (NPP_C) is included for each site that represents the sum of above-ground (ANPP) and below-ground (BNPP) components, expressed in grams of carbon per square meter per year (g C/m2/year). Where BNPP was not reported, it was assumed to be equal to ANPP. A ratio of 0.475 was used to convert dry biomass weight to carbon content. Total NPP was estimated as TNPP (where available), or as the sum of ANPP and BNPP (or from ANPP x 2, if BNPP was not estimated), and then converted to g C/m2/year.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lukas, Roger; Karl, David Michael;

    Nets are towed obliquely at approx. 1 knot, from the surface to approx. 175 m. Towing time is approx. 20 minutes. Zooplankton (weak swimmers >200µm) are collected using oblique tows of a 1 m**2 net (3m length) with 202µm mesh Nitex netting.

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    B2FIND
    Dataset . 1999
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 1999
    License: CC BY
    Data sources: PANGAEA
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 1999
    Data sources: PANGAEA
    0
    citations0
    popularityAverage
    influenceAverage
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      B2FIND
      Dataset . 1999
      Data sources: B2FIND
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 1999
      License: CC BY
      Data sources: PANGAEA
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 1999
      Data sources: PANGAEA
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Wolfe, Kennedy David; Desbiens, Amelia; Mumby, Peter;

    Patterns of movement of marine species can reflect strategies of reproduction and dispersal, species’ interactions, trophodynamics, and susceptibility to change, and thus critically inform how we manage populations and ecosystems. On coral reefs, the density and diversity of metazoan taxa is greatest in dead coral and rubble, which is suggested to fuel food webs from the bottom-up. Yet, biomass and secondary productivity in rubble is predominantly available in some of the smallest individuals, limiting how accessible this energy is to higher trophic levels. We address the bioavailability of motile coral reef cryptofauna based on small-scale patterns of emigration in rubble. We deployed modified RUbble Biodiversity Samplers (RUBS) and emergence traps in a shallow rubble patch at Heron Island, Great Barrier Reef, to detect community-level differences in the directional influx of motile cryptofauna under five habitat accessibility regimes. The mean density (0.13–4.5 ind.cm-3) and biomass (0.14–5.2 mg.cm-3) of cryptofauna were high and varied depending on microhabitat accessibility. Emergent zooplankton represented a distinct community (dominated by the Appendicularia and Calanoida) with the lowest density and biomass, indicating constraints on nocturnal resource availability. Mean cryptofauna density and biomass were greatest when interstitial access within rubble was blocked, driven by the rapid proliferation of small harpacticoid copepods from the rubble surface, leading to trophic simplification. Individuals with high biomass (e.g., decapods, gobies, and echinoderms) were greatest when interstitial access within rubble was unrestricted. Treatments with a closed rubble surface did not differ from those completely open, suggesting that top-down predation does not diminish rubble-derived resources. Our results show that conspecific cues and species’ interactions (e.g., competition and predation) within rubble are most critical in shaping ecological outcomes within the cryptobiome. These findings have implications for prey accessibility through trophic and community size structuring in rubble, which may become increasingly relevant as benthic reef complexity shifts in the Anthropocene. We address the bioavailability of coral reef cryptofauna in rubble based on small-scale patterns of emigration. We adapted the accessibility of Rubble Biodiversity Samplers (RUBS), models used to standardise biodiversity sampling in rubble (Wolfe and Mumby 2020), to explore the local movement patterns of rubble-dwelling fauna, with inference to predation processes within and beyond the cryptobenthos. Five treatments were developed to detect community-level differences in the directional influx of motile cryptofauna under various habitat accessibility regimes. Four of these treatments were developed by modifying accessibility into RUBS (https://www.thingiverse.com/thing:4176644/files) to understand limitations on the directional influx and movement of cryptofauna within coral rubble patches using four treatments; (1) open (completely accessible), (2) interstitial access (top closed), (3) surficial access (sides and bottom closed), and (4) raised (above rubble substratum). The fifth treatment involved a series of emergence plankton traps, designed to target demersal cryptofauna that vertically migrate from within the rubble benthos at night, given emergent zooplankton biomass and diversity are greatest at night. Fieldwork was conducted over several weeks (11th September to 5th October 2021) in a shallow (~3–5 m depth) reef slope site on the southern margin of Heron Island (-23˚26.845’ S, 151˚54.732’ E), Great Barrier Reef, Australia (Fig. 1). All collections were conducted under the Great Barrier Reef Marine Park Authority permit G20/44613.1.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
    0
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    popularityAverage
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Moreira-Saporiti, Agustín; Teichberg, Mirta;

    We studied if functional traits related to resource preemption (light and inorganic nutrients) exert control on space preemption of tropical seagrass meadows. Additionally, we studied if space preemption changed under different eutrophication scenarios. We took seagrass abundance data to study space preemption, seagrass traits data to study their effect on space preemption and eutrophication indicators to evaluate the level of eutrophication at each site/sampling event. The data was collected in Unguja Island (Zanzibar Archipealgo, Tanzania) in seven sites/sampling events (Harbor, Chapwani, Changuu, Bweleo, Fumba, Mangroves and Marumbi). Each site/sampling event comprised a subtidal seagrass meadow (2-4 meters depth) of around 2500 square meters, delimited by the coastline and a fringing reef. The data was taken between the 26.09.2016 to the 05.10.2016. In each site/sampling event, five 50 meters transects were deployed perpendicular to the coast and paralel to each other, approximately separated by 50 meters. The areas enclosed beweeen the transects were names A, B, C and D. Macroalgae biomass was collected as an indicator of eutrophication. Macroalgae biomass was quantified along five 50-m transects per site/sampling event, set perpendicular to the coast and parallel to each other, separated by ~50 meters. We collected the macroalgae present in three random 0.25x0.25 meters quadrats per transect. The macroalgae samples were cleaned of sediments and rinsed with water. They were then dried at 50°C in a forced air oven until constant dry weight. The macroalgae biomass was calculated as the grams of dry weight divided by the area of the quadrat (grams of dry weight per square meter).

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ PANGAEA - Data Publi...arrow_drop_down
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    B2FIND
    Dataset . 2021
    Data sources: B2FIND
    PANGAEA
    Dataset . 2021
    Data sources: PANGAEA
    0
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      B2FIND
      Dataset . 2021
      Data sources: B2FIND
      PANGAEA
      Dataset . 2021
      Data sources: PANGAEA
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Rodriguez Alarcon, Slendy Julieth; Tamme, Riin; Perez Carmona, Carlos;

    Seeds of 52 species of herbaceous plants typical from European grassland ecosystems were obtained from a commercial supplier (Planta naturalis). When species germinated in Petri dishes the seedlings were then transplanted to plastic pots (11 x 11 x 12 cm height, 1L volume). Pots were filled with a mixture of a potting substrate (Biolan Murumuld) and sand. Pots were randomly placed in the greenhouse of the University of Tartu, Estonia. Then, we established monocultures with seven individuals of a single species per pot which were grown under well-watered conditions. One month after transplanting the seedlings to the pots, a drought treatment was applied to half of the pots (five pots per species). The experiment was harvested in late July 2020, when the first individuals started flowering, after month-long drought treatment. Plant traits related to drought responses and resource use strategies were selected and measured for each species following established protocols. These included seven above- and belowground traits: Vegetative plant height (H, cm), Leaf Area (LA, mm2), Specific Leaf Area (SLA, mm2 mg-1), Leaf Dry Matter Content (LDMC, mg g-1), Specific Root Length (SRL, cm g-1), Average root Diameter (AvgD, mm), Root Dry Matter Content (RDMC, mg g-1). Before harvesting, we measured the plant height and collected one leaf per individual for three individuals per pot. Afterward, we collected the aboveground biomass and belowground biomass of all the individuals in each pot. Due to the difficulty in untangling the roots of the different individuals in a pot, root traits were estimated at the pot level. Roots were washed and a sample of finest roots (10-50mg) was collected. Leaves and fine roots were scanned at 300dpi and 600dpi, respectively, using an Epson perfection 3200 Photo scanner for leaves and Epson V700 Photo scanner for fine roots. After scanning, leaves and roots were oven-dried at 60°C for 72h. AvgD and root length were determined using WinRHIZO Pro 2015 (Regent Instruments Inc., Canada), and leaf area with ImageJ software. We averaged all traits values at the species level, attaining a single value for each trait in each treatment. The total aboveground biomass and total belowground biomass of each pot were oven-dried at 60°C for 72h and weighed. Drought is expected to increase in future climate scenarios. Although responses to drought of individual functional traits are relatively well-known, simultaneous changes across multiple traits in response to water scarcity remain poorly understood despite its importance to understand alternative strategies to resist drought. We grew 52 herbaceous species in monocultures under drought and control treatments and characterized the functional space using seven measured above- and belowground traits: plant height, leaf area, specific leaf area, leaf dry matter content, specific root length, average root diameter, and root dry matter content. Then, we estimated how each species occupied this space and the amount of functional space occupied in both treatments using trait probability density functions. We also estimated intraspecific trait variability (ITV) for each species as the dissimilarity in trait values between the individuals of each treatment. We then mapped drought resistance and ITV in the functional space using generalized additive models. The response of species to drought strongly depended on their traits, with species that invested more in root tissues and conserved small size being both more resistant to drought and having higher ITV. We also observed a significant trend of trait displacement towards less conservative strategies. However, these changes depended strongly on the trait values of species in the control treatment, with species with different traits having opposing responses to drought. These contrasting responses resulted in lower trait variability in the species pool in drought compared to control conditions. Our results suggest strong trait filtering acting on conservative species as well as the existence of an optimal part in the functional space to which species converge under drought. Our results show that changes in species trait-space occupancy are key to understand plant strategies to withstand drought, highlighting the importance of individual variation in response to environmental changes, and suggest that community-wide functional diversity and biomass productivity could decrease in a drier future. Knowing these shifts will help to anticipate changes in ecosystem functioning facing climate change. The complete dataset is in the file.

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    ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2022
    License: CC 0
    Data sources: Datacite
    0
    citations0
    popularityAverage
    influenceAverage
    impulseAverage
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    visibilityviews22
    downloaddownloads12
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      ZENODO
      Dataset . 2022
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2022
      License: CC 0
      Data sources: Datacite
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    Authors: Lukas, Roger; Karl, David Michael;

    Nets are towed obliquely at approx. 1 knot, from the surface to approx. 175 m. Towing time is approx. 20 minutes. Zooplankton (weak swimmers >200µm) are collected using oblique tows of a 1 m**2 net (3m length) with 202µm mesh Nitex netting.

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    B2FIND
    Dataset . 1999
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 1999
    Data sources: PANGAEA
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    PANGAEA
    Dataset . 1999
    License: CC BY
    Data sources: PANGAEA
    0
    citations0
    popularityAverage
    influenceAverage
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      B2FIND
      Dataset . 1999
      Data sources: B2FIND
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 1999
      Data sources: PANGAEA
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 1999
      License: CC BY
      Data sources: PANGAEA
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Opreanu, Priscila-Ana;

    Dataset containing meiobenthos data for samples collected during the September 2008 Sesame Cruise in the North-West Black Sea on board of the Romanian R/V Mare Nigrum. Meiobenthos samples were collected in 5 stations, using a multicorer MARK II-400. The dataset includes 5 samples analysed for meiobenthos species composition, abundance and biomass.The entire washed sample was analyzed under the binocular stereomicroscope. Meiobenthic species were identified and enumerated; some meiobenthic species were identified and enumerated only at higher taxonomic level. Taxonomic identification was done at GEOECOMAR.

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    B2FIND
    Dataset . 2015
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 2015
    Data sources: PANGAEA
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 2015
    License: CC BY
    Data sources: PANGAEA
    0
    citations0
    popularityAverage
    influenceAverage
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      B2FIND
      Dataset . 2015
      Data sources: B2FIND
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 2015
      Data sources: PANGAEA
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 2015
      License: CC BY
      Data sources: PANGAEA
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ana Paula Staben Pruchniak (10423459); Graziella dos Santos Portes Silva (10423462); Liliane Schier de Lima (10423465); Sueli Pércio Quináia (4986440);

    Abstract Activated carbon is commonly used as a material for contaminant-adsorption processes in aqueous systems. However, its use is more restricted to charcoal than to coal, for the most part, in view of the fact of the higher cost (~ 40%) if the mineral is a fossil fuel which needs to be extracted from the earth by mining. For this reason, the peach stone that comes from alimentary industrial tailings can be a good choice for the separation of pollutants from aqueous suspensions and other soluble substances. The purpose of this research was the development of a low-cost filter, using stones to remove atrazine from water. Appraisal and characterization studies were performed along with batch experiments to investigate dosing effects of the activated carbon, atrazine concentration, contact time, and adsorption pH on removal procedures. From the results of the experiment, an excellent removal of the analyte in question was observed under conditions that can be considered as close as possible to the environment, such as pH = 6.5, room temperature and 10 minutes of agitation time, always choosing the best alternative with the lowest cost of energy and time. Batch system application has been recommended as versatile for utilization in seasonal problems such as pesticide contamination.

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    figshare
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    figshare
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    Smithsonian figshare
    Dataset . 2020
    License: CC BY
    0
    citations0
    popularityAverage
    influenceAverage
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      figshare
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      figshare
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Smithsonian figshare
      Dataset . 2020
      License: CC BY
  • Authors: T.Ya. Ashikhmina; E. V. Tovstik; S. Yu. Ogorodnikova; I. G. Shirokikh;

    In the course of monitoring near the chemical weapons storage and destruction plant "Maradykovsky", more than 100 strains of Streptomyces were stated from the soil surfaces into the pure culture. In order to find the strains which are tolerant to chemical weapons degradation products, the response of natural isolates of actinomycetes to sodium pyrophosphate, methylphosphonic acid, and arsenic concentration gradient was studied. For each pollutant a special dose was set, which could stimulate and inhibit either the growth of actinomycetes at the stage of spores and at the stage of vegetative growth, or their metabolic activity consisting in production of antibiotics, or cellulose decomposition. Actinomycetes cultures were found, which are promising for creating microbial inoculants for remediation of soils impacted with arsenic and phosphorus xenobiotics.

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    Authors: Snejana Moncheva; Ludmila G Senichkina; Dennis Altukhov;

    The samples were concentrated down to 50 cm**3 by slow decantation after storage for 20 days in a cool and dark place. The species identification was done under light microscope OLIMPUS–BS41 connected to a video-interactive image analysis system at magnification of the ocular 10X and objective – 40X. A Sedgwick-Rafter camera (1ml) was used for counting. 400 specimen were counted for each sample, while rare and large species were checked in the whole sample (Manual of phytoplankton, 2005). Species identification was mainly after Carmelo T. (1997) and Fukuyo, Y. (2000).Taxon-specific phytoplankton abundance and biomass were analysed by Moncheva S., B. Parr, 2005. Manual for Phytoplankton Sampling and Analysis in the Black Sea.The cell biovolume was determined based on morpho-metric measurement of phytoplankton units and the corresponding geometric shapes as described in detail in (Edier, 1979).

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    B2FIND
    Dataset . 2015
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2015
    License: CC BY
    Data sources: PANGAEA
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    PANGAEA
    Dataset . 2015
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2015
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2015
      License: CC BY
      Data sources: PANGAEA
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      PANGAEA
      Dataset . 2015
      Data sources: PANGAEA
  • An extensive compilation of field data on net primary productivity (NPP) of natural and agricultural ecosystems worldwide was synthesized in the 1970s and early 1980s by Prof. H. Lieth, Dr. G. Esser and others. Much of this work was carried out at the University of Osnabrueck, Germany. More than 700 single point estimates of NPP or biomass were extracted from the scientific literature, each with a geographical reference (latitude/longitude). The literature cited dates from 1869 to 1982, with the majority of references from the 1960s and 1970s. Although this data set has not been updated since the 1980s, it represents a wealth of information for use in model development and validation. In the early 1970s, a subset of these NPP data was used by Lieth, Esser and co-workers to develop and test a series of statistical-correlative models of NPP as a function of mean annual temperature and precipitation. The later versions of these models included modifications for soil, seasonality, agriculture, and other human influences ("Osnabrück Biosphere Mode,""High Resolution Biosphere Model," etc.). Most of the 720 unique NPP records (632, or 88 percent) have been matched to a bibliography of 356 references from the primary literature. The original form of this bibliography contained many more references than records, including multiple sources for the same author and study, as well as additional references to data on standing biomass, soils, and so forth. Since this is a useful resource in its own right, an edited and corrected compilation of these 858 references is available here with the cross-references to the NPP records highlighted. Of the 720 unique NPP records, about two-thirds have above-ground NPP estimates that range between 1 and 8530 g/m2/year (dry matter) -- or 2923 g/m2/year, excluding doubtful values, wetlands, and crops/pastures and other likely managed systems. Total NPP, for which more than half of the sites have estimates, ranges from 3 to 9320 g/m2/year (dry matter) -- or 3580 g/m2/year, excluding doubtful values, wetlands, and crops/pastures and other likely managed systems. Each record includes a site identifier, latitude, longitude, author, country, NPP estimates, vegetation type, and other variables. The vegetation-type field begins with a generalized biome type (including tundra, forest, Mediterranean, savanna, grassland, desert, wetland, and a number of managed vegetation types) and is followed by more specific vegetation terminology derived from the original data. Caution is advised in using these biome/vegetation types because they were not defined consistently within the original data set and nearly 200 sites lack any vegetation designation. To achieve completeness in a single synthesis file, a single NPP value (NPP_C) is included for each site that represents the sum of above-ground (ANPP) and below-ground (BNPP) components, expressed in grams of carbon per square meter per year (g C/m2/year). Where BNPP was not reported, it was assumed to be equal to ANPP. A ratio of 0.475 was used to convert dry biomass weight to carbon content. Total NPP was estimated as TNPP (where available), or as the sum of ANPP and BNPP (or from ANPP x 2, if BNPP was not estimated), and then converted to g C/m2/year.

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    Authors: Lukas, Roger; Karl, David Michael;

    Nets are towed obliquely at approx. 1 knot, from the surface to approx. 175 m. Towing time is approx. 20 minutes. Zooplankton (weak swimmers >200µm) are collected using oblique tows of a 1 m**2 net (3m length) with 202µm mesh Nitex netting.

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    B2FIND
    Dataset . 1999
    Data sources: B2FIND
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    PANGAEA
    Dataset . 1999
    License: CC BY
    Data sources: PANGAEA
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    PANGAEA
    Dataset . 1999
    Data sources: PANGAEA
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      B2FIND
      Dataset . 1999
      Data sources: B2FIND
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      PANGAEA
      Dataset . 1999
      License: CC BY
      Data sources: PANGAEA
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      PANGAEA
      Dataset . 1999
      Data sources: PANGAEA
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    Authors: Wolfe, Kennedy David; Desbiens, Amelia; Mumby, Peter;

    Patterns of movement of marine species can reflect strategies of reproduction and dispersal, species’ interactions, trophodynamics, and susceptibility to change, and thus critically inform how we manage populations and ecosystems. On coral reefs, the density and diversity of metazoan taxa is greatest in dead coral and rubble, which is suggested to fuel food webs from the bottom-up. Yet, biomass and secondary productivity in rubble is predominantly available in some of the smallest individuals, limiting how accessible this energy is to higher trophic levels. We address the bioavailability of motile coral reef cryptofauna based on small-scale patterns of emigration in rubble. We deployed modified RUbble Biodiversity Samplers (RUBS) and emergence traps in a shallow rubble patch at Heron Island, Great Barrier Reef, to detect community-level differences in the directional influx of motile cryptofauna under five habitat accessibility regimes. The mean density (0.13–4.5 ind.cm-3) and biomass (0.14–5.2 mg.cm-3) of cryptofauna were high and varied depending on microhabitat accessibility. Emergent zooplankton represented a distinct community (dominated by the Appendicularia and Calanoida) with the lowest density and biomass, indicating constraints on nocturnal resource availability. Mean cryptofauna density and biomass were greatest when interstitial access within rubble was blocked, driven by the rapid proliferation of small harpacticoid copepods from the rubble surface, leading to trophic simplification. Individuals with high biomass (e.g., decapods, gobies, and echinoderms) were greatest when interstitial access within rubble was unrestricted. Treatments with a closed rubble surface did not differ from those completely open, suggesting that top-down predation does not diminish rubble-derived resources. Our results show that conspecific cues and species’ interactions (e.g., competition and predation) within rubble are most critical in shaping ecological outcomes within the cryptobiome. These findings have implications for prey accessibility through trophic and community size structuring in rubble, which may become increasingly relevant as benthic reef complexity shifts in the Anthropocene. We address the bioavailability of coral reef cryptofauna in rubble based on small-scale patterns of emigration. We adapted the accessibility of Rubble Biodiversity Samplers (RUBS), models used to standardise biodiversity sampling in rubble (Wolfe and Mumby 2020), to explore the local movement patterns of rubble-dwelling fauna, with inference to predation processes within and beyond the cryptobenthos. Five treatments were developed to detect community-level differences in the directional influx of motile cryptofauna under various habitat accessibility regimes. Four of these treatments were developed by modifying accessibility into RUBS (https://www.thingiverse.com/thing:4176644/files) to understand limitations on the directional influx and movement of cryptofauna within coral rubble patches using four treatments; (1) open (completely accessible), (2) interstitial access (top closed), (3) surficial access (sides and bottom closed), and (4) raised (above rubble substratum). The fifth treatment involved a series of emergence plankton traps, designed to target demersal cryptofauna that vertically migrate from within the rubble benthos at night, given emergent zooplankton biomass and diversity are greatest at night. Fieldwork was conducted over several weeks (11th September to 5th October 2021) in a shallow (~3–5 m depth) reef slope site on the southern margin of Heron Island (-23˚26.845’ S, 151˚54.732’ E), Great Barrier Reef, Australia (Fig. 1). All collections were conducted under the Great Barrier Reef Marine Park Authority permit G20/44613.1.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
    0
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    popularityAverage
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Moreira-Saporiti, Agustín; Teichberg, Mirta;

    We studied if functional traits related to resource preemption (light and inorganic nutrients) exert control on space preemption of tropical seagrass meadows. Additionally, we studied if space preemption changed under different eutrophication scenarios. We took seagrass abundance data to study space preemption, seagrass traits data to study their effect on space preemption and eutrophication indicators to evaluate the level of eutrophication at each site/sampling event. The data was collected in Unguja Island (Zanzibar Archipealgo, Tanzania) in seven sites/sampling events (Harbor, Chapwani, Changuu, Bweleo, Fumba, Mangroves and Marumbi). Each site/sampling event comprised a subtidal seagrass meadow (2-4 meters depth) of around 2500 square meters, delimited by the coastline and a fringing reef. The data was taken between the 26.09.2016 to the 05.10.2016. In each site/sampling event, five 50 meters transects were deployed perpendicular to the coast and paralel to each other, approximately separated by 50 meters. The areas enclosed beweeen the transects were names A, B, C and D. Macroalgae biomass was collected as an indicator of eutrophication. Macroalgae biomass was quantified along five 50-m transects per site/sampling event, set perpendicular to the coast and parallel to each other, separated by ~50 meters. We collected the macroalgae present in three random 0.25x0.25 meters quadrats per transect. The macroalgae samples were cleaned of sediments and rinsed with water. They were then dried at 50°C in a forced air oven until constant dry weight. The macroalgae biomass was calculated as the grams of dry weight divided by the area of the quadrat (grams of dry weight per square meter).

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    B2FIND
    Dataset . 2021
    Data sources: B2FIND
    PANGAEA
    Dataset . 2021
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2021
      Data sources: B2FIND
      PANGAEA
      Dataset . 2021
      Data sources: PANGAEA
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    Authors: Rodriguez Alarcon, Slendy Julieth; Tamme, Riin; Perez Carmona, Carlos;

    Seeds of 52 species of herbaceous plants typical from European grassland ecosystems were obtained from a commercial supplier (Planta naturalis). When species germinated in Petri dishes the seedlings were then transplanted to plastic pots (11 x 11 x 12 cm height, 1L volume). Pots were filled with a mixture of a potting substrate (Biolan Murumuld) and sand. Pots were randomly placed in the greenhouse of the University of Tartu, Estonia. Then, we established monocultures with seven individuals of a single species per pot which were grown under well-watered conditions. One month after transplanting the seedlings to the pots, a drought treatment was applied to half of the pots (five pots per species). The experiment was harvested in late July 2020, when the first individuals started flowering, after month-long drought treatment. Plant traits related to drought responses and resource use strategies were selected and measured for each species following established protocols. These included seven above- and belowground traits: Vegetative plant height (H, cm), Leaf Area (LA, mm2), Specific Leaf Area (SLA, mm2 mg-1), Leaf Dry Matter Content (LDMC, mg g-1), Specific Root Length (SRL, cm g-1), Average root Diameter (AvgD, mm), Root Dry Matter Content (RDMC, mg g-1). Before harvesting, we measured the plant height and collected one leaf per individual for three individuals per pot. Afterward, we collected the aboveground biomass and belowground biomass of all the individuals in each pot. Due to the difficulty in untangling the roots of the different individuals in a pot, root traits were estimated at the pot level. Roots were washed and a sample of finest roots (10-50mg) was collected. Leaves and fine roots were scanned at 300dpi and 600dpi, respectively, using an Epson perfection 3200 Photo scanner for leaves and Epson V700 Photo scanner for fine roots. After scanning, leaves and roots were oven-dried at 60°C for 72h. AvgD and root length were determined using WinRHIZO Pro 2015 (Regent Instruments Inc., Canada), and leaf area with ImageJ software. We averaged all traits values at the species level, attaining a single value for each trait in each treatment. The total aboveground biomass and total belowground biomass of each pot were oven-dried at 60°C for 72h and weighed. Drought is expected to increase in future climate scenarios. Although responses to drought of individual functional traits are relatively well-known, simultaneous changes across multiple traits in response to water scarcity remain poorly understood despite its importance to understand alternative strategies to resist drought. We grew 52 herbaceous species in monocultures under drought and control treatments and characterized the functional space using seven measured above- and belowground traits: plant height, leaf area, specific leaf area, leaf dry matter content, specific root length, average root diameter, and root dry matter content. Then, we estimated how each species occupied this space and the amount of functional space occupied in both treatments using trait probability density functions. We also estimated intraspecific trait variability (ITV) for each species as the dissimilarity in trait values between the individuals of each treatment. We then mapped drought resistance and ITV in the functional space using generalized additive models. The response of species to drought strongly depended on their traits, with species that invested more in root tissues and conserved small size being both more resistant to drought and having higher ITV. We also observed a significant trend of trait displacement towards less conservative strategies. However, these changes depended strongly on the trait values of species in the control treatment, with species with different traits having opposing responses to drought. These contrasting responses resulted in lower trait variability in the species pool in drought compared to control conditions. Our results suggest strong trait filtering acting on conservative species as well as the existence of an optimal part in the functional space to which species converge under drought. Our results show that changes in species trait-space occupancy are key to understand plant strategies to withstand drought, highlighting the importance of individual variation in response to environmental changes, and suggest that community-wide functional diversity and biomass productivity could decrease in a drier future. Knowing these shifts will help to anticipate changes in ecosystem functioning facing climate change. The complete dataset is in the file.

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    ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2022
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2022
      License: CC 0
      Data sources: Datacite
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    Authors: Lukas, Roger; Karl, David Michael;

    Nets are towed obliquely at approx. 1 knot, from the surface to approx. 175 m. Towing time is approx. 20 minutes. Zooplankton (weak swimmers >200µm) are collected using oblique tows of a 1 m**2 net (3m length) with 202µm mesh Nitex netting.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ B2FINDarrow_drop_down
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    B2FIND
    Dataset . 1999
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 1999
    Data sources: PANGAEA
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 1999
    License: CC BY
    Data sources: PANGAEA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      B2FIND
      Dataset . 1999
      Data sources: B2FIND
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 1999
      Data sources: PANGAEA
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 1999
      License: CC BY
      Data sources: PANGAEA