This readme file was generated on [2023-10-27] by Asa Berglund



GENERAL INFORMATION


Title of Dataset: Effects on the food-web structure and bioaccumulation patterns of organic contaminants in a climate-altered Bothnian Sea mesocosms



Author/Principal Investigator Information

Name: Asa Berglund

ORCID: 0000-0002-7926-2120

Institution: Department of ecology and environmental science

Address: Umeå university, 901 87 Umeå

Email: asa.berglund@umu.se


Author/Associate or Co-investigator Information

Name: Agneta Andersson

ORCID: 0000-0001-7819-9038 

Department of ecology and environmental science

Address: Umeå university, 901 87 Umeå

Email: agneta.andersson@umu.se



Name: Mats Tysklind

ORCID: 0000-0001-8709-6970 

Department of chemistry

Address: Umeå university, 901 87 Umeå

Email: mats.tysklind@umu.se


Date of data collection:
2013-05-30 to 2017-03-15 and 2013-07-03 

Geographic location of data collection: 
Norrbyn, Vasterbotten, Sweden.
63°33'52.4"N 
19°49'55.9"E


Information about funding sources that supported the collection of the data: 
This work was financed by the EcoChange project (Dnr 2009-149) and the Kempe Foundation.

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: Creative commons CC BY


Links to publications that cite or use the data: Effects on the food-web structure and bioaccumulation patterns of organic contaminants in a climate-altered Bothnian Sea mesocosms. Åsa M.M. Berglund, Christine Gallampois, Matyas Ripszam, Henrik Larsson, Daniela A. Figueroa, Evelina Griniene, Pär Byström, Elena Gorokhova, Peter Haglund, Agneta Andersson, Mats Tysklind. 
Front. Mar. Sci. DOI: 10.3389/fmars.2023.1244434


Links to other publicly accessible locations of the data: None


Links/relationships to ancillary data sets: None


Was data derived from another source? No

If yes, list source(s): 


Recommended citation for this dataset: 

A.M.M. Berglund, C. Gallampois, M. Ripszam, H. Larsson, D. Figueroa, E. Griniene, P. Bystrom, E. Gorokhova, P. Haglund, A. Andersson and M. Tysklind 2023. Data repository for “Effects on the food-web structure and bioaccumulation patterns of organic contaminants in a climate-altered Bothnian Sea mesocosms”


DATA & FILE OVERVIEW

File List: 
Biological data.txt
PAR.txt
Fish.txt
POP_water.txt
POP_fish.txt



Relationship between files, if important: Data is identified by the ID of each mesocosm, which is similar in all 4 files

Additional related data collected that was not included in the current data package: None

Are there multiple versions of the dataset? No

If yes, name of file(s) that was updated: 

Why was the file updated? 

When was the file updated? 


METHODOLOGICAL INFORMATION



Description of methods used for collection/generation of data: 

Experimental setup and treatments
The outdoor mesocosm experiment took place during 34 days between May and July 2013, at the Umeå Marine Science Centre (UMF), Sweden (63? 33’N, 19? 49’E). Three experimental treatments were applied, alone or in combination (three replicates each), with tDOM addition, temperature increase and MP addition as treatment factors (Fig. 1A). We used the maximum predicted increase in temperature and tDOM (an increase of 3°C and 30% tDOM, respectively), as suggested earlier (Meier, 2006; Neumann, 2010), and the present day conditions in the region during summer, resulting in 15 and 18°C and 4mg/L and 6mg/L DOC (dissolved organic carbon) for the present-day and climate-altered conditions, respectively. In total 24, mesocosms were established in clean 1 m3 cubic shaped (1×1×1m) polypropylene tanks (Allembalage AB, Jordbo, Sweden), sheltered by a semi-transparent roof. All tanks were simultaneously filled with 947 L sea water (3‰ salinity) collected 1 km offshore in the northern Bothnian sea (63°34’N, 19°54’E) using the large pumps of the UMF field station Flygt 3152.181 (Xylem Sundbyberg Sweden). The water was filtered (1 mm slit filter, Bernoulli System Lund, Sweden) to remove fish and allow natural communities of bacteria, phytoplankton, protozoa and mesozooplankton to populate the mesocosms. The mesocosms were completely immersed in four swimming pools (Ultra Frame Pool 7”, Intex, Long Beach; Ca, USA) filled with seawater and connected to a temperature-control system, that maintain the target water temperatures. A block design included six mesocosms per swimming pool, and the position of each mesocosm was randomized to avoid systematic errors. The temperatures in the pools were recorded daily, yielding an average of 18.0 ± 0.01, 15.2 ± 0.02, 18.0 ± 0.01, and 14.9 ± 0.01°C, for pool 1 – 4 respectively. Individual temperature measurements in each mesocosm was not conducted as each mesocosm was fully immersed in the pools, and hence unlikely would deviate from the temperature in the pools. The mesocosm were aeriated via a compressed air-valve system and to prevent insect colonization mosquito nets were mounted on the top of the mesocosms. 
 
To mimic the increased runoff of organic material, tDOM was prepared using natural humic soil from a typical forest of the area (mix of deciduous and coniferous trees) as described by Ripszam et al. (2015). The humic soil extract was analyzed for DOC, DIN (dissolved inorganic nitrogen, i.e. nitrate, nitrite and ammonium), DIP (dissolved inorganic phosphorous), total N and total P, to calculate the appropriate soil extract volume and nutrient amount for addition. The tDOM-treated mesocosms were pre-treated with 0.5mg/L DOC; after that, tDOM was added weekly (0.14 mg C/L 3 times a week) in concert with water replacement (see below and Fig. 1B). To account for the nutrient addition in the tDOM-treated mesocosms, a corresponding amount of dissolved nitrogen and phosphorous (initial boost: 0.95 µg PO4-P/L, 4.43 µg NO3/L and 0.72 µg NH4/L, followed by 0.20 µg PO4-P/L, 0.94 µg NO3/L and 0.15 NH4/L 3 times a week) was added in control mesocosms (Lignell et al., 2008; Stepanauskas et al., 2002). 
The mesocosms were left to equilibrate, and 4 days after the initial tDOM treatment (hereafter referred to time = 0), 2.5 mL of a methanol spiking solution containing a mixture of 33 MPs was added to half of the mesocosms. For a complete list of compounds and the initial concentrations as well as final concentrations, see Ripszam et al. (2015, Table 1 and Fig 3). The MPs were selected based on the hazardous substance list provided by the European Commission (Directive 2008/105/EC, 2008) and spanned four orders of magnitude of lipophilicity, viz. log octanol-water partition coefficients (log Kow: between 2.1 and 6.4; Table S1). The mixture included legacy POPs, recently and currently used pesticides, high-volume production compounds and some benchmark compounds for modelling (Ripszam et al., 2015).  
Two days after the MP addition, fish was introduced to the mesocosms and kept in the system for 21 days. In total, 10 perch (Perca fluviatilis) larvae (6.3±0.1mm, 0.8±0.05mg) were added as top predators in each mesocosm. Perch was hatched from egg strands, collected from a coastal spawning bay (63°45'14" N, 20°32'18" E) and kept at 16°C until the start of the experiment. During the initial stage (pre-exposure), they were fed a mixture of live zooplankton. One week before the planned experiment termination (day 30), the fish were removed due to low zooplankton abundance and risk of starvation. Thus, on day 23, all surviving perch were collected with a large 3-mm net and killed by overdosing of M222 (Ethyl 3-aminobenzoate methanesulfonate, Fluka; Ethical approval No. A28-13, Umeå University). Samples were kept frozen at –20 °C until chemical analysis was performed. 

Weekly sampling
Twenty liters of water were replaced three times a week with an equal volume of filtered (20 µm) seawater (see Fig. 1B for the sampling scheme). At the same time, nutrients or tDOM (depending on treatment) were added to mesocosms to gradually reach the target of 6 mg/L DOC in the tDOM-treated mesocosms. Once a week, an aliquot of water was sampled, filtered (90 µm), and used to analyze pH, DOC, total nitrogen (Ntot), DIN, total phosphorous (Ptot), DIP, particulate organic phosphorous (PO4-P), primary production (PP), Chlorophyll a (Chl a), bacterial production (BP), and bacterial abundance (BA), phytoplankton, flagellate and ciliates. Mesozooplankton was collected weekly from each mesocosm by three bottom-to-surface hauls with a 65 µm net (14 cm diameter); 40.16 L/sample were filtered. Phytoplankton, flagellates and PO4-P were only counted/analyzed at the start (day -4), mid-point (day 16) and after fish was sampled (day 30, not included in this paper). Light (photosynthetically active radiation, PAR) was measured weekly during mid-day using a PAR Licor sensor (LICOR -193SA) at three depths (top, middle and bottom) and five positions and calculated as an average value for each mesocosm.

Water chemistry
All parameters for water chemistry (DOC, pH, DIN, DIP, Ntot, Ptot, PO4-P) were determined on the day of sampling. DOC was measured in a high-temperature carbon analyzer (Shimadzu TOC-5000, Shimadzu Corporation) with platinum-coated Al2O3 granulate as a catalyst, according to Berglund et al. (2007). A benchtop pH meter (pHenomenal, VWR International AB, Spånga Sweden) with a combined glass electrode (PHENOMENAL 221, avantor, VWR Spånga Sweden), calibrated with three (4.00, 7.00 and 10.01) buffers, AVS Titronorm traceable to SRM from NIST (BDH Chemicals) was used for pH measurements. Concentrations of inorganic nutrients (DIN and DIP) and total nutrient content (Ntot and P tot) were analyzed using a 4-channel auto-analyzer (Quaatro Marine, Braan and Luebbe) according to the Swedish Standards Institute (Grasshoff et al., 1999). The detection limit for DIN and DIP were 1.5 µg DIN/L N and 0.7 µg DIP/L P, respectively. Particulate organic phosphorous (PO4-P) was quantified using the ash-hydrolysis method, with a lower range of measurment from 0.08 µM (Solorzano and Sharp, 1980). 

Biological variables
Primary production, BP and Chl a were analyzed at the day of sampling; all other analyses/counts (BA, phytoplankton, flagellates, ciliates and mesozooplankton) were performed later. Samples for analysis of biomasses and abundances of phytoplankton, heterotrophic flagellates, ciliates and mesozooplankton were fixed with 2% acidic Lugol’s solution according to Utermohl (1958) and stored at 4°C until analysis. 10 µL of glutaraldehyde was added to samples used for BA analyses, and samples were kept for 15 min in the dark followed by storage at -80°C until analysis.
Bacterial production was measured using the [3H-methyl]-thymidine technique (Fuhrman and Azam, 1982). In short, 1 mL of mesocosms water was added to Eppendorf tubes (one control and duplicate samples). In the controls, bacteria were pre-killed by adding 100 µl ice-cold 50 % trichloroacetic acid (TCA) and incubated at -20oC for 5 minutes. Two µL of [3H]-thymidine (84 Ci mmol-1; Perkin Elmer, Massachusetts, USA) were added to each tube to a final concentration 24 nM. The incubation was terminated by adding 100 µL ice-cold 50% TCA and centrifuging at 13,000 rpm for 10 min. After washing the pellet with 5% TCA, 1 mL scintillation cocktail was added before analyses in a scintillator counter (Beckman Coulter LS 6500/Packard Tri-Carb 1600 TR). The incorporated thymidine was converted to cell production using the conversion factor of 1.4x1018 cells mol-1 (Wikner and Hagstrom, 1999). Carbon biomass production was estimated from cell production and average cell carbon biomass according to Fuhrman and Azam (1982).

Bacterial abundance was analyzed using a BD FACSVerseTM flow cytometer (BD Biosciences) fitted with a 488-nm laser (20 mW output). Samples were stained with SYBR Green I (Invitrogen) to a final concentration of 1: 10 000 (Marie et al., 2005) and diluted with filtered (0.2 µm) seawater. Samples were run for 1 min at a flow rate of 30 mL min-1 and 1 µm microspheres (Fluoresbrite plain YG, Polysciences) served as an internal standard in each sample. Forward light scatter (FSC), side light scatter (SSC), and green fluorescence from SYBR Green I (527 ± 15) were measured to estimate bacteria abundance. Bacterial biomass was then calculated using a conversion factor of 20 fg C cell-1 (Lee and Fuhrman, 1987).

Primary production (PP) was measured in situ using the 14C method according to Gargas (1975). Five mL of sample were added to 20 mL transparent polycarbonate tubes in triplicate and one dark tube as control. 14C was added to each tube (7.2 µL, 14C Centralen Denmark, activity 100 µCi mL-1), and incubation was carried out at 80 cm depth inside the pools, after mid-day, for about 3 hours. The activity was stopped by the addition of 150 µL 6M HCl. The samples were analyzed in a scintillation counter (Beckman Coulter LS 6500/Packard Tri-Carb 1600 TR) following addition of 15 mL scintillation cocktail (Optiphase HiSafe 3). Daily PP was calculated as described in Andersson et al. (1996) using factor F according to Gargas (1975). 

Samples for Chl a (100 mL) were filtered onto 25 mm GF/F filters, extracted in 10 mL 96% ethanol overnight and measured with a Perkin Elmer LS 30 fluorometer (433/674 nm excitation/emission wavelengths; Waltham, Middlesex, MA, USA). The protocol was based on US EPA (1997), protocol 446, with the following modifications: a) Ethanol 96% was used in place of 90% acetone and b) glass fibre filters were crushed using ball milling with 5 mm steel beads instead of a mechanical tissue grinder.

Phytoplankton and nanoflagellates were only counted at the start (day -4), mid-point (day 16) and after fish was removed (day 30). Hence, only data for the first two sampling occasions, with relevance for fish, are presented in this paper. Using sedimentation chambers, 10-50 mL of a Lugol-fixed sample were settled for 12-48 hours. The cells were counted using an inverted microscope (Nikon Eclipse Ti) at 100-400x magnification with phase contrast settings (Utermöhl, 1958). Cell biovolume was calculated according to Olenina et al. (2006). Chl ? was used as a proxy for phytoplankton stock, as phytoplankton counts (data not shown) were performed on a limited number of samples, and Chl ? and phytoplankton biomass correlated well (R2=0.66, p<0.0001).

For ciliate analysis, 25–50 mL of the fixed sample were settled for at least 24-48 h in Utermöhl’s chambers and counted with an inverted microscope at 200× magnification, as described in Andersson et al. (2023). The entire bottom of each chamber was surveyed, and an additional subsample was counted if the total number of organisms was below 150. Ciliate biomass was based on their biovolume, which was calculated by their geometric shape using measurements of the cell length and width of at least 20 cells of each species/taxa per sample. Cell carbon content for flagellates and ciliates was calculated according to Menden-Deuer and Lessard (2000).

For mesozooplankton analysis, each sample (entire volume) was counted using a counting chamber and an inverted microscope (Leitz fluovert FS, Leica) at 80× magnification. Copepods were classified according to species and developmental stage (nauplii, copepodites CI–III, CIV–V and adults), whereas cladocerans (Bosmina coregoni) were classified according to species, maturity (adults and juveniles) and sex. Biomass (wet weight) was calculated using abundance data, species- and stage-specific individual weights (Hernroth, 1985). Three mesozooplankton groups were considered for the abundance and biomass aggregation: copepods, cladocerans and rotifers. 

Chemical analyses, MPs
Organic contaminants were extracted from whole fish using 1.5 mL cyclohexane: etylacetate (3:1). Internal standard, 20 µL (Ripszam et al., 2015), was added, and fish samples were homogenized with zirconium beads in a Mini Beadbeater (Biospec. Bartlesville, USA) for 4 min at 3500 oscillations/min. The homogenate was centrifuged at 14 000 rpm for 10 min and the supernatant was collected. The procedure was repeated thrice, and the combined supernatants were evaporated to 500 µL. Lipid removal was accomplished by gel permeation chromatography (GPC) cleanup (Sundkvist et al., 2010). Organic micropollutant concentrations were analyzed by gas chromatography – high-resolution mass spectrometry (GC-HRMS) using an Agilent 6890 GC (Agilent, Santa Clara, CA USA) equipped with an ultra-inert J&W DB-5MS GC column (30m*0.25mm*0.25µm, Agilent, Santa Clara, CA, USA) and a Waters Micromass AutoSpec Ultima HRMS (Waters Corp., Milford, MA, USA) (Sundkvist et al., 2010). The HRMS was operated in electron ionization (EI) mode at >8,000 resolution, utilizing selected ion monitoring (SIM) of two abundant ions of each target compound and internal standard. 

Dissolved MPs in water were determined in the sub-0.7µm fraction by liquid-liquid extraction of 300mL water, passed through GF/F filters. Internal standard (20 µL) was added, and extraction occurred in 100 mL dichloromethane (50 mL once and 25 mL twice). Chemical analysis was performed by GC-MS using an Agilent 7890N GC coupled to an HRT-TOF-MS instrument (Leco, St. Joseph, MI), equipped with an ultra-inert J&W DB-5MS GC column (30m*0.25mm*0.25µm, Agilent, Santa Clara, Ca, USA), as described in Ripszam et al. (2015). One sample (MP15 treatment) was lost during the analysis. If concentrations of any contaminants (in fish or water) exceeded 10% of the highest concentration measured in blanks, compounds were omitted from further analysis. 


References:
Andersson A, Griniene E, Berglund AMM, Brugel S, Gorokhova E, Figueroa D, et al. (2023). Microbial food web changes induced by terrestrial organic matter and elevated temperature in the coastal northern Baltic Sea. Frontiers in Marine Science 10 doi: 10.3389/fmars.2023.1170054
Andersson A, Hajdu S, Haecky P, Kuparinen J, Wikner J. (1996). Succession and growth limitation of phytoplankton in the Gulf of Bothnia (Baltic Sea). Marine Biology 126, 791 - 801., 
Berglund J, Müren U, Båmstedt U, Andersson A. (2007). Efficiency of a phytoplankton-based and a bacteria-based food web in a pelagic marine system. Limnology and Oceanography 52, 121-131, 
Directive 2008/105/EC. (2008) Directive 2008/105/EC. In: Parliament E, editor.
EPA U. (1997) Method 446.0: In Vitro Determination of Chlorophylls a, b, c + c and Pheopigments in Marine And Freshwater Algae by Visible Spectrophotometry. In: National Exposure Research Laboratory Office of Research and Development. U.S. Environmental Protection Agency C, Ohio 45268, editor. 2. U.S. Environmental Protection Agency, Cincinnati, Ohio.
Fuhrman JA, Azam F. (1982). Thymidine incorporation as a measure of heterotrophic bacterioplankton production in marine surface waters - evaluation and field results. Marine Biology 66, 109-120,  doi: 10.1007/bf00397184
Gargas E. (1975). A manual for phytoplankton production studies in the Baltic. Vol publication no 2. Horsholm, Denmark: Water Quality Institute.
Grasshoff K, Kremling K, Ehrhardt M. (1999). Methods of seawater analysis. Weinheim: Wiley-VCH.
Hernroth L. (1985). Recommendations on Methods for Marine Biological Studies in the Baltic Sea: Mesozooplankton Biomass Assessment ; Individual Volume Technique. Lysekil: Institute of Marine Research.
Jobling M. (1995). Fish bioenergetics. Oceanographic Literature Review 9, 785, 
Lee S, Fuhrman JA. (1987). Relationships between biovolume and biomass of naturally dervied marine bacterioplankton. Applied and Environmental Microbiology 53, 1298-1303,  doi: 10.1128/aem.53.6.1298-1303.1987
Lignell R, Hoikkala L, Lahtinen T. (2008). Effects of inorganic nutrients, glucose and solar radiation on bacterial growth and exploitation of dissolved organic carbon and nitrogen in the northern Baltic Sea. Aquatic Microbial Ecology 51, 209-221,  doi: 10.3354/ame01202
Marie D, Simon N, Vaulot D. (2005) Phytoplankton cell counting by flow cytometry. In: Andersen RA, editor. Algal culturing techniques. Academic Press, San Diego, CA, pp. 253–267.
Meier HEM. (2006). Baltic Sea climate in the late twenty-first century: a dynamical downscaling approach using two global models and two emission scenarios. Climate Dynamics 27, 39-68,  doi: 10.1007/s00382-006-0124-x
Menden-Deuer S, Lessard EJ. (2000). Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnology and Oceanography 45, 569-579,  doi: https://doi.org/10.4319/lo.2000.45.3.0569
Neumann T. (2010). Climate-change effects on the Baltic Sea ecosystem: A model study. Journal of Marine Systems 81, 213-224,  doi: http://dx.doi.org/10.1016/j.jmarsys.2009.12.001
Olenina I, Hajdu S, Edler L, Andersson A, Wasmund N, Busch S, et al. (2006). Biovolumes and size-classes of phytoplankton in the Baltic Sea. HELCOM Balt. Sea Environ. Proc. 106
Ripszam M, Gallampois CMJ, Berglund Å, Larsson H, Andersson A, Tysklind M, et al. (2015). Effects of predicted climatic changes on distribution of organic contaminants in brackish water mesocosms. Science of The Total Environment 517, 10-21,  doi: http://dx.doi.org/10.1016/j.scitotenv.2015.02.051
Solorzano L, Sharp JH. (1980). Determination of total dissolved phosphorus and particulate phosphorus in natural waters1. Limnology and Oceanography 25, 754-758,  doi: https://doi.org/10.4319/lo.1980.25.4.0754
Stepanauskas R, Jorgensen NOG, Eigaard OR, Zvikas A, Tranvik LJ, Leonardson L. (2002). Summer inputs of riverine nutrients to the Baltic Sea: Bioavailability and eutrophication relevance. Ecological Monographs 72, 579-597,  doi: 10.2307/3100058
Sundkvist AM, Olofsson U, Haglund P. (2010). Organophosphorus flame retardants and plasticizers in marine and fresh water biota and in human milk. Journal of Environmental Monitoring 12, 943-951,  doi: 10.1039/B921910B
Utermohl H. (1958). Zur vervollkommnung der quantitativen phytoplankton-methodik. Internationale Vereinigung für theoretische und angewandte Limnologie: Mitteilungen 9, 1-38,  doi: 10.1080/05384680.1958.11904091
Wikner J, Hagstrom A. (1999). Bacterioplankton intra-anual variability: importance of hydrography and competition. Aquatic Microbial Ecology 20, 245-260, 



Methods for processing the data: <describe how the submitted data were generated from the raw or collected data>

To account for potential differences in ciliate and mesozooplankton abundances, due to the variations in the communities of the root sample (day -4), the relative change in ciliate and mesozooplankton biomass and abundance were calculated:
Relative change (delta)=  ((t_x- t-4))/t-4) 
where tx is the biomass/abundance at the time of interest and t-4 is the biomasss/abundance at the start of the experiment (day -4). 

Food web efficiency (FWE), defined as the cumulative annual production rate of fish over the pelagic primary and heterotrophic bacterial production was determined as the ratio between fish production (Fishp) and total basal production (PP + BP) (Rand and Stewart, 1998):
FWE=Fishp/(PP+BP)
where fish production, PP and BP estimates were expressed as mg C L-1 d-1. Fish production was estimated as the total mass change of all surviving individuals assuming carbon content to be 20% of the wet weight (Jobling, 1995), and basal production was estimated as the average BP + PP production during the time when fish was present in the mesocosms.

Bioaccumulation factors (BAF) were calculated using lipid normalized (BAFl) and wet weight (BAFww) concentrations in fish and soluble MPs in the sub-0.7 µm fraction:
BAF=  MP concentration in fish (based on wet weight or lipid weight)/MP concentration in water


References: 

Rand PS, Stewart DJ. (1998). Prey fish exploitation, salmonine production, and pelagic food web efficiency in Lake Ontario. Canadian Journal of Fisheries and Aquatic Sciences 55, 318-327,  doi: 10.1139/f97-254




Instrument- or software-specific information needed to interpret the data: <include full name and version of software, and any necessary packages or libraries needed to run scripts>

Not applicable

Standards and calibration information, if appropriate: Se description of methods; Chemical analyses, MPs


Environmental/experimental conditions: Se description of methods; experimental setup and treatments



Describe any quality-assurance procedures performed on the data: 
See standards and calibration information



People involved with sample collection, processing, analysis and/or submission: 
Sample collection: Asa Berglund, Christine Gallampois, Matyas Ripszam, Daniela Figueroa, Henrik Larsson, Evelina Griniene, Elena Gorokhova, Agneta Andersson.
Data processing: Asa Berglund 
Analysis and submission: Asa Berglund, Christine Gallampois, Matyas Ripszam, Henrika Larsson, Daniela Figueroa, Evelina Griniene, Par Bystrom, Elena Gorokhova, Peter Haglund, Agneta Andersson and Mats Tysklind. 



DATA-SPECIFIC INFORMATION FOR: 
Biological data.txt

Number of variables: 35

Number of cases/rows: 120


Variable List: 
time: sampling time in relation to the day of micropollutant addition (day 0)
date: sampling date
temp: temperature treatment in the mesocosm (15 or 18°C)
doc: DOC treatment in the mesocosms (0 = no DOC addition of DOC, 1 = DOC addition)
pop: Micropollutant treatment in mesocosms (0 = no addition of micropollutants, 1 = addition of pollutants)
meso: individual numbering of mesocosms 
pool: identification of the pool that mesocosms were immersed in
DOC: Dissolved organic carbin content (mg L-1)
DIP: Dissolved inorganic phosphorous (µg L-1)
Ptot: Total phosphorous (µg L-1)
Ntot: Total nitrogen (µg L-1)
DIN: Dissolved organic nitrogen (ammonium, nitrate and nitrite; µg L-1)
pH: potential of hydrogen
POP4: particulate organic phosphorous (µg L-1)
PP: Primary production (µmol L-1 day-1)
BP: Heterotrophic bacterial production (µmol L-1 day-1)
Basalprod: Total basal production (PP + BP)(µmol L-1 day-1)
Chl.a: Chlorophyll a (µmol L-1)
Phyto: Phytoplankton (µg C L-1)
BA: Bacterial abundance (individuals µL-1)
Flag.abund: Flagellate abundance (106 individuals/cells L-1)
Cil.abund: Ciliate abundance (103 individuals L-1)
Zoopl.abund: Mesozooplankton abundance (individuals L-1)
Flag.biomass: Flagellate biomass (µg C L-1)
Cil.biomass: Ciliate biomass (µg C L-1)
Zoopl.biomass: Mesozooplankton biomass (µg C L-1)
Cope.biom: Copepod biomass (µg C L-1)
Clad.biom: Cladocera biomass (µg C L-1)
Roti.biom: Rotifer biomass (µg C L-1)
Cope.abund: Copepod abundance (individuals L-1)
Clad.abund: Cladocera abundance (individuals L-1)
Roti.abund: Rotifer abundance (individuals L-1)
Cop_N_abund: Abundance of Copepod naupulii (individuals L-1)  
Cop_I_III_abund: Abundance of Copepod stages I to III (individuals L-1)  
Cop_IV_VI_abund: Abundance of Copepod stages IV to VI (individuals L-1)  

Missing data codes: NA = not analyzed, BDL = Below detection limit

Specialized formats or other abbreviations used: Not applicable

DATA-SPECIFIC INFORMATION FOR: 
PAR.txt

Number of variables: 8

Number of cases/rows: 72


Variable List:
time: sampling time in relation to the day of micropollutant addition (day 0)
date: sampling date
temp: temperature treatment in the mesocosm (15 or 18°C)
doc: DOC treatment in the mesocosms (0 = no DOC addition of DOC, 1 = DOC addition)
pop: Micropollutant treatment in mesocosms (0 = no addition of micropollutants, 1 = addition of pollutants)
meso: individual numbering of mesocosms 
pool: identification of the pool that mesocosms were immersed in
PAR: Photosynthetically active radiation (µmol Quanta m-2 s-1) 

Missing data codes: not applicable

Specialized formats or other abbreviations used: Not applicable


DATA-SPECIFIC INFORMATION FOR: 
Fish.txt

Number of variables: 9

Number of cases/rows: 23


Variable List: 
temp: temperature treatment in the mesocosm (15 or 18°C)
doc: DOC treatment in the mesocosms (0 = no DOC addition of DOC, 1 = DOC addition)
pop: Micropollutant treatment in mesocosms (0 = no addition of micropollutants, 1 = addition of pollutants)
meso: individual numbering of mesocosms 
pool: identification of the pool that mesocosms were immersed in
biomass: fish biomass at the end of the experiment (mg)
surv: number of surviving fish 
dead: number of dead fish
fwe: Food Web Efficiency (%)


Missing data codes: not applicable

Specialized formats or other abbreviations used: Not applicable

DATA-SPECIFIC INFORMATION FOR: 
POP_water.txt

Number of variables: 29

Number of cases/rows: 12


Variable List: 
temp: temperature treatment in the mesocosm (15 or 18°C)
doc: DOC treatment in the mesocosms (0 = no DOC addition of DOC, 1 = DOC addition)
pop: Micropollutant treatment in mesocosms (0 = no addition of micropollutants, 1 = addition of pollutants)
meso: individual numbering of mesocosms
pool: identification of the pool that mesocosms were immersed in
HCBD: soluble hexachlorobutadiene concentration in water (ng L-1)
4.bromophenol: soluble 4-bromophenol concentration in water (ng L-1)
4.bromoaniline: soluble 4-bromoaniline concentration in water (ng L-1)
pentaCBz: soluble pentachlorobenzene concentration in water (ng L-1)
TBP: soluble tributyl phosphate concentration in water (ng L-1)
ethoprophos: soluble ethoprophos concentration in water (ng L-1)
trifluralin: soluble trifluralin concentration in water (ng L-1)
HCH.alfa: soluble alpha hexachlorocychlohexane concentration in water (ng L-1)
HCH.gamma: soluble gamma hexachlorocychlohexane concentration in water (ng L-1)
hexaCBz: soluble alpha hexachlorobenzoene concentration in water (ng L-1)
2.4.6.tribromoaniline: soluble 2,4,6- tribromoaniline concentration in water (ng L-1)
phenanthrene: soluble phenanthrene concentration in water (ng L-1)
anthracene: soluble anthracene concentration in water (ng L-1)
PCB28: soluble polychlorinated biphenyl 28 concentration in water (ng L-1)
chlorothal.dimethyl: soluble chlorothal dimethyl concentration in water (ng L-1)
pendimethalin: soluble pendimethalin concentration in water (ng L-1)
trans.chlorfenvinfos: soluble trans-chlorfenvinfos concentration in water (ng L-1)
picoxystrobin: soluble picoxystrobin concentration in water (ng L-1)
endosulfan.I: soluble endosulfan I concentration in water (ng L-1)
mitotane: soluble mitotane (DDE) concentration in water (ng L-1)
endosulfan.II: soluble endosulfan II concentration in water (ng L-1)
TDCIPP: soluble tris(1,3-diisopropyl)phosphate concentration in water (ng L-1)
TPP: soluble triphenyl phosphate concentration in water (ng L-1)
diflufenican: soluble diflufenican concentration in water (ng L-1)


Missing data codes: BDL = Below detection limit after correction for the blak

Specialized formats or other abbreviations used: Not applicable

DATA-SPECIFIC INFORMATION FOR: 
POP_fish.txt

Number of variables: 29

Number of cases/rows: 12


Variable List: 
temp: temperature treatment in the mesocosm (15 or 18°C)
doc: DOC treatment in the mesocosms (0 = no DOC addition of DOC, 1 = DOC addition)
pop: Micropollutant treatment in mesocosms (0 = no addition of micropollutants, 1 = addition of pollutants)
meso: individual numbering of mesocosms
pool: identification of the pool that mesocosms were immersed in
HCBD: soluble hexachlorobutadiene concentration in fish (pg g-1)
4.bromophenol: soluble 4-bromophenol concentration in fish (pg g-1)
4.bromoaniline: soluble 4-bromoaniline concentration in fish (pg g-1)
pentaCBz: soluble pentachlorobenzene concentration in fish (pg g-1)
TBP: soluble tributyl phosphate concentration in fish (pg g-1)
ethoprophos: soluble ethoprophos concentration in fish (pg g-1)
trifluralin: soluble trifluralin concentration in water fish (pg g-1)
HCH.alfa: soluble alpha hexachlorocychlohexane concentration in fish (pg g-1)
HCH.gamma: soluble gamma hexachlorocychlohexane concentration in fish (pg g-1)
hexaCBz: soluble alpha hexachlorobenzoene concentration in fish (pg g-1)
2.4.6.tribromoaniline: soluble 2,4,6- tribromoaniline concentration in fish (pg g-1)
phenanthrene: soluble phenanthrene concentration in fish (pg g-1)
anthracene: soluble anthracene concentration in fish (pg g-1)
PCB28: soluble polychlorinated biphenyl 28 concentration in fish (pg g-1)
chlorothal.dimethyl: soluble chlorothal dimethyl concentration in fish (pg g-1)
pendimethalin: soluble pendimethalin concentration in fish (pg g-1)
trans.chlorfenvinfos: soluble trans-chlorfenvinfos concentration in fish (pg g-1)
picoxystrobin: soluble picoxystrobin concentration in fish (pg g-1)
endosulfan.I: soluble endosulfan I concentration in fish (pg g-1)
mitotane: soluble mitotane (DDE) concentration in fish (pg g-1)
endosulfan.II: soluble endosulfan II concentration in fish (pg g-1)
TDCIPP: soluble tris(1,3-diisopropyl)phosphate concentration in fish (pg g-1)
TPP: soluble triphenyl phosphate concentration in fish (pg g-1)
diflufenican: soluble diflufenican concentration in fish (pg g-1)

Missing data codes: BDL = Below detection limit after correction for the blak, NA = not analyzed (sample lost during extraction)

Specialized formats or other abbreviations used: Not applicable


