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  • 2
    Publication Date: 2023-10-28
    Description: Antarctic sea surface microlayer (SML) and bulk water samples were collected during the PI-ICE campaign from January until March 2019 at the west coast of the Antarctic Peninsula. SML samples were collected using the glass plate technique, corresponding bulk (subsurface) samples were collected by submerging a plastic bottle below the sea surface. Following chemical parameters were determined: dissolved organic carbon (DOC), particulate organic carbon (POC), total chlorophyll-a, main inorganic ions (chloride, sulfate, sodium, etc.), dissolved free carbohydrates (DFCHO), dissolved combined carbohydrates (DCCHO) and particulate combined carbohydrates (PCCHO). DCCHO and DFCHO were measured from filtered (0.2 µm) seawater after a desalination using electro-dialysis and high-performance anion exchange chromatography coupled with pulsed amperometric detection (HPAEC-PAD). PCCHO were measured from filters (0.2 µm polycarbonate membrane). DFCHO, DCCHO and PCCHO were determined as the sum of individual monosaccharides (e.g. arabinose, glucose, galactose, glucosamine, galactosamine, muramic acid, galacturonic acid, etc.). More details about the analytical procedures can be found in the manuscript. These data were collected in order to improve the understanding of the sea-air transfer of carbohydrates in this pristine environment. A corresponding data set for size-resolved aerosol particles can be found under following doi number (doi:10.1594/PANGAEA.927565).
    Keywords: AC3; Ammonium; Antarctic Peninsula; Arabinose; Arctic Amplification; Bromide; Calcium; carbohydrates; Carbohydrates, dissolved, neutral free; Carbohydrates, dissolved combined; Carbohydrates, particulate hydrolyzable; Carbon, organic, dissolved; Carbon, organic, particulate; chloride; Chloride; Chlorophyll a; Chlorophyll a, epimer; Chlorophyll a, total; Chlorophyll a allomers; DATE/TIME; DEPTH, water; dissolved; DOC; Event label; Fluoride; Formic acid; Fructose; Fucose; Galactosamine; Galactose; Galacturonic acid; Glucosamine; Glucose; Glucuronic acid; Hespérides; LATITUDE; Livingston Island; LONGITUDE; Magnesium; Mannose; Monosaccharides; Muramic acid; Nitrate; Nitrite; Oxalate; particulate; Phosphate; PI-ICE; PI-ICE_WS1; PI-ICE_WS13; PI-ICE_WS14; PI-ICE_WS15; PI-ICE_WS16; PI-ICE_WS17; PI-ICE_WS18; PI-ICE_WS19; PI-ICE_WS2; PI-ICE_WS20; PI-ICE_WS21; PI-ICE_WS3; PI-ICE_WS4; PI-ICE_WS5; PI-ICE_WS6; PI-ICE_WS7; PI-ICE_WS8; PI-ICE campaign; POC; Potassium; Rhamnose; Sample code/label; SML; sodium; Sodium; sugars; Sulfate; surface microlayer; Total chlorophyll; Water sample; WS; Xylose
    Type: Dataset
    Format: text/tab-separated-values, 2011 data points
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  • 3
    Publication Date: 2023-12-06
    Description: Ein Stück Schlamm. Grau, leblos, kalt. Feine Linien ziehen sich hindurch und ein Faden verläuft in einer Zickzack-Linie über die Oberfläche. Es ist eine Sedimentprobe aus dem Pazifischen Ozean, die Stück für Stück ihre Geheimnisse preisgibt. Dr. Lars Max analysiert Sedimentkerne am Alfred-Wegener-Institut und erzählt von der Geschichte unserer Erde und unseres Klimas. Was können wir aus der „grauen“ Vergangenheit lernen?
    Keywords: ANT-XXIX/4; File format; File name; File size; GC; Gravity corer; Helmholtz-Verbund Regionale Klimaänderungen = Helmholtz Climate Initiative (Regional Climate Change); Polarstern; PS81; PS81/265-1; REKLIM; South Atlantic Ocean; Uniform resource locator/link to movie
    Type: Dataset
    Format: text/tab-separated-values, 8 data points
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  • 4
    Publication Date: 2024-02-14
    Description: This data set composes a large amount of quality controlled in situ measurements of major pigments based on HPLC collected from various expeditions across the Atlantic Ocean spanning from 71°S to 84°N, including 11 expeditions with RV Polarstern from the North Atlantic to the Arctic Fram Strait: PS74, PSS76, PS78, PS80, PS85, PS93.2 (https://doi.org/10.1594/PANGAEA.894872), PS99.1 (https://doi.org/10.1594/PANGAEA.905502), PS99.2 ( https://doi.org/10.1594/PANGAEA.894874), PS106 (https://doi.org/10.1594/PANGAEA.899284), PS107 (https://doi.org/10.1594/PANGAEA.894860), PS121 (https://doi.org/10.1594/PANGAEA.941011), four expeditions (two with RV Polarstern and two Atlantic Meridional Transect expeditions with RRS James Clark Ross and RRS Discovery) in the trans-Atlantic Ocean: PS113 ( https://doi.org/10.1594/PANGAEA.911061), PS120, AMT28 and AMT29, and one expedition with RV Polarstern in the Southern Ocean: PS103 (https://doi.org/10.1594/PANGAEA.898941). Chlorophyll a concentration (Chl-a) of six phytoplankton functions groups (PFTs) derived from these pigments have been also included. This published data set has contributed to validate satellite PFT products available on the EU funded Copernicus Marine Service (CMEMS, https://marine.copernicus.eu/), which are derived from multi-sensor ocean colour reflectance data and sea surface temperature using an empirical orthogonal function based approach (Xi et al. 2020; 2021). Description on in situ PFT Chl-a determination from pigment data: PFT Chl-a in this data set were derived using an updated diagnostic pigment analysis (DPA) method (Soppa et al., 2014; Losa et al., 2017) with retuned coefficients by Alvarado et al (2021), that was originally developed by Vidussi et al. (2001), adapted in Uitz et al. (2006) and further refined by Hirata et al. (2011) and Brewin et al. (2015). The values of retuned DPA weighting coefficients for PFT Chl-a determination are: 1.56 for fucoxanthin, 1.53 for peridinin, 0.89 for 19'-hexanoyloxyfucoxanthin, 0.44 for 19'-butanoyloxyfucoxanthin, 1.94 for alloxanthin, 2.63 for total chlorophyll b, and 0.99 for zeaxanthin. The coefficient retuning was based on an updated global HPLC pigment data base for the open ocean (water depth 〉200 m), which was compiled based on the previously published data sets spanning from 1988 to 2012 described in Losa et al. (2017), with updates in Xi et al. (2021) and Álvarez et al. (2022), by adding other newly available HPLC pigment data collected between 2012 and 2018 mainly from SeaBASS (https://seabass.gsfc.nasa.gov/), PANGAEA, British Oceanographic Data Centre (BODC, https://www.bodc.ac.uk/), and Australian Open Access to Ocean Data (AODN, https://portal.aodn.org.au/) (as of February 2020, see Table 1 attached in the 'Additional metadata' for more details on the data sources).
    Keywords: 19-Butanoyloxyfucoxanthin; 19-Hexanoyloxyfucoxanthin; AC3; Alloxanthin; AMT28; AMT28_10-33; AMT28_1-1; AMT28_11-36; AMT28_12-41; AMT28_13-44; AMT28_14-48; AMT28_15-50; AMT28_16-57; AMT28_17-58; AMT28_18-64; AMT28_19-66; AMT28_20-71; AMT28_21-73; AMT28_22-78; AMT28_23-80; AMT28_2-4; AMT28_24-85; AMT28_25-87; AMT28_27-93; AMT28_28-95; AMT28_29-100; AMT28_30-101; AMT28_31-105; AMT28_32-111; AMT28_33-112; AMT28_34-117; AMT28_35-120; AMT28_36-124; AMT28_37-126; AMT28_3-8; AMT28_38-133; AMT28_40-137; AMT28_4-11; AMT28_41-142; AMT28_43-147; AMT28_44-150; AMT28_45-155; AMT28_46-158; AMT28_47-164; AMT28_48-166; AMT28_49-174; AMT28_50-176; AMT28_51-181; AMT28_5-13; AMT28_52-183; AMT28_53-188; AMT28_54-190; AMT28_55-198; AMT28_56-199; AMT28_57-204; AMT28_58-206; AMT28_59-210; AMT28_59-212; AMT28_61-218; AMT28_6-17; AMT28_62-220; AMT28_63-226; AMT28_64-227; AMT28_65-232; AMT28_66-234; AMT28_7-21; AMT28_8-24; AMT28_9-28; AMT29; AMT29_AA; AMT29_AB; AMT29_AC; AMT29_AD; AMT29_AE; AMT29_AF; AMT29_AG; AMT29_AH; AMT29_AI; AMT29_AJ; AMT29_AK; AMT29_AL; AMT29_AM; AMT29_AN; AMT29_AO; AMT29_AP; AMT29_AQ; AMT29_AR; AMT29_AS; AMT29_AV; AMT29_AX; AMT29_BC; AMT29_BD; AMT29_BE; AMT29_BF; AMT29_BG; AMT29_BH; AMT29_BI; AMT29_BJ; AMT29_BK; AMT29_BL; AMT29_BM; AMT29_BN; AMT29_BO; AMT29_BP; AMT29_BQ; AMT29_BR; AMT29_BS; AMT29_BT; AMT29_BU; AMT29_BV; AMT29_BW; AMT29_BX; AMT29_BY; AMT29_BZ; AMT29_CA; AMT29_CB; AMT29_CC; AMT29_CD; AMT29_CE; AMT29_CF; AMT29_CG; AMT29_CH; AMT29_CJ; AMT29_CK; AMT29_CL; AMT29_CM; AMT29_CN; AMT29_CO; AMT29_CP; AMT29_CQ; AMT29_CR; AMT29_CS; AMT29_CT; AMT29_CTD_001; AMT29_CTD_002; AMT29_CTD_003; AMT29_CTD_004; AMT29_CTD_005; AMT29_CTD_006; AMT29_CTD_007; AMT29_CTD_008; AMT29_CTD_009; AMT29_CTD_010; AMT29_CTD_011; AMT29_CTD_013; AMT29_CTD_015; AMT29_CTD_016; AMT29_CTD_017; AMT29_CTD_018; AMT29_CTD_019; AMT29_CTD_020; AMT29_CTD_021; AMT29_CTD_022; AMT29_CTD_024; AMT29_CTD_025; AMT29_CTD_026; AMT29_CTD_027; AMT29_CTD_028; AMT29_CTD_029; AMT29_CTD_030; AMT29_CTD_031; AMT29_CTD_032; AMT29_CTD_034; AMT29_CTD_035; AMT29_CTD_036; AMT29_CTD_037; AMT29_CTD_038; AMT29_CTD_039; AMT29_CTD_041; AMT29_CTD_042; AMT29_CTD_043; AMT29_CTD_044; AMT29_CTD_045; AMT29_CTD_046; AMT29_CTD_047; AMT29_CTD_048; AMT29_CTD_049; AMT29_CTD_050; AMT29_CTD_051; AMT29_CTD_052; AMT29_CTD_053; AMT29_CTD_054; AMT29_CTD_055; AMT29_CU; AMT29_CV; AMT29_CW; AMT29_CX; AMT29_CY; AMT29_CZ; AMT29_DA; AMT29_DB; AMT29_DC; AMT29_DD; AMT29_DE; AMT29_DF; AMT29_DG; AMT29_DH; AMT29_DI; AMT29_DJ; AMT29_DK; AMT29_DL; AMT29_DM; AMT29_DN; AMT29_DO; AMT29_DP; AMT29_DQ; AMT29_DR; AMT29_DS; AMT29_DT; AMT29_DU; AMT29_DV; AMT29_DZ; AMT29_EB; AMT29_EC; AMT29_EE; AMT29_EF; AMT29_EG; AMT29_EI; AMT29_EK; AMT29_EL; AMT29_EM; AMT29_EO; AMT29_EQ; AMT29_ER; AMT29_ES; AMT29_ET; AMT29_EV; ANT-XXXII/2; ANT-XXXIII/4; Arctic Amplification; Arctic Ocean; ARK-XXIV/1; ARK-XXIV/2; ARK-XXIX/2.2; ARK-XXV/1; ARK-XXV/2; ARK-XXVI/1; ARK-XXVII/1; ARK-XXVII/2; ARK-XXVIII/2; ARK-XXX/1.1; ARK-XXX/1.2; ARK-XXXI/1.1,PASCAL; ARK-XXXI/1.2; ARK-XXXI/2; AWI_BioOce; Barents Sea; Biological Oceanography @ AWI; Campaign; Canarias Sea; chlorophyll; Chlorophyll a; Chlorophyll a, Diatoms; Chlorophyll a, Dinoflagellata; Chlorophyll a, Green algae; Chlorophyll a, Haptophyta; Chlorophyll a, Prochlorococcus; Chlorophyll a, Prokaryotes; Chlorophyll a + Divinyl chlorophyll a + Chlorophyllide a; Chlorophyll b + Divinyl chlorophyll b + Chlorophyllide b; Chlorophyllide a; CT; CTD, towed system; CTD/Rosette; CTD/Rosette with Underwater Vision Profiler; CTD001; CTD002; CTD003; CTD004; CTD005; CTD006; CTD007; CTD008; CTD009; CTD010; CTD011; CTD012; CTD013; CTD014; CTD015; CTD016; CTD017; CTD018; CTD019; CTD020; CTD021; CTD022; CTD023; CTD024; CTD025; CTD026; CTD027; CTD028; CTD029; CTD030; CTD031; CTD032; CTD033; CTD034; CTD035; CTD036; CTD037; CTD038; CTD039; CTD040; CTD041; CTD042; CTD043; CTD044; CTD045; CTD046; CTD047; CTD048; CTD049; CTD050; CTD051; CTD052; CTD053; CTD054; CTD055; CTD056; CTD057; CTD058; CTD059; CTD060; CTD061; CTD062; CTD063; CTD-Acoustic Doppler Current Profiler; CTD-ADCP; CTD-RO; CTD-RO_UVP; CTD-twoyo; DATE/TIME; DEPTH, water; Diagnostic Pigment Analysis (DPA); Discovery (2013); Divinyl chlorophyll a; DPA; DY110; EG_I; EG_II; EG_III; EG_IV; Event label; Exploitation of Sentinel-5-P for Ocean Colour Products; FRAM; FRontiers in Arctic marine Monitoring; Fucoxanthin; Global Long-term Observations of Phytoplankton Functional Types from Space; GLOPHYTS; Hand net; HG_I; HG_II; HG_III; HG_IV; HG_IX; HG_V; HG_VI; HG_VIII; HGIV; High Performance Liquid Chromatography (HPLC); HN; HPLC; ICE; Ice station; James Clark Ross; JR18001; Kb0; LATITUDE; Lazarev Sea; LONGITUDE; N3; N4; N5; North Greenland Sea; North Sea; Norwegian Sea; ORDINAL NUMBER; Peridinin; phytoplankton functional types; pigments; Polarstern; PORTWIMS; Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation; PS103; PS103_0_Underway-3; PS103_1-1; PS103_11-1; PS103_15-1; PS103_22-5; PS103_23-5; PS103_2-4; PS103_27-2; PS103_29-3; PS103_3-1; PS103_31-2; PS103_34-6; PS103_39-3; PS103_40-3; PS103_4-1; PS103_43-4; PS103_45-3; PS103_48-1; PS103_5-2; PS103_59-2; PS103_6-6; PS103_67-1; PS103_8-3; PS103_9-1; PS106_18-2; PS106_21-2; PS106_27-6; PS106_28-2; PS106_31-2; PS106_32-2; PS106_45-1; PS106_50-1; PS106_ZODIAK_170527; PS106_ZODIAK_170529; PS106_ZODIAK_170531; PS106_ZODIAK_170601; PS106_ZODIAK_170607; PS106_ZODIAK_170608; PS106_ZODIAK_170617; PS106_ZODIAK_170618; PS106_ZODIAK_170619; PS106_ZODIAK_170624; PS106_ZODIAK_170625; PS106_ZODIAK_170626; PS106_ZODIAK_170627; PS106_ZODIAK_170629; PS106_ZODIAK_170630; PS106_ZODIAK_170701; PS106_ZODIAK_170702; PS106_ZODIAK_170703; PS106_ZODIAK_170705; PS106_ZODIAK_170706; PS106_ZODIAK_170708; PS106_ZODIAK_170709; PS106_ZODIAK_170710; PS106_ZODIAK_170711; PS106_ZODIAK_170713; PS106_ZODIAK_170714; PS106_ZODIAK_170715; PS106/1; PS106/2; PS107; PS107_0_underway-9; PS107_10-4; PS107_12-3; PS107_14-1; PS107_16-3; PS107_18-3; PS107_19-1; PS107_20-8; PS107_21-1; PS107_22-6; PS107_24-1; PS107_28-1; PS107_29-1; PS107_33-6; PS107_34-5; PS107_36-1; PS107_37-1; PS107_40-2; PS107_40-3; PS107_40-4; PS107_40-5; PS107_40-6; PS107_48-1; PS107_6-8; PS107_7-1; PS107_8-1; PS113; PS113_0_underway-5; PS113_11-2; PS113_1-2; PS113_13-2; PS113_14-2; PS113_15-1; PS113_17-2; PS113_18-2; PS113_20-1; PS113_21-1; PS113_22-2; PS113_23-2; PS113_25-1; PS113_26-2; PS113_27-1; PS113_28-1; PS113_29-2; PS113_30-2; PS113_31-1; PS113_3-2; PS113_33-1; PS113_5-2; PS113_6-2; PS113_7-2; PS113_9-2; PS120; PS120_0_underway-10; PS120_11-3; PS120_15-3; PS120_19-3; PS120_20-1; PS120_21-3; PS120_24-3; PS120_3-1; PS120_5-3; PS120_8-3; PS121; PS121_0_Underway-65; PS121_1-2; PS121_12-2; PS121_15-1; PS121_16-5; PS121_24-2; PS121_25-2; PS121_27-2; PS121_28-4; PS121_29-1; PS121_32-2; PS121_33-2; PS121_34-1; PS121_35-3; PS121_36-1; PS121_38-1; PS121_39-1; PS121_40-3; PS121_43-7; PS121_44-3; PS121_45-1; PS121_52-2; PS121_52-6; PS121_5-3; PS121_7-3; PS74; PS74/104-1; PS74/107-1; PS74/108-1; PS74/112-1; PS74/119-1; PS74/120-1; PS74/127-1; PS74/128-1; PS74/132-1; PS74/133-1; PS74/134-1; PS74/1-track; PS74/2-track; PS76; PS76/001-1; PS76/002-1; PS76/005-1; PS76/007-2; PS76/009-1; PS76/017-1; PS76/020-1; PS76/025-1; PS76/026-1; PS76/030-1; PS76/034-3; PS76/039-1; PS76/041-1; PS76/044-1; PS76/049-1; PS76/051-1; PS76/057-1; PS76/058-1; PS76/062-1; PS76/064-1; PS76/068-1; PS76/072-1; PS76/080-1; PS76/082-1; PS76/094-1; PS76/098-1; PS76/102-1; PS76/109-3; PS76/110-1; PS76/111-1; PS76/120-2; PS76/121-1; PS76/122-1; PS76/124-3; PS76/129-1; PS76/132-1; PS76/134-1; PS76/135-1; PS76/136-1; PS76/138-1; PS76/139-1; PS76/157-1; PS76/159-2; PS76/166-1; PS76/167-1; PS76/170-2; PS76/173-1; PS76/174-1; PS76/175-1; PS76/176-1; PS76/178-1; PS76/179-3; PS76/181-1; PS76/182-1; PS76/184-1; PS76/185-1; PS76/194-1; PS76/200-1; PS76/201-1; PS76/203-1; PS76/204-1; PS76/208-5; PS76/210-2; PS76/211-1; PS76/216-1; PS76/220-1; PS76/223-1; PS76/224-1; PS76/227-3; PS76/229-1; PS76/231-1; PS76/233-1; PS76/235-
    Type: Dataset
    Format: text/tab-separated-values, 37522 data points
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  • 5
    Electronic Resource
    Electronic Resource
    s.l. : American Chemical Society
    Journal of medicinal chemistry 14 (1971), S. 834-836 
    ISSN: 1520-4804
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Woodbury, NY : American Institute of Physics (AIP)
    Applied Physics Letters 68 (1996), S. 2186-2188 
    ISSN: 1077-3118
    Source: AIP Digital Archive
    Topics: Physics
    Notes: We report on high efficiency frequency-conversion obtained by four-wave mixing in a single traveling-wave semiconductor optical amplifier. Efficiency in excess of 0 dB is demonstrated for frequency conversion up to 2 THz. Measurements of the signal to the amplified spontaneous emission background ratio are also presented. © 1996 American Institute of Physics.
    Type of Medium: Electronic Resource
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  • 7
    ISSN: 1520-4995
    Source: ACS Legacy Archives
    Topics: Biology , Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 8
    ISSN: 1365-2427
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: SUMMARY. 1. A hypothetical leech population with known initial density, initial weight, final weight and cohort production interval (CPI) was established. Production estimated by the size-frequency method for various growth patterns, mortalities, number of samples per CPI and number of size classes was compared with actual production estimated from daily growth and mortality by the increment-summation method. The population had either perfectly continuous reproduction or a perfectly synchronous cohort.2. When size-classes were delimited in order to equalize the time spent in each size class, the deviations from actual production increased with decreasing number of size-classes and increasing mortality. For a population with perfectly continuous reproduction, production was only overestimated by 32% with an extreme mortality of 2.0% day−1 and three size-classes. For a perfectly synchronous cohort, production was either underestimated or overestimated, depending on the first day of sampling. The deviations from actual production increased considerably with decreasing number of size-classes, increasing mortality and decreasing number of samples per CPI.3. Differences between actual and assumed growth patterns may give underestimates or overestimates of more than one order of magnitude at high mortalities and few size-classes. It is concluded that knowing the actual growth pattern, the size frequency method will give realistic estimates of production in cases when normal cohort methods cannot be used. The estimate can be improved significantly by increasing the number of size classes and the number of samples per CPI.
    Type of Medium: Electronic Resource
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  • 9
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Science Ltd, UK
    Freshwater biology 39 (1998), S. 0 
    ISSN: 1365-2427
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: 1. The macroinvertebrate fauna living on stones in the exposed stony littorals of thirty-nine Danish lakes were examined by multivariate numerical methods. The data were derived from 125 semi-quantitative samples and a species list of 126 taxa. The mean number of individuals per sample was 960, and among the most common taxa were Asellus aquaticus, Gammarus, Oulimnius, Tinodes, Cricotopus and Dicrotendipes.2. The total number of species and fourteen individual taxa were positively correlated to mean depth of the lakes and eleven taxa were correlated to the total phosphorus concentration. The Shannon diversity was negatively correlated to the chlorophyll a concentration ([Chl a]).3. Community patterns were examined by detrended correspondence analysis (DCA), and the relationship between species data and selected environmental variables was analysed by canonical correspondence analysis (CCA). Mean lake depth was found to be the strongest environmental variable in explaining the species data. The [Chl a] and Secchi depth also explained significant variation in the distribution of the stony littoral invertebrates. Wind fetch and relative exposure did not explain any variation in the faunal composition among sites.4. The abilities of the macroinvertebrates to predict the lake trophic state, expressed as log ([Chl a]), were explored by means of weighted averaging (WA) regression and calibration. Two tolerance-weighted WA models using inverse and classical regression for deshrinking are presented. The models were assessed by the root mean square error (RMSE) of prediction, using bootstrapping as cross validation, and by the correlation between observed and inferred log ([Chl a]). The model using inverse deshrinking had a RMSEboot = 0.41 and r2 = 0.63. By using classical regression, the predictability in the ends of the gradient was improved but the RMSE increased: RMSEboot = 0.46.5. Although the factors determining faunal distribution patterns in the Danish lowland lakes were highly multivariate and difficult to disentangle, it seems reasonable to use the WA estimated species optima and tolerances to [Chl a] in a bio-assessment model.
    Type of Medium: Electronic Resource
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  • 10
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    General and Comparative Endocrinology 31 (1977), S. 323-334 
    ISSN: 0016-6480
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Biology , Medicine
    Type of Medium: Electronic Resource
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