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Predicting baseline d13C signatures of a lake food
 

Predicting baseline d13C signatures of a lake food

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Stable Isotope Mass Spectrometry Users Group meeting 2011

Stable Isotope Mass Spectrometry Users Group meeting 2011

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    Predicting baseline d13C signatures of a lake food Predicting baseline d13C signatures of a lake food Presentation Transcript

    • Predicting baseline δ13C signatures of a lake food web using dissolved carbon dioxide Peter Smyntek & Jonathan Grey School of Biological & Chemical Sciences Queen Mary, University of London Stephen Maberly Lake Ecosystem Group Centre for Ecology & Hydrology
    • OutlineStable isotope analysis & lake food webs Archived samples patterns in δ13C & dissolved carbon dioxide (CO2(aq)) Model of isotopic fractionation during photosynthesisPractical applications for using CO2(aq) as a proxy for baseline δ13C
    • A stable isotope picture of a lake food web Pike Perch Arctic charrTrophic Level Indicator δ15N Baseline δ13C Macroinvertebrates Zooplankton Benthic Algae & Detritus Phytoplankton Near shore: -20‰ Offshore: -30‰ δ13C Carbon Source
    • Problem: δ13C signatures at the base of the food web can vary Affects interpretation of food web relationships Windermere offshore baseline δ13C values 2000 - 2005 -16 Monthly samples (May – Sept.) -20δ13C -24(‰) -28 -32 -36 Date What causes variation in baseline δ13C? Can it be predicted?
    • What causes variation in baseline δ13C? Isotopic discrimination during ε photosynthesis (εp) ≈ 15‰ Phytoplankton CO2(aq) δ13C = -25 to -30‰δ13C = -10 to -15‰ HCO3-(aq) δ13C = -1 to -6‰ εp can vary with: - algal species -algal growth rate - availability of CO2(aq) or HCO3-(aq) If variation in εp due to algal species & growth rate is low, can CO2(aq) predict baseline δ13C?
    • MethodsMeasured δ13C values of archived zooplankton samples in Windermere (May – Sept.; 1985 – 2010) Daphnia galeata – herbivore; represents algal δ13C Compared δ13C with biweekly average CO2(aq) concentrations to account for carbon turnover in zooplankton Compared with isotopic fractionation model based on algal physiology
    • Baseline δ13C vs. CO2(aq) in Windermere -16 y = -2.42ln(x) - 22.30 -20 R² = 0.72 -24 Threshold for active uptake ofδ13C (‰) dissolved inorganic carbon? -28 -32 -36 0 10 20 30 40 50 60 70 80 CO2(aq) (µmol L-1) µ
    • Carbon isotopic fractionation model (Cassar et al. 2006) δ13CO2(aq) +103 P Ci P’ Ccεp = ( δ13Cbaseline +103 -1 ) x103 = εt + (εfix - εt) x ε ( P Ci + µ C )( P’ Cc + µ C )εt = isotopic discrimination due to diffusion & active transport = 1‰εfix = isotopic discrimination due to enzymatic carboxylation = 27‰ Algal cell membraneIncorporates: Chloroplast µ1) Algal growth rate (µ) & cellular membrane carbon content (C)2) Permeability of the algal cell (P) δ13Corg Cc & chloroplast (P’) to CO2(aq) Ci3) CO2(aq) concentration in lake (Ci) P’ & in chloroplast (Cc) CO2(aq) P
    • Baseline δ13C vs. CO2(aq) in Windermere -16 y = -2.42ln(x) - 22.30 -20 R² = 0.72 -24δ13C (‰) -28 -32 -36 0 10 20 30 40 50 60 70 80 CO2(aq) (µmol L-1) µ
    • Baseline δ13C vs. CO2(aq) in Windermere -16 y = -2.42ln(x) - 22.30 -20 R² = 0.72 -24δ13C (‰) Model -28 -32 -36 0 10 20 30 40 50 60 70 80 CO2(aq) (µmol L-1) µ
    • Baseline δ13C vs. CO2(aq) in Windermere -16 y = -2.42ln(x) - 22.30 -20 R² = 0.72 -24δ13C (‰) Growth rate = 0.33 d-1 -28 Growth rate = 0.13 d-1 -32 -36 0 10 20 30 40 50 60 70 80 CO2(aq) (µmol L-1) µ
    • Model-predicted vs. Observed baseline δ13C in Windermere -16 y = 0.88x - 3.09 -20 R² = 0.70 Predicted δ13C (‰) -24Fractionation modelpredicts δ13C successfullyusing CO2(aq) -28Provides basis for using -32CO2(aq) as a proxy for δ13Cin productive lakes -36 -36 -32 -28 -24 -20 -16What are the practical applications? Observed δ13C (‰)
    • Practical ApplicationsSupplement direct measurements of baseline δ13C -16 -20 -24 δ13C (‰) -28 Observed -32 δ13C = -30‰ -36
    • Practical ApplicationsSupplement direct measurements of baseline δ13C -16 -20 -24 δ13C (‰) -28 δ13C = -27‰ Observed -32 δ13C = -30‰ -36
    • Practical ApplicationsSupplement direct measurements of baseline δ13C -16 -20 -24 δ13C = -26.5‰ δ13C (‰) Modelled -28 δ13C = -27‰ Observed -32 δ13C = -30‰ -36
    • Practical Applications Estimate and evaluate variation in baseline δ13C -16 Measured standard deviations (May – Sept.) -20 ranged from 0.8 – 4.5‰δ13C -24(‰) -28 -32 -36 Year
    • Practical Applications Estimate and evaluate variation in baseline δ13C -16 Modelled standard deviations (May – Sept.) Modelled -20 ranged from 0.3 – 4.0‰ Observed -24δ13C(‰) -28 -32 -36 Year
    • SummaryCO2(aq) can predict baseline δ13C in productive lakesIsotopic fractionation model indicates δ13C vs. CO2(aq) relationship is consistent with algal physiologyCO2(aq) monitoring can supplement δ13C measurements and improve estimates of temporal variation
    • Acknowledgements• CEH Lake Ecosystem Group - especially: Ian Winfield, Steve Thackeray, Ian Jones, Mitzi DeVille, Ben James, Janice Fletcher, Alex Elliott, Jack Kelly & Heidrun Feuchtmayr• QMUL: Ian Sanders, Nicola Ings and Michelle Jackson• CEH Lancaster: Helen Grant• Freshwater Biological Association• Natural Environment Research Council (NERC)