Predicting baseline d13C signatures of a lake food
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
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)