This document summarizes a study that used dissolved carbon dioxide (CO2(aq)) concentrations to predict baseline carbon isotope (δ13C) signatures in lake food webs. The researchers developed an isotopic fractionation model showing that δ13C values in phytoplankton and zooplankton were consistent with measured CO2(aq) levels. Measured and modeled δ13C values were highly correlated. This relationship allows using CO2(aq) as a proxy to estimate baseline δ13C and its variation over time, supplementing direct isotope measurements. The model improves understanding of factors influencing isotopic signatures at the base of lake food webs.

1. 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

2. Outline
Stable isotope analysis & lake food webs
Archived samples patterns in δ13C &
dissolved carbon dioxide (CO2(aq))
Model of isotopic fractionation during
photosynthesis
Practical applications for using CO2(aq) as a
proxy for baseline δ13C

3. A stable isotope picture of a lake food web
Pike
Perch Arctic charr
Trophic Level Indicator
δ15N
Baseline δ13C
Macroinvertebrates Zooplankton
Benthic Algae
& Detritus Phytoplankton
Near shore: -20‰ Offshore: -30‰
δ13C
Carbon Source

4. 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.)
-20
δ13C -24
(‰) -28
-32
-36
Date
What causes variation in baseline δ13C?
Can it be predicted?

5. 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?

6. Methods
Measured 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

7. Baseline δ13C vs. CO2(aq) in Windermere
-16
y = -2.42ln(x) - 22.30
-20 R² = 0.72
-24
Threshold for active uptake of
δ13C (‰) dissolved inorganic carbon?
-28
-32
-36
0 10 20 30 40 50 60 70 80
CO2(aq) (µmol L-1)
µ

8. Carbon isotopic fractionation model (Cassar et al. 2006)
δ13CO2(aq) +103 P Ci P’ Cc
εp = ( 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
membrane
Incorporates: Chloroplast
µ
1) Algal growth rate (µ) & cellular membrane
carbon content (C)
2) Permeability of the algal cell (P) δ13Corg Cc
& chloroplast (P’) to CO2(aq)
Ci
3) CO2(aq) concentration in lake (Ci) P’
& in chloroplast (Cc) CO2(aq)
P

9. Baseline δ13C vs. CO2(aq) in Windermere
-16
y = -2.42ln(x) - 22.30
-20 R² = 0.72
-24
δ13C (‰)
-28
-32
-36
0 10 20 30 40 50 60 70 80
CO2(aq) (µmol L-1)
µ

10. Baseline δ13C vs. CO2(aq) in Windermere
-16
y = -2.42ln(x) - 22.30
-20 R² = 0.72
-24
δ13C (‰) Model
-28
-32
-36
0 10 20 30 40 50 60 70 80
CO2(aq) (µmol L-1)
µ

11. Baseline δ13C vs. CO2(aq) in Windermere
-16
y = -2.42ln(x) - 22.30
-20 R² = 0.72
-24
δ13C (‰) 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

12. Model-predicted vs. Observed baseline δ13C in Windermere
-16
y = 0.88x - 3.09
-20
R² = 0.70
Predicted
δ13C (‰)
-24
Fractionation model
predicts δ13C successfully
using CO2(aq) -28
Provides basis for using
-32
CO2(aq) as a proxy for δ13C
in productive lakes
-36
-36 -32 -28 -24 -20 -16
What are the practical applications? Observed δ13C (‰)

13. Practical Applications
Supplement direct measurements of baseline δ13C
-16
-20
-24
δ13C (‰)
-28 Observed
-32
δ13C = -30‰
-36

14. Practical Applications
Supplement direct measurements of baseline δ13C
-16
-20
-24
δ13C (‰)
-28 δ13C = -27‰ Observed
-32
δ13C = -30‰
-36

15. Practical Applications
Supplement direct measurements of baseline δ13C
-16
-20
-24 δ13C = -26.5‰
δ13C (‰) Modelled
-28 δ13C = -27‰
Observed
-32
δ13C = -30‰
-36

16. 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

17. Practical Applications
Estimate and evaluate variation in baseline δ13C
-16
Modelled standard deviations (May – Sept.) Modelled
-20 ranged from 0.3 – 4.0‰
Observed
-24
δ13C
(‰) -28
-32
-36
Year

18. Summary
CO2(aq) can predict baseline δ13C in productive lakes
Isotopic fractionation model indicates δ13C vs. CO2(aq)
relationship is consistent with algal physiology
CO2(aq) monitoring can supplement δ13C measurements
and improve estimates of temporal variation

19. 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)