predicting baseline d13c signatures of a lake food
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Predicting baseline δδδδ13C 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 GroupLake Ecosystem Group
Centre for Ecology & Hydrology
Outline
Stable isotope analysis & lake food webs
Archived samples ���� patterns in δδδδ13C &
dissolved carbon dioxide (CO2(aq))
Model of isotopic fractionation during
photosynthesis
Practical applications for using CO2(aq) as a
proxy for baseline δδδδ13C
A stable isotope picture of a lake food web
Pike
Perch Arctic charr
Tro
ph
ic L
ev
el
Ind
ica
tor
15N
Baseline δδδδ13CZooplankton
Phytoplankton
Offshore: -30‰Near shore: -20‰
Macroinvertebrates
Carbon Source
Tro
ph
ic L
ev
el
Ind
ica
tor
δδ δδ1
5
δδδδ13C
Benthic Algae
& Detritus
Baseline δδδδ13C
-24
-20
-16
δδδδ13C
Windermere offshore baseline δδδδ13C values 2000 - 2005
Problem: δδδδ13C signatures at the base of the food web can vary
Affects interpretation of food web relationships
Monthly samples (May – Sept.)
-36
-32
-28
-24δδδδ C
(‰)
Date
What causes variation in baseline δδδδ13C?
Can it be predicted?
Isotopic discrimination during
photosynthesis (εεεεp) ≈ 15‰
Phytoplankton
δδδδ13C = -25 to -30‰ CO2(aq)
δδδδ13C = -10 to -15‰
HCO3-(aq)
What causes variation in baseline δδδδ13C?
εεεεp can vary with:
- algal species
-algal growth rate
- availability of CO2(aq) or HCO3-(aq)
HCO3 (aq)
δδδδ13C = -1 to -6‰
If variation in εεεεp due to algal species & growth rate
is low, can CO2(aq) predict baseline δδδδ13C?
Methods
Measured δδδδ13C values of archived zooplankton samples in
Windermere (May – Sept.; 1985 – 2010)
Daphnia galeata – herbivore; represents algal δδδδ13C
Compared δδδδ13C with biweekly average CO2(aq) concentrations to
account for carbon turnover in zooplankton
Compared with isotopic fractionation model based on algal physiology
y = -2.42ln(x) - 22.30
R² = 0.72
-24
-20
-16
δδδδ13C (‰)
Baseline δδδδ13C vs. CO2(aq) in Windermere
Threshold for active uptake of
dissolved inorganic carbon?
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-32
-28
0 10 20 30 40 50 60 70 80
δδδδ C (‰)
CO2(aq) (µµµµmol L-1)
dissolved inorganic carbon?
Carbon isotopic fractionation model (Cassar et al. 2006)
( ) δδδδ13CO2(aq) +103
δδδδ13Cbaseline +103 - 1 x103εεεεp = ( )( )P Ci
P Ci + µµµµ C
P’ Cc
P’ Cc + µµµµ Cεεεεt + (εεεεfix - εεεεt) x=
εεεεt = isotopic discrimination due to diffusion & active transport = 1‰
εεεεfix = isotopic discrimination due to enzymatic carboxylation = 27‰
Incorporates:
1) Algal growth rate (µµµµ) & cellular
Algal cell
membrane
Chloroplast
membrane1) Algal growth rate (µµµµ) & cellular
carbon content (C)
2) Permeability of the algal cell (P)
& chloroplast (P’) to CO2(aq)
3) CO2(aq) concentration in lake (Ci)
& in chloroplast (Cc) CO2(aq)
Ci
Cc
P
P’
δδδδ13Corg
membrane
y = -2.42ln(x) - 22.30
R² = 0.72
-24
-20
-16
δδδδ13C (‰)
Baseline δδδδ13C vs. CO2(aq) in Windermere
-36
-32
-28
0 10 20 30 40 50 60 70 80
δδδδ C (‰)
CO2(aq) (µµµµmol L-1)
y = -2.42ln(x) - 22.30
R² = 0.72
-24
-20
-16
δδδδ13C (‰)
Baseline δδδδ13C vs. CO2(aq) in Windermere
Model
-36
-32
-28
0 10 20 30 40 50 60 70 80
δδδδ C (‰)
CO2(aq) (µµµµmol L-1)
Model
y = -2.42ln(x) - 22.30
R² = 0.72
-24
-20
-16
δδδδ13C (‰)
Baseline δδδδ13C vs. CO2(aq) in Windermere
Growth rate = 0.33 d-1
-36
-32
-28
0 10 20 30 40 50 60 70 80
δδδδ C (‰)
CO2(aq) (µµµµmol L-1)
Growth rate = 0.33 d
Growth rate = 0.13 d-1
y = 0.88x - 3.09
R² = 0.70
-28
-24
-20
-16
Predicted
δδδδ13C (‰)
Model-predicted vs. Observed baseline δδδδ13C in Windermere
Fractionation model
predicts δδδδ13C successfully
using CO2(aq)
-36
-32
-28
-36 -32 -28 -24 -20 -16
Observed δδδδ13C (‰)
using CO2(aq)
Provides basis for using
CO2(aq) as a proxy for δδδδ13C
in productive lakes
What are the practical applications?
Practical Applications
Supplement direct measurements of baseline δδδδ13C
-28
-24
-20
-16
δδδδ13C (‰)Observed
-36
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-28 Observed
δδδδ13C = -30‰
Practical Applications
Supplement direct measurements of baseline δδδδ13C
-28
-24
-20
-16
δδδδ13C (‰)Observedδδδδ13C = -27‰
-36
-32
-28 Observedδδδδ C = -27‰
δδδδ13C = -30‰
Practical Applications
Supplement direct measurements of baseline δδδδ13C
-28
-24
-20
-16
δδδδ13C (‰) Modelled
Observedδδδδ13C = -27‰
δδδδ13C = -26.5‰
-36
-32
-28Observed
δδδδ C = -27‰
δδδδ13C = -30‰
-24
-20
-16
δδδδ13C
Practical Applications
Measured standard deviations (May – Sept.)
ranged from 0.8 – 4.5‰
Estimate and evaluate variation in baseline δδδδ13C
-36
-32
-28
-24δδδδ13C
(‰)
Year
-24
-20
-16
Modelled
Observed
δδδδ13C
Practical Applications
Modelled standard deviations (May – Sept.)
ranged from 0.3 – 4.0‰
Estimate and evaluate variation in baseline δδδδ13C
-36
-32
-28
-24δδδδ13C
(‰)
Year
Summary
CO2(aq) can predict baseline δδδδ13C in productive lakes
Isotopic fractionation model indicates δδδδ13C vs. CO2(aq)
relationship is consistent with algal physiology
CO2(aq) monitoring can supplement δδδδ13C measurements
and improve estimates of temporal variationand 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• QMUL: Ian Sanders, Nicola Ings and Michelle Jackson
• CEH Lancaster: Helen Grant
• Freshwater Biological Association
• Natural Environment Research Council (NERC)
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