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

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-20

-16

δδδδ13C (‰)

Baseline δδδδ13C vs. CO2(aq) in Windermere

Threshold for active uptake of

dissolved inorganic carbon?

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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

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-20

-16

δδδδ13C (‰)

Baseline δδδδ13C vs. CO2(aq) in Windermere

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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

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-20

-16

δδδδ13C (‰)

Baseline δδδδ13C vs. CO2(aq) in Windermere

Model

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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

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-20

-16

δδδδ13C (‰)

Baseline δδδδ13C vs. CO2(aq) in Windermere

Growth rate = 0.33 d-1

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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

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Predicted

δδδδ13C (‰)

Model-predicted vs. Observed baseline δδδδ13C in Windermere

Fractionation model

predicts δδδδ13C successfully

using CO2(aq)

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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

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δδδδ13C (‰)Observed

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δδδδ13C = -30‰

Practical Applications

Supplement direct measurements of baseline δδδδ13C

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δδδδ13C (‰)Observedδδδδ13C = -27‰

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-28 Observedδδδδ C = -27‰

δδδδ13C = -30‰

Practical Applications

Supplement direct measurements of baseline δδδδ13C

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δδδδ13C (‰) Modelled

Observedδδδδ13C = -27‰

δδδδ13C = -26.5‰

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-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

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-24δδδδ13C

(‰)

Year

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-16

Modelled

Observed

δδδδ13C

Practical Applications

Modelled standard deviations (May – Sept.)

ranged from 0.3 – 4.0‰

Estimate and evaluate variation in baseline δδδδ13C

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-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)