Quantifying aggregation bias in marsh carbon flux estimates caused by rhizosphere oxygen heterogeneity
- 1Universität Hamburg (Hamburg, Germany)
- 2Helmholtz-Zentrum Hereon (Geesthacht, Germany)
Abstract
Oxygen (O ₂ ) in marsh rhizospheres varies strongly at millimetre scales due to radial oxygen loss (ROL), re-oxidation of reduced compounds, hydrology, and sediment structure. Yet many models estimate heterotrophic (microbial) respiration at a single mean O ₂ , which can bias CO ₂ fluxes because the O ₂ response of respiration is curved (nonlinear). We calibrated an enzyme kinetic respiration function to marsh-soil incubations, then compared (I) respiration evaluated at a single mean O ₂ value with (II) the mean of different respiration fluxes over a prescribed rhizosphere O ₂ distribution. To isolate oxygen effects, temperature and the relation between substrate and soil moisture were held fixed; incubations were used only to calibrate carbon kinetics. For a gas-phase, air-equivalent O ₂ proxy, the single mean approach overestimates respiration by ∼ 12 % on average. This bias increases with increasing variation of O ₂ and with the ratio of mean O ₂ to the O ₂ half-saturation constant KM,O2. In the dissolved phase, mean O ₂ ≪ KM,O2 and variation in O ₂ is small, so the bias is negligible due to linear behaviour of the relation in these conditions; when physiology is unit-matched to a similar relative saturation, however, the dissolved case shows a negative bias comparable to the gas-phase one. Our findings suggest reporting the mean and variation of O ₂ together with a calibrated KM,O2 allows modellers to anticipate or correct the bias associated with replacing heterogeneous rhizosphere conditions with a single mean.
Versions
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BibTeX
@article{saadaoui2026quantifying,
title = {{Quantifying aggregation bias in marsh carbon flux estimates caused by rhizosphere oxygen heterogeneity}},
author = {Youssef Saadaoui and Christian Beer and Peter Mueller and Friederike Neiske and Joscha N. Becker and Annette Eschenbach and Philipp Porada},
journal = {Biogeosciences},
year = {2026},
month = oct,
doi = {10.5194/bg-23-6803-2026},
url = {https://bg.copernicus.org/articles/23/6803/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Quantifying aggregation bias in marsh carbon flux estimates caused by rhizosphere oxygen heterogeneity
AU - Youssef Saadaoui
AU - Christian Beer
AU - Peter Mueller
AU - Friederike Neiske
AU - Joscha N. Becker
AU - Annette Eschenbach
AU - Philipp Porada
PY - 2026
DA - 2026/10/02
JO - Biogeosciences
DO - 10.5194/bg-23-6803-2026
UR - https://bg.copernicus.org/articles/23/6803/2026/
AB - Oxygen (O ₂ ) in marsh rhizospheres varies strongly at millimetre scales due to radial oxygen loss (ROL), re-oxidation of reduced compounds, hydrology, and sediment structure. Yet many models estimate heterotrophic (microbial) respiration at a single mean O ₂ , which can bias CO ₂ fluxes because the O ₂ response of respiration is curved (nonlinear). We calibrated an enzyme kinetic respiration function to marsh-soil incubations, then compared (I) respiration evaluated at a single mean O ₂ value with (II) the mean of different respiration fluxes over a prescribed rhizosphere O ₂ distribution. To isolate oxygen effects, temperature and the relation between substrate and soil moisture were held fixed; incubations were used only to calibrate carbon kinetics. For a gas-phase, air-equivalent O ₂ proxy, the single mean approach overestimates respiration by ∼ 12 % on average. This bias increases with increasing variation of O ₂ and with the ratio of mean O ₂ to the O ₂ half-saturation constant KM,O2. In the dissolved phase, mean O ₂ ≪ KM,O2 and variation in O ₂ is small, so the bias is negligible due to linear behaviour of the relation in these conditions; when physiology is unit-matched to a similar relative saturation, however, the dissolved case shows a negative bias comparable to the gas-phase one. Our findings suggest reporting the mean and variation of O ₂ together with a calibrated KM,O2 allows modellers to anticipate or correct the bias associated with replacing heterogeneous rhizosphere conditions with a single mean.
SN - 1726-4189
ER -