ABSTRACT 1176 | POSTER 11

BAYESIAN ESTIMATION OF PHOTOSYNTHESIS-IRRADIANCE PARAMETERS WITH UNCERTAINTY PROPAGATION INTO WATER COLUMN PRIMARY PRODUCTION

Photosynthesis-irradiance (P-I) parameters are central to estimates of marine primary production, yet their uncertainty is rarely quantified in a way that can be consistently propagated into derived quantities such as depth-integrated production. Classical approaches based on nonlinear least-squares fitting yield point estimates for each experiment independently, with approximate confidence intervals that are not readily carried forward into productivity calculations. We present a Bayesian hierarchical framework for estimating P-I parameters from incubation experiments. The model is based on the photoinhibition P-I formulation, with parameters corresponding to the initial slope, the assimilation number, and the photoinhibition factor treated as random variables whose posterior distributions are inferred from the data. A hierarchical structure allows information to be shared across experiments, stabilizing estimates in cases with limited observations while preserving meaningful variability between conditions. The framework produces full posterior distributions for all parameters, enabling direct comparison with classical estimates and explicit quantification of uncertainty. We further demonstrate how these posterior distributions can be propagated into water column primary production estimates, yielding uncertainty-aware depth-integrated production profiles. This approach provides a consistent framework for quantifying production uncertainty in both historical and ongoing P-I studies.

Marija Bacekovic Koloper*, University of Split, [email protected], https://orcid.org/0009-0009-6361-7626

Jann Paul Mattern, University of California Santa Cruz, [email protected]

Žarko Kovač, University of Split, [email protected]

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