Séminaire de David Nerini
Date(s) : 18/05/2026 iCal
14h00 - 15h30
Conditional multivariate functional PCA for the reconstruction of temperature and salinity profiles partially sampled by deep-diving marine mammals
Nadège Fonvieille, Christophe Guinet, Baptiste Picard, David Nerini
We present a statistical method to reconstruct the vertical thermohaline conditions in the Indian Sector of the Southern Ocean, where temperature and salinity profiles are partially sampled by female southern elephant seals. Datasets collected by biologgers provide unprecedented spatial and temporal coverage of ocean conditions. However, the maximum recorded depth varies with the animals’ behaviour, offering only a partial view of the vertical environment. Using multivariate functional Principal Component Analysis (fPCA), a parametric estimation of the covariance structure and mean function from a set of complete bivariate profiles allows the construction of an eigenfunction basis. By accounting for measurement error variance, partially sampled temperature and salinity profiles can be projected into the eigenspace of the complete profiles through conditional estimation of their functional principal coordinates and then reconstructed over the defined domain. For simulated snippet profiles truncated at depth zmax = 250 m and reconstructed over Z = [20, 500] m, reconstruction accuracy increases by 36 % for temperature and 50 % for salinity when incorporating geographical covariates. We then reconstruct 90,000 incomplete profiles from the multivariate functional PCA of 10,000 profiles reaching 500 m, covering approximately 3 million km² around the French subantarctic islands.
Emplacement
Saint-Charles - FRUMAM (2ème étage)
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