Details

Autor(en) / Beteiligte

• Bonamano, Simone
• Madonia, Alice
• Piermattei, Viviana
• Stefanì, Chiara
• Lazzara, Luigi
• Nardello, Ilaria
• Decembrini, Franco
• Marcelli, Marco

Titel

Phyto-VFP: a new bio-optical model of pelagic primary production based on variable fluorescence measures

Ist Teil von

• Journal of marine systems, 2020-04, Vol.204, p.103304, Article 103304

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2020

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Marine primary production (PP) is a key factor in the regulation of the global carbon cycle, with important potential feedback on climate. Seventy percent of marine PP is generated by phytoplankton photosynthesis. However, the phytoplankton productivity rate is dependent on the photo-physiological state of phytoplankton cells, as well as other environmental conditions. To consider these variables appropriately, refine the current estimates of PP, and reduce the laboursome and lengthy methodologies of radiocarbon estimates, we have created “Phyto-VFP” (Variable Fluorescence Phytoplankton Production), a new bio-optical model classified as a Wavelength and Depth-resolved (WDR) model. The model integrates the effect of the photo-acclimation processes on the “active” fraction of the phytoplankton population with the dynamic of the water column, parametrised through a series of laboratory experiments based on in vivo variable fluorescence measures on the marine diatom Skeletonema costatum (Greville) Cleve. The performance of Phyto-VFP was compared with concurrent estimates of radiocarbon (14C) uptakes, under different dynamic and optical conditions, during two oceanographic cruises (SAMCA3 and SAMCA4) in the Mediterranean Sea. The low Root Mean Square Differences (RMSDs) show that Phyto-VFP performs well when estimating phytoplankton PP. When compared to other bio-optical models, Phyto-VFP estimates of PP in coastal waters were closer to radiocarbon measurements than other models could predict [e.g., the Morel model (MM)]. The application of Phyto-VFP to the SAMCA dataset and its comparison to MM allowed the assessment of model performance under three different physical and biological conditions, in which it was possible to analyse how photo-physiological responses of phytoplankton influence PP. •Phyto-VFP estimates phytoplankton Primary Production using variable fluorescence.•Phyto-VFP combines phytoplankton physiological processes with water column dynamics.•The model attained a performance similar to the most widely Primary Production models.•Phyto-VFP shows a higher predictive skill than Morel model in coastal waters.