B007-07
Deciphering the relationship between fluorescence and the rate, limits, and regulation of photosynthesis

Monday, 7 December 2020: 05:54
Virtual
Jennifer E Johnson, Carnegie Institution for Science, Stanford, CA, United States and Joseph A Berry, Carnegie Institution for Science, Global Ecology, Stanford, CA, United States
Abstract:
Solar-induced chlorophyll fluorescence (SIF) is an information-rich signal which contains a wealth of information about gross primary production (GPP). To date, statistical modeling frameworks have been widely used to relate measurements of SIF to measurements of GPP. These approaches tend to focus on characterizing the relationship between SIF and the rate of GPP. However, SIF also has the potential to provide insight into the limits and regulation of GPP. In this presentation, we will discuss the mechanisms that link the leaf-level emission of fluorescence to the physiological factors which limit light capture and utilization and the regulatory processes which coordinate the light and dark reactions. We will present measurements and modeling which demonstrate that there is a unique relationship between the environmental responses of the steady-state fluorescence yield (ΦF), the maximum activity of the rate-limiting enzyme in the light reactions (Cytochrome b6f), and the maximum activity of the rate-limiting enzyme in the dark reactions (Rubisco). These results indicate that in leaf cuvettes where the illumination and viewing geometries are well-characterized, measurements of ΦF can be used to infer leaf-scale photosynthetic functional traits. Under conditions where proximal and remote sensing of SIF can characterize the environmental responses of canopy-scale ΦF with sufficient resolution, it may be possible to use SIF measurements for inferring the photosynthetic functional traits of terrestrial vegetation canopies. Using mechanistic frameworks to relate SIF to the limits and regulation of GPP presents challenges and opportunities that are distinct from those involved in using statistical frameworks to relate SIF to the rate of GPP.