What if we could reconstruct tree metabolism and explore how environmental factors influence it through tree rings?

The climate crisis is pushing forests to a limit. Modern forest management aims to help forests maintain viability and ecosystem functions and services. However, making appropriate management decisions is difficult since our understanding of the connections between climate variations and detailed tree physiological and phenotypic responses is not well interlinked or well represented in current models. We need to open the black box of physiological and metabolic processes that provide the missing link between environmental and phenotypically observable changes. Once achieved, we can refine and test hypotheses about which processes must be better represented and incorporated into models.

New high-resolution bioanalytical mass spectrometers offer high-throughput metabolite identification and compound- and intramolecular position-specific isotope analysis in the natural isotope abundance range. Changes in the commitment of substrates to metabolic pathways and the activation or deactivation of others alter enzyme-specific isotope effects. This leads to differences in reaction products’ intra-molecular and compound-specific isotope compositions. Substantial intramolecular position-specific isotope information of intermediates of metabolic pathways is integrated into the tree ring chemical compounds, allowing to inversely model metabolic fluxes and pathway commitments. By combining disciplines, such as metabolomics, stable isotope ecology and tree ring research, we can use this “multidimensional isotopic fingerprint” in the tree ring archive to unveil the mechanisms of metabolism-environment interaction on scales ranging from cellular regulation to whole plant resource allocation. This will allow retrospective testing of whether processes such as sink control and stomatal growth optimisation respond to selected environmental drivers and affect tree functioning and whether they are correctly incorporated into models. Hence, deciphering past processes will allow us to reveal insights into the future trajectories of forests.