Investigating old‐growth ponderosa pine physiology using tree‐rings, δ13C, δ18O, and a process‐based model

In dealing with predicted changes in environmental conditions outside those experienced today, forest managers and researchers rely on process‐based models to inform physiological processes and predict future forest growth responses. The carbon and oxygen isotope ratios of tree‐ring cellulose (δ13Ccell, δ18Ocell) reveal long‐term, integrated physiological responses to environmental conditions. We incorporated a submodel of δ18Ocell into the widely used Physiological Principles in Predicting Growth (3‐PG) model for the first time, to complement a recently added δ13Ccell submodel. We parameterized the model using previously reported stand characteristics and long‐term trajectories of tree‐ring growth, δ13Ccell, and δ18Ocell collected from the Metolius AmeriFlux site in central Oregon (upland trees). We then applied the parameterized model to a nearby set of riparian trees to investigate the physiological drivers of differences in observed basal area increment (BAI) and δ13Ccell trajectories between upland and riparian trees. The model showed that greater available soil water and maximum canopy conductance likely explain the greater observed BAI and lower δ13Ccell of riparian trees. Unexpectedly, both observed and simulated δ18Ocell trajectories did not differ between the upland and riparian trees, likely due to similar δ18O of source water isotope composition. The δ18Ocell submodel with a Peclet effect improved model estimates of δ18Ocell because its calculation utilizes 3‐PG growth and allocation processes. Because simulated stand‐level transpiration (E) is used in the δ18O submodel, aspects of leaf‐level anatomy such as the effective path length for transport of water from the xylem to the sites of evaporation could be estimated. Process‐based tree growth models incorporate physiological principles that enable them to be widely applied to diverse species and sites, in contrast to empirical growth and yield models. This improves our understanding of how variable environmental conditions influence forest productivity and stand characteristics (Landsberg 2003). A widely used stand‐level process model is Physiological Principles in Predicting Growth (3‐PG), developed by Landsberg and Waring (1997) and since modified by numerous other investigators (Xenakis et al. 2008, Gonzalez‐Benecke et al. 2014, Wei et al. 2014a, Almeida and Sands 2016, Forrester and Tang 2016, Meyer et al. 2018). The 3‐PG model utilizes environmental conditions, stand characteristics, and species‐specific physiological and allometric measurements to accurately predict growth and