Predicting leaf physiology from simple plant and climate attributes: a global GLOPNET analysis.

Knowledge of leaf chemistry, physiology, and life span is essential for global vegetation modeling, but such data are scarce or lacking for some regions, especially in developing countries. Here we use data from 2021 species at 175 sites around the world from the GLOPNET compilation to show that key physiological traits that are difficult to measure (such as photosynthetic capacity) can be predicted from simple qualitative plant characteristics, climate information, easily measured ("soft") leaf traits, or all of these in combination. The qualitative plant functional type (PFT) attributes examined are phylogeny (angiosperm or gymnosperm), growth form (grass, herb, shrub, or tree), and leaf phenology (deciduous vs. evergreen). These three PFT attributes explain between one-third and two-thirds of the variation in each of five quantitative leaf ecophysiological traits: specific leaf area (SLA), leaf life span, mass-based net photosynthetic capacity (Amass), nitrogen content (N(mass)), and phosphorus content (P(mass)). Alternatively, the combination of four simple, widely available climate metrics (mean annual temperature, mean annual precipitation, mean vapor pressure deficit, and solar irradiance) explain only 5-20% of the variation in those same five leaf traits. Adding the climate metrics to the qualitative PFTs as independent factors in the model increases explanatory power by 3-11% for the five traits. If a single easily measured leaf trait (SLA) is also included in the model along with qualitative plant traits and climate metrics, an additional 5-25% of the variation in the other four other leaf traits is explained, with the models accounting for 62%, 65%, 66%, and 73% of global variation in N(mass), P(mass), A(mass), and leaf life span, respectively. Given the wide availability of the summary climate data and qualitative PFT data used in these analyses, they could be used to explain roughly half of global variation in the less accessible leaf traits (A(mass), leaf life span, N(mass), P(mass)); this can be augmented to two-thirds of all variation if climatic and PFT data are used in combination with the readily measured trait SLA. This shows encouraging possibilities of progress in developing general predictive equations for macro-ecology, global scaling, and global modeling.