Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that

SummaryAccurate representation of photosynthesis in terrestrial biosphere models (TBMs) is essential for robust projections of global change. However, current representations vary markedly between TBMs, contributing uncertainty to projections of global carbon fluxes. Here we compared the representation of photosynthesis in seven TBMs by examining leaf and canopy level responses of photosynthetic CO 2 assimilation (A) to key environmental variables: light, temperature, CO 2 concentration, vapor pressure deficit and soil water content. We identified research areas where limited process knowledge prevents inclusion of physiological phenomena in current TBMs and research areas where data are urgently needed for model parameterization or evaluation. We provide a roadmap for new science needed to improve the representation of photosynthesis in the next generation of terrestrial biosphere and Earth system models.

The morphological and biochemical properties of plant canopies are strong predictors of photosynthetic capacity and nutrient cycling. Remote sensing research at the leaf and canopy scales has demonstrated the ability to characterize the biochemical status of vegetation canopies using reflectance spectroscopy, including at the leaf level and canopy level from air- and spaceborne imaging spectrometers. We developed a set of accurate and precise spectroscopic calibrations for the determination of leaf chemistry (contents of nitrogen, carbon, and fiber constituents), morphology (leaf mass per area, Marea), and isotopic composition (δ15N) of temperate and boreal tree species using spectra of dried and ground leaf material. The data set consisted of leaves from both broadleaf and needle-leaf conifer species and displayed a wide range in values, determined with standard analytical approaches: 0.7–4.4% for nitrogen (Nmass), 42–54% for carbon (Cmass), 17–58% for fiber (acid-digestible fiber, ADF), 7–44% for lignin (acid-digestible lignin, ADL), 3–31% for cellulose, 17–265 g/m2 for Marea, and −9.4‰ to 0.8‰ for δ15N. The calibrations were developed using a partial least-squares regression (PLSR) modeling approach combined with a novel uncertainty analysis. Our PLSR models yielded model calibration (independent validation shown in parentheses) R2 and the root mean square error (RMSE) values, respectively, of 0.98 (0.97) and 0.10% (0.13%) for Nmass, R2 = 0.77 (0.73) and RMSE = 0.88% (0.95%) for Cmass, R2 = 0.89 (0.84) and RMSE = 2.8% (3.4%) for ADF, R2 = 0.77 (0.69) and RMSE = 2.4% (3.9%) for ADL, R2 = 0.77 (0.72) and RMSE = 1.4% (1.9%) for leaf cellulose, R2 = 0.62 (0.60) and RMSE = 0.91‰ (1.5‰) for δ15N, and R2 = 0.88 (0.87) with RMSE = 17.2 g/m2 (22.8 g/m2) for Marea. This study demonstrates the potential for rapid and accurate estimation of key foliar traits of forest canopies that are important for ecological research and modeling activities, with a single calibration equation valid over a wide range of northern temperate and boreal species and leaf physiognomies. The results provide the basis to characterize important variability between and within species, and across ecological gradients using a rapid, cost-effective, easily replicated method.

Earth system models (ESMs) use photosynthetic capacity, indexed by the maximum Rubisco carboxylation rate (V cmax), to simulate carbon assimilation and typically rely on empirical estimates, including an assumed dependence on leaf nitrogen determined from soil fertility. In contrast, new theory, based on biochemical coordination and co‐optimization of carboxylation and water costs for photosynthesis, suggests that optimal V cmax can be predicted from climate alone, irrespective of soil fertility. Here, we develop this theory and find it captures 64% of observed variability in a global, field‐measured V cmax dataset for C3 plants. Soil fertility indices explained substantially less variation (32%). These results indicate that environmentally regulated biophysical constraints and light availability are the first‐order drivers of global photosynthetic capacity. Through acclimation and adaptation, plants efficiently utilize resources at the leaf level, thus maximizing potential resource use for growth and reproduction. Our theory offers a robust strategy for dynamically predicting photosynthetic capacity in ESMs.

Researchers from a number of disciplines have long sought the ability to estimate the functional attributes of plant canopies, such as photosynthetic capacity, using remotely sensed data. To date, however, this goal has not been fully realized. In this study, fresh-leaf reflectance spectroscopy (λ=450–2500 nm) and a partial least-squares regression (PLSR) analysis were used to estimate key determinants of photosynthetic capacity—namely the maximum rates of RuBP carboxylation (Vcmax) and regeneration (Jmax)—measured with standard gas exchange techniques on leaves of trembling aspen and eastern cottonwood trees. The trees were grown across an array of glasshouse temperature regimes. The PLSR models yielded accurate and precise estimates of Vcmax and Jmax within and across species and glasshouse temperatures. These predictions were developed using unique contributions from different spectral regions. Most of the wavelengths selected were correlated with known absorption features related to leaf water content, nitrogen concentration, internal structure, and/or photosynthetic enzymes. In a field application of our PLSR models, spectral reflectance data effectively captured the short-term temperature sensitivities of Vcmax and Jmax in aspen foliage. These findings highlight a promising strategy for developing remote sensing methods to characterize dynamic, environmentally sensitive aspects of canopy photosynthetic metabolism at broad scales.