Summary The temperature response of photosynthesis is one of the key factors determining predicted responses to warming in global vegetation models (GVMs). The response may vary geographically, owing to genetic adaptation to climate, and temporally, as a result of acclimation to changes in ambient temperature. Our goal was to develop a robust quantitative global model representing acclimation and adaptation of photosynthetic temperature responses. We quantified and modelled key mechanisms responsible for photosynthetic temperature acclimation and adaptation using a global dataset of photosynthetic CO2 response curves, including data from 141 C3 species from tropical rainforest to Arctic tundra. We separated temperature acclimation and adaptation processes by considering seasonal and common‐garden datasets, respectively. The observed global variation in the temperature optimum of photosynthesis was primarily explained by biochemical limitations to photosynthesis, rather than stomatal conductance or respiration. We found acclimation to growth temperature to be a stronger driver of this variation than adaptation to temperature at climate of origin. We developed a summary model to represent photosynthetic temperature responses and showed that it predicted the observed global variation in optimal temperatures with high accuracy. This novel algorithm should enable improved prediction of the function of global ecosystems in a warming climate.

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.

The physiology, morphology and growth of first-year Betula papyrifera Marsh., Betula alleghaniensis Britton, Ostrya virginiana (Mill.) K. Koch, Acer saccharum Marsh., and Quercus rubra L. seedlings, which differ widely in reported successional affinity and shade tolerance, were compared in a controlled high-resource environment. Relative to late-successional, shade-tolerant Acer and Ostrya species, early-successional, shade-intolerant Betula species had high relative growth rates (RGR) and high rates of photosynthesis, nitrogen uptake and respiration when grown in high light. Fire-adapted Quercus rubra had intermediate photosynthetic rates, but had the lowest RGR and leaf area ratio and the highest root weight ratio of any species. Interspecific variation in RGR in high light was positively correlated with allocation to leaves and rates of photosynthesis and respiration, and negatively related to seed mass and leaf mass per unit area. Despite higher respiration rates, early-successional Betula papyrifera lost a lower percentage of daily photosynthetic CO gain to respiration than other species in high light. A subset comprised of the three Betulaceae family members was also grown in low light. As in high light, low-light grown Betula species had higher growth rates than tolerant Ostrya virainiana. The rapid growth habit of sarly-successional species in low light was associated with a higher proportion of biomass distributed to leaves, lower leaf mass per unit area, a lower proportion of biomass in roots, and a greater height per unit stem mass. Variation in these traits is discussed in terms of reported species ecologies in a resource availability context.

Summary1. The impacts of elevated atmospheric CO 2 and/or O 3 have been examined over 4 years using an open-air exposure system in an aggrading northern temperate forest containing two different functional groups (the indeterminate, pioneer, O 3 -sensitive species Trembling Aspen, Populus tremuloides and Paper Birch, Betula papyrifera , and the determinate, late successional, O 3 -tolerant species Sugar Maple, Acer saccharum ). 2. The responses to these interacting greenhouse gases have been remarkably consistent in pure Aspen stands and in mixed Aspen/Birch and Aspen/Maple stands, from leaf to ecosystem level, for O 3 -tolerant as well as O 3 -sensitive genotypes and across various trophic levels. These two gases act in opposing ways, and even at low concentrations (1·5 × ambient, with ambient averaging 34 -36 nL L − 1 during the summer daylight hours), O 3 offsets or moderates the responses induced by elevated CO 2 . 3. After 3 years of exposure to 560 µ mol mol − 1 CO 2 , the above-ground volume of Aspen stands was 40% above those grown at ambient CO 2 , and there was no indication of a diminishing growth trend. In contrast, O 3 at 1·5 × ambient completely offset the growth enhancement by CO 2 , both for O 3 -sensitive and O 3 -tolerant clones. Implications of this finding for carbon sequestration, plantations to reduce excess CO 2 , and global models of forest productivity and climate change are presented.

The influence of ontogeny, light environment and species on relationships of relative growth rate (RGR) to physiological and morphological traits were examined for first-year northern hardwood tree seedlings. Three Betulaceae species (Betula papyrifera, Betula alleghaniensis and Ostrya virginiana) were grown in high and low light and Quercus rubra and Acer saccharum were grown only in high light. Plant traits were determined at four ages: 41, 62, 83 and 104 days after germination. In high light (610 μmol m s PPFD), across species and ages, RGR was positively related to the proportion of the plant in leaves (leaf weight ratio, LWR; leaf area ratio, LAR), in situ rates of average canopy net photosynthesis (A) per unit mass (A) and per unit area (A), and rates of leaf, stem and root respiration. In low light (127 μmol m s PPFD), RGR was not correlated with A and A whereas RGR was positively correlated with LAR, LWR, and rates of root and stem respiration. RGR was negatively correlated with leaf mass per area in both high and low light. Across light levels, relationships of CO exchange and morphological characteristics with RGR were generally weaker than within light environments. Moreover, relationships were weaker for plant parameters containing a leaf area component (leaf mass per area, LAR and A), than those that were solely mass-based (respiration rates, LWR and A). Across light environments, parameters incorporating the proportion of the plant in leaves and rates of photosynthesis explained a greater amount of variation in RGR (e.g. LWRA, R=0.64) than did any single parameter related to whole-plant carbon gain. RGR generally declined with age and mass, which were used as scalars of ontogeny. LWR (and LAR) also declined for seven of the eight species-light treatments and A declined in four of the five species in high light. Decreasing LWR and A with ontogeny may have been partially responsible for decreasing RGR. Declines in RGR were not due to increased respiration resulting from an increase in the proportion of solely respiring tissue (roots and stems). In general, although LWR declined with ontogeny, specific rates of leaf, stem, and root respiration also decreased. The net result was that whole-plant respiration rates per unit leaf mass decreased for all eight treatments. Identifying the major determinants of variation in growth (e.g. LWRA) across light environments, species and ontogeny contributes to the establishment of a framework for exploring limits to productivity and the nature of ecological success as measured by growth. The generality of these relationships both across the sources of variation we explored here and across other sources of variation in RGR needs further study.

Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes. With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America. Model validation accuracy varied among traits (normalized root mean squared error, 9.1-19.4%; coefficient of determination, 0.28-0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28-81% provided high confidence for multiple traits concurrently. Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental-scale functional diversity.

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