Acclimation Unifies the Scaling of Carbon Assimilation Across Climate Gradients and Levels of Organisation.

The temperature dependence of carbon assimilation-from leaf photosynthesis to ecosystem productivity-is hypothesised to be driven by the kinetics of Rubisco-catalysed carboxylation and electron transport. However, photosynthetic physiology acclimates to changes in temperature, which may decouple temperature dependencies at higher levels of organisation from the acute temperature sensitivity of photosynthesis. Here, we integrate relative growth rate theory, metabolic theory and biochemical photosynthesis theory to develop a carbon budget model of plant growth that accounts for photosynthetic acclimation to temperature. We test its predictions using a novel experimental approach enabling concurrent measurement of the temperature sensitivity of acute photosynthesis, acclimated photosynthesis and growth rate. We demonstrate for the first time that photosynthetic acclimation mediates how carbon assimilation kinetics 'scale up' from leaf photosynthesis to whole-plant growth. We also find that existing models of photosynthetic acclimation are unable to predict features of growth rate responses to temperature in our system.

Hotter is not (always) better: Embracing unimodal scaling of biological rates with temperature.

Rate-temperature scaling relationships have fascinated biologists for nearly two centuries and are increasingly important in our era of global climate change. These relationships are hypothesized to originate from the temperature-dependent kinetics of rate-limiting biochemical reactions of metabolism. Several prominent theories have formalized this hypothesis using the Arrhenius model, which characterizes a monotonic temperature dependence using an activation energy E. However, the ubiquitous unimodal nature of biological temperature responses presents important theoretical, methodological, and conceptual challenges that restrict the promise for insight, prediction, and progress. Here we review the development of key hypotheses and methods for the temperature-scaling of biological rates. Using simulations, we examine the constraints of monotonic models, illustrating their sensitivity to data nuances such as temperature range and noise, and their tendency to yield variable and underestimated E, with critical consequences for climate change predictions. We also evaluate the behaviour of two prominent unimodal models when applied to incomplete and noisy datasets. We conclude with recommendations for resolving these challenges in future research, and advocate for a shift to unimodal models that better characterize the full range of biological temperature responses.

Fast Assimilation-Temperature Response: a FAsTeR method for measuring the temperature dependence of leaf-level photosynthesis.

We present the Fast Assimilation-Temperature Response (FAsTeR) method, a new method for measuring plant assimilation-temperature (AT) response that reduces measurement time and increases data density compared with conventional methods. The FAsTeR method subjects plant leaves to a linearly increasing temperature ramp while taking rapid, nonequilibrium measurements of gas exchange variables. Two postprocessing steps are employed to correct measured assimilation rates for nonequilibrium effects and sensor calibration drift. Results obtained with the new method are compared with those from two conventional stepwise methods. Our new method accurately reproduces results obtained from conventional methods, reduces measurement time by a factor of c. 3.3 (from c. 90 to 27 min), and increases data density by a factor of c. 55 (from c. 10 to c. 550 observations). Simulation results demonstrate that increased data density substantially improves confidence in parameter estimates and drastically reduces the influence of noise. By improving measurement speed and data density, the FAsTeR method enables users to ask fundamentally new kinds of ecological and physiological questions, expediting data collection in short-field campaigns, and improving the representativeness of data across species in the literature.

Gas exchange analysers exhibit large measurement error driven by internal thermal gradients.

Portable gas exchange analysers provide critical data for understanding plant-atmosphere carbon and water fluxes, and for parameterising Earth system models that forecast climate change effects and feedbacks. We characterised temperature measurement errors in the Li-Cor LI-6400XT and LI-6800, and estimated downstream errors in derived quantities, including stomatal conductance (gsw ) and leaf intercellular CO2 concentration (Ci ). The LI-6400XT exhibited air temperature errors (differences between reported air temperature and air temperature measured near the leaf) up to 7.2°C, leaf temperature errors up to 5.3°C, and relative errors in gsw and Ci that increased as temperatures departed from ambient. This caused errors in leaf-to-air temperature relationships, assimilation-temperature curves and CO2 response curves. Temperature dependencies of maximum Rubisco carboxylation rate (Vcmax ) and maximum RuBP regeneration rate (Jmax ) showed errors of 12% and 35%, respectively. These errors are likely to be idiosyncratic and may differ among machines and environmental conditions. The LI-6800 exhibited much smaller errors. Earth system model predictions may be erroneous, as much of their parametrisation data were measured on the LI-6400XT system, depending on the methods used. We make recommendations for minimising errors and correcting data in the LI-6400XT. We also recommend transitioning to the LI-6800 for future data collection.

Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Perú.

Alpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families.