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.

Although tropical forests account for only a fraction of the planet's terrestrial surface, they exchange more carbon dioxide with the atmosphere than any other biome on Earth, and thus play a disproportionate role in the global climate. In the next 20 years, the tropics will experience unprecedented warming, yet there is exceedingly high uncertainty about their potential responses to this imminent climatic change. Here, we prioritize research approaches given both funding and logistical constraints in order to resolve major uncertainties about how tropical forests function and also to improve predictive capacity of earth system models. We investigate overall model uncertainty of tropical latitudes and explore the scientific benefits and inevitable trade-offs inherent in large-scale manipulative field experiments. With a Coupled Model Intercomparison Project Phase 5 analysis, we found that model variability in projected net ecosystem production was nearly 3 times greater in the tropics than for any other latitude. Through a review of the most current literature, we concluded that manipulative warming experiments are vital to accurately predict future tropical forest carbon balance, and we further recommend the establishment of a network of comparable studies spanning gradients of precipitation, edaphic qualities, plant types, and/or land use change. We provide arguments for long-term, single-factor warming experiments that incorporate warming of the most biogeochemically active ecosystem components (i.e. leaves, roots, soil microbes). Hypothesis testing of underlying mechanisms should be a priority, along with improving model parameterization and constraints. No single tropical forest is representative of all tropical forests; therefore logistical feasibility should be the most important consideration for locating large-scale manipulative experiments. Above all, we advocate for multi-faceted research programs, and we offer arguments for what we consider the most powerful and urgent way forward in order to improve our understanding of tropical forest responses to climate change.

ABSTRACT[soil = 41%; foliage = 37%; woody = 14%; coarse woody debris (CWD) = 7%]. When modelled with El Niño Southern Oscillation (ENSO) year temperatures, foliar respiration was 9% greater than when modelled with temperatures from a normal year, which is in the range of carbon sink versus source behaviour for this forest. Our ecosystem respiration estimate from component fluxes was 33% greater than night-time net ecosystem exchange for the same forest, suggesting that studies reporting a large carbon sink for tropical rain forests based solely on eddy flux measurements may be in error.

Both within and between species, leaf physiological parameters are strongly related to leaf dry mass per area (LMA, g/m2), which has been found to increase from forest floor to canopy top in every forest where it has been measured. Although vertical LMA gradients in forests have historically been attributed to a direct phenotypic response to light, an increasing number of recent studies have provided evidence that water limitation in the upper canopy can constrain foliar morphological adaptations to higher light levels. We measured height, light, and LMA of all species encountered along 45 vertical canopy transects across a Costa Rican tropical rain forest. LMA was correlated with light levels in the lower canopy until approximately 18 m sample height and 22% diffuse transmittance. Height showed a remarkably linear relationship with LMA throughout the entire vertical canopy profile for all species pooled and for each functional group individually (except epiphytes), possibly through the influence of gravity on leaf water potential and turgor pressure. Models of forest function may be greatly simplified by estimating LMA-correlated leaf physiological parameters solely from foliage height profiles, which in turn can be assessed with satellite- and aircraft-based remote sensing.