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.

The identification of a common “stress microbiome” indicates tightly controlled relationships between the plant host and bacterial associates and a conserved structure in bacterial communities associated with poplar trees under different growth conditions. The ability of the microbiome to buffer the plant from extreme environmental conditions coupled with the conserved stress microbiome observed in this study suggests an opportunity for future efforts aimed at predictably modulating the microbiome to optimize plant growth.

Background and ObjectivesThis [18F]fluorodeoxyglucose (FDG) PET study evaluates the accuracy of semiquantitative measurement of putaminal hypermetabolism in identifying anti–leucine-rich, glioma–inactivated-1 (LGI1) protein autoimmune encephalitis (AE). In addition, the extent of brain dysmetabolism, their association with clinical outcomes, and longitudinal metabolic changes after immunotherapy in LGI1-AE are examined.MethodsFDG-PET scans from 49 age-matched and sex-matched subjects (13 in LGI1-AE group, 15 in non–LGI1-AE group, 11 with Alzheimer disease [AD], and 10 negative controls [NCs]) and follow-up scans from 8 patients with LGI1 AE on a median 6 months after immunotherapy were analyzed. Putaminal standardized uptake value ratios (SUVRs) normalized to global brain (P-SUVRg), thalamus (P/Th), and midbrain (P/Mi) were evaluated for diagnostic accuracy. SUVRg was applied for all other analyses.ResultsP-SUVRg, P/Th, and P/Mi were higher in LGI1-AE group than in non–LGI1-AE group, AD group, and NCs (all p < 0.05). P/Mi and P-SUVRg differentiated LGI1-AE group robustly from other groups (areas under the curve 0.84–0.99). Mediotemporal lobe (MTL) SUVRg was increased in both LGI1-AE and non–LGI1-AE groups when compared with NCs (both p < 0.05). SUVRg was decreased in several frontoparietal regions and increased in pallidum, caudate, pons, olfactory, and inferior occipital gyrus in LGI1-AE group when compared with that in NCs (all p < 0.05). In LGI1-AE group, both MTL and putaminal hypermetabolism were reduced after immunotherapy. Normalization of regional cortical dysmetabolism associated with clinical improvement at the 6- and 20-month follow-up.DiscussionSemiquantitative measurement of putaminal hypermetabolism with FDG-PET may be used to distinguish LGI1-AE from other pathologies. Metabolic abnormalities in LGI1-AE extend beyond putamen and MTL into other subcortical and cortical regions. FDG-PET may be used in evaluating disease evolution in LGI1-AE.Classification of EvidenceThis study provides Class II evidence that semiquantitative measures of putaminal metabolism on PET can differentiate patients with LGI1-AE from patients without LGI1-AE, patients with AD, or NCs.