Summary Understanding plant thermal tolerance is fundamental to predicting impacts of extreme temperature events that are increasing in frequency and intensity across the globe. Extremes, not averages, drive species evolution, determine survival and increase crop performance. To better prioritize agricultural and natural systems research, it is crucial to evaluate how researchers are assessing the capacity of plants to tolerate extreme events. We conducted a systematic review to determine how plant thermal tolerance research is distributed across wild and domesticated plants, growth forms and biomes, and to identify crucial knowledge gaps. Our review shows that most thermal tolerance research examines cold tolerance of cultivated species; c. 5% of articles consider both heat and cold tolerance. Plants of extreme environments are understudied, and techniques widely applied in cultivated systems are largely unused in natural systems. Lastly, we find that lack of standardized methods and metrics compromises the potential for mechanistic insight. Our review provides an entry point for those new to the methods used in plant thermal tolerance research and bridges often disparate ecological and agricultural perspectives for the more experienced. We present a considered agenda of thermal tolerance research priorities to stimulate efficient, reliable and repeatable research across the spectrum of plant thermal tolerance.

The high temperature responses of photosynthesis and respiration in wheat are an underexamined, yet potential avenue to improving heat tolerance and avoiding yield losses in a warming climate.

Greater availability of leaf dark respiration (Rdark) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of Rdark data is limited because it is cumbersome, time consuming, or destructive to measure. We report a non‐destructive and high‐throughput method of estimating Rdark from leaf hyperspectral reflectance data that was derived from leaf Rdark measured by a destructive high‐throughput oxygen consumption technique. We generated a large dataset of leaf Rdark for wheat (1380 samples) from 90 genotypes, multiple growth stages, and growth conditions to generate models for Rdark. Leaf Rdark (per unit leaf area, fresh mass, dry mass or nitrogen, N) varied 7‐ to 15‐fold among individual plants, whereas traits known to scale with Rdark, leaf N, and leaf mass per area (LMA) only varied twofold to fivefold. Our models predicted leaf Rdark, N, and LMA with r2 values of 0.50–0.63, 0.91, and 0.75, respectively, and relative bias of 17–18% for Rdark and 7–12% for N and LMA. Our results suggest that hyperspectral model prediction of wheat leaf Rdark is largely independent of leaf N and LMA. Potential drivers of hyperspectral signatures of Rdark are discussed.