Global warming events have coincided with turnover of plant species at intervals in Earth history. As mean global temperatures rise, the number, frequency and duration of heat-waves will increase. Ginkgo biloba was grown under controlled climatic conditions at two different day/night temperature regimes (25/20 °C and 35/30 °C) to investigate the impact of heat stress. Photosynthetic CO₂-uptake and electron transport were reduced at the higher temperature, while rates of respiration were greater; suggesting that the carbon balance of the leaves was adversely affected. Stomatal conductance and the potential for evaporative cooling of the leaves was reduced at the higher temperature. Furthermore, the capacity of the leaves to dissipate excess energy was also reduced at 35/30 °C, indicating that photo-protective mechanisms were no longer functioning effectively. Leaf economics were adversely affected by heat stress, exhibiting an increase in leaf mass per area and leaf construction costs. This may be consistent with the selective pressures experienced by fossil Ginkgoales during intervals of global warming such as the Triassic - Jurassic boundary or Early Eocene Climatic Optimum. The physiological and morphological responses of the G. biloba leaves were closely interrelated; these relationships may be used to infer the leaf economics and photosynthetic/stress physiology of fossil plants.

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Plant physiological status is the interaction between the plant genome and the prevailing growth conditions. Accurate characterization of plant physiology is, therefore, fundamental to effective plant phenotyping studies; particularly those focused on identifying traits associated with improved yield, lower input requirements, and climate resilience. Here, we outline the approaches used to assess plant physiology and how these techniques of direct empirical observations of processes such as photosynthetic CO2 assimilation, stomatal conductance, photosystem II electron transport, or the effectiveness of protective energy dissipation mechanisms are unsuited to high-throughput phenotyping applications. Novel optical sensors, remote/proximal sensing (multi- and hyperspectral reflectance, infrared thermography, sun-induced fluorescence), LiDAR, and automated analyses of below-ground development offer the possibility to infer plant physiological status and growth. However, there are limitations to such indirect approaches to gauging plant physiology. These methodologies that are appropriate for the rapid high temporal screening of a number of crop varieties over a wide spatial scale do still require calibration or validation with direct empirical measurement of plant physiological status. The use of deep-learning and artificial intelligence approaches may enable the effective synthesis of large multivariate datasets to more accurately quantify physiological characters rapidly in high numbers of replicate plants. Advances in automated data collection and subsequent data processing represent an opportunity for plant phenotyping efforts to fully integrate fundamental physiological data into vital efforts to ensure food and agro-economic sustainability.