Measuring and modelling responses of Australian grasses to drought

Abstract

Global climate models predict more frequent and intense drought events in the future. Grassland ecosystems are highly sensitive to changes in water availability and ecologically and economically important, so there is a pressing need to assess grassland response to drought. Drought responses are partially determined by the physiological mechanisms (e.g. stomatal closure, hydraulic impairment and leaf senescence) of component species and the timing and intensity of drought events. Thus, detailed understanding of these mechanisms is essential to parameterize mechanistic models that predict drought responses of grasslands. The primary objective of this thesis was to quantify the physiological traits regulating these mechanisms and utilize them to parameterize a model of grass response to drought. The aim of the first data chapter (Chapter 2) was to explore the key physiological traits that respond to drought. In the second data chapter (Chapter 3), I assessed the utility of easily measurable morphological (soft) traits as surrogates for the more difficult to measure physiological (hard) traits in grasses. In the third data chapter (Chapter 4), I evaluate four different formulations of a coupled photosynthesis - stomatal conductance model presented by Tuzet et al. (2003) for their capacity to predict drought responses of C3 grasses. In summary, my PhD research provides insight into the physiological mechanisms regulating grass response to drought, and subsequently provides a basis for modelling drought responses of grasses. The low variability in physiological thresholds among grass species indicate that these mechanisms have not adapted strongly to climate of origin. This result dictates the need for the future studies on alternative mechanisms of drought adaptation in grasses. In addition, my data show that grasses exhibit simultaneous progression of leaf senescence with stomatal closure and hydraulic impairment under drought. This result suggests that substantial improvements in modelling drought responses of grasses could be achieved by incorporating a drought response of leaf senescence in mechanistic models. Accordingly, the leaf senescence data presented here provide a basis for modelling leaf browning responses of grasses under drought.

Date of Award	2022
Original language	English