Drought-related leaf functional traits control spatial and temporal dynamics of live fuel moisture content.

• We examined the spatiotemporal drivers of live fuel moisture content (LFMC). • Models developed from pressure-volume curve data predicted seasonal LFMC. • Leaf dry mass traits predicted maximum LFMC across a large climatic gradient. • Maximum LFMC was correlated with aridity, with lower values in arid sites. • Including leaf traits in LFMC models is likely to improve their performance. Large forest fires generally occur when the moisture content of fuels is low. For live fuels, our understanding of the physiological basis of variation in moisture content has recently advanced. However, process-based models of live fuel moisture content (LFMC) remain elusive. Here, we aim to further our understanding of the role of physiological mechanisms and plant functional traits in driving spatiotemporal variations in LFMC. We examined whether temporal variation in LFMC could be predicted from pressure-volume curve data, which measures leaf water potential and water content on cut shoots dehydrating on a bench. We also examined whether leaf dry mass traits could predict spatial variation in maximum LFMC. We undertook our study in eucalypt forests and woodlands spanning a large climatic gradient in eastern Australia. We found that LFMC models developed from pressure-volume curves reliably predicted seasonal variation in LFMC across four co-occurring species. A two-phase LFMC model, which fit models above and below the turgor loss point (mean absolute error = 3.7-33.2%), performed similarly well to a simple linear model (mean absolute error = 3.4-35.3%). Across a large climatic gradient, the maximum LFMC of 16 species was correlated with specific leaf area (R 2 = 0.54), with the exception of one species with terete terminal stems. Maximum LFMC was highly correlated with aridity (R 2 = 0.82), with lower LFMC observed in more arid sites. Our study demonstrates that spatiotemporal dynamics of LFMC are governed by both leaf dry mass traits and the relationship between leaf water potential and water content, which in turn is determined by traits such as cell wall elasticity. Thus, incorporating these traits into models of LFMC, whether these models are based on drought indices, soil moisture, or remotely sensed imagery, is likely to improve overall model performance, and subsequently improve forecasts of wildfire danger. [ABSTRACT FROM AUTHOR]