The Triassic-Jurassic Boundary marks the third largest mass extinction event in the Phanerozoic, characterised by a rise in CO₂-concentrations from ~600 ppm to ~2100 – 2400ppm, coupled with a ~3.0 – 4.0°C temperature rise. This is hypothesized to have induced major floral turnover, altering vegetation structure, composition and leaf morphology, which in turn are hypothesized to have driven changes in wildfire. However, the effects of elevated CO₂ on fuel properties, such as chemical composition of leaves, are also important in influencing fire behaviour, but yet have not been considered.

We test this by selecting three Triassic analogue species grown experimentally in different atmospheric compositions, and analyse variations in leaf chemistry, and leaf level flammability. These data were used to inform a fire behaviour model.

We find that all three species tested showed a reduction in their volatile component, leading to lower flammability. Accounting for these variations in a model, our results suggest that leaf intrinsic flammability has a measurable impact on modelled fire behaviour.

If scaled up to ecosystem level, periods of elevated CO₂ may therefore be capable of inducing both biochemical and morphological changes in fuel properties, and thus may be capable of influencing fire behaviour.

Plants (Ginkgo biloba, Agathis australis, and Dicksonia antarctica) were grown in ambient or high CO₂ growth chambers for 18 months, then new-growth leaves were selected for analysis.

Intrinisic leaf flammbility data was collected using a Federal Aviation Administration Microcalorimeter (Fire Testing Technology, East Grinstead, UK), using 10-15 mg of dried leaf tissue for each sample and were exposed to a heating program that ramped up to 750°C at a rate of 3°C per second. Two leaves per plant from two plants were analysed each in duplicate. The peak heat release (pHRR: the most intense flux of heat during the combustion of the leaf material, indicates the maximum decomposition rate of the leaves which is related to the volatile gas flux of the material), heat capacity (HRC: the maximum capability of the leaf material to release combustion heat per degree of temperature during pyrolysis; this measure provides an indication of the resistance of the leaves to thermal degradation) and total heat release (THR: the total energy released by the leaf during combustion) was determined for each leaf on a g−1 dry mass basis.

Lignin content was determined as follows: Protein-free cell wall preparations were obtained for 2 leaves per plant from 2 plants by sequential washing in pH 7 potassium phosphate buffer (0.1 M), 1 M NaCl solution, 1% (v/v) Triton-X100 and acetone. Acid-soluble lignin was assayed using the acetyl bromide method (Moreira-Vilar et al 2014), and the absorbance measured at 280 nm.

Leaf volatile compounds were determined as follows:

Volatiles were extracted from dried leaves (2 leaves per plant from 2 plants) ground in glass pestle and mortar under liquid nitrogen (0.01 g (dry weight) in 1 ml hexane with 10 µM butylated hydroxytoluene (BHT; as an internal standard) by sonication and subsequent overnight incubation. Samples were analysed using an Agilent 7200 series accurate mass Q-TOF mass spectrometer coupled to a 7890A GC system (Agilent Technologies, Santa Clara, USA), equipped with an EI (electron ionisation) ion source. 5 μl of each sample was injected into a non-deactivated, baffled glass liner with a 12:1 split ratio (14.448 ml min-1 split flow) and the inlet temperature was maintained at 250°C.   A Zebron semi-volatiles (Phenomenex, Torrance, USA) column (30 m x 250 μm x 0.25 μm) coupled with a 10 m guard column, was maintained at a constant helium flow of 1.2 ml min-1. The temperature of the column was ramped up at a rate of 15°C min-1, from 70°C to 310°C over 16 min, and then held at 310°C for a further 6 minutes. The EI source emission current and voltage were held at 35 μA and 70 eV respectively.  The mass range was set from 50 to 600 m/z, with an acquisition rate of 5 spectra s-1, and a solvent delay of 4 min. Data were analysed using Agilent MassHunter Qualitative Analysis software (version B.07.00) and compounds identified where possible by comparison with standards and NIST (NIST 11 Mass Spectral Library) and Golm libraries (Hummel et al, 2007).