Total and dry mass loss

Dry mass loss of the vegetation types across all experiments ranged from 0 to 100%. The ANOVA of the burning of the vegetation samples show that all individual factors are significant at the 95% level except vegetation type although this factor is significant at a probability of 93% (Table 4.1).

Factor (or interaction) df p Percentage of variation explained Moisture content 1 0.00 9.6 Vegetation 2 0.07 0.1 Initial temperature 2 0.00 1.0 Burn temperature 2 0.00 57.3 Burn time 1 0.00 9.4 Return temp 2 0.01 0.2 Vegetation*initial temp. 4 0.101 0.1

Vegetation *burn temp. 4 0.00 2.2

Vegetation *burn time 2 0.03 0.2

Vegetation *return

temp. 4 0.54 0.0

Initial temp.*burn temp. 4 0.00 4.9

Initial temp.*burn time 2 0.00 1.0

Initial temp.*return

temp. 4 0.49 0.0

Burn temp.*burn time 2 0.00 6.5

Burn temp.*return

temp. 2 0.17 0.1

Burn time*return temp. 2 0.99 0.0

Error 445 7.5

Table 4.1. The significance (probability of factor, interaction or

covariate = 0) and percentage of the original variance explained for the percentage dry mass loss (%dryloss).

explained by the differences in moisture content between the vegetation types. Sphagnum spp. samples always had higher water content than the other vegetation types.

By far the most important individual factor is the burn temperature explaining 57.4% of the variation. Post-hoc testing shows significant differences

between all burn temperatures but the biggest difference is between 400 and 600°C, but with an average dry mass loss of 84.2% at 800°C. The second most important single factor is the burn time (explaining 9.4%) of the

variation in the dataset. The difference between a burn time of two and five minutes is an average dry mass loss of 27.6 to 59.7%.

The initial temperature of the sample between frozen and room temperature explains only 1% of the variation in the dataset but nevertheless is a

significant factor. The post-hoc testing shows that a significant difference, at the 95% level, only lies between the -5°C and the 4 and 22°C factor levels but not between 4 and 22°C, i.e. the effect of initial temperature is an effect of freezing conditions. The difference between the frozen and room

temperature conditions is an average difference of 10.6%, i.e. there would be 10.6% less dry mass loss if the burn took place on a frozen day rather than in summer conditions. The effect of the return temperature explains an even smaller proportion of the variation in the original dataset but

temperature does imply there is a quenching effect and smouldering of vegetation does occur and does lead to continued mass loss. However, the effect is smaller than that due to the initial temperature of the sample and the post-hoc comparison shows that only the difference between -5°C and 22°C is significant. The difference between these two extremes is the difference of 4.5%, i.e. there would be 4.5% less mass loss if the burning took place on a day when the air temperature was below freezing than on a summer’s day.

The most important interaction effect is that between the burn temperature and the burn time (6.5%) of the original variance. It is perhaps not surprising that there would be greater mass loss with vegetation exposed to higher temperatures for longer times. The significance shown to exist between these two factors shows that there is disproportionately higher dry mass loss in moving from a two-minute to a five-minute burn time at a burn temperature of 600°C than if this increase in burn time occurs at either 400 or 800°C. The second most important interaction effect is between the initial

temperature of the sample and the burn temperature – explaining 4.9% of the original variance (Table 4.1). There is little difference between dry mass loss between initial temperature levels at 400 and 800°C; the biggest

difference between levels of initial temperature temperatures occurred when the burn temperatures were 600°C. Initial temperature is also significant in interaction with the burn time although this explains only 1% of the original variance. There are no significant interactions with any other factor and the

Although the importance of vegetation as a single factor is greatly reduced by the inclusion of moisture content as covariate, vegetation as a factor does significantly interact with the burn temperature and the burn time, the

interaction with the temperature explains the most variance. Calluna

vulgaris and Eriophorum spp. behave most like each other at 400 and 800°C with Sphagnum spp. being distinctive. However, at 600°C the Calluna

vulgaris shows a distinctively higher dry mass loss. The interaction between burn time and vegetation shows that the largest difference between the two and five minute burn times exists for Sphagnum spp. and the smallest for Eriophorum spp.

The error term in the optimising model represents all the variance in the original dataset that is not explained by the factors and covariates chosen within the experimental design. In the ideal case it would represent only the proportion of the variance explained by the measurement error, i.e. the irreducible error due to performing the burning experiment. In this case the measurement error (7.5% of the original variance – Table 4.1) could be large due to the constraints upon placing samples in the furnace in an efficient, swift and repeatable manner. There could also be a sampling error involved in selecting samples of vegetation where it could, for example, easily be possible to select variable amounts of woody material within a sample of Calluna vulgaris.