1 2 Fuels and landscape flammability in an Australian alpine environment 3

4 Fraser, Imogen P.1; Williams, Richard J.2; Murphy, Brett P.3; Camac, James S.4; Vesk, Peter 5 A.1

6 1 School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia. Email: 7 [email protected]. Phone: 0430 433 422.

8 2 CSIRO Tropical Ecosystems Research Centre, Winnellie, Northern Territory, Australia

9 3 Research Institute for the Environment and Livelihoods, Charles Darwin University, 10 Darwin, Northern Territory, Australia

11 4 Department of Biological Sciences, Macquarie University, New South Wales, Australia

12 13 Running head: Australian alpine fuels and landscape flammability 14 15 16 Key words: bulk density, fuel load, vegetation structure, fire severity, fire regimes 17 18 19 20 21 22 23 24 25 26 27 28 29 30

1 31 Abstract

32 Factors governing landscape-scale flammability are poorly understood, yet critical to 33 managing fire regimes. Studies of the extent and severity of the 2003 Australian alpine fires 34 revealed marked differences in flammability between major alpine plant communities, with 35 the occurrence and severity of fire greater in heathland compared to grassland. To 36 understand this spatial variation in landscape flammability we documented variation in two 37 physical properties of fuel – load and bulk density – at the life-form and plant community 38 scale. We measured load (mass per unit area) and bulk density (mass per unit volume) of fine 39 fuels (<6 mm) at 56 sites across the Bogong High Plains, southeastern Australia. Fine fuel 40 load was positively correlated with shrub cover, and fine fuel bulk density was negatively 41 correlated with shrub cover. Furthermore, fine fuel load and bulk density were accurately 42 predicted using simple measures of canopy height and shrub cover. We also conducted a 43 burning experiment on individual shrubs and snowgrass (Poa spp.) patches to assess 44 comparative differences in flammability between these life-forms. The burning experiment 45 revealed that shrubs were more flammable than snowgrass as measured by a range of 46 flammability variables. Consequently, our results indicate that treeless alpine landscapes of 47 southeastern Australia are differentially flammable due to inherent life-form differences in 48 both fine fuel load and bulk density. If shrub cover increases in these alpine landscapes, as 49 projected under climate change, then they are likely to become more flammable and may 50 experience more frequent and/or severe fires.

51 Introduction 52 53 Fire patterning across landscapes is typically heterogeneous due to variation in flammability 54 – the availability and propensity of fuel to burn (Turner 2010). Landscape flammability is 55 affected by numerous factors, including vegetation, topography and fire weather (Sullivan et 56 al. 2012), all of which vary and interact spatially and temporally at a range of scales 57 (Bradstock 2010). However differential landscape flammability may also be due to intrinsic 58 fuel properties such as fuel load (mass per unit area), chemical composition and bulk density 59 (mass per unit volume), which may vary spatially but are less subject to short- to medium- 60 term temporal variation. 61 62 Plants vary widely in their physical and chemical properties (Sullivan et al. 2012) and the 63 flammability of a vegetation community depends upon the composition, abundance and

2 64 structural array of the constituent plant species (McCarthy et al. 2001; Scarff and Westoby 65 2006). Fuel load and bulk density are fuel properties that vary spatially and are well-known 66 to be critical determinants of flammability at scales of plant parts through to entire vegetation 67 communities (e.g. Murphy and Russell-Smith 2010; Madrigal et al. 2011; Marino et al. 2011; 68 Hoffmann et al. 2012). Both fuel load and bulk density are integral components of classical 69 and enduring fire behaviour models. For example, fire-line intensity is defined as the product 70 of fuel load, rate of spread and the heat of release of the fuel (Byram 1959). Thus, as fuel 71 load increases, so too does intensity; a phenomenon that has been widely demonstrated 72 (Murphy and Russell-Smith 2010; Hoffmann et al. 2012). Rothermel’s (1972) rate of spread 73 equation explicitly includes bulk density as a term in the denominator. As bulk density 74 declines, potential rate of spread increases (Thomas 1971; Davies et al. 2006; Marino et al. 75 2012). Bulk density is inversely related to aeration of the fuel bed and determines convective 76 and radiative heat transfer rates (Fernandes and Cruz 2012). Variation in these physical fuel 77 properties is likely to drive variation in flammability, producing heterogeneous fire patterns 78 (Turner 2010). 79 80 The alpine vegetation of mainland south-eastern Australia (Williams et al. 2014) provides an 81 excellent case study of how variation in community-level flammability may affect patterns of 82 fire activity at a landscape scale. The Australian alps were burnt by extensive wildfires in the 83 decade 2000-2010. In Victoria, approximately 60% of Victoria’s 6,474 km2 Alpine National 84 Park was burnt in the fires of 2003 (Esplin 2003). Most major forest, woodland and treeless 85 vegetation types were burnt and there were clear community-level differences in fire 86 occurrence and severity across the landscape (Williams et al. 2006; Murphy et al. 2013). In 87 the alpine vegetation of the Bogong High Plains, plant community-level differences in the 88 patterns of burning were very pronounced (Williams et al. 2006). Fire occurrence was 89 greatest in closed heathland (87% of closed heathlands burnt), intermediate in open heathland 90 (59% burnt), and lowest in grassland (15% burnt); closed heathland also burnt more severely 91 than open heathland. On this basis, Williams et al. (2006) proposed that the physical fuel 92 properties of alpine plant communities and their constituent species were critical 93 determinants of the patterns of severity and extent of the 2003 fires. They concluded that 94 alpine shrubs provided the fuels for fire to propagate, and that heathlands were more 95 flammable than grasslands, not only because of the greater abundance of shrubs, but because 96 shrubs were intrinsically more flammable than snowgrass, in part due to differences in fuel 97 structure (Williams et al. 2006). 98

3 99 The fire regimes of the Australian alps are also likely to be sensitive to climate change, 100 through the combined effects of climate change on fire weather and fuels (Williams et al. 101 2014). The bioregion has shown a positive relationship between area burnt and maximum 102 temperature during fire years over the period 1975-2009 (Bradstock et al. 2014). The climate 103 of the treeless, alpine landscapes has warmed and dried since the 1970s (Wahren et al. 2013). 104 On the Bogong High Plains, in Victoria, increases in shrub cover have been documented 105 between 1936 and 1980 (McDougall 2003), and from 1979-2010 (Wahren et al. 2013). 106 Warming and drying is also expected to lead to an increase in the abundance of shrubs within 107 the alpine heathland-grassland vegetation complexes of the mainland alps (Camac et al. 108 2015). 109 110 Thus, understanding how variation in the physical properties of fuels derived from the 111 dominant alpine shrubs and grasses may affect landscape flammability is important for 112 understanding the fire ecology and management of Australian alpine environments, both at 113 present and in the future, as argued by Williams et al. (2006). We investigated how variation 114 in these community-level fuel attributes may affect landscape flammability. We quantified 115 community-level variation in fuel load and structure in the dominant grassland and heathland 116 vegetation types. We also performed a simple burning experiment on the dominant shrubs 117 and snowgrass species to test various measures of flammability. We hypothesized that (1) 118 fuel load and bulk density will vary between heathland and grassland; (2) community-level 119 fuel load will vary positively with shrub cover, but community-level bulk density will be 120 negatively related to shrub cover (Fig. 1); (3) the differences in fuel structure make shrubs 121 more flammable than snowgrass and that (4) the differences in fuel physical characteristics 122 between grassland and heathland are consistent with the observed patterns of burning 123 documented on the Bogong High Plains by Williams et al. (2006). 124

125 Methods 126

127 Field survey 128

129 Study area 130 This study was conducted across the Bogong High Plains in the Alpine National Park (37° S, 131 147° E; about 1600–1860 m altitude), northeastern Victoria, Australia (Fig. 2). Major fires 132 occurred in 1998, 2003, 2006/07. The vegetation is a mosaic of eucalypt woodland and 133 treeless communities, including grassland, open heathland, closed heathland, herbfield and 4 134 wetland (McDougall and Walsh 2007). In this study we focused on the most extensive 135 treeless communities: grassland, open heathland and closed heathland (Suppl. 1 ;McDougall 136 and Walsh 2007). Grassland (shrub cover <20%) is dominated by the snowgrass Poa 137 hiemata. Open heathland is typically dominated by the shrub Grevillea australis with Poa 138 hiemata the dominant inter-shrub species; shrub cover is 20–70% and shrub height is 20–60 139 cm (as in Williams et al. 2006). Closed heathland (shrub cover 70–100%; shrub height 50– 140 150 cm) is dominated by several tall shrub species, including Prostanthera cuneata, Orites 141 lancifolia and Bossiaea foliosa. 142 143 To quantify landscape variation in fuel load and bulk density, we surveyed fuels at 56 144 randomly selected sites that were originally surveyed by Williams et al. (2006) immediately 145 after the 2003 fires. The heathland sites were also resurveyed by Camac et al. (2013). These 146 sites consisted of 16 unburnt heathlands, 20 burnt heathlands, 10 unburnt grasslands and 10 147 burnt grasslands. Burnt sites were defined as those that burnt in 2003, while unburnt sites 148 were likely last burnt in 1939. Burnt sites were classified based on estimated pre-fire shrub 149 cover. The sites spanned the full range of latitude, longitude, slope, elevation and aspect of 150 the Bogong High Plains (Suppl. 2; Camac et al. 2013). 151

152 Vegetation transects 153 154 The fuel surveys commenced in late spring (12/11/2012) and finished mid-summer 155 (5/02/2013). Fuel load and bulk density were sampled from 5–6 quadrats (35 cm × 35 cm; 156 five quadrats in grasslands and six quadrats in heathlands) at 6 m intervals along a 30 m 157 transect line. The number of quadrat replicates was greater in heathlands than grasslands due 158 to the greater vegetation heterogeneity in heathlands. Prior to determining fuel load (via 159 destructive harvesting), measurements of fuel height (+ 1 cm) were taken using a rising plate 160 meter (10 cm diameter disk) in grasslands and open heathlands, and a graduated point quadrat 161 rod (3 mm diameter; 1 cm graduations) in closed heathland. Height was measured at nine 162 random points within each quadrat. The plate disk was made of light, plastic coated paper and 163 did not compress the vegetation in any way. Fuel bulk density was calculated as mass divided 164 by the product of the average height and quadrat area (Cheney et al. 1993). 165 166 Vegetation was sorted into nine classes based on life-form, diameter, vertical position and 167 whether it was live or dead (Suppl. 3). Samples were divided into fine fuels (<6 mm 168 diameter) and coarse fuels (>6 mm diameter). Fine fuels were sorted into one of six classes:

5 169 graminoid, forb, fine litter, live shrub foliage, live shrub twigs, and dead elevated shrub 170 twigs. Coarse fuels were further classed as live shrub stems, coarse litter or dead shrub stems. 171 In heathland, litter was defined as the horizontally layered dead surface fuels (Sullivan et al. 172 2012), and included all shrub, forb and grass matter. In grassland, litter was defined as 173 entirely dead tussocks and un-attached dead grass matter on the ground. Individual tussocks 174 were not sorted into live and dead components as this was too time-consuming. 175 176 To examine life-form differences in fuel load and bulk density, we also measured the fuel 177 load and bulk density of each of the dominant species where a quadrat landed on a 178 homogenous patch (i.e. snowgrass clump; individual shrub > 35 cm diameter). Additional 179 homogenous patches were haphazardly sampled to standardise the sample size for the 180 dominant shrub species. Shrub species were sampled in the vegetation type within which they 181 are a dominant species (i.e. heathland). Grass taxa were sampled in both grassland and open 182 heathland, as they are dominant in both (McDougall and Walsh 2007). 183 184 The fresh mass of all samples was weighed in the field and fuel moisture content determined

185 gravimetrically for a pooled subsample. Samples were oven-dried at 75◦C to a constant dry 186 weight, and the dry weight correction factor applied to the fresh weight of each sample. 187 188 We did not attempt to measure temporal variability in fuel moisture due to the intensive 189 resources required. A summary of fuel moisture content of the dominant taxa over the course 190 of the sampling period is presented in the supplementary material (Suppl. 4 and 5). 191 192 Vegetation cover of the transect was assessed using the line-intercept method (Kent 2011). 193 The cover of each life-form (graminoid, herb and shrub), dominant shrub, subdominant 194 shrub, dominant snowgrass species and bare ground was recorded. 195

196 Flammability experiment 197 198 To characterise the differential flammability of the dominant shrubs and snowgrasses under 199 field conditions, a field-based burning experiment was conducted within the Falls Creek ski 200 resort. The aim of the experimental burns was to quantify variation in flammability between 201 life-forms (shrubs and snowgrasses), as a means of relating landscape-variation in 202 flammability observed during the 2003 fires to inherent differences in life-form flammability 203 observed at the patch-scale during the experiment.

6 204 205 The field burns were conducted at end of the fire season, from March 12–16, 2013. Hence, 206 fuels were likely to have been at maximum curing. The vegetation was a mixture of 207 grassland, open heathland, and low closed heathland and was not burnt in 2003. 208 209 Discrete individuals of two shrub species (Prostanthera cuneata and Grevillea australis) and 210 homogenous patches of two snowgrass species (Poa hiemata and Poa fawcettiae) were 211 selected for burning. For safety reasons, burning was conducted on days when the Forest Fire 212 Danger Index (FFDI; Noble et al. 1980) was below 5. To eliminate confounding effects of 213 temporal variation in flammability, shrub and snowgrass burns were alternated through time. 214 215 Temperature (°C), relative humidity (%) and wind speed at approximately 1.5 m above -1 216 ground (km h ) were recorded immediately before each field burn using a portable 217 thermometer, hygrometer and anemometer respectively (suppl. 6 and 7). Fuel height, patch 218 area, and fuel moisture content were also determined prior to each field burn. 219 220 Shrubs were ignited in the litter layer beneath the edge of the canopy, and snowgrasses on the 221 edge of the broader sample, using two kerosene-based fire-lighting blocks, which remained in 222 place until the fire burnt out. 223 224 Flammability was assessed using several measures. Ignitability was assessed as time to 225 ignition (s) and whether or not the canopy ignited to 25% of canopy area. A successful 226 ignition was defined as being when the flame was sustained in the fuel bed for >5 s, hence 227 time to ignition was calculated as the time difference between ignition source application and 228 fuel ignition, minus 5 s. Flaming duration (s; time difference between ignition and flaming 229 cessation) was used to assess sustainability. 230 231 Combustibility was assessed as peak flame height (m; estimated visually using a graduated 232 stick) and several temperature variables, which were measured as a proxy for heat release. 233 Average temperature was characterised over 5 s intervals using a non-contact infrared 234 thermometer (Professional Infrared Thermometer; Ruby Electronics) pointed at a black 235 aluminium disk (diameter 8 cm), positioned 15 cm above the grass/shrub foliage (as in 236 Bowman et al. 2014). This produced a temperature trace over time. From these data, we 237 calculated peak temperature (°C), rate of temperature increase (°C s-1), area loss rate (m2 s-1) 238 and total temperature release seconds (°C s). Rate of temperature increase was calculated as 239 the difference between the peak and ignition temperatures divided by the time taken to reach 7 240 the peak temperature after ignition. The area loss rate was calculated as the area of the patch 241 burnt divided by the flaming duration. The total temperature release over the duration of 242 flaming was calculated as the sum of the temperature readings made over the duration of 243 flaming divided by the number of readings, multiplied by five (for the 5 s interval between 244 readings). 245 246 Consumability, the proportion of the canopy area burnt (%; Martin et al. 1994), was also 247 assessed. Some authors (Martin et al. 1994; White and Zipperer 2010; Madrigal et al. 2011) 248 consider it to be a measure of flammability. In contrast, we treat this as an outcome of 249 flammability, because the combustion processes driving fuel consumption are captured by the 250 other flammability components. That is, consumability is the outcome of ignition and 251 subsequent combustion. The area of the canopy that burnt was calculated using pre-fire 252 canopy dimensions and an estimate of the proportion of canopy burnt. 253

254 Statistical analysis

255 256 The site transect was the experimental unit for all community-level analyses of fuel structure 257 and load. We present the results for fine fuel load and fine bulk density only as fire 258 predominately propagates via fine fuels (Fernandes 2001; Marino et al. 2012). Results 259 regarding total fuel load and total bulk density can be found in the Supplementary Material 260 (Suppl. 8–10). 261 262 To investigate how fine fuel load and bulk density varied across the landscape, we fitted 263 linear models with slope, aspect, elevation, fire history (burnt or unburnt in 2003), shrub 264 cover and fuel height as predictors. We log-transformed our response variables, bulk density 265 and fuel load, so that the residual errors were normally distributed. Predictor variables (slope, 266 elevation, shrub cover and fuel height) were also log transformed and aspect was sin (aspect - 267 45°) transformed so that accurate estimates of the mean and standard deviation could be 268 obtained for centering and standardizing (Gelman and Hill 2007). All linear models were 269 fitted in R (R Development Core R Development Core Team 2013). We centered the 270 continuous covariates so that they could be interpreted as average responses and slope terms 271 as partial dependencies conditional on other continuous variables at their mean. We also 272 standardized covariates by two standard deviations so that the magnitude of effects could be 273 compared between binary (i.e. fire history) and continuous variables (i.e. slope, aspect, 274 elevation, shrub cover, the cover of bare ground and neighboring life-forms).

8 275 276 Fuel height, shrub cover and slope are non-independent as tall closed heathlands tend to 277 occur on steep slopes (McDougall and Walsh 2007). We expected, a priori, that shrub cover 278 was the variable of primary interest. Single variable models showed that topographic slope 279 and shrub height were also important predictors of fine fuel load. To include combinations of 280 these variables in the same model and avoid issues of co-linearity, we fitted generalized 281 linear models of each of canopy height and slope based on shrub cover, and calculated the 282 residuals of canopy height and slope from the regression line. We then created two variables 283 from these residuals: slope residual and canopy height residual, which are independent of 284 shrub cover. 285 286 We examined multiple parameter combinations to predict fine fuel bulk density and fine fuel 287 load. To reduce the risk of over-fitting the data, the number of variables included in each 288 model was limited to three, such that there were about 10 samples per treatment level (as in 289 Bolker et al. 2009). Shrub cover was included in all multivariate models of fine fuel load and 290 bulk density, and canopy height in all multivariate models of fine fuel load. This is because 291 shrub cover and canopy height were the variables of primary interest. Models were then 292 developed with all possible variable combinations of shrub cover, canopy height and the 293 topographic covariates, given the three variable limit. Models were compared using the small

294 sample size adjustment of Akaike’s Information Criteria (AICc). 295 296 The effect of life-form on the flammability measures (time to ignitition etc.) was assessed 297 visually, using boxplots, as the sample sizes were too small to enable the sound application of 298 inferential statistics. We used the median values of bulk density and fuel load derived from 299 the species-level quadrat data. Fuel moisture content values were those collected prior to each 300 burn. 301

302 Results 303

304 Landscape variation in fine fuel load 305 306 Community-level fine fuel load increased with shrub cover, varying 6-fold (0.89–5.66 kg m- 307 2) across the sites (Fig. 3a; unless otherwise stated, data are means with + 95% confidence 308 limits). Among unburnt sites, closed heathland had a substantially higher fine fuel load (3.2, 309 2.7–4.0 kg m-2) than open heathland (1.9, 1.7–2.3 kg m-2) and grassland sites (1.6, 1.3–1.9 kg

9 310 m-2; Fig. 4a). Among burnt sites, fine fuel load was also higher in closed heathland (1.7, 1.5– 311 1.9 kg m-2) than grassland (1.3, 1.2–1.5 kg m-2), with open heathland (1.4, 1.1–1.8 kg m-2) 312 intermediate and more variable. 313 314 Dead fine fuel load (litter and elevated dead fuels) in heathland was greater in unburnt closed 315 heathland 0.9, 0.7–1.3 kg m-2) than unburnt open heathland (0.4, 0.3–0.5 kg m-2; Suppl. 8). 316 Furthermore, the average proportion of dead fine fuel was greater in closed heathland (33% at 317 burnt sites; 30% at unburnt sites) than open heathland (18% at burnt sites; 19% at unburnt 318 sites). 319 320 Regression models showed that fine fuel load varied substantially with shrub cover. Shrub 321 cover (proportion) and canopy height residual (m) had clear positive effects on fine fuel load, 322 and fire history (i.e. burning 10-years prior) had a clear negative effect (Fig. 5a). Shrub cover 323 had the greatest and most certain effect on fine fuel load, followed by canopy height residual. 324 Slope residual (º) and aspect (º) both had a small and uncertain effect, while elevation (m) 325 had close to no effect. 326 327 The most parsimonious model of fine fuel load included canopy height residual, shrub cover, 328 and fire history as main effects (R2 = 74%; Suppl. 11). The next best model was that with 329 shrub cover and canopy height residual fitted as main effects (R2 = 71%). Given that fire 330 history was binary (i.e. burnt or unburnt in 2003), this predictor variable is only relevant in 331 landscapes affected by fire 10 years prior. Hence, fine fuel load is best predicted as (data are 332 mean + SE): 333

334 Fine fuele xp[ 1mlogit ass (shrub+2can ]htcover) (Equationresid 1)

335 where: α = 0.65 (+ 0.03; β1 = 0.10 (+ 0.01); and β2 = 0.62 (+ 0.09) 336  337 Canopy ht resid (canopy height residual) is calculated as: 338 Can htc aresid n e h x t p [ (shrub 2] cover)(Equation 2) 339 where: α = -2.59; and β = 0.018. 340 341 Based on this model, an increase in shrub cover from 10% to 80% (i.e. grassland with height 342 0.09 m to closed heathland with height 0.5 m) would result, on average, in a doubling of fine 343 fuel load, from 1.5 to 2.9 kg m-2. 344

10 345 Landscape variation in fuel bulk density 346 347 There was a strong, negative relationship between shrub cover and community-level fine fuel 348 bulk density, which varied substantially across the landscape (3.8–23.9 kg m-3; Fig. 3b). 349 Among unburnt sites, fine fuel bulk density was lowest in closed heathland (6.9, 5.6–8.5 kg 350 m-3), intermediate in open heathland (12.3, 10.7–14.0 kg m-3), and highest in grassland (18.0, 351 16.0–20.3 kg m-3; Fig. 4b). Closed heathland had the lowest fine fuel bulk density, regardless 352 of fire history. 353 354 The coefficients of the full model of fine fuel bulk density reveal that shrub cover had a clear 355 negative effect on fine fuel bulk density (Fig. 5b). Shrub cover was the only variable for 356 which the 95% confidence intervals of the coefficient were distinguishable from zero. The 357 independent effect of slope on fine fuel bulk density had little predictive power. 358

359 The five top-ranking models of fine fuel bulk density were within 2 AICc units of each other 360 and, therefore, all of these models have substantial support (Suppl. 12). Given that all 361 candidate models explained a similar amount of variance (R2 = 64–67%), and simpler models 362 are preferable where predictive power is not largely reduced, fine fuel bulk density is best 363 predicted by shrub cover as: 364

365 Fine fuel ebulk x p [logit density (shrub (Equation cover)] 3) 366 where: α = 2.41 (+ 0.04); and β = -0.13 (+ 0.01).

367 368 Based on this model, an increase in shrub cover from 10% to 80% would result in a decrease 369 in fine fuel bulk density from 14.6 to 9.4 kg m-3. 370

371 Life-form differences in fuel properties 372 373 There were clear life-form differences in the canopy component of fine fuel bulk density, 374 which was highest in grass species and lowest in closed heathland shrub species (Fig. 6). For 375 example, the canopy fine fuel bulk density of Poa fawcettiae (median; 24.8 kg m-3) was >7 376 times greater than that of Orites lancifolia (3.3 kg m-3). Closed heathland species had the 377 greatest canopy fuel load, which was similar among open heathland shrub species and grass 378 species. The gross fine fuel load per unit area was typically greater in shrub species than 379 grass species.

11 380 381 382 Flammability experiment 383 384 Flammability, as measured by ignitability, combustibility and sustainability, was 385 overwhelmingly higher in shrubs than snowgrass. The only exception was time to ignition, 386 which varied greatly across the species measured (0–24 seconds; Fig. 7). The other indicator 387 of ignitability, the proportion of patches to reach 25% ignition, was considerably greater in 388 shrubs than snowgrass. The majority of the snowgrass patches failed to ignite beyond the 389 flaming zone of the ignition source. Only three of the 11 snowgrass patches ignited to 25% of 390 the canopy area. Conversely, 11 of the 12 shrubs ignited to 25%. 391 392 Shrubs had considerably higher combustibility than snowgrass. Shrub fires increased in 393 temperature fastest, had the highest maximum temperatures and flame heights, and 394 consequently yielded the greatest cumulative temperature. The median rate of temperature 395 increase was considerably higher in shrub patches (Grevillea australis, 2.4°C s-1; 396 Prostanthera cuneata, 3.6 °C s-1) than in snowgrass patches (Poa fawcettiae, 0.5 °C s-1; Poa 397 hiemata, 0.8 °C s-1). Furthermore, the rate of canopy consumption was greater in shrubs than 398 snowgrasses. Prostanthera cuneata had higher maximum temperatures and total temperature 399 release than Grevillea australis. The area loss rate – a parameter scaled by size – did not 400 differ between these shrubs. 401 402 Flame duration (sustainability) was consistently higher in shrubs (Grevillea australis, 479 s; 403 Prostanthera cuneata, 363 s) than snowgrasses (Poa fawcettiae, 63 s; Poa hiemata, 117 s). 404 Flames in snowgrass patches generally self-extinguished within close proximity to the 405 ignition source. On two occasions, the fire spread gradually across the patch at low intensity. 406 407 Due to the variation in the flammability components, consumption of shrub fuels was greater 408 than that of snowgrass (consumability). The proportion of the canopy burnt was considerably 409 greater in shrubs (Grevillea australis, 90%; Prostanthera cuneata, 95%) than in snowgrasses 410 (Poa fawcettiae, 1%; Poa hiemata, 5%). 411 412 By all measures of flammability and consumability, the dominant shrubs were more 413 flammable than the dominant snowgrasses. This is consistent with the life-form differences in 414 fuel load and bulk density observed. Shrubs were more flammable despite typically having 415 higher average foliar moisture content throughout the 2012-13 growing season than 12 416 snowgrasses (Grevillea australis, 130%; Prostanthera cuneata, 119%; Poa hiemata, 83%; 417 Poa fawcettiae, 64%; Suppl. 13). Moreover, the shrubs were flammable even under the mild 418 fire weather conditions during which the experiment was conducted (FFDI <5). 419 420

421 Discussion 422 423 The substantial differences in fuel load and structure observed at the plant community level 424 between grassland and heathland, and the difference in flammability between shrubs and 425 snowgrass demonstrated in a simple field experiment, are highly consistent with the burning 426 patterns observed in alpine heathland and grassland during the 2003 fires (Williams et al. 427 2006). Closed heathland – the plant community that burnt most frequently and severely 428 during the 2003 fires – is characterised by high shrub cover, tall vegetation, high fine fuel 429 load and low fine fuel bulk density. In contrast, grassland – the least severely and extensively 430 burnt community during the 2003 fires – is characterised by the shortest vegetation, lowest 431 fuel load and highest fine fuel bulk density. The flammability experiments clearly 432 demonstrated that shrubs burnt for longer (sustainability) and consumed more fuel per unit 433 area and unit time (combustibility) than snowgrass patches. While there was no clear 434 difference in time to ignition between the shrubs and snowgrass patches, the shrubs more 435 easily carried a self-propagating flame than the snowgrass patches (ignitability). Thus, we 436 have presented empirical evidence at two spatial scales to help explain observed landscape 437 variation in fire extent and severity. 438

439 Fuel load 440 441 Fine fuel load varied substantially between plant communities, and was highest in closed 442 heathland and lowest in grassland. The total fuel load in unburnt closed heathland (6.56 kg m- 443 2) is very similar to that observed for alpine heathland (6.9 kg m-2) by Wahren, Papst and 444 Tolsma (unpublished data, 2013), and above values reported for lowland shrublands in 445 Australia (0.1–2.65 kg m-2) and globally (0.3–6.49 kg m-2; see Fontaine et. al 2012). 446 Similarly, total fuel load in alpine grassland is above values reported for lowland grassland 447 (0.1–1.2 kg m-2; Table 2) and alpine grassland in France (0.01–0.78 kg m-2 (Redjadj et al. 448 2012; Duparc et al. 2013). Biomass production is positively correlated with moisture 449 availability (Bradstock 2010). Hence, it is likely that the combination of low decomposition 450 rates due to low temperatures (Aerts 2006) and moderate productivity due to high rainfall in

13 451 the Australian alpine environment (Williams et al. 2014) result in comparatively high 452 biomass and hence fuel loads. 453 454 Closed heathland not only had the greatest fine fuel load, but also the greatest load of litter 455 and elevated dead fuels. Dead fuels have a low fuel moisture content and therefore ignite 456 more easily (Sullivan et al. 2012). The spatial distribution of dead fine fuels is a key factor 457 determining moisture and, consequently, fire behaviour (Plucinski 2009; Fontaine et al. 458 2012). Dead branch retention has been suggested as a flammability-enhancing trait (Mutch 459 1970) and is known to affect fire ignition and spread (Anderson and Anderson 2010), 460 combustion rate (Madrigal et al. 2011) and intensity (Schwilk 2003). The high fuel load of 461 elevated dead fuels in closed heathlands contributes to their high flammability. 462 463 Fuel load has been widely demonstrated to increase flammability across a continuum of 464 spatial scales. Etlinger and Beall (2004) found that the oven-dry mass of foliage was the most 465 important determinant of peak heat release and total heat release (combustibility) in 466 individual shrubs. Similarly, fire intensity increases with increasing fuel mass (Byram 1959; 467 Murphy and Russell-Smith 2010; Hoffmann et al. 2012). Closed heathland burnt more 468 severely than open heathland following the 2003 fires (Williams et al. 2006), and this is 469 consistent with the high fuel load measured in closed heathland in this study. 470

471 Fuel structure 472 473 The bulk density of the total and the fine fuel component was lowest in closed heathland and 474 highest in grassland, and there was a strong negative correlation with shrub cover. Alpine 475 heathland and grassland have a higher bulk density than comparable lowland communities. 476 The total bulk density of alpine heathland (open 13.8 kg m-3; closed 13.0 kg m-3) is 477 considerably greater than values measured for lowland shrublands worldwide (1–10.6 kg m-3; 478 Table 2.). The fine fuel bulk density of alpine heathland (6.9 and 12.3 kg m-3 in closed and 479 open heathland respectively) is also greater than values reported for mallee heath (1 kg m-3; 480 Cruz et al. 2010) and Mediterranean shrubland (2.7 kg m-3; Fernandes 2001). Similarly, the 481 bulk density of alpine grassland (18.0 kg m-3) is considerably greater than that of lowland 482 Australian grasslands (0.89–6 kg m-3). The high bulk density of alpine grassland may be due 483 to the needle-like leaves and dense growth form of the Poa tussocks, the retention of dead 484 tillers and dead tussocks as a thin litter layer, and the annual compression of Poa tussocks by 485 the winter snow pack.

14 486 487 Variation in bulk density is a key contributor to differential flammability across the 488 landscape. High fuel bulk density reduces flame impingement and gas flow into the unburnt 489 fuel bed, thereby reducing heat transfer and fuel pre-heating (Rothermel 1972; Tachajapong 490 et al. 2008). The subsequent dampening effect on fire spread has been widely demonstrated 491 (Thomas 1971; Davies et al. 2006; Marino et al. 2012). We conclude that the high bulk 492 density (and hence low aeration) of snowgrass is a major impediment to fire spread in alpine 493 grasslands and may explain why so few were burnt by the 2003 fires, despite there being 494 adequate fuel loads to support fire and severe fire weather. 495 496 Fuels are commonly stratified into discrete layers based on fuel structure (Sullivan et al. 497 2012). Heathland fuels occurred in several overlapping layers: a surface layer of litter; a near 498 surface layer of herbs, snowgrass and prostrate shrubs; and an elevated layer of erect shrubs, 499 producing regions of high vertical continuity. The elevated shrub layer was absent in 500 grassland, patchy in open heathland, and horizontally continuous in closed heathland. Fuel 501 continuity is an important determinant of fire spread (Cruz et al. 2013) and intensity (De Luis 502 et al. 2004). Furthermore, the height of the elevated fuel layer influences flame dimensions, 503 particularly flame height (Cruz et al. 2010; Sullivan et al. 2012) and studies have 504 demonstrated the positive correlation between fuel height and rate of spread in shrublands in 505 Europe, Australia, New Zealand and South Africa (Fernandes et al. 2000; Fernandes 2001; 506 Anderson et al. 2015). Hence, the high vertical and horizontal fuel continuity, and tall fuel 507 layer of closed heathland undoubtedly contributes to their high flammability. 508

509 Predicting fuel load and bulk density 510 511 Globally, considerable research effort has been directed towards the development of size- 512 based models of community-level fuel load. Predictor variables include fuel height, plate 513 drop height, vegetation cover and litter depth, among others (Catchpole and Wheeler 1992). 514 Fuel load in shrublands is also commonly predicted as an exponential function of time since 515 fire (e.g. Marsden-Smedley and Catchpole 1995), whereby fuels accumulate rapidly before 516 levelling at a steady-state. Our study has shown that fuel load can be predicted in both alpine 517 heathland and grassland based on simple measures of canopy height and cover of the 518 dominant shrubs, with reasonably high accuracy (R2 = 72%). Other studies have also found 519 that vegetation height and cover are important predictors of fine fuel load in shrublands (e.g. 520 Pearce et al. 2010). Marsden-Smedley and Catchpole (1995) developed a model to predict

15 521 fuel load in wet heathlands, based on fuel age and total vegetation cover. Fire history also had 522 a clear effect on fuel load in the current study, but was excluded from the predictive model 523 due to the binary nature of the variable. Redjadj et al. (2012) investigated three alternative 524 methods of biomass estimation in subalpine grassland (calibrated visual estimation, an 525 adaptation of the point quadrat method, and rising plate meter) and suggested that while 526 calibrated visual estimation was the most reliable approach (R2 = 82%), the rising plate meter 527 was acceptable above ~300 g m-2, which our grassland loads were. As elevation had no 528 significant effect on fuel mass and bulk density, the model presented in this study can be 529 applied to a range of alpine communities. 530 531 Destructive sampling is commonly used to calculate bulk density in the field (Cheney et al. 532 1993; Hoffmann et al. 2012), however predictive models of bulk density are scarce. Bulk 533 density of Canadian forests has been calculated as a weighted average of species bulk 534 densities (Miller and Urban 1999). Davies et al. (2008) developed the ‘fuel rule’ method to 535 estimate fuel load and bulk density of heathland fuels, based on allometric relationships 536 between fuel height, fuel load and vegetation density. Bulk density in treeless alpine 537 landscapes could also be calculated by combining the predictions of a fuel load model with 538 an estimate of fuel height. We have demonstrated that shrub cover predicts bulk density well 539 (R2 = 64%) in treeless alpine landscapes. The model presented in this study is likely to hold 540 for areas of the Mainland Australian alps dominated by snowgrasses and some mix of several 541 shrub species (Grevillea australis, Phebalium squamulosum, Prostanthera cuneata, Orites 542 lancifolia and Bossiaea foliosa).

543 Flammability experiment 544 545 The flammability experiment clearly confirmed our hypothesis that shrubs are more 546 flammable than snowgrass. The burning experiments revealed that shrubs more easily ignited 547 beyond the flaming zone of the ignition source (ignitability), burnt for longer (sustainability), 548 with more rapid combustion (combustibility), and to a greater extent (consumability) than 549 snowgrass patches. This differential flammability is most likely due to differences in fine fuel 550 bulk density and fuel load, and indicates that shrubs are the primary propagating fuel 551 component of treeless alpine landscapes in Australia. 552 553 The low flammability of alpine snowgrass is in stark contrast to lowland grass species. 554 Grasses have been repeatedly described as enhancing vegetation flammability in various 555 ecosystems, such as mixed conifer forest (Miller and Urban 2000), temperate grassy

16 556 woodlands (Lunt et al. 2012), arid hummock grasslands (Nano et al. 2013) and tropical 557 savannas (Hoffmann et al. 2012; Murphy et al. 2013). Indeed, Hoffmann et al. (2012) found 558 that the abundance of well-aerated grasses was the most important driver of intensity and rate 559 of spread of fire in savanna vegetation. Murphy et al. (2013) concluded that Australia is 560 largely dominated by flammable grass fuels, which typically burn frequently and with a low 561 intensity. Alpine snowgrass plays a unique role in community-level flammability, acting to 562 moderate flammability rather than enhance it. The high bulk density of snowgrass was the 563 likely cause of this low flammability, overwhelming any effects of fuel load and moisture 564 content on the propensity of fire to spread.

565 Ecological implications 566 567 Our results have important implications for the ecology and management of bushfire risk 568 within alpine environments. Most importantly, alpine landscapes are differentially flammable 569 because the dominant shrubs and snowgrasses within the major vegetation types are 570 themselves differentially flammable. This indicates that fire interval and intensity are likely 571 to systematically vary among the dominant alpine plant communities. If bushfire 572 management strategies to reduce bushfire risk are to be employed, then these strategies 573 should be informed by an understanding of the fundamental differences in flammability 574 between the dominant heathland and grassland communities of the Australian alps. 575 576 Livestock grazing has been suggested as a means of managing fuels in Australian alpine 577 environments. Our results are consistent with the conclusions of various studies that livestock 578 grazing, as a stand-alone fuel management practice, is unlikely to reduce the flammability of 579 alpine vegetation at a landscape scale (Wahren et al. 1994; Williams et al. 2006). Cattle 580 preferentially graze on snowgrass, herbs and small shrubs in herbfields, grasslands and open 581 heathlands, and avoid closed heathlands (van Rees 1982; Williams et al. 2006). While 582 livestock grazing may reduce the mass of snowgrass and herbs, our results indicate that these 583 are not the most significant propagating fuels in the alpine landscape. Moreover, grazing has 584 been shown to increase the flammability of Poa tussock grasslands through the selective 585 removal of live shoots and subsequent increase in the proportion of dead fuel (Leonard et al. 586 2010). Hence, it is conceivable that livestock grazing in Australian alpine grasslands may 587 lead to increased flammability through altering the ratio of live to dead fuels. 588 589 Our study also highlights the importance of long-term vegetation monitoring to detect 590 landscape changes in shrub cover. This is particularly important in the context of altered

17 591 vegetation dynamics under climatic change. Increases in shrub cover across the Australian 592 alpine landscape have been detected by long-term vegetation monitoring and aerial 593 photography. A recent analysis of aerial photographs of the Bogong High Plains showed that 594 between 1936 and 1980 the cover of closed heathland increased while the cover of grassland 595 decreased. This was mostly due to a shift of grassland to open heathland and from open 596 heathland to closed heathland (McDougall 2003). Similarly, long-term monitoring 597 undertaken between 1979 and 2010 revealed a decrease in graminoid cover, and increase in 598 forb and shrub cover (Wahren et al. 2013). These changes in vegetation cover have been 599 accompanied by an elevated mean growing season temperature and a decrease in annual 600 precipitation (Wahren et al. 2013). Rising global temperatures are predicted to lead to further 601 increases in shrub cover through a range of interactive biotic and abiotic effects (Camac et al. 602 2015). If shrub cover increases in these alpine landscapes, then our results suggest that this 603 will be associated with increases in fuel load and fuel porosity, ultimately leading to 604 increased landscape flammability. 605 606 Acknowledgements 607 608 Grant Cotton, Alice Crowe, Fabio de Freitas, Verity Fyfe, Joe Hall, David King, Will Neil, 609 Chryso Petrou, Alex Rooke, Nick Bell, Karina Salmon, Samantha Wetherbee, Miranda Wier, 610 Ruby Wilson and Thomas Rolf for their help collecting the field data. Ben Derrick, Falls 611 Creek Resort Management and the team at Parks Victoria, Mt Beauty Office, for the 612 provision of resources and assistance in the field. Robert Chalwell, former Department of 613 Sustainability and Environment, for organizing the permit for the experimental burns at short 614 notice. Dr Chris Lucas, Bureau of Meteorology, Melbourne, for calculating the forest fire 615 danger index values. Warwick Papst for sound advice on site selection. The manuscript was 616 improved based on the comments of three anonymous reviewers. This research was 617 supported by the Australian Government’s National Environmental Research Program- 618 Environmental Decisions, Australian Research Council Centre of Excellence for 619 Environmental Decisions, and the CSIRO Climate Adaptation Flagship. Murphy was 620 supported by a grant from the Australian Research Council (DE130100434). 621 622 623 624

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798 799 800 801 802 803 804 805 806 807 808 809 810 811 812

26 813 TABLES 814 815 Table 1: Bulk density (kg m-3) reported for shrubland and grassland ecosystems 816 globally. 817 Vegetation community Fuel type Bulk density Time since Source (kg/ m3) fire (years) Alpine open heathland Fine 12.26 74 Present study (Southeastern Australia) Total 13.04

Alpine closed heathland Fine 6.86 74 Present study (Southeastern Australia) Total 13.04

Mallee heath Litter 51.30 21 Cruz et al. (2010) (Southern Australia) Total 1.08 Mediterranean shrubland Fine 2.7 Not specified Fernandes (2001) (Portugal) Total 2.3 Coastal heathland Total c 4.4–4.7 Not specified Davies et al. (2009) (UK) Gorse and heather Total 2.43–10.6 Not specified Thomas (1971) (England) Alpine grassland Fine 18.01 74 Present study (Southeastern Australia) Total 13.76 Savannah at forest boundary Total c. 1–6 Not specified Hoffmann et al. (South America) (2012) Themeda australis grassland Total 0.89 Not specified Cheney et al. (1993) (North Australia) Eriachne sp. grassland Total 1.25 Not specified Cheney et al. (1993) (Northern Australia) 818 819 820 821 822 823 824 825 27 826 FIGURES 827

828 829 830 Fig. 1: Proposed relationship between shrub cover, flammability, fuel mass and bulk 831 density in the Southeast Australian Alps. The burning patterns of the 2003 fires 832 (Williams et al. 2006) indicate that shrub cover and flammability are positively related; 833 severity was highest in closed heathland, and fire occurrence was greater in closed 834 heathland than open heathland. Fine fuel load is predicted to increase with shrub cover, 835 driving flammability upwards. Fine fuel bulk density is predicted to decrease with 836 shrub cover, also driving flammability upwards. 837 838 839 840 841 842 843 844 845 846 847

28 848 849 850 851 Fig. 2: Location of the survey sites across the Bogong High Plains. Symbols represent 852 survey sites burnt (full) or unburnt (empty) in 2003. The shapes of the symbols 853 distinguish pre-fire vegetation types: grassland (square); open heathland (circle); or 854 closed heathland (diamond). White shading indicates alpine and treeless subalpine 855 areas, derived from the vegetation class ‘Other Grasslands, Herblands, Sedgelands and 856 Rushlands’ in the National Vegetation Information System version 3.1 (Anon. 2006). 857 Grey shading indicates eucalypt forest. 858 859 860 861 862

29 863 864 865 Fig. 3: (a) Fine fuel load (kg m-2) and (b) fine fuel bulk density (kg m-3) as a function of 866 shrub cover (%). The grey shading denotes the community shrub cover thresholds. A 867 linear model has been fitted to log fine fuel load and log fine fuel bulk density, and the 868 predictions (mean, 95% confidence intervals) have been back-transformed and plotted. 869 r2 is indicated. Points are grassland (square), open heathland (circle) and closed 870 heathland (diamond), and have been separated as burnt (full) or unburnt (empty). 871 872

873 874 Fig. 4: (a) Fine fuel load (kg m-2) and (b) fine fuel bulk density (kg m-3) in unburnt 875 (empty circle) and burnt (full circle) communities. Communities are: closed heathland; 876 open heathland and grassland. Data are mean (lower, upper 95% confidence limit), 877 which have been calculated for log fine fuel load and log fine fuel bulk density, and back 878 transformed. 879 880 30 881

882 883 884 Fig. 5: Mean effects (+ 95% confidence intervals) of the coefficients of the full model of 885 (a) log fine fuel load, and (b) log fine fuel bulk density. Independent variables along the 886 y-axis have been centered and standardized and include: fire history (burnt or unburnt 887 in 2003), aspect, elevation, slope residual (slope resid; the residual of the model of slope 888 predicted by shrub cover), canopy height residual (can ht resid; the residual of the 889 model of canopy height predicted by shrub cover) and shrub cover. A coefficient is 890 statistically significant when its confidence intervals are distinguishable from 0. 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908

31 909 910 Fig. 6: Variation in total fine fuel load (a; all fine fuels in quadrat), canopy fine fuel load 911 (b; canopy fuels only) and canopy fine fuel bulk density between a selection of closed 912 heathland shrub species (dark grey boxes: Orites lancifolia, Bossiaae foliosa and 913 Prostanthera cuneata), open heathland shrub species (light grey boxes: Grevillea 914 australis and Hovea montana, Asterolasia trymalioides) and snowgrass species (empty 915 boxes: Poa fawcettiae and Poa hiemata). Sample size (n) is indicated. The boxes capture 916 the median, 25th and 75th percentiles, and the dashed lines extend to 1.5 times the 917 interquartile range of the data. Circles represent outliers. 32 918 919 Fig. 7: Variation in a selection of flammability parameters measured with species burnt. 920 Closed heathland shrub species (dark grey boxes: Prostanthera cuneata), open 921 heathland shrub species (light grey boxes: Grevillea australis) and snowgrass species 922 (empty boxes: Poa fawcettiae and Poa hiemata). Sample size (n) is indicated. The boxes 923 capture the median, 25th and 75th percentiles, and the dashed lines extend to 1.5 times 924 the interquartile range of the data. Circles represent outliers. 925 926

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Minerva Access is the Institutional Repository of The University of Melbourne

Author/s: Fraser, IP; Williams, RJ; Murphy, BP; Camac, J; Vesk, PA

Title: Fuels and landscape flammability in an Australian alpine environment

Date: 2016-09-01

Citation: Fraser, I. P., Williams, R. J., Murphy, B. P., Camac, J. & Vesk, P. A. (2016). Fuels and landscape flammability in an Australian alpine environment. AUSTRAL ECOLOGY, 41 (6), pp.657-670. https://doi.org/10.1111/aec.12355.

Persistent Link: http://hdl.handle.net/11343/216903

File Description: Accepted version