Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Determinants of flammability in savanna grass species

Determinants of flammability in savanna grass species Journal of Ecology 2016, 104, 138–148 doi: 10.1111/1365-2745.12503 Determinants of flammability in savanna grass species 1 2 1 3 Kimberley J. Simpson , Brad S. Ripley , Pascal-Antoine Christin , Claire M. Belcher , 4 1 1 Caroline E. R. Lehmann , Gavin H. Thomas and Colin P. Osborne * 1 2 Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK; Department of Botany, Rhodes University, PO Box 94, Grahamstown 6140, South Africa; College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4PS, UK; and School of GeoSciences, University of Edinburgh, Edinburgh EH9 3JN, UK Summary 1. Tropical grasses fuel the majority of fires on Earth. In fire-prone landscapes, enhanced flammabil- ity may be adaptive for grasses via the maintenance of an open canopy and an increase in spa- tiotemporal opportunities for recruitment and regeneration. In addition, by burning intensely but briefly, high flammability may protect resprouting buds from lethal temperatures. Despite these potential benefits of high flammability to fire-prone grasses, variation in flammability among grass species, and how trait differences underpin this variation, remains unknown. 2. By burning leaves and plant parts, we experimentally determined how five plant traits (biomass quantity, biomass density, biomass moisture content, leaf surface-area-to-volume ratio and leaf effec- tive heat of combustion) combined to determine the three components of flammability (ignitability, sustainability and combustibility) at the leaf and plant scales in 25 grass species of fire-prone South African grasslands at a time of peak fire occurrence. The influence of evolutionary history on flammability was assessed based on a phylogeny built here for the study species. 3. Grass species differed significantly in all components of flammability. Accounting for evolution- ary history helped to explain patterns in leaf-scale combustibility and sustainability. The five mea- sured plant traits predicted components of flammability, particularly leaf ignitability and plant combustibility in which 70% and 58% of variation, respectively, could be explained by a combina- tion of the traits. Total above-ground biomass was a key driver of combustibility and sustainability with high biomass species burning more intensely and for longer, and producing the highest pre- dicted fire spread rates. Moisture content was the main influence on ignitability, where species with higher moisture contents took longer to ignite and once alight burnt at a slower rate. Biomass den- sity, leaf surface-area-to-volume ratio and leaf effective heat of combustion were weaker predictors of flammability components. 4. Synthesis. We demonstrate that grass flammability is predicted from easily measurable plant func- tional traits and is influenced by evolutionary history with some components showing phylogenetic signal. Grasses are not homogenous fuels to fire. Rather, species differ in functional traits that in turn demonstrably influence flammability. This diversity is consistent with the idea that flammability may be an adaptive trait for grasses of fire-prone ecosystems. Key-words: biomass moisture content, biomass quantity, determinants of plant community diver- sity and structure, fire regime, functional traits, phylogeny, poaceae, resprouting persistence, recovery and recruitment (Emerson & Gillespie Introduction 2008). Fire is also multidimensional and its effects on vegeta- Fire is a disturbance that has shaped plant traits and floral tion depend on the characteristics of the local fire regime communities for over 420 million years (Glasspool, Edwards (Keeley et al. 2011), which can vary considerably in fre- & Axe 2004; Bond, Woodward & Midgley 2005) and acts as quency, intensity, size and season (Archibald et al. 2013). a powerful selective filter for functional traits related to plant Different fire regimes can lead to the assembly of distinct populations and communities that are functionally clustered for diverse traits (Pausas & Bradstock 2007; Verdu & Pausas *Correspondence author: E-mail: c.p.osborne@shef.ac.uk 2007; Silva & Batalha 2010; Forrestel, Donoghue & Smith © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Determinants of flammability in savanna grasses 139 2014). For example, resprouting species are favoured in fre- Despite these predicted benefits of frequent fire to fire- quent, low-intensity fire regimes, and obligate seeders that prone grasses, interspecific variation in the flammability of persist via seedling recruitment are favoured in infrequent, such species has been little explored (Ripley et al. 2010), in high-intensity fire regimes (Pausas & Bradstock 2007; Pausas contrast to knowledge about interspecific variation in post-fire & Keeley 2014). response among grass species (Ripley et al. 2015). A histori- Plant flammability may both influence and be influenced by cal belief persists that grasses and other herbaceous plants fire regime (He, Lamont & Downes 2011; Pausas et al. 2012) vary little in their flammability, which has led to the diversity but species variation in flammability has received relatively of herbaceous fuels being reduced to one or few fuel classes little attention (but see Scarff & Westoby 2006; Murray, in fire behaviour modelling (e.g. Anderson 1982). Given the Hardstaff & Phillips 2013; Grootemaat et al. 2015). Flamma- considerable known variation in the flammability of woody bility is an emergent property of a plant’s chemical and physi- species (Schwilk 2003; Scarff & Westoby 2006; Pausas et al. cal traits. However, the identification of these traits in several 2012; Murray, Hardstaff & Phillips 2013), such presumptions fire-prone taxa, particularly herbaceous species, has not been are unfounded. Substantial changes in grassland community achieved. Flammability as a vegetation property consists of flammability resulting from invasion by non-native grasses several interdependent components (Anderson 1970) that can provide evidence to suggest considerable interspecific varia- each be quantified. Ignitability (the ease of ignition), com- tion in grass flammability (Hughes, Vitousek & Tunison 1991; Rossiter et al. 2003). In addition, recent evidence bustibility (the intensity of combustion) and sustainability (the maintenance of burning over time) are flammability compo- shows that grass traits relating to post-fire recovery are shaped nents and can be measured at multiple scales. For example, by fire regime (Forrestel, Donoghue & Smith 2014; Ripley ignitability is often measured as ignition delay at the leaf or et al. 2015), suggesting that traits relating to flammability plant scale, while the rate of fire spread is a measure of may be responding in similar ways, resulting in intra- and ignitability that operates at the community scale (Gill & interspecific variation in flammability. Zylstra 2005). Physical and chemical traits influencing some or all compo- Plant flammability is a key determinant of fire behaviour nents of flammability relate to the quantity, quality, moisture (Bond & van Wilgen 1996; Beckage, Platt & Gross 2009). In content and aeration of biomass (Bond & van Wilgen 1996; woody plants, flammability varies considerably between and Gill & Moore 1996). Biomass quantity is critical to com- within species (e.g. Fonda 2001; Saura-Mas et al. 2010; Pau- bustibility and fire spread rate because it directly influences sas et al. 2012; Cornwell et al. 2015), and minor changes in fire energy output rate (Byram 1959; Rothermel 1972). Bio- vegetation composition have repeatedly demonstrated signifi- mass moisture content determines the extent to which fuels cant alterations in vegetation flammability and fire regime absorb heat energy, with high values associated with delayed (Rossiter et al. 2003; Brooks et al. 2004; Belcher et al. ignition and low combustion and fire spread rates (Pyne 2010). Flammability may act as a means by which plants 1984; Nelson 2001). Biomass surface-area-to-volume (SA/V) modify fire regimes to engender favourable conditions ratio influences curing and reaction rates within fires (Papio (Schwilk 2003). For example, slow-growing, woody, obligate & Trabaud 1991; Gill & Moore 1996), with high values seeder species, such as Pinus species, require infrequent linked to rapid ignition, and rapid rates of combustion and intense fire to complete their life cycle. High-temperature fire spread. Increasing biomass density, defined as the mass crown fires are vital for releasing stored seeds from the of biomass per unit volume of fuel bed, raises fuel connectiv- retained mature cones of these serotinous species and enhanc- ity, therefore enhancing combustibility and fire spread rate. ing recruitment opportunities of seedlings via mortality of This relationship applies up to a certain threshold beyond neighbouring trees (Lamont et al. 1991; Keeley et al. 2011). which poor ventilation will limit drying and combustion rates In contrast, resprouting perennial grasses, which dominate (Rothermel 1972). Intrinsic properties of plant material, such grasslands and savannas (Uys 2000; Allan & Southgate 2002; as heat of combustion, affect combustibility and fire spread Overbeck & Pfadenhauer 2007), may benefit from very fre- rate through the amount of heat energy released during com- quent fire (Archibald et al. 2013). These shade-intolerant spe- plete combustion. Sustainability is often inversely related to cies require the regular removal of standing dead biomass combustibility and ignitability (e.g. de Magalh~aes & Schwilk (Everson, Everson & Tainton 1988) and woody growth (Bond 2012). Therefore, plant traits likely to enhance combustion 2008), which may be aided by high plant flammability. Sur- and spread rate may indirectly reduce flaming duration. In face fires in grassy systems are characterized by rapid com- contrast, high biomass quantity increases combustion and bustion and spread, low fire residence times and cool burn spread, but is also likely to enhance sustainability, as more temperatures (Bradstock & Auld 1995; Archibald et al. fuel takes longer to burn. Plant traits important to flammabil- 2013). Such fire characteristics are advantageous to resprout- ity have been identified in a number of fire-prone taxa (e.g. ing grass species, protecting basal meristems from excessive Ganteaume et al. 2013; Schwilk & Caprio 2011). However, heat through biomass that burns rapidly (Gagnon et al. 2010). the traits that influence grass flammability, and more generally In addition, high flammability, if linked to efficient post-fire the flammability of herbaceous species, have not been empiri- recovery, may provide enhanced regeneration opportunities cally established or explored. for these species by killing neighbouring plants and reducing We examined three components of flammability, at multiple post-fire competition (Bond & Midgley 1995). scales, for 25 species common in fire-prone South African © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 140 K. J. Simpson et al. grasslands. Five structural and chemical plant traits, known to For measurements of leaf SA/V ratio and EHoC, leaves were removed from a randomly selected tiller of each individual. Total leaf influence vegetation flammability, were measured and corre- area was measured on digital images using the computer program lated with flammability trait values (see Table 1). We hypoth- WinDIAS (Delta-T Devices, Cambridge, U.K.) that determines leaf esized that (i) there is significant interspecific variation in area by selecting pixels of a pre-defined colour range. Leaf thickness flammability among grass species and that (ii) the measured was measured, at the middle of the leaf and excluding the midrib, for plant traits can explain this variation, with each trait contribut- three leaves per tiller using digital callipers (accurate to 0.01 mm), ing to flammability components in different ways (see and an average value was calculated. Leaf SA/V ratio was calculated Table 1 for specific predictions). We also expected that from the average leaf area and leaf thickness of each species. flammability and plant traits covary due to the interdependent The heat of combustion is the energy released as heat when bio- relationships between flammability components and plant mass undergoes complete combustion with oxygen, which typically traits. The strong phylogenetic patterns in grass distributions relates to C:N ratio, lignin content and the presence of flammable across fire-frequency gradients (e.g. Visser et al. 2012; For- compounds (Philpot 1969; Bond & van Wilgen 1996). We measured the EHoC, which is the heat of combustion of pyrolysate vapours, restel, Donoghue & Smith 2014) led us to predict that (iii) and does not assume that all char is consumed. Compared to mea- flammability is influenced by evolutionary history and con- surements that involve the full thermal decomposition of biomass tains a phylogenetic signal. (such as in bomb calorimetry), EHoC is a more realistic estimate of the energy released from a wildfire in which combustion is incom- Materials and methods plete, and most of the energy is released from burning the pyrolysate vapours. Oven-dried leaf samples of known mass (5.0  0.4 mg) were conditioned at room temperature and humidity before being PLANT MATERIAL analysed in a microscale combustion calorimeter following the manu- Plants were collected during the natural fire season in July 2014 in facturer’s guidelines (FAA Micro Calorimeter, Fire Testing Technol- grassland and Nama-Karoo habitats near Grahamstown in the Eastern ogy Ltd, East Grinstead, UK). Each sample was held in nitrogen and Cape of South Africa (see Table S1 in Supporting Information for site heated at a rate of 3 °C per second driving off the volatile gases that details). Fire return times over the 2000–2006 period were 2.3 years were ignited and completely oxidized, and heat release was quantified for vegetation surrounding Grahamstown (Tansey et al. 2008). by oxygen depletion calorimetry (Tewarson 2002). Total heat release Seven individuals of 25 species, representing 5 grass subfamilies, was divided by the sample mass to provide the EHoC (kJ g ). Due were collected for study (see Table S2). All species were native to to the high repeatability of this trait measurement, material from three the region except Cenchrus setaceus, a North African invasive species randomly chosen individuals per species was tested in duplicate, to (Milton 2004). For each species, seven randomly selected, healthy- give an average value per individual and per species. looking adult plants were dug up while keeping their shoot For plant-scale traits, the height (maximum vertical distance from architecture intact. Plants were stored in sealed plastic bags at room ground level to the tallest point) and width (maximum horizontal temperature for a maximum of 48 h to minimize changes in moisture spread) of each clump was determined. Biomass density was mea- content. A specimen of each species was deposited at the Selmar sured using a novel method, which determined the vertical biomass Schonland Herbarium (Rhodes University). distribution for each individual. For this, the biomass of each clump was divided at five or more equal intervals along its vertical height, so that intervals were 2.5, 5, 10 or 15 cm in length depending on the STRUCTURAL AND CHEMICAL TRAITS plant height, and started at ground level. Each clump was cut with scissors at the selected intervals. The fresh and dry biomass of each A section of each individual (approximately one-third of the entire section were weighed to four decimal places, the latter after oven dry- plant), with its below-ground biomass and soil removed, was used to ing at 70 °C to a constant weight. Cumulative dry biomass was calcu- measure five structural and chemical plant traits. Biomass quantity, lated at each vertical height interval from ground level. Linear models density and moisture content were measured at the plant scale, while were fitted to the logged cumulative dry biomass and vertical height effective heat of combustion (EHoC) and SA/V ratio were measured for each individual. The slope of this relationship was used as a proxy at the leaf scale. Table 1. Matrix summarizing the predicted relationships between plant and flammability traits. Flammability traits were determined at different scales (L, leaf; P, plant; C, community) and represent three flammability components. Symbols reflect the direction of the relationship (‘+’: posi- tive; ‘’: negative; ‘0’: none; ‘N/A’: could not be tested). Influence is either direct or indirect (in parentheses) Plant trait Biomass Leaf Leaf effective Flammability Biomass Biomass moisture SA/V heat of 1 1 1 Flammability trait component Scale quantity (g) density (g cm ) content (g g ) ratio combustion (J g ) Time to ignition (s) Ignitability L N/A N/A  + 0 Predicted rate Ignitability C ++  ++ of fire spread (m s ) Flaming time (s) Sustainability L, P + ()(+)()() Combustion rate (g s ) Combustibility L, P ++  ++ © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 Determinants of flammability in savanna grasses 141 for biomass density, in g cm , with high values indicating densely occurred if large pieces of plant material fell off the balance during a packed biomass. For each clump, dry biomass values were combined burn. The width parameter used to fit the Boltzmann curve reflects to give the total dry biomass, and moisture content was calculated by the time period in which mass was drastically reduced and was used dividing the difference between fresh and dry biomass by the dry bio- as a plant-scale measurement of sustainability (flaming time). Three mass. seconds of data either side of the inflection point were selected and a linear regression fitted. The slope of this regression represents the maximum combustion rate in g s . As preliminary results found this FLAMMABILITY combustibility trait to be strongly driven by the biomass of the sam- ple, interspecific comparisons were standardized for mass. Therefore, Flammability was represented by three components: ignitability, com- maximum combustion rate was plotted against mass change for each bustibility and sustainability (Anderson 1970). All components were species, and linear models were fitted to the fresh, dry and combined measured for each individual at the leaf scale via epiradiator tests. In data sets. As there was no change in mass common to all 25 species, addition, combustibility and sustainability were determined at the the y-intercept extracted from the model fitted to the combined data plant scale by burning partial plant canopies. Plant-scale measurement set was used to characterize the intrinsic combustibility of each spe- of ignitability was beyond the scope of this experiment; however, a cies. The combined data set was used as the slopes of the models fit- community-level measure was obtained by estimating the rate of fire ted to the fresh and dry data did not differ significantly for any spread for each individual by parameterizing Rothermel’s (1972) fire species, and model fit was improved by combining the data sets. Any spread model with plant trait data. Leaf- and plant-scale flammability unpaired samples were excluded to ensure a balanced data set of fresh components were measured both on fresh and dry biomass to deter- and dry samples. The y-intercept differed significantly between fresh mine the effect of moisture content. The ‘fresh’ clump was kept in a and dry models for three species (Panicum sp., Hyparrhenia hirta sealed plastic bag at room temperature, and the ‘dry’ clump was first and Merxmuellera stricta) and in these cases, the y-intercept was dried at 70 °C for a minimum of 48 h. extracted from linear models fitted to the fresh data set. Leaf-scale ignitability, sustainability and combustibility were mea- Forward fire spread rate values, the community-scale measure of sured as time to ignition, flaming time and mass loss rate, respec- ignitability, were predicted for each individual using Rothermel’s tively, using a Quartz infrared 500 W epiradiator (Helios, Italquartz, (1972) surface fire spread model as implemented using the ros() func- Milan, Italy) in a fume cupboard with a constant vertical windspeed 1 tion in the Rothermel package (Vacchiano & Ascoli 2014) in R (R of 0.1 m s . As application of leaf material directly to the epiradia- Core Team 2013). Fire behaviour was simulated for each individual tor’s silica disc surface always caused instantaneous combustion, by parameterizing the model with data for the following traits: leaf 2-mm wire mesh was positioned 1 cm above the epiradiator’s surface. SA/V ratio, leaf EHoC, biomass moisture content, plant height and The background temperature at the mesh surface (without fuel), mea- fuel load (biomass quantity divided by the estimated cover area). See sured by a thermocouple connected to a data-logger, ranged between Table S3 for a details of the procedure followed and model assump- 370 and 400 °C. Samples of 0.2 g (0.001 g) leaf material were cut tions. into 2-cm segments to standardize between samples and applied to the centre of the mesh. The 0.2 g mass was used because preliminary studies found that smaller masses failed to ignite, while larger fuel PHYLOGENETIC ANALYSIS masses increased the risk that other fuel properties, particularly fuel height, influenced flammability values. Smaller samples were used for We constructed a phylogeny that was initially based on a previously Aristida congesta subsp. barbicollis due to the low leaf mass of this generated data set for grasses composed of the plastid markers species. Each test was filmed at 25 frames s , and (i) time to igni- trnKmatK, ndhF and rbcL (Grass Phylogeny Working Group II 2012) tion (TTI; the time between sample application to the epiradiator and and augmented here. For ten species not represented in this previous first flaming) and (ii) flaming time (FT; the time from ignition to data set, a fragment of trnKmatK was PCR-amplified from genomic flame extinction) were subsequently determined. As samples were DNA, following protocols and primers described previously (Grass completely combusted by applying them to the epiradiator, an average Phylogeny Working Group II 2012). The newly generated sequences leaf combustion rate was obtained by dividing the mass of samples have been submitted to NCBI database (Benson et al. 2012) under by FT. Species average values for TTI and FT were obtained for the accession numbers KP860326 to KP860336. The new markers fresh and dry material. The influence of leaf moisture content on were manually aligned to the data set, which consisted of 606 taxa these flammability traits was determined as the difference in values and 5649 aligned bp. This initial data set was downsized to 70 spe- between fresh and dry samples of each individual and averaged per cies, including all the taxa studied here and representatives of all species. grass lineages. A time-calibrated phylogenetic tree was obtained through Bayesian inference as implemented in BEAST (Bayesian evo- As canopy architecture influences grass flammability (Martin lutionary analysis by sampling trees; Drummond & Rambaut 2007). 2010), a method that measures plant-scale flammability traits was uti- A general time-reversible substitution model with a gamma-shape lized. Fresh and dry plant material from each individual were clamped parameter and a proportion of invariants (GTR+G+I) were used. The on a stand on a four-point balance (Mark 205A; Bel Engineering, log-normal relaxed clock was selected. The tree prior was modelled Monza, Italy) and burnt in a fume cupboard with a constant by a Yule process. The monophyly of the BEP-PACMAD clade was 0.1 m s vertical wind speed (see Figure S1 for diagram of the set- enforced, leaving Puelia olyriformis as the outgroup. The calibration up used). Samples were ignited by directing a Bunsen burner flame to prior for the age of the BEP-PACMAD crown was set to a normal the side of the base of the clump at a 45° angle and a 5 cm distance for a maximum of 3 s (less if ignition happened earlier). This resulted distribution, with a mean of 51.2 and a standard deviation of 0.001 in successful ignition in all individuals. Mass loss was logged at 0.2-s (mean based on Christin et al. 2014). Two independent runs were intervals and the sigmoidal relationship produced was fitted with a conducted for 10 000 000 generations, sampling a tree every 1000 Boltzmann equation. Data were excluded if fitting the relationship generations. The convergence of the runs and the appropriateness of was not possible due to noise around the curve (n = 40/350), which the burn-in period, set to 2 000 000 generations, were verified using © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 142 K. J. Simpson et al. Tracer (Rambaut A, Drummond AJ (2007) Tracer v1.4, available at P < 0.001), sustainability (F = 3.02, P < 0.001) and 24,144 http://beast.bio.ed.ac.uk/Tracer). Median ages were mapped on the combustibility (F = 2.97, P < 0.001). Ignition delays 24,144 maximum-credibility tree. The relationships among the species studied ranged from 1.0 s (H. hirta) to 4.0 s (C. setaceus) with a here were extracted from this tree and used for comparative analyses. mean across species of 1.7 s. The mean flaming duration across species was 6.3 s and ranged from 4.3 s (A. congesta subsp. barbicollis) to 7.6 s (Eragrostis plana). Connected to DATA ANALYSIS flaming duration was average combustion rate, with E. plana Statistical analyses were carried out in the R environment (R Core 1 burning at the slowest rate (27 mg s ) and A. congesta Team 2013). Data were log-transformed to improve normality and to subsp. barbicollis at the fastest (49 mg s ). meet model assumptions where necessary. At the plant scale, intrinsic combustibility (for a given bio- Analysis of variance (ANOVA) was used to determine whether plant mass) differed by <2.5-fold across species, ranging from and flammability traits differed significantly between species. The influ- 1 1 0.064 g s (Eustachys paspaloides) to 0.163 g s (The- ence of species, and state (‘fresh’ or ‘dry’), on leaf-scale flammability meda triandra). When investigating the relationship between was determined by two-way ANOVA. As biomass quantity values for the combustion rate and biomass, the bivariate mixed effects plant-scale burns are not representative of the species (i.e. for each spe- cies, clumps were subsampled and a range of masses were burnt), a spe- model revealed that within-species slopes (pooled cies effect on the relationship between maximum combustion rate and mean = 0.594, HPD: 0.507 to 0.707) and across-species biomass quantity was tested using the R package MCMCglmm (Had- slopes (mean = 0.797, HPD: 0.067 to 1.385) did not differ field 2010). This approach implements Markov chain Monte Carlo rou- significantly (mean slope difference (Db) = 0.212, HPD: tines for fitting generalized linear mixed models, while accounting for 0.521 to 0.683) when accounting for phylogeny (Fig. 2). non-independence and correlated random effects arising from phyloge- This common relationship was extrapolated while taking into netic relationships (Hadfield 2010). We fitted flammability (maximum account intrinsic combustibility differences, allowing combus- combustion rate) and biomass quantity as a bivariate normal response, tion values to be predicted for the species mean total biomass. and species as a random effect. Models were run for 500 000 iterations These predicted values of whole-plant combustion rates varied with a burn-in of 1000 iterations, a thinning interval of 500 and weakly- >20-fold among species, ranging from 0.06 g s (A. con- informative priors (V = diag(2), nu = 0.002). The 95% highest poste- gesta subsp. barbicollis) to 1.28 g s (M. disticha; Fig. 2). rior densities (HPD) of within-species and across-species slopes and the Fuel models based on the traits of C. setaceus predicted no difference between slopes were estimated while accounting for phy- logeny and used to assess whether slopes differed among species. fire spread, because biomass moisture content values To test the hypotheses put forward in Table 1 and to establish the exceeded the moisture of extinction, defined as the fuel mois- strength and direction of plant trait contributions to flammability com- ture content above which a steady rate of fire spread is not ponents, a MCMC multi-response generalized linear mixed model possible. Of the remaining species that spread fire, the esti- approach was used again. Traits were separated into leaf and plant mated rate of spread differed substantially (25-fold; Table S4) scale to ensure appropriate comparisons, using the same prior and and varied significantly between species (ANOVA: specifications as before. The fit of the models to data was established F = 42.42, P < 0.001). 24,150 by fitting linear models between the observed flammability trait val- Substantial interspecific variation was also found in the five ues and those predicted by the models. The contribution of plant traits traits measured as explanatory traits for flammability (Fig. 1; to fire spread rate was tested to determine whether strong relation- see Table S5). Biomass moisture content values of the non- ships occurred across species when accounting for phylogeny, while native C. setaceus were substantially higher than the other acknowledging that some circularity is involved because spread rate was predicted based on the values of these traits. species. However, species still differed significantly for this To explore the pattern of covariance among plant and flammability trait when C. setaceus was excluded (ANOVA: F = 14.39, 23,144 traits, principal component analyses were performed using the prin- P < 0.001). The measurement of biomass density (i.e. vertical comp function (R core team 2013). Linear regressions were also used biomass distribution) produced consistent values within spe- to establish the relationships among plant and flammability traits, with cies (Fig. S2; species average CV = 28%), but considerable the latter being split into leaf-scale and plant-scale traits for analyses differences among species with slope values ranging from to ensure an appropriate comparison. The relationships between 0.155 (Eragrostis lehmanniana) to 0.831 (M. stricta). flammability traits measured at different scales were also established Collection site did not influence flammability traits. Of the using linear regressions. plant traits, vertical biomass distribution (P = 0.008) and leaf The influence of evolutionary history was established for each EHoC (P = 0.046) were the only ones affected by collection plant and flammability trait by testing for the presence of a phyloge- site (see Table S7). netic signal. This was done using the pgls function in the caper pack- age (Orme et al. 2012) which estimated Pagel’s k. TRAIT CONTRIBUTIONS TO FLAMMABILITY Results Measured plant traits significantly predicted the components of flammability, particularly ignitability and plant-scale com- FLAMMABILITY VARIATION AMONG SPECIES bustibility, in which 70% and 58% of variation could be All flammability components varied considerably across spe- explained by the plant traits, respectively (Tables 2 and 3). cies (Fig. 1; Table S4). At the leaf-scale, significant inter- Variation in sustainability could be explained to a lesser specific variation was found in ignitability (F = 5.02, extent by plant traits at the leaf (47%) and plant scale (37%), 24,144 © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 Determinants of flammability in savanna grasses 143 Fig. 1. The evolutionary relationships between species and average values of explanatory plant traits (solid circles) and flammability traits (open circles). Trait values are indicated by the size of the circles. A nonzero phylogenetic signal was found for leaf SA/V ratio (Pagel’s k = 1; P = 1 for k = 1; P < 0.001 for k = 0), leaf flaming time (Pagel’s k = 0.45; P = 1.0 for k = 1; P < 0.001 for k = 0) and leaf combustion rate (Pagel’s k = 0.99; P = 0.93 for k = 1; P = 0.037 for k = 0). as well as variation in leaf-scale combustibility (39%). The direction of relationships between plant and flammability traits is consistent with those predicted in Table 1, but there are exceptions. Both biomass density and leaf SA/V ratio were expected to correlate positively with predicted spread rate, but instead correlated negatively (Table 3). Moisture content was key in determining leaf-scale flamma- bility components (Table 2; Table S6). Ignitability was partic- ularly influenced by moisture content, with fresh leaf material taking 42% longer to ignite on average than dry leaf material across species, with a maximum increase of 288% seen for C. setaceus (1.0 s dry vs. 4.0 s fresh). Once alight, fresh leaf material also burned on average for 7% longer at a 3% lower combustion rate compared to dry leaf material across species. Fig. 2. Relationships between biomass quantity and maximum com- Leaf SA/V ratio significantly influenced sustainability, with bustion rate across 25 grass species. The mean slopes of within- high values associated with low flaming duration. The EHoC species relationships (grey lines) and across-species relationships of leaf material alone contributed little to overall leaf-scale (black dotted line) for maximum combustion rate with biomass burned do not differ significantly when phylogeny is accounted for. flammability when compared to moisture or SA/V ratio Data points are shown as grey circles. Estimates of whole-plant com- (Table 2). bustion rates (black diamonds) showed substantial variation (>20- At the plant scale, biomass quantity was by far the stron- fold). These values were calculated by extrapolating the common gest driver of sustainability and combustibility (Table 3). across-species relationship (black dashed line) to species mean total Plants with greater biomass burnt at a faster rate and for biomass values while taking into account the intrinsic combustibility differences among species. longer. Biomass density and moisture content significantly © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 144 K. J. Simpson et al. contributed to plant-scale combustibility, such that plants with determining the overall predicted rate of spread (Table 3; high density and low moisture content combusted most Fig. S2). rapidly (Table 3). The EHoC of leaf material significantly contributed to sustainability with high values associated with TRAIT COVARIANCE short flaming times (Table 3). Leaf SA/V ratio did not signifi- cantly contribute to plant-scale combustibility or sustainabil- Principal components analysis (PCA) and linear regressions ity. were used to explore patterns of covariance among the plant Biomass load, moisture content, density and leaf SA/V and flammability trait variables, with the latter being split into ratio all contributed highly to predicted fire spread rate when leaf-scale and plant-scale traits (Fig. 3). For the plant traits, taking phylogeny into account (Table 3). Fuel load con- the first two principal components accounted for 67.6% of the tributed directly to reaction intensity and indirectly to the total variance. The first axis related to the chemical properties propagating flux ratio, via bulk density. Biomass moisture of biomass and how it is arranged spatially (leaf EHoC, bio- content contributed to spread rate by increasing the heat mass moisture content and density had the highest axis load- required for ignition and damping the reaction intensity (see ings). Leaf SA/V ratio loaded most heavily on the second Fig. S2). Leaf SA/V ratio influenced reaction intensity and axis, followed by biomass moisture content and density. Only the proportion of this reaching adjacent fuel (propagating flux biomass quantity did not fall as clearly on the first two princi- ratio), as well as the proportion of fuel raised to ignition tem- pal components, which we believe is due to the high variation perature (effective heating number; Fig. S2). Leaf EHoC con- within the data (CV = 89.0%). For the leaf-scale flammability tributed to the reaction intensity but played a small part in traits, the first two principal components accounted for 95.1% Table 2. The contribution of plant traits to leaf-scale flammability components as determined by MCMC phylogenetic generalized linear mixed models. Values represent posterior mean estimates of the slopes, the upper and lower 95% confidence intervals and P values (those in bold are significant at P = 0.05). In combination, species mean trait values of leaf moisture content, SA/V ratio and effective heat of combustion (EHoC) 2 2 significantly predicted ignitability (F = 398.3, P < 0.001, R = 0.70), sustainability (F = 147.5, P < 0.001, R = 0.47) and combustibility 1,166 1,166 (F = 105.4 P < 0.001, R = 0.39) 1,166 Leaf moisture content* Leaf SA/V ratio log Leaf EHoC Ignitability (time to ignition) Estimate 0.691 0.174e-3 0.135e-4 (95% CI) (0.620 to 0.760) (0.420e-3 to 0.872 e-5) (0.527e-4 to 0.290e-4) P value <0.001 0.17 0.49 Sustainability (flaming time) Estimate 0.492 0.876e-3 0.159e-4 (95% CI) (0.421 to 0.567) (0.142e-2 to -0.359 e-4) (0.626e-4 to 0.113e-3) P value <0.001 0.002 0.741 Combustibility (combustion rate) Estimate 0.303e-2 0.522e-5 0.227e-6 (95% CI) (0.406e-2 to 0.170e-2) (0.547e-5 to 0.164e-4) (0.254e-5 to 0.193e-5) P value <0.001 0.36 0.86 *Parameter characterized as: the species mean difference in ignition delay (for ignitability) or flaming duration (for sustainability and combustibil- ity) between fresh and dry leaf material for each individual. Table 3. The contribution of plant traits to plant-scale flammability components as determined by MCMC phylogenetic generalized linear mixed models. Values represent posterior mean estimates of the slopes, the upper and lower 95% confidence intervals and P values (those in bold are significant at P = 0.05). Values represent posterior mean estimates of the slopes, the upper and lower 95% confidence intervals and P values (those in bold are significant at P = 0.05). In combination, the five plant traits significantly predicted sustainability (F = 90.07, P < 0.001, 1,151 2 2 2 R = 0.37), combustibility (F = 210.8, P < 0.001, R = 0.58) and ignitability (F = 184.2, P < 0.001, R = 0.51) 1,151 1,173 log Biomass log Biomass log Biomass quantity density moisture content Leaf SA/V ratio log Leaf EHoC* Sustainability Estimate 0.434 0.614 1.036 0.050 0.012 (flaming time) (95% CI) (0.350 to 0.517) (2.162 to 0.889) (0.688 to 2.753) (0.162 to 0.055) (0.023 to 0.001) P value <0.001 0.443 0.252 0.363 0.060 Combustibility Estimate 0.035 0.149 0.108 0.105e-2 0.580e-4 (maximum (95% CI) (0.028 to 0.041) (0.021 to 0.277) (0.250 to 0.027) (0.858e-2 to 0.012) (0.101e-2 to 0.103e-2) combustion rate) P value <0.001 0.024 0.116 0.910 0.826 Ignitability Estimate 2.002 0.061 0.034 0.128e-2 0.121e-3 (predicted (95% CI) (0.951 to 3.015) (0.094 to 0.033) (0.044 to 0.025) (0.789e3 to 0.169e-2) (0.993e-4 to 0.360e-3) spread rate) P value <0.001 <0.001 <0.001 <0.001 0.309 *Species mean values. © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 Determinants of flammability in savanna grasses 145 Fig. 3. Principal components analysis biplots of explanatory plant traits (a) and flammability traits at the leaf scale (b) and plant scale (c). The tables within each plot indicate the slope and significance of linear regressions between each pair of variables. Data for all traits were log- transformed to improve normality except leaf SA/V ratio. EHoC is the leaf effective heat of combustion. P < 0.1; *, P < 0.05; ***, P < 0.001. of the total variance. Leaf flaming time and combustion rate significantly in multiple components of flammability. This find- were negatively correlated (P < 0.001), and fell in opposing ing suggests that static classifications of grassy and herbaceous directions on the first PCA axis (Fig. 3), which reflects how vegetation as homogenous fuels mask considerable interspecific combustion rate was derived from flaming time. Time to igni- and community variation in flammability. Consequently, fire tion was unrelated to flaming time and combustion rate and behaviour predictions based on such fuel models may lose was orthogonal to both in the PCA (Fig. 3). For plant-scale accuracy when community composition is not accounted for. flammability traits, 71.8% of total variance is accounted for A substantial proportion of variation in ignitability and by the first two principal components. Traits did not separate combustibility (70% and 58%, respectively) can be explained on the first axis, but did on the second axis which related to by a combination of the five plant traits measured here. For burning intensity. High rates of plant combustion were associ- sustainability, a smaller proportion of variation was accounted ated with rapid predicted fire spread rates (P < 0.001) and for (37%), perhaps because this component is not only driven marginally with longer flaming times (P = 0.071; Fig. 3). by plant traits, but is also directly influenced by combustibil- The relationships between flammability traits measured at ity. Additionally, some variation in sustainability could be different scales were variable, with a significantly positive accounted for by traits relating to leaf chemistry, such as correlation found for ignitability (leaf time to ignition vs. nitrogen, phosphorus and tannin concentrations (Grootemaat predicted rate of spread; P = 0.025), but no significant corre- et al. 2015) that were not measured in this study. Biomass lation for combustibility (leaf-scale combustion rate vs. quantity was the key trait influencing plant-scale flammability plant-scale combustion rate; P = 0.29). components and also determined the influence of an individ- ual plant on local fire characteristics. The importance of bio- mass quantity for combustibility, sustainability and fire spread INFLUENCE OF EVOLUTIONARY HISTORY ON rates in the field is illustrated by the elevated flammability of FLAMMABILITY landscapes caused by the raised fuel load production of non- Support for a phylogenetic signal was found for leaf-scale native grasses (Hughes, Vitousek & Tunison 1991; D’Antonio combustibility (Pagel’s k = 0.99; P = 0.93 for likelihood & Vitousek 1992; Rossiter et al. 2003). While making a rela- ratio test against k = 1; P = 0.037 against k = 0) and sustain- tively small contribution to flammability components once ability (Pagel’s k = 0.45; P = 0.67 against k = 1; P = 0.011 alight, biomass moisture content was key to ignitability, with against k = 0), but not for the other flammability traits. Of higher moisture contents requiring more energy to dry and the plant traits, there was a strong phylogenetic signal for leaf heat biomass to the point of ignition (Trollope 1978; Gill & SA/V ratio (Pagel’s k = 1.00; P = 1.00 against k = 1; Moore 1996; Alessio et al. 2008; Plucinski & Anderson P < 0.001 against k = 0), with closely related species tending 2008). By influencing ignitability, and therefore the likelihood to have similar values of leaf SA/V ratio. No phylogenetic of fire occurring in the first place, moisture content exerts a signal was found in the other plant traits. strong influence on vegetation flammability. Our finding of high interspecific variation in EHoC (effective heat of com- bustion) also conflicts with the notion that grass energy con- Discussion tent is an almost constant value (Trollope 1984). However, This large comparative study of grass flammability provides EHoC contributed little to leaf-scale flammability components, strong support for the hypothesis that grass species vary supporting the idea that this intrinsic property is less © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 146 K. J. Simpson et al. important in determining flammability than fuel mass, struc- grasses that have higher fuel loads, rapid combustion is not ture and moisture content (Bond & van Wilgen 1996). associated with lowered burning durations and a subsequent Despite this small importance overall, the EHoC marginally reduction in heat transfer to the soil and below-ground plant contributed to plant-scale flaming time. parts. The interspecific variation in flammability components The inconsistent relationships between components of observed across a set of species that commonly coexist in the flammability, and within flammability components measured field further suggests a role for interspecific competition in at different scales, suggest that descriptions of flammability promoting flammability as an adaptive trait. Potentially, should incorporate all suitable components and should be enhanced plant flammability can increase the mortality of taken at an appropriate scale. The mixed covariance between neighbouring, less fire-tolerant individuals and thereby reduce flammability components found here suggests that one cannot post-fire competition (Bond & Midgley 1995). Furthermore, always be used as a proxy for the others. Therefore, studies some evidence provides intriguing support for a link between that consider one or even two components of flammability high flammability and ecological success in fire-prone grass- may mask the complexity of vegetation flammability (Ander- land species (Ripley et al. 2015). The influence of flammabil- son 1970). Similar to the findings of Martin (2010), we find ity at the species level on grassland community-level support for the importance of incorporating plant architecture flammability has not been determined. However, findings into measurements of grass flammability. Inconsistencies from other vegetation fuel types show that flammability tends to be driven by the most flammable species of a community, between combustibility at the leaf- and plant-scale highlight that other factors (such as biomass quantity and density) are such that fuel loads are non-additive (van Altena et al. 2012; key determinants of combustibility at the plant scale. Bench- de Magalh~aes & Schwilk 2012). The knowledge gained in scale measurements of flammability have been criticized as this study can be used in further work to determine whether not being representative of flammability in the field (Fernan- high flammability is an adaptation to life in frequently burnt des & Cruz 2012), and our findings emphasize the need for environments for grasses and has thus been a fundamental caution when extrapolating flammability traits between differ- trait in grass evolution. In addition, the knowledge of inter- ent scales. In comparison with leaf-scale studies, the flamma- specific variation in grass flammability obtained here can lead bility component values obtained here are more representative to a better understanding of wildfire behaviour, particularly in of flammability in the field because they are measured at the grassland ecosystems. This could potentially contribute to an plant scale and on field-state plants that are at the phenologi- improvement of global carbon modelling and lead to new cal stage most appropriate to fire occurrence. insights about ecosystem feedback to fire. The phylogenetic signal found in some flammability com- ponents (leaf-scale combustibility and sustainability) suggests Acknowledgements that evolutionary history may partially explain patterns of Research support was provided by a Natural Environment Research Council grass flammability and the strong sorting of grass lineages studentship to K.J.S., Royal Society University Research Fellowship across fire-frequency gradients (Uys, Bond & Everson 2004; URF120119 to P.A.C. and URF120016 to G.H.T. and a European Research Council Starter Grant ERC-2013-StG-335891-ECOFLAM to C.M.B. Author Visser et al. 2012; Forrestel, Donoghue & Smith 2014). How- contributions: K.J.S., G.H.T., B.S.R., C.M.B., C.E.R.L. and C.P.O. designed ever, conclusions on phylogenetic signal derived from a small the study. K.J.S., B.S.R. and P.A.C. generated the data. K.J.S., P.A.C., B.S.R., phylogeny must remain cautious due to low statistical power G.H.T. and C.P.O. analysed the data. K.J.S. wrote the manuscript with the help of all the authors. We thank Tony Palmer, Claire Adams and Nosipho Plaatjie (Boettiger, Coop & Ralph 2012). for their support in the laboratory and field, Albert Phillimore for assistance Through their flammability, plants may modify the fire with the MCMCglmm analyses and James Simpson for his help with graphics. regime they experience in order to increase their fitness in We also thank Hans Cornelissen and two anonymous referees for their con- fire-prone environments (Schwilk 2003). Resprouting grasses structive comments on the manuscript. are likely to benefit from frequent fires that remove standing biomass and maintain an open canopy, because they are typi- Data accessibility cally intolerant of shading (Everson, Everson & Tainton Trait data: Species average values uploaded as online supporting information; 1988; Bond 2008). The grasses studied here showed high raw data available in DRYAD entry doi: 10.5061/dryad.2c506. ignitability, combustibility and predicted fire spread rates, Sequence data: GenBank accession numbers available as online supporting when compared to woody vegetation fuels (e.g. Pausas et al. information. 2012; Ganteaume et al. 2013). Furthermore, grasses are able Phylogeny: Nexus file available in DRYAD entry doi: 10.5061/dryad.2c506. to regrow quickly after fire. This combination of high MCMCglmm R Script: Available in DRYAD entry doi: 10.5061/ dryad.2c506. flammability and rapid regrowth drives a fire regime charac- terized by high fire frequency (Grigulis et al. 2005). Plant- scale combustion rate was marginally positively related to References flaming time, with high biomass plants burning at a faster rate Alessio, G.A., Penuelas, J., Llusia, J., Ogaya, R., Estiarte, M. & De Lillis, M. and for longer. This finding is in contrast with other studies (2008) Influence of water and terpenes on flammability in some dominant (e.g. de Magalh~aes & Schwilk 2012) that found a negative Mediterranean species. International Journal of Wildland Fire, 17, 274–286. Allan, G.E. & Southgate, R.I. (2002) Fire regimes in the spinifex landscapes of relationship between the two. It also does conflicts with the Australia. Flammable Australia: the Fire Regimes and Biodiversity of a Con- idea of high flammability providing resprouting plants protec- tinent (eds R.A. Bradstock, J.E. Williams & A.M. Gill), pp. 145–176. tion against lethal temperatures (Gagnon et al. 2010), as for Cambridge University Press, Cambridge, UK. © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 Determinants of flammability in savanna grasses 147 van Altena, C., van Logtestijn, R.S.P., Cornwell, W.K. & Cornelissen, J.H.C. Glasspool, I.J., Edwards, D. & Axe, L. (2004) Charcoal in the Silurian as evi- (2012) Species composition and fire: non-additive mixture effects on ground dence for the earliest wildfire. Geology, 32, 381–383. fuel flammability. Frontiers in Plant Science, 3, 63. Grass Phylogeny Working Group II (2012) New grass phylogeny resolves deep Anderson, H.E. (1970) Forest fuel ignitability. Fire Technology, 6, 312–319. evolutionary relationships and discovers C origins. New Phytologist, 193, Anderson, H.E. (1982). Aids to determining fuel models for estimating fire 304–312. behavior. USDA Forest Service, Intermountain Forest and Range Experiment Grigulis, K., Lavorel, S., Davies, I.D., Dossantos, A., Lloret, F. & Montserrat, Station. General Technical Report INT-122, 22. V. (2005) Landscape-scale positive feedbacks between fire and expansion of Archibald, S., Lehmann, C.E.R., Gomez-Dans, J.L. & Bradstock, R.A. (2013) the large tussock grass, Ampelodesmos mauritanica in Catalan shrublands. Defining pyromes and global syndromes of fire regimes. Proceedings of the Global Change Biology, 11, 1042–1053. National Academy of Sciences of the United States of America, 110, 6442– Grootemaat, S., Wright, I.J., van Bodegom, P.M., Cornelissen, J.H.C. & 6447. Cornwell, W.K. (2015) Burn or rot: leaf traits explain why flammability and Beckage, B., Platt, W.J. & Gross, L.J. (2009) Vegetation, fire, and feedbacks: a decomposability are decoupled across species. Functional Ecology, 29, disturbance mediated model of savannas. The American Naturalist, 174, 1486–1497. 805–818. Hadfield, J.D. (2010) MCMC methods for multi-response generalized linear Belcher, C.M., Mander, L., Rein, G., Jervis, F.X., Haworth, M., Hesselbo, S.P., mixed models: the MCMCglmm R package. Journal of Statistical Software, Glasspool, I.J. & McElwain, J.C. (2010) Increased fire activity at the Trias- 33,1–22. sic/Jurassic boundary in Greenland due to climate-driven floral change. Nat- He, T., Lamont, B.B. & Downes, K.S. (2011) Banksia born to burn. New Phy- ure Geoscience, 3, 426–429. tologist, 191, 184–196. Benson, D.A., Karsch-Mizrachi, I., Clark, K., Lipman, D.J., Ostell, J. & Sayers, Hughes, F., Vitousek, P.M. & Tunison, T. (1991) Alien grass invasion and fire E.W. (2012) GenBank. Nucleic Acids Research, 40, D48–D53. in the seasonal submontane zone of Hawaii. Ecology, 72, 743–746. Boettiger, C., Coop, G. & Ralph, P. (2012) Is your phylogeny informative? Keeley, J.E., Pausas, J.G., Rundel, P.W., Bond, W.J. & Bradstock, R.A. (2011) Measuring the power of comparative methods. Evolution, 66, 2240–2251. Fire as an evolutionary pressure shaping plant traits. Trends in Plant Science, Bond, W.J. (2008) What limits trees in C grasslands and savannas? Annual 16, 406–411. Review of Ecology Evolution and Systematics, 39, 641–659. Lamont, B.B., Le Maitre, D.C., Cowling, R.M. & Enright, N.J. (1991) Canopy Bond, W.J. & Midgley, J.J. (1995) Kill thy neighbor – an individualistic argu- seed storage in woody plants. Botanical Review, 57, 277–317. ment for the evolution of flammability. Oikos, 73,79–85. de Magalh~aes, R.M.Q. & Schwilk, D.W. (2012) Leaf traits and litter flammabil- Bond, W.J. & van Wilgen, B.W. (1996) Fire and plants. Population and Com- ity: evidence for non-additive mixture effects in a temperate forest. Journal munity Biology Series, 14. Chapman & Hall, London, UK. of Ecology, 100, 1153–1163. Bond, W.J., Woodward, F.I. & Midgley, G.F. (2005) The global distribution of Martin, T. (2010) Photosynthetic and evolutionary determinants of the response ecosystems in a world without fire. New Phytologist, 165, 525–537. of selected C and C (NADP-ME) grasses to fire. MSc Thesis, Rhodes 3 4 Bradstock, R.A. & Auld, T.D. (1995) Soil temperature during experimental University, Grahamstown, South Africa. bushfire in relation to fire intensity: consequences for legume germination Milton, S.J. (2004) Grasses as invasive alien plants in South Africa. South Afri- and fire management in south-eastern Australia. Journal of Applied Ecology, can Journal of Science, 100,69–75. 32,76–84. Murray, B.R., Hardstaff, L.K. & Phillips, M.L. (2013) Differences in leaf Brooks, M.L., D’Antonio, C.M., Richardson, D.M., Grace, J.B., Keeley, J.E., flammability, leaf traits and flammability-trait relationships between native DiTomaso, J.M., Hobbs, R.J., Pellant, M. & Pyke, D. (2004) Effects of inva- and exotic plant species of dry sclerophyll forest. PLoS One, 8, e79205. sive alien plants on fire regimes. BioScience, 54, 677–688. Nelson, R.M. Jr (2001) Water relations of forest fuels. Forest Fires: Behavior Byram, G.M. (1959) Combustion of forest fuels. Forest Fire: Control and Use and Ecological Effects (eds E.A. Johnson & K. Miyanishi), pp. 79–149. Aca- (ed K.P. Davis), pp. 61–89. McGraw-Hill, New York, NY, USA. demic Press, San Diego, CA, USA. Christin, P.-A., Spriggs, E., Osborne, C.P., Stromberg, C.A.E., Salamin, N. & Orme, D., Freckleton, F.P., Thomas, G.H., Petzoldt, T., Fritz, S., Isaac, N. & Edwards, E.J. (2014) Molecular dating, evolutionary rates, and the age of the Pearse, W. (2012) The Caper package: comparative analysis of phylogenet- grasses. Systematic Biology, 63, 153–165. ics and evolution in R. Available at: http://cran.r-project.org/web/packages/ Cornwell, W.K., Elvira, A., van Kempen, L., van Logtestijn, R.S.P., Aproot, A. caper. & Cornelissen, J.H.C. (2015) Flammability across the gymnosperm phy- Overbeck, G.E. & Pfadenhauer, J. (2007) Adaptive strategies in burned sub- logeny: the importance of litter particle size. New Phytologist, 206, 672–681. tropical grassland in southern Brazil. Flora, 202,27–49. D’Antonio, C.M. & Vitousek, P.M. (1992) Biological invasions by exotic Papio, C. & Trabaud, L. (1991) Comparative-study of the aerial structure of 5 grasses, the grass/fire cycle, and global change. Annual Review of Ecology shrubs of mediterranean shrublands. Forest Science, 37, 146–159. and Systematics, 23,63–87. Pausas, J.G. & Bradstock, R.A. (2007) Fire persistence traits of plants along a Drummond, A.J. & Rambaut, A. (2007) BEAST: bayesian evolutionary analy- productivity and disturbance gradient in mediterranean shrublands of south- sis by sampling trees. BMC Evolutionary Biology, 7, 214. east Australia. Global Ecology and Biogeography, 16, 330–340. Emerson, B.C. & Gillespie, R.G. (2008) Phylogenetic analysis of community Pausas, J.G. & Keeley, J.E. (2014) Evolutionary ecology of resprouting and assembly and structure over space and time. Trends in Ecology & Evolution, seeding in fire-prone ecosystems. New Phytologist, 204,55–65. 23, 619–630. Pausas, J.G., Alessio, G.A., Moreira, B. & Corcobado, G. (2012) Fires enhance Everson, C.S., Everson, T.M. & Tainton, N.M. (1988) Effects of intensity and flammability in Ulex parviflorus. New Phytologist, 193,18–23. height of shading on the tiller initiation of 6 grass species from the highland Philpot, C.W. (1969) Seasonal changes in heat content and ether extractive sourveld of natal. South African Journal of Botany, 54, 315–318. content of chamise. Research Paper INT-61. USDA Forest Service, Inter- Fernandes, P.M. & Cruz, M.G. (2012) Plant flammability experiments offer mountain Forest and Range Experiment Station, Ogden, UT, USA. limited insight into vegetation-fire dynamics interactions. New Phytologist, Plucinski, M.P. & Anderson, W.R. (2008) Laboratory determination of factors 194, 606–609. influencing successful point ignition in the litter layer of shrubland vegeta- Fonda, R.W. (2001) Burning characteristics of needles from eight pine species. tion. International Journal of Wildland Fire, 17, 628–637. Forest Science, 47, 390–396. Pyne, S.J. (1984) Introduction to Wildland Fire – Fire Management in the Uni- Forrestel, E.J., Donoghue, M.J. & Smith, M.D. (2014) Convergent phylogenetic ted States. Wiley, New York, NY, USA. and functional responses to altered fire regimes in mesic savanna grasslands R Core Team (2013) R: A Language and Environment for Statistical Comput- of North America and South Africa. New Phytologist, 203, 1000–1011. ing. R Foundation for Statistical Computing, Vienna, Austria. Available at Gagnon, P.R., Passmore, H.A., Platt, W.J., Myers, J.A., Paine, C.E.T. & http://www.R-project.org/. Harms, K.E. (2010) Does pyrogenicity protect burning plants? Ecology, 91, Ripley, B., Donald, G., Osborne, C.P., Abraham, T. & Martin, T. (2010) 3481–3486. Experimental investigation of fire ecology in the C and C subspecies of 3 4 Ganteaume, A., Jappiot, M., Lampin, C., Guijarro, M. & Hernando, C. (2013) Alloteropsis semialata. Journal of Ecology, 98, 1196–1203. Flammability of some ornamental species in wildland-urban interfaces in Ripley, B., Visser, V., Christin, P.-A., Archibald, S., Martin, T. & Osborne, southeastern France: laboratory assessment at particle level. Environmental C.P. (2015) Fire ecology of C and C grasses depends on evolutionary 3 4 Management, 52, 467–480. history and frequency of burning but not photosynthetic type. Ecology, 96, Gill, A.M. & Moore, P.H.R. (1996) Ignitability of Leaves of Australian Plants, 2679–2691. p. 34. CSIRO Plant Industry, Canberra, Australia. Rossiter, N.A., Setterfield, S.A., Douglas, M.M. & Hutley, L.B. (2003) Testing Gill, A.M. & Zylstra, P. (2005) Flammability of Australian forests. Australian the grass-fire cycle: alien grass invasion in the tropical savannas of northern Forestry, 68,87–93. Australia. Diversity and Distribution, 9, 169–176. © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 148 K. J. Simpson et al. Rothermel, R.C. (1972) A mathematical model for predicting fire spread in Supporting Information wildland fuels. Research Paper INT-115. USDA Forest Service, Intermoun- tain Forest and Range Experiment Station, Ogden, UT, USA. Additional Supporting Information may be found in the online ver- Saura-Mas, S., Paula, S., Pausas, J.G. & Lloret, F. (2010) Fuel loading and flammability in the Mediterranean basin woody species with different post-fire sion of this article: regenerative strategies. International Journal of Wildland Fire, 19, 783–794. Scarff, F.R. & Westoby, M. (2006) Leaf litter flammability in some semi-arid Figure S1. Schematic drawing of the set-up used to measure plant- Australian woodlands. Functional Ecology, 20, 745–752. scale combustibility and sustainability. Schwilk, D.W. (2003) Flammability is a niche construction trait: canopy archi- tecture affects fire intensity. American Naturalist, 162, 725–733. Schwilk, D.W. & Caprio, A.C. (2011) Scaling from leaf traits to fire behaviour: Figure S2. Cumulative dry biomass over vertical plant height for the community composition predicts fire severity in a temperate forest. Journal grass species. of Ecology, 99, 970–980. Silva, I.A. & Batalha, M.A. (2010) Phylogenetic structure of Brazilian savannas Figure S3. The influence of plant traits on components of Rother- under different fire regimes. Journal of Vegetation Science, 21, 1003–1013. Tansey, K., Gregoire, J.-M., Defourny, P., Leigh, R., Pekel, J.-F., van Bogaert, mel’s (1972) fire spread rate model. E. & Bartholome, E. (2008) A new, global, multi-annual (2000–2007) burnt area product at 1 km resolution. Geophysical Research Letters, 35, L01401. Table S1. Climate data from plant collection sites. Tewarson, A. (2002) Generation of heat and chemical compounds in fires. The SFPE Handbook of Fire Protection Engineering, 3rd edn (eds P.J. DiNenno, Table S2. Grass species names, collection site and GenBank acces- D. Drysdale, C.L. Beyler & W.D. Walton), pp. 3–82. National Fire Protec- tion Association, Quincy, MD, USA. sion details. Trollope, W.S.W. (1978) Fire behaviour – a preliminary study. Proceedings of the Grassland Society of South Africa, 13, 123–128. Table S3. Plant traits values used to model the forward rate of fire Trollope, W.S.W. (1984) Fire in savanna. Ecological Effects of Fire in South spread (m min ). African Ecosystems (eds V. Booysen & N.M. Tainton), pp. 200–217. Springer-Verlag, Berlin, Germany. Uys, R.G. (2000) The effects of different burning regimes on grassland phytodiver- Table S4. Species mean flammability component values. sity. MSc thesis, Botany Department, University of Cape Town, South Africa. Uys, R.G., Bond, W.J. & Everson, T.M. (2004) The effect of different fire Table S5. Species mean plant trait values. regimes on plant diversity in southern African grasslands. Biological Conser- vation, 118, 489–499. Vacchiano, G. & Ascoli, D. (2014) An implementation of the Rothermel fire Table S6. Results of analysis of variance (two-way ANOVA with inter- spread model in the R programming language. Fire Technology, 50, 823– action) of leaf-scale flammability by species and state (fresh or dry). Verdu, M. & Pausas, J.G. (2007) Fire drives phylogenetic clustering in Mediter- Table S7. Mean plant trait values for the three collection sites. ranean Basin woody plant communities. Journal of Ecology, 95, 1316–1323. Visser, V., Woodward, F.I., Freckleton, R.P. & Osborne, C.P. (2012) Environ- mental factors determining the phylogenetic structure of C grass communi- ties. Journal of Biogeography, 39, 232–246. Received 19 March 2015; accepted 26 October 2015 Handling Editor: Hans Cornelissen © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Ecology Wiley

Loading next page...
 
/lp/wiley/determinants-of-flammability-in-savanna-grass-species-Npjc0vO06F

References (84)

Publisher
Wiley
Copyright
Journal of Ecology © 2016 British Ecological Society
ISSN
0022-0477
eISSN
1365-2745
DOI
10.1111/1365-2745.12503
Publisher site
See Article on Publisher Site

Abstract

Journal of Ecology 2016, 104, 138–148 doi: 10.1111/1365-2745.12503 Determinants of flammability in savanna grass species 1 2 1 3 Kimberley J. Simpson , Brad S. Ripley , Pascal-Antoine Christin , Claire M. Belcher , 4 1 1 Caroline E. R. Lehmann , Gavin H. Thomas and Colin P. Osborne * 1 2 Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK; Department of Botany, Rhodes University, PO Box 94, Grahamstown 6140, South Africa; College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4PS, UK; and School of GeoSciences, University of Edinburgh, Edinburgh EH9 3JN, UK Summary 1. Tropical grasses fuel the majority of fires on Earth. In fire-prone landscapes, enhanced flammabil- ity may be adaptive for grasses via the maintenance of an open canopy and an increase in spa- tiotemporal opportunities for recruitment and regeneration. In addition, by burning intensely but briefly, high flammability may protect resprouting buds from lethal temperatures. Despite these potential benefits of high flammability to fire-prone grasses, variation in flammability among grass species, and how trait differences underpin this variation, remains unknown. 2. By burning leaves and plant parts, we experimentally determined how five plant traits (biomass quantity, biomass density, biomass moisture content, leaf surface-area-to-volume ratio and leaf effec- tive heat of combustion) combined to determine the three components of flammability (ignitability, sustainability and combustibility) at the leaf and plant scales in 25 grass species of fire-prone South African grasslands at a time of peak fire occurrence. The influence of evolutionary history on flammability was assessed based on a phylogeny built here for the study species. 3. Grass species differed significantly in all components of flammability. Accounting for evolution- ary history helped to explain patterns in leaf-scale combustibility and sustainability. The five mea- sured plant traits predicted components of flammability, particularly leaf ignitability and plant combustibility in which 70% and 58% of variation, respectively, could be explained by a combina- tion of the traits. Total above-ground biomass was a key driver of combustibility and sustainability with high biomass species burning more intensely and for longer, and producing the highest pre- dicted fire spread rates. Moisture content was the main influence on ignitability, where species with higher moisture contents took longer to ignite and once alight burnt at a slower rate. Biomass den- sity, leaf surface-area-to-volume ratio and leaf effective heat of combustion were weaker predictors of flammability components. 4. Synthesis. We demonstrate that grass flammability is predicted from easily measurable plant func- tional traits and is influenced by evolutionary history with some components showing phylogenetic signal. Grasses are not homogenous fuels to fire. Rather, species differ in functional traits that in turn demonstrably influence flammability. This diversity is consistent with the idea that flammability may be an adaptive trait for grasses of fire-prone ecosystems. Key-words: biomass moisture content, biomass quantity, determinants of plant community diver- sity and structure, fire regime, functional traits, phylogeny, poaceae, resprouting persistence, recovery and recruitment (Emerson & Gillespie Introduction 2008). Fire is also multidimensional and its effects on vegeta- Fire is a disturbance that has shaped plant traits and floral tion depend on the characteristics of the local fire regime communities for over 420 million years (Glasspool, Edwards (Keeley et al. 2011), which can vary considerably in fre- & Axe 2004; Bond, Woodward & Midgley 2005) and acts as quency, intensity, size and season (Archibald et al. 2013). a powerful selective filter for functional traits related to plant Different fire regimes can lead to the assembly of distinct populations and communities that are functionally clustered for diverse traits (Pausas & Bradstock 2007; Verdu & Pausas *Correspondence author: E-mail: c.p.osborne@shef.ac.uk 2007; Silva & Batalha 2010; Forrestel, Donoghue & Smith © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Determinants of flammability in savanna grasses 139 2014). For example, resprouting species are favoured in fre- Despite these predicted benefits of frequent fire to fire- quent, low-intensity fire regimes, and obligate seeders that prone grasses, interspecific variation in the flammability of persist via seedling recruitment are favoured in infrequent, such species has been little explored (Ripley et al. 2010), in high-intensity fire regimes (Pausas & Bradstock 2007; Pausas contrast to knowledge about interspecific variation in post-fire & Keeley 2014). response among grass species (Ripley et al. 2015). A histori- Plant flammability may both influence and be influenced by cal belief persists that grasses and other herbaceous plants fire regime (He, Lamont & Downes 2011; Pausas et al. 2012) vary little in their flammability, which has led to the diversity but species variation in flammability has received relatively of herbaceous fuels being reduced to one or few fuel classes little attention (but see Scarff & Westoby 2006; Murray, in fire behaviour modelling (e.g. Anderson 1982). Given the Hardstaff & Phillips 2013; Grootemaat et al. 2015). Flamma- considerable known variation in the flammability of woody bility is an emergent property of a plant’s chemical and physi- species (Schwilk 2003; Scarff & Westoby 2006; Pausas et al. cal traits. However, the identification of these traits in several 2012; Murray, Hardstaff & Phillips 2013), such presumptions fire-prone taxa, particularly herbaceous species, has not been are unfounded. Substantial changes in grassland community achieved. Flammability as a vegetation property consists of flammability resulting from invasion by non-native grasses several interdependent components (Anderson 1970) that can provide evidence to suggest considerable interspecific varia- each be quantified. Ignitability (the ease of ignition), com- tion in grass flammability (Hughes, Vitousek & Tunison 1991; Rossiter et al. 2003). In addition, recent evidence bustibility (the intensity of combustion) and sustainability (the maintenance of burning over time) are flammability compo- shows that grass traits relating to post-fire recovery are shaped nents and can be measured at multiple scales. For example, by fire regime (Forrestel, Donoghue & Smith 2014; Ripley ignitability is often measured as ignition delay at the leaf or et al. 2015), suggesting that traits relating to flammability plant scale, while the rate of fire spread is a measure of may be responding in similar ways, resulting in intra- and ignitability that operates at the community scale (Gill & interspecific variation in flammability. Zylstra 2005). Physical and chemical traits influencing some or all compo- Plant flammability is a key determinant of fire behaviour nents of flammability relate to the quantity, quality, moisture (Bond & van Wilgen 1996; Beckage, Platt & Gross 2009). In content and aeration of biomass (Bond & van Wilgen 1996; woody plants, flammability varies considerably between and Gill & Moore 1996). Biomass quantity is critical to com- within species (e.g. Fonda 2001; Saura-Mas et al. 2010; Pau- bustibility and fire spread rate because it directly influences sas et al. 2012; Cornwell et al. 2015), and minor changes in fire energy output rate (Byram 1959; Rothermel 1972). Bio- vegetation composition have repeatedly demonstrated signifi- mass moisture content determines the extent to which fuels cant alterations in vegetation flammability and fire regime absorb heat energy, with high values associated with delayed (Rossiter et al. 2003; Brooks et al. 2004; Belcher et al. ignition and low combustion and fire spread rates (Pyne 2010). Flammability may act as a means by which plants 1984; Nelson 2001). Biomass surface-area-to-volume (SA/V) modify fire regimes to engender favourable conditions ratio influences curing and reaction rates within fires (Papio (Schwilk 2003). For example, slow-growing, woody, obligate & Trabaud 1991; Gill & Moore 1996), with high values seeder species, such as Pinus species, require infrequent linked to rapid ignition, and rapid rates of combustion and intense fire to complete their life cycle. High-temperature fire spread. Increasing biomass density, defined as the mass crown fires are vital for releasing stored seeds from the of biomass per unit volume of fuel bed, raises fuel connectiv- retained mature cones of these serotinous species and enhanc- ity, therefore enhancing combustibility and fire spread rate. ing recruitment opportunities of seedlings via mortality of This relationship applies up to a certain threshold beyond neighbouring trees (Lamont et al. 1991; Keeley et al. 2011). which poor ventilation will limit drying and combustion rates In contrast, resprouting perennial grasses, which dominate (Rothermel 1972). Intrinsic properties of plant material, such grasslands and savannas (Uys 2000; Allan & Southgate 2002; as heat of combustion, affect combustibility and fire spread Overbeck & Pfadenhauer 2007), may benefit from very fre- rate through the amount of heat energy released during com- quent fire (Archibald et al. 2013). These shade-intolerant spe- plete combustion. Sustainability is often inversely related to cies require the regular removal of standing dead biomass combustibility and ignitability (e.g. de Magalh~aes & Schwilk (Everson, Everson & Tainton 1988) and woody growth (Bond 2012). Therefore, plant traits likely to enhance combustion 2008), which may be aided by high plant flammability. Sur- and spread rate may indirectly reduce flaming duration. In face fires in grassy systems are characterized by rapid com- contrast, high biomass quantity increases combustion and bustion and spread, low fire residence times and cool burn spread, but is also likely to enhance sustainability, as more temperatures (Bradstock & Auld 1995; Archibald et al. fuel takes longer to burn. Plant traits important to flammabil- 2013). Such fire characteristics are advantageous to resprout- ity have been identified in a number of fire-prone taxa (e.g. ing grass species, protecting basal meristems from excessive Ganteaume et al. 2013; Schwilk & Caprio 2011). However, heat through biomass that burns rapidly (Gagnon et al. 2010). the traits that influence grass flammability, and more generally In addition, high flammability, if linked to efficient post-fire the flammability of herbaceous species, have not been empiri- recovery, may provide enhanced regeneration opportunities cally established or explored. for these species by killing neighbouring plants and reducing We examined three components of flammability, at multiple post-fire competition (Bond & Midgley 1995). scales, for 25 species common in fire-prone South African © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 140 K. J. Simpson et al. grasslands. Five structural and chemical plant traits, known to For measurements of leaf SA/V ratio and EHoC, leaves were removed from a randomly selected tiller of each individual. Total leaf influence vegetation flammability, were measured and corre- area was measured on digital images using the computer program lated with flammability trait values (see Table 1). We hypoth- WinDIAS (Delta-T Devices, Cambridge, U.K.) that determines leaf esized that (i) there is significant interspecific variation in area by selecting pixels of a pre-defined colour range. Leaf thickness flammability among grass species and that (ii) the measured was measured, at the middle of the leaf and excluding the midrib, for plant traits can explain this variation, with each trait contribut- three leaves per tiller using digital callipers (accurate to 0.01 mm), ing to flammability components in different ways (see and an average value was calculated. Leaf SA/V ratio was calculated Table 1 for specific predictions). We also expected that from the average leaf area and leaf thickness of each species. flammability and plant traits covary due to the interdependent The heat of combustion is the energy released as heat when bio- relationships between flammability components and plant mass undergoes complete combustion with oxygen, which typically traits. The strong phylogenetic patterns in grass distributions relates to C:N ratio, lignin content and the presence of flammable across fire-frequency gradients (e.g. Visser et al. 2012; For- compounds (Philpot 1969; Bond & van Wilgen 1996). We measured the EHoC, which is the heat of combustion of pyrolysate vapours, restel, Donoghue & Smith 2014) led us to predict that (iii) and does not assume that all char is consumed. Compared to mea- flammability is influenced by evolutionary history and con- surements that involve the full thermal decomposition of biomass tains a phylogenetic signal. (such as in bomb calorimetry), EHoC is a more realistic estimate of the energy released from a wildfire in which combustion is incom- Materials and methods plete, and most of the energy is released from burning the pyrolysate vapours. Oven-dried leaf samples of known mass (5.0  0.4 mg) were conditioned at room temperature and humidity before being PLANT MATERIAL analysed in a microscale combustion calorimeter following the manu- Plants were collected during the natural fire season in July 2014 in facturer’s guidelines (FAA Micro Calorimeter, Fire Testing Technol- grassland and Nama-Karoo habitats near Grahamstown in the Eastern ogy Ltd, East Grinstead, UK). Each sample was held in nitrogen and Cape of South Africa (see Table S1 in Supporting Information for site heated at a rate of 3 °C per second driving off the volatile gases that details). Fire return times over the 2000–2006 period were 2.3 years were ignited and completely oxidized, and heat release was quantified for vegetation surrounding Grahamstown (Tansey et al. 2008). by oxygen depletion calorimetry (Tewarson 2002). Total heat release Seven individuals of 25 species, representing 5 grass subfamilies, was divided by the sample mass to provide the EHoC (kJ g ). Due were collected for study (see Table S2). All species were native to to the high repeatability of this trait measurement, material from three the region except Cenchrus setaceus, a North African invasive species randomly chosen individuals per species was tested in duplicate, to (Milton 2004). For each species, seven randomly selected, healthy- give an average value per individual and per species. looking adult plants were dug up while keeping their shoot For plant-scale traits, the height (maximum vertical distance from architecture intact. Plants were stored in sealed plastic bags at room ground level to the tallest point) and width (maximum horizontal temperature for a maximum of 48 h to minimize changes in moisture spread) of each clump was determined. Biomass density was mea- content. A specimen of each species was deposited at the Selmar sured using a novel method, which determined the vertical biomass Schonland Herbarium (Rhodes University). distribution for each individual. For this, the biomass of each clump was divided at five or more equal intervals along its vertical height, so that intervals were 2.5, 5, 10 or 15 cm in length depending on the STRUCTURAL AND CHEMICAL TRAITS plant height, and started at ground level. Each clump was cut with scissors at the selected intervals. The fresh and dry biomass of each A section of each individual (approximately one-third of the entire section were weighed to four decimal places, the latter after oven dry- plant), with its below-ground biomass and soil removed, was used to ing at 70 °C to a constant weight. Cumulative dry biomass was calcu- measure five structural and chemical plant traits. Biomass quantity, lated at each vertical height interval from ground level. Linear models density and moisture content were measured at the plant scale, while were fitted to the logged cumulative dry biomass and vertical height effective heat of combustion (EHoC) and SA/V ratio were measured for each individual. The slope of this relationship was used as a proxy at the leaf scale. Table 1. Matrix summarizing the predicted relationships between plant and flammability traits. Flammability traits were determined at different scales (L, leaf; P, plant; C, community) and represent three flammability components. Symbols reflect the direction of the relationship (‘+’: posi- tive; ‘’: negative; ‘0’: none; ‘N/A’: could not be tested). Influence is either direct or indirect (in parentheses) Plant trait Biomass Leaf Leaf effective Flammability Biomass Biomass moisture SA/V heat of 1 1 1 Flammability trait component Scale quantity (g) density (g cm ) content (g g ) ratio combustion (J g ) Time to ignition (s) Ignitability L N/A N/A  + 0 Predicted rate Ignitability C ++  ++ of fire spread (m s ) Flaming time (s) Sustainability L, P + ()(+)()() Combustion rate (g s ) Combustibility L, P ++  ++ © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 Determinants of flammability in savanna grasses 141 for biomass density, in g cm , with high values indicating densely occurred if large pieces of plant material fell off the balance during a packed biomass. For each clump, dry biomass values were combined burn. The width parameter used to fit the Boltzmann curve reflects to give the total dry biomass, and moisture content was calculated by the time period in which mass was drastically reduced and was used dividing the difference between fresh and dry biomass by the dry bio- as a plant-scale measurement of sustainability (flaming time). Three mass. seconds of data either side of the inflection point were selected and a linear regression fitted. The slope of this regression represents the maximum combustion rate in g s . As preliminary results found this FLAMMABILITY combustibility trait to be strongly driven by the biomass of the sam- ple, interspecific comparisons were standardized for mass. Therefore, Flammability was represented by three components: ignitability, com- maximum combustion rate was plotted against mass change for each bustibility and sustainability (Anderson 1970). All components were species, and linear models were fitted to the fresh, dry and combined measured for each individual at the leaf scale via epiradiator tests. In data sets. As there was no change in mass common to all 25 species, addition, combustibility and sustainability were determined at the the y-intercept extracted from the model fitted to the combined data plant scale by burning partial plant canopies. Plant-scale measurement set was used to characterize the intrinsic combustibility of each spe- of ignitability was beyond the scope of this experiment; however, a cies. The combined data set was used as the slopes of the models fit- community-level measure was obtained by estimating the rate of fire ted to the fresh and dry data did not differ significantly for any spread for each individual by parameterizing Rothermel’s (1972) fire species, and model fit was improved by combining the data sets. Any spread model with plant trait data. Leaf- and plant-scale flammability unpaired samples were excluded to ensure a balanced data set of fresh components were measured both on fresh and dry biomass to deter- and dry samples. The y-intercept differed significantly between fresh mine the effect of moisture content. The ‘fresh’ clump was kept in a and dry models for three species (Panicum sp., Hyparrhenia hirta sealed plastic bag at room temperature, and the ‘dry’ clump was first and Merxmuellera stricta) and in these cases, the y-intercept was dried at 70 °C for a minimum of 48 h. extracted from linear models fitted to the fresh data set. Leaf-scale ignitability, sustainability and combustibility were mea- Forward fire spread rate values, the community-scale measure of sured as time to ignition, flaming time and mass loss rate, respec- ignitability, were predicted for each individual using Rothermel’s tively, using a Quartz infrared 500 W epiradiator (Helios, Italquartz, (1972) surface fire spread model as implemented using the ros() func- Milan, Italy) in a fume cupboard with a constant vertical windspeed 1 tion in the Rothermel package (Vacchiano & Ascoli 2014) in R (R of 0.1 m s . As application of leaf material directly to the epiradia- Core Team 2013). Fire behaviour was simulated for each individual tor’s silica disc surface always caused instantaneous combustion, by parameterizing the model with data for the following traits: leaf 2-mm wire mesh was positioned 1 cm above the epiradiator’s surface. SA/V ratio, leaf EHoC, biomass moisture content, plant height and The background temperature at the mesh surface (without fuel), mea- fuel load (biomass quantity divided by the estimated cover area). See sured by a thermocouple connected to a data-logger, ranged between Table S3 for a details of the procedure followed and model assump- 370 and 400 °C. Samples of 0.2 g (0.001 g) leaf material were cut tions. into 2-cm segments to standardize between samples and applied to the centre of the mesh. The 0.2 g mass was used because preliminary studies found that smaller masses failed to ignite, while larger fuel PHYLOGENETIC ANALYSIS masses increased the risk that other fuel properties, particularly fuel height, influenced flammability values. Smaller samples were used for We constructed a phylogeny that was initially based on a previously Aristida congesta subsp. barbicollis due to the low leaf mass of this generated data set for grasses composed of the plastid markers species. Each test was filmed at 25 frames s , and (i) time to igni- trnKmatK, ndhF and rbcL (Grass Phylogeny Working Group II 2012) tion (TTI; the time between sample application to the epiradiator and and augmented here. For ten species not represented in this previous first flaming) and (ii) flaming time (FT; the time from ignition to data set, a fragment of trnKmatK was PCR-amplified from genomic flame extinction) were subsequently determined. As samples were DNA, following protocols and primers described previously (Grass completely combusted by applying them to the epiradiator, an average Phylogeny Working Group II 2012). The newly generated sequences leaf combustion rate was obtained by dividing the mass of samples have been submitted to NCBI database (Benson et al. 2012) under by FT. Species average values for TTI and FT were obtained for the accession numbers KP860326 to KP860336. The new markers fresh and dry material. The influence of leaf moisture content on were manually aligned to the data set, which consisted of 606 taxa these flammability traits was determined as the difference in values and 5649 aligned bp. This initial data set was downsized to 70 spe- between fresh and dry samples of each individual and averaged per cies, including all the taxa studied here and representatives of all species. grass lineages. A time-calibrated phylogenetic tree was obtained through Bayesian inference as implemented in BEAST (Bayesian evo- As canopy architecture influences grass flammability (Martin lutionary analysis by sampling trees; Drummond & Rambaut 2007). 2010), a method that measures plant-scale flammability traits was uti- A general time-reversible substitution model with a gamma-shape lized. Fresh and dry plant material from each individual were clamped parameter and a proportion of invariants (GTR+G+I) were used. The on a stand on a four-point balance (Mark 205A; Bel Engineering, log-normal relaxed clock was selected. The tree prior was modelled Monza, Italy) and burnt in a fume cupboard with a constant by a Yule process. The monophyly of the BEP-PACMAD clade was 0.1 m s vertical wind speed (see Figure S1 for diagram of the set- enforced, leaving Puelia olyriformis as the outgroup. The calibration up used). Samples were ignited by directing a Bunsen burner flame to prior for the age of the BEP-PACMAD crown was set to a normal the side of the base of the clump at a 45° angle and a 5 cm distance for a maximum of 3 s (less if ignition happened earlier). This resulted distribution, with a mean of 51.2 and a standard deviation of 0.001 in successful ignition in all individuals. Mass loss was logged at 0.2-s (mean based on Christin et al. 2014). Two independent runs were intervals and the sigmoidal relationship produced was fitted with a conducted for 10 000 000 generations, sampling a tree every 1000 Boltzmann equation. Data were excluded if fitting the relationship generations. The convergence of the runs and the appropriateness of was not possible due to noise around the curve (n = 40/350), which the burn-in period, set to 2 000 000 generations, were verified using © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 142 K. J. Simpson et al. Tracer (Rambaut A, Drummond AJ (2007) Tracer v1.4, available at P < 0.001), sustainability (F = 3.02, P < 0.001) and 24,144 http://beast.bio.ed.ac.uk/Tracer). Median ages were mapped on the combustibility (F = 2.97, P < 0.001). Ignition delays 24,144 maximum-credibility tree. The relationships among the species studied ranged from 1.0 s (H. hirta) to 4.0 s (C. setaceus) with a here were extracted from this tree and used for comparative analyses. mean across species of 1.7 s. The mean flaming duration across species was 6.3 s and ranged from 4.3 s (A. congesta subsp. barbicollis) to 7.6 s (Eragrostis plana). Connected to DATA ANALYSIS flaming duration was average combustion rate, with E. plana Statistical analyses were carried out in the R environment (R Core 1 burning at the slowest rate (27 mg s ) and A. congesta Team 2013). Data were log-transformed to improve normality and to subsp. barbicollis at the fastest (49 mg s ). meet model assumptions where necessary. At the plant scale, intrinsic combustibility (for a given bio- Analysis of variance (ANOVA) was used to determine whether plant mass) differed by <2.5-fold across species, ranging from and flammability traits differed significantly between species. The influ- 1 1 0.064 g s (Eustachys paspaloides) to 0.163 g s (The- ence of species, and state (‘fresh’ or ‘dry’), on leaf-scale flammability meda triandra). When investigating the relationship between was determined by two-way ANOVA. As biomass quantity values for the combustion rate and biomass, the bivariate mixed effects plant-scale burns are not representative of the species (i.e. for each spe- cies, clumps were subsampled and a range of masses were burnt), a spe- model revealed that within-species slopes (pooled cies effect on the relationship between maximum combustion rate and mean = 0.594, HPD: 0.507 to 0.707) and across-species biomass quantity was tested using the R package MCMCglmm (Had- slopes (mean = 0.797, HPD: 0.067 to 1.385) did not differ field 2010). This approach implements Markov chain Monte Carlo rou- significantly (mean slope difference (Db) = 0.212, HPD: tines for fitting generalized linear mixed models, while accounting for 0.521 to 0.683) when accounting for phylogeny (Fig. 2). non-independence and correlated random effects arising from phyloge- This common relationship was extrapolated while taking into netic relationships (Hadfield 2010). We fitted flammability (maximum account intrinsic combustibility differences, allowing combus- combustion rate) and biomass quantity as a bivariate normal response, tion values to be predicted for the species mean total biomass. and species as a random effect. Models were run for 500 000 iterations These predicted values of whole-plant combustion rates varied with a burn-in of 1000 iterations, a thinning interval of 500 and weakly- >20-fold among species, ranging from 0.06 g s (A. con- informative priors (V = diag(2), nu = 0.002). The 95% highest poste- gesta subsp. barbicollis) to 1.28 g s (M. disticha; Fig. 2). rior densities (HPD) of within-species and across-species slopes and the Fuel models based on the traits of C. setaceus predicted no difference between slopes were estimated while accounting for phy- logeny and used to assess whether slopes differed among species. fire spread, because biomass moisture content values To test the hypotheses put forward in Table 1 and to establish the exceeded the moisture of extinction, defined as the fuel mois- strength and direction of plant trait contributions to flammability com- ture content above which a steady rate of fire spread is not ponents, a MCMC multi-response generalized linear mixed model possible. Of the remaining species that spread fire, the esti- approach was used again. Traits were separated into leaf and plant mated rate of spread differed substantially (25-fold; Table S4) scale to ensure appropriate comparisons, using the same prior and and varied significantly between species (ANOVA: specifications as before. The fit of the models to data was established F = 42.42, P < 0.001). 24,150 by fitting linear models between the observed flammability trait val- Substantial interspecific variation was also found in the five ues and those predicted by the models. The contribution of plant traits traits measured as explanatory traits for flammability (Fig. 1; to fire spread rate was tested to determine whether strong relation- see Table S5). Biomass moisture content values of the non- ships occurred across species when accounting for phylogeny, while native C. setaceus were substantially higher than the other acknowledging that some circularity is involved because spread rate was predicted based on the values of these traits. species. However, species still differed significantly for this To explore the pattern of covariance among plant and flammability trait when C. setaceus was excluded (ANOVA: F = 14.39, 23,144 traits, principal component analyses were performed using the prin- P < 0.001). The measurement of biomass density (i.e. vertical comp function (R core team 2013). Linear regressions were also used biomass distribution) produced consistent values within spe- to establish the relationships among plant and flammability traits, with cies (Fig. S2; species average CV = 28%), but considerable the latter being split into leaf-scale and plant-scale traits for analyses differences among species with slope values ranging from to ensure an appropriate comparison. The relationships between 0.155 (Eragrostis lehmanniana) to 0.831 (M. stricta). flammability traits measured at different scales were also established Collection site did not influence flammability traits. Of the using linear regressions. plant traits, vertical biomass distribution (P = 0.008) and leaf The influence of evolutionary history was established for each EHoC (P = 0.046) were the only ones affected by collection plant and flammability trait by testing for the presence of a phyloge- site (see Table S7). netic signal. This was done using the pgls function in the caper pack- age (Orme et al. 2012) which estimated Pagel’s k. TRAIT CONTRIBUTIONS TO FLAMMABILITY Results Measured plant traits significantly predicted the components of flammability, particularly ignitability and plant-scale com- FLAMMABILITY VARIATION AMONG SPECIES bustibility, in which 70% and 58% of variation could be All flammability components varied considerably across spe- explained by the plant traits, respectively (Tables 2 and 3). cies (Fig. 1; Table S4). At the leaf-scale, significant inter- Variation in sustainability could be explained to a lesser specific variation was found in ignitability (F = 5.02, extent by plant traits at the leaf (47%) and plant scale (37%), 24,144 © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 Determinants of flammability in savanna grasses 143 Fig. 1. The evolutionary relationships between species and average values of explanatory plant traits (solid circles) and flammability traits (open circles). Trait values are indicated by the size of the circles. A nonzero phylogenetic signal was found for leaf SA/V ratio (Pagel’s k = 1; P = 1 for k = 1; P < 0.001 for k = 0), leaf flaming time (Pagel’s k = 0.45; P = 1.0 for k = 1; P < 0.001 for k = 0) and leaf combustion rate (Pagel’s k = 0.99; P = 0.93 for k = 1; P = 0.037 for k = 0). as well as variation in leaf-scale combustibility (39%). The direction of relationships between plant and flammability traits is consistent with those predicted in Table 1, but there are exceptions. Both biomass density and leaf SA/V ratio were expected to correlate positively with predicted spread rate, but instead correlated negatively (Table 3). Moisture content was key in determining leaf-scale flamma- bility components (Table 2; Table S6). Ignitability was partic- ularly influenced by moisture content, with fresh leaf material taking 42% longer to ignite on average than dry leaf material across species, with a maximum increase of 288% seen for C. setaceus (1.0 s dry vs. 4.0 s fresh). Once alight, fresh leaf material also burned on average for 7% longer at a 3% lower combustion rate compared to dry leaf material across species. Fig. 2. Relationships between biomass quantity and maximum com- Leaf SA/V ratio significantly influenced sustainability, with bustion rate across 25 grass species. The mean slopes of within- high values associated with low flaming duration. The EHoC species relationships (grey lines) and across-species relationships of leaf material alone contributed little to overall leaf-scale (black dotted line) for maximum combustion rate with biomass burned do not differ significantly when phylogeny is accounted for. flammability when compared to moisture or SA/V ratio Data points are shown as grey circles. Estimates of whole-plant com- (Table 2). bustion rates (black diamonds) showed substantial variation (>20- At the plant scale, biomass quantity was by far the stron- fold). These values were calculated by extrapolating the common gest driver of sustainability and combustibility (Table 3). across-species relationship (black dashed line) to species mean total Plants with greater biomass burnt at a faster rate and for biomass values while taking into account the intrinsic combustibility differences among species. longer. Biomass density and moisture content significantly © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 144 K. J. Simpson et al. contributed to plant-scale combustibility, such that plants with determining the overall predicted rate of spread (Table 3; high density and low moisture content combusted most Fig. S2). rapidly (Table 3). The EHoC of leaf material significantly contributed to sustainability with high values associated with TRAIT COVARIANCE short flaming times (Table 3). Leaf SA/V ratio did not signifi- cantly contribute to plant-scale combustibility or sustainabil- Principal components analysis (PCA) and linear regressions ity. were used to explore patterns of covariance among the plant Biomass load, moisture content, density and leaf SA/V and flammability trait variables, with the latter being split into ratio all contributed highly to predicted fire spread rate when leaf-scale and plant-scale traits (Fig. 3). For the plant traits, taking phylogeny into account (Table 3). Fuel load con- the first two principal components accounted for 67.6% of the tributed directly to reaction intensity and indirectly to the total variance. The first axis related to the chemical properties propagating flux ratio, via bulk density. Biomass moisture of biomass and how it is arranged spatially (leaf EHoC, bio- content contributed to spread rate by increasing the heat mass moisture content and density had the highest axis load- required for ignition and damping the reaction intensity (see ings). Leaf SA/V ratio loaded most heavily on the second Fig. S2). Leaf SA/V ratio influenced reaction intensity and axis, followed by biomass moisture content and density. Only the proportion of this reaching adjacent fuel (propagating flux biomass quantity did not fall as clearly on the first two princi- ratio), as well as the proportion of fuel raised to ignition tem- pal components, which we believe is due to the high variation perature (effective heating number; Fig. S2). Leaf EHoC con- within the data (CV = 89.0%). For the leaf-scale flammability tributed to the reaction intensity but played a small part in traits, the first two principal components accounted for 95.1% Table 2. The contribution of plant traits to leaf-scale flammability components as determined by MCMC phylogenetic generalized linear mixed models. Values represent posterior mean estimates of the slopes, the upper and lower 95% confidence intervals and P values (those in bold are significant at P = 0.05). In combination, species mean trait values of leaf moisture content, SA/V ratio and effective heat of combustion (EHoC) 2 2 significantly predicted ignitability (F = 398.3, P < 0.001, R = 0.70), sustainability (F = 147.5, P < 0.001, R = 0.47) and combustibility 1,166 1,166 (F = 105.4 P < 0.001, R = 0.39) 1,166 Leaf moisture content* Leaf SA/V ratio log Leaf EHoC Ignitability (time to ignition) Estimate 0.691 0.174e-3 0.135e-4 (95% CI) (0.620 to 0.760) (0.420e-3 to 0.872 e-5) (0.527e-4 to 0.290e-4) P value <0.001 0.17 0.49 Sustainability (flaming time) Estimate 0.492 0.876e-3 0.159e-4 (95% CI) (0.421 to 0.567) (0.142e-2 to -0.359 e-4) (0.626e-4 to 0.113e-3) P value <0.001 0.002 0.741 Combustibility (combustion rate) Estimate 0.303e-2 0.522e-5 0.227e-6 (95% CI) (0.406e-2 to 0.170e-2) (0.547e-5 to 0.164e-4) (0.254e-5 to 0.193e-5) P value <0.001 0.36 0.86 *Parameter characterized as: the species mean difference in ignition delay (for ignitability) or flaming duration (for sustainability and combustibil- ity) between fresh and dry leaf material for each individual. Table 3. The contribution of plant traits to plant-scale flammability components as determined by MCMC phylogenetic generalized linear mixed models. Values represent posterior mean estimates of the slopes, the upper and lower 95% confidence intervals and P values (those in bold are significant at P = 0.05). Values represent posterior mean estimates of the slopes, the upper and lower 95% confidence intervals and P values (those in bold are significant at P = 0.05). In combination, the five plant traits significantly predicted sustainability (F = 90.07, P < 0.001, 1,151 2 2 2 R = 0.37), combustibility (F = 210.8, P < 0.001, R = 0.58) and ignitability (F = 184.2, P < 0.001, R = 0.51) 1,151 1,173 log Biomass log Biomass log Biomass quantity density moisture content Leaf SA/V ratio log Leaf EHoC* Sustainability Estimate 0.434 0.614 1.036 0.050 0.012 (flaming time) (95% CI) (0.350 to 0.517) (2.162 to 0.889) (0.688 to 2.753) (0.162 to 0.055) (0.023 to 0.001) P value <0.001 0.443 0.252 0.363 0.060 Combustibility Estimate 0.035 0.149 0.108 0.105e-2 0.580e-4 (maximum (95% CI) (0.028 to 0.041) (0.021 to 0.277) (0.250 to 0.027) (0.858e-2 to 0.012) (0.101e-2 to 0.103e-2) combustion rate) P value <0.001 0.024 0.116 0.910 0.826 Ignitability Estimate 2.002 0.061 0.034 0.128e-2 0.121e-3 (predicted (95% CI) (0.951 to 3.015) (0.094 to 0.033) (0.044 to 0.025) (0.789e3 to 0.169e-2) (0.993e-4 to 0.360e-3) spread rate) P value <0.001 <0.001 <0.001 <0.001 0.309 *Species mean values. © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 Determinants of flammability in savanna grasses 145 Fig. 3. Principal components analysis biplots of explanatory plant traits (a) and flammability traits at the leaf scale (b) and plant scale (c). The tables within each plot indicate the slope and significance of linear regressions between each pair of variables. Data for all traits were log- transformed to improve normality except leaf SA/V ratio. EHoC is the leaf effective heat of combustion. P < 0.1; *, P < 0.05; ***, P < 0.001. of the total variance. Leaf flaming time and combustion rate significantly in multiple components of flammability. This find- were negatively correlated (P < 0.001), and fell in opposing ing suggests that static classifications of grassy and herbaceous directions on the first PCA axis (Fig. 3), which reflects how vegetation as homogenous fuels mask considerable interspecific combustion rate was derived from flaming time. Time to igni- and community variation in flammability. Consequently, fire tion was unrelated to flaming time and combustion rate and behaviour predictions based on such fuel models may lose was orthogonal to both in the PCA (Fig. 3). For plant-scale accuracy when community composition is not accounted for. flammability traits, 71.8% of total variance is accounted for A substantial proportion of variation in ignitability and by the first two principal components. Traits did not separate combustibility (70% and 58%, respectively) can be explained on the first axis, but did on the second axis which related to by a combination of the five plant traits measured here. For burning intensity. High rates of plant combustion were associ- sustainability, a smaller proportion of variation was accounted ated with rapid predicted fire spread rates (P < 0.001) and for (37%), perhaps because this component is not only driven marginally with longer flaming times (P = 0.071; Fig. 3). by plant traits, but is also directly influenced by combustibil- The relationships between flammability traits measured at ity. Additionally, some variation in sustainability could be different scales were variable, with a significantly positive accounted for by traits relating to leaf chemistry, such as correlation found for ignitability (leaf time to ignition vs. nitrogen, phosphorus and tannin concentrations (Grootemaat predicted rate of spread; P = 0.025), but no significant corre- et al. 2015) that were not measured in this study. Biomass lation for combustibility (leaf-scale combustion rate vs. quantity was the key trait influencing plant-scale flammability plant-scale combustion rate; P = 0.29). components and also determined the influence of an individ- ual plant on local fire characteristics. The importance of bio- mass quantity for combustibility, sustainability and fire spread INFLUENCE OF EVOLUTIONARY HISTORY ON rates in the field is illustrated by the elevated flammability of FLAMMABILITY landscapes caused by the raised fuel load production of non- Support for a phylogenetic signal was found for leaf-scale native grasses (Hughes, Vitousek & Tunison 1991; D’Antonio combustibility (Pagel’s k = 0.99; P = 0.93 for likelihood & Vitousek 1992; Rossiter et al. 2003). While making a rela- ratio test against k = 1; P = 0.037 against k = 0) and sustain- tively small contribution to flammability components once ability (Pagel’s k = 0.45; P = 0.67 against k = 1; P = 0.011 alight, biomass moisture content was key to ignitability, with against k = 0), but not for the other flammability traits. Of higher moisture contents requiring more energy to dry and the plant traits, there was a strong phylogenetic signal for leaf heat biomass to the point of ignition (Trollope 1978; Gill & SA/V ratio (Pagel’s k = 1.00; P = 1.00 against k = 1; Moore 1996; Alessio et al. 2008; Plucinski & Anderson P < 0.001 against k = 0), with closely related species tending 2008). By influencing ignitability, and therefore the likelihood to have similar values of leaf SA/V ratio. No phylogenetic of fire occurring in the first place, moisture content exerts a signal was found in the other plant traits. strong influence on vegetation flammability. Our finding of high interspecific variation in EHoC (effective heat of com- bustion) also conflicts with the notion that grass energy con- Discussion tent is an almost constant value (Trollope 1984). However, This large comparative study of grass flammability provides EHoC contributed little to leaf-scale flammability components, strong support for the hypothesis that grass species vary supporting the idea that this intrinsic property is less © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 146 K. J. Simpson et al. important in determining flammability than fuel mass, struc- grasses that have higher fuel loads, rapid combustion is not ture and moisture content (Bond & van Wilgen 1996). associated with lowered burning durations and a subsequent Despite this small importance overall, the EHoC marginally reduction in heat transfer to the soil and below-ground plant contributed to plant-scale flaming time. parts. The interspecific variation in flammability components The inconsistent relationships between components of observed across a set of species that commonly coexist in the flammability, and within flammability components measured field further suggests a role for interspecific competition in at different scales, suggest that descriptions of flammability promoting flammability as an adaptive trait. Potentially, should incorporate all suitable components and should be enhanced plant flammability can increase the mortality of taken at an appropriate scale. The mixed covariance between neighbouring, less fire-tolerant individuals and thereby reduce flammability components found here suggests that one cannot post-fire competition (Bond & Midgley 1995). Furthermore, always be used as a proxy for the others. Therefore, studies some evidence provides intriguing support for a link between that consider one or even two components of flammability high flammability and ecological success in fire-prone grass- may mask the complexity of vegetation flammability (Ander- land species (Ripley et al. 2015). The influence of flammabil- son 1970). Similar to the findings of Martin (2010), we find ity at the species level on grassland community-level support for the importance of incorporating plant architecture flammability has not been determined. However, findings into measurements of grass flammability. Inconsistencies from other vegetation fuel types show that flammability tends to be driven by the most flammable species of a community, between combustibility at the leaf- and plant-scale highlight that other factors (such as biomass quantity and density) are such that fuel loads are non-additive (van Altena et al. 2012; key determinants of combustibility at the plant scale. Bench- de Magalh~aes & Schwilk 2012). The knowledge gained in scale measurements of flammability have been criticized as this study can be used in further work to determine whether not being representative of flammability in the field (Fernan- high flammability is an adaptation to life in frequently burnt des & Cruz 2012), and our findings emphasize the need for environments for grasses and has thus been a fundamental caution when extrapolating flammability traits between differ- trait in grass evolution. In addition, the knowledge of inter- ent scales. In comparison with leaf-scale studies, the flamma- specific variation in grass flammability obtained here can lead bility component values obtained here are more representative to a better understanding of wildfire behaviour, particularly in of flammability in the field because they are measured at the grassland ecosystems. This could potentially contribute to an plant scale and on field-state plants that are at the phenologi- improvement of global carbon modelling and lead to new cal stage most appropriate to fire occurrence. insights about ecosystem feedback to fire. The phylogenetic signal found in some flammability com- ponents (leaf-scale combustibility and sustainability) suggests Acknowledgements that evolutionary history may partially explain patterns of Research support was provided by a Natural Environment Research Council grass flammability and the strong sorting of grass lineages studentship to K.J.S., Royal Society University Research Fellowship across fire-frequency gradients (Uys, Bond & Everson 2004; URF120119 to P.A.C. and URF120016 to G.H.T. and a European Research Council Starter Grant ERC-2013-StG-335891-ECOFLAM to C.M.B. Author Visser et al. 2012; Forrestel, Donoghue & Smith 2014). How- contributions: K.J.S., G.H.T., B.S.R., C.M.B., C.E.R.L. and C.P.O. designed ever, conclusions on phylogenetic signal derived from a small the study. K.J.S., B.S.R. and P.A.C. generated the data. K.J.S., P.A.C., B.S.R., phylogeny must remain cautious due to low statistical power G.H.T. and C.P.O. analysed the data. K.J.S. wrote the manuscript with the help of all the authors. We thank Tony Palmer, Claire Adams and Nosipho Plaatjie (Boettiger, Coop & Ralph 2012). for their support in the laboratory and field, Albert Phillimore for assistance Through their flammability, plants may modify the fire with the MCMCglmm analyses and James Simpson for his help with graphics. regime they experience in order to increase their fitness in We also thank Hans Cornelissen and two anonymous referees for their con- fire-prone environments (Schwilk 2003). Resprouting grasses structive comments on the manuscript. are likely to benefit from frequent fires that remove standing biomass and maintain an open canopy, because they are typi- Data accessibility cally intolerant of shading (Everson, Everson & Tainton Trait data: Species average values uploaded as online supporting information; 1988; Bond 2008). The grasses studied here showed high raw data available in DRYAD entry doi: 10.5061/dryad.2c506. ignitability, combustibility and predicted fire spread rates, Sequence data: GenBank accession numbers available as online supporting when compared to woody vegetation fuels (e.g. Pausas et al. information. 2012; Ganteaume et al. 2013). Furthermore, grasses are able Phylogeny: Nexus file available in DRYAD entry doi: 10.5061/dryad.2c506. to regrow quickly after fire. This combination of high MCMCglmm R Script: Available in DRYAD entry doi: 10.5061/ dryad.2c506. flammability and rapid regrowth drives a fire regime charac- terized by high fire frequency (Grigulis et al. 2005). Plant- scale combustion rate was marginally positively related to References flaming time, with high biomass plants burning at a faster rate Alessio, G.A., Penuelas, J., Llusia, J., Ogaya, R., Estiarte, M. & De Lillis, M. and for longer. This finding is in contrast with other studies (2008) Influence of water and terpenes on flammability in some dominant (e.g. de Magalh~aes & Schwilk 2012) that found a negative Mediterranean species. International Journal of Wildland Fire, 17, 274–286. Allan, G.E. & Southgate, R.I. (2002) Fire regimes in the spinifex landscapes of relationship between the two. It also does conflicts with the Australia. Flammable Australia: the Fire Regimes and Biodiversity of a Con- idea of high flammability providing resprouting plants protec- tinent (eds R.A. Bradstock, J.E. Williams & A.M. Gill), pp. 145–176. tion against lethal temperatures (Gagnon et al. 2010), as for Cambridge University Press, Cambridge, UK. © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 Determinants of flammability in savanna grasses 147 van Altena, C., van Logtestijn, R.S.P., Cornwell, W.K. & Cornelissen, J.H.C. Glasspool, I.J., Edwards, D. & Axe, L. (2004) Charcoal in the Silurian as evi- (2012) Species composition and fire: non-additive mixture effects on ground dence for the earliest wildfire. Geology, 32, 381–383. fuel flammability. Frontiers in Plant Science, 3, 63. Grass Phylogeny Working Group II (2012) New grass phylogeny resolves deep Anderson, H.E. (1970) Forest fuel ignitability. Fire Technology, 6, 312–319. evolutionary relationships and discovers C origins. New Phytologist, 193, Anderson, H.E. (1982). Aids to determining fuel models for estimating fire 304–312. behavior. USDA Forest Service, Intermountain Forest and Range Experiment Grigulis, K., Lavorel, S., Davies, I.D., Dossantos, A., Lloret, F. & Montserrat, Station. General Technical Report INT-122, 22. V. (2005) Landscape-scale positive feedbacks between fire and expansion of Archibald, S., Lehmann, C.E.R., Gomez-Dans, J.L. & Bradstock, R.A. (2013) the large tussock grass, Ampelodesmos mauritanica in Catalan shrublands. Defining pyromes and global syndromes of fire regimes. Proceedings of the Global Change Biology, 11, 1042–1053. National Academy of Sciences of the United States of America, 110, 6442– Grootemaat, S., Wright, I.J., van Bodegom, P.M., Cornelissen, J.H.C. & 6447. Cornwell, W.K. (2015) Burn or rot: leaf traits explain why flammability and Beckage, B., Platt, W.J. & Gross, L.J. (2009) Vegetation, fire, and feedbacks: a decomposability are decoupled across species. Functional Ecology, 29, disturbance mediated model of savannas. The American Naturalist, 174, 1486–1497. 805–818. Hadfield, J.D. (2010) MCMC methods for multi-response generalized linear Belcher, C.M., Mander, L., Rein, G., Jervis, F.X., Haworth, M., Hesselbo, S.P., mixed models: the MCMCglmm R package. Journal of Statistical Software, Glasspool, I.J. & McElwain, J.C. (2010) Increased fire activity at the Trias- 33,1–22. sic/Jurassic boundary in Greenland due to climate-driven floral change. Nat- He, T., Lamont, B.B. & Downes, K.S. (2011) Banksia born to burn. New Phy- ure Geoscience, 3, 426–429. tologist, 191, 184–196. Benson, D.A., Karsch-Mizrachi, I., Clark, K., Lipman, D.J., Ostell, J. & Sayers, Hughes, F., Vitousek, P.M. & Tunison, T. (1991) Alien grass invasion and fire E.W. (2012) GenBank. Nucleic Acids Research, 40, D48–D53. in the seasonal submontane zone of Hawaii. Ecology, 72, 743–746. Boettiger, C., Coop, G. & Ralph, P. (2012) Is your phylogeny informative? Keeley, J.E., Pausas, J.G., Rundel, P.W., Bond, W.J. & Bradstock, R.A. (2011) Measuring the power of comparative methods. Evolution, 66, 2240–2251. Fire as an evolutionary pressure shaping plant traits. Trends in Plant Science, Bond, W.J. (2008) What limits trees in C grasslands and savannas? Annual 16, 406–411. Review of Ecology Evolution and Systematics, 39, 641–659. Lamont, B.B., Le Maitre, D.C., Cowling, R.M. & Enright, N.J. (1991) Canopy Bond, W.J. & Midgley, J.J. (1995) Kill thy neighbor – an individualistic argu- seed storage in woody plants. Botanical Review, 57, 277–317. ment for the evolution of flammability. Oikos, 73,79–85. de Magalh~aes, R.M.Q. & Schwilk, D.W. (2012) Leaf traits and litter flammabil- Bond, W.J. & van Wilgen, B.W. (1996) Fire and plants. Population and Com- ity: evidence for non-additive mixture effects in a temperate forest. Journal munity Biology Series, 14. Chapman & Hall, London, UK. of Ecology, 100, 1153–1163. Bond, W.J., Woodward, F.I. & Midgley, G.F. (2005) The global distribution of Martin, T. (2010) Photosynthetic and evolutionary determinants of the response ecosystems in a world without fire. New Phytologist, 165, 525–537. of selected C and C (NADP-ME) grasses to fire. MSc Thesis, Rhodes 3 4 Bradstock, R.A. & Auld, T.D. (1995) Soil temperature during experimental University, Grahamstown, South Africa. bushfire in relation to fire intensity: consequences for legume germination Milton, S.J. (2004) Grasses as invasive alien plants in South Africa. South Afri- and fire management in south-eastern Australia. Journal of Applied Ecology, can Journal of Science, 100,69–75. 32,76–84. Murray, B.R., Hardstaff, L.K. & Phillips, M.L. (2013) Differences in leaf Brooks, M.L., D’Antonio, C.M., Richardson, D.M., Grace, J.B., Keeley, J.E., flammability, leaf traits and flammability-trait relationships between native DiTomaso, J.M., Hobbs, R.J., Pellant, M. & Pyke, D. (2004) Effects of inva- and exotic plant species of dry sclerophyll forest. PLoS One, 8, e79205. sive alien plants on fire regimes. BioScience, 54, 677–688. Nelson, R.M. Jr (2001) Water relations of forest fuels. Forest Fires: Behavior Byram, G.M. (1959) Combustion of forest fuels. Forest Fire: Control and Use and Ecological Effects (eds E.A. Johnson & K. Miyanishi), pp. 79–149. Aca- (ed K.P. Davis), pp. 61–89. McGraw-Hill, New York, NY, USA. demic Press, San Diego, CA, USA. Christin, P.-A., Spriggs, E., Osborne, C.P., Stromberg, C.A.E., Salamin, N. & Orme, D., Freckleton, F.P., Thomas, G.H., Petzoldt, T., Fritz, S., Isaac, N. & Edwards, E.J. (2014) Molecular dating, evolutionary rates, and the age of the Pearse, W. (2012) The Caper package: comparative analysis of phylogenet- grasses. Systematic Biology, 63, 153–165. ics and evolution in R. Available at: http://cran.r-project.org/web/packages/ Cornwell, W.K., Elvira, A., van Kempen, L., van Logtestijn, R.S.P., Aproot, A. caper. & Cornelissen, J.H.C. (2015) Flammability across the gymnosperm phy- Overbeck, G.E. & Pfadenhauer, J. (2007) Adaptive strategies in burned sub- logeny: the importance of litter particle size. New Phytologist, 206, 672–681. tropical grassland in southern Brazil. Flora, 202,27–49. D’Antonio, C.M. & Vitousek, P.M. (1992) Biological invasions by exotic Papio, C. & Trabaud, L. (1991) Comparative-study of the aerial structure of 5 grasses, the grass/fire cycle, and global change. Annual Review of Ecology shrubs of mediterranean shrublands. Forest Science, 37, 146–159. and Systematics, 23,63–87. Pausas, J.G. & Bradstock, R.A. (2007) Fire persistence traits of plants along a Drummond, A.J. & Rambaut, A. (2007) BEAST: bayesian evolutionary analy- productivity and disturbance gradient in mediterranean shrublands of south- sis by sampling trees. BMC Evolutionary Biology, 7, 214. east Australia. Global Ecology and Biogeography, 16, 330–340. Emerson, B.C. & Gillespie, R.G. (2008) Phylogenetic analysis of community Pausas, J.G. & Keeley, J.E. (2014) Evolutionary ecology of resprouting and assembly and structure over space and time. Trends in Ecology & Evolution, seeding in fire-prone ecosystems. New Phytologist, 204,55–65. 23, 619–630. Pausas, J.G., Alessio, G.A., Moreira, B. & Corcobado, G. (2012) Fires enhance Everson, C.S., Everson, T.M. & Tainton, N.M. (1988) Effects of intensity and flammability in Ulex parviflorus. New Phytologist, 193,18–23. height of shading on the tiller initiation of 6 grass species from the highland Philpot, C.W. (1969) Seasonal changes in heat content and ether extractive sourveld of natal. South African Journal of Botany, 54, 315–318. content of chamise. Research Paper INT-61. USDA Forest Service, Inter- Fernandes, P.M. & Cruz, M.G. (2012) Plant flammability experiments offer mountain Forest and Range Experiment Station, Ogden, UT, USA. limited insight into vegetation-fire dynamics interactions. New Phytologist, Plucinski, M.P. & Anderson, W.R. (2008) Laboratory determination of factors 194, 606–609. influencing successful point ignition in the litter layer of shrubland vegeta- Fonda, R.W. (2001) Burning characteristics of needles from eight pine species. tion. International Journal of Wildland Fire, 17, 628–637. Forest Science, 47, 390–396. Pyne, S.J. (1984) Introduction to Wildland Fire – Fire Management in the Uni- Forrestel, E.J., Donoghue, M.J. & Smith, M.D. (2014) Convergent phylogenetic ted States. Wiley, New York, NY, USA. and functional responses to altered fire regimes in mesic savanna grasslands R Core Team (2013) R: A Language and Environment for Statistical Comput- of North America and South Africa. New Phytologist, 203, 1000–1011. ing. R Foundation for Statistical Computing, Vienna, Austria. Available at Gagnon, P.R., Passmore, H.A., Platt, W.J., Myers, J.A., Paine, C.E.T. & http://www.R-project.org/. Harms, K.E. (2010) Does pyrogenicity protect burning plants? Ecology, 91, Ripley, B., Donald, G., Osborne, C.P., Abraham, T. & Martin, T. (2010) 3481–3486. Experimental investigation of fire ecology in the C and C subspecies of 3 4 Ganteaume, A., Jappiot, M., Lampin, C., Guijarro, M. & Hernando, C. (2013) Alloteropsis semialata. Journal of Ecology, 98, 1196–1203. Flammability of some ornamental species in wildland-urban interfaces in Ripley, B., Visser, V., Christin, P.-A., Archibald, S., Martin, T. & Osborne, southeastern France: laboratory assessment at particle level. Environmental C.P. (2015) Fire ecology of C and C grasses depends on evolutionary 3 4 Management, 52, 467–480. history and frequency of burning but not photosynthetic type. Ecology, 96, Gill, A.M. & Moore, P.H.R. (1996) Ignitability of Leaves of Australian Plants, 2679–2691. p. 34. CSIRO Plant Industry, Canberra, Australia. Rossiter, N.A., Setterfield, S.A., Douglas, M.M. & Hutley, L.B. (2003) Testing Gill, A.M. & Zylstra, P. (2005) Flammability of Australian forests. Australian the grass-fire cycle: alien grass invasion in the tropical savannas of northern Forestry, 68,87–93. Australia. Diversity and Distribution, 9, 169–176. © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148 148 K. J. Simpson et al. Rothermel, R.C. (1972) A mathematical model for predicting fire spread in Supporting Information wildland fuels. Research Paper INT-115. USDA Forest Service, Intermoun- tain Forest and Range Experiment Station, Ogden, UT, USA. Additional Supporting Information may be found in the online ver- Saura-Mas, S., Paula, S., Pausas, J.G. & Lloret, F. (2010) Fuel loading and flammability in the Mediterranean basin woody species with different post-fire sion of this article: regenerative strategies. International Journal of Wildland Fire, 19, 783–794. Scarff, F.R. & Westoby, M. (2006) Leaf litter flammability in some semi-arid Figure S1. Schematic drawing of the set-up used to measure plant- Australian woodlands. Functional Ecology, 20, 745–752. scale combustibility and sustainability. Schwilk, D.W. (2003) Flammability is a niche construction trait: canopy archi- tecture affects fire intensity. American Naturalist, 162, 725–733. Schwilk, D.W. & Caprio, A.C. (2011) Scaling from leaf traits to fire behaviour: Figure S2. Cumulative dry biomass over vertical plant height for the community composition predicts fire severity in a temperate forest. Journal grass species. of Ecology, 99, 970–980. Silva, I.A. & Batalha, M.A. (2010) Phylogenetic structure of Brazilian savannas Figure S3. The influence of plant traits on components of Rother- under different fire regimes. Journal of Vegetation Science, 21, 1003–1013. Tansey, K., Gregoire, J.-M., Defourny, P., Leigh, R., Pekel, J.-F., van Bogaert, mel’s (1972) fire spread rate model. E. & Bartholome, E. (2008) A new, global, multi-annual (2000–2007) burnt area product at 1 km resolution. Geophysical Research Letters, 35, L01401. Table S1. Climate data from plant collection sites. Tewarson, A. (2002) Generation of heat and chemical compounds in fires. The SFPE Handbook of Fire Protection Engineering, 3rd edn (eds P.J. DiNenno, Table S2. Grass species names, collection site and GenBank acces- D. Drysdale, C.L. Beyler & W.D. Walton), pp. 3–82. National Fire Protec- tion Association, Quincy, MD, USA. sion details. Trollope, W.S.W. (1978) Fire behaviour – a preliminary study. Proceedings of the Grassland Society of South Africa, 13, 123–128. Table S3. Plant traits values used to model the forward rate of fire Trollope, W.S.W. (1984) Fire in savanna. Ecological Effects of Fire in South spread (m min ). African Ecosystems (eds V. Booysen & N.M. Tainton), pp. 200–217. Springer-Verlag, Berlin, Germany. Uys, R.G. (2000) The effects of different burning regimes on grassland phytodiver- Table S4. Species mean flammability component values. sity. MSc thesis, Botany Department, University of Cape Town, South Africa. Uys, R.G., Bond, W.J. & Everson, T.M. (2004) The effect of different fire Table S5. Species mean plant trait values. regimes on plant diversity in southern African grasslands. Biological Conser- vation, 118, 489–499. Vacchiano, G. & Ascoli, D. (2014) An implementation of the Rothermel fire Table S6. Results of analysis of variance (two-way ANOVA with inter- spread model in the R programming language. Fire Technology, 50, 823– action) of leaf-scale flammability by species and state (fresh or dry). Verdu, M. & Pausas, J.G. (2007) Fire drives phylogenetic clustering in Mediter- Table S7. Mean plant trait values for the three collection sites. ranean Basin woody plant communities. Journal of Ecology, 95, 1316–1323. Visser, V., Woodward, F.I., Freckleton, R.P. & Osborne, C.P. (2012) Environ- mental factors determining the phylogenetic structure of C grass communi- ties. Journal of Biogeography, 39, 232–246. Received 19 March 2015; accepted 26 October 2015 Handling Editor: Hans Cornelissen © 2015 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society., Journal of Ecology, 104, 138–148

Journal

Journal of EcologyWiley

Published: Jan 1, 2016

Keywords: ; ; ; ; ; ; ;

There are no references for this article.