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Keywords:

  • alternate stable states;
  • disturbance ecology;
  • disturbance interactions;
  • fire;
  • grasslands;
  • grazing;
  • herbivory

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

1. Vegetation fires can have major social, economic and ecological consequences. Research into fire behaviour has aimed to give managers greater ability to predict and control fires. Fire and grazing are important and interacting disturbances in grasslands. Fire is known to widely affect grazing patterns, but the effects of grazing on the incidence of fire are less well known. There have been few tests of the idea that ‘grazing reduces blazing’, which has popular and political currency in some countries. This study addresses the hypothesis that grazing affects fire potential in native grasslands.

2. Paired grazed and ungrazed quadrats were established at five lawn and five tussock grassland sites. Fuel load, percentage dead fuel and the number of days sustaining fires where possible were compared between treatments.

3. In lawn grasslands, grazing markedly reduced fire potential through the removal of plant biomass and by preventing the vegetation escaping into the unpalatable and flammable tussock state. Grazing led to increased fire potential in tussock grasslands where animals selectively removed live shoots, leaving a high proportion of dead fuel.

4. The difference in flammability responses to grazing between lawn and tussock grassland appeared to be due to differences in palatability that in turn may relate to soil fertility and the constancy of intense grazing. These differences mean that grazing lawns and tussock grasslands are likely to be subject to differing disturbance regimes. This association between disturbance regime and vegetation structural type suggests that lawns and tussock grasslands represent alternative stable states within the grassland ecosystem.

5.Synthesis and applications. Grazing is only likely to reduce the probability of fire where the bulk of the vegetation consists of potential food for grazing animals. It is likely that the negative relationship between vegetation palatability and fire potential applies to grasslands generally and possibly to many other vegetation types. Grassland managers may need to manipulate disturbance regimes in order to maintain vegetation structural heterogeneity and thereby promote landscape-scale biodiversity.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Vegetation fires potentially have major social, economic and ecological impacts. This has motivated a large amount of research into fire behaviour (e.g. Albini 1976; Cheney & Sullivan 1997). This research has often aimed to give managers greater ability to prevent or control wildfires and/or use controlled burning for hazard reduction or ecological purposes. Many studies have focused on the influence of fuel characteristics on fire behaviour. However, with the exception of time since fire, there has been less emphasis on understanding the factors that influence fuel characteristics. Biomass removal by grazing animals is likely to be one of the most important of these factors in grassland ecosystems.

Grazing is a major shaper of grassland structure and species composition (McNaughton 1984; Collins 1987; Lunt 1997; Leonard & Kirkpatrick 2004). Most grassy ecosystems are subject to both fire and grazing. The interactive effect of fire and grazing on grassland species composition is often distinct from the single effects of either disturbance (e.g. Collins 1987; Kirkpatrick et al. 2005). Fuhlendorf et al. (2008) have argued that the interaction of fire and grazing should be viewed as a single disturbance process. It has also been widely observed that fire influences the spatial patterning of grazing, with, in most cases, grazers being attracted to recently burned areas (Archibald & Bond 2004; Fuhlendorf & Engle 2004; Archibald et al. 2005; Murphy & Bowman 2007). Depending on the proportion of the grassland burnt and the density of grazers, this ‘magnet effect’ may result in either increased or decreased heterogeneity within the landscape (Archibald et al. 2005).

The obvious way in which grazing may affect the likelihood of fire in grasslands is by reducing fuel loads. Fire and herbivores have been characterized as ‘competing’ consumers of plant biomass (Bond 2005; Bond & Keeley 2005). This ‘competition’ forms the basis of the ‘grazing reduces blazing’ argument put forward by advocates of grazing domestic livestock in conservation reserves as a means of controlling wildfires (Williams et al. 2006). There is some evidence to support the ‘grazing reduces blazing’ proposition. Decreased fire frequency and intensity caused by the reduction in grass fuel loads by grazing has been identified as facilitating expansion of woody species in grassy vegetation worldwide (e.g. Adamoli, Sennhauser & Acero 1990; van Langevelde et al. 2003; Briggs et al. 2005). Grazing, especially by very large populations of animals, may also reduce the extent of fires at a landscape scale by forming ‘fire breaks’ within the grassland matrix (McNaughton 1992).

The degree to which grazing reduces fuel loads is determined by the density of grazers, their rate of food intake and plant growth rates (Noy-Meir 1975; McNaughton 1992). The impact of grazing on grassland fuel loads varies between components of the vegetation due to variation in feeding preferences and the behavioural, morphological and physiological traits that influence food intake (Farnsworth, Focardi & Beecham 2002). For example, it has been widely observed that grazers target short, regenerating grasses in preference to long, rank foliage (McNaughton 1984; Archibald & Bond 2004; Murphy & Bowman 2007).

Grazers could also alter grassland fire potential by reducing the amount of dead material present in the fuel array. The long-term absence of disturbance in grassland results in the accumulation of large amounts of dead plant material (Morgan & Lunt 1999). Grazing animals have been thought to inhibit the accumulation of dead biomass through consumption of foliage and trampling (Whalley 2005). Where grazing pressure is sufficient to form grazing lawns, plants are maintained in a state of continuous regeneration (McNaughton 1984) and the proportion of dead material present in the sward may be very low.

This study examines the degree to which vertebrate grazing affects fire potential (i.e. the likelihood that given a source of ignition, a fire will sustain and spread) in native grasslands in Tasmania, Australia. The central hypothesis is encapsulated by the proposition that ‘grazing reduces blazing’. Subsidiary hypotheses are that the effect of grazing on fire potential is affected by grassland structure, grazing intensity and environmental conditions.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Experimental design and data collection

The study was carried out at 10 native grassland sites in Tasmania, Australia (Table 1). Collectively the sites were representative of the major grassland types occurring in Tasmania. The sites were evenly divided between ‘tussock’ and ‘lawn’ structural types. Tussock sites were mostly dominated by large tussock-forming Poa species, although one site dominated by Themeda triandra also exhibited a tussock structure (botanical nomenclature follows Buchanan 2009). Lawn sites were dominated by Austrodanthonia spp. or Themeda and were characterized by a short (≤5 cm), more or less continuous sward. The grasslands studied ranged in size from approximately 10 to 700 ha. Vegetation structure was uniform within the area sampled in each grassland. The sites occurred within a matrix of forest or woodland, with a sharp boundary between the grassland and surrounding vegetation. All sites were grazed by several species of native mammals (Table 1). Staghorn Hill, Limekiln and Stockers Bottom were also intermittently grazed by sheep at low densities (<1 animal per hectare), while cattle were present at the Vale of Belvoir site from December to May each year at very low density (approximately 0·1 animal per hectare). Tasmania experiences a relatively equable maritime climate with rainfall being approximately evenly distributed throughout the year. Grassfires can potentially occur from late spring to late autumn (September to May). However, wildfires are most likely to occur in January to March.

Table 1.   Site attributes
SiteVegetation structureRainfall (mm year−1)Dominant grass taxaGrazing species
  1. Grazing species: BW, Bennett’s wallaby Macropus rufogriseus; TP, Tasmanian pademelon Thylogale labillardierii; CW, common wombat Vombatus ursinus; EGK, Eastern grey kangaroo Macropus giganteus; sheep, Ovis aries; cattle, Bos taurus.

Lime BayLawn742Austrodanthonia spp.BW, TP
LimekilnLawn533Themeda triandraBW, TP, EGK, Sheep
London LakesLawn940Austrodanthonia spp.BW, TP
Staghorn HillLawn575Themeda triandra, Austrodanthonia spp.BW, TP, EGK, Sheep
Stocker’s BottomLawn560Themeda triandra, Austrodanthonia spp.BW, TP, EGK, Sheep
Vale of BelvoirTussock2120Poa labillardiereiBW, TP, CW Cattle
Epping ForestTussock587Themeda triandra, Austrodanthonia spp.BW, TP, EGK
Iris RiverTussock2283Poa hiemataBW, TP, CW
Paradise NorthTussock1360Poa labillardiereiBW, TP, CW
Paradise PlainsTussock1360Poa labillardiereiBW, TP, CW

Six sites (Staghorn Hill, Limekiln, Stocker’s Bottom, London Lakes, Paradise Plains and Vale of Belvoir) were established in August to September 2005. At these sites eight 50 × 50-m2 cells were laid out. Vegetation and topography within each cell was approximately uniform. Atypical areas (e.g. rocky, poorly drained) within cells were excluded from sampling. Within each cell two 1 × 1-m2 quadrats were randomly located. The remaining eight sites were established in October to December 2006. Within these sites 10 pairs of quadrats were randomly located. At all sites one quadrat within each pair was enclosed within a 2 × 2 × 1·5-m3 chicken wire fence, which excluded vertebrate grazers, while the other member of the pair was left open to grazing. Quadrats were therefore evenly divided between grazed and ungrazed treatments, which allowed the effects of grazing to be assessed. The area over which quadrats were spread within each site ranged between approximately 2 and 10 ha.

Quadrats were surveyed at the time they were established and at varying intervals up to 6 months for 18–24 months (≈550–730 days). The percentage covers of all vascular plant species present were estimated using a 100 cell grid as an aid. The average height of each species was measured to the nearest centimetre. A double sampling approach (Catchpole & Wheeler 1992) was used to estimate fuel load (t ha−1) from height and cover data (Leonard 2009a). A visual estimate of the percentage of dead fuel within each quadrat was also made.

At each survey, vertebrate herbivore dung pellets within quadrats were counted and cleared from quadrats. Sheep and macropod densities were estimated from dung pellet counts, adjusted for losses due to decay and trampling, using published daily pellet output rates (Lange & Willcocks 1978; Johnson & Jarman 1987; Vernes 1999). Animal densities derived using this method were in close agreement with densities observed in spotlight surveys (Leonard 2009b). Wombat densities were estimated from spotlight surveys. A value for total grazing intensity was calculated by converting densities of all grazer species into dry sheep equivalents (DSE ha−1) according to species field metabolic rates, which were derived from average species body mass using the allometric scaling equations given in Nagy, Girard & Brown (1999) (Table 2). A detailed account of the method used to calculate total grazing intensity is given in Leonard (2009b).

Table 2.   Average body mass, field metabolic rate (FMR) and dry sheep equivalents (DSE) values for grazers
 Mass (kg)FMR (kJ day−1)DSE
Sheep40·074601·00
Eastern grey kangaroo16·832390·43
Bennett’s wallaby8·421280·29
Tasmanian pademelon5·516440·22
Common wombat26·042340·57

At sites established in 2005 a soil sample was collected from each 50 × 50-m2 cell. Samples extended from the soil surface to a depth of approximately 15 cm. At the 2006 sites, five samples to the same depth were collected from random locations within each site. Samples from each site were bulked and analysed for pH, conductivity and N, P, K, Ca, Mg, Mn, Zn, Cu, B and organic content by Allison Laboratories, Hobart, Tasmania.

Data analysis

Fuel loads in grazed and ungrazed quadrats at each site after 18 months were compared using paired t-tests. The percentage difference in fuel load between quadrat pairs [((ungrazed − grazed)/grazed) × 100] at 18 months was calculated. One-way anova was used to test for differences in this variable between lawn and tussock structural groups. A linear mixed effects model (Pinheiro & Bates 2000) was used to test for variation in percentage dead fuel amongst grazing treatment/structural group combinations. The dependent variable in this model was the mean value for percentage dead fuel at each survey time within each site. The interaction of grazing treatment and structural group was fitted as a fixed factor. Site was fitted as a random factor, thereby accounting for the lack of independence between samples taken within sites at different times. Mixed effects modelling was carried out using the ‘nmle’ package (Pinheiro et al. 2008) in R 2.7.1 (R Core Development Team 2008). Pairwise contrasts were carried out to test for differences in percentage dead fuel amongst treatment/structure combinations.

Fire potential (i.e. whether, once ignited, fire would be expected to be sustaining) was ascertained for each quadrat for each day of the study using the rule set given in Leonard (2009a). This rule set was derived using logistic regression and classification tree analysis of fuel and weather data from 111 test fires (33 sustaining, 78 non-sustaining). To summarize, for fires to be sustaining, all of the following conditions must be met: result of equation

  • image

dead fuel moisture <24·2%, fuel load >0·8 t ha−1, percentage dead fuel ≥60%; if fuel load is between 0·8 and 2·2 t ha−1, wind speed >2·5 km h−1. Daily values for fuel load and percentage dead fuel for each quadrat for each day of the study were interpolated from quadrat survey data, assuming a linear rate of change between measurements. Daily weather conditions at 15:00-hours at each site were estimated from data collected at nearby Bureau of Meteorology stations calibrated by data collected from the sites (Leonard 2009b). Daily dead fuel moisture contents at 15:00-hours were calculated using the equation derived by Marsden-Smedley & Catchpole (2001) for buttongrass Gymnoschoenus sphaerocephalus moorland fuels. This model performed better than commonly used grassland fuel moisture models (Noble, Bary & Gill 1980; Purton 1982) in Tasmanian conditions (S.W.J. Leonard, unpublished data). The fire danger rating at 15:00-hours (Noble, Bary & Gill 1980) was found to approximate the daily maximum.

The percentage of potential fire days (i.e. days on which fire was predicted to be sustaining) over the entire study period was calculated for each quadrat. Paired t-tests were used to test for differences between grazed and ungrazed treatments at each site. In order to examine the effect of grazing on fire potential across all sites, the difference in percentage of potential fire days (grazed − ungrazed) between each pair of quadrats was calculated. This value is henceforth called the fire potential difference index (FPDI). Negative FPDI values indicate that grazed quadrats were predicted to be burnable less often than ungrazed quadrats, while positive FPDI values show the reverse. One-sample t-tests were used to determine whether mean FPDI values differed from zero. One-way anova was used to test for variation in FPDI and grazing intensity between grassland structural types and amongst sites dominated by different grass genera (Poa, Themeda and Austrodanthonia). Relationships between FPDI and grazing intensity and soil nutrient levels were examined using generalized linear models. As only a single value per site for each soil variable was obtained, the site mean for FPDI was used in these analyses. All analyses were carried out using the Minitab 15 software package (Minitab Inc. 2006) except where noted above.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

After 18 months there was markedly more fuel present in ungrazed quadrats than in grazed quadrats at all lawn sites (Table 3). Tussock sites also exhibited a tendency for fuel loads to be greater in ungrazed quadrats than in grazed quadrats, although this difference was statistically significant only at Iris River. The mean percentage difference in fuel load between grazed and ungrazed treatments was significantly higher at lawn sites than at tussock sites (tussock 114·24%, lawn 1264·63%. = 29·5, < 0·001).

Table 3.   Mean values (±SE) and results of paired t-tests for fuel loads (t ha−1) in grazed and ungrazed quadrats after 18 months
 StructureGrazedUngrazedP
  1. Bold type indicates significant difference ( 0·05).

Staghorn HillLawn0·59 ± 0·054·17 ± 0·770·002
Stockers BottomLawn0·14 ± 0·023·61 ± 0·630·001
London LakesLawn0·24 ± 0·041·80 ± 0·600·033
LimekilnLawn0·15 ± 0·021·03 ± 0·180·002
Lime BayLawn0·05 ± 0·020·25 ± 0·060·007
Paradise PlainsTussock4·56 ± 1·465·71 ± 1·460·539
Iris RiverTussock2·04 ± 0·134·42 ± 0·42<0·001
Paradise NorthTussock4·32 ± 1·046·37 ± 0·850·097
Vale of BelvoirTussock7·08 ± 1·388·60 ± 1·950·512
Epping ForestTussock3·07 ± 0·835·61 ± 1·880·177

There was a significant interactive effect of structure and grazing treatment on percentage dead fuel (= 3·23, = 0·03). The percentage of dead fuel in grazed tussock quadrats was significantly higher than in all other treatment/structure combinations (Fig. 1).

image

Figure 1.  Mean values for percentage dead fuel in grazing treatment/grassland structure combinations. Bars indicate standard error. Different letters indicate significant difference between groups ( 0·05).

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At all lawn sites, except for Lime Bay where there were no potential fire days in either treatment, there were significantly less potential fire days in grazed quadrats than in ungrazed quadrats (Table 4). Grazed quadrats were predicted to be burnable more often than ungrazed quadrats at the Epping Forest, Paradise North and Iris River tussock sites.

Table 4.   Mean values and results of paired t-tests for differences in percentage potential fire days over study period (≈550–730 days)
Site % potential fire days (±SE)
StructureGrazedUngrazedP
  1. Bold type indicates significant difference ( 0·05).

Staghorn HillLawn3·4 ± 1·315·5 ± 1·6<0·001
LimekilnLawn1·0 ± 0·77·2 ± 1·30·021
Stocker’s BottomLawn4·7 ± 0·517·0 ± 2·7<0·001
London LakesLawn1·0 ± 0·56·4 ± 1·70·038
Lime BayLawn0·0 ± 0·00·0 ± 0·0
Paradise PlainsTussock15·2 ± 3·612·7 ± 1·60·334
Vale of BelvoirTussock10·8 ± 2·011·5 ± 2·00·783
Paradise NorthTussock22·6 ± 3·710·6 ± 3·00·019
Iris RiverTussock3·6 ± 1·00·8 ± 0·30·013
Epping ForestTussock22·4 ± 5·314·9 ± 5·70·004

There were significant differences in mean FPDI between tussock and lawn structural groups (= 51·34, < 0·001; Table 5). At lawn sites grazing decreased the percentage of days on which sustaining fires were predicted, while at tussock sites grazed quadrats were predicted to be burnable more often than ungrazed quadrats (Table 5). Mean FPDI was significantly lower at sites dominated by Austrodanthonia than at sites dominated by Poa, with there being no consistent effect of grazing on fire potential at Themeda-dominated sites (= 11·36, < 0·001; Table 5).

Table 5.   Results of one-sample t-tests of hypothesis that mean fire potential difference index (FPDI) equals zero
 MeanTP
  1. Negative FPDI values indicate that grazing decreases fire potential, while positive FPDI values indicate the reverse situation (see text for derivation of FPDI). Bold type indicates significant difference ( 0·05).

Tussock6·04·00<0·001
Lawn−7·6−6·80<0·001
Poa5·12·820·008
Themeda−1·7−0·830·417
Austrodanthonia−7·1−4·46<0·001

Mean grazing intensity on lawns was almost twice that of tussock grasslands (lawn 1·52 DSE per hectare, tussock 0·80 DSE per hectare, = 13·11, < 0·001). There was a significant interactive effect of grazing intensity and grassland structure on FPDI (Table 6). Within lawns grazing intensity had a significant negative relationship with FPDI (Fig. 2; regression equation: FPDI −4·34 − 2·17 × grazing intensity, F1,40 = 7·42, = 0·01, adjusted R2 = 0·14). However, there was no significant relationship between these variables at tussock sites. Mean grazing intensity was significantly higher at sites dominated by Austrodanthonia than at Poa-dominated sites, with Themeda sites not differing from either of these groups (Austrodanthonia 1·37 DSE per hectare, Themeda 0·99 DSE per hectare, Poa 0·84 DSE per hectare, = 6·78, = 0·002).

Table 6.   Results of generalized linear modelling of fire potential difference index (FPDI) vs. grazing intensity and grassland structure
 Coefficient ± SETPFGLMPGLMAdjusted R2
  1. d.f. 3, 84. Bold type indicates significant relationship ( 0·05).

Structure−3·85 ± 3·97−0·120·3920·72<0·0010·40
Graze8·16 ± 4·721·720·09   
Structure × graze−10·32 ± 4·83−2·140·04   
Intercept−0·49 ± 3·97−0·120·90   
image

Figure 2.  Relationship between fire potential difference index (FPDI) and grazing intensity (DSE per hectare). Solid circles, tussock quadrats pairs; open circles, lawn quadrat pairs. Negative FPDI values indicate that grazing decreases fire potential, while positive FPDI values indicate the reverse situation (see text for derivation of FPDI). Regression line is for FPDI vs. grazing intensity within lawn sites.

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Site mean FPDI was negatively related to soil Zn, Ca and K concentrations (Table 7). In all cases grazed quadrats were predicted to be burnable less often than ungrazed quadrats as levels of these nutrients increased (Fig. 3).

Table 7.   Results of regression of fire potential difference index (FPDI) vs. soil nutrient levels
 CoefficientInterceptPAdjusted R2
  1. Bold type indicates significant relationship ( 0·05).

N−1·022·570·5640·00
LOI−0·092·980·5260·00
pH0·80−3·300·8360·00
COND−0·044·840·3570·00
P0·71−4·580·7610·00
log10K−40·9095·960·0130·50
Ca−0·0046·310·0490·23
Mg−0·013·990·1380·11
Mn−0·024·150·070·19
Zn−4·0918·48<0·0010·63
Cu−3·135·300·0870·16
B1·04−3·080·3890·00
image

Figure 3.  Fire potential difference index (FPDI) for potential fire days vs. soil nutrient levels (p.p.m.). Solid circles, tussock sites; open circles, lawn sites. Note log10 transformation for K. Negative FPDI values indicate that grazing decreases fire potential, while positive FPDI values indicate the reverse situation (see text for derivation of FPDI).

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Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The major pattern in the data was that the effect of grazing exclusion differed between lawn and tussock grasslands. At lawn sites ungrazed quadrats had greater fuel loads than grazed quadrats, while at tussock sites the difference in fuel load between treatments was comparatively minor. However, grazing exclusion also decreased the proportion of dead fuel at tussock sites, with no clear difference at lawn sites. This difference in the effects of grazing treatment on fuel characteristics between grassland structural types flowed through into predictions of fire potential. Grazing exclusion increased fire potential at lawn sites by increasing the amount of time there was sufficient fuel to carry fire. At tussock sites grazing exclusion decreased the number of days on which there was sufficient dead fuel to carry fire, thus reducing fire potential. The exception to this overall pattern was the Lime Bay lawn site. At this site neither fuel loads nor dead fuel levels were sufficient to allow sustaining fires on any day of the study. Moisture limitation, caused by shallow soils (<5 cm) and extreme drought conditions, meant that productivity at this site was so low that even with the exclusion of grazing there was never enough fuel to carry fire within the study period.

The difference in flammability between lawns and tussock grasslands appears to be caused by differences in their palatability. Most lawn sites in the current study were dominated by species of Austrodanthonia. These are amongst the most palatable native grasses occurring in Tasmania, having high levels of digestibility and protein (Mitchell 2002). Themeda, which was co-dominant in some lawns, also has a high protein content when actively growing. However, the protein content of dormant or dead Themeda leaves, which comprise the bulk of the biomass at Themeda-dominated tussock sites, is approximately half that of living foliage. Poa species, the most common dominants of tussock sites, are likely to be even less palatable, having less protein and higher silica content than Austrodanthonia or Themeda (Mitchell 2002). The large tussocks formed by most Poa species also tend to accumulate dead foliage, further reducing their palatability. In tussock grasslands the most nutritious portion of the vegetation consists of actively growing grass shoots and small-statured grasses and forbs occurring between the tussocks. Our study indicates that grazers target these green components of the vegetation, thereby increasing the proportion of dead material. Observations of feeding Bennett’s wallabies Macropus rufogriseus indicate that they are capable of highly precise forage selection and can remove green leaves from grass tussocks while leaving dead foliage intact. Macropod selectivity for the most nutritious portion of vegetation has been observed in a number of species and vegetation types (Dawson 1989).

At lawn sites most of the plant biomass constitutes potential food for grazers. These sites supported high herbivore populations, which consumed a large proportion of the potential fuel, thereby markedly reducing the number of days on which fires could be sustaining. By contrast, at tussock sites, where most of the vegetation is unpalatable, grazer populations were lower and their feeding was concentrated on only a portion of the vegetation. Therefore, grazing had little effect on fuel loads, and, by increasing the proportion of dead fuel, led to an increase in fire potential. The formation and maintenance of grazing lawns involves a positive feedback between grazing and forage quality and availability (McNaughton 1984; Cromsigt & Olff 2008). The results of our study indicate that this feedback also limits the potential for fire to occur in these systems.

The trend for the reduction in fire potential associated with grazing to become more pronounced with higher soil Ca, K and Zn levels further indicates that vegetation palatability is a determinant of the effect of grazing on fuel characteristics. Positive relationships between grazing intensity and cationic nutrients (Na and Mg) have also been observed in southern African grasslands (Stock, Bond & van de Vijer 2010). In both the southern African study and our study there was no relationship between grazing intensity and soil N levels. This is in contrast to a number of studies in which areas that were foci for grazing had elevated soil N levels due to enhanced N cycling through urine deposition by grazers (McNaughton, Banyikwa & McNaughton 1997; Knapp et al. 1999). However, Stock, Bond & van de Vijer (2010) found that grazing lawns had higher concentrations of foliar N, P, Na, K and Mg than bunch grassland. Foliar nutrient content was not examined in our study. However, the differences in the dominant grass taxa between lawns and tussock grasslands mean that it is likely that a similar pattern occurs in Tasmanian grasslands. These observations suggest that, in Tasmania and southern Africa at least, it is the relative palatability of the plants comprising grazing lawns, which in turn is due to a combination of high soil fertility and intense grazing, that explains the attractiveness of lawns to grazers, rather than enhanced nutrient cycling rates caused by the animals themselves.

Bond (2005) characterized fire and herbivores as ‘competing’ consumers of plant biomass, although differing in that fire can consume a much broader range of plant material than herbivores as it has no requirement for protein or other nutrients. The observations made in the current study support these ideas. At sites dominated by palatable species, the consumption of biomass by grazers greatly reduced the likelihood of fire occurring. However, where the bulk of plant biomass was unpalatable, grazers had little impact on fuel loads, and thus fire potential was much higher. In fact, at these sites, the preferential consumption of the most palatable portion of the vegetation actually increased its flammability. It is likely that importance of palatability in determining the outcome of the ‘competition’ between herbivores and fire is not unique to grasslands. In fact it would be expected that palatability, along with other constraints on the ability of animals to consume plant biomass, would influence the relative importance of herbivory and fire in most vegetation types. Grazing lawns, in which virtually all vegetation biomass becomes food rather than fuel, probably represent one extreme of the palatability vs. flammability spectrum. Vegetation types dominated by scleromorphic shrubs and sedges are likely to be towards the other extreme. The traits characterizing scleromorphic species, i.e. thick, fibrous, nutrient-poor and often toxic foliage, deter herbivory (Turner 1994). Heaths dominated by these species are fire prone, and fire plays a major role in their ecology (Keith, McCaw & Whelan 2002). The effect of herbivory on the flammability of heath is likely to be minor. The findings of Williams et al. (2006) mirror those of the current study in that grazing by cattle had no effect on the extent or intensity of wildfire in the Australian Alps because the dominant heath vegetation was not favoured for feeding.

The difference in the effect of grazing on fire potential between lawn and tussock grasslands means that they are likely to experience different disturbance regimes. Within lawns the dominant disturbance is clearly grazing with fire being a rare event, probably only occurring in extreme weather conditions. By contrast, fire is likely to be a regular occurrence in tussock grasslands. The association between vegetation structure and disturbance regime suggests that grazing lawns and tussock grasslands represent alternate stable states (Scheffer et al. 2001). A change in disturbance regime within a particular grassland patch may therefore cause a shift from one state to the other. The London Lakes site appears to provide an example of this. The current Austrodanthonia lawn grassland at London Lakes occurs in an area that previously consisted of grassy woodland, which was cleared several decades ago. The understorey of remaining woodland adjacent to the present grassland is dominated by Poa and has a tussock structure. It appears that in the wake of clearing grazers targeted the regenerating vegetation such that Poa was prevented from re-establishing, leading to the development of the present lawn. While in this case it was exogenous disturbance that apparently initiated the change from tussock to lawn, similar transitions have been observed following fire (Meers & Adams 2003; Archibald & Bond 2004; Fuhlendorf & Engle 2004). This effect may be particularly pronounced when the disturbed area is small relative to the surrounding undisturbed vegetation (Gill & Bradstock 1995).

Grasslands may also follow the opposite trajectory, that is, from lawn to tussock structure, if grazing intensity is relaxed sufficiently. In our study most ungrazed lawn quadrats underwent such a transition. A number of these quadrats continued to exhibit a tussock structure more than a year after exclosures were removed at the end of the experiment. This observation supports the idea of lawn and tussock structures as alternative stable states and further highlights the primacy of grazing in preventing the transition of lawns to the unpalatable and more flammable tussock grassland. Outside of an experimental setting, a lawn to tussock transition may occur through grazers being attracted away from existing lawns by the appearance of regenerating patches following fire at other locations (Bond & Archibald 2003). A sudden, major decrease in grazer populations through epidemic disease, extreme environmental conditions or intense predation could allow grasses to escape into an unpalatable and more flammable state (Kirkpatrick 2007). Data from our study suggest a threshold in grazing intensity delineating lawn and tussock grasslands at around 1 DSE. At any particular site the level of grazing required to prevent a transition from a lawn to tussock state will depend on the growth rate of the vegetation. For example, the Lime Bay and Limekiln sites were maintained as lawns by relatively low levels of grazing (∼0·4 DSE). Productivity at these sites appeared to be lower than at the other lawn sites. In both cases this was probably the result of soil moisture being limiting due to low rainfall at Limekiln and shallow soils at Lime Bay.

The findings of our study have several implications for the management of native vegetation. Firstly, within vegetation generally, they indicate that herbivory is only likely to be effective in reducing the probability of fire where the bulk of the vegetation consists of potential food for grazing or browsing animals (Valderrabano & Torrano 2000; Williams et al. 2006). Indeed, in largely unpalatable vegetation, selective consumption by herbivores of actively growing material may increase the likelihood of fire by increasing the proportion of dead fuel. Within grassy ecosystems the coexistence of lawn and tussock structures is common throughout the world (e.g. Bakker, de Leeuw & van Wieren 1983; Knapp et al. 1999; Posse, Anchorena & Collantes 2000; Archibald et al. 2005). Such heterogeneity is likely to enhance biodiversity at a landscape scale (Fuhlendorf & Engle 2004; Leonard & Kirkpatrick 2004), and therefore managers must ensure that a disturbance regime is applied that is conducive to maintaining this situation. For example, where grazing lawns occur within a matrix of tussock grassland, frequent, extensive fires may result in the reduction in the extent of lawns and promotion of homogenous tussock grassland, while patch burning will tend to promote grazing lawn formation (Bond & Archibald 2003). Burning within lawn grasslands will rarely be a necessary (or possible) management action. However, in instances where vegetation growth outstrips consumption by grazers (e.g. following above average rainfall during the growth season), burning may be required to prevent a transition from lawn to tussock structure.

The results of our study show that the outcome of the ‘competition’ (Bond 2005; Bond & Keeley 2005) between fire and grazing in Tasmanian native grasslands is not simply one of ‘grazing reducing blazing’. Instead, the relationship between grazing and fire potential is largely determined by the palatability of the vegetation, which in turn is influenced by interactions between the plant species present, soil fertility, grazing intensity and site disturbance history. It is likely that the negative relationship between vegetation palatability and fire potential pertains to grassy ecosystems generally and possibly to a broad range of vegetation types. Further research elucidating these inter-relationships would make a valuable contribution to the understanding of grassland ecology and the conservation management of this vegetation.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Many thanks to Jenny Styger for outstanding assistance in the field. Thanks also to the various landowners who generously provided access to study sites. This project was funded by the Australian Research Council (DPO665083).

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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