SEARCH

SEARCH BY CITATION

Keywords:

  • C4 grasses;
  • carbon storage;
  • CO 2 ;
  • fire management;
  • fire regimes;
  • global change;
  • northern Australia;
  • tree demography;
  • tropical savanna;
  • woody thickening

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Implications and Conclusions
  8. Acknowledgements
  9. References
  10. Biosketch
  11. Supporting Information

Aim

Many tropical savannas are undergoing a trend of increasing woody biomass, or ‘woody thickening’. Management to reduce fire frequency and intensity in savannas could substantially increase the amount of carbon stored in woody biomass. We addressed two questions: (1) are northern Australian savannas thickening; and (2) to what extent, and by what demographic processes, does fire affect woody biomass accumulation?

Location

Three large national parks, covering 24,000 km2, in monsoonal northern Australia.

Methods

We examined changes in woody biomass carbon stocks – inferred from tree basal area and the density of woody understorey plants – over a 10-year period in 136 savanna monitoring plots. We statistically assessed these changes in relation to fire frequency and severity. We used a meta-analysis to identify general trends in woody cover in Australian savannas over the last half-century.

Results

Woody biomass carbon stocks were relatively stable across the three national parks, but rates of change were statistically indistinguishable from earlier findings of a weak thickening trend. Change was negatively correlated with fire frequency, particularly the frequency of severe fires. High frequencies of severe fires decreased rates of accumulation of biomass by existing trees (through reductions in tree growth and death of individual stems), rather than whole-tree mortality and suppression of recruitment. However, across northern Australia, our meta-analysis identified a general, albeit weak, trend of woody thickening.

Main conclusions

The drivers of northern Australia's weak thickening trend are uncertain, but likely candidates include increasing atmospheric CO2 concentration and water availability, and pastoral intensification. We demonstrate that changes to fire management have the potential to either increase or decrease rates of woody thickening relative to any underlying trend. Understanding how savanna fires affect woody biomass, and how fire effects are mediated by climate and CO2, are essential research priorities to predict the fate of savannas.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Implications and Conclusions
  8. Acknowledgements
  9. References
  10. Biosketch
  11. Supporting Information

The tropical savanna biome plays a prominent role in the terrestrial carbon cycle, contributing approximately 30% of global terrestrial net primary production, roughly equivalent to that of tropical forests (Grace et al., 2006). Over the last decade, the woody biomass dynamics of savannas have received considerable attention, in large part due to the presumed instability of tree–grass coexistence (Sankaran et al., 2005; Bond & Midgley, 2012). Indeed, trends of woody thickening (i.e. increasing woody plant abundance) have been observed across a number of savanna systems spanning Africa, Australia, North and South America (Archer et al., 1995; Burrows et al., 2002; Cabral et al., 2003; Heisler et al., 2003; Sharp & Whittaker, 2003; Durigan & Ratter, 2006; Lehmann et al., 2009; Bond & Midgley, 2012; Buitenwerf et al., 2012). Land-use intensification, changed patterns of fire and rainfall and increasing atmospheric carbon dioxide concentration ([CO2]) have all been posited as drivers of these trends; however, the relative importance of each remains unresolved.

Savannas are characterized by frequent low-intensity fires (Murphy et al., 2013), causing much of the carbon captured by vegetation to be released back to the atmosphere, so that savannas store less carbon than other productive tropical biomes (Grace et al., 2006). However, recent work suggests that northern Australia's mesic savannas are a weak carbon sink, with several estimates of the rate of sequestration in the range 1–2 t C ha−1 year−1 (Cook et al., 2005; Beringer et al., 2007). However, the nature of the carbon sink is poorly defined, with little evidence to support or refute the notion that the sink is largely due to woody thickening, possibly as a result of increasing [CO2] (Bond & Midgley, 2012) and rainfall, or decreasing evaporative demand (Roderick & Farquhar, 2004). Given that savanna fires result in the loss of carbon from vegetation to the atmosphere, reducing fire frequencies could lead to substantial increases in woody biomass and thus greater carbon storage (Grace et al., 2006; Beringer et al., 2007; Murphy et al., 2010).

World-wide evidence demonstrates dramatic increases in woody biomass following fire exclusion in savannas (San José et al., 1998; Tilman et al., 2000; Higgins et al., 2007). Much of the work on this topic comes from a number of savanna fire experiments in Africa. For example, Brookman-Amissah et al. (1980) described massive negative effects of annual fire regimes, especially of high intensity fires late in the dry season, on rates of recovery of woody biomass following tree clearing in a Ghanaian savanna. Similar findings have been obtained in mesic savannas in other parts of West and southern Africa (Trapnell, 1959; Rose Innes, 1972). In a 40-year study in the semi-arid savannas of Kruger National Park, South Africa, complete fire exclusion resulted in very large increases in woody biomass, triennial and biennial fires resulted in smaller increases, and annual fires resulted in declines in woody biomass (Higgins et al., 2007). Two similar, although shorter-term, fire experiments undertaken in northern Australia's Kakadu National Park suggested that fire exclusion leads to an increase in woody biomass, frequent severe fires lead to a decrease in biomass and frequent mild fires lead to little change (Williams et al., 1999; Russell-Smith et al., 2003). Similarly, remote sensing of tree cover over the late 20th century across Kakadu (Lehmann et al., 2009) showed that changes in cover were negatively related to fire frequency.

The recent development of an extensive decadal vegetation and fire monitoring dataset, from three large national parks in northern Australia, provides an opportunity to contrast the magnitude of changes in woody biomass carbon stocks, and the components of those changes, in response to fire, and to explore the proximate causes of woody thickening. Using this dataset, in conjunction with a meta-analysis of the published literature, we address two questions: (1) are northern Australian savannas thickening; and (2) to what extent, and by what demographic processes, does fire affect woody biomass accumulation?

Materials and Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Implications and Conclusions
  8. Acknowledgements
  9. References
  10. Biosketch
  11. Supporting Information

Study area

The data derive from three northern Australian national parks (Fig. 1) – Kakadu (19,092 km2), Nitmiluk (2924 km2) and Litchfield (1464 km2) – dominated by eucalypt (Eucalyptus and Corymbia spp.) savanna woodlands. The climate is monsoonal, with an intense summer monsoon season. Mean annual rainfall ranges from c. 900 mm in the south-east to c. 1500 mm in the north-west (Fig. 1). Around 42%, 66% and 51% of Kakadu, Nitmiluk and Litchfield National Parks, respectively, burn each year (Bushfires NT, unpublished data).

image

Figure 1. Location of 136 vegetation monitoring plots in mesic savanna throughout Kakadu, Nitmiluk and Litchfield National Parks, northern Australia. Contour lines represent mean annual rainfall (mm).

Download figure to PowerPoint

Estimation of live woody biomass carbon stocks

Between December 1994 and February 1997, 220 vegetation monitoring plots were established throughout the three parks. At each plot, three vegetation inventories were carried out at approximately 5-year intervals (i.e. spanning 10 years). We restricted our current analysis to the 136 plots located in savanna (Fig. 1).

Live woody biomass was assessed as two separate components: trees (adults only) and woody understorey (shrubs and juvenile trees). The carbon stored in live adult trees was estimated from a complete inventory of live trees in plots measuring 40 m × 20 m. For the initial inventory, all live trees with diameter at breast height (d.b.h.; 130 cm) ≥ 5 cm were permanently tagged and their d.b.h. was recorded. In the two subsequent inventories, we recorded d.b.h. and survival of tagged trees, and recruitment of new trees, with permanent tags added to all new trees. Using allometric equations, live tree basal area (m2 ha−1) was converted to tree carbon stock, following Cook et al. (2005) and Murphy et al. (2010), including both aboveground and belowground tree biomass (t C ha−1).

As adult trees were permanently tagged in the monitoring plots, we were able to partition changes in tree carbon stocks into three components: (1) changes to the biomass of trees that survived each 5-year monitoring period; (2) changes in biomass attributable to the death of existing trees; and (3) changes in biomass attributable to the recruitment of new trees into the adult size class (d.b.h. ≥ 5 cm).

Inventories of shrubs and juvenile trees (< 5 cm d.b.h.), were also made during the vegetation assessments, to estimate woody understorey carbon stock. All live woody plants in three height classes were counted: small (< 50 cm); medium (50–200 cm); and large (> 200 cm). Within each 40 m × 20 m adult tree plot, small woody plants were counted in two 40 m × 1 m transects, and medium and large woody plants were counted in a single 40 m × 10 m transect. Using allometric height:biomass relationships specific to the region (J. Russell-Smith, unpublished data) we estimated the aboveground biomass of a typical woody plant for each size class as 23 ± 5 g (± 95% confidence interval, CI, = 28) for small, 179 ± 48 g (n = 48) for medium and 892 ± 291 g (n = 10) for large individuals. To estimate biomass of the woody understorey, including belowground biomass, we assumed a root:shoot ratio of 0.50, based on results for juveniles (<  10 cm d.b.h.) of Eucalyptus tetrodonta from Kakadu (Werner & Murphy, 2001). The biomass of a typical woody understorey plant was converted to carbon stock, assuming a carbon content of 49% (Cook et al., 2005). Estimated carbon stock per individual was multiplied by the density of individuals per unit area, to produce estimated woody understorey carbon stock (t C ha−1). Understorey individuals were not permanently marked, so it was not possible to partition overall change in understorey carbon stock into its individual components (i.e. recruitment, mortality, individual growth).

Fire frequency

A detailed fire history was available for each monitoring plot (Russell-Smith & Edwards, 2006; Murphy & Russell-Smith, 2010). Each plot was visited once or twice each year throughout the 10-year study, and scored as burnt or unburnt in each season (early or late dry season). If burnt, the fire was categorized as mild, moderate or severe, using a severity index based on the scorch height of leaves (Russell-Smith & Edwards, 2006). Leaf scorch height has been shown to be closely related to measured Byram fire-line intensity in these savannas (R2 = 0.85) (Williams et al., 1998), underpinning the validity of the fire severity classification. Mild fires suggest Byram fire-line intensities of < 1 MW m−1, moderate fires 1–2 MW m−1, and severe fires > 2 MW m−1. Using the 10-year fire histories, we calculated the frequency of mild, moderate and severe fires for each plot for each 5-year interval.

Statistical analysis

We examined five response variables: the overall change in carbon stock of (1) live adult trees and (2) woody understorey, as well as change in tree carbon stock that could be attributed to (3) existing trees that survived each 5-year monitoring period, (4) existing trees that died, and (5) trees that recruited into the adult size class (> 5 cm d.b.h.). Change was assessed in each of the two 5-year monitoring periods, and expressed as an annualized rate:

  • display math

where ‘time’ is the duration of the monitoring period in years.

We examined the effect of four variables on changes in woody biomass carbon stocks, three related to fire frequency: (1) mild fire frequency; (2) moderate fire frequency; (3) severe fire frequency; and three other variables that we expected to be important: (4) tree or woody understorey biomass in the plot at the start of the monitoring period (log-transformed); (5) the amount of rainfall in the plot during the monitoring period, estimated from interpolated monthly rainfall grids (0.05° resolution; Australian Bureau of Meteorology, Canberra); and (6) landform (i.e. either lowland or sandstone plateau), affecting moisture infiltration and maximum rooting depth.

For each of the five response variables, models representing all combinations of the six explanatory variables were constructed as linear mixed effects models in the program R (version 2.15.1), using the package nlme (Pinheiro et al., 2012). Mixed effects models allowed us to account for the repeated measurement of each plot (i.e. in each 5-year monitoring period), and hence the lack of independence of observations, a key assumption of standard statistical models such as ordinary least squares regression. By specifying ‘plot’ as a random intercept, each plot is allowed to differ in its intercept, such that the repeat observations from each plot can be correlated without violating any assumption of the model. Each response variable was appropriately transformed to ensure normally distributed errors.

Models were evaluated using a robust form of Akaike's information criterion, AICc, a model selection index favouring both model fit and simplicity (Burnham & Anderson, 2002). Lower values of AICc indicate greater support for a model, relative to other models in the same candidate set. From AICc, Akaike weights (wi) were calculated for each model, and these are equivalent to the probability of a given model being the best in the candidate set. The importance of each variable was evaluated using w+, the sum of wi for all models in which that variable occurred. For each variable, w+ is equivalent to the probability of the best model containing that variable. We considered that w+ values of < 0.73 were indicative of substantial model selection uncertainty, and that a relationship between the response and the explanatory variable in question was not well supported by the data. A w+ value of 0.73 is equivalent to an AICc difference of 2 units between the models containing the variable under examination and those not containing it. An AICc difference of 2 units is a common ‘rule of thumb’ used to assess clear evidence of an effect.

There was some evidence that the three fire frequency variables were weakly correlated, raising the possibility of multicollinearity. We assessed the extent to which multicollinearity was a problem by calculating the variance inflation factor (VIF) for each fire frequency variable (Kutner et al., 2004). For each fire frequency variable, VIF was very low (≤ 1.1), well within the common ‘rule of thumb’ of < 5, indicating that multicollinearity was unlikely to be a problem in our analysis.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Implications and Conclusions
  8. Acknowledgements
  9. References
  10. Biosketch
  11. Supporting Information

Changes in woody biomass

Between 1994 and 2004 there was almost no net change in woody biomass carbon stocks in the savannas across the region. Carbon stocks in tree and understorey woody biomass were 25.1 ± 2.2 t C ha−1 and 0.64 ± 0.08 t C ha−1, respectively, in both 1994 and 2004 (Fig. 2), representing a net change of 0.00 ± 0.11 t C ha−1 year−1 (± 95% CI). A one-sample t-test suggested that net change was not significantly different from zero [t(135) = −0.04, = 0.96]. There was a slight increase between 1994 and 1999 (0.30 ± 0.20 t C ha−1 year−1), offset almost exactly by a subsequent decrease between 1999 and 2004. The slight decrease in woody biomass carbon stocks in the second half of the study period parallels a slight increase in the overall number of fires (2.1–2.9 fires site−1) and the number of severe fires (0.1–0.3 fires site−1). Generalized linear models (negative binomial errors) suggested these differences in the number of fires were statistically significant (< 0.0001 and < 0.01, respectively).

image

Figure 2. Schematic representation of changes in woody biomass carbon stocks for 136 vegetation monitoring plots in mesic savanna, northern Australia, over a 10-year period (1994–2004). The relative contributions of growth, recruitment and mortality to change in carbon stocks are indicated. Values represent means ± 95% confidence intervals.

Download figure to PowerPoint

Not only was there very little net change in carbon stocks, but few individual plots experienced large changes (Fig. 3). For example, only three plots (< 3%) experienced either positive or negative changes as large as 1.7 t C ha−1 year−1 in magnitude, i.e. the rate of woody thickening suggested by Beringer et al. (2007) for a site near Darwin.

image

Figure 3. Frequency distribution of annualized changes in woody biomass carbon stocks for 136 vegetation monitoring plots in mesic savanna, northern Australia, over a 10-year period (1994–2004).

Download figure to PowerPoint

The effects of fire on woody biomass

Inspection of changes in tree and woody understorey carbon stocks indicates a negative effect of frequent, severe fires (Fig. 4). Generally, plots that were subject to one or more severe fires in a 5-year period experienced declines in tree and woody understorey carbon stocks (Fig. 4a,e). For trees, the negative effect of fire was most apparent in changes in carbon stock of surviving individuals (Fig. 4b), with frequent severe fires substantially reducing the rate of accumulation of biomass by individual trees. For changes that were due to the death or recruitment of trees, there was no clear relationship with fire frequency (Fig. 4c,d).

image

Figure 4. Mean annual change (%) in carbon stock (t C ha−1) in live (a–d) adult tree and (e) woody understorey biomass, in relation to fire regimes of varying fire frequency and severity for 136 vegetation monitoring plots in mesic savanna, northern Australia. From left to right along the x-axis, fires are increasingly frequent and severe. Change in tree carbon stock is separated into that due to: (b) survival of existing trees; (c) death of existing trees; and (d) recruitment of new trees. Standard errors are shown.

Download figure to PowerPoint

These observations were confirmed by model selection, with very strong support for effects of fire frequency on changes in both tree and woody understorey carbon stocks. There was strong support for the effect of severe fire frequency on change in tree carbon stock overall (+ 0.99), and for the effect of fire frequencies in general, regardless of severity, on changes in carbon stock of surviving individuals (w+ ≥ 0.98; Fig. 5). However, there was very little support for the effect of fire frequencies on change in carbon stocks attributable to either the death or recruitment of trees. Both severe and mild fire frequencies affected woody understorey carbon stock (w+ of 0.89 and 0.86, respectively).

image

Figure 5. Summary of the importance of the three fire frequency variables examined (mild, moderate and severe fire frequency) as determinants of various components of change in woody biomass carbon stock for 136 vegetation monitoring plots in mesic savanna, northern Australia. w+ is equivalent to the probability of a given variable occurring in the best model, and therefore reflects the weight of evidence of a relationship between that variable and change in carbon stock. The dashed line indicates a w+ value of 0.72, considered a useful ‘rule of thumb’ to assess clear evidence of an effect.

Download figure to PowerPoint

Where there were detectable relationships between change in carbon stock and fire frequency, the relationships were consistently negative, with severe fires causing substantially greater declines than moderate and mild fires (Fig. 6). A regime of frequent severe fires (≥ 0.2 fires year−1, i.e. 1 in 5 years) is clearly associated with declines in both tree and woody understorey carbon stocks.

image

Figure 6. The modelled relationship between annual change in carbon stocks in (a–d) adult tree biomass and (e) woody understorey biomass and the frequency of mild, moderate and severe fires for 136 vegetation monitoring plots in mesic savanna, northern Australia. The predicted effects are based on multi-model averaging of all candidate models, weighted according to wi. The shaded areas represent the 95% confidence interval associated with fire frequency. NS, not significant.

Download figure to PowerPoint

That frequent severe fires reduce carbon stocks appears to be due primarily to reductions in the rate of biomass accumulation by surviving trees (Fig. 6b), rather than tree mortality or recruitment (Fig. 6c,d).

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Implications and Conclusions
  8. Acknowledgements
  9. References
  10. Biosketch
  11. Supporting Information

Are Australian savannas thickening?

We demonstrate that across a large area of mesic savanna in northern Australia there was little net change in woody biomass over a decade (1994–2004), in stark contrast to a number of recent reports of very high rates of woody biomass increase in the region's savannas (Chen et al., 2003; Beringer et al., 2007; Bowman et al., 2008). This absence of a strong thickening trend is despite the decade being the wettest on record, averaging 1399 mm annually across the region, compared to a long-term average of 1210 mm (Australian Bureau of Meteorology, 2012). The absence of a strong trend is also commensurate with the findings of Lehmann et al. (2009). Although they demonstrated a statistically significant increase in tree cover across the savannas of Kakadu between 1984 and 2004, they found that the magnitude of the change was very small, just +1.2% of ground area per decade. Based on a relationship between tree cover and basal area (R2 = 0.64) provided by Lehmann et al. (2009), we estimate that their observed increase in tree cover represents an increase in tree carbon stocks of just 0.07 t C ha−1 year−1, well within the 95% confidence interval of our estimate (0.0 ±0.11 t C ha−1 year−1). The conversion of savanna woody cover to biomass using allometric equations is a well-established practice (Asner et al., 2003; Fensham & Fairfax, 2003; Fensham et al., 2003; Suganuma et al., 2006). In the preceding 20-year period (1964–1984), the trend detected by Lehmann et al. (2009) was substantially greater, at +3.7%, equating to a change in carbon stocks of 0.22 t C ha−1 year−1.

The allometric approach that we used to estimate woody biomass is the standard approach for studying woody biomass dynamics (e.g. San José et al., 1998; Tilman et al., 2000; Burrows et al., 2002; Higgins et al., 2007; Lewis et al., 2009). However, there are a large number of potential sources of error in such biomass estimates that are rarely acknowledged, let alone quantified. In our case, the largest sources of error include: (1) the inherent error associated with the species-specific and generic allometric equations used (Nickless et al., 2011); and (2) the use of a ‘generic’ relationship where species-specific relationships are not available. Species-specific equations were only available for six species for aboveground tree biomass, and none for belowground tree biomass and shrub biomass. Systematic under- or overestimation of woody biomass carbon stocks would result in an equivalent under- or overestimation of absolute change. Unfortunately, we are unable to quantify the likely extent of such systematic error in our results. This is an important limitation of our study, but one shared with the vast majority of other examinations of woody biomass change over time (e.g. Tilman et al., 2000; Burrows et al., 2002; Higgins et al., 2007; Lewis et al., 2009).

The absence of a strong trend of increasing woody biomass carbon stocks in the savannas of our study area, inferred from both our results and those of Lehmann et al. (2009), contrasts strongly with some recent reports of dramatic thickening in the same region. Bowman et al. (2008) used historical aerial photography to show that savannas at a single location in Kakadu thickened between 1964 and 2004, with tree cover increasing from 48% to 65%, equivalent to an annual increase of about 0.4 t C ha−1 year−1 in tree carbon stocks. However, the findings of the more spatially extensive study of Lehmann et al. (2009) suggest that these high rates of thickening are atypical. Attribution of drivers of the local tree cover increase reported by Bowman et al. (2008) has been subject to debate, largely owing to the unique disturbance history of that study area. Petty et al. (2007) suggested that the tree cover change was related to the site's proximity to floodplains and high densities of feral Asian water buffalo (Bubalus bubalis) in the 1960s and 1970s, before their virtual extermination from Kakadu by the late 1980s. Work in Kakadu has demonstrated that buffalo promote the growth and survival of juvenile savanna trees, by reducing the abundance of grasses, which compete with juvenile trees and provide fuel for fires (Werner, 2005; Petty et al., 2007). However, Bowman et al. (2008) found virtually no relationship between rates of tree cover change and buffalo track densities (observed in historical aerial photographs), and attributed the thickening trend to larger-scale drivers such as increasing rainfall and [CO2], as well as their interactions with changing fire regimes. It is important to note that localized changes in tree cover and woody biomass do not preclude large-scale drivers. Indeed, high spatial variation in rates of woody thickening, reportedly associated with elevated [CO2], is consistent with several recent African studies (Higgins et al., 2007; Buitenwerf et al., 2012; Higgins & Scheiter, 2012).

Several localized studies near Darwin (Fig. 1) have reported very high rates of thickening (Chen et al., 2003; Beringer et al., 2007), although these rates are difficult to reconcile with our results and we suggest they are not indicative of a long-term or wider trend. Based on repeated woody biomass inventories, Chen et al. (2003) reported a dramatic increase in woody biomass carbon stocks, at a rate of 3.1 t C ha−1 year−1, equivalent to around 6% annually. However, the timeframe of that study was ≤ 2 years, and the study sites were not burnt during that time. Over longer time-scales, occasional severe fires would destroy at least some of the accumulated biomass. In the same area, Beringer et al. (2007) used flux-tower measurements to estimate a carbon sink of 2.0 t C ha−1 year−1 over 5 years and suggested that 1.2 t C ha−1 year−1 (60%) and 0.5 t C ha−1 year−1 (15%) of this was due to increases in tree and woody understorey biomass, respectively. In our study, only 3 of 136 plots experienced either positive or negative changes in woody biomass carbon stocks as large as these values. We suggest that the study sites of Chen et al. (2003) and Beringer et al. (2007) are thickening at an atypically high rate. Cook et al. (2005) and Hutley & Beringer (2010) attributed these high rates of biomass increase at least partly to the extensive tree damage suffered during severe Tropical Cyclone Tracy in 1974, with tree populations yet to fully recover. Thus, in examining rates of carbon sequestration it is important to use regional scale data collected over sufficiently long periods to capture the effects of periodic disturbance. Short-term, local data should only be used with extreme caution if the goal is to extrapolate sequestration rates to the wider region.

In north-eastern Australia, Burrows et al. (2002) reported that a 270,000 km2 area of savanna had thickened by 0.24 t C ha−1 year−1 in aboveground live tree biomass over the 1980s and 1990s. These authors attributed the thickening to land-use changes, namely the intensification of livestock grazing and coincident reductions in fire frequencies and intensities. Fensham et al. (2003) reported substantially smaller increases in aboveground live tree biomass over a much longer time period in north-eastern Australia (around 0.13 t C ha−1 year−1), emphasizing that thickening may reflect long periods of recovery from infrequent severe droughts that caused mass tree mortality. Lending support to this hypothesis, Fensham et al. (2009) found an equivalent decrease in woody cover over the period 1990–2002, coincident with the severe drought of the mid-1990s.

With some exceptions, when the results of the longer term and large-scale quantitative studies of Australian savannas are collectively examined there is a trend of increasing woody cover over the last half-century (Fig. 7). Across these studies – excluding the results of Burrows et al. (2002) and the present study, which did not measure cover directly and have strong spatio-temporal overlap with Fensham et al. (2003) and Lehmann et al. (2009), respectively – the mean increase reported is 0.08 ± 0.05% of ground area year−1 (± bootstrapped 95% CI), significantly greater than zero. Hence, we conclude that northern Australia's savannas have thickened over the latter half of the 20th century, and that the trend is generally consistent across northern Australia. However, it is important to note that the magnitude of the trend is very small, and this may explain why it was not detected using less precise historical descriptive records (e.g. Fensham, 2008). Significantly though, the trend of increasing woody cover was similar in the mesic savannas of Kakadu (Lehmann et al., 2009) and the more xeric savannas of north-eastern (Fensham et al., 2003) and north-western Australia (Fensham & Fairfax, 2003). Periodic severe droughts and extensive tree dieback are not known to occur in Kakadu, and thus the drought explanation provided by Fensham et al. (2009) cannot account for the weak thickening trend observed over 40 years in Kakadu by Lehmann et al. (2009).

image

Figure 7. Changes in remotely assessed woody cover in northern Australian savannas, derived from published studies: 1. Lehmann et al. (2009), Kakadu National Park; 2. This study, coastal Northern Territory; 3. Fensham et al. (2003), central Queensland; 4. Burrows et al. (2002), central Queensland; 5. Sharp & Bowman (2004a), north-western Australia; 6. Fensham et al. (2009), north-central Queensland; 7. Sharp & Whittaker (2003), north-western Australia; 8. Fensham & Fairfax (2003), north-western Australia; 9. Sharp & Bowman (2004b), north-western Australia. Note that the cover values of 4 (Burrows et al., 2002) were shifted upwards by 2% for the sake of clarity. The sizes of the circles are proportional to the natural logarithm of the size of the study area (× 100 km2). The asterisks denote cover estimates derived from tree basal area. Details on the derivation of the cover values are provided in Appendix S1 in Supporting Information.

Download figure to PowerPoint

It is important to acknowledge that there are potential sources of error in the aerial photographic studies included in our meta-analysis. Most notably, some studies did not describe how differences in the scale of photographs were accounted for, despite this being a substantial source of error. Fensham et al. (2002) point out that increasing the scale of photographs (e.g. from 1:100,000 to 1:25,000) leads to an apparent increase in cover. Specifically, in the study of Lehmann et al. (2009), photo-scale decreased from 1:16,000 to 1:25,000 between 1964 and 1984, which could have potentially dampened the thickening trend they observed. In the cases of Sharp & Whittaker (2003) and Sharp & Bowman (2004a,b), although not explicitly stated in those papers, the scale of the photographs used was consistently 1:50,000 (B.R. Sharp, New Zealand Ministry of Fisheries, Wellington, New Zealand, pers. comm.).

A frequently cited cause of thickening in savannas is overgrazing by domestic livestock (e.g. Archer et al., 1995; Sharp & Whittaker, 2003). By reducing grass biomass, overgrazing can reduce fire frequency and intensity and increase resource availability – changes which could be expected to favour the establishment of woody plants. Given that there are reports of intensification of grazing by domestic livestock in recent decades throughout northern Australia (Garnett & Crowley, 1995; Sharp & Whittaker, 2003), this seems a plausible explanation for the observed pattern of thickening. However, the general consistency of the pattern across a range of sites with different land-use histories (i.e. conservation versus pastoralism), albeit without replication, suggests that localized changes in land use are not solely responsible, and that processes operating at regional to continental scales are also important. Plausible explanations include: (1) increased [CO2] (Bond & Midgley, 2012; Higgins & Scheiter, 2012); (2) increased rainfall, with northern Australia subject to a trend of increasing rainfall over the latter half of the 20th century (Smith, 2004); and (3) increased soil moisture due to decreased evaporation (Roderick & Farquhar, 2004).

Does fire frequency affect woody biomass?

While woody biomass was relatively stable over the period 1994–2004 across our large, 24,000 km2 study area, changes in woody biomass were clearly related to fire frequency and severity. The strong decline in woody biomass in response to severe fires is consistent with the findings of two large-scale Australian fire experiments (Kapalga: Williams et al., 1999; Munmarlary: Russell-Smith et al., 2003), where annual late dry-season fires substantially reduced tree basal area, relative to unburnt areas (by c. 6.8% annually at Kapalga and 0.2–3.8% annually at Munmarlary, where fires were less intense). Our results are also commensurate with the natural experiment of Lehmann et al. (2009), who showed that annual fires, predominantly in the early dry season, reduced tree canopy cover in Kakadu by c. 0.6% annually, relative to unburnt areas. Our results also parallel those from Africa. Brookman-Amissah et al. (1980) found that following tree clearing in Ghana, tree basal area in unburnt areas increased at about 7 and 14 times the rate observed in areas subject to annual early and late dry-season fires, respectively. The 40-year Kruger fire experiment in South Africa produced similar results, with annual fires reducing woody biomass by about 1.5% annually, relative to triennially burnt areas (Higgins et al., 2007). A number of studies have also shown the large positive effect that complete fire exclusion has on woody biomass (e.g. Brookman-Amissah et al., 1980; San José et al., 1998; Tilman et al., 2000). In sum, our results show strong concordance with published results from savannas around the world. Low fire frequencies generally lead to an increase in tree biomass, while high fire frequencies, especially of severe fires, generally lead to a decrease in tree biomass.

An important finding of this study is that observed decreases in tree biomass following severe fires are not driven by mortality of individual trees, but primarily by decreases in the rates of biomass accumulation of surviving trees (Figs 5 & 6). The large negative effect of fire on individual tree growth rates in these savannas has been reported by Murphy et al. (2010), who attributed the effect to the carbon costs of rebuilding the canopy following fire. The effect of severe fires on tree growth is complemented by the partial or complete loss of individual stems, without whole-tree mortality, following severe fires (e.g. Williams et al., 1999), leading to large decreases in whole-tree biomass. Unfortunately, we cannot distinguish between the processes of individual stem (cf. whole-tree) recruitment, mortality and growth, because individual trees, rather than stems, were tagged. In relation to the effects of fire on growth of surviving trees, an important limitation of our study is that we cannot rule out that severe fires reduce the bark thickness of trees, leading to an underestimation of growth rates and biomass following a severe fire. Simple calculations based on the stand structures observed in our study suggest that the effect of severe fires on the biomass accumulation of surviving trees (shown in Fig. 4b) could be the result of a loss of 2 mm of bark thickness due to each severe fire. We suspect that substantially less bark than this would be lost, but are unable to provide evidence to support that conclusion.

The limited role that whole-tree recruitment and mortality play in the negative relationship between fire frequency and tree biomass was also demonstrated recently by Higgins et al. (2007) using the Kruger Park fire experiment in South Africa. Unexpectedly, they found that tree density was unresponsive to fire frequency (see also Buitenwerf et al., 2012), although tree biomass was strongly limited by frequent fires. They attributed this effect to high rates of fire-induced topkill (death of aboveground parts) of savanna trees, but low rates of whole-tree mortality. Higgins et al. (2007) concluded that the frequent experimental fires limited tree biomass not by inducing tree mortality or preventing recruitment, but by restricting trees to a suppressed, juvenile state. A similar effect has been found in a South American savanna system (Hoffmann et al., 2009). Like Higgins et al. (2007) and Hoffmann et al. (2009), we observed that frequent fires strongly limit the biomass of individual trees. However, these previous studies emphasized that fire primarily limits the transition between sapling and adult size classes, as did a recent northern Australian study (Prior et al., 2010). Our results suggest that even when individuals reach small adult size classes (≥ 5 cm d.b.h.), fire plays an important role in further preventing transition to larger size classes.

Implications and Conclusions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Implications and Conclusions
  8. Acknowledgements
  9. References
  10. Biosketch
  11. Supporting Information

The general trend of woody thickening across Australian savannas, albeit weak (Fig. 7), has important implications for the continental carbon budget. For example, Burrows et al. (2002) estimated that savanna tree biomass was sequestering about 0.67 t C ha−1 year−1 in central Queensland, and then extrapolated this figure to all grazed savanna woodlands of Queensland (60 Mha) to suggest a carbon sink of 35 Mt C year−1, equivalent to 22% of Australia's 2010 greenhouse gas emissions (Department of Climate Change & Energy Efficiency, 2012). Clearly, if the thickening trend is part of a broader global pattern, then the amount of carbon sequestered annually in savanna woody biomass is likely to be enormous. There is some evidence that increasing woody biomass in savanna systems is paralleled by increasing soil carbon stocks (Coetsee et al., 2010); however, the generality of such an effect is highly uncertain. For example, Jackson et al. (2002) found that invasion of North American grasslands by woody plants could lead to increases or decreases in soil carbon stocks, depending on where sites fell along a rainfall gradient – low rainfall sites tended to gain soil carbon, while high rainfall sites tended to lose soil carbon. Despite this uncertainty, changes in soil carbon stocks are likely to be very important, given that soil organic carbon is by far the largest pool in these savannas, representing about 75% of ecosystem carbon (Chen et al., 2003). In sum, we currently have a poor understanding of the roles played by increasing [CO2], climate and land-use change (e.g. intensification of pastoralism and fire management) in increasing carbon storage in savannas, so a key research priority should be resolving the existence, nature and causes of such trends, as well as their likely longevity.

Although Australian savannas do appear to be thickening, our results clearly demonstrate that high fire frequencies, and especially the frequency of severe fires, can potentially offset such trends. However, there is little evidence to suggest a current trend of increasingly frequent or severe fires in northern Australia. Certainly, within the three national parks examined in this study there is evidence that fire frequencies have been fairly stable over the last 30 years (Gill et al., 2000; Edwards et al., 2001). Alarmingly, the ongoing and rapid spread of exotic Gamba grass (Andropogon gayanus) across northern Australia's mesic savannas has the potential to transform fire regimes in coming decades, dramatically increasing typical fire intensities (Setterfield et al., 2010) and reducing tree biomass.

In contrast, active management to reduce the frequency of intense fires may lead to increases in woody biomass over time, and there have been suggestions that accompanying increases in ecosystem carbon storage may generate tradeable carbon credits (Richards et al., 2011). For example, in northern Australia several large greenhouse gas abatement projects based on savanna fire management have been developed in recent years (Russell-Smith et al., 2009). Such projects use prescribed burning in the early dry season to decrease the extent of intense fires late in the dry season, with the aim of reducing emissions of the potent greenhouse gases methane and nitrous oxide (Russell-Smith et al., 2009; Richards et al., 2012). Our results suggest that the moderation of fire regimes associated with these projects is also likely to increase carbon storage in woody biomass, but the extent to which woody biomass can increase in these savannas is highly uncertain. This is a research question that is probably best addressed using process-based models that explicitly incorporate the effects of fire as well as resource constraints (e.g. Liedloff & Cook, 2007; Scheiter & Higgins, 2009). Quantifying the long-term response of savanna woody biomass to changes in fire management, water availability and [CO2] remains a key research challenge, critical for predicting future changes to the ecology and carbon cycle of the savanna biome.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Implications and Conclusions
  8. Acknowledgements
  9. References
  10. Biosketch
  11. Supporting Information

We thank the parks staff who undertook the vegetation and fire assessments, and numerous botanists who assisted. Parks Australia and the Northern Territory Parks and Wildlife Service provided financial support for the monitoring programme. This work was funded by the Australian Research Council (DP0878177 and DE130100434) and the Australian Government's National Environmental Research Program.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Implications and Conclusions
  8. Acknowledgements
  9. References
  10. Biosketch
  11. Supporting Information
  • Archer, S., Schimel, D.S. & Holland, E.A. (1995) Mechanisms of shrubland expansion: land use, climate or CO2? Climatic Change, 29, 9199.
  • Asner, G.P., Archer, S., Hughes, R.F., Ansley, R.J. & Wessman, C.A. (2003) Net changes in regional woody vegetation cover and carbon storage in Texas Drylands, 1937-1999. Global Change Biology, 9, 316335.
  • Australian Bureau of Meteorology (2012) Australian rainfall and temperature surface data. Available at: http://www.bom.gov.au/cgi-bin/silo/cli_var/area_timeseries.pl.
  • Beringer, J., Hutley, L.B., Tapper, N.J. & Cernusak, L.A. (2007) Savanna fires and their impact on net ecosystem productivity in North Australia. Global Change Biology, 13, 9901004.
  • Bond, W.J. & Midgley, G.F. (2012) Carbon dioxide and the uneasy interactions of trees and savannah grasses. Philosophical Transactions of the Royal Society B: Biological Sciences, 367, 601612.
  • Bowman, D.M.J.S., Riley, J.E., Boggs, G.S., Lehmann, C.E.R. & Prior, L.D. (2008) Do feral buffalo (Bubalus bubalis) explain the increase of woody cover in savannas of Kakadu National Park, Australia? Journal of Biogeography, 35, 19761988.
  • Brookman-Amissah, J., Hall, J.B., Swaine, M.D. & Attakorah, J.Y. (1980) A re-assessment of a fire protection experiment in north-eastern Ghana savanna. Journal of Applied Ecology, 17, 8599.
  • Buitenwerf, R., Bond, W.J., Stevens, N. & Trollope, W.S.W. (2012) Increased tree densities in South African savannas: >50 years of data suggests CO2 as a driver. Global Change Biology, 18, 675684.
  • Burnham, K.P. & Anderson, D.R. (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer-Verlag, New York.
  • Burrows, W.H., Henry, B.K., Back, P.V., Hoffmann, M.B., Tait, L.J., Anderson, E.R., Menke, N., Danaher, T., Carter, J.O. & McKeon, G.M. (2002) Growth and carbon stock change in eucalypt woodlands in northeast Australia: ecological and greenhouse sink implications. Global Change Biology, 8, 769784.
  • Cabral, A.C., De Miguel, J.M., Rescia, A.J., Schmitz, M.F. & Pineda, F.D. (2003) Shrub encroachment in Argentinean savannas. Journal of Vegetation Science, 14, 145152.
  • Chen, X.Y., Hutley, L.B. & Eamus, D. (2003) Carbon balance of a tropical savanna of northern Australia. Oecologia, 137, 405416.
  • Coetsee, C., Bond, W.J. & February, E.C. (2010) Frequent fire affects soil nitrogen and carbon in an African savanna by changing woody cover. Oecologia, 162, 10271034.
  • Cook, G.D., Liedloff, A.C., Eager, R.W., Chen, X., Williams, R.J., O'Grady, A.P. & Hutley, L.B. (2005) The estimation of carbon budgets of frequently burnt tree stands in savannas of northern Australia, using allometric analysis and isotopic discrimination. Australian Journal of Botany, 53, 621630.
  • Department of Climate Change and Energy Efficiency (2012) Australian National Greenhouse Accounts: National Inventory Report, 2010. Department of Climate Change and Energy Efficiency, Canberra.
  • Durigan, G. & Ratter, J.A. (2006) Successional changes in cerrado and cerrado/forest ecotonal vegetation in western São Paulo State, Brazil, 1962-2000. Edinburgh Journal of Botany, 63, 119130.
  • Edwards, A., Hauser, P., Anderson, M., McCartney, J., Armstrong, M., Thackway, R., Allan, G.E., Hempel, C. & Russell-Smith, J. (2001) A tale of two parks: contemporary fire regimes of Litchfield and Nitmiluk National Parks, monsoonal northern Australia. International Journal of Wildland Fire, 10, 7989.
  • Fensham, R.J. (2008) Leichardt's maps: 100 years of change in vegetation structure in Queensland. Journal of Biogeography, 35, 141156.
  • Fensham, R.J. & Fairfax, R.J. (2003) Assessing woody cover change in north-west Australian savanna using aerial photography. International Journal of Wildland Fire, 12, 359367.
  • Fensham, R.J., Fairfax, R.J., Holman, J.E. & Whitehead, P.J. (2002) Quantitative assessment of vegetation structural attributes from aerial photography. International Journal of Remote Sensing, 23, 22932317.
  • Fensham, R.J., Low Choy, S.J., Fairfax, R.J. & Cavallaro, P.C. (2003) Modelling trends in woody vegetation structure in semi-arid Australia as determined from aerial photography. Journal of Environmental Management, 68, 421436.
  • Fensham, R.J., Fairfax, R.J. & Ward, D.P. (2009) Drought-induced tree death in savanna. Global Change Biology, 15, 380387.
  • Garnett, S.T. & Crowley, G.M. (1995) Ecology and conservation of the Golden-shouldered Parrot. Queensland Department of Environment and Heritage, Brisbane.
  • Gill, A.M., Ryan, P.G., Moore, P.H.R. & Gibson, M. (2000) Fire regimes of World Heritage Kakadu National Park, Australia. Austral Ecology, 25, 616625.
  • Grace, J., San Jose, J., Meir, P., Miranda, H.S. & Montes, R.A. (2006) Productivity and carbon fluxes of tropical savannas. Journal of Biogeography, 33, 387400.
  • Heisler, J.L., Briggs, J.M. & Knapp, A.K. (2003) Long-term patterns of shrub expansion in a C4-dominated grassland: fire frequency and the dynamics of shrub cover and abundance. American Journal of Botany, 90, 423428.
  • Higgins, S.I. & Scheiter, S. (2012) Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally. Journal of Biogeography, 488, 209212.
  • Higgins, S.I., Bond, W.J., February, E.C., Bronn, A., Euston-Brown, D.I.W., Enslin, B., Govender, N., Rademan, L., O'Regan, S., Potgieter, A.L.F., Scheiter, S., Sowry, R., Trollope, L. & Trollope, W.S.W. (2007) Effects of four decades of fire manipulation on woody vegetation structure in savanna. Ecology, 88, 11191125.
  • Hoffmann, W.A., Adasme, R., Haridasan, M., de Carvalho, M.T., Geiger, E.L., Pereira, M.A., Gotsch, S.G. & Franco, A.C. (2009) Tree topkill, not mortality, governs the dynamics of savanna–forest boundaries under frequent fire in central Brazil. Ecology, 90, 13261337.
  • Hutley, L.B. & Beringer, J. (2010) Disturbance and climatic drivers of carbon dynamics of a north Australian tropical savanna. Ecosystem function in savannas: measurement and modeling at landscape to global scales (ed. by M.J. Hill and N.P. Hanan), pp. 5775. CRC Press, Boca Raton, FL.
  • Jackson, R.B., Banner, J.L., Jobbágy, E.G., Pockman, W.T. & Wall, D.H. (2002) Ecosystem carbon loss with woody plant invasion of grasslands. Nature, 418, 623626.
  • Kutner, M.H., Nachtsheim, C. & Neter, J. (2004) Applied linear regression models. McGraw-Hill Irwin, Boston, MA.
  • Lehmann, C.E.R., Prior, L.D. & Bowman, D.M.J.S. (2009) Decadal dynamics of tree cover in an Australian tropical savanna. Austral Ecology, 34, 601612.
  • Lewis, S.L., Lopez-Gonzalez, G., Sonké, B. et al. (2009) Increasing carbon storage in intact African tropical forests. Nature, 457, 10031006.
  • Liedloff, A.C. & Cook, G.D. (2007) Modelling the effects of rainfall variability and fire on tree populations in an Australian tropical savanna with the Flames simulation model. Ecological Modelling, 201, 269282.
  • Murphy, B.P. & Russell-Smith, J. (2010) Fire severity in a northern Australian savanna landscape: the importance of time since previous fire. International Journal of Wildland Fire, 19, 4651.
  • Murphy, B.P., Russell-Smith, J. & Prior, L.D. (2010) Frequent fires reduce tree growth rates in northern Australian savannas: implications for tree demography and carbon sequestration. Global Change Biology, 16, 331343.
  • Murphy, B.P., Bradstock, R.A., Boer, M.M., Carter, J., Cary, G.J., Cochrane, M.A., Fensham, R.J., Russell-Smith, J., Williamson, G.J. & Bowman, D.M.J.S. (2013) Fire regimes of Australia: a pyrogeographic model system. Journal of Biogeography, 6, 10481058.
  • Nickless, A., Scholes, R.J. & Archibald, S. (2011) A method for calculating the variance and confidence intervals for tree biomass estimates obtained from allometric equations. South African Journal of Science, 107, 8695.
  • Petty, A.M., Werner, P.A., Lehmann, C.E.R., Riley, J.E., Banfai, D.S. & Elliott, L.P. (2007) Savanna responses to feral buffalo in Kakadu National Park, Australia. Ecological Monographs, 77, 441463.
  • Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. & R Development Core Team (2012) nlme: linear and nonlinear mixed effects models. Available at: http://cran.r-project.org/web/packages/nlme/index.html (last accessed 16 July 2012).
  • Prior, L.D., Williams, R.J. & Bowman, D. (2010) Experimental evidence that fire causes a tree recruitment bottleneck in an Australian tropical savanna. Journal of Tropical Ecology, 26, 595603.
  • Richards, A.E., Cook, G.D. & Lynch, B.T. (2011) Optimal fire regimes for soil carbon storage in tropical savannas of northern Australia. Ecosystems, 14, 503518.
  • Richards, A.E., Andersen, A.N., Schatz, J., Eager, R., Dawes, T.Z., Hadden, K., Scheepers, K. & van der Geest, M. (2012) Savanna burning, greenhouse gas emissions and indigenous livelihoods: introducing the Tiwi Carbon Study. Austral Ecology, 37, 712723.
  • Roderick, M.L. & Farquhar, G.D. (2004) Changes in Australian pan evaporation from 1970 to 2002. International Journal of Climatology, 24, 10771090.
  • Rose Innes, R. (1972) Fire in West African vegetation. Proceedings of Tall Timbers Fire Ecology Conference, 11, 175199.
  • Russell-Smith, J. & Edwards, A.C. (2006) Seasonality and fire severity in savanna landscapes of monsoonal northern Australia. International Journal of Wildland Fire, 15, 541550.
  • Russell-Smith, J., Whitehead, P.J., Cook, G.D. & Hoare, J.L. (2003) Response of Eucalyptus-dominated savanna to frequent fires: lessons from Munmarlary, 1973–1996. Ecological Monographs, 73, 349375.
  • Russell-Smith, J., Murphy, B.P., Meyer, C.P., Cook, G.D., Maier, S., Edwards, A.C., Schatz, J. & Brocklehurst, P. (2009) Improving estimates of savanna burning emissions for greenhouse accounting in northern Australia: limitations, challenges, applications. International Journal of Wildland Fire, 18, 118.
  • San José, J.J., Montes, R.A. & Farinas, M.R. (1998) Carbon stocks and fluxes in a temporal scaling from a savanna to a semi-deciduous forest. Forest Ecology and Management, 105, 251262.
  • Sankaran, M., Hanan, N.P., Scholes, R.J. et al. (2005) Determinants of woody cover in African savannas. Nature, 438, 846849.
  • Scheiter, S. & Higgins, S.I. (2009) Impacts of climate change on the vegetation of Africa: an adaptive dynamic vegetation modelling approach. Global Change Biology, 15, 22242246.
  • Setterfield, S.A., Rossiter-Rachor, N.A., Hutley, L.B., Douglas, M.M. & Williams, R.J. (2010) Turning up the heat: the impacts of Andropogon gayanus (gamba grass) invasion on fire behaviour in northern Australian savannas. Diversity and Distributions, 16, 854861.
  • Sharp, B.R. & Bowman, D.M.J.S. (2004a) Patterns of long-term woody vegetation change in sandstone-plateau savanna woodland, Northern Territory, Australia. Journal of Tropical Ecology, 20, 259270.
  • Sharp, B.R. & Bowman, D.M.J.S. (2004b) Net woody vegetation increase confined to seasonally inundated lowlands in an Australian tropical savanna, Victoria River District, Northern Territory. Austral Ecology, 29, 667683.
  • Sharp, B.R. & Whittaker, R.J. (2003) The irreversible cattle-driven transformation of a seasonally flooded Australia savanna. Journal of Biogeography, 30, 783802.
  • Smith, I. (2004) An assessment of recent trends in Australian rainfall. Australian Meteorological Magazine, 53, 163173.
  • Suganuma, H., Abe, Y., Taniguchi, M., Tanouchi, H., Utsugi, H., Kojima, T. & Yamada, K. (2006) Stand biomass estimation method by canopy coverage for application to remote sensing in an arid area of Western Australia. Forest Ecology and Management, 222, 7587.
  • Tilman, D., Reich, P., Phillips, H., Menton, M., Patel, A., Vos, E., Peterson, D. & Knops, J. (2000) Fire suppression and ecosystem carbon storage. Ecology, 81, 26802685.
  • Trapnell, C.G. (1959) Ecological results of woodland burning experiments in Northern Rhodesia. Journal of Ecology, 47, 129168.
  • Werner, P.A. (2005) Impact of feral water buffalo and fire on growth and survival of mature savanna trees: an experimental field study in Kakadu National Park, northern Australia. Austral Ecology, 30, 625647.
  • Werner, P.A. & Murphy, P.G. (2001) Size-specific biomass allocation and water content of above- and below-ground components of three Eucalyptus species in a northern Australian savanna. Australian Journal of Botany, 49, 155167.
  • Williams, R.J., Gill, A.M. & Moore, P.H.R. (1998) Seasonal changes in fire behaviour in a tropical savanna in northern Australia. International Journal of Wildland Fire, 8, 227239.
  • Williams, R.J., Cook, G.D., Gill, A.M. & Moore, P.H. (1999) Fire regime, fire intensity and tree survival in a tropical savanna in northern Australia. Australian Journal of Ecology, 24, 5059.

Biosketch

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Implications and Conclusions
  8. Acknowledgements
  9. References
  10. Biosketch
  11. Supporting Information

Brett Murphy's research focuses on the role of fire in shaping and maintaining tropical vegetation, especially mosaics of flammable savanna and fire-sensitive rain forest. Ultimately, he wants to know how to best manage fire in northern Australia's savanna landscapes to maximize benefits to biodiversity.

Author contributions: B.P.M. and J.R.-S. conceived the study; B.P.M. performed the analyses; and B.P.M. and C.E.R.L. led the writing, with extensive input from M.J.L. and J.R.-S.

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Implications and Conclusions
  8. Acknowledgements
  9. References
  10. Biosketch
  11. Supporting Information
FilenameFormatSizeDescription
jbi12204-sup-0001-AppendixS1.docWord document71KAppendix S1 Estimation of changes in savanna woody cover in northern Australia.

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.