Restoration thinning has minor and temporary effects on understorey fuels in a regrowth eucalypt floodplain forest under conservation management

Study area

The study was conducted in Murray Valley National Park in south-eastern Australia (Fig. 1). The study area comprises inland riverine forest with a monodominant canopy of Eucalyptus camaldulensis Dehnh. (river red gum) with an understorey of grasses, sedges, rushes, and ephemeral herbs where composition varies with hydrology and micro-topography (Keith 2004).

Fig. 1.

Locations of (a) the study area in Australia, (b) study sites in relation to Murray Valley National Park, major rivers, and the Yarrawonga Weir, (c) study sites and treatment plots within river red gum forests mapped as Site Quality 1 and Site Quality 2.

Prior to gazettal as a national park in 2010, the area was subject to various types of timber extraction for ~150 years (Donovan 1997). In the late 1800s and early 1900s, these forests were intensively logged, with many parts of the forest clearfelled (Donovan 1997). Clearcutting was followed by periods of mass recruitment (Donovan 1997), which may have been exacerbated by changes in flooding regimes due to river regulation (Bren 1992). Post-disturbance recruitment has resulted in a nine-fold increase in tree density, on average, across the forest (McGregor et al. 2016). Estimated densities of trees >10 cm diameter at breast height (DBH) were ~17 trees per ha in the 1860s (pre-logging) and ~147 trees per ha in 2010 (McGregor et al. 2016). Basal area was estimated to have increased to only a small degree, from 13 to 15 m² per ha (McGregor et al. 2016), indicating the replacement of large trees with many smaller regrowth trees.

The region experiences low winter-dominant rainfall (~400 mm per year) and high evaporation (~1800 mm per year) (Bureau of Meteorology 2024), with the forest relying on river flows during cyclical annual flooding to meet water requirements. River regulation since the 1930s has reduced the frequency, duration, timing, and extent of flooding within the site (Dexter et al. 1986; Mac Nally et al. 2011). Temporal variation in water availability across the study area is primarily a function of river flow upstream from the site, which fluctuated substantially across the study period (see Supplementary material Fig. S1). Water availability also varies spatially across the site due to variations in topography and flooding susceptibility. Site Quality mapping was undertaken in the 1950s to delineate this variation (Baur 1984), with areas mapped as Site Quality 1 (SQ1) or Site Quality 2 (SQ2). Areas mapped as SQ1 are generally wetter, having taller trees (>21 m), shallower ground water (<6 m), and more regular flooding than areas mapped as SQ2, which are generally drier, have shorter trees (<21 m), less flooding, and deeper groundwater (2–9 m). Site Quality mapping for the study area is in Fig. 1.

Thinning experiment

Twenty-two sites were established within the study area (Fig. 1), each consisting of three 9-ha (300 m × 300 m) treatment plots (66 plots in total). Site and plot locations were planned using desktop spatial data then located in the field with a GPS. Sites were equally stratified among the two categories of Site Quality (i.e. 11 per each Site Quality) based on prior Site Quality mapping (Baur 1984). Sites were also selected to span a range of (pre-thinning) tree densities based on prior tree density mapping (Bowen et al. 2012). Observed initial tree densities were quantified in the field prior to thinning in each plot by tallying all trees >10 cm DBH within 10 20-m × 50-m subplots (multi-stemmed trees were counted as a single tree). Initial tree density rates were intended to provide a plot-specific measure of the severity of post-logging structural change, which is thought to vary spatially across the study area depending on disturbance history. The initial tree density of study plots ranged from 262 to 1856 trees per ha (mean 742 trees per ha). Sites had not been logged or experienced fire for at least 10 years prior to 2010.

Each of the three 9-ha treatment plots within each of the 22 sites was assigned at random to one of three levels of thinning: (1) control with no thinning applied; (2) thinning applied with trees retained at 7-m intervals; and (3) thinning applied with trees retained at 15-m intervals. Trees selected for retention within thinned plots were marked, and all remaining trees <40 cm DBH were then removed. Any trees with visible hollows or any dead trees >20 cm DBH were not removed to avoid habitat disturbance. A mechanical harvester (30 tonne) removed targeted trees and freshly cut stumps were sprayed with glyphosate herbicide to reduce coppicing using a vehicle mounted sprayer. A skidder (7 tonne) and forwarder (12 tonne) were used to place thinned logs into skel trucks. Thinned logs were removed from the site using historical tracks where possible. All other material (e.g. timber <10 cm diameter, branches, bark, leaves) was left on site. Thinning was undertaken between April 2016 and August 2017, avoiding periods of flooding. After prescribed thinning was completed, tree density measurements were repeated within each plot. Due to high variation in tree densities prior to thinning, post-thinning tree densities varied within thinning treatments (from 131–633 [mean 263] trees per ha in 15-m spacing plots, and from 167–1694 [mean 412] trees per ha in 7-m spacing plots). We therefore calculated the proportion of trees removed in each plot and used this as the measure of thinning intensity for analysis. Within thinned plots, thinning intensity (the proportion of trees removed) ranged from 0.08 to 0.86 (mean 0.54).

Measuring fuel attributes

Within each of the three 9-ha treatment plots within each of the 22 sites, we assessed 10 understorey fuel attributes 1 year prior and annually for 5 years after thinning. Details and methods are in Table 1. Monitoring and thinning dates for each plot were used to calculate the time since thinning in decimal years for each fuel attribute on each monitoring occasion.

Data analysis

For each of the 10 fuel attributes we ran separate Bayesian hierarchical generalised linear mixed models. Predictors included time since thinning (decimal years), initial tree density (trees per ha prior to thinning), thinning intensity (proportion of trees removed), and Site Quality (SQ1, SQ2), along with all their two-way, three-way and four-way interactions. We included interactions between all four predictor variables in our models because we assumed that the effect of thinning intensity was dependent on pre-thinning tree density and that this relationship may vary with water availability (Site Quality) and may also vary over time. The risk of overfitting was minimised by ensuring that the number of observations per model term exceeded the recommended minimum of 15 (Babyak 2004). Prior to analysis, initial tree density was log transformed to meet the assumptions of a normal distribution, and all continuous predictors were standardised to z-scores with a mean of zero and standard deviation of one. Models included random nested intercepts for survey year, site, plot, and subplot, to account for annual sampling, spatial blocking of plots within sites, subplots within plots, and repeat sampling of subplots over time.

All fuel attributes were log transformed prior to analysis. Near-surface vegetation cover, the dead proportion of near-surface vegetation, elevated vegetation cover, the dead proportion of elevated vegetation, and average canopy base height were modelled using a Gaussian distribution. All other attributes were modelled using student’s T distribution.

Models were fitted in R ver. 4.1.0 (R Core Team 2023) using the brms package (Bürkner 2017). This approach provided a stable and efficient computing environment for running multiple complex models with different response variables. Non-informative priors (mean = 0, s.d. = 10) were set for all fuel attribute models. Models were built using 4000 iterations of four chains, with 1000 warm-up chains and thinning of 1. All R-hats were equal to or greater than 1, and model convergence and dispersion tests were further assessed by visual inspection. We assessed model fit by conducting posterior predictive checks (Gabry et al. 2019) to ensure posterior predictions did not systematically deviate from the observed data (Supplementary material Fig. S3).

To evaluate the statistical significance of all predictor terms and interactions in each model, we compared the 89% high density interval of posterior distributions with the region of practical equivalence (ROPE) (Supplementary material Fig. S4). Where these did not overlap, the null hypothesis of no effect on the dependent variable was rejected (Kruschke 2018; Makowski et al. 2019), indicating a significant effect. Summarised results of ROPE tests for each predictor and interaction in each model are in Table 2. To examine the direction and magnitude of significant effects, we extracted draws from the posterior predictive distribution from each model across levels of interest. For initial tree density, we extracted predictions for unthinned controls at three levels of initial tree density (at the 20th, 50th, and 80th percentiles, which corresponded to densities of 450, 700, and 900 trees per ha) across both levels of Site Quality at yearly intervals throughout the study period. To examine the effects of thinning, we extracted predictions for four thinning intensities (at 0 and the midpoints of the tertiles for thinned plots (i.e. control, proportion of trees removed = 0; light thinning, proportion of trees removed = 0.25; moderate thinning, proportion of trees removed = 0.58; and heavy thinning, proportion of trees removed = 0.76). We extracted predictions across both levels of Site Quality at yearly post-thinning intervals and we compared predictions for light, moderate, and heavy thinning with controls. Where 95% credible intervals for the predicted differences did not include zero, this was considered to be evidence of a significant effect. We included each survey year as a random effect in predictions and matched this with the years since thinning predictor. We predicted to average levels for other random effects. Plots of back-transformed predicted values were used to investigate the size and direction of effects.

Fuel hazard classification

Fuel hazard assessments translate fuel attribute measurements into fuel hazard classes that provide an indication of fire spread rate, flame height, and difficulty to control (Gould et al. 2007; Cheney et al. 2012; McCaw 2013). We used mean values from model predictions to estimate fuel hazard ratings for surface, near surface and elevated fuel layers across the dataset based on classification methods described in Hines et al. (2010), where fuel hazard is categorised based on combinations of fuel attributes in each understorey layer. Some attribute combinations within the data were undefined according to the Hines et al. (2010) hazard rating categories and we followed the approach of Pickering et al. (2023) for extending classification tables. We used these guides to determine fuel hazard scores for surface fuels (litter), near-surface fuels, and elevated fuels.

We assessed overall fuel hazard scores across thinned and unthinned plots over time using classification methods described in Hines et al. (2010) where levels of bark, elevated and combined surface and near-surface fuel hazard are combined to give an overall fuel hazard rating. Although we did not measure bark hazard in the field, for the purpose of this assessment we assigned the lowest bark hazard rating of ‘Low to Moderate’ for all plots. E. camaldulensis bark is smooth and sheds in short ribbons or flakes (Harden 2002), and Hines et al. (2010) describe trees with ‘slab bark, smooth bark, and small flakes’ as never exceeding a moderate level of hazard.

Litter

There was no evidence of any significant effects of initial tree density or thinning on the percent cover or depth of litter (Table 2). Throughout the study, mean litter cover was above 90% and mean litter depth was below 10 mm (Fig. 2). These predictions resulted in a surface fuel hazard rating of ‘High’ throughout the study across all sites and treatments (Table 3).

Fig. 2.

Posterior predictions (mean and 95% credible interval) for litter cover (Lit. cov.), litter depth (Lit. depth), near-surface vegetation cover (NS cover), dead proportion of near-surface vegetation (NS dead), average height of near-surface vegetation (NS ht.), elevated vegetation cover (Elev. cover), dead proportion of elevated vegetation (Elev. dead), average height of elevated vegetation (Elev. ht.), height to base of canopy (Canopy ht.), and volume of coarse woody debris (CWD) at various levels of initial tree density across time since thinning and Site Quality. Predictions are for unthinned control plots.

Near-surface vegetation

Mean near-surface cover was low throughout the study, fluctuating from ~1% to ~10% in control plots over time (Fig. 2). The cover of near-surface vegetation was significantly affected by a three-way interaction of initial tree density, time, and Site Quality (Table 2), where near-surface cover was increased to a very small degree (<5% change in cover) with decreasing tree density in SQ1 during the first year of the study (Fig. 2). There was also evidence of a significant thinning by time effect (Table 2). Thinning reduced near-surface vegetation cover in the first year after thinning by up to ~3%, but this trend reversed within 5 years after thinning by which time cover in thinned plots was up to 2.5% higher than in controls (Fig. 3, Table 4). Effect sizes were greatest for heavy thinning (Fig. 3).

Fig. 3.

Posterior predictions (mean and 95% credible interval) for the effects of thinning on litter cover (Lit. cov.), litter depth (Lit. depth), near-surface vegetation cover (NS cover), dead proportion of near-surface vegetation (NS dead), average height of near-surface vegetation (NS ht.), elevated vegetation cover (Elev. cover), dead proportion of elevated vegetation (Elev. dead), average height of elevated vegetation (Elev. ht.), height to base of canopy (Canopy ht.), and volume of coarse woody debris (CWD) across time since thinning and Site Quality. Plots show the predicted differences for various levels of thinning intensity compared to unthinned controls at moderate initial tree density (~700 trees per ha).

The mean proportion of near-surface cover that was dead varied between ~0.4 and ~0.6 across study years in control plots (Fig. 2). Thinning initially significantly increased the dead proportion of near-surface vegetation by up to 0.05, but the trend reversed over time (Fig. 3, Tables 2, 4) with heavy thinning reducing the proportion of dead near-surface cover by more than 0.05 in the fifth year after thinning. Moderate and light thinning had effects of a smaller magnitude (Fig. 3).

There were no significant effects of any of the predictors on the average height of near-surface vegetation (Table 2), which ranged from ~0.45 m to ~0.75 m (Fig. 2).

These predictions produced a near-surface fuel hazard rating of ‘Low’ throughout the study across all sites and treatments (Table 3).

Elevated vegetation

Predicted elevated vegetation cover was low across the study duration, with mean values under 5% (Fig. 2). Thinning significantly reduced elevated vegetation cover relative to control plots for the first 3 years after thinning (Table 2, Fig. 3); however, differences were very small. Mean elevated vegetation cover was reduced by a maximum of approximately 1.7% by heavy thinning (Table 4), and by smaller amounts by moderate and light thinning (Fig. 3). However, elevated vegetation cover in thinned plots had returned to similar levels as in control plots by the fourth year after thinning (Fig. 3, Table 4).

The mean value of the proportion of elevated cover that was standing dead material in control plots ranged from ~0.35 to ~0.55 depending on the year (Fig. 2), with the highest proportions occurring when total cover was lowest. The proportion dead was significantly increased by up to 0.07 by thinning (Table 2, Fig. 3, Table 4), with heavy thinning having the largest effect. The effect diminished with time since thinning (Table 2) and there was no longer a difference between thinned and control plots by the fifth year after thinning (Fig. 3, Table 4).

In control plots, the mean height of elevated vegetation ranged from ~2.2 m to ~3.3 m (Fig. 2) and there was a three-way interaction of initial tree density, time, and Site Quality (Table 2), which suggested small (<1 m) effects of increased elevated vegetation height in lower tree densities in later years of the study in SQ1 (Fig. 2, Table 2). Thinning significantly decreased the average height of elevated vegetation across all years of the study (Table 2), with height reductions up to ~1.6 m for heavy thinning, and smaller effects for moderate and light thinning (Fig. 3, Table 4).

Predicted values equated to an elevated fuel hazard rating of ‘Moderate’ throughout the study across all sites and treatments (Table 3).

Height to the base of the canopy

Mean height to the canopy base in control plots ranged from ~1.2 m to ~3.6 m across the study (Fig. 2). Heavy thinning significantly increased the average height to the base of the canopy by up to 1.78 m in the first year after thinning, with smaller effects for lower thinning intensities (Table 2, Fig. 3, Table 4). The trend waned over time (Table 2) and by the fourth year after thinning the mean predicted average canopy base height was similar to control levels (Fig. 3, Table 4).

Coarse woody debris

Site Quality was the only predictor with a significant effect on the volume of coarse woody debris within the study (Table 2), with lower mean values in SQ2 (approximately 30–45 m³ per ha) than SQ1 (approximately 45–65 m³ per ha) (Fig. 2).

Overall fuel hazard

Based on the fuel hazard ratings for surface, near-surface, and elevated fuel layers (assessed based on modelled results from field measurements) and for bark (assessed based on the ubiquitous bark type in mono-dominant river red gum forests) the overall fuel hazard rating was ‘Moderate’ across the study and did not vary with thinning (Table 3).

Effect of stand structure on fuel attributes

Contrary to our predictions, tree density was not a strong predictor of understorey fuels within the study, having only minor and sporadic effects on near-surface vegetation cover and elevated vegetation height. Additionally, the effect of thinning on understorey fuels did not depend on tree densities prior to thinning. This contrasts with findings in conifer forests in North America, where previously logged, high-density stands do exhibit higher cover of elevated fuels, lower canopy base heights, and greater proportions of dead vegetation compared to unlogged, low-density stands, making them more prone to severe canopy fire (Naficy et al. 2010; Taylor et al. 2014a). Other studies in Australian eucalypt forests have also found increased fuel hazards and fire severity in stands recovering from previous disturbance compared with old growth stands, and this has been linked to young dense tree regrowth, shrub proliferation, and changes in understorey composition (Taylor et al. 2014b; Wilson et al. 2018; Lindenmayer et al. 2021). In contrast to many other eucalypt forests, river red gum forests are dominated by a single tree species and lack a shrubby mid-storey (Chesterfield 1986; Donovan 1997), thus shrub proliferation is not expected to occur. However, we expected dense tree regrowth to contribute to fuel loads in disturbed sites, as the elevated fuel layer in these forests is comprised primarily of river red gum saplings. The absence of this trend in our study is likely due to the considerable time since previous disturbance. The high-density stands seen in river red gum forests today primarily resulted from episodic germination events following logging, much of which occurred more than five decades ago (McGregor et al. 2016). Our results suggest that any effects of historical woody recruitment processes on sub-canopy fuels have now subsided, and there is currently little differentiation in the understorey fuel attributes that we measured in this study in relation to total tree density. This aligns with descriptions by Zylstra et al. (2023) where the increased flammability in previously disturbed tall wet forest stands drops off after approximately 50 years when regrowth trees have grown beyond the height of the understorey vegetation.

Effect of site quality on fuel attributes

Site Quality (an indicator of spatial variation in water availability) was not a strong driver of understorey fuels or thinning responses. However, there was short-term temporal variation in many understorey fuel attributes during the 5-year study. We consider the most likely driver to be fluctuations in soil water availability associated with flooding conditions and weather. In river red gum forests, tree growth and condition are closely linked to water availability (Robertson et al. 2001; Wen et al. 2009; Gorrod et al. 2024), and the species composition and growth of ground layer vegetation varies temporally with cycles of forest floor inundation and drying (Robertson et al. 2001). In our study, most fuel attributes changed more from year-to-year than between levels of Site Quality, with the exception of coarse woody debris volume that was consistently greater in wetter sites (SQ1) than drier sites (SQ2). Our control plots demonstrated that irrespective of thinning, near-surface vegetation cover peaked in the first year after our thinning treatment was applied, which followed a period of high river flows and inundation of the study area. Subsequently, the proportion of near-surface vegetation cover that was dead increased for several years, which coincided with an extended period of relatively low river flows. Although spatial variation in Site Quality was not a strong predictor of fuel attributes in the study area, it is likely that temporal hydrological patterns are a driver of fuel dynamics in river red gum forests.

Effects of thinning on fuel attributes

Thinning generally had a less substantial effect on fuel attributes than we expected. Statistically significant effects did not occur for all fuel attributes, and where they occurred, magnitudes of change were often so small as to be ecologically insignificant. Thinning in our study initially decreased total near-surface and elevated vegetation cover (by less than 5%) but increased the proportion that was dead compared with control plots (by less than 0.1). An initial decrease in live understorey vegetation was expected, due to thinning removing and damaging small saplings and disturbing ground covers during treatments. Reductions in understorey fuel loads have been demonstrated within the first year after thinning in other eucalypt forests (Proctor and McCarthy 2015; Furlaud et al. 2023). However, after the initial small loss of vegetation cover, both near-surface and elevated vegetation in thinned plots increased relative to control plots over the course of our study, while the proportion that was dead decreased. In the post-thinning period, groundcovers may proliferate due to increased resource availability (Thomas et al. 1999; Moore et al. 2006; Davis and Puettmann 2009), and seedling recruitment and resprouting are promoted, which creates a new cohort of small trees (O’Hara et al. 2010; Harrington and Devine 2011; Volkova et al. 2017). Other studies in both conifer and eucalypt forests have shown that post-thinning regrowth can lead to increased understorey vegetation cover relative to control treatments within 3–17 years (Thomas et al. 1999; Phillips and Waldrop 2008; Proctor and McCarthy 2015). Within our 5-year study period, both near-surface and elevated vegetation cover in thinned plots had returned to or exceeded levels in unthinned plots, suggesting that any small effect of thinning in reducing fuels associated with understorey vegetation cover was highly transient, and offset by vigourous regrowth within a few years.

The effects of thinning on vertical fuel connectivity in our study were more substantial and prolonged, but these effects are also likely to wane or reverse over time. Thinning decreased the average height of elevated vegetation across the study (by up to 1.6 m) and this effect persisted for at least 5 years after thinning, while the height to the base of the canopy was increased (by as much as 1.8 m) for the first 4 years. This suggests that thinning increased the distance between understorey fuels and the canopy for several years, when the risk of canopy scorch may be reduced. This reflects other studies in regrowth forests that have demonstrated reduced vertical fuel connectivity following thinning programs that remove small trees with aim to lessen the risk of high severity canopy fires (Brown et al. 2004; Donovan et al. 2022; Ritter et al. 2022). However, our results also point to the rapid increase in elevated vegetation cover in thinned plots, and this was likely due to thinning-stimulated recruitment. Gorrod et al. (2025) reported that thinning substantially increased the abundance of resprouts and seedlings in thinned plots during this thinning trial. In some areas, coppice stems exceeded the number of trees removed by thinning, and thinning promoted up to 7500 additional seedlings per ha in an episodic recruitment event that occurred during the study. Therefore, the decrease in the average height of elevated vegetation after thinning was presumably due to new dense small recruits that on average replaced the taller saplings. Ager et al. (2007) similarly reported that thinning in high-density conifer forests stimulated seedling regeneration that led to reduced average vegetation heights relative to unthinned treatments. As this new cohort of river red gum recruits grows, the average height of elevated vegetation in thinned plots will eventually increase, and the height to the canopy base will likely continue to decrease as more young trees reach the canopy. Thus, a single thinning treatment is unlikely to decrease vertical fuel connectivity in river red gum forests over the longer term; in fact, there is likely to be a period of increased fuel connectivity and fire risk as post-thinning regrowth matures. Zylstra et al. (2023) describe how post-disturbance recruitment can increase biomass within the understorey relative to the canopy, thereby increasing forest flammability and potential flame heights until growth and succession processes shift biomass back into the canopy over time. This aligns with the findings that long-undisturbed eucalypt forests typically have less vertical fuel connectivity relative to those more recently disturbed (Wilson et al. 2021), and that susceptibility to high-severity canopy fire peaks around 10–40 years after stand disturbance (Taylor et al. 2014b).

Surprisingly, our results showed no effect of thinning on litter cover or depth, or on coarse woody debris. Other studies have found that thinning increased the abundance of fine and/or coarse surface fuels in eucalypt forests (Proctor and McCarthy 2015; Volkova et al. 2017; Volkova and Weston 2019), and thinning debris has been implicated in increased fire severity in thinned stands (Taylor et al. 2021a, 2021b). Although non-log material was left on site in our study, it may be that the relative lack of branches on the thin straight trees that were targeted for removal, and possibly the lack of shrubby understorey, enabled thinning operations to be undertaken without the accumulation of large amounts of thinning debris. Approximately half the sites in our study also experienced flooding soon after thinning, which may have removed or accelerated decomposition of leaf litter and debris. Our results indicate there were also no subsequent effects of thinning on litter or coarse woody debris, such as may occur through differences in tree vigour or susceptibility to wind damage after thinning (Pukkala et al. 2016).

Fuel hazards in river red gum forests

Neither tree density nor thinning changed the hazard rating for any assessment category within our study, including overall fuel hazard. Across all thinned and unthinned treatments during the study, fuel hazard was consistently rated as ‘High’ for surface fuels (litter), ‘Low’ for near-surface fuels, ‘Moderate’ for elevated fuels, and ‘Low-Moderate’ for bark fuels, giving an unchanging score of ‘Moderate’ for overall fuel hazard. Our findings may be related to the ecology of the study system with a single-species tree layer, minimal shrubs, an herbaceous understorey, and abundant episodic tree recruitment. In contrast, Proctor and McCarthy (2015) reported that thinning reduced the overall fuel hazard rating in a mixed eucalypt forest for about a decade after treatment, although they quantified this rating using modified weightings for fuel hazard components, including an increased weighting for bark hazard. Additionally, their study system had higher levels of near-surface and elevated vegetation cover prior to thinning and thus had higher fuel hazard ratings in control plots. Differences among fuel hazard assessment methods may affect hazard scores. For example, Cheney et al. (2012) suggest that elevated fuel assessments should exclude vegetation >2 m in height. However, our approach, which followed Hines et al. (2010), did not use these cut-offs. Given the very small numerical changes in vegetation cover that we recorded, and the low amounts of total elevated fuel (<5%), these differences are unlikely to change the overall hazard rating in our study.

We suspect that the ‘Moderate’ hazard rating for elevated fuels in our study overestimated fire risks. Elevated fuels were assigned a fuel hazard rating of ‘Moderate’ based on cover being <20% but dead proportions being >0.3 (Hines et al. 2010; Pickering et al. 2023). However, as mean total elevated cover remained under 5%, the minimum value of 0.5% that we used for the presence of very small amounts of dead cover likely overestimated dead proportion values. With such low levels of total elevated vegetation cover, a fuel hazard rating of ‘Low’ seems more appropriate, irrespective of the dead proportion. This reflects recent findings by Cruz (2024) that commonly used fuel hazard classification systems may be an unreliable indicator of on-ground fuel quantities and a poor predictor of potential fire behaviour. Cruz et al. (2022) suggest that directly quantitative understorey fuel metrics such as the product of height and cover values are more suitable predictors of fire behaviour.

River red gum forests are typically less fire-prone than many other eucalypt forests (Colloff 2014; NSW Department of Planning and Environment 2022). While fire risk in river red gum forests may be virtually null during periods of inundation, periods of drought may substantially increase flammability and fire activity (NSW Department of Planning and Environment 2022). As more extreme fire weather is predicted for parts of Australia with ongoing climate change (Jones et al. 2022), there is an increasing need to understand fire risks and mitigation strategies across a broader range of landscapes, especially those that serve as biodiversity conservation reserves. Studies such as this, which gather empirical data about fuel dynamics within understudied systems, will assist in the development of more refined techniques for assessing, predicting, and managing fire risks in Australian eucalypt forests (Cruz et al. 2018; Hanan et al. 2022).