Rapid response of habitat structure and aboveground carbon storage to altered fire regimes in tropical savanna

Rapid response of habitat structure and aboveground carbon storage to altered fire regimes in tropical savanna Shaun R. Levick1,2,3, Anna E. Richards2, Garry D. Cook2, Jon Schatz2, Marcus Guderle1, Richard J. Williams2, Parash Subedi3, Susan E. Trumbore1, and Alan N. Andersen3 1Max Planck Institute for Biogeochemistry, Hans-Knoell-Str. 10, 07745 Jena, Germany 2CSIRO Land and Water, PMB 44, Winnellie, 0822 NT, Australia 3Research Institute for the Environment and Livelihoods, Charles Darwin University, NT 0909, Australia Correspondence: Shaun R. Levick (shaun.levick@csiro.au)


Introduction
Fire is an integral component of the functioning of savanna ecosystems, exerting top-down control on woody vegetation structure (Bond and Keeley, 2005;Sankaran et al., 2005). Savanna fires restrict vegetation vertical growth through a fire-trap mechanism, whereby young trees are constrained to low woody resprouts under high fire frequencies (Higgins et al., 2000;Freeman to reach mid-and upper canopy heights, with long-term consequences for size-class distribution and structural heterogeneity (Helm and Witkowski, 2012;Levick et al., 2015a).
Three-dimensional (3D) heterogeneity of vegetation has long been valued as a key factor promoting faunal diversity through increased niche diversity (MacArthur and MacArthur, 1961;MacArthur, 1964). The structural modifications that fires impart on savanna vegetation have been shown to impact both vertebrate (Woinarski et al., 2004) and invertebrate  5 taxa. Fire-driven structural changes in savanna vegetation also have important implications for climate regulation, as savanna fires contribute significantly to atmospheric emissions of greenhouse gases through biomass combustion (Hurst et al., 1994;van der Werf et al., 2010). Despite the importance of quantifying fire induced changes to 3D structure in savanna vegetation, current understanding of magnitudes and spatial patterns remains limited, and savanna fires represent large uncertainty in global vegetation models (Higgins et al., 2007;Scheiter et al., 2013). Gaining better understanding of how different fire regimes impact 10 savanna vegetation structure is becoming increasingly urgent in the face of changing climate and land-management conditions that are triggering variations in the timing, frequency, intensity and duration of fires in the tropical biome (Alencar et al., 2015).
Fire frequency in Australian savannas is particularly high, with many regions burning twice in every three years on average (Beringer et al., 2014). Many of these fires occur late in the dry season, producing high intensity burns that result in simplified vegetation structure (Bowman et al., 1988;Lehmann et al., 2009;Ondei et al., 2017). There are widespread concerns that 15 such fire regimes are linked to dramatic declines in faunal populations, through the removal of ground layer vegetation (Lawes et al., 2015;Legge et al., 2015;. Methane and nitrous oxide emissions from savanna fires are included in Australias national greenhouse-gas accounts, and are responsible for approximately 3% of total accountable greenhousegas emissions (Meyer et al., 2012). There is considerable interest in reducing the frequency and intensity of fires in northern Australia through strategic early dry season (April to July) burning, in order to reduce both greenhouse gas emissions and 20 certain components of biodiversity decline . As such, the Australian Government has implemented legislation enabling landowners to claim carbon credits for reducing greenhouse gas emissions from savanna fires through early dry season burning (Carbon Farming Initiative -Emissions Abatement through Savanna Fire Management Methodology Determination 2015, Department of Environment and Energy). Such changes to fire regimes in northern Australia are also likely to increase carbon sequestration in the landscape (Murphy et al., 2010;Richards et al., 2012), although there is currently 25 no approved methodology for incorporating this into the national accounts. While much attention is currently being given to reducing the extent and frequency of late season fires in northern Australia, it is important to recognise that savannas have evolved with fire (Bond and Keeley, 2005;Durigan and Ratter, 2016) and excluding fire would be detrimental to certain savanna specialists that favour more open and grassy habitat. The challenge is finding the best mix of patches of different regimes across connected landscapes. 30 Understanding of how different fire regimes impact habitat structure and carbon dynamics in tropical savannas can be enhanced through detailed 3D measurements of vegetation structure at sites subject to long-term, replicated experimental fire treatments. Traditional field-based inventory techniques are limited in their ability to quantify 3D structure, but light-detection and ranging (LiDAR) can now achieve this with high accuracy and precision in a repeatable and transferable manner (Lefsky et al., 2002;Levick and Rogers, 2008). Airborne LiDAR has a proven record in providing detailed 3D representations of 35 savanna vegetation structure across time and space (Smit et al., 2010;Levick et al., 2012Levick et al., , 2015b, but has yet to be used for assessing vegetation biomass and structural diversity responses to experimental fires in savannas. Northern Australia has a long history of savanna fire experiments , including the ongoing Burning for Biodiversity experiment at the Territory Wildlife Park that has applied six fire treatments in three replicated blocks since 2004 (Scott et al., 2010). Here we integrate field-based measurements of vegetation structure with airborne LiDAR to determine how 5 variation in fire frequency and season affects the 3D habitat structure and aboveground carbon storage of woody vegetation. Our specific aims are to : i) explore how vegetation carbon storage and structural diversity respond to increasing fire frequency; and ii) quantify the structural impact of late-season fires compared to early-season fires. We use airborne LiDAR data to provide greater spatial coverage than can be achieved with field sampling alone, and to gain better understanding of how reliably LiDAR could be used to assess savanna carbon dynamics in instances where field data may not be available or attainable. Pseudopogonatherum contortum (Brongn.) A.Camus, Sarga intrans F.Muell. ex Benth. and Eriachne triseta Nees ex Steud (Scott et al., 2010) . The soils are relatively shallow (0.5 to 1 m deep) gravelly red earths (Petroferric Red Kandosol) (Isbell, 2002) of the Kay land system within the Koolpinyah land surface group, and have developed predominantly from deeply weathered sandstones, siltstones and shales (Wood et al., 1985). The climate is wet-dry tropical with greater than 90% of annual rainfall (mean 1401 mm) falling in the wet season from November to April, and mean monthly maximum and minimum 20 temperatures between 33.1°C and 20.9°C (Bureau of Meteorology, Commonwealth of Australia).
The fire experiment consists of 18 1-ha plots grouped into 3 blocks (A, B, C) arranged along a north-south transect ( Figure   1). Soil depth increases from north to south, and the C block has higher soil moisture given its proximity to a small drainage line. Six fire treatments were randomly assigned to each block at the start of the experiment: unburnt plots (U) and plots burnt at fire return intervals of 1 (E1), 2 (E2), 3 (E3) and 5 (E5) years in the early dry season (June) and plots burnt every 2 years 25 (L2) in the late dry season (Table 1). Prior to implementation of the burning treatments in 2004, all areas had been unburnt for at least 14 years when fire records started (except for a fire in 1992 and again in 2000 in the A block only).
During each experimental burn, fire intensity was estimated using the established relationship between rate of spread and fuel load (Williams et al., 1998). Rate of fire spread was determined from thermocouples linked to electronic stop watches positioned 5 cm above the soil surface, in the flaming combustion zone. Six timers were used in each 1 ha plot, arranged in a  star pickets and flagging tape. Fuel loads were determined prior to each fire by direct harvest and weighing. Ten replicate 0.5 m x 0.5 m fuel samples were cut for each plot. Fuel heat content was assumed to be 20 000 kJ per kg dry weight.

Field-based estimation of above ground woody biomass
In each of the 18 plots, two 30 x 30 m subplots were established at the north-west and south-east corners, at least 10 m away from plot edges. In each subplot the species identity, location, height and diameter of all woody plants >2 m in height was recorded. The location of each individual plant was recorded to 0.3 m accuracy using a differential GPS with postprocessing (Trimble Inc.). Tree heights were recorded with a standard height pole (plants <8 m) or clinometer (plants >8 m), 5 and stem diameter was recorded at 1.3 m with a diameter tape for all woody species except for the multi-stemmed shrubs Calytrix exstipulata and Exocarpus latifolius, in which case diameter was recorded at the stem base (0.1 m above the ground).
Aboveground biomass was calculated for each individual tree using the equation developed by (Williams et al., 2005): whereby AGB = aboveground biomass (kg), D = stem diameter (m), and H = tree height (m). Individual tree biomasses 10 were then summed for each 30 X 30 m subplot. Estimated biomass values were converted to carbon terms on a per hectare basis assuming 50 % of biomass was carbon (t C ha −1 ). This approach did not consider the contribution of small (<2 m) multi-stemmed shrubs to the carbon pool.

Airborne LiDAR surveying and processing
We mapped 150 ha of the study area with airborne LiDAR in June 2013, 9 years after the beginning of the experiment. The

Upscaling aboveground woody biomass estimates with airborne LiDAR
The normalized airborne LiDAR returns were clipped to the spatial extent of each field-measured 30 X 30 m subplot. Using  scores for each of the models were compared to identify the most parsimonious model.

Results
3.1 Estimation of above ground woody biomass from airborne LiDAR 5 Airborne LiDAR proved valuable for upscaling woody biomass measurements from the field-plots to the full extent of the fire experiment (Figure 3). Only three woody canopy structural variables were retained in the step-wise linear regression procedure: mean canopy height (MCH), total canopy cover (Cov1m), and overstory canopy cover (Cov10m): The distribution of model residuals showed no spatial trend nor relationship with the fire treatment. The degree of residual 10 error (RMSE = 7.35 t C ha −1 ), provided acceptable confidence for inclusion of modelled biomass values in further analyses.

Effects of fire regime on woody canopy cover and aboveground biomass
Canopy cover decreased along the experimental gradient of fire frequency and season, ranging from about 75 % (SE = 1.7) in unburnt plots to 45 % (SE = 2.3) in late season bienniel plots (Figure 4a). These differences in canopy cover translated into similar patterns of biomass variation across the experiment (Figure 4b). The highest within-treatment variability for both cover 15 and biomass was found in the early season annual plots (E1).
The best model explaining variation in both woody cover and biomass was one in which fire treatment, block position, and the interaction between them was included (   These results point to an important source of environmental variation arising from block position, which represents a gradient in soil depth and moisture availability across the experimental site. When we consider the spectrum of increasing fire intensity occurring across the experimental treatments, we found that correlations between the reductions in aboveground biomass and fire intensity decreased along the soil depth and moisture availability gradient ( Figure 5). In carbon terms, the early biennial fires on average caused a reduction of 10 t C ha −1 compared 5 to unburnt plots, whereas late biennial fires almost doubled that reduction to 19 t C ha −1 .

Fire effects on vertical habitat structure
In addition to the observed patterns in woody canopy cover and aboveground biomass, our LiDAR-based assessment also revealed substantial variation in canopy height profile distributions, derived from the number of LiDAR returns from different height levels ( Figure 6). Most profiles were bimodal, with a peak at 1-2 m height and a smaller peak at 10-15 m. The clearest bimodal response was found in the early season triannual burns (Figure 6c), whereas early season annual and 5-yr burn profiles were more uniform (Figure 6a,d).
Keeping fire frequency constant (biennial) and exploring the effects of fire season highlighted the large influence of late season versus early season burns (Figure 7). Compared with no fire, early season biennial fires reduced cover across all heights, but especially below 7m, and late season biennial fire reduced cover even further throughout, generating a vertical profile 5 similar in shape but with much lower frequency of occurrence (Figure 7). The late season fire profile contained significantly less canopy in all height classes compared to the unburnt (no overlap of error bars), but the most marked effects were in the lower height classes (shrub layer).

Discussion
Airborne LiDAR provided direct measures of canopy cover and height distribution, and the derived metrics successfully pre-10 dicted field-based estimates of aboveground biomass. The synoptic view that airborne LiDAR provided enabled us to map changes in biomass under different fire regimes, in addition to exploring differences in vegetation vertical profiles across the full expanse of the fire experiment.

Carbon storage consequences of altered fire regimes
Ten years of experimental burning imparted large structural differences in woody canopy across the plots of the Territory Wildlife Park fire experiment. Fire effects were most pronounced at the extremes of the experimental spectrum, with highest cover and biomass occurring under complete fire exclusion and lowest values of woody canopy structure obtained under biennial late season burning. The directionality of these trends was persistent across the underlying gradient of increasing soil 5 depth and moisture, but the magnitude and slope of the effects was greater in the A and B block with shallower, drier soils ( Figure 5). The lower magnitude of carbon reduction in the lower lying "C" block likely stems from the sparse herbaceous cover in these plots which results in patchy, low intensity fires.
Recent research into woody biomass trends in the region (from long-term field monitoring plots) indicate that woody biomass has been relatively stable over decadal periods, with minor evidence of woody thickening, and that biomass is negatively data in our study to directly corroborate this finding, but the patterns of reduced cover throughout the height profile do suggest mortality and the consumption of trees by fire, rather than just reduction in growth rates.
Similar investigations in southern African savannas have found that fire frequency itself had little bearing on woody cover, but that the presence of fire alone was a stronger predictor of reduced woody cover (Devine et al., 2015). In our study however, we found that cover and biomass were reduced as fire frequency increased (Figure 4), with the exception to the trend being experiments like those obtained at Kapalga experiment, our finding are in agreement with the diminished basal areas observed there under very high intensity late season fires .
There is increasing interest in understanding the effect of different fire regimes on carbon stored in Australian savannas (Murphy et al., 2013;Cook et al., 2015) and recent studies (Cook et al., 2016) have shown higher carbon stocks in dead organic matter under lower fire frequencies. At the Territory Wildlife Park fire plots the early biennial fire caused a reduction of 10 t C 5 ha −1 on average compared to unburnt plots, whereas late biennial fires almost doubled that average reduction to 19 t C ha −1 ( Figure 5). These patterns are consistent with the trend of lower greenhouse gas emissions under early dry season fires, relative to late fires (Meyer et al., 2012) and point to the importance of available fuel load and its characteristics (greater herbaceous volume and lower moisture content late in the dry season) in understanding fire induced structural change in savannas. This is further emphasised by the variation in response to fire along the environmental gradient of the experimental site. 10 Murphy et al. (2013) suggested that the moderation of fire regimes in northern Australia is likely to increase carbon storage in woody biomass, but the extent to which woody biomass can increase in these savannas is highly uncertain. Our results reduce some of this uncertainty, by providing quantification of the degree carbon stored in unburnt plots deviates from a range of different fire frequencies.

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Different fire regimes imparted a diverse array of vertical structural profiles on woody vegetation. Although woody canopy cover and aboveground biomass displayed subtle responses among the early season fire frequency treatments, we found that each fire regime generated a relatively unique niche space in terms of vertical profile distribution. These niches were most divergent in the understory height classes (< 5 m). Tracking these profiles over time into the future might reveal increased height of divergence as cohorts grow taller. Alternatively, these understorey height curves may represent stable persistent 20 equilibrium resprout heights that define the optimal of resprouts that are able to persist within the flame zone under a particular fire regime (Freeman et al., 2017).
These vertical profile findings highlight the powerful role that fire management can play in shaping three-dimensional habitat in ecosystems. The challenge this presents to land-managers is deciding which of this range of profiles is optimal for their specific management objectives. We still lack explicit understanding of how different organisms utilize three-dimensional 25 space, and it is increasingly evident that no one profile is optimal. Mid-story shrubs and trees provide key food resources for birds and small mammals, and high ground cover reduces predation risk by feral cats (Davies et al., 2016). Conversely, habitat simplification through late season burning was found to promote longer-term abundance of Frilled-neck lizards in Kakadu National Park, despite high initial direct mortality rates (Corbett et al., 2003;Andersen et al., 2005). As such, it is likely that a mix of patches at the landscape scale, spanning a diverse range of vertical profiles, is needed from a wildlife conservation 30 perspective. The relative proportions and spatial arrangement of these patches needs targeted and deeper investigation.

Limitations and future directions
Our findings in this study provide quantification of the magnitudes of fire regime effects on woody structure in a tropical savanna. When generalizing to other savanna regions however, the following limitations should be to be taken into consideration.
First, prior to the establishment of the TWP fire experiment in 2004 the vegetation was unburnt since 1990. Fourteen years of fire exclusion is rare in these tropical landscapes, so the starting conditions are atypical.

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Second, despite the good results obtained in upscaling field-based woody biomass estimates with airborne LiDAR (Figure 3), future efforts should focus on reducing the level of uncertainty in the LiDAR-biomass model. Greater confidence in biomass/carbon prediction could be achieved by turning to individual tree-based segmentation approaches. Developments in terrestrial LiDAR in particular show great promise for providing individual tree volumes and biomass estimates that can be scaled, together with their uncertainties, to plot and landscape scales (Calders et al., 2014;Levick et al., 2016). Furthermore, 10 the rich 3D models that terrestrial LiDAR provide will open up new avenues for exploring actual 3D structural metrics.
Last, our analyses in this study rely on differences between treatments at a single point in time to infer the mechanisms underpinning woody structural modification. Although typical for this type of investigation, the single time point approach should ideally be complimented with time-series analyses of before and after fire events to better constrain the mechanisms underpinning structural change. 15

Conclusions
We quantified the magnitude of aboveground carbon reduction under different regimes by integrating airborne LiDAR, fieldsurveys, and an ongoing fire regime experiment. Our results highlight the impact of late season burning on both carbon storage and on canopy vertical profile structure. Clear relationships between biodiversity and fire regimes have proven difficult to establish in savannas, despite many attempts at linking floral and faunal diversity directly to fire regime patterns. The range of 20 vertical profile responses that we have illustrated here under different experimental fire treatments could hold the key to unlocking stronger links between fire management and biodiversity responses. High-resolution LiDAR can expose the structural consequences of different management actions, and make them more easily accessible for integration with biodiversity and ecosystem process studies.