Quantifying the environmental limits to fire spread in grassy ecosystems.

Modeling fire spread as an infection process is intuitive: An ignition lights a patch of fuel, which infects its neighbor, and so on. Infection models produce nonlinear thresholds, whereby fire spreads only when fuel connectivity and infection probability are sufficiently high. These thresholds are fundamental both to managing fire and to theoretical models of fire spread, whereas applied fire models more often apply quasi-empirical approaches. Here, we resolve this tension by quantifying thresholds in fire spread locally, using field data from individual fires (n = 1,131) in grassy ecosystems across a precipitation gradient (496 to 1,442 mm mean annual precipitation) and evaluating how these scaled regionally (across 533 sites) and across time (1989 to 2012 and 2016 to 2018) using data from Kruger National Park in South Africa. An infection model captured observed patterns in individual fire spread better than competing models. The proportion of the landscape that burned was well described by measurements of grass biomass, fuel moisture, and vapor pressure deficit. Regionally, averaging across variability resulted in quasi-linear patterns. Altogether, results suggest that models aiming to capture fire responses to global change should incorporate nonlinear fire spread thresholds but that linear approximations may sufficiently capture medium-term trends under a stationary climate.

Forest reorganisation effects on fuel moisture content can exceed changes due to climate warming in wet temperate forests.

The distributions of vegetation and fire activity are changing rapidly in response to climate warming. In many regions, climate effects on dead fuel moisture content (FMC) are expected to increase future wildfire activity. However, forest FMC is largely driven by microclimate conditions, which are moderated from open weather by vegetation canopies. As shifts in vegetation increase under climate warming, the extent to which future fire activity will be driven by climate directly or associated vegetation shifts remains unresolved. Here, we present a study aimed at quantifying the relative magnitudes of (i) direct climate warming, and (ii) vegetation change, on FMC. Field sites to evaluate these effects were established in a natural laboratory of altered forest states to mature wet temperate forest in south-eastern Australia. FMC was estimated using a process-based model and 48 years of reconstructed climate data. Canopy effects on microclimate were captured by transferring inputs from climate to microclimate using models parameterised with field observations. To evaluate the relative magnitude of climate and vegetation effects, we calculated the maximum difference in mean annual FMC across annual climate replicates and compared this to FMC differences across reorganising forest sites. Our results show vegetation effects on FMC can exceed those related to expected climate change. Changes to forest structure and composition increased (+15.7%) and decreased (-12.3%) mean annual FMC, with a larger negative effect when forest cover was completely removed (-18.5%). In contrast, the largest climate effect on FMC was -6.6% across 48-years of data. Our study demonstrates that the magnitude of vegetation effects on FMC can exceed expected climate change effects. Models of future fire activity that do not account for changing vegetation effects on microclimate are omitting a key biophysical control on FMC and therefore may not be accurately predicting future fire activity.

Tree species explain only half of explained spatial variability in plant water sensitivity.

Spatiotemporal patterns of plant water uptake, loss, and storage exert a first-order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional-scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto-regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species-specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range.

Plant hydraulic modelling of leaf and canopy fuel moisture content reveals increasing vulnerability of a Mediterranean forest to wildfires under extreme drought.

Fuel moisture content (FMC) is a crucial driver of forest fires in many regions world-wide. Yet, the dynamics of FMC in forest canopies as well as their physiological and environmental determinants remain poorly understood, especially under extreme drought. We embedded a FMC module in the trait-based, plant-hydraulic SurEau-Ecos model to provide innovative process-based predictions of leaf live fuel moisture content (LFMC) and canopy fuel moisture content (CFMC) based on leaf water potential ( ψ Leaf ). SurEau-Ecos-FMC relies on pressure-volume (p-v) curves to simulate LFMC and vulnerability curves to cavitation to simulate foliage mortality. SurEau-Ecos-FMC accurately reproduced ψ Leaf and LFMC dynamics as well as the occurrence of foliage mortality in a Mediterranean Quercus ilex forest. Several traits related to water use (leaf area index, available soil water, and transpiration regulation), vulnerability to cavitation, and p-v curves (full turgor osmotic potential) had the greatest influence on LFMC and CFMC dynamics. As the climate gets drier, our results showed that drought-induced foliage mortality is expected to increase, thereby significantly decreasing CFMC. Our results represent an important advance in our capacity to understand and predict the sensitivity of forests to wildfires.

Global increase in wildfire risk due to climate-driven declines in fuel moisture.

There is mounting concern that global wildfire activity is shifting in frequency, intensity, and seasonality in response to climate change. Fuel moisture provides a powerful means of detecting changing fire potential. Here, we use global burned area, weather reanalysis data, and the Canadian fire weather index system to calculate fuel moisture trends for multiscale biogeographic regions across a gradient in vegetation productivity. We quantify the proportion of days in the local fire season between 1979 and 2019, where fuel moisture content is below a critical threshold indicating extreme fire potential. We then associate fuel moisture trends over that period to vegetation productivity and comment on its implications for projected anthropogenic climate change. Overall, there is a strong drying trend across realms, biomes, and the productivity gradient. Even where a wetting trend is observed, this often indicates a trend toward increasing fire activity due to an expected increase in fuel production. The detected trends across the productivity gradient lead us to conclude global fire activity will increase with anthropogenic climate change.

Estimation of moisture in live fuels in the mediterranean: Linear regressions and random forests.

The live fuel moisture content is an important factor in estimating the risk of forest fires and their rate of spread. However, due to a lack of research, the FMC values in the Mediterranean region of Andalusia, Spain, must be obtained by sample collection. This study is therefore the first to provide tools for estimating the moisture content of the most widespread plant species in Andalusia. First, samples were collected to estimate the moisture content of the plants; these data were collected from May 2007 to the present. Each species has its own range of moistures that depend on the time of year and the physiological state in which they are found. Secondly, an extensive database was obtained for each day of sample collection from the nearest weather station with free access. The statistics are performed at 12 solar hours on the day of sample collection and 24 h before collection, and then at 7 days, 14 days, 1 month, 3 months and 6 months before the day of collection. Finally, this database was statistically analyzed in two ways: Multiple linear regressions and random forest for each species. The predictive capacity of random forest is superior (R2 > 0.89) to that obtained in linear regression (R2 < 0.86). The highest root mean square error obtained in the case of the random forest is 0.74479 while in the linear regressions it was 1.29184. Consequently, uncertainty regarding fire behavior in the case of forest fires is reduced.

Trajectories of wildfire behavior under climate change. Can forest management mitigate the increasing hazard?

Mediterranean forests and fire regimes are closely intertwined. Global change is likely to alter both forest dynamics and wildfire activity, ultimately threatening the provision of ecosystem services and posing greater risks to society. In this paper we evaluate future wildfire behavior by coupling climate projections with simulation models of forest dynamics and wildfire hazard. To do so, we explore different forest management scenarios reflecting different narratives related to EU forestry (promotion of carbon stocks, reduction of water vulnerability, biomass production and business-as-usual) under the RCP 4.5 and RCP 8.5 climate pathways in the period 2020-2100. We used as a study model pure submediterranean Pinus nigra forests of central Catalonia (NE Spain). Forest dynamics were simulated from the 3rd National Forest Inventory (143 stands) using SORTIE-nd software based on climate projections under RCPs 4.5 and 8.5. The climate products were also used to estimate fuel moisture conditions (both live and dead) and wind speed. Fuel parameters and fire behavior were then simulated, selecting crown fire initiation potential and rate of spread as key indicators. The results revealed consistent trade-offs between forest dynamics, climate and wildfire. Despite the clear influence exerted by climate, forest management modulates fire behavior, resulting in different trends depending on the climatic pathway. In general, the maintenance of current practices would result in the highest rates of crown fire activity, while management for water vulnerability reduction is postulated as the best alternative to surmount the increasingly hazardous conditions envisaged in RCP 8.5.

Estimating Fuel Moisture in Grasslands Using UAV-Mounted Infrared and Visible Light Sensors.

Predicting wildfire behavior is a complex task that has historically relied on empirical models. Physics-based fire models could improve predictions and have broad applicability, but these models require more detailed inputs, including spatially explicit estimates of fuel characteristics. One of the most critical of these characteristics is fuel moisture. Obtaining moisture measurements with traditional destructive sampling techniques can be prohibitively time-consuming and extremely limited in spatial resolution. This study seeks to assess how effectively moisture in grasses can be estimated using reflectance in six wavelengths in the visible and infrared ranges. One hundred twenty 1 m-square field samples were collected in a western Washington grassland as well as overhead imagery in six wavelengths for the same area. Predictive models of vegetation moisture using existing vegetation indices and components from principal component analysis of the wavelengths were generated and compared. The best model, a linear model based on principal components and biomass, showed modest predictive power (r² = 0.45). This model performed better for the plots with both dominant grass species pooled than it did for each species individually. The presence of this correlation, especially given the limited moisture range of this study, suggests that further research using samples across the entire fire season could potentially produce effective models for estimating moisture in this type of ecosystem using unmanned aerial vehicles, even when more than one major species of grass is present. This approach would be a fast and flexible approach compared to traditional moisture measurements.

Effect of moisture content and fuel type on emissions from vegetation using a steady state combustion apparatus.

Emission measurements are available in the literature for a wide variety of field burns and laboratory experiments, although previous studies do not always isolate the effect of individual features such as fuel moisture content (FMC). This study explores the effect of FMC on gaseous and particulate emissions from flaming and smouldering combustion of four different wildland fuels found across the United States. A custom linear tube-heater apparatus was built to steadily produce emissions in different combustion modes over a wide range of FMC. Results showed that when compared with flaming combustion, smouldering combustion showed increased emissions of CO, particulate matter and unburned hydrocarbons, corroborating trends in the literature. CO and particulate matter emissions in the flaming mode were also significantly correlated with FMC, which had little influence on emissions for smouldering mode combustion, when taking into account the dry mass of fuel burned. These variations occurred for some vegetative fuel species but not others, indicating that the type of fuel plays an important role. This may be due to the chemical makeup of moist and recently live fuels, which is discussed and compared with previous measurements in the literature.

Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize.

BACKGROUND: Vegetation water content is one of the important biophysical features of vegetation health, and its remote estimation can be utilized to real-timely monitor vegetation water stress. Here, we compared the responses of canopy water content (CWC), leaf equivalent water thickness (EWT), and live fuel moisture content (LFMC) to different water treatments and their estimations using spectral vegetation indices (VIs) based on water stress experiments for summer maize during three consecutive growing seasons 2013-2015 in North Plain China. RESULTS: Results showed that CWC was sensitive to different water treatments and exhibited an obvious single-peak seasonal variation. EWT and LFMC were less sensitive to water variation and EWT stayed relatively stable while LFMC showed a decreasing trend. Among ten hyperspectral VIs, green chlorophyll index (CIgreen), red edge normalized ratio (NRred edge), and red-edge chlorophyll index (CIred edge) were the most sensitive VIs responding to water variation, and they were optimal VIs in the prediction of CWC and EWT. CONCLUSIONS: Compared to EWT and LFMC, CWC obtained the best predictive power of crop water status using VIs. This study demonstrated that CWC was an optimal indicator to monitor maize water stress using optical hyperspectral remote sensing techniques.

Foliar Moisture Content from the Spectral Signature for Wildfire Risk Assessments in Valparaíso-Chile.

Fuel moisture content (FMC) proved to be one of the most relevant parameters for controlling fire behavior and risk, particularly at the wildland-urban interface (WUI). Data relating FMC to spectral indexes for different species are an important requirement identified by the wildfire safety community. In Valparaíso, the WUI is mainly composed of Eucalyptus Globulus and Pinus Radiata-commonly found in Mediterranean WUI areas-which represent the 97.51% of the forests plantation inventory. In this work we study the spectral signature of these species under different levels of FMC. In particular, we analyze the behavior of the spectral reflectance per each species at five dehydration stages, obtaining eighteen spectral indexes related to water content and, for Eucalyptus Globulus, the area of each leave-associated with the water content-is also computed. As the main outcome of this research, we provide a validated linear regression model associated with each spectral index and the fuel moisture content and moisture loss, per each species studied.

Foliar uptake of fog in coastal California shrub species.

Understanding plant water uptake is important in ecosystems that experience periodic drought. In many Mediterranean-type climates like coastal California, plants are subject to significant drought and wildfire disturbance. During the dry summer months, coastal shrub species are often exposed to leaf wetting from overnight fog events. This study sought to determine whether foliar uptake of fog occurs in shrub species and how this uptake affects physiology and fuel condition. In a controlled greenhouse experiment, dominant California shrub species were exposed to isotopically labeled fog water and plant responses were measured. Potted plants were covered at the base to prevent root uptake. The deuterium label was detected in the leaves of four out of five species and in the stems of two of the species. While there was a minimal effect of foliar water uptake on live fuel moisture, several species had lower xylem tension and greater photosynthetic rates after overnight fog treatments, especially Salvia leucophylla. Coastal fog may provide a moisture source for many species during the summer drought, but the utilization of this water source may vary based on foliar morphology, phenology and plant water balance. From this study, it appears that drought-deciduous species (Artemisia californica and Salvia leucophylla) benefit more from overnight fog events than evergreen species (Adenostoma fasciculatum, Baccharis pilularis and Ceanothus megacarpus). This differential response to fog exposure among California shrub species may affect species distributions and physiological tolerances under future climate scenarios.

Flammability properties of British heathland and moorland vegetation: models for predicting fire ignition.

Temperate ecosystems, for example British heathlands and moorlands, are predicted to experience an increase in severe summer drought and wildfire frequency over the next few decades. The development of fire ignition probability models is fundamental for developing fire-danger rating systems and predicting wildfire outbreaks. This work assessed the flammability properties of the fuel complex of British moorlands as a function of their moisture content under laboratory conditions. Specifically, we aimed to develop: (1) models of the probability of fire ignition in peat/litter fuel-beds (litter of four different plant species, Sphagnum moss and peat); (2) flammability properties in terms of ignitability, sustainability, consumability and combustibility of these peat/litter fuel-beds; (3) the probability of ignition in a canopy-layer of Calluna vulgaris (the most dominant heath/moor species in Britain) as a function of its dead-fuel proportion and moisture content; (4) the efficacy of standardized smouldering and flaming ignition sources in developing sustained ignitions. For this, a series of laboratory experiments simulating the fuel structure of moor vegetation were performed. The flammability properties in peat/litter fuel-beds were influenced strongly by the fuel moisture content. There were small differences in moisture thresholds for experiencing initial flaming ignitions (35-59%), however, the threshold for sustained ignitions (i.e. spreading a fixed distance from the ignition point) varied across a much wider range (19-55%). Litter/peat fuel-beds were classified into three groups: fuel-beds with high ignitability and combustibility, fuel-beds with high levels of sustainability, and fuel-beds with low levels in all flammability descriptors. The probability of ignition in the upper Calluna-vegetation layer was influenced by both the proportion of dead fuels and their moisture content, ranging from 19% to 35% of moisture as dead fuel proportion increased. Smouldering sources were more efficient in igniting peat/litter fuel-beds but in the Calluna-vegetation layer flaming sources performed better. This work can assist in improving the predictions of fire-rating systems implemented in British moorlands, by providing better warnings based on critical moisture thresholds for various fuel types.

Accelerated rise in wildfire carbon emissions from Arctic continuous permafrost.

Wildfires over permafrost put perennially frozen carbon at risk. However, wildfire emissions from biomass burning over the diverse range of permafrost regions and their share in global wildfire emissions have not been revealed. The results showed a dramatic increase in wildfire carbon emissions from permafrost regions over the period 1997-2021. The share of permafrost in global wildfire CO2 emissions increased from 2.42% in 1997 to 20.86% in 2021. Accelerating wildfire emissions from continuous permafrost region is the single largest contributor to increased emissions in northern permafrost regions. Fire-induced emissions from 2019 to 2021 alone accounted for approximately 40% of the 25-year total CO2 emissions from continuous permafrost regions. The rise in wildfire emissions from continuous permafrost regions is explained by desiccation within a 5-10 cm soil depth, where wildfires combust belowground fuel. These findings highlight the acceleration of fire-induced carbon emissions from continuous permafrost regions, which disturb the organic carbon stock and accelerate the positive feedback between permafrost degradation and climate warming, thus stimulating permafrost towards a climatic tipping point.

Composition of inorganic elements in fine particulate matter emitted during surface fire in relation to moisture content of forest floor combustibles.

The moisture content of combustible material on the forest floor is constantly changing due to environmental factors, which have a direct impact on the composition and emission intensity of particulate matter released during fire. In this study, an indoor biomass combustion analysis device was used to analyze the emission characteristics of fine particulate matter (PM2.5) from combustion of herbaceous combustible materials with different moisture contents (0%, 15%, and 30%). The composition of inorganic elements in PM2.5 (Zn, K, Mg, Ca, and other 13 measurable elements) were determined by inductively coupled plasma-mass spectrometer (ICP-MS). The results showed that the PM2.5 emission factor increased significantly with the increase of moisture content of combustible materials in the range of 11.63 ± 0.55 for dry samples to 36.71 ± 1.21 g/kg for samples with 30% moisture content. The main elemental components of PM2.5 were K, Zn, Ca, Mg, and Na and K, Ca, Mg, and Na emission factors increased with the increase of moisture content of combustibles. The proportion of macronutrients in PM2.5 released by combustion of each herb increased as the moisture content increased, but the proportion of trace elements gradually decreased. There was a good correlation between elemental composition of PM2.5 and that of herbaceous combustibles. The results provide evidence that the moisture content of combustible materials has a significant effect on the emission of inorganic elements in particulate matter, and hence cautions should be exercised during fuel reduction treatments, such as early prescribed fire.

Climate change, fuel and fire behaviour in a eucalypt forest.

A suite of models was used to examine the links between climate, fuels and fire behaviour in dry eucalypt forests in south-eastern Australia. Predictions from a downscaled climate model were used to drive models of fuel amount, the moisture content of fuels and two models of forest fire behaviour at a location in western Sydney in New South Wales, Australia. We found that a warming and drying climate produced lower fine fuel amounts, but greater availability of this fuel to burn due to lower moisture contents. Changing fuel load had only a small effect on fuel moisture. A warmer, drier climate increased rate of spread, an important measure of fire behaviour. Reduced fuel loads ameliorated climate-induced changes in fire behaviour for one model. Sensitivity analysis of the other fire model showed that changes in fuel amount induced changes in fire behaviour of a similar magnitude to that caused directly by sensitivity to climate. Projection of changes in fire risk requires modelling of changes in vegetation as well as changes in climate. Better understanding of climate change effects on vegetation structure is required.

Forest fire risk estimation in a typical temperate forest in Northeastern China using the Canadian forest fire weather index: case study in autumn 2019 and 2020.

China's forest cover has increased by approximately 10% as a result of sustainable forest management since the late 1970s. The forest ecosystem area affected by fire is increasing at an alarming rate of approximately 600,000 ha per year. The northeastern part of China, with a forest cover of 41.6%, has the greatest percentage of acres affected by forest fires. This study combines field and satellite weather data to determine factors that influence dead fuel moisture content (FMC). It assesses the use of the Canadian forest fire weather index to determine the daily forest fire danger in a typical temperate forest in Northeastern China during autumn. Based on the Wilcoxon test for paired samples, the observed and predicted values of FMC showed similar variation in eight of eleven sampling sites (72.7%), with a p value > 0.05. Three sampling plots presented lower predicted values of FMC than observed values (27.3%), with a p value < 0.05. The calculation of fire risk using the Canadian Forest Fire Weather Rating System (CFFDRS) in Maoer Mountain forest ecosystems presented low, medium or high risk; thus, the CFFDRS is suitable for determining fire danger in our study region. Along with these results, this study served to compare the use of FMC-metre field data and China Weather Station data to evaluate fire danger. The results of this study led us to suggest the multiplication of meteorological stations in fire-prone regions.

Predicting potential wildfire severity across Southern Europe with global data sources.

The large environmental and socioeconomic impacts of wildfires in Southern Europe require the development of efficient generalizable tools for fire danger analysis and proactive environmental management. With this premise, we aimed to study the influence of different environmental variables on burn severity, as well as to develop accurate and generalizable models to predict burn severity. To address these objectives, we selected 23 wildfires (131,490 ha) across Southern Europe. Using satellite imagery and geospatial data available at the planetary scale, we spatialized burn severity as well as 20 pre-burn environmental variables, which were grouped into climatic, topographic, fuel load-type, fuel load-moisture and fuel continuity predictors. We sampled all variables and divided the data into three independent datasets: a training dataset, used to perform univariant regression models, random forest (RF) models by groups of variables, and RF models including all predictors (full and parsimonious models); a second dataset to analyze interpolation capacity within the training wildfires; and a third dataset to study extrapolation capacity to independent wildfires. Results showed that all environmental variables determined burn severity, which increased towards the mildest climatic conditions, sloping terrain, high fuel loads, and coniferous vegetation. In general, the highest predictive and generalization capacities were found for fuel load proxies obtained though multispectral imagery, both in the individual analysis and by groups of variables. The full and parsimonious models outperformed all, the individual models, models by groups, and formerly developed predictive models of burn severity, as they were able to explain up to 95%, 59% and 25% of variance when applied to the training, interpolation and extrapolation datasets respectively. Our study is a benchmark for progress in the prediction of fire danger, provides operational tools for the identification of areas at risk, and sets the basis for the design of pre-burn management actions.

Convergence in critical fuel moisture and fire weather thresholds associated with fire activity in the pyroregions of Mediterranean Europe.

Wildfires are becoming an increasing threat to many communities worldwide. There has been substantial progress towards understanding the proximal causes of increased fire activity in recent years at regional and national scales. However, subcontinental scale examinations of the commonalities and differences in the drivers of fire activity across different regions are rare in the Mediterranean zone of the European Union (EUMed). Here, we first develop a new classification of EUMed pyroregions, based on grouping different ecoregions with similar seasonal patterns of burned area. We then examine the thresholds associated with fire activity in response to different drivers related to fuel moisture, surface meteorology and atmospheric stability. We document an overarching role for variation in dead fuel moisture content (FMd), or its atmospheric proxy of vapor pressure deficit (VPD), as the major driver of fire activity. A proxy for live fuel moisture content (EVI), wind speed (WS) and the Continuous Haines Index (CH) played secondary, albeit important, roles. There were minor differences in the actual threshold values of FMd (10-12%), EVI (0.29-0.36) and CH (4.9-5.5) associated with the onset of fire activity across pyroregions with peak fire seasons in summer and fall, despite very marked differences in mean annual burned area and fire size range. The average size of fire events increased with the number of drivers exceeding critical thresholds and reaching increasingly extreme values of a driver led to disproportionate increases in the likelihood of a fire becoming a large fire. For instance, the percentage of fires >500 ha increased from 2% to 25% as FMd changed from the wettest to the driest quantile. Our study is among the first to jointly address the roles of fuel moisture, surface meteorology and atmospheric stability on fire activity in EUMed and provides novel insights on the interactions across fire activity triggers.

Fuel trait effects on flammability of native and invasive alien shrubs in coastal fynbos and thicket (Cape Floristic Region).

In June 2017, extreme fires along the southern Cape coast of South Africa burnt native fynbos and thicket vegetation and caused extensive damage to plantations and residential properties. Invasive alien plants (IAPs) occur commonly in the area and were thought to have changed the behaviour of these fires through their modification of fuel properties relative to that of native vegetation. This study experimentally compared various measures of flammability across groups of native and alien invasive shrub species in relation to their fuel traits. Live plant shoots of 30 species (10 species each of native fynbos, native thicket, and IAPs) were sampled to measure live fuel moisture, dry biomass, fuel bed porosity and the proportions of fine-, coarse- and dead fuels. These shoots were burnt experimentally, and flammability measured in terms of maximum temperature (combustibility), completeness of burn (consumability), and time-to-ignition (ignitability). Multiple regression models were used to assess the relationships between flammability responses and fuel traits, while the Kruskal-Wallis H test was used to establish if differences existed in flammability measures and fuel traits among the vegetation groups. Dry biomass significantly enhanced, while live fuel moisture significantly reduced, maximum temperature, whereas the proportion of fine fuels significantly increased completeness of burn. Unlike other similar studies, the proportion of dead fuels and fuel bed porosity were not retained by any of the models to account for variation in flammability. Species of fynbos and IAPs generally exhibited greater flammability in the form of higher completeness of burn and more rapid ignition than species of thicket. Little distinction in flammability and fuel traits could be made between species of fynbos and IAPs, except that fynbos species had a greater proportion of fine fuels. Thicket species had higher proportions of coarse fuels and greater dry biomass (~fuel loading) than species of fynbos and IAPs. Live fuel moisture did not differ among the vegetation groups, contrary to the literature often ascribing variation in flammability to fuel moisture differences. The fuel traits investigated only explained 21-53% of the variation in flammability and large variation was evident among species within vegetation groups suggesting that species-specific and in situ community-level investigations are warranted, particularly in regard fuel moisture and chemical contents.