Environments affected by fire

Recent landscape‐scale wildfires in eastern Australia have made apparent the need for a greater understanding of the flammability dynamics of forested ecosystems. Fire severity is a measure of the impact of a fire on vegetation, but little is known about the landscape‐scale response of the fire‐prone dry sclerophyll forests of eastern Australia to different levels of fire severity. Species in these forests have multiple responses to fire, which can be dependent on the fire severity. In this study, we aimed to determine the effect of fire severity on the vegetation structure, and therefore flammability, of these forests. We addressed two hypotheses that 1) High severity fire would result in a denser understory than low severity fire after 5 years and that 2) High severity fire would reduce the vertical separation between understory and canopy after 5 years. Field surveys of 38 forest sites with differing fire severity but standardised time since fire and forest type, in Sydney region of New South Wales, Australia, were used to test these hypotheses. We found lower canopy cover and greater understory cover (0.5–4 m height) after high severity fire compared with sites which burnt at low severity. Vertical separation was less between the canopy and understory at sites after high severity fire than after low severity fire. The greater quantity of understory fuel and greater vertical continuity in fuel structure observed suggests a potential increase in forest flammability after high severity fire compared with lower severity fires in these forest types.

Fire drives animal population dynamics across many ecosystems. Yet, we still lack an understanding of how most species recover from fire and the effects of fire severity and patchiness on recovery processes. This information is crucial for fire‐mediated biodiversity conservation, particularly as fire regimes change globally. We conducted an experiment to test whether post‐fire recovery is driven by in situ survival or recolonisation, and to determine whether this varies with fires of increasing percentage area burnt (burn cover) and severity. We used the pale field rat Rattus tunneyi as a model, because it represents the extinction process for a suite of mammal species suffering population collapse across Australia's northern savannas. Our treatments spanned a gradient from patchy, low severity fires (simulating early dry season management burns) to thorough, high severity fires (simulating wildfires). We performed capture–mark–recapture, vegetation and aerial surveys before, 6 weeks after and 1 year after fire. Six weeks after fire, pale field rats were only captured in unburnt patches of vegetation, and capture rates were proportional to the amount of unburnt habitat. One year later, both vegetation and pale field rat populations recovered across all sites. However, population recovery after low severity fires was likely achieved through in situ survival and reproduction in unburnt micro‐refuges, compared to recolonisation driving recovery after high severity fires. Synthesis and applications. Pale field rat persistence is strongly dependent on the retention of unburnt habitat patches within fire‐affected areas. Management strategies that increase micro‐refugia within burnt areas may facilitate pale field rat population recovery. Globally, building recovery mechanisms into fire management will be vital for supporting the long‐term persistence of fire‐affected species.

Soil organic carbon (SOC) is an important component of the ecosystem carbon pool, and fire is one of the important disturbances in forest ecosystems. With global warming, there has been a gradual increase in boreal forest fires, which has a nonnegligible impact on the SOC dynamics in forests. The CENTURY model was employed in our study to simulate the changes in SOC stocks in boreal forests of the Great Xing’an Mountains, China under different fire severity conditions. Fire severity was represented by the metric of difference normalized burn ratio (dNBR) derived from 30-m Landsat-8 imageries. Changes in forest SOC stocks following fire disturbance were predicted under four future Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). We found that the CENTURY model had good results in simulating the SOC stocks in the postfire of China’s boreal forests. Forest SOC dynamics responded differently to fire severities and the larger SOC loss was associated with increasing fire severity. Importantly, a feedback mechanism was found between climate change and SOC stocks, which reduces SOC stocks with increasing temperatures. High-severity forest fires tended to cause serious damage to the SOC pool and delay forest SOC recovery time; after such events, forest SOC stocks cannot be fully recovered to the prefire levels (6.74% loss). In addition, higher CO2 emissions and warmer temperatures significantly affected the recovery of SOC stocks after fire disturbance, resulting in larger SOC losses. Overall, we projected losses of 10.14%, 12.06%, 12.41%, and 15.70% of SOC stocks after high-severity fires in four RCP scenarios, respectively. Our findings emphasize the importance of fire disturbance and climate change on future dynamics of SOC stocks in China’s boreal forests, providing a scientific basis for future boreal forest management and fire management.

<p>Fire is an important element of the ecosystems, nevertheless, high severity fires can have negative impacts on the ecosystems as consequence of the high temperatures reaches. High temperatures normally have detrimental impacts on soil properties. The objective of this work was to determine the relationship of spectral reflectance and soil pH, electrical conductivity (EC), carbonates (CaCO<sub>3</sub>) and total carbon (TC) content after a medium to high severity wildfire occurred in Croatia using linear and nonlinear calibration models.</p><p>Soils were sampled 2 days after a medium to high severity wildfire in Zadar County, Croatia. A total of 120 soil samples (0-5 cm) were collected from three different treatments (n= 40 per treatment): control (C), mean severity (MS), high severity (HS). Soil pH, EC, CaCO<sub>3</sub> and TC content were determined using standard laboratory methods.  Soil spectral measurements were carried out using a portable spectroradiometer (20 per treatment, 60 in total). Linear statistical model - partial least squares regression (PLSR) and non-linear - artificial neural network (ANN) were generated to estimate changes in soil pH, EC, CaCO<sub>3</sub> and TC content based on the original spectral reflectance and its first derivative in form of principal components (PC). One-way ANOVA revealed pH values were significantly different in all three treatments. EC, CaCO<sub>3</sub> and TC were significantly higher in HS plots compared with the other treatments.</p><p>Different wildfire severity indicated very collinear soil spectral response, but with certain variations of reflectance intensity. Control samples showed a higher reflectance than MS and HS samples. This is attributed to the low pH and TC content. Low reflectance of MS and HS samples could be explained by their increased pH and TC values. Soil pH was the only parameter that showed a high R<sup>2</sup> and low root mean squared error (RMSE) after Savitzky Golay smoothing and the first derivation. In PLSR model, strong to very strong correlation and low RMSE were obtained. ANN model also showed a high R<sup>2</sup> and lower RMSE for all properties except pH. Both models showed satisfactory results for prediction of the studied soil properties. ANN model predicted EC, CaCO<sub>3</sub>, and TC better, while PLSR proved to be a better model for pH prediction.</p><p><strong>Key words:</strong> soil reflectance, fire severity, principal components, partial least squares regression, neural networks</p><p>Acknowledgements</p><p>This work was supported by Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO).</p>

This paper aims to evaluate the ability of VNIR proximal soil spectroscopy to determine post-fire soil chemical properties and discriminate fire severity based on soil spectra. A total of 120 topsoil samples (0–3 cm) were taken from 6 ha of unburned (control (CON)) and burned areas (moderate fire severity (MS) and high fire severity (HS)) in Mediterranean Croatia within one year after the wildfire. Partial least squares regression (PLSR) and an artificial neural network (ANN) were used to build calibration models of soil pH, electrical conductivity (EC), CaCO3, plant-available phosphorus (P2O5) and potassium (K2O), soil organic carbon (SOC), exchangeable calcium (exCa), magnesium (exMg), potassium (exK), sodium (exNa), and cation exchange capacity (CEC), based on soil reflectance data. In terms of fire severity, CON samples exhibited higher average reflectance than MS and HS samples due to their lower SOC content. The PCA results pointed to the significance of the NIR part of the spectrum for extracting the variance in reflectance data and differentiation between the CON and burned area (MS and HS). DA generated 74.2% correctly classified soil spectral samples according to the fire severity. Both PLSR and ANN calibration techniques showed sensitivity to extract information from soil features based on hyperspectral reflectance, most successfully for the prediction of SOC, P2O5, exCa, exK, and CEC. This study confirms the usefulness of soil spectroscopy for fast screening and a better understanding of soil chemical properties in post-fire periods.

Brachypodium sylvaticum, a shade-tolerant, forest dwelling, and aggressive invasive grass native to Eurasia, is a noxious weed in California, Oregon, and Washington. This species could cause ecosystem collapse by altering forest fire regimes. To examine interactions with fire, we divided two Willamette National Forest sites into eight units and randomly selected half for treatment with prescribed fire in spring 2011. We assessed the effect ofB. sylvaticumon fire (severity and intensity) as well as the effect of fire onB. sylvaticum(cover, seedling emergence, and dispersal). We found thatB. sylvaticumcover decreased fire severity but had no effect on intensity. Furthermore, fire severity influencedB. sylvaticumcover; in areas receiving low-severity fire, the grass increased from 21 ± 15.05 to 34 ± 15.81%, but in areas of high-severity fire, cover remained consistently around 0% (0 ± 0% cover in yr 1 to 0.2 5± 0.25% in yr 3). In the field, prescribed fire decreased seedling emergence by 32% compared to controls, but not in an associated greenhouse experiment. However, in the greenhouse, severely burned plots had zero emergence, compared to 0.29 ± 0.14 seedlings low-severity m−2plot. Fire severity also influenced dispersal in the field; we monitored plots with < 0.5% coverB. sylvaticuminitially; when these plots experienced low severity fire, they had greaterB. sylvaticumcover (increasing 1,200%), suggesting increased dispersal with less severe fires. High-severity dispersal plots did not experience increased cover. High severity fires have the potential to control the grass, but low-severity fires will likely increase its cover.

High severity fire and mixed conifer forest-chaparral dynamics in the southern Cascade Range, USA

High severity wildfire events are a feature of forests globally and are likely to be more prevalent with climate change. As a disturbance process, fire has the potential to change important ecological functions, such as decomposition, through its impact on biodiversity. Despite the recognised importance of decomposition in terms of fuel loads and energy flow, little is known about the post-fire effects of fire severity on decomposition by litter-dwelling macroinvertebrate detritivores. We tested the hypotheses that: 1) increasing fire severity is associated with decreased rates of leaf litter decomposition by macroinvertebrate detritivores; and 2) the abundance and biomass of macroinvertebrate detritivores decreases with increasing fire severity, while body size increases. We used a litterbag experiment at long-unburnt, ground-burnt and crown-burnt sites (n = 7 for all treatments) to test the effect of fire severity on: a) macroinvertebrate-driven break-down of litter fuel loads; and b) the size and abundance of macroinvertebrate detritivores three years after fire. Microhabitat conditions differed among fire severity classes. Macroinvertebrate exclusion reduced litter decomposition by 34.7%. Macroinvertebrate detritivores were larger and less abundant following higher severity fires, possibly as a result of fire-induced changes in habitat structure. Opposing effects of fire severity on macroinvertebrate abundance and body size resulted in both similar detritivore biomass and, most interestingly, no differences in leaf litter decomposition under different fire severities. This suggests that the diversity of macroinvertebrates enhances functional resilience of litter decomposition to fire and that litter-breakdown is not inhibited within three years following a high severity fire in this forest type and where recolonisation sources are readily available. We found no support for the hypothesis that high severity fires reduce litter decomposition and therefore increase the likelihood of future fires.

First evaluation of fire severity retrieval from PRISMA hyperspectral data

Status and trends of fire activity in southern California yellow pine and mixed conifer forests

Two drivers of global change that affect ecosystem function include wildland fire regimes characterised by frequent, severe fires and increased atmospheric nitrogen (N) deposition. However, their combined effect on the post-fire recovery of Mediterranean forests is still little known. We assessed the interactive effects of two fire severities and N addition on the rate and timing of seed germination of three woody species with different post-fire regeneration strategies in fire-prone forests: Pinus pinaster, an obligate-seeder species, and two facultative-seeder species, Pterospartum tridentatum (high-resprouting and low-seeding ability) and Halimium lasianthum (low-resprouting and high-seeding ability). Seeds were subjected to six combinations of temperature [control (no heat treatment), 60 °C (moderate fire severity) and 120 °C (high fire severity) for 5 min] and N fertilisation (without N and with addition of 4.17 g Nm−2 of solid granules of ammonium nitrate, equivalent to three times the current estimate of airborne N deposition in the study area) under laboratory conditions. We found that N fertilisation had a significant, negative effect on the rate of seed germination of the three species under study. Additionally, we detected no differences in P. pinaster germination among thermal treatments; while both P. tridentatum and H. lasianthum had significantly higher germination rates when submitted to high fire-severity conditions. Moreover, the average time of seed germination increased with N fertilisation for P. pinaster but increased after the thermal treatments for H. lasianthum. These results suggest that increased N availability under intense wildfire regimes may hinder post-fire seed germination, regardless of the species’ regeneration strategy, in fire-prone pine forests.

This study leveraged Convolutional Neural Network (CNN) models to estimate canopy height in Southeast Australian forests before and after the 2019–2020 bushfire event, using inputs from Sentinel-1, Sentinel-2, spectral indices, and terrain features and GEDI canopy height (rh99) as target variables. Our research consisted of three primary objectives: (1) an evaluation of GEDI rh99 canopy height in relation to an nDSM derived from airborne LiDAR, (2) the development of two CNN models for pre- and post-fire scenarios, and (3) utilizing the trained CNN models to generate pre- and post-fire canopy height maps and canopy height change maps across the continuous landscape. GEDI rh99 accuracy analysis revealed R 2 = 0.85, RMSE = 7.25 m and nRMSE = 0.23 for pre-fire GEDI and R 2 = 0.78, RMSE = 10.1 m and nRMSE = 0.3 for post-fire GEDI. The pre-fire CNN model achieved R 2 = 0.75, RMSE = 7.29 m and nRMSE = 0.22, and post-fire CNN model achieved R 2 = 0.62, RMSE = 11.67 m, and nRMSE = 0.34 when validated against nDSM. Bias analysis was conducted, revealing that GEDI rh99 underestimated nDSM across all canopy height ranges, and slope has no significant impact on GEDI accuracy. Notably, post-fire GEDI rh99 exhibited significant underestimation in high to extreme fire severity. The CNN models’ prediction displayed overestimation in short forests < 20 m and increasing underestimation in taller forests > 20 m and underestimation in high and extreme fire severity. Subsequent investigation indicated that canopy layer thinning in high fire severity conditions resulted in weak GEDI waveform signals and consequent bias. Additionally, the GEDI rh99 bias was propagated to the CNN models, together with insensitivity of spectral band values to canopy height in dense and burnt forests potentially contributed to models’ bias. Finally, we demonstrated CNN models’ ability to monitor forest recovery by generating canopy height maps spanning East Gippsland from 2019 to 2023.

I investigated the response to fire of Arbutus arizonica, an important but little-studied tree species of fire-prone Madrean pine-oak communities of the southwestern United States and northern Mexico. Arbutus arizonica exhibited a marked ability to tolerate both moderate-severity and high-severity fire. Although all individuals were top-killed by high-severity fire, the shoots of nearly half of the trees survived moderate-severity fire, and most top-killed trees in both types of fire resprouted. Tree response to fire appeared to be governed by fire severity and tree size. Top-survival and epicormic resprouting after top-kill were both higher after moderate-severity than high-severity fire and increased with tree size. Levels of root crown resprouting, however, exhibited the opposite pattern. The results of this study suggest that A. arizonica has a capacity, similar to that found for sympatric Quercus species, to persist in a fire-prone environment. Resumen Investigue la reaccion al fuego de Arbu...

Context Obligate-seeding woody plants with long reproductive maturity periods and no soil seed banks are threatened with decline as climate change drives more frequent and severe fires, such as the extensive 2019–2020 wildfires in south-eastern Australia. Aims This study aimed to investigate the effects of fire intervals and severity on the persistence of one such species, Banksia cunninghamii (Hairpin Banksia), in temperate forests. Methods We measured post-fire seedling recruitment of B. cunninghamii at 25 sites in Victoria, burned at various severities in the 2019–2020 wildfires and with differing prior fire intervals. A Bayesian framework was used to model the relationship between seedling numbers, fire severity and fire interval. A spatial analysis compared a species distribution model for B. cunninghamii with fire severity and fire intervals. Key results There was a low chance of B. cunninghamii recruitment (&lt;25%) at sites that either had burned eucalypt canopies or a preceding fire interval of less than 12 years. Sixty-seven percent of its distribution in the south-east of the state of Victoria was mapped as burned at high severity (burned eucalypt canopies) between 1998 and 2020, or burned at shorter than 12 year intervals between 1960 and 2020, although some B. cunninghamii populations will have persisted due to the patchiness of past burns. Conclusions Banksia cunninghamii is vulnerable to local extinctions in the wildfire-affected areas if fires occur again before plants reach maturity, or if high fire severity destroys seeds. Implications More frequent and severe wildfires mean that burn planning needs to consider the reproductive cycles of serotinous obligate-seeding plants.

Sustainable forest management that employs the emulation of natural disturbance paradigm develops plans based on disturbance-driven forest succession. However, most research on forest succession has focused primarily on postdisturbance species change, often ignoring predisturbance legacies. We used the FORECAST ecosystem management model and a newly created natural regeneration submodel to examine the interaction of fire severity, fire frequency, and natural regeneration to produce multicycle dynamics of white spruce (Picea glauca (Moench) Voss), lodgepole pine (Pinus contorta Dougl. ex Loud.), and trembling aspen (Populus tremuloides Michx.) in a western Canadian boreal forest. We simulated 19 different scenarios in a factorial disturbance experiment of three severities and six frequencies and a no-disturbance scenario. Our simulations resulted in a wide diversity of boreal forest community types, including trembling aspen dominated mixedwoods with high-frequency (75 year), high-severity (100% mortality) fires, and nearly pure lodgepole pine stands at midfrequency (100–125 year) and high-severity fires. With the unvarying disturbance regimes we used, a variety of different but recurring temporal patterns emerged. We show that the loss of seed source legacies reinforces the disturbance-driven species dominance, demonstrating community inertia. This study provides a long-term perspective to boreal forest management that demonstrates the role of disturbance and legacies in long-term dynamics.