Tropical woody plants store ∼230 petagrams of carbon (PgC) in their aboveground living biomass. This review suggests that these stocks are currently growing in primary forests at rates that have ...decreased in recent decades. Droughts are an important mechanism in reducing forest C uptake and stocks by decreasing photosynthesis, elevating tree mortality, increasing autotrophic respiration, and promoting wildfires. Tropical forests were a C source to the atmosphere during the 2015-2016 El Niño-related drought, with some estimates suggesting that up to 2.3 PgC were released. With continued climate change, the intensity and frequency of droughts and fires will likely increase. It is unclear at what point the impacts of severe, repeated disturbances by drought and fires could exceed tropical forests' capacity to recover. Although specific threshold conditions beyond which ecosystem properties could lead to alternative stable states are largely unknown, the growing body of scientific evidence points to such threshold conditions becoming more likely as climate and land use change across the tropics.
Droughts have reduced forest carbon uptake and stocks by elevating tree mortality, increasing autotrophic respiration, and promoting wildfires.
Threshold conditions beyond which tropical forests are pushed into alternative stable states are becoming more likely as effects of droughts intensify.
Aim
Over the past several decades, wildfires have become larger, more frequent, and/or more severe in many areas. Simultaneously, anthropogenic ignitions are steadily growing. We have little ...understanding of how increasing anthropogenic ignitions are changing modern fire regimes.
Location
Conterminous United States.
Time period
1984–2016.
Major taxa studied
Vegetation.
Methods
We aggregated fire radiative power (FRP)‐based fire intensity, event size, burned area, frequency, season length, and ignition type data from > 1.8 million government records and remote sensing data at a 50‐km resolution. We evaluated the relationship between fire physical characteristics and ignition type to determine if and how modern U.S.A. fire regimes are changing sensu stricto given increased anthropogenic ignitions, and how those patterns vary over space and time.
Results
At a national scale, wildfires occur over longer fire seasons (17% increase) and have become larger (78%) and more frequent (12%), but not necessarily more intense. Further, human ignitions have increased 9% proportionally. The proportion of human ignitions has a negative relationship with fire size and FRP and a positive relationship with fire frequency and season length. Areas dominated by lightning ignitions experience fires that are 2.4 times more intense and 9.2 times larger. Areas dominated by human ignitions experience fires that are twice as frequent and have a fire season that is 2.4 times longer. The effect of human ignitions on fire characteristics varies regionally. Ecoregions in the eastern U.S.A. and in some parts of the coastal western U.S.A. have no areas dominated by lightning ignitions. For the remaining ecoregions, more intense and larger fires are associated with lightning ignitions, and longer season lengths are associated with human ignitions.
Main conclusions
Increasing anthropogenic ignitions – in tandem with climate and land cover change – are contributing to a ‘new normal’ of fire activity across continental scales.
•We evaluate fire spread in peat in Kalimantan, Indonesia based on satellite data.•∼70% of fires start in non-forest,<20% in oil palm, and<10% near settlements.•Most fires started in oil palm and ...near settlements do not escape.•However, the density of ignitions in these land use/land cover classes is high.•Efforts to control fires should include reducing ignitions in non-forest areas.
Fire disturbance in many tropical forests, including peat swamps, has become more frequent and extensive in recent decades. These fires compromise a variety of ecosystem services, among which mitigating global climate change through carbon storage is particularly important for peat swamps. Indonesia holds the largest amount of tropical peat carbon globally, and mean annual CO2 emissions from decomposition of deforested and drained peatlands and associated fires in Southeast Asia have been estimated at ∼2000 Mt y-1. A key component to understanding and therefore managing fire in the region is identifying the land use/land cover classes associated with fire ignitions. We assess the oft-asserted claim that escaped fires from oil palm concessions and smallholder farms near settlements are the primary sources of fire in a peat-swamp forest area in Central Kalimantan, Indonesia, equivalent to around a third of Kalimantan's total peat area. We use the MODIS Active Fire product from 2000 to 2010 to evaluate the fire origin and spread on the land use/land cover classes of legal, industrial oil palm concessions (the only type of legal concession in the study area), non-forest, and forest, as well as in relation to settlement proximity. We find that most fires (68–71%) originate in non-forest, compared to oil palm concessions (17%–19%), and relatively few (6–9%) are within 5km of settlements. Moreover, most fires started within oil palm concessions and in close proximity to settlements stay within those boundaries (90% and 88%, respectively), and fires that do escape constitute only a small proportion of all fires on the landscape (2% and 1%, respectively). Similarly, a small proportion of fire detections in forest originate from oil palm concessions (2%) and within close proximity to settlements (2%). However, fire ignition density in oil palm (0.055 ignitions km−2) is comparable to that in non-forest (0.060km-2 ignitions km-2), which is approximately ten times that in forest (0.006 ignitions km−2). Ignition density within 5km of settlements is the highest at 0.125 ignitions km−2. Furthermore, increased anthropogenic activity in close proximity to oil palm concessions and settlements produces a detectable pattern of fire activity. The number of ignitions decreases exponentially with distance from concessions; the number of ignitions initially increases with distance from settlements, and, around from 7.2km, then decreases with distance from settlements. These results refute the claim that most fires originate in oil palm concessions, and that fires escaping from oil palm concessions and settlements constitute a major proportion of fires in this study region. However, there is a potential for these land use types to contribute substantially to the fire landscape if their area expands. Effective fire management in this area should therefore target not just oil palm concessions, but also non-forested, degraded areas where ignitions and fires escaping into forest are most likely to occur.
Fire is a common tool for land conversion and management associated with oil palm production. Fires can cause biodiversity and carbon losses, emit pollutants that deteriorate air quality and harm ...human health, and damage property. The Roundtable on Sustainable Palm Oil (RSPO) prohibits the use of fire on certified concessions. However, efforts to suppress fires are more difficult during El Niño conditions and on peatlands. In this paper, we address the following questions for oil palm concessions developed prior to 2012 in Sumatra and Kalimantan, the leading producers of oil palm both within Indonesia and globally: (1) for the period 2012-2015, did RSPO-certified concessions have a lower density of fire detections, fire ignitions, or 'escaped' fires compared with those concessions that are not certified? and (2) did this pattern change with increasing likelihood of fires in concessions located on peatland and in dry years? These questions are particularly critical in fuel-rich peatlands, of which approximately 46% of the area was designated as oil palm concession as of 2010. We conducted propensity scoring to balance covariate distributions between certified and non-certified concessions, and we compare the density of fires in certified and non-certified concessions using Kolmogorov-Smirnov tests based on moderate resolution imaging spectroradiometer Active Fire Detections from 2012-2015 clustered into unique fire events. We find that fire activity is significantly lower on RSPO certified concessions than non-RSPO certified concessions when the likelihood of fire is low (i.e., on non-peatlands in wetter years), but not when the likelihood of fire is high (i.e., on non-peatlands in dry years or on peatlands). Our results provide evidence that RSPO has the potential to reduce fires, though it is currently only effective when fire likelihood is relatively low. These results imply that, in order for this mechanism to reduce fire, additional strategies will be needed to control fires in oil palm plantations in dry years and on peatlands.
Night-time provides a critical window for slowing or extinguishing fires owing to the lower temperature and the lower vapour pressure deficit (VPD). However, fire danger is most often assessed based ...on daytime conditions
, capturing what promotes fire spread rather than what impedes fire. Although it is well appreciated that changing daytime weather conditions are exacerbating fire, potential changes in night-time conditions-and their associated role as fire reducers-are less understood. Here we show that night-time fire intensity has increased, which is linked to hotter and drier nights. Our findings are based on global satellite observations of daytime and night-time fire detections and corresponding hourly climate data, from which we determine landcover-specific thresholds of VPD (VPD
), below which fire detections are very rare (less than 95 per cent modelled chance). Globally, daily minimum VPD increased by 25 per cent from 1979 to 2020. Across burnable lands, the annual number of flammable night-time hours-when VPD exceeds VPD
-increased by 110 hours, allowing five additional nights when flammability never ceases. Across nearly one-fifth of burnable lands, flammable nights increased by at least one week across this period. Globally, night fires have become 7.2 per cent more intense from 2003 to 2020, measured via a satellite record. These results reinforce the lack of night-time relief that wildfire suppression teams have experienced in recent years. We expect that continued night-time warming owing to anthropogenic climate change will promote more intense, longer-lasting and larger fires.
Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network ...(NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters.
Indonesian fire events generate significant impacts on ecosystems, society, and climate regionally and globally. Following severe burning in 2015, Indonesia prioritized targeted fire prevention to ...reduce crop destruction, haze, forest degradation, and carbon emissions. We show that such efforts resulted in a qualified success. Fire activity during 2016–2019 averaged ~23% of expected levels across 627 target communities (11 Mha), waning to 70% during the severe 2019 fire season, which was delayed ~30–50 days despite relatively dry conditions. Small/medium-scale and agro-industrial landholdings targeted by fire prevention burned extensively and comparatively, yet they accounted for a relatively limited 12–22% and 18–26% of fire activity over 2013–2017 respectively upon considering fire ignition and dissemination patterns. Small/medium landholdings appeared as a net ‘fire propagator’, with up to half of associated fire activity affecting other lands. Conversely, agro-industrial lands appeared as net ‘fire receivers’, with up to half of their fire activity originating from adjacent degraded lands. Successful fire prevention represents a boon for Indonesian forest restoration and carbon-emission reduction schemes. However, more effective fire prevention must focus on degraded lands vulnerable to the agricultural incursion, from which ignition fires propagate comparably to small/medium landholdings and for which almost half of fire activity stemmed from ignitions thereon.
Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we ...integrate a 30-yr wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero-inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99% interval coverage for the number of fires and 93% coverage for fire sizes over a six year withheld data set. Dryness and air temperature strongly predict extreme wildfire probabilities. Housing density has a hump-shaped relationship with fire occurrence, with more fires occurring at intermediate housing densities. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.
Context
Addressing ecosystem degradation in the Anthropocene will require ecological restoration across large spatial extents. Identifying areas where natural regeneration will occur without direct ...resource investment will improve scalability of restoration actions.
Objectives
An ecoregion in need of large scale restoration is the Great Basin of the Western US, where increasingly large and frequent wildfires threaten ecosystem integrity and its foundational shrub species. We develop a framework to forecast where post-wildfire regeneration of sagebrush cover (
Artemisia
spp.) is likely to occur within the burnt areas across the region (> 900,000 km
2
).
Methods
First, we parameterized population models using Landsat satellite-derived time series of sagebrush cover. Second, we evaluated the out-of-sample performance by predicting natural regeneration in wildfires not used for model training. This model assessment reproduces a management-oriented scenario: making restoration decisions shortly after wildfires with minimal local information. Third, we asked how accounting for increasingly fine-scale spatial heterogeneity could improve model forecasting accuracy.
Results
Regional-level models revealed that sagebrush post-fire recovery is slow, estimating > 80-year time horizon to reach an average cover at equilibrium of 16.6% (CI95% 9–25). Accounting for wildfire and within-wildfire spatial heterogeneity improved out-of-sample forecasts, resulting in a mean absolute error of 3.5 ± 4.3% cover, compared to the regional model with an error of 7.2 ± 5.1% cover.
Conclusions
We demonstrate that combining population models and non-parametric spatial matching provides a flexible framework for forecasting plant population recovery. Models for population recovery applied to Landsat-derived time series will assist restoration decision-making, including identifying priority targets for restoration.
Context
Dynamic feedbacks between physical structure and ecological function drive ecosystem productivity, resilience, and biodiversity maintenance. Detailed maps of canopy structure enable ...comprehensive evaluations of structure–function relationships. However, these relationships are scale-dependent, and identifying relevant spatial scales to link structure to function remains challenging.
Objectives
We identified optimal scales to relate structure heterogeneity to ecological resistance, measured as the impacts of wildfire on canopy structure, and ecological resilience, measured as native shrub recruitment. We further investigated whether structural heterogeneity can aid spatial predictions of shrub recruitment.
Methods
Using high-resolution imagery from unoccupied aerial systems (UAS), we mapped structural heterogeneity across ten semi-arid landscapes, undergoing a disturbance-mediated regime shift from native shrubland to dominance by invasive annual grasses. We then applied wavelet analysis to decompose structural heterogeneity into discrete scales and related these scales to ecological metrics of resilience and resistance.
Results
We found strong indicators of scale dependence in the tested relationships. Wildfire effects were most prominent at a single scale of structural heterogeneity (2.34 m), while the abundance of shrub recruits was sensitive to structural heterogeneity at a range of scales, from 0.07 – 2.34 m. Structural heterogeneity enabled out-of-site predictions of shrub recruitment (R
2
= 0.55). The best-performing predictive model included structural heterogeneity metrics across multiple scales.
Conclusions
Our results demonstrate that identifying structure–function relationships requires analyses that explicitly account for spatial scale. As high-resolution imagery enables spatially extensive maps of canopy heterogeneity, models for scale dependence will aid our understanding of resilience mechanisms in imperiled arid ecosystems.
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