Chantelle A. Burton’s research while affiliated with Met Office and other places

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Publications (7)


Limiting controls on tree cover
a–c Shows the relative standard limitation for each control and d–f normalised sensitivity of each factor. Purple shows areas limited by mean annual environmental stresses (S), yellow by human pressure from population density and land use (L), Cyan by Mean annual Precipitation (P) and dots by Mean Annual Temperature and Shortwave Radiation (T). Red represents co-limitation by S&L, blue by S&P, and green by L&P. Shades show the relative importance of the limitation, with darker, intense shades indicating a stronger impact, lighter shaded (and none-capitalised letter in legend) less impact, and white indicating little or no limitation - by definition coinciding with high tree cover. From top-bottom maximum stress, human pressure, and MAP limitation at 10% likelihood.
The percentage reduction in tree cover area by each environmental and human stresses
Each row represents a different stress (top-bottom): a, b Fire (using burnt area), c, d heat stress, e, f windthrow, and g, h seasonal rainfall distribution. These are followed by human pressures: i, j population density, k, l urban area, m, n cropland area and o, p pasture area. For each stress or human pressure, two maps are shown: the left map represents the 10th percentile, and the right map represents the 90th percentile of the likely range of the stress’ impacts, illustrating the range of uncertainty in the estimated tree cover reduction. This figure allows for a visual comparison of both the magnitude of tree cover reduction by each stress and the confidence level (percentile range) associated with these reductions.
Pairwise comparison of the likelihood that the stress or human pressure in each column reduces tree cover more than the one in each row
Each map in the grid shows indicates the likelihood of the column stress having a greater impact on tree cover reduction than the row stress. Blue areas represent regions where the column stress is more likely to cause a higher reduction in tree cover, while brown areas represent regions where the row stress has a higher likelihood. The stress or pressure’s first two letters or initials are listed next to the relevant colour for each map. For example, the top left blue areas show where Hs (Heat stress) reduces tree cover more than Ba (Burnt area). White indicates equal likelihood, and lighter shades of blue or brown show a slight likelihood difference between the column and row stress. The colour gradients allow for a visual comparison of how different stresses or pressures are likely to impact tree cover in various locations.
The impact of burnt area on tree cover in bioclimate space
Dots indicate grid cells with (x-axis) Mean annual rainfall and (y-axis) rainfall seasonality for a, c and maximum temperature of the warmest month for b, d. a, b Colour indicated fire impact on tree extent and c, d percentage deciduous cover vs. evergreen (see methods).
The percentage reduction in tree cover area by fire
a, b Fire as per Fig. 2, c, d fire without direct human influence on tree cover and e, f fire without influence from human impact on tree cover or burnt area. g, h Shows the added impact on tree cover fire would have without humans influencing tree cover, while i, j shows fires added impact without human influence on tree cover or burnt area. Columns show 10% and 90% percentiles accounting for framework uncertainty.

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Fire, environmental and anthropogenic controls on pantropical tree cover
  • Article
  • Full-text available

November 2024

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Explaining tropical tree cover distribution in areas of intermediate rainfall is challenging, with fire’s role in limiting tree cover particularly controversial. We use a novel Bayesian approach to provide observational constraints on the strength of the influence of humans, fire, rainfall seasonality, heat stress, and wind throw on tropical tree cover. Rainfall has the largest relative impact on tree cover (11.6–39.6%), followed by direct human pressures (29.8–36.8%), heat stress (10.5–23.3%) and rainfall seasonality (6.3–22.8%). Fire has a smaller impact (0.2–3.2%) than other stresses, increasing to 0.3–5.2% when excluding human influence. However, we found a potential vulnerability of eastern Amazon and Indonesian forests to fire, with up to 2% forest loss for a 1% increase in burnt area. Our results suggest that vegetation models should focus on fire development for emerging fire regimes in tropical forests and revisit the linkages between rainfall, non-fire disturbances, land use and broad-scale vegetation distributions.

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Fig. 1 | Biomes reconstruction during the LGM (left) as modelled in Sato et al 10 . (middle) using bias correction and (right) using clustering, driven by an ensemble of LGM climate reconstructions. Dots in a represent the pollen core locations used to correct reconstructions; colours in a and b indicate the biome reconstructed using pollen spectra. Biomes are: Thf, tropical humid forest; Tdf, tropical dry forest; wtf, warm temperate forest; tef, temperate evergreen forest; tdf, temperate deciduous forest; bef, boreal evergreen forest; bdf, boreal deciduous forest; Ts, tropical savannah; sw, sclerophyll woodland; tp, temperate parkland; bp, boreal parkland; dg, dry grass/shrubland; hd, hot desert; st, shrub tundra; t, tundra. WTs
Fig. 2 | Height (top) and Fractional cover (bottom) before (left) after bias correction (right). Evergreen fraction, leaf type and growing degree days (GDD) are also bias-corrected but contribute less to changes in biome distribution (see Supplementary Figs. 2-7)
Fig. 3 | Forest and Savanna area, number of fragments and fragmentation index. Columns show uncorrected and bias-corrected simulations when considering "woodland /tall savanna" as part of the savanna ("combined with savanna") or forest
Fig. 4 | Possible images of clusters. Clusters are identified in Figs. 1c and f. Top-row artwork by Jennifer Lobo. Bottom row colour images were AI-generated. See "Vegetation assemblage imagery" in methods for generation.
Fig. 5 | The area and fragmentation for biome clustering, as per Fig. 3. Bioclimatic clusters are identified in Fig. 1.
Niche-dependent forest and savanna fragmentation in Tropical South America during the Last Glacial Maximum Check for updates

September 2024

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66 Reads

npj Biodiversity

The refugia hypothesis, often used to explain Amazonia's high biodiversity, initially received ample support but has garnered increasing criticism over time. Palynological, phylogenetic, and vegetation model reconstruction studies have been invoked to support the opposing arguments of extensive fragmentation versus a stable Amazonian Forest during Pleistocene glacial maxima. Here, we test the past existence of forest fragments and savanna connectivity by bias-correcting vegetation distributions from a Dynamic Vegetation Model (DVM) driven by paleoclimate simulations for South America during the Last Glacial Maximum (LGM). We find evidence for fragmented forests akin to refugia with extensive tropical humid forests to the west and forest islands in central/southern Amazonia. Drier ecosystems of Northern Llanos, Caatinga and Cerrado may have merged into continuous savanna/grasslands that dominated the continent. However, our reconstructions suggest taller, dense woodland/tropical savanna vegetation and areas of similar bioclimate connected disparate forest fragments across Amazonia. This ecotonal biome may have acted as a corridor for generalist forest and savanna species, creating connectivity that allows for range expansion during glacial periods. Simultaneously, it could have served as a barrier for specialists, inducing diversification through the formation of 'semi-refugia'.


FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept

August 2024

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101 Reads

As fire seasons in Brazil lengthen and intensify, the need to enhance fire simulations and comprehend fire drivers becomes crucial. Yet determining what drivers burning in different Brazilian biomes is a major challenge, with the highly uncertain relationship between drivers and fire. Finding ways to acknowledge and quantify that uncertainty is critical in ascertaining the causes of Brazil’s changing fire regimes. We propose FLAME (Fire Landscape Analysis using Maximum Entropy), a new fire model that integrates Bayesian inference with the Maximum Entropy (MaxEnt) concept, enabling probabilistic reasoning and uncertainty quantification. FLAME utilizes bioclimatic, land cover and human driving variables to model fires. We apply FLAME to Brazilian biomes, evaluating its performance against observed data for three categories of fires: all fires (ALL), fires reaching natural vegetation (NAT), and fires in non-natural vegetation (NON). We assessed burned area responses to variable groups. The model showed adequate performance for all biomes and fire categories. Maximum temperature and precipitation together are important factors influencing burned area in all biomes. The number of roads and amount of forest boundaries (edge densities), and forest, pasture and soil carbon showed higher uncertainties among the responses. The potential response of these variables displayed similar spatial likelihood of the observations given the model, between the ALL, NAT and NON categories. Overall, the uncertainties were larger for the NON-category, particularly for Pampas and Pantanal. Customizing variable selection and fire categories based on biome characteristics could contribute to a more biome-focused and contextually relevant analysis. Moreover, prioritizing regional-scale analysis is essential for decision-makers and fire management strategies. FLAME is easily adaptable to be used in various locations and periods, serving as a valuable tool for more informed and effective fire prevention measures.


State of Wildfires 2023-2024

August 2024

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491 Reads

Earth System Science Data

Climate change contributes to the increased frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regionalised research efforts. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023-February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use and forecast future risks under different climate scenarios. During the 2023-2024 fire season, 3.9 × 10 6 km 2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totalling 2.4 Pg C. Global fire C emissions were increased by record emissions in Canadian boreal forests (over 9 times the average) and reduced by low emissions from African savannahs. Notable events included record-breaking fire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawaii (100 deaths) and Chile (131 deaths). Over 232 000 people were evacuated Earth Syst. Sci. Data, 16, 3601-3685, 2024 https://doi.


State of Wildfires 2023–2024

August 2024

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412 Reads

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10 Citations

Climate change contributes to the increased frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regionalised research efforts. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use and forecast future risks under different climate scenarios. During the 2023–2024 fire season, 3.9×106 km² burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totalling 2.4 Pg C. Global fire C emissions were increased by record emissions in Canadian boreal forests (over 9 times the average) and reduced by low emissions from African savannahs. Notable events included record-breaking fire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawaii (100 deaths) and Chile (131 deaths). Over 232 000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece, a combination of high fire weather and an abundance of dry fuels increased the probability of fires, whereas burned area anomalies were weaker in regions with lower fuel loads and higher direct suppression, particularly in Canada. Fire weather prediction in Canada showed a mild anomalous signal 1 to 2 months in advance, whereas events in Greece and Amazonia had shorter predictability horizons. Attribution analyses indicated that modelled anomalies in burned area were up to 40 %, 18 %, and 50 % higher due to climate change in Canada, Greece, and western Amazonia during the 2023–2024 fire season, respectively. Meanwhile, the probability of extreme fire seasons of these magnitudes has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude to 2023 in Canada are projected to occur 6.3–10.8 times more frequently under a medium–high emission scenario (SSP370). This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society's resilience to wildfires and promote advances in preparedness, mitigation, and adaptation. New datasets presented in this work are available from 10.5281/zenodo.11400539 (Jones et al., 2024) and 10.5281/zenodo.11420742 (Kelley et al., 2024a).


State of Wildfires 2023–24

June 2024

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608 Reads

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2 Citations

Climate change is increasing the frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regional research concentration. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use, and forecast future risks under different climate scenarios. During the 2023–24 fire season, 3.9 million km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totaling 2.4 Pg C. This was driven by record emissions in Canadian boreal forests (over 9 times the average) and dampened by reduced activity in African savannahs. Notable events included record-breaking wildfire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawai’i (100 deaths) and Chile (131 deaths). Over 232,000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece a combination of high fire weather and an abundance of dry fuels increased the probability of fires by 4.5-fold and 1.9–4.1-fold, respectively, whereas fuel load and direct human suppression often modulated areas with anomalous burned area. The fire season in Canada was predictable three months in advance based on the fire weather index, whereas events in Greece and Amazonia had shorter predictability horizons. Formal attribution analyses indicated that the probability of extreme events has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude are projected to occur 2.22–9.58 times more frequently in Canada under high emission scenarios. Without mitigation, regions like Western Amazonia could see up to a 2.9-fold increase in extreme fire events. For the 2024–25 fire season, seasonal forecasts highlight moderate positive anomalies in fire weather for parts of western Canada and South America, but no clear signal for extreme anomalies is present in the forecast. This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society’s resilience to wildfires and promote advances in preparedness, mitigation, and adaptation.


Citations (1)


... Fires around the world are another important source of CO 2 in many ecosystems. In the last fire season, 2.4 Pg C was emitted to the atmosphere, which was 16% above the average due to some extreme events at different ecosystems in South and North America, Europe, and Hawaii, but is expected that at the end of this century fires of these magnitudes will be between 6 and 10 times more frequent under a moderate climate change scenario [6]. These increases are associated with weather; future drier and warmer conditions will favor wildfires, but also more available biomass fuel is projected in future through the enhancement of the primary productivity of forests in a richer CO 2 atmosphere [7]. ...

Reference:

Effects of Prescribed Burns on Soil Respiration in Semi-Arid Grasslands
State of Wildfires 2023–2024