Humans have substantially extended fire seasons in all biomes on Earth

Humans have substantially extended fire seasons in all biomes on Earth | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Humans have substantially extended fire seasons in all biomes on Earth Todd Ellis, David Bowman, Grant Williamson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5606101/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Sep, 2025 Read the published version in Nature Ecology & Evolution → Version 1 posted You are reading this latest preprint version Abstract The conjunction of lightning ignitions and dry vegetation has shaped fire regimes throughout geological time. These natural fire regimes have in turn influenced the adaptation of biotas globally. Anthropogenic fire use, however, radically transformed worldwide fire regimes by extending seasons to the limits bounded by periods of high fuel moisture. Conversely, active fire suppression has typically limited the occurrence and extent of lightning ignited fires – particularly where population density is higher. Disaggregating contemporary human- and lightning-caused seasonal fire patterns globally can shed light on the magnitude of the anthropogenic change to fire regimes among biomes. Using global datasets of flammability moisture thresholds and fuel moisture we define the global distribution of fire seasons. We then use a global record of lightning strike density to disaggregate the fire season into mixed (i.e., lightning present) and anthropogenic ignitions periods, before describing the respective importance of these periods amongst biomes. We assess these the breadth of these ignitions periods against a daily satellite burned area record (2001–2023) and contextualise the occurrence of areas burned within mixed and anthropogenic ignitions periods against vegetation productivity and human population density using the established productivity-fire activity relationship. Collectively, we show that current anthropogenic influences have transformed worldwide fire regimes by substantially lengthening fire seasons, irrespective of local land use and fire suppression practices, population density, lightning occurrence, and biome type. Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts/Environmental health Earth and environmental sciences/Ecology/Fire ecology Earth and environmental sciences/Ecology/Climate-change ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Fire seasons represent a chief organising principle in pyrogeography and fire management, with the onset, peak, and cessation of these periods defining the temporal boundaries within which ecoregions are most prone to wildfire occurrence (Flannigan et al., 2013 ; 2016 ; Jolly et al., 2015 ; Westerling, 2016 ). Fire seasons are primarily shaped by climate and human activity (Jones et al., 2022 ), with bioclimatic seasonality controlled by fuel availability, moisture content (Ellis et al., 2022 , 2024 ), and naturally occurring ignitions (e.g., lightning; Bradstock, 2010 ). Biotas have both adapted and contributed to the evolution of such bioclimatically determined fire regimes through geological time (Bowman et al., 2009; 2011 ). The global expansion of the human footprint through the Quaternary Period, however, has resulted in the encroachment of the 400-million-year-old naturally occurring bioclimatic fire regime in favour of anthropogenically managed fire (Bowman et al., 2011 ; 2020 ; Glikson 2013 ; Le Page et al., 2010 ). Pre-historical fire records indicate that the development and spread of Indigenous fire management in the late-Pleistocene and Holocene have been shown to have both positive and negative environmental effects (Adeleye et al., 2021 ; McWethy et al., 2013 ; 2016 ; Mooney et al., 2011 ), with the strongest impact in naturally ignition-limited systems such as New Zealand (Perry et al., 2012 ). Anthropogenic climate change is causing an ongoing expansion of many fire season boundaries (Abatzoglou et al., 2016 ; Jolly et al., 2015 ). The expansion of the fire season boundaries increases the potential for uncontrollable wildfire by drying out essential moisture reserves in the soil column (Abram et al., 2021 ). Additionally, this expansion increases the temporal range of extreme fire weather (Abatzoglou et al., 2019 ; Flannigan et al., 2013 ) and associated events such as pyrocumulonimbi storms (Di Virgilio et al., 2019 ; Sharples et al., 2016 ), as well as the expansion of the fire activity across the nighttime barrier – a period of lower temperatures and higher humidity that has typically served as a bulwark against large, multi-day wildfires (Balch et al., 2022 ; Luo et al., 2024 ). The lengthening seasonality and peak period of extreme fire weather has caused higher socio-economic and ecological costs in recent decades (Boer et al., 2017 ; Bowman et al., 2017 ; Duane et al., 2021 ; Ellis et al., 2022 ; Jain et al., 2022 ; Jolly et al., 2015 ), a trend that is expected to continue throughout the 21st century (Abatzoglou et al., 2019 ; Abatzoglou et al., 2021 ; Flannigan et al., 2013 ; Wotton et al., 2017 ). Such fire disasters serve as an indicator that these shifts in fire seasonality are already having deleterious impacts on both human and natural systems (Boer et al., 2020 ; Collins et al., 2021 ; Higuera and Abatzoglou, 2020 ; Nolan et al., 2020 ; van Oldenborgh et al., 2021 ). Although human ignitions vastly exceed lightning ignitions, lightning remains the leading cause of total burned area attributed to wildfire in landscapes where it co-occurs with the warmest and driest months (Abatzoglou et al., 2016 ; Jones et al., 2022 ; Veraverbeke et al., 2017 ). This notably includes, for example, higher-latitude boreal forests where human population density is low and, consequently, management practices such as suppression or controlled burning are most costly (Abatzoglou et al., 2016 ; Balch et al., 2017 ; Jones et al., 2022 ; 2024 ; Pausas and Bond, 2020 ; Veraverbeke et al., 2017 ). Contemporary fire management attempts to suppress all lightning and anthropogenic wildfires within the peak of the fire season when fuels are maximally dry and fires likely to be uncontrollable (Bowman et al., 2011 ; Kelley et al., 2019 ). By contrast, managers of flammable ecosystems target the shoulders of the bioclimatic fire season with controlled burning, which extends the recorded fire season into the cooler, more moist months up until the point fires are unable to propagate (Balch et al., 2017 ; Calef et al., 2017 ; Pereira et al., 2022 ). Illuminating the natural occurrence of fires in ecoregions and biomes under ubiquitous anthropogenic influence – whether directly (management) or indirectly (climate change) – is of both practical and fundamental importance. Under any future scenario, lightning remains a largely uncontrollable foundation of fire regimes. For many ecoregions, a lengthening of the period when fuels are available to burn further enables major lightning ignitions to expand beyond the boundaries of the naturally expected fire season (e.g., Calef et al., 2017 ) while also lengthening the potential period for human-caused ignitions. The drying of fuels also carries the potential to increase lightning ignition efficiency during these periods, further increasing the potential for large or uncontrollable lightning-ignited fires (Hessilt et al., 2022 ; Jain et al., 2024 ; Rao et al., 2023 ). Because the coincidence of lightning with the bioclimatic fire season and periods of high fuel availability is a major driver of burned area, it is important to know how this coincidence varies across different ecoregions. The disaggregation of lightning-caused from current anthropogenic seasonal fire patterns, however, has received limited attention or concentrated on regional studies (e.g., Balch et al., 2017 ; Coogan et al., 2020 ; Styger et al., 2018 ). Gauging the magnitude of the difference between where fire seasons co-occur with lightning activity facilitates understanding how and where humans may have altered natural fire regimes. Shifts in the timing of fires can affect plant and animal phenological cycles, and the recognition that fire regimes are strongly shaped by humans reframes discussion around what a ‘natural’ fire regime is – a concept central to carbon accounting of wildfire emissions (Bowman et al., 2023 ). The demarcation of the fire season as driven by exclusively human activity or the convergence of human and lightning activity also provides a useful baseline for managers to track the transformative effects of climate change on fire regimes. Such changes affect the time window for fuel management by, for example, cultural burning or the need to schedule firefighting resources to suppress uncontrolled fires (see Fig. 1). To accomplish this disaggregation of the period of mixed anthropogenic and lightning ignitions from the period of purely anthropogenic ignitions, we use a global dataset of ecoregional fuel moisture flammability thresholds representing over 700 ecoregions hierarchically grouped by biome and biogeographic realm (Ellis et al., 2024 ; Olson et al., 2001 ). We define the fire season as the period within which a given ecoregion may experience wildfire, with uncontrollable fire likely in the driest period of the fire season. To differentiate the naturally occurring from the purely anthropogenic ignitions periods, we intersected our identified fire seasons with global lightning density data. The overlapping period between our moisture-derived fire season and the highest density of lightning activity subsequently represents the mixed ignitions period where both lightning and anthropogenic ignitions are possible, while any remainder reflects the anthropogenic ignitions period within which fires can propagate only under deliberate human promotion. We compared the lengths of mixed and anthropogenic ignitions periods to the fire season itself to identify those regions where anthropogenic or lightning ignitions may be the primary drivers of fire seasonality, as well as highlight the transformative effects humans have had and continue to have in constraining global fire seasonality. We contextualise the role humans have had in shaping fire seasonality by exploring the associations between ignitions periods using global data on vegetation productivity, population density, and satellite burned area records. 2 Materials and methods 2.1 Geospatial classification Throughout our analyses, we used the well-established global classificatory system of terrestrial ecoregions (Olson et al., 2001 ; supplementary information). Each individual ecoregion is also associated with one of 14 unique terrestrial biome types representing comparable habitats across the globe regardless of location (e.g., temperate coniferous forests). Additionally, these identified biome types represent the potential habitat type rather than true habitat, and thus do not consider the effects of human land-use patterns on shaping the true habitat. At the coarsest spatial scale, each ecoregion falls within one of eight biogeographic realms which, in turn, are representative of continents or equivalent landmasses. The Nearctic realm, for example, primarily defines the North American continent and contains 117 distinct ecoregions representing 10 of the 14 potential biome types. 2.2 Defining fire seasons We used the ecoregional flammability thresholds identified in Ellis et al. ( 2024 ) as important meteorological switches constraining local fire ignition and spread (Bradstock, 2010 ) for 772 distinct ecoregions worldwide. These thresholds were identified using the dead fine fuel moisture (DFFMC) component (Van Wagner, 1987 ) of the Global Fire Weather Index dataset (1979–2019: Jain et al., 2022 ; McElhinny et al., 2020) as drawn from ERA5 reanalysis records (Hersbach et al., 2020 ) and featuring a 0.25° spatial resolution. For each ecoregion with an associated flammability threshold, we defined bioclimatic fire seasons dependent on the distribution of reanalysis-derived daily DFFMC distributions and their association with fire via the flammability threshold. To accomplish this, we first simplified grid cell-level DFFMC records to get a 366-day record representing the count of each day of the year (i.e., 1-366) where the DFFMC fell below the relevant ecoregional flammability threshold over the latest climate normal period (1991–2020). We reduced the noise in these distributions by applying a seven-day circular convolution filter, which applies a rolling average that treats the first and last values (i.e., January 1st and December 31st ) as sequential. We then modally centred this timeseries to account for spatial differences in seasonality (e.g., peak dryness potentially occurring in December and January). Finally, we identified the fire season for each grid cell by extracting the highest density continuous interval containing 95% of the days (centred around peak dryness) falling below the ecoregional flammability threshold between 1991 and 2020. Note that this can obscure some nuance in cells with multimodal seasonal dryness. Although leap years (i.e., the 366th day of the year) were included in the distribution, they were not identified as fire season initiation or cessation dates, and thus the final seasonal definitions did not need additional recoding to a 365-day scale. While we identified fire seasons for every grid cell with the potential for one, we used global land cover classification data (Hansen et al., 2000 ) to exclude all bare earth cells from final analyses and reporting. Altogether, the bioclimatic fire season as defined above reflects a potential fire season as constrained by fuel moisture. For these same ecoregions, we also defined a naturally occurring ignitions period using daily lightning occurrence data from the World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC: Kaplan & Lau, 2021 ). These data represent a global gridded timeseries record of lightning stroke density with a 30-arcminute spatial resolution and a daily temporal resolution between 2010 and 2023 as at the date of analysis. First, we used bilinear resampling to match ERA5’s 0.25° spatial resolution and transformed the values of density to stroke counts using grid cell area. To identify lightning seasonality, we applied the same approach (modal centring; 95% highest density continuous interval) used to identify the fuel moisture-based fire season. To provide context for the importance of lightning in driving ignitions in each region – particularly if a region has little lightning activity that occurs sporadically year-round (e.g., New Zealand) – we calculated the mean daily lightning occurrence during the week-long period around the peak (i.e., mode) of the lightning season. While this period of lightning activity can occur at any time throughout a year, it only represents the naturally occurring fire season where it co-occurs with our moisture-derived fire season (Fig. 1 ). Because anthropogenic ignitions can occur concurrently with lightning ignitions, we label this period as the ‘mixed ignitions period.’ Any period of our defined fire seasons outside of the mixed ignition period represents the ‘anthropogenic ignitions period’ where ignitions are exclusively anthropogenic (Fig. 1 ). 2.3 Seasonal convergence To assess the validity of our moisture-derived fire seasons, we first compared the lengths of our mixed and anthropogenic ignitions periods at the grid cell level, as well as summarised by biome type and weighted by total represented area. Additionally, we used the full available MODIS MCD64A1 Burned Area Product (2001–2023: Giglio et al., 2021 ) to calculate the percent of burned area that occur within the mixed or anthropogenic ignitions period at the grid cell level. Note that the MODIS burned area record includes uncertainty around the recorded day of burn, which can impact our fire season or ignitions period delineation if that uncertainty overlaps with initiation or cessation dates. These assessments are useful for identifying whether regional fire patterns are primarily constrained by the potential for either mixed or anthropogenic ignitions, as well as understanding where fire seasonality has been substantially altered due to anthropogenic factors (e.g., African savannas: Bond and Zaloumis, 2016 ). Finally, we visualised the relationship between net primary productivity, population density, and percent burned area captured by the mixed or anthropogenic ignitions period. To do this, we used interpolated 2020 population density data with a 30-arcsecond spatial resolution bilinearly resampled to ERA5’s 0.25° from the Gridded Population of the World dataset (Center for International Earth Science Information Network, 2018 ). For primary productivity, we used the resampled mean annual MODIS MOD17A3 product derived from the 2001–2023 record (Running et al., 2015 ) as a measure of each ecoregion’s overall vegetation productivity. It is within this context – where these complex variables meet – that we evaluate the role humans have in both directly and indirectly shaping global fire seasonality. See supplementary information for a full summary of all data sources and represented time periods. 3 Results Based on our climatological analyses, there are strongly contrasting patterns in fire season length among biomes and ecoregions, with potential fire seasons and ignitions periods identified for 751 distinct ecoregions over 197,256 grid cells and representing 10.9 billion hectares after excluding bare earth (Figs. 2 a-c, 3 a-b; Table 1 ). Weighted by the grid cell area, the mean fire season length for our global dataset was 164.4 days (SD ± 80; Fig. 2 a), while the mean lightning season length was 185.7 days (± 90.1; Fig. 2 d-e) with an average of 8.3 (± 11.3) daily lightning strikes during the peak week of lightning activity per grid cell. The mean length of the mixed ignitions period (lightning present) was 108.9 days (± 75.1; Fig. 3 a), while the mean length of the anthropogenic ignitions period (lightning absent) was 55.5 days (± 53.8; Fig. 3 b). Summarised at the biome level, overall fire seasons ranged between 82.1 days (± 26.1) for 33 tundra ecoregions representing 0.7 G ha and 200.6 days (± 99.8) for 206 tropical moist forest ecoregions representing 1.9 G ha. The lengths of mixed ignitions periods ranged between 40.9 days (± 31.5) for the tundra ecoregions, and 152 days (± 64.4) for 82 temperate broadleaf forest ecoregions representing 1.3 G ha. On the other hand, the lengths of anthropogenic ignitions periods ranged between 24.6days (± 44.4) for 39 Mediterranean forest ecoregions, and 97.1 days (± 65.1) for 45 tropical grassland ecoregions representing 1.9 G ha. The development of this anthropogenic ignitions period reflects an inherent increase in the length of the total fire season (Fig. 4 ). Total burned area represented by each ignitions period is globally dominant within these anthropogenic ignitions period (46.3%) as compared to the mixed ignitions period (23.7%). This global average, however, is driven by tropical savanna regions that burn annually and does not reflect the underlying variation across space. For example, many temperate coniferous and boreal forest ecoregions primarily burn within the mixed ignitions period when lightning is present (Figs. 2 d, 3 c), although lightning activity is low within this region (Fig. 2 e; Table 1 ). See supplementary information for complete realm- and biome-level descriptive statistics that reflect the complex spatial variation in fire seasonality and ignition periods. In sum, humans have expanded the fire season in nearly all ecoregions on Earth by introducing the potential for ignitions within an exclusively anthropogenic ignitions period (Table 1 ; Figs. 3 b,d, 4 ). Table 1 Summary statistics for global and biome-level fire season and ignition period definitions. Bare earth environments are excluded. Both mean and standard deviation are weighted by the total represented area. The percent of burned area represented reflect total burned area in the complete annual MODIS burned area product records (2001–2023: Giglio et al., 2018), meaning regions that frequently burn such as tropical grasslands are over-represented in regional averaging. Note that the presented fire season will not inherently capture all burned area (e.g., out-of-season fuel treatments), and thus the burned area captured within ignitions periods does not sum to 100%. Totals Mean (± std. dev.) % Season length Ignitions period length Burnt area represented by ignitions period Biome Ecoregions Grid cells Area (G ha) Fire Lightning Peak daily lightning strikes Mixed Anthropogenic Mixed Anthropogenic All biomes 751 197,256 10.9 164.4 (± 80) 185.7 (± 90.1) 8.3 (± 11.3) 108.9 (± 75.1) 55.5 (± 53.8) 23.7% 46.3% Boreal forests 28 39,086 1.5 111.7 (± 24.6) 92.6 (± 25.1) 1.4 (± 1.3) 84.1 (± 21.1) 27.6 (± 18.9) 64.9% 13.8% Deserts 88 17,196 1.1 188.2 (± 66.9) 203.9 (± 66.9) 6.7 (± 8.9) 135 (± 60) 53.2 (± 56.2) 43.4% 30.3% Flooded grasslands 24 1,439 0.1 163.9 (± 68.6) 202.3 (± 68.1) 8.6 (± 8) 74.9 (± 71.8) 89 (± 57.6) 15.1% 51.6% Mediterranean forests 39 4,651 0.3 169.8 (± 65) 250.5 (± 66.3) 4.3 (± 4.5) 145.2 (± 52.3) 24.6 (± 44.4) 59.5% 11.0% Montane grasslands 48 6,042 0.4 174.1 (± 90.3) 158.1 (± 63.8) 4.8 (± 7) 94 (± 59.7) 80.1 (± 67.2) 13.8% 74.9% Temp. broadleaf forests 82 23,038 1.3 197.4 (± 66.4) 183 (± 65.4) 6.2 (± 7.8) 152 (± 49.7) 45.4 (± 43.7) 49.8% 20.7% Temp. coniferous forests 53 7,261 0.4 168 (± 83.2) 167.9 (± 72.8) 7.7 (± 12.4) 124.9 (± 64.4) 43.1 (± 45.1) 57.9% 19.1% Temp. grasslands 40 17,699 1.0 181.2 (± 54) 163.9 (± 64.3) 7.5 (± 9.4) 143.4 (± 52.7) 37.8 (± 27.9) 55.9% 15.5% Trop. coniferous forests 16 993 0.1 129.6 (± 67.4) 239.3 (± 62.1) 30.6 (± 20.8) 81.6 (± 61.2) 48 (± 45.5) 51.4% 34.5% Trop. dry forests 49 3,964 0.3 137.3 (± 75) 252.5 (± 70.8) 18.4 (± 19.2) 73.2 (± 60.1) 64.1 (± 58.3) 28.7% 49.5% Trop. grasslands 45 24,956 1.9 156.2 (± 79.4) 206.3 (± 71) 10.6 (± 8.9) 59.1 (± 72.6) 97.1 (± 65.1) 13.7% 51.4% Trop. moist forests 206 25,990 1.9 200.6 (± 99.8) 277.2 (± 62.4) 16.1 (± 14.7) 142.6 (± 97.3) 58 (± 53.7) 42.9% 40.7% Tundra 33 24,941 0.7 82.1 (± 26.1) 44.7 (± 35.7) 0.3 (± 0.3) 40.9 (± 31.5) 41.1 (± 26.2) 65.7% 23.2% 3.1 Fire seasonality as shaped by productivity and human population density Human population density and productivity simultaneously act as major constraints on fire seasonality (Fig. 5; Bowman et al., 2011 ; 2014 ; Pausas and Ribeiro, 2013 ). Our global analysis shows that higher human population is closely associated with more area burned exclusive to the anthropogenic ignitions period (Fig. 5b). Ecoregions with intermediate productivity and human population are mixed in the concentration of burned area within either ignitions period. In ecoregions with low (deserts, tundra) to moderate (boreal and coniferous forests) productivity and lower human population density, most area burned occurs during the mixed ignitions period, while those regions at the extremes of low productivity and high human populations have burned area limited primarily within the anthropogenic ignitions period (Fig. 5b). D Figure 5 Relationship between net primary productivity, population density, burned area, and ignitions period. Fire activity in a) is represented by the total burned area represented by underlying ecoregions and seasons, while b) show the mean percent of total burned area represented during those seasons. Letters represent the mean burned area as derived from the MODIS burned area product for underlying biome types. Bare earth is excluded from the represented points. 4 Discussion It is well understood that humans have increasingly transformed and controlled the global footprint of fire through the Quaternary (~ 2mya; Glikson et al., 2013) but disaggregating the natural from the purely anthropogenic ignitions period has received little prior attention (Coogan et al., 2020 ; Parisien et al., 2023 ). Biotas that evolved with lightning ignitions have shaped fire regimes over geological time, but anthropogenic influences have altered these natural fire regimes (Bowman et al., 2011 ), with the grasslands of New Zealand (Perry et al., 2012 ) and the savannas of both Africa (Bond and Zaloumis, 2016 ) and northern Australia (Bird et al., 2024 ) being prime examples. Our analyses illustrate the profound human influence in shaping fire regimes in nearly every ecoregion on Earth, a scale previously unconsidered. We use a hierarchical approach to identify fire seasons, reflecting the season within which a given ecoregion or grid cell may experience dry meteorological conditions conducive to wildfire ignitions. We then disaggregate our identified fire seasons into mixed (co-occurring with lightning) and anthropogenic ignitions periods (Figs. 1 – 3 ). We assessed this approach using global satellite burned area records (2001–2023: Figs. 3 c-d). This assessment further demonstrates our global approach meaningfully captures ecoregional variation in fire seasonality corresponding to recognised gradients in productivity and human population (Fig. 5; Bowman et al., 2011 ; 2014 ; Pausas and Ribeiro, 2013 ). As discussed below, this disaggregation of mixed and purely anthropogenic ignitions periods is an essential step in understanding how humans have shaped the fire activity across all landscapes on Earth, pushing the fire season to the limits of or beyond natural lightning occurrence (Figs. 2 d-e, 3 a-b, 4 , 5b). This framework is important to understand the likely effects of fire management and climate change in shaping future fire seasonality. 4.1 Human expansion of the fire season We have demonstrated a near ubiquitous expansion of fire activity beyond the conjunction of fuel dryness and lightning that defines the mixed ignitions period (Fig. 4 ). In geologic time, the mixed ignitions period would correspond to a naturally occurring fire season, meaning that the length of the moisture-derived fire season today is, on average, 55 days (or one-third) longer as it would otherwise be without human activity providing a year-round ignition source (Table 1 ; Fig. 4 ). The seasonal asynchrony that often occurs between human and lightning ignitions inherently creates an exclusively anthropogenic ignitions period across most of the Earth’s ecoregions (Figs. 3 b, 4 ). This anthropogenic effect is shaped by gradients of population density and productivity (Figs. 5a-b). The large extent of burned area outside the mixed ignitions period reflects high densities of year-round human ignitions, particularly in more populated regions (Fig. 5b) or ecoregions with ancient anthropogenic ignitions patterns (e.g., African savannas where lightning co-occurs with peak moisture availability: Bond and Zaloumis, 2016 ; Jones et al., 2022 ). This can include a mix of planned and unplanned ignitions, although it should be noted that planned burning can still occur outside of these defined fire seasons to reduce the future fire potential within the fire season (Table 1 ). Within the productivity gradient (Bowman et al., 2014 ; Krawchuk & Moritz, 2011 ; Pauas & Ribeiro, 2013), human influence is smallest in lower productivity ecoregions where human populations are sparse (e.g., tundra). The anthropogenic effect is greatest in higher productivity ecoregions (e.g., tropical grasslands and rainforests; Figs. 3 d, 4 ), reflecting the use of fire in land-use (agriculture and forestry) and landcover conversion (tropical deforestation: Le Page et al., 2010 ). Within the intermediate productivity zone (~ 1.2 to 5.4 t C ha − 1 year − 1 : Ellis et al., 2024 ), the anthropogenic effect is more complex, reflecting a mix of population densities, land-use patterns, and landscape flammability (e.g., boreal forests as compared to tropical savannas; Figs. 4 –5). The introduction of anthropogenic ignitions has introduced an evolutionary filter for all biotas resulting in a reassortment of species distributions and ecosystems in many ecoregions (Pausas & Keeley, 2009 ). For instance, in temperate forests and shrublands where humans have substantially increased fire frequency and fire season lengths (e.g., Table 1 ), fire sensitive species have been driven into fire refugia (Meddens et al., 2018 ). Additionally, humans use fire in productive ecoregions to drastically change or maintain landcover. For instance, the creation of the anthropogenic ignitions period has contributed to the conversion tropical forest to pasture and croplands (Le Page et al., 2010 ; Fig. 5b) and is visible in our disaggregation of burned area by ignitions period within ecological transition lines on the South American and African continents (Figs. 3 c-d; supplementary information). 4.2 Fire suppression and the distortion of the natural fire season In contrast to the anthropogenic ignitions that expand fire seasons, humans also suppress fire activity, particularly during periods of peak fuel dryness that often characterise the mixed ignitions period (Bowman et al., 2011 ; Fig. 1 ). Fire suppression has transformed the fire ecology of many forested and temperate ecoregions over the last century by excluding frequent low- to moderate-intensity wildfires (Kreider et al., 2024 ). This transformation has resulted in increased forest biomass and changes to species compositions, making forests more prone to extreme wildfires (Tubbesing et al., 2020 ). The application of prescribed or cultural burning practices or other fuel treatments applied at the shoulders of the moisture-defined fire season has the potential to restore ecologically resilient fire regimes (Mariani et al., 2022 ). This can stave off uncontrollable lightning-ignited wildfires that are a perverse ultimate outcome of fire suppression. However, inherent institutional and cultural barriers to treatments such as prescribed or cultural burning make this approach difficult to implement at scale successfully and consistently (e.g., Prichard et al., 2021 ; Schultz et al., 2019 ). 4.3 Climate change The impacts of climatic change on burned area manifest differently amongst ecoregions (Jones et al., 2022 ), but there is a clear global trend towards drying burnable fuels (Ellis et al., 2022 ). This trend carries with it the potential to further lengthen both mixed and anthropogenic ignitions periods based on local lightning activity. Longer fire seasons are likely to be exploited by humans, potentially leading to year-round fire activity in an increasing number of ecoregions. This has profound implications for fire management, particularly sharing of firefighting resources between ecoregions (Abatzoglou et al., 2021 ; Ribeiro et al., 2024 ). The trajectory of lightning ignitions under climate change is more uncertain. Fire seasons may expand into periods of lightning activity where fuels were previously too moist to burn (e.g., Fill et al., 2019 ). Conversely, lightning seasonality and lightning storm intensity may change, thereby increasing the potential for lightning ignitions (Romps et al., 2014 ; Styger et al., 2018 ). The unpredictability of lightning ignitions is particularly challenging to forecast and manage. Nonetheless, there is evidence of increasing lightning ignitions from a range of ecoregions globally (e.g., Veraverbeke et al., 2017 ). Ecoregions with fire seasons driven exclusively by lightning activity are rare since global industrialisation and are primarily limited to high-latitude tundra or boreal and broadleaf forests with minimal human population density and where fire management is most costly (e.g., Figs. 3 c-d, 4 ; ; Balch et al., 2017 ; Calef et al., 2017 ; Pereira et al., 2022 ; supplementary information). Many boreal forest and tundra ecoregions, however, are at the cusp of ecological transformation driven by an increase in the frequency, size, and severity of wildfires due to climate change affecting both fuel dryness and lightning ignitions (Abbott et al., 2020; Hanes et al., 2019 ; Jones et al., 2015 ; Talucci et al., 2022 ). These projected increases could amplify climate change because of the heightened likelihood of fire disturbances releasing greenhouse gases (particularly CO 2 and CH 4 ) from organic soils and proliferating thermokarst landscapes (Hu et al., 2015 ). With drying future fuels, there is an implicit increase in fire potential in both the anthropogenic and the mixed ignitions periods. This is evident due to the incomplete overlap of the lightning season with the mixed ignitions period in these ecoregions (Table 1 ; supplementary information). The pace and scale of ecological transformation in boreal forest and tundra ecoregions critically depends on ecological effects that shape landscape flammability such as the abundance of deciduous species (Mack et al., 2021 ; Whitman et al., 2019 ) and the future conjunction of lightning and fuel dryness (Hessilt et al., 2022 ; Jones et al., 2024 ; Rao et al., 2023 ). The increase in lightning occurrence and ignitions within the mixed ignitions period is happening or is expected to happen across ecoregions more broadly (Calef et al., 2017 ; Jones et al., 2024 ; Mariani et al., 2018; Romps et al., 2014 ). For example, in the temperate broadleaf forests of the Australian island state of Tasmania, the frequency of dry lightning has greatly increased in recent decades, even overtaking anthropogenic fire ignitions as the primary source of burned area (Styger et al., 2018 ). In addition to climate change lengthening both the anthropogenic and mixed ignitions periods for many ecoregions, the intensity of fuel dryness is likely to increase thereby increasing the potential of uncontrollable fires (Jain et al., 2022 ), particularly during the mixed ignitions period where lightning is a dominant driver of burned area (Jain et al., 2024 ; Rao et al., 2023 ). In the past, high nighttime fuel moistures enabled more effective fire management, although climate change is weakening this bulwark due to increasing fuel dryness (Balch et al., 2022 ; Boer et al., 2017 ; Luo et al., 2024 ). Climatic change is also likely to increase the severity of fire weather within both the mixed and anthropogenic ignitions periods, promoting profound ecological changes (Enright et al., 2015 ; Scheffer et al., 2009 ). Our study provides an important baseline for landscape managers and researchers to quantify and track the climate change-induced transformation to fire regimes, particularly where these shifts may create more flammable, difficult-to-manage landscapes. 5 Conclusion Our use of ecoregion-specific fuel moisture thresholds to identify the onset and cessation of fire seasons provides a critical point for future research and fire management. Furthermore, we disaggregate ecoregional ignition periods based on the presence of lightning, showing for the first time the full scope of the anthropogenic influence on global fire seasonality. We assessed our seasonal definitions using global MODIS burned area data, finding concordance between satellite fire occurrence and both our fire seasons and ignitions periods. We show that anthropogenic factors have extended the fire season beyond its natural boundaries as represented by the mixed ignition period, leading to drier conditions and increased wildfire potential in nearly all ecoregions on Earth. Additionally, fire suppression has perversely increased the risk of large uncontrollable fires within the driest period of the mixed ignition period. Anthropogenic climate change is further exacerbating the potential for wildfire due to the lengthening of fire seasons and intensity of fuel dryness that increase lightning ignition efficiency. Lightning patterns, too, are being disrupted by anthropogenic climate change, further shifting the occurrence and distribution of ecoregional lightning activity beyond pre-existing bounds although the trajectory of these changes remains uncertain. The expansion of the mixed ignitions period is of particular concern due to the increasing efficiency of lightning ignitions that can result in large uncontrollable fires. This study provides an important framework to understand and track changes to fire seasons in response to changing natural and anthropogenic ignition patterns. Declarations 6 Data availability The grid cell-level fire seasons and related statistics developed as part of this project are in review and will be freely available via the University of Tasmania Research Data Portal. 7 Acknowledgements The authors would like to thank Dr. Piyush Jain of Natural Resources Canada ( [email protected] ) for their work in pre-processing the ERA5 reanalysis data previously used in Ellis et al. (2022; 2024). This work was supported by the New South Wales Bushfire Risk Management Research Hub and funded by the New South Wales Department of Planning, Industry, and Environment. 8 Author contributions T.M.E., G.J.W., and D.M.J.S.B. conceptualized the project goals and scope. T.M.E. designed and scripted all final methods used throughout the project, with some significant data pre-processing by G.J.W. T.M.E. analysed the data, produced the figures, and wrote the manuscript with significant input from D.M.J.S.B. and G.J.W. 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Tech. rep., The Canadian Forest Service: Petawawa National Forestry Institution. Westerling, A. L. (2016, June). Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring. Philosophical Transactions of the Royal Society B: Biological Sciences, 371 , 20150178. doi: 10.1098/rstb.2015.0178 Whitman, E., Parisien, M.-A., Thompson, D. K., & Flannigan, M. D. (2019, December). Short-interval wildfire and drought overwhelm boreal forest resilience. Scientific Reports, 9 . doi: 10.1038/s41598-019-55036-7 Wotton, B. M., Flannigan, M. D., & Marshall, G. A. (2017, August). Potential climate change impacts on fire intensity and key wildfire suppression thresholds in Canada. Environmental Research Letters, 12 , 095003. doi: 10.1088/1748-9326/aa7e6e Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5606101","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":392327339,"identity":"9f91706c-d504-4749-9425-b600dc37bf67","order_by":0,"name":"Todd Ellis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYHACAwmGAgseMPMDELOxE1DPA9ZiIAHWwjgDpIWZSC1gDjNYIyEt9uzNG298MJCQkW/gMfxs82ubPB8zA+OHjzl4bOE5Vmw5A+gwgwM8xtK5fbcN25gZmCVnbsOjRSLHTJoHpIWBd4N0bs9tRqAWNmZeQlr+ALXIN/Bu/m3Zc9ueOC3gEDvAu02a4cftRMJazgD90gNy2GH+b5a9DbeT25gZm/H6hb0dGGI/Kmzs5dvbkm/8+HPbdn5788EPH/FoQQBQdDC2gViMDcSoh4E/pCgeBaNgFIyCkQIApV1ENc7/nMMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4410-8676","institution":"University of Tasmania","correspondingAuthor":true,"prefix":"","firstName":"Todd","middleName":"","lastName":"Ellis","suffix":""},{"id":392327340,"identity":"6d6aab22-231e-457b-934e-b65c665a490c","order_by":1,"name":"David Bowman","email":"","orcid":"https://orcid.org/0000-0001-8075-124X","institution":"University of Tasmania","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Bowman","suffix":""},{"id":392327341,"identity":"b6f50f62-5a9d-4fd1-b3c5-da24659f6a4f","order_by":2,"name":"Grant Williamson","email":"","orcid":"https://orcid.org/0000-0002-3469-7550","institution":"University of Tasmania","correspondingAuthor":false,"prefix":"","firstName":"Grant","middleName":"","lastName":"Williamson","suffix":""}],"badges":[],"createdAt":"2024-12-09 06:05:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5606101/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5606101/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41559-025-02862-w","type":"published","date":"2025-09-22T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73486204,"identity":"89a249c0-e9d2-45b2-83b7-9c79d9191702","added_by":"auto","created_at":"2025-01-10 12:27:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172336,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of key fire seasonality concepts using simulated mean dead fine fuel moisture content (DFFMC: %), mean indexed lightning strokes, and total burned area (ha) data. This hypothetical ecoregion is modelled after high-latitude North American temperate coniferous forests. Our fire seasons were designed using ecoregional flammability thresholds (Ellis et al., 2024): This ecoregion has a flammability threshold of 16%. The fire season ranges from May to early October, with the driest period occurring between July and August. The period of the highest-density (95%) lightning activity ranges from July to September. The overlap of lightning occurrence and the fire season represents our defined mixed ignitions period, while the fire season outside of these bounds represents the anthropogenic ignitions period. This delineation highlights when and where burned area occurrence is dominated either by the potential for natural (i.e., lightning) or anthropogenic factors depending on local fuel dryness and lightning activity. Note that burned area can occur outside of our identified fire seasons and represents pre- or post-seasonal prescribed burning to limit in-season fire occurrence. During the mixed ignitions period, lightning occurrence represents the most uncontrollable source of wildfire ignitions, and subsequently can be the greatest cause of burned area for ecoregions such as this example.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5606101/v1/6630e04ec019fc049ec7a958.png"},{"id":73486205,"identity":"0dc9c334-384e-4ea1-8cf0-a6f0a60d8149","added_by":"auto","created_at":"2025-01-10 12:27:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1207715,"visible":true,"origin":"","legend":"\u003cp\u003ea) Fire season length as determined by the associated ecoregional flammability threshold (Ellis et al., 2024) for ERA5-based DFFMC records during the latest climate normal (1991 – 2020) and calculated at the grid cell level. b) and c) show the fire season initiation and cessation dates as day of year (1-365), respectively. d) Lightning season lengths based on the World Wide Lightning Location Network Global Lightning Climatology (2010 – 2023: Kaplan \u0026amp; Lau 2021) transformed to number of strikes per grid cell. e) shows the mean number of daily lightning strikes in a seven-day period centred around the modal peak between 2010 – 2023.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5606101/v1/4d8112c426010fc112260a98.png"},{"id":73486208,"identity":"6764ded8-1adb-4726-8e63-9b141cb7c713","added_by":"auto","created_at":"2025-01-10 12:27:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":582940,"visible":true,"origin":"","legend":"\u003cp\u003eLength in number of days of both the a) mixed ignitions period and b) anthropogenic ignitions period, as well was the percent of the grid cell-level MODIS burned area (2001 – 2023) that occurred within the c) mixed and d) anthropogenic ignitions periods.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5606101/v1/d3403178aae6a8dc6eb24d98.png"},{"id":73486218,"identity":"6cc2fa1b-eb90-4f88-b134-4c6005f058c4","added_by":"auto","created_at":"2025-01-10 12:27:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":316085,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of fire season (x axis) and mixed ignitions period (y axis) lengths averaged by biome type. Points represent biome-level means weighted by total represented land area. The displacement of biome-level means in relation to the 1:1 line highlights those biomes where the anthropogenic ignitions period has extended the fire season beyond its natural limits (as represented by the mixed ignitions period)..\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5606101/v1/cb07d8e0587351220ac933d6.png"},{"id":73486419,"identity":"0d62452b-6400-4d5f-bb52-d2967a4dc77f","added_by":"auto","created_at":"2025-01-10 12:35:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":303317,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between net primary productivity, population density, burned area, and ignitions period. Fire activity in a) is represented by the total burned area represented by underlying ecoregions and seasons, while b) show the mean percent of total burned area represented during those seasons. Letters represent the mean burned area as derived from the MODIS burned area product for underlying biome types. Bare earth is excluded from the represented points.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5606101/v1/28b272d5ba6dd4030a8ea85a.png"},{"id":91956159,"identity":"b4acf67f-933e-4ece-a1c6-03d81ee7e087","added_by":"auto","created_at":"2025-09-23 07:11:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3335872,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5606101/v1/65969e48-4f84-4138-b505-6dbc36cfa05b.pdf"},{"id":73486210,"identity":"772495af-edc0-4df5-9cca-3b6c606d3770","added_by":"auto","created_at":"2025-01-10 12:27:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":546584,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"0201BSupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5606101/v1/cad42e9aadb6b8d7ef3ad9cf.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Humans have substantially extended fire seasons in all biomes on Earth","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eFire seasons represent a chief organising principle in pyrogeography and fire management, with the onset, peak, and cessation of these periods defining the temporal boundaries within which ecoregions are most prone to wildfire occurrence (Flannigan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jolly et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Westerling, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Fire seasons are primarily shaped by climate and human activity (Jones et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with bioclimatic seasonality controlled by fuel availability, moisture content (Ellis et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and naturally occurring ignitions (e.g., lightning; Bradstock, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Biotas have both adapted and contributed to the evolution of such bioclimatically determined fire regimes through geological time (Bowman et al., 2009; \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The global expansion of the human footprint through the Quaternary Period, however, has resulted in the encroachment of the 400-million-year-old naturally occurring bioclimatic fire regime in favour of anthropogenically managed fire (Bowman et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Glikson \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Le Page et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Pre-historical fire records indicate that the development and spread of Indigenous fire management in the late-Pleistocene and Holocene have been shown to have both positive and negative environmental effects (Adeleye et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; McWethy et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mooney et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with the strongest impact in naturally ignition-limited systems such as New Zealand (Perry et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnthropogenic climate change is causing an ongoing expansion of many fire season boundaries (Abatzoglou et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jolly et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The expansion of the fire season boundaries increases the potential for uncontrollable wildfire by drying out essential moisture reserves in the soil column (Abram et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, this expansion increases the temporal range of extreme fire weather (Abatzoglou et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Flannigan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and associated events such as pyrocumulonimbi storms (Di Virgilio et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sharples et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), as well as the expansion of the fire activity across the nighttime barrier \u0026ndash; a period of lower temperatures and higher humidity that has typically served as a bulwark against large, multi-day wildfires (Balch et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The lengthening seasonality and peak period of extreme fire weather has caused higher socio-economic and ecological costs in recent decades (Boer et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bowman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Duane et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ellis et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jain et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jolly et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), a trend that is expected to continue throughout the 21st century (Abatzoglou et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Abatzoglou et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Flannigan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wotton et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Such fire disasters serve as an indicator that these shifts in fire seasonality are already having deleterious impacts on both human and natural systems (Boer et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Collins et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Higuera and Abatzoglou, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nolan et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; van Oldenborgh et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough human ignitions vastly exceed lightning ignitions, lightning remains the leading cause of total burned area attributed to wildfire in landscapes where it co-occurs with the warmest and driest months (Abatzoglou et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Veraverbeke et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This notably includes, for example, higher-latitude boreal forests where human population density is low and, consequently, management practices such as suppression or controlled burning are most costly (Abatzoglou et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Balch et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pausas and Bond, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Veraverbeke et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Contemporary fire management attempts to suppress all lightning and anthropogenic wildfires within the peak of the fire season when fuels are maximally dry and fires likely to be uncontrollable (Bowman et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kelley et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By contrast, managers of flammable ecosystems target the shoulders of the bioclimatic fire season with controlled burning, which extends the recorded fire season into the cooler, more moist months up until the point fires are unable to propagate (Balch et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Calef et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pereira et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIlluminating the natural occurrence of fires in ecoregions and biomes under ubiquitous anthropogenic influence \u0026ndash; whether directly (management) or indirectly (climate change) \u0026ndash; is of both practical and fundamental importance. Under any future scenario, lightning remains a largely uncontrollable foundation of fire regimes. For many ecoregions, a lengthening of the period when fuels are available to burn further enables major lightning ignitions to expand beyond the boundaries of the naturally expected fire season (e.g., Calef et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) while also lengthening the potential period for human-caused ignitions. The drying of fuels also carries the potential to increase lightning ignition efficiency during these periods, further increasing the potential for large or uncontrollable lightning-ignited fires (Hessilt et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jain et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rao et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Because the coincidence of lightning with the bioclimatic fire season and periods of high fuel availability is a major driver of burned area, it is important to know how this coincidence varies across different ecoregions. The disaggregation of lightning-caused from current anthropogenic seasonal fire patterns, however, has received limited attention or concentrated on regional studies (e.g., Balch et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Coogan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Styger et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Gauging the magnitude of the difference between where fire seasons co-occur with lightning activity facilitates understanding how and where humans may have altered natural fire regimes. Shifts in the timing of fires can affect plant and animal phenological cycles, and the recognition that fire regimes are strongly shaped by humans reframes discussion around what a \u0026lsquo;natural\u0026rsquo; fire regime is \u0026ndash; a concept central to carbon accounting of wildfire emissions (Bowman et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The demarcation of the fire season as driven by exclusively human activity or the convergence of human and lightning activity also provides a useful baseline for managers to track the transformative effects of climate change on fire regimes. Such changes affect the time window for fuel management by, for example, cultural burning or the need to schedule firefighting resources to suppress uncontrolled fires (see Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eTo accomplish this disaggregation of the period of mixed anthropogenic and lightning ignitions from the period of purely anthropogenic ignitions, we use a global dataset of ecoregional fuel moisture flammability thresholds representing over 700 ecoregions hierarchically grouped by biome and biogeographic realm (Ellis et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Olson et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). We define the fire season as the period within which a given ecoregion may experience wildfire, with uncontrollable fire likely in the driest period of the fire season. To differentiate the naturally occurring from the purely anthropogenic ignitions periods, we intersected our identified fire seasons with global lightning density data. The overlapping period between our moisture-derived fire season and the highest density of lightning activity subsequently represents the mixed ignitions period where both lightning and anthropogenic ignitions are possible, while any remainder reflects the anthropogenic ignitions period within which fires can propagate only under deliberate human promotion. We compared the lengths of mixed and anthropogenic ignitions periods to the fire season itself to identify those regions where anthropogenic or lightning ignitions may be the primary drivers of fire seasonality, as well as highlight the transformative effects humans have had and continue to have in constraining global fire seasonality. We contextualise the role humans have had in shaping fire seasonality by exploring the associations between ignitions periods using global data on vegetation productivity, population density, and satellite burned area records.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Geospatial classification\u003c/h2\u003e \u003cp\u003eThroughout our analyses, we used the well-established global classificatory system of terrestrial ecoregions (Olson et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; supplementary information). Each individual ecoregion is also associated with one of 14 unique terrestrial biome types representing comparable habitats across the globe regardless of location (e.g., temperate coniferous forests). Additionally, these identified biome types represent the potential habitat type rather than true habitat, and thus do not consider the effects of human land-use patterns on shaping the true habitat. At the coarsest spatial scale, each ecoregion falls within one of eight biogeographic realms which, in turn, are representative of continents or equivalent landmasses. The Nearctic realm, for example, primarily defines the North American continent and contains 117 distinct ecoregions representing 10 of the 14 potential biome types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Defining fire seasons\u003c/h2\u003e \u003cp\u003eWe used the ecoregional flammability thresholds identified in Ellis et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) as important meteorological switches constraining local fire ignition and spread (Bradstock, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) for 772 distinct ecoregions worldwide. These thresholds were identified using the dead fine fuel moisture (DFFMC) component (Van Wagner, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) of the Global Fire Weather Index dataset (1979\u0026ndash;2019: Jain et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; McElhinny et al., 2020) as drawn from ERA5 reanalysis records (Hersbach et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and featuring a 0.25\u0026deg; spatial resolution. For each ecoregion with an associated flammability threshold, we defined bioclimatic fire seasons dependent on the distribution of reanalysis-derived daily DFFMC distributions and their association with fire via the flammability threshold. To accomplish this, we first simplified grid cell-level DFFMC records to get a 366-day record representing the count of each day of the year (i.e., 1-366) where the DFFMC fell below the relevant ecoregional flammability threshold over the latest climate normal period (1991\u0026ndash;2020). We reduced the noise in these distributions by applying a seven-day circular convolution filter, which applies a rolling average that treats the first and last values (i.e., January 1st and December 31st ) as sequential. We then modally centred this timeseries to account for spatial differences in seasonality (e.g., peak dryness potentially occurring in December and January). Finally, we identified the fire season for each grid cell by extracting the highest density continuous interval containing 95% of the days (centred around peak dryness) falling below the ecoregional flammability threshold between 1991 and 2020. Note that this can obscure some nuance in cells with multimodal seasonal dryness. Although leap years (i.e., the 366th day of the year) were included in the distribution, they were not identified as fire season initiation or cessation dates, and thus the final seasonal definitions did not need additional recoding to a 365-day scale. While we identified fire seasons for every grid cell with the potential for one, we used global land cover classification data (Hansen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) to exclude all bare earth cells from final analyses and reporting. Altogether, the bioclimatic fire season as defined above reflects a potential fire season as constrained by fuel moisture.\u003c/p\u003e \u003cp\u003eFor these same ecoregions, we also defined a naturally occurring ignitions period using daily lightning occurrence data from the World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC: Kaplan \u0026amp; Lau, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These data represent a global gridded timeseries record of lightning stroke density with a 30-arcminute spatial resolution and a daily temporal resolution between 2010 and 2023 as at the date of analysis. First, we used bilinear resampling to match ERA5\u0026rsquo;s 0.25\u0026deg; spatial resolution and transformed the values of density to stroke counts using grid cell area. To identify lightning seasonality, we applied the same approach (modal centring; 95% highest density continuous interval) used to identify the fuel moisture-based fire season. To provide context for the importance of lightning in driving ignitions in each region \u0026ndash; particularly if a region has little lightning activity that occurs sporadically year-round (e.g., New Zealand) \u0026ndash; we calculated the mean daily lightning occurrence during the week-long period around the peak (i.e., mode) of the lightning season. While this period of lightning activity can occur at any time throughout a year, it only represents the naturally occurring fire season where it co-occurs with our moisture-derived fire season (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Because anthropogenic ignitions can occur concurrently with lightning ignitions, we label this period as the \u0026lsquo;mixed ignitions period.\u0026rsquo; Any period of our defined fire seasons outside of the mixed ignition period represents the \u0026lsquo;anthropogenic ignitions period\u0026rsquo; where ignitions are exclusively anthropogenic (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Seasonal convergence\u003c/h2\u003e \u003cp\u003eTo assess the validity of our moisture-derived fire seasons, we first compared the lengths of our mixed and anthropogenic ignitions periods at the grid cell level, as well as summarised by biome type and weighted by total represented area. Additionally, we used the full available MODIS MCD64A1\u003c/p\u003e \u003cp\u003eBurned Area Product (2001\u0026ndash;2023: Giglio et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to calculate the percent of burned area that occur within the mixed or anthropogenic ignitions period at the grid cell level. Note that the MODIS burned area record includes uncertainty around the recorded day of burn, which can impact our fire season or ignitions period delineation if that uncertainty overlaps with initiation or cessation dates. These assessments are useful for identifying whether regional fire patterns are primarily constrained by the potential for either mixed or anthropogenic ignitions, as well as understanding where fire seasonality has been substantially altered due to anthropogenic factors (e.g., African savannas: Bond and Zaloumis, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Finally, we visualised the relationship between net primary productivity, population density, and percent burned area captured by the mixed or anthropogenic ignitions period. To do this, we used interpolated 2020 population density data with a 30-arcsecond spatial resolution bilinearly resampled to ERA5\u0026rsquo;s 0.25\u0026deg; from the Gridded Population of the World dataset (Center for International Earth Science Information Network, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For primary productivity, we used the resampled mean annual MODIS MOD17A3 product derived from the 2001\u0026ndash;2023 record (Running et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) as a measure of each ecoregion\u0026rsquo;s overall vegetation productivity. It is within this context \u0026ndash; where these complex variables meet \u0026ndash; that we evaluate the role humans have in both directly and indirectly shaping global fire seasonality. See supplementary information for a full summary of all data sources and represented time periods.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eBased on our climatological analyses, there are strongly contrasting patterns in fire season length among biomes and ecoregions, with potential fire seasons and ignitions periods identified for 751 distinct ecoregions over 197,256 grid cells and representing 10.9\u0026nbsp;billion hectares after excluding bare earth (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-c, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Weighted by the grid cell area, the mean fire season length for our global dataset was 164.4 days (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;80; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), while the mean lightning season length was 185.7 days (\u0026plusmn;\u0026thinsp;90.1; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed-e) with an average of 8.3 (\u0026plusmn;\u0026thinsp;11.3) daily lightning strikes during the peak week of lightning activity per grid cell. The mean length of the mixed ignitions period (lightning present) was 108.9 days (\u0026plusmn;\u0026thinsp;75.1; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), while the mean length of the anthropogenic ignitions period (lightning absent) was 55.5 days (\u0026plusmn;\u0026thinsp;53.8; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Summarised at the biome level, overall fire seasons ranged between 82.1 days (\u0026plusmn;\u0026thinsp;26.1) for 33 tundra ecoregions representing 0.7 G ha and 200.6 days (\u0026plusmn;\u0026thinsp;99.8) for 206 tropical moist forest ecoregions representing 1.9 G ha. The lengths of mixed ignitions periods ranged between 40.9 days (\u0026plusmn;\u0026thinsp;31.5) for the tundra ecoregions, and 152 days (\u0026plusmn;\u0026thinsp;64.4) for 82 temperate broadleaf forest ecoregions representing 1.3 G ha. On the other hand, the lengths of anthropogenic ignitions periods ranged between 24.6days (\u0026plusmn;\u0026thinsp;44.4) for 39 Mediterranean forest ecoregions, and 97.1 days (\u0026plusmn;\u0026thinsp;65.1) for 45 tropical grassland ecoregions representing 1.9 G ha. The development of this anthropogenic ignitions period reflects an inherent increase in the length of the total fire season (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Total burned area represented by each ignitions period is globally dominant within these anthropogenic ignitions period (46.3%) as compared to the mixed ignitions period (23.7%). This global average, however, is driven by tropical savanna regions that burn annually and does not reflect the underlying variation across space. For example, many temperate coniferous and boreal forest ecoregions primarily burn within the mixed ignitions period when lightning is present (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), although lightning activity is low within this region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). See supplementary information for complete realm- and biome-level descriptive statistics that reflect the complex spatial variation in fire seasonality and ignition periods. In sum, humans have expanded the fire season in nearly all ecoregions on Earth by introducing the potential for ignitions within an exclusively anthropogenic ignitions period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb,d, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics for global and biome-level fire season and ignition period definitions. Bare earth environments are excluded. Both mean and standard deviation are weighted by the total represented area. The percent of burned area represented reflect total burned area in the complete annual MODIS burned area product records (2001\u0026ndash;2023: Giglio et al., 2018), meaning regions that frequently burn such as tropical grasslands are over-represented in regional averaging. Note that the presented fire season will not inherently capture all burned area (e.g., out-of-season fuel treatments), and thus the burned area captured within ignitions periods does not sum to 100%.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTotals\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eMean (\u0026plusmn;\u0026thinsp;std. dev.)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSeason length\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003eIgnitions period length\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u003cb\u003eBurnt area represented by ignitions period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEcoregions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGrid cells\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eArea (G ha)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eFire\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eLightning\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ePeak daily lightning strikes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eMixed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eAnthropogenic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eMixed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003eAnthropogenic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll biomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197,256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164.4 (\u0026plusmn;\u0026thinsp;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e185.7 (\u0026plusmn;\u0026thinsp;90.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.3 (\u0026plusmn;\u0026thinsp;11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e108.9 (\u0026plusmn;\u0026thinsp;75.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.5 (\u0026plusmn;\u0026thinsp;53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e46.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoreal forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39,086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111.7 (\u0026plusmn;\u0026thinsp;24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92.6 (\u0026plusmn;\u0026thinsp;25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4 (\u0026plusmn;\u0026thinsp;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e84.1 (\u0026plusmn;\u0026thinsp;21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.6 (\u0026plusmn;\u0026thinsp;18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e64.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e13.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeserts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e188.2 (\u0026plusmn;\u0026thinsp;66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e203.9 (\u0026plusmn;\u0026thinsp;66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.7 (\u0026plusmn;\u0026thinsp;8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e135 (\u0026plusmn;\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e53.2 (\u0026plusmn;\u0026thinsp;56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e43.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e30.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlooded grasslands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163.9 (\u0026plusmn;\u0026thinsp;68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e202.3 (\u0026plusmn;\u0026thinsp;68.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.6 (\u0026plusmn;\u0026thinsp;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74.9 (\u0026plusmn;\u0026thinsp;71.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e89 (\u0026plusmn;\u0026thinsp;57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e15.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e51.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediterranean forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e169.8 (\u0026plusmn;\u0026thinsp;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e250.5 (\u0026plusmn;\u0026thinsp;66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.3 (\u0026plusmn;\u0026thinsp;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e145.2 (\u0026plusmn;\u0026thinsp;52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.6 (\u0026plusmn;\u0026thinsp;44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e59.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e11.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMontane grasslands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e174.1 (\u0026plusmn;\u0026thinsp;90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e158.1 (\u0026plusmn;\u0026thinsp;63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.8 (\u0026plusmn;\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e94 (\u0026plusmn;\u0026thinsp;59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e80.1 (\u0026plusmn;\u0026thinsp;67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e74.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp. broadleaf forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23,038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e197.4 (\u0026plusmn;\u0026thinsp;66.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e183 (\u0026plusmn;\u0026thinsp;65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.2 (\u0026plusmn;\u0026thinsp;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e152 (\u0026plusmn;\u0026thinsp;49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e45.4 (\u0026plusmn;\u0026thinsp;43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e49.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e20.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp. coniferous forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e168 (\u0026plusmn;\u0026thinsp;83.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e167.9 (\u0026plusmn;\u0026thinsp;72.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.7 (\u0026plusmn;\u0026thinsp;12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e124.9 (\u0026plusmn;\u0026thinsp;64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e43.1 (\u0026plusmn;\u0026thinsp;45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e57.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e19.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp. grasslands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e181.2 (\u0026plusmn;\u0026thinsp;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e163.9 (\u0026plusmn;\u0026thinsp;64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.5 (\u0026plusmn;\u0026thinsp;9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e143.4 (\u0026plusmn;\u0026thinsp;52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.8 (\u0026plusmn;\u0026thinsp;27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e55.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e15.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrop. coniferous forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e129.6 (\u0026plusmn;\u0026thinsp;67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e239.3 (\u0026plusmn;\u0026thinsp;62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.6 (\u0026plusmn;\u0026thinsp;20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81.6 (\u0026plusmn;\u0026thinsp;61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48 (\u0026plusmn;\u0026thinsp;45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e51.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e34.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrop. dry forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e137.3 (\u0026plusmn;\u0026thinsp;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e252.5 (\u0026plusmn;\u0026thinsp;70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.4 (\u0026plusmn;\u0026thinsp;19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73.2 (\u0026plusmn;\u0026thinsp;60.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e64.1 (\u0026plusmn;\u0026thinsp;58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e28.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e49.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrop. grasslands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156.2 (\u0026plusmn;\u0026thinsp;79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e206.3 (\u0026plusmn;\u0026thinsp;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.6 (\u0026plusmn;\u0026thinsp;8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e59.1 (\u0026plusmn;\u0026thinsp;72.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97.1 (\u0026plusmn;\u0026thinsp;65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e51.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrop. moist forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e200.6 (\u0026plusmn;\u0026thinsp;99.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e277.2 (\u0026plusmn;\u0026thinsp;62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.1 (\u0026plusmn;\u0026thinsp;14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e142.6 (\u0026plusmn;\u0026thinsp;97.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e58 (\u0026plusmn;\u0026thinsp;53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e42.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e40.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTundra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.1 (\u0026plusmn;\u0026thinsp;26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.7 (\u0026plusmn;\u0026thinsp;35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3 (\u0026plusmn;\u0026thinsp;0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.9 (\u0026plusmn;\u0026thinsp;31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41.1 (\u0026plusmn;\u0026thinsp;26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e65.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e23.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Fire seasonality as shaped by productivity and human population density\u003c/h2\u003e \u003cp\u003eHuman population density and productivity simultaneously act as major constraints on fire seasonality (Fig.\u0026nbsp;5; Bowman et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pausas and Ribeiro, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Our global analysis shows that higher human population is closely associated with more area burned exclusive to the anthropogenic ignitions period (Fig.\u0026nbsp;5b). Ecoregions with intermediate productivity and human population are mixed in the concentration of burned area within either ignitions period. In ecoregions with low (deserts, tundra) to moderate (boreal and coniferous forests) productivity and lower human population density, most area burned occurs during the mixed ignitions period, while those regions at the extremes of low productivity and high human populations have burned area limited primarily within the anthropogenic ignitions period (Fig.\u0026nbsp;5b).\u003c/p\u003e \u003cp\u003eD\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 5\u003c/b\u003e Relationship between net primary productivity, population density, burned area, and ignitions period. Fire activity in a) is represented by the total burned area represented by underlying ecoregions and seasons, while b) show the mean percent of total burned area represented during those seasons. Letters represent the mean burned area as derived from the MODIS burned area product for underlying biome types. Bare earth is excluded from the represented points.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIt is well understood that humans have increasingly transformed and controlled the global footprint of fire through the Quaternary (~\u0026thinsp;2mya; Glikson et al., 2013) but disaggregating the natural from the purely anthropogenic ignitions period has received little prior attention (Coogan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Parisien et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Biotas that evolved with lightning ignitions have shaped fire regimes over geological time, but anthropogenic influences have altered these natural fire regimes (Bowman et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with the grasslands of New Zealand (Perry et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and the savannas of both Africa (Bond and Zaloumis, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and northern Australia (Bird et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) being prime examples.\u003c/p\u003e \u003cp\u003eOur analyses illustrate the profound human influence in shaping fire regimes in nearly every ecoregion on Earth, a scale previously unconsidered. We use a hierarchical approach to identify fire seasons, reflecting the season within which a given ecoregion or grid cell may experience dry meteorological conditions conducive to wildfire ignitions. We then disaggregate our identified fire seasons into mixed (co-occurring with lightning) and anthropogenic ignitions periods (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We assessed this approach using global satellite burned area records (2001\u0026ndash;2023: Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-d). This assessment further demonstrates our global approach meaningfully captures ecoregional variation in fire seasonality corresponding to recognised gradients in productivity and human population (Fig.\u0026nbsp;5; Bowman et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pausas and Ribeiro, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As discussed below, this disaggregation of mixed and purely anthropogenic ignitions periods is an essential step in understanding how humans have shaped the fire activity across all landscapes on Earth, pushing the fire season to the limits of or beyond natural lightning occurrence (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed-e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, 5b). This framework is important to understand the likely effects of fire management and climate change in shaping future fire seasonality.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Human expansion of the fire season\u003c/h2\u003e \u003cp\u003eWe have demonstrated a near ubiquitous expansion of fire activity beyond the conjunction of fuel dryness and lightning that defines the mixed ignitions period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In geologic time, the mixed ignitions period would correspond to a naturally occurring fire season, meaning that the length of the moisture-derived fire season today is, on average, 55 days (or one-third) longer as it would otherwise be without human activity providing a year-round ignition source (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The seasonal asynchrony that often occurs between human and lightning ignitions inherently creates an exclusively anthropogenic ignitions period across most of the Earth\u0026rsquo;s ecoregions (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This anthropogenic effect is shaped by gradients of population density and productivity (Figs.\u0026nbsp;5a-b). The large extent of burned area outside the mixed ignitions period reflects high densities of year-round human ignitions, particularly in more populated regions (Fig.\u0026nbsp;5b) or ecoregions with ancient anthropogenic ignitions patterns (e.g., African savannas where lightning co-occurs with peak moisture availability: Bond and Zaloumis, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This can include a mix of planned and unplanned ignitions, although it should be noted that planned burning can still occur outside of these defined fire seasons to reduce the future fire potential within the fire season (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Within the productivity gradient (Bowman et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Krawchuk \u0026amp; Moritz, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pauas \u0026amp; Ribeiro, 2013), human influence is smallest in lower productivity ecoregions where human populations are sparse (e.g., tundra). The anthropogenic effect is greatest in higher productivity ecoregions (e.g., tropical grasslands and rainforests; Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), reflecting the use of fire in land-use (agriculture and forestry) and landcover conversion (tropical deforestation: Le Page et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Within the intermediate productivity zone (~\u0026thinsp;1.2 to 5.4 t C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e: Ellis et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the anthropogenic effect is more complex, reflecting a mix of population densities, land-use patterns, and landscape flammability (e.g., boreal forests as compared to tropical savannas; Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;5).\u003c/p\u003e \u003cp\u003eThe introduction of anthropogenic ignitions has introduced an evolutionary filter for all biotas resulting in a reassortment of species distributions and ecosystems in many ecoregions (Pausas \u0026amp; Keeley, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For instance, in temperate forests and shrublands where humans have substantially increased fire frequency and fire season lengths (e.g., Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), fire sensitive species have been driven into fire refugia (Meddens et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, humans use fire in productive ecoregions to drastically change or maintain landcover. For instance, the creation of the anthropogenic ignitions period has contributed to the conversion tropical forest to pasture and croplands (Le Page et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fig.\u0026nbsp;5b) and is visible in our disaggregation of burned area by ignitions period within ecological transition lines on the South American and African continents (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-d; supplementary information).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Fire suppression and the distortion of the natural fire season\u003c/h2\u003e \u003cp\u003eIn contrast to the anthropogenic ignitions that expand fire seasons, humans also suppress fire activity, particularly during periods of peak fuel dryness that often characterise the mixed ignitions period (Bowman et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Fire suppression has transformed the fire ecology of many forested and temperate ecoregions over the last century by excluding frequent low- to moderate-intensity wildfires (Kreider et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This transformation has resulted in increased forest biomass and changes to species compositions, making forests more prone to extreme wildfires (Tubbesing et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The application of prescribed or cultural burning practices or other fuel treatments applied at the shoulders of the moisture-defined fire season has the potential to restore ecologically resilient fire regimes (Mariani et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This can stave off uncontrollable lightning-ignited wildfires that are a perverse ultimate outcome of fire suppression. However, inherent institutional and cultural barriers to treatments such as prescribed or cultural burning make this approach difficult to implement at scale successfully and consistently (e.g., Prichard et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schultz et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Climate change\u003c/h2\u003e \u003cp\u003eThe impacts of climatic change on burned area manifest differently amongst ecoregions (Jones et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but there is a clear global trend towards drying burnable fuels (Ellis et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This trend carries with it the potential to further lengthen both mixed and anthropogenic ignitions periods based on local lightning activity. Longer fire seasons are likely to be exploited by humans, potentially leading to year-round fire activity in an increasing number of ecoregions. This has profound implications for fire management, particularly sharing of firefighting resources between ecoregions (Abatzoglou et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ribeiro et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The trajectory of lightning ignitions under climate change is more uncertain. Fire seasons may expand into periods of lightning activity where fuels were previously too moist to burn (e.g., Fill et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Conversely, lightning seasonality and lightning storm intensity may change, thereby increasing the potential for lightning ignitions (Romps et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Styger et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The unpredictability of lightning ignitions is particularly challenging to forecast and manage. Nonetheless, there is evidence of increasing lightning ignitions from a range of ecoregions globally (e.g., Veraverbeke et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEcoregions with fire seasons driven exclusively by lightning activity are rare since global industrialisation and are primarily limited to high-latitude tundra or boreal and broadleaf forests with minimal human population density and where fire management is most costly (e.g., Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-d, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; ; Balch et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Calef et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pereira et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; supplementary information). Many boreal forest and tundra ecoregions, however, are at the cusp of ecological transformation driven by an increase in the frequency, size, and severity of wildfires due to climate change affecting both fuel dryness and lightning ignitions (Abbott et al., 2020; Hanes et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Talucci et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These projected increases could amplify climate change because of the heightened likelihood of fire disturbances releasing greenhouse gases (particularly CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e) from organic soils and proliferating thermokarst landscapes (Hu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). With drying future fuels, there is an implicit increase in fire potential in both the anthropogenic and the mixed ignitions periods. This is evident due to the incomplete overlap of the lightning season with the mixed ignitions period in these ecoregions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; supplementary information). The pace and scale of ecological transformation in boreal forest and tundra ecoregions critically depends on ecological effects that shape landscape flammability such as the abundance of deciduous species (Mack et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Whitman et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the future conjunction of lightning and fuel dryness (Hessilt et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rao et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The increase in lightning occurrence and ignitions within the mixed ignitions period is happening or is expected to happen across ecoregions more broadly (Calef et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mariani et al., 2018; Romps et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For example, in the temperate broadleaf forests of the Australian island state of Tasmania, the frequency of dry lightning has greatly increased in recent decades, even overtaking anthropogenic fire ignitions as the primary source of burned area (Styger et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to climate change lengthening both the anthropogenic and mixed ignitions periods for many ecoregions, the intensity of fuel dryness is likely to increase thereby increasing the potential of uncontrollable fires (Jain et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), particularly during the mixed ignitions period where lightning is a dominant driver of burned area (Jain et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rao et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the past, high nighttime fuel moistures enabled more effective fire management, although climate change is weakening this bulwark due to increasing fuel dryness (Balch et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Boer et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Climatic change is also likely to increase the severity of fire weather within both the mixed and anthropogenic ignitions periods, promoting profound ecological changes (Enright et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Scheffer et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Our study provides an important baseline for landscape managers and researchers to quantify and track the climate change-induced transformation to fire regimes, particularly where these shifts may create more flammable, difficult-to-manage landscapes.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eOur use of ecoregion-specific fuel moisture thresholds to identify the onset and cessation of fire seasons provides a critical point for future research and fire management. Furthermore, we disaggregate ecoregional ignition periods based on the presence of lightning, showing for the first time the full scope of the anthropogenic influence on global fire seasonality. We assessed our seasonal definitions using global MODIS burned area data, finding concordance between satellite fire occurrence and both our fire seasons and ignitions periods. We show that anthropogenic factors have extended the fire season beyond its natural boundaries as represented by the mixed ignition period, leading to drier conditions and increased wildfire potential in nearly all ecoregions on Earth. Additionally, fire suppression has perversely increased the risk of large uncontrollable fires within the driest period of the mixed ignition period. Anthropogenic climate change is further exacerbating the potential for wildfire due to the lengthening of fire seasons and intensity of fuel dryness that increase lightning ignition efficiency. Lightning patterns, too, are being disrupted by anthropogenic climate change, further shifting the occurrence and distribution of ecoregional lightning activity beyond pre-existing bounds although the trajectory of these changes remains uncertain. The expansion of the mixed ignitions period is of particular concern due to the increasing efficiency of lightning ignitions that can result in large uncontrollable fires. This study provides an important framework to understand and track changes to fire seasons in response to changing natural and anthropogenic ignition patterns.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e6 Data availability\u003c/h2\u003e\n\u003cp\u003eThe grid cell-level fire seasons and related statistics developed as part of this project are in review and will be freely available via the University of Tasmania Research Data Portal.\u003c/p\u003e\n\u003ch2\u003e7 Acknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank Dr. Piyush Jain of Natural Resources Canada ([email protected]) for their work in pre-processing the ERA5 reanalysis data previously used in Ellis et al. (2022; 2024). This work was supported by the New South Wales Bushfire Risk Management Research Hub and funded by the New South Wales Department of Planning, Industry, and Environment.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e\n\u003ch2\u003e8 Author contributions\u003c/h2\u003e\n\u003cp\u003eT.M.E., G.J.W., and D.M.J.S.B. conceptualized the project goals and scope. T.M.E. designed and scripted all final methods used throughout the project, with some significant data pre-processing by G.J.W. T.M.E. analysed the data, produced the figures, and wrote the manuscript with significant input from D.M.J.S.B. and G.J.W. All authors contributed critical feedback through all stages of the project and gave final approval for publication\u003c/p\u003e\n\u003ch2\u003e9 Conflict of interest statement\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbatzoglou, J. T., Battisti, D. 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Environmental Research Letters, \u003cem\u003e12\u003c/em\u003e, 095003. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1088/1748-9326/aa7e6e\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/aa7e6e\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5606101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5606101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe conjunction of lightning ignitions and dry vegetation has shaped fire regimes throughout geological time. These natural fire regimes have in turn influenced the adaptation of biotas globally. Anthropogenic fire use, however, radically transformed worldwide fire regimes by extending seasons to the limits bounded by periods of high fuel moisture. Conversely, active fire suppression has typically limited the occurrence and extent of lightning ignited fires \u0026ndash; particularly where population density is higher. Disaggregating contemporary human- and lightning-caused seasonal fire patterns globally can shed light on the magnitude of the anthropogenic change to fire regimes among biomes. Using global datasets of flammability moisture thresholds and fuel moisture we define the global distribution of fire seasons. We then use a global record of lightning strike density to disaggregate the fire season into mixed (i.e., lightning present) and anthropogenic ignitions periods, before describing the respective importance of these periods amongst biomes. We assess these the breadth of these ignitions periods against a daily satellite burned area record (2001\u0026ndash;2023) and contextualise the occurrence of areas burned within mixed and anthropogenic ignitions periods against vegetation productivity and human population density using the established productivity-fire activity relationship. Collectively, we show that current anthropogenic influences have transformed worldwide fire regimes by substantially lengthening fire seasons, irrespective of local land use and fire suppression practices, population density, lightning occurrence, and biome type.\u003c/p\u003e","manuscriptTitle":"Humans have substantially extended fire seasons in all biomes on Earth","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 12:27:39","doi":"10.21203/rs.3.rs-5606101/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-ecology-and-evolution","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natecolevol","sideBox":"Learn more about [Nature Ecology \u0026 Evolution](http://www.nature.com/natecolevol/)","snPcode":"","submissionUrl":"","title":"Nature Ecology \u0026 Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c96d77cc-e562-4924-bc48-8923d017f259","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41820372,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts/Environmental health"},{"id":41820373,"name":"Earth and environmental sciences/Ecology/Fire ecology"},{"id":41820374,"name":"Earth and environmental sciences/Ecology/Climate-change ecology"}],"tags":[],"updatedAt":"2025-09-23T07:10:55+00:00","versionOfRecord":{"articleIdentity":"rs-5606101","link":"https://doi.org/10.1038/s41559-025-02862-w","journal":{"identity":"nature-ecology-and-evolution","isVorOnly":false,"title":"Nature Ecology \u0026 Evolution"},"publishedOn":"2025-09-22 04:00:00","publishedOnDateReadable":"September 22nd, 2025"},"versionCreatedAt":"2025-01-10 12:27:39","video":"","vorDoi":"10.1038/s41559-025-02862-w","vorDoiUrl":"https://doi.org/10.1038/s41559-025-02862-w","workflowStages":[]},"version":"v1","identity":"rs-5606101","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5606101","identity":"rs-5606101","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}