Philippa Kaur

Since the bone discontinuities noted in the three vertebrae analyzed show no clear sign of bone overgrowth, it is pivotal to rule out the possibility that we are dealing with preservation damages before proposing an accurate diagnosis for the lesions. The close-up view examination of the abnormalities shows that their edges have clear signs of smoothing and rounding (Fig. 1), which represent important evidence of osteoblastic activity18,19. Additionally, the similar color of the cortical damage and normal bone can be used as secondary evidence to rule out post-mortem processes as a possible origin of the alterations, since recent destructive processes are lighter than the rest of the bone19. Therefore, as taphonomic processes can be ruled out, the pointed evidence strongly suggests that the discontinuities observed are of pathological origin. More specifically, these breaks found in all three vertebrae are indicative of bone fracture.Based on fracture analysis criteria applied here20, which consider the location and morphological pattern of the fractures, we classified the fractures noted in all vertebrae as traumas belonging to Type A (vertebral body compression), Group A2 (split fractures), and subgroup A2.1 (sagittal split fracture). This diagnosis implies that the traumatic episode was likely caused by a compressive force on the vertebral column, which split the vertebral bodies in the sagittal plane. This type of injury is considered stable—i.e., the fracture does not have a tendency to displace after reduction—and neurological deficit is uncommon20,22,23. Although stable traumas cause only moderate pain, without generating significant movement limitations20, the Eremotherium individual here analyzed died with unhealed bones, as there is no evidence of callus formation.The absence of other skeletal signs that point to the presence of another type of disease concomitantly to the fractures allows us to reject the possibility that they have been generated as a result of a pre-existing disease (e.g., infection, neoplasm). We also consider that the vertebral injuries were not caused by repetitive force (stress fractures) because this type of injury is commonly characterized as a nondisplaced line or crack in the bone, called hairline fracture3. Those refer to situations where the broken bone fragments are not visibly out of alignment and exhibit very little relative displacement21. Although the Eremotherium vertebrae fractures’ can be described as nondisplaced, they also have a noticeable gap between their edges that is mostly narrow with wider parts in the middle, something found in split fractures20 but that is not characteristic of hairline fractures. Lastly, the subgroup C1.2.1 (rotational sagittal split fracture) might be a source of confusion due to similar morphological pattern with subgroup A2.1 (sagittal split fracture). However, in subgroup C1.2.1 there are compressive and rotational forces acting simultaneously, producing total separation into two parts20, which clearly did not occur in the vertebrae analyzed here.In humans, compression fractures are most commonly caused by osteoporosis, although infection, neoplasm and trauma can also be etiological factors23,24,25. However, as aforementioned, the absence of other pathological skeletal marks is an important characteristic to take note as it serves to disregard the possibility of the fractures’ genesis to be secondary to another pathology. As such, in this case, osteoporosis, infection and neoplasm are unlikely etiologies. On the other hand, a compression fracture in a healthy individual is commonly generated after a severe traumatic event such as a fall from great height23,26. This scenario seems to better explain the origin of the vertebral fractures in the case of the Eremotherium ground sloth herein studied.The three fractured vertebrae were recovered in the Toca das Onças site (Fig. 2), a small cave considered as one of the richest paleontological sites of the Brazilian Quaternary15. Two complete skeletons of Eremotherium laurillardi and fragments belonging to at least thirteen other individuals, together with several other bones assigned to different smaller species are known to this cave14. It comprises of a single dry chamber that can only be entered through vertical entrances approximately 4.5 m high (Figs. 2b–d and 3). Two different hypotheses concerning the depositional process of Toca da Onças were previously proposed: (1) the animals climbed down into the cave in search of water14; or (2) due to the vertical character of the cave entrance, it could have functioned as a natural trap where animals accidentally fell into the cave15.Figure 2Location map of the Toca das Onças site and images of the cave. (a) Detail of the location, (b) cave entrance area view, (c) view from inside the cave, (d) Cave entrance detail. Scale bars 10 m in (b) and 5 m in (c). This figure was generated by Adobe Photoshop CS6 software (https://www.adobe.com/br/products/photoshop.html).Full size imageFigure 3Schematic representation of the Toca das Onças site. (a) Ground plan of the cave illustrating its morphology and dimension, (b) Cross-section illustrating the abyss-shaped entrance.Full size imageThe first hypothesis would indicate that the animal fell into the cave during an attempt to climb down. However, there is no report in the literature indicating that Eremotherium laurillardi could have been a climbing animal. In addition, the vertical morphology of the cave entrance would be a limiting factor for climbing behavior (see Fig. 3).Therefore, based on the type of fracture (compression sagittal split fracture) observed in the three vertebrae of Eremotherium as well as the inferred origin mechanism (fall from a great height), the presence of the individual here analyzed in the fossil accumulation of Toca das Onças is more likely explained by the second hypothesis. This idea is not particularly new as ‘entrapment due to fall’ has been described as a fossil accumulation mode to several other caves worldwide (e.g.,27,28). However, the use of bones fractures as an indicator of fossil accumulation mode is an interesting novelty. Of course, a detailed taphonomic investigation in the Toca das Onças still needs to be conducted in order to accurately interpret the formation of this important Quaternary fossil accumulation from Brazil.In sum, we suggest that the animal accidentally fell into the cave, fractured at least three sequential vertebrae (12th, 13th thoracic vertebrae and 1st lumbar vertebra) after the impact on the ground, survived for a while, but succumbed trapped inside the cave without food and water (Fig. 4). Other animals found in the cave, but without signs of bone fracture, may have fallen and not fractured their bones or not survived after the fall, especially the smaller ones. Finally, the proposal of falls to explain the unusual record of giant ground sloth fossils preserving much of its skeleton in caves, as reported for Toca das Onças site, contrasts with the better-documented pattern of skeletal accumulation via hydraulic action.Figure 4Artistic reconstruction of the suggested fall of the individual Eremotherium laurillardi into the cave. Artwork by Júlia d’Oliveira.Full size image More

Our compiled dataset consisted of 1674 records of marine mammal records after removing duplicate reports. It included 660 reports of sightings, 59 reports of induced mortalities or hunting records, 240 reports of incidental mortalities, 632 unique stranding records (live / dead), and 83 records which could not be categorised because of incomplete information.SightingsA total of 660 opportunistic sightings (number of individuals, ni = 3299) were recorded throughout the Indian coastline between 1748 and 2017 (Fig. 1a, 2a, 3a). Sighting data on the east coast (species = 18, ni = 1105) was mostly restricted to Odisha and Tamil Nadu (representing 97% of total east coast sightings). On the west coast (ni = 1297), Maharashtra (ni = 549), Gujarat (ni = 248) and Karnataka (ni = 307) contributed to highest sighting records (representing 85% of total west coast sightings). Sightings from the islands also contributed to 24.85% of the dataset (Andaman & Nicobar Islands = 24.37%, Lakshadweep = 0.48%). Highest incidence of sightings was for DFP (ni = 1894) followed by dugongs (ni = 959), BW (ni = 58) and SBW (ni = 17).Figure 1Marine mammal records obtained from data compiled between years 1748 – 2017 along the east coast, west coast and the islands of India for the groups i.e., baleen whales (BW), dolphins and finless porpoise (DFP), sperm and beaked whales (SBW) and dugongs, given as color-coded stacked bars where (a) sighting records—records where live animals were sighted (b) induced mortalities—records where animals were reported hunted or killed or were driven ashore, (c) incidental mortalities—records where animals were found dead after entanglement in fishing nets or being struck by vessels and (d) stranding records—records where dead or live animals were found washed ashore, or floating near shore or stranded alive and were attempted for rescue.Full size imageFigure 2Marine mammal records obtained every year from the data compiled between years 1748–2017 along Indian coastline given as cumulative numbers for each group i.e., baleen whales (BW), dolphins and finless porpoise (DFP), sperm and beaked whales (SBW) and dugongs, as color-coded stacked bars, where (a) sighting records—records where live animals were sighted (b) induced mortalities—records where animals were reported hunted or killed or were driven ashore, (c) incidental mortalities—records where animals were found dead after entanglement in fishing nets or being struck by vessels and (d) stranding records—records where dead or live animals were found washed ashore, or floating near shore or stranded alive and were attempted for rescue.Full size imageFigure 3Bubble plots showing distribution of marine mammal records obtained from data compiled between years 1748–2017 along the Indian coastline for each group i.e., baleen whales (BW), dolphins and finless porpoise (DFP), sperm and beaked whales (SBW) and dugongs, as color-coded stacked bars, where (a) sighting—records where live animals were sighted (b) induced mortalities—records where animals were reported hunted or killed or were driven ashore, (c) incidental mortalities—records where animals were found dead after entanglement in fishing nets or being struck by vessels and (d) strandings—records where dead or live animals were found washed ashore, or floating near shore or stranded alive and were attempted for rescue. Size of the bubble indicates number of individuals. These maps were created using ArcGIS 10.5 (https://desktop.arcgis.com/en/arcmap/10.3/map/working-with-layers/about-symbolizing-layers-to-represent-quantity.htm).Full size imageInduced mortalitiesA total of 59 incidences (ni = 102) were recorded of marine mammals being hunted/ captured between the years 1748–2017 (Fig. 1b, 2b, 3b). The total number of animals hunted/ captured deliberately is similar along east coast (ni = 33), west coast (ni = 29) and islands (ni = 36). Out of all marine mammal species, 90% of the animals hunted at the east coast were dugong D. dugon (ni = 30, all from Tamil Nadu). On the west coast, records of hunting incidences of finless porpoise Neophocaena phocaenoides were highest (79% of total records on west coast, Goa ni = 17, Kerala ni = 4, Karnataka and Maharashtra ni = 1). In the islands (i.e., Andaman and Nicobar Islands), 94% of the hunting records were of dugongs (ni = 34).Incidental mortalitiesA total of 240 net entanglements (ni = 1356) were reported along the Indian coast between the years 1748 and 2017 (Fig. 1c, 2c, 3c). Similar counts of individuals entangled along east (ni = 670) and west coast (ni = 654) were obtained with low reporting from the islands (ni = 26). Fourteen species were reported entangled from both east and west coast with only 4 species recorded from the islands. D. dugon was found to be most frequently entangled along the east coast (63 incidences, ni = 594, contributing to 56% of the total numbers on east coast), followed by Tursiops sp. (11 incidences, ni = 14, 9% of the east coast dataset). On the west coast, Tursiops sp. was the most frequently entangled (18 incidences, ni = 117, contributing to 18% of the west coast dataset), followed by N. phocaenoides (17 incidences, ni = 34, contributing to 17% of the dataset). The total number of DFP being entangled from west coast (ni = 623) were higher than east coast (ni = 68). More dugong individuals were entangled along east coast (i.e., from Tamil Nadu; ni = 594) as compared to the west coast (i.e., Gujarat; ni = 3) and Islands (i.e., Andaman and Nicobar; ni = 19). D. dugon was the most frequently entangled species in the islands (19 incidences, ni = 19, contributing to 79% of the total numbers in islands dataset) followed by false killer whale Pseudorca crassidens (3 incidences, ni = 5, contributing to 12% of the islands dataset). Very few BW or SBW (11 incidences, ni = 11) were recorded accidently entangled throughout the Indian coastline.StrandingsMarine mammals stranding reports consisted of 91.93% dead (ni = 581) and 8.07% live strandings (ni = 51) (Figs. 1d, 2d, 3d). Considering mass strandings as strandings with ni  > 2 (excluding mother and calf;33,34), 8.5% of all reports were mass strandings (21 strandings, ni = 1054). Most of the records did not have information about the sex of the stranded animal (83%), the age class (88%) or the state of decomposition of the carcass (53%). Highest strandings were reported of dugongs (strandings = 190, ni = 228), followed by BW (strandings = 178, ni =  = 190), DFP (strandings = 157, ni =  = 552) and SBW (strandings = 47, individuals = 48). There were 54 incidences (ni = 54, 9% of total stranding data) where the animal was not identified reliably to include in either of the groups.Species composition and frequencies of strandings were different on east coast, west coast and in the islands (Fig. 1, Table 1). Twenty-two species were reported as stranded on the east coast with D. dugon as the most frequently stranded species (83 incidences, ni = 107, ~ 29% of all records), followed by Indo-Pacific humpback dolphin Sousa chinensis, (31 incidences, ni = 108, ~ 10% of all records). On the west coast, out of 20 species reported as stranded, Balaenoptera musculus was most frequent (28 incidences, ni = 29, ~ 12% of all records) followed by N. phocaenoides (23 incidences, ni = 39, ~ 10% of all records). In the islands, 13 species were reported as stranded, D. dugon (93 incidences, ni = 102, contributing to 77% of the total animals found on the islands) followed by strandings of sperm whale Physeter macrocephalus (8 incidences, ni = 8, contributing to 6% of the data; Table 1).

a. Baleen whales

Table 1 Number of stranding events reported for marine mammals between 1748–2017 in India from the east coast, the west coast and Lakshadweep and Andaman & Nicobar archipelagos.Full size tableA total of 178 BW strandings (ni = 190) were reported. Most species were unidentified (east coast ni= 27, west coast ni = 58, islands ni = 4; i.e., 47% of the data). Identified strandings comprised of 6 species (see Table 1), some of which were later found to be misidentification (no confirmed evidence for common Minke Whale Balaenoptera acutorostrata, Sei Whale Balaenoptera borealis and Fin Whale Balaenoptera physalus from Indian waters; MMRCNI, 2018). Higher number of strandings occurred on the west coast (ni = 126), as compared to east coast (ni = 60). The east and west coast reported all six species of BW, whereas only three species stranded on the islands. B. borealis (misidentified) was the most stranded species across the east coast (12 incidences, ni = 12, contributing to 11% of the data) whereas blue whale Balaenoptera musculus was the most frequent across the west coast (28 incidences, ni = 29, contributing to 11% of the data). Baleen whale strandings were rare in the islands (4 incidences, ni = 4).Forty-seven SBW strandings (ni = 48) were reported along the Indian coast. More SBW stranded on the east coast (ni = 23) as compared to the west coast (ni = 13) and the islands (ni = 12). P. macrocephalus was most frequently reported (70% of all SBW records, east coast ni = 20, west coast ni = 6, islands ni = 8).There were 157 strandings (ni =552) of DFP belonging to 14 species. Twenty-one of these events were mass strandings (ni  > 2). The largest mass stranding event (ni = 147) occurred of short-finned pilot whale Globicephala macrorhynchus along the west coast (Tamil Nadu). Higher number of DFP strandings were recorded from east coast (ni = 418) as compared to west coast (ni = 83) and the islands (ni = 51; Table 1). East coast received a higher diversity of stranded DFP (number of species = 11) as compared to west coasts (number of species = 9) and the islands (number of species = 3). S. chinensis was the most frequently stranded species along the east coast (31 incidences, ni = 108, contributing to 33% of the data) whereas N. phocaenoides was the most frequent along the west coast (23 incidences, ni = 39, contributing to 37% of the data; Table 1).

d. Dugongs

The current distribution of dugongs in India is in the shallow coastal waters of Gujarat, Tamil Nadu and Andaman & Nicobar Islands37,38. There are 190 stranding events recorded between the years 1893 and 2017. The highest number of stranded dugongs were recorded from Tamil Nadu (ni = 107) closely followed by Andaman and Nicobar Islands (ni = 102) and few records from Gujarat (ni = 19).Temporal stranding patternsOur analysis of temporal trends for the last 42 years (1975–2017) showed that the mean number of strandings along the Indian coast was 11.25 ± SE 1.39 / year. The number of stranding reports show an increasing trend for two decades after 1975, dropping between 1995 and 2004. We observed a distinct rise in strandings post 2005 (18.23 ± SE 2.98 / year) with the highest reports from 2015–17 (27.66 ± SE 8.51/year) (Fig. 4).

a. Baleen whales

Figure 4A beanplot of decadal trends in marine mammal stranding in India from data compiled between years 1975–2017. Data prior to 1975 was discontinuous over the years to be considered for decadal trends. The data for last decade considered here includes only two years (2015–17) where increased reporting is evident. The bold horizontal lines indicate the mean number of strandings in each decade whereas the smaller horizontal lines indicate stranding numbers recorded for each year within the decade.Full size imageOn the west coast, mean stranding rate throughout the years (1975–2017) was 0.0010 ± SE 0.0014 strandings/km, and a steady rise was observed in rate of reported strandings after 2010. A seasonal trend was observed as well, with a peak in the month of September (sr = 0.0061 ± SE 0.0016 strandings/km), i.e., towards the end of monsoon season, and lowest strandings were recorded in the month of June (sr = 0.0016 ± SE 0.006 strandings/ km) (Fig. 5).Figure 5Temporal patterns (annual and monthly stranding rates / 100 km of coastline) in strandings of marine mammal records obtained from data compiled between years 1975–2017 along east and west coast of India for each group where (a) annual stranding rate and (b) monthly stranding rate for baleen whales (BW); (c) annual stranding rate and (d) monthly stranding rate for dolphins and finless porpoise (DFP); (e) annual stranding rate and (f) monthly stranding rate for sperm and beaked whales (SBW) and (g) annual stranding rate and (h) monthly stranding rate for dugongs.Full size imageThe mean stranding rate of BW on the east coast through 1975–2017 was 0.0013 ± SE 0.0017 strandings/km, but no specific trends were observed according to years or seasons. Stranding rates of BW did not differ between east and west coast (Mann–Whitney U test, U = 390, U standardized = -0.025, p value  > 0.05).The stranding rates of SBW differed significantly along both the coasts (Mann Whitney U test, U = 192, U standardized = 0.0, p value  More

Present fire-climate classificationTo identify the different regions of the planet with suitable climatic conditions for fire activity, we compare the global distribution of climate indicators based on temperature and precipitation, with satellite-derived GFED4 burned area data21 (Fig. 1). Starting from four general climates (Tr-tropical, Ar-arid, Te-temperate and Bo-boreal) based on the Köppen–Geiger climate classification main categories22, we create four fire-prone classes using climate thresholds to define the patterns observed in Fig. 1. Each category is characterised by the element that boosts fire activity during the FS: low precipitation, high temperatures or a combination of both. The classification is made by contrasting the probability distribution of the climatic variables at data points associated with high fire activity vs. points with low fire activity within the main Köppen-Geiger categories (see Threshold Selection in Methods section for a detailed explanation).Fig. 1: Burned area observations and climate drivers.a 1996–2016 maximum annual burned area (BAmax) and monthly burned area time series for selected regions. b Average monthly precipitation percentage from the annual total for the fire season (PPFS). c Average monthly temperature anomaly from the annual mean for the fire season (TAFS).Full size imageThe environmental conditions associated with fire occurrence emerge more clearly in this comparison, yielding the different threshold sets in Table 1 that determine the fire-prone months at any location (the selection method is detailed in the Methods section). We define those years with at least 1-month meeting the thresholds, as fire-prone years (FPY). Depending on the number of FPY at each location, the categories of Table 1 are sub-divided into recurrent (r), occasional (o) and infrequent (i) (see Methods). The average number of fire-prone months during the FPY is defined as the potential FS length (PFSL), i.e., the season with climatic characteristics prone to fire activity.Table 1 Fire classification defining criteria.Full size tableFigure 2a depicts the global map of the burned areas classified according to the selected thresholds (Table 1). Savanna fires are responsible for the largest proportion of burned area on the global scale21. The FS in these areas is longer than in other climates (see Supplementary Fig. 1) and, despite savanna fires being also dependent on ignition patterns and human policies and practices, the FS is tied to a pronounced seasonal cycle of precipitation23,24,25, with fire occurring mainly during the dry part of the cycle. Because of this, the Tropical – dry season fire class (Tr-ds) coincides with the distribution of the tropical savanna climate. In Fig. 2, boreal fires are represented as hot season fires (Bo-hs) due to the large positive temperature anomaly existing in those locations during the FS (Fig. 1c). In fact, temperature variations explain much of the variability in boreal burned area26,27. Temperate fires are classified as dry and hot season (Te-dhs) because they affect regions where the dry season coincides with the warm season (Fig. 1b, c). Here, high temperatures and precipitation seasonality determine fire activity and inter-annual burned area variability, e.g., in Western North America28,29,30,31 and Southern Europe32,33. Fire activity in arid regions occurs during warm months, but the relation with precipitation is more complex. The FS is associated with a hot season in cooler (MAT  27.5 °C), the FS starts right at the beginning of the dry season (e.g., the Sahel, Supplementary Fig. 12) while where MATs are more moderate, between 18.5 and 27.5 °C, it takes longer to develop (e.g., Central Australia and the Kalahari desert, Supplementary Figs. 12 and 13). Due to the dependency between fires and the existence of fuel in arid climates, we named this class Arid fuel limited (Ar-fl). A more in-depth discussion about the definition of this fire-climate class can be found in the section entitled Threshold selection for each climate of the Supplementary Information.Fig. 2: Fire-prone region classification.a With observed burned area data as a reference: not classified (NC, white) and misclassified (C, black) areas with BAmax = 0 ha, unclassified (NC, grey) and classified (Tr-ds, Ar-fl, Te-dhs and Bo-hs) areas with BAmax  > 0 ha. Each class is subdivided into three subcategories depending on the recurrence of the fire-prone conditions: recurrent (r), occasional (o) and infrequent (i). b Present (1996–2016) fire-prone climatic regions. c Future (2070–2099) fire-prone climatic regions with shaded grey representing a  0 ha) or fireless (BA = 0 ha). This reveals a two-way relation between fires and climate: fires take place under specific climatic conditions, and most places with these climatic conditions are indeed fire-prone, which supports our earlier hypothesis. Fire activity is controlled by weather, resources to burn and ignitions, as represented through the fire regime triangle12,20. On broad temporal scales and large spatial scales, temperature and precipitation have an important impact on fire because these climate variables influence vegetation type and the abundance, composition, moisture content, and structure of fuels34. Although ignitions may be driving fires to a greater extent than temperature or precipitation at specific locations or events35, they do not seem to limit fire activity at coarse spatial and temporal resolutions, implying that where fuels are sufficient and atmospheric conditions are conducive to combustion, the potential for ignition exists, either by lightning or human causes13,20. For all these reasons, we can identify specific climates that are prone to fires.The areas classified as fire-prone in Fig. 2b comprise 99.26% of the observed global mean annual burned area in Supplementary Fig. 2. This percentage is above 85% for all four general climates (Supplementary Fig. 20). The percentage of land area with non-zero burned area data classified as fire-prone is 91.22%. Considering for each location only the obtained FPY, the percentage of the observed burned area classified is 90.36%. Furthermore, the PFS obtained in the fire-climate classification (Fig. 3b) also correlates well with the timing of observed fire incidence, as globally 87.91% of the observed mean burned area occurs during the identified months of PFS at classified fire-prone locations.Fig. 3: Potential fire season.a Future minus present potential fire season length (PFSL) difference in months (ΔPFSL). b Present potential fire season. c Future potential fire season.Full size imageUnclassified regions (in grey in Fig. 2a) correspond for the most part to those with the least burned area or those where agricultural practices modify the climatic seasonality of fires. In addition, as the classification is conceived from a climatic point of view, locations with fire activity associated with specific meteorological conditions that are not appreciable at the monthly temporal resolution, are probably not well identified. For example, a week of extremely high temperatures could be almost unnoticeable in the monthly mean temperature, but not in fire activity. Similarly, months with the same total precipitation may have different fire activity if the precipitation falls concentrated in a few days or is distributed throughout the month. Furthermore, the short temporal sampling period of the burned area data could also be influencing our results. Locations with long fire cycles may not be well represented in the data.Future fire-climate classificationA future fire-climate classification map is derived by applying the thresholds obtained in the present fire-climate classification to future climatology variables from multiple coupled model intercomparison project phase 5 (CMIP5) global circulation model (GCM) outputs, considering the RCP8.5 scenario (the worst-case climate change scenario of the CMIP5). Two contrasting approaches can be taken for analysing future fire activity, one that considers quick vegetation adaptation to the new climatic conditions, and another that does not. These two approaches clearly diverge in the boreal regions, where the biome (mainly taiga) is strongly conditioned by the low temperatures and where future temperature changes at the end of the 21st century will have a greater amplitude. It is expected that the boreal forest of these areas will not be immediately replaced by a temperate mixed forest where the average annual temperature exceeds the range of values typical of the taiga biome. Terrestrial vegetation compositional and structural change could occur during the 21st century where vegetation disturbance is accelerated or amplified by human activity, but equilibrium states may not be reached until the 22nd century or beyond36.Based on the assumption that during the future period (2070–2099) the vegetation will not be fully adapted to the new climatic conditions, and since the present Köppen–Geiger climate classification (on which we base our Tr, Ar, Te and Bo categories) closely corresponds to the different existent biomes22, we analyse only the projected changes in the specific fire-climate classification variables, maintaining the general division of Tropical, Arid, Temperate and Boreal regions as is in present climate conditions. The future fire-climate classification is shown in Fig. 2c.We note that we determine future fire activity from relationships of the latter with the present climate; however, these relationships might not be stationary. Our approach does not contemplate possible future changes in precipitation frequency if they are not noticeable in monthly precipitation amounts. Areas with the rising incidence of extreme precipitation events due to global warming37 may experience an increase in monthly precipitation but a decrease in rainy days, which may lead us to consider the conditions there less favourable for fire activity than they actually will be.Future changes in global fire activityModelled future fire-prone regions experience significant variations with respect to the present (Fig. 2b, c). Due to global warming, the Bo-hs fire class pertaining to boreal forests would spread over a larger area, reaching most of Northern Scandinavia and undergoing a southward and northward expansion in Canada, Alaska and Russia. This category may experience a percentual expansion of 47.0% according to our results. This expansion is more accentuated for the combination of the highest recurrence subcategories Bo-hs-r and Bo-hs-o, reaching a value of 111.5%.The conjunction of Te-dhs-r and Te-dhs-o fire classes of midlatitudes also undergoes a considerable expansion of 24.5% in the area (Fig. 2b, c). The most remarkable changes are expected in Southern China and Southern Europe. A large part of Europe transitions from an infrequent fire category to a more frequent fire category with Csa and Csb Mediterranean climates38.The Tr-ds fire classes with frequent fire-prone conditions in the Tropics presents fewer spatial changes (Fig. 2b, c), with a spatial contraction of 6.3%. The most important differences are found in South America. Some of the climate model results considered here indicate also that some parts of the Eastern Amazon rainforest will move from a non-fire class to Tr-ds fire class, as other studies have suggested39.The Arid fire-prone classes Ar-fl-r and Ar-fl-o would increase its area by 5.0%. Projected changes in the extent of this class are very sensitive to changes in annual precipitation, conducive to vegetation and fuel reduction or increment, thus there is significant uncertainty in the proximity of desert regions (Fig. 2c).Clearer conclusions can be drawn from the FPY and PFSL calculation (Figs. 3 and 4). The number of months meeting the set of conditions in Table 1 yields the estimated PFSL (Fig. 3b), and the number of years with at least 1-month meeting the thresholds, the FPY. In the boreal regions, we obtain a general lengthening of the PFS. The PFS of these areas is conditioned by temperature, so the amplified warming of Artic zones40 is expected to make the FS longer. Notwithstanding, in certain parts of Eastern Asia, the intense warming is counterbalanced by an increase of the precipitation in certain warm months (see Supplementary Figs. 21 and 22), leading to a slight shortening of our estimated PFS. There is evidence, however, that temperature increases may lead to drier fuels in the future despite the precipitation increase, thus augmenting fire risk, as some investigations have shown for Canada41. Our results agree in general with several other studies that have previously pointed towards an increase of the FSL in boreal areas1,17,42, even when some suggest a more pronounced lengthening in more northerly latitudes1,17. In terms of the frequency of years with fire-prone conditions, the conclusions are even clearer. A general increase of the FPY is observed, especially for northerly latitudes, where the differences reach values of more than +4 years per decade (Fig. 4a). This possible increase in fire activity in boreal areas may result in significant peatland combustion and a release of the large quantities of soil carbon that they store into the atmosphere43. These greenhouse gas emissions may create a positive feedback loop, leading to a further increase in temperature, which in turn will enhance boreal wildfire incidence and more peatland burning.Fig. 4: Fire-prone years.a Future minus a present number of years with at least one month classified as fire-prone per decade (ΔFPY). b Present fire-prone years per decade. c Future fire-prone years per decade.Full size imageThe Te-dhs fire class, corresponding to temperate climates, would also experience a general lengthening of the PFS (Fig. 3). A future precipitation decline may be especially significant in Southern Europe (Supplementary Fig. 21), associated with an increased anticyclonic circulation yielding more stable conditions44, while the temperature rise would be quite homogeneous among all Te-dhs fire-climate class areas. The FS drought intensification around the Mediterranean, together with the general warming (Supplementary Fig. 21), would lead to a lengthening of the PFS of around 2 months (Fig. 3a), but summer months could also experience this precipitation decline (Supplementary Fig. 22), meaning that the FS would be more severe. The Western US, which has already experienced over the last decades the lengthening of the FS45 and the increase of large fires46 and extreme wildfire weather47,48 due to climate change, may also experience an FS lengthening by the end of the 21st century. Some authors18,48,49,50 have studied projected fire future changes from other points of view (occurrence of very large fires, wildfire potential, etc.), finding also a general increase of fire severity by the end of the century in some of these Te-dhs fire regions. The interannual recurrence of fire-prone conditions will significantly increase in countries like France, Italy or Eastern China (Fig. 4a).The PFSL of the Tropical Tr-ds fire-climate class presents slight differences between present and future values (Fig. 3). Some areas of the Northern African savanna may experience a shortening of the PFS, while Southern Africa shows a lengthening. A dipole pattern of wetting in tropical Eastern Africa and drying in Southern Africa51 could be the reason for these future changes. There is a contrasting influence of ENSO in present African fire patterns52, which suggests that the future pattern of precipitation variations in Central Africa may be associated with ENSO future changes under climate change conditions53. Although the quantification of ENSO changes in a warmer climate is still an issue that continues to be investigated, an expansion and strengthening of ENSO teleconnections is confirmed by some authors53,54,55. The general increase in precipitation along all seasons in western equatorial Africa would lead to a significant decrease in the recurrence of interannual fire-prone conditions (Fig. 4a).Our results show that fire-prone areas in Temperate and especially Boreal climates are projected to undergo the most significant expansion and lengthening of the potential FS at the end of the XXI century driven by rising temperatures. In the Tropics, little change is expected in these respects. Notwithstanding, global warming is likely to make fire risk more severe mostly everywhere, and in particular in some regions such as Mediterranean Europe and the Eastern Amazon, where an important decrease in precipitation is also predicted during the PFS. More favourable fire conditions will potentially increment fire activity and burned areas in many places. In others, especially in the Tropics, increasing suppression efforts and a cease to agricultural and pastoral practices like vegetation clearing by fire, replaced by more intensive farming, could counteract the impact of a warmer climate. A reduction of these human-caused fires in the Tropics could bring global burned area down2, despite rising trends elsewhere, given the vast contribution of Tropical fires to the burned areas at the global scale (Fig. 1). More

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DatasetsWe use the Amazon basin (http://worldmap.harvard.edu/data/geonode:amapoly_ivb, accessed 28 January 2021) as our region of study. To determine the grid cells that are contained within Brazil for a subset of analysis, we use the ‘maps’ package in R (v.3.3.0; https://CRAN.R-project.org/package=maps). This is also used in the plotting of country outlines. The main dataset used to determine forest health is from VODCA33, of which we use the Ku-band product. These data are available at 0.25° × 0.25° at a monthly resolution from January 1988 to December 2016. We also use NOAA AVHRR NDVI34. For precipitation data, we use the CHIRPS dataset40 downloaded from Google Earth Engine at a monthly resolution. Finally, to determine land cover types, we used the IGBP MODIS land cover dataset MCD12C1 (ref. 37). All these datasets are at a higher spatial resolution than the VODCA dataset and thus we downscale them to match the lower resolution. Our SST data comes from HadISST49, where we define a North Atlantic region (15–70° W, 5–25° N), for which we take the spatial mean. The mean monthly cycle is then removed to produce anomalies.For the vegetation datasets that we measure the resilience indicators on (below), we use STL decomposition (seasonal and trend decomposition using Loess)51 using the stl() function in R. This splits time series in each grid cell into an overall trend, a repeating annual cycle (by using the ‘periodic’ option for the seasonal window) and a residual component. We use the residual component in our resilience analysis. The first 3 yr of data had large jumps in VOD which were seen when testing other regions of the world as well as in the Amazon region. Hence, we restrict our analysis to the period January 1991 to December 2016.To test the robustness of the detrending, we also vary the size of the trend window in the stl() function. The results from these alternatively detrended time series are shown in Supplementary Fig. 4. The results are also robust to varying the window used to calculate the seasonal component rather than using ‘periodic’; at the strictest plausible value of 13, we still see the same increases in AR(1) (Supplementary Fig. 5).For the AMO index shown in Supplementary Fig. 13, data come from the Kaplan SST dataset and can be downloaded from https://psl.noaa.gov/data/timeseries/AMO/.Grid cell selectionWe use the IGBP MODIS land cover dataset at the resolution described above to determine which grid cells to use in our analysis. The dataset is available at an annual resolution from 2001 to 2018 (but we only use the time series up to 2016 to match the time span of our VOD and NDVI datasets). To focus on changes in forest resilience, we use grid cells where the evergreen BL fraction is ≥80% in 2001. Grid cells are treated as human land-use area if the built-up, croplands or vegetation mosaics fraction is >0%. We remove grid cells that have human land use in them from our forest analysis, regardless of if there is ≥80% BL fraction in the grid cell.We measure the minimum distance between forested Amazon basin grid cells and human land-use grid cells in 2016 (believing this to be the most cautious and least biased way to measure distance) using the latitude and longitude of each grid point and computing the great-circle distance. We use human land-use grid cells over a larger area than the basin, so that we can determine the closest distance to human land use, regardless of whether this human land use lies within the basin. We also measure the minimum distance from human land use or roads in Brazil, where we have reliable data on state and federal roads (https://datacatalog.worldbank.org/dataset/brazil-road-network-federal-and-state-highways). As in the main text, we reiterate that these minimum distances can be viewed as the maximum distance from human land use as our data will not include roads for the full Amazon basin, or non-federal or non-state roads in Brazil that will have human activity associated with them.To ensure that the pattern of changes in resilience is not a consequence of more settlements being in the southeast of the region, combined with the gradient of rainfall from northwest to southeast typical of the rainforest, we measure the correlation between MAP and the distances from the urban grid cells, which is very weak (Spearman’s ρ = 0.109, P  More