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

Froehner, S., Rizzi, J., Vieira, L. M. & Sanez, J. PAHs in water, sediment and biota in an area with port activities. Arch. Environ. Contam. Toxicol. 75, 236–246. https://doi.org/10.1007/s00244-018-0538-6 (2018).CAS 
Article 
PubMed 

Google Scholar 
Anyanwu, I. N., Sikoki, F. D. & Semple, K. T. Risk assessment of PAHs and N-PAH analogues in sediment cores from the Niger Delta. Mar. Pollut. Bull. 161, 111684. https://doi.org/10.1016/j.marpolbul.2020.111684 (2020).CAS 
Article 
PubMed 

Google Scholar 
Han, B., Cui, D. Y., Liu, A., Li, Q. & Zheng, L. Distribution, sources, and risk assessment of polycyclic aromatic hydrocarbons (PAHs) in surface sediments from Daya Bay, South China. Environ. Sci. Pollut. Res. 28, 25858–25865. https://doi.org/10.1007/s11356-020-11956-w (2021).CAS 
Article 

Google Scholar 
Li, J. W. et al. Polycyclic aromatic hydrocarbons in water, sediment, soil, and plants of the Aojiang River waterway in Wenzhou, China. J. Hazardous Mater. 173, 75–81. https://doi.org/10.1016/j.jhazmat.2009.08.050 (2010).CAS 
Article 

Google Scholar 
Honda, M. & Suzuki, N. Toxicities of polycyclic aromatic hydrocarbons for aquatic animals. Int. J. Environ. Res. Public Health 17, 1363. https://doi.org/10.3390/ijerph17041363 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Lu, G. N., Tao, X. Q., Dang, Z., Yi, X. Y. & Yang, C. Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors. Cent. Eur. J. Chem. 6, 310–318. https://doi.org/10.2478/s11532-008-0010-y (2008).CAS 
Article 

Google Scholar 
Yuan, H. Z., Zhang, E. L., Lin, Q., Wang, R. & Liu, E. F. Sources appointment and ecological risk assessment of polycyclic aromatic hydrocarbons (PAHs) in sediments of Erhai Lake, a low-latitude and high-altitude lake in southwest China. Environ. Sci. Pollut. Res. 23, 4430–4441. https://doi.org/10.1007/s11356-015-5626-9 (2016).CAS 
Article 

Google Scholar 
Souza, M. R. R. et al. Concentration, distribution and source apportionment of polycyclic aromatic hydrocarbons (PAH) in Poxim River sediments, Brazil. Mar. Pollut. Bull. 127, 478–483. https://doi.org/10.1016/j.marpolbul.2017.12.045 (2018).CAS 
Article 
PubMed 

Google Scholar 
Davis, E. et al. Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in small craft harbor (SCH) surficial sediments in Nova Scotia, Canada. Sci. Total Environ. 691, 528–537. https://doi.org/10.1016/j.scitotenv.2019.07.114 (2019).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Dreyer, A., Radke, M., Turunen, J. & Blodau, C. Long-term change of polycyclic aromatic hydrocarbon deposition to peatlands of eastern Canada. Environ. Sci. Technol. 39, 3918–3924. https://doi.org/10.1021/es0481880 (2005).CAS 
Article 
PubMed 
ADS 

Google Scholar 
Ma, W. L. et al. Polycyclic aromatic hydrocarbons in water, sediment and soil of the Songhua River Basin, China. Environ. Monitoring Assessment 185, 8399–8409. https://doi.org/10.1007/s10661-013-3182-7 (2013).CAS 
Article 

Google Scholar 
Yang, Y. Y. et al. Distributions, compositions, and ecological risk assessment of polycyclic aromatic hydrocarbons and phthalic acid esters in surface sediment of Songhua river, China. Mar. Pollut. Bull. 152, 10923. https://doi.org/10.1016/j.marpolbul.2020.110923 (2020).CAS 
Article 

Google Scholar 
Rahmanpoor, S., Ghafourian, H., Hashtroudi, S. M. & Bastami, K. D. Distribution and sources of polycyclic aromatic hydrocarbons in surface sediments of the Hormuz strait, Persian Gulf. Mar. Pollut. Bull. 78, 224–229. https://doi.org/10.1016/j.marpolbul.2013.10.032 (2014).CAS 
Article 
PubMed 

Google Scholar 
Aghadadashi, V., Mehdinia, A., Bakhtiari, A. R., Mohammadi, J. & Moradi, M. Source, spatial distribution, and toxicity potential of Polycyclic Aromatic Hydrocarbons in sediments from Iran’s environmentally hot zones, the Persian Gulf. Ecotoxicol. Environ. Saf. 173, 514–525. https://doi.org/10.1016/j.ecoenv.2019.02.029 (2019).CAS 
Article 
PubMed 

Google Scholar 
Zhang, L., Zhu, B., Gao, J. H. & Kang, H. Q. Impact of Taihu Lake on city ozone in the Yangtze River Delta. Adv. Atmos. Sci. 34, 226–234. https://doi.org/10.1007/s00376-016-6099-6 (2017).CAS 
Article 

Google Scholar 
Huang, S. B., Qiao, M., Wang, H. & Wang, Z. J. Organchlorinated pesticides in surface sediments of meiliang bay in Taihu Lake, China. J. Environ. Sci. Health Part a-Toxic/Hazardous Substances Environ. Eng. 41, 223–234. https://doi.org/10.1080/10934520500354664 (2006).CAS 
Article 

Google Scholar 
Su, H. L. et al. Distribution characteristics and risk assessments of PAHs in fish from Lake Taihu, China. Hum. Ecol. Risk Assessment 21, 1753–1765. https://doi.org/10.1080/10807039.2014.975003 (2015).CAS 
Article 

Google Scholar 
Wang, W. W., Qu, X. L., Lin, D. H. & Yang, K. Octanol-water partition coefficient (logKow) dependent movement and time lagging of polycyclic aromatic hydrocarbons (PAHs) from emission sources to lake sediments: A case study of Taihu Lake, China. Environ. Pollut. 288, 117709. https://doi.org/10.1016/j.envpol.2021.117709 (2021).CAS 
Article 
PubMed 

Google Scholar 
Peng, X. Z., Zhang, G., Zheng, L. P., Mai, B. X. & Zeng, S. W. The vertical variations of hydrocarbon pollutants and organochlorine pesticide residues in a sediment core in Lake Taihu, East China. Geochem.-Exploration Environ. Anal. 5, 99–104. https://doi.org/10.1144/1467-7873/03-038 (2005).CAS 
Article 

Google Scholar 
Zhang, Y., Lu, Y. & Zhao, W. Y. Spatial distribution of polycyclic aromatic hydrocarbons from Lake Taihu, China. Bull. Environ. Contam. Toxicol. 87, 80–85. https://doi.org/10.1007/s00128-011-0292-1 (2011).CAS 
Article 
PubMed 

Google Scholar 
Dong, Y. B. et al. Polycyclic aromatic hydrocarbons in sediments from typical Algae, Macrophyte Lake Bay and adjoining river of Taihu Lake, China: Distribution, sources, and risk assessment. Water 13, 470. https://doi.org/10.3390/w13040470 (2021).CAS 
Article 

Google Scholar 
Chen, P. & Liang, J. Polycyclic aromatic hydrocarbons in green space soils in Shanghai: Source, distribution, and risk assessment. J. Soils Sediments 21, 967–977. https://doi.org/10.1007/s11368-020-02838-2 (2021).CAS 
Article 

Google Scholar 
Xia, Z. et al. New approaches to reduce sample processing times for the determination of polycyclic aromatic compounds in environmental samples. Chemosphere 274, 129738. https://doi.org/10.1016/j.chemosphere.2021.129738 (2021).CAS 
Article 
PubMed 
ADS 

Google Scholar 
Sun, T., Wang, Y. H., Tian, J. M. & Kong, X. G. Characteristics of PAHs in soils under different land-use types and their associated health risks in the northern Taihu Basin, China. J. Soils Sediments 22, 134–145. https://doi.org/10.1007/s11368-021-03050-6 (2022).CAS 
Article 

Google Scholar 
Abdollahi, S. et al. Contamination levels and spatial distributions of heavy metals and PAHs in surface sediment of Imam Khomeini Port, Persian Gulf, Iran. Mar. Pollut. Bull. 72, 336–345. https://doi.org/10.1016/j.marpolbul.2013.01.025 (2013).CAS 
Article 

Google Scholar 
Santos, E. et al. Polycyclic aromatic hydrocarbons (PAH) in superficial water from a tropical estuarine system: Distribution, seasonal variations, sources and ecological risk assessment. Mar. Pollut. Bull. 127, 352–358. https://doi.org/10.1016/j.marpolbul.2017.12.014 (2018).CAS 
Article 
PubMed 

Google Scholar 
Long, E. R., Field, L. J. & Macdonald, D. D. Predicting toxicity in marine sediments with numerical sediment quality guidelines. Environ. Toxicol. Chem. 17, 714–727. https://doi.org/10.1002/etc.5620170428 (1998).CAS 
Article 

Google Scholar 
Han, B., Liu, A., He, S., Li, Q. & Zheng, L. Composition, content, source, and risk assessment of PAHs in intertidal sediment in Shilaoren Bay, Qingdao, China. Mar. Pollut. Bull. 159, 111499. https://doi.org/10.1016/j.marpolbul.2020.111499 (2020).CAS 
Article 
PubMed 

Google Scholar 
Abba, E. J., Unnikrishnan, S., Kumar, R., Yeole, B. & Chowdhury, Z. Fine aerosol and PAH carcinogenicity estimation in outdoor environment of Mumbai City, India. Int. J. Environ. Health Res. 22, 134–149. https://doi.org/10.1080/09603123.2011.613112 (2012).CAS 
Article 
PubMed 

Google Scholar 
Ji, H., Zhang, D. & Shinohara, R. Size distribution and estimated carcinogenic potential of particulate polycyclic aromatic hydrocarbons collected at a downtown site in Kumamoto, Japan, in Spring. J. Health Sci. 53, 700–707. https://doi.org/10.1248/jhs.53.700 (2007).CAS 
Article 

Google Scholar 
Lei, P., Zhang, H. & Shan, B. Q. Vertical records of sedimentary PAHs and their freely dissolved fractions in porewater profiles from the northern bays of Taihu Lake, Eastern China. RSC Adv. 6, 98835–98844. https://doi.org/10.1039/c6ra11180g (2016).CAS 
Article 
ADS 

Google Scholar 
Li, A. L. et al. Sedimentary archive of Polycyclic Aromatic Hydrocarbons and perylene sources in the northern part of Taihu Lake, China. Environ. Pollut. 246, 198–206. https://doi.org/10.1016/j.envpol.2018.11.112 (2019).CAS 
Article 
PubMed 

Google Scholar 
Zhang, Y. et al. Potential source contributions and risk assessment of PAHs in sediments from Taihu Lake, China: Comparison of three receptor models. Water Res. 46, 3065–3073. https://doi.org/10.1016/j.watres.2012.03.006 (2012).CAS 
Article 
PubMed 
ADS 

Google Scholar 
Qiao, M., Wang, C. X., Huang, S. B., Wang, D. H. & Wang, Z. J. Composition, sources, and potential toxicological significance of PAHs in the surface sediments of the Meiliang Bay, Taihu Lake, China. Environ. Int. 32, 28–33. https://doi.org/10.1016/j.envint.2005.04.005 (2016).CAS 
Article 

Google Scholar 
Sun, L., Zang, S. Y. & Sun, H. J. Sources and history of PAHs in lake sediments from oil-producing and industrial areas, northeast China. Int. J. Environ. Sci. Technol. 11, 2051–2060. https://doi.org/10.1007/s13762-013-0396-8 (2014).CAS 
Article 

Google Scholar 
Guo, J. Y. et al. Screening level of PAHs in sediment core from Lake Hongfeng, Southwest China. Arch. Environ. Contam. Toxicol. 60, 590–596. https://doi.org/10.1007/s00244-010-9568-4 (2011).CAS 
Article 
PubMed 

Google Scholar 
Zhang, Z. D. et al. A study on PAHs in the surface soil of the region around Qinghai Lake in the Tibet plateau: Evaluation of distribution characteristics, sources and ecological risks. Environ. Res. Commun. 3, 041005. https://doi.org/10.1088/2515-7620/abf3d9 (2021).Article 

Google Scholar 
Li, C. C. et al. Spatial distribution, potential risk assessment, and source apportionment of polycyclic aromatic hydrocarbons (PAHs) in sediments of Lake Chaohu, China. Environ. Sci. Pollut. Res. 21, 12028–12039. https://doi.org/10.1007/s11356-014-3137-8 (2014).CAS 
Article 

Google Scholar 
Romo-Gomez, C., Monks, S., Pulido-Flores, G. & Gordillo-Martinez, A. J. Determination of polycyclic aromatic hydrocarbons (PAHs) in superficial water and sediment of Lake Tecocomulco, Mexico. Interciencia 35, 905–911 (2010).
Google Scholar 
Yuan, Z. J. et al. Polycyclic aromatic hydrocarbons (PAHs) in urban stream sediments of Suzhou Industrial Park, an emerging eco-industrial park in China: Occurrence, sources and potential risk. Ecotoxicol. Environ. Saf. 214, 112095. https://doi.org/10.1016/10.1016/j.ecoenv.2021.112095 (2021).CAS 
Article 
PubMed 

Google Scholar 
Shen, B. B., Wu, J. L. & Zhao, Z. H. Residues of organochlorine pesticides and polycyclic aromatic hydrocarbons in surface waters, soils and sediments of the Kaidu River catchment, northwest China. Int. J. Environ. Pollut. 63, 104–116. https://doi.org/10.1504/IJEP.2018.10014155 (2018).CAS 
Article 

Google Scholar 
Yan, W., Chi, J. S., Wang, Z. Y., Huang, W. X. & Zhang, G. Spatial and temporal distribution of polycyclic aromatic hydrocarbons (PAHs) in sediments from Daya Bay, South China. Environ. Pollut. 157, 1823–1830. https://doi.org/10.1016/j.envpol.2009.01.023 (2009).CAS 
Article 
PubMed 

Google Scholar 
Arias, A. H. et al. Presence, distribution, and origins of polycyclic aromatic hydrocarbons (PAHs) in sediments from Bahia Blanca estuary, Argentina. Environ. Monitor. Assess. 160, 301–314. https://doi.org/10.1007/s10661-008-0696-5 (2010).CAS 
Article 

Google Scholar 
Yunker, M. B. et al. PAHs in the Fraser River basin: A critical appraisal of PAH ratios as indicators of PAH source and composition. Org. Geochem. 33, 489–515. https://doi.org/10.1016/S0146-6380(02)00002-5 (2002).CAS 
Article 

Google Scholar 
Tobiszewski, M. & Namiesnik, J. PAH diagnostic ratios for the identification of pollution emission sources. Environ. Pollut. 162, 110–119. https://doi.org/10.1016/j.envpol.2011.10.025 (2012).CAS 
Article 
PubMed 

Google Scholar 
Chen, M. H., Li, C. H., Ye, C. & Xu, S. H. Distribution, sources and risk assessment of polycyclic aromatic hydrocarbon in sediments from Zhushan Bay littoral zone, Lake Taihu. J. Environ. Eng. Technol. 4, 199–205 (2014) (in Chinese).
Google Scholar 
Zhao, Q., Yu, Q. & Chen, L. M. Particulate matter and particle-bound polycyclic aromatic hydrocarbons in the Dapu road tunnel in Shanghai. Int. J. Environ. Pollut. 41, 21–37. https://doi.org/10.1504/IJEP.2010.032243 (2010).CAS 
Article 

Google Scholar 
Tian, Y. Z. et al. Source contributions and spatiotemporal characteristics of PAHs in sediments: Using three-way source apportionment approach. Environ. Toxicol. Chem. 33, 1747–1753. https://doi.org/10.1002/etc.2628 (2014).CAS 
Article 
PubMed 

Google Scholar 
Zhang, F. et al. Polycyclic aromatic hydrocarbons (PAHs) and Pb isotopic ratios in a sediment core from Shilianghe Reservoir, eastern China: Implying pollution sources. Appl. Geochem. 66, 140–148. https://doi.org/10.1016/j.apgeochem.2015.12.010 (2016).CAS 
Article 
ADS 

Google Scholar 
Harrison, R. M., Smith, D. J. T. & Luhana, L. Source apportionment of atmospheric polycyclic aromatic hydrocarbons collected from an urban location in Birmingham, UK. Environ. Sci. Technol. 30, 825–832. https://doi.org/10.1021/es950252d (1996).CAS 
Article 
ADS 

Google Scholar 
Simcik, M. F., Eisenreich, S. J. & Lioy, P. J. Source apportionment and source/sink relationships of PAHs in the coastal atmosphere of Chicago and Lake Michigan. Atmos. Environ. 33, 5071–5079. https://doi.org/10.1016/S1352-2310(99)00233-2 (1999).CAS 
Article 
ADS 

Google Scholar 
Yang, J., Xu, W. L. & Cheng, H. Y. Seasonal variations and sources of airborne polycyclic aromatic hydrocarbons (PAHs) in Chengdu, China. Atmosphere 9, 63. https://doi.org/10.3390/atmos9020063 (2018).CAS 
Article 
ADS 

Google Scholar 
Cetin, B. Investigation of PAHs, PCBs and PCNs in soils around a Heavily Industrialized Area in Kocaeli, Turkey: Concentrations, distributions, sources and toxicological effects. Sci. Total Environ. 560, 160–169. https://doi.org/10.1016/j.scitotenv.2016.04.037 (2016).CAS 
Article 
PubMed 
ADS 

Google Scholar 
Ali-Taleshi, M. S., Squizzato, S., Riyahi Bakhtiari, A., Moeinaddini, M. & Masiol, M. Using a hybrid approach to apportion potential source locations contributing to excess cancer risk of PM25-bound PAHs during heating and non-heating periods in a megacity in the Middle East. Environ. Res. 201, 111617. https://doi.org/10.1016/j.envres.2021.111617 (2021).CAS 
Article 
PubMed 

Google Scholar 
Xu, J. et al. Historical trends of concentrations, source contributions and toxicities for PAHs in dated sediment cores from five lakes in western China. Sci. Total Environ. 470, 519–526. https://doi.org/10.1016/j.scitotenv.2013.10.022 (2014).CAS 
Article 
PubMed 
ADS 

Google Scholar 
Wei, H., Liu, G. B., Yong, T. & Qin, Z. Emission of polycyclic aromatic hydrocarbons from different types of motor vehicles’ exhaust. Environ. Earth Sci. 74, 5557–5564. https://doi.org/10.1007/s12665-015-4570-9 (2015).CAS 
Article 

Google Scholar  More

Ballard JWO, Melvin RG, Katewa SD, Maas K (2007) Mitochondrial DNA variation is associated with measurable differences in life‐history traits and mitochondrial metabolism in Drosophila simulans. Evolution 61:1735–1747. https://doi.org/10.1111/j.1558-5646.2007.00133.xCAS 
Article 
PubMed 

Google Scholar 
Balloux F, Handley LJL, Jombart T, Liu H, Manica A (2009) Climate shaped the worldwide distribution of human mitochondrial DNA sequence variation. P R Soc B-Biol Sci 276:3447–3455. https://doi.org/10.1098/rspb.2009.0752CAS 
Article 

Google Scholar 
Boore JL (1999) Animal mitochondrial genomes. Nucleic Acids Res 27:1767–1780. https://doi.org/10.1093/nar/27.8.1767CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Brijs J, Sandblom E, Sundh H, Grans A, Hinchcliffe J, Ekstrom A, Sundell K, Olsson C, Axelsson M, Pichaud N (2017) Increased mitochondrial coupling and anaerobic capacity minimizes aerobic costs of trout in the sea. Sci Rep. 7:45778. https://doi.org/10.1038/srep45778CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Caballero S, Duchene S, Garavito MF, Slikas B, Baker CS (2015) Initial evidence for adaptive selection on the NADH subunit two of freshwater dolphins by analyses of mitochondrial genomes. Plos One 10:e0123543. https://doi.org/10.1371/journal.pone.0123543CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Camus MF, Wolff JN, Sgro CM, Dowling DK (2017) Experimental support that natural selection has shaped the latitudinal distribution of mitochondrial haplotypes in Australian Drosophila melanogaster. Mol Biol Evol 34:2600–2612CAS 
Article 

Google Scholar 
Carapelli A, Fanciulli PP, Frati F, Leo C (2019) Mitogenomic data to study the taxonomy of Antarctic springtail species (Hexapoda: Collembola) and their adaptation to extreme environments. Polar Biolo 1:1–8. https://doi.org/10.1007/s00300-019-02466-8Article 

Google Scholar 
Chang DD, Clayton DA (1986) Identification of primary transcriptional start sites of mouse mitochondrial DNA: accurate in vitro initiation of both heavy and light strand transcripts. Mol Cell Biol 6:1446–1453. https://doi.org/10.1128/MCB.6.5.1446CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Chen H, Sun S, Norenburg JL, Sundberg P (2014) Mutation and selection cause codon usage and bias in mitochondrial genomes of ribbon worms (Nemertea). Plos One 9:e85631. https://doi.org/10.1371/journal.pone.0085631CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Clayton DA (1991) Replication and transcription of vertebrate mitochondrial DNA. Annu Rev Cell Biol 7:453–478. https://doi.org/10.1146/annurev.cb.07.110191.002321CAS 
Article 
PubMed 

Google Scholar 
Consuegra S, John E, Verspoor E, De Leaniz CG (2015) Patterns of natural selection acting on the mitochondrial genome of a locally adapted fish species. Genet Sel Evol 47:1–10. https://doi.org/10.1186/s12711-015-0138-0CAS 
Article 

Google Scholar 
Crofts AR (2004) The cytochrome bc 1 complex: function in the context of structure. Annu Rev Physiol 66:689–733. https://doi.org/10.1146/annurev.physiol.66.032102.150251CAS 
Article 
PubMed 

Google Scholar 
Da Fonseca RR, Johnson WE, O’Brien SJ, Ramos MJ, Antunes A (2008) The adaptive evolution of the mammalian mitochondrial genome. BMC Genomics 9:119Article 

Google Scholar 
de Oliveira JL, Morales AC, Hurst LD, Urrutia AO, Thompson CR, Wolf JB (2021) Inferring adaptive codon preference to understand sources of selection shaping codon usage bias. Mol Biol Evol 38:3247–3266. https://doi.org/10.1093/molbev/msab099CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dowling DK, Friberg U, Lindell J (2008) Evolutionary implications of non-neutral mitochondrial genetic variation. Trends Ecol Evol 23:546–554. https://doi.org/10.1016/j.tree.2008.05.011Article 
PubMed 

Google Scholar 
Egan JP, Bloom DD, Kuo CH, Hammer MP, Tongnunui P, Iglesias SP, Sheaves M, Grudpan C, Simons AM (2018) Phylogenetic analysis of trophic niche evolution reveals a latitudinal herbivory gradient in Clupeoidei (herrings, anchovies, and allies). Mol Phylogenet Evol 124:151–161. https://doi.org/10.1016/j.ympev.2018.03.011Article 
PubMed 

Google Scholar 
Elbassiouny AA, Lovejoy NR, Chang BS (2020) Convergent patterns of evolution of mitochondrial oxidative phosphorylation (OXPHOS) genes in electric fishes. Philos T R Soc B 375:20190179. https://doi.org/10.1098/rstb.2019.0179CAS 
Article 

Google Scholar 
Evans DH, Piermarini PM, Choe KP (2005) The multifunctional fish gill: dominant site of gas exchange, osmoregulation, acid-base regulation, and excretion of nitrogenous waste. Physiol Rev 85:97–177CAS 
Article 

Google Scholar 
Foote AD, Morin PA, Durban JW, Pitman RL, Wade P, Willerslev E, Gilbert MT, Da Fonseca RR (2011) Positive selection on the killer whale mitogenome. Biol Lett 7:116–118. https://doi.org/10.1098/rsbl.2010.0638CAS 
Article 
PubMed 

Google Scholar 
Ganias K (2014) Biology and ecology of sardines and anchovies. CRC Press, Boca RatonBook 

Google Scholar 
Garvin MR, Bielawski JP, Gharrett AJ (2011) Positive Darwinian selection in the piston that powers protonpumps in complex I of the mitochondria of Pacific salmon. Plos One 6:e24127CAS 
Article 

Google Scholar 
Garvin MR, Bielawski JP, Sazanov LA, Gharrett AJ (2015a) Review and meta‐analysis of natural selection in mitochondrial complex I in metazoans. J Zool Syst Evol Res 53:1–17. https://doi.org/10.1111/jzs.12079Article 

Google Scholar 
Garvin MR, Thorgaard GH, Narum SR (2015b) Differential expression of genes that control respiration contribute to thermal adaptation in redband trout (Oncorhynchus mykiss gairdneri). Genome Biol Evol 7:1404–1414. https://doi.org/10.1093/gbe/evv078CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gershoni M, Levin L, Ovadia O, Toiw Y, Shani N, Dadon S, Barzilai N, Bergman A, Atzmon G, Wainstein J, Tsur A (2014) Disrupting mitochondrial–nuclear coevolution affects OXPHOS complex I integrity and impacts human health. Genome Biol Evol 6:2665–2680. https://doi.org/10.1093/gbe/evu208CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hershberg R, Petrov DA (2008) Selection on codon bias. Annu Rev Genet 42:287–299. https://doi.org/10.1146/annurev.genet.42.110807.091442CAS 
Article 
PubMed 

Google Scholar 
Hughes LC, Somoza GM, Nguyen BN, Bernot JP, Gonzalez-Castro M, Díaz de Astarloa JM, Ortí G (2017) Transcriptomic differentiation underlying marine to freshwater transitions in the South American silversides Odontesthes argentinensis and O. bonariensis (Atheriniformes). Ecol Evol 7:5258–5268. https://doi.org/10.1002/ece3.3133Article 
PubMed 
PubMed Central 

Google Scholar 
Hwang PP, Lee TH (2007) New insights into fish ion regulation and mitochondrion-rich cells. Comp Biochem Phys A 148:479–497. https://doi.org/10.1016/j.cbpa.2007.06.416CAS 
Article 

Google Scholar 
Jacobsen MW, Da Fonseca RR, Bernatchez L, Hansen MM (2016) Comparative analysis of complete mitochondrial genomes suggests that relaxed purifying selection is driving high nonsynonymous evolutionary rate of the NADH2 gene in whitefish (Coregonus ssp.). Mol Phyl Evol 95:161–170. https://doi.org/10.1016/j.ympev.2015.11.008CAS 
Article 

Google Scholar 
Jia W, Higgs PG (2008) Codon usage in mitochondrial genomes: distinguishing context-dependent mutation from translational selection. Mol Biol Evol 25:339–351. https://doi.org/10.1093/molbev/msm259CAS 
Article 
PubMed 

Google Scholar 
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Zídek A, Potapenko A, Bridgland A (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589. https://doi.org/10.1038/s41586-021-03819-2CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kadenbach B (2003) Intrinsic and extrinsic uncoupling of oxidative phosphorylation. BBA-Bioenerg 1604:77–94. https://doi.org/10.1016/S0005-2728(03)00027-6CAS 
Article 

Google Scholar 
Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S et al. (2012) Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649. https://doi.org/10.1093/bioinformatics/bts199Article 
PubMed 
PubMed Central 

Google Scholar 
Kumar S, Stecher G, Tamura K (2016) MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 33:1870–1874. https://doi.org/10.1093/molbev/msw054CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lai KP, Li JW, Gu J, Chan TF, Tse WK, Wong CK (2015) Transcriptomic analysis reveals specific osmoregulatory adaptive responses in gill mitochondria-rich cells and pavement cells of the Japanese eel. BMC Genomics 16:1072. https://doi.org/10.1186/s12864-015-2271-0CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lajbner Z, Pnini R, Camus MF, Miller J, Dowling DK (2018) Experimental evidence that thermal selection shapes mitochondrial genome evolution. Sci Rep.-UK 8:9500. https://doi.org/10.1038/s41598-018-27805-3CAS 
Article 

Google Scholar 
Lam SH, Lui EY, Li Z, Cai S, Sung WK, Mathavan S, Lam TJ, Ip YK (2014) Differential transcriptomic analyses revealed genes and signaling pathways involved in iono-osmoregulation and cellular remodeling in the gills of euryhaline Mozambique tilapia, Oreochromis mossambicus. BMC Genomics 15:921. https://doi.org/10.1186/1471-2164-15-921CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lavoue S, Konstantinidis P, Chen WJ (2014) Progress in clupeiform systematics. In: Ganias K (eds) Biology and ecology of sardines and anchovies. CRC Press, Boca Raton
Google Scholar 
Lavoue S, Miya M, Musikasinthorn P, Chen WJ, Nishida M (2013) Mitogenomic evidence for an Indo-west pacific origin of the Clupeoidei (Teleostei: Clupeiformes). Plos One 8:e56485. https://doi.org/10.1371/journal.pone.0056485CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li Y, Park JS, Deng JH, Bai Y (2006) Cytochrome c oxidase subunit IV is essential for assembly and respiratory function of the enzyme complex. J Bioenerg Biomembr 38:283–291. https://doi.org/10.1007/s10863-006-9052-zCAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Liu Y, Yang Q, Zhao F (2021) Synonymous but not silent: the codon usage code for gene expression and protein folding. Annu Rev Biochem 20:375–401. https://doi.org/10.1146/annurev-biochem-071320-112701CAS 
Article 

Google Scholar 
Lowell BB, Spiegelman BM (2000) Towards a molecular understanding of adaptive thermogenesis. Nature 404.6778:652–660. https://doi.org/10.1038/35007527Article 

Google Scholar 
McLean MJ, Wolfe KH, Devine KM (1998) Base composition skews, replication orientation and gene orientation in 12 prokaryote genomes. J Mol Evol 47:691–696. https://doi.org/10.1007/PL00006428CAS 
Article 
PubMed 

Google Scholar 
Mishmar D, Ruiz-Pesini E, Golik P, Macaulay V, Clark AG, Hosseini S et al. (2003) Natural selection shaped regional mtDNA variation in humans. P Natl Acad Sci USA 100:171–176CAS 
Article 

Google Scholar 
Morales HE, Pavlova A, Joseph L, Sunnucks P (2015) Positive and purifying selection in mitochondrial genomes of a bird with mitonuclear discordance. Mol Ecol 24:2820–2837CAS 
Article 

Google Scholar 
Morales HE, Pavlova A, Amos N, Major R, Bragg J, Kilian A, Greening C, Sunnucks P (2016) Mitochondrial nuclear interactions maintain a deep mitochondrial split in the face of nuclear gene flow. Bio Rxiv 1:095596
Google Scholar 
Mossman JA, Jennifer YG, Navarro F, Rand DM (2019) Mitochondrial DNA fitness depends on nuclear genetic background in Drosophila. G3-Genes Genom Genet 9:1175–1188. https://doi.org/10.1534/g3.119.400067CAS 
Article 

Google Scholar 
Murrell B, Joel OW, Sasha M, Thomas W, Konrad S, Pond SLK (2012) Detecting individual sites subject to episodic diversifying selection. Plos Genet 8:e1002764CAS 
Article 

Google Scholar 
Murrell B, Moola S, Mabona A, Weighill T, Sheward D, Kosakovsky Pond SL, Scheffler K (2013) FUBAR: a fast, unconstrained bayesian approximation for inferring selection. Mol Bio Evol 30:1196–1205CAS 
Article 

Google Scholar 
Perna NT, Kocher TD (1995) Patterns of nucleotide composition at four fold degenerate sites of animal mitochondrial genomes. J Mol Evol 41:353–358. https://doi.org/10.1007/BF00186547CAS 
Article 
PubMed 

Google Scholar 
Philippe H, Brinkmann H, Lavrov DV, Littlewood DTJ, Manuel M, Worheide G, Baurain D (2011) Resolving difficult phylogenetic questions: why more sequences are not enough. Plos Biol 9:e1000602CAS 
Article 

Google Scholar 
Plotkin JB, Kudla G (2011) Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet 12:32–42. https://doi.org/10.1038/nrg2899CAS 
Article 
PubMed 

Google Scholar 
Pond SLK, Frost SD (2005) Datamonkey: rapid detection of selective pressure on individual sites of codon alignments. Bioinformatics 21:2531–2533. https://doi.org/10.1093/bioinformatics/bti320CAS 
Article 
PubMed 

Google Scholar 
Posada D (2008) jModelTest: phylogenetic model averaging. Mol Biol Evol 7:1253–1256. https://doi.org/10.1093/molbev/msn083CAS 
Article 

Google Scholar 
R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/Rand DM, Kann LM (1998) Mutation and selection at silent and replacement sites in the evolution of animal mitochondrial DNA. Genetica 102/103:393–407. https://doi.org/10.1023/A:1017006118852CAS 
Article 

Google Scholar 
Ruiz-Pesini E, Mishmar D, Brandon M, Procaccio V, Wallace DC (2004) Effects of purifying and adaptive selection on regional variation in human mtDNA. Science 303:223–226CAS 
Article 

Google Scholar 
Satoh TP, Sato Y, Masuyama N, Miya M, Nishida M (2010) Transfer RNA gene arrangement and codon usage in vertebrate mitochondrial genomes: a new insight into gene order conservation. BMC Genomics 11:479. https://doi.org/10.1186/1471-2164-11-479CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Schreiber AM, Specker JL (2000) Metamorphosis in the summer flounder, Paralichthysdentatus: thyroidal status influences gill mitochondria-rich cells. Gen Comp Endocr 117:238–250. https://doi.org/10.1006/gcen.1999.7407CAS 
Article 
PubMed 

Google Scholar 
Schwede T, Kopp J, Guex N, Peitsch MC (2003) SWISS-MODEL: an automated protein homology-modelling server. Nucleic Acids Res 31:3381–3385. https://doi.org/10.1093/nar/gkg520CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sebastian W, Sukumaran S, Zacharia PU, Muraleedharan KR, Dinesh Kumar PK, Gopalakrishnan A (2020) Signals of selection in the mitogenome provide insights into adaptation mechanisms in heterogeneous habitats in a widely distributed pelagic fish. Sci Rep.-UK 10:1–4. https://doi.org/10.1038/s41598-020-65905-1CAS 
Article 

Google Scholar 
Shadel GS, Clayton DA (1997) Mitochondrial DNA maintenance in vertebrates. Annu Rev Biochem 66:409–435. https://doi.org/10.1146/annurev.biochem.66.1.409CAS 
Article 
PubMed 

Google Scholar 
Shen YY, Liang L, Zhu ZH, Zhou WP, Irwin DM, Zhang YP (2010) Adaptive evolution of energy metabolism genes and the origin of flight in bats. Proc Natl Acad Sci USA 107:8666–8671. https://doi.org/10.1073/pnas.0912613107Article 
PubMed 
PubMed Central 

Google Scholar 
Stier A, Bize P, Habold C, Bouillaud F, Massemin S, Criscuolo F (2014) Mitochondrial uncoupling prevents cold-induced oxidative stress: a case study using UCP1 knockout mice. J Exp Biol 217:624–630CAS 
PubMed 

Google Scholar 
Sueoka N (1988) Directional mutation pressure and neutral molecular evolution. Proc Natl Acad Sci 85:2653–2657. https://doi.org/10.1073/pnas.85.8.2653CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Teske PR, Sandoval-Castillo J, Golla TR, Emami-Khoyi A, Tine M, von der Heyden S, Beheregaray LB (2019) Thermal selection as a driver of marine ecological speciation. Proc R Soc B 286:20182023. https://doi.org/10.1098/rspb.2018.2023CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tsukihara T, Aoyama H, Yamashita E, Tomizaki T, Yamaguchi H, Shinzawa-Itoh K, Nakashima R, Yaono R, Yoshikawa S (1996) The whole structure of the 13-subunit oxidized cytochrome c oxidase at 2.8 A. Science 272:1136–1144. https://doi.org/10.1126/science.272.5265.1136CAS 
Article 
PubMed 

Google Scholar 
Whitehead A, Roach JL, Zhang S, Galvez F (2012) Salinity-and population-dependent genome regulatory response during osmotic acclimation in the killifish (Fundulus heteroclitus) gill. J Exp Biol 215:1293–1305. https://doi.org/10.1242/jeb.062075Article 
PubMed 

Google Scholar 
Woolley S, Johnson J, Smith MJ, Crandall KA, McClellan DA (2003) TreeSAAP: selection on amino acid properties using phylogenetic trees. Bioinformatics 19:671–672. https://doi.org/10.1093/bioinformatics/btg043CAS 
Article 
PubMed 

Google Scholar 
Wright F (1990) The ‘effective number of codons’ used in a. Gene Gene 87:23–29. https://doi.org/10.1016/0378-1119(90)90491-9CAS 
Article 
PubMed 

Google Scholar 
Xia X (2005) Mutation and selection on the anticodon of tRNA genes in vertebrate mitochondrial genomes. Gene 345:13–20. https://doi.org/10.1016/j.gene.2004.11.019CAS 
Article 
PubMed 

Google Scholar 
Xia X (2013) DAMBE5: a comprehensive software package for data analysis in molecular biology and evolution. Mol Biol Evol 30:1720–1728. https://doi.org/10.1093/molbev/mst064CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Xia X (2019) Is there a mutation gradient along vertebrate mitochondrial genome mediated by genome replication? Mitochondrion 46:30–40. https://doi.org/10.1016/j.mito.2018.06.004CAS 
Article 
PubMed 

Google Scholar 
Zhang X, Wen H, Wang H, Ren Y, Zhao J, Li Y (2017) RNA-Seq analysis of salinity stress-responsive transcriptome in the liver of spotted sea bass (Lateolabrax maculatus). Plos One 12:e0173238. https://doi.org/10.1371/journal.pone.0173238CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhao F, Zhou Z, Dang Y, Na H, Adam C, Lipzen A, Ng V, Grigoriev IV, Liu Y (2021) Genome-wide role of codon usage on transcription and identification of potential regulators. Proc Natl Acad Sci USA 9:118. https://doi.org/10.1073/pnas.2022590118CAS 
Article 

Google Scholar  More

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When Mariuá, a 1.5-year-old female jaguar, set foot in our breeding centre in Argentina in December 2018, we did not know that she would make history. Two years later, she walked out with two cubs: the first jaguars to roam the 1.4 million hectares of the Iberá wetlands of northeastern Argentina for at least 70 years. Mariuá and her cubs have started to reverse a process that some had thought irreversible.Within decades, one million species out of a total of some eight million could go extinct globally1. Hunting, habitat loss and ecosystem degradation are propelling this unprecedented biodiversity crisis. Current extinction rates are 100 to 1,000 times higher than in the past several million years.Argentina is no exception. Over the past 150 years, 5 bird and 4 mammal species have gone extinct. Today, about 17% of the country’s 3,000 vertebrate species are imperilled2, and 13 out of the 18 extant species of large mammal, from anteaters to tapirs, are experiencing catastrophic declines, in terms of both number and geographical range (see http://cma.sarem.org.ar).In 1998, we started a rewilding programme in Argentina to try to reverse this appalling loss. Our non-profit foundation, Fundación Rewilding Argentina, was spun out from the US non-profit organization Tompkins Conservation. We create protected areas where we can reintroduce native species, re-establish their interactions, restore ecosystem functionality and build valuable ecotourism based on wildlife viewing.Both rewilding and ecotourism can be controversial. We think that our work is an instructive example of how active restoration of crucial species, when done responsibly, can benefit both ecosystems and local people. It should be in the toolkit for meeting the 2030 biodiversity targets that will be discussed at the Convention on Biological Diversity’s Conference of the Parties in Kunming, China, next month.Three stepsThe popularity of rewilding projects is growing. These include: wolves brought back to Yellowstone National Park in Wyoming, beavers to England, bison and musk ox to northern Russia, leopards to Mozambique and Tasmanian devils to mainland Australia. The International Union for Conservation of Nature reports that, since 2008, at least 418 reintroduction projects have been started3. Most of these projects occur in protected areas and involve one or a few species. Our work in Argentina is broader.As a first step, we acquire private lands with philanthropic funds, reintroduce many species and form government-protected areas that are donated to federal and provincial governments. So far, we have purchased and donated about 400,000 hectares, with an estimated market value of US$91 million. This has created and enlarged six national parks, one national reserve and two provincial parks. Another 100,000 hectares are being donated. Together, these lands comprise a little over 10% of the total terrestrial area currently managed by the National Parks Administration of Argentina.The second step is to restore ecosystems, mainly by reintroducing species at an unprecedented scale. We spend more than $3 million each year on rewilding activities in three regions: the Iberá wetlands in the northeast, the dry Chaco forests in the north and the Patagonian steppe and coast in the south. Most often, we work with species deemed to have large impacts at the ecosystem level, such as large predators and herbivores.

Jaguars now roam Argentina’s Iberá wetlands for the first time in more than 70 years.Credit: Matías Rebak

Thus far, we have successfully reintroduced pampas deer, giant anteaters and collared peccaries (a pig-like, hoofed animal). We have also started founding populations of jaguars, coypus (large aquatic rodents), Wolffsohn’s viscachas (rodents that resemble a large chinchilla), red-and-green macaws and bare-faced curassows (birds related to chickens and pheasants). We are currently working on the reintroduction of 14 species.As they become abundant, reintroduced species re-weave the fabric of ecological relationships. For example, jaguars (Panthera onca) and macaws (Ara chloropterus) are reviving a crucial interaction: predation. Jaguars have begun to prey on eight species, including native rodents and feral hogs, which could limit those populations and thus benefit vegetation growth. The macaws are consuming 49 plant species, which could enhance seed dispersal, although this remains to be tested.
Include the true value of nature when rebuilding economies after coronavirus
Third, we invest heavily in infrastructure, capacity building and publicity to create an economy based on ecotourism. The species we work with are often highly charismatic, which benefits local communities, creating an economic incentive to conserve native wildlife and habitats. We organize workshops and courses so that locals can train as nature guides, cooks, craftspeople and more. In Iberá, where our work is most advanced, tourist visits increased by 87% between 2015 and 2021, according to official data from the Iberá wetland management agency. There were more than 50,000 visitors last year, despite the COVID-19 pandemic.All of these steps are important: simply setting aside protected areas is not enough. Globally, most modern ecosystems are ecologically damaged4, even in long-standing protected areas5. In Argentina, for example, functional populations of jaguars are missing from 19 of 22 national parks where historical distribution data suggest this key apex predator should occur.Jaguars and capybarasOur flagship project is the rewilding of the Iberá wetland. There, we are working on the restoration of nine species, including jaguars, which were eradicated from this area more than 70 years ago. We have now established a founding population of eight individuals: one adult male and three adult females, two of which (including Mariuá) were each released with two cubs aged four months. Our goal is to release a total of 20 individuals by 2027.Of all the species we work with, giant otters (Pteronura brasiliensis) and macaws have been the most difficult. Both species are extinct in the wild in Argentina. Bureaucratic hurdles have made sourcing wild individuals from neighbouring countries impossible.We obtained two pairs of giant otters from European zoos, and are holding them in pens in the core of Iberá. After several attempts, one pair bred successfully and the female gave birth to three cubs, producing the first litter born in the country for more than 30 years. We plan to release this family to the wild next year.

This female giant river otter, together with a male and their three cubs, will be released to the wild in Argentina next year to create a founding population.Credit: Matías Rebak

We source macaws, which have been extinct in the wild in Argentina for 100 years, from zoos, wildlife shelters and breeding centres. Because of their captive origin, we must give them the opportunity to practise flying in an aviary. We provide them with native foods, so that they learn what to eat, and we use a remote-controlled stuffed fox to teach them to avoid predators. This training isn’t always successful. Out of the 87 macaws that we have worked with, 48 were healthy and skilled enough to release. Two founding populations now thrive in the wild; one of them began reproducing in 2020.Efforts elsewhere have demonstrated the powerful effects of restoring species. In the northeast Pacific Ocean, reintroduced sea otters (Enhydra lutris) have voraciously eaten sea urchins, which in turn has allowed the return of lush kelp forests6. In Yellowstone Park, some researchers argue that reintroduced wolves have discouraged herbivores from foraging along stream edges, which might have increased tree growth and stabilized stream banks7. In Mozambique’s Gorongosa Park, the return of wildebeest and other large herbivores has curtailed Mimosa pigra, an undesirable invasive shrub8.
Biodiversity needs every tool in the box: use OECMs
Our rewilding work in Argentina could also have profound impacts. Close monitoring of the female jaguars and their cubs in the Iberá wetland has shown that they are largely feeding on the most abundant native prey: capybaras (Hydrochoerus hydrochaeris). Reducing the number of capybaras is expected to allow more vegetation to thrive, providing habitat for arthropods and small vertebrates, and possibly increasing carbon sequestration9. It could also help to reduce the transmission of sarcoptic mange, a density-dependent disease plaguing the capybara population. Jaguars also prey on foxes, which might benefit threatened bird species. We are working with several academic institutions to test how the return of the jaguar is reshaping the ecosystem.Challenges and caveatsAs our rewilding work gained momentum, critics ramped up from different fronts. At first, some were fearful of our policy of acquiring private lands with funds provided largely by foreign philanthropists. Those concerns faded when we began donating the land to federal and provincial governments.Then, ranchers argued that we were taking agricultural land out of production and reintroducing or boosting populations of animals that would conflict with their livestock. For example, in Patagonia, we established several protected areas where pumas (Puma concolor) and guanacos (Lama guanicoe, a relative of the llama) thrive. For almost a century, ranchers have trapped, shot and poisoned these animals, blaming them for killing sheep and competing for forage, respectively. We are conducting research to quantify the impact of pumas and guanacos on livestock, and offering alternative job opportunities based on wildlife viewing.

Red-and-green macaws went extinct in Argentina in the late 1800s. Rewilding efforts that began in 2016 have now established two founding populations in the Iberá wetlands.Credit: Matías Rebak

Federal and state managers, and often academics, argue that some founding populations of reintroduced species are too small and genetically related to create a viable, long-term population. This is true in some cases. But careful releases of unrelated animals can sidestep this issue. Worries about the spread of diseases when translocating individuals is also often invoked as a reason to halt rewilding activities. We implement thorough health checks and rigorous quarantines to decrease the risk of introducing unwanted diseases in the regions where we work.Concerns are sometimes raised about whether reintroduced species will recreate historical conditions, or instead create something new. Rewilding, however, seeks to regenerate and maintain ecological processes and biodiversity, rather than reaching some specific, historical equilibrium10. We think it is preferable to assume the uncertainties in trying to restore ecosystems, rather than accepting their degraded state.
Protect the last of the wild
Another worry is the possible impacts that tourism can have on climate, biodiversity and society — for instance, on water use, aviation emissions, road building and so on. Our strategy is to limit visitor numbers and avoid crowding by constructing multiple access gates on existing dirt roads.There are many policies that hinder rather than help rewilding. In Argentina, the laws that regulate transportation of wildlife species are built on the assumption that such activities always represent a threat to conservation. Wild animals can typically be imported to the country only through an airport in Buenos Aires. Because of this, an animal that could be driven in a truck from Brazil in a few hours must instead fly more than 1,500 kilometres and then be driven all the way back to its release area. Receiving wild animals at another international port, or moving them around within the country, requires special permits that often take months to obtain. Regulations could be altered to ease rewilding efforts while still policing the illegal wildlife trade.Next stepsNature-based tourism has been growing globally at rates of more than 4% per year, particularly in low- and middle-income countries11. Charismatic fauna, including large predators, are becoming increasingly important. In the Brazilian Pantanal, the world’s largest wetland, wildlife viewing — mostly of jaguars — generated an annual revenue of $6.8 million in 2015. This is three times the revenue obtained from traditional cattle ranching in that region12.With about 97% of the planet’s land surface ravaged by humans4, nature is facing its last stand. Urgent measures are needed not only to halt but also to reverse ecosystem and biodiversity loss. The active reintroduction of key species is one powerful way to heal some degraded ecosystems.This daunting task should not fall solely to non-profit organizations that have limited funds and staff, like us. The United Nations launched its Decade on Ecosystem Restoration in June 2021, calling for massive restoration efforts worldwide to heal nature and the climate. To achieve meaningful results at a global scale, rewilding needs the support of many stakeholders and effective international cooperation. Crucially, it requires the active involvement of governments to facilitate, fund and lead restoration efforts.

Nature 603, 225-227 (2022)
doi: https://doi.org/10.1038/d41586-022-00631-4

ReferencesIPBES. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (eds Diaz, S. et al.) (IPBES, 2019).
Google Scholar 
Bauni, V. et al. Zookeys 1085, 101–127 (2022).Article 

Google Scholar 
Soorae, P. S. (ed.) Global Conservation Translocation Perspectives: 2021. Case Studies from Around the Globe (IUCN SSC Conservation Translocation Specialist Group, Environment Agency — Abu Dhabi & Calgary Zoo, 2021).
Google Scholar 
Plumptre, A. J. et al. Front. For. Glob. Chang. https://doi.org/10.3389/ffgc.2021.626635 (2021).Article 

Google Scholar 
Jones, K. R. et al. Science 360, 788–791 (2018).PubMed 
Article 

Google Scholar 
Estes, J. A. & Duggins, D. O. Ecol. Monogr. 65, 75–100 (1995).Article 

Google Scholar 
Allen, B. L. et al. Food Webs 12, 64–75 (2017).Article 

Google Scholar 
Guyton, J. A. et al. Nature Ecol. Evol. 4, 712–724 (2020).PubMed 
Article 

Google Scholar 
Schmitz, O. J. et al. Science 362, eaar3213 (2018).PubMed 
Article 

Google Scholar 
Svenning, J. C., Munk, M. & Schweiger, A. in Rewilding (eds Pettorelli, N., Durant, S. M. & Du Toit, J. T.) 73–98 (Cambridge Univ. Press, 2019).
Google Scholar 
Balmford, A. et al. PLoS Biol. 7, e1000144 (2009).PubMed 
Article 

Google Scholar 
Tortato, F. R., Izzo, T. J., Hoogesteijn, R. & Peres, C. A. Glob. Ecol. Conserv. 11, 106–114 (2017).Article 

Google Scholar 
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The authors declare no competing interests.

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Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).PubMed 
PubMed Central 

Google Scholar 
Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl. Acad. Sci. 115, E10397–E10406 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cardoso, P. et al. Scientists’ warning to humanity on insect extinctions. Biol. Conserv. 242, 108426 (2020).
Google Scholar 
Habel, J. C., Samways, M. J. & Schmitt, T. Mitigating the precipitous decline of terrestrial European insects: Requirements for a new strategy. Biodivers. Conserv. 28, 1343–1360 (2019).
Google Scholar 
Brühl, C. A. & Zaller, J. G. Biodiversity decline as a consequence of an inappropriate environmental risk assessment of pesticides. Front. Environ. Sci. 7, 2013–2016 (2019).
Google Scholar 
Seastedt, T. R. & Crossley, D. A. The influence of arthropods on ecosystems. Bioscience 34, 157–161 (1984).
Google Scholar 
Brussaard, L. et al. Biodiversity and ecosystem functioning in soil. Ambio 26, 563–570 (1997).
Google Scholar 
Symondson, W. O. C., Sunderland, K. D. & Greenstone, M. H. Can generalist predators be effective biocontrol agents?. Annu. Rev. Entomol. 47, 561–594 (2002).CAS 
PubMed 

Google Scholar 
Goulson, D. The insect apocalypse, and why it matters. Curr. Biol. 29, R967–R971 (2019).CAS 
PubMed 

Google Scholar 
Kremen, C. et al. Pollination and other ecosystem services produced by mobile organisms: A conceptual framework for the effects of land-use change. Ecol. Lett. 10, 299–314 (2007).PubMed 

Google Scholar 
Schowalter, T. D., Noriega, J. A. & Tscharntke, T. Insect effects on ecosystem services: Introduction. Basic Appl. Ecol. 26, 1–7 (2018).
Google Scholar 
Dangles, O. & Casas, J. Ecosystem services provided by insects for achieving sustainable development goals. Ecosyst. Serv. 35, 109–115 (2019).
Google Scholar 
van der Sluijs, J. P. Insect decline, an emerging global environmental risk. Curr. Opin. Environ. Sustain. 46, 39–42 (2020).
Google Scholar 
Metcalfe, H., Hassall, K. L., Boinot, S. & Storkey, J. The contribution of spatial mass effects to plant diversity in arable fields. J. Appl. Ecol. 56, 1560–1574 (2019).PubMed 
PubMed Central 

Google Scholar 
Winter, S. et al. Effects of vegetation management intensity on biodiversity and ecosystem services in vineyards: A meta-analysis. J. Appl. Ecol. 55, 2484–2495 (2018).PubMed 
PubMed Central 

Google Scholar 
Blaise, C. et al. The key role of inter-row vegetation and ants on predation in Mediterranean organic vineyards. Agric. Ecosyst. Environ. 311, 107237 (2021).
Google Scholar 
Hoffmann, C. et al. Can flowering greencover crops promote biological control in German vineyards?. Insects 8, 121 (2017).PubMed Central 

Google Scholar 
Eckert, M., Mathulwe, L. L., Gaigher, R., der Merwe, L. J. & Pryke, J. S. Native cover crops enhance arthropod diversity in vineyards of the Cape Floristic Region. J. Insect Conserv. 24, 133–149 (2019).
Google Scholar 
Sáenz-Romo, M. G. et al. Ground cover management in a Mediterranean vineyard: Impact on insect abundance and diversity. Agric. Ecosyst. Environ. 283, 106571 (2019).
Google Scholar 
Capó-Bauçà, S., Marqués, A., Llopis-Vidal, N., Bota, J. & Baraza, E. Long-term establishment of natural green cover provides agroecosystem services by improving soil quality in a Mediterranean vineyard. Ecol. Eng. 127, 285–291 (2019).
Google Scholar 
Garcia, L. et al. Management of service crops for the provision of ecosystem services in vineyards: A review. Agric. Ecosyst. Environ. 251, 158–170 (2018).
Google Scholar 
Nicholls, C. I., Altieri, M. A. & Ponti, L. Enhancing plant diversity for improved insect pest management in Northern California organic vineyards. Acta Hortic. 785, 263–278 (2008).
Google Scholar 
Franin, K., Barić, B. & Kuštera, G. The role of ecological infrastructure on beneficial arthropods in vineyards. Spanish J. Agric. Res. 14, e303 (2016).
Google Scholar 
Shapira, I. et al. Habitat use by crop pests and natural enemies in a Mediterranean vineyard agroecosystem. Agric. Ecosyst. Environ. 267, 109–118 (2018).
Google Scholar 
Judt, C. et al. Diverging effects of landscape factors and inter-row management on the abundance of beneficial and herbivorous arthropods in andalusian vineyards (Spain). Insects 10, 320 (2019).PubMed Central 

Google Scholar 
Geldenhuys, M., Gaigher, R., Pryke, J. S. & Samways, M. J. Diverse herbaceous cover crops promote vineyard arthropod diversity across different management regimes. Agric. Ecosyst. Environ. 307, 107222 (2021).CAS 

Google Scholar 
Medail, F. & Quezel, P. Biodiversity hotspots in the Mediterranean Basin: Setting global conservation priorities. Conserv. Biol. https://doi.org/10.1046/j.1523-1739.1999.98467.x (1999).Article 

Google Scholar 
Carrère, P. La structure du vignoble du Vaucluse. Etudes Conjonct. 9, 931–949 (1957).
Google Scholar 
Nentwig, W. et al. Spiders of Europe. (2020). www.araneae.nmbe.ch.Tronquet, M. Catalogue des coléoptères de France. Rev. l’Assoc. Roussillonnaise d’Entomol. 23, 1–10 (2014).
Google Scholar 
Rosseel, Y. Lavaan: An R package for structural equation modeling. J. Stat. Softw. 48, 2 (2012).
Google Scholar 
Grace, J. B. Structural equation modeling and natural systems. Struct. Equ. Model. Nat. Syst. https://doi.org/10.1017/CBO9780511617799 (2006).Article 

Google Scholar 
Fiera, C. et al. Effects of vineyard inter-row management on the diversity and abundance of plants and surface-dwelling invertebrates in Central Romania. J. Insect Conserv. 24, 175–185 (2020).PubMed 
PubMed Central 

Google Scholar 
de Pedro, L., Perera-Fernández, L. G., López-Gallego, E., Pérez-Marcos, M. & Sanchez, J. A. The effect of cover crops on the ciodiversity and abundance of ground-dwelling arthropods in a Mediterranean pear orchard. Agrono 10, 580 (2020).
Google Scholar 
Ebeling, A. et al. Plant diversity impacts decomposition and herbivory via changes in aboveground arthropods. PLoS ONE 9, e106529 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Cobb, T. P., Langor, D. W. & Spence, J. R. Biodiversity and multiple disturbances: Boreal forest ground beetle (Coleoptera: Carabidae) responses to wildfire, harvesting, and herbicide. Can. J. For. Res. 37, 1310–1323 (2007).
Google Scholar 
Hendrickx, F. et al. How landscape structure, land-use intensity and habitat diversity affect components of total arthropod diversity in agricultural landscapes. J. Appl. Ecol. 44, 340–351 (2007).
Google Scholar 
Melbourne, B. A. Bias in the effect of habitat structure on pitfall traps: An experimental evaluation. Aust. J. Ecol. 24, 228–239 (1999).
Google Scholar 
Welti, E. A. R., Prather, R. M., Sanders, N. J., de Beurs, K. M. & Kaspari, M. Bottom-up when it is not top-down: Predators and plants control biomass of grassland arthropods. J. Anim. Ecol. 89, 1286–1294 (2020).PubMed 

Google Scholar 
Gonçalves, F. et al. Do soil management practices affect the activity density, diversity, and stability of soil arthropods in vineyards?. Agric. Ecosyst. Environ. 294, 106863 (2020).
Google Scholar 
Muscas, E. et al. Effects of vineyard floor cover crops on grapevine vigor, yield, and fruit quality, and the development of the vine mealybug under a Mediterranean climate. Agric. Ecosyst. Environ. 237, 203–212 (2017).
Google Scholar 
Nicholls, C. I., Parrella, M. P. & Altieri, M. A. Reducing the abundance of leafhoppers and thrips in a northern California organic vineyard through maintenance of full season floral diversity with summer cover crops. Agric. For. Entomol. 2, 107–113 (2000).
Google Scholar 
Vogelweith, F. & Thiéry, D. Cover crop differentially affects arthropods, but not diseases, occurring on grape leaves in vineyards. Aust. J. Grape Wine Res. 23, 426–431 (2017).
Google Scholar 
Hanna, R., Zalom, F. G. & Roltsch, W. J. Relative impact of spider predation and cover crop on population dynamics of Erythroneura variabilis in a raisin grape vineyard. Entomol. Exp. Appl. 107, 177–191 (2003).
Google Scholar 
Burgio, G. et al. Habitat management of organic vineyard in Northern Italy: the role of cover plants management on arthropod functional biodiversity. Bull. Entomol. Res. 106, 759–768 (2016).CAS 
PubMed 

Google Scholar 
Wisniewska, J. & Prokopy, R. Do spiders (Araneae) feed on rose leafhopper (Edwardsiana rosae; Auchenorrhyncha: Cicadellidae) pests of apple trees? (2013).Malumbres-Olarte, J., Vink, C. J., Ross, J. G., Cruickshank, R. H. & Paterson, A. M. The role of habitat complexity on spider communities in native alpine grasslands of New Zealand. Insect Conserv. Divers. 6, 124–134 (2013).
Google Scholar 
Wilson, H. et al. Summer flowering cover crops support wild bees in vineyards. Environ. Entomol. 47, 63–69 (2018).PubMed 

Google Scholar 
Kratschmer, S. et al. Tillage intensity or landscape features: What matters most for wild bee diversity in vineyards?. Agric. Ecosyst. Environ. 266, 142–152 (2018).
Google Scholar 
Gardarin, A., Pigot, J. & Valantin-Morison, M. The hump-shaped effect of plant functional diversity on the biological control of a multi-species pest community. Sci. Rep. 11, 1–14 (2021).
Google Scholar 
Serra, G., Lentini, A., Verdinelli, M. & Delrio, G. Effects of cover crop management on grape pests in a Mediterranean environment. IOBC/WPRS Bull. (2006).Sáenz-Romo, M. G. et al. Effects of ground cover management on insect predators and pests in a Mediterranean vineyard. Insects 10, 421 (2019).PubMed Central 

Google Scholar 
Barry, J. P., Baxter, C. H., Sagarin, R. D. & Gilman, S. E. Climate-related, long-term faunal changes in a California rocky intertidal community. Science 267, 672–675 (1995).ADS 
CAS 
PubMed 

Google Scholar 
Ewald, J. A. et al. Influences of extreme weather, climate and pesticide use on invertebrates in cereal fields over 42 years. Glob. Chang. Biol. 21, 3931–3950 (2015).ADS 
PubMed 

Google Scholar 
Celette, F., Findeling, A. & Gary, C. Competition for nitrogen in an unfertilized intercropping system: The case of an association of grapevine and grass cover in a Mediterranean climate. Eur. J. Agron. https://doi.org/10.1016/j.eja.2008.07.003 (2009).Article 

Google Scholar 
Ruiz-Colmenero, M., Bienes, R. & Marques, M. J. Soil and water conservation dilemmas associated with the use of green cover in steep vineyards. Soil Tillage Res. 117, 211–223 (2011).
Google Scholar  More

Middle and Late Pleistocene climates of MSA occupationsWe first examined MSA occupations (n = 84, Fig. 2) spanning the Middle to Late Pleistocene using simulated climate data (see Methods). We extracted mean annual temperature (bio01) and total annual precipitation (bio12) values from the climate model33 within a 50 km radii, centred on the occupation’s mid-age date range rounded to the nearest 1000 year (kyr) time slice, to characterise environments across the wider logistical landscape (following Blinkhorn and Grove10,11). The climatic conditions for each occupation can be found in Supplementary Table S1 and are illustrated in Fig. 1.Figure 2Distribution of the eastern African Middle Stone Age occupations studied. This map was created in ArcGIS 10.5 using an SRTM (NASA).Full size imageWe found that average temperatures at eastern African MSA occupations varied between 9 °C and 25 °C, with 59 occupations falling within the 68% confidence interval of 14–23 °C. The warmest environments occupied were found in coastal regions, such as Abdur along the Red Sea coast of modern-day Eritrea (25 °C) and Panga Ya Saidi situated on the Kenyan coast (24 °C), as well as in the Lower Omo Valley of southwestern Ethiopia (24–23 °C). These hot environments were inhabited during MIS 5 and MIS 7. On the other hand, the coldest environments inhabited were at high altitude, at Fincha Habera in the Bale Mountains of southern Ethiopia (9–10 °C) and at Kenyan Rift Valley occupations of Marmonet Drift (10–14 °C) and Enkapune ya Muto (13 °C), most of which date to MIS 3. Average precipitation levels experienced by Middle to Late Pleistocene MSA populations in eastern Africa ranged between 396 and 1593 mm, with 59 occupations falling within the 68% confidence interval of 620-1150 mm, corresponding to the precipitation bracket of sub-humid landscapes. The wettest habitats were located on islands within and along the shore of Lake Victoria at Rusinga Nyamtia (1593 mm) and Karungu (1374-1499 mm) in MIS 3 and 5, as well as within the Ethiopian Rift Valley at Gademotta (1368 mm), the Ethiopian Highlands at Mochena Borango (1270-1297 mm), and the Kenyan Rift Valley at Marmonet Drift (1173-1368 mm) in MIS 3, 5 and 7. On the other hand, the driest occupations occurred at Laas Geel in Somaliland during MIS 3 (396 mm) as well as within the Lower Omo Valley (534-582 mm) during MIS 5 and 7.Classifying biomes and ecotones at MSA occupationsWe then used the modelled biome dataset (biome4output)33 to classify the local ecology of each MSA occupation within a 50 km radius. We found that 38% of the occupations (n = 32) had access to only tropical xerophytic shrubland within their logistical landscape (see Fig. 3. for modern examples of this biome), and a further 42% with this biome among others within a 50 km radius (n = 35). Tropical xerophytic shrubland was persistently occupied throughout the Middle to Late Pleistocene (Fig. 1), and whilst it was the most prevalent biome type available, representing 61.9% of the biomes present during occupational phases across the region (Supplementary Fig. S2 and Table S2), eastern African MSA adaptive systems were likely specialised for engagement with tropical xerophytic shrubland, and its modulation may therefore have influenced patterns of Middle to Late Pleistocene human distribution. Nonetheless, the proportion of occupations with access to tropical xerophytic shrubland was significantly higher using a 2-sample proportion test than the proportion of the biome available across the region throughout MSA occupational phases (Z-value = 3.38, p-value = 0.0007; Supplementary Table S2), suggesting preferential occupation of tropical xerophytic shrubland and emphasising it as an important ecosystem for MSA populations.Figure 3Examples of xerophytic shrubland environments in modern eastern Africa, including typical species (sp.). (A) Acacia tortilis (B) Commiphora sp. (C) Acacia sp. and Duosperma eremophilum. (D) Hyphaene compressa, Acacia sp., Salvadora persica, Cyperacea and Lawsonia inermis (E) Acacia sp. and Duosperma eremophilum, (F) Acacia tortilis (background: Commiphora sp. Capparaceae sp. Tephrosia sp. and Indigofera spinosa).Full size imageIn total, 57% of the occupations had a logistical landscape falling on the boundary between multiple biomes (n = 48; Supplementary Table S1). The majority of these ecotonal sites are situated between ‘open’ and ‘closed’ biome types, supporting the assertion of Basell9 that access to wooded ecologies was vital for MSA populations. Forest biomes made up relatively low proportions of the available environments available throughout the Middle to Late Pleistocene; however, importantly, we found the proportions of forest biomes occupied by MSA occupations to be significantly higher than would be expected based on the prevalence of these biomes, especially in MIS 3 and MIS 7 (see Supplementary Fig. S2 and Table S2), supporting the contention that MSA hominins preferred the rarer habitats that were near to woods and forests. The most common ecotone occupied during the eastern African MSA was that between tropical xerophytic shrubland and temperate conifer forest, which is seen as far north as Goda Buticha in southeastern Ethiopia, and as far south as Mumba in Tanzania. However, the region to the east of Lake Victoria shows the most intense occupation of this ecotone, the boundary of which fluctuates through time and space (Supplementary Table S1).We found that MIS 7 saw the preferential occupation of closed ecotones between temperate conifer forest and warm mixed forest, as well as tropical xerophytic shrubland and associated ecotones which are generally occupied throughout the period. MIS 5 saw a slight increase in habitat diversity, though expansions primarily involved the tracking of tropical xerophytic shrubland environments (as shown by all occupations in MIS 5 having access to this biome within 50 km) with exposure to new ecotones occurring at the peripheries. This can be seen at occupations distributed widely across the region; for example, certain occupations at Omo would have involved engagement with deserts alongside tropical xerophytic shrubland, whereas some MSA populations at Panga Ya Saidi had access to tropical deciduous forest and tropical savannah environments within their logistical landscape. MIS 3 saw the greatest variety in the ecologies occupied, where expansions can be seen into new and previously uninhabited environments, such as steppe tundra and warm mixed forest, with a distinct emphasis on temperate conifer forest rather than tropical xerophytic shrubland. Importantly, a chi-square test revealed that the relative proportions of biomes in the region do not differ significantly between the Marine Isotope Stages (χ2 = 9.07, p-value = 0.99), strongly suggesting that variation in the environments occupied through time reflects a shift in preference as opposed to fluctuation in the underlying ecology (see Supplementary Table S2).Characterising MSA environments throughout the Middle to Late PleistoceneWe used cluster analyses to group the occupations based on their climatic values to assess patterns in habitat choice. To do this, we scaled and combined the temperature and precipitation data and employed an automated clustering algorithm (the average silhouette method) to ascertain the optimal number (k) of clusters in the data. The algorithm found ten clusters to represent the best division of the data (Fig. 4, Supplementary Fig. S1).Figure 4Hierarchical clustering of the occupations according to mean annual temperature and total annual precipitation. K means clustering identified ten clusters as the optimal division of the dendrogram, which have been highlighted here as well as the range of environmental conditions occupied by each cluster and the percentage of cells within 50 km of that biome for all occupations within that cluster.Full size imageMost of the occupations (n = 45) fall within warm to temperate sub-humid clusters (2,4,5 and 7) with a broad temperature range of 13–19 °C and a precipitation range of 613-1297 mm. These clusters are dominated by tropical xerophytic shrubland and temperate conifer forest environments and their ecotones. We found that only two clusters (8,9) did not include occupations with access to tropical xerophytic shrubland, indicating that this biome was present across a large portion of the MSA climatic range, except at the coldest extreme. We found that the coldest cluster, cluster 9 (temperature range 9–10 °C), was the most ecotonal, with all occupations situated at high altitude where populations would have had access to steppe tundra, temperate conifer forest, temperate sclerophyll woodland and warm mixed forest, the complex topography allowing diverse biomes to appear closer together than is usually possible34. Extremely humid occupations from around Lake Victoria (Karungu and Rusinga Nyamita) formed cluster 10 (1374-1593 mm precipitation). These occupations have moderate temperatures (16–18 °C) and occupy an ecotone between tropical xerophytic shrubland and temperate conifer forest. Panga Ya Saidi and Laas Geel form their own respective clusters (3 and 6) due to their distinctively hot temperatures; however, at Panga Ya Saidi, this is coupled with moist sub-humid conditions and a diverse tropical environment (24 °C, 996-1153 mm), whereas Laas Geel possesses the lowest annual precipitation of all the occupations (18 °C, 396 mm), making its hot-dry environment unique for the eastern African MSA. However, the occupation at Laas Geel falls within the tropical xerophytic shrubland biome, with access to some open conifer woodland within 50 km, suggesting that whilst occupying a climatic extreme, this distinct habitat represents an extension of the types of environments that eastern African MSA populations were already well-adapted to.Phased habitability modelsWe used the precipitation and temperature data from the occupations as the parameters to produce phased ‘habitability’ models for the more abundantly populated interglacial phases of the MSA, demonstrating the extent of the landscape that experienced comparable climatic settings to occupations dated within that period. The climatic range produced by each phased subset was projected throughout every 1000-year time interval for that MIS, and then the percentage of ‘habitable’ cells (i.e., cells that remain within that climatic range) was calculated to identify areas that were persistently habitable, as well as the geographic range and temporal scope of impersistent habitable landscapes.Figure 5 demonstrates the temperature, precipitation, and combined habitability models for each phase. MIS 9 shows the most limited habitable zone out of the interglacial phases, however the lower number of occupations available to construct the distribution likely has impacted the construction of the models. MIS 7 marks a period of expansion, with the region surrounding Lake Victoria and the Eastern Rift Valley Lakes and the Ethiopian Highlands showing the most persistent habitability across the region. For temperature, large areas of the Horn and modern-day Sudan show less persistent habitability (ca. 40–50% cells falling within the temperature range of 12–23 °C seen at MIS 7 occupations), with pockets of unsuitability along the coast of the Baab el Mandeb and the border between modern-day Ethiopian and Somalia. However, arid zones of the southern Sahara are completely uninhabitable in terms of precipitation (0% of cells fall within the precipitation range of 582-1368 mm at MIS 7 occupations), as is the tip of the Horn. Precipitation is thus the limiting factor when considering habitability for MIS 7, as the area deemed habitable in terms of precipitation is more geographically restricted than that derived from temperature. MIS 5 sees the largest increase in habitable area for temperature, with all cells showing temperature values within the MIS 5 occupation range of 13–25 °C for at least 60% of the period. Precipitation habitability, that we considered here to be ranging between 554-1385 mm, is however more fragmented, with pockets of uninhabitability forming around the northeast edge of Lake Victoria, in the region to the south of Lake Tana, and within modern-day Tanzania. Like MIS 7, this means that habitability is limited by precipitation in MIS 5. However, the habitability models for MIS 3 demonstrates the opposite pattern. Temperature habitability, defined as between 9–19 °C by the sites dating to MIS 3, shows the most restricted distribution of all the models, with habitable areas concentrated to the areas around Lake Victoria and the Ethiopian highlands, which are linked towards the southeast of Lake Turkana. Yet, MIS 3 shows the most persistent and widely distributed zone of habitability for precipitation, where much of eastern Africa, except towards the Sahara and the very tip of the Horn of Africa, remains persistently within the range of precipitation values experienced by MIS3 occupations (396-1593 mm). Overall, these models propose that interglacial MSA occupations, especially in MIS 5, may have been much more spatially diverse than presently known, however we note that these distributions are based purely on climatic data and ignore the potential effects of volcanic eruptions and subsequent ashfalls that have also been argued to have conditioned habitability in this region9.Figures 5Mean annual temperature (top), total annual precipitation (middle) and combined (bottom) phased models of habitability, demonstrating the percentage of time intervals (1000 years per interval) that remain within the climatic range of the occupations dated to that Marine Isotope Stage (MIS). The palaeocoastline has been estimated based on the predicted mean sea-level for each MIS.Full size imageFigures 6Scatterplots of the Mantel test results (Table 1, Supplementary Table S4–S5) between the pairwise distance matrix of toolkit composition (top) and raw material use (bottom) and the other distances matrices excluding the two binary variables, site type and method.Full size imageExploring the relationship between climate and Middle Stone Age occupationsWe then examined the extent to which patterns of variability in stone tool assemblage composition and raw material use correlated with environmental conditions within a 50 km radius at the mid-age of occupation of each assemblage, as well as a suite of other variables recorded by Blinkhorn and Grove11 (see Methods and Supplementary Methods S1 details). Figure 6 demonstrates the relationships between these variables and toolkit composition and raw material use, revealed using simple Mantel tests (Table 1 and Supplementary Table S4–S5). We found that MSA assemblage composition was correlated with differences in both mean annual temperature (adj. p = 0.001; Table 1) and total annual precipitation (adj. p = 0.003; Table 1), and raw material use also shows statistically significant relationships with both mean annual temperature (adj. p = 0.001; Table 1) and total annual precipitation (adj. p = 0.003; Table 1). With the use of Pleistocene climate models at high temporal resolutions, these results refine the findings of Blinkhorn and Grove11, which relied on comparisons of the climatic extremes of the LGM and LIG.Table 1 Simple Mantel test results for the effects of precipitation and temperature on toolkit composition and raw material. Statistical significance highlighted at p  More