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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

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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

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Climate dataMonthly climate data (air temperature at 2 m and cloudiness) with a spatial resolution of 0.5° were obtained from the CRU Time Series 4.0.15 We extracted data from 1982 to 2018 to match the time series of satellite vegetation observations. The VPD was calculated as the difference between saturated water-vapour pressure and actual water-vapour pressure31. Temperature and vapour-pressure data used for the VPD calculation were obtained from CRU.Soil moisture dataThe daily root-zone soil moisture with a spatial resolution of 0.25° for the period 1980–2018 was obtained from the Global Land Evaporation Amsterdam Model (GLEAM v.3.3a)32. The dataset is based on radiation and air temperature from a reanalysis, a combination of gauge-based, reanalysis-based and satellite-based precipitation and satellite-based vegetation optical depth.Fire emission dataMonthly carbon emissions from biomass burning were obtained from the fourth-generation Global Fire Emission Database33. This dataset has a spatial resolution of 0.25° and provides global data on the burning area and emissions on three-hourly, daily and monthly timescales and estimates the contributions of different fire types. Emissions data can be obtained for different substances, such as carbon (C), dry matter (DM), carbon dioxide (CO2), carbon monoxide (CO) and methane (CH4).Satellite vegetation greenness dataThe satellite-based NDVI archived from the MODIS NDVI dataset with a spatial resolution of 0.5° and a temporal resolution of 16 days was used here to detect vegetation greenness changes. In addition, the solar-induced chlorophyll fluorescence product was used as a proxy of vegetation photosynthesis. We furthermore used the four-day clear-sky CSIF time series (2000–2019) with a spatial resolution of 0.05° × 0.05° from ref. 34 (https://osf.io/8xqy6/).GPP based on NIRvThe NIRv is a newly developed satellite vegetation index combining NDVI and near-infrared band reflectivity of vegetation and is recognized as a proxy of GPP35,36. We obtained the 0.05° NIRv_GPP from 1982 to 2018 from ref. 37. This product was produced by upscaling the relationships between NIRv and observed GPP to the global scale and was judged to perform well in capturing interannual trends of GPP37.Atmospheric CO2 dataIn situ observations of daily CO2 concentration at Point Barrow were obtained from the National Oceanic and Atmospheric Administration/Earth System Research Laboratory network. According to analyses of atmospheric transport and mixing processes, the CO2 signals detected at Barrow are suggested to be an integrated measure of carbon fluxes over both the high latitudes and the middle latitudes20.Ecosystem carbon fluxesSimulations of ecosystem carbon fluxes (GPP, TER and NEE) derived from process-based model simulations (TRENDY), empirical models based on flux tower observations (FLUXCOM) and atmospheric CO2 inversion models were jointly used for the investigation of net ecosystem carbon exchange over the northern middle and high latitudes.The TRENDY dataset is an ensemble of dynamic global vegetation model (DGVM) simulations that are forced by CRU–National Centers for Environmental Prediction historical climate and CO2 inputs38. The DGVMs use a bottom‐up approach to simulate terrestrial CO2 fluxes (for example, GPP, TER and NEE), and were extensively used to explore the mechanisms driving changes in carbon uptake and fluxes. The simulated GPP, TER and NEE from nine models of TRENDYv.8 (Supplementary Table 1) were used in this study. The S2 experiment, which considered the effect of both observed changes of CO2 and climate on ecosystem carbon fluxes, was selected for studying the changes of ecosystem carbon fluxes before and after the temperature shift.The FLUXCOM dataset is an upscaling product using empirical models forced by eddy-covariance data from 224 flux towers, remote sensing data and climate data8,9,10. It provides estimates of global energy and carbon fluxes (http://www.fluxcom.org/). The empirical models were trained by three machine learning algorithms, including Random Forests, Artificial Neural Networks and Multivariate Adaptive Regression Spline, and thus provide a series of estimates of global carbon fluxes. We used the FLUXCOM carbon fluxes data driven by the European Centre for Medium-Range Weather Forecasts Reanalysis v.5 (ERA5) climate reanalysis from 1979 to 2018.The atmospheric CO2 inversion datasets provide estimates of NEE over land from long-term atmospheric CO2 measurements using atmospheric transport models. Three atmospheric CO2 inversion products were used here: monthly net biome production with a spatial resolution of 3.75° × 2.5° from the JENA CarboScope (version s76_vo2020) for the period 1976–2019, long-term global CO2 fluxes estimated by the NICAM-based Inverse Simulation for Monitoring CO2 (NISMON-CO2) between 1990 and 2019 and the Copernicus Atmosphere Monitoring Service12 (CAMS v.19r1) dataset between 1979 and 2019.Eddy-covariance CO2 observation dataThe eddy-covariance measurements of carbon fluxes from tower sites were obtained from the Integrated Carbon Observation System 2018 and the FLUXNET Network 2015. We selected 48 eddy-covariance CO2 observation sites with 10 yr continuous data (Supplementary Table 2) located north of 25° N and extracted temperature and NEE data from September to November to explore the change of ecosystem carbon exchange in autumn.NEE estimationThe monthly NEE was estimated as the difference between TER and GPP. The autumn (September to November) GPP and TER derived from TRENDY and FLUXCOM over the study region were obtained by aggregating GPP and TER from each grid cell weighted by the grid-cell area. The NEE derived from atmospheric CO2 inversions was directly used and compared against those from TRENDY and FLUXCOM. To compare the NEE before and after the temperature turning point, we divided the NEE time series into two periods: 1982–2003 and 2004–2018.Calculation of the AZCWe used observations of CO2 from Point Barrow to characterize the trends in the zero-crossing date of CO2 (downward in spring and upward in autumn). These trends roughly correspond to the beginning of net carbon uptake in spring and the beginning of net carbon release in autumn. According to the method of ref. 39, we obtained the detrended seasonal CO2 curve by separating the seasonal cycle from the long-term trend and short-term variations, fitting a function consisting of a quadratic polynomial for the long-term trend and four harmonics for the annual cycle to the daily data. The residuals from this function fit are then obtained. A 1.5-month and a 390-day full-width half-maximum-value averaging filter were used for the digital filtering of residuals to remove the short-term variations and the long-term trend, respectively. Then we got the zero-crossing dates when the detrended seasonal CO2 curve crosses the zero line from positive to negative and negative to positive, respectively.The autumn carbon release is calculated as the amount of CO2 released between the autumn zero-crossing date and the first week of September following ref. 21.Identification of turning point of temperatureWe used the piecewise linear regression method to determine the turning point of the mean autumn (September to November) temperature during 1982–2018 over the area north of 25° N. In addition, a moving t-test method was used to verify the turning-point identification. Then, the temporal trends of the mean autumn temperature before and after the turning point were calculated using the Mann–Kendall non-parametric trend test method, and the confidence intervals were determined using Sen’s slope statistics. According to the temperature trends before and after the turning point, we further identified the CAs as where the autumn temperature shows a decreasing trend after the turning point (2004) relative to that before the turning point, and WAs as regions outside the CAs. To maintain spatial integrity and continuity, we ignored the significance of the temperature trend when dividing the CAs and WAs.To verify that our analysis is not affected by the division of the time period and regions, we also identified the temperature turning point at each grid point using the piecewise linear regression method and then extracted those grid points with significant temperature change and significant NEE change (P  More