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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

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