How Climate Shaped Human Migration

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How Climate Shaped Human Migration Apr 17, 2022 0:52:48 GMT

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Post by Admin on Apr 17, 2022 0:52:48 GMT

Discussion
The main conclusions of our analysis are robust with respect to the existing uncertainties in species attribution, particularly for the period from 1 to 0.3 Ma, and the dating of archaeological layers, as demonstrated by key HSM calculations with four different scenarios that accounted for these factors (Methods and Extended Data Fig. 10). Although our study is based on species-stratified fossil and archaeological input data, our calculation of species overlap as HSM co-variability allowed us to treat potential species transitions and successions in human evolutionary history quantitatively and to identify their spatiotemporal characteristics. To the best of our knowledge, such research has not been reported thus far. The HSM captures regionally distributed patchworks of habitable areas in agreement with a general multiregional perspective41 (Figs. 1 and 4). According to our CEM, southern and eastern Africa as well as the region north of the Intertropical Convergence Zone emerge as potential long-term refugia for various types of archaic humans. As the climate changed on orbital timescales, these refugia shifted geographically, creating population patterns with greater complexity. Further analysis of the pan-African connectivity of refugia in our HSM dataset, as shown in the inset in Fig. 4, will increase understanding of hominin dispersal, interbreeding and cladogenetic transitions as well as potential cultural exchanges.

In summary, we demonstrated that astronomically forced climate shifts were a key factor in driving hominin species distributions42 and dispersal and were probably important for diversification43.

Methods
2Ma simulation
We conducted the 2Ma simulation with the Community Earth System Model (CESM), version 1.2, at an ocean and atmosphere resolution of approximately 3.75° × 3.75°. The model uses bathymetry of the Last Glacial Maximum and time-varying forcings of greenhouse gases15, ice sheets15 and astronomical insolation conditions16. CESM1.2 has a relatively low standard equilibrium climate sensitivity (ECS) of 2.4 °C per CO2 doubling, which lies outside the likely range of estimates45 (3.7 ± 1.2 °C) obtained with other climate model simulations conducted as part of the Coupled Model Intercomparison Project, phase 6. However, this value is within the lower range of recent estimates compiled by the Intergovernmental Panel on Climate Change sixth assessment report46 of Working Group 1, which identifies a very likely ECS range of 2–5 °C. To obtain a more realistic response to past long-wave radiative forcings in our palaeo-climate model simulation and to implicitly capture radiative effects of other CO2-correlated forcings47 from dust, vegetation, N2O or CH4, we therefore scaled the range of the applied CO2 forcing15 by a factor of 1.5. The resulting effective ECS, which includes non-CO2 greenhouse gas forcings, was in our case approximately 3.8 °C. Our result is in reasonable agreement with the Coupled Model Intercomparison Project phase 6 estimate and previous palaeo-climate estimates18,19 of 3.2 °C, which were obtained from reconstructions of the global mean surface temperature and radiative forcings covering the last 784,000 years. Amplification of the CO2 forcing in CESM1.2 led to a realistic representation of the amplitude of global mean, tropical and Antarctic temperature changes (Extended Data Figs. 1b and 2a, b) and to a simulated temperature range between Last Glacial Maximum and Late Holocene conditions of approximately 5.9 °C. This result is in close agreement with recent palaeo-proxy-based estimates20. Similar to previous long-term transient climate model simulations conducted with Earth system models of intermediate complexity7,48, the CESM1.2 simulations use an orbital acceleration factor of 5, which means that the 2-million-year orbital history is squeezed into 400,000 model years in CESM. The complete model trajectory is based on 21 individual chunks that were run in parallel, with each covering at least one interglacial–glacial cycle (Supplementary Table 2). Moreover, each chunk overlaps with the next chunk so that the issue of initial conditions and spin-up time can be evaluated properly. The final climate trajectory is obtained by combining the individual chunks and by using sliding linear interpolation in the chunk-overlap periods. The model simulation has been evaluated against numerous palaeo-proxy-based data (Fig. 1 and Extended Data Fig. 1). Unlike other Earth system models49,50,51, the 2Ma simulation conducted with CESM1.2 does not generate strong internal millennial-scale variability such as that shown by Dansgaard–Oeschger cycles. The CESM1.2 data are provided on the climate data server of the Institute for Basic Science (IBS) Center for Climate Physics at climatedata.ibs.re.kr.

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How Climate Shaped Human Migration Apr 17, 2022 17:45:12 GMT

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Post by Admin on Apr 17, 2022 17:45:12 GMT

Extended dataset of archaeological and fossil hominin data
The SDM for the Homo genus was derived from a recent compilation of archaeological and fossil data21. The original data compilation published in 2020 (ref. 21) included 2,754 radiometric age estimates for fossil hominin occurrences, each accompanied by the confidence interval around the estimate, the fossil site name and the archaeological layer within the site (where available) from which the dated sample was derived, the geographical coordinates of the site and the possible attribution to one or more than one Homo species. Confident attributions to a single species generated a core record, whereas instances with multiple attributions formed an extended record. Six different species were recognized: H. habilis, H. ergaster, H. erectus, H. heidelbergensis, H. neanderthalensis and H. sapiens. The updated record, as shown in Supplementary Table 1, contains 3,245 data entries restricted to the temporal age interval of 2 Ma–30 ka; those from Australia and the Americas were excluded. Further, we combined H. habilis and H. ergaster into a single African Oldowan toolmaker species. Each occurrence is attributed to a given species depending on the presence of unambiguous anatomical remains, either singly or in connection to a specific lithic tool industry. This helped to guide identification if this was not otherwise possible from the bones and teeth alone (398 entries, 12%), the age limits of the individual species or the stone tool industry. For example, an occurrence in Africa older than the first appearance of H. sapiens at Jebel Irhoud55 yet younger than the first appearance of H. heidelbergensis at Melka Kunture56 is attributed to H. heidelbergensis. Moreover, French Mousterian stone tools have been unambiguously assigned to H. neanderthalensis, whereas Aurignacian tools were attributed to H. sapiens. When these criteria were applied, the core record included 94.5% of the attributions, 48.5% of which refer to H. neanderthalensis and 37.5% of which refer to H. sapiens. Where neither of these criteria was met (in the original compilation, the SDM acknowledges attribution uncertainty), we accounted for this by testing the stability of our results with respect to different versions of the SDM (Extended Data Fig. 10). For example, transitional industries (for example, the Levantine Mousterian or Lincombian–Ranisian–Jerzmanowician industries) received multiple attributions because they fit either H. sapiens or H. neanderthalensis in terms of toolmaker identity57,58. A detailed explanation of this approach is provided as supplementary material for ref. 21 (https://ars.els-cdn.com/content/image/1-s2.0-S2590332220304760-mmc1.pdf).

A second source of uncertainty stems from dating. Although approximately 50% of the entries refer to the 14C method (>90% of which are based on accelerator mass spectrometry), other dating methods such as electron spin resonance (14% of the sample), thermoluminescence (12%) and optically stimulated luminescence (12%) are less precise than radiocarbon dating. Nonetheless, multiple datings are present for individual fossil sites, even within a single stratigraphic layer at the site. To account for uncertainties in species attribution and age, we ran our analyses according to the four different approaches described below.

1.
Multiple dates, tier 1. Only the core record, which excludes entries with uncertain species attributions, and all age estimates available for each archaeological layer are used. Multiple age estimates per layer are possible, and the age uncertainty for each is included in our analysis. This subdivision includes 3,060 data entries. Although the main analysis in our study is based on this case, we need to consider possible sampling biases due to the higher weights given to archaeological layers with multiple dates (Figs. 1–4 and Extended Data Fig. 10).

2.
Multiple dates, tier 2. The extended record, in which ambiguous species attributions are treated by randomly choosing among the possible candidate species, is used along with multiple age estimates (including uncertainties) per layer. This subdivision includes 3,245 (all) data entries (Extended Data Fig. 10).

3.
Single date, tier 1. Multiple age estimates for a single archaeological layer are combined in this approach to provide a minimum and maximum age for the layer. Each archaeological layer has only one entry, thereby eliminating possible sampling biases in the estimation of our CEM. This subdivision includes 1,567 data entries (Extended Data Fig. 10).

4.
Single date, tier 2. Age estimates for archaeological layers are treated as those in the single date, tier 1 category except that the extended record rather than the core record is used. This subdivision includes 1,652 data entries (Extended Data Fig. 10).

We acknowledge that our species subdivisions may be controversial and that these do not necessarily require constancy of morphology, habitat and behaviour. However, even though some species attributions such as H. heidelbergensis could be questioned, we remain confident that the majority of the record presents little challenge considering that 86% of the core data belong to the well-defined, widely accepted H. neanderthalensis or H. sapiens record and tool-making traditions. Thus, even though some species attributions might be considered invalid, widely accepted constraints are used. Clearly, to the best of current knowledge, 500,000-year-old remains in Africa can belong toneither H. sapiens nor H. habilis59, irrespective of whether the name H. heidelbergensis is considered appropriate. To further reduce uncertainties, we tested the robustness of our main findings with four alternative scenarios (Extended Data Fig. 10) for species attribution and dating and excluding uncertain and poorly dated species (for example, Homo floresiensis, Homo naledi, Homo bodoensis, Homo longi and Denisovans), which are restricted to too few fossil sites for which no climatic variability can possibly be ascertained or do not currently include any other locality or remains in their definition. The final species assignments used in our study should be interpreted here as plausible working hypotheses.

Random climate trajectory
To address the question of whether the actual climate trajectory influenced the distribution of fossil and archaeological data, we developed a CEM and HSM in which fossil data were assigned to randomly chosen climate states from the CESM1.2 simulation under the constraint that the climate range selected must overlap with the total fossil age range of the respective species. We randomized the time variability of the four-dimensional climate data vector (annual mean temperature, annual mean precipitation, minimum precipitation and NPP) while keeping the co-variability among the climate vector components, as well as the mean state, invariant. The original HSM (H), which is based on the real trajectory of CESM1.2, and the model (Hscr) that we trained from a scrambled trajectory were then compared. The time-averaged differences between the models for H. sapiens, H. neanderthalensis and H. heidelbergensis were then interpreted as an indication of whether the realistic climate evolution influenced the observed hominin distributions in space and time relative to a system that maintains its orbital climate co-variance and mean state (Extended Data Fig. 8a–c) but does not consider the exact time evolution of glacial–interglacial and orbital cycles. The time-averaged difference between H(x,y,t) in the original HSM and Hscr(x,y,t) in the HSM derived from time-randomized climate data was then tested at each grid point using a paired t-test.