Homo erectus adapted to steppe-desert climate extremes one million years ago
Bibliographic record
Abstract
Questions about when early members of the genus Homo adapted to extreme environments like deserts and rainforests have traditionally focused on Homo sapiens. Here, we present multidisciplinary evidence from Engaji Nanyori in Tanzania’s Oldupai Gorge, revealing that Homo erectus thrived in hyperarid landscapes one million years ago. Using biogeochemical analyses, precise chronometric dating, palaeoclimate simulations, biome modeling, fire history reconstructions, palaeobotanical studies, faunal assemblages, and archeological evidence, we reconstruct an environment dominated by semidesert shrubland. Despite these challenges, Homo erectus repeatedly occupied fluvial landscapes, leveraging water sources and ecological focal points to mitigate risk. These findings suggest archaic humans possessed an ecological flexibility previously attributed only to later hominins. This adaptability likely facilitated the expansion of Homo erectus into the arid regions of Africa and Eurasia, redefining their role as ecological generalists thriving in some of the most challenging landscapes of the Middle Pleistocene. Homo erectus adapted to hyper-arid climatic conditions one million years ago through the strategic use of rivers and ponds, and this facilitated geographic expansion within and beyond Africa, as shown by multi-proxy data from Oldupai Gorge, Tanzania.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".