Fine-scaled climate variation in equatorial Africa revealed by modern and fossil primate teeth
Bibliographic record
Abstract
Variability in resource availability is hypothesized to be a significant driver of primate adaptation and evolution, but most paleoclimate proxies cannot recover environmental seasonality on the scale of an individual lifespan. Oxygen isotope compositions (δ 18 O values) sampled at high spatial resolution in the dentitions of modern African primates ( n = 2,352 near weekly measurements from 26 teeth) track concurrent seasonal precipitation, regional climatic patterns, discrete meteorological events, and niche partitioning. We leverage these data to contextualize the first δ 18 O values of two 17 Ma Afropithecus turkanensis individuals from Kalodirr, Kenya, from which we infer variably bimodal wet seasons, supported by rainfall reconstructions in a global Earth system model. Afropithecus ’ δ 18 O fluctuations are intermediate in magnitude between those measured at high resolution in baboons ( Papio spp.) living across a gradient of aridity and modern forest-dwelling chimpanzees ( Pan troglodytes verus ). This large-bodied Miocene ape consumed seasonally variable food and water sources enriched in 18 O compared to contemporaneous terrestrial fauna ( n = 66 fossil specimens). Reliance on fallback foods during documented dry seasons potentially contributed to novel dental features long considered adaptations to hard-object feeding. Developmentally informed microsampling recovers greater ecological complexity than conventional isotope sampling; the two Miocene apes ( n = 248 near weekly measurements) evince as great a range of seasonal δ 18 O variation as more time-averaged bulk measurements from 101 eastern African Plio-Pleistocene hominins and 42 papionins spanning 4 million y. These results reveal unprecedented environmental histories in primate teeth and suggest a framework for evaluating climate change and primate paleoecology throughout the Cenozoic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".