Historical records reveal the distinctive associations of human disturbance and extreme climate change with local extinction of mammals
Bibliographic record
Abstract
Accelerated anthropogenic impacts and climatic changes are widely considered to be responsible for unprecedented species extinction. However, determining their effects on extinction is challenging owing to the lack of long-term data with high spatial and temporal resolution. In this study, using historical occurrence records of 11 medium- to large-sized mammal species or groups of species in China from 905 BC to AD 2006, we quantified the distinctive associations of anthropogenic stressors (represented by cropland coverage and human population density) and climatic stressors (represented by air temperature) with the local extinction of these mammals. We found that both intensified human disturbances and extreme climate change were associated with the increased local extinction of the study mammals. In the cold phase (the premodern period of China), climate cooling was positively associated with increased local extinction, while in the warm phase (the modern period) global warming was associated with increased local extinction. Interactive effects between human disturbance and temperature change with the local extinction of elephants, rhinos, pandas, and water deer were found. Large-sized mammals, such as elephants, rhinos, and pandas, showed earlier and larger population declines than small-sized ones. The local extinction sensitivities of these mammals to the human population density and standardized temperature were estimated during 1700 to 2000. The quantitative evidence for anthropogenic and climatic associations with mammalian extinction provided insights into the driving processes of species extinction, which has important implications for biodiversity conservation under accelerating global changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".