Disentangling the effects of climate and people on Sahel vegetation dynamics
Bibliographic record
Abstract
Abstract. The Sahel belt of Africa has been the focus of intensive scientific research since the 1960s, spurred on by the chronic vulnerability of its population to recurring drought and the threat of long-term land degradation. But satellite sensors have recently shown that much of the region has experienced significant increases in photosynthetic activity since the early 1980s, thus re-energizing long-standing debates about the role that people play in shaping land surface status, and thus climate at regional scales. In this paper, we test the hypothesis that people have had a measurable impact on vegetation dynamics in the Sahel for the period 1982–2002. We compare potential natural vegetation dynamics predicted by a process-based ecosystem model with satellite-derived greenness observations, and map the agreement between the two across a geographic grid at a spatial resolution of 0.5°. As aggregated data-model agreement is very good, any local differences between the two could be due to human impact. We then relate this agreement metric to state-of-the-art data sets on demographics, pasture, and cropping. Our findings suggest that demographic and agricultural pressures in the Sahel are unable to account for differences between simulated and observed vegetation dynamics, even for the most densely populated areas. But we do identify a weak, positive correlation between data-model agreement and pasture intensity at the Sahel-wide level. This indicates that herding or grazing does not appreciably affect vegetation dynamics in the region. Either people have not had a significant impact on vegetation dynamics in the Sahel or the identification of a human "footprint" is precluded by inconsistent or subtle vegetation response to complex socio-environmental interactions, and/or limitations in the data used for this study. We do not exclude the possibility of a greater human influence on vegetation dynamics over the coming decades with changing land use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".