Multidecadal variability in Atlas cedar growth in Northwest Africa during the last 850 years: Implications for dieback and conservation of an endangered species
Bibliographic record
Abstract
Widespread forest dieback is a phenomenon of global concern that requires an improved understanding of the relationship between tree growth and climate to support conservation efforts. One priority for conservation is the Atlas cedar ( Cedrus atlantica ), an endangered species exhibiting dieback throughout its North African range. In this study, we evaluate the long-term context for recent dieback and develop a projection of future C. atlantica growth by exploring the periodic variability of its growth through time. First, we present a new C. atlantica tree-ring chronology (1150–2013 CE) from the Middle Atlas mountains, Morocco. We then compare the new chronology to existing C. atlantica chronologies in Morocco and use principal components analysis (PCA) to isolate the common periodic signal from the seven longest available records (PCA 7 , 1271–1984 CE) in the Middle and High Atlas portions of the C. atlantica range. PCA 7 captures 55.7% of the variance and contains significant multidecadal (˜95 yr, ˜57 yr, ˜21 yr) periodic components, revealed through spectral and wavelet analyses. Parallel analyses of historical climate data (1901–2016 CE) suggests that the multidecadal growth signal originates primarily in growing season (spring and summer) precipitation variability, compounded by slow-changing components of summer and winter temperatures. Finally, we model the long-term growth patterns between 1271–1984 CE using a small number (three to four) of harmonic components, illustrating that suppressed growth since the 1970s – a factor implicated in the dieback of this species – is consistent with recurrent climatically-driven growth declines. Forward projection of this model suggests two climatically-favourable periods for growth in the 21st century that may enhance current conservation actions for the long-term survival of the C. atlantica in the Middle and High Atlas mountains.
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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 | 0.000 | 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".