Shrub facilitation promotes advancing of the <i>Fagus sylvatica</i> treeline across the Apennines (Italy)
Bibliographic record
Abstract
Abstract Questions Elevational treelines are expected to shift upwards in response to warming climate. However, worldwide upward shifts of treelines are inconsistent because local scale factors can affect the response to temperature. In this study, we explore the hypothesis that in the Apennines, where the current altitudinal treeline position is largely depressed because of past human activity, shrubs act as nurse plants promoting the upward migration of Fagus sylvatica . Location A 500–km‐long latitudinal gradient in the Apennines, Italy. Methods We selected, along this gradient, nine treeline sites with different elevations, rock substrates, and physiognomic types including Juniperus communis , Pinus mugo , Vaccinium myrtillus shrublands, and grasslands. Here, in 68 transects we collected and analysed spatially fine‐scale distribution data of F. sylvatica individuals in relation to both their age and their position, associated or not with shrubs. Results Fagus sylvatica regeneration is rare in open secondary grasslands at 1,600–2,100 m a.s.l., highlighting a bottleneck in the regeneration phase of this species. On the contrary, we systematically observed a strong association between shrubs and F. sylvatica individuals. Compared with the adjacent grassland, F. sylvatica regeneration was 58.3 times higher under Pinus mugo , 131.5 timeshigher under Juniperus communis and 102.4 times higher under Vaccinium myrtillus . The age structure of F. sylvatica population indicates that, in the last 50 years, recruitment under shrubs is continuous, while in grassland it is episodic. Conclusions Above the current treeline of the Apennines, F. sylvatica individuals develop only in the presence of shrubs, which act as nurse species. Shrubs are a necessary condition for F. sylvatica re‐colonization of the high‐altitude open areas affected, in the last centuries, by intense human land use.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".