Spatial patterns of tree recruitment in a relict population of<i>Pinus uncinata</i>: forest expansion through stratified diffusion
Bibliographic record
Abstract
Abstract Aim To infer future changes in the distribution of isolated relict tree populations at the limit of a species’ geographical range, a deep understanding of the regeneration niche and the spatial pattern of tree recruitment is needed. Location A relictPinus uncinatapopulation located at the south‐western limit of distribution of the species in the Iberian System of north‐eastern Spain. Methods Pinus uncinataindividuals were mapped within a 50 × 40‐m plot, and their size, age and reproductive status were estimated. Data on seed dispersal were obtained from a seed‐release experiment. The regeneration niche of the species was assessed based on the associations of seedling density with substrate and understorey cover. The spatial pattern of seedlings was described using point‐pattern (Ripley'sK) and surface‐pattern (correlograms, Moran'sI) analyses. Statistical and inverse modelling were used to characterize seedling clustering. Results Pine seedlings appeared aggregated in 6‐m patches. Inverse modelling estimated a longer mean dispersal distance (27 m), which corresponded to the size of a large cluster along the north to north‐eastward direction paralleled by an eastward trend of increasing seedling age. The two spatial scales of recruitment were related to two dispersal processes. The small‐scale clustering of seedlings was due to local seed dispersal in open areas near the edge ofCalluna vulgarismats: the regeneration niche. The long‐range expansion might be caused by less frequent medium‐distance dispersal events due to the dominant north‐westerly winds. Main conclusions To understand future range shifts of marginal tree populations, data on seed dispersal, regeneration niche and spatial pattern of recruitment at local scales should be obtained. The monitoring of understorey communities should be a priority in order to predict correctly shifts in tree species range in response to global warming.
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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 | 0.000 |
| 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".