Vegetation and soil seed bank relationships across microhabitats in an abandoned<i>Quercus suber</i>parkland under simulated fire
Bibliographic record
Abstract
Mediterranean agro-forestry systems are undergoing rapid change due to abandonment. This turns formerly cultivated or grazed oak-tree parklands (i.e., savanna-type formations called “dehesas”) into flammable formations of scattered trees within a matrix of shrubs with open spaces. Wildfires can now occur, threatening the persistence of these formations. Fire-prone shrublands commonly regenerate after fire from seeds stored in the soil. Understanding the relationships between standing vegetation and the soil seed bank across microhabitats can help predict the response of the system in case of fire. Here we investigated these relationships in an abandoned Quercus suber (cork oak) dehesa in central Spain. Vegetation and soil were sampled and assigned to different microhabitats: under the trees (TRC), in dense shrub cover (DSC), and in low shrub cover (LSC). A heat shock was applied to half of each sample to simulate fire; the other half served as control. Both sets of samples were then germinated in a greenhouse. Almost 90% of the species were herbs, while the rest were woody shrubs. The number of species recorded in TRC and DSC was lower than in LSC, in which species richness, particularly herbs, was maximal. Heating increased the total number of species that germinated, but mean species richness per sample was not altered. Heating markedly increased the number of germinations in all microhabitats, particularly those of woody species. Furthermore, the germination of shrubby species increased in the 3 microhabitats, notably in TRC. While the standing plant community was well differentiated among microhabitats, this was not the case for the soil seed bank, which was homogeneous across microhabitats, with or without heating. We conclude that the high density of shrubby seeds found in TRC or in the other microhabitats presents substantial threats to the persistence of Q. suber parklands in case of fire.
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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 | 0.000 |
| 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 | 0.002 |
| 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".