Changes in land use and physiological transitions of a <i>Juniperus thurifera</i> forest: from decline to recovery
Bibliographic record
Abstract
Forest decline is frequently associated with infection; however, infections habitually affect trees that have been previously debilitated by environmental stress. Nevertheless, the causes and physiology of noninfectious forest decline are not well known. Some Juniperus thurifera L. forests presented severe (noninfectious) declines, with defoliations over 50%. The goal of this study was to determine the causes and characterize the physiology of this noninfectious decline, and we hypothesized that it could be related to environmental stress from increasing interspecific competition resulting from land abandonment. We randomly assigned 60 trees to either a control group or one of two competition-release treatments, i.e., vegetation clearing or soil ploughing. We characterized the physiological state of the trees both before treatment application and 14 months after treatment application. The J. thurifera trees that were declining experienced significantly lower soil nutrient and water availability, which resulted in lower leaf nutrient concentrations, lower photosynthetic rates, higher water stress, and arrested growth and reproduction. We confirmed that competition release increased nutrient availability and acquisition, reduced water deficit, improved photosynthetic rates, and abruptly stopped defoliation. Competition plays an increasingly critical role in forest conservation, particularly with the reported increase in the number of species colonizing previously unfeasible habitats due to global change and the absence of traditional activities that used to buffer competition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".