Strategy Space and the Disturbance Spectrum: A Life‐History Model for Tree Species Coexistence
Bibliographic record
Abstract
The disturbance spectrum consists of disturbance patterns differing in type, size, intensity, and frequency. It is proposed that tree life-history traits are adaptations to particular disturbance regimes. Four independent axes are proposed to define the dominant dimensions of tree strategy space: shade tolerance, tree height, capacity for vegetative reproduction, and seed dispersal distance. A fitness model was developed to elucidate interactions between the proposed life-history traits. The model shows how alternate life-history sets can coexist when disturbance patterns fluctuate in space and time. Variable disturbance regimes were shown, based on data and simulation results, to enhance species coexistence, as predicted. The strategy space model accurately predicts the number of common tree species for the eastern United States, boreal Canada, and southwestern piñon-juniper woodlands. The model also provides an explanation for latitudinal gradients in tree species richness in North America and Europe. The proposed model predicts a relationship between disturbance characteristics and the species composition of a forest that allows for the coexistence of large numbers of species. The life-history traits of size, growth rate, life span, shade tolerance, age of reproduction, seed dispersal distance, and vegetative reproduction are all incorporated into the model.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".