Alternate successional pathway yields alternate pattern of functional diversity
Bibliographic record
Abstract
Abstract Question In Eastern Canada, wildfires turn Picea mariana forests into Kalmia angustifolia dominated heath or P. mariana forest depending on burn severity. These alternate end‐points of succession provide an opportunity to test assumptions concerning alternate successional trajectories dominated by distinct plant functional groups. Disturbance effects on functional diversity ( FD ) have been studied largely in single post‐disturbance communities, but rarely applied to alternate successional pathways. Do post‐fire Kalmia heaths have lower FD than forest communities, and does heath formation select for a narrow range of traits leading to biotic homogenization? Location Terra Nova National Park, Newfoundland, Canada. Methods Based on five functional traits (specific leaf area, leaf dry matter content, seed mass, height, specific root length), we measured functional trait dispersion within (alpha FD ) and between sites (beta FD ). We calculated overall FD metrics in nine heath and four forest sites as well as metrics for three functional groups (trees, shrubs, and herbs) in each site. We also performed a complementary taxonomic diversity ( TD ) analysis to establish links between FD and TD . Results We could not detect a difference in overall alpha TD between the two successional communities but found significant loss of alpha and beta FD in heaths and within tree and herb groups. Overall beta FD was lower in heaths than forests. It was also lower for trees and herbs, indicating an increase in functional similarity (functional homogenization) of the majority of life forms. Conclusions Between the two successional pathways, trait space occupancy was lower in heaths than forests. Heath formation in post‐fire communities consistently restricts functional dispersion (low alpha FD ) of tree and herb traits leading to functional homogenization (low beta FD ). When a successional trajectory leads to heath formation, it is accompanied by a loss of functional diversity.
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 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 | 0.001 | 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 | 0.001 |
| 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 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".