Ecological controls on post‐fire vegetation assembly at multiple spatial scales in eastern<scp>N</scp>orth<scp>A</scp>merican boreal forests
Bibliographic record
Abstract
Abstract Question In fire‐prone boreal forests, to what extent does fire severity influence understorey plant community assembly? What are the abiotic and biotic factors controlling understorey community regeneration at regional, landscape and local scales? Location Black spruce‐dominated boreal forest in eastern Canada. Methods A taxonomic and a trait‐based approach were combined to evaluate the relative influence of habitat characteristics, burn severity and biotic processes on understorey regeneration at multiple spatial scales. Sampling of understorey vegetation cover was carried out in 133 recently burned plots located in 14 different fires across a 600‐km transect. The spatial hierarchy for sampling consisted of five fire regime zones (regional scale), two to four fires within each zone (landscape) and two to four toposequences within each fire (local). The environmental control of fire severity and habitat characteristics on understorey regeneration was assessed using a canonical redundancy analysis (RDA) and variation partitioning. We investigated environmental and biotic filters of species traits at different scales by modelling trait–spatial assemblages with Moran eigen vector maps (MEMs). Results In spite of the large variability of environmental conditions covered by our sampling design, low depths of burn were measured in the large majority of the studied sites. Incomplete consumption of the forest floor is frequently observed in eastern boreal forests characterized by long fire cycles.In situbiological legacies persisted through the low‐severity fires, which conserved the pre‐fire relationships between plant communities and their environment. As a result, fire severity was neither the unique nor the dominant control on post‐fire regeneration. Habitat characteristics explained a three times higher proportion of variation in species composition. Biotic controls on trait assemblage increased at the two finer scales. Conclusions Severity alone cannot predict understorey vegetation assembly at different scales in the low‐severity fire regime characteristic of the eastern North American boreal forest.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".