Disentangling legacy effects from environmental filters of postfire assembly of boreal tree assemblages
Bibliographic record
Abstract
Disturbance plays a key role in driving ecological responses by creating opportunities for new ecological communities to assemble and by directly influencing the outcomes of assembly. Legacy effects (such as seed banks) and environmental filters can both influence community assembly, but their effects are impossible to separate with observational data. Here, we used seeding experiments in sites covering a broad range of postdisturbance conditions to tease apart the effects of seed availability, environmental factors, and disturbance characteristics on early community assembly after fire. We added seed of four common boreal trees to experimental plots in 55 replicate sites in recently burned areas of black spruce forest in northwestern North America. Seed addition treatments increased the probability of occurrence for all species, indicating a widespread potential for seed limitation to affect patterns of recruitment after fire. Small-seeded. species (aspen and birch) were most sensitive to environmental factors such as soil moisture and organic layer depth, suggesting a role for niche-based environmental filtering in community assembly. Fire characteristics related to severity and frequency were also important drivers of seedling regeneration, indicating the potential for disturbance to mediate environmental filters and legacy effects on seed availability. Because effects of seed availability are typically impossible to disentangle from environmental constraints on recruitment in observational studies, legacy effects contingent on vegetation history may be misinterpreted as being driven by strong environmental filters. Results from the seeding experiments suggest that vegetation legacies affecting seed availability play a pivotal role in shaping patterns of community assembly after fire in these low-diversity boreal forests.
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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 | 0.000 | 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) | 0.000 | 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".