Functional Ecology of Forest, Heath, and Shrub Savannah Alternate States in Eastern Canada
Bibliographic record
Abstract
In eastern Canada, alternation of wildfire regime due to fire suppression creates alternate vegetation states converting black spruce forest to heath and shrub savannah (SS). We compared the taxonomic diversity (TD) and functional diversity (FD) of post-fire forest, heath, and SS alternate states to determine if community FD can explain their persistence. We hypothesized that (i) species diversity (TD and FD) would be the highest in forest followed by SS and heath due to decreased interspecific competition and niche differentiation, (ii) differences between TD and FD indices would be greater in communities with high TD in forest due to high trait differentiation and richness, and (iii) changes in community trait values would indicate niche limitations and resource availability. We conducted this study in Terra Nova National Park, Newfoundland, Canada. We calculated functional dispersion (alpha FD), functional pairwise dissimilarity (beta FD), Shannon’s diversity (alpha TD), and Bray–Curtis dissimilarity (beta TD) from species cover. We used five functional traits (specific root length, specific leaf area, leaf dry matter content, height, and seed mass) related to nutrient acquisition, productivity, and growth. We found lower beta diversity in forest than heath and SS; forest also had higher species diversity and greater breadth in niche space utilization. SS was functionally similar to heath but lower than forest in functional dispersion and functional divergence. It had the highest functional richness and evenness. There was no difference in functional evenness between forest and heath. Functional beta diversity was the highest in forest, and did not differ between heath and SS. Resource acquisition and availability was the greatest in forest and the lowest in heath. We suspect that this might be due to forest having the highest functional trait turnover and niche utilization. We conclude that alternate vegetation states originating from alterations to the natural fire regime negatively impact ecosystem function.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".