Plant diversity patterns across karst and non-karst substrates in old-growth coastal temperate rainforests of Vancouver Island, Canada
Bibliographic record
Abstract
Karst landscapes, which cover ∼10 % of British Columbia, are globally significant for their hydrology and biodiversity, yet comparative studies of vascular plant diversity on karst versus non-karst substrates remain scarce in Canada and worldwide. We evaluated tree, shrub, and herb communities in 90 plots and 360 nested subplots across three paired old-growth sites on Vancouver Island, assessing species richness, Shannon diversity (alpha), compositional dissimilarity (beta), and regional heterogeneity (gamma). Karst forests were consistently species-poor at local scales, with markedly low shrub and herb diversity, yet supported compositionally distinct assemblages that contributed disproportionately to beta diversity. Non-karst sites, in contrast, maintained significantly higher richness, alpha, and gamma diversity ( p < 0.001) across all vegetation layers, with indicator species including mountain hemlock ( Tsuga mertensiana ), yellow cedar ( Callitropsis nootkatensis ), and false azalea ( Menziesia ferruginea ). These results underscore the role of bedrock geology in structuring biodiversity and provide rare empirical data from temperate karst ecosystems: karst forests enhance landscape-scale heterogeneity through distinct assemblages, whereas non-karst forests sustain greater taxonomic and structural diversity. Recognizing these complementary roles is essential for developing conservation strategies that maintain biodiversity, ecosystem function, and long-term resilience in coastal temperate rainforests. • Non-karst old-growth forests had higher vascular plant richness and diversity. • Tree, shrub, and herb composition differed by bedrock substrate. • Bray-Curtis dissimilarity revealed substantial beta diversity in community composition. • Indicator species identified for non-karst old-growth forest communities.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".