Distribution of plant functional groups across grassland-forest ecotones: testing the assumptions
Bibliographic record
Abstract
Ecotones, transition zones found at abrupt discontinuities in vegetation, are a part of every landscape and have long been considered hotspots for biodiversity and conservation of both plants and animals. However, many assumptions about ecotone characteristics have not been rigorously tested. The most prevalent claim in the literature is that ecotones support higher species richness than adjacent habitats. Patterns of higher species richness in ecotones has been hypothesized to arise from ecological processes ranging from spatial mass effect, increased environmental heterogeneity, seed predation or introduction by animals or insects, to increased dispersal ability by exotic generalists. The purpose of this project is to document patterns of plant functional group richness and abundance across grassland-aspen ecotones in the Lac du Bois grasslands north of Kamloops, British Columbia. Specifically, this research addresses the following questions: 1) Are ecotones more species-rich than surrounding areas in both north-and south-facing aspects? 2) What is the relationship between functional diversity and species richness across the grassland-aspen ecotones? and 3) How does the method of ecotone definition (statistical versus visual) and data analysis (blocking versus gradient approach) impact the results?\nTwenty ecotones (10 south-facing and 10 north-facing ecotones) were intensively sampled along 35 m transects for richness and abundance of herbaceous plant species, aspen saplings, soil pH and moisture and tree canopy cover. To compare techniques, the location of each ecotone was defined both statistically using moving window regression analysis and visually using the treeline as an approximate centre. Ecotone locations varied greatly when the statistical method was compared with the visual method. Overall, the results did not support the assumption that ecotones are more species rich than adjacent habitats. However there was variation between richness and abundance of other functional groups (shade tolerance, dispersal method and drought tolerance, for example) in ecotones compared to adjacent habitats. This research also found a strong influence of aspect on the results, especially when grasslands and ecotones were compared.
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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 | 0.039 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".