The ones we left behind: Comparing plot sampling and floristic habitat sampling for estimating bryophyte diversity
Bibliographic record
Abstract
ABSTRACT An efficient method for estimating bryophyte diversity in forest stands must consider more than just the dominant forest mesohabitat. We compared two methodologies commonly used for estimating diversity in forest ecosystems. Floristic habitat sampling (FHS) utilizes stratification of all forest mesohabitats, which includes the natural diversity of microhabitats found within and stratifies a mosaic of mesohabitats (e.g. forest, streams, seeps, and cliffs) and microhabitats (e.g. rocks logs, etc.) that are often not considered in forest research projects that use plot sampling to estimate species diversity. In Canadian cedar hemlock forest, FHS methodology recorded more than twice as many bryophyte species as plot sampling (PS). A comparison of the dominant forest mesohabitat concluded that plot sampling was not as efficient as FHS in estimating bryophyte diversity and that plot sampling can result in different interpretations of species diversity. Rare species ordination of stands sampled using FHS showed strong clustering of sites with respect to biogeoclimatic zones and age since the last major disturbance (fire or logging) as compared with rare species ordinations from PS data, which showed no delineation of stands along temporal gradients. Plot sampling has many useful applications in ecology, but floristic habitat sampling is more efficient for quantifying overall bryophyte diversity. FHS provides an excellent way to record a comprehensive list of species.
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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 | 0.060 | 0.093 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".