Construction of neighbourhood diversity indices with stem mapping data
Bibliographic record
Abstract
Typical use of diversity indices in forest stands does not reflect within-stand variation in species and structure. Despite numerous efforts to design stand diversity indices that capture spatial variation in two- or three-dimensional space, the application of these indices is limited due to their complexity or lack of ecological relevance. In this research note, we illustrate the application of diversity indices at the neighbourhood scale using stem mapping data from a boreal mixedwood stand in northeastern Ontario, which was assessed before and after partial harvesting. Among the three diversity indices (Shannon species diversity, Shannon structural diversity by height class distribution, and structural diversity by height variation), neighbourhood structural diversity by height variation was less dependent on neighbourhood size (between 4 and 12 trees) and more sensitive to stand structural change than Shannon structural diversity by height class distribution. Despite general increases in Shannon species diversity and structural diversity by height variation at both stand and neighbourhood scales after the harvest — an indication of higher diversity — within-stand variation (coefficients of variation) in neighbourhood diversity values decreased, suggesting that residual stands were spatially more uniform after the harvest. Therefore, the neighbourhood diversity indices are useful for capturing spatial variation in species and structural diversity, especially in managed stands in which spatial distributions and patterns can be significantly modified by management interventions.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".