Designing Cluster Plots for Sampling Local Plant Species Composition for Biodiversity Management
Bibliographic record
Abstract
Aim of study: Cluster plot designs are widely used in national forest inventory systems to assess current forest resources. By spreading subplots apart, a cluster plot could potentially capture a large variety of local plant species. This aspect has rarely been examined in the past. This study is conducted to understand how design factors of a cluster plot affect estimates of local plant species composition.Area of study: Two large census forest plots in Taiwan and Peninsular Malaysia over 25 ha with different species richness were used.Material and methods: Design factors of a cluster plot were plot configuration (PCONFIG), plot area (PAREA), cluster layout (CLAYOUT), and extent of ground area covered by a cluster (CEXTENT). Jaccard and Sørensen similarity indices were used to compare species compositional similarity between two cluster plot designs. A simulation study was carried out.Main results: Results were consistent among the study sites and similarity indices. PAREA, CLAYOUT, and CEXTENT notably influenced how species composition was sampled. Larger PAREA increased similarity in species composition between two cluster plot designs. Square and rectangle CLAYOUT had the most dissimilar species composition between them. Larger CEXTENT decreased similarity in species composition.Research highlights: We recommend that for CEXTENT ≤ 1000 m2 and PAREA ≤ 500 m2, a cluster plot of rectangle CLAYOUT is preferred for information gain. The study could potentially benefit forest managers designing cluster plots for plant diversity assessment.Keywords: Biodiversity assessment; composition similarity; national forest inventory; species diversity; sampling design; sampling efficiency.Abbreviation used: extent of ground area covered by a cluster (CEXTENT); cluster layout (CLAYOUT); Jaccard similarity index (JAC); plot area (PAREA); plot configuration (PCONFIG); Sørensen similarity index (SOR).
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".