Evaluation of Forest Recovery over Time and Space Using Permanent Plots Monitored over 30 Years in a Jamaican Montane Rain Forest
Bibliographic record
Abstract
Conservation of tropical forest biodiversity increasingly depends on its recovery following severe human disturbance. Our ability to measure recovery using current similarity indices suffers from two limitations: different sized individuals are treated as equal, and the indices are proportionate (a community with twice the individuals of every species as compared with the reference community would be assessed as identical). We define an alternative recovery index for trees - the Tanner index, as the mean of the quantitative Bray-Curtis similarity indices of species composition for stem density and for basal area. We used the new index to compare the original (pre-gap) and post-gap composition of five experimental gap plots (each 90-100 m(2)) and four control plots over 24-35 years in the Blue Mountains of Jamaica. After 24-35 years, these small gaps surrounded by undisturbed forest had recovered 68% of the sum of per species stem density and 29% of the sum of per species basal area, a recovery index of 47%. Four endemic species were especially reduced in density and basal area. With the incorporation of basal area and stem density, our index reduces over-estimations of forest recovery obtained using existing similarity indices (by 24%-41%), and thus yields more accurate estimates of forest conservation status. Finally, our study indicates that the two kinds of comparisons: 1) over time between pre-gap and post-gap composition and 2) over space between gap plots and spatial controls (space-for-time substitution) yield broadly similar results, which supports the value of using space-for-time substitutions in studying forest recovery, at least in this tropical montane forest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".