Detecting landscape-level changes in tree biomass and biodiversity: methodological constraints and challenges of plot-based approaches
Bibliographic record
Abstract
Understanding how human-impacted landscapes are changing is crucial for effective adaptive management and payment for ecosystem services programs. Landscape-level shifts in land use pose challenges not seen in typical ecological studies of well-protected forests. In human-modified landscapes, forests are often monitored using unique sets of randomized plots at each visit rather than re-censusing in the same permanent plots. We contrast field-based forest change monitoring using these two techniques and investigate whether sampling more plots or bigger plots better detects forest changes. Our empirical analysis employs long-term data sets from old-growth, second-growth, and managed tropical forests. We find that resampling in permanent plots reduces variation among subsequent censuses, but more importantly, it enables more powerful statistical tests. Increasing the number of plots improves detection of forest biomass changes more effectively than enlarging existing plot sizes, cost considerations being equal. This effect arises from more extensive capture of spatial heterogeneity by sampling in a greater number of locations. We further show that typical sampling techniques poorly assess the biodiversity of tropical forests and struggle to identify big changes in populations of common species. We conclude with practical suggestions for forest sampling in human-impacted tropical landscapes, including defining monitoring goals and delineating forests vs. entire landscapes as study areas.
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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 | 0.149 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".