Biomass, productivity and allocation patterns in tropical old-growth and logged-over forests in Ghana
Bibliographic record
Abstract
Understanding how tropical forest structure and function change during the decades after logging is a key research challenge. This thesis reports functional traits, forest structure, biomass, net primary productivity (NPP) and allocation, as well as their controlling factors in an old-growth forest and a 54-year-old logged-over forest in Ghana. By analyzing root traits, I found fine-root biomass, root length, surface area, and root tissue density were higher in the logged-over forest, whereas the old-growth forest had higher specific root length and specific root area. I also found divergent exploitation strategies between the two forests; plants in the old-growth forest produced thinner roots, which increase resource uptake efficiency, while plants in the logged-over forest had thicker roots, which are associated with greater resource conservation. Through correlation analysis, I found that fine-root mass correlated positively to relative humidity, while absorbed photosynthetically active radiation and fine-root biomass were also positively correlated. Fine-root mass and soil K were also positively correlated, and fine-root necromass correlated positively with soil P. I then explored the relationships between leaf traits, taxonomic (e.g., species richness) or structural (e.g., tree diameter) variables and aboveground biomass (AGB) or coarse wood productivity (CWP) in the two forests. Leaf K related positively to tree biomass in the logged-over forest. Leaf N and P were significantly and positively related to tree productivity in the old-growth forest and logged-over forest. AGB and CWP were mostly explained by the structural variables. The shape and magnitude of the relationships between tree species richness and AGB or CWP differed between the two forests. In addition, I found that leaf area index, mean tree diameter and height were similar between the two forests, but stand density and basal area were higher in the logged-over forest than in the old-growth forest. Total biomass and annual NPP were comparable in both forests, but there was a shift in NPP allocation between wood and fine roots. I conclude that the forest structure, biomass and productivity of the logged-over forest have largely recovered, but the legacy of logging still persists, which is reflected in differences in functional traits and allocation patterns.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".