Spatial patterns of light-demanding tree species in the Yangambi rainforest (Democratic Republic of Congo)
Bibliographic record
Abstract
We are studying the factors of spatial distribution of light-demanding tree species in an undisturbed natural forest to contribute to the elucidation of the enigma of the persistence of light-demanding tree species in the canopy of the Congo Basin rainforests. Our objective was to analyze the current spatial distribution combined with the spatial autocorrelation of light-demanding stems species and to carry out a Canonical Correspondence Analysis (CCA) to discuss the role that different factors may have played in determining the observed spatial pattern. These species were subdivided into three regeneration guilds, based on their growth speed and longevity: short-lived pioneer (SLP), long-lived pioneer (LLP), and non-pioneer light-demanding (NPLD) species. We also compared the distribution of these species to that of Gilbertiodendron dewevrei, a typical extremely aggregative and locally hyperdominant shade-tolerant species. For this purpose, we carried out a selective inventory of all stems (at least 10 cm DBH) of light-demanding species in the forest around the Moni River, an area rich in individuals of light-demanding species. This inventory was carried out along eight transects equidistant by 450 m. The total length of these transects was 50.125 km (an area of 250.625 ha). The sampling units consisted of contiguous plots (200 m x 50 m) centered on the transect (long side parallel to the transect). Each plot was divided into 8 sub-plots of 25 m x 50 m. The parameters directly measured in the field were diameter at breast height (DBH), wetland indicators, topography, and slope. The last three parameters were measured in each sub-plot. The wetland indicators were spring, stream (stream 1 to about 3 m wide that can be forded), river (stream more than 3 m wide and that cannot be forded), temporary or periodically flooded swamp, permanent swamp, and absence of wetland. The modalities of the topography parameter were ridge, plateau, slope break, upward slope, downward slope, and lowland. The slope was measured using the SUUNTO clinometer. The longitude and latitude were deduced from the Cartesian coordinates (X and Y) of each tree, which were measured directly during the inventory. The altitude and distance from the nearest watercourse were deduced from the Geographic Information System (GIS). The altitude was derived from the digital terrain model of the Yangambi region, while the distance to the nearest watercourse was derived from a distance to watercourses map of the Yangambi region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.047 | 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 teacher head, 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".