Partitioning beta diversity in a tropical karst seasonal rainforest in Southern China
Bibliographic record
Abstract
Both deterministic and stochastic processes have been linked to forest community assembly; however, their contribution to beta diversity has not been properly explored, and no studies to date have investigated their impacts on sparse depleted soils in forests that contain widespread exposed limestone karst. We found that the pairwise differences in species composition between quadrates was determined by a balanced variation in abundance, whereby the individuals of some species at one site were substituted by an equivalent number of individuals of different species at another site. Both the total beta diversity and its balanced variation in abundance declined with increasing sampling grain size. Our research indicated that environmental differences exert a strong influence on beta diversity, particularly total beta diversity and its balanced abundance variation in larger grain sizes. It was evident that deterministic and stochastic processes worked together, and that deterministic processes were more important than stochastic processes in the regulation of beta diversity in this heterogeneous tropical karst seasonal rainforest of Southern China. However, in future research a functional trait based approach will be required to tease out the relative degree of deterministic and stochastic processes toward an assessment of the temporal changes in species composition.
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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) | 0.000 | 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".