Individual Species-Area Relationship of Woody Plant Communities in a Heterogeneous Subtropical Monsoon Rainforest
Bibliographic record
Abstract
The spatial structure of species richness is often characterized by the species-area relationship (SAR). However, the SAR approach rarely considers the spatial variability of individual plants that arises from species interactions and species' habitat associations. Here, we explored how the interactions of individual plants of target species influence SAR patterns at a range of neighborhood distances. We analyzed the data of 113,988 woody plants of 110 species from the Fushan Forest Dynamics Plot (25 ha), northern Taiwan, which is a subtropical rainforest heavily influenced by typhoons. We classified 34 dominant species into 3 species types (i.e., accumulator, repeller, or no effect) by testing how the individual species-area relationship (i.e., statistics describing how neighborhood species richness changes around individuals) of target species departs (i.e., positively, negatively, or with no obvious trend) from a null model that accounts for habitat association. Deviation from the null model suggests that the net effect of species' interactions increases (accumulate) or decreases (repel) neighborhood species richness. We found that (i) accumulators were dominant at small interaction distances (<10-30 m); (ii) the detection of accumulator species was lower at large interaction distances (>30 m); (iii) repellers were rarely detected; and (iv) large-sized and abundant species tended to be accumulators. The findings suggest that positive species interactions have the potential to accumulate neighborhood species richness, particularly through size- and density-dependent mechanisms. We hypothesized that the frequently disturbed environment of this subtropical rainforest (e.g., typhoon-driven natural disturbances such as landslides, soil erosion, flooding, and windthrow) might create the spatial heterogeneity of species richness and promote positive species interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".