Species–area relationships explained by the joint effects of dispersal limitation and habitat heterogeneity
Bibliographic record
Abstract
Species-area relationships (SARs) characterize the spatial distribution of species diversity in community ecology, but the biological mechanisms underlying the SARs have not been fully explored. Here, we examined the roles of dispersal limitation and habitat heterogeneity in shaping SARs in two large-scale forest plots. One is a 24-ha subtropical forest in Gutianshan National Nature Reserve, China. The other is a 50-ha tropical rain forest in Barro Colorado Island, Panama. Spatial point pattern models were applied to investigate the contributions of dispersal and habitat heterogeneity and their interactions to the formation of the SARs in the two sites. The results showed that, although dispersal and habitat heterogeneity each could significantly contribute to the SARs, each alone was insufficient to explain the SARs. Their joint effects sufficiently explained the real SARs, suggesting that heterogeneous habitat and dispersal limitation are two predominant mechanisms for maintaining the spatial distributions of the species in these two forests. These results add to our understanding of the ecological processes underlying the spatial variation of SARs in natural forest communities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".