Can Neutral Theory Predict the Responses of Amazonian Tree Communities to Forest Fragmentation?
Bibliographic record
Abstract
We use Hubbell's neutral theory to predict the impact of habitat fragmentation on Amazonian tree communities. For forest fragments isolated for about two decades, we generate neutral predictions for local species extinction, changes in species composition within fragments, and increases in the probability that any two trees within a fragment are conspecific. We tested these predictions using fragment and intact forest data from the Biological Dynamics of Forest Fragments Project in central Amazonia. To simulate complete demographic isolation, we excluded immigrants--species absent from a fragment or intact forest plot in the initial census but present in its last census--from our tests. The neutral theory correctly predicted the rate of species extinction from different plots as a function of the diversity and mortality rate of trees in each plot. However, the rate of change in species composition was much faster than predicted in fragments, indicating that different tree species respond differently to environmental changes. This violates the key assumption of neutral theory. When immigrants were included in our calculations, they increased the disparity between predicted and observed changes in fragments. Overall, neutral theory accurately predicted the pace of local extinctions in fragments but consistently underestimated 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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".