Cascading community and ecosystem consequences of introduced coconut palms (<i>Cocos</i> <i>nucifera</i>) in tropical islands
Bibliographic record
Abstract
Biological invasions are a pervasive and dominant form of anthropogenic disturbance. However, we seldom have the opportunity to evaluate the long-term, indirect, and often slow-moving cascading effects of invasions at the community and ecosystem scale. Here we synthesize the collective knowledge from 10 years of study on the influence of the deep historical introduction of coconut palms (Cocos nucifera L.) across a series of islets at Palmyra Atoll. Through a suite of pathways, we find this palm drives near-complete ecosystem state change when it becomes dominant. Abiotic conditions are transformed, with major soil nutrients 2.7–11.5 times lower and water stress 15% elevated in palm-dominated forests compared with native forest. Faunal communities are likewise dramatically altered, not only in composition but also in behavior, body size, and body condition. Biotic interactions, including herbivory rates, palatability, and seed predation, are likewise changed. Cumulatively, these changes transform food webs, leading to dramatically shortened and simplified food chains in invaded ecosystems. Many of these changes appear to create slow-acting feedback loops that favor the palm at the expense of native species. Given the widespread nature of this historical introduction, many island and coastal regions of tropical oceans may be similarly transformed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".