Biochar Rescues Native Trees in the Biodiversity Hotspot of Mauritius
Bibliographic record
Abstract
Many tropical invasive species have allelopathic effects that contribute to their success in native plant communities. Pyrolyzed biomass (“biochar”) can sorb toxic compounds, including allelochemicals produced by invasive plants, potentially reducing their inhibitory effects on native species. Strawberry guava (Psidium cattleianum) is among the most important allelopathic invasive species on tropical islands and recognized as the most serious threat among invasive species in the global biodiversity hotspot of Mauritius. We investigated the effects of additions of locally produced biochar on native tree species in a field experiment conducted in areas invaded by strawberry guava within Mauritius’ largest national park. Growth and survivorship of native tree species were monitored over 2.5 years in plots subjected to four treatments: non-weeded, weeded, weeded + 25 t/ha biochar, and weeded + 50 t/ha biochar. Native tree growth and survivorship were strongly suppressed by strawberry guava. Biochar treatments dramatically increased native tree performance, with more than a doubling in growth, and substantially increased native tree survivorship and species diversity, while suppressing strawberry guava regeneration, consistent with growth-promoting properties and sorption of allelochemicals. We conclude that biochars, including “sustainable biochars” produced from locally accessible biomass using low-tech pyrolysis systems, have considerable potential to counteract effects of allelopathic invaders and increase the capacity for native species regeneration in tropical island ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".