Independent, But Not Synergistic, Effects of Landscape Structure And Climate Drive Pollination of A Tropical Plant, Heliconia Tortuosa
Bibliographic record
Abstract
Abstract Context Land-cover and climate change are predicted to affect pollination and plant reproduction but few studies have tested how these stressors interact to drive pollination success.Objectives Using a 9-year dataset we tested whether climate interacts synergistically with forest loss and fragmentation to affect pollination of a tropical understory herb, Heliconia tortuosa. We hypothesized that hot and/or dry conditions might amplify effects of habitat loss and fragmentation, leading to declines in plant reproduction.Methods We collected data on pollen tubes, fruits and seeds of H. tortuosa in a mensurative experiment representing gradients in forest amount and patch size (N=40 focal-patch landscapes). We modeled these reproductive metrics as a function of landscape composition, configuration, precipitation, temperature, and statistical interactions among these variables.Results We found little support for synergistic landscape and climate effects. However, probability of fruit production decreased in wet years, and increased in large patches embedded in more contiguous forest landscapes. Counterintuitively, small patches in heavily deforested landscapes also exhibited a high probability of fruit – perhaps due to constraints on hummingbird movement. Amount of H. tortuosa fruit decreased in wet years and in deforested landscapes. Conclusion Although we did not detect synergistic effects, climate and landscape structure do have independent impacts on plant reproduction. Unfortunately, the regional climate is predicted to become wetter and the forest remains fragmented, with potential negative consequences for Heliconia reproduction. Decline in this common species is likely to have cascading consequences for hummingbird pollinators, and subsequently other hummingbird dependent plant species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".