Elevated inbreeding in <i>Heliconia tortuosa</i> is determined by tropical forest stand age, isolation and loss of hummingbird functional diversity
Bibliographic record
Abstract
Forest conversion and habitat loss are major threats to biological diversity. Forest regeneration can mitigate the negative effects of old-growth forest loss on species diversity, but less is known about the extent to which forest loss reduces genetic diversity in remnant populations and whether secondary forests play a role in the maintenance of genetic diversity. We quantified genetic diversity in a tropical hummingbird-pollinated understorey herb, Heliconia tortuosa, across a landscape mosaic of primary and secondary forest regrowth. Using microsatellite genotypes from >850 adult and juvenile plants within 33 forest patches and extensive bird surveys, we examined the effect of contemporary and historical landscape features including forest age (primary vs. secondary forest), stand isolation and pollinator assemblages on genetic diversity and levels of inbreeding in H. tortuosa. We found that inbreeding was up to three times higher in secondary forest, and this effect was amplified with reductions in primary forest in the surrounding landscape through reduced observed heterozygosity in isolated fragments. Inbreeding in forest patches was negatively correlated with the local frequency of specialist long-distance foraging traplining hummingbirds. Traplining hummingbirds therefore appear to facilitate mating among unrelated plants-an inference we tested using empirically parameterized simulations. Higher levels of inbreeding in H. tortuosa are therefore associated with reduced functional diversity of hummingbirds in secondary forests and forest patches isolated from primary forests. Our findings suggest a cryptic consequence of primary forest loss and secondary forest regeneration through the disruption of mutualistic interactions resulting in the erosion of genetic diversity in a common understorey plant.
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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 | 0.000 |
| 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 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".