A dominant plant (<i>Caesalpinia spinosa</i>) drives plant-pollinator interactions for an understory species in arid Southern Peru
Bibliographic record
Abstract
Background/Question/Methods In arid environments, dominant plants such as shrubs or trees typically facilitate other plant species within their understory. These direct positive effects are relatively well studied; however, the indirect effects arising from this interaction are not. Here, the magnet hypothesis for pollination was tested using the dominant plant Caesalpinia spinosa and its associated understory species Grindelia glutinosa . We predicted that visitation rate (a proxy of pollination), abundance, and diversity of arthropod pollinators should increase for the target understory species when associated with C. spinosa . To test this hypothesis, pollination visitation to the understory target was recorded using iPod Nanos and pan traps both under C. spinosa and in open adjacent microsites. The experiment was replicated twice – when C. spinosa was flowering, and when it was in vegetative state (but with the understory species in flower in both instances). Results/Conclusions We found higher abundance and diversity of visitors in microsites associated with the dominant plant, and this facilitation was greater when the dominant was also in flower. Arthropod communities occurring in open microsites were subsamples of the arthropod communities visiting understory microsites. The most dominant taxa in both microsites were Hymenoptera and Diptera. Overall, this study demonstrates that dominant plants increase pollination visitation for understory species, which provides support for the magnet hypothesis. Moreover, this study also provides evidence of indirect effects mediated by dominant plants that go beyond plant-plant interactions. Finally, we show that dominant plants are important for understory species because they enhance pollinator services locally.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".