Complex indirect effects of epiphytic bromeliads on the invertebrate food webs of their support tree
Bibliographic record
Abstract
Abstract Ecosystem engineers are species that affect others through the provision of habitat rather than consumptive resources. The extent to which ecosystem engineers can indirectly affect entire food webs, however, is poorly understood. Epiphytic tank bromeliads (Bromeliaceae) are ecosystem engineers that are common throughout the Neotropics, and are associated with a variety of predatory arthropods. Here, we examine if bromeliads, by increasing predator densities, indirectly benefit their support tree through reduction in herbivorous insects and leaf damage. We observed and manipulated bromeliad densities in Costa Rican orange orchards, and measured impacts on leaf damage and arboreal and bromeliad invertebrate communities in two different seasons. Our results show that bromeliads are associated with predatory and herbivorous invertebrates but not leaf damage. Bromeliads were correlated with increased densities of their associated predators, especially ants and hunting spiders, but we could not confirm a causal link. Associations with bromeliads changed over time, with seasonal shifts interfering with responses to our manipulations. Bromeliads had a reduced association with predators in the dry season. Moreover, a null association between bromeliads and herbivorous invertebrates in the dry season unexpectedly became positive in the wet season. In summary, we have only limited evidence that bromeliads indirectly promote the top‐down control of arboreal herbivores; instead, our manipulations suggest that bromeliads increase herbivore densities in the wet season. This research suggests that although bromeliads may act as ecosystem engineers, indirectly influencing the invertebrate food web on support trees, their effects are trophically complex and seasonally dependent. Abstract in Spanish is available with online material.
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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) | 0.000 | 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".