Biogeographic context dependence of trophic cascade strength in bromeliad food webs
Bibliographic record
Abstract
Ecosystem functions and the biomass of lower trophic levels are frequently controlled by predators. The strength of top-down control in these trophic cascades can be affected by the identity and diversity of predators, prey, and resources, as well as environmental conditions such as temperature, moisture, and nutrient loading, which can all impact interaction strength between trophic levels. Few studies have been able to replicate a complete community over a large geographic area to compare the full trophic cascade in a manipulative experiment. Here, we identify geographic dependency in trophic cascade strength, and the driving factors and specific mechanisms behind it, by combining geographically replicated experiments with a novel approach of community analogues of common garden and transplant experiments. We studied a predator-detritivore-detritus food web in bromeliads in Puerto Rico, Costa Rica, and Brazil. We found that interaction strengths between resources, consumers, and predators were strongly site-specific, but the exact mechanism differed between trophic levels. Large bodied predators created strong interaction strengths between predator and consumer trophic levels, reducing consumer abundance regardless of the geographic location, whereas small-bodied predators created weak interactions with no impact on consumer abundances in any site. In contrast, the interaction strength between consumers and resources varied among sites, depending on the dominant species of leaf detritus. More labile leaf species in Costa Rica created a strong consumer-resource interaction and therefore strong trophic cascade, whereas tougher leaf species in Brazil created a weak consumer-resource interaction, and an overall weaker trophic cascade. Our study highlights the importance of replicating experiments over geographic scales to understand general patterns of ecological processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".