Predicted rainfall changes disrupt trophic interactions in a tropical aquatic ecosystem
Bibliographic record
Abstract
Changes in the distribution of rainfall and the occurrence of extreme rain events will alter the size and persistence of aquatic ecosystems. Such alterations may affect the structure of local aquatic communities in terms of species composition, and by altering species interactions. In many aquatic ecosystems, leaf litter sustains detrital food webs and could regulate the responses of communities to changes in rainfall. Few empirical studies have focused on how rainfall changes will affect aquatic communities and none have evaluated if basal resource diversity can increase resistance to such rainfall effects. In this study, we used water-holding terrestrial bromeliads, a tropical aquatic ecosystem, to test how predicted rainfall changes and litter diversity may affect community composition and trophic interactions. We used structural equation modeling to investigate the combined effects of rainfall changes and litter diversity on trophic interactions. We demonstrated that changes in rainfall disrupted trophic relationships, even though there were only minor direct effects on species abundance, richness, and community composition. Litter diversity was not able to reduce the impact of changes in rainfall on trophic interactions. We suggest that changes in rainfall can alter the way in which species interact with each other, decreasing the linkages among trophic groups. Such reductions in biotic interactions under climate change will have critical consequences for the functioning of tropical aquatic ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| 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".