Mismatch in microbial food webs: predators but not prey perform better in their local biotic and abiotic conditions
Bibliographic record
Abstract
Understanding how trophic levels respond to changes in abiotic and biotic conditions is key for predicting how food webs will react to environmental perturbations. Different trophic levels may respond disproportionately to change, with lower levels more likely to react faster, as they typically consist of smaller-bodied species with higher reproductive rates. This response could cause a mismatch between trophic levels, in which predators and prey will respond differently to changing abiotic or biotic conditions. This mismatch between trophic levels could result in altered top-down and bottom-up control and changes in interaction strength. To determine the possibility of a mismatch, we conducted a reciprocal-transplant experiment involving Sarracenia purpurea food webs consisting of bacterial communities as prey and a subset of six morphologically similar protozoans as predators. We used a factorial design with four temperatures, four bacteria and protozoan biogeographic origins, replicated four times. This design allowed us to determine how predator and prey dynamics were altered by abiotic (temperature) conditions and biotic (predators paired with prey from either their local or non-local biogeographic origin) conditions. We found that prey reached higher densities in warmer temperature regardless of their temperature of origin. Conversely, predators achieved higher densities in the temperature condition and with the prey from their origin. These results confirm that predators perform better in abiotic and biotic conditions of their origin while their prey do not. This mismatch between trophic levels may be especially significant under climate change, potentially disrupting ecosystem functioning by disproportionately affecting top-down and bottom-up control.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".