Agricultural land-use change seasonally rewires stream food webs: a case study from headwater streams in the Lake Erie watershed
Bibliographic record
Abstract
Human impacts, like agricultural land-use change, alter natural patterns of resource availability and consumer response through space and time threatening the stability of ecosystems. In streams, removal of riparian zones and nutrient loading through agriculture may alter seasonal asynchronous resource fluxes and lead to food web rewiring and stability loss. Here, we seasonally sampled three streams across an agricultural gradient to determine how agricultural land-use change rewires seasonal stream food web structures. We show that agricultural land-use change seasonally rewired trophic interactions through reduced terrestrial energy use and trophic position. Additionally, agricultural land-use change drove steeper biomass size spectrum slopes and shifted fish communities to high abundances of small individuals. Collectively, our results suggest agricultural land-use change may be homogenizing stream food webs towards productive, single-energy channel dominated webs with faster turnover rates. Theory predicts such changes are indicative of instability, suggesting agriculture may be destabilizing stream food webs. Importantly, our results indicate agricultural land-use strategies that aim to retain riparian zones and reduce nutrient run-off may be important for reducing potential destabilizing effects. As agriculture is expected to increase to support growing human populations, elucidating strategies to maintain resilient stream food webs in agricultural landscapes is essential.
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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) | 0.001 | 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 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".