Do novel ecosystems follow predictable trajectories? Testing the trophic surge hypothesis in reservoirs using fish
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Hydroelectric reservoirs are novel ecosystems that provide a variety of important services. To manage these ecosystems and their fish populations effectively, we need to develop conceptual frameworks for predicting their short‐ and long‐term responses. To advance this goal, we revisited and tested the “trophic surge hypothesis, TSH .” The TSH has been widely cited in the literature, but has not been empirically tested across numerous reservoirs. The TSH suggests that fish populations should show a hump‐shaped pattern (i.e., the non‐equilibrium phase) after river impoundment. As such, we assembled 40 recruitment and 109 adult fish abundance time series from 19 species distributed across seven reservoirs from temperate and boreal regions, and applied curve fitting analyses and model selection. We found that the hump‐shaped pattern was the predominant pattern across individual time series, providing moderate support for the TSH . Fish recruitment increased substantially during reservoir filling and was followed by an increase in adult fish lagging 3–4 years behind. The non‐equilibrium phase was transient and lasted roughly eight years for recruits, whereas it could be much longer for adults. When time series were combined across regions and sites, the support for the TSH was weaker. However, we observed significant variability in the duration, timing, and magnitude of the surge across individual time series and found that the total flooded area was the most influential predictor to explain this variability. In conclusion, the TSH and related metrics can be a useful and general predictive framework to understand how fish populations may respond to impoundment. In particular, long‐term management recommendations could be short‐sighted if formulated before convincing evidence has emerged to show that the reservoir reached its new trophic equilibrium.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it