Fish population dynamics and diversity in boreal and temperate reservoirs: A quantitative synthesis
Bibliographic record
Abstract
Abstract River impoundments are commonly cited as key disturbances to freshwater aquatic ecosystems. Dams alter natural hydrological regimes, homogenize river system dynamics at a global scale, can act as barriers for migratory species and may facilitate species invasions. In this synthesis, we examined the short- and long-term effects of impoundment on fish population dynamics and community structure. At the population level, we tested the “trophic surge hypothesis”, which predicts a hump-shaped response of fish abundance through time after impoundment. We tested the hypothesis on 40 recruitment time series and 125 adult abundance time series from 19 species and nine reservoirs distributed in temperate and boreal regions. At the community level, we compared diversity metrics (richness, evenness, diversity) on two datasets: 1) between reservoirs and reference ecosystems (lakes, rivers, and streams) and 2) over time (before and after impoundment and over time). At the population level, the trophic surge hypothesis was supported in more than 55% of the time series but we observed significant variation across species, reservoirs and regions. Fish recruitment increased substantially during reservoir filling and shortly after impoundment, and was usually followed by an increase in adult fish. The surge was transient and vanished after 3-4 years for recruits and after 10 years for adults. However, we are lacking long time series to conclude about population patterns in the trophic equilibrium phase. At the community level, we did not find any strong directional patterns in species diversity metrics when comparing reservoirs to reference lakes but found higher diversity and evenness in reservoirs and impounded streams/rivers relative to unimpounded streams/rivers. We did not find directional patterns when looking at a change over time. Variability in the reported diversity results across studies may be related to the ability to tease apart the unique effects of impoundment and water regulation from other stressors such as propagule pressure and eutrophication, as well as the comparability of the reference system. In conclusion, fish populations benefited quickly but transiently from impoundment, and longer time series are needed to conclude about population dynamics and equilibrium in aging reservoirs in order to develop management recommendations.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".