Reservoir refilling enhances growth and recruitment of an endangered remnant riverine fish
Bibliographic record
Abstract
Rapid increases in native riverine fish populations associated with trophic upsurge immediately following reservoir construction are well documented. Repeated upsurge periods and extended benefits to populations are, however, less understood. We used sclerochronology to investigate fish growth and netting surveys to estimate recruitment and abundance of a lacustrine population of an Australian riverine fish, the Macquarie perch (Macquaria australasica) in Lake Dartmouth. Record low inflows from 1997 to 2008 caused the reservoir to shrink to its lowest volume since construction. Refilling began in 2008, reaching 99% capacity in 2013. We hypothesized that fish growth, recruitment, and abundance would increase in response to the refilling of the lake, reproducing a similar response to the initial filling period. Our findings supported this hypothesis. Macquarie perch growth, recruitment, and abundance were enhanced during the refilling of Lake Dartmouth. Growth, best explained by the effects of dam height, change in dam height, temperature (and their interactions), and recruitment, were highest during the first years of refilling when lake levels and temperatures were low. We propose one or a combination of varying levels of intraspecific competition (low during initial filling and high following population expansion) and improved riverine conditions for reproductive success as the most plausible explanation. Our results suggest extended periods of low lake levels followed by rapid inundation events are likely to enhance recruitment and population growth opportunities for this species. While reservoir construction in general impacts negatively on native fish populations, the potential to offset these impacts for conservation management purposes should be considered.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".