Contrasting responses of Arctic charr and brown trout to compensatory nutrient enrichment in an oligotrophicated reservoir
Bibliographic record
Abstract
Many large lakes in northern Scandinavia have become oligotrophicated due to hydroelectric water regulation in the twentieth century, causing a loss of littoral habitat and negative consequences for ecosystem productivity, fish populations, and fisheries. Compensatory nutrient enrichment is a potential remediation method that has successfully been carried out in Canada and the US. Here we assessed the response of Arctic charr (Salvelinus alpinus) and brown trout (Salmo trutta) to nutrient addition in a whole lake experiment in Stor-Mjölkvattnet, Sweden, with nearby Burvattnet as a reference. Nitrate and phosphate were added for eight consecutive years. The study also included sampling the seventh year after discontinuation of nutrient addition, which allowed us to investigate how long nutrient enrichment would be effective on fish growth. Populations of Arctic charr and brown trout responded quickly and vigorously to the treatment, with approximately a doubling of the catch per unit effort. Nutrient addition had a consistent positive effect on charr length, weight, and condition at a given age, with a median response to nutrient addition (as measured by Shapley values) of 32 mm, 45 g, and 0.087 g cm−3 × 100. The response in length and weight was strongest in the age classes 4+ and 5+. The corresponding responses of trout were 13 mm, 32 g, and 0.044 g cm−3 × 100, respectively. Seven years after the enrichment had ended, charr at ages ≤6+ years were back to their previous state before treatment, i.e. slow growing and in bad condition. The older age-classes of charr (≥7+), however, were in good condition, suggesting that those fish, as young had experienced the excellent conditions prevailing in the last years of nutrient enrichment and largely kept this advantage. We conclude that compensatory nutrient addition is a useful method for restoring charr populations and reversible.
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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 | 0.001 | 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) | 0.000 | 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".