Acidification and eutrophication: insights into wildfowlfish competition.
Bibliographic record
Abstract
Wildfowl managers are often advised to discourage the introduction of fish into wetlands because competition between fish and wildfowl for invertebrate prey negatively affects the quality of breeding habitat and duckling growth rates.In the last three decades, research on this competition has been performed under the umbrella of two management issues: the effects of acidification on wildfowl and fish in oligotrophic (low-nutrient) lakes and the effects of biomanipulations (fish removals to reduce nuisance algae) on lake communities in mesotrophic to hypertrophic lakes.In both types of studies, regardless of the fish and bird fauna involved, the focus has been on the effects of fish extirpations or removals on invertebrates and thus on the birds that compete with the fish for invertebrate prey.However, some of the ways in which fish are removed or lost from lakes may not necessarily result in benefits to wildfowl, because (1) in acidic lakes, low pH can also have negative effects on the birds and (2) some methods used to remove fish in biomanipulations can negatively affect the very invertebrates upon which birds feed.Thus, although these fish removal/extinction-based studies have addressed the issues of acid precipitation and eutrophication in lakes, they do not always unequivocally show that fish are responsible for reduced invertebrate abundance and reduction of wildfowl habitat quality.Removal studies may not, therefore, provide an adequate basis for advising managers to discourage fish introductions into wetlands.The merits and pitfalls of these fish removal/ extinction-based studies of wildfowl-fish competition are reviewed.Additionally, a more direct approach to studying wildfowl-fish competition and to assessing the effects of fish introductions on invertebrates and wildfowl is suggested -namely, adding fish experimentally to wetlands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".