Do non‐native fish as prey favour the conservation of the threatened indigenous Eurasian otter?
Bibliographic record
Abstract
Summary 1. Biological invasions are considered a major threat to biodiversity. Most research has focused on the distribution, biology and impacts of non‐native species on native fauna and flora. However, few studies have explored their role as prey for native predators of conservation concern. 2. To assess the incidence and intensity of predation by the Eurasian otter Lutra lutra on established non‐native fish species, data were collated from the published literature. To be selected, studies had to cover at least 1 year, analyse more than 100 spraints and report the study period and percentage relative frequency (%RF) of all prey fish species. 3. To permit reliable, time‐related comparisons with %RF of non‐native fishes in otter diet, we also reviewed available information about both the distribution of non‐native fishes and history of their introductions to European countries, revealing a decrease with longitude in the number of naturalised non‐native fishes taken (ranging between 5 and 34) and their percentage in each fish assemblage. 4. Our selective criteria were met by 30 dietary studies from 44 study areas in 15 European countries during 1970–2010. The extent to which otters rely on non‐native fishes was almost negligible (mean %RF = 4.8), with the number of non‐native fishes preyed upon by otters decreasing with both latitude and longitude. 5. The %RF of non‐native fish in the diet increased slightly with time, with otters preying significantly more on non‐native fish in study areas where alterations of the fish assemblage had been highlighted in the reference papers. No relationship was found between otter diet breadth and the occurrence of non‐native fishes in their diet. 6. The current role of non‐native species in otter diet suggests that effective otter conservation management plans should focus on the maintenance and/or enhancement of native fish assemblages.
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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 | 0.000 | 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 | 0.001 |
| 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 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".