Predicting harvest of non-native signal crayfish in lakes — a role for changing climate?
Bibliographic record
Abstract
The signal crayfish (Pacifastacus leniusculus) was introduced to Sweden in 1960, and it has a high commercial and recreational value, but it may also have negative effects on native ecosystems. To better predict how climate warming will affect population dynamics of this cool-water crayfish, we explored the role of temperature and density dependence as explanatory factors of the subsequent years’ catch rates of commercially sized signal crayfish in four Swedish lakes. We found air temperatures to be good proxies for water temperatures in all lakes, except during winter. We could only obtain water temperature data for Lake Vättern, and winter temperature data were therefore only included in the analysis of catch-per-unit-effort patterns in this lake. Our results indicate that increasing mean air temperatures will potentially affect the population dynamics of cool-water freshwater crayfish species such as the signal crayfish. Based on data from four lakes, it seems that the population dynamics of signal crayfish are lake-specific and could be affected by either recruitment during the juvenile stage, the survival and growth of adults, or both. Increased fluctuations in water temperature during winter may potentially influence adult survival. To better predict the effects of global warming on the dynamics of cool-water crayfish populations, we suggest that future studies should investigate recruitment in crayfish along temperature gradients and the influence of variations in water temperature on winter mortality.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".