Temperature and food quantity effects on the harpacticoid copepod Nitocra spinipes: Combining in vivo bioassays with population modeling
Bibliographic record
Abstract
The harpacticoid copepod Nitocra spinipes has become a popular model species for toxicity testing over the past few decades. However, the combined influence of temperature and food shortage, two climate change-related stressors, has never been assessed in this species. Consequently, effects of three temperatures (15, 20 and 25°C) and six food regimes (between 0 and 5 × 105 algal cells/mL) on the life cycle of N. spinipes were examined in this study. Similarly to other copepod species, development times and brood sizes decreased with rising temperatures. Mortality was lowest in the 20°C temperature setup, indicating a close-by temperature optimum for this species. Decreasing food concentrations led to increased development times, higher mortality and a reduction in brood size. A sex ratio shift toward more females per male was observed for increasing temperatures, while no significant relationship with food concentration was found. Temperature and food functions for each endpoint were integrated into an existing individual-based population model for N. spinipes which in the future may serve as an extrapolation tool in environmental risk assessment. The model was able to accurately reproduce the experimental data in subsequent verification simulations. We suggest that temperature, food shortage, and potentially other climate change-related stressors should be considered in environmental risk assessment of chemicals to account for non-optimal exposure conditions that may occur in the field. Furthermore, we advocate combining in vivo bioassays with population modeling as a cost effective higher tier approach to assess such considerations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".