Considering non-predatory death in the estimation of copepod early life stage mortality and survivorship
Bibliographic record
Abstract
Estimation of early stage mortality is essential for predicting copepod population dynamics and ecological linkages. Standard methods do not distinguish among types of mortality, nor do they consider that samples may include dead individuals, which can lead to misinterpretation and bias. Here, we develop theory to explain how non-predatory death, or “expiration”, influences in situ abundances. We present an amended population dynamics model that accounts for the production of non-viable eggs, expiration of live individuals and losses of dead individuals. This model is used to derive generalizations of four vertical mortality estimation methods, including the widely used Vertical Life Table approach. These new formulae are applied to data for Calanus finmarchicus in the Labrador Sea to illustrate the potential effects of reduced viability on estimated early stage loss rates and survivorship. Results show that even slight reductions in viability can impart significant changes, with the nature of the effect varying among methods, consistent with previous studies. We explain the reasons for these differences and how the common practice of aggregating stages masks the ecological significance of egg viability. Our analysis reinforces previous recommendations for scientists to consider expiration in their estimates of mortality, and in designing their empirical studies.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".