Daphnia defense strategies in fishless lakes and ponds: one size does not fit all
Bibliographic record
Abstract
Body size and neck spine development in Daphnia greatly influence this animal’s vulnerability to predation by the size-selective invertebrate planktivore Chaoborus. We develop a stage-classified matrix population model for Daphnia that investigates the interaction and evolution of these two traits in situations (fishless lakes and ponds) where Chaoborus predation constitutes the major source of mortality. This model produces fitness landscapes for these traits in ten distinct Daphnia environments that are characterized by Chaoborus size (medium-sized Chaoborus americanus or large Chaoborustrivittatus), Chaoborus density (0–1.0 L−1) and food level (high or low). Larger Daphnia phenotypes are favored in both high and low food environments that contain C. americanus, and also in a high food situation with C. trivittatus. The environments with C. trivittatus and low food availability, however, select for very small, as well as very large, Daphnia phenotypes (small phenotypes are favored more at high Chaoborus densities), but not those that are intermediate in size. The development of neck spines is advantageous in all situations with Chaoborus, but high food environments that contain C. americanus favor their elimination following juvenile development, while the other model environments favor their retention (to various degrees) after maturity. These model predictions describe alternative antipredator strategies, two of which correspond closely with phenotypic patterns exhibited by two species of Daphnia (Daphnia pulex and Daphnia minnehaha) that commonly coexist with Chaoborus in fishless lakes and ponds.
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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 | 0.003 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".