Changes in water chemistry can disable plankton prey defenses
Bibliographic record
Abstract
The effectiveness of antipredator defenses is greatly influenced by the environment in which an organism lives. In aquatic ecosystems, the chemical composition of the water itself may play an important role in the outcome of predator-prey interactions by altering the ability of prey to detect predators or to implement defensive responses once the predator's presence is perceived. Here, we demonstrate that low calcium concentrations (<1.5 mg/L) that are found in many softwater lakes and ponds disable the ability of the water flea, Daphnia pulex to respond effectively to its predator, larvae of the phantom midge, Chaoborus americanus. This low-calcium environment prevents development of the prey's normal array of induced defenses, which include an increase in body size, formation of neck spines, and strengthening of the carapace. We estimate that this inability to access these otherwise effective defenses results in a 50-186% increase in the vulnerability of the smaller juvenile instars of Daphnia, the stages most susceptible to Chaoborus predation. Such a change likely contributes to the observed lack of success of daphniids in most low-calcium freshwater environments, and will speed the loss of these important zooplankton in lakes where calcium levels are in decline.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".