MétaCan
Menu
Back to cohort
Record W2094617961 · doi:10.1073/pnas.1209938109

Changes in water chemistry can disable plankton prey defenses

2012· article· en· W2094617961 on OpenAlexaff
Howard P. Riessen, Robert Dallas Linley, Ianina Altshuler, Max Rabus, Thomas Söllradl, Hauke Clausen‐Schaumann, Christian Laforsch, Norman D. Yan

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of WindsorOntario Science CentreYork University
Fundersnot available
KeywordsDaphnia pulexPredationDaphniaBiologyZooplanktonEcologyPredatorCarapacePlanktonOrganismMidgeKairomoneZoologyLarvaCrustacean

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.252
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations76
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicAquatic Ecosystems and Phytoplankton DynamicsFrench-language works237,207