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Record W2592540071 · doi:10.1093/beheco/arx021

Altered physical and social conditions produce rapidly reversible mating systems in water striders

2017· article· en· W2592540071 on OpenAlexaff
Andrew Sih, Pierre‐Olivier Montiglio, Tina W. Wey, Sean Fogarty

Bibliographic record

VenueBehavioral Ecology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversité du Québec à MontréalMcGill University
Fundersnot available
KeywordsHaremBiologyMatingPolygynyPromiscuityMating systemGerridaeEcologyZoologyDemography

Abstract

fetched live from OpenAlex

Mating systems can vary within-species but the environmental drivers and behavioral mechanisms underlying this variation are seldom investigated experimentally. We experimentally assessed how individual behavioral plasticity in response to changes in pool and group size resulted in fundamental shifts in mating systems in water striders. We observed the same animals in larger and smaller pools, mimicking variation in pool size in natural streams, and observed a rapid, reversible change in the entire mating system. In large pools, striders exhibited scramble promiscuity with intense sexual conflict. Most males were active, harassing and driving females into hiding. Matings were frequent and typically lasted for more than 100 min. In contrast, when placed in small pools, the same animals often exhibited harem polygyny where the largest male drove other males into hiding, but allowed females to be relatively active. Matings were less frequent and of much shorter duration. Harem polygyny took several days to emerge after animals were moved to small pools, while these same animals returned to scramble promiscuity within hours after being moved to larger pools. Such variability in mating systems likely has important implications for the evolution of individual mating tactics.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.057
GPT teacher head0.305
Teacher spread0.248 · 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 designObservational
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

Citations14
Published2017
Admission routes1
Has abstractyes

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