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Record W2907667633 · doi:10.1101/324202

Active sensing with light improves predator detection in a diurnal fish

2018· preprint· en· W2907667633 on OpenAlexafffund
Matteo Santon, Pierre‐Paul Bitton, Jasha Dehm, Roland Fritsch, Ulrike K. Harant, Nils Anthes, Nico K. Michiels

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaVolkswagen FoundationDeutsche Forschungsgemeinschaft
KeywordsPredatorBiologyCichlidAcclimatizationFish <Actinopterygii>DownwellingFisheryZoologyPredationEcologyUpwelling

Abstract

fetched live from OpenAlex

Abstract Active sensing by means of light is rare. In vertebrates, it is known only from chemiluminescent fish with light organs below their pupils, an anatomical arrangement that is ideal to generate eyeshine in the pupils of nearby organisms. Here, we test whether diurnal fish can achieve the same by redirecting sunlight through reflection instead. We recently showed that small (< 5 cm), benthic, marine triplefin fish actively redirect downwelling light using their iris. We hypothesized that this mechanism allows triplefins to improve detection of a cryptic organism by generating eyeshine in its pupil. Here, we tested this by attaching small dark hats to triplefins to shade their iris from downwelling light. Two controls consisted of triplefins with a clear or no hat. These treatments test the prediction that light redirection increases the visual detection ability of triplefins. To this end, we placed treated fish in a tank with a display compartment containing either a stone as the control stimulus, or a scorpionfish, i.e. a cryptic, motionless triplefin predator with retroreflective eyes. After overnight acclimatization, we determined the average distance triplefins kept from the display compartment over two days. Both in the laboratory ( n = 15 replicates per treatment) and in a similar field experiment at 15 m depth ( n = 43 replicates per treatment) fish kept longer distances from the scorpionfish than from the stone. This response varied between hat treatments: shaded triplefins stayed significantly closer to the scorpionfish in the laboratory and in one of two orientations tested in the field. A follow-up field experiment at 10 m depth revealed the immediate response of triplefins to a scorpionfish. At first, many individuals ( n = 80) moved towards it, with shaded triplefins getting significantly closer. All individuals then gradually moved to a safer distance at the opposite half of the tank. Visual modelling supported the experimental results by showing that triplefins can redirect enough light with their iris to increase a scorpionfish’s pupil brightness above detection threshold at a distance of 7 cm under average field conditions and at more than 12 cm under favorable conditions. We conclude that triplefins are generally good in the visual detection of a cryptic predator, but can significantly improve this ability when able to redirect downwelling light with their iris and induce eyeshine in the predator’s pupil. We discuss the consequences of “diurnal active photolocation” for visual detection and camouflage among fish species.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2018
Admission routes2
Has abstractyes

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