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How does the yangtze river dolphin see?

The yangtze river dolphin (Lipotes vexillifer) is a mammal in the order Cetacea. Its eyes belong to the vision type Small prey mammal (UV).

Measured in this species: foveas and night vision. One measured dial: a value other than colour or sharpness is measured in this species; colour and sharpness are not measured here. Every value below carries its evidence level and sources; nothing is typed by hand.

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What stands out

The six dials

Evidence levels: how the tiers work. "Measured" means a value measured in this species; "Estimated" values come from a close relative or an eye-size formula.

Vision values for the yangtze river dolphin (Lipotes vexillifer), catalogue-v1
DialValueEvidenceSources
ColourColour receptors
1 receptor class: 524 nm (MWS (green))
receptor set of nearest measured relative Tursiops truncatus (same order Cetacea)
Group default[1]
SharpnessAcuity
3 cycles per degree
median of 12 relatives in order Cetacea: Tursiops truncatus, Orcinus orca, Lagenorhynchus obliquidens, Balaenoptera acutorostrata, Delphinus delphis, Pseudorca crassidens
Group default[2][3][4][5]
Field of viewBinocular overlap
75°
median of 41 relatives in class Mammalia: Octodon degus, Octodon lunatus, Equus caballus, Ovis aries, Bos taurus, Capra hircus
Group default[6][7][8][9]
Sharp zones (foveas)Number of foveas
0
fovea_present / area_centralis_type (retinal topography; count 1 = fovea present, 0 = none)
Measured[10]
Fovea type
area centralis		Measured[10]
Night visionActivity pattern
diurnal
mode of 3 rows (of 3 rows): diurnal; photopic
Measured (not re-verified)[11][12][13]
Rods vs cones
cone-dominated
nocturnal -> rod-dominated; crepuscular / cathemeral / mixed -> mixed; diurnal -> cone-dominated
Derived[11][12][13]
Motion (flicker fusion)Flicker fusion frequency
60 Hz
median of 21 relatives in class Mammalia: Rattus norvegicus, Cavia porcellus, Mus musculus, Felis catus, Macaca mulatta, Macaca nemestrina
Group default[14][15][16][17]

Related animals

More mammals: all mammals with measured vision data.

Sources

  1. Frazer SA, Baghalian M, et al. 2024. Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD). GigaScience 13:giae073; VPOD v1.3 data release. doi.org/10.5281/zenodo.19051998
  2. Caves EM, Fernandez-Juricic E, Kelley LA (2024) Ecological and morphological correlates of visual acuity in birds. J Exp Biol 227(2): jeb246063. Supplementary Table S1.. doi.org/10.1242/jeb.246063
  3. de Sousa AA et al. 2022. A natural history of vision loss: insight from evolution for human visual function. Neurosci Biobehav Rev 134:104550 (mmc, acuity compilation). doi.org/10.1016/j.neubiorev.2022.104550
  4. Kirk EC, Kay RF 2004. The evolution of high visual acuity in the Anthropoidea. In Anthropoid Origins, Table 1 (behavioural acuity). doi.org/10.1007/978-1-4419-8873-7_20
  5. Kirk & Kay 2004 Table 2 (anatomical acuity). doi.org/10.1007/978-1-4419-8873-7_20
  6. Heesy CP 2004. On the relationship between orbit orientation and binocular visual field overlap in mammals. Anat Rec 281A:1104, Table 1. doi.org/10.1002/ar.a.20116
  7. Heffner RS, Heffner HE 1992. Visual factors in sound localization in mammals. J Comp Neurol 317:219, Table 1 (via Evo-M1 sensory merge). doi.org/10.1002/cne.903170302
  8. Vega-Zuniga T, Medina FS, Fredes F, et al. 2013. Does nocturnality drive binocular vision? Octodontine rodents as a case study. PLoS ONE 8: e84199.. doi.org/10.1371/journal.pone.0084199
  9. Vega-Zuniga T, Medina FS, Marín G, Letelier JC, Palacios AG, Němec P, Schleich CE, Mpodozis J. (2017). Selective binocular vision loss in two subterranean caviomorph rodents: Spalacopus cyanus and Ctenomys talarum. Scientific reports
  10. Kopania EEK, Clark NL. 2025. Mammalian retinal specializations for high acuity vision evolve in response to both foraging strategies and morphological constraints. Evolution Letters 9: qrae072. Supplementary Tables S1-S2.. doi.org/10.1093/evlett/qrae072
  11. Wilman et al. 2014 EltonTraits 1.0, MamFuncDat.txt. doi.org/10.6084/m9.figshare.3559887.v1
  12. Schmitz L, Motani R. 2011. Science 332:705-708, SOM. doi.org/10.1126/science.1200043
  13. Moura et al. 2024. A phylogeny-informed characterisation of global tetrapod traits addresses data gaps and biases. PLoS Biol 22:e3002658. TetrapodTraits v3.0.1.. doi.org/10.5281/zenodo.22536349
  14. Haarlem CS, Hynes C, Jackson AL, Mitchell KJ, O'Connell RG, Healy K. 2026. Pace of ecology drives the tempo of visual perception across the animal kingdom. Nature Ecology & Evolution (doi:10.1038/s41559-026-02994-7). Figshare dataset 10.6084/m9.figshare.30556475. doi.org/10.6084/m9.figshare.30556475
  15. Healy K, McNally L, Ruxton GD, Cooper N, Jackson AL. 2013. Metabolic rate and body size are linked with perception of temporal information. Animal Behaviour 86:685-696. Table 1. doi.org/10.1016/j.anbehav.2013.06.018
  16. Inger R, Bennie J, Davies TW, Gaston KJ. 2014. Potential biological and ecological effects of flickering artificial light. PLoS ONE 9(5): e98631. Table 3. doi.org/10.1371/journal.pone.0098631
  17. Lafitte A, Sordello R, Legrand M, Nicolas V, Obein G, Reyjol Y. 2022. A flashing light may not be that flashy: A systematic review on critical fusion frequencies. PLoS ONE 17(12): e0279718. S10 File (CFF database). doi.org/10.1371/journal.pone.0279718

Every value cites its sources (all sources). Data: catalogue-v1, built 2026-09-29. Accuracy notes: how accurate is this?