Pace of ecology drives the tempo of visual perception across the animal kingdom
Laughlin, S. B. The metabolic cost of information—a fundamental factor in visual ecology. In Ecology of Sensing (eds Barth, F. G. & Schmid, A.) 169–185 (Springer, 2001); https://doi.org/10.1007/978-3-662-22644-5_9
Land, M. F. & Nilsson, D.-E. Animal Eyes (Oxford Univ. Press, 2012).
von Uexküll, J. in Instinctive Behavior 5–80 (International Univ. Press, 1934).
Donner, K. Temporal vision: measures, mechanisms and meaning. J. Exp. Biol. 224, jeb222679 (2021).
Reeves, A. in Handbook of Perception and Action (eds Prinz, W. & Bridgeman, B.) Vol. 1, 11–24 (Academic, 1996).
Brozek, J. & Keys, A. Changes in flicker-fusion frequency with age. J. Consult. Psychol. 9, 87–90 (1945).
Misiak, H. Age and sex differences in critical flicker frequency. J. Exp. Psychol. 37, 318–332 (1947).
Chatterjee, P., Mohan, U., Krishnan, A. & Sane, S. P. Evolutionary constraints on flicker fusion frequency in Lepidoptera. J. Comp. Physiol. A 206, 671–681 (2020).
Inger, R., Bennie, J., Davies, T. W. & Gaston, K. J. Potential biological and ecological effects of flickering artificial light. PLoS ONE 9, 98631 (2014).
Petie, R., Hall, M. R., Hyldahl, M. & Garm, A. Visual orientation by the crown-of-thorns starfish (Acanthaster planci). Coral Reefs 35, 1139–1150 (2016).
Kelly, D. H. Visual responses to time-dependent stimuli. I. Amplitude sensitivity measurements. J. Opt. Soc. Am. 51, 422–429 (1961).
Rider, A. T., Bruce Henning, G. & Stockman, A. Light adaptation controls visual sensitivity by adjusting the speed and gain of the response to light. PLoS ONE 14, e0220358 (2019).
Autrum, H. The electrophysiological analysis of the visual system in insects. Exp. Cell Res. 14, 429–439 (1958).
Boström, J. E. et al. Ultra-rapid vision in birds. PLoS ONE 11, 3–9 (2016).
Potier, S., Lieuvin, M., Pfaff, M. & Kelber, A. How fast can raptors see?. J. Exp. Biol. 223, jeb209031 (2020).
Frank, T. M., Johnsen, S. & Cronin, T. W. Light and vision in the deep-sea benthos: II. Vision in deep-sea crustaceans. J. Exp. Biol. 215, 3344–3353 (2012).
Healy, K., McNally, L., Ruxton, G. D., Cooper, N. & Jackson, A. L. Metabolic rate and body size are linked with perception of temporal information. Anim. Behav. 86, 685–696 (2013).
D’Eath, R. B. Can video images imitate real stimuli in animal behaviour experiments?. Biol. Rev. 73, 267–292 (1998).
Howard, J., Dubs, A. & Payne, R. The dynamics of phototransduction in insects—a comparative study. J. Comp. Physiol. 154, 707–718 (1984).
Mebourou, E. K., Bernáth, B., Schenker, D. & Guerin, P. M. Vision and the genesis of survival strategies in tsetse flies: a laboratory study. J. Insect Physiol. 107, 212–223 (2018).
Bobkova, M. V., Tartakovskaya, O. S., Borissenko, S. L., Zhukov, V. V. & Meyer-Rochow, V. B. Restoration of morphological and functional integrity in the regenerating eye of the giant African land snail Achatina fulica. Acta Zool. 85, 1–14 (2004).
Fritsches, K. A., Brill, R. W. & Warrant, E. J. Warm eyes provide superior vision in swordfishes. Curr. Biol. 15, 55–58 (2005).
Weihs, D. Stability versus maneuverability in aquatic locomotion. Integr. Comp. Biol. 42, 127–134 (2002).
Nabawy, M. R. A., Sivalingam, G., Garwood, R. J., Crowther, W. J. & Sellers, W. I. Energy and time optimal trajectories in exploratory jumps of the spider Phidippus regius. Sci. Rep. 8, 7142 (2018).
Van Leeuwen, J. L., De Groot, J. H. & Kier, W. M. Evolutionary mechanics of protrusible tentacles and tongues. Neth. J. Zool. 50, 113–139 (2000).
Gilbert, C. Visual control of cursorial prey pursuit by tiger beetles (Cicindelidae). J. Comp. Physiol. 181, 217–230 (1997).
Fleming, S. M. & Michel, M. Sensory horizons and the functions of conscious vision. Behav. Brain Sci. https://doi.org/10.1017/S0140525X25000068 (2025).
McComb, D. M., Frank, T. M., Hueter, R. E. & Kajiura, S. M. Temporal resolution and spectral sensitivity of the visual system of three coastal shark species from different light environments. Physiol. Biochem. Zool. 83, 299–307 (2010).
Haarlem, C. S., O’Connell, R. G., Mitchell, K. J. & Jackson, A. L. The speed of sight: individual variation in critical flicker fusion thresholds. PLoS ONE 19, 1–13 (2024).
Umino, Y. et al. The relationship between slow photoresponse recovery rate and temporal resolution of vision. J. Neurosci. 32, 14364–14373 (2012).
Hellmer, C. B., Bohl, J. M., Hall, L. M., Koehler, C. C. & Ichinose, T. Dopaminergic modulation of signal processing in a subset of retinal bipolar cells. Front. Cell. Neurosci. 14, 253 (2020).
Nagy, J., Ebbinghaus, B., Hoon, M. & Sinha, R. GABAA presynaptic inhibition regulates the gain and kinetics of retinal output neurons. eLife 10, e60994 (2021).
Samaha, J. & Postle, B. R. The speed of alpha-band oscillations predicts the temporal resolution of visual perception. Curr. Biol. 25, 2985–2990 (2015).
Haarlem, C. S., Mitchell, K. J., Jackson, A. L. & Connell, R. G. O. Individual peak alpha frequency correlates with visual temporal resolution, but only under specific task conditions. Eur. J. Neurosci. 60, 5591–5604 (2024).
Greenwood, V. J. et al. Does the flicker frequency of fluorescent lighting affect the welfare of captive European starlings? Appl. Anim. Behav. Sci. 86, 145–159 (2004).
Harrison, S. E. & Gray, S. M. Effects of light pollution on bluegill foraging behavior. Trans. Am. Fish. Soc. 153, 152–162 (2024).
Lafitte, A. et al. Does a flashing artificial light have more or conversely less impacts on animals than a continuous one? A systematic review. Nat. Conserv. 54, 149–177 (2023).
Lafitte, A. et al. A flashing light may not be that flashy: a systematic review on critical fusion frequencies. PLoS ONE 17, e0279718 (2022).
de Souza, J. M. & Ventura, D. F. Comparative study of temporal summation and response form in hymenopteran photoreceptors. J. Comp. Physiol.165, 237–245 (1989).
Myhrvold, N. P. et al. An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles. Ecology 96, 3109–3109 (2015).
Froese, R. & Pauly, D. (eds.) FishBase 2000: Concepts, Design and Data Sources (ICLARM, 2000).
Palomares, M. L. D. & Pauly, D. (eds) SeaLifeBase www.sealifebase.org (accessed 23 December 2024).
Ortega Hidalgo, M. M., Iparraguirre Bolaños, E. & Brea San-Nicolás, C. Biomass assessment in annelids: a photogrammetric method suitable for hatchlings and adults developed for Eisenia andrei. Span. J. Soil Sci. 7, 1–16 (2017).
Nowak, R. Walker’s Mammals of the World (Johns Hopkins Univ. Press, 1999).
Handbook of the Birds of the World (Lynx Nature Books, 1992).
Parham, J. F. et al. Best practices for justifying fossil calibrations. Syst. Biol. 61, 346–359 (2012).
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).
Kuhn, T. S., Mooers, A. & Thomas, G. H. A simple polytomy resolver for dated phylogenies. Methods Ecol. Evol. 2, 427–436 (2011).
Pyron, R. A. & Burbrink, F. T. Early origin of viviparity and multiple reversions to oviparity in squamate reptiles. Ecol. Lett. 17, 13–21 (2014).
Thomson, R. C., Spinks, P. Q. & Bradley Shaffer, H. A global phylogeny of turtles reveals a burst of climate-associated diversification on continental margins. Proc. Natl Acad. Sci. USA 118, e2012215118 (2021).
Alexander Pyron, R. & Wiens, J. J. A large-scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians. Mol. Phylogenet. Evol. 61, 543–583 (2011).
Betancur-R, R. et al. The tree of life and a new classification of bony fishes. PLoS Curr. https://doi.org/10.1371/currents.tol.53ba26640df0ccaee75bb165c8c26288 (2013).
Stein, R. W. et al. Global priorities for conserving the evolutionary history of sharks, rays and chimaeras. Nat. Ecol. Evol. 2, 288–298 (2018).
Benjamin Redelings, B. et al. Open tree of life synthetic tree. Version ott 3.7.2. Zenodo https://doi.org/10.5281/zenodo.3937741 (2019).
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
R: a language and environment for statistical computing (R Foundation, 2025); https://www.r-project.org/
Hammer, D. X., Schmitz, H., Schmitz, A., Grady Rylander, H. & Welch, A. J. Sensitivity threshold and response characteristics of infrared detection in the beetle Melanophila acuminata (Coleoptera: Buprestidae). Comp. Biochem. Physiol. A 128, 805–819 (2001).
Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).
Guillerme, T. & Healy, K. mulTree: a package for running MCMCglmm analysis on multiple trees. Zenodo https://doi.org/10.5281/zenodo.12902 (2014).
Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–511 (1992).
Haarlem, C.S. Supplementary files for “pace of ecology drives the tempo of visual perception across the Animal Kingdom”. figshare https://doi.org/10.6084/m9.figshare.30556475 (2025).
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