The interplay of group size and flow velocity modulates fish exploratory behaviour.
Artificial intelligence
Deep learning
Fish movement
Group behaviour
Hydrodynamics
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
08 Jun 2024
08 Jun 2024
Historique:
received:
19
03
2024
accepted:
04
06
2024
medline:
9
6
2024
pubmed:
9
6
2024
entrez:
8
6
2024
Statut:
epublish
Résumé
Social facilitation is a well-known phenomenon where the presence of organisms belonging to the same species enhances an individual organism's performance in a specific task. As far as fishes are concerned, most studies on social facilitation have been conducted in standing-water conditions. However, for riverine species, fish are most commonly located in moving waters, and the effects of hydrodynamics on social facilitation remain largely unknown. To bridge this knowledge gap, we designed and performed flume experiments where the behaviour of wild juvenile Italian riffle dace (Telestes muticellus) in varying group sizes and at different mean flow velocities, was studied. An artificial intelligence (AI) deep learning algorithm was developed and employed to track fish positions in time and subsequently assess their exploration, swimming activity, and space use. Results indicate that energy-saving strategies dictated space use in flowing waters regardless of group size. Instead, exploration and swimming activity increased by increasing group size, but the magnitude of this enhancement (which quantifies social facilitation) was modulated by flow velocity. These results have implications for how future research efforts should be designed to understand the social dynamics of riverine fish populations, which can no longer ignore the contribution of hydrodynamics.
Identifiants
pubmed: 38851769
doi: 10.1038/s41598-024-63975-z
pii: 10.1038/s41598-024-63975-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
13186Subventions
Organisme : Horizon 2020
ID : 860800
Informations de copyright
© 2024. The Author(s).
Références
Guerin, B. Social Facilitation (Editions de la Maison des Sciences de l’Homme, 1993).
doi: 10.1017/CBO9780511628214
Uzsák, A., Dieffenderfer, J., Bozkurt, A. & Schal, C. Social facilitation of insect reproduction with motor-driven tactile stimuli. Proc. R. Soc. B Biol. Sci. 281, 20140325 (2014).
doi: 10.1098/rspb.2014.0325
Pays, O. et al. The effect of social facilitation on vigilance in the eastern gray kangaroo, macropus giganteus. Behav. Ecol. 20, 469 (2009).
doi: 10.1093/beheco/arp019
Jackson, A. L., Ruxton, G. D. & Houston, D. C. the effect of social facilitation on foraging success in vultures: A modelling study. Biol. Lett. 4, 311 (2008).
pubmed: 18364309
pmcid: 2610049
doi: 10.1098/rsbl.2008.0038
Ryer, C. H. & Olla, B. L. Social mechanisms facilitating exploitation of spatially variable ephemeral food patches in a pelagic marine fish. Anim. Behav. 44, 69 (1992).
doi: 10.1016/S0003-3472(05)80756-0
Baird, T. A., Ryer, C. H. & Olla, B. L. social enhancement of foraging on an ephemeral food source in juvenile walleye pollock, theragra chalcogramma. Environ. Biol. Fish. 31, 307 (1991).
doi: 10.1007/BF00000697
Suboski, M. D. et al. Alarm reaction in acquisition and social transmission of simulated-predator recognition by Zebra Danio fish (Brachydanio Rerio). J. Comp. Psychol. 104, 101 (1990).
doi: 10.1037/0735-7036.104.1.101
Karplus, I., Katzenstein, R. & Goren, M. Predator recognition and social facilitation of predator avoidance in coral reef fish dascyllus marginatus juveniles. Mar. Ecol. Prog. Ser. 319, 215 (2006).
doi: 10.3354/meps319215
Magurran, A. E. & Pitcher, T. J. Foraging, timidity and shoal size in minnows and goldfish. Behav. Ecol. Sociobiol. 12, 147 (1983).
doi: 10.1007/BF00343206
Ward, A. J. W. Social facilitation of exploration in mosquitofish (Gambusia Holbrooki). Behav. Ecol. Sociobiol. 66, 223 (2012).
doi: 10.1007/s00265-011-1270-7
Webster, M. M., Ward, A. J. W. & Hart, P. J. B. Boldness is influenced by social context in threespine sticklebacks (Gasterosteus aculeatus). Behaviour 144, 351 (2007).
doi: 10.1163/156853907780425721
Magnhagen, C. & Bunnefeld, N. Express your personality or go along with the group: what determines the behaviour of shoaling perch?. Proc. R. Soc. B Biol. Sci. 276, 3369 (2009).
doi: 10.1098/rspb.2009.0851
Fish, F. E., Fegely, J. F. & Xanthopoulos, C. J. Burst-and-coast swimming in schooling fish (Notemigonus Crysoleucas) with implications for energy economy. Comp. Biochem. Physiol. A Physiol. 100, 633 (1991).
doi: 10.1016/0300-9629(91)90382-M
Herskin, J. & Steffensen, J. F. Energy savings in sea bass swimming in a school: Measurements of tail beat frequency and oxygen consumption at different swimming speeds. J. Fish Biol. 53, 366 (1998).
doi: 10.1111/j.1095-8649.1998.tb00986.x
Svendsen, J. C., Skov, J., Bildsoe, M. & Steffensen, J. F. Intra-school positional preference and reduced tail beat frequency in trailing positions in schooling roach under experimental conditions. J. Fish Biol. 62, 834 (2003).
doi: 10.1046/j.1095-8649.2003.00068.x
Marras, S. et al. Fish swimming in schools save energy regardless of their spatial position. Behav. Ecol. Sociobiol. 69, 19 (2015).
doi: 10.1007/s00265-014-1834-4
Mozzi, G. et al. Aggregation in Riverine Fish: A Review from a Fish Passage Perspective. In International School of Hydraulics 265–280 (2023).
Kent, M. I. A., Lukeman, R., Lizier, J. T. & Ward, A. J. W. Speed-mediated properties of schooling. R. Soc. Open Sci. 6, 181482 (2019).
pubmed: 30891275
pmcid: 6408369
doi: 10.1098/rsos.181482
Chicoli, A. et al. The effects of flow on schooling devario aequipinnatus: School structure, startle response and information transmission. J. Fish Biol. 84, 1401 (2014).
pubmed: 24773538
pmcid: 4040972
doi: 10.1111/jfb.12365
Ashraf, I. et al. Simple phalanx pattern leads to energy saving in cohesive fish schooling. Proc. Natl. Acad. Sci. U. S. A. 114, 9599 (2017).
pubmed: 28839092
pmcid: 5594674
doi: 10.1073/pnas.1706503114
Katz, Y., Tunstrøm, K., Ioannou, C. C., Huepe, C. & Couzin, I. D. Inferring the structure and dynamics of interactions in schooling fish. Proc. Natl. Acad. Sci. U. S. A. 108, 18720 (2011).
pubmed: 21795604
pmcid: 3219116
doi: 10.1073/pnas.1107583108
Li, L. et al. Vortex phase matching as a strategy for schooling in robots and in fish. Nat. Commun. 11, 1 (2020).
De Bie, J., Manes, C. & Kemp, P. S. Collective behaviour of fish in the presence and absence of flow. Anim. Behav. 167, 151 (2020).
doi: 10.1016/j.anbehav.2020.07.003
Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291 (2007).
pubmed: 17437562
doi: 10.1111/j.1469-185X.2007.00010.x
Palmer, M. & Ruhi, A. linkages between flow regime, biota, and ecosystem processes implications for river restoration. Science 365, 6459 (2019).
doi: 10.1126/science.aaw2087
Deinet, S. et al. The Living Planet Index (LPI) for Migratory Freshwater Fish (World Fish Migration Foundation, 2020).
Schiavon, A. et al. Navigating the drought: Upstream migration of a small-sized cypriniformes (Telestes Muticellus) in response to drying in a partially intermittent mountain stream. Knowl. Manag. Aquat. Ecosyst. https://doi.org/10.1051/kmae/2024003 (2024).
doi: 10.1051/kmae/2024003
Videler, J. J. & Wardle, C. S. Fish swimming stride by stride: Speed limits and endurance. Rev. Fish Biol. Fish. 1, 23 (1991).
doi: 10.1007/BF00042660
Ashraf, M. U., Nyqvist, D., Comoglio, C. & Manes, C. The effect of in-flume habituation time and fish behaviour on estimated swimming performance. J. Ecohydraul. https://doi.org/10.1080/24705357.2024.2306411 (2024).
doi: 10.1080/24705357.2024.2306411
Schumann, S. et al. Social buffering of oxidative stress and cortisol in an endemic cyprinid fish. Sci. Rep. 13, 20579 (2023).
pubmed: 37996569
pmcid: 10667237
doi: 10.1038/s41598-023-47926-8
Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. You only look once: Unified, real-time object detection. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016).
The MathWorks Inc., MATLAB Version: 9.13.0 (R2022b), 9.13.0.
Kalman, R. E. A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960).
doi: 10.1115/1.3662552
Nyqvist, D. et al. PIT-tagging Italian spined loach (Cobitis Bilineata)–methodology, survival, and behavioral effects. J. Fish Biol. 102, 575–580 (2022).
pubmed: 36514841
doi: 10.1111/jfb.15289
R Core Team, R: A Language and Environment for Statistical Computing, 4.0.5.
Silverman, B. W. Density Estimation for Statistics and Data Analysis Vol. 26 (CRC Press, 1986).
Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).
doi: 10.1007/978-3-319-24277-4
A. Kassambara, Rstatix: Pipe-Friendly Framework for Basic Statistical Tests, R package version 0.7.2.
Terpstra, T. J. The asymptotic normality and consistency of Kendall’s test against trend, when ties are present in one ranking. Indag. Math. 55, 327 (1952).
doi: 10.1016/S1385-7258(52)50043-X
Jonckheere, A. R. A distribution-free k-sample test against ordered alternatives. Biometrika 41, 133 (1954).
doi: 10.1093/biomet/41.1-2.133
Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583 (1952).
doi: 10.1080/01621459.1952.10483441
Friedman, M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32, 675 (1937).
doi: 10.1080/01621459.1937.10503522
Wilcoxon, F. Individual Comparisons by Ranking Methods,in Breakthroughs in Statistics Methodology and Distribution (Springer, 1992).
Bonferroni, C. Teoria Statistica Delle Classi e Calcolo Delle Probabilita. Pubbl. Del R Ist Super. Sci. Econ. Commericiali Firenze 8, 3 (1936).
Percie Du Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. BMJ Open Sci. 4, 1769 (2020).
Mikheev, V. N. & Andreev, O. A. Two-phase exploration of a novel environment in the guppy, poecilia reticulata. J. Fish Biol. 42, 375 (1993).
Ashraf, M. U. et al. Fish swimming performance: Effect of flume length and different fatigue definitions. In Advances in Hydraulic Research (eds Kalinowska, M. B. et al.) (Springer Nature Switzerland, 2024).
Vezza, P. et al. Rethinking swimming performance tests for bottom-dwelling fish: The case of european glass eel (Anguilla Anguilla). Sci. Rep. 10, 16416 (2020).
pubmed: 33009464
pmcid: 7532191
doi: 10.1038/s41598-020-72957-w
Kerr, J. R., Manes, C. & Kemp, P. S. Assessing hydrodynamic space use of brown trout, salmo trutta, in a complex flow environment: A return to first principles. J. Exp. Biol. 219, 3480 (2016).
pubmed: 27591311
Nadler, L. E., Killen, S. S., McClure, E. C., Munday, P. L. & McCormick, M. I. Shoaling reduces metabolic rate in a gregarious coral reef fish species. J. Exp. Biol. 219, 2802 (2016).
pubmed: 27655821
pmcid: 5047653
doi: 10.1242/jeb.139493
Zelnik, P. R. & Goldspink, G. The effect of exercise on plasma cortisol and blood sugar levels in the rainbow trout, salmo Gairdnerii Richardson. J. Fish Biol. 19, 37 (1981).
doi: 10.1111/j.1095-8649.1981.tb05809.x
Li, X. et al. Effect of flow velocity on the growth, stress and immune responses of turbot (Scophthalmus Maximus) in recirculating aquaculture systems. Fish Shellfish Immunol. 86, 1169 (2019).
pubmed: 30599254
doi: 10.1016/j.fsi.2018.12.066
Krause, J. & Ruxton, G. D. Living in Groups (OUP Oxford, 2002).
doi: 10.1093/oso/9780198508175.001.0001
Killen, S. S., Marras, S., Steffensen, J. F. & Mckenzie, D. J. Aerobic capacity influences the spatial position of individuals within fish schools. Proc. R. Soc. B Biol. Sci. 279, 357 (2012).
doi: 10.1098/rspb.2011.1006
Liao, J. C. A review of fish swimming mechanics and behaviour in altered flows. Philos. Trans. R. Soc. B Biol. Sci. 362, 1973 (2007).
doi: 10.1098/rstb.2007.2082
Cano-Barbacil, C. et al. Key factors explaining critical swimming speed in freshwater fish: A review and statistical analysis for iberian species. Sci. Rep. 10, 18947 (2020).
pubmed: 33144649
pmcid: 7609642
doi: 10.1038/s41598-020-75974-x
Katopodis, C. & Gervais, R. Fish Swimming Performance Database and Analyses (Canadian Science Advisory Secretariat CSAS, 2016).
Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57 (2018).
pubmed: 29203921
doi: 10.1038/s41559-017-0402-5
Freeman, M. C., Bowen, Z. H., Bovee, K. D. & Irwin, E. R. Flow and habitat effects on juvenile fish abundance in natural and altered flow regimes. Ecol. Appl. 11, 179 (2001).
doi: 10.1890/1051-0761(2001)011[0179:FAHEOJ]2.0.CO;2
Kuriqi, A., Pinheiro, A. N., Sordo-Ward, A. & Garrote, L. Influence of hydrologically based environmental flow methods on flow alteration and energy production in a run-of-river hydropower plant. J. Clean. Prod. 232, 1028 (2019).
doi: 10.1016/j.jclepro.2019.05.358
Jowett, I. G., Richardson, J. & Bonnett, M. L. Relationship between flow regime and fish abundances in a gravel-bed river, New Zealand. J. Fish Biol. 66, 1419 (2005).
doi: 10.1111/j.0022-1112.2005.00693.x
Hockley, F. A., Wilson, C. A. M. E., Brew, A. & Cable, J. Fish responses to flow velocity and turbulence in relation to size, sex and parasite load. J. R. Soc. Interface 11, 20130814 (2014).
pubmed: 24284893
pmcid: 3869156
doi: 10.1098/rsif.2013.0814
Bunt, C. M., Castro-Santos, T. & Haro, A. Performance of fish passage structures at upstream barriers to migration. River Res. Appl. 28, 457 (2012).
doi: 10.1002/rra.1565
Noonan, M. J., Grant, J. W. A. & Jackson, C. D. A quantitative assessment of fish passage efficiency. Fish Fish. 13, 450 (2012).
doi: 10.1111/j.1467-2979.2011.00445.x
Clay, C. H. Design of Fishways and Other Fish Facilities (CRC Press, 1995).
Schiavon, A. et al. Survival and swimming performance of a small-sized cypriniformes (Telestes muticellus) tagged with passive integrated transponders. J. Limnol. https://doi.org/10.4081/jlimnol.2023.2129 (2023).
doi: 10.4081/jlimnol.2023.2129
Silva, A. T., Katopodis, C., Santos, J. M., Ferreira, M. T. & Pinheiro, A. N. Cyprinid swimming behaviour in response to turbulent flow. Ecol. Eng. 44, 314 (2012).
doi: 10.1016/j.ecoleng.2012.04.015
Lemasson, B. H., Haefner, J. W. & Bowen, M. D. Schooling increases risk exposure for fish navigating past artificial barriers. PLoS One 9, e108220 (2014).
pubmed: 25268736
pmcid: 4182462
doi: 10.1371/journal.pone.0108220
Nyqvist, D., Tarena, F., Candiotto, A. & Comoglio, C. Individual activity levels and presence of conspecifics affect fish passage rates over an in-flume barrier. Freshw. Fish Ecol. https://doi.org/10.1111/eff.12787 (2024).
doi: 10.1111/eff.12787
Albayrak, I., Boes, R. M., Kriewitz-Byun, C. R., Peter, A. & Tullis, B. P. Fish guidance structures: Hydraulic performance and fish guidance efficiencies. J. Ecohydraul. 5, 113 (2020).
doi: 10.1080/24705357.2019.1677181
Berdahl, A. et al. collective animal navigation and migratory culture: From theoretical models to empirical evidence. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170009 (2018).
doi: 10.1098/rstb.2017.0009
Okasaki, C., Keefer, M. L., Westley, P. A. H. & Berdahl, A. Collective navigation can facilitate passage through human-made barriers by homeward migrating pacific salmon: Collective navigation improves passage. Proc. R. Soc. B Biol. Sci. 287, 20202137 (2020).
doi: 10.1098/rspb.2020.2137
Quaranta, E., Katopodis, C., Revelli, R. & Comoglio, C. Turbulent flow field comparison and related suitability for fish passage of a standard and a simplified low-gradient vertical slot fishway. River Res. Appl. 33, 1295 (2017).
doi: 10.1002/rra.3193