INTRODUCTIONTop

Aggregations are groups of individuals that form a coherent and cohesive whole (Parrish et al. 1997Parrish J.K., Hamner W.M., Prewitt C.T. 1997. Introduction-from individuals to aggregations: unifying properties, global framework, and the holy grails of congregation. In: Parrish J.K., Hamner W.M. (eds), Animal groups in three dimensions. Cambridge Univ. Press, Cambridge, pp. 1-14.), ranging from two to millions of individuals. Aggregation behaviour occurs in a wide variety of organisms, from unicellular bacteria to whales (Parrish and Edelstein-Keshet 1999Parrish J.K., Edelstein-Keshet L. 1999. Complexity, pattern, and evolutionary trade-offs in animal aggregation. Science 284: 99-101.), and has been described in at least 50% of all marine and freshwater fish species (López et al. 2012López U., Gautrais J., Couzin I.D., et al. 2012. From behavioural analyses to models of collective motion in fish schools. Interface Focus 2: 693-707.). Two types of aggregations have been described for fish: (1) schools, synchronized and self-organized swimming groups formed by individuals that display coordinated movement patterns; and (2) shoals, passive aggregations of individuals that may not be actively attracted to these aggregations but are grouped because of physical processes (e.g. light or food) or for social reasons (individuals interact within the group). The former has a structure measured in polarity and synchrony, while the latter has no implications of structure or function (Hoare et al. 2000Hoare D.J., Krause J., Peuhkuri N., et al. 2000. Body size and shoaling in fish. J. Fish Biol. 57: 1351-1366., López et al. 2012López U., Gautrais J., Couzin I.D., et al. 2012. From behavioural analyses to models of collective motion in fish schools. Interface Focus 2: 693-707., Miller and Gerlai 2012Miller N., Gerlai R. 2012. From Schooling to Shoaling: Patterns of collective motion in zebrafish (Danio rerio). PLoS ONE 7: e48865.).

Many fish species switch between polarized schools and loosely organized shoals (even competitive aggregations) following a change in the risk of predation (Magurran 1990Magurran A.E. 1990. The adaptive significance of schooling as an anti-predator defence in fish. Ann. Zool. Fenn. 27: 51-66.). Consequently, the stability of fish aggregations can be influenced by ecological factors. However, recent studies have demonstrated that environmental variables (e.g. water quality or physical disturbances) can act as powerful stressors that decrease the fitness of fish aggregations and are expressed as a change in their behaviour. Therefore, environmental conditions may lead to immediate modification of swimming behaviour, i.e. between schooling and shoaling behaviour (Hasler et al. 2009Hasler C.T., Suski C.D., Hanson K.C., et al. 2009. Effects of water temperature on laboratory swimming performance and natural activity levels of adult largemouth bass. Can. J. Zool. 87: 589-596., Miller and Gerlai 2012Miller N., Gerlai R. 2012. From Schooling to Shoaling: Patterns of collective motion in zebrafish (Danio rerio). PLoS ONE 7: e48865., Sadoul et al. 2014Sadoul B., Mengues P.E., Friggens N.C., et al. 2014. A new method for measuring group behaviours of fish shoals from recorded videos taken in near aquaculture conditions. Aquaculture 430: 179-187.).

In central Chile, intense upwelling-favourable conditions driven by southwesterly winds are present throughout the year but are more intense during the austral spring and summer (Shaffer et al. 1999Shaffer G., Hormazabal S., Pizarro O., et al. 1999. Seasonal and interannual variability of currents and temperature off central Chile. J. Geophys. Res. 104: 29951-29961., Narváez et al. 2004Narváez D.A., Poulin E., Leiva G., et al. 2004. Seasonal and spatial variation of nearshore hydrographic conditions in central Chile. Cont. Shelf Res. 24: 279-292.). At a local scale, central Chile is characterized by a micro-tidal dynamic and wind-induced currents (Aiken et al. 2007Aiken C.M., Navarrete S.A., Castillo M.I., et al. 2007. Along-shore larval dispersal kernels in a numerical ocean model of the central Chilean coast. Mar. Ecol. Prog. Ser. 339: 13-24.). Local winds have a strong seasonal and diurnal signal, the latter marked mostly by the sea breeze during the austral spring and summer, which is described as an afternoon intensification of onshore winds (Narváez et al. 2004Narváez D.A., Poulin E., Leiva G., et al. 2004. Seasonal and spatial variation of nearshore hydrographic conditions in central Chile. Cont. Shelf Res. 24: 279-292.).

In the nearshore, temperatures can change abruptly in response to wind forcing, since these variables are strongly and significantly correlated (Narváez et al. 2004Narváez D.A., Poulin E., Leiva G., et al. 2004. Seasonal and spatial variation of nearshore hydrographic conditions in central Chile. Cont. Shelf Res. 24: 279-292.). Hence the question arises: can changes in local winds and/or water temperature influence the swimming behaviour of fish larvae prior to settlement? The aim of this study is to describe in situ swimming behaviour of larval aggregations of the triplefin Helcogrammoides chilensis (Cancino, 1960) in subtidal rocky reefs of central Chile and to evaluate whether local environmental conditions may alter their behaviour. We postulate that events of intense local wind may disrupt schooling behaviour into a more dispersed and lose shoal, while in calm conditions aggregation of organisms will be favoured.

MATERIALS AND METHODSTop

Model species

Triplefins (family Tripterygiidae), particularly Helcogrammoides chilensis, are small-bodied, cryptobenthic fish characterized by a bipartite life cycle. They spawn benthic eggs and show parental care up until a planktonic larva with pigmented eyes, small yolk sacs and open mouth hatches (Ruck 1973Ruck J.G. 1973. Development of Tripterygion capito and F. robustum (Pisces: Tripterygiidae). Zool. Publ. Vic. Univ. Wellingt. 63: 1-10, 1980Ruck J.G. 1980. Early development of Forsterygion varium, Gilloblennius decemdigitatus, and G. tripennis (Pisces: Tripterygiidae). N. Z. J. Mar. Freshw. Res. 14: 313-326.). Their pelagic larval duration is approximately 2 to 4 months (Mansur et al. 2014Mansur L., Plaza G., Landaeta M.F., et al. 2014. Planktonic duration in fourteen species of intertidal rocky fishes from the south-eastern Pacific Ocean. Mar. Freshw. Res. 65: 901-909.), after which they settle as juveniles in the rocky exposed shores (McDermontt and Shima 2006McDermontt C.J., Shima J.S. 2006. Ontogenetic shift in microhabitat preference of a temperate reef fish Forsterygion lapillum: implications for population limitation. Mar. Ecol. Prog. Ser. 320: 259-266.).

Helcogrammoides chilensis inhabits the coast of Chile from 20°18′S to 36°45′S in the southern Pacific, in shallow waters near the coast (Williams and Springer 2001Williams J.T., Springer V.G. 2001. Review of the South American Antartic triplefin fish genus Helcogrammoides (Perciformes: Tripterygiidae). Rev. Biol. Trop. 49: 117-123., Cancino et al. 2010Cancino C., Farías K., Lampas S., et al. 2010. Descripción de los complejos estructurales óseos en Helcogrammoides chilensis (Blennioidei: Tripterygiidae) de la zona central de Chile. Rev. Biol. Mar. Oceanogr. 45: 671-682.). It has carnivorous habits, with a diet mostly composed of copepods and amphipods (Muñoz and Ojeda 1998Muñoz A.A., Ojeda F.P. 1998. Guild structure of carnivorous intertidal fishes of the Chilean coast: implications of ontogenetic dietary shifts. Oecologia 114: 563-573.). Its pelagic larval stages are found throughout the year in nearshore waters (Hernández-Miranda et al. 2003Hernández-Miranda E., Palma A.T., Ojeda F.P. 2003. Larval fish assemblages in nearshore coastal waters off central Chile: Temporal and spatial patterns. Estuar. Coast. Shelf. Sci. 56: 1075-1092., Landaeta et al. 2009Landaeta M.F., Schrebler K., Bustos C.A., et al. 2009. Temporal fluctuations of nearshore icthyoplankton off Valparaíso, central Chile, during the ENSO cycle 1997-2000. Rev. Biol. Mar. Oceanogr. 44: 571-582., 2015Landaeta M.F., Zavala-Muñoz F., Palacios-Fuentes P., et al. 2015. Spatial and temporal variations of coastal fish larvae, ectoparasites and oceanographic conditions off central Chile. Rev. Biol. Mar. Oceanogr. 50: 563-574.), with maximum abundances between October and April (Palacios-Fuentes et al. 2014Palacios-Fuentes P., Landaeta M.F., Jahnsen-Guzmán N., et al. 2014. Hatching patterns and larval growth of a triplefin from central Chile inferred by otolith microstructure analysis. Aquat. Ecol. 48: 259-266.). H. chilensis larvae have a long pelagic larval duration (78-113 days; Mansur et al. 2014Mansur L., Plaza G., Landaeta M.F., et al. 2014. Planktonic duration in fourteen species of intertidal rocky fishes from the south-eastern Pacific Ocean. Mar. Freshw. Res. 65: 901-909.) given their slow growth rates (~0.15 mm day^–1; Palacios-Fuentes et al. 2014Palacios-Fuentes P., Landaeta M.F., Jahnsen-Guzmán N., et al. 2014. Hatching patterns and larval growth of a triplefin from central Chile inferred by otolith microstructure analysis. Aquat. Ecol. 48: 259-266.).

H. chilensis can be considered a model species due to its life history traits, which are similar to those of other cryptobenthic species that inhabit rocky or reef shores in temperate waters (e.g. benthic eggs, parental care, carnivorous, pelagic larvae and slow growth) (Caie and Shima 2019Caie P., Shima J.S. 2019. Patterns of selective predation change with ontogeny but not density in a marine fish. Oecologia 189: 123-132.). Recently, Tripterygiids have been considered part of a group of cryptobenthic reef fishes (species shorter than 5 cm in length) which fuel reef trophodynamics, owing to their unique larval dynamics (high abundance, rapid growth and extreme mortality) (Brandl et al. 2019Brandl S.J., Tornabene L., Goatley C.H.R., et al. 2019. Demographic dynamics of the smallest marine vertebrates fuel coral-reef ecosystem functioning. Science 364: 1189-1192.).

Study area

The subtidal rocky reefs of the semi-protected area of Montemar, Valparaíso Bay, central Chile (32°57′S, 77°33′W) (Fig. 1A), are characterized by their shallow depth (~1 m during neap tide, ~3 m during spring tides) and moderate swell. These reefs are subject to seasonal and diurnal oceanographic changes generated by events such as coastal storms that occur in winter, sea breeze in summer and daily tidal variation. The region is dominated by a semi-diurnal tidal regime that explains 98% of the sea level variability (Díaz-Astudillo et al. 2017Díaz-Astudillo M., Castillo M.I., Cáceres M.A., et al. 2017. Oceanographic and lunar forcing affects nearshore larval fish assemblages from temperate rocky reefs. Mar. Biol. Res. 13: 1015-1026.). Sea level in the very nearshore is dominated by both tides and surge waves, but there is no information about sea level variability in intertidal or subtidal pools of central Chile. The seafloor is mostly lithic, composed of coarse sand and eroded shells. These reefs are the habitat of small and medium-size cryptobenthic fish species (Stepien 1990Stepien C.A. 1990. Population structure, diets and biogeographic relationships of a rocky intertidal fish assemblage in central Chile: high levels of herbivory in a temperate system. Bull. Mar. Sci. 47: 598-612.), housing large abundances of H. chilensis which settle preferentially on lithic substratum (Pérez-Matus et al. 2016Pérez-Matus A., Sánchez F., González-But J.C., et al. 2016. Understory algae associations and predation risk influence broad-scale kelp habitat use in a temperate reef fish. Mar. Ecol. Prog. Ser. 559: 147-158.).

Field work

During the late austral summer and early autumn, larval fish aggregations from intertidal rocky reefs nearby Montemar (Fig. 1A) were video-recorded (1080 p resolution) with a GoPro Hero3+ Black Edition camera (2014, 32 recordings from 19 February to 16 April) and a GoPro Hero4+ Silver Edition camera (2016, 29 recordings from 3 January to 15 March). The larvae were recorded by three observers (PPF, MFL, MAR) each day between 10:00 and 14:00 h, a similar time frame to that used in previous studies (e.g. Leis et al. 2014Leis J.M., Paris C.B., Irisson J-O., et al. 2014. Orientation of fish larva in situ is consistent among locations, years and methods, but varies with time of day. Mar. Ecol. Prog. Ser. 505: 193-208.) and suitable considering our study’s aim to determine inter-daily behavioural responses.

Larval fish aggregations were recorded as long as they could be followed underwater and at an appropriate distance to avoid major disturbances in fish behaviour (~20 cm). In situ observations of Helcogrammoides larvae, juveniles and adults show that the presence of the diver does not generate significant differences in fish pool abundances (Javier Polanco, pers. comm.). Recordings were not available for weekends or days of extremely high tides or dangerous surges (2014: 3 days; 2016: 6 days).

Haphazard individuals were captured periodically with hand nets (10×13 cm, 2 mm mesh opening), then sacrificed in 1 mL of benzocaine BZ-20 (20 mg per 100 mL) dissolved in 1 L of seawater and immediately preserved in 96% ethanol. Then individuals were observed and photographed with a camera Moticam 2500 (5 Mpx) attached to an Olympus ZS-61 stereomicroscope (Olympus Corporation, Shinjukuku, Tokyo, Japan), and later identified as the triplefin Helcogrammoides chilensis (Cancino, 1960) according to Pérez (1979)Pérez R. 1979. Postembryonic development of Tripterygion chilensis Cancino, 1955, in Valparaíso bay (Tripterygiidae: Perciformes). Rev. Biol. Mar. 16: 19-329..

Video analyses

The length of each video segment in which larvae appeared was determined to estimate sampling effort in seconds (s). Each video was examined to count the number of fish larvae in the aggregations per frame every 10 s (higher frequency than proposed by Hoare et al. 2004Hoare D.J., Couzin I.D., Godin J.G.J., et al. 2004. Context-dependent group size choice in fish. Anim. Behav. 67: 155-164. to obtain a finer temporal scale). The final abundance was considered as the maximum observed value. A second revision was conducted to establish the time (in seconds) that the aggregations of fish larvae spent in schooling (i.e. when individuals were grouped, swimming polarized and coordinated in the same direction), shoaling (i.e. when individuals were grouped and stayed together as a whole, but with each larva swimming in its own direction), or solitary behaviour (i.e. when a single larva was found per frame) (Fig. 1B-D). This time was then standardized using the sampling effort (s) to obtain comparable measurements. If there was more than one video segment containing larvae per day, the information was pooled together so that each day contained only one data value. Days without larval aggregations were not considered for further analysis.

Meteorological and oceanographic data acquisition

Wind speed and direction were measured every 5 minutes from 6 January to 20 April 2014 and from 3 January to 15 March 2016 with a Davis Vantage Pro2 meteorological station installed ~20 m away from the subtidal pool of the study (Fig. 1). Wind speeds under 1 m s^–1 were assigned by the station as “calm winds”. To estimate the along-shore and cross-shore wind stress (τ), the wind vector was separated into its orthogonal components u (east-west) and v (north-south). A constant drag coefficient (C_D) of 1.3 10^–3 and a constant air density (ρ_A) of 1.2 kg m^–3 were used, following Shaffer et al. (1997)Shaffer G., Pizarro O., Djurfeldt L., et al. 1997. Circulation and low-frequency variability near the Chilean coast: remotely forced fluctuations during the 1991-92 El Niño. J. Phys. Oceanogr. 27: 217-235.. The kinetic energy dissipation rate (ε, W m^-3) was derived from wind speed as a proxy of wind-induced turbulence following the model developed by MacKenzie and Leggett (1993)MacKenzie B.R., Leggett W.C. 1993. Wind-based models for estimating the dissipation rates of turbulent energy in aquatics environments: empirical comparisons. Mar. Ecol. Progr. Ser. 94: 207-216., using Z=2 m (mean water depth of the aggregations), C_D=1.3 10^–3, ρ_A=1.2 kg m^–3, a seawater density of ρ_w=1025 kg m^–3 and a von Karmann constant of 0.4. Wind stress components and wind-induced turbulence were then plotted through time to detect any temporal patterns that may influence the behaviour of larval fish.

In 2014 water temperature (°C) was measured directly from the intertidal pools during each sampling day using a Hanna HI9828 multiparameter probe. In 2016 a HOBO data logger anchored 50 m away from the pool was used to determine the water temperature at 3 m depth during the sampling period (Fig. 1A).

Statistical analyses

To explore relations between the observed behaviours and environmental variability, a principal component analysis (PCA, using correlation matrix) was performed using a biological matrix containing the proportion of time spent in each behaviour per date and an environmental matrix with four explanatory variables: water temperature, u- and v- wind stress and wind-induced turbulence. Spearman correlations were performed between larval behaviour proportions and the four environmental variables. Prior to analysis, proportional data were transformed using an arcsine function. Differences in environmental variables between each year were evaluated using Mann-Whitney U tests. Statistical analyses were performed using the Past 4.0 software (Hammer et al. 2001Hammer Ø., Harper D.A.T., Ryan P.D. 2001. PAST: Paleontological Statistics software package for education and data analysis. Palaeontologia Electronica 4: 4.).

RESULTSTop

Larval fish aggregations

In 2014, larval fish observations varied between 1 and 148 individuals (mean 37.82±41.09) (Fig. 2A), with two main peaks of +60 individuals, the first on 28 February and the second during the first fortnight of April. In 2016, abundances ranged between 1 and 70 individuals (28.52±15.69) (Fig. 2B), with only one peak which started in mid-January and lasted until early February. Larval abundances were more variable in 2014 than in 2016 (Levene test, F=1.63, P<0.01).

All types of behaviour were observed. In 2014 (Fig. 2C), schooling behaviour was dominant from 19 February to 11 March, later changing into a solitary condition until 26 March, followed by predominate shoaling behaviour until 16 April. In contrast, in 2016 (Fig. 2D) a dominant schooling period was observed between 5 January and 10 February, later shifting into solitary behaviour until 9 March.

In both years, the majority of individuals observed in solitary behaviour were large (~3.7 cm) and had highly pigmented bodies, corresponding to juveniles of H. chilensis.

Environmental conditions

In 2014 water temperatures decreased gradually over the course of the study period, fluctuating between a maximum of 17.12°C on 5 February and a minimum of 11.98°C on 3 March (mean 14.23±1.26°C) (Fig. 3A). Strong fluctuations of about 4°C over periods of less than three days during the first week of February, the first days of March and the second week of April can be associated with strong southwesterly wind events (Fig. 3A). Wind stress in the v component (maximum v-stress of 0.08 N m^–2, minimum of –0.11 N m^–2) showed lower variation than the u component (maximum u-stress of 0.02 N m^–2, minimum of –0.17 N m^–2), illustrating the high variability of cross-shelf winds and the lower kinetic energy of meridional winds. Westerly winds were more intense than easterly winds during summer. Turbulent dissipation rates showed a similar pattern to wind stress, fluctuating between 0 and 0.004 W m^-3 (mean of 7.9 10^–5±0.0002 W m^–3), with high turbulence events during the first week of February and the second week of April.

In 2016 water temperatures fluctuated between a minimum of 14°C on 22 February and a maximum of 18.9°C on 22 January, with a mean of 16.8±1.17°C. A gradual decrease of the temperature was observed, given that higher temperatures were observed during late January and lower ones in mid-March (Fig. 3B). Winds were predominantly from the southwest and highly variable in intensity, with temporary windows of up to 8 days of calm winds (<1 m s^–1) between events of intense winds. Similar values of wind stress were observed in both components, varying between –0.13 N m^–2 and 0.04 N m^–2 in the east-west component and between –0.13 N m^–2 and 0.06 N m^–2 in the north-south component (Fig. 3B). Two particularly strong southwest wind events were observed in mid-January and late February, causing abrupt decreases in water temperatures. Turbulent dissipation rates were generally low, except during windows of strong winds in mid-January, late February and late April. They varied between 0 and 0.008 W m^–3, with a mean of 7.5 10^–5±0.0002 W m^–3.

Water temperatures were significantly higher in 2016 than in 2014 (Mann-Whitney U=26, P<0.001), while no differences were found in u-wind stress (Mann-Whitney U=353, P=0.322) and v-wind stress (Mann-Whitney U=326, P=0.161) between the years. Nonetheless, wind-induced turbulence was significantly higher in 2014 (Mann-Whitney U=247.5, P=0.004).

Biophysical interactions

The PCA for 2014 explained 65.6% of the variability in the first three axes (Fig. 4A), with the first accounting for 26.7% of the variance, which mainly showed a positive (negative) correlation between u-wind stress and wind-induced turbulence with solitary (shoaling) behaviour. The second axis (22.6% of the variance) illustrated a positive relationship between the water temperature and schooling behaviour, while the third axis explained 16.3% of the variance and showed a positive (negative) correlation between v-wind stress and solitary (schooling) behaviour (Table 1). In 2016, the first three axes of the PCA explained 83.5% of the variability (Fig. 4B). Axis 1 (38.4%) showed a positive correlation between wind stress (in both components) and wind-induced turbulence with solitary behaviour, and a negative correlation between water temperatures and solitary individuals. Axis 2 (28.6%) showed a positive (negative) relation between u- and v-wind stress and schooling (solitary) behaviour. Axis 3 (16.4%) showed a positive correlation between u-wind stress and shoaling behaviour (Table 1).

The Spearman correlations showed that in 2014 wind-induced turbulence showed a significant correlation with solitary (positive correlation, ρ=0.45, P<0.05) and shoaling (negative correlation, ρ=–0.44, P<0.01) behaviour, meaning that when turbulence was high the proportion of solitary fish increased while aggregations in the form of shoals decreased. In 2016 water temperatures were correlated negatively with solitary behaviour (ρ=–0.51, P<0.05) and positively with shoaling behaviour (ρ=0.43, P<0.05). Shoaling was also correlated negatively with wind stress in both the u- (ρ=–0.45, P<0.05) and v- (ρ=–0.41, P<0.05) components. (Table 2).

DISCUSSIONTop

Social aggregations are mainly studied as ecological responses to food stimuli or reproduction tactics (López et al. 2012López U., Gautrais J., Couzin I.D., et al. 2012. From behavioural analyses to models of collective motion in fish schools. Interface Focus 2: 693-707., Maury 2017Maury O. 2017. Can schooling regulate marine populations and ecosystems? Prog. Oceanogr. 156: 91-103.). Hence, behaviour patterns associated with physical forcing have been scarcely explored. This is the first attempt to study larval fish aggregation behaviour in Chilean waters, and one of the few that relate larval aggregations with environmental conditions using in situ observations. Most of the previous fish swimming behaviour studies have been performed with adult individuals of demersal and pelagic fish under controlled conditions (Leis 2010Leis J.M. 2010. Ontogeny of behaviour in larvae of marine demersal fishes. Ichthyol. Res. 57: 325-342., López et al. 2012López U., Gautrais J., Couzin I.D., et al. 2012. From behavioural analyses to models of collective motion in fish schools. Interface Focus 2: 693-707., Santana-Garcon et al. 2014Santana-Garcon J., Leis J.M., Newman S.J., et al. 2014. Presettlement schooling behaviour of a priacanthid, the Purplespotted Bigeye Priacanthus tayenus (Priacanthidae: Teleostei). Environ. Biol. Fish. 97: 277-283., and references therein), which may not completely reflect the behaviours that would be observed in natural conditions (López et al. 2012López U., Gautrais J., Couzin I.D., et al. 2012. From behavioural analyses to models of collective motion in fish schools. Interface Focus 2: 693-707.). Therefore, in situ studies associated with physical forcing can provide significant information to support experiment- and model-based assumptions about fish behaviour. In this study, only a fraction of collective behaviour (i.e. shoaling and solitary) was influenced by environmental variables.

Schooling behaviour, which requires a complex level of interaction among individuals and energy resources to swim together in a synchronized and self-organized manner (Miller and Gerlai 2012Miller N., Gerlai R. 2012. From Schooling to Shoaling: Patterns of collective motion in zebrafish (Danio rerio). PLoS ONE 7: e48865.), did not respond to the environmental variables selected for this study. Schooling is a density-dependent regulation in marine metapopulations that is adopted when the benefits of increased survival outweigh the costs of increased competition among individuals (López et al. 2012López U., Gautrais J., Couzin I.D., et al. 2012. From behavioural analyses to models of collective motion in fish schools. Interface Focus 2: 693-707.), fulfilling an important ecological function and response of auto-organized groups (Maury 2017Maury O. 2017. Can schooling regulate marine populations and ecosystems? Prog. Oceanogr. 156: 91-103.). Thus, it is possible that the environmental conditions found during the study period were not sufficiently stressful to induce the schooling behaviour.

However, shoaling and solitary behaviour showed a different interaction with local meteorological and oceanographic conditions. Long-term time-series of water temperature data have proven that Montemar, along with the rest of the central Chilean coast, is directly influenced by mesoscale upwelling (Aravena et al. 2014Aravena G., Broitman B., Stenseth N.C. 2014. Twelve years of change in coastal upwelling along the central-northern coast of Chile: spatially heterogeneous responses to climatic variability. PLoS ONE 9: e90276.), resulting in the entrance of colder water into nearshore areas. This probably occurred in 2014, when higher wind-induced turbulence and a cooler water column were observed than in 2016, although no differences were found in local wind stress. Additionally, the austral summer of 2016 was influenced by a positive ENSO phase, which warmed up the nearshore waters of central Chile, reducing the intensity of coastal winds (Díaz-Astudillo et al. 2019Díaz-Astudillo M., Landaeta M.F., Bernal-Durán V., et al. 2019. The influence of regional and local oceanography in early stages of marine fishes from temperate rocky reefs. Mar. Biol. 166: 42.).

During the austral summer of 2014, higher wind-induced turbulence was observed along with an increase in the frequency of solitary individuals and a reduction in shoaling aggregations. Accordingly, a peak of solitary individuals (possible presettled specimens due to their size and intense pigmentation) was observed during the second and third week of March (i.e. early autumn), coinciding with the lowest larval abundances, decreasing water temperatures and moderate to high turbulence. Studies of fish larvae swimming speeds have shown that they increase with ontogeny (Leis 2006Leis J.M. 2006. Are Larvae of Demersal Fishes Plankton or Nekton? Adv. Mar. Biol. 51: 57-141.), meaning that bigger larvae have better swimming abilities. Therefore, it is expected that older and bigger (solitary) individuals will be found during periods of high turbulence, as was observed in 2014. Wellenreuther and Clements (2008)Wellenreuther M., Clements K.D. 2008. Determinants of habitat association in a sympatric clade of marine fishes. Mar. Biol. 154: 393-402. described that active processes (i.e. behaviour) can highly affect settlement, in which larval conduct plays an important role in determining the settlement location. Therefore, presettled triplefin larvae, which show better swimming skills, are expected to have a greater impact on settlement and the ecological processes of this species. A similar scenario was observed in 2016, when a peak in solitary individuals between mid-February and the first days of March (i.e. late summer), coincided with a high turbulence event, decreasing temperatures and lower larval abundances. However, these relationships were not significant, probably due to the lower magnitude of wind-induced turbulence. Some triplefin species from temperate waters around New Zealand have their maximum recruitment during the late austral summer and autumn (i.e. between February and April) (Wellenreuther and Clements 2008Wellenreuther M., Clements K.D. 2008. Determinants of habitat association in a sympatric clade of marine fishes. Mar. Biol. 154: 393-402.), suggesting that solitary behaviour in triplefins may occur prior to settlement on rocky reefs. Accordingly, the pre-settlement larvae of H. chilensis showed solitary behaviour before settlement, coinciding with the transition between the austral summer and autumn, along with a decrease in seawater temperatures.

When studying the in situ swimming behaviour of temperate fish, Hindell et al. (2003)Hindell J.S., Jenkins G.P., Moran S.M., et al. 2003. Swimming ability and behaviour of post-larvae of a temperate marine fish re-entrained in the pelagic environment. Oecologia 135: 158-166. found that the movement of postlarvae was a result of both swimming and local currents. Though coastal currents were not assessed in this study because of the lack of subtidal circulation data, evidence shows that mesoscale circulation in Valparaíso Bay is driven by regional winds (Aiken 2008Aiken C.M., Castillo M.I., Navarrete S.A. 2008. A simulation of the Chilean coastal current and associated topographic upwelling near Valparaíso, Chile. Cont. Shelf Res. 28: 2371-2381.), suggesting that coastal currents in the subtidal rocky reefs of Montemar are probably controlled by local winds. In this study, shoaling behaviour was inversely related to wind-induced turbulence in 2014 and to wind stress in 2016. A possible reason for this is that higher winds might suppress fish shoals by generating currents that 1) are strong enough to transport fish outside the reefs, or 2) encourage fish to hide in rock cracks as a form of protective conduct. In either case the proportion of shoals would diminish, but further in situ observations are needed to clarify the underlying mechanism, which should incorporate local currents and sea level as additional environmental variables. By contrast, in periods of weaker or calm winds, shoaling was the prevailing behaviour.

In a broader context, given the correlation found between environmental variables and some aggregated behaviours, what would the implications regarding future survival or recruitment be? Early life experiences of fish can shape an individual’s fitness (Caie and Shima 2019Caie P., Shima J.S. 2019. Patterns of selective predation change with ontogeny but not density in a marine fish. Oecologia 189: 123-132.), a concept known as the carry-over effect (Pechenik 2006Pechenik J.A. 2006. Larval experience and latent effects - metamorphosis is not a new beginning. Integr. Comp. Biol. 46: 323-333.). Juvenile survival and metapopulation connectivity can be moderated by larval growth rates (Paralabrax clathratus, Shima and Findlay 2002Shima J.S., Findlay A.M. 2002. Pelagic larval growth rate impacts benthic settlement and survival of a temperate reef fish. Mar. Ecol. Prog. Ser. 235: 303-309.) and larval quality (Forsterygion lapillum, Shima and Swearer 2009Shima J.S, Swearer S.E. 2009. Larval quality is shaped by matrix effects: Implications for connectivity in a marine metapopulation. Ecology 90: 1255-1267.), respectively, both of which can be influenced by biotic factors and environmental conditions. Hence, the changes in the environmental conditions observed in our study were perceived by larvae (shown by a correlated change in their behaviour) and are expected to have an effect on how many individuals can settle in the rocky intertidal zone and/or on post-settlement survival.

Nevertheless, in situ studies, like this one, still have some limitations. Firstly, it is challenging and expensive to increase the sampling period, which forces us to limit the study objective to a certain time of the day and/or of the year. Future studies should strive to increase diving hours to address diurnal (Leis et al. 2014Leis J.M., Paris C.B., Irisson J-O., et al. 2014. Orientation of fish larva in situ is consistent among locations, years and methods, but varies with time of day. Mar. Ecol. Prog. Ser. 505: 193-208.) and annual variability in fish larval behaviour. This also implies more time to analyse videos and data. Secondly, the very nature of the data limits the variety and power of the statistical analysis that can be performed. Either way, any future attempts to carry out in situ behavioural studies will have to improve the methodological logistics and explore, or develop, statistical methods which better suit the data, possibly leading to more generalized conclusions. Although the data presented in this study about the influence of wind-induced turbulence on fish schools will set a basis for studying the future of rocky reef fishes, especially taking into account the recent increase in extreme coastal storms, which erode the coasts of central Chile (Martínez et al. 2018Martínez C., Contreras-López M., Winckler P., et al. 2018. Coastal erosion in central Chile: A new hazard? Ocean Coast. Man. 156: 141-155.). Also, in future studies, this oceanographic influence on fish larval behaviour ought to unveil its impact beyond the pelagic larval stage, as well as its carry-over effects on fitness and the dynamics of juvenile and adult populations.

ACKNOWLEDGEMENTSTop

We appreciate the fieldwork and help of Mirna Castillo, Bastián Fernández-General, and Victor Molina-Valdivia (Universidad de Valparaíso, Chile). We also thank two anonymous reviewers who helped improve our manuscript and Francesca Search for her help with the English revision of our final version.

Funding

This work was supported by the National Agency for Research and Development (ANID), partially by a scholarship granted to PPF (ANID/Sholarship Program/Doctorado Nacional/2015 – 21150181) and a FONDECYT National Project granted to MLF (FONDECYT 1150296).

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