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Spring and summer ichthyoplankton assemblages in a temperate Patagonian gulf: an overview of temporal and spatial patterns on their structure

Abstract

Knowing about the spatio-temporal patterns in the structure of ichthyoplankton assemblages allows inferring about the spawning behaviour of adult fishes, understanding the recruitment dynamics, and predicting the potential effects of mid- and long-term changes. Here, we studied the ichthyoplankton assemblages from the San José Gulf (Northern Patagonia, Argentina) and investigated their changes in space and time. To do that, we took monthly samples during two consecutive years, in spring and summer. A total of 2088 larvae were caught; they comprised 36 taxa, from which 14 were identified to species, two to genus, one to family and one to order. There were large differences in the structure of the assemblages between years, coincidently with marked changes in the surface water temperature. The structure of the ichthyoplankton assemblages also showed significant differences between the spring and summer: Helcogrammoides cunninghami, Dules auriga and larvae belonging to the family Engraulidae contributed most to these differences. The species diversity was higher in the colder year than in the warmer one. We discuss the potential role of environmental and oceanographic features on the interannual variability in the early stages of coastal fishes within a small gulf.

Key words
environmental variability; fish larvae; small gulfs; Southwest Atlantic Ocean; temperate waters

INTRODUCTION

The biology and early life-history of fishes, together with environmental and hydrographical factors, influence the occurrence, distribution, and abundance of fish larvae, and model the spatio-temporal patterns in the structure (sensu Begon 2006BEGON M, TOWNSEND CR & HARPER JL. 2006. Ecology: from individuals to ecosystems. 4th ed., Blackwell Publishing Ltd, UK, 759 p.) of ichthyoplankton assemblages (e.g., Fig. 1 in Doyle et al. 1993DOYLE MJ, MORSE WW & KENDALL AW. 1993. A comparison of larval assemblages in the temperate zone of the Northeast Pacific and Northwest Atlantic Oceans. B Mar Sci 53: 588-644.). The study of these patterns can provide information both about the localization (in space and time) and about the spawning strategies of the adult fishes (Doyle et al. 1993DOYLE MJ, MORSE WW & KENDALL AW. 1993. A comparison of larval assemblages in the temperate zone of the Northeast Pacific and Northwest Atlantic Oceans. B Mar Sci 53: 588-644., Paulic & Papst 2013PAULIC JE & PAPST MH. 2013. Larval and early juvenile fish distribution and assemblage structure in the Canadian Beaufort Sea during July–August, 2005. J Marine Syst 127: 46-54.). At the spatial scale, topographic features such as bathymetry and bottom type, together with other mechanisms that affect the primary production (e.g., upwellings, marine fronts) as well as the behavior of the adults, determine the spawning areas, while physical processes influence the final distribution patterns of the early stages (Sabatés 1990SABATÉS A. 1990. Distribution pattern of larval fish populations in the Northwestern Mediterranean. Mar Ecol Prog Ser 59: 75-82., Sabatés & Olivar 1996SABATÉS A & OLIVAR MP. 1996. Variation of larval fish distributions associated with variability in the location of a shelf-slope front. Mar Ecol Prog Ser 135: 11-20., Alemany et al. 2006ALEMANY F, DEUDERO S, MORALES-NIN B, LÓPEZ-JURADO JL, JANSÀ J, PALMER M & PALOMERA I. 2006. Influence of physical environmental factors on the composition and horizontal distribution of summer larval fish assemblages off Mallorca island (Balearic archipelago, Western Mediterranean). J Plankton Res 28: 1-15.). At the temporal scale, the assemblages’ composition may vary broadly (Guan et al. 2015GUAN L, DOWER JF, MCKINNELL SM, PEPIN P, PAKHOMOV EA & HUNT PV. 2015. A comparison of spring larval fish assemblages in the Strait of Georgia (British Columbia, Canada) between the early 1980s and late 2000s. Prog Oceanogr 138: 45-57.). Larval fish assemblages in coastal waters undergo important changes in abundance and composition (Álvarez et al. 2012ÁLVAREZ I, CATALÁN IA, JORDI A, PALMER M, SABATÉS A & BASTERRETXEA G. 2012. Drivers of larval fish assemblage shift during the spring-summer transition in the coastal Mediterranean. Estuar Coast Shelf Sci 97: 127-135.) due to the seasonality of reproductive timing for different species (e.g., Barletta-Bergan et al. 2002BARLETTA-BERGAN A, BARLETTA M & SAINT-PAULA U. 2002. Structure and seasonal dynamics of larval fish in the Caeté River Estuary in North Brazil. Estuar Coast Shelf Sci 54: 193-206., Ramos et al. 2006RAMOS S, COWEN RK, RÉ P & BORDALO AA. 2006. Temporal and spatial distributions of larval fish assemblages in the Lima estuary (Portugal). Estuar Coast Shelf Sci 66: 303-314., Sabatés et al. 2007SABATÉS A, OLIVAR MP, SALAT J, PALOMERA I & ALEMANY F. 2007. Physical and biological processes controlling the distribution of fish larvae in the NW Mediterranean. Prog Oceanogr 74: 355-376., Primo et al. 2010PRIMO AL, AZEITEIRO UM, MARQUES C & PARDAL MA. 2010. Impact of climate variability on ichthyoplankton communities: an example of a small temperate estuary. Estuar Coast Shelf Sci 91: 484-491.). In temperate regions, these seasonal changes are mainly related to water temperature, photoperiod, and food availability (McKaye 1984MCKAYE KR. 1984. Behavioural aspects of cichlid reproductive strategies: patterns of territoriality and brood defence in Central American substratum spawners and African mouth brooders. In: Potts GW & Wootton RJ (Eds), Fish reproduction: strategies and tactics, Academic Press, London, p. 245-273., Payne 1986PAYNE AI. 1986. The ecology of tropical lakes and rivers. Chichester, New York, J Wiley & Sons, 301 p.). Less cyclical phenomena (e.g., thermal anomalies, and circulation and stratification patterns) modifying the oceanographic environment may also cause interannual variation in the spawning strategies, which may encompass timing, duration, and location of the spawning (Doyle et al. 1993DOYLE MJ, MORSE WW & KENDALL AW. 1993. A comparison of larval assemblages in the temperate zone of the Northeast Pacific and Northwest Atlantic Oceans. B Mar Sci 53: 588-644.). Hence, the structure of ichthyoplankton assemblages may change with a combination of multiple factors (Auth 2008AUTH TD. 2008. Distribution and community structure of ichthyoplankton from the northern and central California Current in May 2004–06. Fish Oceanogr 17: 316-331., Brodeur et al. 2008BRODEUR RD, PETERSON WT, AUTH TD, SOULEN HL, PARNEL MM & EMERSON AA. 2008. Abundance and diversity of coastal fish larvae as indicators of recent changes in ocean and climate conditions in the Oregon upwelling zone. Mar Ecol Prog Ser 366: 187-202.), including, among others, temperature and salinity (Auth & Brodeur 2006AUTH TD & BRODEUR RD. 2006. Distribution and community structure of ichthyoplankton off the coast of Oregon, USA, in 2000 and 2002. Mar Ecol Prog Ser 319: 199-213., Parnel et al. 2008PARNEL MM, EMMETT RL & BRODEUR RD. 2008. Ichthyoplankton community in the Columbia River plume off Oregon: effects of fluctuating oceanographic conditions. Fish Bull 106: 161-173.), overfishing and composition of the copepod community (Bui et al. 2010BUI AOV, OUELLET P, CASTONGUAY M & BRÊTHES JC. 2010. Ichthyoplankton community structure in the northwest Gulf of St. Lawrence (Canada): past and present. Mar Ecol Prog Ser 412: 189-205.), spawning stock biomass and spawning location, and the occurrence of oceanographic currents that may advect eggs and larvae to different areas (Auth et al. 2011AUTH TD, BRODEUR RD, SOULEN HL, CIANNELLI L & PETERSON WT. 2011. The response of fish larvae to decadal changes in environmental forcing factors off the Oregon coast. Fish Oceanogr 20: 314-328.).

Some information on these sources of spatio-temporal variation exists for large semi-enclosed environments of the Northern Hemisphere, such as the Gulf of Alaska or the Strait of Georgia (Lanksbury et al. 2005LANKSBURY JA, DUFFY-ANDERSON JT, MIER KL & WILSON MT. 2005. Ichthyoplankton abundance, distribution, and assemblage structure in the Gulf of Alaska during September 2000 and 2001. Estuar Coast Shelf Sci 64: 775-785., Guan et al. 2015GUAN L, DOWER JF, MCKINNELL SM, PEPIN P, PAKHOMOV EA & HUNT PV. 2015. A comparison of spring larval fish assemblages in the Strait of Georgia (British Columbia, Canada) between the early 1980s and late 2000s. Prog Oceanogr 138: 45-57.). However, smaller gulfs, potentially more dependent on environmental variability, have received less attention as is the case of the San José Gulf (SJG), Argentina, located in the Southern Hemisphere. The SJG (42°20’S, 64°19’W) is a shallow, semi-enclosed basin from Northern Patagonia, located in the transition region between two biogeographic provinces (Balech & Ehrlich 2008BALECH E & EHRLICH M. 2008. Biogeographic scheme of the Argentine Sea. Rev Inv Desarr Pesq 19: 45-75., Galván et al. 2009GALVÁN DE, VENERUS LA & IRIGOYEN AJ. 2009. The reef-fish fauna of the Northern Patagonian Gulfs, Argentina, Southwestern Atlantic. The Open Fish Sci J 2: 90-98.). The gulf is divided into two oceanographic domains (West and East domains) by a thermal front that forms in spring and summer (Amoroso & Gagliardini 2010AMOROSO RO & GAGLIARDINI DA. 2010. Inferring complex hydrographic processes using remote-sensed images: turbulent fluxes in the patagonian gulfs and implications for scallop metapopulation dynamics. J Coast Res 26: 320-332.). Waters in the western domain are homogeneous throughout the year, but stratification occurs in the eastern domain during spring and summer (Amoroso et al. 2011AMOROSO RO, PARMA AM, ORENSANZ JM & GAGLIARDINI DA. 2011. Zooming the macroscope: medium-resolution remote sensing as a framework for the assessment of a small-scale fishery. ICES J Mar Sci 68: 696-706., Crespi-Abril et al. 2014CRESPI-ABRIL A, VILLANUEVA GOMILA GL, VENERUS L & BARÓN PJ. 2014. Spatial distribution of cephalopod paralarvae in San José Gulf (Northern Patagonia, Argentina): the role of tidal circulation in larval dispersal. Fish Res 152: 13-20.). Recent work attributed changes in the mesozooplankton community to this particular oceanographic feature (Hernández Moresino et al. 2017HERNÁNDEZ MORESINO R, DI MAURO R, CRESPI-ABRIL AC, VILLANUEVA-GOMILA GL, COMPAIRE JC & BARÓN PJ. 2017. Contrasting structural patterns of the mesozooplankton community result from the development of a frontal system in San José Gulf, Patagonia. Estuar Coast Shelf Sci 193: 1-11.). The area involving Península Valdés and the SJG has a long and complex history of protection, which dates back to 1967 (A. Cinti et al., unpublished data). It was declared as a Natural World Heritage Site by UNESCO in 1999, and now it is also included within the Valdés Biosphere Reserve1 1 http://www.unesco.org/new/en/natural-sciences/environment/ecological-sciences/biosphere-reserves/latin-america-and-the-caribbean/argentina/valdes (accessed 18.05.18). , mainly due to its importance for the reproduction and conservation of charismatic marine birds and mammals. Other components of this ecosystem, however, have never been included in the conservation agendas. Paradoxically, despite the economic relevance of the marine resources from the Patagonian region, whose productivity sustains highly lucrative industrial fisheries (Góngora et al. 2012GÓNGORA ME, GONZÁLEZ-ZEVALLOS D, PETTOVELLO A & MENDÍA L. 2012. Caracterización de las principales pesquerías del golfo San Jorge Patagonia, Argentina. Lat Am J Aquat Res 40: 1-11., Irusta et al. 2016IRUSTA CG, MACCHI G, LOUGE E, RODRIGUES K, D´ATRI LL, VILLARINO MF, SANTOS B & SIMONAZZI M. 2016. Biology and fishery of the argentine hake (Merluccius hubbsi). Rev Invest Desarr Pesq 28: 9-36.), the ichthyoplankton assemblages of the Argentine Sea, particularly south of 41°S, have not been studied in detail (but see some preliminary information in Ciechomski et al. 1975CIECHOMSKI JD, CASSIA MC & WEISS G. 1975. Distribución de huevos, larvas y juveniles de peces en los sectores surbonaerenses, patagónico y fueguino del Mar Epicontinental Argentino en relación con las condiciones ambientales, en noviembre 1973-enero 1974. Ecosur 2: 219-248., 1981, Ehrlich et al. 1999EHRLICH MD, SÁNCHEZ RP, CIECHOMSKI JD, MACHINANDIARENA L & PÁJARO M. 1999. Ichthyoplankton composition, distribution and abundance on the southern patagonian shelf and adjacent Waters. INIDEP Doc Cient 5: 37-65., Acha et al. 2012ACHA EM, ORDUNA M, RODRIGUEZ K, MILITELLI MI & BRAVERMAN M. 2012. Caracterización de la zona de “El Rincón” (Provincia de Buenos Aires) como área de reproducción de peces costeros. Rev Invest Desarr Pesq 21: 31-43., and M. Sylvester et al., unpublished data). The lack of systematic monitoring of ichthyoplankton assemblages within the Northern Patagonian gulfs of Argentina and along the country’s inner continental shelf has led to an information gap that also reaches the taxonomy. Only less than 20% of the early stages of the bony fishes inhabiting the Argentine Sea were described, and some descriptions of the early stages of even commercially important species were made only recently (e.g., Betti et al. 2009BETTI P, MACHINANDIARENA L & EHRLICH MD. 2009. Larval development of Argentine hake Merluccius hubbsi. J Fish Biol 74: 235-249., Derisio et al. 2012DERISIO C, BETTI P, DÍAZ DE ASTARLOA JM & MACHINANDIARENA L. 2012. Larval development of Etropus longimanus (Paralichthyidae) and Symphurus trewavasae (Cynoglossidae) off the Buenos Aires coast, Argentina. Sci Mar 76: 19-29., Villanueva Gomila et al. 2015VILLANUEVA GOMILA GL, EHRLICH MD & VENERUS LA. 2015. Early life history of the Argentine sea bass Acanthistius patachonicus (Jenyns 1840) (Pisces: Serranidae). Fish Bull 113: 456-467.).

The main goal in this study was to investigate the spatio-temporal patterns in the structure of larval fish assemblages within a small semi-enclosed basin, the SJG, strongly influenced by its surrounding waters, and to shed light about the potential effects of water temperature and oceanographic features on the ichthyoplankton composition. The main questions that we address in this work comprise: i) Do the ichthyoplankton assemblages of the SJG show any temporal differences at the seasonal and/or annual scales?; ii) Do the ichthyoplankton assemblages show clear spatial patterns within the SJG?; and iii) Which environmental factors may affect the structure of ichthyoplankton assemblages within the SJG?

MATERIALS AND METHODS

Study site

The northwestern margin of the SJG presents a narrow mouth (6.9 km width) that connects it with the much larger and deeper San Matías Gulf (SMG), through which up to 15% of the total water volume of the SJG flows in and out during each semidiurnal tidal cycle (Rivas 1990RIVAS A. 1990. Heat-balance and annual variation of mean temperature in the north-patagonian gulfs. Oceanol Acta 13: 265-272.) (Fig. 1). The asymmetrical position of the gulf mouth combined with a strong year-round semilunar tidal circulation produces a homogeneous structure of the water column in the western area, while the eastern side is less affected allowing spring-summer stratification (Amoroso et al. 2011AMOROSO RO, PARMA AM, ORENSANZ JM & GAGLIARDINI DA. 2011. Zooming the macroscope: medium-resolution remote sensing as a framework for the assessment of a small-scale fishery. ICES J Mar Sci 68: 696-706., Crespi-Abril et al. 2014CRESPI-ABRIL A, VILLANUEVA GOMILA GL, VENERUS L & BARÓN PJ. 2014. Spatial distribution of cephalopod paralarvae in San José Gulf (Northern Patagonia, Argentina): the role of tidal circulation in larval dispersal. Fish Res 152: 13-20.). As a result, a frontal system forms during approximately half of the year, which divides the SJG into a western (WD) and an eastern (ED) hydrographic domain (Amoroso & Gagliardini 2010AMOROSO RO & GAGLIARDINI DA. 2010. Inferring complex hydrographic processes using remote-sensed images: turbulent fluxes in the patagonian gulfs and implications for scallop metapopulation dynamics. J Coast Res 26: 320-332.). The complexity of this system is further enhanced by the arrival of fertilized waters from the coastal, vertically-mixed side of the tidal front that develops off Península Valdés (Amoroso & Gagliardini 2010AMOROSO RO & GAGLIARDINI DA. 2010. Inferring complex hydrographic processes using remote-sensed images: turbulent fluxes in the patagonian gulfs and implications for scallop metapopulation dynamics. J Coast Res 26: 320-332.). In a recent article where the samples collected in the survey described below were used, the hydrodynamic differences between domains were associated with changes in the structure of the mesozooplankton community (Hernández Moresino et al. 2017HERNÁNDEZ MORESINO R, DI MAURO R, CRESPI-ABRIL AC, VILLANUEVA-GOMILA GL, COMPAIRE JC & BARÓN PJ. 2017. Contrasting structural patterns of the mesozooplankton community result from the development of a frontal system in San José Gulf, Patagonia. Estuar Coast Shelf Sci 193: 1-11.). Furthermore, seasonal and interannual variations in the abundance and composition of the mesozooplankton were associated with differences in the chlorophyll-a concentration and water temperature in the SJG (Hernández Moresino et al. 2017HERNÁNDEZ MORESINO R, DI MAURO R, CRESPI-ABRIL AC, VILLANUEVA-GOMILA GL, COMPAIRE JC & BARÓN PJ. 2017. Contrasting structural patterns of the mesozooplankton community result from the development of a frontal system in San José Gulf, Patagonia. Estuar Coast Shelf Sci 193: 1-11.).

Figure 1
Location of the oceanographic stations in the San José Gulf. WD and ED indicate the western and eastern domains, respectively (sensu Amoroso & Gagliardini 2010AMOROSO RO & GAGLIARDINI DA. 2010. Inferring complex hydrographic processes using remote-sensed images: turbulent fluxes in the patagonian gulfs and implications for scallop metapopulation dynamics. J Coast Res 26: 320-332.). The black line indicates the approximate location of the San José frontal system, which divides the gulf in two domains. The size of the triangles is proportional to the total number of tows made in each station (from 1 to 9) throughout the study.

Sampling

A total of 155 ichthyoplankton samples were collected on a monthly basis in spring and summer during a 2-year survey that we conducted aboard outboard motorboats in the SJG, covering a regular grid of 25 stations (of which 5 to 23 were surveyed each month) (Fig. 1), from October 2011 to March 2012 and from October 2012 to March 2013 (henceforth ‘Year 1’ and ‘Year 2’, respectively). No samples were available for January and November 2012. Ichthyoplankton tows were made with a Hensen net with a mouth diameter of 70 cm fitted with 300-μm mesh. A General Oceanics R2020 flowmeter (Miami, United States) was mounted in the mouth of the net to estimate the volume of filtered water. Towing speed ranged between 77 and 154 cm·s-1. Depending on bottom depth, the tows were oblique (mean bottom depth ± SD: 33 ± 9 m) or horizontal (mean bottom depth± SD: 14 ± 5 m), to increase the volume of water sampled for 15 minutes. Horizontal tows were made close to the seafloor. Maximum depth during each ichthyoplankton tow was recorded by a depth sensor attached to the mouth of the net. Samples were fixed immediately after collection and preserved in 5% formalin. Larvae were quantified and identified in the laboratory to the lowest possible taxonomic level based on their morphology (Neira et al. 1998NEIRA FJ, MISKIEWICZ AG & TRNSKI T. 1998. Methods. In: Neira FJ, Miskiewicz AG & Trnski T (Eds). Larvae of temperate Australian fishes. Laboratory guide for larval fish identification University of Western Australia Press, Nedlands, p. 11-19.).

Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua Level-2 files were acquired for the study area from the NASA ocean color web site (https://oceancolor.gsfc.nasa.gov). We obtained the daytime sea surface temperature (SST) 11 μM product that uses the 11 and 12 μM bands. Values of satellite SST used were the averages of all unmasked pixels within a 3 × 3-pixel box centered on the in-situ target. Temporal and spatial resolution was 24 h and 1.1 km, respectively. Due to the cloud cover usually present over the study area, the data acquired daily or even every week had many gaps. To solve this and also due to the strong spring winds that did not allow us to perform all tows on consecutive days within each month, we averaged the daily SST values obtained for each oceanographic station in the period elapsed between the first and the last tow made in each month. The validity of the satellite data was corroborated throughout the study area (results not showed). To describe the mid-term SST regime in the SJG, we used a Hovmöller diagram (Hovmöller 1949HOVMÖLLER E. 1949. The trough-and-ridge diagram. Tellus 1: 62-66.). Thermal anomalies were estimated by subtracting the monthly 12-year SST average (the data used to calculate these mean values covered the period 2003-2014) to each monthly SST, for each gulf domain.

Finally, we modelled the daily SST during the spring and summer for each gulf domain and year, using the following sinusoidal model, modified from McCloskey (1986)MCCLOSKEY JW. 1986. Seasonal temperature patterns of selected cities in and around Ohio. Ohio J Sci 86: 8-10.:

T d = m + p s i n ( 2 π d 182 + h ) (1)

where Td is the SST at day d; d is an integer varying between 1 and 177, which represents each Julian day from 1 October to 26 March (when sampling ended on Year 2), covering the whole sampling period; m is the mean yearly SST; p, the amplitude for temperature variation; and h, a phase shift parameter indicating the beginning of the temperature cycle.

Data analysis

Due to a large number of zeros observed in the abundance data, we were not able to use the raw data for the analyses. Instead, we used the weighted average density of larvae (individuals·m-3) of each taxon and the average SST by month and year (irrespective of the sampling stations), for each oceanographic domain (East and West). All the subsequent analyses were made from a Bray-Curtis similarity matrix (Bray & Curtis 1957BRAY JR & CURTIS JT. 1957. An ordination of the upland forest communities of southern Wisconsin. Ecol Monogr 27: 325-349.), constructed from that data set (Warton et al. 2012WARTON D, WRIGHT ST & WANG Y. 2012. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol Evol 3: 89-101.). This matrix has 18 rows, which represents a combination of domain × month × year, and 16 columns, one per fish taxa. Larvae present in less than 2% of the stations sampled (i.e., in three or less out of 155 stations), were removed from the analyses. We also did not include some unidentified taxa for which there were a limited number of individuals that covered a narrow size range, and hence did not allow assigning them univocally to a taxon (see Figure S1 Figures S1, S2 and S3. Tables SI and SII. - Supplementary Material).

As the distribution of the larval density values was not normal, we used a randomization test (Manly 1991MANLY BFJ. 1991. Randomization and Monte Carlo methods in biology, 3rd edition, Chapman and Hall, Boca Raton, FL, 281 p.) to contrast the overall median larval density between years. We chose the median instead of the mean to avoid the unduly influence of a few extreme density values. We run the randomization test by constructing Monte Carlo distributions (n = 50000) of the difference between median density values, under the null hypothesis of no differences between years, and ran a two-tailed test.

To explore the spatio-temporal variation of the ichthyoplankton assemblages in a single 2D plot, we ran a non-metric multidimensional scaling ordination (nMDS) (Clarke & Warwick 2001CLARKE KR & WARWICK RM. 2001. Change in marine communities: an approach to statistical analysis and interpretation. 2nd edition. Natural Environment Research Council, Plymouth Marine Laboratory, Plymouth, 176 p.). In addition, to evaluate potential differences in the structure of the ichthyoplankton assemblages due to the effects of ‘Gulf domain’, ‘Season’, ‘Year’, ‘SST’ and ‘Thermal anomaly’, we ran a multivariate analysis of variance with permutations (PERMANOVA: Anderson 2001ANDERSON MJ. 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecol 26: 32-46.) (Table I). This analysis assumes that the samples (i.e., rows of the original data matrix) are exchangeable under a true null hypothesis, which implies that the multivariate observations are independent and identically distributed (Anderson 2001ANDERSON MJ. 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecol 26: 32-46.). All the factors tested were considered fixed, and the number of permutations used for fitting the PERMANOVA models was 999. As the first model run pointed to seasonal and yearly effects on the community structure rather than spatial (i.e., ‘Gulf domain’) effects, we evaluated a model containing the interaction between both temporal factors, ‘Season’ and ‘Year’, as well as the other variables listed in Table I. In this model, data were randomized freely among the interaction cells. Before conducting the PERMANOVA analyses, we checked the assumption of similar multivariate dispersion of points among groups with the ‘betadisper’ function included in the ‘vegan’ package (Oksanen 2017OKSANEN J. 2017. Vegan: ecological diversity. R package version 2.4-3. https://github.com/vegandevs/vegan.
https://github.com/vegandevs/vegan...
) of the R software (R Development Core Team 2014R DEVELOPMENT CORE TEAM. 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. www.r-project.org.
www.r-project.org...
).

Table I
Explanatory variables tested in the PERMANOVA analysis. The numbers in parentheses indicate the number of levels in each factor. SJG: San José Gulf.

To identify indicator species for each group of samples characterized by the relevant variables identified in the prior analyses (i.e., PERMANOVA and nMDS), we estimated the “Indicator species values” index (IndVal, Dufrêne & Legendre 1997DUFRÊNE M & LEGENDRE P. 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol Monogr 67: 345-366.). IndVal is the product of two values: A (specificity) and B (fidelity). The specificity is based on the abundance of a particular species in a group relative to its abundance in all groups, and the fidelity is the percent frequency of one particular taxon in each group. Indicator values range from 0 (no indication) to 100 (perfect indication) (Dufrêne & Legendre 1997DUFRÊNE M & LEGENDRE P. 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol Monogr 67: 345-366., Suntsov et al. 2012SUNTSOV A, KOSLOW JA & WATSON W. 2012. The spatial structure of coastal ichthyoplankton assemblages off Central and Southern California. CCOFI Rep 53: 153-170.). A maximum ranking of 100 occurs when a single taxon is present in all samples of a group and only occurs in that group of samples (Dufrêne & Legendre 1997DUFRÊNE M & LEGENDRE P. 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol Monogr 67: 345-366.). The significance of the IndVal measure for each species was tested using the ‘multipatt’ function included in the ‘indicspecies’ package of the R software (De Cáceres et al. 2012DE CÁCERES M, LEGENDRE P, WISER SK & BROTONS L. 2012. Using species combinations in indicator value analyses. Methods Ecol Evol 3: 973-982.).

The richness and diversity indexes traditionally used by ecologists have several limitations because they are highly sensitive to the number of individuals sampled, the number of samples, and the size of the surveyed area (Colwell et al. 2012COLWELL RK, CHAO A, GOTELLI NJ, LIN S-Y, MAO CX, CHAZ DON RL & LONGINO JT. 2012. Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages. J Plant Ecol 5: 3-21., Chao et al. 2014CHAO A, GOTELLI NJ, HSIEH TC, SANDER EL, MA KH, COLWELL RK & ELLISON AM. 2014. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol Monogr 84: 45-67.). To overcome many of these shortcomings, we used rarefaction curves based on Hill numbers (Gotelli & Colwell 2011GOTELLI NJ & COLWELL RK. 2011. Estimating species richness. In: Magurran AE & McGill BJ (Eds), Biological diversity: frontiers in measurement and assessment, Oxford University Press, Oxford, UK, p. 39-54., Colwell et al. 2012COLWELL RK, CHAO A, GOTELLI NJ, LIN S-Y, MAO CX, CHAZ DON RL & LONGINO JT. 2012. Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages. J Plant Ecol 5: 3-21., Chao et al. 2014CHAO A, GOTELLI NJ, HSIEH TC, SANDER EL, MA KH, COLWELL RK & ELLISON AM. 2014. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol Monogr 84: 45-67.). We compared the species richness and diversity indexes of the ichthyoplankton assemblages between groups of samples characterized by the relevant variables identified in the prior analyses. For these analyses, density data in the number of larvae·100 m-3 were averaged for each species per season or year (see Results) and rounded to obtain integer values. We evaluated first- (q = 0 or species richness) and second-order (q = 1 or Shannon diversity index, which is the exponential of Shannon entropy) Hill numbers (Jost 2007JOST L. 2007. Partitioning diversity into independent alpha and beta components. Ecology 88: 2427-2439.). We extrapolated each curve to the double of the overall density (sample size) and estimated the 95% confidence bands through the bootstrap method (100 replicates) (Chao et al. 2014CHAO A, GOTELLI NJ, HSIEH TC, SANDER EL, MA KH, COLWELL RK & ELLISON AM. 2014. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol Monogr 84: 45-67.). The significance of the differences in richness and diversity between the groups identified by PERMANOVA analysis was assessed through the overlapping of the confidence bands in the plots. We considered that richness or diversity differed when the corresponding confidence bands for each group did not overlap.

Multivariate statistical analyses, rarefaction curves, and the estimation of the ‘Indicator species values’ were performed with the packages ‘iNEXT’ (Hsieh et al. 2016HSIEH TC, MA KH & CHAO A. 2016. iNEXT: An R package for interpolation and extrapolation of species diversity (Hill numbers). Methods Ecol Evol 7: 1451-1456.), ‘vegan’ (Oksanen 2017OKSANEN J. 2017. Vegan: ecological diversity. R package version 2.4-3. https://github.com/vegandevs/vegan.
https://github.com/vegandevs/vegan...
), ‘indicspecies’ (De Cáceres 2013DE CÁCERES M. 2013. How to use the indicspecies package. R package version 1.7.1. https://cran.rproject.org/web/packages/indicspecies/vignettes/indicspeciesTutorial.pdf
https://cran.rproject.org/web/packages/i...
), and ‘stats’ of the R software (R Development Core Team 2014R DEVELOPMENT CORE TEAM. 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. www.r-project.org.
www.r-project.org...
). Plots of density, nMDS, and rarefaction curves were built using the ‘ggplot2’ package for R (Wickham 2009WICKHAM H. 2009. ggplot2: Elegant graphics for data analysis. Springer-Verlag, New York.). Parameters for the SST models were obtained with the ‘nls2’ R package (Grothendieck 2007GROTHENDIECK G. 2007. nls2: Non-linear regression with brute force. R package version 0.1-2. Available from: http://CRAN.R-project.org/package=nls2.
http://CRAN.R-project.org/package=nls2...
). Aleatorization tests were run with an ad-hoc R code written by the authors.

RESULTS

A total of 2088 larvae were caught during the ichthyoplankton surveys conducted in the SJG. These larvae comprised 36 taxa, from which 14 were identified to species, two to genus, one to family and one to order (Table II). A total of 332 larvae were unidentified, 189 of them were yolk-sac larvae, or their preservation state was too poor for identifying them properly. The remaining larvae (n = 143), ranging in numbers from 1 to 40 individuals, belonged to 18 different taxa not assigned to any particular species, genus, family, or order (See Figure S1).

Table II
Larvae collected in the San José Gulf between October 2011 and March 2012 (Year 1), and between October 2012 and March 2013 (Year 2). %F: Frequency of occurrence (percentage of tows containing each species). * Identified species removed from the analyses due their low %F value.

SST differed between gulf domains; it was also higher in Year 1 compared to Year 2, particularly during summer (Fig. 2). Maximum modeled SST in SJG and the differences in mean SST between domains were both larger in Year 1 compared to Year 2 (Fig. 2 and see Figure S2 Figures S1, S2 and S3. Tables SI and SII. ). Maximum modelled SST for the ED and WD were 19.2°C and 17.8°C in the Year 1, and 17.6°C and 16.2°C in Year 2, respectively. The ED was always warmer than the WD. In spring, the difference between minimum modelled SST values for both domains were 1.1°C and 0.7°C for years 1 and 2, respectively. In summer, the difference between maximum modelled SST values were 1.4°C for both years. At a broader temporal scale, Year 1 was dominated by positive thermal anomalies, while during Year 2, negative thermal anomalies were more common (Fig. 3). Overall, the ED had more extreme thermal anomalies than the WD throughout the period 2003-2014 (ED range: -1.68°C to 3.43°C, and WD range: -1.38°C to 1.29°C).

Figure 2
Sinusoidal satellite sea surface temperature (SST) models (solid lines) in the San José Gulf for Year 1: October 2011 to March 2012 (red: East domain and light-red: West domain), and Year 2: October 2012 to March 2013 (blue: East domain and light-blue: West domain). Shaded areas represent 95% confidence regions for each fitted curve. Raw data are showed as open circles. Note that the confidence regions overlap in spring, but they separate during summer. Thermal amplitude was greater during the first year sampled.
Figure 3
Hovmöller diagram showing the thermal anomalies in SST in the San José Gulf for the West (left panel) and East (right panel) domains. Only the period 2008-2014 is shown for clarity. The scale bar indicates the range in thermal anomalies in SST.

The overall larval density differed largely between years: it was three times higher in the Year 1 than in the Year 2 (14.4 larvae·100 m-3 vs. 4.8 larvae·100 m-3, respectively; p < 0.001) (Fig. 4). In Year 1, larval density was maximum in late spring (December) (Fig. 4a) while in Year 2, most larvae were caught in summer (February) (Fig. 4b). Five abundant species dominated the samples during the whole study period: Helcogrammoides cunninghami, Raneya brasiliensis, Pseudopercis semifasciata, Sebastes oculatus, and larvae belonging to the family Engraulidae (Table II and see Figure S3 Figures S1, S2 and S3. Tables SI and SII. ). Those species accounted for approximately 72% and 60% of all the larvae caught, in Years 1 and 2, respectively (see Figure S3). Each species occurred in the water column during particular periods. For example, larvae belonging to the family Engraulidae were sampled during all the months surveyed. Other species such as Percophis brasiliensis and Xystreurys rasile were collected only in one month (Fig. 4).

Figure 4
Bubble plots showing the mean monthly density of the larvae sampled within the San José Gulf during two consecutive years: a) Year 1: October 2011 to March 2012, and b) Year 2: October 2012 to March 2013. The diameter of the dots is proportional to larval density (number of larvae·100 m-3). The ranges for larval density were 0.01 larvae·100 m-3 – 268.36 larvae·100 m-3, and 0.03 larvae·100 m-3 – 21.91 larvae·100 m-3 for Year 1 and 2, respectively. A.pat: Acanthistius patachonicus, A.chi: Agonopsis chiloensis, Athe: Atherinopsidae, D.aur: Dules auriga, Eng: Engraulidae, H.cun: Helcogrammoides cunninghami, M.hub: Merluccius hubbsi, Par: Paralichthys spp., P.bra: Percophis brasiliensis, Pleu: Pleuronectiformes, P.sem: Pseudopercis semifasciata, R.bra: Raneya brasiliensis, S.ocu: Sebastes oculatus, S.bra: Stromateus brasiliensis, S.tre: Symphurus trewavasae, S.fol: Syngnathus folletti, T.lat: Trachurus lathami, X.ras: Xystreurys rasile, NI: Unidentified species. X: Without data.

A total of 16 taxa were included in the multivariate statistical analyses (Table II). The analysis of similarity using nMDS suggested some differences among the ichthyoplankton assemblages: one defined by the season (spring vs. summer) and the other, less clear, defined by the year (Fig. 5). The PERMANOVA models showed that the factor ‘Gulf domain’ and the covariate ‘Thermal anomaly’ had no effect on the structure of the larval fish assemblages of the SJG (Supplementary Material - Tables SI and SII Figures S1, S2 and S3. Tables SI and SII. ). In contrast, the PERMANOVA model that included the factors ‘Season’ and ‘Year’, the covariate ‘SST’, and the interaction ‘Season × Year’, revealed significant effects of the main effects and the covariate on the structure of the larval assemblages of the SJG, while the interaction was not significant (Table III).

Figure 5
Non-metric multidimensional scaling (nMDS) ordination of larval fish assemblages from the San José Gulf, using a Bray-Curtis dissimilarity matrix of larval abundance (individuals·m-3), averaged by month and gulf domain. Spring: October to December (circles), summer: January to March (triangles), Year 1: October 2011 to March 2012 (green symbols), Year 2: October 2012 to March 2013 (orange symbols). W and E: West and East domains. Stress = 0.11.
Table III
Results of the PERMANOVA model testing the effects of Season (S), Year (Y), their interaction (S×Y), and Sea Surface Temperature (SST) on the structure of the ichthyoplankton assemblages. * Significant (p < 0.05).

Larvae belonging to the family Engraulidae were the most abundant species, and they were collected during the whole sampling period; hence, they were not a good indicator species. The species with the highest IndVal index for the spring were rocky reef fishes including: H. cunninghami, that appeared in all stations sampled during that season (i.e., B = 1) and was largely (but not completely) restricted to it (i.e., A = 0.93) (p < 0.01); S. oculatus (A = 1 and B = 0.80; p < 0.01), and Agonopsis chiloensis (A = 1 and B = 0.50; p < 0.05), that appeared only in some spring stations. Pseudopercis semifasciata (A = 0.97 and B = 0.80; p < 0.05) was also caught in some spring stations, but it was not restricted to that season. Finally, Dules auriga is a good indicator species for the samples collected in summer (A = 0.98 and B = 0.88; p < 0.01).

The species richness and diversity of the larvae assemblages were compared between seasons and years, the temporal factors identified by the PERMANOVA analysis. The spring and summer had overlapping confidence intervals; hence, both indexes did not differ between seasons (Fig. 6a, b). Regarding the variable ‘Year’, the curves did not reach a plateau for Year 2 due to the low number of individuals collected. However, our results suggested a greater diversity in Year 2 (Fig. 6d). The species richness, however, was similar between years when only the species included in the analyses were considered (15 species in Year 1 and 13 species in Year 2) (Fig. 6c), as well as when all the taxa caught were taken into account (36 species in Year 1 and 28 species in Year 2).

Figure 6
Sample size-based interpolation (solid lines) and extrapolation (dotted lines) sampling curves with 95% confidence bands (shaded areas) for the seasons (plots a and b) and years (plots c and d) separately, for species richness and Shannon diversity index. The solid symbols represent the index values associated to the overall density sampled at each season or year. Spring: October to December (circles and red lines), summer: January to March (triangles and light-blue lines). Year 1: October 2011 to March 2012 (circles and green lines) and Year 2: October 2012 to March 2013 (triangles and orange lines).

DISCUSSION

This study revealed for the first time the temporal and spatial sources of variation in the structure of fish larval assemblages inhabiting the SJG, a small basin located in the Southwest Atlantic. The gap in the taxonomic knowledge that hampered the identification of about 15% of the larvae collected is often a pervasive problem in ichthyoplankton research due to the rapid morphological changes occurring during the early ontogeny (Hernandez et al. 2013HERNANDEZ FJ, CARASSOU L, GRAHAM WM & POWERS SP. 2013. Evaluation of the taxonomic sufficiency approach for ichthyoplankton community analysis. Mar Ecol Prog Ser 491: 77-90.). Here, we lacked complete developmental series for most species that would have allowed the comparison of larvae with juvenile fishes to identify them properly. The larval development of 27 bony fish species, of the ca. 50 species inhabiting the SJG, was described. However, in this study, we were able to identify most larvae at the species, genus, family, or order levels. Overall, our analyses included 86.4% of the larvae collected.

Our results showed that the structure of the larval assemblages in SJG varied at different temporal scales: both the nMDS and PERMANOVA revealed significant differences between seasons and years. The relationships between larval fish dynamics and environmental variability are complex, usually non-linear, and involve multiple environmental factors (Hsieh et al. 2005HSIEH C, REISS C, WATSON W, ALLEN MJ, HUNTER JR, LEA RN, ROSENBLATT RH, SMITH PE & SUGIHARA G. 2005. A comparison of long-term trends and variability in populations of larvae of exploited and unexploited fishes in the Southern California region: a community approach. Prog Oceanogr 67: 160-185., Auth et al. 2011AUTH TD, BRODEUR RD, SOULEN HL, CIANNELLI L & PETERSON WT. 2011. The response of fish larvae to decadal changes in environmental forcing factors off the Oregon coast. Fish Oceanogr 20: 314-328.). While the multiple effects of water temperature (e.g., Auth 2008AUTH TD. 2008. Distribution and community structure of ichthyoplankton from the northern and central California Current in May 2004–06. Fish Oceanogr 17: 316-331., Guan et al. 2017GUAN L, DOWER JF, MCKINNELL SM, PEPIN P, PAKHOMOV EA & HUNT BPV. 2017. Interannual variability in the abundance and composition of spring larval fish assemblages in the Strait of Georgia (British Columbia, Canada) from 2007 to 2010. Fish Oceanogr 26: 638-654.) on the biology of the species comprising the larval fish assemblages in Patagonian waters are mostly unknown, our study showed an association between the structure of the larval assemblages and SST. Indeed, Year 1 was warmer than Year 2, coincidently with a greater density of fish larvae caught. The overall density of larvae was three times higher during Year 1 and, irrespective of the variety of reproductive strategies showed by different fishes; all the species caught were more abundant during that period. Many studies associate changes in the structure of the ichthyoplankton assemblages with changes in water temperature (Genner et al. 2010GENNER MJ, HALLIDAY NC, SIMPSON SD, SOUTHWARD SJ, HAWKINS SJ & SIMS DW. 2010. Temperature-driven phenological changes within a marine larval fish assemblage. J Plankton Res 32: 699-708., Kono et al. 2016KONO Y, SASAKI H, KURIHARA Y, FUJIWARA A, YAMAMOTO J & SAKURAI Y. 2016. Distribution pattern of Polar cod (Boreogadus saida) larvae and larval fish assemblages in relation to oceanographic parameters in the northern Bering Sea and Chukchi Sea. Polar Biol 39: 1039-1048.) because it affects growth, egg maturation, spawning time (Bui et al. 2010BUI AOV, OUELLET P, CASTONGUAY M & BRÊTHES JC. 2010. Ichthyoplankton community structure in the northwest Gulf of St. Lawrence (Canada): past and present. Mar Ecol Prog Ser 412: 189-205.), and spawning migrations (Genner et al. 2010GENNER MJ, HALLIDAY NC, SIMPSON SD, SOUTHWARD SJ, HAWKINS SJ & SIMS DW. 2010. Temperature-driven phenological changes within a marine larval fish assemblage. J Plankton Res 32: 699-708.).

Other factors associated with water temperature and temporal concurrency with the spawning of adults may exert their influence on the repetitive nature of larval groups (Hoffmeyer et al. 2009HOFFMEYER MS, MENÉNDEZ MC, BIANCALANA F, NIZOVOY AM & TORRES ER. 2009. Ichthyoplankton spatial pattern in the inner shelf off Bahía Blanca Estuary, SW Atlantic Ocean. Estuar Coast Shelf Sci 84: 383-392.). For example, dominant currents and other oceanographic processes may cause the presence of certain species in a particular area due to their interaction with spawning areas, which persist from year to year (Gray & Miskiewicz 2000GRAY CA & MISKIEWICZ AG. 2000. Larval fish assemblages in south-east Australian coastal waters: seasonal and spatial structure. Estuar Coast Shelf Sci 50: 549-570., Acha et al. 2012ACHA EM, ORDUNA M, RODRIGUEZ K, MILITELLI MI & BRAVERMAN M. 2012. Caracterización de la zona de “El Rincón” (Provincia de Buenos Aires) como área de reproducción de peces costeros. Rev Invest Desarr Pesq 21: 31-43.). The input of nutrients into SJG occurs mainly through shelf waters due to the absence of other important discharging sources (e.g., extreme rainfalls, river discharge, and human settlements) (Amoroso & Gagliardini 2010AMOROSO RO & GAGLIARDINI DA. 2010. Inferring complex hydrographic processes using remote-sensed images: turbulent fluxes in the patagonian gulfs and implications for scallop metapopulation dynamics. J Coast Res 26: 320-332.). Due to the morphology of the basin and its geographic characteristics, water temperature inside the SJG is strongly influenced by the heat exchange with the atmosphere (Rivas 1990RIVAS A. 1990. Heat-balance and annual variation of mean temperature in the north-patagonian gulfs. Oceanol Acta 13: 265-272.). Thus, during a warm year (like, for example, Year 1), it is expected than SJG waters increase their temperature faster than the adjacent continental shelf. Coincidently with this hypothesis, the ED presented more extreme positive thermal anomalies than the WD, whose temperature strongly depends on the exchange with shelf waters. The heat exchange between water masses through the narrow mouth of the SJG moderates the water temperature fluctuations within it. The mitigation of the rise in water temperature in warm years would demand the input of shelf water at a lower temperature than usual, or a greater volume of water exchanged (A. Rivas, personal communication). This process might have caused a greater input of nutrients, phyto, and zooplankton, including fish larvae, from shelf waters into the SJG during the first year sampled. A greater volume of water exchanged could also have increased the availability of phyto and zooplankton, enhancing the survival of fish larvae. Although the environmental mechanisms that drive and sustain the chlorophyll-a cycle in this area have not been fully studied (Williams et al. 2018WILLIAMS GN, SOLÍS ME & ESTEVES JL. 2018. Satellite-measured phytoplankton and environmental factors in North Patagonian Gulfs. In: Hoffmeyer M, Sabatini M, Brandini F, Calliari D & Santinelli NH (Eds), Plankton ecology of the Southwestern Atlantic. From the subtropical to the subantarctic realm, 1st ed., Switzerland: Springer, p. 307-325.), the concentration of chlorophyll-a in the Argentine Continental Shelf was higher in Year 1 than in Year 2 (Andreo et al. 2016ANDREO VC, DOGLIOTTI AI & TAURO CB. 2016. Remote sensing of phytoplankton blooms in the continental shelf and shelf-break of Argentina: spatio-temporal changes and phenology. IEEE J-STARS 9: 5315-5323.). Furthermore, the interannual variability in the ichthyoplankton structure was coincident with changes in the density and composition of other mesozooplanktonic groups in the SJG during the period studied (Hernández Moresino et al. 2017HERNÁNDEZ MORESINO R, DI MAURO R, CRESPI-ABRIL AC, VILLANUEVA-GOMILA GL, COMPAIRE JC & BARÓN PJ. 2017. Contrasting structural patterns of the mesozooplankton community result from the development of a frontal system in San José Gulf, Patagonia. Estuar Coast Shelf Sci 193: 1-11.). Copepods, the main food source for fish larvae (e.g., Spinelli et al. 2012SPINELLI ML, PÁJARO M, MARTOS P, ESNAL GB, SABATINI M & CAPITANIO FL. 2012. Potential zooplankton preys (Copepoda and Appendicularia) for Engraulis anchoita in relation to early larval and spawning distributions in the Patagonian frontal system (SW Atlantic Ocean). Sci Mar 76: 39-47., Temperoni & Viñas 2013TEMPERONI B & VIÑAS MD. 2013. Food and feeding of Argentine hake (Merluccius hubbsi) larvae in the Patagonian nursery ground. Fish Res 148: 47-55.), were more abundant in SJG during the spring-summer of 2011-2012 than during the same seasons in 2012-2013, coincidently with a higher concentration of chlorophyll-a (Hernández Moresino et al. 2017HERNÁNDEZ MORESINO R, DI MAURO R, CRESPI-ABRIL AC, VILLANUEVA-GOMILA GL, COMPAIRE JC & BARÓN PJ. 2017. Contrasting structural patterns of the mesozooplankton community result from the development of a frontal system in San José Gulf, Patagonia. Estuar Coast Shelf Sci 193: 1-11.). This relationship between chlorophyll concentrations and zooplankton abundance has also been observed in the spawning area of other fishes of the Argentine Sea such as Merluccius hubbsi (Temperoni et al. 2014TEMPERONI B, VIÑAS MD, MARTOS P & MARRARI M. 2014. Spatial patterns of copepod biodiversity in relation to a tidal front system in the main spawning and nursery area of the Argentine hake Merluccius hubbsi. J Mar Syst 139: 433-445.) and Engraulis anchoita (one of the species belonging to the family Engraulidae, Marrari et al. 2013MARRARI M, SIGNORINI S, MCCLAIN C, PÁJARO M, MARTOS P, VIÑAS MD, HANSEN J, DI MAURO R, CEPEDA G & BURATTI C. 2013. Reproductive success of the Argentine anchovy, Engraulis anchoita, in relation to environmental variability at a midshelf front (Southwestern Atlantic Ocean). Fish Oceanogr 22: 247-261.).

Both the species richness and diversity play an important role in the processes related to ecosystem functioning and promote production and ecosystem stability (Bui et al. 2010BUI AOV, OUELLET P, CASTONGUAY M & BRÊTHES JC. 2010. Ichthyoplankton community structure in the northwest Gulf of St. Lawrence (Canada): past and present. Mar Ecol Prog Ser 412: 189-205.). Our PERMANOVA results indicated a change in the composition of the larval assemblages between seasons, which adds to seasonal variations in the densities of particular dominant taxa (i.e., larvae belonging to the family Engraulidae, H. cunninghami, P. semifasciata, and S. oculatus were more abundant in spring) and to a greater number of species caught in summer than in spring (15 vs. 9 species, respectively). These findings agree with the general tendencies observed through the rarefaction curves (i.e., they showed a greater species richness and diversity in summer). However, no significant differences were evident from this analysis in contrast to what had been expected for temperate waters (Berrios & Vargas 2000BERRIOS VL & VARGAS ME. 2000. Estructura del ensamble de peces intermareales de la costa rocosa del norte de Chile. Rev Biol Mar Oceanog 35: 73-81.). The significant variation in the structure of the assemblages between years, identified by the PERMANOVA analysis, could be associated with the changes in diversity showed by the rarefaction curves. Species richness was similar between years; thus, the greater diversity found in Year 2 could be related to changes in the densities. In this context, the higher evenness in Year 2 (Fig. 4) could explain the greater diversity estimated for this year (Magurran 2004MAGURRAN AE. 2004. Measuring biological diversity. Blackwell Publishing, Oxford, UK, 264 p.). Therefore, while the switches in the density of the dominant species contributed most to the seasonal changes in the assemblages, at the inter-annual scales the changes in the assemblages were the result of variations both in dominant and subdominant species. Nevertheless, conclusions should be taken with caution when analysing the comparisons of the rarefaction curves between years because the number of individuals collected in Year 2 was insufficient to obtain consistent estimates of richness and diversity (i.e., the rarefaction curves did not reach an asymptote).

All the indicator species (i.e., H. cunninghami, P. semifasciata, S. oculatus, A. chiloensis, and D. auriga), are rocky reef fishes and were also abundant in a preliminary ichthyoplankton study within the SJG (M. Sylvester et al., unpublished data). Together with the fact that most larvae caught were in a preflexion stage suggests that the SJG is an important area for the reproduction and breeding at least for these species.

The general knowledge about the composition of the ichthyoplankton assemblages of the Argentine Sea is highly heterogeneous. More attention was put to study the ecology of the larval fish assemblages in certain areas, such as the coast of Buenos Aires Province (Hoffmeyer et al. 2009HOFFMEYER MS, MENÉNDEZ MC, BIANCALANA F, NIZOVOY AM & TORRES ER. 2009. Ichthyoplankton spatial pattern in the inner shelf off Bahía Blanca Estuary, SW Atlantic Ocean. Estuar Coast Shelf Sci 84: 383-392., Acha et al. 2012ACHA EM, ORDUNA M, RODRIGUEZ K, MILITELLI MI & BRAVERMAN M. 2012. Caracterización de la zona de “El Rincón” (Provincia de Buenos Aires) como área de reproducción de peces costeros. Rev Invest Desarr Pesq 21: 31-43.). The SJG and the Península Valdés region represent a transition zone between template waters (warm-temperate Argentine Province), with species ranging from Brazil to Northern Patagonia, and cold waters (cold temperate Magellanic Province), with species typical of higher latitudes occurring up to the mouth of Río de la Plata (Balech & Ehrlich 2008BALECH E & EHRLICH M. 2008. Biogeographic scheme of the Argentine Sea. Rev Inv Desarr Pesq 19: 45-75.). This may be the reason why the larval fish assemblage from the SJG shares so many species with that from the coast of Buenos Aires Province (e.g., E. anchoita, Stromateus brasiliensis, Acanthistius patachonicus, Odontesthes spp., Syngnathus folletti, and P. brasiliensis), and with that found to the south of 46°S (e.g., A. chiloensis, S. oculatus, and H. cunninghami), where the hydrographic features of the Argentine Sea are different (Ciechomski et al. 1975CIECHOMSKI JD, CASSIA MC & WEISS G. 1975. Distribución de huevos, larvas y juveniles de peces en los sectores surbonaerenses, patagónico y fueguino del Mar Epicontinental Argentino en relación con las condiciones ambientales, en noviembre 1973-enero 1974. Ecosur 2: 219-248., Ehrlich et al. 1999EHRLICH MD, SÁNCHEZ RP, CIECHOMSKI JD, MACHINANDIARENA L & PÁJARO M. 1999. Ichthyoplankton composition, distribution and abundance on the southern patagonian shelf and adjacent Waters. INIDEP Doc Cient 5: 37-65., Hoffmeyer et al. 2009HOFFMEYER MS, MENÉNDEZ MC, BIANCALANA F, NIZOVOY AM & TORRES ER. 2009. Ichthyoplankton spatial pattern in the inner shelf off Bahía Blanca Estuary, SW Atlantic Ocean. Estuar Coast Shelf Sci 84: 383-392., Acha et al. 2012ACHA EM, ORDUNA M, RODRIGUEZ K, MILITELLI MI & BRAVERMAN M. 2012. Caracterización de la zona de “El Rincón” (Provincia de Buenos Aires) como área de reproducción de peces costeros. Rev Invest Desarr Pesq 21: 31-43.). The scarce information available about the species composition of the ichthyoplankton assemblages from Northern Patagonia (41°S – 46°S) matches our results: R. brasiliensis, H. cunninghami, species belonging to the Engraulidae, S. oculatus, and P. semifasciata are the most common species reported at those latitudes (Ciechomski et al. 1981CIECHOMSKI JD, EHRLICH MD, LASTA CA & SÁNCHEZ RP. 1981. Campañas de investigación pesquera realizadas en el mar argentino por los B/I “Shinkai Maru” y “Walther Herwig” y el B/P “Marburg” años 1978 y 1979. Contr Inst Nac Inv Desarr Pesq 383: 59-79., Acha et al. 2018ACHA EM, EHRLICH MD, MUELBERT JH, PÁJARO M, BRUNO D, MACHINANDIARENA L & CADAVEIRA M. 2018. Ichthyoplankton Associated to the Frontal Regions of the Southwestern Atlantic. In: Hoffmeyer M, Sabatini M, Brandini F, Calliari D & Santinelli NH (Eds), Plankton ecology of the Southwestern Atlantic. From the subtropical to the subantarctic realm, 1st ed., Switzerland: Springer, Cham, p. 219-246., L. Fernández Goya & P. Betti, unpublished data). Comparatively, the large number of taxa found in this study may be attributable to the high sampling effort made during two consecutive years on a monthly frequency. In a recent review describing the ichthyoplankton associated to frontal systems of the Southwest Atlantic, Acha et al. (2018)ACHA EM, EHRLICH MD, MUELBERT JH, PÁJARO M, BRUNO D, MACHINANDIARENA L & CADAVEIRA M. 2018. Ichthyoplankton Associated to the Frontal Regions of the Southwestern Atlantic. In: Hoffmeyer M, Sabatini M, Brandini F, Calliari D & Santinelli NH (Eds), Plankton ecology of the Southwestern Atlantic. From the subtropical to the subantarctic realm, 1st ed., Switzerland: Springer, Cham, p. 219-246. reported the occurrence of 19 species, one genus, two families and one order of fishes for a series of tidal fronts located between the north of Península Valdés (42°S) and Isla de los Estados (55°S). Not only the number of taxa identified in this study (n = 18) vs. in Acha et al. (2018)ACHA EM, EHRLICH MD, MUELBERT JH, PÁJARO M, BRUNO D, MACHINANDIARENA L & CADAVEIRA M. 2018. Ichthyoplankton Associated to the Frontal Regions of the Southwestern Atlantic. In: Hoffmeyer M, Sabatini M, Brandini F, Calliari D & Santinelli NH (Eds), Plankton ecology of the Southwestern Atlantic. From the subtropical to the subantarctic realm, 1st ed., Switzerland: Springer, Cham, p. 219-246. (n = 23), but also the identity of the species found were similar, even when a punctual area was surveyed in our work compared to the large coastal extension encompassing 13 latitude degrees reviewed in that work.

Finally, it is well known that the spawning areas of some fish species present in the SJG are associated with frontal systems (e.g., Engraulidae, M. hubbsi, and A. patachonicus, among others; see Hansen et al. 2001HANSEN JE, MARTOS P & MADIROLAS A. 2001. Relationship between spatial distribution of the Patagonian stock of Argentine anchovy, Engraulis anchoita, and sea temperatures during late spring to early summer. Fish Oceanogr 10: 193-206., Álvarez-Colombo et al. 2011ÁLVAREZ-COLOMBO G ET AL. 2011. Distribution and behavior of Argentine hake larvae: evidence of a biophysical mechanism for self-recruitment in Northern Patagonian shelf waters. Cienc Mar 37: 633-657., Acha et al. 2018ACHA EM, EHRLICH MD, MUELBERT JH, PÁJARO M, BRUNO D, MACHINANDIARENA L & CADAVEIRA M. 2018. Ichthyoplankton Associated to the Frontal Regions of the Southwestern Atlantic. In: Hoffmeyer M, Sabatini M, Brandini F, Calliari D & Santinelli NH (Eds), Plankton ecology of the Southwestern Atlantic. From the subtropical to the subantarctic realm, 1st ed., Switzerland: Springer, Cham, p. 219-246., M.I. Militelli et al., unpublished data). However, although a frontal system develops in the SJG during spring and summer, together with the occurrence of thermal stratification in the East domain (Amoroso & Gagliardini 2010AMOROSO RO & GAGLIARDINI DA. 2010. Inferring complex hydrographic processes using remote-sensed images: turbulent fluxes in the patagonian gulfs and implications for scallop metapopulation dynamics. J Coast Res 26: 320-332.), we did not find any spatial effects in the structure of the ichthyoplankton assemblages between domains. This lack of spatial effect could be due to the small dimension of the SJG and its shallow waters, which leads to reduced influence of stratification compared to tidal forcing (Tonini et al. 2013TONINI MH, PALMA ED & PIOLA AR. 2013. A numerical study of gyres, thermal fronts and seasonal circulation in austral semi-enclosed gulfs. Cont Shelf Res 65: 97-110., Tonini & Palma 2017TONINI MH & PALMA ED. 2017. Tidal dynamics on the North Patagonian Argentinean gulfs. Estuar Coast Shelf Sci 189: 115-130.). In relation to this, our findings suggest that the structure of ichthyoplankton assemblages in small, shallow gulfs such as the SJG, might be stronger influenced by fluctuations in environmental and hydrographic variables such as winds (direction and intensity), atmospheric temperature, and water currents (Bailey et al. 1999BAILEY KM, BOND NA & STABENO PJ. 1999. Anomalous transport of walleye pollock larvae linked to ocean and atmospheric patterns in May 1996. Fish Oceanogr 8: 264-273., Lanksbury et al. 2005LANKSBURY JA, DUFFY-ANDERSON JT, MIER KL & WILSON MT. 2005. Ichthyoplankton abundance, distribution, and assemblage structure in the Gulf of Alaska during September 2000 and 2001. Estuar Coast Shelf Sci 64: 775-785., Guan et al. 2015GUAN L, DOWER JF, MCKINNELL SM, PEPIN P, PAKHOMOV EA & HUNT PV. 2015. A comparison of spring larval fish assemblages in the Strait of Georgia (British Columbia, Canada) between the early 1980s and late 2000s. Prog Oceanogr 138: 45-57.) than by local factors such as, for example, the thermal stratification observed in the ED mainly between January and March (Amoroso et al. 2011AMOROSO RO, PARMA AM, ORENSANZ JM & GAGLIARDINI DA. 2011. Zooming the macroscope: medium-resolution remote sensing as a framework for the assessment of a small-scale fishery. ICES J Mar Sci 68: 696-706., Crespi-Abril et al. 2014CRESPI-ABRIL A, VILLANUEVA GOMILA GL, VENERUS L & BARÓN PJ. 2014. Spatial distribution of cephalopod paralarvae in San José Gulf (Northern Patagonia, Argentina): the role of tidal circulation in larval dispersal. Fish Res 152: 13-20.). Improving our knowledge about the interactions between ichthyoplankton and oceanographic and environmental features is crucial to provide a better understanding of the fluctuations of ichthyoplankton assemblages in small basis. This study represents a first step in that direction.

ACKNOWLEGMENTS

We want to thank M López, P Fiorda, R Hernández-Moresino, L Getino, G Trobbiani, N Ortíz, D Remenar, I D`Ercole, M Camarero, and C Owen for their help during surveys. J Dignani designed and calibrated the depth recorder for the net. A Rivas and JP Pisoni made useful comments on an earlier version of this manuscript. Thanks also to the NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group (NASA OB.DAAC, Greenbelt, MD, USA) for the production and distribution of MODIS data. Fieldwork was conducted within a World Natural Heritage Site and was authorized by the Secretaría de Turismo y Áreas Protegidas del Chubut.

Funding: This work was supported by the Agencia Nacional de Promoción Científica y Tecnológica (PICT 2010–2461) (granted to L Venerus); Conservation, Research, and Education Opportunities International (granted to GL Villanueva-Gomila).

  • 1
    http://www.unesco.org/new/en/natural-sciences/environment/ecological-sciences/biosphere-reserves/latin-america-and-the-caribbean/argentina/valdes (accessed 18.05.18).

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Publication Dates

  • Publication in this collection
    19 Oct 2020
  • Date of issue
    2020

History

  • Received
    5 Nov 2018
  • Accepted
    22 July 2019
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