Interactive effects of haloclines and food patches on the vertical distribution of 3 species of temperate invertebrate larvae

Journal of Experimental Marine Biology and Ecology 367 (2008) 131–141

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Journal of Experimental Marine Biology and Ecology j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / j e m b e

Interactive effects of haloclines and food patches on the vertical distribution of 3 species of temperate invertebrate larvae Jessica A. Sameoto ⁎, Anna Metaxas Department of Oceanography, Dalhousie University 1355 Oxford St, Halifax, Nova Scotia, Canada B3H 4J1

a r t i c l e

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Article history: Received 14 November 2007 Received in revised form 29 August 2008 Accepted 5 September 2008 Keywords: Food patches Haloclines Invertebrate larvae Larval density Salinity Vertical distribution

a b s t r a c t In laboratory experiments, we examined the effect of haloclines and determined whether the presence of food patches overrides this effect on larval vertical distribution of the sea star Asterias rubens, the sea urchin Strongylocentrotus droebachiensis and the mussel Mytilus edulis. We experimentally constructed haloclines in which the salinity of the bottom water layer was 35 and that of the top layer was 21, 24, 27, and 30 (21/35, 24/35, 27/35, and 30/35) for A. rubens and S. droebachiensis, and 24, 27, 30 and 32 (24/35, 27/35, 30/35, and 32/35) for M. edulis. For each species and stage, additional halocline treatments (A. rubens: 24/32 and 27/32; 4-arm S. droebachiensis: 21/29 and 24/32; 6-arm S. droebachiensis: 24/29 and 24/32; M. edulis: 27/32 and 30/32) were used to determine whether the larval response to inhibitory salinity gradients was due to the absolute salinity of the top layer or the relative salinity difference between the two layers. Also, we measured the density of A. rubens and M. edulis to determine whether the specific gravity of larvae can explain the observed vertical distributions. Larvae aggregated at and below the halocline and these aggregations were more pronounced with increasing strength of the vertical salinity gradient. Threshold salinities in the top layer which inhibited ~ 100% of the larvae from crossing the halocline were 24 for A. rubens and M. edulis, and 21 for S. droebachiensis. These distributional patterns were not the result of larval density, which was greater than all treatment water densities for M. edulis and S. droebachiensis and lower for A. rubens. The effect of the presence of a food patch at inhibitory haloclines (A. rubens: 24/35 and 27/35; 4-arm S. droebachiensis: 21/34 and 24/34; M. edulis: 27/35) was determined by using three algal densities: 0, 5000 or 10 000 cells ml- 1 Thalassiosira pseudonana in either the top or the bottom water layer. For both A. rubens and M. edulis, the number of larvae at the halocline increased in the presence of a food patch, but this effect did not depend on algal density in the patch. For 4-arm S. droebachiensis, there was no effect of the presence of a food patch on larval vertical distribution. Our results suggest that low salinity may act as a barrier to vertical movement and that the presence of food patches above the halocline may strengthen the larval aggregation response to inhibitory haloclines. © 2008 Elsevier B.V. All rights reserved.

1. Introduction For many benthic marine invertebrates, the larval dispersal phase can be important in regulating the dynamics of adult populations (Grosberg, 1982; Gaines et al., 1985; Minchinton and Scheibling, 1991). Historically, larval dispersal has typically been considered a passive process (see Scheltema, 1986) and, with few exceptions (e.g. some larval crustaceans; Luckenbach and Orth, 1992), most larvae probably cannot control their horizontal dispersal (Chia et al., 1984; Shanks, 1995). However, the ability of larvae to control their vertical displacement in the water column can have significant consequences on larval transport since overlying water layers often vary in the strength and direction of their currents (Mann, 1988; Young, 1995; Dekshenieks et al., 1996; Hill, 1998; Metaxas, 2001).

⁎ Corresponding author. Tel.: +1 902 494 3021; fax: +1 902 494 3877. E-mail address: [email protected] (J.A. Sameoto). 0022-0981/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jembe.2008.09.003

Until recently, it was argued that at large scales and coarse resolution (km’s to 100 km’s), larval distributions can be explained solely by hydrodynamics (e.g. McQuaid and Phillips, 2000; Tilburg et al., 2006). However, attempts to predict larval distributions have frequently failed to agree with field observations, suggesting that biological effects, such as behaviour, need to be considered in models of dispersal (Katz et al., 1994; Tilburg et al., 2005). In fact, increasingly, it is being recognized that hydrodynamic models must include a larval behavioural component to successfully predict dispersal and population connectivity (e.g. Dekshenieks et al., 1996; Siegel et al., 2003; Aiken et al., 2007). At small spatial scales (millimeters to meters), larvae receive multiple physical and biological cues in their environment which may lead them to modify their vertical displacement. Many observations suggest that the structure of the water column, manifested as density stratification, can influence larval vertical displacement (e.g. Tremblay and Sinclair, 1990; Manuel et al., 2000; Rawlinson et al., 2004). Laboratory studies have demonstrated the ability of larvae to aggregate in response to haloclines on small scales

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(mm’s to cm’s), and that this response can become more pronounced with increasing strength of the discontinuity until a gradient is reached that completely inhibits vertical dispersal (Harder, 1968; Metaxas and Young, 1998b). In the field, increased concentrations of phytoplankton, the main food source for invertebrate larvae, often coincide spatially with density discontinuities (Viličić et al., 1989; Raby et al., 1994). However, the importance of phytoplankton patches as a cue that can induce larvae to regulate their vertical position is not well established. In the laboratory, a response by echinoid larvae manifested as controlled swimming has been observed to both the presence and the quality of food patches (Metaxas and Young, 1998a; Burdett-Coutts and Metaxas, 2004). In the field, the vertical distribution of bivalve veligers was found to be positively correlated with chlorophyll a concentration, but only when the water column was stratified (Raby et al., 1994). In contrast, Tremblay and Sinclair (1990) found that aggregations of sea scallop larvae of Placopecten magellanicus were not related to food concentration. Salinity stratification and food supply may be environmental conditions that influence the distribution of larvae by inducing a behavioural response. However, the behavioural patterns incorporated to date in transport models only include diurnal or ontogenetic vertical migrations. To account for the influence of features such as density gradients and biological features in the water column on dispersal patterns, first order estimates of larval behaviour in response to these features, and their cues, are required. Moreover, since larvae likely encounter numerous cues simultaneously, their interactive effects on larval behaviour should be considered. For example, although salinity gradients may act as barriers to larval movement, few studies have measured the strength of inhibitory gradients and none have addressed the possible effect of a positive cue (such as the presence of a food patch) in overriding this barrier. In this study, we examined the response of larvae of three invertebrate species, the sea star A. rubens, the sea urchin S. droebachiensis, and the mussel M. edulis, to strongly stratified salinity gradients (2-3 cm), in the laboratory, to identify the strength of the halocline required to inhibit larval vertical dispersal, and determined the effect of larval density on observed distributions. Since all three species coexist in the subtidal zone of Atlantic Canada, we hypothesized that larval response to salinity should be similar across species. Additionally, we examined whether the presence of a food patch of different algal densities and at different locations relative to the halocline (above, below) can override the larval response to an inhibitory salinity gradient. Since most invertebrate larvae spend several weeks in the plankton and can be transported 100s km’s away from their release point, they most likely encounter several physical and biological features in the water column. Our study provides first order estimates of behaviour in response to two such features, which can be incorporated into transport models and increase their predictive ability. Studies, such as ours, that can unconfound the effects of simultaneous multiple cues on larval behaviour can only be done under controlled conditions in the laboratory. Their results can then be tested using a combination of empirical observations and predictive modeling. 2. Materials and methods 2.1. Larval cultures 2.1.1. Fertilization Adults of the sea star Asterias rubens were collected from the subtidal zone near Halifax, NS, Canada in July 2005. Spawning was induced in the laboratory by injecting ~ 3 ml of 100 µM 1methyladenine into the coelomic cavity. Females were spawned into beakers containing 150 ml of 0.45-μm filter seawater, whereas males were dry spawned to prolong sperm longevity. Fertilization was

achieved by mixing sperm and eggs from 6 different combinations of 5 females and 2 males to generate 6 parental pairs. Fertilization success was 90.6-97.7% (n = 67 to 128), as indicated by an elevated perivitelline membrane. Adults of the sea urchin Strongylocentrotus droebachiensis were collected from the subtidal zone near Halifax, NS, Canada, in March 2006. Spawning was induced in the laboratory by injecting ~ 2 ml of 0.55 M KCl through the peristomial membrane. As for A. rubens, females were spawned into beakers containing 150 ml of 0.45-μm filter seawater, whereas males were dry spawned. Fertilization was achieved by mixing sperm and eggs from different combinations of 6 females and 7 males to generate 10 parental pairs for experiments using 4-arm stage larvae, whereas combinations of a different set of 7 females and 6 males were used to generate 13 pairs for experiments with 6-arm stage larvae. (The 4- and 6-arm larvae used in the experiments were not siblings.) Fertilization success was 91.6-99.2% (n = 88 to 31) and 94.5-100% (n = 97 to 121) for larvae reared to 4- and 6-arm stage, respectively. Two-day old, D-stage veligers of the mussel Mytilus edulis were obtained from the Invertebrate Division of the Bedford Institute of Oceanography, Dartmouth, NS, Canada in September 2005. Gametes were obtained by temperature shocking adult mussels. Mussels were kept in flow-through tanks at 10 °C in 25-µm filtered seawater of ambient salinity and were spawned by placing individuals in 20 °C (25 °C, if necessary) 1-µm filtered seawater. Gametes were collected and filtered (100-µm filter for eggs and 40-µm for sperm) to remove fecal material. Fertilization was achieved by mixing eggs and sperm and fertilization success was N95%. 2.2. Larval rearing Larvae were reared on a high ration (5000 cells ml- 1 for A. rubens and S. droebachiensis, and 30 000 cells ml- 1 for M. edulis) mixed microalgal diet of Isochrysis galbana and Tetraselmis sp. in 4-L glass culture jars containing 0.45-μm filtered seawater with a salinity of 34. For A. rubens and S. droebachiensis, cultures were stirred continuously with motorized paddles. All cultures were kept in a temperaturecontrolled room at 12 ± 0.5 °C, 10 ± 0.5 °C and 16 ± 0.5 °C for A. rubens, S. droebachiensis, and M. edulis, respectively, and fluorescent lights provided a light intensity (at culture jar level) of ~ 50-100 µE m- 2 s- 1 on a 12 h light : 12 h dark cycle. The water in the culture jars was changed and microalgae added every other day. 2.3. Experimental treatments 2.3.1. Establishment of haloclines We established sharp haloclines (~2-3 cm) in Plexiglas rectangular columns (30 cm height, 10 cm width, 9.5 cm depth), marked in 0.5-cm increments. Columns were first filled to 6 cm above the bottom with 0.45-µm filtered seawater of the lower salinity. Subsequently, water of higher salinity was gravity-fed through a glass tube to the bottom of the column, until the surface of the low salinity layer reached 16 cm above the bottom. The glass tube was then carefully removed to avoid disturbing the halocline. Water samples were taken with a Pasteur pipette at 0.5-cm intervals and the salinities measured using a temperature-compensated hand held refractometer (Fisher Scientific) to establish the salinity profile of each column. Haloclines were defined as the distance from the top of the high salinity layer to the bottom of the low salinity layer. To prevent direct light penetration from above, the tops of the columns were covered with 4-cm thick Styrofoam caps. 2.4. Larval response to haloclines We established a total of seven treatments (six haloclines and one control) for each species. Four treatments were used to determine the

J.A. Sameoto, A. Metaxas / Journal of Experimental Marine Biology and Ecology 367 (2008) 131–141

maximal salinity in the top layer that affected larval vertical distribution and inhibited ~100% of larvae from crossing into the top layer, hereafter referred to as the threshold salinity. The salinity of the bottom layer in the four treatments was 35 (ambient). For A. rubens and S. droebachiensis, the tested salinities of the top layer were 21, 24, 27, and 30 (treatments henceforth referred to as 21/35, 24/35, 27/35, and 30/35). For M. edulis, the tested salinities of the top layer were 24, 27, 30, and 32 (henceforth referred to as 24/35, 27/35, 30/35, and 32/35). Two further halocline treatments were selected to determine whether the larval response to inhibitory salinity gradients was due to the absolute salinity of the top layer or the relative salinity difference between the two layers. These treatments were: for A. rubens, 24/32 and 27/32; for 4-arm S. droebachiensis, 21/29 and 24/32; for 6-arm S. droebachiensis, 24/29 and 24/32; and for M. edulis, 27/32 and 30/32. No differences were observed in larval swimming behaviour between salinities of 32 and 35 for A. rubens and M. edulis, or between 29 and 35 for S. droebachiensis. Control columns filled with seawater of salinity 35 were used to determine larval vertical distribution in the absence of a halocline. The different salinities were made by mixing dechlorinated freshwater with 0.45-µm filtered seawater. 2.5. Larval response to food patches at haloclines Experimental haloclines were constructed using inhibitory salinity gradients as defined in the previously described set of experiments. The marine diatom Thalassiosira pseudonana was added to the seawater used in the top or bottom layer, prior to its introduction in the experimental column, thus stabilizing a food patch in that layer. For treatments where algae were in the top water layer, preliminary observations indicated that the position of the food patches coincided with the upper salinity layer and remained static throughout the experimental period (and thus was unaffected by algal sinking) (Sameoto, unpublished data). This observation is also consistent with Metaxas and Young (1998a) for the marine diatom Thalassiosira weissflogii. To test the effect of a food patch, we used haloclines of 24/ 35 and 27/35 for A. rubens, 21/34 and 24/34 for 4-arm S. droebachiensis, and 27/35 for M. edulis. During the period when food

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patch experiments of S. droebachiensis were run, ambient seawater salinity dropped to 34. However, no difference in larval swimming was observed between salinities of 34 and 35. To establish whether there was an effect of algal density in the food patch on larval response, we used three levels of diatom density: 0 cells ml- 1 (as a control), and 5000 and 10 000 cells ml- 1. Procedural control columns with a homogeneous water column with salinity of 35 for A. rubens and M. edulis, and 34 for S. droebachiensis, and no phytoplankton were also run, but not included in the statistical analyses. 2.6. Experimental design Approximately 100 to 200 larvae, for A. rubens and M. edulis, and 50 to 100 larvae for S. droebachiensis, were introduced into each column 3 cm above the bottom. Larvae were allowed to swim for 40 minutes, when we visually recorded the number of larvae in each 0.5-cm increment starting from the bottom of the column. In preliminary studies, we determined the precision of visually counting larvae in each height interval to be N95%. Pilot experiments showed that larval distributions at 40 minutes are maintained for at least 1 h (Sameoto, unpub. data; Metaxas, unpub. data). For all experiments, we used a repeated block design (where a block consisted of a single column of each treatment) and ran a total of 4 blocks. Consequently, a total of 4 columns for each treatment were run. For the halocline experiments, we used 7 treatments in each block. For each species, all blocks were run within a period of 48 h. For the food patch experiment, we used 6 treatments for A. rubens, 10 for 4-arm S. droebachiensis, and 3 for M. edulis. For each species, all 4 blocks were run within 48 h, except for M. edulis where all 4 blocks were run within 24 h. We used 11-d old bipinnaria larvae of A. rubens, 5-d 4-arm and 18-d old 6-arm plutei of S. droebachiensis (only 4-arm larvae were used in the food patch experiment), and 11-d old veligers of M. edulis. All experiments were conducted during the diurnal phase of the larval diel cycle and fluorescent lights provided a light intensity (at the level of the columns) of ~50-100 µE m- 2 s- 1 in a temperaturecontrolled room at 12 ± 0.5 °C, 10 ± 0.5 °C and 16 ± 0.5 °C, for A. rubens, S. droebachiensis, and M. edulis, respectively.

Fig. 1. A. rubens. Vertical distribution in experimental columns of 11-d old bipinnaria larvae placed in seven halocline treatments, 40 minutes after introduction of larvae. Bars indicate mean percentage of larvae and error bars are standard error of the mean (n = 4). Lines indicate the salinity structure in each treatment.

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Fig. 2. S. droebachiensis (4-arm). Vertical distribution in experimental columns of 5-d old echinoplutei placed in seven halocline treatments, 40 minutes after introduction of larvae. Bars indicate mean percentage of larvae and error bars are standard error of the mean (n = 4). Lines indicate the salinity structure in each treatment.

2.7. Statistical analyses To simplify the comparisons between treatments, 0.5-cm intervals were pooled based on their position relative to the halocline to create 3 levels of the factor ‘Position’: (1) below the halocline, (2) at the

halocline, and (3) above the halocline. Since the haloclines were approximately 2.5 cm wide, we established the depth of the center of the halocline and pooled the 0.5-cm bins that fell 1 cm above and 1 cm below the center of the halocline to determine the number of larvae ‘at the halocline’. Since the control columns did not have a halocline, we

Fig. 3. S. droebachiensis (6-arm). Vertical distribution in experimental columns of 18-d old echinoplutei placed in seven halocline treatments, 40 minutes after introduction of larvae. Bars indicate mean percentage of larvae and error bars are standard error of the mean (n = 4). Lines indicate the salinity structure in each treatment.

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Fig. 4. M. edulis. Vertical distribution in experimental columns of 11-d old veliger larvae placed in seven halocline treatments, 40 minutes after introduction of larvae. Bars indicate mean percentage of larvae and error bars are standard error of the mean (n = 4). Lines indicate the salinity structure in each treatment.

used the height that corresponded to the average halocline midpoint from treatments with a halocline as a reference and pooled all 0.5-cm bins that fell 1 cm above and 1 cm below this point to determine the number of larvae ‘at the halocline’. Bins that fell below and above the range of bins ‘at the halocline’ were pooled to determine the number of larvae ‘below the halocline’ and ‘above the halocline’, respectively. For each species and stage, we examined the independence of the factors larval position, block, and salinity gradient (for the halocline experiments) or algal density (for the food patch experiments), by loglinear analysis of three-way contingency tables. With this analysis, main and interactive effects can be measured by fitting log-linear equations to observed frequencies. G-tests examine the goodness of fit of observed frequencies to expected frequencies, based on approximating the χ2 distribution. When there is a good fit of the data to the model, the Gvalue is low and therefore not significant, indicating that the factors or treatments are independent (Sokal and Rohlf, 1981). Firstly, we examined the fit of the data to the most complex model which contained three terms (Position, Block, and either Salinity Gradient or Algal Density) and the corresponding two-way interaction terms. The model did not fit the data well, as indicated by significant three-way interactions, and Block × Position interactions were also observed for most tests. Consequently, we examined the independence of Position

Table 1 A. rubens. Analysis by two-way loglinear models of the independence between the vertical position of larvae and the salinity of the top water layer in experimental columns, 40 minutes after introduction of bipinnaria Treatment Comparisons

Block 1 G

Block 2 p

G

Block 3 p

G

Block 4 p

G

p

35 vs 21/35 247.6 b 0.001⁎ 286.4 b 0.001⁎ 306.7 b0.001⁎ 276.0 b0.001⁎ 35 vs 24/35 200.2 b 0.001⁎ 243.6 b 0.001⁎ 245.0 b0.001⁎ 195.5 b0.001⁎ 35 vs 27/35 162.2 b 0.001⁎ 139.6 b 0.001⁎ 90.9 b0.001⁎ 103.9 b 0.001⁎ 35 vs 30/35 73.1 b 0.001⁎ 76.7 b 0.001⁎ 15.0 b0.001⁎ 30.8 b 0.001⁎ 30/35 vs 21/35 192.6 b 0.001⁎ 240.5 b 0.001⁎ 315.1 b0.001⁎ 311.0 b 0.001⁎ 30/35 vs 24/35 145.9 b 0.001⁎ 212.1 b 0.001⁎ 234.8 b0.001⁎ 223.4 b 0.001⁎ 30/35 vs 27/35 114.5 b 0.001⁎ 83.0 b 0.001⁎ 98.6 b0.001⁎ 99.9 b 0.001⁎ 27/35 vs 21/35 26.6 b 0.001⁎ 55.4 b 0.001⁎ 103.5 b0.001⁎ 106.4 b 0.001⁎ 27/35 vs 24/35 0.7 0.688 48.1 b 0.001⁎ 55.5 b0.001⁎ 61.8 b 0.001⁎ 24/35 vs 21/35 22.2 b 0.001⁎ 0.2 0.886 30.3 b0.001⁎ 2.3 0.322 Analysis is done separately for each block because of 3-way interactions (αcritical = 0.00125, df= 2, ⁎ significant p-values).

and either Salinity Gradient or Algal Density for each block with twoway models. In one case, heterogeneity among blocks was found due to a departure in the direction of response of one column and this column was removed from the analysis. We considered all statistical tests significant at α = 0.05, unless otherwise stated, and used adjusted alpha values, where αcritical = [0.05/(no. of comparisons within each species or stage for a given model)], for multiple comparisons. 2.8. Density of larvae To establish the role of the specific gravity of larvae in their vertical distribution, we estimated the density of A. rubens and M. edulis. Density fluids, ranging from 1.010 g ml- 1 to 1.054 g ml- 1, were created by mixing 0.15 M NaCl with a stock solution of Percoll™ isosmotic with seawater (mean± SE = 978.3 ± 1.4 mmol kg- 1, n = 3). The osmolality of the stock solution was checked using a vapor pressure osmometer (Vapro 5520, Wescor Inc.). The density of the Percoll™ solutions was tested using calibration beads of known density (Amersham Biosciences), and with a temperature-compensated refractometer (Fisher Scientific). Discontinuous density gradients were established in 14-ml polypropylene test tubes (~1.5 cm diameter and 9.5 cm deep) by slowly layering solutions of

Table 2 S. droebachiensis (4-arm) Treatment comparison

Block 1 G

Block 2 p

35 vs 21/35 113.7 b 0.001⁎ 35 vs 24/35 10.4 0.006 35 vs 27/35 0.1 0.944 35 vs 30/35 1.5 0.479 30/35 vs 21/35 129.9 b 0.001⁎ 30/35 vs 24/35 16.8 b 0.001⁎ 30/35 vs 27/35 1.0 0.598 27/35 vs 21/35 155.5 b 0.001⁎ 27/35 vs 24/35 16.3 b 0.001⁎ 24/35 vs 21/35 81.6 b 0.001⁎

Block 3 G

Block 4

G

p

p

G

155.8 21.3 0.0 0.0 171.6 24.6 0.0 177.4 25.8 110.2

b0.001⁎ 142.5 b 0.001⁎ 139.5 b0.001⁎ 38.2 b 0.001⁎ 36.2 1.00 7.3 0.025 7.4 1.00 0.0 1.00 23.6 b0.001⁎ 162.8 b 0.001⁎ 271.3 b0.001⁎ 44.0 b 0.001⁎ 72.4 1.00 8.5 0.014 4.5 b0.001⁎ 104.6 b 0.001⁎ 170.6 b0.001⁎ 19.5 0.001⁎ 38.9 b0.001⁎ 42.3 b 0.001⁎ 67.3

p b 0.001⁎ b 0.001⁎ 0.025 b 0.001⁎ b 0.001⁎ b 0.001⁎ 0.104 b 0.001⁎ b 0.001⁎ b 0.001⁎

Analysis by two-way loglinear models of the independence between the vertical position of larvae and the salinity in the top water layer in experimental columns, 40 minutes after introduction of larvae. Analysis is done separately for each block because of 3-way interactions (αcritical = 0.00125, df = 2, ⁎ significant p-values).

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Table 3 S. droebachiensis (6-arm). Analysis by two-way loglinear models of the independence between the vertical position of larvae and the salinity of the top water layer in experimental columns, 40 minutes after introduction of larvae Treatment comparison

Block 1 G

Block 2 p

35 vs 21/35 111.9 b 0.001⁎ 35 vs 24/35 62.9 b 0.001⁎ 35 vs 27/35 0.5 0.782 35 vs 30/35 2.3 0.310 30/35 vs 21/35 196.9 b 0.001⁎ 30/35 vs 24/35 118.6 b 0.001⁎ 30/35 vs 27/35 0.6 0.730 27/35 vs 21/35 134.6 b 0.001⁎ 27/35 vs 24/35 78.8 b 0.001⁎ 24/35 vs 21/35 15.3 b 0.001⁎

Block 3

Block 4

G

p

G

p

G

104.0 75.3 3.3 9.7 115.6 81.8 5.1 95.2 62.8 11.7

b 0.001⁎ b 0.001⁎ 0.194 0.008 b 0.001⁎ b 0.001⁎ 0.076 b 0.001⁎ b 0.001⁎ 0.003

127.7 87.6 13.0 5.4 128.7 90.1 10.2 95.1 59.1 2.6

b 0.001⁎ 158.0 b 0.001⁎ b 0.001⁎ 60.5 b 0.001⁎ 0.002 7.6 0.023 0.068 0.6 0.723 b 0.001⁎ 131.1 b 0.001⁎ b 0.001⁎ 50.4 b 0.001⁎ 0.006 4.1 0.130 b 0.001⁎ 133.5 b 0.001⁎ b 0.001⁎ 54.0 b 0.001⁎ .272 13.2 0.002

p

Analysis is done separately for each block (αcritical = 0.00125, df = 2, ⁎ significant p-values).

Table 4 M. edulis. Analysis by two-way loglinear models of the independence between the vertical position of larvae and the salinity of the top water layer in experimental columns, 40 minutes after introduction of veligers Treatment Comparisons

Block 1 G

p

G

Block 2 p

G

Block 3 p

G

Block 4 p

35 vs 24/35 35 vs 27/35 35 vs 30/35 35 vs 32/35 32/35 vs 24/35 32/35 vs 27/35 32/35 vs 30/35 30/35 vs 24/35 30/35 vs 27/35 27/35 vs 24/35

162.7 57.9 27.3 33.9 275.0 132.4 37.3 53.9 17.2 30.8

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

113.2 54.4 26.3 1.6 97.4 42.2 19.9 13.4 0.5 14.2

b 0.001⁎ b 0.001⁎ b 0.001⁎ 0.443 b 0.001⁎ b 0.001⁎ b 0.001⁎ 0.001⁎ 0.773 b 0.001⁎

176.1 214.8 75.4 140.4 23.4 29.7 0.2 16.1 19.2 0.1

b 0.001⁎ b 0.001⁎ b 0.001⁎ b 0.001⁎ b 0.001⁎ b 0.001⁎ 0.910 b 0.001⁎ b 0.001⁎ 0.979

93.6 68.4 42.6 28.9 30.0 16.1 6.3 18.7 1.3 18.4

b 0.001⁎ b 0.001⁎ b 0.001⁎ b 0.001⁎ b 0.001⁎ b 0.001⁎ 0.043 b 0.001⁎ 0.519 b 0.001⁎

Analysis is done separately for each block because of 3-way interaction (αcritical = 0.00125, df= 2, ⁎ significant p-values).

progressively lower density using a syringe fitted with a wide-bore needle. Layers were 2 ml deep. Approximately 30 larvae were gently pipetted into the top layer and gradients were centrifuged at 400× g for 15 min. Larval position relative to the density layers was then recorded. For each species, 4 replicate test tubes were run. 3. Results 3.1. Larval response to haloclines The position of larvae in the experimental columns was dependent on the halocline treatment for all species and developmental stages. For A. rubens, 4-arm S. droebachiensis and M. edulis, a significant three-way interaction between Halocline, Block and Position was detected (p b 0.01). Subsequent two-way models of the independence of Halocline

and Position within each Block were also highly significant (p b 0.0001). For 6-arm S. droebachiensis, there was no 3-way interaction (p = 0.21); however, since the partial association between Block and Position was marginally significant (p = 0.05) we examined all possible individual pairs of the factors Halocline and Position for each species and stage. In the absence of a halocline, larvae of all species and stages swam upwards towards the surface of the water column (Figs.1–4). For A. rubens, 40 to 50% of larvae were near the air-water interface, whereas 90 and 70% of 4- and 6-arm stage larvae of S. droebachiensis, respectively, were in the upper 3 cm’s of the water column (Figs.1–3). Unlike the two other species, larvae of M. edulis were more evenly distributed throughout the water column, and only 10 to 20% of larvae were near the surface (Fig. 4). For all species, the presence of a halocline had a pronounced effect on larval vertical distribution. However, the threshold salinity gradient that completely inhibited vertical dispersal varied among species. When introduced into the column, larvae swam upwards and when they reached the discontinuity, they either crossed through into the top layer within a few minutes or remained immediately below the top boundary of the halocline. The proportion of larvae above the halocline decreased with decreasing salinity in the top layer. At and below the threshold salinity gradient, larvae did not cross into the top water layer (Figs. 1–4). For A. rubens, the threshold salinity that larvae did not cross into was 24 (Fig. 1). Larval position differed significantly between all pairs of halocline treatments in at least 3 of 4 blocks, except between 24/35 and 21/35, where differences were indicated only in 2 of 4 blocks (Fig. 1, Table 1). For both stages of S. droebachiensis, position in each of the 24/35 and 21/35 treatments differed significantly from that in 35, 30/35, and 27/35 treatments. Position also varied significantly between 24/35 and 21/35 for 4-arm, but not for 6-arm larvae (Figs. 2, 3, Tables 2, 3). For 4- and 6-arm S. droebachiensis, larvae were highly aggregated (N50%) at the halocline and did not cross into the top layer when salinity was 21. For 4-arm larvae, some aggregation (~ 20%) occurred at the halocline when the top salinity was 24, but most larvae (~ 70%) crossed through into the top layer. For the 6-arm stage, most larvae (~65%) were found at the halocline and only ~ 20% were above the halocline when salinity in the top layer was 24 thus it appears that for this species, the threshold salinity lies between 24 and 21. For M. edulis, position was significantly different between all pairs of halocline treatments except between 32/35 and 30/35, and 30/35 and 27/35, where only 2 of 4 blocks indicated significant differences (Fig. 4, Table 4). Larvae did not cross the salinity gradient of 24/35; however, larval aggregation (~25%) occurred at the halocline when the salinity of the top layer was as high as 30. For each species, larval vertical distribution appeared to be related to both the absolute salinity of the top layer and the relative salinity difference between the two layers. For A. rubens, position differed significantly between 27/32 and 27/35 across all blocks and in all cases more larvae (20-40%) were observed above the halocline in the 27/32 treatment (Fig. 1, Table 5). However, between 24/32 and 24/35 position

Table 5 Two-way loglinear models of the independence between larval position in experimental columns and halocline strength for each of 3 species in experimental columns, 40 minutes after introduction of larvae Species

Treatment Comparison

Block 1 G

p

Block 2 G

p

G

Block 3 p

G

Block 4 p

27/32 vs 27/35 24/32 vs 24/35

59.1 33.5

b0.001⁎ b0.001⁎

45.6 1.9

b0.001⁎ 0.396

50.5 2.4

b0.001⁎ 0.304

25.9 13.4

b 0.001⁎ 0.001⁎

24/32 vs 24/35 21/29 vs 21/35 24/32 vs 24/35 24/29 vs 24/35

2.2 1.9 26.9 16.3

0.340 0.390 b0.001⁎ b0.001⁎

10.7 6.9 17.0 22.8

0.005⁎ 0.032 b0.001⁎ b0.001⁎

11.5 7.3 19.4 13.5

0.003⁎ 0.027 b0.001⁎ 0.001⁎

16.7 10.9 0.5 6.4

b0.001⁎ 0.004⁎ 0.777 0.041

30/32 vs 30/35 27/32 vs 27/35

34.4 22.1

b0.001⁎ b0.001⁎

6.9 18.5

0.032 b0.001⁎

14.1 70.0

0.001⁎ b0.001⁎

3.9 29.1

0.143 b 0.001⁎

A. rubens

S. droebachiensis 4-arm stage 6-arm stage M. edulis

Analysis is done separately for each block because of 3-way interactions (αcritical = 0.0125, df = 2, ⁎ significant p-values).

J.A. Sameoto, A. Metaxas / Journal of Experimental Marine Biology and Ecology 367 (2008) 131–141

137

Fig. 5. A. rubens. Vertical distribution of 11-d old bipinnaria, 40 minutes after introduction of larvae, in experimental columns with two salinity gradients inhibitory to vertical dispersal and with food patches of three algal densities (0, 5000, 10 000 cells ml- 1) positioned in the top salinity layer. Bars indicate mean percentage of larvae and error bars are standard error of the mean (n = 4 for all except for 24/35 0 cells ml- 1 where n = 3). Lines indicate the salinity structure in each treatment and stippled areas indicate the position of food patches.

differed only in 2 of 4 blocks, (Fig. 1, Table 5) and the direction of the response varied between blocks. For 4-arm S. droebachiensis, position varied significantly between 24/32 and 24/35 in 3 of 4 blocks (Table 5), and more larvae (20-35%) were above the halocline at 24/32 (Fig. 2). However, no differences in position were observed between treatments 21/29 and 21/35. For 6-arm larvae of S. droebachiensis, position of larvae varied significantly between 24/32 and 24/35, and between 24/29 and 24/35. However, although differences were detected in 3 of 4 blocks (Table 5), only one block had more larvae (18-30%) above the halocline at 24/32 and at 24/29 than at 24/35. For M. edulis, we observed differences in position between 30/32 and 30/35 for 2 of 4 blocks (Table 5), and more larvae (17-34%) were above the halocline at 30/32 than at 30/35 (Fig. 4). Differences between 27/32 and 27/35 were observed for all blocks (Table 5), and more larvae (11-23%) were consistently found above the halocline at 27/32 (Fig. 4). 3.2. Larval response to food patches The effect of the presence of a food patch on larval vertical distribution varied among salinity gradients and among species. For A. rubens, there was no three-way interaction among Algal Density, Position, and Block for 27/35. However, since there was a significant Position × Block interaction (pb 0.0001), we examined the dependence of Position on Algal Density within each Block. For all blocks, Position was dependent on Algal Density (Fig. 5, Table 6). Paired comparisons between algal densities showed more larvae at the halocline (12-34%) and fewer above the halocline (10-36%) in treatments containing algae than in the control treatment (0 cells ml- 1). However, the strength of the effect did not depend on the concentration of the algal patch (5000 vs. 10 000 cells ml- 1). In contrast, for 24/35, the presence of a food patch in the top salinity layer had no effect on larval vertical distribution (Fig. 5, Table 6). For 4-arm S. droebachiensis, at 24/34, Position was dependent on Algal Density in the top water layer for most blocks (Fig. 6, Table 7), but it did not vary consistently within blocks. At 21/34, Position was independent of Algal Density in the top layer (Fig. 6, Table 7). The presence of a food patch in the bottom water layer did not have a pronounced effect on Position at either 24/34 or 21/34.

For M. edulis, Position was dependent on Algal Density for most blocks (Fig. 7, Table 8). Within each block, more larvae were observed at the halocline (6-21%) and fewer above the halocline (7-22%), in treatments containing algae compared with the control treatment (0 cells ml- 1). However, the strength of the effect did not depend on the concentration of the algal patch (5000 vs. 10 000 cells ml- 1). 4. Larval density The density of M. edulis veligers (N1.052 g ml- 1) was greater, and that of A. rubens bipinnaria was lower (1.010-1.015 g ml- 1), than seawater of all salinities and temperatures used in our experiments (Table 9). While all larvae were alive when introduced into the density gradient media,

Table 6 A. rubens. Analysis by loglinear models of independence between the vertical position of larvae in experimental columns (P), and the factors Algal Density (A) and Block (B), for two halocline treatments (27/35 and 24/35), 40 minutes after introduction of bipinnaria into the experimental column(⁎ significant p-values, two-way model: αcritical = 0.0125) Model Three-way model A+B+P+ [A × P] + [B × P] 27/35 24/35 Two-way model A + P + [A × P] 27/35 Block 1 Block 2 Block 3 Block 4 24/35 Block 1 Block 2 Block 3 Block 4 1

df

G

p

12 12

16.9 27.2

0.155 0.007⁎

4 4 4 4

14.2 100.8 76.6 49.3

0.007⁎ b 0.001⁎ b 0.001⁎ b 0.001⁎

4 4 4 N/A1

12.9 10.6 8.9 N/A1

0.011⁎ 0.032 0.063 N/A1

Block 4 for 0 cells ml- 1 was removed from analysis. See text.

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Fig. 6. S. droebachiensis (4-arm). Vertical distribution of 5-d old echinoplutei, 40 minutes after introduction of larvae, in experimental columns with two salinity gradients inhibitory -1 to vertical dispersal and with food patches of three algal-densities (0, 5000, 10 000 cells ml ) positioned in the top and bottom salinity layers. Bars indicate mean percentage of larvae and error bars are standard error of the mean (n = 4). Lines indicate the salinity structure in each treatment and stippled areas indicate the position of food patches.

100% mortality was observed after centrifugation for all species and stages. 5. Discussion 5.1. Effect of the salinity gradient For all species, there was a pronounced effect of the presence of a halocline on larval vertical distribution. In the absence of a halocline, all species and stages swam upwards and echinoderm larvae aggregated near the surface of the water column. Since the experimental columns had caps that did not permit light penetration from above, this was not a phototactic response. Rather, this response is likely the result of negative geotaxis (Young, 1995). In the presence of a halocline, the proportion of larvae above the halocline decreased with decreasing salinity of the top layer, and at or below the threshold salinity gradient, few or no larvae crossed the halocline. Interestingly, larval vertical distributions relative to salinity gradients in which salinity in the top layer was greater than the threshold salinity were often bimodal, with a peak near the surface of the water column and another around the halocline. Bimodal depth distributions of zooplankton may be caused

by asynchronous vertical movement (Pearre, 1979). However, Metaxas and Young (1998a) demonstrated that individual larvae swim upwards continuously until they reach a position relative to a discontinuity where they remain in these positions for at least 30 minutes. In this study, vertical movement was relatively synchronized among larvae and vertical distributions did not change for at least 60 min (Sameoto unpub. data). This suggests that the observed larval response to a halocline is non-uniform across a population, and may be an individual or offspring-specific characteristic. The importance of the relative salinity difference between the two layers in determining larval distribution depended on the salinity of the top water layer. When the top salinity was greater than the threshold, more larvae crossed the halocline as the salinity difference decreased. However, when the top salinity was the threshold salinity, the salinity difference between the layers had no effect on larval distribution. Similarly, in halocline experiments for the sea urchins Echinometra lucunter (2-arm larvae) and Arbacia punctulata (4-arm larvae), Metaxas and Young (1998b) observed that larvae did not cross into a top water layer of salinity 21 and observed no difference in larval distribution between salinity treatments of 21/33 and 21/30.

J.A. Sameoto, A. Metaxas / Journal of Experimental Marine Biology and Ecology 367 (2008) 131–141 Table 7 S. droebachiensis (4-arm). Analysis by loglinear models of independence between the vertical position of larvae in experimental columns (P), and the factors Algal Density (A) and Block (B), for two halocline treatments (24/34 and 21/34), and two food patch locations (top and bottom salinity layer), 40 minutes after introduction of larvae into the experimental column (⁎ significant p-values, two-way model: αcritical = 0.0125) Model

df

Three-way model A + B + P + [A × P] + [B × P] 24/34 12 21/34 12 Two-way model A + P + [A × P] 24/34 Block 1 4 Block 2 4 Block 3 4 Block 4 4 21/34 Block 1 4 Block 2 4 Block 3 4 Block 4 4

Top Salinity Layer

Bottom Salinity Layer

G

p

G

p

34.9 16.6

b 0.001⁎ 0.164

33.9 33.4

b 0.001⁎ b 0.001⁎

15.9 40.6 2.4 13.0

0.003⁎ b 0.001⁎ 0.661 0.011⁎

33.3 6.9 7.3 15.9

b 0.001⁎ 0.139 0.122 0.003⁎

6.7 8.3 5.5 3.3

0.154 0.080 0.243 0.511

11.3 10.6 18.4 6.9

0.0229 0.031 0.001⁎ 0.138

Salinity-induced physiological stress may have influenced the observed larval vertical distributions. Hyposaline conditions can have important consequences for the development and survival of marine invertebrates. For example, Qui et al. (2002) showed that developmental time of the mussel Mytilus edulis from a swimming embryo to a D-stage veliger was 45 h at a salinity of 20, but only 26 h at a salinity of 25. For the mud snail Ilyanassa obsoleta, reduced salinities have been shown to result in reduced larval size (Richmond and Woodin, 1996) and larvae of the sea urchin Lytechinus variegatus have significantly decreased survival in salinities b27.5 (Roller and Stickle, 1993). Aggregations at the halocline may therefore result from larval avoidance of low salinity environments and would therefore be an adaptive response that increases survival. Alternatively, larvae may sense, and respond to, changes in viscosity associated with changes in salinity. Due to their small size and slow swimming velocities, viscous forces govern larval movement. Changes in viscosity have been found to alter swimming behaviour in larvae of the sand dollar, Dendraster excentricus, when the relative difference in dynamic viscosity was 27% (Podolsky and Emlet, 1993). The maximal difference in dynamic viscosity in our experiments was between ambient salinity of 35 and salinity of 21 at 16 °C, and was only ~2.8 %

139

Table 8 M. edulis. Analysis by loglinear models of independence between the vertical position of larvae in experimental columns (P), and the factors Algal Density (A) and Block (B), for the halocline treatment 27/35, 40 minutes after introduction of veligers into the experimental column (⁎ significant p-values, two-way model: αcritical = 0.0125) Model Three-way model A + B + P + [A × P] + [B × P] Two-way model A + P + [A × P] Block 1 Block 2 Block 3 Block 4

df

G

P

12

38.076

b 0.0001⁎

4 4 4 4

37.146 61.600 4.637 14.826

b 0.0001⁎ b 0.0001⁎ 0.3267 0.0051⁎

(Dorsey, 1940). Thus, it is unlikely that changes in viscosity between the water layers allowed larvae to detect the halocline. The threshold salinity and the magnitude of the salinity gradient required to elicit aggregation varied among species, and may reflect species-specific differences in salinity tolerance. In the laboratory, 4-arm stage larvae of the sea urchin Echinometra lucunter, aggregated at a salinity gradient of 24/33, but not at 27/33 (Metaxas and Young, 1998b) and short-term (24-hr) exposure to salinities ≤24 significantly reduced survival relative to salinities of 27 and 33 (Metaxas, 1998). In our study, M. edulis appeared to be the most sensitive species to changes in salinity, and A. rubens appeared to be more sensitive than S. droebachiensis. Interestingly, fewer 6- than 4-arm stage larvae crossed into a salinity of 24. Older larvae may avoid entrainment in low salinity waters, thereby maintaining a position lower in the water column that prevents their dispersal away from potential adult habitats. The density of larvae did not appear to have contributed to the observed aggregations at the halocline. The density range of A. rubens was lower than the density of seawater with salinity 21 (at 10 °C), but larvae did not pass through into this water layer. Larvae also aggregated at the bottom of water layers of salinity 24 and 27, which have densities much greater than that of A. rubens. In the absence of swimming, larvae of S. droebachiensis and M. edulis would sink in all treatment salinities. Pennington and Strathmann (1990) found that 4-arm echinoplutei of Tripneustes gratilla have a density of ~1.06 g ml- 1, which is significantly greater than seawater densities. Although Strongylocentrotus sp. have relatively thin skeletal rods compared to T. gratilla, they are still negatively buoyant (Pennington and Strathmann, 1990). The density of M. edulis was also greater than that of seawater at all tested salinities. Unlike the other two larval types, larval bivalves have a calcified larval shell which likely contributes to its increased density. These results suggest that larvae of all species and stages are actively regulating their vertical position.

Fig. 7. M. edulis. Vertical distribution of 11-d old veliger larvae, 40 minutes after introduction of larvae, in experimental columns with a salinity gradient inhibitory to vertical dispersal and with food patches of three algal-densities (0, 5000, 10 000 cells ml- 1) positioned in the top salinity layer. Bars indicate mean percentage of larvae and error bars are standard error of the mean (n = 4). Lines indicate the salinity structure in each treatment and stippled areas indicate the position of food patches.

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Table 9 Density of seawater (g ml- 1) used in experimental treatments, at 0 kPa, as a function of salinity and temperature [based on calculations from Fofonoff and Millard Jr (1983)] Salinity 21 24 27 30 35

Temperature (°C) 10

12

16

1.016 1.018 1.021 1.023 1.027

1.016 1.018 1.020 1.023 1.027

1.015 1.017 1.020 1.022 1.026

5.2. Effect of the presence of a food patch at inhibitory salinity gradients For both A. rubens and M. edulis, there was an effect of the presence of a food patch on larval vertical distribution relative to an inhibitory halocline. More larvae aggregated at the halocline when a food patch was present, but there was no difference in the degree of aggregation between the two levels of algal density. Interestingly, for A. rubens, this was only observed when the salinity of the top layer was greater than the threshold salinity. This may also occur with M. edulis, however, our experiment only tested a salinity in the top layer of 27, which is greater than the threshold salinity of this species. Since the top of the halocline is the location in the water column where larvae would first encounter the phytoplankton, they may cease their ascent when they sense the algae and, consequently, aggregate at that location. It is possible that larvae also responded to the presence of a food patch when the top water layer was the threshold salinity, but that the effects of the halocline and the algal patch were confounded. For 4-arm larvae of S. droebachiensis, the presence of a food patch did not have an effect on larval vertical distribution relative to inhibitory salinity gradients. Echinoplutei of Echinometra lucunter detected and responded to algal densities as low as 2500 cells ml- 1 at a non-inhibitory halocline of 24/33 (Metaxas and Young, 1998a). Similarly, Burdett-Coutts and Metaxas (2004) found 4-arm larvae of S. droebachiensis to respond to high densities (10 000 cells ml- 1), but not to low densities (5000 cells ml- 1) of phytoplankton located at a halocline of 27/33. In contrast, in our study, larval vertical distribution was not altered when algal patches were present. This suggests that the response of S. droebachiensis to the presence of algae may depend on the steepness of the halocline gradient. 6. Conclusion Larval horizontal transport is determined by the interactive effects of the position of larvae in the water column and hydrodynamics. Therefore, boundaries, such as haloclines that inhibit vertical movement will affect larval distribution. Our study suggests that larvae have the ability to influence their vertical position in the water column relative to salinity gradients and that the presence of a food patch at a halocline may strengthen the aggregation response. Our results also indicate that for strongly stratified conditions, salinity has a much greater influence than the presence of food patches on larval distribution. Interestingly, all three species coexist in the subtidal zone of Atlantic Canada, thus their larvae are likely exposed to similar conditions while in the plankton. However, differences in vertical distribution observed between species in response to the same salinity gradients suggest that larval dispersal in the water column may be influenced by species-specific salinity tolerance. Such speciesspecific differences in larval behaviour can be used to fine-tune the behavioural component in transport models that attempt to predict larval dispersal. Acknowledgments We thank Robert Scheibling for comments on a previous version of this manuscript, Andria Roy and Matthew Beck for assisting with

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