# Environmental drivers of the fine-scale distribution of a gelatinous zooplankton community across a mesoscale front

Jessica Y. Luo, Benjamin Grassian, Dorothy Tang, Jean-Olivier Irisson, Adam

T. Greer, Cedric M. Guigand, Sam Mcclatchie, Robert K. Cowen

► To cite this version:

Jessica Y. Luo, Benjamin Grassian, Dorothy Tang, Jean-Olivier Irisson, Adam T. Greer, et al. Environmental drivers of the fine-scale distribution of a gelatinous zooplankton community across a mesoscale front. Medio Ambiente y Comportamiento Humano, 2014, 510, pp.129-149. 10.3354/meps10908. hal-03502777

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# Environmental drivers of the fine-scale distribution of a gelatinous zooplankton community across a mesoscale front

Jessica Y. Luo<fnref n="1" />,*, Benjamin Grassian<fnref n="1" />, Dorothy Tang<fnref n="1" />, Jean-Olivier Irisson<fnref n="2" />, Adam T. Greer<fnref n="1" />,<fnref n="3" />, Cedric M. Guigand<fnref n="1" />, Sam McClatchie<fnref n="4" />, Robert K. Cowen<fnref n="1" />,<fnref n="5" />

**ABSTRACT:** Mesoscale fronts occur frequently in many coastal areas and often are sites of elevated productivity; however, knowledge of the fine-scale distribution of zooplankton at these fronts is lacking, particularly within the mid-trophic levels. Furthermore, small (<13 cm) gelatinous zooplankton are ubiquitous, but are under-studied, and their abundances underestimated due to inadequate sampling technology. Using the *In Situ* Ichthyoplankton Imaging System (ISIIS), we describe the fine-scale distribution of small gelatinous zooplankton at a sharp salinity-driven front in the Southern California Bight. Between 15 and 17 October 2010, over 129000 hydromedusae, ctenophores, and siphonophores within 44 taxa, and nearly 650000 pelagic tunicates were imaged in 5450 m<fnref n="3" /> of water. Organisms were separated into 4 major assemblages which were largely associated with depth-related factors. Species distribution modeling using boosted regression trees revealed that hydromedusae and tunicates were primarily associated with temperature and depth, siphonophores with dissolved oxygen (DO) and chlorophyll a fluorescence, and ctenophores with DO. The front was the least influential out of all environmental variables modeled. Additionally, except for 6 taxa, all other taxa were not aggregated at the front. Results provide new insights into the biophysical drivers of gelatinous zooplankton distributions and the varying influence of mesoscale fronts in structuring zooplankton communities.

**KEY WORDS:** Gelatinous zooplankton · Jellyfish · Fronts · Community dynamics · Aggregations · Environmental drivers · Imaging systems · Southern California Bight

Resale or republication not permitted without written consent of the publisher

## INTRODUCTION

Gelatinous zooplankton, which include medusae, ctenophores, siphonophores, and pelagic tunicates, are pervasive consumers in marine environments (Purcell & Arai 2001). Many have life histories that result in dramatic boom and bust cycles that have the potential to disrupt fisheries, human activities, and

nutrient cycling (Pitt et al. 2009, Purcell 2012). Physical factors that typically influence the patchy accumulation of plankton (e.g. fronts, eddies, etc.) also drive gelatinous zooplankton aggregations (Graham et al. 2001), which may result in the rapid predation of plankton prey. To date, most attention has been paid to the large, conspicuous scyphomedusae despite the ubiquity and abundance of the smaller gelat-

<footnote n="1">¹Marine Biology and Fisheries, Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, 4600 Rickenbacker Causeway, Miami, Florida 33149, USA</footnote>

<footnote n="2">²Ocean Observatory of Villefranche-sur-Mer, University of Pierre and Marie Curie, 181 Chemin du Lazaret, 06230 Villefranche-sur-Mer, France</footnote>

<footnote n="3">³College of Engineering, University of Georgia, 200 D.W. Brooks Dr., Athens, Georgia 30602, USA</footnote>

<footnote n="4">⁴Southwest Fisheries Science Center, NOAA Fisheries, 8901 La Jolla Shores Drive, La Jolla, California 92037, USA</footnote>

<footnote n="5">⁵Hatfield Marine Science Center, Oregon State University, 2030 SE Marine Science Drive, Newport, Oregon 97365, USA</footnote>
inous zooplankton (mms to cms in size) in coastal areas (e.g. Pagès & Gili 1991, Pavez et al. 2010). Their small size, fragility, and transparency have rendered small gelatinous zooplankton difficult to sample. Thus, their distribution and ecological impact are virtually unknown.

Due to the limitations in conventional net sampling, which destroys or disrupts most gelatinous zooplankton, many scientists have called for *in situ* approaches to resolve their distributions, including blue water diving, manned submersibles, remotely operated vehicles (ROVs), and towed camera systems (Hamner et al. 1975, Raskoff 2002, Haddock 2004). Diving and submersibles were used in early studies on gelatinous zooplankton (Hamner et al. 1975, Mackie & Mills 1983, Madin 1988), but these techniques are less applicable for systematic studies of jellyfish due to limitations in duration and spatial coverage (diving) and operational cost (submersibles). ROVs have been used extensively to examine distributions of jellyfish (Silguero & Robison 2000, Raskoff 2001) but require the careful maneuvering of the vehicle to traverse long distances. Furthermore, since many gelatinous zooplankton are small and transparent, they may be difficult to see and quantify accurately using front-lighting with vehicles such as submersibles and ROVs. One solution has been to use back-lighting to create silhouettes for imaging transparent organisms (Ortner et al. 1981, Samson et al. 2001).

Towed camera systems are considered best suited for fine (i.e. m to 100s of m) to coarse- (i.e. 1 to 100 km) scale distributional studies, as they can easily sample over long distances without careful vehicle maneuvering (Graham et al. 2003). However, very few towed camera systems are capable of sampling sufficiently large volumes of water to quantify large, rare organisms such as jellyfish (but see Graham et al. 2003, Madin et al. 2006), and none has combined a large sampling volume with back-lit imaging for studying gelatinous zooplankton. The *In Situ* Ichthyoplankton Imaging System (ISIIS) is a towed-camera system with focused shadowgraph imaging and sufficiently large field-of-view that samples large volumes of water for quantitative measurement (size and abundance) of mesozooplankton (Cowen & Guigand 2008). It has been used in various coastal environments to study the fine to coarse-scale distribution of plankton, including gelatinous zooplankton (McClatchie et al. 2012, Cowen et al. 2013, Greer et al. 2013).

Fronts and other mesoscale features are often sites of enhanced plankton biomass and aggregations of upper trophic level nekton (Owen 1981, Olson & Backus 1985, Franks 1992, Bakun 2006), contributing

disproportionally to regional productivity and trophic ecology. However, fronts can vary in terms of spatial extent and temporal duration, ranging from large, quasi-permanent fronts to transient fronts formed by mesoscale eddy filaments, which may be defined by gradients in temperature, density, or chlorophyll *a* (chl *a*) fluorescence (Castelao et al. 2006, Kahru et al. 2012). Mesoscale fronts are common in coastal areas, but are still poorly understood in terms of their oceanography and how they structure biological communities (Landry et al. 2012). While studies on the complex plankton community dynamics at a front have included mid-trophic level organisms, most have focused on fish and crustaceans (Backus et al. 1969, Angel 1989, Koubbi 1993, Pakhomov et al. 1994, Lara-Lopez et al. 2012). Gelatinous zooplankton, as iono- and osmo-conformers that adjust slowly to even small salinity gradients, also tend to aggregate at density discontinuities (Mills 1984). Large medusae and ctenophores are known to bloom and aggregate at convergent surface fronts (see review in Graham et al. 2001), but little is currently known about whether the strong effects of frontal dynamics similarly affect the small gelatinous zooplankton, particularly considering the broad trophic roles they play.

The California Current is a wide, moderately productive, eastern boundary current, offshore from a zone of seasonal wind-driven upwelling. Numerous small-scale interactions with the upwelling zone, combined with small mesoscale variability, produce many filaments, eddies, and fronts off the central California coast. South of Point Conception, where the angle of the coast changes and the California Current moves far offshore, the Southern California Bight (SCB) experiences less upwelling, and is bounded on the west by a series of submarine ridges and seamounts called the Santa Rosa-Cortes Ridge (SRR). The SRR is a site where an inshore, fast-moving poleward flow meets a slower, offshore equatorward flow resulting in a seasonally persistent convergent frontal feature (Todd et al. 2011). This mesoscale feature is generated by the meeting of a filament of the California Current with SCB water, which is a complex mixture including Inshore Countercurrent and Equatorial Pacific water (McClatchie et al. 2012).

A dense aggregation of a small narcomedusa, *Solmaris rhodoloma*, was observed at a convergent, mesoscale front at the SRR in the SCB (McClatchie et al. 2012). Here, we extend McClatchie et al.'s (2012) study to investigate the broader gelatinous zooplankton community at the SRR front. We test the
expectation that, as with *S. rhodoloma*, the front would play a dominant role in the distributions of the other members of the gelatinous zooplankton community as well. To do so, we first describe the fine to sub-mesoscale distributions of 46 taxa of gelatinous zooplankton to identify assemblage structure and dynamics, and then examine relationships between organisms and the frontal and water column environments using a non-linear modeling technique.

# MATERIALS AND METHODS

## Sampling site and instrumentation

This study was conducted south of San Nicolas Island in the SCB on 15 to 17 October 2010 (Fig. 1). As described in McClatchie et al. (2012), a frontal feature within the SCB was detected using remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) data. The area with the highest SST gradients was selected as the sampling region, and within this area, the strongest SST gradients were located south of San Nicolas Island at the SRR. MODIS SST images were obtained on 26 September, 10 October, and 28 October 2010. An autonomous glider undulating between

the surface and 500 m along CalCOFI line 90 (cf. Rudnick et al. 2004, Todd et al. 2011) also passed through the target frontal area from 2 to 8 October 2010 and provided an independent measurement of a subsurface feature which appeared to be a convergent front (see Section 3.2 in McClatchie et al. 2012).

We deployed the ISIIS to measure the horizontal and vertical distribution of the zooplankton and associated physical properties (temperature, salinity, oxygen, and chl *a* fluorescence) in the study area. ISIIS was towed behind the R/V 'Bell M. Shimada' at a speed of 5 knots (2.5 m s⁻¹) and sampled the water column in a tow-yo fashion between the surface and ca. 130 m depth. We sampled the front in 3 parallel cross-front transects spaced ca. 25 km apart on consecutive days during the study period. Sampling from 17:00–23:36, 16:35–23:59, and 20:53–01:29 h (next day), we towed from east to west in the first 2 transects, and then from west to east in the last transect. These E–W transects were ~45 to 65 km in length and spanned a N–S distance of ~50 km (Fig. 1). ISIIS sampled down and up along the 3 transects 24, 26, and 18 times over 63.25, 66.40, and 43.11 km, respectively. Sensors on ISIIS measured conductivity, temperature, and depth (Sea-Bird Electronics 49 Fastcat CTD sensor), dissolved oxygen (DO) (SBE 43), and fluorometry (Eco FL-RT, Wetlabs chlorophyll-*a* fluorescence). A Doppler velocity log (DVL, Navquest 600) measured instrument velocity and pitch. ISIIS is equipped with a 2048-pixel line scan camera (Dalsa Piranha 2 P2-22-02k40) with a 13 cm field-of-view, 50 cm depth-of-field, and 66 µm pixel resolution. The imaging output is recorded as a continuous image that is parsed into equivalent image frames (2048 × 2048 pixels) at 17 frames s⁻¹. Traveling at a mean velocity of 2.47 m s⁻¹, ISIIS sampled the water column at 166 l s⁻¹. Sampling volumes for Transects 1 to 3 were 4001, 4428 and 2696 m³, respectively, totaling 11 125 m³, although for this study we sub-sampled for downcasts only (5450 m³ water). The combination of a fine pixel resolution with a large sampling volume on ISIIS allows for the simultaneous resolution of particles as small as 700 µm (e.g. small copepods and appendicularians) as well as larger mesozooplankton within the 5 to 13 cm size range (Cowen et al. 2013).

## Image analysis

Analysis of ISIIS images was done manually, except for 1 taxonomic group (Ctenophora), which was analyzed with assistance from an image segmenta-

<figure><img src="image_2.png" /><figcaption>Fig. 1. Sampling transects in the Southern California Bight (SCB) during 15 to 17 October 2010. Transects were sampled on consecutive days, with Transect 1 sampled on 15 Oct and Transect 3 sampled on 17 Oct. Thin gray lines: 1000 m isobaths</figcaption></figure>
tion program. All images from each downcast were visually quantified for siphonophores, hydromedusae, and doliolids. Abundances of *Solmaris rhodoloma* were reported previously (McClatchie et al. 2012). Due to high abundances, appendicularians were sub-sampled and quantified in one-fifth of every 20th image. Within the hydromedusae, siphonophores, and ctenophores, individual organisms were identified to the lowest possible taxonomic group, many of which included species-level identifications. Species- or genus-level classifications were verified by taxonomic experts. Doliolids were identified only to order, and appendicularians to class.

Ctenophores were analyzed in every image in all downcasts with the assistance of the ISIIS image segmentation software (Tsechpenakis et al. 2007, 2008; downloaded from http://cs.iupui.edu/~gavriil/vital/MVISIIS). The segmentation software identifies and extracts regions of interest (ROIs) from raw images by first eliminating the noise and then using a 1st order Conditional Random Field to detect targets. We optimized the program for segmenting ctenophores (using the following inputs: filter = 20.4, length of major axis = 13, area = 1.7), achieving a >90% segmentation accuracy rate. We then sorted the ctenophores from the other ROIs and identified them manually to the lowest possible taxonomic group.

## Data analysis

### Physical environment

Environmental variables from ISIIS sensors (temperature, salinity, chl a fluorescence, DO, and calculated seawater density) were interpolated throughout the water column to create background maps. For simplicity, we chose to use a simple interpolation based on Euclidean distances. To do so, we had to scale the horizontal and vertical coordinates to isotropy (original values of environmental variables were not changed). First, we estimated the anisotropy between environmental variables in the vertical and horizontal direction by inspecting directional variograms for each variable to find the most appropriate scaling factor for the coordinates (1800:1). Then, we rescaled the horizontal coordinate according to the scaling factor and linearly interpolated between casts. R packages 'gstat' and 'akima' were used. (Pebesma 2004, Akima et al. 2013).

In this study, the front was primarily delineated by salinity, and defined as the location of the greatest rate of change in salinity values. Isohalines between

33 and 34 were inspected, and the locations where the isohalines were closest together were delineated as the front.

Physical data (non-interpolated environmental variables and location data) and biological counts were both placed into 1 m depth bins and then matched with each other using the sample time stamp. The concentration of each organism per bin ($c_b$; no. of ind. m⁻³) was calculated from counts ($a_b$) and volume sampled ($vol_b$):

$$c_b = \frac{a_b}{vol_b} = \frac{a_b}{0.13 \times 0.50 \times v_b(t_{bf} - t_{bi})}$$

where 0.13 is the ISIIS vertical field of view in m, 0.5 is the ISIIS depth of field in m, $v_b$ is the average instrument velocity per time bin (m s⁻¹), $t_{bf}$ and $t_{bi}$ the final and initial times when ISIIS crossed each depth bin. This differs from McClatchie et al. (2012) in that previously, concentrations of organisms were calculated using counts per 1 m³ along-path sampling volume, so the depth range for each bin varied by instrument descent speed. The current method standardizes to volume m⁻¹ depth bin.

### Species distribution and assemblage analysis

All species were ranked according to total abundance and percent present in the water column. Those that were present in <1% of total depth bins and <0.02% of the total population were considered 'Rare' and no further analyses were performed. Multi-species unknown ctenophores and hydromedusae were also excluded. Single-species unknown hydromedusae were included, as well as the 'very small hydromedusae' (vsh). A simple 'Frontal Aggregation Factor' (FAF), defined as mean concentration within the front / mean concentration outside the front, was calculated for all organisms. Kruskal-Wallis tests were performed on the mean concentration of organisms (1) inside vs. outside of the front, and (2) in the front vs. east of the front. Large and small *S. rhodoloma* (McClatchie et al. 2012) were distinguished for initial abundance and aggregation tests, but were grouped together for all further tests.

To examine the spatial co-occurrence of species, we performed a correspondence analysis (CA) on log-transformed concentration data, and then used hierarchical clustering with Ward's method on the first 4 CA axes to identify assemblages. The CA biplot did not show a horseshoe effect, so Detrended Correspondence Analysis (DCA) was not necessary. The CA was conducted in R v.3.0.1 (R Development
Core Team 2013) using package 'vegan' (Oksanen et al. 2013). To look at positive and negative associations of species with each other, we constructed a correlation heatmap using the non-parametric Spearman's rank correlation coefficient.

## Species distribution modeling

Species-environment relationships were evaluated using boosted regression trees (BRTs), an ensemble statistical method that combines regression trees with boosting, a machine learning technique (Elith et al. 2008). The R package 'gbm' was used (Ridgeway 2013). BRTs optimize predictive performance using the iterative development of a large group of small regression trees constructed with random subsets of the data. We chose to use BRTs because most organisms in our dataset exhibited non-monotonic, non-linear relationships to environmental gradients, thus rendering linear models inappropriate. Tree-based methods, which include BRTs, also provide an estimate of the relative influence of each environmental variable to species distributions (Friedman 2001, Ridgeway 2012). Models were constructed with 6 explanatory variables: front (a categorical variable indicating location with respect to the front), and 5 continuous variables (depth, temperature, salinity, oxygen, and fluorometry). Since most environmental variables were

correlated with depth, depth was explicitly included in the model to account for variation that was attributed to depth and not the frontal feature. We used learning rates as close to the recommend 0.01 to 0.001 range as possible, a maximum of 10000 trees, and 5-fold cross validation. The interaction depth of each tree was set to 3. Using fitted model values, pseudo-R² values were calculated as 1 - mean squared error / total squared error. Species 'maps' were then plotted using the results of the BRTs: concentrations were plotted by depth and longitude and overlaid onto an interpolated plot of the variable with the highest contribution to the model.

# RESULTS

## Physical environment

The physical environment in the study area was dominated by a salinity-driven front delineated by the 33.3 and 33.45 isohalines (Fig. 2). The temperature profile was relatively similar on each side of the front (see Fig. S1 in the Supplement at www.int-res.com/articles/suppl/m510p129_supp.pdf). Maximum chl a fluorescence occurred on the east side of the front, approximately 20 to 30 km from the front (Fig. S2). DO was higher in the waters to the west of the front (Fig. S3). There was a slight delay in the DO

sensor, resulting in an offset of 3 to 6 m between the downcasts and upcasts (Fig. S3), though within the downcasts, the values showed no step structure. In general, the waters to the west of the front were marked by low salinity, high DO and low chl a. The waters to the east of the front were marked by high salinity, low DO and high surface chl a.

## Species abundance

A total of 664 650 appendicularians, 107 626 hydromedusae, 11 934 siphonophores, 4425 doliolids, and 4413 ctenophores were counted in ca. 700 000 frames. Mean concentrations (±SD) were 105.45 ± 257.6, 0.81 ± 11.2, 0.28 ± 1.3, 0.98 ± 4.5, and 0.06 ± 0.44 ind. m⁻³, respectively. Hydromedusae were split into 22 taxa, including 7 narcomedusae, 6 trachymedusae, 1 lep-

<figure><img src="image_3.png" /><figcaption>Fig. 2. Interpolated salinity profile of the sampled transects in the Southern California Bight. Distance (in km) is measured along each transect. Top: Transect 1; middle: Transect 2; lower: Transect 3. Contour lines mark the 33.3 and 33.45 isohalines. Tick marks indicate the starting and ending locations of each tow-yo cast. Biological data for the downcasts only were analyzed</figcaption></figure>
tomedusa, 1 anthomedusa, and 6 unknown species, including the catch-all vsh group. Siphonophores were classified into 9 taxa, consisting of 5 calycophoran and 4 physonect species. There were 12 species of ctenophores and 1 unknown group. All 3 major appendicularian families were present in the study area, but were not distinguished here. Doliolids were also lumped into 1 group. In total, 46 gelatinous zooplankton taxa were quantified (Fig. 3).

The most abundant organisms by far were the appendicularians, followed by *Solmaris rhodoloma*, vsh, and *Liriope tetraphylla* (all hydromedusae). The most abundant siphonophore was *Sphaeronectes* sp., which consisted of over 95% *Sphaeronectes koellikeri* and the remainder *Sphaeronectes fragilis*. The most abundant ctenophore was the lobate *Ocyropsis maculata*. A total of 16 rare taxa were excluded from further analysis. Excluding the unknown ctenophores and 1 group of unknown hydromedusae, 28 taxa were further analyzed (Table 1).

FAF values, along with the Kruskal-Wallis tests, showed that most organisms were not aggregated at the front (Table 1). Since more organisms were present to the east of the front (SCB waters) than the west (California Current waters), we also calculated an adjusted FAF, defined as the mean concentration within the front / mean concentration to the east of the front (data not shown). All organisms with FAF < 2 also had an $FAF_{adj} < 1.25$ (n = 40), which means that organisms within the front were only 25% more abundant than inshore (east) of the front. We considered those to be 'not aggregated' at the front. Six organisms had FAF values between 2 and 2.35 and $FAF_{adj} < 2$, but only 3 (doliolids, *Solmaris* sp. 2, and *Aegina citrea*) were significant in both Kruskal-Wallis tests; these were considered 'mildly aggregated'. *Solmundella bitentaculata* and h15 (hydromedusa) were 'moderately aggregated,' with FAF between

3 and 3.5. Large *S. rhodoloma* was 'highly aggregated,' with $FAF = 3.9$ and $FAF_{adj} = 11.9$. Out of all sampled taxa, 43 were not aggregated, 5 were mildly to moderately aggregated, and only 1 taxon was highly aggregated at the front.

## Assemblage analysis

CA was performed on the 28 taxa to evaluate species co-occurrence. CA results were then evaluated using cluster analysis, which revealed 4 assemblages and 2 outlier species (Fig. 4). The largest assemblage, A, comprised 13 taxa including the pelagic tunicates, 6 hydromedusae, 2 ctenophores, and a siphonophore. Assemblage B consisted of only siphonophores and ctenophores. Assemblage C contained 2 ctenophores, and assemblage D had 2 siphonophores and 3 hydromedusae. The outliers were also hydromedusae. Hydromedusae and tunicates tended to group together, and siphonophores and ctenophores tended to group together.

Relationships among taxa and among assemblages were further examined by the correlation heatmap, which shows correlations between taxa as well as positive (co-occurrence) and negative (absence/avoidance) relationships (Fig. 4). Correlation strength was greatest in assemblage A (mean $\rho$ = 0.26) and declined in subsequent assemblages (mean $\rho$ = 0.18, 0.08, 0.04 for assemblage B, C, and D, respectively). The strongest correlations in assemblage A were between *S. rhodoloma* and vsh ($\rho$ = 0.62, p < 0.01), and *S. rhodoloma* and *L. tetraphylla* ($\rho$ = 0.55, p < 0.01). Though *Muggiaea atlantica* clustered into assemblage B, it exhibited strong positive correlations with assemblage A taxa. The strongest negative correlations were between *O. maculata* and *S. rhodoloma* ($\rho$ = -0.18, p < 0.05) and vsh ($\rho$ = -0.16,

Fig. 3. (a–av) *In Situ* Ichthyoplankton Imaging System (ISIIS) images of all taxa found in the Southern California Bight. (a–t) **Hydromedusae**: (a) *Liriope tetraphylla*; (b) *Solmaris rhodoloma*; (c) very small hydromedusae; (d) *Solmundella bitentaculata*; (e) h15; (f) *Rhopalonema velatum*; (g) *Pegantha* sp. 1; (h) *Haliscera conica*; (i) *Aglantha* sp. (likely *A. digitale*); (j) *Haliscera* sp. 2; (k) *Solmaris* sp. 2; (l) *Arctapodema* sp.; (m) unknown hydro 2; (n) *Pegantha* sp. 2; (o) *Aegina* aff. *Citrea*; (p) *Annatiara affinis*; (q) *Eutonia scintillans*; (r) unknown hydro 1; (s) Pandeidae; (t) unknown hydro 3. (u–ae) **Siphonophores**: (u) *Chelophyes* sp.; (v) *Lensia multicristata*; (w) *Sphaeronectes fragilis*; (x) *Nanomia bijuga*; (y) *Forskalia* sp.; (z) *Agalma elegans*; (aa) *Muggiaea atlantica*; (ab) *Sphaeronectes koellikeri*; (ac) *Cordagalma* sp.; (ad) *Lilyopsis rosea*; (ae) Prayidae. For siphonophores, the 2 *Sphaeronectes* species (w, ab) were grouped together, though the majority (>95%) were *S. koellikeri*. The diphyid siphonophores other than *M. atlantica* were grouped together, and this group is likely dominated by 2 taxa (u, v). (af–ar) **Ctenophores**: (af) *Haeckelia beehlri*; (ag) juvenile lobata; (ah, ai) larval lobates; (aj, ak) *Ocyropsis maculata*, see possible developmental sequence from ah–ak; (al) *Hormiphora californiensis*; (am) *Velamen parallellum*; (an) undescribed Mertensiidae; (ao) *Bolinopsis vitrea*; (ap) Beroida; (aq) *Thalassocalyce inconstans*; (ar) *Charistephane* sp. Not pictured: *Bolinopsis infundibulum* and *Pleurobrachia* sp. (as–av) **Pelagic tunicates**: (as) doliolid; (at) Oikopleuridae appendicularian house; (au) Fritillaridae appendicularian; and (av) Kowalevskiidae appendicularian.

Not shown: large *Bathochordaeus* appendicularians
<figure><img src="image_4.png" /></figure>
<table><caption>Table 1. Abundances and concentrations for all sampled taxa, binned into 1 m depth bins. Dark grey shading indicates rare taxa and light grey shading indicates the 2 unknown groups that were not included in further analyses. Table columns indicate: percent of the total sampled water column in which species was found, maximum concentration in a 1 m depth bin, maximum counts in a 1 m depth bin, total counts, the frontal aggregation factor (FAF), significance of a Kruskal-Wallis (K-W) test of mean concentrations in vs. out of the front (i/o F), and the significance of a K-W test of mean concentrations in the front vs. east of the front (F/E). The second K-W test significance is reported because the eastern (inshore) portion of the sampling region had more organisms overall. *Solmaris rhodoloma* (all) was included as well as the large (>5 mm bell diameter) and small (<5 mm bell diameter) size fractions. FAF is calculated as mean concentration inside the front / mean concentration outside the front. FAF values >3 are in **bold**. K-W test significance level symbols: \*p ≤ 0.1, \*\*p ≤ 0.05, and \*\*\*p ≤ 0.01. Short dash (–) is not significant</caption><thead><tr><th>Taxon</th><th>Group</th><th>% Present</th><th>Max conc. (no. m<sup>-3</sup>)</th><th>Max counts (no. m<sup>-3</sup>)</th><th>Total abund.</th><th>FAF</th><th>K-W (i/o F)</th><th>K-W (F/E)</th></tr></thead><tbody><tr><td>Appendicularians</td><td>Tunicates</td><td>24.15</td><td>3695.51</td><td>8700</td><td>644650</td><td>0.99</td><td>*</td><td>***</td></tr><tr><td><i>Solmaris rhodoloma</i></td><td>Hydromedusae</td><td>19.23</td><td>861.77</td><td>2300</td><td>79350</td><td>2.75</td><td>***</td><td>**</td></tr><tr><td>Large size</td><td></td><td>8.96</td><td>772.11</td><td>775</td><td>25450</td><td><b>3.91</b></td><td>***</td><td>***</td></tr><tr><td>Small size</td><td></td><td>16.58</td><td>757.72</td><td>2010</td><td>54010</td><td>2.27</td><td>***</td><td>–</td></tr><tr><td>vsh ('very small hydromedusae')</td><td>Hydromedusae</td><td>15.64</td><td>156.23</td><td>394</td><td>15347</td><td>2.17</td><td>***</td><td>–</td></tr><tr><td><i>Liriope tetraphylla</i></td><td>Hydromedusae</td><td>15.47</td><td>92.33</td><td>258</td><td>7814</td><td>1.58</td><td>***</td><td>–</td></tr><tr><td><i>Sphaeronectes</i> sp.</td><td>Siphonophores</td><td>28.63</td><td>28.56</td><td>14</td><td>4717</td><td>1.29</td><td>***</td><td>–</td></tr><tr><td>Doliolids</td><td>Tunicates</td><td>10.24</td><td>78.46</td><td>88</td><td>4425</td><td>2.33</td><td>***</td><td>***</td></tr><tr><td><i>Muggiaea atlantica</i></td><td>Siphonophores</td><td>14.31</td><td>23.29</td><td>39</td><td>2686</td><td>1.45</td><td>***</td><td>–</td></tr><tr><td><i>Nanomia bijuga</i></td><td>Siphonophores</td><td>19.69</td><td>22.66</td><td>11</td><td>2596</td><td>1.01</td><td>*</td><td>***</td></tr><tr><td><i>Ocyropsis maculata</i></td><td>Ctenophores</td><td>14.56</td><td>12.71</td><td>10</td><td>1820</td><td>1.08</td><td>**</td><td>***</td></tr><tr><td><i>Solmundella bitentaculata</i></td><td>Hydromedusae</td><td>8.37</td><td>20.63</td><td>29</td><td>1727</td><td><b>3.44</b></td><td>***</td><td>***</td></tr><tr><td>h15</td><td>Hydromedusae</td><td>5.51</td><td>37.03</td><td>45</td><td>1245</td><td><b>3.22</b></td><td>***</td><td>***</td></tr><tr><td>Diphyidae</td><td>Siphonophores</td><td>8.64</td><td>9.74</td><td>11</td><td>1047</td><td>1.06</td><td>–</td><td>–</td></tr><tr><td>Beroida</td><td>Ctenophores</td><td>6.14</td><td>19.77</td><td>8</td><td>703</td><td>0.89</td><td>–</td><td>***</td></tr><tr><td><i>Hormiphora californiensis</i></td><td>Ctenophores</td><td>4.37</td><td>9.76</td><td>10</td><td>517</td><td>1.34</td><td>*</td><td>–</td></tr><tr><td>Larval Lobata</td><td>Ctenophores</td><td>4.65</td><td>10.52</td><td>5</td><td>503</td><td>0.93</td><td>–</td><td>***</td></tr><tr><td><i>Agalma elegans</i></td><td>Siphonophores</td><td>3.83</td><td>11.77</td><td>5</td><td>452</td><td>1.77</td><td>***</td><td>**</td></tr><tr><td>Unknown-hydro</td><td>Hydromedusae</td><td>2.62</td><td>19.6</td><td>18</td><td>390</td><td>1.33</td><td>***</td><td>***</td></tr><tr><td><i>Pegantha</i> sp. 1</td><td>Hydromedusae</td><td>2.28</td><td>11.06</td><td>9</td><td>333</td><td>0.49</td><td>–</td><td>***</td></tr><tr><td><i>Rhopalonema velatum</i></td><td>Hydromedusae</td><td>2.76</td><td>4.7</td><td>3</td><td>267</td><td>1.26</td><td>–</td><td>–</td></tr><tr><td><i>Velamen parallelum</i></td><td>Ctenophores</td><td>2.49</td><td>5.7</td><td>3</td><td>243</td><td>1.41</td><td>***</td><td>*</td></tr><tr><td><i>Haliscera conica</i></td><td>Hydromedusae</td><td>2.45</td><td>5.75</td><td>3</td><td>235</td><td>1.68</td><td>***</td><td>–</td></tr><tr><td><i>Lilyopsis rosea</i></td><td>Siphonophores</td><td>2.11</td><td>6.73</td><td>2</td><td>196</td><td>1.27</td><td>–</td><td>–</td></tr><tr><td><i>Aglantha</i> sp.</td><td>Hydromedusae</td><td>1.85</td><td>5.17</td><td>4</td><td>195</td><td>0.98</td><td>–</td><td>***</td></tr><tr><td><i>Thalassocalyce inconstans</i></td><td>Ctenophores</td><td>1.83</td><td>5.21</td><td>2</td><td>174</td><td>1.36</td><td>*</td><td>–</td></tr><tr><td><i>Haliscera</i> sp. 2</td><td>Hydromedusae</td><td>1.63</td><td>4.96</td><td>5</td><td>164</td><td>1.53</td><td>**</td><td>–</td></tr><tr><td><i>Solmaris</i> sp. 2</td><td>Hydromedusae</td><td>1.44</td><td>9.26</td><td>4</td><td>160</td><td>2.02</td><td>***</td><td>**</td></tr><tr><td>Prayidae</td><td>Siphonophores</td><td>1.51</td><td>5.11</td><td>2</td><td>144</td><td>0.49</td><td>***</td><td>**</td></tr><tr><td>Unknown</td><td>Ctenophores</td><td>1.35</td><td>4.99</td><td>3</td><td>128</td><td>1.2</td><td>–</td><td>*</td></tr><tr><td>Mertensiidae, undesc</td><td>Ctenophores</td><td>1.27</td><td>5.72</td><td>2</td><td>117</td><td>1.53</td><td>***</td><td>–</td></tr><tr><td><i>Haeckelia beehlri</i></td><td>Ctenophores</td><td>1.06</td><td>4.32</td><td>2</td><td>97</td><td>0.85</td><td>–</td><td>–</td></tr><tr><td><i>Cordagalma</i> sp.</td><td>Siphonophores</td><td>0.94</td><td>5.13</td><td>2</td><td>87</td><td>1.36</td><td>–</td><td>–</td></tr><tr><td>Juvenile Lobata</td><td>Ctenophores</td><td>0.82</td><td>4.11</td><td>2</td><td>78</td><td>1.05</td><td>–</td><td>–</td></tr><tr><td>Unk-hydro-3</td><td>Hydromedusae</td><td>0.69</td><td>4.09</td><td>3</td><td>68</td><td>0.67</td><td>–</td><td>–</td></tr><tr><td><i>Arctapodema</i> sp.</td><td>Hydromedusae</td><td>0.67</td><td>4.4</td><td>2</td><td>63</td><td>0.47</td><td>*</td><td>***</td></tr><tr><td><i>Eutonia scintillans</i></td><td>Hydromedusae</td><td>0.58</td><td>4.84</td><td>3</td><td>57</td><td>1.25</td><td>–</td><td>–</td></tr><tr><td><i>Annatiara affinis</i></td><td>Hydromedusae</td><td>0.51</td><td>3.86</td><td>2</td><td>48</td><td>0.85</td><td>–</td><td>–</td></tr><tr><td><i>Pegantha</i> sp. 2</td><td>Hydromedusae</td><td>0.42</td><td>6.04</td><td>3</td><td>45</td><td>0.33</td><td>–</td><td>–</td></tr><tr><td><i>Aegina</i> aff. <i>citrea</i></td><td>Hydromedusae</td><td>0.46</td><td>2.89</td><td>2</td><td>44</td><td>2.02</td><td>*</td><td>**</td></tr><tr><td>Pandeidae</td><td>Hydromedusae</td><td>0.31</td><td>3.37</td><td>1</td><td>28</td><td>1.33</td><td>–</td><td>–</td></tr><tr><td>Unk-hydro-2</td><td>Hydromedusae</td><td>0.24</td><td>2.19</td><td>2</td><td>24</td><td>0.45</td><td>–</td><td>–</td></tr><tr><td>Unk-hydro-1</td><td>Hydromedusae</td><td>0.23</td><td>5.21</td><td>2</td><td>22</td><td>0.12</td><td>*</td><td>*</td></tr><tr><td><i>Bolinopsis</i> sp.</td><td>Ctenophores</td><td>0.1</td><td>2.38</td><td>1</td><td>9</td><td>1.46</td><td>–</td><td>–</td></tr><tr><td><i>Charistephane</i> sp.</td><td>Ctenophores</td><td>0.1</td><td>2.81</td><td>1</td><td>9</td><td>1.39</td><td>–</td><td>–</td></tr><tr><td><i>Dryodora glandiformis</i></td><td>Ctenophores</td><td>0.1</td><td>3.69</td><td>1</td><td>9</td><td>1.1</td><td>–</td><td>–</td></tr><tr><td><i>Forskalia</i> sp.</td><td>Siphonophores</td><td>0.1</td><td>2.24</td><td>1</td><td>9</td><td>2.35</td><td>–</td><td>–</td></tr><tr><td><i>Pleurobrachia</i> sp.</td><td>Ctenophores</td><td>0.04</td><td>1.17</td><td>3</td><td>6</td><td>0</td><td>–</td><td>–</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>Fig. 4. Assemblages and community-level relationships between gelatinous zooplankton. Left: cluster dendrogram of the first 4 axes of the canonical correspondence (CA) species scores. Four main groups are indicated in boxes. Right: correlation heatmap constructed using the non-parametric Spearman's rank correlation coefficient *r*<sub>s</sub>. Blue: positive correlation; red: negative correlation. Shading indicates strength of correlation. Assemblage groupings from CA results are marked in boxes. The level of statistical significance for each correlation coefficient is given in the lower triangle: **p** < 0.01, **p** < 0.05, and \*p* < 0.1</figcaption></figure>

p < 0.05). Except for some assemblage B taxa, most other organisms exhibited weak negative correlations (though some significant at p < 0.05) with assemblage A taxa (Fig. 4).

## Gelatinous zooplankton and their physical environment

There are notable trends in the location and environmental conditions in which different taxa were found (Fig. 5). Of the gelatinous zooplankton, the taxa that occupied the shallowest and deepest areas were the hydromedusae. Both groups of siphonophore and ctenophore species were present in the mid-depths, though as a group, the siphonophores

tended to aggregate in the upper mid-depths compared to ctenophores, which occupied the lower mid-depths. They were also present in a broad range of temperatures and DO levels. All organisms occupied waters within a narrow range of salinities. By depth, assemblage A was shallowest (mean 16.5 m), followed by B, C, and D (mean 48.9, 90.0, 84.4 m, respectively); the 2 outliers had a mean depth of 102.2 m. Assemblages were generally organized by depth-related factors rather than factors that varied along the horizontal plane.

To further evaluate the relative influence of the environmental variables on species distributions, statistical modeling was performed on all 28 taxa using BRTs, although only 17 taxa were present in sufficient numbers for accurate modeling. The 11 taxa
<figure><img src="image_6.png" /><figcaption>Fig. 5. Boxplots of depth, temperature, oxygen, fluorometry (V; measure of chl *a* fluorescence), and salinity for all 28 taxa of gelatinous zooplankton, as grouped by assemblage (see Fig. 3 for full species names). Orange box in the salinity column indicates the isohalines that mark the front. Blue: hydromedusae; purple: pelagic tunicates; red: ctenophores; green: siphonophores. The left (lower) and right (upper) sides of the boxes indicate the 25th and 75th percentiles, respectively; the line in the middle of the box is the median, and the left and right whiskers extend from the lowest and highest values within 1.5 × IQR, where IQR is the inter-quartile range. Data beyond the whiskers are outliers and are plotted as semi-transparent points. Darker points indicate where outliers overlap each other</figcaption></figure>
with low model R² values (<0.11), with the exception of the larval lobates, were only found in maximum concentrations of <9 ind. m⁻³. The remaining 17 taxa were found in densities >9 ind. m⁻³ and reasonable models could be fit to them. Of the modeled taxa, the number of trees fit (Table S1) ranged from 4637 for *Solmaris* sp. 2, to 9996 for both Beroida and *O. maculata*. The majority of models had more than 6000 trees. Model R² values ranged from 0.13 for *Hormiphora californiensis* to 0.93 for *S. rhodoloma* (Fig. 6). Modeled taxa include 10 of 11 organisms in assemblage A, 4 of 8 in assemblage B, 1 of 2 in assemblage C, and 2 of 5 in assemblage D.

BRT model results show that overall, temperature and depth had the highest relative influence for the largest number of organisms (Fig. 6). Temperature was the most important variable for 6 species, chl *a* was most important for 4 species, depth for 4 species, DO for 2 species, and salinity for only 1 species. Groupings by assemblage revealed that the first half of assemblage A was primarily influenced by temperature and depth, and the second half of A through C (with some exceptions) was primarily influenced by chl *a* fluorescence and DO.

BRT modeling results showed high consistency in the relative influence of variables within broad taxonomic groups, regardless of assemblage affiliation (Fig. 6). The most influential variables for hydromedusae were temperature (mean 27%) and depth (mean 25%). Appendicularians were also primarily associated with depth (45%) but doliolids with chl *a* (35%). Siphonophores were primarily associated with DO (mean 26%) and chl *a* (mean 22%). Model results for ctenophores were more varied: *H. californiensis* was associated with chl *a* (28%) and temperature (27%), Beroida with temperature (23%), and *O. maculata* with DO (48%).

Position relative to the front, a categorical variable, was the least influential of all the environmental variables, and did not factor in as most important for any of the 17 taxa. Salinity ranked highest in only 1 model (diphyidae), and its average contribution (13%) was greater than that of the front position (5.9%). Compared to other groups,

<figure><img src="image_7.png" /><figcaption>Fig. 6. Results of the boosted regression tree (BRT) analysis for 17 taxa of gelatinous zooplankton, with associated model R² values, grouped by assemblages. Bar plots show the percent influence of each variable (D = depth, T = temperature, O = oxygen, F = chl *a* fluorescence, S = salinity, Fr = front). Blue: hydromedusae; purple: pelagic tunicates; red: ctenophores; green: siphonophores. Peg. = Pegantha; Apps = Appendicularians; SORH = *Solmaris rhodoloma*; vsh = very small hydromedusae; SOBI = *Solmundella bitentaculata*; LITE = *Liriope tetraphylla*; HOCA = *Hormiphora californiensis*; AGEL = *Agalma elegans*; MUAT = *Muggiaea atlantica*; NABI = *Nanomia bijuga*; Sphaero. = Sphaeronectes; Beroid = Beroida; OCMA = *Ocyropsis maculata*; Diphy = Diphyidae; Sol sp.2 = Solmaris sp. 2</figcaption></figure>
siphonophores were most influenced by salinity (mean 16%), but this still only ranked as fourth (out of 6) most important variable to the group. Front position contributed the least to models of appendicularians (0.87%), diphyidae (1.8%) and *M. atlantica* (1.9%), and most to 3 hydromedusae (h15 and *S. bitentaculata*, both 10%; *S. rhodoloma*, 12%) and *Sphaeronectes* sp. (8.2%). Large *S. rhodoloma* was also modeled separately (though not shown) and had salinity (18%) as the second most important variable and the front as the fourth (15%) most important variable. Overall, the influence of the front in BRT models was lowest for pelagic tunicates, and highest for hydromedusae. Appendicularians, *O. maculata* and *Solmaris* sp. 2 were least associated with both salinity and the front in their models.

Based on model results, abundances of 6 representative taxa (3 hydromedusae, doliolids, 1 siphono-

phore and 1 ctenophore), were plotted on top of their most influential variable(s) (Figs. 7–12). *S. bitentaculata* was found mostly in shallow waters >15°C, though some were found deeper in the water column on the east side of the front in Transects 1 and 2 (Fig. 7). *Pegantha* sp. were found in waters >16°C, but mostly on the west side of the transects where fluorometry was <0.2 V (Fig. 8). Though *Sphaeronectes* sp. were found throughout the water column, the highest concentrations were found in waters 10 to 15°C and DO 3 to 4.5 ml l⁻¹ (Fig. 9). *O. maculata* was found deeper, generally where DO was <3.5 ml l⁻¹ (Fig. 10). The dominant hydromedusa in this study, *S. rhodoloma*, was found in highest concentrations in temperatures ca. 17°C and at the front (Fig. 11; see also McClatchie et al. 2012). Highest concentrations of doliolids, which are also known prey of *S. rhodoloma*, were found where fluorometry was >0.25 V, particularly around 0.3 V (Fig. 12).

<figure><img src="image_8.png" /><figcaption>Fig. 7. Density of *Solmundella bitentaculata* plotted on interpolated temperature. Orange arrows: location of front. Size of bubble indicates concentration of organisms found in a 1 m depth bin. Distance (in km) is measured along each transect. Panel rows in each figure indicate transect (top: Transect 1; middle: Transect 2; bottom: Transect 3)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 8. Density of *Pegantha* sp. plotted on interpolated fluorometry with contours of 10, 13, and 16°C isothermals. See Fig. 7 for further details</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Fig. 9. Density of *Sphaeronectes* sp. plotted on interpolated oxygen with 10, 13, and 16°C isothermals. See Fig. 7 for further details</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 10. Density of *Ocyropsis maculata* plotted on inter-polated oxygen. See Fig. 7 for further details</figcaption></figure>

DISCUSSION

We present, for the first time, the fine-scale distri-
bution of a community of small gelatinous zooplank-
ton (<1 mm bell diameter medusae to 20+ cm long
siphonophores) and their associated environment
across a mesoscale oceanic front. Mesoscale fronts
are biologically important because of their frequent
occurrence in coastal zones (Kahru et al. 2012), and
potential to aggregate both predators and prey
(Bakun 2006). We quantified 46 gelatinous zooplank-
ton taxa, and as expected, the abundance distribu-
tion by taxa was highly skewed, with the top 5 spe-
cies representing a disproportionate amount of the
total community. Coefficient of variation (SD/mean)
values were highest for the hydromedusae, indica-
ting that they were the most patchily distributed. Pre-
vious studies have described gelatinous zooplankton
communities using net sampling systems that inte-

grate over 100s of m in the horizontal and at least 10s
of m of depth (e.g. Pagès et al. 2001, Pavez et al.
2010). Our results provide further support for the
diversity and high abundance of small gelatinous
zooplankton, which are likely to be major players
within the planktonic environment. This examination
of community composition and potential interactions
has led to a set of possible hypotheses for how fronts
structure communities of mid-trophic level organ-
isms.

Physical processes structuring populations and
frontal dynamics

In this study, BRTs, which allowed us to assess the
relative importance of a suite of environmental vari-
ables in structuring populations, revealed tempera-
ture and depth as the most important factors structur-
<figure><img src="image_12.png" /><figcaption>Fig. 11. *Solmaris rhodoloma* plotted on interpolated temperature. See Fig. 7 for further details</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 12. Doliolids plotted on interpolated fluorometry. See Fig. 7 for further details</figcaption></figure>

ing gelatinous zooplankton distributions. This is largely due to the numerical dominance of hydromedusae (most influenced by temperature) and appendicularians (most influenced by depth). The positive physiological effect of increased temperatures has been shown to increase respiration and growth rates, and shorten developmental times in hydromedusae (Larson 1987, Matsakis 1993, Møller & Riisgård 2007, Ma & Purcell 2005). Many field studies have identified the combined effects of temperature and salinity in driving populations, with higher abundances typically occurring in warm, salty water (Purcell 2005). Thus, it is sometimes difficult to separate the 2 factors, though Purcell (2005) states that temperature has 'the most obvious effects' on medusae abundance and production. It is unknown why changing temperatures would have a greater effect on hydromedusae versus ctenophores and siphonophores, and should be a subject for further investigation.

What is surprising about the BRT results is not the high influence of temperature and depth in structuring populations, but the low influence of the front and salinity variables, particularly given the strong salinity-driven front in this sampling region. The front contributed >10% in only 3 organisms (all hydromedusae), even though they all had moderate to high FAF values. Salinity was also not as influential as we would have expected, with ≤20% contribution in all models except one. The current understanding for how convergent fronts structure biological communities consists of (1) the direct growth and passive advection of primary producers to variations in light and nutrients at the convergence zone, and (2) the accumulation of motile phytoplankton and primary consumers through buoyancy regulation and vertical movements, leading to (3) the active aggregation of predators using sensory cues to hone in on the accumulations of pri-
mary consumers (Okubo 1978, Owen 1981, Olson & Backus 1985, Franks 1992, Olson 2002, Bakun 2006, Landry et al. 2012). However, this scenario fails to address the myriad intermediate trophic levels whose behaviors may be altered by the presence of a top predator, or organisms whose distribution might be primarily regulated by other water column characteristics than just the productivity and convergent flow at the front.

Given the primary role of temperature and depth for the majority of surface dwelling organisms, the small role of salinity and the front overall, and the low level of aggregation in species at the front, we propose some possible hypotheses for the observed data:

(1) The convergence zone(s) at fronts is only important for organisms directly in the path of the convergent flow. All other organisms are structured by water mass effects (i.e. some organisms are present in one water mass and not the other). ('Physical structuring effect')

(2) Fronts function as a proximate structuring factor for organisms, with ultimate controlling factors being those that exert a physiological or biological constraint on the organism (e.g. temperature, predation). This is an extension of hypothesis 1, but with a behavioral component. ('Hierarchical structuring effect')

(3) Frontal zones are initially structured by bottom-up effects (current understanding), but over time, become dominated by top-down effects through succession. The dominant processes change through the course of frontal development. ('Temporal succession effect')

The physical structuring hypothesis (Hyp. 1) focuses on water mass differences driving compositional differences, and attributes observed differences in community structure at the frontal gradient to physical factors alone. The interaction between depth and the convergent front results in its influence being evident only at certain depths and not at others. However, aggregation was observed in 6 shallow taxa, but not in others present in similar depths, which suggests that there are processes occurring at shallow depths other than convergence alone, such as predation or predator avoidance. Though past studies have attributed different assemblages of zooplankton to water mass differences (e.g. Pagès et al. 2001, Palma & Silva 2006) and frontal boundaries (Pavez et al. 2010), many were conducted on scales too coarse (sometimes integrating 50 to 100 m) to tease out predation effects. Since many plankton patches are <5 m in vertical thick-

ness (Benoit-Bird et al. 2013b), integrating even 20 m in the vertical would mask trophic interactions. One possible way of testing this hypothesis is to observe plankton community structure at fronts in the absence of top predators, which would likely occur at the beginning stages of frontal development. Furthermore, it would be important to obtain fine-scale current velocities. Coupled with organism depth and orientation, which is possible to measure with ISIIS (Greer et al. 2013), the relative influence of convergent flow on organisms by depth could be modeled.

The hierarchical structuring hypothesis (Hyp. 2) states that predation, predator avoidance, or physiological constraints are ultimate drivers for population distributions, while the front is a proximate, regulating factor. In the present study, large *Solmaris rhodoloma*, which comprised 24% of the hydromedusae and was aggregated in the front in concentrations exceeding 800 ind. m⁻³, was the dominant predator. *S. rhodoloma* are obligate predators on other gelatinous zooplankton in a range of sizes, such as doliolids and possibly appendicularians and siphonophores (Purcell & Mills 1988, Larson et al. 1989, Raskoff 2002). Their aggregation in the top 50 m at the front could have cascading top-down effects on their prey. However, in deeper waters, many organisms may be structured by other water mass effects, which is evident in some of the BRT results. For example, lobate ctenophores such as *Ocyropsis maculata* appear primarily influenced by DO levels (48% in BRT model), with highest concentrations occurring between 2 and 3 ml l⁻¹ DO, suggesting that these ctenophores are primarily attracted to an intermediate DO level. This is consistent with some experimental work showing that lobate ctenophores have an affinity for low DO environments, which do not affect their feeding ability (Kolesar 2006, Kolesar et al. 2010). Proximate vs. ultimate factors also have been used to explain zooplankton diel vertical migrations (Hays 2003, Cohen & Forward 2009). Examining the effects of fronts with different characteristics (e.g. temperature, density, or chl a driven fronts) on zooplankton communities, particularly gelatinous zooplankton communities, would help determine which factors are regulating (proximate) and which are controlling (ultimate).

The final possible explanation (Hyp. 3) is that the community structure observed in this study was originally formed according to bottom-up effects (Bakun 1996, Olson 2002), but became dominated by top-down effects as the predator
aggregation became established. Our observation
may be of a frontal community post-predator
aggregation. Recent studies of end-to-end pelagic
food webs suggest that bottom-up effects may be
primary in regulating spatial aggregations of pred-
ators (Benoit-Bird & McManus 2012, Benoit-Bird
et al. 2013a). This is consistent with the current
understanding of how fronts structure biological
communities. Indeed, a recent SCB frontal study
conducted in October 2008 found elevated concen-
trations of zooplankton (all non-gelatinous, except
the appendicularians and chaetognaths) at a meso-
scale front (Ohman et al. 2012). However, in our
study, organisms were generally not aggregated at
the front, except the large *S. rhodoloma* and a
few other mildly to moderately aggregating hydro-
medusae and doliolids. Additionally, the chl a
fluorescence maximum was located 20 to 30 km
inshore (east) of the front in every transect. Thus,
it is possible that after the development of the
front, there was a succession of dominant organ-
isms in progressively higher trophic levels (like a
spring bloom; cf. Porter 1977, Muller et al. 1991,
Lochte et al. 1993), culminating in the ‘bloom’ of *S.
rhodoloma*. This jellyfish bloom could have then
consumed or driven away other gelatinous zoo-
plankton (their prey) at the frontal discontinuity (a
top-down effect), which could explain why there
are so few organisms aggregating at the front.
With respect to the BRT models, it is possible
that the association between *S. rhodoloma* and the
front was greater during the initial development of
the front, but its influence decreased as the aggre-
gation became established and the population
structured by other factors. Since this is a snapshot
study, it is not possible to determine at what point
during the frontal development and population
succession sampling occurred. This highlights the
need for more comprehensive studies of mesoscale
fronts, particularly those that track the frontal
community structure at multiple time points.

Community composition and assemblage analyses

Aside from a few historical reports (Torrey 1904,
1909, Ritter 1905, Alvariño 1980, Alvariño & Kim-
brell 1987), there have been no recent studies on
the gelatinous zooplankton community as a whole,
spanning the major functional groups (hydro-
medusae, siphonophores, ctenophores, and pelagic
tunicates) in the California Current. However, there
seems to be a general coherence between the spe-

cies we found and other reports from the region
(cf. Mills & Haddock 2007, Mills et al. 2007, Mills
& Rees 2007). In fact, nearly all of the taxa we re-
ported in our study, with the exception of the
ctenophores, were documented in Alvariño (1980).
The only other comprehensive study on ctenophore
distribution and assemblages (that we know of)
is from Harbison et al. (1978), so our description of
the ctenophore community distribution, particularly
for lesser described species such as *O. maculata*
and *Thalassocalyce inconstans*, is new for this
region.

The species imaged in this study were grouped
into 4 assemblages using results from CA, but
when combined with correlation analysis and BRT
modeling, there were some unexpected diver-
gences within groups and commonalities between
groups, which suggest that these assemblages
are dynamic. Though negative correlations were
observed between assemblage A and the others,
similar negative correlations between assemblages
B through D were largely absent. Also, assemblage
D and the 2 outliers were slightly positively cor-
related, instead of being more separated as the
clustering might suggest. Correlation analysis uses
abundance/concentration data instead of in CA,
where the chi-squared distance essentially reflects
only presence-absence data in all but the most
abundant taxa. Thus, correlation analyses arrived
at slightly different results and instead, suggested
support for 3 assemblages.

In contrast, the BRT results suggested that there
were differences in processes within assemblages.
This is highlighted in 2 cases. (1) *S. rhodoloma*
was highly correlated with its known doliolid prey
as well as some suspected siphonophore prey (Lar-
son et al. 1989, Raskoff 2002, McClatchie et al.
2012), yet the physical variables that most associ-
ated with doliolid and shallow siphonophore popu-
lations were different from those most associated
with *S. rhodoloma* populations. Most likely, prey
populations (doliolids and small siphonophores)
are being consumed, thus effectively occupying a
slightly different environmental niche than their
predators. Indeed, BRT results for doliolids and
the siphonophore *Muggiaea atlantica* were nearly
identical. (2) Two siphonophores, *M. atlantica* and
*Sphaeronectes* sp., were positively correlated and
grouped in the same assemblage, yet the depths of
their peak abundances were offset from one
another and they showed different environmental
influences. These 2 calycorphoans are of a similar
shape, size, and feeding strategy, and presumably
would be feeding on the same prey field (Mackie et al. 1987). While there may be species-specific physiology that would render one taxa more sensitive to temperature or DO, an alternative scenario is that one siphonophore competitively excluded the other, which has resulted in the apparent resource partitioning. This explanation was also proposed by Buecher & Gibbons (1999) in a distribution study on pelagic cnidarians in the Mediterranean. These examples of community interactions extend the concept of an assemblage beyond merely species presence-absence co-occurrences, which is often used in isolation to describe assemblages in gelatinous zooplankton, larval fish, and other zooplankton (Pagès et al. 2001, Thibault-Botha et al. 2004, Richardson et al. 2010). The processes captured in these analyses (co-occurrence, correlation between abundances, and environmental drivers) each explain different components of species distributions and assemblage characteristics, and are complementary. Assemblages are likely dynamic in non-structured planktonic habitats, and they may reflect some niche separation between plankton taxonomic groups and species.

## Using ISIIS to assess gelatinous zooplankton abundance

It is difficult to use traditional sampling techniques to accurately quantify small gelatinous zooplankton, as they are easily broken apart by nets. Remsen et al. (2004) suggested that nets underestimate pelagic tunicates by over 3 times and cnidarians/ctenophores by 12 times. Even though a formal comparison of gelatinous zooplankton abundances as measured by ISIIS vs. other techniques is not possible in this case, a few broad-stroke comparisons suggest that ISIIS is at least as efficient for estimating small gelatinous zooplankton abundances as net and ROV systems, is able to quantify fine-scale hotspots of high plankton densities, and may be imaging an order of magnitude more small cnidarians than nets typically capture.

While not strictly equal, comparisons with other studies in coastal regions, including California Current and other eastern boundary current sites, reveal similarities in a few taxa but differences for many others. For common siphonophores, 3 yr of ROV sampling in Monterey Bay revealed maximum concentrations of *Nanomia bijuga* and diphyids to be 0.7 and 0.033 ind. m⁻³, respectively (Robison et

al. 1998, Silguero & Robison 2000). In comparison, our peak concentrations of *N. bijuga* and diphyid siphonophores were 23 and 9.7 ind. m⁻³, respectively, which is 33 and 300 times greater than previously reported. In other upwelling systems, a similar order of magnitude difference was also observed for small siphonophores and a common hydromedusa (Pagès et al. 2001, Palma & Silva 2006, Pavez et al. 2010). However, Raskoff (2001) found blooms of *S. rhodoloma* in similar densities as our study, though we were able to localize high density hotspots within the larger aggregation, with concentrations >1000 ind. m⁻³ (McClatchie et al. 2012). Future work should include systematic comparisons between ISIIS and other sampling systems for estimating gelatinous zooplankton concentrations.

## Species-specific behavior

We also observed some taxa-specific behaviors that are worth briefly noting. In 2 sampling transects, the distribution of *Liriope tetraphylla*, *S. rhodoloma* and *Solmundella bitentaculata* shifted from mid-depths in the afternoon to the surface in the evening, which suggests that these taxa are vertically migrating to the surface at dusk. The most pronounced pattern was observed in *L. tetraphylla* (Fig. S4 in the Supplement at www.int-res.com/articles/suppl/m510p129_supp.pdf); organisms were found deeper on the east side of Transects 1 and 2, where the sampling started at 17:00 and 16:35 h, respectively, and continued through the night. Average depths of *L. tetraphylla* in Transects 1 to 2 decreased 45 m in ~2 h, such that during the middle of the transect (~19:30 h), organisms were within 15 m of the surface. Note that this change in depth was not observed in Transect 3 because sampling commenced 2 h later (~21:00 h). This behavior is consistent with previous reports on *L. tetraphylla* (Moreira 1973). *S. bitentaculata* and *S. rhodoloma* have not previously been shown to vertically migrate, though their relative, *Solmissus albescens*, can migrate hundreds of meters daily (Mills & Goy 1988). However, since this study was not designed to investigate vertical migrations, these observations should be further tested in a more targeted study.

The distributions of unidentified larval lobates and *O. maculata* were in close proximity (but non-overlapping) with one another, with larval lobates occurring higher in the water column. The relative abundance and concentrations of both are com-
parable, and a series of life stages are shown in Fig. 3ah–ak, with pictures consistent with that of reported development cycles of *Ocyropsis crystallina* (Chiu 1963). If the larval lobates are indeed young life stages of *O. maculata*, then this would be the first documentation of ontogenetic vertical migration in ctenophores.

## CONCLUSIONS

We document a high density community of small cnidarians, ctenophores, and pelagic tunicates around a mesoscale front in the Southern California Bight. One extension of our work is to quantify the contribution of small gelatinous zooplankton and pelagic tunicates to carbon cycling in the pelagic oceans. Gelatinous zooplankton have extremely high turnover rates, with appendicularians able to turn over 3 times their body mass daily in house production (Sato et al. 2001). Furthermore, growth rates in jellyfish, after standardization based on carbon content, are 2 times greater than that of pelagic vertebrates (Pitt et al. 2013). In combination with the high densities we documented, this suggests that small gelatinous zooplankton may have a small but significant impact on global carbon cycling. Further investigation on the role of small gelatinous zooplankton in carbon and other nutrient cycling is warranted.

Our description of the fine-scale distribution and physical environment of nearly 50 gelatinous zooplankton taxa allowed us to precisely model the effects of physical variables on species distribution and density. Our results highlight the gaps in our knowledge on how fronts structure planktonic communities, and we propose several hypotheses for further investigation. However, we also fully acknowledge the major limitation of our study in its temporal scale: this is a snapshot of a front over 3 d. Further study needs to be done to investigate the seasonal and annual cycles of gelatinous zooplankton production at fronts and in the California Current system. Furthermore, as mesoscale fronts are incredibly prevalent over a wide band along the California coast, and have already shown multi-decadal increasing trends (Kahru et al. 2012), it is important to understand the influence of different types of fronts (chl a, temperature, and salinity fronts) on plankton communities, to describe baseline conditions, and also to predict shifts in community structure with changing ocean conditions.

*Acknowledgements*. We thank Phil Pugh, Steve Haddock, Dhugal Lindsay, Bill Browne and Casey Dunn for contributing their taxonomic expertise in identifying organisms. Jenna Binstein, Michelle Giorgilli and Allison LaChanse helped with image analysis. Su Sponaugle and Rob Condon contributed helpful discussions and review of earlier drafts. We thank Shin-ichi Uye and 3 anonymous reviewers, whose comments helped improve the manuscript. We thank the Captain and crew of the R/V 'Bell M. Shimada'. Partial funding for this study comes from the NOAA Fisheries Service. J.Y.L. was supported by University of Miami's Maytag Fellowship and a NSF Graduate Research Fellowship.

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Proofs received from author(s): August 26, 2014