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Seasonal, interannual, and spatial patterns of community composition over the eastern Bering Sea shelf in cold years. Part I: zooplankton

Seasonal, interannual, and spatial patterns of community composition over the eastern Bering Sea... Abstract Ecosystem structure and function in the eastern Bering Sea are impacted by seasonal, interannual, and spatial variation of the zooplankton community. Zooplankton abundance, community composition and individual responses of key taxa, in spring, summer, and fall were evaluated across ecoregions during three years with high sea-ice, 2008–2010 (cold years). Interannual variations were greatest in spring, but less pronounced compared with intra-annual variations. Intra-annual variations were greatest in the south middle domain in spring and the north middle domain in all seasons. Models using environmental variables were able to explain 69–77% of zooplankton community variation within each season. Among individual taxa, Calanus marshallae/glacialis had delayed stage progression in spring 2009 compared with 2008 and 2010 on the south middle shelf, likely due to late ice retreat and cold temperatures that increased development times. In contrast, stage progression was fastest in summer 2008 likely due to warmer temperatures. Our findings indicate that intra-annual variation of zooplankton community composition, life history stage, and abundance within a cold period may affect the amount of high–lipid zooplankton prey (e.g. Neocalanus and Calanus spp. copepods and euphausiids) available seasonally for forage fish (e.g. age-0 walleye pollock) to grow to a sufficient size (to avoid size-dependent predation) and have sufficient lipid stores (to avoid starvation) to survive the first winter at sea. Introduction The eastern Bering Sea shelf is a highly productive ecosystem for fish, marine mammals and seabirds, with valuable commercial fisheries (e.g. groundfish, salmon, and crab) and subsistence harvests. These tertiary consumers, including humans, rely on the transfer of primary production to higher trophic levels through the zooplankton community. Accordingly, understanding seasonal, interannual and spatial variations in zooplankton assemblages (relative abundance of different species and stages), with an emphasis on responses of individual taxa that are key prey items, is important for predicting shifts in productivity of fish and other predators. Prior studies on zooplankton communities and the overlap of fish predators and zooplankton prey have focused on interannual variability between warm and cold climate stanzas within a single season (Coyle et al., 2011; Eisner et al., 2014; Moss et al., 2009; Stabeno et al., 2012b; Siddon et al., 2013; Eisner et al., 2015). In contrast, the current study evaluates within and between season patterns over a cold stanza (three consecutive cold years). Seasonal comparisons of zooplankton communities across varied shelf regions allow us to make inferences about factors influencing ecosystem dynamics in the Bering Sea and similar high latitude shelf ecosystems (e.g. Chukchi Sea). The overall goal is to explain seasonal variance in zooplankton communities and the underlying factors driving these variations. We also discuss how these variations have potential ripple effects across the ecosystem and impact fisheries community dynamics (Siddon et al., this issue). Regional climate forcing via variations in sea ice extent and timing has large impacts on the eastern Bering Sea ecosystem (Hunt et al., 2011; Stabeno et al., 2012a,b,, 2016). Since 2000, there have been multi-year periods (stanzas) of low sea ice (warm) or high sea ice (cold) conditions (Stabeno et al., 2012b). The timing of sea ice retreat and extent of ice coverage varies in relation to the strength and position of the Aleutian Low, with winds from the south in warm years and from the north in cold years (Stabeno et al., 2012b). Sea ice affects the seasonal progression of oceanographic conditions (e.g. stratification, hydrography, phytoplankton bloom timing; Sigler et al., 2015). Importantly, in years with late ice retreat, the spring phytoplankton bloom generally occurs earlier as stratification sets up due to ice melt (ice edge bloom), rather than later due to warming (open water bloom) (Hunt et al., 2002; Sigler et al., 2015, 2016). This early spring primary production may be beneficial to large lipid-rich crustacean zooplankton (e.g. large copepods such as Calanus and Neocalanus spp., and euphausiids) survival and growth (e.g. Baier and Napp, 2003; Hunt et al., 2016), resulting in increased lipid storage and enhanced overwinter survival of age-0 walleye pollock Gadus chalcogrammus (hereafter termed pollock; Coyle et al., 2008, 2011; Heintz et al., 2013; Eisner et al., 2015; Renner et al., 2016; Sigler et al., 2016). Therefore, variations in sea ice in spring can affect the production of phytoplankton, zooplankton, and forage fish throughout the growing season. Spatial variability in marine communities in the eastern Bering Sea is often partitioned along- and across-shelf by water masses and fronts. Frontal regions can serve as natural cross-shelf boundaries for water masses acting to separate and concentrate planktonic prey (Kachel et al., 2002; Eisner et al., 2014, 2015), which impacts the distribution of zooplanktivorous fish, sea birds, and marine mammals (Schneider, 1982; Moore et al., 2002; Jahncke et al., 2005; Baker and Hollowed, 2014). Regions of the shelf have been classified as inner <50 m bathymetry), middle (50–100 m), and outer (100–170 m) domains delineated by frontal regions (Coachman, 1986) and further divided south and north of ∼60°N latitude (Figure 1) (Stabeno et al., 2010). Recently, further subdivision into 16 smaller ecoregions has been proposed based on bathymetric, oceanographic, ecosystem and fisheries characteristics (Ortiz et al., 2012). Ecosystem modeling outputs have been partitioned by ecoregion (Ortiz et al., 2016); therefore, it is important to explore the applicability of these ecoregion designations at different trophic levels. Little is known about the applicability of ecoregion designations for lower tropic levels, in particular, although differences were seen for phytoplankton biomass in late summer with higher biomass observed further north within the south middle shelf (Ecoregion 6 compared with 3) likely due to nutrient inflow through the Pribilof Canyon (Figure 1) (Eisner et al., 2016). Figure 1. View largeDownload slide The map and inset show the eastern Bering Sea study area. Ecoregions are indicated by solid lines and numbers, and bathymetry is shown by light gray contours. The inner shelf is designated by Ecoregions 2, 7 (south inner) and 11 (north inner); the middle shelf by Ecoregions 3 and 6 (south middle), 9 and 10 (north middle); the outer shelf by Ecoregions 4 (south outer) and 8 (north outer); off-shelf by Ecoregions 15 and 16; Pribilof Islands by Ecoregion 5; and Alaska Peninsula by Ecoregion 1. Figure 1. View largeDownload slide The map and inset show the eastern Bering Sea study area. Ecoregions are indicated by solid lines and numbers, and bathymetry is shown by light gray contours. The inner shelf is designated by Ecoregions 2, 7 (south inner) and 11 (north inner); the middle shelf by Ecoregions 3 and 6 (south middle), 9 and 10 (north middle); the outer shelf by Ecoregions 4 (south outer) and 8 (north outer); off-shelf by Ecoregions 15 and 16; Pribilof Islands by Ecoregion 5; and Alaska Peninsula by Ecoregion 1. In this article, we investigate the seasonal progression of individual zooplankton species and assemblages on the eastern Bering Sea shelf over three consecutive cold years. Specifically we ask: (i) Do zooplankton communities show intra-annual and interannual differences? (ii) Do communities show differences among pre-defined ecoregions? (iii) How do season, ecoregion, year and environmental covariates (such as water temperature, salinity, and sea ice) account for variability in community composition? (iv) Does the abundance and stage composition of important prey species, such as Calanus spp. (C. marshallae and C. glacialis) show intra-annual and interannual differences? Finally, we discuss zooplankton community patterns in relation to ichthyoplankton and juvenile fish community patterns (i.e., predator field) over the same time period—described in Siddon et al. (this issue), in an effort to broaden our ecosystem perspective and further evaluate trophic linkages in this region. Methods Zooplankton and oceanographic sampling Samples were collected in 2008–2010 from April through July as a part of the multi-disciplinary Bering Sea Project (Bering Sea Ecosystem Study and Bering Sea Integrated Ecosystem Research Programme, Wiese et al., 2012), and from mid-August through early October by the Alaska Fisheries Science Center (AFSC) Bering Arctic Subarctic Integrated Survey (BASIS) (Supplementary Material S1). Stations were located across the shelf from 54.5°N to 62.8°N (northernmost location in spring and summer surveys). Zooplankton was sampled with a 25 cm diameter CalVET (PairVET) system (Smith et al., 1985) with 150 µm mesh nets, with General Oceanics flowmeters mounted inside the nets to monitor the volume filtered. The nets were fished vertically during the day from 100 m to the surface or from near-bottom to the surface at depths < 100 m. All samples were preserved in 5% formalin:seawater for later sorting. Vertical profiles of temperature and salinity were collected at each station with SBE 25 or SBE 9 (Seabird Electronics, Bellevue, WA, USA) conductivity-temperature-depth (CTD) profilers. An additional dataset was collected in the southeastern Bering Sea in April/May 2008–2010 by the AFSC Ecosystems and Fisheries Oceanography Coordinated Investigations (EcoFOCI) programme. Zooplankton was sampled using 60 and 20 cm diameter MARMAP style Bongo and PairVET frames bracketed together and fitted with 333 µm (or 505 µm for a subset of stations in 2009) and 150 µm mesh nets, respectively. Nets were towed obliquely to the surface from 300 m, or from 10 m off the bottom at depths <300 m. All nets were equipped with calibrated flowmeters to monitor the volume filtered. Oceanographic data were concurrently collected with a SBE 19 CTD that provided real-time measurements over the towed path. Collections from the Bering Sea Project and BASIS were processed at the University of Alaska Fairbanks according to established protocols (e.g. Coyle et al., 2008), and the combined data set is termed BSP/BASIS, hereafter. Briefly, each sample was poured into a sorting tray and large organisms, primarily shrimp and jellyfish, were removed and counted. The samples were then sequentially split using a Folsom splitter until the smallest subsample contained about 200 specimens of the most abundant taxa. All taxa in the smallest subsamples were identified, staged, and counted. Each larger subsample was examined to identify and count the larger, less abundant taxa. Collections from EcoFOCI were sorted at the Polish Plankton Sorting and Identification Center (Szczecin, Poland) to the lowest taxonomic level and developmental stage possible. Since only selected taxa were counted from 150 µm samples, additional taxa counts from 333 µm (or 505 µm) samples were used to complement the analysis of EcoFOCI data (see Supplementary Material S3). Samples from the 505 µm net were used for stations sampled 8–18 May 2009 because the 333 net was not available. The 333 and 505 µm nets were assumed to provide similar counts for the larger size (later stage) taxa enumerated (Siefert and Incze, 1989; Boeing and Duffy-Anderson, 2008); all taxa counted from these nets were larger than 505 µm. Due to differences in taxonomic resolution and mesh sizes, analyses of community structure were conducted separately for the EcoFOCI dataset. Cnidarians and ctenophores as well as mysids, gammarids, shrimp, and juvenile and adult euphausiids were removed from both datasets, since sampling for these species groups was not quantitative due to small net size and volume of water filtered. Taxa and stages selected for the analyses are listed in Supplementary Material S2 (BSP/BASIS dataset) and Supplementary Material S3 (EcoFOCI dataset). Data analyses Station selection and ecoregions Stations were sampled in 13 or 7 of the 16 ecoregions (Ortiz et al., 2012) for BSP/BASIS and EcoFOCI data, respectively (Figures 2 and 3). Stations along the Alaska Peninsula but inshore of the ecoregion boundary were included in Ecoregion 1. Figure 2. View largeDownload slide Zooplankton clusters (symbols) are shown by season and year for the BSP/BASIS dataset. Ecoregions are shown by numbers (described in Figure 1). The heatmap in Table 1 shows the taxa/stage in each cluster. Symbols designate the season that the cluster was typically observed: squares, diamonds and circles designate spring, summer and fall, respectively. (See on-line version for color figure.) Figure 2. View largeDownload slide Zooplankton clusters (symbols) are shown by season and year for the BSP/BASIS dataset. Ecoregions are shown by numbers (described in Figure 1). The heatmap in Table 1 shows the taxa/stage in each cluster. Symbols designate the season that the cluster was typically observed: squares, diamonds and circles designate spring, summer and fall, respectively. (See on-line version for color figure.) Figure 3. View largeDownload slide Spring zooplankton clusters (triangles) are shown for the EcoFOCI dataset. Ecoregions are shown by numbers (described in Figure 1). The heatmap in Table 2 shows the taxa/stage in each cluster. (See on-line version for color figure.) Figure 3. View largeDownload slide Spring zooplankton clusters (triangles) are shown for the EcoFOCI dataset. Ecoregions are shown by numbers (described in Figure 1). The heatmap in Table 2 shows the taxa/stage in each cluster. (See on-line version for color figure.) Environmental variables We selected water-mass properties correlated to the distribution of zooplankton communities in prior studies in the eastern Bering Sea (Baier and Napp, 2003; Coyle et al., 2008; Eisner et al., 2014). For each station, we included mean temperature and salinity above and below the mixed layer depth, estimated as the depth where σt is 0.10 kg m−3 higher than the value at 5 m (Danielson et al., 2011). Mean values of temperature and salinity over the water-column were excluded from analysis due to high correlations (>94%) with those observed below the mixed layer depth. Bathymetric depth, latitude, and longitude were included as covariates. Sea ice variables were also included as covariates since presence of sea ice correlates with growth and survival of important copepod taxa (e.g. Calanus spp.). Station-specific variables included the number of days of sea ice coverage during the prior winter as well as the day of year when ice last occurred. Sea ice was assumed present if coverage was >15% over a 60 km2 box centered at each 0.5° latitude and 1° longitude (Eisner et al., 2014). Sea ice data were obtained from the Advanced Microwave Scanning Radiometer on Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua from the National Snow and Ice Data Center (S. Salo, NOAA PMEL, pers. comm.). Categorical variables (factors) included Season, Ecoregion, and Year. Multivariate analyses Patterns in community structure were first analyzed with classification (cluster analysis) and ordination (nonmetric multidimensional scaling (nMDS)) techniques using Bray-Curtis dissimilarity of abundance between stations. Next, quantitative analyses using distance-based linear models and permutational multivariate analysis of variance (termed permutational MANOVA or PERMANOVA) were applied to each season to test year and ecoregion effects as well as environmental effects on zooplankton community composition (PRIMER-E, Anderson et al., 2008; Clarke et al., 2014). Advantages of using these distance-based, permutational techniques are: (i) flexibility of the dissimilarity measure that best describes distance between groups, and (ii) relaxed distributional assumptions, such as normality. To allow for more even contribution of small and large relative abundances and consequently make the variance-to-mean ratio closer to one, a fourth root transformation was applied to the abundance data prior to statistical analyses. Extremely rare species, occurring at <3% of stations within a dataset (“Rare” in Supplementary Materials S2 and S3), were removed from the analyses because they did not significantly contribute to spatial or temporal patterns of community composition. Cluster analysis and nMDS were both used to determine station groups with similar zooplankton composition. The groups were determined by drawing a line across the dendrogram from a hierarchical agglomerative cluster analysis using group average linkage, at a particular level of similarity (Supplementary Materials S4 and S6). This level was based on a combination of: (i) the presence of longer branches (indicating stability), (ii) groupings suggested by similarity profile analysis, a procedure that tests for randomness at each sequential node of the dendrogram (Clarke et al., 2014), as well as (iii) biological interpretation (Romesburg, 2004). Clusters were then verified by nMDS by superimposing the cluster groups on the ordination to check that groups were separated sufficiently (Supplementary Materials S5 and S7). The resulting clusters were mapped geographically. Clusters were designated by bold capital letters for the BSP/BASIS dataset and by bold lower case letters for the EcoFOCI dataset. To test for significant differences in community structure among seasons, ecoregions, and years, analysis of similarity global and pairwise tests were applied. This analysis uses ranks of a resemblance matrix (in our case, Bray-Curtis dissimilarity) to estimate the overall differences among groups relative to within groups using an R-statistic (Clarke et al., 2014). For R = 0, groups are not different, whereas R = 1 indicates groups are different and do not overlap. A PERMANOVA test, which partitions the variance and tests for effects on the Bray-Curtis dissimilarity, was used to determine significant effects and percent contribution of the factors Season, Ecoregion, and Year, to overall variability. This was followed by a distance-based linear regression model which determined what environmental variables might explain the significant temporal and spatial effects (Anderson et al., 2008). A forward selection option based on an adjusted R2 criterion was used to select the most important environmental variables. For each season, the environmental variables chosen by the regression model were then included in order of importance as covariates in a second PERMANOVA using Type I sum of squares (sequential), followed by the factors. This yielded relative components of variation, so as to determine which factors and environmental covariates explained the most variability in zooplankton community composition (Underwood and Petraitis, 1993). Separate regression models and PERMANOVAs with covariates were applied to each season. Finally, to test if zooplankton communities varied by predefined ecoregions, PERMANOVA pairwise analyses were conducted among the ecoregions within each domain for each season and year. Multivariate tests of dispersion (Anderson et al., 2008) were conducted to test if differences observed in PERMANOVAs were due, at least partially, to unequal variances. Abundances and stage composition of predominant copepods Untransformed abundance data from BSP/BASIS and EcoFOCI datasets were combined to obtain higher resolution estimates of seasonal changes. Monthly mean abundances (all stages combined) were graphed by year and averaged over all years for five common copepod species that are potential prey items for fish. We evaluated variations for taxa common to the south middle shelf and near the Pribilof Islands and Alaska Peninsula (Ecoregions 1, 3, 5, 6 combined) and south outer shelf (Ecoregion 4) (Figure 1). Taxa included Calanus spp. and Pseudocalanus spp. on the middle shelf, and Eucalanus bungii, Metridia pacifica, and Neocalanus cristatus on the outer shelf. In addition, stage-specific abundances (Adult Females [AFs], Adult Males [AMs], Copepodites [C] stages 2–5) for Calanus spp. were used to evaluate changes in stage composition averaged over two week intervals for April–September on the south middle shelf and near the Pribilof Islands and Alaska Peninsula combined. Stage compositions were compared among years, and related to the timing of sea ice retreat and the spring chlorophyll a maximum at Bering Sea Mooring 2 (M2, Pacific Marine Environmental Lab (PMEL); Figure 1) as determined by Sigler et al. (2015). The Calanus spp. C1 stage was evaluated separately, since this information was not available for the 2010 EcoFOCI dataset. In-situ mean temperature from CTD profiles was compared with stage compositions (% of total as C4 or C5) to evaluate the effect of temperature on Calanus spp. stage progression. Results Do zooplankton communities show intra-annual and interannual differences? Assemblage groups for the BSP/BASIS data set were determined from the dendrogram by slicing at a similarity of 68.2% (Supplementary Material S4). The nMDS analysis with cluster groups superimposed indicated that this similarity level was sufficient to separate assemblage groups (Supplementary Material S5). In spring, patterns of zooplankton assemblages were very similar in 2008 and 2009 (Figure 2). The spatial pattern of zooplankton community composition was similar among ecoregions on the inner shelf (Ecoregion 2, 7, 11), middle shelf (Ecoregion 3, 6, 9, 10), and north outer shelf (Ecoregion 8; no data available for Ecoregion 4). Cluster Q dominated the inner and middle shelves. Cluster Q was characterized by small copepods: Oithona spp. copepodites, late stages of Acartia spp., and late stages and adults of Pseudocalanus spp. (Table 1). On the north middle shelf, Cluster P occurred in both 2008 and 2009, but was more widespread in 2009. Cluster P had similar species to Cluster Q, but lower abundances of Acartia spp. and higher abundances of Oithona spp. copepodites. Finally, on the north outer shelf, Cluster D was widespread and was characterized by the more oceanic species: Neocalanus and Metridia spp., in addition to Microcalanus and Oithona spp. copepodites and adults. A different pattern, more similar to patterns observed in summer, was observed in the spring of 2010 likely because this survey occurred ∼1 month later in the year (Supplementary Material S1). The northern shelf was characterized primarily by Cluster T, distinguished by Acartia longiremis adults, early stage Calanus spp. copepodites, and euphausiid nauplii. Clusters M, S, and J also appeared in 2010, but not the other two springs. Cluster S was more prevalent on the inner and middle shelf and was characterized by Acartia spp., early stage Calanus spp., Oithona spp., and early stage Pseudocalanus spp. Cluster M had a very similar makeup, but higher numbers of later stages of Calanus spp. and higher Metridia spp. abundance. Cluster J was similar to Cluster D, but had higher abundances of Oncea spp., Metridia spp., E. bungii, early stage Calanus spp. copepodites, and euphausiid nauplii. Table 1. Heatmap shows the taxa common to one or more clusters in Figure 2. Gray scale is used to code mean abundance (fourth root transformed) of each taxa within each cluster. Clusters are ordered (left to right) by season and shelf domain (I, inner; M, middle; O, outer; SB, shelf-break) in which the cluster commonly occurred. For brevity, clusters with < 3 stations are not shown. (See on-line version for color.)     Spatial patterns in zooplankton assemblage were similar during both summer and fall across all years. Summer was characterized by Cluster S on the middle and inner shelves (Figure 2). As the water deepened on the outer shelf there was a transition to Cluster I (2009 south shelf) or Cluster M, followed by Cluster K further offshore. Fritillaria spp. and echinoderm larvae as well as Oithona and Oncea spp. characterized Cluster I. Cluster K had a similar makeup to Cluster M (described in spring section), but with reduced Calanus spp. and higher Metridia spp. and E. bungii abundances. One final cluster, Cluster U, is of interest during the summer. Cluster U was characterized by high abundances of Pseudocalanus spp. and late stage Calanus spp. (C4–C5). Cluster U was present in some areas during the summer, but was more prevalent during fall in the south middle shelf, particularly in 2008 and 2010. Otherwise, the fall was dominated by Cluster V, a cluster characterized by high abundances of Acartia, Oithona, Pseudocalanus, and Fritillaria spp., polychaete larvae, and the pteropod Limacina helicina. Zooplankton assemblage patterns for spring EcoFOCI data were similarly evaluated using cluster analysis by slicing the dendrogram at a similarity of 70.2% (Supplementary Material S6). The nMDS analysis with cluster groups superimposed indicated that this similarity level was sufficient to separate assemblages (Supplementary Material S7). EcoFOCI data were concentrated along the southern portion of the middle shelf, north of the Alaska Peninsula and near the Pribilof Islands (Figures 1 and 3). Cluster k was distributed along the inner and middle shelf in both 2008 and 2010. Cluster k was characterized by Acartia spp., Calanus spp. adults and early stages, euphausiid nauplii, Oithona spp., and the highest overall abundances of Pseudocalanus spp. (Table 2). Along the outer shelf, Cluster f was prevalent in 2008 and 2010 (Figure 3). Cluster f was characterized by higher abundances of Metridia and Oithona spp. while the typical inshore taxa (e.g. Acartia spp.) were less abundant (Table 2). The spring 2009 data had a different distribution of clusters in comparison with 2008 and 2010. The inner and middle shelves were represented by Clusters m and n, two clusters that were characterized by lower abundances overall compared with the two other springs, particularly in Calanus, Oithona, and Pseudocalanus spp., and euphausiid nauplii. Table 2. Heatmap shows the taxa common to one or more clusters in Figure 3. Gray scale is used to code mean abundance (fourth root transformed) of each taxa within each cluster. Clusters are ordered (left to right) by season and shelf domain (M, middle; O/SB, outer/shelf-break) in which the cluster commonly occurred. For brevity, clusters with <3 stations are not shown. (See on-line version for color.)     Do communities show differences among predefined ecoregions? PERMANOVAs and tests of dispersion were conducted among the ecoregions within the north and south middle domains for each season and year. Sample sizes were too low to conduct similar analyses for south inner domain ecoregions, and other domains consisted of only one ecoregion (Figure 1). Our results indicated that the ecoregions within the south middle domain (Ecoregions 3 and 6) had significant differences in community composition in 2008 and 2009 in spring, 2010 but not 2009 in summer, and 2010 but not 2008 or 2009 in fall (Supplementary Material S8). Sample sizes were too low to test 2010 in spring and 2008 in summer. Ecoregions within the north middle domain (Ecoregions 9 and 10) were different in all years and seasons tested, although the significant differences in community composition for summer 2009 may be at least partially attributed to differences in dispersion (P < 0.05, Supplementary Material S8). Sample sizes were too low to test 2008 and 2010 in fall. How do season, ecoregion, year and environmental covariates account for variability in community composition? As suggested by the prior cluster results, PERMANOVA estimates of components of variation for factors (Year, Season, Ecoregion) using the entire BSP/BASIS dataset indicated that Season and Ecoregion explained more variability (24 and 21%, respectively) than Year (4%) with interactions among factors also important (Supplementary Material S9). The permutational tests of dispersion did not show unequal variance between years 2008 and 2009 or between summer and fall seasons, but did show unequal variance among remaining years and seasons, and for ∼ 25% of the ecoregion pairs tested (pairs with sufficient sample sizes) indicating that differences were at least partially attributable to unequal variance among ecoregions. Analysis of similarity indicated that seasonal differences were greater between spring and summer, than summer and fall (R = 0.417 and 0.349, respectively, Supplementary Material S10). Differences among years were greatest in spring, particularly for the EcoFOCI dataset (R = 0.093 and 0.130 for BSP/BASIS and EcoFOCI, respectively). Fall communities did not show significant differences among years (P > 0.05). PERMANOVA estimates of components of variation by season, using both datasets, indicated that more variability in community structure was explained by Ecoregion (28–43%) than Year (4–11%) (Table 3). The permutational tests of dispersion indicated variances were significantly different for summer communities between 2008 and 2010, and between 2009 and 2010, and among some Ecoregions and Ecoregion/Year interactions, indicating that differences were at least partially attributable to unequal variances among groups. Table 3. PERMANOVA (Type 1 sequential) estimates of components of variation (%) are shown by season for Year, Ecoregion, and interactions among these factors without (w/o) and with (w) environmental covariates (Env).     Env include temperature and salinity (T, S) below the pycnocline (Tb, Sb) and above the pycnocline (Ta, Sa), ice variables (coverage (Ice C): number of days of coverage during the prior winter, retreat (Ice R): day of retreat), and latitude (Lat), longitude (Long), and bathymetry (Depth). Residual (Res) unexplained variability and total explained variability (1-Res) are shown. N indicates number of samples. The BSP/BASIS analysis excludes Ecoregion 15, 16, and 8. Ice variables were not available for the EcoFOCI dataset. The order of Env in each PERMANOVA sequential model is shown. The Env that explains the highest variability in community structure for a single variable (based on analysis from the regression model) is the first variable in each column. The addition of environmental covariates: below and above pycnocline temperature (T) and salinity (S), spatial variables (latitude, longitude, bathymetric depth), and sea ice variables (where applicable) in PERMANOVA models lowered the residual variability from an average of 48% to an average of 27% (Table 3). Adding covariates appeared to account for much of the variability in Ecoregion, since variability explained by Ecoregion dropped from 28–43% without covariates to 3–8% with covariates included in the model. Variability explained by Year remained relatively constant with or without covariates. In spring, sea ice variables explained the most variability for the BSP/BASIS data, and below pycnocline T and S explained the most variability for the EcoFOCI data (sea ice data unavailable for this dataset). In contrast, in fall depth explained the most variability. In summer, single variables explained only a small amount (<10%) of the variability. Overall, factors and environmental covariates combined were able to explain considerable variability in community structure (76–77% in spring, 71% in summer and 69% in fall). Below pycnocline T or S singly explained the most variability in zooplankton community structure using marginal tests in the distance-based linear regression model (top variables in Table 3). Below pycnocline T and S were plotted for each season and year (Supplementary Materials S11 and S12) to help visualize changes in these water mass parameters and compare T and S distributions with changes in zooplankton assemblages (Figures 2 and 3). In spring, inshore to offshore gradations (lower to higher values) were seen for below pycnocline T and S for the BSP/BASIS data set for all years; however, for the EcoFOCI data, this pattern was disrupted for 2009 with an intrusion of colder water at ∼ 55°N, 164–165°W (Supplementary Material S12). Below pycnocline T of −1.8 to −1.5 °C (suggesting the influence of sea ice) was observed at many stations in the northern portion of the survey area on the inner and middle shelf in spring 2008 and 2009. In summer and fall, below pycnocline T was lower on the middle shelf than on the inner and outer shelves due to the presence of the “cold pool”, cold (<2 °C) near bottom water that forms on the middle shelf in winter during ice production. In contrast, below pycnocline S still showed increases from the inner to the outer shelf. Does the abundance and stage composition of important prey species show intra-annual and interannual differences? Single species abundances of selected zooplankton taxa (all stages combined including both BSP/BASIS and EcoFOCI datasets) were averaged by month to investigate higher resolution changes in abundance in taxa common to the south middle or outer shelves (Figure 4). On the middle shelf, Pseudocalanus spp. and Calanus spp. both increased from April – June, although the percent rate of increase was higher for Calanus spp. Calanus spp. abundances dropped off in July–August with lowest levels in September (Figure 4). In contrast, Pseudocalanus spp. levels continued to remain constant from June – August and had highest levels in September. On the outer shelf, M. pacifica showed similar increases as Calanus spp., although peak abundances were a month later (in July). E. bungii had peak abundances in June or July depending on the year. N. cristatus demonstrated a much different phenology with highest abundances in April–May, decreases in June–July, and very low numbers in August–September. The only obvious interannual trend was indicated by N. cristatus, which showed lower values in 2009 than in 2008 and 2010 during April and May (no spring peak), as observed in cluster analysis. Figure 4. View largeDownload slide Mean copepod abundances (No. m− 3, all stages combined) are shown by month and year for the south middle and outer shelves in the southeast Bering Sea for combined BSP/BASIS and EcoFOCI datasets. (See on-line version for color figure.) Figure 4. View largeDownload slide Mean copepod abundances (No. m− 3, all stages combined) are shown by month and year for the south middle and outer shelves in the southeast Bering Sea for combined BSP/BASIS and EcoFOCI datasets. (See on-line version for color figure.) The stage composition of Calanus spp. over 2-week intervals shows that adults were dominant in early spring, followed by early then late stage copepodites with C5 dominating by late summer (Figure 5). Early copepodite stage C2 appeared later in 2009 than in the other two years, along with a later sea ice retreat and later spring bloom (Figure 5). Calanus spp. C1 abundances also appeared later in 2009 than in 2008 (Supplementary Material S13). The percent of C4 and C5 was low at lower temperature (<0 °C) (Figure 6). Figure 5. View large Download slide Calanus spp. stage (C2-Adult) proportions are shown by week for the south middle shelf, Pribilof Island and Alaska Peninsula ecoregions for 2008, 2009 and 2010 using combined BSP/BASIS and EcoFOCI datasets. The arrows indicate the timing of sea ice retreat and the spring chlorophyll a maximum at Mooring 2 (Sigler et al., 2014, Supplementary Material). (See on-line version for color figure.) Figure 5. View large Download slide Calanus spp. stage (C2-Adult) proportions are shown by week for the south middle shelf, Pribilof Island and Alaska Peninsula ecoregions for 2008, 2009 and 2010 using combined BSP/BASIS and EcoFOCI datasets. The arrows indicate the timing of sea ice retreat and the spring chlorophyll a maximum at Mooring 2 (Sigler et al., 2014, Supplementary Material). (See on-line version for color figure.) Figure 6. View largeDownload slide Fraction of Calanus spp. stage C4 and C5 (late stages combined) and mean water column temperature are shown for data in Figure 5. Figure 6. View largeDownload slide Fraction of Calanus spp. stage C4 and C5 (late stages combined) and mean water column temperature are shown for data in Figure 5. Discussion Our goal was to evaluate the seasonal progression of individual zooplankton species and assemblages over three consecutive cold years and relate our findings to forage fish productivity on the eastern Bering Sea shelf. Overall, the zooplankton community composition varied spatially from the inner to outer shelf. Spatial variations also were observed between pre-defined ecoregions during spring for the south middle shelf and all seasons for the north middle shelf. Communities varied temporally by season, with lower variation seen interannually. The main differences in community composition were changes in the relative abundance of copepod stages and species, and these changes were correlated to below pycnocline temperature and salinity (i.e. subsurface water masses). Sea ice coverage and retreat timing were also related to community variations in spring, and we suggest sea-ice impacts zooplankton communities in two ways. Sea ice dynamics impacts the water temperature as well as timing of the ice-associated primary production, both of which relate directly to development and growth rates of zooplankton (e.g. Calanus spp.) life history stage progression (Baier and Napp, 2003). Zooplankton from the EcoFOCI dataset and larval fish communities concurrently collected showed similar interannual variations with unique communities observed in spring 2009 (Siddon et al., this issue). Variations in zooplankton assemblages, single species, and environmental drivers Spatial variations across this broad region were similar to past studies. Variations (observed from cluster analysis) were larger across the shelf than from north to south, as seen in prior community analysis for late summer/early fall data (Eisner et al., 2014, 2015). The cross-shelf variations seen in the eastern Bering Sea have been well-documented (Cooney and Coyle, 1982; Smith and Vidal, 1986; Vidal and Smith, 1986; Coyle and Pinchuk, 1996, 2002; Napp et al., 2000; Eisner et al., 2014, 2015) with, for example, Acartia spp. found inshore, Calanus spp. found primarily in middle shelf waters, and Neocalanus spp., M. pacifica, and E. bungii found on the outer shelf. The cluster analyses suggested that ecoregion designations proposed by Ortiz et al. (2012) did not describe the patterns in community composition as well as the traditional domain classifications (Stabeno et al., 2010) used in prior community analysis of zooplankton in the eastern Bering Sea (Eisner et al., 2014). Although data in some season/ecoregion combinations were lacking, PERMANOVA analyses indicated that the ecoregion designations were appropriate for evaluating changes in zooplankton communities in some specific areas and seasons (e.g. early in the seasonal progression within the south middle domain and during all seasons within the north middle domain). In addition to the spatial variability, we observed additional patterns in zooplankton abundance related to seasonal changes in oceanographic conditions and unrelated to the cross-shelf dynamics. Variability in the rate of development for life history stages accounts for much of the observed seasonal patterns, particularly for copepods. Small copepod taxa with several generations per year (e.g. Pseudocalanus, Acartia, and Oithona spp.) had adult stages in all seasons with early copepodite stages (C1–C4) highest in summer or fall, as has been described in Coyle et al. (2008). In contrast, large copepods such as Calanus spp. overwinter as copepodite stage C5, metamorphose into adults in late winter, spawn in spring, and progress through nauplii stages (N1–N6), then copepodites stages (C1–C5) during spring and summer, leading to dominance of C5 in fall, as observed in our study. The sharp increase in relative abundance of C4–C5 Calanus spp. copepodites observed when water temperature increased from 0 to 3 °C, indicates that seasonal warm-up accelerates copepodite development rates either directly by influencing metabolic rates or indirectly by facilitating excess in food supply (algal bloom). The percent of C4 and C5 plateaus above 3 °C as most of the copepodites mature to C5 during late summer, prior to overwintering (Supplementary Material S14). Accordingly, the earlier molting into later copepodite stages in 2008 compared with 2009 and 2010 is likely related to warmer temperatures in summer 2008 as evidenced from M2 data (Stabeno et al., 2012b) and our data (Figure 6). Likewise, Ershova et al. (2015), observed a strong relationship between mean developmental stage of Calanus glacialis and surface temperature in the Chukchi Sea. Variations in sea ice retreat may have led to the unique patterns in zooplankton assemblages observed in spring 2009 (Figure 3). Baier and Napp (2003) found that Calanus spp. C5 copepodites molt into AFs well in advance of the spring bloom (February) with eggs produced immediately after. In a heavy ice year (1995), eggs were released early (January–February), C1 coincided with the spring bloom in April with many early stages (C1–C3) in May. In a year with no ice in (1996), eggs were released late (March) and C1s were observed with the spring bloom in May. Thus, the appearance of C1 seems to relate to the timing of the spring bloom in both early/no ice and late ice years, and the spring bloom generally occurs earlier in years with late ice retreat (ice persisting until after March 15) (Hunt et al., 2011; Sigler et al., 2015, 2016). All 3 years in the current study were late ice retreat years, although 2009 had the latest ice retreat on the southeastern middle shelf (near M2) compared with 2008 and 2010, and a much later spring bloom (late May/early June) similar in timing to warm year open water blooms (Sigler et al., 2015). Therefore, Calanus spp. stage progression in 2009 appeared to be delayed by the later spring bloom (i.e. a lack of adequate food resources) as well as colder temperatures which slowed growth. Similarly, studies in a shallow arctic fjord, Rijpfjorden in north-eastern Svalbard, Norway, showed that while C. glacialis egg production was triggered by ice algae, the growth of nauplii and copepodites was fueled by higher water temperatures and pelagic production (phytoplankton) later in the spring (Leu et al., 2011). Pseudocalanus spp. and M. pacifica spawning also appeared to be somewhat delayed in 2009, suggested by fewer early copepodite stages in spring 2009 (Figure 3; Table 2). Eisner et al. (2014) found that years with late sea ice retreat (colder water temperatures) and early ice associated spring blooms had higher abundances of Pseudocalanus spp. and Calanus spp. during August/September compared with years with early ice retreat and late spring blooms. In the current study, Calanus spp. and Pseudocalanus spp. total seasonal abundances did not appear lower in 2009 than in the other two years, suggesting that the delay to the start of stage progression did not reduce the numbers over the growing season, perhaps due to smaller variations in sea ice and bloom timing among cold years than between warm and cold temperature stanzas (Sigler et al., 2015). The high contribution of sea ice to zooplankton community variation in spring (33% of the variation is explained by ice coverage and retreat timing; Table 3), further supports the importance of sea ice and subsequent spring bloom timing to copepod stage progression (Sigler et al., 2016). Season and ecoregion explained more variability in community composition than year for the three cold years of our study, likely due to the reduced interannual variability in environmental variables observed within climate stanzas (Eisner et al. 2014). Bottom temperature and salinity for all years had a similar moderate impact (explained 8–13% of the variance) on community structure during all seasons for the BSP/BASIS dataset. However, for the spring Eco-FOCI data set, bottom temperature and salinity accounted for a much higher amount of variability in community structure (44%). This suggests that bottom water mass characteristics were important in structuring zooplankton communities, particularly during spring on the southern shelf. Bering Sea water masses have different concentrations of nutrients, phytoplankton, and microzooplankton (Stabeno et al., 2012a; Stoecker et al., 2014), which can lead to variations in prey fields for zooplankton. Similarly, an analysis of late summer BASIS zooplankton community data collected from 2003 to 2009 on the southern middle shelf, indicated bottom temperature and salinity were significantly correlated to large zooplankton community composition (Eisner et al., 2014). On the Chukchi Sea shelf, bottom temperature was commonly found to be the strongest factor shaping zooplankton assemblages for data collected 1947–2012 during summer months (July–September) (Ershova et al., 2015). Although we explained considerable variability (69–77%) in community structure within a season, models could be improved by the addition of variables such as currents, onshore and offshore flow, and other factors that impact copepod stage progression, such as microzooplankton abundance and predation. Overlap of zooplankton and fish assemblages The linkages among physics and zooplankton (prey source) to the quality of larval fish nursery grounds (or effective juvenile habitats) are often poorly understood (Sheaves et al., 2006). Several criteria are required to define habitat function including the effects of scale, complexity and connectivity, critical resources and processes supporting juveniles, and factors affecting sustainability of the fishery (Sheaves et al., 2006). This information can, in turn, inform ecological forecasting. Our current research attempts to aid in this effort by describing the spatial and temporal overlap between zooplankton, and larval and juvenile fish, including diet information, and environmental factors potentially influencing both communities. This information has broader applications for understanding potential processes affecting larval and juvenile habitats in other marine ecosystems. Calanus, Pseudocalanus, and Metridia spp. were important in diets of larval and juvenile pollock and Pacific cod Gadus macrocephalus in the eastern Bering Sea based on data collected in 2008 at a subset of stations in the current study (Strasburger et al., 2014). Pollock consumed early and late stage M. pacifica and Pseudocalanus spp. nauplii in spring, late stage Pseudocalanus spp. nauplii and copepodites C1 to adult and A. longiremis C4-adult in summer, and late stage Calanus spp. copepodites (C4 and C5) and adult Pseudocalanus spp. in fall. Pacific cod consumed Calanus spp. C1–C2, late stage M. pacifica nauplii and copepod eggs in spring, Calanus spp. C3 and Pseudocalanus C4 to adult in summer, and euphausiids (Thysanoessa spp.) and Limocina helicina in fall. The pollock and Pacific cod diets mirror the seasonal phenology of zooplankton in our current study. Similarly, from warm to cold periods, increases in Calanus spp. were observed in the water column and in diets of juvenile pollock and juvenile salmon in the southeast Bering Sea (Coyle et al., 2011). Euphausiids as well as Calanus spp. are important lipid-rich prey for juvenile pollock and Pacific cod (Moss et al., 2009; Coyle et al., 2011; Strasburger et al., 2014; Buckley et al., 2016; Hunt et al., 2016). Euphausiid nauplii and calyptopsis were in lower abundance in spring 2009 in the EcoFOCI dataset. Bi et al. (2015) found that the euphausiid, Thysanoessa raschii, located on the inner and middle shelf, was the most common euphausiid taxa on the Bering Sea shelf in 2008–2010, with the lowest proportions in 2009 (86, 36, and 65%, respectively). Accordingly, the high proportions of T. raschii in 2008 may have partially led to the high recruitment observed for the 2008 pollock year class (Ianelli et al., 2015). An important finding of Siddon et al. (this issue) was that larval and juvenile pollock during all seasons were less abundant in 2009. In spring 2009, deviations in zooplankton assemblages on the middle shelf near the Alaska Peninsula (Figure 3) were mirrored by deviations in concurrently collected ichthyoplankton assemblage data; many stations were dominated by adult copepods, but not copepodites, and showed a lack of pollock or had no fish larvae of any species (Siddon et al., this issue). We postulate that in 2009 the delay in the timing of sea ice retreat and in the spring bloom (Figure 5) which in turn appeared to delay stage progression for Pseudocalanus and Metridia spp. could have led to reduced prey availability (e.g. reductions in Pseudocalanus spp. nauplii and Metridia spp. copepodites) for larval pollock and other ichthyoplankton that prey on these items. The fish assemblage composition therefore may be altered by changes in the zooplankton prey base, in addition to variations in currents and other environmental factors (Siddon et al., this issue). In 2008 and 2010, the fish clusters with the highest pollock contributions often spatially overlapped with zooplankton clusters with high abundances of Pseudocalanus spp. (spring) or Calanus spp. (summer and fall). For example, in fall the pollock-dominated fish cluster on the south middle shelf overlapped with the two zooplankton clusters (U, G) that had the highest abundances of late stage Calanus spp. (C4, C5) (Table 2). This suggests that high quality zooplankton prey in appropriate size ranges were geographically available (co-located) for pollock during 2008 and 2010. In addition, the earlier appearance of late stage Calanus spp. C5 (larger and more lipid-rich than earlier copepodite stages, Liu and Hopcroft, 2007; Sigler et al., 2016) in 2008 may provide a longer period of high fat resources for higher trophic level consumers. Age-0 pollock had the highest abundances in 2008 (Siddon et al., this issue) as well as high lipid content, which enabled high overwinter survival (Heintz et al., 2013). Conclusions Evaluations of seasonal variations in zooplankton community composition and environmental variables help offer insight into changes in fish abundance, growth, and survival in the eastern Bering Sea. The overlap of suitable zooplankton prey with larval and juvenile fish predators is important to understanding trophic transfer of energy through the food web and fisheries recruitment (Siddon et al., 2013). High quality lipid-rich zooplankton prey (e.g. Neocalanus and Calanus spp. copepods and euphausiids) are critical for fish to grow to a sufficient size (to avoid size-dependent predation) and have enough lipid stores (to avoid starvation) to survive the first winter at sea (Heintz et al., 2013; Sigler et al., 2016). Environmental factors such as sea ice, spring bloom timing, and temperature can impact zooplankton species composition and stage progression even within a cold year climate stanza, as evidenced by the delay in stage progression of Calanus spp. in 2009, a year with a relatively late spring bloom. The low abundances of pollock larvae and juveniles in 2009 were possibly related to changes in spring zooplankton assemblage structure as well as environmental forcing. A characterization of the seasonality of zooplankton species and stage progression between and within climate stanzas will aid in ecosystem and multispecies fisheries modeling efforts in this region. This research also can be used to further our understanding of seasonal variations in zooplankton, factors driving these changes and potential impacts on ichthyoplankton and juvenile fish in other subarctic ecosystems. Supplementary data Supplementary material is available at the ICESJMS online version of the article. Acknowledgements We thank the staff and crew of the NOAA ship Oscar Dyson, the USCGC Healy, R/V/Knorr, and R/V Thomas Thompson for help and effort in collecting oceanographic and zooplankton samples. We are grateful for the assistance in field sampling, data processing and analysis from UAF and NOAA scientific staff and volunteers. We thank K. Coyle and his lab for assistance and guidance regarding BASIS zooplankton sampling and analysis, as well as the Polish Plankton Sorting and Identification Center for their contributions to the EcoFOCI dataset. Zooplankton collections and analysis for the Bering Sea Project were supported by the National Science Foundation as part of the BEST programme (Award Nos. ARC-0816805, ARC-0946402, and ARC-1107203). Funding This research was also generously supported by NOAA Fisheries; this is contribution EcoFOCI-N887 to NOAA's North Pacific Climate Regimes and Ecosystem Productivity research programme. References Anderson M. J., Gorley R. N., Clarke K. R. 2008. PERMANOVA for PRIMER: Guide to Software and Statistical Methods . PRIMER-E, Plymouth, UK. Baier C. T., Napp J. M. 2003. Climate-induced variability in Calanus marshallae populations. Journal of Plankton Research , 25: 771– 782. Google Scholar CrossRef Search ADS   Baker M. R., Hollowed A. B. 2014. Delineating ecological regions in marine systems: Integrating physical structure and community composition to inform spatial management in the eastern Bering Sea. Deep Sea Research II , 109: 215– 240. Google Scholar CrossRef Search ADS   Bi H., Yu H., Pinchuk A. I., Harvey H. R. 2015. Variability in euphausiid populations on the eastern Bering Sea shelf during the recent cooling event in summer 2008-2010. Deep Sea Research I , 95: 12– 19. Google Scholar CrossRef Search ADS   Boeing W. J., Duffy-Anderson J. T. 2008. Ichthyoplankton dynamics and biodiversity in the Gulf of Alaska: responses to environmental change. Ecological Indicators , 8: 292– 302. Google Scholar CrossRef Search ADS   Buckley T. W., Ortiz I., Kotwicki S., Aydin K. 2016. Summer diet composition of walleye pollock and predator–prey relationships with copepods and euphausiids in the eastern Bering Sea, 1987–2011. Deep Sea Research II , 134: 302– 311. Google Scholar CrossRef Search ADS   Clarke K. R., Gorley R. N., Somerfield P. J., Warwick R. M. 2014. Change in marine communities: an approach to statistical analysis and interpretation . 3rd edn. PRIMER-E, Plymouth, UK. Coachman L. K. 1986. Circulation, water masses and fluxes on the southeastern Bering Sea shelf. Continental Shelf Research , 5: 23– 108. Google Scholar CrossRef Search ADS   Cooney R. T., Coyle K. O. 1982. Trophic implication of cross-shelf copepod distributions in the southeastern Bering Sea. Marine Biology , 70: 187– 196. Google Scholar CrossRef Search ADS   Coyle K. O., Pinchuk A. I. 1996. Zooplankton of the Bering Sea: a review of the Russian-language literature. In Ecology of the Bering Sea: a review of the Russian Literature  pp. 97– 133. Ed. by Mathisen O. A., Coyle K. O.. University of Alaska Sea Grant College Program, Fairbanks, Alaska. Coyle K. O., Pinchuk A. I. 2002. Climate-related differences in zooplankton density and growth on the inner shelf of the southeastern Bering Sea. Progress in Oceanography , 55: 177– 194. Google Scholar CrossRef Search ADS   Coyle K. O., Pinchuk A. I., Eisner L. B., Napp J. M. 2008. Zooplankton species composition, abundance and biomass on the eastern Bering Sea shelf during summer: the potential role of water column stability and nutrients in structuring the zooplankton community. Deep Sea Research II , 55: 1775– 1791. Google Scholar CrossRef Search ADS   Coyle K. O., Eisner L. B., Mueter F. J., Pinchuk A. I., Janout M. A., Cieciel K. D., Farley E. V., Andrews A. G. 2011. Climate change in the southeastern Bering Sea: impacts on pollock stocks and implications for the oscillating control hypothesis. Fisheries Oceanography , 20: 139– 156. Google Scholar CrossRef Search ADS   Danielson S., Eisner L., Weingartner T., Aagaard K. 2011. Thermal and haline variability over the central Bering Sea shelf: Seasonal and interannual perspectives. Continental Shelf Research , 31: 539– 554. Google Scholar CrossRef Search ADS   Eisner L., Napp J., Mier K., Pinchuk A., Andrews A. 2014. Climate-mediated changes in zooplankton community structure for the eastern Bering Sea. Deep Sea Research II , 109: 157– 171. Google Scholar CrossRef Search ADS   Eisner L., Siddon E., Strasburger W. 2015. Spatial and temporal changes in assemblage structure of zooplankton and pelagic fish across varying climate conditions in the eastern Bering Sea. Izvestia TINRO , 181: 141– 160. Eisner L., Gann J., Ladd C., Cieciel K., Mordy C. 2016. Late summer early fall phytoplankton biomass (chlorophyll a) in the eastern Bering Sea: spatial and temporal variations and factors affecting chlorophyll a concentrations. Deep Sea Research II , 134: 100– 114. Google Scholar CrossRef Search ADS   Ershova E. A., Hopcroft R. R., Kosobokova K. N., Matsuno K., Nelson R. J., Yamaguchi A., Eisner L. B. 2015. Long-term changes in summer zooplankton communities of the western Chukchi Sea, 1945–2012. Oceanography , 28: 100– 115. Google Scholar CrossRef Search ADS   Heintz R. A., Siddon E. C., Farley E. VJr, Napp J. M. 2013. Climate-related changes in the nutritional condition of young-of-the-year walleye pollock (Theragra chalcogramma) from the eastern Bering Sea. Deep Sea Research II , 94: 150– 156. Google Scholar CrossRef Search ADS   Hunt G. L.Jr, Stabeno P. J., Walters G., Sinclair E., Brodeur R. D., Napp J. M., Bond N. A. 2002. Climate change and control of the southeastern Bering Sea pelagic ecosystem. Deep Sea Research II , 49: 5821– 5853. Google Scholar CrossRef Search ADS   Hunt G. L.Jr., Coyle K. O., Eisner L. B., Farley E. V., Heintz R., Mueter F., Napp J. M., et al.   2011. Climate impacts on eastern Bering Sea food webs: A synthesis of new data and an assessment of the Oscillating Control Hypothesis. ICES Journal of Marine Science , 68: 1230– 1243. Google Scholar CrossRef Search ADS   Hunt G. L.Jr., Ressler P. H., Gibson G. A., DeRobertis A., Aydin K., Sigler M. F., Ortiz I., et al.   2016. Euphausiids in the eastern Bering Sea: A synthesis of recent studies of euphausiid production, consumption and population control. Deep Sea Research II , 134: 204– 222. Google Scholar CrossRef Search ADS   Ianelli J. N., Honkalehto T., Barbeaux S., Kotwicki S. 2015. Assessment of Alaska pollock stock in the Eastern Bering Sea. In Stock Assessment and Fishery Evaluation Report for the Groundfish Resources of the Bering Sea/Aleutian Islands Regions . Report, North Pacific Fisheries Management Council. Anchorage, AK , 151 p. Jahncke J., Coyle K. O., Zeeman S. I., Kachel N. B., Hunt G. L. 2005. Distribution of foraging shearwaters relative to inner front of SE Bering Sea. Marine Ecology Progress Series , 305: 219– 233. Google Scholar CrossRef Search ADS   Kachel N. B., Hunt G. L.Jr., Salo S. A., Schumacher J. D., Stabeno P. J., Whitledge T. E. 2002. Characteristics and variability of the inner front of the southeastern Bering Sea. Deep Sea Research II , 49: 5889– 5909. Google Scholar CrossRef Search ADS   Liu H., Hopcroft R. R. 2007. A comparison of seasonal growth and development of the copepods Calanus marshallae and C-pacificus in the northern Gulf of Alaska. Journal of Plankton Research , 29: 569– 581. Google Scholar CrossRef Search ADS   Leu E., Søreide J. E., Hessen D. O., Falk-Petersen S., Berge J. 2011. Consequences of changing sea-ice cover for primary and secondary producers in the European Arctic shelf seas: Timing, quantity, and quality. Progress in Oceanography , 90: 18– 32. Google Scholar CrossRef Search ADS   Moore S. E., Waite J. M., Friday N. A., Honkalehto T. 2002. Cetacean distribution and relative abundance on the central-eastern and the southeastern Bering Sea shelf with reference to oceanographic domains. Progress in Oceanography , 55: 249– 261. Google Scholar CrossRef Search ADS   Moss J., Murphy J., Farley E., Eisner L., Andrews A. 2009. Juvenile pink and chum salmon distribution, diet, and growth in the Northern Bering and Chukchi Seas 2009. North Pacific Anadromous Fish Commission, Bulletin N , 5: 191– 196. Napp J. M., Kendall A. W., Schumacher J. D. 2000. A synthesis of biological and physical processes affecting the feeding environment of larval walleye pollock (Theragra chalcogramma) in the eastern Bering Sea. Fisheries Oceanography , 9: 147– 162. Google Scholar CrossRef Search ADS   Ortiz I., Wiese F., Greig A. 2012. Marine Regions Boundary Data for the Bering Sea Shelf and Slope. Version 1.0. UCAR/NCAR - Earth Observing Laboratory. doi:10.5065/D6DF6P6C. Ortiz I., Aydin A., Hermann A. J., Gibson G. A., Punt A. E., Wiese F. K., Eisner L. B., et al.   2016. Climate to fish: Synthesizing field work, data and models in a 39-year retrospective analysis of seasonal processes on the eastern Bering Sea shelf and slope. Deep-Sea Research II , 134: 390– 412. Google Scholar CrossRef Search ADS   Renner M., Salo S., Eisner L. B., Ressler P. H., Ladd C., Kuletz K., Santora J. A., et al.   2016. Timing of ice retreat alters seabird abundances and distributions in the southeast Bering Sea. Biology Letters , 12: 20160276. Google Scholar CrossRef Search ADS PubMed  Romesburg H. C. 2004. Cluster Analysis for Researchers . Lulu.com, Morrisville, NC. 334 pp. (Reprint of 1984 edition, with minor revisions.) Schneider D. 1982. Fronts and seabird aggregations in the southeastern Bering Sea. Marine Ecology Progress Series , 10: 101– 103. Google Scholar CrossRef Search ADS   Sheaves M., Baker R., Johnston R. 2006. Marine nurseries and effective juvenile habitats: An alternative view. Marine Ecology Progress Series , 318: 303– 306. Google Scholar CrossRef Search ADS   Siddon E. C., Kristiansen T., Mueter F. J., Holsman K. K., Heintz R. A. 2013. Spatial match-mismatch between juvenile fish and prey provides a mechanism for recruitment variability across contrasting climate conditions in the eastern Bering Sea. PLoS One , 8: e84526. Google Scholar CrossRef Search ADS PubMed  Siddon E. C., Duffy-Anderson J. T., Mier K. L., Busby M., Eisner L. B. 2018. Seasonal, interannual, and spatial patterns of community composition over the eastern Bering Sea shelf in cold years. Part II: ichthyoplankton and juvenile fish. ICES Journal of Marine Science , 75: 87– 101. Siefert D. L. W., Incze L. S. 1989. Zooplankton of Shelikof Strait, Alaska, April to August 1986: Data from Fisheries-Oceanography Coordinated Investigations (FOCI) Cruises . Seattle, WA, Alaska Fisheries Science Center. 169 pp. Sigler M. F., Stabeno P. J., Eisner L. B., Napp J. M., Mueter F. J. 2015. Spring and fall phytoplankton blooms in a productive subarctic ecosystem, the eastern Bering Sea, during 1995-2011. Deep Sea Research II , 109: 71– 83. Google Scholar CrossRef Search ADS   Sigler M. F., Napp J. M., Stabeno P. J., Heintz R. A., Lomas M. W., Hunt G. L.Jr. 2016. Variation in annual production of copepods, euphausiids, and juvenile walleye pollock in the southeastern Bering Sea. Deep Sea Research II , 134: 223– 234. Google Scholar CrossRef Search ADS   Smith P. E., Flerx W., Hewitt R. P. 1985. The CalCOFI vertical egg tow (CalVET) net. An egg production method for estimating spawning biomass of pelagic fish: application to the northern anchovy Engraulis mordox , pp. 23– 33. In Ed. by Lasker R.. NOAA Technical Report, NMFS 36, US Department of Commerce, Washington, DC. Smith S. L., Vidal J. 1986. Variations in the distribution, abundance, and development of copepods in the southeastern Bering Sea in 1980 and 1981. Continental Shelf Research , 5: 215– 239. Google Scholar CrossRef Search ADS   Stabeno P., Napp J., Mordy C., Whitledge T. 2010. Factors influencing physical structure and lower trophic levels of the eastern Bering Sea shelf in 2005: Sea ice, tides and winds. Progress in Oceanography , 85: 180– 196. Google Scholar CrossRef Search ADS   Stabeno P. J., Farley E. V., Kachel N., Moore S., Mordy C., Napp J. M., Overland J. E., et al.   2012a. A comparison of the physics, of the northern and southern shelves of the eastern Bering Sea and some implications to the ecosystem. Deep Sea Research II , 65–70, 14– 30. Stabeno P. J., Kachel N. B., Moore S. E., Napp J. M., Sigler M., Yamaguchi A., Zerbini A. N. 2012b. Comparison of warm and cold years on the southeastern Bering Sea shelf and some implications for the ecosystem. Deep Sea Research II , 65–70: 31– 45. Stabeno P. J., Danielson S., Kachel N. B., Mordy C. W. 2016. Currents on eastern Bering Sea shelf. Deep Sea Research II , 134: 1– 426. Google Scholar CrossRef Search ADS   Stoecker D. K., Weigel A. C., Stockwell D. A., Lomas M. W. 2014. Microzooplankton: Abundance, biomass and contribution to chlorophyll in the eastern Bering Sea in summer. Deep Sea Research II , 109: 134– 144. Google Scholar CrossRef Search ADS   Strasburger W. W., Hillgruber N., Pinchuk A. I., Mueter F. J. 2014. Feeding ecology of age-0 walleye Pollock (Gadus chalcogramma) and Pacific cod (Gadus macrocephalus) in the southeastern Bering Sea. Deep Sea Research II , 109: 172– 180. Google Scholar CrossRef Search ADS   Underwood A. J., Petraitis P. S. 1993. Structure of intertidal assemblages in different locations: how can local processes be compared. In Species diversity in ecological communities, pp. 38–51 . University of Chicago Press, Chicago. Vidal J., Smith S. L. 1986. Biomass, growth, and development of populations of herbivorous zooplankton in the southeastern Bering Sea during spring. Deep Sea Research , 33: 523– 556. Google Scholar CrossRef Search ADS   Wiese F. K., Wiseman W. J., van Pelt T. I. 2012. Bering Sea linkages. Deep Sea Research II , 65-70: 2– 5. Google Scholar CrossRef Search ADS   Published by International Council for the Exploration of the Sea 2017. This work is written by US Government employees and is in the public domain in the US. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ICES Journal of Marine Science Oxford University Press

Seasonal, interannual, and spatial patterns of community composition over the eastern Bering Sea shelf in cold years. Part I: zooplankton

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Published by International Council for the Exploration of the Sea 2017. This work is written by US Government employees and is in the public domain in the US.
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1095-9289
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10.1093/icesjms/fsx156
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Abstract

Abstract Ecosystem structure and function in the eastern Bering Sea are impacted by seasonal, interannual, and spatial variation of the zooplankton community. Zooplankton abundance, community composition and individual responses of key taxa, in spring, summer, and fall were evaluated across ecoregions during three years with high sea-ice, 2008–2010 (cold years). Interannual variations were greatest in spring, but less pronounced compared with intra-annual variations. Intra-annual variations were greatest in the south middle domain in spring and the north middle domain in all seasons. Models using environmental variables were able to explain 69–77% of zooplankton community variation within each season. Among individual taxa, Calanus marshallae/glacialis had delayed stage progression in spring 2009 compared with 2008 and 2010 on the south middle shelf, likely due to late ice retreat and cold temperatures that increased development times. In contrast, stage progression was fastest in summer 2008 likely due to warmer temperatures. Our findings indicate that intra-annual variation of zooplankton community composition, life history stage, and abundance within a cold period may affect the amount of high–lipid zooplankton prey (e.g. Neocalanus and Calanus spp. copepods and euphausiids) available seasonally for forage fish (e.g. age-0 walleye pollock) to grow to a sufficient size (to avoid size-dependent predation) and have sufficient lipid stores (to avoid starvation) to survive the first winter at sea. Introduction The eastern Bering Sea shelf is a highly productive ecosystem for fish, marine mammals and seabirds, with valuable commercial fisheries (e.g. groundfish, salmon, and crab) and subsistence harvests. These tertiary consumers, including humans, rely on the transfer of primary production to higher trophic levels through the zooplankton community. Accordingly, understanding seasonal, interannual and spatial variations in zooplankton assemblages (relative abundance of different species and stages), with an emphasis on responses of individual taxa that are key prey items, is important for predicting shifts in productivity of fish and other predators. Prior studies on zooplankton communities and the overlap of fish predators and zooplankton prey have focused on interannual variability between warm and cold climate stanzas within a single season (Coyle et al., 2011; Eisner et al., 2014; Moss et al., 2009; Stabeno et al., 2012b; Siddon et al., 2013; Eisner et al., 2015). In contrast, the current study evaluates within and between season patterns over a cold stanza (three consecutive cold years). Seasonal comparisons of zooplankton communities across varied shelf regions allow us to make inferences about factors influencing ecosystem dynamics in the Bering Sea and similar high latitude shelf ecosystems (e.g. Chukchi Sea). The overall goal is to explain seasonal variance in zooplankton communities and the underlying factors driving these variations. We also discuss how these variations have potential ripple effects across the ecosystem and impact fisheries community dynamics (Siddon et al., this issue). Regional climate forcing via variations in sea ice extent and timing has large impacts on the eastern Bering Sea ecosystem (Hunt et al., 2011; Stabeno et al., 2012a,b,, 2016). Since 2000, there have been multi-year periods (stanzas) of low sea ice (warm) or high sea ice (cold) conditions (Stabeno et al., 2012b). The timing of sea ice retreat and extent of ice coverage varies in relation to the strength and position of the Aleutian Low, with winds from the south in warm years and from the north in cold years (Stabeno et al., 2012b). Sea ice affects the seasonal progression of oceanographic conditions (e.g. stratification, hydrography, phytoplankton bloom timing; Sigler et al., 2015). Importantly, in years with late ice retreat, the spring phytoplankton bloom generally occurs earlier as stratification sets up due to ice melt (ice edge bloom), rather than later due to warming (open water bloom) (Hunt et al., 2002; Sigler et al., 2015, 2016). This early spring primary production may be beneficial to large lipid-rich crustacean zooplankton (e.g. large copepods such as Calanus and Neocalanus spp., and euphausiids) survival and growth (e.g. Baier and Napp, 2003; Hunt et al., 2016), resulting in increased lipid storage and enhanced overwinter survival of age-0 walleye pollock Gadus chalcogrammus (hereafter termed pollock; Coyle et al., 2008, 2011; Heintz et al., 2013; Eisner et al., 2015; Renner et al., 2016; Sigler et al., 2016). Therefore, variations in sea ice in spring can affect the production of phytoplankton, zooplankton, and forage fish throughout the growing season. Spatial variability in marine communities in the eastern Bering Sea is often partitioned along- and across-shelf by water masses and fronts. Frontal regions can serve as natural cross-shelf boundaries for water masses acting to separate and concentrate planktonic prey (Kachel et al., 2002; Eisner et al., 2014, 2015), which impacts the distribution of zooplanktivorous fish, sea birds, and marine mammals (Schneider, 1982; Moore et al., 2002; Jahncke et al., 2005; Baker and Hollowed, 2014). Regions of the shelf have been classified as inner <50 m bathymetry), middle (50–100 m), and outer (100–170 m) domains delineated by frontal regions (Coachman, 1986) and further divided south and north of ∼60°N latitude (Figure 1) (Stabeno et al., 2010). Recently, further subdivision into 16 smaller ecoregions has been proposed based on bathymetric, oceanographic, ecosystem and fisheries characteristics (Ortiz et al., 2012). Ecosystem modeling outputs have been partitioned by ecoregion (Ortiz et al., 2016); therefore, it is important to explore the applicability of these ecoregion designations at different trophic levels. Little is known about the applicability of ecoregion designations for lower tropic levels, in particular, although differences were seen for phytoplankton biomass in late summer with higher biomass observed further north within the south middle shelf (Ecoregion 6 compared with 3) likely due to nutrient inflow through the Pribilof Canyon (Figure 1) (Eisner et al., 2016). Figure 1. View largeDownload slide The map and inset show the eastern Bering Sea study area. Ecoregions are indicated by solid lines and numbers, and bathymetry is shown by light gray contours. The inner shelf is designated by Ecoregions 2, 7 (south inner) and 11 (north inner); the middle shelf by Ecoregions 3 and 6 (south middle), 9 and 10 (north middle); the outer shelf by Ecoregions 4 (south outer) and 8 (north outer); off-shelf by Ecoregions 15 and 16; Pribilof Islands by Ecoregion 5; and Alaska Peninsula by Ecoregion 1. Figure 1. View largeDownload slide The map and inset show the eastern Bering Sea study area. Ecoregions are indicated by solid lines and numbers, and bathymetry is shown by light gray contours. The inner shelf is designated by Ecoregions 2, 7 (south inner) and 11 (north inner); the middle shelf by Ecoregions 3 and 6 (south middle), 9 and 10 (north middle); the outer shelf by Ecoregions 4 (south outer) and 8 (north outer); off-shelf by Ecoregions 15 and 16; Pribilof Islands by Ecoregion 5; and Alaska Peninsula by Ecoregion 1. In this article, we investigate the seasonal progression of individual zooplankton species and assemblages on the eastern Bering Sea shelf over three consecutive cold years. Specifically we ask: (i) Do zooplankton communities show intra-annual and interannual differences? (ii) Do communities show differences among pre-defined ecoregions? (iii) How do season, ecoregion, year and environmental covariates (such as water temperature, salinity, and sea ice) account for variability in community composition? (iv) Does the abundance and stage composition of important prey species, such as Calanus spp. (C. marshallae and C. glacialis) show intra-annual and interannual differences? Finally, we discuss zooplankton community patterns in relation to ichthyoplankton and juvenile fish community patterns (i.e., predator field) over the same time period—described in Siddon et al. (this issue), in an effort to broaden our ecosystem perspective and further evaluate trophic linkages in this region. Methods Zooplankton and oceanographic sampling Samples were collected in 2008–2010 from April through July as a part of the multi-disciplinary Bering Sea Project (Bering Sea Ecosystem Study and Bering Sea Integrated Ecosystem Research Programme, Wiese et al., 2012), and from mid-August through early October by the Alaska Fisheries Science Center (AFSC) Bering Arctic Subarctic Integrated Survey (BASIS) (Supplementary Material S1). Stations were located across the shelf from 54.5°N to 62.8°N (northernmost location in spring and summer surveys). Zooplankton was sampled with a 25 cm diameter CalVET (PairVET) system (Smith et al., 1985) with 150 µm mesh nets, with General Oceanics flowmeters mounted inside the nets to monitor the volume filtered. The nets were fished vertically during the day from 100 m to the surface or from near-bottom to the surface at depths < 100 m. All samples were preserved in 5% formalin:seawater for later sorting. Vertical profiles of temperature and salinity were collected at each station with SBE 25 or SBE 9 (Seabird Electronics, Bellevue, WA, USA) conductivity-temperature-depth (CTD) profilers. An additional dataset was collected in the southeastern Bering Sea in April/May 2008–2010 by the AFSC Ecosystems and Fisheries Oceanography Coordinated Investigations (EcoFOCI) programme. Zooplankton was sampled using 60 and 20 cm diameter MARMAP style Bongo and PairVET frames bracketed together and fitted with 333 µm (or 505 µm for a subset of stations in 2009) and 150 µm mesh nets, respectively. Nets were towed obliquely to the surface from 300 m, or from 10 m off the bottom at depths <300 m. All nets were equipped with calibrated flowmeters to monitor the volume filtered. Oceanographic data were concurrently collected with a SBE 19 CTD that provided real-time measurements over the towed path. Collections from the Bering Sea Project and BASIS were processed at the University of Alaska Fairbanks according to established protocols (e.g. Coyle et al., 2008), and the combined data set is termed BSP/BASIS, hereafter. Briefly, each sample was poured into a sorting tray and large organisms, primarily shrimp and jellyfish, were removed and counted. The samples were then sequentially split using a Folsom splitter until the smallest subsample contained about 200 specimens of the most abundant taxa. All taxa in the smallest subsamples were identified, staged, and counted. Each larger subsample was examined to identify and count the larger, less abundant taxa. Collections from EcoFOCI were sorted at the Polish Plankton Sorting and Identification Center (Szczecin, Poland) to the lowest taxonomic level and developmental stage possible. Since only selected taxa were counted from 150 µm samples, additional taxa counts from 333 µm (or 505 µm) samples were used to complement the analysis of EcoFOCI data (see Supplementary Material S3). Samples from the 505 µm net were used for stations sampled 8–18 May 2009 because the 333 net was not available. The 333 and 505 µm nets were assumed to provide similar counts for the larger size (later stage) taxa enumerated (Siefert and Incze, 1989; Boeing and Duffy-Anderson, 2008); all taxa counted from these nets were larger than 505 µm. Due to differences in taxonomic resolution and mesh sizes, analyses of community structure were conducted separately for the EcoFOCI dataset. Cnidarians and ctenophores as well as mysids, gammarids, shrimp, and juvenile and adult euphausiids were removed from both datasets, since sampling for these species groups was not quantitative due to small net size and volume of water filtered. Taxa and stages selected for the analyses are listed in Supplementary Material S2 (BSP/BASIS dataset) and Supplementary Material S3 (EcoFOCI dataset). Data analyses Station selection and ecoregions Stations were sampled in 13 or 7 of the 16 ecoregions (Ortiz et al., 2012) for BSP/BASIS and EcoFOCI data, respectively (Figures 2 and 3). Stations along the Alaska Peninsula but inshore of the ecoregion boundary were included in Ecoregion 1. Figure 2. View largeDownload slide Zooplankton clusters (symbols) are shown by season and year for the BSP/BASIS dataset. Ecoregions are shown by numbers (described in Figure 1). The heatmap in Table 1 shows the taxa/stage in each cluster. Symbols designate the season that the cluster was typically observed: squares, diamonds and circles designate spring, summer and fall, respectively. (See on-line version for color figure.) Figure 2. View largeDownload slide Zooplankton clusters (symbols) are shown by season and year for the BSP/BASIS dataset. Ecoregions are shown by numbers (described in Figure 1). The heatmap in Table 1 shows the taxa/stage in each cluster. Symbols designate the season that the cluster was typically observed: squares, diamonds and circles designate spring, summer and fall, respectively. (See on-line version for color figure.) Figure 3. View largeDownload slide Spring zooplankton clusters (triangles) are shown for the EcoFOCI dataset. Ecoregions are shown by numbers (described in Figure 1). The heatmap in Table 2 shows the taxa/stage in each cluster. (See on-line version for color figure.) Figure 3. View largeDownload slide Spring zooplankton clusters (triangles) are shown for the EcoFOCI dataset. Ecoregions are shown by numbers (described in Figure 1). The heatmap in Table 2 shows the taxa/stage in each cluster. (See on-line version for color figure.) Environmental variables We selected water-mass properties correlated to the distribution of zooplankton communities in prior studies in the eastern Bering Sea (Baier and Napp, 2003; Coyle et al., 2008; Eisner et al., 2014). For each station, we included mean temperature and salinity above and below the mixed layer depth, estimated as the depth where σt is 0.10 kg m−3 higher than the value at 5 m (Danielson et al., 2011). Mean values of temperature and salinity over the water-column were excluded from analysis due to high correlations (>94%) with those observed below the mixed layer depth. Bathymetric depth, latitude, and longitude were included as covariates. Sea ice variables were also included as covariates since presence of sea ice correlates with growth and survival of important copepod taxa (e.g. Calanus spp.). Station-specific variables included the number of days of sea ice coverage during the prior winter as well as the day of year when ice last occurred. Sea ice was assumed present if coverage was >15% over a 60 km2 box centered at each 0.5° latitude and 1° longitude (Eisner et al., 2014). Sea ice data were obtained from the Advanced Microwave Scanning Radiometer on Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua from the National Snow and Ice Data Center (S. Salo, NOAA PMEL, pers. comm.). Categorical variables (factors) included Season, Ecoregion, and Year. Multivariate analyses Patterns in community structure were first analyzed with classification (cluster analysis) and ordination (nonmetric multidimensional scaling (nMDS)) techniques using Bray-Curtis dissimilarity of abundance between stations. Next, quantitative analyses using distance-based linear models and permutational multivariate analysis of variance (termed permutational MANOVA or PERMANOVA) were applied to each season to test year and ecoregion effects as well as environmental effects on zooplankton community composition (PRIMER-E, Anderson et al., 2008; Clarke et al., 2014). Advantages of using these distance-based, permutational techniques are: (i) flexibility of the dissimilarity measure that best describes distance between groups, and (ii) relaxed distributional assumptions, such as normality. To allow for more even contribution of small and large relative abundances and consequently make the variance-to-mean ratio closer to one, a fourth root transformation was applied to the abundance data prior to statistical analyses. Extremely rare species, occurring at <3% of stations within a dataset (“Rare” in Supplementary Materials S2 and S3), were removed from the analyses because they did not significantly contribute to spatial or temporal patterns of community composition. Cluster analysis and nMDS were both used to determine station groups with similar zooplankton composition. The groups were determined by drawing a line across the dendrogram from a hierarchical agglomerative cluster analysis using group average linkage, at a particular level of similarity (Supplementary Materials S4 and S6). This level was based on a combination of: (i) the presence of longer branches (indicating stability), (ii) groupings suggested by similarity profile analysis, a procedure that tests for randomness at each sequential node of the dendrogram (Clarke et al., 2014), as well as (iii) biological interpretation (Romesburg, 2004). Clusters were then verified by nMDS by superimposing the cluster groups on the ordination to check that groups were separated sufficiently (Supplementary Materials S5 and S7). The resulting clusters were mapped geographically. Clusters were designated by bold capital letters for the BSP/BASIS dataset and by bold lower case letters for the EcoFOCI dataset. To test for significant differences in community structure among seasons, ecoregions, and years, analysis of similarity global and pairwise tests were applied. This analysis uses ranks of a resemblance matrix (in our case, Bray-Curtis dissimilarity) to estimate the overall differences among groups relative to within groups using an R-statistic (Clarke et al., 2014). For R = 0, groups are not different, whereas R = 1 indicates groups are different and do not overlap. A PERMANOVA test, which partitions the variance and tests for effects on the Bray-Curtis dissimilarity, was used to determine significant effects and percent contribution of the factors Season, Ecoregion, and Year, to overall variability. This was followed by a distance-based linear regression model which determined what environmental variables might explain the significant temporal and spatial effects (Anderson et al., 2008). A forward selection option based on an adjusted R2 criterion was used to select the most important environmental variables. For each season, the environmental variables chosen by the regression model were then included in order of importance as covariates in a second PERMANOVA using Type I sum of squares (sequential), followed by the factors. This yielded relative components of variation, so as to determine which factors and environmental covariates explained the most variability in zooplankton community composition (Underwood and Petraitis, 1993). Separate regression models and PERMANOVAs with covariates were applied to each season. Finally, to test if zooplankton communities varied by predefined ecoregions, PERMANOVA pairwise analyses were conducted among the ecoregions within each domain for each season and year. Multivariate tests of dispersion (Anderson et al., 2008) were conducted to test if differences observed in PERMANOVAs were due, at least partially, to unequal variances. Abundances and stage composition of predominant copepods Untransformed abundance data from BSP/BASIS and EcoFOCI datasets were combined to obtain higher resolution estimates of seasonal changes. Monthly mean abundances (all stages combined) were graphed by year and averaged over all years for five common copepod species that are potential prey items for fish. We evaluated variations for taxa common to the south middle shelf and near the Pribilof Islands and Alaska Peninsula (Ecoregions 1, 3, 5, 6 combined) and south outer shelf (Ecoregion 4) (Figure 1). Taxa included Calanus spp. and Pseudocalanus spp. on the middle shelf, and Eucalanus bungii, Metridia pacifica, and Neocalanus cristatus on the outer shelf. In addition, stage-specific abundances (Adult Females [AFs], Adult Males [AMs], Copepodites [C] stages 2–5) for Calanus spp. were used to evaluate changes in stage composition averaged over two week intervals for April–September on the south middle shelf and near the Pribilof Islands and Alaska Peninsula combined. Stage compositions were compared among years, and related to the timing of sea ice retreat and the spring chlorophyll a maximum at Bering Sea Mooring 2 (M2, Pacific Marine Environmental Lab (PMEL); Figure 1) as determined by Sigler et al. (2015). The Calanus spp. C1 stage was evaluated separately, since this information was not available for the 2010 EcoFOCI dataset. In-situ mean temperature from CTD profiles was compared with stage compositions (% of total as C4 or C5) to evaluate the effect of temperature on Calanus spp. stage progression. Results Do zooplankton communities show intra-annual and interannual differences? Assemblage groups for the BSP/BASIS data set were determined from the dendrogram by slicing at a similarity of 68.2% (Supplementary Material S4). The nMDS analysis with cluster groups superimposed indicated that this similarity level was sufficient to separate assemblage groups (Supplementary Material S5). In spring, patterns of zooplankton assemblages were very similar in 2008 and 2009 (Figure 2). The spatial pattern of zooplankton community composition was similar among ecoregions on the inner shelf (Ecoregion 2, 7, 11), middle shelf (Ecoregion 3, 6, 9, 10), and north outer shelf (Ecoregion 8; no data available for Ecoregion 4). Cluster Q dominated the inner and middle shelves. Cluster Q was characterized by small copepods: Oithona spp. copepodites, late stages of Acartia spp., and late stages and adults of Pseudocalanus spp. (Table 1). On the north middle shelf, Cluster P occurred in both 2008 and 2009, but was more widespread in 2009. Cluster P had similar species to Cluster Q, but lower abundances of Acartia spp. and higher abundances of Oithona spp. copepodites. Finally, on the north outer shelf, Cluster D was widespread and was characterized by the more oceanic species: Neocalanus and Metridia spp., in addition to Microcalanus and Oithona spp. copepodites and adults. A different pattern, more similar to patterns observed in summer, was observed in the spring of 2010 likely because this survey occurred ∼1 month later in the year (Supplementary Material S1). The northern shelf was characterized primarily by Cluster T, distinguished by Acartia longiremis adults, early stage Calanus spp. copepodites, and euphausiid nauplii. Clusters M, S, and J also appeared in 2010, but not the other two springs. Cluster S was more prevalent on the inner and middle shelf and was characterized by Acartia spp., early stage Calanus spp., Oithona spp., and early stage Pseudocalanus spp. Cluster M had a very similar makeup, but higher numbers of later stages of Calanus spp. and higher Metridia spp. abundance. Cluster J was similar to Cluster D, but had higher abundances of Oncea spp., Metridia spp., E. bungii, early stage Calanus spp. copepodites, and euphausiid nauplii. Table 1. Heatmap shows the taxa common to one or more clusters in Figure 2. Gray scale is used to code mean abundance (fourth root transformed) of each taxa within each cluster. Clusters are ordered (left to right) by season and shelf domain (I, inner; M, middle; O, outer; SB, shelf-break) in which the cluster commonly occurred. For brevity, clusters with < 3 stations are not shown. (See on-line version for color.)     Spatial patterns in zooplankton assemblage were similar during both summer and fall across all years. Summer was characterized by Cluster S on the middle and inner shelves (Figure 2). As the water deepened on the outer shelf there was a transition to Cluster I (2009 south shelf) or Cluster M, followed by Cluster K further offshore. Fritillaria spp. and echinoderm larvae as well as Oithona and Oncea spp. characterized Cluster I. Cluster K had a similar makeup to Cluster M (described in spring section), but with reduced Calanus spp. and higher Metridia spp. and E. bungii abundances. One final cluster, Cluster U, is of interest during the summer. Cluster U was characterized by high abundances of Pseudocalanus spp. and late stage Calanus spp. (C4–C5). Cluster U was present in some areas during the summer, but was more prevalent during fall in the south middle shelf, particularly in 2008 and 2010. Otherwise, the fall was dominated by Cluster V, a cluster characterized by high abundances of Acartia, Oithona, Pseudocalanus, and Fritillaria spp., polychaete larvae, and the pteropod Limacina helicina. Zooplankton assemblage patterns for spring EcoFOCI data were similarly evaluated using cluster analysis by slicing the dendrogram at a similarity of 70.2% (Supplementary Material S6). The nMDS analysis with cluster groups superimposed indicated that this similarity level was sufficient to separate assemblages (Supplementary Material S7). EcoFOCI data were concentrated along the southern portion of the middle shelf, north of the Alaska Peninsula and near the Pribilof Islands (Figures 1 and 3). Cluster k was distributed along the inner and middle shelf in both 2008 and 2010. Cluster k was characterized by Acartia spp., Calanus spp. adults and early stages, euphausiid nauplii, Oithona spp., and the highest overall abundances of Pseudocalanus spp. (Table 2). Along the outer shelf, Cluster f was prevalent in 2008 and 2010 (Figure 3). Cluster f was characterized by higher abundances of Metridia and Oithona spp. while the typical inshore taxa (e.g. Acartia spp.) were less abundant (Table 2). The spring 2009 data had a different distribution of clusters in comparison with 2008 and 2010. The inner and middle shelves were represented by Clusters m and n, two clusters that were characterized by lower abundances overall compared with the two other springs, particularly in Calanus, Oithona, and Pseudocalanus spp., and euphausiid nauplii. Table 2. Heatmap shows the taxa common to one or more clusters in Figure 3. Gray scale is used to code mean abundance (fourth root transformed) of each taxa within each cluster. Clusters are ordered (left to right) by season and shelf domain (M, middle; O/SB, outer/shelf-break) in which the cluster commonly occurred. For brevity, clusters with <3 stations are not shown. (See on-line version for color.)     Do communities show differences among predefined ecoregions? PERMANOVAs and tests of dispersion were conducted among the ecoregions within the north and south middle domains for each season and year. Sample sizes were too low to conduct similar analyses for south inner domain ecoregions, and other domains consisted of only one ecoregion (Figure 1). Our results indicated that the ecoregions within the south middle domain (Ecoregions 3 and 6) had significant differences in community composition in 2008 and 2009 in spring, 2010 but not 2009 in summer, and 2010 but not 2008 or 2009 in fall (Supplementary Material S8). Sample sizes were too low to test 2010 in spring and 2008 in summer. Ecoregions within the north middle domain (Ecoregions 9 and 10) were different in all years and seasons tested, although the significant differences in community composition for summer 2009 may be at least partially attributed to differences in dispersion (P < 0.05, Supplementary Material S8). Sample sizes were too low to test 2008 and 2010 in fall. How do season, ecoregion, year and environmental covariates account for variability in community composition? As suggested by the prior cluster results, PERMANOVA estimates of components of variation for factors (Year, Season, Ecoregion) using the entire BSP/BASIS dataset indicated that Season and Ecoregion explained more variability (24 and 21%, respectively) than Year (4%) with interactions among factors also important (Supplementary Material S9). The permutational tests of dispersion did not show unequal variance between years 2008 and 2009 or between summer and fall seasons, but did show unequal variance among remaining years and seasons, and for ∼ 25% of the ecoregion pairs tested (pairs with sufficient sample sizes) indicating that differences were at least partially attributable to unequal variance among ecoregions. Analysis of similarity indicated that seasonal differences were greater between spring and summer, than summer and fall (R = 0.417 and 0.349, respectively, Supplementary Material S10). Differences among years were greatest in spring, particularly for the EcoFOCI dataset (R = 0.093 and 0.130 for BSP/BASIS and EcoFOCI, respectively). Fall communities did not show significant differences among years (P > 0.05). PERMANOVA estimates of components of variation by season, using both datasets, indicated that more variability in community structure was explained by Ecoregion (28–43%) than Year (4–11%) (Table 3). The permutational tests of dispersion indicated variances were significantly different for summer communities between 2008 and 2010, and between 2009 and 2010, and among some Ecoregions and Ecoregion/Year interactions, indicating that differences were at least partially attributable to unequal variances among groups. Table 3. PERMANOVA (Type 1 sequential) estimates of components of variation (%) are shown by season for Year, Ecoregion, and interactions among these factors without (w/o) and with (w) environmental covariates (Env).     Env include temperature and salinity (T, S) below the pycnocline (Tb, Sb) and above the pycnocline (Ta, Sa), ice variables (coverage (Ice C): number of days of coverage during the prior winter, retreat (Ice R): day of retreat), and latitude (Lat), longitude (Long), and bathymetry (Depth). Residual (Res) unexplained variability and total explained variability (1-Res) are shown. N indicates number of samples. The BSP/BASIS analysis excludes Ecoregion 15, 16, and 8. Ice variables were not available for the EcoFOCI dataset. The order of Env in each PERMANOVA sequential model is shown. The Env that explains the highest variability in community structure for a single variable (based on analysis from the regression model) is the first variable in each column. The addition of environmental covariates: below and above pycnocline temperature (T) and salinity (S), spatial variables (latitude, longitude, bathymetric depth), and sea ice variables (where applicable) in PERMANOVA models lowered the residual variability from an average of 48% to an average of 27% (Table 3). Adding covariates appeared to account for much of the variability in Ecoregion, since variability explained by Ecoregion dropped from 28–43% without covariates to 3–8% with covariates included in the model. Variability explained by Year remained relatively constant with or without covariates. In spring, sea ice variables explained the most variability for the BSP/BASIS data, and below pycnocline T and S explained the most variability for the EcoFOCI data (sea ice data unavailable for this dataset). In contrast, in fall depth explained the most variability. In summer, single variables explained only a small amount (<10%) of the variability. Overall, factors and environmental covariates combined were able to explain considerable variability in community structure (76–77% in spring, 71% in summer and 69% in fall). Below pycnocline T or S singly explained the most variability in zooplankton community structure using marginal tests in the distance-based linear regression model (top variables in Table 3). Below pycnocline T and S were plotted for each season and year (Supplementary Materials S11 and S12) to help visualize changes in these water mass parameters and compare T and S distributions with changes in zooplankton assemblages (Figures 2 and 3). In spring, inshore to offshore gradations (lower to higher values) were seen for below pycnocline T and S for the BSP/BASIS data set for all years; however, for the EcoFOCI data, this pattern was disrupted for 2009 with an intrusion of colder water at ∼ 55°N, 164–165°W (Supplementary Material S12). Below pycnocline T of −1.8 to −1.5 °C (suggesting the influence of sea ice) was observed at many stations in the northern portion of the survey area on the inner and middle shelf in spring 2008 and 2009. In summer and fall, below pycnocline T was lower on the middle shelf than on the inner and outer shelves due to the presence of the “cold pool”, cold (<2 °C) near bottom water that forms on the middle shelf in winter during ice production. In contrast, below pycnocline S still showed increases from the inner to the outer shelf. Does the abundance and stage composition of important prey species show intra-annual and interannual differences? Single species abundances of selected zooplankton taxa (all stages combined including both BSP/BASIS and EcoFOCI datasets) were averaged by month to investigate higher resolution changes in abundance in taxa common to the south middle or outer shelves (Figure 4). On the middle shelf, Pseudocalanus spp. and Calanus spp. both increased from April – June, although the percent rate of increase was higher for Calanus spp. Calanus spp. abundances dropped off in July–August with lowest levels in September (Figure 4). In contrast, Pseudocalanus spp. levels continued to remain constant from June – August and had highest levels in September. On the outer shelf, M. pacifica showed similar increases as Calanus spp., although peak abundances were a month later (in July). E. bungii had peak abundances in June or July depending on the year. N. cristatus demonstrated a much different phenology with highest abundances in April–May, decreases in June–July, and very low numbers in August–September. The only obvious interannual trend was indicated by N. cristatus, which showed lower values in 2009 than in 2008 and 2010 during April and May (no spring peak), as observed in cluster analysis. Figure 4. View largeDownload slide Mean copepod abundances (No. m− 3, all stages combined) are shown by month and year for the south middle and outer shelves in the southeast Bering Sea for combined BSP/BASIS and EcoFOCI datasets. (See on-line version for color figure.) Figure 4. View largeDownload slide Mean copepod abundances (No. m− 3, all stages combined) are shown by month and year for the south middle and outer shelves in the southeast Bering Sea for combined BSP/BASIS and EcoFOCI datasets. (See on-line version for color figure.) The stage composition of Calanus spp. over 2-week intervals shows that adults were dominant in early spring, followed by early then late stage copepodites with C5 dominating by late summer (Figure 5). Early copepodite stage C2 appeared later in 2009 than in the other two years, along with a later sea ice retreat and later spring bloom (Figure 5). Calanus spp. C1 abundances also appeared later in 2009 than in 2008 (Supplementary Material S13). The percent of C4 and C5 was low at lower temperature (<0 °C) (Figure 6). Figure 5. View large Download slide Calanus spp. stage (C2-Adult) proportions are shown by week for the south middle shelf, Pribilof Island and Alaska Peninsula ecoregions for 2008, 2009 and 2010 using combined BSP/BASIS and EcoFOCI datasets. The arrows indicate the timing of sea ice retreat and the spring chlorophyll a maximum at Mooring 2 (Sigler et al., 2014, Supplementary Material). (See on-line version for color figure.) Figure 5. View large Download slide Calanus spp. stage (C2-Adult) proportions are shown by week for the south middle shelf, Pribilof Island and Alaska Peninsula ecoregions for 2008, 2009 and 2010 using combined BSP/BASIS and EcoFOCI datasets. The arrows indicate the timing of sea ice retreat and the spring chlorophyll a maximum at Mooring 2 (Sigler et al., 2014, Supplementary Material). (See on-line version for color figure.) Figure 6. View largeDownload slide Fraction of Calanus spp. stage C4 and C5 (late stages combined) and mean water column temperature are shown for data in Figure 5. Figure 6. View largeDownload slide Fraction of Calanus spp. stage C4 and C5 (late stages combined) and mean water column temperature are shown for data in Figure 5. Discussion Our goal was to evaluate the seasonal progression of individual zooplankton species and assemblages over three consecutive cold years and relate our findings to forage fish productivity on the eastern Bering Sea shelf. Overall, the zooplankton community composition varied spatially from the inner to outer shelf. Spatial variations also were observed between pre-defined ecoregions during spring for the south middle shelf and all seasons for the north middle shelf. Communities varied temporally by season, with lower variation seen interannually. The main differences in community composition were changes in the relative abundance of copepod stages and species, and these changes were correlated to below pycnocline temperature and salinity (i.e. subsurface water masses). Sea ice coverage and retreat timing were also related to community variations in spring, and we suggest sea-ice impacts zooplankton communities in two ways. Sea ice dynamics impacts the water temperature as well as timing of the ice-associated primary production, both of which relate directly to development and growth rates of zooplankton (e.g. Calanus spp.) life history stage progression (Baier and Napp, 2003). Zooplankton from the EcoFOCI dataset and larval fish communities concurrently collected showed similar interannual variations with unique communities observed in spring 2009 (Siddon et al., this issue). Variations in zooplankton assemblages, single species, and environmental drivers Spatial variations across this broad region were similar to past studies. Variations (observed from cluster analysis) were larger across the shelf than from north to south, as seen in prior community analysis for late summer/early fall data (Eisner et al., 2014, 2015). The cross-shelf variations seen in the eastern Bering Sea have been well-documented (Cooney and Coyle, 1982; Smith and Vidal, 1986; Vidal and Smith, 1986; Coyle and Pinchuk, 1996, 2002; Napp et al., 2000; Eisner et al., 2014, 2015) with, for example, Acartia spp. found inshore, Calanus spp. found primarily in middle shelf waters, and Neocalanus spp., M. pacifica, and E. bungii found on the outer shelf. The cluster analyses suggested that ecoregion designations proposed by Ortiz et al. (2012) did not describe the patterns in community composition as well as the traditional domain classifications (Stabeno et al., 2010) used in prior community analysis of zooplankton in the eastern Bering Sea (Eisner et al., 2014). Although data in some season/ecoregion combinations were lacking, PERMANOVA analyses indicated that the ecoregion designations were appropriate for evaluating changes in zooplankton communities in some specific areas and seasons (e.g. early in the seasonal progression within the south middle domain and during all seasons within the north middle domain). In addition to the spatial variability, we observed additional patterns in zooplankton abundance related to seasonal changes in oceanographic conditions and unrelated to the cross-shelf dynamics. Variability in the rate of development for life history stages accounts for much of the observed seasonal patterns, particularly for copepods. Small copepod taxa with several generations per year (e.g. Pseudocalanus, Acartia, and Oithona spp.) had adult stages in all seasons with early copepodite stages (C1–C4) highest in summer or fall, as has been described in Coyle et al. (2008). In contrast, large copepods such as Calanus spp. overwinter as copepodite stage C5, metamorphose into adults in late winter, spawn in spring, and progress through nauplii stages (N1–N6), then copepodites stages (C1–C5) during spring and summer, leading to dominance of C5 in fall, as observed in our study. The sharp increase in relative abundance of C4–C5 Calanus spp. copepodites observed when water temperature increased from 0 to 3 °C, indicates that seasonal warm-up accelerates copepodite development rates either directly by influencing metabolic rates or indirectly by facilitating excess in food supply (algal bloom). The percent of C4 and C5 plateaus above 3 °C as most of the copepodites mature to C5 during late summer, prior to overwintering (Supplementary Material S14). Accordingly, the earlier molting into later copepodite stages in 2008 compared with 2009 and 2010 is likely related to warmer temperatures in summer 2008 as evidenced from M2 data (Stabeno et al., 2012b) and our data (Figure 6). Likewise, Ershova et al. (2015), observed a strong relationship between mean developmental stage of Calanus glacialis and surface temperature in the Chukchi Sea. Variations in sea ice retreat may have led to the unique patterns in zooplankton assemblages observed in spring 2009 (Figure 3). Baier and Napp (2003) found that Calanus spp. C5 copepodites molt into AFs well in advance of the spring bloom (February) with eggs produced immediately after. In a heavy ice year (1995), eggs were released early (January–February), C1 coincided with the spring bloom in April with many early stages (C1–C3) in May. In a year with no ice in (1996), eggs were released late (March) and C1s were observed with the spring bloom in May. Thus, the appearance of C1 seems to relate to the timing of the spring bloom in both early/no ice and late ice years, and the spring bloom generally occurs earlier in years with late ice retreat (ice persisting until after March 15) (Hunt et al., 2011; Sigler et al., 2015, 2016). All 3 years in the current study were late ice retreat years, although 2009 had the latest ice retreat on the southeastern middle shelf (near M2) compared with 2008 and 2010, and a much later spring bloom (late May/early June) similar in timing to warm year open water blooms (Sigler et al., 2015). Therefore, Calanus spp. stage progression in 2009 appeared to be delayed by the later spring bloom (i.e. a lack of adequate food resources) as well as colder temperatures which slowed growth. Similarly, studies in a shallow arctic fjord, Rijpfjorden in north-eastern Svalbard, Norway, showed that while C. glacialis egg production was triggered by ice algae, the growth of nauplii and copepodites was fueled by higher water temperatures and pelagic production (phytoplankton) later in the spring (Leu et al., 2011). Pseudocalanus spp. and M. pacifica spawning also appeared to be somewhat delayed in 2009, suggested by fewer early copepodite stages in spring 2009 (Figure 3; Table 2). Eisner et al. (2014) found that years with late sea ice retreat (colder water temperatures) and early ice associated spring blooms had higher abundances of Pseudocalanus spp. and Calanus spp. during August/September compared with years with early ice retreat and late spring blooms. In the current study, Calanus spp. and Pseudocalanus spp. total seasonal abundances did not appear lower in 2009 than in the other two years, suggesting that the delay to the start of stage progression did not reduce the numbers over the growing season, perhaps due to smaller variations in sea ice and bloom timing among cold years than between warm and cold temperature stanzas (Sigler et al., 2015). The high contribution of sea ice to zooplankton community variation in spring (33% of the variation is explained by ice coverage and retreat timing; Table 3), further supports the importance of sea ice and subsequent spring bloom timing to copepod stage progression (Sigler et al., 2016). Season and ecoregion explained more variability in community composition than year for the three cold years of our study, likely due to the reduced interannual variability in environmental variables observed within climate stanzas (Eisner et al. 2014). Bottom temperature and salinity for all years had a similar moderate impact (explained 8–13% of the variance) on community structure during all seasons for the BSP/BASIS dataset. However, for the spring Eco-FOCI data set, bottom temperature and salinity accounted for a much higher amount of variability in community structure (44%). This suggests that bottom water mass characteristics were important in structuring zooplankton communities, particularly during spring on the southern shelf. Bering Sea water masses have different concentrations of nutrients, phytoplankton, and microzooplankton (Stabeno et al., 2012a; Stoecker et al., 2014), which can lead to variations in prey fields for zooplankton. Similarly, an analysis of late summer BASIS zooplankton community data collected from 2003 to 2009 on the southern middle shelf, indicated bottom temperature and salinity were significantly correlated to large zooplankton community composition (Eisner et al., 2014). On the Chukchi Sea shelf, bottom temperature was commonly found to be the strongest factor shaping zooplankton assemblages for data collected 1947–2012 during summer months (July–September) (Ershova et al., 2015). Although we explained considerable variability (69–77%) in community structure within a season, models could be improved by the addition of variables such as currents, onshore and offshore flow, and other factors that impact copepod stage progression, such as microzooplankton abundance and predation. Overlap of zooplankton and fish assemblages The linkages among physics and zooplankton (prey source) to the quality of larval fish nursery grounds (or effective juvenile habitats) are often poorly understood (Sheaves et al., 2006). Several criteria are required to define habitat function including the effects of scale, complexity and connectivity, critical resources and processes supporting juveniles, and factors affecting sustainability of the fishery (Sheaves et al., 2006). This information can, in turn, inform ecological forecasting. Our current research attempts to aid in this effort by describing the spatial and temporal overlap between zooplankton, and larval and juvenile fish, including diet information, and environmental factors potentially influencing both communities. This information has broader applications for understanding potential processes affecting larval and juvenile habitats in other marine ecosystems. Calanus, Pseudocalanus, and Metridia spp. were important in diets of larval and juvenile pollock and Pacific cod Gadus macrocephalus in the eastern Bering Sea based on data collected in 2008 at a subset of stations in the current study (Strasburger et al., 2014). Pollock consumed early and late stage M. pacifica and Pseudocalanus spp. nauplii in spring, late stage Pseudocalanus spp. nauplii and copepodites C1 to adult and A. longiremis C4-adult in summer, and late stage Calanus spp. copepodites (C4 and C5) and adult Pseudocalanus spp. in fall. Pacific cod consumed Calanus spp. C1–C2, late stage M. pacifica nauplii and copepod eggs in spring, Calanus spp. C3 and Pseudocalanus C4 to adult in summer, and euphausiids (Thysanoessa spp.) and Limocina helicina in fall. The pollock and Pacific cod diets mirror the seasonal phenology of zooplankton in our current study. Similarly, from warm to cold periods, increases in Calanus spp. were observed in the water column and in diets of juvenile pollock and juvenile salmon in the southeast Bering Sea (Coyle et al., 2011). Euphausiids as well as Calanus spp. are important lipid-rich prey for juvenile pollock and Pacific cod (Moss et al., 2009; Coyle et al., 2011; Strasburger et al., 2014; Buckley et al., 2016; Hunt et al., 2016). Euphausiid nauplii and calyptopsis were in lower abundance in spring 2009 in the EcoFOCI dataset. Bi et al. (2015) found that the euphausiid, Thysanoessa raschii, located on the inner and middle shelf, was the most common euphausiid taxa on the Bering Sea shelf in 2008–2010, with the lowest proportions in 2009 (86, 36, and 65%, respectively). Accordingly, the high proportions of T. raschii in 2008 may have partially led to the high recruitment observed for the 2008 pollock year class (Ianelli et al., 2015). An important finding of Siddon et al. (this issue) was that larval and juvenile pollock during all seasons were less abundant in 2009. In spring 2009, deviations in zooplankton assemblages on the middle shelf near the Alaska Peninsula (Figure 3) were mirrored by deviations in concurrently collected ichthyoplankton assemblage data; many stations were dominated by adult copepods, but not copepodites, and showed a lack of pollock or had no fish larvae of any species (Siddon et al., this issue). We postulate that in 2009 the delay in the timing of sea ice retreat and in the spring bloom (Figure 5) which in turn appeared to delay stage progression for Pseudocalanus and Metridia spp. could have led to reduced prey availability (e.g. reductions in Pseudocalanus spp. nauplii and Metridia spp. copepodites) for larval pollock and other ichthyoplankton that prey on these items. The fish assemblage composition therefore may be altered by changes in the zooplankton prey base, in addition to variations in currents and other environmental factors (Siddon et al., this issue). In 2008 and 2010, the fish clusters with the highest pollock contributions often spatially overlapped with zooplankton clusters with high abundances of Pseudocalanus spp. (spring) or Calanus spp. (summer and fall). For example, in fall the pollock-dominated fish cluster on the south middle shelf overlapped with the two zooplankton clusters (U, G) that had the highest abundances of late stage Calanus spp. (C4, C5) (Table 2). This suggests that high quality zooplankton prey in appropriate size ranges were geographically available (co-located) for pollock during 2008 and 2010. In addition, the earlier appearance of late stage Calanus spp. C5 (larger and more lipid-rich than earlier copepodite stages, Liu and Hopcroft, 2007; Sigler et al., 2016) in 2008 may provide a longer period of high fat resources for higher trophic level consumers. Age-0 pollock had the highest abundances in 2008 (Siddon et al., this issue) as well as high lipid content, which enabled high overwinter survival (Heintz et al., 2013). Conclusions Evaluations of seasonal variations in zooplankton community composition and environmental variables help offer insight into changes in fish abundance, growth, and survival in the eastern Bering Sea. The overlap of suitable zooplankton prey with larval and juvenile fish predators is important to understanding trophic transfer of energy through the food web and fisheries recruitment (Siddon et al., 2013). High quality lipid-rich zooplankton prey (e.g. Neocalanus and Calanus spp. copepods and euphausiids) are critical for fish to grow to a sufficient size (to avoid size-dependent predation) and have enough lipid stores (to avoid starvation) to survive the first winter at sea (Heintz et al., 2013; Sigler et al., 2016). Environmental factors such as sea ice, spring bloom timing, and temperature can impact zooplankton species composition and stage progression even within a cold year climate stanza, as evidenced by the delay in stage progression of Calanus spp. in 2009, a year with a relatively late spring bloom. The low abundances of pollock larvae and juveniles in 2009 were possibly related to changes in spring zooplankton assemblage structure as well as environmental forcing. A characterization of the seasonality of zooplankton species and stage progression between and within climate stanzas will aid in ecosystem and multispecies fisheries modeling efforts in this region. This research also can be used to further our understanding of seasonal variations in zooplankton, factors driving these changes and potential impacts on ichthyoplankton and juvenile fish in other subarctic ecosystems. Supplementary data Supplementary material is available at the ICESJMS online version of the article. Acknowledgements We thank the staff and crew of the NOAA ship Oscar Dyson, the USCGC Healy, R/V/Knorr, and R/V Thomas Thompson for help and effort in collecting oceanographic and zooplankton samples. We are grateful for the assistance in field sampling, data processing and analysis from UAF and NOAA scientific staff and volunteers. We thank K. Coyle and his lab for assistance and guidance regarding BASIS zooplankton sampling and analysis, as well as the Polish Plankton Sorting and Identification Center for their contributions to the EcoFOCI dataset. Zooplankton collections and analysis for the Bering Sea Project were supported by the National Science Foundation as part of the BEST programme (Award Nos. ARC-0816805, ARC-0946402, and ARC-1107203). Funding This research was also generously supported by NOAA Fisheries; this is contribution EcoFOCI-N887 to NOAA's North Pacific Climate Regimes and Ecosystem Productivity research programme. References Anderson M. J., Gorley R. N., Clarke K. R. 2008. PERMANOVA for PRIMER: Guide to Software and Statistical Methods . PRIMER-E, Plymouth, UK. Baier C. T., Napp J. M. 2003. Climate-induced variability in Calanus marshallae populations. Journal of Plankton Research , 25: 771– 782. Google Scholar CrossRef Search ADS   Baker M. R., Hollowed A. B. 2014. Delineating ecological regions in marine systems: Integrating physical structure and community composition to inform spatial management in the eastern Bering Sea. Deep Sea Research II , 109: 215– 240. Google Scholar CrossRef Search ADS   Bi H., Yu H., Pinchuk A. I., Harvey H. R. 2015. Variability in euphausiid populations on the eastern Bering Sea shelf during the recent cooling event in summer 2008-2010. Deep Sea Research I , 95: 12– 19. Google Scholar CrossRef Search ADS   Boeing W. J., Duffy-Anderson J. T. 2008. Ichthyoplankton dynamics and biodiversity in the Gulf of Alaska: responses to environmental change. Ecological Indicators , 8: 292– 302. Google Scholar CrossRef Search ADS   Buckley T. W., Ortiz I., Kotwicki S., Aydin K. 2016. Summer diet composition of walleye pollock and predator–prey relationships with copepods and euphausiids in the eastern Bering Sea, 1987–2011. Deep Sea Research II , 134: 302– 311. Google Scholar CrossRef Search ADS   Clarke K. R., Gorley R. N., Somerfield P. J., Warwick R. M. 2014. Change in marine communities: an approach to statistical analysis and interpretation . 3rd edn. PRIMER-E, Plymouth, UK. Coachman L. K. 1986. Circulation, water masses and fluxes on the southeastern Bering Sea shelf. Continental Shelf Research , 5: 23– 108. Google Scholar CrossRef Search ADS   Cooney R. T., Coyle K. O. 1982. Trophic implication of cross-shelf copepod distributions in the southeastern Bering Sea. Marine Biology , 70: 187– 196. Google Scholar CrossRef Search ADS   Coyle K. O., Pinchuk A. I. 1996. Zooplankton of the Bering Sea: a review of the Russian-language literature. In Ecology of the Bering Sea: a review of the Russian Literature  pp. 97– 133. Ed. by Mathisen O. A., Coyle K. O.. University of Alaska Sea Grant College Program, Fairbanks, Alaska. Coyle K. O., Pinchuk A. I. 2002. Climate-related differences in zooplankton density and growth on the inner shelf of the southeastern Bering Sea. Progress in Oceanography , 55: 177– 194. Google Scholar CrossRef Search ADS   Coyle K. O., Pinchuk A. I., Eisner L. B., Napp J. M. 2008. Zooplankton species composition, abundance and biomass on the eastern Bering Sea shelf during summer: the potential role of water column stability and nutrients in structuring the zooplankton community. Deep Sea Research II , 55: 1775– 1791. Google Scholar CrossRef Search ADS   Coyle K. O., Eisner L. B., Mueter F. J., Pinchuk A. I., Janout M. A., Cieciel K. D., Farley E. V., Andrews A. G. 2011. Climate change in the southeastern Bering Sea: impacts on pollock stocks and implications for the oscillating control hypothesis. Fisheries Oceanography , 20: 139– 156. Google Scholar CrossRef Search ADS   Danielson S., Eisner L., Weingartner T., Aagaard K. 2011. Thermal and haline variability over the central Bering Sea shelf: Seasonal and interannual perspectives. Continental Shelf Research , 31: 539– 554. Google Scholar CrossRef Search ADS   Eisner L., Napp J., Mier K., Pinchuk A., Andrews A. 2014. Climate-mediated changes in zooplankton community structure for the eastern Bering Sea. Deep Sea Research II , 109: 157– 171. Google Scholar CrossRef Search ADS   Eisner L., Siddon E., Strasburger W. 2015. Spatial and temporal changes in assemblage structure of zooplankton and pelagic fish across varying climate conditions in the eastern Bering Sea. Izvestia TINRO , 181: 141– 160. Eisner L., Gann J., Ladd C., Cieciel K., Mordy C. 2016. Late summer early fall phytoplankton biomass (chlorophyll a) in the eastern Bering Sea: spatial and temporal variations and factors affecting chlorophyll a concentrations. Deep Sea Research II , 134: 100– 114. Google Scholar CrossRef Search ADS   Ershova E. A., Hopcroft R. R., Kosobokova K. N., Matsuno K., Nelson R. J., Yamaguchi A., Eisner L. B. 2015. Long-term changes in summer zooplankton communities of the western Chukchi Sea, 1945–2012. Oceanography , 28: 100– 115. Google Scholar CrossRef Search ADS   Heintz R. A., Siddon E. C., Farley E. VJr, Napp J. M. 2013. Climate-related changes in the nutritional condition of young-of-the-year walleye pollock (Theragra chalcogramma) from the eastern Bering Sea. Deep Sea Research II , 94: 150– 156. Google Scholar CrossRef Search ADS   Hunt G. L.Jr, Stabeno P. J., Walters G., Sinclair E., Brodeur R. D., Napp J. M., Bond N. A. 2002. Climate change and control of the southeastern Bering Sea pelagic ecosystem. Deep Sea Research II , 49: 5821– 5853. Google Scholar CrossRef Search ADS   Hunt G. L.Jr., Coyle K. O., Eisner L. B., Farley E. V., Heintz R., Mueter F., Napp J. M., et al.   2011. Climate impacts on eastern Bering Sea food webs: A synthesis of new data and an assessment of the Oscillating Control Hypothesis. ICES Journal of Marine Science , 68: 1230– 1243. Google Scholar CrossRef Search ADS   Hunt G. L.Jr., Ressler P. H., Gibson G. A., DeRobertis A., Aydin K., Sigler M. F., Ortiz I., et al.   2016. Euphausiids in the eastern Bering Sea: A synthesis of recent studies of euphausiid production, consumption and population control. Deep Sea Research II , 134: 204– 222. Google Scholar CrossRef Search ADS   Ianelli J. N., Honkalehto T., Barbeaux S., Kotwicki S. 2015. Assessment of Alaska pollock stock in the Eastern Bering Sea. In Stock Assessment and Fishery Evaluation Report for the Groundfish Resources of the Bering Sea/Aleutian Islands Regions . Report, North Pacific Fisheries Management Council. Anchorage, AK , 151 p. Jahncke J., Coyle K. O., Zeeman S. I., Kachel N. B., Hunt G. L. 2005. Distribution of foraging shearwaters relative to inner front of SE Bering Sea. Marine Ecology Progress Series , 305: 219– 233. Google Scholar CrossRef Search ADS   Kachel N. B., Hunt G. L.Jr., Salo S. A., Schumacher J. D., Stabeno P. J., Whitledge T. E. 2002. Characteristics and variability of the inner front of the southeastern Bering Sea. Deep Sea Research II , 49: 5889– 5909. Google Scholar CrossRef Search ADS   Liu H., Hopcroft R. R. 2007. A comparison of seasonal growth and development of the copepods Calanus marshallae and C-pacificus in the northern Gulf of Alaska. Journal of Plankton Research , 29: 569– 581. Google Scholar CrossRef Search ADS   Leu E., Søreide J. E., Hessen D. O., Falk-Petersen S., Berge J. 2011. Consequences of changing sea-ice cover for primary and secondary producers in the European Arctic shelf seas: Timing, quantity, and quality. Progress in Oceanography , 90: 18– 32. Google Scholar CrossRef Search ADS   Moore S. E., Waite J. M., Friday N. A., Honkalehto T. 2002. Cetacean distribution and relative abundance on the central-eastern and the southeastern Bering Sea shelf with reference to oceanographic domains. Progress in Oceanography , 55: 249– 261. Google Scholar CrossRef Search ADS   Moss J., Murphy J., Farley E., Eisner L., Andrews A. 2009. Juvenile pink and chum salmon distribution, diet, and growth in the Northern Bering and Chukchi Seas 2009. North Pacific Anadromous Fish Commission, Bulletin N , 5: 191– 196. Napp J. M., Kendall A. W., Schumacher J. D. 2000. A synthesis of biological and physical processes affecting the feeding environment of larval walleye pollock (Theragra chalcogramma) in the eastern Bering Sea. Fisheries Oceanography , 9: 147– 162. Google Scholar CrossRef Search ADS   Ortiz I., Wiese F., Greig A. 2012. Marine Regions Boundary Data for the Bering Sea Shelf and Slope. Version 1.0. UCAR/NCAR - Earth Observing Laboratory. doi:10.5065/D6DF6P6C. Ortiz I., Aydin A., Hermann A. J., Gibson G. A., Punt A. E., Wiese F. K., Eisner L. B., et al.   2016. Climate to fish: Synthesizing field work, data and models in a 39-year retrospective analysis of seasonal processes on the eastern Bering Sea shelf and slope. Deep-Sea Research II , 134: 390– 412. Google Scholar CrossRef Search ADS   Renner M., Salo S., Eisner L. B., Ressler P. H., Ladd C., Kuletz K., Santora J. A., et al.   2016. Timing of ice retreat alters seabird abundances and distributions in the southeast Bering Sea. Biology Letters , 12: 20160276. Google Scholar CrossRef Search ADS PubMed  Romesburg H. C. 2004. Cluster Analysis for Researchers . Lulu.com, Morrisville, NC. 334 pp. (Reprint of 1984 edition, with minor revisions.) Schneider D. 1982. Fronts and seabird aggregations in the southeastern Bering Sea. Marine Ecology Progress Series , 10: 101– 103. Google Scholar CrossRef Search ADS   Sheaves M., Baker R., Johnston R. 2006. Marine nurseries and effective juvenile habitats: An alternative view. Marine Ecology Progress Series , 318: 303– 306. Google Scholar CrossRef Search ADS   Siddon E. C., Kristiansen T., Mueter F. J., Holsman K. K., Heintz R. A. 2013. Spatial match-mismatch between juvenile fish and prey provides a mechanism for recruitment variability across contrasting climate conditions in the eastern Bering Sea. PLoS One , 8: e84526. Google Scholar CrossRef Search ADS PubMed  Siddon E. C., Duffy-Anderson J. T., Mier K. L., Busby M., Eisner L. B. 2018. Seasonal, interannual, and spatial patterns of community composition over the eastern Bering Sea shelf in cold years. Part II: ichthyoplankton and juvenile fish. ICES Journal of Marine Science , 75: 87– 101. Siefert D. L. W., Incze L. S. 1989. Zooplankton of Shelikof Strait, Alaska, April to August 1986: Data from Fisheries-Oceanography Coordinated Investigations (FOCI) Cruises . Seattle, WA, Alaska Fisheries Science Center. 169 pp. Sigler M. F., Stabeno P. J., Eisner L. B., Napp J. M., Mueter F. J. 2015. Spring and fall phytoplankton blooms in a productive subarctic ecosystem, the eastern Bering Sea, during 1995-2011. Deep Sea Research II , 109: 71– 83. Google Scholar CrossRef Search ADS   Sigler M. F., Napp J. M., Stabeno P. J., Heintz R. A., Lomas M. W., Hunt G. L.Jr. 2016. Variation in annual production of copepods, euphausiids, and juvenile walleye pollock in the southeastern Bering Sea. Deep Sea Research II , 134: 223– 234. Google Scholar CrossRef Search ADS   Smith P. E., Flerx W., Hewitt R. P. 1985. The CalCOFI vertical egg tow (CalVET) net. An egg production method for estimating spawning biomass of pelagic fish: application to the northern anchovy Engraulis mordox , pp. 23– 33. In Ed. by Lasker R.. NOAA Technical Report, NMFS 36, US Department of Commerce, Washington, DC. Smith S. L., Vidal J. 1986. Variations in the distribution, abundance, and development of copepods in the southeastern Bering Sea in 1980 and 1981. Continental Shelf Research , 5: 215– 239. Google Scholar CrossRef Search ADS   Stabeno P., Napp J., Mordy C., Whitledge T. 2010. Factors influencing physical structure and lower trophic levels of the eastern Bering Sea shelf in 2005: Sea ice, tides and winds. Progress in Oceanography , 85: 180– 196. Google Scholar CrossRef Search ADS   Stabeno P. J., Farley E. V., Kachel N., Moore S., Mordy C., Napp J. M., Overland J. E., et al.   2012a. A comparison of the physics, of the northern and southern shelves of the eastern Bering Sea and some implications to the ecosystem. Deep Sea Research II , 65–70, 14– 30. Stabeno P. J., Kachel N. B., Moore S. E., Napp J. M., Sigler M., Yamaguchi A., Zerbini A. N. 2012b. Comparison of warm and cold years on the southeastern Bering Sea shelf and some implications for the ecosystem. Deep Sea Research II , 65–70: 31– 45. Stabeno P. J., Danielson S., Kachel N. B., Mordy C. W. 2016. Currents on eastern Bering Sea shelf. Deep Sea Research II , 134: 1– 426. Google Scholar CrossRef Search ADS   Stoecker D. K., Weigel A. C., Stockwell D. A., Lomas M. W. 2014. Microzooplankton: Abundance, biomass and contribution to chlorophyll in the eastern Bering Sea in summer. Deep Sea Research II , 109: 134– 144. Google Scholar CrossRef Search ADS   Strasburger W. W., Hillgruber N., Pinchuk A. I., Mueter F. J. 2014. Feeding ecology of age-0 walleye Pollock (Gadus chalcogramma) and Pacific cod (Gadus macrocephalus) in the southeastern Bering Sea. Deep Sea Research II , 109: 172– 180. Google Scholar CrossRef Search ADS   Underwood A. J., Petraitis P. S. 1993. Structure of intertidal assemblages in different locations: how can local processes be compared. In Species diversity in ecological communities, pp. 38–51 . University of Chicago Press, Chicago. Vidal J., Smith S. L. 1986. Biomass, growth, and development of populations of herbivorous zooplankton in the southeastern Bering Sea during spring. Deep Sea Research , 33: 523– 556. Google Scholar CrossRef Search ADS   Wiese F. K., Wiseman W. J., van Pelt T. I. 2012. Bering Sea linkages. Deep Sea Research II , 65-70: 2– 5. Google Scholar CrossRef Search ADS   Published by International Council for the Exploration of the Sea 2017. This work is written by US Government employees and is in the public domain in the US.

Journal

ICES Journal of Marine Science – Oxford University Press

Published: Jan 1, 2018

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